From 765960e413a7126bc72eae31b9e2cd87e94aab73 Mon Sep 17 00:00:00 2001 From: Gabriel Date: Fri, 12 Jun 2026 14:36:00 +0800 Subject: [PATCH 01/28] [refactor](be) Refactor file table reader stack ### What problem does this PR solve? Issue Number: N/A Related PR: N/A Problem Summary: Refactor the file table reader stack around the format_v2 reader implementation. This includes the new file reader abstractions, parquet reader components, table reader adapters for Hive/Iceberg/Paimon/JDBC, ColumnMapper filter and projection handling, expression clone support used by file-local filter rewrites, Iceberg row lineage materialization, and related BE unit tests and design notes. This commit is the squashed result of rebasing the current branch onto master. ### Release note None ### Check List (For Author) - Test: No need to test (history rewrite only in this step; no code changes beyond the already rebased branch content) - Behavior changed: No - Does this need documentation: No --- .gitignore | 1 + .../data_type_datetimev2_serde.cpp | 139 + .../data_type_datetimev2_serde.h | 2 + .../data_type_datev2_serde.cpp | 24 + .../data_type_serde/data_type_datev2_serde.h | 2 + .../data_type_decimal_serde.cpp | 152 + .../data_type_serde/data_type_decimal_serde.h | 2 + .../data_type_nullable_serde.cpp | 37 +- .../data_type_nullable_serde.h | 2 + .../data_type_number_serde.cpp | 98 + .../data_type_serde/data_type_number_serde.h | 3 + .../core/data_type_serde/data_type_serde.cpp | 48 + be/src/core/data_type_serde/data_type_serde.h | 9 + .../data_type_string_serde.cpp | 42 + .../data_type_serde/data_type_string_serde.h | 3 + .../data_type_serde/data_type_time_serde.cpp | 48 + .../data_type_serde/data_type_time_serde.h | 2 + .../data_type_serde/decoded_column_view.h | 102 + be/src/exec/operator/file_scan_operator.cpp | 29 +- be/src/exec/operator/file_scan_operator.h | 2 + be/src/exec/scan/file_scanner.cpp | 1 - be/src/exec/scan/file_scanner.h | 4 +- be/src/exec/scan/file_scanner_v2.cpp | 883 +++ be/src/exec/scan/file_scanner_v2.h | 135 + be/src/exec/scan/split_source_connector.h | 29 + be/src/exprs/runtime_filter_expr.cpp | 11 + be/src/exprs/runtime_filter_expr.h | 1 + be/src/exprs/short_circuit_evaluation_expr.h | 35 +- be/src/exprs/vbloom_predicate.h | 7 + be/src/exprs/vcase_expr.h | 11 + be/src/exprs/vcast_expr.h | 12 + be/src/exprs/vcolumn_ref.h | 13 + be/src/exprs/vcompound_pred.h | 5 + be/src/exprs/vcondition_expr.h | 15 + be/src/exprs/vdirect_in_predicate.h | 6 + be/src/exprs/vectorized_fn_call.h | 6 + be/src/exprs/vexpr.cpp | 59 +- be/src/exprs/vexpr.h | 11 +- be/src/exprs/vin_predicate.h | 11 +- be/src/exprs/vliteral.cpp | 6 - be/src/exprs/vliteral.h | 23 +- be/src/exprs/vslot_ref.cpp | 41 +- be/src/exprs/vslot_ref.h | 15 +- be/src/exprs/vtopn_pred.h | 5 + be/src/format/CMakeLists.txt | 3 + be/src/format/csv/csv_reader.cpp | 4 +- be/src/format/generic_reader.h | 1 + be/src/format/json/new_json_reader.cpp | 4 +- be/src/format/native/native_reader.cpp | 4 +- be/src/format/orc/vorc_reader.cpp | 4 +- be/src/format/parquet/vparquet_reader.cpp | 4 +- .../format/table/deletion_vector_reader.cpp | 19 +- be/src/format/table/deletion_vector_reader.h | 35 +- be/src/format/table/iceberg_reader_mixin.h | 3 - be/src/format_v2/column_data.h | 407 ++ be/src/format_v2/column_mapper.cpp | 1603 ++++++ be/src/format_v2/column_mapper.h | 257 + be/src/format_v2/column_mapper_nested.cpp | 949 ++++ be/src/format_v2/column_mapper_nested.h | 90 + be/src/format_v2/expr/cast.cpp | 131 + be/src/format_v2/expr/cast.h | 68 + be/src/format_v2/expr/delete_predicate.cpp | 117 + be/src/format_v2/expr/delete_predicate.h | 60 + .../expr/equality_delete_predicate.cpp | 159 + .../expr/equality_delete_predicate.h | 71 + be/src/format_v2/file_reader.cpp | 199 + be/src/format_v2/file_reader.h | 279 + be/src/format_v2/jni/jdbc_reader.cpp | 192 + be/src/format_v2/jni/jdbc_reader.h | 58 + be/src/format_v2/jni/jni_table_reader.cpp | 319 ++ be/src/format_v2/jni/jni_table_reader.h | 112 + .../parquet/parquet_column_schema.cpp | 283 + .../format_v2/parquet/parquet_column_schema.h | 92 + .../parquet/parquet_file_context.cpp | 163 + .../format_v2/parquet/parquet_file_context.h | 42 + be/src/format_v2/parquet/parquet_profile.h | 32 + be/src/format_v2/parquet/parquet_reader.cpp | 489 ++ be/src/format_v2/parquet/parquet_reader.h | 141 + be/src/format_v2/parquet/parquet_scan.cpp | 536 ++ be/src/format_v2/parquet/parquet_scan.h | 160 + .../format_v2/parquet/parquet_statistics.cpp | 1264 +++++ be/src/format_v2/parquet/parquet_statistics.h | 141 + be/src/format_v2/parquet/parquet_type.cpp | 348 ++ be/src/format_v2/parquet/parquet_type.h | 91 + .../parquet/reader/column_reader.cpp | 538 ++ .../format_v2/parquet/reader/column_reader.h | 196 + .../reader/global_rowid_column_reader.cpp | 86 + .../reader/global_rowid_column_reader.h | 49 + .../parquet/reader/list_column_reader.cpp | 170 + .../parquet/reader/list_column_reader.h | 53 + .../parquet/reader/map_column_reader.cpp | 226 + .../parquet/reader/map_column_reader.h | 57 + .../reader/nested_column_materializer.cpp | 72 + .../reader/nested_column_materializer.h | 39 + .../parquet/reader/parquet_leaf_reader.cpp | 577 ++ .../parquet/reader/parquet_leaf_reader.h | 134 + .../reader/row_position_column_reader.cpp | 78 + .../reader/row_position_column_reader.h | 45 + .../parquet/reader/scalar_column_reader.cpp | 309 ++ .../parquet/reader/scalar_column_reader.h | 82 + .../parquet/reader/struct_column_reader.cpp | 228 + .../parquet/reader/struct_column_reader.h | 62 + be/src/format_v2/parquet/selection_vector.h | 167 + be/src/format_v2/schema_projection.cpp | 127 + be/src/format_v2/schema_projection.h | 43 + be/src/format_v2/table/hive_reader.cpp | 143 + be/src/format_v2/table/hive_reader.h | 42 + be/src/format_v2/table/iceberg_reader.cpp | 774 +++ be/src/format_v2/table/iceberg_reader.h | 167 + be/src/format_v2/table/paimon_reader.cpp | 192 + be/src/format_v2/table/paimon_reader.h | 44 + be/src/format_v2/table_reader.cpp | 643 +++ be/src/format_v2/table_reader.h | 1195 +++++ be/src/io/file_factory.cpp | 13 +- be/src/io/file_factory.h | 5 +- be/test/CMakeLists.txt | 1 + .../data_type_serde_decoded_values_test.cpp | 1693 ++++++ .../runtime_filter_expr_sampling_test.cpp | 46 + .../scan/vfile_scanner_exception_test.cpp | 303 ++ be/test/format_v2/column_mapper_test.cpp | 2002 +++++++ be/test/format_v2/expr/cast_test.cpp | 1005 ++++ .../format_v2/expr/delete_predicate_test.cpp | 155 + .../expr/equality_delete_predicate_test.cpp | 181 + be/test/format_v2/expr/table_expr_test.cpp | 123 + .../parquet/parquet_column_reader_test.cpp | 3744 +++++++++++++ .../format_v2/parquet/parquet_reader_test.cpp | 2868 ++++++++++ .../format_v2/table_reader_request_test.cpp | 50 + be/test/format_v2/table_reader_test.cpp | 4717 +++++++++++++++++ docs/doris-iceberg-parquet-api-design.md | 511 ++ ...ew-parquet-reader-column-index-refactor.md | 404 ++ docs/ut-coverage-plan.md | 92 + .../org/apache/doris/qe/SessionVariable.java | 17 +- gensrc/thrift/Exprs.thrift | 4 + gensrc/thrift/Opcodes.thrift | 2 + gensrc/thrift/PaloInternalService.thrift | 1 + .../test_outfile_parquet_complex_type.out | 12 + .../parquet/test_hive_read_parquet.out | 12 +- .../test_hive_read_parquet_comlex_type.out | 40 +- .../export/test_export_external_table.out | 60 +- .../hive/test_external_catalog_hive.out | 128 +- .../test_outfile_parquet_complex_type.groovy | 11 + 141 files changed, 35511 insertions(+), 253 deletions(-) create mode 100644 be/src/core/data_type_serde/decoded_column_view.h create mode 100644 be/src/exec/scan/file_scanner_v2.cpp create mode 100644 be/src/exec/scan/file_scanner_v2.h create mode 100644 be/src/format_v2/column_data.h create mode 100644 be/src/format_v2/column_mapper.cpp create mode 100644 be/src/format_v2/column_mapper.h create mode 100644 be/src/format_v2/column_mapper_nested.cpp create mode 100644 be/src/format_v2/column_mapper_nested.h create mode 100644 be/src/format_v2/expr/cast.cpp create mode 100644 be/src/format_v2/expr/cast.h create mode 100644 be/src/format_v2/expr/delete_predicate.cpp create mode 100644 be/src/format_v2/expr/delete_predicate.h create mode 100644 be/src/format_v2/expr/equality_delete_predicate.cpp create mode 100644 be/src/format_v2/expr/equality_delete_predicate.h create mode 100644 be/src/format_v2/file_reader.cpp create mode 100644 be/src/format_v2/file_reader.h create mode 100644 be/src/format_v2/jni/jdbc_reader.cpp create mode 100644 be/src/format_v2/jni/jdbc_reader.h create mode 100644 be/src/format_v2/jni/jni_table_reader.cpp create mode 100644 be/src/format_v2/jni/jni_table_reader.h create mode 100644 be/src/format_v2/parquet/parquet_column_schema.cpp create mode 100644 be/src/format_v2/parquet/parquet_column_schema.h create mode 100644 be/src/format_v2/parquet/parquet_file_context.cpp create mode 100644 be/src/format_v2/parquet/parquet_file_context.h create mode 100644 be/src/format_v2/parquet/parquet_profile.h create mode 100644 be/src/format_v2/parquet/parquet_reader.cpp create mode 100644 be/src/format_v2/parquet/parquet_reader.h create mode 100644 be/src/format_v2/parquet/parquet_scan.cpp create mode 100644 be/src/format_v2/parquet/parquet_scan.h create mode 100644 be/src/format_v2/parquet/parquet_statistics.cpp create mode 100644 be/src/format_v2/parquet/parquet_statistics.h create mode 100644 be/src/format_v2/parquet/parquet_type.cpp create mode 100644 be/src/format_v2/parquet/parquet_type.h create mode 100644 be/src/format_v2/parquet/reader/column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/column_reader.h create mode 100644 be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/global_rowid_column_reader.h create mode 100644 be/src/format_v2/parquet/reader/list_column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/list_column_reader.h create mode 100644 be/src/format_v2/parquet/reader/map_column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/map_column_reader.h create mode 100644 be/src/format_v2/parquet/reader/nested_column_materializer.cpp create mode 100644 be/src/format_v2/parquet/reader/nested_column_materializer.h create mode 100644 be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/parquet_leaf_reader.h create mode 100644 be/src/format_v2/parquet/reader/row_position_column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/row_position_column_reader.h create mode 100644 be/src/format_v2/parquet/reader/scalar_column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/scalar_column_reader.h create mode 100644 be/src/format_v2/parquet/reader/struct_column_reader.cpp create mode 100644 be/src/format_v2/parquet/reader/struct_column_reader.h create mode 100644 be/src/format_v2/parquet/selection_vector.h create mode 100644 be/src/format_v2/schema_projection.cpp create mode 100644 be/src/format_v2/schema_projection.h create mode 100644 be/src/format_v2/table/hive_reader.cpp create mode 100644 be/src/format_v2/table/hive_reader.h create mode 100644 be/src/format_v2/table/iceberg_reader.cpp create mode 100644 be/src/format_v2/table/iceberg_reader.h create mode 100644 be/src/format_v2/table/paimon_reader.cpp create mode 100644 be/src/format_v2/table/paimon_reader.h create mode 100644 be/src/format_v2/table_reader.cpp create mode 100644 be/src/format_v2/table_reader.h create mode 100644 be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp create mode 100644 be/test/format_v2/column_mapper_test.cpp create mode 100644 be/test/format_v2/expr/cast_test.cpp create mode 100644 be/test/format_v2/expr/delete_predicate_test.cpp create mode 100644 be/test/format_v2/expr/equality_delete_predicate_test.cpp create mode 100644 be/test/format_v2/expr/table_expr_test.cpp create mode 100644 be/test/format_v2/parquet/parquet_column_reader_test.cpp create mode 100644 be/test/format_v2/parquet/parquet_reader_test.cpp create mode 100644 be/test/format_v2/table_reader_request_test.cpp create mode 100644 be/test/format_v2/table_reader_test.cpp create mode 100644 docs/doris-iceberg-parquet-api-design.md create mode 100644 docs/new-parquet-reader-column-index-refactor.md create mode 100644 docs/ut-coverage-plan.md diff --git a/.gitignore b/.gitignore index 7a61c598c99f75..d3976f46132e8c 100644 --- a/.gitignore +++ b/.gitignore @@ -153,3 +153,4 @@ compile_commands.json .github .worktrees/ +.worktree_initialized diff --git a/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp b/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp index 92a5106b4815a8..340d5b8c951a15 100644 --- a/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp +++ b/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp @@ -28,6 +28,7 @@ #include "core/data_type/data_type_decimal.h" #include "core/data_type/data_type_number.h" #include "core/data_type/primitive_type.h" +#include "core/data_type_serde/decoded_column_view.h" #include "core/types.h" #include "core/value/vdatetime_value.h" #include "exprs/function/cast/cast_to_datetimev2_impl.hpp" @@ -43,6 +44,85 @@ enum { namespace doris { static const int64_t micro_to_nano_second = 1000; +namespace { + +#pragma pack(1) +struct DecodedInt96Timestamp { + int64_t nanos_of_day; + int32_t julian_day; + + int64_t to_timestamp_micros() const { + static constexpr int32_t JULIAN_EPOCH_OFFSET_DAYS = 2440588; + static constexpr int64_t MICROS_IN_DAY = 86400000000; + static constexpr int64_t NANOS_PER_MICROSECOND = 1000; + return (julian_day - JULIAN_EPOCH_OFFSET_DAYS) * MICROS_IN_DAY + + nanos_of_day / NANOS_PER_MICROSECOND; + } +}; +#pragma pack() +static_assert(sizeof(DecodedInt96Timestamp) == 12); + +Status append_datetimev2_from_epoch_micros(ColumnDateTimeV2::Container& data, + int64_t timestamp_micros) { + static constexpr int64_t MICROS_PER_SECOND = 1000000; + static constexpr int64_t MICROS_PER_MINUTE = MICROS_PER_SECOND * 60; + static constexpr int64_t MICROS_PER_HOUR = MICROS_PER_MINUTE * 60; + static constexpr int64_t MICROS_PER_DAY = MICROS_PER_HOUR * 24; + static const int64_t EPOCH_DAYNR = calc_daynr(1970, 1, 1); + + int64_t days_since_epoch = timestamp_micros / MICROS_PER_DAY; + int64_t micros_of_day = timestamp_micros % MICROS_PER_DAY; + if (micros_of_day < 0) { + micros_of_day += MICROS_PER_DAY; + --days_since_epoch; + } + + const int64_t daynr = EPOCH_DAYNR + days_since_epoch; + if (daynr <= 0) { + return Status::DataQualityError( + "Decoded DATETIMEV2 timestamp is out of range: micros={}, daynr={}", + timestamp_micros, daynr); + } + + DateV2Value datetime_value; + if (!datetime_value.get_date_from_daynr(static_cast(daynr))) { + return Status::DataQualityError( + "Decoded DATETIMEV2 timestamp is out of range: micros={}, daynr={}", + timestamp_micros, daynr); + } + + const auto hour = static_cast(micros_of_day / MICROS_PER_HOUR); + micros_of_day %= MICROS_PER_HOUR; + const auto minute = static_cast(micros_of_day / MICROS_PER_MINUTE); + micros_of_day %= MICROS_PER_MINUTE; + const auto second = static_cast(micros_of_day / MICROS_PER_SECOND); + const auto microsecond = static_cast(micros_of_day % MICROS_PER_SECOND); + datetime_value.unchecked_set_time(datetime_value.year(), datetime_value.month(), + datetime_value.day(), hour, minute, second, microsecond); + data.push_back(datetime_value); + return Status::OK(); +} + +void append_datetimev2_from_utc_epoch_micros(ColumnDateTimeV2::Container& data, + int64_t timestamp_micros, + const cctz::time_zone& timezone) { + static constexpr int64_t MICROS_PER_SECOND = 1000000; + + int64_t epoch_seconds = timestamp_micros / MICROS_PER_SECOND; + int64_t micros_of_second = timestamp_micros % MICROS_PER_SECOND; + if (micros_of_second < 0) { + micros_of_second += MICROS_PER_SECOND; + --epoch_seconds; + } + + DateV2Value datetime_value; + datetime_value.from_unixtime(epoch_seconds, timezone); + datetime_value.set_microsecond(static_cast(micros_of_second)); + data.push_back(datetime_value); +} + +} // namespace + // NOLINTBEGIN(readability-function-size) // NOLINTBEGIN(readability-function-cognitive-complexity) Status DataTypeDateTimeV2SerDe::from_string_batch(const ColumnString& col_str, @@ -451,6 +531,65 @@ Status DataTypeDateTimeV2SerDe::read_column_from_arrow(IColumn& column, return Status::OK(); } +Status DataTypeDateTimeV2SerDe::read_column_from_decoded_values( + IColumn& column, const DecodedColumnView& view) const { + if (view.value_kind != DecodedValueKind::INT64 && view.value_kind != DecodedValueKind::INT96) { + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("DATETIMEV2 decoded reader expects INT64 or INT96 source")); + } + if (view.values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded value buffer is null for {}", column.get_name()); + } + auto& data = assert_cast(column).get_data(); + const auto old_size = data.size(); + if (view.value_kind == DecodedValueKind::INT96) { + const auto* values = reinterpret_cast(view.values); + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(DateV2Value()); + continue; + } + auto st = append_datetimev2_from_epoch_micros(data, values[row].to_timestamp_micros()); + if (!st.ok()) { + if (decoded_column_view_can_null_on_conversion_failure(view)) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + continue; + } + data.resize(old_size); + return st; + } + } + return Status::OK(); + } + + const auto* values = reinterpret_cast(view.values); + static const auto utc_timezone = cctz::utc_time_zone(); + const auto& timezone = view.timezone == nullptr ? utc_timezone : *view.timezone; + const int64_t second_mask = view.time_unit == DecodedTimeUnit::MILLIS ? 1000 : 1000000; + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(DateV2Value()); + continue; + } + const int64_t timestamp_micros = values[row] * (1000000 / second_mask); + if (view.timestamp_is_adjusted_to_utc) { + append_datetimev2_from_utc_epoch_micros(data, timestamp_micros, timezone); + } else { + auto st = append_datetimev2_from_epoch_micros(data, timestamp_micros); + if (!st.ok()) { + if (decoded_column_view_can_null_on_conversion_failure(view)) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + continue; + } + data.resize(old_size); + return st; + } + } + } + return Status::OK(); +} + Status DataTypeDateTimeV2SerDe::write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& result, int64_t row_idx, bool col_const, diff --git a/be/src/core/data_type_serde/data_type_datetimev2_serde.h b/be/src/core/data_type_serde/data_type_datetimev2_serde.h index 0389432a621730..34d0373eba1c34 100644 --- a/be/src/core/data_type_serde/data_type_datetimev2_serde.h +++ b/be/src/core/data_type_serde/data_type_datetimev2_serde.h @@ -88,6 +88,8 @@ class DataTypeDateTimeV2SerDe : public DataTypeNumberSerDe(column).get_data(); + const auto* values = reinterpret_cast(view.values); + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(DateV2Value()); + continue; + } + DateV2Value date_v2; + date_v2.get_date_from_daynr(values[row] + date_threshold); + data.push_back(date_v2); + } + return Status::OK(); +} + Status DataTypeDateV2SerDe::write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& result, int64_t row_idx, bool col_const, diff --git a/be/src/core/data_type_serde/data_type_datev2_serde.h b/be/src/core/data_type_serde/data_type_datev2_serde.h index 0375f9be4b4b23..ff985d61345d5a 100644 --- a/be/src/core/data_type_serde/data_type_datev2_serde.h +++ b/be/src/core/data_type_serde/data_type_datev2_serde.h @@ -86,6 +86,8 @@ class DataTypeDateV2SerDe : public DataTypeNumberSerDe +NativeType decode_big_endian_signed_integer(const uint8_t* data, int length) { + if constexpr (std::is_same_v) { + NativeType value = data != nullptr && length > 0 && (data[0] & 0x80) != 0 ? NativeType(-1) + : NativeType(0); + for (int i = 0; i < length; ++i) { + value = (value << 8) + NativeType(data[i]); + } + return value; + } else { + using UnsignedNativeType = + std::conditional_t, unsigned __int128, + std::make_unsigned_t>; + UnsignedNativeType value = data != nullptr && length > 0 && (data[0] & 0x80) != 0 + ? static_cast(-1) + : 0; + for (int i = 0; i < length; ++i) { + value = static_cast((value << 8) | data[i]); + } + return static_cast(value); + } +} + +template +bool decoded_decimal_value_fits(const typename PrimitiveTypeTraits::CppType::NativeType& value, + UInt32 precision) { + return value >= min_decimal_value(precision).value && + value <= max_decimal_value(precision).value; +} + +template +bool decoded_decimal_int_value_fits(Int128 value, UInt32 precision) { + using NativeType = typename PrimitiveTypeTraits::CppType::NativeType; + if constexpr (std::is_same_v) { + const auto wide_value = wide::Int256(value); + return decoded_decimal_value_fits(wide_value, precision); + } else { + return value >= static_cast(min_decimal_value(precision).value) && + value <= static_cast(max_decimal_value(precision).value); + } +} + +template +Status read_decimal_decoded_value(const DecodedColumnView& view, UInt32 precision, int64_t row, + typename PrimitiveTypeTraits::CppType* result) { + using FieldType = typename PrimitiveTypeTraits::CppType; + using NativeType = typename FieldType::NativeType; + NativeType native_value; + if (view.value_kind == DecodedValueKind::INT32) { + const auto* values = reinterpret_cast(view.values); + const auto value = static_cast(values[row]); + if (!decoded_decimal_int_value_fits(value, precision)) { + return Status::DataQualityError("Decoded decimal value is out of range"); + } + native_value = NativeType(value); + } else if (view.value_kind == DecodedValueKind::INT64) { + const auto* values = reinterpret_cast(view.values); + const auto value = static_cast(values[row]); + if (!decoded_decimal_int_value_fits(value, precision)) { + return Status::DataQualityError("Decoded decimal value is out of range"); + } + native_value = NativeType(value); + } else { + const auto& value = (*view.binary_values)[row]; + const auto length = view.value_kind == DecodedValueKind::FIXED_BINARY + ? view.fixed_length + : cast_set(value.size); + if (length > static_cast(sizeof(NativeType))) { + return Status::DataQualityError("Decoded decimal binary value is too wide: length={}", + length); + } + native_value = decode_big_endian_signed_integer( + reinterpret_cast(value.data), length); + } + if (!decoded_decimal_value_fits(native_value, precision)) { + return Status::DataQualityError("Decoded decimal value is out of range"); + } + *result = FieldType {native_value}; + return Status::OK(); +} + +template +Status read_decimal_decoded_values(IColumn& column, const DecodedColumnView& view, + UInt32 precision) { + if (view.value_kind == DecodedValueKind::INT32 || view.value_kind == DecodedValueKind::INT64) { + if (view.values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded value buffer is null for {}", column.get_name()); + } + } else if (view.binary_values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded binary values are null for {}", column.get_name()); + } + auto& data = assert_cast&>(column).get_data(); + const auto old_size = data.size(); + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(typename PrimitiveTypeTraits::CppType()); + continue; + } + if (view.value_kind == DecodedValueKind::BINARY || + view.value_kind == DecodedValueKind::FIXED_BINARY) { + const auto& value = (*view.binary_values)[row]; + const auto length = view.value_kind == DecodedValueKind::FIXED_BINARY + ? view.fixed_length + : cast_set(value.size); + if (value.data == nullptr && length > 0) { + if (decoded_column_view_can_null_on_conversion_failure(view)) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + continue; + } + return Status::Corruption("Decoded decimal binary value is null for {} at row {}", + column.get_name(), row); + } + } + typename PrimitiveTypeTraits::CppType value; + auto st = read_decimal_decoded_value(view, precision, row, &value); + if (!st.ok()) { + if (decoded_column_view_can_null_on_conversion_failure(view)) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + continue; + } + data.resize(old_size); + st.prepend(fmt::format( + "Failed to decode decimal value for {} at row {}: ", column.get_name(), row)); + return st; + } + data.push_back(value); + } + return Status::OK(); +} + +} // namespace template Status DataTypeDecimalSerDe::from_string_batch(const ColumnString& str, ColumnNullable& column, @@ -381,6 +515,24 @@ Status DataTypeDecimalSerDe::read_column_from_arrow(IColumn& column, return Status::OK(); } +template +Status DataTypeDecimalSerDe::read_column_from_decoded_values( + IColumn& column, const DecodedColumnView& view) const { + if constexpr (T == TYPE_DECIMAL32 || T == TYPE_DECIMAL64 || T == TYPE_DECIMAL128I || + T == TYPE_DECIMAL256) { + if (view.value_kind == DecodedValueKind::INT32 || + view.value_kind == DecodedValueKind::INT64 || + view.value_kind == DecodedValueKind::BINARY || + view.value_kind == DecodedValueKind::FIXED_BINARY) { + return read_decimal_decoded_values(column, view, precision); + } + } + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("Unsupported decoded values for {} from source kind {}", + get_name(), static_cast(view.value_kind))); +} + template Status DataTypeDecimalSerDe::write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& result, diff --git a/be/src/core/data_type_serde/data_type_decimal_serde.h b/be/src/core/data_type_serde/data_type_decimal_serde.h index 0185672e024718..089835a21be955 100644 --- a/be/src/core/data_type_serde/data_type_decimal_serde.h +++ b/be/src/core/data_type_serde/data_type_decimal_serde.h @@ -107,6 +107,8 @@ class DataTypeDecimalSerDe : public DataTypeSerDe { const cctz::time_zone& ctz) const override; Status read_column_from_arrow(IColumn& column, const arrow::Array* arrow_array, int64_t start, int64_t end, const cctz::time_zone& ctz) const override; + Status read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const override; Status write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& row_buffer, int64_t row_idx, bool col_const, const FormatOptions& options) const override; diff --git a/be/src/core/data_type_serde/data_type_nullable_serde.cpp b/be/src/core/data_type_serde/data_type_nullable_serde.cpp index a93f8d6126c7d5..7c6ce46e1cd960 100644 --- a/be/src/core/data_type_serde/data_type_nullable_serde.cpp +++ b/be/src/core/data_type_serde/data_type_nullable_serde.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include "core/assert_cast.h" #include "core/column/column.h" @@ -31,10 +31,12 @@ #include "core/column/column_vector.h" #include "core/data_type_serde/data_type_serde.h" #include "core/data_type_serde/data_type_string_serde.h" +#include "core/data_type_serde/decoded_column_view.h" #include "exprs/function/cast/cast_base.h" #include "format/transformer/vcsv_transformer.h" #include "util/jsonb_document.h" #include "util/jsonb_writer.h" +#include "util/simd/bits.h" namespace doris { class Arena; @@ -350,6 +352,39 @@ Status DataTypeNullableSerDe::read_column_from_arrow(IColumn& column, ctz); } +Status DataTypeNullableSerDe::read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const { + auto& nullable_column = assert_cast(column); + auto& null_map = nullable_column.get_null_map_data(); + const auto old_size = null_map.size(); + auto& nested_column = nullable_column.get_nested_column(); + const auto old_nested_size = nested_column.size(); + null_map.resize(null_map.size() + view.row_count); + if (view.null_map == nullptr) { + // No null value + memset(null_map.data() + old_size, 0, view.row_count); + } else { + // TODO: skip if no null in map + auto* dst = null_map.data() + old_size; + memcpy(dst, view.null_map, view.row_count); + // If there are all null values, we can skip reading nested column and just insert defaults. + if (simd::count_zero_num(reinterpret_cast(view.null_map), view.row_count) == + 0) { + nested_column.insert_many_defaults(view.row_count); + return Status::OK(); + } + } + DecodedColumnView nested_view = view; + nested_view.conversion_failure_null_map = &null_map; + nested_view.conversion_failure_null_map_offset = old_size; + auto st = nested_serde->read_column_from_decoded_values(nested_column, nested_view); + if (!st.ok()) { + null_map.resize(old_size); + nested_column.resize(old_nested_size); + } + return st; +} + bool DataTypeNullableSerDe::write_column_to_mysql_text(const IColumn& column, BufferWritable& bw, int64_t row_idx, const FormatOptions& options) const { diff --git a/be/src/core/data_type_serde/data_type_nullable_serde.h b/be/src/core/data_type_serde/data_type_nullable_serde.h index 6e069444483b87..ee1eab51941ecb 100644 --- a/be/src/core/data_type_serde/data_type_nullable_serde.h +++ b/be/src/core/data_type_serde/data_type_nullable_serde.h @@ -86,6 +86,8 @@ class DataTypeNullableSerDe : public DataTypeSerDe { const cctz::time_zone& ctz) const override; Status read_column_from_arrow(IColumn& column, const arrow::Array* arrow_array, int64_t start, int64_t end, const cctz::time_zone& ctz) const override; + Status read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const override; Status write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& row_buffer, int64_t row_idx, bool col_const, const FormatOptions& options) const override; diff --git a/be/src/core/data_type_serde/data_type_number_serde.cpp b/be/src/core/data_type_serde/data_type_number_serde.cpp index 5f518aa6bbdc5d..cc6fd59366fa0a 100644 --- a/be/src/core/data_type_serde/data_type_number_serde.cpp +++ b/be/src/core/data_type_serde/data_type_number_serde.cpp @@ -20,6 +20,8 @@ #include #include +#include +#include #include "common/exception.h" #include "common/status.h" @@ -27,6 +29,7 @@ #include "core/data_type/define_primitive_type.h" #include "core/data_type/primitive_type.h" #include "core/data_type_serde/data_type_serde.h" +#include "core/data_type_serde/decoded_column_view.h" #include "core/packed_int128.h" #include "core/types.h" #include "core/value/timestamptz_value.h" @@ -43,6 +46,65 @@ #include "util/unaligned.h" namespace doris { +namespace { + +template +const NativeType* decoded_values_as(const DecodedColumnView& view) { + return reinterpret_cast(view.values); +} + +template +bool decoded_number_value_fits(SourceType value) { + if constexpr (std::is_floating_point_v) { + return true; + } else if constexpr (std::is_same_v) { + return value == SourceType(0) || value == SourceType(1); + } else if constexpr (std::is_signed_v) { + const auto int128_value = static_cast(value); + return int128_value >= static_cast(std::numeric_limits::lowest()) && + int128_value <= static_cast(std::numeric_limits::max()); + } else { + const auto uint128_value = static_cast(value); + if constexpr (std::is_signed_v) { + return uint128_value <= + static_cast(std::numeric_limits::max()); + } else { + return uint128_value <= + static_cast(std::numeric_limits::max()); + } + } +} + +template +Status read_number_decoded_values(IColumn& column, const DecodedColumnView& view) { + if (view.values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded value buffer is null for {}", column.get_name()); + } + auto& data = + assert_cast::ColumnType&>(column).get_data(); + const auto old_size = data.size(); + const auto* values = decoded_values_as(view); + for (int64_t row = 0; row < view.row_count; ++row) { + using DorisCppType = typename PrimitiveTypeTraits::CppType; + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(DorisCppType()); + continue; + } + if (!decoded_number_value_fits(values[row])) { + if (decoded_column_view_can_null_on_conversion_failure(view)) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + continue; + } + data.resize(old_size); + return Status::DataQualityError("Decoded value is out of range for {} at row {}", + column.get_name(), row); + } + data.push_back(static_cast(values[row])); + } + return Status::OK(); +} + +} // namespace // Type map的基本结构 template struct TypeMap { @@ -157,6 +219,42 @@ Status DataTypeNumberSerDe::write_column_to_arrow(const IColumn& column, cons return Status::OK(); } +template +Status DataTypeNumberSerDe::read_column_from_decoded_values( + IColumn& column, const DecodedColumnView& view) const { + if constexpr (T == TYPE_BOOLEAN) { + if (view.value_kind == DecodedValueKind::BOOL) { + return read_number_decoded_values(column, view); + } + } else if constexpr (T == TYPE_TINYINT || T == TYPE_SMALLINT || T == TYPE_INT || + T == TYPE_BIGINT || T == TYPE_LARGEINT) { + if (view.value_kind == DecodedValueKind::INT32) { + return read_number_decoded_values(column, view); + } + if (view.value_kind == DecodedValueKind::UINT32) { + return read_number_decoded_values(column, view); + } + if (view.value_kind == DecodedValueKind::INT64) { + return read_number_decoded_values(column, view); + } + if (view.value_kind == DecodedValueKind::UINT64) { + return read_number_decoded_values(column, view); + } + } else if constexpr (T == TYPE_FLOAT) { + if (view.value_kind == DecodedValueKind::FLOAT) { + return read_number_decoded_values(column, view); + } + } else if constexpr (T == TYPE_DOUBLE) { + if (view.value_kind == DecodedValueKind::DOUBLE) { + return read_number_decoded_values(column, view); + } + } + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("Unsupported decoded values for {} from source kind {}", + get_name(), static_cast(view.value_kind))); +} + template Status DataTypeNumberSerDe::deserialize_one_cell_from_json(IColumn& column, Slice& slice, const FormatOptions& options) const { diff --git a/be/src/core/data_type_serde/data_type_number_serde.h b/be/src/core/data_type_serde/data_type_number_serde.h index b57f9f9d21298d..0e0a3acfc1aed7 100644 --- a/be/src/core/data_type_serde/data_type_number_serde.h +++ b/be/src/core/data_type_serde/data_type_number_serde.h @@ -117,6 +117,9 @@ class DataTypeNumberSerDe : public DataTypeSerDe { Status read_column_from_arrow(IColumn& column, const arrow::Array* arrow_array, int64_t start, int64_t end, const cctz::time_zone& ctz) const override; + Status read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const override; + Status write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& row_buffer, int64_t row_idx, bool col_const, const FormatOptions& options) const override; diff --git a/be/src/core/data_type_serde/data_type_serde.cpp b/be/src/core/data_type_serde/data_type_serde.cpp index ac688ae6c307a3..728cafab3469fd 100644 --- a/be/src/core/data_type_serde/data_type_serde.cpp +++ b/be/src/core/data_type_serde/data_type_serde.cpp @@ -34,6 +34,54 @@ namespace doris { DataTypeSerDe::~DataTypeSerDe() = default; +bool decoded_column_view_can_null_on_conversion_failure(const DecodedColumnView& view) { + return !view.enable_strict_mode && view.conversion_failure_null_map != nullptr; +} + +void decoded_column_view_insert_null_on_conversion_failure(IColumn& column, + const DecodedColumnView& view, + int64_t row) { + DORIS_CHECK(decoded_column_view_can_null_on_conversion_failure(view)); + DORIS_CHECK(row >= 0); + DORIS_CHECK(row < view.row_count); + DORIS_CHECK(view.conversion_failure_null_map_offset >= 0); + const auto null_map_row = view.conversion_failure_null_map_offset + row; + DORIS_CHECK(null_map_row >= 0); + DORIS_CHECK(static_cast(null_map_row) < view.conversion_failure_null_map->size()); + column.insert_default(); + (*view.conversion_failure_null_map)[null_map_row] = 1; +} + +Status decoded_column_view_handle_conversion_failure(IColumn& column, const DecodedColumnView& view, + const Status& status) { + if (!decoded_column_view_can_null_on_conversion_failure(view)) { + return status; + } + for (int64_t row = 0; row < view.row_count; ++row) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + } + return Status::OK(); +} + +Status DataTypeSerDe::read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const { + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("read_column_from_decoded_values is not supported for {}", + get_name())); +} + +Status DataTypeSerDe::read_field_from_decoded_value(const IDataType& data_type, Field* field, + const DecodedColumnView& view) const { + DORIS_CHECK(field != nullptr); + DORIS_CHECK(view.row_count == 1); + auto column = data_type.create_column(); + RETURN_IF_ERROR(read_column_from_decoded_values(*column, view)); + DORIS_CHECK(column->size() == 1); + column->get(0, *field); + return Status::OK(); +} + DataTypeSerDeSPtrs create_data_type_serdes(const DataTypes& types) { DataTypeSerDeSPtrs serdes; serdes.reserve(types.size()); diff --git a/be/src/core/data_type_serde/data_type_serde.h b/be/src/core/data_type_serde/data_type_serde.h index 7c007c6558ddf3..bc542455b2fbc4 100644 --- a/be/src/core/data_type_serde/data_type_serde.h +++ b/be/src/core/data_type_serde/data_type_serde.h @@ -27,6 +27,7 @@ #include "common/cast_set.h" #include "common/status.h" #include "core/column/column_nullable.h" +#include "core/data_type_serde/decoded_column_view.h" #include "core/field.h" #include "core/string_buffer.hpp" #include "core/types.h" @@ -485,6 +486,14 @@ class DataTypeSerDe { int64_t start, int64_t end, const cctz::time_zone& ctz) const = 0; + // Read already decoded column values into a Doris column. The input view is format-neutral: + // file readers translate their decoder output into DecodedColumnView, while SerDe owns + // the Doris-type-specific materialization into IColumn. + virtual Status read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const; + virtual Status read_field_from_decoded_value(const IDataType& data_type, Field* field, + const DecodedColumnView& view) const; + // ORC serializer virtual Status write_column_to_orc(const std::string& timezone, const IColumn& column, const NullMap* null_map, diff --git a/be/src/core/data_type_serde/data_type_string_serde.cpp b/be/src/core/data_type_serde/data_type_string_serde.cpp index 08d87cc38cac92..3edd443cc73168 100644 --- a/be/src/core/data_type_serde/data_type_string_serde.cpp +++ b/be/src/core/data_type_serde/data_type_string_serde.cpp @@ -19,11 +19,40 @@ #include "core/column/column_string.h" #include "core/data_type/define_primitive_type.h" +#include "core/data_type_serde/decoded_column_view.h" #include "util/jsonb_document_cast.h" #include "util/jsonb_utils.h" #include "util/jsonb_writer.h" namespace doris { +namespace { + +template +Status read_string_decoded_values(IColumn& column, const DecodedColumnView& view) { + if (view.binary_values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded binary values are null for {}", column.get_name()); + } + auto& string_column = assert_cast(column); + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + string_column.insert_default(); + continue; + } + const auto& value = (*view.binary_values)[row]; + if (value.data == nullptr && value.size > 0) { + if (decoded_column_view_can_null_on_conversion_failure(view)) { + decoded_column_view_insert_null_on_conversion_failure(column, view, row); + continue; + } + return Status::Corruption("Decoded string binary value is null for {} at row {}", + column.get_name(), row); + } + string_column.insert_data(value.data, value.size); + } + return Status::OK(); +} + +} // namespace template Status DataTypeStringSerDeBase::serialize_column_to_json(const IColumn& column, @@ -313,6 +342,19 @@ Status DataTypeStringSerDeBase::read_column_from_arrow( return Status::OK(); } +template +Status DataTypeStringSerDeBase::read_column_from_decoded_values( + IColumn& column, const DecodedColumnView& view) const { + if (view.value_kind != DecodedValueKind::BINARY && + view.value_kind != DecodedValueKind::FIXED_BINARY) { + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("Unsupported decoded values for {} from source kind {}", + get_name(), static_cast(view.value_kind))); + } + return read_string_decoded_values(column, view); +} + template Status DataTypeStringSerDeBase::write_column_to_orc( const std::string& timezone, const IColumn& column, const NullMap* null_map, diff --git a/be/src/core/data_type_serde/data_type_string_serde.h b/be/src/core/data_type_serde/data_type_string_serde.h index 79c8450835d39c..81b80eab4a5cbf 100644 --- a/be/src/core/data_type_serde/data_type_string_serde.h +++ b/be/src/core/data_type_serde/data_type_string_serde.h @@ -203,6 +203,9 @@ class DataTypeStringSerDeBase : public DataTypeSerDe { Status read_column_from_arrow(IColumn& column, const arrow::Array* arrow_array, int64_t start, int64_t end, const cctz::time_zone& ctz) const override; + Status read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const override; + Status write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& result, int64_t row_idx, bool col_const, const FormatOptions& options) const override { diff --git a/be/src/core/data_type_serde/data_type_time_serde.cpp b/be/src/core/data_type_serde/data_type_time_serde.cpp index e57fd08a271339..c40e671793c848 100644 --- a/be/src/core/data_type_serde/data_type_time_serde.cpp +++ b/be/src/core/data_type_serde/data_type_time_serde.cpp @@ -20,11 +20,38 @@ #include "core/data_type/data_type_decimal.h" #include "core/data_type/data_type_number.h" #include "core/data_type/primitive_type.h" +#include "core/data_type_serde/decoded_column_view.h" #include "core/value/time_value.h" #include "exprs/function/cast/cast_base.h" #include "exprs/function/cast/cast_to_time_impl.hpp" namespace doris { +namespace { + +TimeValue::TimeType read_time_decoded_value(const DecodedColumnView& view, int64_t row) { + int64_t micros = 0; + if (view.value_kind == DecodedValueKind::INT32) { + const auto* values = reinterpret_cast(view.values); + micros = static_cast(values[row]) * 1000; + } else { + const auto* values = reinterpret_cast(view.values); + micros = values[row]; + if (view.time_unit == DecodedTimeUnit::MILLIS) { + micros *= 1000; + } else if (view.time_unit == DecodedTimeUnit::NANOS) { + micros /= 1000; + } + } + const bool negative = micros < 0; + const int64_t abs_micros = std::abs(micros); + return TimeValue::make_time( + abs_micros / TimeValue::ONE_HOUR_MICROSECONDS, + (abs_micros % TimeValue::ONE_HOUR_MICROSECONDS) / TimeValue::ONE_MINUTE_MICROSECONDS, + (abs_micros % TimeValue::ONE_MINUTE_MICROSECONDS) / TimeValue::ONE_SECOND_MICROSECONDS, + abs_micros % TimeValue::ONE_SECOND_MICROSECONDS, negative); +} + +} // namespace Status DataTypeTimeV2SerDe::write_column_to_mysql_binary(const IColumn& column, MysqlRowBinaryBuffer& result, @@ -145,6 +172,27 @@ Status DataTypeTimeV2SerDe::from_string_strict_mode(StringRef& str, IColumn& col return Status::OK(); } +Status DataTypeTimeV2SerDe::read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const { + if (view.value_kind != DecodedValueKind::INT32 && view.value_kind != DecodedValueKind::INT64) { + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("TIMEV2 decoded reader expects INT32 or INT64 source")); + } + if (view.values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded value buffer is null for {}", column.get_name()); + } + auto& data = assert_cast(column).get_data(); + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(TimeValue::TimeType()); + continue; + } + data.push_back(read_time_decoded_value(view, row)); + } + return Status::OK(); +} + template Status DataTypeTimeV2SerDe::from_int_batch(const typename IntDataType::ColumnType& int_col, ColumnNullable& target_col) const { diff --git a/be/src/core/data_type_serde/data_type_time_serde.h b/be/src/core/data_type_serde/data_type_time_serde.h index db703616b497cf..e3fccf379c913a 100644 --- a/be/src/core/data_type_serde/data_type_time_serde.h +++ b/be/src/core/data_type_serde/data_type_time_serde.h @@ -67,6 +67,8 @@ class DataTypeTimeV2SerDe : public DataTypeNumberSerDe Status from_decimal_strict_mode_batch(const typename DecimalDataType::ColumnType& decimal_col, IColumn& target_col) const; + Status read_column_from_decoded_values(IColumn& column, + const DecodedColumnView& view) const override; int get_scale() const override { return _scale; } protected: diff --git a/be/src/core/data_type_serde/decoded_column_view.h b/be/src/core/data_type_serde/decoded_column_view.h new file mode 100644 index 00000000000000..88932bde3cfc2f --- /dev/null +++ b/be/src/core/data_type_serde/decoded_column_view.h @@ -0,0 +1,102 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/status.h" +#include "core/column/column_nullable.h" +#include "core/string_ref.h" + +namespace cctz { +class time_zone; +} // namespace cctz + +namespace doris { + +class IColumn; + +// 已解码 column batch 的物理值来源类型。 +// 该枚举只描述通用内存布局，不包含 Parquet/ORC/Arrow 等格式专有类型。 +enum class DecodedValueKind { + BOOL, + INT32, + UINT32, + INT64, + UINT64, + INT96, + FLOAT, + DOUBLE, + BINARY, + FIXED_BINARY, +}; + +enum class DecodedTimeUnit { + UNKNOWN, + MILLIS, + MICROS, + NANOS, +}; + +struct DecodedColumnView { + DecodedValueKind value_kind = DecodedValueKind::INT32; + DecodedTimeUnit time_unit = DecodedTimeUnit::UNKNOWN; + int64_t row_count = 0; + int decimal_precision = -1; + int decimal_scale = -1; + int fixed_length = -1; + bool timestamp_is_adjusted_to_utc = false; + const uint8_t* values = nullptr; + const uint8_t* null_map = nullptr; + const std::vector* binary_values = nullptr; + const cctz::time_zone* timezone = nullptr; + bool enable_strict_mode = false; + NullMap* conversion_failure_null_map = nullptr; + int64_t conversion_failure_null_map_offset = 0; +}; + +inline bool decoded_column_view_row_is_null(const DecodedColumnView& view, int64_t row) { + return view.null_map != nullptr && view.null_map[row] != 0; +} + +inline bool decoded_column_view_has_non_null_value(const DecodedColumnView& view) { + if (view.null_map == nullptr) { + return view.row_count > 0; + } + + // TODO(gabriel): optimize null map check with SIMD or bitset if needed. + for (int64_t row = 0; row < view.row_count; ++row) { + if (view.null_map[row] == 0) { + return true; + } + } + return false; +} + +bool decoded_column_view_can_null_on_conversion_failure(const DecodedColumnView& view); + +void decoded_column_view_insert_null_on_conversion_failure(IColumn& column, + const DecodedColumnView& view, + int64_t row); + +Status decoded_column_view_handle_conversion_failure(IColumn& column, const DecodedColumnView& view, + const Status& status); + +} // namespace doris diff --git a/be/src/exec/operator/file_scan_operator.cpp b/be/src/exec/operator/file_scan_operator.cpp index 2a87f413a15bd6..d4035d37e27106 100644 --- a/be/src/exec/operator/file_scan_operator.cpp +++ b/be/src/exec/operator/file_scan_operator.cpp @@ -24,6 +24,7 @@ #include "exec/operator/olap_scan_operator.h" #include "exec/operator/scan_operator.h" #include "exec/scan/file_scanner.h" +#include "exec/scan/file_scanner_v2.h" #include "exec/scan/scanner_context.h" #include "format/format_common.h" #include "storage/storage_engine.h" @@ -119,10 +120,32 @@ Status FileScanLocalState::_init_scanners(std::list* scanners) { _max_scanners); shard_num = std::max(shard_num, 1U); _kv_cache = std::make_unique(shard_num); + const TFileScanRangeParams* scan_params = nullptr; + if (state()->get_query_ctx() != nullptr && + state()->get_query_ctx()->file_scan_range_params_map.count(parent_id()) > 0) { + scan_params = &state()->get_query_ctx()->file_scan_range_params_map[parent_id()]; + } else { + scan_params = _split_source->get_params(); + } + const bool is_load = + state()->desc_tbl().get_tuple_descriptor(scan_params->src_tuple_id) != nullptr; + // TODO: Use scanner v2 for all queries. + const bool use_file_scanner_v2 = + state()->query_options().__isset.enable_file_scanner_v2 && + state()->query_options().enable_file_scanner_v2 && !is_load && + _split_source->all_scan_ranges_match(*scan_params, FileScannerV2::is_supported); + _operator_profile->add_info_string("UseScannerV2", use_file_scanner_v2 ? "true" : "false"); for (int i = 0; i < _max_scanners; ++i) { - std::unique_ptr scanner = FileScanner::create_unique( - state(), this, p._limit, _split_source, _scanner_profile.get(), _kv_cache.get(), - &p._colname_to_slot_id); + ScannerSPtr scanner; + if (use_file_scanner_v2) { + scanner = FileScannerV2::create_shared(state(), this, p._limit, _split_source, + _scanner_profile.get(), _kv_cache.get(), + &p._colname_to_slot_id); + } else { + scanner = FileScanner::create_shared(state(), this, p._limit, _split_source, + _scanner_profile.get(), _kv_cache.get(), + &p._colname_to_slot_id); + } RETURN_IF_ERROR(scanner->init(state(), _conjuncts)); scanners->push_back(std::move(scanner)); } diff --git a/be/src/exec/operator/file_scan_operator.h b/be/src/exec/operator/file_scan_operator.h index d4e31195a4459a..c47488fa357c77 100644 --- a/be/src/exec/operator/file_scan_operator.h +++ b/be/src/exec/operator/file_scan_operator.h @@ -29,6 +29,7 @@ namespace doris { class FileScanner; +class FileScannerV2; } // namespace doris namespace doris { @@ -56,6 +57,7 @@ class FileScanLocalState final : public ScanLocalState { private: friend class FileScanner; + friend class FileScannerV2; PushDownType _should_push_down_bloom_filter() const override { return PushDownType::UNACCEPTABLE; } diff --git a/be/src/exec/scan/file_scanner.cpp b/be/src/exec/scan/file_scanner.cpp index d5c18a38b851c0..8f27a3d330ff68 100644 --- a/be/src/exec/scan/file_scanner.cpp +++ b/be/src/exec/scan/file_scanner.cpp @@ -1771,7 +1771,6 @@ Status FileScanner::_init_expr_ctxes() { if (is_file_slot) { _is_file_slot.emplace(slot_id); _file_slot_descs.emplace_back(it->second); - _file_col_names.push_back(it->second->col_name()); } _column_descs.push_back(col_desc); diff --git a/be/src/exec/scan/file_scanner.h b/be/src/exec/scan/file_scanner.h index 2113cdaf475ff1..cfa84cd68ab04b 100644 --- a/be/src/exec/scan/file_scanner.h +++ b/be/src/exec/scan/file_scanner.h @@ -133,8 +133,6 @@ class FileScanner : public Scanner { bool _cur_reader_eof = false; // File source slot descriptors std::vector _file_slot_descs; - // col names from _file_slot_descs - std::vector _file_col_names; // Unified column descriptors for init_reader (includes file, partition, missing, synthesized cols) std::vector _column_descs; @@ -147,6 +145,7 @@ class FileScanner : public Scanner { // dest slot name to index in _dest_vexpr_ctx; std::unordered_map _dest_slot_name_to_idx; // col name to default value expr + // TODO: only used by json reader. Could we delete this? std::unordered_map _col_default_value_ctx; // the map values of dest slot id to src slot desc // if there is not key of dest slot id in dest_sid_to_src_sid_without_trans, it will be set to nullptr @@ -193,7 +192,6 @@ class FileScanner : public Scanner { std::shared_ptr _io_ctx; // Whether to fill partition columns from path, default is true. - bool _fill_partition_from_path = true; std::unordered_map> _partition_col_descs; std::unordered_map _partition_value_is_null; diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp new file mode 100644 index 00000000000000..6a5e093baeb863 --- /dev/null +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -0,0 +1,883 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "exec/scan/file_scanner_v2.h" + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "common/cast_set.h" +#include "common/config.h" +#include "common/consts.h" +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/block/column_with_type_and_name.h" +#include "core/column/column.h" +#include "core/data_type/data_type.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" +#include "core/data_type_serde/data_type_serde.h" +#include "core/string_ref.h" +#include "exec/common/util.hpp" +#include "exec/operator/scan_operator.h" +#include "exprs/vexpr.h" +#include "exprs/vexpr_context.h" +#include "exprs/vslot_ref.h" +#include "format/format_common.h" +#include "format_v2/column_mapper.h" +#include "format_v2/jni/jdbc_reader.h" +#include "format_v2/table/hive_reader.h" +#include "format_v2/table/iceberg_reader.h" +#include "format_v2/table/paimon_reader.h" +#include "format_v2/table_reader.h" +#include "io/fs/file_meta_cache.h" +#include "io/io_common.h" +#include "runtime/descriptors.h" +#include "runtime/exec_env.h" +#include "runtime/runtime_state.h" +#include "service/backend_options.h" +#include "storage/id_manager.h" + +namespace doris { +namespace { + +std::string table_format_name(const TFileRangeDesc& range) { + return range.__isset.table_format_params ? range.table_format_params.table_format_type + : "NotSet"; +} + +TFileFormatType::type get_range_format_type(const TFileScanRangeParams& params, + const TFileRangeDesc& range) { + return range.__isset.format_type ? range.format_type : params.format_type; +} + +bool is_supported_table_format(const TFileRangeDesc& range) { + const auto table_format = table_format_name(range); + return table_format == "NotSet" || table_format == "tvf" || table_format == "hive" || + table_format == "iceberg" || table_format == "paimon"; +} + +bool is_supported_jdbc_table_format(const TFileRangeDesc& range) { + return table_format_name(range) == "jdbc"; +} + +bool is_partition_slot(const TFileScanSlotInfo& slot_info, const std::string& column_name) { + if (column_name.starts_with(BeConsts::GLOBAL_ROWID_COL) || + column_name == BeConsts::ICEBERG_ROWID_COL) { + return false; + } + return slot_info.__isset.category ? slot_info.category == TColumnCategory::PARTITION_KEY + : !slot_info.is_file_slot; +} + +bool is_scanner_materialized_virtual_column(const std::string& column_name) { + return column_name == BeConsts::ICEBERG_ROWID_COL; +} + +bool parse_non_negative_int(std::string_view value, int32_t* result) { + DORIS_CHECK(result != nullptr); + int32_t parsed = -1; + const auto* begin = value.data(); + const auto* end = begin + value.size(); + const auto [ptr, ec] = std::from_chars(begin, end, parsed); + if (ec != std::errc() || ptr != end || parsed < 0) { + return false; + } + *result = parsed; + return true; +} + +std::string access_path_to_string(const std::vector& path) { + return fmt::format("{}", fmt::join(path, ".")); +} + +format::ColumnDefinition* find_or_add_child(format::ColumnDefinition* parent, int32_t id, + std::string name, DataTypePtr type) { + DORIS_CHECK(parent != nullptr); + for (auto& child : parent->children) { + if ((child.has_identifier_field_id() && child.get_identifier_field_id() == id) || + child.name == name) { + return &child; + } + } + parent->children.push_back({ + .identifier = Field::create_field(id), + .name = std::move(name), + .type = std::move(type), + .children = {}, + .default_expr = nullptr, + .is_partition_key = false, + }); + return &parent->children.back(); +} + +const format::ColumnDefinition* find_schema_child_by_path( + const format::ColumnDefinition* schema_column, const std::string& child_path) { + if (schema_column == nullptr) { + return nullptr; + } + int32_t parsed_field_id = -1; + if (parse_non_negative_int(child_path, &parsed_field_id)) { + const auto child_it = std::ranges::find_if( + schema_column->children, [&](const format::ColumnDefinition& child) { + return child.has_identifier_field_id() && + child.get_identifier_field_id() == parsed_field_id; + }); + return child_it == schema_column->children.end() ? nullptr : &*child_it; + } + const auto child_it = std::ranges::find_if(schema_column->children, [&](const auto& child) { + if (to_lower(child.name) == to_lower(child_path)) { + return true; + } + return std::ranges::any_of(child.name_mapping, [&](const std::string& alias) { + return to_lower(alias) == to_lower(child_path); + }); + }); + return child_it == schema_column->children.end() ? nullptr : &*child_it; +} + +int32_t schema_field_id(const format::ColumnDefinition* schema_column) { + if (schema_column == nullptr || !schema_column->has_identifier_field_id()) { + return -1; + } + return schema_column->get_identifier_field_id(); +} + +struct AccessPathNode { + bool project_all = false; + std::map children; +}; + +void merge_access_path_node(AccessPathNode* dst, const AccessPathNode& src) { + DORIS_CHECK(dst != nullptr); + if (dst->project_all) { + return; + } + if (src.project_all) { + dst->project_all = true; + dst->children.clear(); + return; + } + for (const auto& [path, child] : src.children) { + merge_access_path_node(&dst->children[path], child); + } +} + +void insert_access_path(AccessPathNode* root, const std::vector& path, + size_t path_idx) { + DORIS_CHECK(root != nullptr); + if (root->project_all) { + return; + } + if (path_idx >= path.size()) { + root->project_all = true; + root->children.clear(); + return; + } + insert_access_path(&root->children[path[path_idx]], path, path_idx + 1); +} + +Status build_nested_children_from_access_node(format::ColumnDefinition* column, + const DataTypePtr& type, const AccessPathNode& node, + const std::string& path, + const format::ColumnDefinition* schema_column); + +Status build_struct_children_from_access_node(format::ColumnDefinition* column, + const DataTypeStruct& struct_type, + const AccessPathNode& node, const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + for (const auto& [child_path, child_node] : node.children) { + // Currently we do not support accessing struct children by position (e.g. "col.0") because it can be ambiguous and error-prone when the struct schema evolves. We only support accessing struct children by name (e.g. "col.child"). If needed, we can consider adding support for position-based access in the future with careful design and consideration. + if (child_path == "OFFSET" || child_path == "*" || child_path == "KEYS" || + child_path == "VALUES") { + return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + + // Try to find the child field in the schema column first. If not found, fallback to find the child field in the struct type by name (case-insensitive). + const auto* schema_child = find_schema_child_by_path(schema_column, child_path); + int32_t field_id = schema_field_id(schema_child); + std::string field_name = schema_child == nullptr ? child_path : schema_child->name; + DataTypePtr field_type = schema_child == nullptr ? nullptr : schema_child->type; + if (field_id < 0 || field_type == nullptr) { + for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { + if (to_lower(struct_type.get_element_name(field_idx)) == to_lower(field_name)) { + field_id = cast_set(field_idx); + field_name = struct_type.get_element_name(field_idx); + field_type = struct_type.get_element(field_idx); + break; + } + } + } + + if (field_id < 0 || field_type == nullptr) { + return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + // TODO: For TVF Parquet files without field ids, this fallback uses the struct ordinal as + // the table child identifier. BY_NAME mapping should instead keep a string identifier and + // let TableColumnMapper resolve the file-local child id from the Parquet schema. + auto* child = find_or_add_child(column, field_id, field_name, field_type); + RETURN_IF_ERROR(build_nested_children_from_access_node( + child, child->type, child_node, path + "." + child_path, schema_child)); + } + return Status::OK(); +} + +Status build_map_children_from_access_node(format::ColumnDefinition* column, + const DataTypeMap& map_type, const AccessPathNode& node, + const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + AccessPathNode key_node; + AccessPathNode value_node; + bool need_key = false; + bool need_value = false; + + for (const auto& [child_path, child_node] : node.children) { + if (child_path == "OFFSET") { + return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + if (child_path == "KEYS") { + need_key = true; + merge_access_path_node(&key_node, child_node); + continue; + } + if (child_path == "VALUES") { + need_key = true; + key_node.project_all = true; + key_node.children.clear(); + need_value = true; + merge_access_path_node(&value_node, child_node); + continue; + } + if (child_path == "*") { + need_key = true; + key_node.project_all = true; + key_node.children.clear(); + need_value = true; + merge_access_path_node(&value_node, child_node); + continue; + } + return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + if (need_key && !need_value) { + // Keep value readable until the downstream map materialization path can construct a table + // Map column from keys only. + need_value = true; + value_node.project_all = true; + value_node.children.clear(); + } + + DataTypes entry_child_types; + Strings entry_child_names; + if (need_key) { + entry_child_types.push_back(map_type.get_key_type()); + entry_child_names.push_back("key"); + } + if (need_value) { + entry_child_types.push_back(map_type.get_value_type()); + entry_child_names.push_back("value"); + } + if (entry_child_types.empty()) { + return Status::OK(); + } + + auto entry_type = std::make_shared(entry_child_types, entry_child_names); + auto* entry_child = find_or_add_child(column, 0, "entries", entry_type); + const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + const auto* value_schema = schema_column != nullptr && schema_column->children.size() > 1 + ? &schema_column->children[1] + : nullptr; + if (need_key) { + auto* key_child = find_or_add_child(entry_child, 0, "key", map_type.get_key_type()); + RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, key_node, + path + ".KEYS", key_schema)); + } + if (need_value) { + auto* value_child = find_or_add_child(entry_child, 1, "value", map_type.get_value_type()); + RETURN_IF_ERROR(build_nested_children_from_access_node( + value_child, value_child->type, value_node, path + ".VALUES", value_schema)); + } + return Status::OK(); +} + +Status build_nested_children_from_access_node(format::ColumnDefinition* column, + const DataTypePtr& type, const AccessPathNode& node, + const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + if (node.project_all || node.children.empty()) { + // If project_all is true or there is no specific child path, we need to project all children of the complex type. + return Status::OK(); + } + + const auto nested_type = remove_nullable(type); + switch (nested_type->get_primitive_type()) { + case TYPE_STRUCT: + return build_struct_children_from_access_node( + column, assert_cast(*nested_type), node, path, + schema_column); + case TYPE_ARRAY: { + if (node.children.size() != 1 || !node.children.contains("*")) { + return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", + path, column->name); + } + const auto& array_type = assert_cast(*nested_type); + auto* child = find_or_add_child(column, 0, "element", array_type.get_nested_type()); + const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + return build_nested_children_from_access_node(child, child->type, node.children.at("*"), + path + ".*", element_schema); + } + case TYPE_MAP: + return build_map_children_from_access_node( + column, assert_cast(*nested_type), node, path, schema_column); + default: + return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", + path, column->name); + } +} + +Status build_nested_children_from_access_paths(format::ColumnDefinition* column, + const TColumnAccessPaths& access_paths, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + if (is_scanner_materialized_virtual_column(column->name)) { + return Status::OK(); + } + if (!is_complex_type(remove_nullable(column->type)->get_primitive_type())) { + return Status::OK(); + } + + AccessPathNode root; + // Build tree for AccessPathNode. + // For example, for access paths ["a.b", "a.c", "d"], the tree will be: + // root + // ├── a + // │ ├── b + // │ └── c + // └── d + for (const auto& access_path : access_paths) { + // TODO: Support META access paths if needed. Currently FileScannerV2 only supports DATA access paths. + if (access_path.type != TAccessPathType::DATA || !access_path.__isset.data_access_path) { + return Status::NotSupported("FileScannerV2 only supports DATA access paths for slot {}", + column->name); + } + const auto& path = access_path.data_access_path.path; + if (path.empty()) { + insert_access_path(&root, path, 0); + continue; + } + int32_t top_level_id = -1; + if (to_lower(path.front()) != to_lower(column->name) && + (!parse_non_negative_int(path.front(), &top_level_id) || + !column->has_identifier_field_id() || + top_level_id != column->get_identifier_field_id())) { + return Status::NotSupported("FileScannerV2 access path {} does not match slot {}", + access_path_to_string(path), column->name); + } + insert_access_path(&root, path, 1); + } + // Recursively build nested children for the column based on the AccessPathNode tree. + return build_nested_children_from_access_node(column, column->type, root, column->name, + schema_column); +} + +Status build_nested_children_from_access_paths(format::ColumnDefinition* column, + const SlotDescriptor* slot_desc, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + DORIS_CHECK(slot_desc != nullptr); + return build_nested_children_from_access_paths(column, slot_desc->all_access_paths(), + schema_column); +} + +Status rewrite_slot_refs_to_global_index( + VExprSPtr* expr, + const std::unordered_map& slot_id_to_global_index) { + DORIS_CHECK(expr != nullptr); + if (*expr == nullptr) { + return Status::OK(); + } + if ((*expr)->is_slot_ref()) { + const auto* slot_ref = assert_cast(expr->get()); + const auto global_index_it = slot_id_to_global_index.find(slot_ref->slot_id()); + if (global_index_it == slot_id_to_global_index.end()) { + DORIS_CHECK(slot_ref->slot_id() >= 0); + const auto global_index = format::GlobalIndex(cast_set(slot_ref->slot_id())); + *expr = VSlotRef::create_shared(cast_set(global_index.value()), + cast_set(global_index.value()), -1, + slot_ref->data_type(), slot_ref->column_name()); + RETURN_IF_ERROR(expr->get()->prepare(nullptr, RowDescriptor(), nullptr)); + return Status::OK(); + } + const auto global_index = global_index_it->second; + *expr = VSlotRef::create_shared(cast_set(global_index.value()), + cast_set(global_index.value()), -1, + slot_ref->data_type(), slot_ref->column_name()); + RETURN_IF_ERROR(expr->get()->prepare(nullptr, RowDescriptor(), nullptr)); + return Status::OK(); + } + auto children = (*expr)->children(); + for (auto& child : children) { + if (child == nullptr) { + continue; + } + RETURN_IF_ERROR(rewrite_slot_refs_to_global_index(&child, slot_id_to_global_index)); + } + (*expr)->set_children(std::move(children)); + return Status::OK(); +} + +} // namespace + +#ifdef BE_TEST +Status FileScannerV2::TEST_build_nested_children_from_access_paths( + format::ColumnDefinition* column, const TColumnAccessPaths& access_paths) { + return build_nested_children_from_access_paths(column, access_paths, nullptr); +} + +Status FileScannerV2::TEST_build_nested_children_from_access_paths( + format::ColumnDefinition* column, const TColumnAccessPaths& access_paths, + const format::ColumnDefinition* schema_column) { + return build_nested_children_from_access_paths(column, access_paths, schema_column); +} +#endif + +bool FileScannerV2::is_supported(const TFileScanRangeParams& params, const TFileRangeDesc& range) { + const auto format_type = get_range_format_type(params, range); + if (format_type == TFileFormatType::FORMAT_PARQUET) { + return is_supported_table_format(range); + } else if (format_type == TFileFormatType::FORMAT_JNI) { + return is_supported_jdbc_table_format(range); + } else { + LOG(WARNING) << "Unsupported file format type " << format_type << " for file scanner v2"; + return false; + } +} + +FileScannerV2::FileScannerV2(RuntimeState* state, FileScanLocalState* local_state, int64_t limit, + std::shared_ptr split_source, + RuntimeProfile* profile, ShardedKVCache* kv_cache, + const std::unordered_map* colname_to_slot_id) + : Scanner(state, local_state, limit, profile), + _split_source(std::move(split_source)), + _kv_cache(kv_cache) { + (void)colname_to_slot_id; + if (state->get_query_ctx() != nullptr && + state->get_query_ctx()->file_scan_range_params_map.count(local_state->parent_id()) > 0) { + _params = &(state->get_query_ctx()->file_scan_range_params_map[local_state->parent_id()]); + } else { + _params = _split_source->get_params(); + } +} + +Status FileScannerV2::init(RuntimeState* state, const VExprContextSPtrs& conjuncts) { + RETURN_IF_ERROR(Scanner::init(state, conjuncts)); + _get_block_timer = + ADD_TIMER_WITH_LEVEL(_local_state->scanner_profile(), "FileScannerV2GetBlockTime", 1); + _file_counter = + ADD_COUNTER_WITH_LEVEL(_local_state->scanner_profile(), "FileNumber", TUnit::UNIT, 1); + _file_read_bytes_counter = ADD_COUNTER_WITH_LEVEL(_local_state->scanner_profile(), + "FileReadBytes", TUnit::BYTES, 1); + _file_read_calls_counter = ADD_COUNTER_WITH_LEVEL(_local_state->scanner_profile(), + "FileReadCalls", TUnit::UNIT, 1); + _file_read_time_counter = + ADD_TIMER_WITH_LEVEL(_local_state->scanner_profile(), "FileReadTime", 1); + _file_cache_statistics = std::make_unique(); + _file_reader_stats = std::make_unique(); + RETURN_IF_ERROR(_init_io_ctx()); + _io_ctx->file_cache_stats = _file_cache_statistics.get(); + _io_ctx->file_reader_stats = _file_reader_stats.get(); + _io_ctx->is_disposable = _state->query_options().disable_file_cache; + return Status::OK(); +} + +Status FileScannerV2::_open_impl(RuntimeState* state) { + RETURN_IF_CANCELLED(state); + RETURN_IF_ERROR(Scanner::_open_impl(state)); + RETURN_IF_ERROR(_split_source->get_next(&_first_scan_range, &_current_range)); + if (_first_scan_range) { + RETURN_IF_ERROR(_create_table_reader_for_format(_current_range, &_table_reader)); + DORIS_CHECK(_table_reader != nullptr); + RETURN_IF_ERROR(_init_expr_ctxes()); + RETURN_IF_ERROR(_init_table_reader(_current_range)); + } + return Status::OK(); +} + +Status FileScannerV2::_get_block_impl(RuntimeState* state, Block* block, bool* eof) { + while (true) { + RETURN_IF_CANCELLED(state); + if (!_has_prepared_split) { + RETURN_IF_ERROR(_prepare_next_split(eof)); + if (*eof) { + return Status::OK(); + } + } + + { + SCOPED_TIMER(_get_block_timer); + RETURN_IF_ERROR(_table_reader->get_block(block, eof)); + } + if (*eof) { + _state->update_num_finished_scan_range(1); + _has_prepared_split = false; + *eof = false; + continue; + } + return Status::OK(); + } +} + +Status FileScannerV2::_prepare_next_split(bool* eos) { + bool has_next = _first_scan_range; + if (!_first_scan_range) { + RETURN_IF_ERROR(_split_source->get_next(&has_next, &_current_range)); + } + _first_scan_range = false; + if (!has_next || _should_stop) { + *eos = true; + return Status::OK(); + } + DORIS_CHECK(_table_reader != nullptr); + _current_range_path = _current_range.path; + RETURN_IF_ERROR(_prepare_table_reader_split(_current_range)); + COUNTER_UPDATE(_file_counter, 1); + _has_prepared_split = true; + *eos = false; + return Status::OK(); +} + +Status FileScannerV2::_init_table_reader(const TFileRangeDesc& range) { + const auto format_type = get_range_format_type(*_params, range); + format::FileFormat file_format; + RETURN_IF_ERROR(_to_file_format(format_type, &file_format)); + DORIS_CHECK(_table_reader != nullptr); + + format::TableColumnPredicates table_column_predicates; + RETURN_IF_ERROR(_build_table_column_predicates(&table_column_predicates)); + VExprContextSPtrs table_conjuncts; + RETURN_IF_ERROR(_build_table_conjuncts(&table_conjuncts)); + RETURN_IF_ERROR(_table_reader->init({ + .projected_columns = _projected_columns, + .column_predicates = std::move(table_column_predicates), + .conjuncts = std::move(table_conjuncts), + .format = file_format, + .scan_params = const_cast(_params), + .io_ctx = _io_ctx, + .runtime_state = _state, + .scanner_profile = _local_state->scanner_profile(), + .allow_missing_columns = false, // TODO + .push_down_agg_type = _local_state->get_push_down_agg_type(), + })); + return Status::OK(); +} + +Status FileScannerV2::_create_table_reader_for_format( + const TFileRangeDesc& range, std::unique_ptr* reader) const { + DORIS_CHECK(reader != nullptr); + const auto table_format = table_format_name(range); + if (table_format == "NotSet" || table_format == "tvf") { + *reader = std::make_unique(); + } else if (table_format == "hive") { + *reader = hive::HiveReader::create_unique(); + } else if (table_format == "iceberg") { + *reader = std::make_unique(); + } else if (table_format == "paimon") { + *reader = paimon::PaimonReader::create_unique(); + } else if (table_format == "jdbc") { + *reader = std::make_unique(); + } else { + return Status::NotSupported("FileScannerV2 does not support table format {}", table_format); + } + return Status::OK(); +} + +Status FileScannerV2::_prepare_table_reader_split(const TFileRangeDesc& range) { + std::map partition_values; + RETURN_IF_ERROR(_generate_partition_values(range, &partition_values)); + RETURN_IF_ERROR(_table_reader->prepare_split({ + .partition_values = std::move(partition_values), + .cache = _kv_cache, + .current_range = range, + .global_rowid_context = _create_global_rowid_context(range), + })); + return Status::OK(); +} + +bool FileScannerV2::_should_enable_file_meta_cache() const { + return ExecEnv::GetInstance()->file_meta_cache()->enabled() && + _split_source->num_scan_ranges() < config::max_external_file_meta_cache_num / 3; +} + +std::optional FileScannerV2::_create_global_rowid_context( + const TFileRangeDesc& range) const { + if (!_need_global_rowid_column) { + return std::nullopt; + } + auto& id_file_map = _state->get_id_file_map(); + DORIS_CHECK(id_file_map != nullptr); + const auto file_id = id_file_map->get_file_mapping_id( + std::make_shared(_local_state->cast().parent_id(), + range, _should_enable_file_meta_cache())); + return format::GlobalRowIdContext { + .version = IdManager::ID_VERSION, + .backend_id = BackendOptions::get_backend_id(), + .file_id = file_id, + }; +} + +Status FileScannerV2::_generate_partition_values( + const TFileRangeDesc& range, std::map* partition_values) const { + DORIS_CHECK(partition_values != nullptr); + partition_values->clear(); + if (!range.__isset.columns_from_path_keys || !range.__isset.columns_from_path) { + return Status::OK(); + } + DORIS_CHECK(range.columns_from_path_keys.size() == range.columns_from_path.size()); + for (size_t idx = 0; idx < range.columns_from_path_keys.size(); ++idx) { + const auto& key = range.columns_from_path_keys[idx]; + const auto it = _partition_slot_descs.find(key); + if (it == _partition_slot_descs.end()) { + continue; + } + const auto& value = range.columns_from_path[idx]; + const bool is_null = range.__isset.columns_from_path_is_null && + idx < range.columns_from_path_is_null.size() && + range.columns_from_path_is_null[idx]; + Field field; + DORIS_CHECK(it->second.slot_desc != nullptr); + RETURN_IF_ERROR(_parse_partition_value(it->second.slot_desc, value, is_null, &field)); + partition_values->emplace(it->second.canonical_name, std::move(field)); + } + return Status::OK(); +} + +Status FileScannerV2::_parse_partition_value(const SlotDescriptor* slot_desc, + const std::string& value, bool is_null, + Field* field) const { + DORIS_CHECK(slot_desc != nullptr); + DORIS_CHECK(field != nullptr); + if (is_null) { + *field = Field::create_field(Null()); + return Status::OK(); + } + const auto data_type = remove_nullable(slot_desc->get_data_type_ptr()); + auto column = data_type->create_column(); + auto serde = data_type->get_serde(); + DataTypeSerDe::FormatOptions options; + options.converted_from_string = true; + StringRef ref(value.data(), value.size()); + RETURN_IF_ERROR(serde->from_string(ref, *column, options)); + DORIS_CHECK(column->size() == 1); + *field = (*column)[0]; + return Status::OK(); +} + +Status FileScannerV2::_init_expr_ctxes() { + _slot_id_to_desc.clear(); + _slot_id_to_global_index.clear(); + _partition_slot_descs.clear(); + for (const auto* slot_desc : _output_tuple_desc->slots()) { + _slot_id_to_desc.emplace(slot_desc->id(), slot_desc); + } + DORIS_CHECK(_table_reader != nullptr); + RETURN_IF_ERROR(_build_projected_columns(*_table_reader)); + return Status::OK(); +} + +Status FileScannerV2::_build_projected_columns(const format::TableReader& table_reader) { + _projected_columns.clear(); + _projected_columns.reserve(_params->required_slots.size()); + _need_global_rowid_column = false; + format::ProjectedColumnBuildContext build_context { + .scan_params = _params, + .range = &_current_range, + .runtime_state = _state, + }; + + for (size_t slot_idx = 0; slot_idx < _params->required_slots.size(); ++slot_idx) { + const auto& slot_info = _params->required_slots[slot_idx]; + const auto it = _slot_id_to_desc.find(slot_info.slot_id); + if (it == _slot_id_to_desc.end()) { + return Status::InternalError("Unknown source slot descriptor, slot_id={}", + slot_info.slot_id); + } + auto column = _build_table_column(it->second); + if (column.name.starts_with(BeConsts::GLOBAL_ROWID_COL)) { + _need_global_rowid_column = true; + } + RETURN_IF_ERROR(_build_default_expr(slot_info, &column.default_expr)); + build_context.schema_column.reset(); + RETURN_IF_ERROR(table_reader.annotate_projected_column(slot_info, &build_context, &column)); + // Build the column's nested children based on the column's access paths and the schema column (if exists). + // The access paths are generated from the slot's access path expressions which means a projected column can have a subset of the schema column's nested children. + RETURN_IF_ERROR(build_nested_children_from_access_paths( + &column, it->second, + build_context.schema_column.has_value() ? &*build_context.schema_column : nullptr)); + if (is_partition_slot(slot_info, column.name)) { + column.is_partition_key = true; + _partition_slot_descs.emplace( + column.name, + PartitionSlotInfo {.slot_desc = it->second, .canonical_name = column.name}); + for (const auto& alias : column.name_mapping) { + _partition_slot_descs.emplace( + alias, + PartitionSlotInfo {.slot_desc = it->second, .canonical_name = column.name}); + } + } + const auto global_index = format::GlobalIndex(slot_idx); + _slot_id_to_global_index.emplace(slot_info.slot_id, global_index); + _projected_columns.push_back(std::move(column)); + } + RETURN_IF_ERROR(table_reader.validate_projected_columns(build_context)); + return Status::OK(); +} + +Status FileScannerV2::_build_default_expr(const TFileScanSlotInfo& slot_info, + VExprContextSPtr* ctx) const { + DORIS_CHECK(ctx != nullptr); + if (slot_info.__isset.default_value_expr && !slot_info.default_value_expr.nodes.empty()) { + return VExpr::create_expr_tree(slot_info.default_value_expr, *ctx); + } + + if (_params->__isset.default_value_of_src_slot) { + const auto it = _params->default_value_of_src_slot.find(slot_info.slot_id); + if (it != _params->default_value_of_src_slot.end() && !it->second.nodes.empty()) { + return VExpr::create_expr_tree(it->second, *ctx); + } + } + return Status::OK(); +} + +format::ColumnDefinition FileScannerV2::_build_table_column(const SlotDescriptor* slot_desc) { + DORIS_CHECK(slot_desc != nullptr); + format::ColumnDefinition column; + // TODO(gabriel): why always BY_NAME here? + column.identifier = Field::create_field(slot_desc->col_name()); + column.name = slot_desc->col_name(); + column.type = slot_desc->get_data_type_ptr(); + return column; +} + +Status FileScannerV2::_build_table_column_predicates( + format::TableColumnPredicates* predicates) const { + DORIS_CHECK(predicates != nullptr); + predicates->clear(); + const auto& slot_predicates = _local_state->cast()._slot_id_to_predicates; + for (const auto& [slot_id, slot_predicate_list] : slot_predicates) { + const auto it = _slot_id_to_desc.find(slot_id); + if (it == _slot_id_to_desc.end()) { + continue; + } + const auto global_index_it = _slot_id_to_global_index.find(slot_id); + if (global_index_it == _slot_id_to_global_index.end()) { + continue; + } + (*predicates)[global_index_it->second] = slot_predicate_list; + } + return Status::OK(); +} + +Status FileScannerV2::_build_table_conjuncts(VExprContextSPtrs* conjuncts) const { + DORIS_CHECK(conjuncts != nullptr); + conjuncts->clear(); + conjuncts->reserve(_conjuncts.size()); + for (const auto& conjunct : _conjuncts) { + VExprSPtr root; + RETURN_IF_ERROR(format::clone_table_expr_tree(conjunct->root(), &root)); + RETURN_IF_ERROR(rewrite_slot_refs_to_global_index(&root, _slot_id_to_global_index)); + conjuncts->push_back(VExprContext::create_shared(std::move(root))); + } + return Status::OK(); +} + +TFileFormatType::type FileScannerV2::_get_current_format_type() const { + return get_range_format_type(*_params, _current_range); +} + +Status FileScannerV2::_to_file_format(TFileFormatType::type format_type, + format::FileFormat* file_format) { + DORIS_CHECK(file_format != nullptr); + switch (format_type) { + case TFileFormatType::FORMAT_PARQUET: + *file_format = format::FileFormat::PARQUET; + return Status::OK(); + case TFileFormatType::FORMAT_JNI: + *file_format = format::FileFormat::JNI; + return Status::OK(); + default: + return Status::NotSupported("FileScannerV2 does not support file format {}", + to_string(format_type)); + } +} + +Status FileScannerV2::_init_io_ctx() { + _io_ctx = std::make_shared(); + _io_ctx->query_id = &_state->query_id(); + return Status::OK(); +} + +Status FileScannerV2::close(RuntimeState* state) { + if (!_try_close()) { + return Status::OK(); + } + if (_table_reader != nullptr) { + RETURN_IF_ERROR(_table_reader->close()); + _table_reader.reset(); + } + return Scanner::close(state); +} + +void FileScannerV2::try_stop() { + Scanner::try_stop(); + if (_io_ctx) { + _io_ctx->should_stop = true; + } +} + +void FileScannerV2::update_realtime_counters() { + if (_file_reader_stats == nullptr) { + return; + } + const int64_t bytes_read = _file_reader_stats->read_bytes; + COUNTER_SET(_file_read_bytes_counter, bytes_read); + COUNTER_SET(_file_read_calls_counter, cast_set(_file_reader_stats->read_calls)); + COUNTER_SET(_file_read_time_counter, cast_set(_file_reader_stats->read_time_ns)); +} + +} // namespace doris diff --git a/be/src/exec/scan/file_scanner_v2.h b/be/src/exec/scan/file_scanner_v2.h new file mode 100644 index 00000000000000..7925bc0c7dfdd7 --- /dev/null +++ b/be/src/exec/scan/file_scanner_v2.h @@ -0,0 +1,135 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "common/factory_creator.h" +#include "common/status.h" +#include "core/block/block.h" +#include "exec/operator/file_scan_operator.h" +#include "exec/scan/scanner.h" +#include "exec/scan/split_source_connector.h" +#include "exprs/vexpr_fwd.h" +#include "format_v2/column_mapper.h" +#include "format_v2/table_reader.h" +#include "gen_cpp/Descriptors_types.h" +#include "gen_cpp/PlanNodes_types.h" +#include "io/io_common.h" +#include "runtime/runtime_profile.h" + +namespace doris { + +class RuntimeState; +class SlotDescriptor; +class TFileRangeDesc; +class TFileScanRangeParams; +class ShardedKVCache; + +class FileScannerV2 final : public Scanner { + ENABLE_FACTORY_CREATOR(FileScannerV2); + +public: + static constexpr const char* NAME = "FileScannerV2"; + + static bool is_supported(const TFileScanRangeParams& params, const TFileRangeDesc& range); +#ifdef BE_TEST + static Status TEST_build_nested_children_from_access_paths( + format::ColumnDefinition* column, const std::vector& access_paths); + static Status TEST_build_nested_children_from_access_paths( + format::ColumnDefinition* column, const std::vector& access_paths, + const format::ColumnDefinition* schema_column); +#endif + + FileScannerV2(RuntimeState* state, FileScanLocalState* parent, int64_t limit, + std::shared_ptr split_source, RuntimeProfile* profile, + ShardedKVCache* kv_cache, + const std::unordered_map* colname_to_slot_id); + + Status init(RuntimeState* state, const VExprContextSPtrs& conjuncts) override; + Status _open_impl(RuntimeState* state) override; + Status close(RuntimeState* state) override; + void try_stop() override; + std::string get_name() override { return FileScannerV2::NAME; } + std::string get_current_scan_range_name() override { return _current_range_path; } + void update_realtime_counters() override; + +protected: + Status _get_block_impl(RuntimeState* state, Block* block, bool* eof) override; + +private: + TFileFormatType::type _get_current_format_type() const; + Status _init_io_ctx(); + Status _init_expr_ctxes(); + Status _prepare_next_split(bool* eos); + Status _init_table_reader(const TFileRangeDesc& range); + Status _create_table_reader_for_format(const TFileRangeDesc& range, + std::unique_ptr* reader) const; + Status _prepare_table_reader_split(const TFileRangeDesc& range); + bool _should_enable_file_meta_cache() const; + std::optional _create_global_rowid_context( + const TFileRangeDesc& range) const; + Status _generate_partition_values(const TFileRangeDesc& range, + std::map* partition_values) const; + Status _parse_partition_value(const SlotDescriptor* slot_desc, const std::string& value, + bool is_null, Field* field) const; + Status _build_projected_columns(const format::TableReader& table_reader); + Status _build_default_expr(const TFileScanSlotInfo& slot_info, VExprContextSPtr* ctx) const; + static format::ColumnDefinition _build_table_column(const SlotDescriptor* slot_desc); + Status _build_table_column_predicates(format::TableColumnPredicates* predicates) const; + Status _build_table_conjuncts(VExprContextSPtrs* conjuncts) const; + static Status _to_file_format(TFileFormatType::type format_type, + format::FileFormat* file_format); + + struct PartitionSlotInfo { + const SlotDescriptor* slot_desc = nullptr; + std::string canonical_name; + }; + + const TFileScanRangeParams* _params = nullptr; + std::shared_ptr _split_source; + bool _first_scan_range = false; + bool _has_prepared_split = false; + TFileRangeDesc _current_range; + std::string _current_range_path; + + std::unique_ptr _table_reader; + std::vector _projected_columns; + bool _need_global_rowid_column = false; + std::unordered_map _slot_id_to_desc; + std::unordered_map _slot_id_to_global_index; + std::unordered_map _partition_slot_descs; + + std::unique_ptr _file_cache_statistics; + std::unique_ptr _file_reader_stats; + std::shared_ptr _io_ctx; + ShardedKVCache* _kv_cache = nullptr; + + RuntimeProfile::Counter* _get_block_timer = nullptr; + RuntimeProfile::Counter* _file_counter = nullptr; + RuntimeProfile::Counter* _file_read_bytes_counter = nullptr; + RuntimeProfile::Counter* _file_read_calls_counter = nullptr; + RuntimeProfile::Counter* _file_read_time_counter = nullptr; +}; + +} // namespace doris diff --git a/be/src/exec/scan/split_source_connector.h b/be/src/exec/scan/split_source_connector.h index 5926baff303cbf..320f6f90d0dd02 100644 --- a/be/src/exec/scan/split_source_connector.h +++ b/be/src/exec/scan/split_source_connector.h @@ -17,6 +17,8 @@ #pragma once +#include + #include "common/config.h" #include "core/custom_allocator.h" #include "runtime/runtime_state.h" @@ -45,6 +47,15 @@ class SplitSourceConnector { virtual TFileScanRangeParams* get_params() = 0; + virtual bool all_scan_ranges_match( + const TFileScanRangeParams& params, + const std::function& + predicate) { + (void)params; + (void)predicate; + return false; + } + protected: template , typename V2 = std::vector> requires(std::is_same_v, @@ -125,6 +136,24 @@ class LocalSplitSourceConnector : public SplitSourceConnector { throw Exception( Status::FatalError("Unreachable, params is got by file_scan_range_params_map")); } + + bool all_scan_ranges_match( + const TFileScanRangeParams& params, + const std::function& + predicate) override { + if (_scan_ranges.empty()) { + return false; + } + for (const auto& scan_range : _scan_ranges) { + const auto& file_scan_range = scan_range.scan_range.ext_scan_range.file_scan_range; + for (const auto& range : file_scan_range.ranges) { + if (!predicate(params, range)) { + return false; + } + } + } + return true; + } }; /** diff --git a/be/src/exprs/runtime_filter_expr.cpp b/be/src/exprs/runtime_filter_expr.cpp index e335e8ed4da1e7..f6dc40d9178de4 100644 --- a/be/src/exprs/runtime_filter_expr.cpp +++ b/be/src/exprs/runtime_filter_expr.cpp @@ -63,6 +63,17 @@ RuntimeFilterExpr::RuntimeFilterExpr(const TExprNode& node, VExprSPtr impl, doub _filter_id(filter_id), _sampling_frequency(sampling_frequency) {} +Status RuntimeFilterExpr::clone_node(VExprSPtr* cloned_expr) const { + DORIS_CHECK(cloned_expr != nullptr); + DORIS_CHECK(_impl != nullptr); + VExprSPtr cloned_impl; + RETURN_IF_ERROR(_impl->deep_clone(&cloned_impl)); + *cloned_expr = RuntimeFilterExpr::create_shared(clone_texpr_node(), std::move(cloned_impl), + _ignore_thredhold, _null_aware, _filter_id, + _sampling_frequency); + return Status::OK(); +} + Status RuntimeFilterExpr::prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) { RETURN_IF_ERROR_OR_PREPARED(_impl->prepare(state, desc, context)); diff --git a/be/src/exprs/runtime_filter_expr.h b/be/src/exprs/runtime_filter_expr.h index ceb4324d9d56f5..e3f34a5fb605b6 100644 --- a/be/src/exprs/runtime_filter_expr.h +++ b/be/src/exprs/runtime_filter_expr.h @@ -80,6 +80,7 @@ class RuntimeFilterExpr final : public VExpr { } VExprSPtr get_impl() const override { return _impl; } + Status clone_node(VExprSPtr* cloned_expr) const override; void attach_profile_counter(std::shared_ptr rf_input_rows, std::shared_ptr rf_filter_rows, diff --git a/be/src/exprs/short_circuit_evaluation_expr.h b/be/src/exprs/short_circuit_evaluation_expr.h index 47a37b360c6e90..7240207aacad71 100644 --- a/be/src/exprs/short_circuit_evaluation_expr.h +++ b/be/src/exprs/short_circuit_evaluation_expr.h @@ -63,6 +63,13 @@ class ShortCircuitIfExpr final : public ShortCircuitExpr { ~ShortCircuitIfExpr() override = default; const std::string& expr_name() const override { return IF_NAME; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + node.__set_short_circuit_evaluation(true); + *cloned_expr = ShortCircuitIfExpr::create_shared(node); + return Status::OK(); + } Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, size_t count, ColumnPtr& result_column) const override; @@ -76,6 +83,18 @@ class ShortCircuitCaseExpr final : public ShortCircuitExpr { ShortCircuitCaseExpr(const TExprNode& node); ~ShortCircuitCaseExpr() override = default; const std::string& expr_name() const override { return CASE_NAME; } + bool has_else_expr() const { return _has_else_expr; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + TCaseExpr case_node; + case_node.__set_has_case_expr(false); + case_node.__set_has_else_expr(_has_else_expr); + node.__set_case_expr(case_node); + node.__set_short_circuit_evaluation(true); + *cloned_expr = ShortCircuitCaseExpr::create_shared(node); + return Status::OK(); + } Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, size_t count, ColumnPtr& result_column) const override; @@ -91,6 +110,13 @@ class ShortCircuitIfNullExpr final : public ShortCircuitExpr { ~ShortCircuitIfNullExpr() override = default; const std::string& expr_name() const override { return IFNULL_NAME; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + node.__set_short_circuit_evaluation(true); + *cloned_expr = ShortCircuitIfNullExpr::create_shared(node); + return Status::OK(); + } Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, size_t count, ColumnPtr& result_column) const override; @@ -104,10 +130,17 @@ class ShortCircuitCoalesceExpr final : public ShortCircuitExpr { ShortCircuitCoalesceExpr(const TExprNode& node) : ShortCircuitExpr(node) {} ~ShortCircuitCoalesceExpr() override = default; const std::string& expr_name() const override { return COALESCE_NAME; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + node.__set_short_circuit_evaluation(true); + *cloned_expr = ShortCircuitCoalesceExpr::create_shared(node); + return Status::OK(); + } Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, size_t count, ColumnPtr& result_column) const override; private: inline static const std::string COALESCE_NAME = "coalesce"; }; -} // namespace doris \ No newline at end of file +} // namespace doris diff --git a/be/src/exprs/vbloom_predicate.h b/be/src/exprs/vbloom_predicate.h index f23bde0d9ad3fd..410bb5c8d370b3 100644 --- a/be/src/exprs/vbloom_predicate.h +++ b/be/src/exprs/vbloom_predicate.h @@ -59,6 +59,13 @@ class VBloomPredicate final : public VExpr { std::shared_ptr get_bloom_filter_func() const override { return _filter; } uint64_t get_digest(uint64_t seed) const override; + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto cloned = VBloomPredicate::create_shared(clone_texpr_node()); + cloned->set_filter(_filter); + *cloned_expr = std::move(cloned); + return Status::OK(); + } private: Status _do_execute(VExprContext* context, const Block* block, const uint8_t* __restrict filter, diff --git a/be/src/exprs/vcase_expr.h b/be/src/exprs/vcase_expr.h index 97b2551091d100..6787283f0c5d23 100644 --- a/be/src/exprs/vcase_expr.h +++ b/be/src/exprs/vcase_expr.h @@ -59,6 +59,17 @@ class VCaseExpr final : public VExpr { void close(VExprContext* context, FunctionContext::FunctionStateScope scope) override; const std::string& expr_name() const override; std::string debug_string() const override; + bool has_else_expr() const { return _has_else_expr; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + TCaseExpr case_node; + case_node.__set_has_case_expr(false); + case_node.__set_has_else_expr(_has_else_expr); + node.__set_case_expr(case_node); + *cloned_expr = VCaseExpr::create_shared(node); + return Status::OK(); + } private: template diff --git a/be/src/exprs/vcast_expr.h b/be/src/exprs/vcast_expr.h index c3f2526794b3b8..f0f3ead95d56af 100644 --- a/be/src/exprs/vcast_expr.h +++ b/be/src/exprs/vcast_expr.h @@ -57,6 +57,11 @@ class VCastExpr : public VExpr { const DataTypePtr& get_target_type() const; virtual std::string cast_name() const { return "CAST"; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VCastExpr::create_shared(clone_texpr_node()); + return Status::OK(); + } uint64_t get_digest(uint64_t seed) const override { auto res = VExpr::get_digest(seed); @@ -94,6 +99,13 @@ class TryCastExpr final : public VCastExpr { size_t count, ColumnPtr& result_column) const override; ~TryCastExpr() override = default; std::string cast_name() const override { return "TRY CAST"; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + node.__set_is_cast_nullable(_original_cast_return_is_nullable); + *cloned_expr = TryCastExpr::create_shared(node); + return Status::OK(); + } private: DataTypePtr original_cast_return_type() const; diff --git a/be/src/exprs/vcolumn_ref.h b/be/src/exprs/vcolumn_ref.h index e4485e5815e02f..33ade77defaaba 100644 --- a/be/src/exprs/vcolumn_ref.h +++ b/be/src/exprs/vcolumn_ref.h @@ -81,6 +81,19 @@ class VColumnRef final : public VExpr { } } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + TColumnRef column_ref; + column_ref.__set_column_id(_column_id); + column_ref.__set_column_name(_column_name); + node.__set_column_ref(column_ref); + auto cloned = VColumnRef::create_shared(node); + cloned->set_gap(_gap.load()); + *cloned_expr = std::move(cloned); + return Status::OK(); + } + std::string debug_string() const override { std::stringstream out; out << "VColumnRef(slot_id: " << _column_id << ",column_name: " << _column_name diff --git a/be/src/exprs/vcompound_pred.h b/be/src/exprs/vcompound_pred.h index 0c8fd46d8bdf37..51945d2ccba62e 100644 --- a/be/src/exprs/vcompound_pred.h +++ b/be/src/exprs/vcompound_pred.h @@ -58,6 +58,11 @@ class VCompoundPred : public VectorizedFnCall { #endif const std::string& expr_name() const override { return _expr_name; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VCompoundPred::create_shared(clone_texpr_node()); + return Status::OK(); + } Status evaluate_inverted_index(VExprContext* context, uint32_t segment_num_rows) override { segment_v2::InvertedIndexResultBitmap res; diff --git a/be/src/exprs/vcondition_expr.h b/be/src/exprs/vcondition_expr.h index ceb6ed1396d78f..6beade74b3e3d6 100644 --- a/be/src/exprs/vcondition_expr.h +++ b/be/src/exprs/vcondition_expr.h @@ -65,6 +65,11 @@ class VectorizedIfExpr : public VConditionExpr { size_t count, ColumnPtr& result_column) const override; const std::string& expr_name() const override { return IF_NAME; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VectorizedIfExpr::create_shared(clone_texpr_node()); + return Status::OK(); + } inline static const std::string IF_NAME = "if"; protected: @@ -123,6 +128,11 @@ class VectorizedIfNullExpr : public VectorizedIfExpr { public: VectorizedIfNullExpr(const TExprNode& node) : VectorizedIfExpr(node) {} const std::string& expr_name() const override { return IF_NULL_NAME; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VectorizedIfNullExpr::create_shared(clone_texpr_node()); + return Status::OK(); + } inline static const std::string IF_NULL_NAME = "ifnull"; Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, @@ -137,6 +147,11 @@ class VectorizedCoalesceExpr : public VConditionExpr { size_t count, ColumnPtr& result_column) const override; VectorizedCoalesceExpr(const TExprNode& node) : VConditionExpr(node) {} const std::string& expr_name() const override { return NAME; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VectorizedCoalesceExpr::create_shared(clone_texpr_node()); + return Status::OK(); + } inline static const std::string NAME = "coalesce"; }; diff --git a/be/src/exprs/vdirect_in_predicate.h b/be/src/exprs/vdirect_in_predicate.h index 882c8d4de69030..fed92a35a23afb 100644 --- a/be/src/exprs/vdirect_in_predicate.h +++ b/be/src/exprs/vdirect_in_predicate.h @@ -68,6 +68,12 @@ class VDirectInPredicate final : public VExpr { std::shared_ptr get_set_func() const override { return _filter; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VDirectInPredicate::create_shared(clone_texpr_node(), _filter); + return Status::OK(); + } + bool get_slot_in_expr(VExprSPtr& new_root) const { if (!get_child(0)->is_slot_ref()) { return false; diff --git a/be/src/exprs/vectorized_fn_call.h b/be/src/exprs/vectorized_fn_call.h index 4d8a1011169be2..1d7de6d936f8fe 100644 --- a/be/src/exprs/vectorized_fn_call.h +++ b/be/src/exprs/vectorized_fn_call.h @@ -98,6 +98,12 @@ class VectorizedFnCall : public VExpr { segment_v2::AnnRangeSearchRuntime& runtime, bool& suitable_for_ann_index) override; + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VectorizedFnCall::create_shared(clone_texpr_node()); + return Status::OK(); + } + protected: FunctionBasePtr _function; std::string _expr_name; diff --git a/be/src/exprs/vexpr.cpp b/be/src/exprs/vexpr.cpp index 65dcd4eb9de2df..ee0d8e11cc91ee 100644 --- a/be/src/exprs/vexpr.cpp +++ b/be/src/exprs/vexpr.cpp @@ -372,6 +372,51 @@ VExpr::VExpr(DataTypePtr type, bool is_slotref) } } +TExprNode VExpr::clone_texpr_node() const { + TExprNode node; + node.__set_node_type(_node_type); + node.__set_opcode(_opcode); + node.__set_type(create_type_desc(remove_nullable(_data_type)->get_primitive_type(), + static_cast(_data_type->get_precision()), + static_cast(_data_type->get_scale()))); + node.__set_is_nullable(_data_type->is_nullable()); + node.__set_num_children(get_num_children()); + node.__set_fn(_fn); + return node; +} + +Status VExpr::clone_node(VExprSPtr* cloned_expr) const { + DORIS_CHECK(cloned_expr != nullptr); + return Status::NotSupported("Cannot clone expression {} for file-local rewrite", expr_name()); +} + +Status VExpr::deep_clone(VExprSPtr* cloned_expr, + const VExprCloneNodeOverride& clone_node_override) const { + DORIS_CHECK(cloned_expr != nullptr); + + VExprSPtr cloned; + if (clone_node_override) { + RETURN_IF_ERROR(clone_node_override(*this, &cloned)); + } + if (cloned == nullptr) { + RETURN_IF_ERROR(clone_node(&cloned)); + } + DORIS_CHECK(cloned != nullptr); + + VExprSPtrs cloned_children; + cloned_children.reserve(_children.size()); + for (const auto& child : _children) { + DORIS_CHECK(child != nullptr); + VExprSPtr cloned_child; + RETURN_IF_ERROR(child->deep_clone(&cloned_child, clone_node_override)); + cloned_children.push_back(std::move(cloned_child)); + } + cloned->set_children(std::move(cloned_children)); + cloned->reset_prepare_state(); + *cloned_expr = std::move(cloned); + return Status::OK(); +} + Status VExpr::prepare(RuntimeState* state, const RowDescriptor& row_desc, VExprContext* context) { ++context->_depth_num; if (context->_depth_num > config::max_depth_of_expr_tree) { @@ -401,6 +446,15 @@ Status VExpr::open(RuntimeState* state, VExprContext* context, return Status::OK(); } +void VExpr::reset_prepare_state() { + _prepared = false; + _prepare_finished = false; + _open_finished = false; + for (auto& child : _children) { + child->reset_prepare_state(); + } +} + void VExpr::close(VExprContext* context, FunctionContext::FunctionStateScope scope) { for (auto& i : _children) { i->close(context, scope); @@ -751,8 +805,9 @@ Status VExpr::get_const_col(VExprContext* context, return Status::OK(); } - if (_constant_col != nullptr) { - DCHECK(column_wrapper != nullptr); + if (_constant_col != nullptr && column_wrapper == nullptr) { + return Status::OK(); + } else if (_constant_col != nullptr) { *column_wrapper = _constant_col; return Status::OK(); } diff --git a/be/src/exprs/vexpr.h b/be/src/exprs/vexpr.h index 46ac59ed093a97..8f834e6d060e83 100644 --- a/be/src/exprs/vexpr.h +++ b/be/src/exprs/vexpr.h @@ -24,6 +24,7 @@ #include #include +#include #include #include #include @@ -79,6 +80,7 @@ struct AnnRangeSearchRuntime; // the relatioinship between threads and classes. using Selector = IColumn::Selector; +using VExprCloneNodeOverride = std::function; struct AnnRangeSearchEvaluationResult { // Indicates whether the expr row_bitmap has been updated. @@ -248,6 +250,10 @@ class VExpr { static bool contains_blockable_function(const VExprContextSPtrs& ctxs); + Status deep_clone(VExprSPtr* cloned_expr, + const VExprCloneNodeOverride& clone_node_override = {}) const; + virtual Status clone_node(VExprSPtr* cloned_expr) const; + bool is_nullable() const { return _data_type->is_nullable(); } PrimitiveType result_type() const { return _data_type->get_primitive_type(); } @@ -262,6 +268,7 @@ class VExpr { virtual const VExprSPtrs& children() const { return _children; } void set_children(const VExprSPtrs& children) { _children = children; } void set_children(VExprSPtrs&& children) { _children = std::move(children); } + void reset_prepare_state(); virtual std::string debug_string() const; static std::string debug_string(const VExprSPtrs& exprs); static std::string debug_string(const VExprContextSPtrs& ctxs); @@ -269,7 +276,7 @@ class VExpr { static ColumnPtr filter_column_with_selector(const ColumnPtr& origin_column, const Selector* selector, size_t count) { if (selector == nullptr) { - DCHECK_EQ(origin_column->size(), count); + DCHECK_EQ(origin_column->size(), count) << origin_column->get_name(); return origin_column; } DCHECK_EQ(count, selector->size()); @@ -362,6 +369,8 @@ class VExpr { virtual uint64_t get_digest(uint64_t seed) const; protected: + TExprNode clone_texpr_node() const; + /// Simple debug string that provides no expr subclass-specific information std::string debug_string(const std::string& expr_name) const { std::stringstream out; diff --git a/be/src/exprs/vin_predicate.h b/be/src/exprs/vin_predicate.h index 1d3ad4e4ce7c3e..bfdccfb6c1b586 100644 --- a/be/src/exprs/vin_predicate.h +++ b/be/src/exprs/vin_predicate.h @@ -60,6 +60,15 @@ class VInPredicate MOCK_REMOVE(final) : public VExpr { Status evaluate_inverted_index(VExprContext* context, uint32_t segment_num_rows) override; uint64_t get_digest(uint64_t seed) const override { return 0; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + auto node = clone_texpr_node(); + TInPredicate in_predicate; + in_predicate.__set_is_not_in(_is_not_in); + node.__set_in_predicate(in_predicate); + *cloned_expr = VInPredicate::create_shared(node); + return Status::OK(); + } private: FunctionBasePtr _function; @@ -70,4 +79,4 @@ class VInPredicate MOCK_REMOVE(final) : public VExpr { uint32_t _in_list_value_count_threshold = 10; bool _is_args_all_constant = false; }; -} // namespace doris \ No newline at end of file +} // namespace doris diff --git a/be/src/exprs/vliteral.cpp b/be/src/exprs/vliteral.cpp index 551839f699e2e6..9b93d7097274ee 100644 --- a/be/src/exprs/vliteral.cpp +++ b/be/src/exprs/vliteral.cpp @@ -37,12 +37,6 @@ namespace doris { class VExprContext; -void VLiteral::init(const TExprNode& node) { - Field field; - field = _data_type->get_field(node); - _column_ptr = _data_type->create_column_const(1, field); -} - Status VLiteral::prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) { RETURN_IF_ERROR_OR_PREPARED(VExpr::prepare(state, desc, context)); return Status::OK(); diff --git a/be/src/exprs/vliteral.h b/be/src/exprs/vliteral.h index b1b8e89157d420..89988e2ba31142 100644 --- a/be/src/exprs/vliteral.h +++ b/be/src/exprs/vliteral.h @@ -24,6 +24,7 @@ #include "common/status.h" #include "core/data_type/data_type.h" #include "core/data_type_serde/data_type_serde.h" +#include "core/field.h" #include "exprs/vexpr.h" namespace doris { @@ -39,10 +40,19 @@ class VLiteral : public VExpr { VLiteral(const TExprNode& node, bool should_init = true) : VExpr(node), _expr_name(_data_type->get_name()) { if (should_init) { - init(node); + Field field; + field = _data_type->get_field(node); + _column_ptr = _data_type->create_column_const(1, field); } } + VLiteral(const DataTypePtr& type, const Field& field) : VExpr(type, false) { + _data_type = type; + _column_ptr = _data_type->create_column_const(1, field); + _node_type = TExprNodeType::LITERAL; + _expr_name = _data_type->get_name(); + } + #ifdef BE_TEST VLiteral() = default; MOCK_FUNCTION std::string value() const; @@ -67,13 +77,18 @@ class VLiteral : public VExpr { bool equals(const VExpr& other) override; uint64_t get_digest(uint64_t seed) const override; + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + Field field; + _column_ptr->get(0, field); + *cloned_expr = VLiteral::create_shared(_data_type, field); + return Status::OK(); + } protected: + VLiteral(const DataTypePtr& type) : VExpr(type, false) {} ColumnPtr _column_ptr; std::string _expr_name; - -private: - void init(const TExprNode& node); }; } // namespace doris diff --git a/be/src/exprs/vslot_ref.cpp b/be/src/exprs/vslot_ref.cpp index 87aad6b977ecbe..f02ef50d5751c3 100644 --- a/be/src/exprs/vslot_ref.cpp +++ b/be/src/exprs/vslot_ref.cpp @@ -41,10 +41,28 @@ VSlotRef::VSlotRef(const doris::TExprNode& node) VSlotRef::VSlotRef(const SlotDescriptor* desc) : VExpr(desc->type(), true), _slot_id(desc->id()), _column_id(-1), _column_name(nullptr) {} +VSlotRef::VSlotRef(int slot_id, int column_id, int column_uniq_id, const DataTypePtr& type, + std::string column_name) + : VExpr(type, true), + _slot_id(slot_id), + _column_id(column_id), + _column_uniq_id(column_uniq_id), + _owned_column_name(std::move(column_name)), + _column_name(&_owned_column_name) {} + Status VSlotRef::prepare(doris::RuntimeState* state, const doris::RowDescriptor& desc, VExprContext* context) { - RETURN_IF_ERROR_OR_PREPARED(VExpr::prepare(state, desc, context)); DCHECK_EQ(_children.size(), 0); + if (_prepared) { + return Status::OK(); + } + if (_column_id >= 0 && _column_name != nullptr) { + _prepared = true; + _prepare_finished = true; + return Status::OK(); + } + _prepared = true; + RETURN_IF_ERROR(VExpr::prepare(state, desc, context)); if (_slot_id == -1) { _prepare_finished = true; return Status::OK(); @@ -109,6 +127,27 @@ DataTypePtr VSlotRef::execute_type(const Block* block) const { return block->get_by_position(_column_id).type; } +Status VSlotRef::clone_node(VExprSPtr* cloned_expr) const { + DORIS_CHECK(cloned_expr != nullptr); + if (_column_id >= 0 && _column_name != nullptr) { + *cloned_expr = VSlotRef::create_shared(_slot_id, _column_id, _column_uniq_id, _data_type, + *_column_name); + return Status::OK(); + } + auto node = clone_texpr_node(); + TSlotRef slot_ref; + slot_ref.__set_slot_id(_slot_id); + node.__set_slot_ref(slot_ref); + node.__set_label(_column_label); + auto cloned = VSlotRef::create_shared(node); + auto* cloned_slot_ref = static_cast(cloned.get()); + cloned_slot_ref->_column_id = _column_id; + cloned_slot_ref->_column_uniq_id = _column_uniq_id; + cloned_slot_ref->_column_name = _column_name; + *cloned_expr = std::move(cloned); + return Status::OK(); +} + const std::string& VSlotRef::expr_name() const { return *_column_name; } diff --git a/be/src/exprs/vslot_ref.h b/be/src/exprs/vslot_ref.h index ef61edc384c2f2..a67bdc1953cd0a 100644 --- a/be/src/exprs/vslot_ref.h +++ b/be/src/exprs/vslot_ref.h @@ -31,12 +31,14 @@ class TExprNode; class Block; class VExprContext; -class VSlotRef MOCK_REMOVE(final) : public VExpr { +class VSlotRef : public VExpr { ENABLE_FACTORY_CREATOR(VSlotRef); public: VSlotRef(const TExprNode& node); VSlotRef(const SlotDescriptor* desc); + VSlotRef(int slot_id, int column_id, int column_uniq_id, const DataTypePtr& type, + std::string column_name); #ifdef BE_TEST VSlotRef() = default; void set_slot_id(int slot_id) { _slot_id = slot_id; } @@ -58,6 +60,7 @@ class VSlotRef MOCK_REMOVE(final) : public VExpr { int column_id() const { return _column_id; } MOCK_FUNCTION int slot_id() const { return _slot_id; } + int column_uniq_id() const { return _column_uniq_id; } bool equals(const VExpr& other) override; @@ -67,16 +70,24 @@ class VSlotRef MOCK_REMOVE(final) : public VExpr { column_ids.insert(_column_id); } - MOCK_FUNCTION const std::string& column_name() const { return *_column_name; } + virtual const std::string& column_name() const { return *_column_name; } uint64_t get_digest(uint64_t seed) const override; double execute_cost() const override { return 0.0; } + Status clone_node(VExprSPtr* cloned_expr) const override; + +protected: + VSlotRef(int slot_id, int column_id, int column_uniq_id) + : _slot_id(slot_id), _column_id(column_id), _column_uniq_id(column_uniq_id) { + _node_type = TExprNodeType::SLOT_REF; + } private: int _slot_id; int _column_id; int _column_uniq_id = -1; + std::string _owned_column_name; const std::string* _column_name = nullptr; const std::string _column_label; }; diff --git a/be/src/exprs/vtopn_pred.h b/be/src/exprs/vtopn_pred.h index 526787bccc65dc..a8723a35a89d34 100644 --- a/be/src/exprs/vtopn_pred.h +++ b/be/src/exprs/vtopn_pred.h @@ -63,6 +63,11 @@ class VTopNPred : public VExpr { } int source_node_id() const { return _source_node_id; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = VTopNPred::create_shared(clone_texpr_node(), _source_node_id, nullptr); + return Status::OK(); + } Status prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) override { _predicate = &state->get_query_ctx()->get_runtime_predicate(_source_node_id); diff --git a/be/src/format/CMakeLists.txt b/be/src/format/CMakeLists.txt index ef9dab92c00f97..bc0325f3e0f252 100644 --- a/be/src/format/CMakeLists.txt +++ b/be/src/format/CMakeLists.txt @@ -22,6 +22,9 @@ set(LIBRARY_OUTPUT_PATH "${BUILD_DIR}/src/format") set(EXECUTABLE_OUTPUT_PATH "${BUILD_DIR}/src/format") file(GLOB_RECURSE SRC_FILES CONFIGURE_DEPENDS *.cpp) +file(GLOB_RECURSE FORMAT_V2_SRC_FILES CONFIGURE_DEPENDS + ${CMAKE_CURRENT_SOURCE_DIR}/../format_v2/*.cpp) +list(APPEND SRC_FILES ${FORMAT_V2_SRC_FILES}) # Lance reader requires Rust static library (BUILD_RUST_READERS=ON) if (NOT BUILD_RUST_READERS) diff --git a/be/src/format/csv/csv_reader.cpp b/be/src/format/csv/csv_reader.cpp index 731f9e61049713..fa54ac5777c33e 100644 --- a/be/src/format/csv/csv_reader.cpp +++ b/be/src/format/csv/csv_reader.cpp @@ -651,8 +651,8 @@ Status CsvReader::_create_file_reader(bool need_schema) { need_schema)); } else { _file_description.mtime = _range.__isset.modification_time ? _range.modification_time : 0; - io::FileReaderOptions reader_options = - FileFactory::get_reader_options(_state, _file_description); + io::FileReaderOptions reader_options = FileFactory::get_reader_options( + _state ? _state->query_options() : _default_query_options, _file_description); io::FileReaderSPtr file_reader; if (_io_ctx_holder) { file_reader = DORIS_TRY(io::DelegateReader::create_file_reader( diff --git a/be/src/format/generic_reader.h b/be/src/format/generic_reader.h index d849d595056adb..aa6477b769570e 100644 --- a/be/src/format/generic_reader.h +++ b/be/src/format/generic_reader.h @@ -299,6 +299,7 @@ class GenericReader : public ProfileCollector { // ---- get_columns cache ---- bool _get_columns_cached = false; std::unordered_map _cached_name_to_type; + const TQueryOptions _default_query_options; }; /// Provides an accessor for the current batch's row positions within the file. diff --git a/be/src/format/json/new_json_reader.cpp b/be/src/format/json/new_json_reader.cpp index 8d53b6009e6bef..1aa19574b39a58 100644 --- a/be/src/format/json/new_json_reader.cpp +++ b/be/src/format/json/new_json_reader.cpp @@ -498,8 +498,8 @@ Status NewJsonReader::_open_file_reader(bool need_schema) { need_schema)); } else { _file_description.mtime = _range.__isset.modification_time ? _range.modification_time : 0; - io::FileReaderOptions reader_options = - FileFactory::get_reader_options(_state, _file_description); + io::FileReaderOptions reader_options = FileFactory::get_reader_options( + _state ? _state->query_options() : _default_query_options, _file_description); io::FileReaderSPtr file_reader; if (_io_ctx_holder) { file_reader = DORIS_TRY(io::DelegateReader::create_file_reader( diff --git a/be/src/format/native/native_reader.cpp b/be/src/format/native/native_reader.cpp index 029d7ff2024f20..3632b6e4e0a1c9 100644 --- a/be/src/format/native/native_reader.cpp +++ b/be/src/format/native/native_reader.cpp @@ -137,8 +137,8 @@ Status NativeReader::init_reader() { _scan_params.broker_addresses.end()); } - io::FileReaderOptions reader_options = - FileFactory::get_reader_options(_state, file_description); + io::FileReaderOptions reader_options = FileFactory::get_reader_options( + _state ? _state->query_options() : _default_query_options, file_description); auto reader_res = _io_ctx_holder ? io::DelegateReader::create_file_reader( _profile, system_properties, file_description, reader_options, diff --git a/be/src/format/orc/vorc_reader.cpp b/be/src/format/orc/vorc_reader.cpp index 48a1894f02e276..06393c3195e430 100644 --- a/be/src/format/orc/vorc_reader.cpp +++ b/be/src/format/orc/vorc_reader.cpp @@ -382,8 +382,8 @@ Status OrcReader::_create_file_reader() { if (_file_input_stream == nullptr) { _file_description.mtime = _scan_range.__isset.modification_time ? _scan_range.modification_time : 0; - io::FileReaderOptions reader_options = - FileFactory::get_reader_options(_state, _file_description); + io::FileReaderOptions reader_options = FileFactory::get_reader_options( + _state ? _state->query_options() : _default_query_options, _file_description); io::FileReaderSPtr inner_reader; if (_io_ctx_holder != nullptr) { inner_reader = DORIS_TRY(io::DelegateReader::create_file_reader( diff --git a/be/src/format/parquet/vparquet_reader.cpp b/be/src/format/parquet/vparquet_reader.cpp index 31078c7b8b0d0c..600cbfbfaa7d21 100644 --- a/be/src/format/parquet/vparquet_reader.cpp +++ b/be/src/format/parquet/vparquet_reader.cpp @@ -313,8 +313,8 @@ Status ParquetReader::_open_file() { ++_reader_statistics.open_file_num; _file_description.mtime = _scan_range.__isset.modification_time ? _scan_range.modification_time : 0; - io::FileReaderOptions reader_options = - FileFactory::get_reader_options(_state, _file_description); + io::FileReaderOptions reader_options = FileFactory::get_reader_options( + _state ? _state->query_options() : _default_query_options, _file_description); if (_io_ctx_holder) { _file_reader = DORIS_TRY(io::DelegateReader::create_file_reader( _profile, _system_properties, _file_description, reader_options, diff --git a/be/src/format/table/deletion_vector_reader.cpp b/be/src/format/table/deletion_vector_reader.cpp index bfe34a5f555f94..d7e33c923d95b7 100644 --- a/be/src/format/table/deletion_vector_reader.cpp +++ b/be/src/format/table/deletion_vector_reader.cpp @@ -54,9 +54,9 @@ Status DeletionVectorReader::_create_file_reader() { return Status::EndOfFile("stop read."); } - _file_description.mtime = _range.__isset.modification_time ? _range.modification_time : 0; + _file_description.mtime = _desc.modification_time; io::FileReaderOptions reader_options = - FileFactory::get_reader_options(_state, _file_description); + FileFactory::get_reader_options(_state->query_options(), _file_description); _file_reader = DORIS_TRY(io::DelegateReader::create_file_reader( _profile, _system_properties, _file_description, reader_options, io::DelegateReader::AccessMode::RANDOM, _io_ctx)); @@ -64,20 +64,13 @@ Status DeletionVectorReader::_create_file_reader() { } void DeletionVectorReader::_init_file_description() { - _file_description.path = _range.path; - _file_description.file_size = _range.__isset.file_size ? _range.file_size : -1; - if (_range.__isset.fs_name) { - _file_description.fs_name = _range.fs_name; - } + _file_description.path = _desc.path; + _file_description.file_size = _desc.file_size; + _file_description.fs_name = _desc.fs_name; } void DeletionVectorReader::_init_system_properties() { - if (_range.__isset.file_type) { - // for compatibility - _system_properties.system_type = _range.file_type; - } else { - _system_properties.system_type = _params.file_type; - } + _system_properties.system_type = _params.file_type; _system_properties.properties = _params.properties; _system_properties.hdfs_params = _params.hdfs_params; if (_params.__isset.broker_addresses) { diff --git a/be/src/format/table/deletion_vector_reader.h b/be/src/format/table/deletion_vector_reader.h index 0663f3b28490ef..968344a8496bc7 100644 --- a/be/src/format/table/deletion_vector_reader.h +++ b/be/src/format/table/deletion_vector_reader.h @@ -36,6 +36,22 @@ struct IOContext; } // namespace io namespace doris { +struct DeleteFileDesc { + enum class Format { + PAIMON, + ICEBERG, + }; + + std::string key = ""; + std::string path = ""; + std::string fs_name = ""; + int64_t start_offset = 0; + int64_t size = 0; + int64_t file_size = -1; + int64_t modification_time = 0; + Format format = Format::PAIMON; +}; + class DeletionVectorReader { ENABLE_FACTORY_CREATOR(DeletionVectorReader); @@ -43,7 +59,22 @@ class DeletionVectorReader { DeletionVectorReader(RuntimeState* state, RuntimeProfile* profile, const TFileScanRangeParams& params, const TFileRangeDesc& range, io::IOContext* io_ctx) - : _state(state), _profile(profile), _range(range), _params(params), _io_ctx(io_ctx) {} + : _state(state), _profile(profile), _params(params), _io_ctx(io_ctx) { + _desc = DeleteFileDesc { + .key = "", + .path = range.path, + .fs_name = range.__isset.fs_name ? range.fs_name : "", + .start_offset = range.start_offset, + .size = range.size, + .file_size = range.__isset.file_size ? range.file_size : -1, + .modification_time = range.__isset.modification_time ? range.modification_time : 0}; + } + DeletionVectorReader(RuntimeState* state, RuntimeProfile* profile, + const TFileScanRangeParams& params, const DeleteFileDesc& desc, + io::IOContext* io_ctx) + : _state(state), _profile(profile), _params(params), _io_ctx(io_ctx) { + _desc = desc; + } ~DeletionVectorReader() = default; Status open(); Status read_at(size_t offset, Slice result); @@ -56,7 +87,7 @@ class DeletionVectorReader { private: RuntimeState* _state = nullptr; RuntimeProfile* _profile = nullptr; - const TFileRangeDesc& _range; + DeleteFileDesc _desc; const TFileScanRangeParams& _params; io::IOContext* _io_ctx = nullptr; diff --git a/be/src/format/table/iceberg_reader_mixin.h b/be/src/format/table/iceberg_reader_mixin.h index bd049342195695..2bc15f18cf141a 100644 --- a/be/src/format/table/iceberg_reader_mixin.h +++ b/be/src/format/table/iceberg_reader_mixin.h @@ -343,9 +343,6 @@ class IcebergReaderMixin : public BaseReader, public TableSchemaChangeHelper { // id -> block column name std::unordered_map _id_to_block_column_name; - // File column names used during init - std::vector _file_col_names; - std::function()> _create_topn_row_id_column_iterator; diff --git a/be/src/format_v2/column_data.h b/be/src/format_v2/column_data.h new file mode 100644 index 00000000000000..e6b08d549b3524 --- /dev/null +++ b/be/src/format_v2/column_data.h @@ -0,0 +1,407 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "common/consts.h" +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/field.h" +#include "exprs/vexpr_fwd.h" + +namespace doris::format { + +// File-local top-level column id. +// +// Scope: +// - Only valid inside one physical file schema returned by FileReader::get_schema(). +// - For Parquet, this is the top-level field ordinal in the new reader schema. +// - The synthetic row-position column also uses this type, with a reserved negative id. +// +// Do not use this for table/global column unique ids, block positions, nested child ids, or +// slot ids. Nested child ids are carried by LocalColumnIndex::index below. +class LocalColumnId { +public: + constexpr LocalColumnId() = default; + explicit constexpr LocalColumnId(int32_t id) : _id(id) {} + + static constexpr LocalColumnId invalid() { return LocalColumnId(); } + + constexpr int32_t value() const { return _id; } + constexpr bool is_valid() const { return _id >= 0; } + + constexpr bool operator==(const LocalColumnId& other) const { return _id == other._id; } + constexpr bool operator!=(const LocalColumnId& other) const { return !(*this == other); } + constexpr bool operator<(const LocalColumnId& other) const { return _id < other._id; } + +private: + int32_t _id = -1; +}; + +// Position of a file-local column in the Block produced by one FileScanRequest. +// +// This is assigned by TableColumnMapper/TableReader after predicate/non-predicate columns are +// deduplicated. It is not a file schema id and it is not stable across requests. Use value() only +// at the boundary where an existing Block or expression API still expects a size_t/int position. +class LocalIndex { +public: + constexpr LocalIndex() = default; + explicit constexpr LocalIndex(size_t index) : _index(index) {} + + constexpr size_t value() const { return _index; } + constexpr bool operator==(const LocalIndex& other) const { return _index == other._index; } + constexpr bool operator<(const LocalIndex& other) const { return _index < other._index; } + +private: + size_t _index = 0; +}; + +// Position of a table/global output column in the final Block returned by TableReader. +// +// This type is reserved for boundaries that need to refer to caller-visible column order. It must +// not be used to index a file-local Block, because schema evolution and lazy materialization can +// make file-local order different from table output order. +class GlobalIndex { +public: + constexpr GlobalIndex() = default; + explicit constexpr GlobalIndex(size_t index) : _index(index) {} + + constexpr size_t value() const { return _index; } + constexpr bool operator==(const GlobalIndex& other) const { return _index == other._index; } + constexpr bool operator<(const GlobalIndex& other) const { return _index < other._index; } + +private: + size_t _index = 0; +}; + +// Index of a split-local constant/default value used to materialize columns that are not read from +// the physical file, such as partition columns, added columns with default values, and virtual +// table-format columns. +// +// It is separate from LocalIndex because constants do not occupy a position in the file reader +// output block unless an expression explicitly materializes them. +class ConstantIndex { +public: + constexpr ConstantIndex() = default; + explicit constexpr ConstantIndex(size_t index) : _index(index) {} + + constexpr size_t value() const { return _index; } + constexpr bool operator==(const ConstantIndex& other) const { return _index == other._index; } + constexpr bool operator<(const ConstantIndex& other) const { return _index < other._index; } + +private: + size_t _index = 0; +}; + +inline std::ostream& operator<<(std::ostream& os, const LocalColumnId& id) { + return os << id.value(); +} + +inline std::ostream& operator<<(std::ostream& os, const LocalIndex& index) { + return os << index.value(); +} + +inline std::ostream& operator<<(std::ostream& os, const GlobalIndex& index) { + return os << index.value(); +} + +inline std::ostream& operator<<(std::ostream& os, const ConstantIndex& index) { + return os << index.value(); +} + +// A split/file-local constant value used to materialize a table/global column without reading a +// physical file column. +// +// Common producers are partition values, schema-evolution default expressions, generated columns +// and table-format virtual columns. The entry is keyed by ConstantIndex in ConstantMap; global_index +// keeps the link back to the caller-visible output column. +struct ConstantEntry { + GlobalIndex global_index; + VExprContextSPtr expr; + DataTypePtr type; +}; + +// Per mapping/split collection of constants. +// +// ConstantIndex only has meaning within this container. Keeping constants separate from LocalIndex +// makes it explicit that these values do not occupy positions in the file reader output Block. +class ConstantMap { +public: + ConstantIndex add(ConstantEntry entry) { + const auto index = ConstantIndex(_entries.size()); + _entries.push_back(std::move(entry)); + return index; + } + + const ConstantEntry& get(ConstantIndex index) const { + DORIS_CHECK(index.value() < _entries.size()); + return _entries[index.value()]; + } + + void clear() { _entries.clear(); } + bool empty() const { return _entries.empty(); } + size_t size() const { return _entries.size(); } + + const std::vector& entries() const { return _entries; } + +private: + std::vector _entries; +}; + +// Target of a localized filter. +// +// A filter can either reference a file-local Block position or a constant entry. Unset entries mean +// the filter cannot be evaluated below the table-reader finalize stage. +struct FilterEntry { + enum class Kind { + UNSET, + LOCAL, + CONSTANT, + }; + + static FilterEntry local(LocalIndex index) { + return {.kind = Kind::LOCAL, .index = index.value()}; + } + + static FilterEntry constant(ConstantIndex index) { + return {.kind = Kind::CONSTANT, .index = index.value()}; + } + + bool is_set() const { return kind != Kind::UNSET; } + bool is_local() const { return kind == Kind::LOCAL; } + bool is_constant() const { return kind == Kind::CONSTANT; } + + LocalIndex local_index() const { + DORIS_CHECK(is_local()); + return LocalIndex(index); + } + + ConstantIndex constant_index() const { + DORIS_CHECK(is_constant()); + return ConstantIndex(index); + } + + Kind kind = Kind::UNSET; + size_t index = 0; +}; + +enum ColumnType { + DATA_COLUMN = 0, // normal data column + ROW_NUMBER = 1, // row number in a file + GLOBAL_ROWID = 2, // global unique row id across files, used by TopN filter +}; + +struct GlobalRowIdContext { + uint8_t version = 0; + int64_t backend_id = 0; + uint32_t file_id = 0; +}; + +// Column schema definition shared by table/global projection and file-local schema matching. +// +// ColumnDefinition intentionally carries schema identity only. FE column unique ids are translated +// to GlobalIndex at the FileScannerV2 boundary and must not appear in table/file reader APIs. +struct ColumnDefinition { + // Typed identifier value used to match a column against another schema. + // + // - TYPE_NULL: no explicit identifier. BY_NAME falls back to ColumnDefinition::name. + // - TYPE_INT: interpreted by TableColumnMapperOptions::mode as a field id or file position. + // - TYPE_STRING: explicit name identifier. + // + // This is not the id that FileReader uses to read data. For example, a Parquet column can be + // matched by its optional Parquet field_id, while the reader still addresses it by a file-local + // ordinal. + Field identifier; + // Reader-local id of this node inside the file schema returned by FileReader::get_schema(). + // Top-level fields use the root column ordinal and nested fields use the child ordinal under + // their parent. -1 means unset; special virtual file columns may use other negative ids. + // Table/global ColumnDefinition values can leave this as -1 because they are not read directly + // by a FileReader. + int32_t local_id = -1; + // Logical table column name. This is also the matching name for by-name file formats. + std::string name; + // Historical or external names for the same logical field. Table formats such as Iceberg can + // use this to resolve partition path keys after column rename. + std::vector name_mapping {}; + DataTypePtr type; + // Projected nested table children. Children use table/global identifiers; they are resolved to + // file-local child ids by TableColumnMapper before reaching FileReader. + std::vector children {}; + // Expression used to materialize missing/default/generated values when the column is not read + // directly from the file. + VExprContextSPtr default_expr = nullptr; + // Partition columns are constants from split metadata and should not be matched against file + // schema unless table-format logic explicitly asks for it. + bool is_partition_key = false; + // File-local column kind. For table/global columns this remains DATA_COLUMN. + ColumnType column_type = ColumnType::DATA_COLUMN; + + bool has_identifier() const { return !identifier.is_null(); } + bool has_identifier_field_id() const { return identifier.get_type() == TYPE_INT; } + bool has_identifier_name() const { return identifier.get_type() == TYPE_STRING; } + + // DuckDB-style helper for BY_FIELD_ID matching. The mapper binds the matching mode once, so a + // TYPE_INT identifier is interpreted as a field id only by the field-id matcher. + int32_t get_identifier_field_id() const { + DORIS_CHECK(has_identifier_field_id()); + return identifier.get(); + } + // DuckDB-style helper for BY_NAME matching. When no explicit string identifier is present, the + // logical column name is the identifier. + const std::string& get_identifier_name() const { + if (identifier.is_null()) { + return name; + } + DORIS_CHECK(has_identifier_name()); + return identifier.get(); + } + // Helper for BY_INDEX matching. BY_INDEX reuses the TYPE_INT identifier as the table-side file + // position, matching DuckDB's typed identifier plus mapper-mode interpretation. + int32_t get_identifier_position() const { + DORIS_CHECK(has_identifier_field_id()); + return identifier.get(); + } + + // Helper for reader-local projection and scan requests. + int32_t file_local_id() const { + if (local_id != -1) { + return local_id; + } + return get_identifier_field_id(); + } + + std::string debug_string() const; +}; + +static constexpr int ROW_POSITION_COLUMN_ID = -10001; +static constexpr const char* ROW_POSITION_COLUMN_NAME = "__file_row_position"; +static constexpr int GLOBAL_ROWID_COLUMN_ID = -10002; + +inline ColumnDefinition row_position_column_definition() { + ColumnDefinition field; + field.identifier = Field::create_field(ROW_POSITION_COLUMN_ID); + field.local_id = ROW_POSITION_COLUMN_ID; + field.name = ROW_POSITION_COLUMN_NAME; + field.type = std::make_shared(); + field.column_type = ColumnType::ROW_NUMBER; + return field; +} + +inline ColumnDefinition global_rowid_column_definition() { + ColumnDefinition field; + field.identifier = Field::create_field(BeConsts::GLOBAL_ROWID_COL); + field.local_id = GLOBAL_ROWID_COLUMN_ID; + field.name = BeConsts::GLOBAL_ROWID_COL; + field.type = std::make_shared(); + field.column_type = ColumnType::GLOBAL_ROWID; + return field; +} + +// Recursive file-local projection path. +// +// For a root entry in FileScanRequest::{predicate_columns, non_predicate_columns}, index is the +// top-level file column id and column_id() is valid. For children, index is the file-local child id +// under the parent node. This is the reader schema local id, not an Iceberg/Parquet field id, not a +// table child id, and not a child output ordinal. +// +// project_all_children=true means the whole subtree under this node is needed. When false, children +// lists the selected child paths. File readers can use this to avoid constructing readers for +// unprojected nested children. +struct LocalColumnIndex { + int32_t index = -1; + bool project_all_children = true; + std::vector children {}; + + static LocalColumnIndex top_level(LocalColumnId column_id) { + return {.index = column_id.value()}; + } + + static LocalColumnIndex local(int32_t local_id) { return {.index = local_id}; } + + static LocalColumnIndex partial_local(int32_t local_id) { + return {.index = local_id, .project_all_children = false}; + } + + LocalColumnId column_id() const { return LocalColumnId(index); } + int32_t local_id() const { return index; } + std::string debug_string() const; +}; + +inline bool is_full_projection(const LocalColumnIndex* projection) { + return projection == nullptr || projection->project_all_children; +} + +inline bool is_partial_projection(const LocalColumnIndex* projection) { + return projection != nullptr && !projection->project_all_children; +} + +inline const LocalColumnIndex* find_child_projection(const LocalColumnIndex* projection, + int32_t local_id) { + if (is_full_projection(projection)) { + return nullptr; + } + const auto child_it = std::find_if( + projection->children.begin(), projection->children.end(), + [&](const LocalColumnIndex& child) { return child.local_id() == local_id; }); + return child_it == projection->children.end() ? nullptr : &*child_it; +} + +inline bool is_child_projected(const LocalColumnIndex* projection, int32_t local_id) { + return is_full_projection(projection) || find_child_projection(projection, local_id) != nullptr; +} + +// Merge two projection trees that point to the same file-local node. +// +// A full projection dominates a partial projection. Two partial projections are merged by child id +// and recursively union their child paths. The caller must only merge projections for the same +// root/child node. +inline Status merge_local_column_index(LocalColumnIndex* target, const LocalColumnIndex& source) { + DORIS_CHECK(target != nullptr); + DORIS_CHECK(target->index == source.index); + if (target->project_all_children) { + return Status::OK(); + } + if (source.project_all_children) { + target->project_all_children = true; + target->children.clear(); + return Status::OK(); + } + for (const auto& source_child : source.children) { + auto target_child_it = std::find_if( + target->children.begin(), target->children.end(), + [&](const LocalColumnIndex& child) { return child.index == source_child.index; }); + if (target_child_it == target->children.end()) { + target->children.push_back(source_child); + continue; + } + RETURN_IF_ERROR(merge_local_column_index(&*target_child_it, source_child)); + } + return Status::OK(); +} + +} // namespace doris::format diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp new file mode 100644 index 00000000000000..c63080a82074ca --- /dev/null +++ b/be/src/format_v2/column_mapper.cpp @@ -0,0 +1,1603 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/column_mapper.h" + +#include +#include +#include +#include +#include +#include + +#include "common/consts.h" +#include "common/exception.h" +#include "common/status.h" +#include "core/data_type/convert_field_to_type.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" +#include "core/data_type/primitive_type.h" +#include "exprs/short_circuit_evaluation_expr.h" +#include "exprs/vcase_expr.h" +#include "exprs/vcast_expr.h" +#include "exprs/vcompound_pred.h" +#include "exprs/vcondition_expr.h" +#include "exprs/vdirect_in_predicate.h" +#include "exprs/vectorized_fn_call.h" +#include "exprs/vexpr_context.h" +#include "exprs/vin_predicate.h" +#include "exprs/vliteral.h" +#include "exprs/vtopn_pred.h" +#include "format_v2/column_mapper_nested.h" +#include "format_v2/expr/cast.h" +#include "format_v2/file_reader.h" +#include "format_v2/schema_projection.h" +#include "format_v2/table_reader.h" +#include "gen_cpp/Exprs_types.h" + +namespace doris::format { + +namespace { + +std::string mapping_mode_to_string(TableColumnMappingMode mode) { + switch (mode) { + case TableColumnMappingMode::BY_FIELD_ID: + return "BY_FIELD_ID"; + case TableColumnMappingMode::BY_NAME: + return "BY_NAME"; + case TableColumnMappingMode::BY_INDEX: + return "BY_INDEX"; + } + return "UNKNOWN"; +} + +bool column_has_name(const ColumnDefinition& column, const std::string& name) { + if (to_lower(column.name) == to_lower(name)) { + return true; + } + if (column.has_identifier_name() && to_lower(column.get_identifier_name()) == to_lower(name)) { + return true; + } + return std::ranges::any_of(column.name_mapping, [&](const std::string& alias) { + return to_lower(alias) == to_lower(name); + }); +} + +bool column_names_match(const ColumnDefinition& lhs, const ColumnDefinition& rhs) { + if (column_has_name(rhs, lhs.name)) { + return true; + } + if (lhs.has_identifier_name() && column_has_name(rhs, lhs.get_identifier_name())) { + return true; + } + return std::ranges::any_of(lhs.name_mapping, [&](const std::string& alias) { + return column_has_name(rhs, alias); + }); +} + +class ColumnMatcher { +public: + virtual ~ColumnMatcher() = default; + virtual const ColumnDefinition* find( + const ColumnDefinition& table_column, + const std::vector& file_schema) const = 0; +}; + +class FieldIdMatcher final : public ColumnMatcher { +public: + const ColumnDefinition* find(const ColumnDefinition& table_column, + const std::vector& file_schema) const override { + if (!table_column.has_identifier_field_id()) { + return nullptr; + } + const auto field_id = table_column.get_identifier_field_id(); + const auto field_it = std::ranges::find_if(file_schema, [&](const ColumnDefinition& field) { + return field.has_identifier_field_id() && field.get_identifier_field_id() == field_id; + }); + return field_it == file_schema.end() ? nullptr : &*field_it; + } +}; + +class NameMatcher final : public ColumnMatcher { +public: + const ColumnDefinition* find(const ColumnDefinition& table_column, + const std::vector& file_schema) const override { + const auto field_it = std::ranges::find_if(file_schema, [&](const ColumnDefinition& field) { + return column_names_match(table_column, field); + }); + return field_it == file_schema.end() ? nullptr : &*field_it; + } +}; + +class PositionMatcher final : public ColumnMatcher { +public: + const ColumnDefinition* find(const ColumnDefinition& table_column, + const std::vector& file_schema) const override { + if (!table_column.has_identifier_field_id()) { + return nullptr; + } + const auto position = table_column.get_identifier_position(); + if (position < 0 || static_cast(position) >= file_schema.size()) { + return nullptr; + } + return &file_schema[static_cast(position)]; + } +}; + +const ColumnMatcher& matcher_for_mode(TableColumnMappingMode mode) { + static const FieldIdMatcher field_id_matcher; + static const NameMatcher name_matcher; + static const PositionMatcher position_matcher; + switch (mode) { + case TableColumnMappingMode::BY_FIELD_ID: + return field_id_matcher; + case TableColumnMappingMode::BY_NAME: + return name_matcher; + case TableColumnMappingMode::BY_INDEX: + return position_matcher; + } + return field_id_matcher; +} + +std::string virtual_column_type_to_string(TableVirtualColumnType type) { + switch (type) { + case TableVirtualColumnType::INVALID: + return "INVALID"; + case TableVirtualColumnType::ROW_ID: + return "ROW_ID"; + case TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER: + return "LAST_UPDATED_SEQUENCE_NUMBER"; + case TableVirtualColumnType::ICEBERG_ROWID: + return "ICEBERG_ROWID"; + } + return "UNKNOWN"; +} + +std::string filter_conversion_type_to_string(FilterConversionType type) { + switch (type) { + case FilterConversionType::COPY_DIRECTLY: + return "COPY_DIRECTLY"; + case FilterConversionType::CAST_FILTER: + return "CAST_FILTER"; + case FilterConversionType::READER_EXPRESSION: + return "READER_EXPRESSION"; + case FilterConversionType::FINALIZE_ONLY: + return "FINALIZE_ONLY"; + case FilterConversionType::CONSTANT: + return "CONSTANT"; + } + return "UNKNOWN"; +} + +std::string data_type_debug_string(const DataTypePtr& type) { + return type == nullptr ? "null" : type->get_name(); +} + +const Field* find_partition_value(const ColumnDefinition& table_column, + const std::map& partition_values) { + const auto find_by_name = [&](const std::string& name) -> const Field* { + const auto value_it = partition_values.find(name); + return value_it == partition_values.end() ? nullptr : &value_it->second; + }; + if (const auto* value = find_by_name(table_column.name); value != nullptr) { + return value; + } + if (table_column.has_identifier_name()) { + if (const auto* value = find_by_name(table_column.get_identifier_name()); + value != nullptr) { + return value; + } + } + for (const auto& alias : table_column.name_mapping) { + if (const auto* value = find_by_name(alias); value != nullptr) { + return value; + } + } + return nullptr; +} + +std::string field_debug_string(const Field& field) { + std::ostringstream out; + out << "Field{type=" << type_to_string(field.get_type()) << ", value="; + switch (field.get_type()) { + case TYPE_NULL: + out << "null"; + break; + case TYPE_INT: + out << field.get(); + break; + case TYPE_BIGINT: + out << field.get(); + break; + case TYPE_STRING: + out << field.get(); + break; + default: + out << field.to_debug_string(0); + break; + } + out << "}"; + return out.str(); +} + +template +std::string join_debug_strings(const std::vector& values, Formatter formatter) { + std::ostringstream out; + out << "["; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + out << ", "; + } + out << formatter(values[i]); + } + out << "]"; + return out.str(); +} + +} // namespace + +struct FileSlotRewriteInfo { + size_t block_position = 0; + DataTypePtr file_type; + DataTypePtr table_type; + std::string file_column_name; +}; + +struct RewriteContext { + RuntimeState* runtime_state = nullptr; + std::vector created_exprs {}; + + void add_created_expr(VExprSPtr expr) { created_exprs.push_back(std::move(expr)); } + + Status prepare_created_exprs(VExprContext* context) const { + DORIS_CHECK(context != nullptr); + RowDescriptor row_desc; + for (const auto& expr : created_exprs) { + if (dynamic_cast(expr.get()) != nullptr && runtime_state == nullptr) { + return Status::InvalidArgument( + "RuntimeState is required to prepare rewritten cast expression {}", + expr->expr_name()); + } + RETURN_IF_ERROR(expr->prepare(runtime_state, row_desc, context)); + } + return Status::OK(); + } +}; + +static VExprSPtr create_file_slot_ref(const VSlotRef& slot_ref, + const FileSlotRewriteInfo& rewrite_info, + RewriteContext* rewrite_context) { + auto ref = + VSlotRef::create_shared(slot_ref.slot_id(), cast_set(rewrite_info.block_position), + -1, rewrite_info.file_type, rewrite_info.file_column_name); + rewrite_context->add_created_expr(ref); + return ref; +} + +static bool is_cast_expr(const VExprSPtr& expr) { + return dynamic_cast(expr.get()) != nullptr; +} + +static bool is_binary_comparison_predicate(const VExprSPtr& expr) { + if (expr == nullptr || expr->get_num_children() != 2 || + (expr->node_type() != TExprNodeType::BINARY_PRED && + expr->node_type() != TExprNodeType::NULL_AWARE_BINARY_PRED)) { + return false; + } + switch (expr->op()) { + case TExprOpcode::EQ: + case TExprOpcode::EQ_FOR_NULL: + case TExprOpcode::NE: + case TExprOpcode::GE: + case TExprOpcode::GT: + case TExprOpcode::LE: + case TExprOpcode::LT: + return true; + default: + return false; + } +} + +std::string TableColumnMapperOptions::debug_string() const { + std::ostringstream out; + out << "TableColumnMapperOptions{mode=" << mapping_mode_to_string(mode) + << ", allow_missing_columns=" << allow_missing_columns << "}"; + return out.str(); +} + +std::string ColumnDefinition::debug_string() const { + std::ostringstream out; + out << "ColumnDefinition{name=" << name << ", identifier=" << field_debug_string(identifier) + << ", name_mapping=" + << join_debug_strings(name_mapping, [](const std::string& name) { return name; }) + << ", local_id=" << local_id << ", type=" << data_type_debug_string(type) << ", children=" + << join_debug_strings(children, + [](const ColumnDefinition& child) { return child.debug_string(); }) + << ", has_default_expr=" << (default_expr != nullptr) + << ", is_partition_key=" << is_partition_key << "}"; + return out.str(); +} + +std::string LocalColumnIndex::debug_string() const { + std::ostringstream out; + out << "LocalColumnIndex{index=" << index << ", project_all_children=" << project_all_children + << ", children=" + << join_debug_strings(children, + [](const LocalColumnIndex& child) { return child.debug_string(); }) + << "}"; + return out.str(); +} + +std::string ColumnMapping::debug_string() const { + std::ostringstream out; + out << "ColumnMapping{global_index=" << global_index + << ", table_column_name=" << table_column_name << ", file_local_id="; + if (file_local_id.has_value()) { + out << *file_local_id; + } else { + out << "null"; + } + out << ", constant_index="; + if (constant_index.has_value()) { + out << *constant_index; + } else { + out << "null"; + } + out << ", file_column_name=" << file_column_name + << ", original_file_type=" << data_type_debug_string(original_file_type) + << ", original_file_children=" + << join_debug_strings(original_file_children, + [](const ColumnDefinition& child) { return child.debug_string(); }) + << ", file_type=" << data_type_debug_string(file_type) + << ", table_type=" << data_type_debug_string(table_type) + << ", has_projection=" << (projection != nullptr) << ", child_mappings=" + << join_debug_strings(child_mappings, + [](const ColumnMapping& child) { return child.debug_string(); }) + << ", is_trivial=" << is_trivial << ", is_constant=" << constant_index.has_value() + << ", filter_conversion=" << filter_conversion_type_to_string(filter_conversion) + << ", virtual_column_type=" << virtual_column_type_to_string(virtual_column_type) + << ", has_default_expr=" << (default_expr != nullptr) << "}"; + return out.str(); +} + +std::string TableColumnMapper::debug_string() const { + std::ostringstream out; + out << "TableColumnMapper{options=" << _options.debug_string() << ", mappings=" + << join_debug_strings(_mappings, + [](const ColumnMapping& mapping) { return mapping.debug_string(); }) + << ", hidden_mappings=" + << join_debug_strings(_hidden_mappings, + [](const ColumnMapping& mapping) { return mapping.debug_string(); }) + << ", constant_count=" << _constant_map.size() << "}"; + return out.str(); +} + +static const FileSlotRewriteInfo* find_slot_rewrite_info( + const VExprSPtr& expr, + const std::map& global_to_file_slot, + const VSlotRef** slot_ref) { + if (expr == nullptr) { + return nullptr; + } + VExprSPtr slot_expr = expr; + const bool input_is_cast = is_cast_expr(expr) && expr->get_num_children() == 1; + if (is_cast_expr(expr) && expr->get_num_children() == 1) { + slot_expr = expr->children()[0]; + } + if (!slot_expr->is_slot_ref()) { + return nullptr; + } + const auto* candidate_slot_ref = assert_cast(slot_expr.get()); + const auto rewrite_it = global_to_file_slot.find(slot_ref_global_index(*candidate_slot_ref)); + if (rewrite_it == global_to_file_slot.end()) { + return nullptr; + } + if (input_is_cast && !expr->data_type()->equals(*rewrite_it->second.table_type)) { + return nullptr; + } + if (slot_ref != nullptr) { + *slot_ref = candidate_slot_ref; + } + return &rewrite_it->second; +} + +static bool filter_conversion_has_local_source(FilterConversionType conversion) { + switch (conversion) { + case FilterConversionType::COPY_DIRECTLY: + case FilterConversionType::CAST_FILTER: + case FilterConversionType::READER_EXPRESSION: + return true; + case FilterConversionType::FINALIZE_ONLY: + case FilterConversionType::CONSTANT: + return false; + } + return false; +} + +static bool column_predicate_can_use_local_source(FilterConversionType conversion) { + switch (conversion) { + case FilterConversionType::COPY_DIRECTLY: + return true; + case FilterConversionType::CAST_FILTER: + case FilterConversionType::READER_EXPRESSION: + case FilterConversionType::FINALIZE_ONLY: + case FilterConversionType::CONSTANT: + return false; + } + return false; +} + +static bool table_filter_has_only_local_entries( + const TableFilter& table_filter, const std::map& filter_entries) { + for (const auto global_index : table_filter.global_indices) { + const auto entry_it = filter_entries.find(global_index); + if (entry_it == filter_entries.end() || !entry_it->second.is_local()) { + return false; + } + } + return true; +} + +static VExprSPtr unwrap_literal_for_file_cast(const VExprSPtr& expr, + const DataTypePtr& table_type) { + if (expr == nullptr) { + return nullptr; + } + if (expr->is_literal()) { + return expr; + } + if (is_cast_expr(expr) && expr->get_num_children() == 1 && expr->children()[0]->is_literal() && + expr->children()[0]->data_type()->equals(*table_type)) { + return expr->children()[0]; + } + return nullptr; +} + +static Field literal_field_from_expr(const VExpr& literal_expr) { + DORIS_CHECK(literal_expr.is_literal()); + const auto* literal = dynamic_cast(&literal_expr); + DORIS_CHECK(literal != nullptr); + Field field; + literal->get_column_ptr()->get(0, field); + return field; +} + +// Table filter localization clones an already-prepared table expr and then rewrites it to file +// slots. Only split-local literals and BE cast nodes need table-reader-specific clone behavior; +// plain slot refs and literals use their own VExpr::clone_node(). +static Status clone_table_expr_node(const VExpr& expr, VExprSPtr* cloned_expr) { + DORIS_CHECK(cloned_expr != nullptr); + if (const auto* split_literal = dynamic_cast(&expr)) { + *cloned_expr = std::make_shared( + split_literal->data_type(), literal_field_from_expr(expr), + split_literal->original_type(), split_literal->original_field()); + } else if (const auto* vcast_expr = dynamic_cast(&expr); + vcast_expr != nullptr && vcast_expr->node_type() == TExprNodeType::CAST_EXPR) { + *cloned_expr = Cast::create_shared(vcast_expr->data_type()); + } + return Status::OK(); +} + +Status clone_table_expr_tree(const VExprSPtr& expr, VExprSPtr* cloned_expr) { + DORIS_CHECK(cloned_expr != nullptr); + if (expr == nullptr) { + *cloned_expr = nullptr; + return Status::OK(); + } + return expr->deep_clone(cloned_expr, clone_table_expr_node); +} + +static VExprSPtr original_table_literal(const VExprSPtr& literal_expr, + RewriteContext* rewrite_context = nullptr) { + DORIS_CHECK(literal_expr != nullptr); + DORIS_CHECK(literal_expr->is_literal()); + const auto* rewritten_literal = dynamic_cast(literal_expr.get()); + if (rewritten_literal == nullptr) { + return literal_expr; + } + auto literal = VLiteral::create_shared(rewritten_literal->original_type(), + rewritten_literal->original_field()); + if (rewrite_context != nullptr) { + rewrite_context->add_created_expr(literal); + } + return literal; +} + +static ColumnDefinition hidden_column_from_slot_ref(const VSlotRef& slot_ref) { + ColumnDefinition column; + column.name = slot_ref.column_name(); + column.identifier = Field::create_field(column.name); + column.type = slot_ref.data_type(); + return column; +} + +static void collect_top_level_slot_columns(const VExprSPtr& expr, + std::map* columns) { + DORIS_CHECK(columns != nullptr); + if (expr == nullptr) { + return; + } + if (expr->is_slot_ref()) { + const auto* slot_ref = assert_cast(expr.get()); + columns->try_emplace(slot_ref_global_index(*slot_ref), + hidden_column_from_slot_ref(*slot_ref)); + return; + } + for (const auto& child : expr->children()) { + collect_top_level_slot_columns(child, columns); + } +} + +static std::vector projected_file_children_from_child_mappings( + const std::vector& original_file_children, + const std::vector& child_mappings) { + std::vector result; + result.reserve(child_mappings.size()); + for (const auto* child_mapping : present_child_mappings_in_file_order(child_mappings)) { + const auto file_child_it = std::ranges::find_if( + original_file_children, [&](const ColumnDefinition& file_child) { + return file_child.file_local_id() == *child_mapping->file_local_id; + }); + DORIS_CHECK(file_child_it != original_file_children.end()); + ColumnDefinition projected_child = *file_child_it; + projected_child.type = child_mapping->file_type; + projected_child.children = child_mapping->projected_file_children; + result.push_back(std::move(projected_child)); + } + return result; +} + +static VExprSPtr rewrite_literal_to_file_type(const VExprSPtr& literal_expr, + const FileSlotRewriteInfo& rewrite_info, + RewriteContext* rewrite_context) { + DORIS_CHECK(literal_expr != nullptr); + DORIS_CHECK(literal_expr->is_literal()); + const auto original_literal = original_table_literal(literal_expr, rewrite_context); + const Field original_field = literal_field(original_literal); + if (rewrite_info.file_type->equals(*original_literal->data_type())) { + return original_literal; + } + Field file_field; + try { + convert_field_to_type(original_field, *rewrite_info.file_type, &file_field, + original_literal->data_type().get()); + } catch (const Exception&) { + return nullptr; + } + if (file_field.is_null()) { + return nullptr; + } + if (file_field.get_type() != remove_nullable(rewrite_info.file_type)->get_primitive_type()) { + return nullptr; + } + auto literal = std::make_shared( + rewrite_info.file_type, file_field, original_literal->data_type(), original_field); + rewrite_context->add_created_expr(literal); + return literal; +} + +static bool rewrite_binary_slot_literal_predicate( + const VExprSPtr& expr, + const std::map& global_to_file_slot, + RewriteContext* rewrite_context) { + if (!is_binary_comparison_predicate(expr)) { + return false; + } + auto children = expr->children(); + const VSlotRef* slot_ref = nullptr; + const FileSlotRewriteInfo* rewrite_info = + find_slot_rewrite_info(children[0], global_to_file_slot, &slot_ref); + int slot_child_idx = 0; + int literal_child_idx = 1; + if (rewrite_info == nullptr) { + rewrite_info = find_slot_rewrite_info(children[1], global_to_file_slot, &slot_ref); + slot_child_idx = 1; + literal_child_idx = 0; + } + if (rewrite_info == nullptr || slot_ref == nullptr) { + return false; + } + auto literal_expr = + unwrap_literal_for_file_cast(children[literal_child_idx], rewrite_info->table_type); + if (literal_expr == nullptr) { + return false; + } + + auto rewritten_literal = + rewrite_literal_to_file_type(literal_expr, *rewrite_info, rewrite_context); + if (rewritten_literal == nullptr) { + children[literal_child_idx] = original_table_literal(literal_expr, rewrite_context); + expr->set_children(std::move(children)); + return false; + } + + children[slot_child_idx] = create_file_slot_ref(*slot_ref, *rewrite_info, rewrite_context); + children[literal_child_idx] = std::move(rewritten_literal); + expr->set_children(std::move(children)); + return true; +} + +static bool rewrite_in_slot_literal_predicate( + const VExprSPtr& expr, + const std::map& global_to_file_slot, + RewriteContext* rewrite_context) { + if (expr->node_type() != TExprNodeType::IN_PRED || expr->get_num_children() < 2) { + return false; + } + auto children = expr->children(); + const VSlotRef* slot_ref = nullptr; + const FileSlotRewriteInfo* rewrite_info = + find_slot_rewrite_info(children[0], global_to_file_slot, &slot_ref); + if (rewrite_info == nullptr || slot_ref == nullptr) { + return false; + } + + VExprSPtrs rewritten_literals; + rewritten_literals.reserve(children.size() - 1); + for (size_t child_idx = 1; child_idx < children.size(); ++child_idx) { + auto literal_expr = + unwrap_literal_for_file_cast(children[child_idx], rewrite_info->table_type); + if (literal_expr == nullptr) { + return false; + } + auto rewritten_literal = + rewrite_literal_to_file_type(literal_expr, *rewrite_info, rewrite_context); + if (rewritten_literal == nullptr) { + for (size_t restore_idx = 1; restore_idx < children.size(); ++restore_idx) { + auto restore_literal = unwrap_literal_for_file_cast(children[restore_idx], + rewrite_info->table_type); + if (restore_literal != nullptr) { + children[restore_idx] = + original_table_literal(restore_literal, rewrite_context); + } + } + expr->set_children(std::move(children)); + return false; + } + rewritten_literals.push_back(std::move(rewritten_literal)); + } + + children[0] = create_file_slot_ref(*slot_ref, *rewrite_info, rewrite_context); + for (size_t literal_idx = 0; literal_idx < rewritten_literals.size(); ++literal_idx) { + children[literal_idx + 1] = std::move(rewritten_literals[literal_idx]); + } + expr->set_children(std::move(children)); + return true; +} + +static VExprSPtr rewrite_table_expr_to_file_expr( + const VExprSPtr& expr, + const std::map& global_to_file_slot, + RewriteContext* rewrite_context) { + if (expr == nullptr) { + return nullptr; + } + DORIS_CHECK(rewrite_context != nullptr); + if (rewrite_binary_slot_literal_predicate(expr, global_to_file_slot, rewrite_context)) { + return expr; + } + if (rewrite_in_slot_literal_predicate(expr, global_to_file_slot, rewrite_context)) { + return expr; + } + if (is_struct_element_expr(expr)) { + auto children = expr->children(); + if (children[0]->is_slot_ref()) { + const auto* slot_ref = assert_cast(children[0].get()); + const auto rewrite_it = global_to_file_slot.find(slot_ref_global_index(*slot_ref)); + if (rewrite_it != global_to_file_slot.end()) { + // struct_element must see the actual file struct layout. Casting the parent struct + // to the output projection can hide filter-only children such as `s.id` in + // `SELECT s.name WHERE s.id > 5`. + children[0] = create_file_slot_ref(*slot_ref, rewrite_it->second, rewrite_context); + expr->set_children(std::move(children)); + return expr; + } + } + children[0] = + rewrite_table_expr_to_file_expr(children[0], global_to_file_slot, rewrite_context); + expr->set_children(std::move(children)); + return expr; + } + if (expr->is_slot_ref()) { + const auto* slot_ref = assert_cast(expr.get()); + const auto rewrite_it = global_to_file_slot.find(slot_ref_global_index(*slot_ref)); + if (rewrite_it != global_to_file_slot.end()) { + const auto& rewrite_info = rewrite_it->second; + auto file_slot = create_file_slot_ref(*slot_ref, rewrite_info, rewrite_context); + if (rewrite_info.file_type->equals(*rewrite_info.table_type)) { + return file_slot; + } + auto cast_expr = Cast::create_shared(rewrite_info.table_type); + cast_expr->add_child(std::move(file_slot)); + rewrite_context->add_created_expr(cast_expr); + return cast_expr; + } + return expr; + } + // The input is a split-local cloned tree. A previous split-local clone may already have + // inserted Cast(slot). Keep that rewrite idempotent: rewrite the cast child from table slot to + // the current split's file slot, and drop the cast when the current split no longer needs it. + if (is_cast_expr(expr) && expr->get_num_children() == 1) { + const auto& child = expr->children()[0]; + if (child->is_slot_ref()) { + const auto* slot_ref = assert_cast(child.get()); + const auto rewrite_it = global_to_file_slot.find(slot_ref_global_index(*slot_ref)); + if (rewrite_it != global_to_file_slot.end() && + expr->data_type()->equals(*rewrite_it->second.table_type)) { + auto rewritten_child = + create_file_slot_ref(*slot_ref, rewrite_it->second, rewrite_context); + if (rewrite_it->second.file_type->equals(*rewrite_it->second.table_type)) { + return rewritten_child; + } + expr->set_children({std::move(rewritten_child)}); + return expr; + } + } + } + + VExprSPtrs rewritten_children; + rewritten_children.reserve(expr->children().size()); + for (const auto& child : expr->children()) { + rewritten_children.push_back( + rewrite_table_expr_to_file_expr(child, global_to_file_slot, rewrite_context)); + } + expr->set_children(std::move(rewritten_children)); + return expr; +} + +static constexpr const char* ROW_LINEAGE_ROW_ID = "_row_id"; +static constexpr const char* ROW_LINEAGE_LAST_UPDATED_SEQ_NUMBER = "_last_updated_sequence_number"; + +static TableVirtualColumnType row_lineage_virtual_column_type(const std::string& column_name) { + if (column_name == ROW_LINEAGE_ROW_ID) { + return TableVirtualColumnType::ROW_ID; + } + if (column_name == ROW_LINEAGE_LAST_UPDATED_SEQ_NUMBER) { + return TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER; + } + return TableVirtualColumnType::INVALID; +} + +// Returns true when the current file type is not the exact nested type the scan should expose. +// This is about building the projected file-side type/projection, not about whether TableReader +// later needs to rematerialize the complex value back to table layout. +static bool needs_projected_file_type_rebuild(const ColumnMapping& mapping) { + if (!is_complex_type(mapping.file_type->get_primitive_type())) { + return false; + } + if (mapping.child_mappings.empty()) { + return false; + } + DORIS_CHECK(mapping.file_type != nullptr); + DORIS_CHECK(mapping.table_type != nullptr); + if (remove_nullable(mapping.file_type)->get_primitive_type() != + remove_nullable(mapping.table_type)->get_primitive_type()) { + return true; + } + if (!mapping.table_type->equals(*mapping.file_type)) { + return true; + } + for (const auto& child_mapping : mapping.child_mappings) { + // Rename-only child mappings do not change the file-side projected shape. If field-id + // matching maps table child `renamed_b` to file child `b`, the file reader can still expose + // the original file type as long as child count/order/types are unchanged. + if (!child_mapping.file_local_id.has_value() || + needs_projected_file_type_rebuild(child_mapping)) { + return true; + } + } + return false; +} + +static bool has_projected_file_children(const ColumnMapping& mapping) { + if (mapping.original_file_children.empty() || mapping.projected_file_children.empty()) { + return false; + } + if (mapping.original_file_children.size() != mapping.projected_file_children.size()) { + return true; + } + for (size_t idx = 0; idx < mapping.original_file_children.size(); ++idx) { + if (mapping.original_file_children[idx].file_local_id() != + mapping.projected_file_children[idx].file_local_id()) { + return true; + } + } + return false; +} + +static bool needs_nested_file_projection(const ColumnMapping& mapping) { + if (has_projected_file_children(mapping)) { + // Return True if the projected child column is missing / re-ordered + return true; + } + return std::ranges::any_of(mapping.child_mappings, [](const ColumnMapping& child_mapping) { + return needs_nested_file_projection(child_mapping); + }); +} + +// Build the projected file type according to the pruned complex projection. For example, if we +// have a struct column `s` with children `id` and `name`, and the projection only keeps `s.name`, +// then we need to build the projected file type of `s` to only contain `name` so that the file +// reader can read it correctly. +static Status build_projected_child_type(const DataTypePtr& file_type, + const std::vector& child_mappings, + DataTypePtr* projected_type) { + DORIS_CHECK(file_type != nullptr); + DORIS_CHECK(projected_type != nullptr); + DataTypes child_types; + Strings child_names; + child_types.reserve(child_mappings.size()); + child_names.reserve(child_mappings.size()); + for (const auto* child_mapping : present_child_mappings_in_file_order(child_mappings)) { + child_types.push_back(child_mapping->file_type); + child_names.push_back(child_mapping->file_column_name); + } + return rebuild_projected_type(file_type, child_types, child_names, projected_type); +} + +// Build the complex column projection according to the ColumnMapping which is re-ordered by the file-schema's order. +static Status build_complex_projection(const ColumnMapping& mapping, LocalColumnIndex* projection) { + if (projection == nullptr) { + return Status::InvalidArgument("projection is null"); + } + DORIS_CHECK(mapping.file_local_id.has_value()); + *projection = LocalColumnIndex::local(*mapping.file_local_id); + projection->project_all_children = mapping.child_mappings.empty(); + projection->children.clear(); + for (const auto* child_mapping : present_child_mappings_in_file_order(mapping.child_mappings)) { + LocalColumnIndex child_projection; + RETURN_IF_ERROR(build_complex_projection(*child_mapping, &child_projection)); + projection->children.push_back(std::move(child_projection)); + } + if (!projection->project_all_children && projection->children.empty()) { + return Status::NotSupported("Projection for complex column {} contains no file children", + mapping.file_column_name); + } + return Status::OK(); +} + +using FilterProjectionMap = std::map; + +// Update the mapping's file type according to the projection, and determine whether the projection +// is trivial (i.e. the projected file type is the same as the table type, so no need to +// rematerialize the complex value back to table layout after reading from file). +static Status apply_projection_to_mapping_file_type(const LocalColumnIndex& projection, + ColumnMapping* mapping) { + DORIS_CHECK(mapping != nullptr); + if (mapping->original_file_type == nullptr) { + mapping->original_file_type = mapping->file_type; + } + if (mapping->original_file_type == nullptr || + !is_complex_type(remove_nullable(mapping->original_file_type)->get_primitive_type())) { + return Status::OK(); + } + ColumnDefinition field; + field.type = mapping->original_file_type; + field.children = mapping->original_file_children; + ColumnDefinition projected_field; + RETURN_IF_ERROR(project_column_definition(field, projection, &projected_field)); + mapping->file_type = std::move(projected_field.type); + mapping->projected_file_children = std::move(projected_field.children); + mapping->is_trivial = + mapping->table_type != nullptr && mapping->table_type->equals(*mapping->file_type); + return Status::OK(); +} + +static Status merge_filter_projection(const FilterProjectionMap* filter_projections, + LocalColumnIndex* projection) { + DORIS_CHECK(projection != nullptr); + if (filter_projections == nullptr) { + return Status::OK(); + } + const auto filter_projection_it = filter_projections->find(projection->column_id()); + if (filter_projection_it == filter_projections->end()) { + return Status::OK(); + } + // Merge predicate-only nested paths into the root projection that is about to be scanned. + // Example: `SELECT s.a WHERE s.b > 1` first builds the output projection `s -> a` from + // ColumnMapping, while build_filter_projection_map() records `s -> b`. This merge produces + // one file scan projection `s -> a,b`. + RETURN_IF_ERROR(merge_local_column_index(projection, filter_projection_it->second)); + return Status::OK(); +} + +static void sort_projection_children_by_file_id(LocalColumnIndex* projection) { + DORIS_CHECK(projection != nullptr); + if (projection->project_all_children) { + return; + } + for (auto& child : projection->children) { + sort_projection_children_by_file_id(&child); + } + std::ranges::sort(projection->children, + [](const LocalColumnIndex& lhs, const LocalColumnIndex& rhs) { + return lhs.local_id() < rhs.local_id(); + }); +} + +static Status add_scan_column(FileScanRequest* file_request, ColumnMapping* mapping, + std::vector* scan_columns, bool is_predicate_column, + const FilterProjectionMap* filter_projections = nullptr) { + const auto file_column_id = LocalColumnId(mapping->file_local_id.value()); + if (!is_predicate_column && + std::ranges::find_if(file_request->predicate_columns, [&](const LocalColumnIndex& p) { + return p.column_id() == file_column_id; + }) != file_request->predicate_columns.end()) { + // This column is already projected for predicates, so skip adding it to non-predicate columns. + return Status::OK(); + } + // local_positions is the global read-column index for this scan request, so it also + // deduplicates predicate_columns and non_predicate_columns across all filter/projection paths. + const bool newly_added = file_request->local_positions.count(file_column_id) == 0; + if (newly_added) { + file_request->local_positions.emplace(file_column_id, + LocalIndex(file_request->local_positions.size())); + } + LocalColumnIndex projection = LocalColumnIndex::top_level(file_column_id); + if (needs_nested_file_projection(*mapping)) { + RETURN_IF_ERROR(build_complex_projection(*mapping, &projection)); + } + if (is_predicate_column) { + DCHECK(filter_projections != nullptr); + // If a projected complex root is also used by a predicate, rebuild the predicate scan + // projection from the output mapping before merging predicate-only children. For + // `SELECT s.a WHERE s.b > 1`, build_complex_projection() produces `s -> a` and + // merge_filter_projection() adds `s -> b`, so the predicate column reads both children. + RETURN_IF_ERROR(merge_filter_projection(filter_projections, &projection)); + } + auto existing_projection_it = std::ranges::find_if( + *scan_columns, + [&](const LocalColumnIndex& p) { return p.column_id() == file_column_id; }); + auto exists = existing_projection_it != scan_columns->end(); + if (exists) { + // Merge with existing projection if already exists, to avoid duplicated projections when the same column is used in multiple predicates and/or projections. + RETURN_IF_ERROR(merge_local_column_index(&*existing_projection_it, projection)); + sort_projection_children_by_file_id(&*existing_projection_it); + } else { + scan_columns->push_back(std::move(projection)); + } + if (is_predicate_column) { + // The predicate projection now contains both the output children and filter-only children + // for this root, so the duplicate non-predicate root can be removed without losing output + // data. + auto it = std::ranges::find_if( + file_request->non_predicate_columns, + [&](const LocalColumnIndex& p) { return p.column_id() == file_column_id; }); + if (it != file_request->non_predicate_columns.end()) { + file_request->non_predicate_columns.erase(it); + } + } + return Status::OK(); +} + +static const LocalColumnIndex* find_scan_projection( + const std::vector& scan_columns, LocalColumnId file_column_id) { + const auto projection_it = + std::ranges::find_if(scan_columns, [&](const LocalColumnIndex& projection) { + return projection.column_id() == file_column_id; + }); + return projection_it == scan_columns.end() ? nullptr : &*projection_it; +} + +// Apply the final scan projection of one root file column back to its ColumnMapping. This updates +// mapping.file_type/projected_file_children from the original file schema to the exact shape that +// FileReader will return. +// +// Example: for `SELECT s.a WHERE s.b > 1`, add_scan_column() keeps only one predicate scan +// projection `s -> a,b`. Applying that projection changes the mapping's file type from the full +// file struct `s` to the projected file struct `s`, so later filter rewrite and +// TableReader final materialization use the same column shape as the file-local block. +static Status apply_scan_projection_to_mapping_file_type(const FileScanRequest& file_request, + ColumnMapping* mapping) { + DORIS_CHECK(mapping != nullptr); + DORIS_CHECK(mapping->file_local_id.has_value()); + const auto file_column_id = LocalColumnId(*mapping->file_local_id); + // Predicate columns are the actual scan projection when a column is used by row-level filters: + // add_scan_column() removes the duplicate non-predicate projection in that case. + const auto* projection = find_scan_projection(file_request.predicate_columns, file_column_id); + if (projection == nullptr) { + projection = find_scan_projection(file_request.non_predicate_columns, file_column_id); + } + DORIS_CHECK(projection != nullptr); + return apply_projection_to_mapping_file_type(*projection, mapping); +} + +// Build extra scan projections required only by row-level filters on nested struct children. +// +// Example: for `SELECT s.a FROM t WHERE s.b.c > 1`, the output projection may only contain `s.a`, +// but the file reader must also read `s.b.c` to evaluate the predicate. This function collects the +// filter path `s -> b -> c`, resolves it against the root mapping's original file schema, and +// records a root projection like `s -> b -> c` in filter_projections. When add_scan_column() adds +// the same root as a predicate column, it rebuilds that root from the projection mapping, merges +// this filter-only projection into it, and removes the duplicate non-predicate root entry. +static Status build_filter_projection_map(const std::vector& table_filters, + const std::vector& mappings, + FilterProjectionMap* filter_projections) { + DORIS_CHECK(filter_projections != nullptr); + filter_projections->clear(); + for (const auto& table_filter : table_filters) { + if (table_filter.conjunct == nullptr) { + continue; + } + // Collect all the nested struct paths in the filter conjunct. For example, for a filter + // like `s.id > 5 AND s.name = 'abc'`, collect `s -> id` and `s -> name`, then build scan + // projections for those paths from the file schema. + std::vector paths; + collect_nested_struct_paths(table_filter.conjunct->root(), &paths); + for (const auto& path : paths) { + auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { + return mapping.global_index == path.root_global_index; + }); + if (mapping_it == mappings.end() || !mapping_it->file_local_id.has_value() || + path.selectors.empty()) { + continue; + } + + LocalColumnIndex child_projection; + RETURN_IF_ERROR(build_file_child_projection_from_schema( + mapping_it->original_file_children, path.selectors, &child_projection)); + if (child_projection.local_id() < 0) { + continue; + } + auto root_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); + root_projection.children.push_back(std::move(child_projection)); + auto filter_projection_it = filter_projections->find(root_projection.column_id()); + if (filter_projection_it == filter_projections->end()) { + filter_projections->emplace(root_projection.column_id(), + std::move(root_projection)); + continue; + } + RETURN_IF_ERROR( + merge_local_column_index(&filter_projection_it->second, root_projection)); + } + } + return Status::OK(); +} + +static bool needs_complex_rematerialize(const ColumnMapping& mapping) { + return !mapping.is_trivial && !mapping.child_mappings.empty(); +} + +static void rebuild_projection(ColumnMapping* mapping, LocalIndex block_position) { + DORIS_CHECK(mapping->file_local_id.has_value()); + if (mapping->is_trivial || needs_complex_rematerialize(*mapping)) { + mapping->projection = VExprContext::create_shared(VSlotRef::create_shared( + cast_set(block_position.value()), cast_set(block_position.value()), -1, + mapping->file_type, mapping->file_column_name)); + return; + } + + auto expr = Cast::create_shared(mapping->table_type); + expr->add_child(VSlotRef::create_shared(cast_set(block_position.value()), + cast_set(block_position.value()), -1, + mapping->file_type, mapping->file_column_name)); + mapping->projection = VExprContext::create_shared(expr); +} + +// Build file slot rewrite info from the localized filter targets. Only local targets can enter +// file-reader expressions; constant and unset targets stay above the file reader. +static std::map build_file_slot_rewrite_map( + const std::vector& mappings, + const std::map& filter_entries) { + std::map global_to_file_slot; + for (const auto& mapping : mappings) { + const auto entry_it = filter_entries.find(mapping.global_index); + if (entry_it == filter_entries.end() || !entry_it->second.is_local()) { + continue; + } + DORIS_CHECK(mapping.file_local_id.has_value()); + global_to_file_slot.emplace( + mapping.global_index, + FileSlotRewriteInfo {.block_position = entry_it->second.local_index().value(), + .file_type = mapping.file_type, + .table_type = mapping.table_type, + .file_column_name = mapping.file_column_name}); + } + return global_to_file_slot; +} + +static const ColumnDefinition* find_file_child_by_table_column( + const ColumnDefinition& table_column, const std::vector& file_children, + TableColumnMappingMode mode) { + return matcher_for_mode(mode).find(table_column, file_children); +} + +static const ColumnDefinition* find_file_child_for_complex_wrapper( + const ColumnDefinition& table_child, const ColumnDefinition& file_field, + TableColumnMappingMode mode) { + if (file_field.children.empty()) { + return nullptr; + } + const auto* file_child = + find_file_child_by_table_column(table_child, file_field.children, mode); + if (file_child != nullptr) { + return file_child; + } + if (remove_nullable(file_field.type)->get_primitive_type() == TYPE_MAP && + file_field.children.size() == 1 && column_has_name(table_child, "entries")) { + return &file_field.children[0]; + } + return nullptr; +} + +Status TableColumnMapper::_create_by_index_mapping(const ColumnDefinition& table_column, + const std::vector& file_schema, + ColumnMapping* mapping) { + DORIS_CHECK(mapping != nullptr); + DORIS_CHECK(!table_column.is_partition_key); + + // Key contract: in BY_INDEX mode, `ColumnDefinition::identifier` TYPE_INT is interpreted as the + // 0-based position of this column inside `file_schema`. FE writes the physical file position + // of each non-partition projected column into that identifier. This interpretation allows: + // - sparse projection: read only a subset of file columns (for example only `_col2` + // and `_col4`); + // - column reordering: table column order differs from file column order; + // - no many-to-one mapping: FE must guarantee that each file position is referenced by at + // most one table column. + const auto file_index = table_column.get_identifier_position(); + + // Case A: file_index is in range, so build a direct positional mapping. + // The file column name (for example `_col0`) is intentionally ignored here. + if (file_index >= 0 && static_cast(file_index) < file_schema.size()) { + return _create_direct_mapping(table_column, file_schema[static_cast(file_index)], + mapping); + } + + // Case B: file_index is out of range, which means the file does not contain this column. + // Route it through the missing-column path used by schema evolution. + // B1: the table column carries a default_expr injected by FE, so use the constant branch and + // materialize that value for every row. + if (table_column.default_expr != nullptr) { + _set_constant_mapping(mapping, table_column.default_expr); + return Status::OK(); + } + // B2: if missing columns are not allowed, fail explicitly instead of silently producing + // NULLs and hiding the issue. + if (!_options.allow_missing_columns) { + return Status::InvalidArgument( + "Table column '{}' (file_index={}) is out of range for file schema of size {}", + table_column.name, file_index, file_schema.size()); + } + // B3: if missing columns are allowed, keep the mapping empty + // (`file_column_id` remains `nullopt`) and let the upper finalize stage fill + // NULL/default values. + return Status::OK(); +} + +void TableColumnMapper::_set_constant_mapping(ColumnMapping* mapping, VExprContextSPtr expr) { + DORIS_CHECK(mapping != nullptr); + DORIS_CHECK(expr != nullptr); + mapping->default_expr = std::move(expr); + mapping->constant_index = _constant_map.add(ConstantEntry { + .global_index = mapping->global_index, + .expr = mapping->default_expr, + .type = mapping->table_type, + }); + mapping->filter_conversion = FilterConversionType::CONSTANT; +} + +Status TableColumnMapper::_create_mapping_for_column(const ColumnDefinition& table_column, + GlobalIndex global_index, + ColumnMapping* mapping) { + DORIS_CHECK(mapping != nullptr); + *mapping = ColumnMapping {}; + mapping->global_index = global_index; + mapping->table_column_name = table_column.name; + mapping->table_type = table_column.type; + const auto row_lineage_type = row_lineage_virtual_column_type(table_column.name); + if (const auto* partition_value = find_partition_value(table_column, _partition_values); + table_column.is_partition_key && partition_value != nullptr) { + // Partition values are split constants and must take precedence over defaults. + _set_constant_mapping(mapping, VExprContext::create_shared(VLiteral::create_shared( + mapping->table_type, *partition_value))); + } else if (_options.mode == TableColumnMappingMode::BY_INDEX && + !table_column.is_partition_key && table_column.has_identifier_field_id()) { + // BY_INDEX interprets ColumnDefinition::identifier as physical file position. + RETURN_IF_ERROR(_create_by_index_mapping(table_column, _file_schema, mapping)); + } else if (const auto* file_field = _find_file_field(table_column, _file_schema)) { + // Normal physical file column mapping. + RETURN_IF_ERROR(_create_direct_mapping(table_column, *file_field, mapping)); + if (row_lineage_type != TableVirtualColumnType::INVALID) { + // Iceberg v3 rewritten files may physically contain row lineage metadata fields. + // File non-null values must be preserved, while file NULLs still inherit from data file + // metadata in IcebergTableReader. Therefore the mapping has a real file source plus a + // virtual post-materialization step, and filters must wait for finalize output. + mapping->virtual_column_type = row_lineage_type; + mapping->filter_conversion = FilterConversionType::FINALIZE_ONLY; + } + } else if (row_lineage_type != TableVirtualColumnType::INVALID) { + // Iceberg row lineage metadata fields are optional in data files. Missing fields are exposed + // as all-NULL table columns first; IcebergTableReader fills inherited values only when the + // split carries first_row_id / last_updated_sequence_number metadata. + // FE may attach a default_expr to these hidden metadata columns, but the Iceberg v3 + // inheritance rule must take precedence over the generic missing-column default path. + mapping->virtual_column_type = row_lineage_type; + } else if (table_column.name == BeConsts::ICEBERG_ROWID_COL) { + // Doris internal Iceberg row locator is never a physical Iceberg data column. It is built + // from file path, row position and partition metadata for delete/update/merge. + mapping->virtual_column_type = TableVirtualColumnType::ICEBERG_ROWID; + } else if (table_column.default_expr != nullptr) { + // Missing schema-evolution column with an explicit default expression. + _set_constant_mapping(mapping, table_column.default_expr); + } else { + if (table_column.is_partition_key) { + return Status::InvalidArgument( + "Table column '{}' (global_index={}) does not have a matching partition value", + table_column.name, mapping->global_index.value()); + } + if (!_options.allow_missing_columns) { + return Status::InvalidArgument( + "Table column '{}' (global_index={}) does not have a matching file column", + table_column.name, mapping->global_index.value()); + } + } + return Status::OK(); +} + +Status TableColumnMapper::_create_hidden_filter_mapping(const ColumnDefinition& table_column, + GlobalIndex global_index, + ColumnMapping* mapping) { + auto status = _create_mapping_for_column(table_column, global_index, mapping); + if (mapping->file_local_id.has_value() || mapping->constant_index.has_value() || + mapping->virtual_column_type != TableVirtualColumnType::INVALID) { + return Status::OK(); + } + if (_options.mode == TableColumnMappingMode::BY_NAME) { + return status; + } + + // Predicate-only slot refs carry the table name/type but do not carry the table-format field + // id used by BY_FIELD_ID or the file position used by BY_INDEX. Use a name fallback only for + // hidden filter localization; projected columns still obey the requested mapping mode. + const auto* file_field = + matcher_for_mode(TableColumnMappingMode::BY_NAME).find(table_column, _file_schema); + if (file_field == nullptr) { + return status; + } + ColumnMapping fallback_mapping; + fallback_mapping.global_index = global_index; + fallback_mapping.table_column_name = table_column.name; + fallback_mapping.table_type = table_column.type; + RETURN_IF_ERROR(_create_direct_mapping(table_column, *file_field, &fallback_mapping)); + *mapping = std::move(fallback_mapping); + return Status::OK(); +} + +Status TableColumnMapper::_build_hidden_filter_mappings( + const std::vector& table_filters) { + _hidden_mappings.clear(); + + std::map filter_columns; + for (const auto& table_filter : table_filters) { + if (table_filter.conjunct != nullptr) { + collect_top_level_slot_columns(table_filter.conjunct->root(), &filter_columns); + } + } + + // TableColumnPredicates only carry GlobalIndex and predicate objects. They do not provide the + // top-level column name/type needed to build a hidden mapping, so a predicate-only column can + // be hidden-mapped only when the same root slot also appears in a conjunct. + for (const auto& [global_index, table_column] : filter_columns) { + if (_find_mapping(global_index) != nullptr) { + // Ignore columns that are already mapped by the projected columns + continue; + } + ColumnMapping mapping; + RETURN_IF_ERROR(_create_hidden_filter_mapping(table_column, global_index, &mapping)); + if (mapping.file_local_id.has_value() || mapping.constant_index.has_value() || + mapping.virtual_column_type != TableVirtualColumnType::INVALID) { + _hidden_mappings.push_back(std::move(mapping)); + } + } + return Status::OK(); +} + +Status TableColumnMapper::create_mapping(const std::vector& projected_columns, + const std::map& partition_values, + const std::vector& file_schema) { + clear(); + _partition_values = partition_values; + _file_schema = file_schema; + for (size_t column_idx = 0; column_idx < projected_columns.size(); ++column_idx) { + ColumnMapping mapping; + RETURN_IF_ERROR(_create_mapping_for_column(projected_columns[column_idx], + GlobalIndex(column_idx), &mapping)); + _mappings.push_back(std::move(mapping)); + } + return Status::OK(); +} + +std::vector TableColumnMapper::_filter_visible_mappings() const { + std::vector mappings; + mappings.reserve(_mappings.size() + _hidden_mappings.size()); + mappings.insert(mappings.end(), _mappings.begin(), _mappings.end()); + mappings.insert(mappings.end(), _hidden_mappings.begin(), _hidden_mappings.end()); + return mappings; +} + +Status TableColumnMapper::_build_filter_entries(const FileScanRequest& file_request) { + _filter_entries.clear(); + const auto mappings = _filter_visible_mappings(); + for (const auto& mapping : mappings) { + FilterEntry entry; + if (mapping.constant_index.has_value()) { + entry = FilterEntry::constant(*mapping.constant_index); + } else if (mapping.file_local_id.has_value() && + filter_conversion_has_local_source(mapping.filter_conversion)) { + const auto local_position_it = + file_request.local_positions.find(LocalColumnId(*mapping.file_local_id)); + if (local_position_it != file_request.local_positions.end()) { + entry = FilterEntry::local(local_position_it->second); + } + } + _filter_entries.emplace(mapping.global_index, entry); + } + return Status::OK(); +} + +Status TableColumnMapper::create_scan_request( + const std::vector& table_filters, + const TableColumnPredicates& table_column_predicates, + const std::vector& projected_columns, FileScanRequest* file_request, + RuntimeState* runtime_state) { + // FileReader evaluates expressions against a file-local block. This mapper owns the + // table-column to file-column conversion, so it also owns the file-local block positions. + file_request->predicate_columns.clear(); + file_request->non_predicate_columns.clear(); + file_request->local_positions.clear(); + file_request->conjuncts.clear(); + file_request->delete_conjuncts.clear(); + file_request->column_predicate_filters.clear(); + _filter_entries.clear(); + // 1. Build referenced non-predicate columns + for (size_t column_idx = 0; column_idx < projected_columns.size(); ++column_idx) { + const auto global_index = GlobalIndex(column_idx); + auto* mapping = _find_mapping(global_index); + if (mapping != nullptr && mapping->file_local_id.has_value()) { + // A file column can be read lazily as a non-predicate column only when it is not used + // by row-level expression filters. Single-column ColumnPredicate filters are pruning + // hints only and must not force row-level predicate materialization. + bool used_by_filter = false; + for (const auto& table_filter : table_filters) { + const auto& global_indices = table_filter.global_indices; + if (std::find(global_indices.begin(), global_indices.end(), global_index) != + global_indices.end() && + filter_conversion_has_local_source(mapping->filter_conversion)) { + used_by_filter = true; + break; + } + } + if (!used_by_filter) { + RETURN_IF_ERROR(add_scan_column(file_request, mapping, + &file_request->non_predicate_columns, false)); + } + } + } + // 2. Build referenced predicate columns + // Hidden filter mappings must be built before localizing filters, so that they can be localized together with visible mappings and referenced by localized filter expressions. + RETURN_IF_ERROR(_build_hidden_filter_mappings(table_filters)); + RETURN_IF_ERROR( + localize_filters(table_filters, table_column_predicates, file_request, runtime_state)); + // 3. Rebuild output projection expressions for projected columns. localize_filters() has + // already applied the final scan projection to mapping.file_type/projected_file_children before + // rewriting filter expressions. + for (auto& mapping : _mappings) { + if (!mapping.file_local_id.has_value()) { + continue; + } + auto position_it = + file_request->local_positions.find(LocalColumnId(*mapping.file_local_id)); + DORIS_CHECK(position_it != file_request->local_positions.end()) + << file_request->local_positions.size() << " " << *mapping.file_local_id << " " + << mapping.file_column_name; + rebuild_projection(&mapping, position_it->second); + } + return Status::OK(); +} + +ColumnMapping* TableColumnMapper::_find_mapping(GlobalIndex global_index) { + for (auto& mapping : _mappings) { + if (mapping.global_index == global_index) { + return &mapping; + } + } + return nullptr; +} + +ColumnMapping* TableColumnMapper::_find_filter_mapping(GlobalIndex global_index) { + if (auto* mapping = _find_mapping(global_index); mapping != nullptr) { + return mapping; + } + for (auto& mapping : _hidden_mappings) { + if (mapping.global_index == global_index) { + return &mapping; + } + } + return nullptr; +} + +Status TableColumnMapper::localize_filters(const std::vector& table_filters, + const TableColumnPredicates& table_column_predicates, + FileScanRequest* file_request, + RuntimeState* runtime_state) { + FilterProjectionMap filter_projections; + auto filter_mappings = _filter_visible_mappings(); + RETURN_IF_ERROR( + build_filter_projection_map(table_filters, filter_mappings, &filter_projections)); + for (const auto& table_filter : table_filters) { + for (const auto& global_index : table_filter.global_indices) { + auto* mapping = _find_filter_mapping(global_index); + if (mapping == nullptr || !mapping->file_local_id.has_value() || + !filter_conversion_has_local_source(mapping->filter_conversion)) { + continue; + } + RETURN_IF_ERROR(add_scan_column(file_request, mapping, &file_request->predicate_columns, + true, &filter_projections)); + } + } + // Rebuild the file type for every scan-local mapping before expression rewrite. Predicate-only + // hidden mappings must see the same projected file type as the file reader will produce. + for (auto& mapping : _mappings) { + if (mapping.file_local_id.has_value() && + file_request->local_positions.contains(LocalColumnId(*mapping.file_local_id))) { + RETURN_IF_ERROR(apply_scan_projection_to_mapping_file_type(*file_request, &mapping)); + } + } + for (auto& mapping : _hidden_mappings) { + if (mapping.file_local_id.has_value() && + file_request->local_positions.contains(LocalColumnId(*mapping.file_local_id))) { + RETURN_IF_ERROR(apply_scan_projection_to_mapping_file_type(*file_request, &mapping)); + } + } + RETURN_IF_ERROR(_build_filter_entries(*file_request)); + + // Build the complete table-slot rewrite map after all predicate columns have been assigned. + // This keeps expression localization independent from filter iteration order. + filter_mappings = _filter_visible_mappings(); + const auto global_to_file_slot = build_file_slot_rewrite_map(filter_mappings, _filter_entries); + for (const auto& table_filter : table_filters) { + if (table_filter.conjunct != nullptr && + table_filter_has_only_local_entries(table_filter, _filter_entries)) { + RewriteContext rewrite_context {.runtime_state = runtime_state}; + VExprSPtr rewrite_root; + const auto clone_status = + clone_table_expr_tree(table_filter.conjunct->root(), &rewrite_root); + if (!clone_status.ok()) { + continue; + } + auto localized_root = rewrite_table_expr_to_file_expr(rewrite_root, global_to_file_slot, + &rewrite_context); + auto localized_conjunct = VExprContext::create_shared(std::move(localized_root)); + RETURN_IF_ERROR(rewrite_context.prepare_created_exprs(localized_conjunct.get())); + file_request->conjuncts.push_back(std::move(localized_conjunct)); + } + } + for (const auto& [global_index, predicates] : table_column_predicates) { + const auto* mapping = _find_filter_mapping(global_index); + const auto entry_it = _filter_entries.find(global_index); + if (mapping == nullptr || !mapping->file_local_id.has_value() || predicates.empty() || + entry_it == _filter_entries.end() || !entry_it->second.is_local() || + !column_predicate_can_use_local_source(mapping->filter_conversion) || + mapping->file_type == nullptr) { + continue; + } + FileColumnPredicateFilter column_predicate_filter; + column_predicate_filter.file_column_id = LocalColumnId(*mapping->file_local_id); + column_predicate_filter.target = + FileNestedPredicateTarget(column_predicate_filter.file_column_id); + const auto file_primitive_type = remove_nullable(mapping->file_type)->get_primitive_type(); + for (const auto& predicate : predicates) { + DORIS_CHECK(predicate != nullptr); + if (predicate->primitive_type() == file_primitive_type) { + column_predicate_filter.predicates.push_back(predicate); + } + } + if (column_predicate_filter.predicates.empty()) { + continue; + } + file_request->column_predicate_filters.push_back(std::move(column_predicate_filter)); + } + for (const auto& table_filter : table_filters) { + if (table_filter.conjunct == nullptr || + !table_filter_has_only_local_entries(table_filter, _filter_entries)) { + continue; + } + std::vector nested_column_predicate_filters; + collect_nested_column_predicate_filters(table_filter.conjunct->root(), filter_mappings, + &nested_column_predicate_filters); + for (auto& column_predicate_filter : nested_column_predicate_filters) { + merge_column_predicate_filter(std::move(column_predicate_filter), + &file_request->column_predicate_filters); + } + } + return Status::OK(); +} + +const ColumnDefinition* TableColumnMapper::_find_file_field( + const ColumnDefinition& table_column, + const std::vector& file_schema) const { + if (table_column.name.starts_with(BeConsts::GLOBAL_ROWID_COL)) { + const auto field_it = std::ranges::find_if(file_schema, [](const ColumnDefinition& field) { + return field.column_type == ColumnType::GLOBAL_ROWID; + }); + return field_it == file_schema.end() ? nullptr : &*field_it; + } + return matcher_for_mode(_options.mode).find(table_column, file_schema); +} + +Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_column, + const ColumnDefinition& file_field, + ColumnMapping* mapping) const { + DORIS_CHECK(mapping != nullptr); + DORIS_CHECK(file_field.local_id >= 0 || file_field.local_id == GLOBAL_ROWID_COLUMN_ID); + mapping->file_local_id = file_field.local_id; + mapping->table_column_name = table_column.name; + mapping->file_column_name = file_field.name; + mapping->original_file_type = file_field.type; + mapping->original_file_children = file_field.children; + mapping->projected_file_children = file_field.children; + mapping->file_type = file_field.type; + mapping->is_trivial = mapping->table_type->equals(*mapping->file_type); + mapping->filter_conversion = mapping->is_trivial ? FilterConversionType::COPY_DIRECTLY + : FilterConversionType::CAST_FILTER; + mapping->child_mappings.clear(); + + if (!table_column.children.empty()) { + DORIS_CHECK(is_complex_type(mapping->file_type->get_primitive_type())); + for (const auto& table_child : table_column.children) { + const auto* file_child = + find_file_child_for_complex_wrapper(table_child, file_field, _options.mode); + if (file_child == nullptr) { + if (!_options.allow_missing_columns) { + return Status::InvalidArgument( + "Table child column '{}' does not have a matching file child " + "under column '{}'", + table_child.name, table_column.name); + } + ColumnMapping child_mapping; + child_mapping.table_column_name = table_child.name; + child_mapping.file_column_name = table_child.name; + child_mapping.table_type = table_child.type; + child_mapping.file_type = table_child.type; + child_mapping.filter_conversion = FilterConversionType::FINALIZE_ONLY; + mapping->child_mappings.push_back(std::move(child_mapping)); + continue; + } + ColumnMapping child_mapping; + child_mapping.table_column_name = table_child.name; + child_mapping.table_type = table_child.type; + RETURN_IF_ERROR(_create_direct_mapping(table_child, *file_child, &child_mapping)); + mapping->child_mappings.push_back(std::move(child_mapping)); + } + if (needs_projected_file_type_rebuild(*mapping)) { + // If complex projection prunes some children, we have to rebuild the projected file type to make sure the reader expression can find the correct child types by name. + RETURN_IF_ERROR(build_projected_child_type(mapping->file_type, mapping->child_mappings, + &mapping->file_type)); + mapping->projected_file_children = projected_file_children_from_child_mappings( + mapping->original_file_children, mapping->child_mappings); + DCHECK(mapping->table_type != nullptr); + mapping->is_trivial = mapping->table_type->equals(*mapping->file_type); + // TODO: ? READER_EXPRESSION + mapping->filter_conversion = mapping->is_trivial + ? FilterConversionType::COPY_DIRECTLY + : FilterConversionType::READER_EXPRESSION; + } + } + return Status::OK(); +} + +} // namespace doris::format diff --git a/be/src/format_v2/column_mapper.h b/be/src/format_v2/column_mapper.h new file mode 100644 index 00000000000000..2ab2d63149dbcc --- /dev/null +++ b/be/src/format_v2/column_mapper.h @@ -0,0 +1,257 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "core/field.h" +#include "exprs/vexpr_fwd.h" +#include "format_v2/file_reader.h" + +namespace doris { +class ColumnPredicate; +class RuntimeState; +} // namespace doris + +namespace doris::format { + +struct ColumnDefinition; +struct TableFilter; + +// Table-level simple predicates grouped by table/global output position. The key is not +// LocalColumnId: TableColumnMapper resolves it through ColumnMapping before creating file pruning +// hints. +using TableColumnPredicates = std::map>>; + +enum class TableColumnMappingMode { + // Match by ColumnDefinition::identifier TYPE_INT as field id. + BY_FIELD_ID, + // Match by ColumnDefinition::identifier TYPE_STRING, or logical name when identifier is null. + BY_NAME, + // Match top-level columns by file position. This mainly serves Hive1 ORC style files whose + // column names are placeholder values such as `_col0` / `_col1`, where position is the only + // reliable way to select the correct column. + BY_INDEX, +}; + +enum TableVirtualColumnType { + INVALID = 0, // not a virtual column + // Iceberg v3 row lineage metadata column `_row_id`. Physical non-null values + // are preserved; NULL or missing values inherit first_row_id + row_position. + ROW_ID = 1, + // Iceberg v3 row lineage metadata column `_last_updated_sequence_number`. + // Physical non-null values are preserved; NULL or missing values inherit the + // data file's last_updated_sequence_number. + LAST_UPDATED_SEQUENCE_NUMBER = 2, + // Doris internal Iceberg row locator column `__DORIS_ICEBERG_ROWID_COL__`. + // It is a struct used by delete/update/merge, not the Iceberg `_row_id`. + ICEBERG_ROWID = 3, +}; + +enum class FilterConversionType { + COPY_DIRECTLY, // filter can be copied directly from file layer without any change, e.g. column type and table type are the same and no complex nested projection is involved. + CAST_FILTER, // filter can be converted from file layer by adding a cast, e.g. column type is nullable but table type is not, or file column has a trivial nested projection but table column has a complex nested projection. + READER_EXPRESSION, + FINALIZE_ONLY, // filter cannot be converted to file layer and should be evaluated at table reader finalize phase, e.g. predicates on ICEBERG_ROW_ID column which is generated by IcebergReader. + CONSTANT, +}; + +// Nested global-to-local child mapping. The root index points either to a request-local slot or to +// a child id, depending on the owner. child_mapping keeps the recursive table-child to file-child +// relationship explicit instead of encoding it in ColumnMapping flags. +struct IndexMapping { + int32_t index = -1; + std::map> child_mapping; +}; + +// Recursive result produced after one table/global column is assigned to a file-local source. +struct ColumnMapResult { + std::optional local_column_id; + std::optional column_index; + std::optional mapping; +}; + +// Final mapping entry from one global result column to one file-local source. +struct ColumnMapEntry { + IndexMapping mapping; + DataTypePtr local_type; + DataTypePtr global_type; + FilterConversionType filter_conversion = FilterConversionType::FINALIZE_ONLY; +}; + +// Collection of final result-column mappings produced for one file/split. +struct ResultColumnMapping { + std::map global_to_local; +}; + +// 单个 table column 到 file column 的映射结果。 +// 这是 table 层和 file 层的核心边界对象。 +struct ColumnMapping { + // Position of the top-level projected column in the table/global output block. Table-level + // filters and column predicates refer to this index after FileScannerV2 translates FE ids at + // the scanner boundary. + GlobalIndex global_index; + std::string table_column_name; + // File-reader local id for the mapped node. + // + // For a root mapping it is convertible to LocalColumnId. For a nested mapping it is the + // LocalColumnIndex child id under the parent projection. This is deliberately separated from + // ColumnDefinition::identifier, which is the table-to-file matching key such as Parquet/Iceberg + // field_id or column name. + // + // Empty means the table column is constant, missing, partition-only, or virtual. + std::optional file_local_id; + std::string file_column_name; + // Full file type/children before nested projection pruning. Used to rebuild projected types + // and to localize nested filters that reference children not present in the output projection. + DataTypePtr original_file_type; + std::vector original_file_children; + // File children after applying the scan projection. The order follows the physical file schema, + // not table child order. TableReader uses this to map table-output children back to the + // file-local block layout when projection, predicate-only children, and schema evolution mix. + std::vector projected_file_children; + // Split/file-local constant entry when this mapping is produced from partition/default/virtual + // expression instead of physical file data. + std::optional constant_index; + // Effective file type after applying casts/remaps/nested projection pruning. + DataTypePtr file_type; + // Target table/global type after final materialization. + DataTypePtr table_type; + + // 最终输出表达式。用于把 file-local value 转成 table/global value，例如 cast、 + // default、partition、generated column 或复杂列 remap。 + VExprContextSPtr projection; + + // Mapping tree for nested table children. The order follows table output children, while file + // children can be pruned/reordered through each child mapping's file-reader local id. + std::vector child_mappings; + // True when file value can be used directly as table value without cast or child remap. + bool is_trivial = false; + // How filters referencing this table/global column can be converted below table-reader + // finalize. This is metadata for localize_filters() and future constant-filter evaluation. + FilterConversionType filter_conversion = FilterConversionType::FINALIZE_ONLY; + TableVirtualColumnType virtual_column_type = TableVirtualColumnType::INVALID; + VExprContextSPtr default_expr; + + std::string debug_string() const; +}; + +struct TableColumnMapperOptions { + TableColumnMappingMode mode = TableColumnMappingMode::BY_FIELD_ID; + bool allow_missing_columns = true; + + std::string debug_string() const; +}; + +Status clone_table_expr_tree(const VExprSPtr& expr, VExprSPtr* cloned_expr); + +// 通用 table schema 到 file schema 映射层。 +// Iceberg 会使用 BY_FIELD_ID；普通 by-name 场景可以复用该组件，但不应把它命名成 +// Iceberg-only 组件。 +class TableColumnMapper { +public: + explicit TableColumnMapper(TableColumnMapperOptions options = {}) + : _options(std::move(options)) {} + virtual ~TableColumnMapper() = default; + + // 建立 table schema 到 file schema 的列映射。 + // 输出的 ColumnMapping 描述 table column 如何从 file column、常量列或表达式得到； + // 后续 projection、filter localization 和 table block finalize 都应复用这份映射。 + virtual Status create_mapping(const std::vector& projected_columns, + const std::map& partition_values, + const std::vector& file_schema); + + // 把 table-level scan 请求转换成 file-local scan 请求。table_filters 保留 row-level + // 过滤语义并转换成 file-local conjuncts；table_column_predicates 只转换成 file-layer + // pruning hints，不参与 batch row filtering。 + virtual Status create_scan_request(const std::vector& table_filters, + const TableColumnPredicates& table_column_predicates, + const std::vector& projected_columns, + FileScanRequest* file_request, + RuntimeState* runtime_state = nullptr); + + // 将 table-level filter 定位到文件 schema。 + // trivial mapping 可以直接复制结构化谓词；类型变化时可以尝试安全 cast；无法安全 + // 下推的表达式应通过 reader_expression_map 或 table-level finalize/filter fallback 处理。 + virtual Status localize_filters(const std::vector& table_filters, + const TableColumnPredicates& table_column_predicates, + FileScanRequest* file_request, + RuntimeState* runtime_state = nullptr); + void clear() { + _mappings.clear(); + _hidden_mappings.clear(); + _constant_map.clear(); + _filter_entries.clear(); + _file_schema.clear(); + _partition_values.clear(); + } + const std::vector& mappings() const { return _mappings; } + const std::map& filter_entries() const { return _filter_entries; } + const ConstantMap& constant_map() const { return _constant_map; } + std::string debug_string() const; + +private: + const ColumnDefinition* _find_file_field( + const ColumnDefinition& table_column, + const std::vector& file_schema) const; + Status _create_direct_mapping(const ColumnDefinition& table_column, + const ColumnDefinition& file_field, ColumnMapping* mapping) const; + + Status _create_by_index_mapping(const ColumnDefinition& table_column, + const std::vector& file_schema, + ColumnMapping* mapping); + Status _build_filter_entries(const FileScanRequest& file_request); + Status _build_result_column_mapping(const FileScanRequest& file_request); + + void _set_constant_mapping(ColumnMapping* mapping, VExprContextSPtr expr); + Status _create_mapping_for_column(const ColumnDefinition& table_column, + GlobalIndex global_index, ColumnMapping* mapping); + Status _create_hidden_filter_mapping(const ColumnDefinition& table_column, + GlobalIndex global_index, ColumnMapping* mapping); + Status _build_hidden_filter_mappings(const std::vector& table_filters); + std::vector _filter_visible_mappings() const; + + ColumnMapping* _find_mapping(GlobalIndex global_index); + ColumnMapping* _find_filter_mapping(GlobalIndex global_index); + + TableColumnMapperOptions _options; + // Column mapping for each projected column, in the same order as projected_columns. Each entry + // describes how to get one table/global column from file-local sources, and carries metadata + // for filter localization and result finalize. + std::vector _mappings; + // Predicate-only top-level columns are not output projection columns, so keep their mappings + // here. They are visible only to filter localization and file-reader predicate construction. + std::vector _hidden_mappings; + std::map _filter_entries; + ConstantMap _constant_map; + // Split-local schema state retained from create_mapping() so create_scan_request() can build + // hidden mappings for top-level filter slots that are absent from projected_columns. + std::vector _file_schema; + std::map _partition_values; +}; + +} // namespace doris::format diff --git a/be/src/format_v2/column_mapper_nested.cpp b/be/src/format_v2/column_mapper_nested.cpp new file mode 100644 index 00000000000000..f711a9d5020bd6 --- /dev/null +++ b/be/src/format_v2/column_mapper_nested.cpp @@ -0,0 +1,949 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/column_mapper_nested.h" + +#include +#include +#include +#include +#include + +#include "common/cast_set.h" +#include "common/exception.h" +#include "core/assert_cast.h" +#include "core/data_type/convert_field_to_type.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" +#include "core/data_type/primitive_type.h" +#include "exprs/create_predicate_function.h" +#include "exprs/vexpr.h" +#include "exprs/vin_predicate.h" +#include "format_v2/expr/cast.h" +#include "gen_cpp/Exprs_types.h" +#include "storage/predicate/null_predicate.h" +#include "storage/predicate/predicate_creator.h" + +namespace doris::format { + +namespace { + +static bool is_cast_expr(const VExprSPtr& expr) { + return dynamic_cast(expr.get()) != nullptr; +} + +static bool is_binary_comparison_predicate(const VExprSPtr& expr) { + if (expr == nullptr || expr->get_num_children() != 2 || + (expr->node_type() != TExprNodeType::BINARY_PRED && + expr->node_type() != TExprNodeType::NULL_AWARE_BINARY_PRED)) { + return false; + } + switch (expr->op()) { + case TExprOpcode::EQ: + case TExprOpcode::EQ_FOR_NULL: + case TExprOpcode::NE: + case TExprOpcode::GE: + case TExprOpcode::GT: + case TExprOpcode::LE: + case TExprOpcode::LT: + return true; + default: + return false; + } +} + +static bool is_null_predicate_function(const VExprSPtr& expr, bool* is_null) { + DORIS_CHECK(is_null != nullptr); + if (expr == nullptr || expr->node_type() != TExprNodeType::FUNCTION_CALL || + expr->get_num_children() != 1) { + return false; + } + if (expr->fn().name.function_name == "is_null_pred") { + *is_null = true; + return true; + } + if (expr->fn().name.function_name == "is_not_null_pred") { + *is_null = false; + return true; + } + return false; +} + +static bool is_signed_integer_type(PrimitiveType type) { + switch (type) { + case TYPE_TINYINT: + case TYPE_SMALLINT: + case TYPE_INT: + case TYPE_BIGINT: + case TYPE_LARGEINT: + return true; + default: + return false; + } +} + +static int primitive_integer_width(PrimitiveType type) { + switch (type) { + case TYPE_TINYINT: + return 1; + case TYPE_SMALLINT: + return 2; + case TYPE_INT: + return 4; + case TYPE_BIGINT: + return 8; + case TYPE_LARGEINT: + return 16; + default: + return 0; + } +} + +static bool is_decimal_type(PrimitiveType type) { + switch (type) { + case TYPE_DECIMAL32: + case TYPE_DECIMAL64: + case TYPE_DECIMALV2: + case TYPE_DECIMAL128I: + case TYPE_DECIMAL256: + return true; + default: + return false; + } +} + +static bool is_order_preserving_safe_cast(const DataTypePtr& from_type, + const DataTypePtr& to_type) { + if (from_type == nullptr || to_type == nullptr) { + return false; + } + const auto from_nested_type = remove_nullable(from_type); + const auto to_nested_type = remove_nullable(to_type); + if (from_nested_type->equals(*to_nested_type)) { + return true; + } + + const auto from_primitive_type = from_nested_type->get_primitive_type(); + const auto to_primitive_type = to_nested_type->get_primitive_type(); + if (is_signed_integer_type(from_primitive_type) && is_signed_integer_type(to_primitive_type)) { + return primitive_integer_width(to_primitive_type) >= + primitive_integer_width(from_primitive_type); + } + if (from_primitive_type == TYPE_FLOAT && to_primitive_type == TYPE_DOUBLE) { + return true; + } + if (is_decimal_type(from_primitive_type) && is_decimal_type(to_primitive_type)) { + return from_nested_type->get_scale() == to_nested_type->get_scale() && + to_nested_type->get_precision() >= from_nested_type->get_precision(); + } + return false; +} + +static bool parse_struct_child_selector(const VExprSPtr& expr, StructChildSelector* selector) { + DORIS_CHECK(selector != nullptr); + if (expr == nullptr || !expr->is_literal()) { + return false; + } + const Field field = literal_field(expr); + switch (field.get_type()) { + case TYPE_STRING: + case TYPE_CHAR: + case TYPE_VARCHAR: + selector->by_name = true; + selector->name = std::string(field.as_string_view()); + return true; + case TYPE_BOOLEAN: + selector->by_name = false; + selector->ordinal = field.get() ? 1 : 0; + return selector->ordinal > 0; + case TYPE_TINYINT: + selector->by_name = false; + if (field.get() <= 0) { + return false; + } + selector->ordinal = cast_set(field.get()); + return true; + case TYPE_SMALLINT: + selector->by_name = false; + if (field.get() <= 0) { + return false; + } + selector->ordinal = cast_set(field.get()); + return true; + case TYPE_INT: + selector->by_name = false; + if (field.get() <= 0) { + return false; + } + selector->ordinal = cast_set(field.get()); + return true; + case TYPE_BIGINT: + selector->by_name = false; + if (field.get() <= 0) { + return false; + } + selector->ordinal = cast_set(field.get()); + return true; + default: + return false; + } +} + +static bool extract_nested_struct_path(const VExprSPtr& expr, NestedStructPath* path) { + DORIS_CHECK(path != nullptr); + if (!is_struct_element_expr(expr)) { + return false; + } + + // Process for element_at(struct, 'field') or element_at(struct, 1) expression. + StructChildSelector selector; + if (!parse_struct_child_selector(expr->children()[1], &selector)) { + return false; + } + + const auto& parent = expr->children()[0]; + if (parent->is_slot_ref()) { + const auto* slot_ref = assert_cast(parent.get()); + path->root_global_index = slot_ref_global_index(*slot_ref); + path->selectors.clear(); + path->selectors.push_back(std::move(selector)); + return true; + } + + // Process for element_at(element_at(struct, 'field'), 'field') or + // element_at(element_at(struct, 1), 1) expression. + if (!extract_nested_struct_path(parent, path)) { + return false; + } + path->selectors.push_back(std::move(selector)); + return true; +} + +static bool extract_nested_struct_path_for_pruning(const VExprSPtr& expr, NestedStructPath* path) { + DORIS_CHECK(path != nullptr); + // Simple `ELEMENT_AT` + if (extract_nested_struct_path(expr, path)) { + return true; + } + + // `ELEMENT_AT` with `CAST` + if (!is_cast_expr(expr) || expr->get_num_children() != 1) { + return false; + } + const auto& child = expr->children()[0]; + if (!is_order_preserving_safe_cast(child->data_type(), expr->data_type())) { + return false; + } + // A safe widening cast is null-preserving and keeps the comparison ordering of the nested + // primitive leaf, so file-layer pruning can target the original leaf statistics. The row-level + // filter still evaluates the original cast expression after read. + return extract_nested_struct_path_for_pruning(child, path); +} + +static const ColumnDefinition* resolve_file_child(const std::vector& children, + const StructChildSelector& selector) { + if (selector.by_name) { + const auto child_it = std::ranges::find_if(children, [&](const ColumnDefinition& child) { + return child.name == selector.name; + }); + return child_it == children.end() ? nullptr : &*child_it; + } + if (selector.ordinal == 0 || selector.ordinal > children.size()) { + return nullptr; + } + return &children[selector.ordinal - 1]; +} + +static const DataTypeStruct* struct_type_or_null(const DataTypePtr& type) { + if (type == nullptr) { + return nullptr; + } + const auto nested_type = remove_nullable(type); + if (nested_type->get_primitive_type() != TYPE_STRUCT) { + return nullptr; + } + return assert_cast(nested_type.get()); +} + +static std::optional struct_child_index(const ColumnMapping& mapping, + const StructChildSelector& selector) { + const auto* struct_type = struct_type_or_null(mapping.table_type); + if (struct_type == nullptr) { + return std::nullopt; + } + if (selector.by_name) { + const auto position = struct_type->try_get_position_by_name(selector.name); + if (!position.has_value()) { + return std::nullopt; + } + return cast_set(*position); + } + if (selector.ordinal == 0 || selector.ordinal > struct_type->get_elements().size()) { + return std::nullopt; + } + return cast_set(selector.ordinal - 1); +} + +// Get the global child index for a child mapping. If the mapping's table type is struct, resolve +// the child index by the child mapping's table column name; otherwise, use the fallback child index. +static int32_t child_mapping_global_index(const ColumnMapping& mapping, + const ColumnMapping& child_mapping, + size_t fallback_child_idx) { + const auto* struct_type = struct_type_or_null(mapping.table_type); + if (struct_type == nullptr) { + return cast_set(fallback_child_idx); + } + const auto position = struct_type->try_get_position_by_name(child_mapping.table_column_name); + DORIS_CHECK(position.has_value()) << "Cannot find child '" << child_mapping.table_column_name + << "' in table type " << mapping.table_type->get_name(); + return cast_set(*position); +} + +static const ColumnMapping* resolve_mapped_child(const ColumnMapping& mapping, + int32_t global_child_index) { + for (size_t child_idx = 0; child_idx < mapping.child_mappings.size(); ++child_idx) { + const auto& child_mapping = mapping.child_mappings[child_idx]; + if (child_mapping_global_index(mapping, child_mapping, child_idx) == global_child_index) { + return &child_mapping; + } + } + return nullptr; +} + +// Resolve a table-side nested struct path through the existing ColumnMapping tree and build the +// corresponding file-local projection. For example, if table column `s` has children +// `{a, renamed_b}` and file column `s` has children `{a, b}`, the filter path +// `struct_element(s, 'renamed_b')` is resolved to the file projection `s -> b` by following the +// child mapping instead of matching the table child name against the file schema. Return false when +// the path is not represented in child_mappings, so callers can fall back to original file schema +// for predicate-only children that were not part of the output projection. +static bool resolve_nested_projection_with_mapping(const NestedStructPath& path, + const std::vector& mappings, + LocalColumnIndex* root_projection) { + DORIS_CHECK(root_projection != nullptr); + *root_projection = {}; + if (path.selectors.empty()) { + return false; + } + const auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { + return mapping.global_index == path.root_global_index; + }); + if (mapping_it == mappings.end() || !mapping_it->file_local_id.has_value()) { + return false; + } + + *root_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); + auto* current_projection = root_projection; + const auto* current_mapping = &*mapping_it; + + // Traverse the ColumnMapping tree according to the table-side struct selectors and emit the + // corresponding file-local child ids. If the path is not fully represented in child_mappings, + // return false so callers can choose the file-schema fallback for filter-only children. + for (size_t selector_idx = 0; selector_idx < path.selectors.size(); ++selector_idx) { + const auto global_child_index = + struct_child_index(*current_mapping, path.selectors[selector_idx]); + if (!global_child_index.has_value()) { + *root_projection = {}; + return false; + } + const auto* child_mapping = resolve_mapped_child(*current_mapping, *global_child_index); + if (child_mapping == nullptr || !child_mapping->file_local_id.has_value()) { + *root_projection = {}; + return false; + } + + auto child_projection = LocalColumnIndex::partial_local(*child_mapping->file_local_id); + child_projection.project_all_children = selector_idx + 1 == path.selectors.size(); + current_projection->children.push_back(std::move(child_projection)); + current_projection = ¤t_projection->children.back(); + current_mapping = child_mapping; + } + return true; +} + +static bool table_root_is_struct(const ColumnMapping& mapping) { + return struct_type_or_null(mapping.table_type) != nullptr; +} + +static const ColumnDefinition* resolve_file_leaf_from_projection( + const std::vector& children, const LocalColumnIndex& projection) { + const auto child_it = std::ranges::find_if(children, [&](const ColumnDefinition& child) { + return child.file_local_id() == projection.local_id(); + }); + if (child_it == children.end()) { + return nullptr; + } + if (projection.children.empty()) { + return &*child_it; + } + if (projection.children.size() != 1) { + return nullptr; + } + return resolve_file_leaf_from_projection(child_it->children, projection.children[0]); +} + +struct NestedPredicateTarget { + LocalColumnIndex file_projection; + FileNestedPredicateTarget file_target; + std::string leaf_name; + DataTypePtr leaf_type; +}; + +static std::unique_ptr build_struct_predicate_target_from_projection( + const std::vector& children, const LocalColumnIndex& projection) { + const auto child_it = std::ranges::find_if(children, [&](const ColumnDefinition& child) { + return child.file_local_id() == projection.local_id(); + }); + if (child_it == children.end()) { + return nullptr; + } + std::unique_ptr nested_child; + if (!projection.children.empty()) { + if (projection.children.size() != 1) { + return nullptr; + } + nested_child = build_struct_predicate_target_from_projection(child_it->children, + projection.children[0]); + if (nested_child == nullptr) { + return nullptr; + } + } + return std::make_unique(child_it->file_local_id(), child_it->name, + std::move(nested_child)); +} + +static bool build_struct_predicate_target(const ColumnMapping& root_mapping, + const LocalColumnIndex& root_projection, + FileNestedPredicateTarget* file_target) { + DORIS_CHECK(file_target != nullptr); + if (!root_projection.column_id().is_valid() || root_projection.children.size() != 1) { + return false; + } + auto struct_target = build_struct_predicate_target_from_projection( + root_mapping.original_file_children, root_projection.children[0]); + if (struct_target == nullptr) { + return false; + } + *file_target = FileNestedPredicateTarget(root_projection.column_id(), std::move(struct_target)); + return true; +} + +static bool resolve_nested_predicate_target(const NestedStructPath& path, + const std::vector& mappings, + NestedPredicateTarget* target) { + DORIS_CHECK(target != nullptr); + const auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { + return mapping.global_index == path.root_global_index; + }); + if (mapping_it == mappings.end() || !mapping_it->file_local_id.has_value() || + path.selectors.empty()) { + return false; + } + + if (resolve_nested_projection_with_mapping(path, mappings, &target->file_projection)) { + DORIS_CHECK(target->file_projection.children.size() == 1); + const auto* file_leaf = resolve_file_leaf_from_projection( + mapping_it->original_file_children, target->file_projection.children[0]); + if (file_leaf == nullptr || file_leaf->type == nullptr) { + return false; + } + target->leaf_type = remove_nullable(file_leaf->type); + if (is_complex_type(target->leaf_type->get_primitive_type())) { + return false; + } + target->leaf_name = file_leaf->name; + if (!build_struct_predicate_target(*mapping_it, target->file_projection, + &target->file_target)) { + return false; + } + return true; + } + + if (!table_root_is_struct(*mapping_it)) { + return false; + } + LocalColumnIndex child_projection; + if (!build_file_child_projection_from_schema(mapping_it->original_file_children, path.selectors, + &child_projection) + .ok() || + child_projection.local_id() < 0) { + return false; + } + const auto* file_leaf = + resolve_file_leaf_from_projection(mapping_it->original_file_children, child_projection); + if (file_leaf == nullptr || file_leaf->type == nullptr) { + return false; + } + target->leaf_type = remove_nullable(file_leaf->type); + if (is_complex_type(target->leaf_type->get_primitive_type())) { + return false; + } + target->leaf_name = file_leaf->name; + target->file_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); + target->file_projection.children.push_back(std::move(child_projection)); + if (!build_struct_predicate_target(*mapping_it, target->file_projection, + &target->file_target)) { + return false; + } + return true; +} + +static VExprSPtr original_table_literal_for_nested_predicate(const VExprSPtr& literal_expr) { + DORIS_CHECK(literal_expr != nullptr); + DORIS_CHECK(literal_expr->is_literal()); + const auto* rewritten_literal = dynamic_cast(literal_expr.get()); + if (rewritten_literal == nullptr) { + return literal_expr; + } + return VLiteral::create_shared(rewritten_literal->original_type(), + rewritten_literal->original_field()); +} + +static std::optional to_column_predicate_type(TExprOpcode::type opcode) { + switch (opcode) { + case TExprOpcode::EQ: + return PredicateType::EQ; + case TExprOpcode::NE: + return PredicateType::NE; + case TExprOpcode::GT: + return PredicateType::GT; + case TExprOpcode::GE: + return PredicateType::GE; + case TExprOpcode::LT: + return PredicateType::LT; + case TExprOpcode::LE: + return PredicateType::LE; + default: + return std::nullopt; + } +} + +static TExprOpcode::type reverse_comparison_opcode(TExprOpcode::type opcode) { + switch (opcode) { + case TExprOpcode::GT: + return TExprOpcode::LT; + case TExprOpcode::GE: + return TExprOpcode::LE; + case TExprOpcode::LT: + return TExprOpcode::GT; + case TExprOpcode::LE: + return TExprOpcode::GE; + default: + return opcode; + } +} + +static std::shared_ptr create_comparison_column_predicate( + PredicateType predicate_type, uint32_t column_id, const std::string& column_name, + const DataTypePtr& data_type, const Field& value) { + switch (predicate_type) { + case PredicateType::EQ: + return create_comparison_predicate(column_id, column_name, data_type, + value, false); + case PredicateType::NE: + return create_comparison_predicate(column_id, column_name, data_type, + value, false); + case PredicateType::GT: + return create_comparison_predicate(column_id, column_name, data_type, + value, false); + case PredicateType::GE: + return create_comparison_predicate(column_id, column_name, data_type, + value, false); + case PredicateType::LT: + return create_comparison_predicate(column_id, column_name, data_type, + value, false); + case PredicateType::LE: + return create_comparison_predicate(column_id, column_name, data_type, + value, false); + default: + return nullptr; + } +} + +static bool extract_child_id_path_from_projection(const LocalColumnIndex& root_projection, + std::vector* file_child_id_path) { + DORIS_CHECK(file_child_id_path != nullptr); + file_child_id_path->clear(); + const auto* current_projection = &root_projection; + while (!current_projection->children.empty()) { + if (current_projection->children.size() != 1) { + file_child_id_path->clear(); + return false; + } + current_projection = ¤t_projection->children[0]; + file_child_id_path->push_back(current_projection->local_id()); + } + return !file_child_id_path->empty(); +} + +static std::shared_ptr build_nested_comparison_predicate( + const VExprSPtr& literal_expr, TExprOpcode::type opcode, LocalColumnId root_file_column_id, + const std::string& leaf_name, const DataTypePtr& file_leaf_type) { + if (literal_expr == nullptr || !literal_expr->is_literal() || file_leaf_type == nullptr) { + return nullptr; + } + const auto predicate_type = to_column_predicate_type(opcode); + if (!predicate_type.has_value()) { + return nullptr; + } + const auto original_literal = original_table_literal_for_nested_predicate(literal_expr); + const Field original_field = literal_field(original_literal); + Field file_field; + try { + convert_field_to_type(original_field, *file_leaf_type, &file_field, + original_literal->data_type().get()); + } catch (const Exception&) { + return nullptr; + } + if (file_field.is_null()) { + return nullptr; + } + try { + return create_comparison_column_predicate(*predicate_type, + cast_set(root_file_column_id.value()), + leaf_name, file_leaf_type, file_field); + } catch (const Exception&) { + return nullptr; + } +} + +static std::shared_ptr build_nested_in_list_predicate( + const VExprSPtrs& literal_exprs, LocalColumnId root_file_column_id, + const std::string& leaf_name, const DataTypePtr& file_leaf_type) { + if (literal_exprs.empty() || file_leaf_type == nullptr) { + return nullptr; + } + + auto value_column = file_leaf_type->create_column(); + for (const auto& literal_expr : literal_exprs) { + if (literal_expr == nullptr || !literal_expr->is_literal()) { + return nullptr; + } + const auto original_literal = original_table_literal_for_nested_predicate(literal_expr); + const Field original_field = literal_field(original_literal); + Field file_field; + try { + convert_field_to_type(original_field, *file_leaf_type, &file_field, + original_literal->data_type().get()); + } catch (const Exception&) { + return nullptr; + } + if (file_field.is_null()) { + return nullptr; + } + value_column->insert(file_field); + } + + std::shared_ptr values; + try { + values.reset(create_set(file_leaf_type->get_primitive_type(), literal_exprs.size(), false)); + ColumnPtr value_column_ptr = std::move(value_column); + values->insert_range_from(value_column_ptr, 0, value_column_ptr->size()); + return create_in_list_predicate( + cast_set(root_file_column_id.value()), leaf_name, file_leaf_type, values, + false); + } catch (const Exception&) { + return nullptr; + } +} + +static std::shared_ptr build_nested_null_predicate( + bool is_null, LocalColumnId root_file_column_id, const std::string& leaf_name, + const DataTypePtr& file_leaf_type) { + if (file_leaf_type == nullptr) { + return nullptr; + } + const auto leaf_primitive_type = remove_nullable(file_leaf_type)->get_primitive_type(); + return NullPredicate::create_shared(cast_set(root_file_column_id.value()), leaf_name, + is_null, leaf_primitive_type); +} + +static bool set_nested_column_filter_target(const NestedPredicateTarget& target, + FileColumnPredicateFilter* column_filter) { + DORIS_CHECK(column_filter != nullptr); + std::vector file_child_id_path; + if (!extract_child_id_path_from_projection(target.file_projection, &file_child_id_path)) { + return false; + } + column_filter->file_column_id = target.file_projection.column_id(); + column_filter->file_child_id_path = std::move(file_child_id_path); + column_filter->target = target.file_target; + return true; +} + +static bool extract_nested_binary_comparison_filter(const VExprSPtr& expr, + const std::vector& mappings, + FileColumnPredicateFilter* column_filter) { + DORIS_CHECK(column_filter != nullptr); + if (!is_binary_comparison_predicate(expr)) { + return false; + } + NestedStructPath path; + VExprSPtr literal_expr; + TExprOpcode::type opcode = expr->op(); + if (extract_nested_struct_path_for_pruning(expr->children()[0], &path) && + expr->children()[1]->is_literal()) { + literal_expr = expr->children()[1]; + } else if (extract_nested_struct_path_for_pruning(expr->children()[1], &path) && + expr->children()[0]->is_literal()) { + literal_expr = expr->children()[0]; + opcode = reverse_comparison_opcode(opcode); + } else { + return false; + } + + NestedPredicateTarget target; + if (!resolve_nested_predicate_target(path, mappings, &target)) { + return false; + } + auto predicate = build_nested_comparison_predicate(literal_expr, opcode, + target.file_projection.column_id(), + target.leaf_name, target.leaf_type); + if (predicate == nullptr) { + return false; + } + if (!set_nested_column_filter_target(target, column_filter)) { + return false; + } + column_filter->predicates.push_back(std::move(predicate)); + return true; +} + +static bool extract_nested_in_list_filter(const VExprSPtr& expr, + const std::vector& mappings, + FileColumnPredicateFilter* column_filter) { + DORIS_CHECK(column_filter != nullptr); + if (expr == nullptr || expr->node_type() != TExprNodeType::IN_PRED || + expr->get_num_children() < 2) { + return false; + } + if (const auto* in_predicate = dynamic_cast(expr.get()); + in_predicate != nullptr && in_predicate->is_not_in()) { + return false; + } + + NestedStructPath path; + if (!extract_nested_struct_path_for_pruning(expr->children()[0], &path)) { + return false; + } + + VExprSPtrs literal_exprs; + literal_exprs.reserve(expr->get_num_children() - 1); + for (size_t child_idx = 1; child_idx < expr->children().size(); ++child_idx) { + if (!expr->children()[child_idx]->is_literal()) { + return false; + } + literal_exprs.push_back(expr->children()[child_idx]); + } + + NestedPredicateTarget target; + if (!resolve_nested_predicate_target(path, mappings, &target)) { + return false; + } + auto predicate = build_nested_in_list_predicate( + literal_exprs, target.file_projection.column_id(), target.leaf_name, target.leaf_type); + if (predicate == nullptr) { + return false; + } + if (!set_nested_column_filter_target(target, column_filter)) { + return false; + } + column_filter->predicates.push_back(std::move(predicate)); + return true; +} + +static bool extract_nested_null_filter(const VExprSPtr& expr, + const std::vector& mappings, + FileColumnPredicateFilter* column_filter) { + DORIS_CHECK(column_filter != nullptr); + bool is_null = false; + if (!is_null_predicate_function(expr, &is_null)) { + return false; + } + + NestedStructPath path; + if (!extract_nested_struct_path_for_pruning(expr->children()[0], &path)) { + return false; + } + + NestedPredicateTarget target; + if (!resolve_nested_predicate_target(path, mappings, &target)) { + return false; + } + auto predicate = build_nested_null_predicate(is_null, target.file_projection.column_id(), + target.leaf_name, target.leaf_type); + if (predicate == nullptr) { + return false; + } + if (!set_nested_column_filter_target(target, column_filter)) { + return false; + } + column_filter->predicates.push_back(std::move(predicate)); + return true; +} + +} // namespace + +SplitLocalFileLiteral::SplitLocalFileLiteral(const DataTypePtr& file_type, const Field& file_field, + DataTypePtr original_type, Field original_field) + : VLiteral(file_type, file_field), + _original_type(std::move(original_type)), + _original_field(std::move(original_field)) {} + +GlobalIndex slot_ref_global_index(const VSlotRef& slot_ref) { + DORIS_CHECK(slot_ref.column_id() >= 0); + return GlobalIndex(cast_set(slot_ref.column_id())); +} + +bool is_struct_element_expr(const VExprSPtr& expr) { + return expr != nullptr && expr->get_num_children() == 2 && + expr->fn().name.function_name == "struct_element"; +} + +Field literal_field(const VExprSPtr& literal_expr) { + DORIS_CHECK(literal_expr != nullptr); + DORIS_CHECK(literal_expr->is_literal()); + const auto* literal = dynamic_cast(literal_expr.get()); + DORIS_CHECK(literal != nullptr); + Field field; + literal->get_column_ptr()->get(0, field); + return field; +} + +// Collect nested struct leaf references that can be turned into file-reader projections and +// primitive pruning predicates. For example, from `s.a > 1 AND element_at(s, 'b') = 2`, this +// records two paths rooted at `s`: `s -> a` and `s -> b`. Non-struct expressions are traversed +// recursively, while a recognized struct path is emitted once so the caller can merge it into the +// scan projection for that top-level file column. +void collect_nested_struct_paths(const VExprSPtr& expr, std::vector* paths) { + DORIS_CHECK(paths != nullptr); + if (expr == nullptr) { + return; + } + NestedStructPath path; + if (extract_nested_struct_path_for_pruning(expr, &path)) { + paths->push_back(std::move(path)); + return; + } + for (const auto& child : expr->children()) { + collect_nested_struct_paths(child, paths); + } +} + +std::vector present_child_mappings_in_file_order( + const std::vector& child_mappings) { + std::vector result; + result.reserve(child_mappings.size()); + for (const auto& child_mapping : child_mappings) { + if (child_mapping.file_local_id.has_value()) { + result.push_back(&child_mapping); + } + } + std::ranges::sort(result, [](const ColumnMapping* lhs, const ColumnMapping* rhs) { + DORIS_CHECK(lhs->file_local_id.has_value()); + DORIS_CHECK(rhs->file_local_id.has_value()); + return *lhs->file_local_id < *rhs->file_local_id; + }); + return result; +} + +// Build the nested child projection under a top-level file column by walking file schema children +// directly. The returned projection does not include the root column id; callers attach it under a +// `LocalColumnIndex::partial_local(root_id)` when merging into the scan request. +Status build_file_child_projection_from_schema(const std::vector& children, + std::span selectors, + LocalColumnIndex* projection) { + DORIS_CHECK(projection != nullptr); + if (selectors.empty()) { + return Status::InvalidArgument("Nested struct selector path is empty"); + } + const auto* child = resolve_file_child(children, selectors.front()); + if (child == nullptr) { + return Status::OK(); + } + *projection = LocalColumnIndex::local(child->file_local_id()); + projection->project_all_children = selectors.size() == 1; + projection->children.clear(); + if (selectors.size() == 1) { + return Status::OK(); + } + if (child->children.empty() || + remove_nullable(child->type)->get_primitive_type() != TYPE_STRUCT) { + *projection = LocalColumnIndex {}; + return Status::OK(); + } + LocalColumnIndex child_projection; + RETURN_IF_ERROR(build_file_child_projection_from_schema(child->children, selectors.subspan(1), + &child_projection)); + if (child_projection.local_id() < 0) { + *projection = LocalColumnIndex {}; + return Status::OK(); + } + projection->children.push_back(std::move(child_projection)); + return Status::OK(); +} + +// Merge predicates that target the same physical file column or nested leaf. For example, +// `WHERE s.b > 1 AND s.b < 10` produces two predicates for the same target `s -> b`; keeping them +// in one FileColumnPredicateFilter lets the file reader apply both pruning checks to the same leaf +// instead of carrying duplicate target entries. +void merge_column_predicate_filter(FileColumnPredicateFilter column_filter, + std::vector* filters) { + DORIS_CHECK(filters != nullptr); + auto existing_filter_it = std::ranges::find_if(*filters, [&](const auto& existing_filter) { + return existing_filter.same_target_as(column_filter); + }); + if (existing_filter_it == filters->end()) { + filters->push_back(std::move(column_filter)); + return; + } + existing_filter_it->predicates.insert(existing_filter_it->predicates.end(), + column_filter.predicates.begin(), + column_filter.predicates.end()); +} + +// Extract file-column pruning predicates from localized row-level conjuncts that reference nested +// struct leaves. This is separate from file_request->conjuncts: conjuncts do row filtering, while +// FileColumnPredicateFilter carries primitive leaf predicates for file/page/statistics pruning. +// +// Example: for `WHERE s.b.c > 10 AND element_at(s, 'd') IS NOT NULL`, this function emits pruning +// filters for the nested targets `s -> b -> c` and `s -> d`. The caller only invokes it after +// table_filter_has_only_local_entries() succeeds, so each root slot already has a file-local scan +// source in _filter_entries. +void collect_nested_column_predicate_filters(const VExprSPtr& expr, + const std::vector& mappings, + std::vector* filters) { + DORIS_CHECK(filters != nullptr); + if (expr == nullptr) { + return; + } + if (expr->node_type() == TExprNodeType::COMPOUND_PRED && + expr->op() == TExprOpcode::COMPOUND_AND) { + for (const auto& child : expr->children()) { + collect_nested_column_predicate_filters(child, mappings, filters); + } + return; + } + FileColumnPredicateFilter column_filter; + if (extract_nested_binary_comparison_filter(expr, mappings, &column_filter) || + extract_nested_in_list_filter(expr, mappings, &column_filter) || + extract_nested_null_filter(expr, mappings, &column_filter)) { + merge_column_predicate_filter(std::move(column_filter), filters); + } +} + +} // namespace doris::format diff --git a/be/src/format_v2/column_mapper_nested.h b/be/src/format_v2/column_mapper_nested.h new file mode 100644 index 00000000000000..03eff5d3a5a6a5 --- /dev/null +++ b/be/src/format_v2/column_mapper_nested.h @@ -0,0 +1,90 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "core/field.h" +#include "exprs/vexpr_fwd.h" +#include "exprs/vliteral.h" +#include "exprs/vslot_ref.h" +#include "format_v2/column_mapper.h" +#include "format_v2/file_reader.h" + +namespace doris::format { + +struct StructChildSelector { + bool by_name = true; + std::string name; + size_t ordinal = 0; +}; + +struct NestedStructPath { + GlobalIndex root_global_index; + std::vector selectors; +}; + +// A split-local literal produced by slot-literal predicate localization. This wrapper keeps the +// original table literal so a cloned conjunct can be localized again for another split. +class SplitLocalFileLiteral final : public VLiteral { +public: + SplitLocalFileLiteral(const DataTypePtr& file_type, const Field& file_field, + DataTypePtr original_type, Field original_field); + + const DataTypePtr& original_type() const { return _original_type; } + const Field& original_field() const { return _original_field; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + Field file_field; + get_column_ptr()->get(0, file_field); + *cloned_expr = std::make_shared(_data_type, file_field, + _original_type, _original_field); + return Status::OK(); + } + +private: + DataTypePtr _original_type; + Field _original_field; +}; + +GlobalIndex slot_ref_global_index(const VSlotRef& slot_ref); +bool is_struct_element_expr(const VExprSPtr& expr); +Field literal_field(const VExprSPtr& literal_expr); + +void collect_nested_struct_paths(const VExprSPtr& expr, std::vector* paths); + +std::vector present_child_mappings_in_file_order( + const std::vector& child_mappings); + +Status build_file_child_projection_from_schema(const std::vector& children, + std::span selectors, + LocalColumnIndex* projection); + +void merge_column_predicate_filter(FileColumnPredicateFilter column_filter, + std::vector* filters); + +void collect_nested_column_predicate_filters(const VExprSPtr& expr, + const std::vector& mappings, + std::vector* filters); + +} // namespace doris::format diff --git a/be/src/format_v2/expr/cast.cpp b/be/src/format_v2/expr/cast.cpp new file mode 100644 index 00000000000000..76686b73ac9360 --- /dev/null +++ b/be/src/format_v2/expr/cast.cpp @@ -0,0 +1,131 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/expr/cast.h" + +#include +#include +#include + +#include + +#include "common/status.h" +#include "core/block/block.h" +#include "core/block/column_with_type_and_name.h" +#include "core/block/columns_with_type_and_name.h" +#include "exprs/function/simple_function_factory.h" +#include "exprs/vexpr_context.h" +#include "exprs/vliteral.h" + +namespace doris { + +Status Cast::prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) { + RETURN_IF_ERROR_OR_PREPARED(VExpr::prepare(state, desc, context)); + if (_children.size() != 1) { + return Status::InternalError( + fmt::format("Cast should have exactly 1 child expr, but got {}", _children.size())); + } + ColumnsWithTypeAndName argument_template; + argument_template.reserve(_children.size()); + if (_children[0]->is_literal()) { + // For some functions, he needs some literal columns to derive the return type. + auto literal_node = std::dynamic_pointer_cast(_children[0]); + argument_template.emplace_back(literal_node->get_column_ptr(), _children[0]->data_type(), + _children[0]->expr_name()); + } else { + argument_template.emplace_back(nullptr, _children[0]->data_type(), + _children[0]->expr_name()); + } + + _expr_name = fmt::format("CAST(arguments={},return={})", _children[0]->data_type()->get_name(), + _data_type->get_name()); + // get the function. won't prepare function. + _function = SimpleFunctionFactory::instance().get_function( + "CAST", argument_template, _data_type, + {.new_version_unix_timestamp = state->query_options().new_version_unix_timestamp}, + state->be_exec_version()); + if (_function == nullptr) { + return Status::InternalError("Could not find function {} ", _expr_name); + } + VExpr::register_function_context(state, context); + _prepare_finished = true; + return Status::OK(); +} + +Status Cast::open(RuntimeState* state, VExprContext* context, + FunctionContext::FunctionStateScope scope) { + DCHECK(_prepare_finished); + for (auto& i : _children) { + RETURN_IF_ERROR(i->open(state, context, scope)); + } + RETURN_IF_ERROR(VExpr::init_function_context(state, context, scope, _function)); + if (scope == FunctionContext::FRAGMENT_LOCAL) { + RETURN_IF_ERROR(VExpr::get_const_col(context, nullptr)); + } + _open_finished = true; + return Status::OK(); +} + +void Cast::close(VExprContext* context, FunctionContext::FunctionStateScope scope) { + VExpr::close_function_context(context, scope, _function); + VExpr::close(context, scope); +} + +Status Cast::execute_column_impl(VExprContext* context, const Block* block, + const Selector* selector, size_t count, + ColumnPtr& result_column) const { + return _do_execute(context, block, selector, count, result_column); +} + +std::string Cast::debug_string() const { + return _expr_name; +} + +Status Cast::_do_execute(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const { + DCHECK(_open_finished || block == nullptr) << debug_string(); + if (_children.size() != 1) { + return Status::InternalError( + fmt::format("Cast should have exactly 1 child expr, but got {}", _children.size())); + } + if (is_const_and_have_executed()) { // const have executed in open function + result_column = get_result_from_const(count); + return Status::OK(); + } + + Block temp_block; + ColumnNumbers args(1); + + ColumnPtr tmp_arg_column; + RETURN_IF_ERROR(_children[0]->execute_column(context, block, selector, count, tmp_arg_column)); + auto arg_type = _children[0]->execute_type(block); + temp_block.insert({tmp_arg_column, arg_type, _children[0]->expr_name()}); + args[0] = 0; + + uint32_t num_columns_without_result = temp_block.columns(); + // prepare a column to save result + temp_block.insert({nullptr, _data_type, _expr_name}); + + RETURN_IF_ERROR(_function->execute(context->fn_context(_fn_context_index), temp_block, args, + num_columns_without_result, count)); + result_column = temp_block.get_by_position(num_columns_without_result).column; + DCHECK_EQ(result_column->size(), count); + RETURN_IF_ERROR(result_column->column_self_check()); + return Status::OK(); +} + +} // namespace doris diff --git a/be/src/format_v2/expr/cast.h b/be/src/format_v2/expr/cast.h new file mode 100644 index 00000000000000..37452ba905ea6d --- /dev/null +++ b/be/src/format_v2/expr/cast.h @@ -0,0 +1,68 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include + +#include "common/object_pool.h" +#include "common/status.h" +#include "exprs/function_context.h" +#include "exprs/vexpr.h" + +namespace doris { +class RowDescriptor; +class RuntimeState; +class TExprNode; +class Block; +class VExprContext; +} // namespace doris + +namespace doris { + +class Cast final : public VExpr { + ENABLE_FACTORY_CREATOR(Cast); + +public: + Cast(const DataTypePtr& type) { + _node_type = TExprNodeType::CAST_EXPR; + _opcode = TExprOpcode::CAST; + _data_type = type; + } + ~Cast() override = default; + Status prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) override; + Status open(RuntimeState* state, VExprContext* context, + FunctionContext::FunctionStateScope scope) override; + void close(VExprContext* context, FunctionContext::FunctionStateScope scope) override; + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override; + std::string debug_string() const override; + uint64_t get_digest(uint64_t seed) const override { return 0; } + const std::string& expr_name() const override { return _expr_name; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = Cast::create_shared(_data_type); + return Status::OK(); + } + +private: + Status _do_execute(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const; + std::string _expr_name; + FunctionBasePtr _function; +}; +} // namespace doris diff --git a/be/src/format_v2/expr/delete_predicate.cpp b/be/src/format_v2/expr/delete_predicate.cpp new file mode 100644 index 00000000000000..cdbcf7d5314117 --- /dev/null +++ b/be/src/format_v2/expr/delete_predicate.cpp @@ -0,0 +1,117 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/expr/delete_predicate.h" + +#include +#include +#include + +#include +#include +#include + +#include "common/status.h" +#include "core/block/block.h" +#include "core/block/column_numbers.h" +#include "core/block/column_with_type_and_name.h" +#include "core/block/columns_with_type_and_name.h" + +namespace doris { + +DeletePredicate::DeletePredicate(const std::vector& deleted_rows) + : VExpr(), _deleted_rows(deleted_rows) { + _node_type = TExprNodeType::PREDICATE; + _opcode = TExprOpcode::DELETE; + _data_type = std::make_shared(); +} + +Status DeletePredicate::prepare(RuntimeState* state, const RowDescriptor& desc, + VExprContext* context) { + RETURN_IF_ERROR_OR_PREPARED(VExpr::prepare(state, desc, context)); + _expr_name = "DeletePredicate"; + _prepare_finished = true; + return Status::OK(); +} + +Status DeletePredicate::open(RuntimeState* state, VExprContext* context, + FunctionContext::FunctionStateScope scope) { + DCHECK(_prepare_finished); + RETURN_IF_ERROR_OR_PREPARED(VExpr::open(state, context, scope)); + _open_finished = true; + return Status::OK(); +} + +void DeletePredicate::close(VExprContext* context, FunctionContext::FunctionStateScope scope) { + VExpr::close(context, scope); +} + +/** + * DeletePredicate is derived from 2 cases: + * 1. All row IDs indicates deleted rows. (e.g. Delete rows with row_id in (1, 2, 3)) + * 2. Bit vector indicates whether each row is deleted or not. (e.g. Bit vector[0,1,0,0,1] indicates row 1 and row 4 are deleted) + * + * So DeletePredicate should have exactly 1 child expr, which is the slot of row id. + * Row IDs should be generated by file reader as a virtual column in `block`. + **/ +Status DeletePredicate::execute(VExprContext* context, Block* block, int* result_column_id) const { + if (_children.size() != 1) { + return Status::InternalError(fmt::format( + "DeletePredicate should have exactly 1 child expr, but got {}", _children.size())); + } + int slot = -1; + RETURN_IF_ERROR(_children[0]->execute(context, block, &slot)); + const auto& row_ids = + assert_cast(*block->get_by_position(slot).column).get_data(); + const auto count = row_ids.size(); + auto res_col = ColumnBool::create(count, 0); + if (_deleted_rows.empty()) { + block->insert({std::move(res_col), std::make_shared(), expr_name()}); + *result_column_id = static_cast(block->get_columns().size() - 1); + return Status::OK(); + } + if (count == 0) { + block->insert({std::move(res_col), std::make_shared(), expr_name()}); + *result_column_id = static_cast(block->get_columns().size() - 1); + return Status::OK(); + } + const int64_t* delete_rows = _deleted_rows.data(); + const int64_t* delete_rows_end = delete_rows + _deleted_rows.size(); + const int64_t* start_pos = std::lower_bound(delete_rows, delete_rows_end, row_ids[0]); + int64_t start_index = start_pos - delete_rows; + const int64_t* end_pos = std::upper_bound(start_pos, delete_rows_end, row_ids[count - 1]); + const int64_t end_index = end_pos - delete_rows; + + while (start_index < end_index) { + int64_t delete_row = delete_rows[start_index]; + if (const auto it = std::ranges::lower_bound(row_ids, delete_row); + it != row_ids.end() && *it == delete_row) { + const size_t index = it - row_ids.begin(); + res_col->get_data()[index] = true; + } + ++start_index; + } + block->insert({std::move(res_col), std::make_shared(), expr_name()}); + *result_column_id = static_cast(block->get_columns().size() - 1); + return Status::OK(); +} + +std::string DeletePredicate::debug_string() const { + return _expr_name; +} + +} // namespace doris diff --git a/be/src/format_v2/expr/delete_predicate.h b/be/src/format_v2/expr/delete_predicate.h new file mode 100644 index 00000000000000..3a95c31d8bfe14 --- /dev/null +++ b/be/src/format_v2/expr/delete_predicate.h @@ -0,0 +1,60 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include + +#include "common/object_pool.h" +#include "common/status.h" +#include "exprs/function_context.h" +#include "exprs/vexpr.h" + +namespace doris { +class RowDescriptor; +class RuntimeState; +class TExprNode; +class Block; +class VExprContext; +} // namespace doris + +namespace doris { + +class DeletePredicate final : public VExpr { + ENABLE_FACTORY_CREATOR(DeletePredicate); + +public: + DeletePredicate(const std::vector& deleted_rows); + ~DeletePredicate() override = default; + Status execute(VExprContext* context, Block* block, int* result_column_id) const override; + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + return Status::InternalError("Not implement DeletePredicate::execute_column_impl"); + } + Status prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) override; + Status open(RuntimeState* state, VExprContext* context, + FunctionContext::FunctionStateScope scope) override; + void close(VExprContext* context, FunctionContext::FunctionStateScope scope) override; + std::string debug_string() const override; + uint64_t get_digest(uint64_t seed) const override { return 0; } + const std::string& expr_name() const override { return _expr_name; } + +private: + std::string _expr_name; + const std::vector& _deleted_rows; +}; +} // namespace doris \ No newline at end of file diff --git a/be/src/format_v2/expr/equality_delete_predicate.cpp b/be/src/format_v2/expr/equality_delete_predicate.cpp new file mode 100644 index 00000000000000..dd5ff0305bc15f --- /dev/null +++ b/be/src/format_v2/expr/equality_delete_predicate.cpp @@ -0,0 +1,159 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/expr/equality_delete_predicate.h" + +#include + +#include + +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/block/column_with_type_and_name.h" +#include "core/column/column_nullable.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_number.h" + +namespace doris { +namespace { + +bool column_value_equal(const ColumnPtr& lhs, size_t lhs_row, const ColumnPtr& rhs, + size_t rhs_row) { + if (lhs->is_nullable() && rhs->is_nullable()) { + return lhs->compare_at(lhs_row, rhs_row, *rhs, -1) == 0; + } + if (lhs->is_nullable()) { + const auto& nullable_lhs = assert_cast(*lhs); + return !nullable_lhs.is_null_at(lhs_row) && + nullable_lhs.get_nested_column().compare_at(lhs_row, rhs_row, *rhs, -1) == 0; + } + if (rhs->is_nullable()) { + const auto& nullable_rhs = assert_cast(*rhs); + return !nullable_rhs.is_null_at(rhs_row) && + lhs->compare_at(lhs_row, rhs_row, nullable_rhs.get_nested_column(), -1) == 0; + } + return lhs->compare_at(lhs_row, rhs_row, *rhs, -1) == 0; +} + +} // namespace + +EqualityDeletePredicate::EqualityDeletePredicate(Block delete_block, std::vector field_ids) + : VExpr(), _delete_block(std::move(delete_block)), _field_ids(std::move(field_ids)) { + _node_type = TExprNodeType::PREDICATE; + _opcode = TExprOpcode::DELETE; + _data_type = std::make_shared(); + _expr_name = "EqualityDeletePredicate"; + DCHECK_EQ(_delete_block.columns(), _field_ids.size()); + _delete_hashes = _build_hashes(_delete_block); + for (size_t row = 0; row < _delete_hashes.size(); ++row) { + _delete_hash_map.emplace(_delete_hashes[row], row); + } +} + +Status EqualityDeletePredicate::prepare(RuntimeState* state, const RowDescriptor& desc, + VExprContext* context) { + RETURN_IF_ERROR_OR_PREPARED(VExpr::prepare(state, desc, context)); + _expr_name = "EqualityDeletePredicate"; + _prepare_finished = true; + return Status::OK(); +} + +Status EqualityDeletePredicate::open(RuntimeState* state, VExprContext* context, + FunctionContext::FunctionStateScope scope) { + DCHECK(_prepare_finished); + for (auto& child : _children) { + RETURN_IF_ERROR(child->open(state, context, scope)); + } + if (scope == FunctionContext::FRAGMENT_LOCAL) { + RETURN_IF_ERROR(VExpr::get_const_col(context, nullptr)); + } + _open_finished = true; + return Status::OK(); +} + +void EqualityDeletePredicate::close(VExprContext* context, + FunctionContext::FunctionStateScope scope) { + VExpr::close(context, scope); +} + +Status EqualityDeletePredicate::execute(VExprContext* context, Block* block, + int* result_column_id) const { + if (_children.size() != _field_ids.size()) { + return Status::InternalError( + "EqualityDeletePredicate should have {} child exprs, but got {}", _field_ids.size(), + _children.size()); + } + + Block data_key_block; + for (const auto& child : _children) { + Block eval_block = *block; + int slot = -1; + RETURN_IF_ERROR(child->execute(context, &eval_block, &slot)); + const auto& key_column = eval_block.get_by_position(slot); + data_key_block.insert({key_column.column, key_column.type, key_column.name}); + } + + const auto rows = data_key_block.rows(); + auto res_col = ColumnBool::create(rows, 0); + if (_delete_hash_map.empty() || rows == 0) { + block->insert({std::move(res_col), std::make_shared(), expr_name()}); + *result_column_id = static_cast(block->columns() - 1); + return Status::OK(); + } + + auto data_hashes = _build_hashes(data_key_block); + auto& result_data = res_col->get_data(); + for (size_t row = 0; row < rows; ++row) { + const auto range = _delete_hash_map.equal_range(data_hashes[row]); + for (auto it = range.first; it != range.second; ++it) { + if (_equal(data_key_block, row, it->second)) { + result_data[row] = true; + break; + } + } + } + + block->insert({std::move(res_col), std::make_shared(), expr_name()}); + *result_column_id = static_cast(block->columns() - 1); + return Status::OK(); +} + +std::vector EqualityDeletePredicate::_build_hashes(const Block& block) { + std::vector hashes(block.rows(), 0); + for (const auto& column : block.get_columns()) { + column->update_hashes_with_value(hashes.data(), nullptr); + } + return hashes; +} + +bool EqualityDeletePredicate::_equal(const Block& data_block, size_t data_row, + size_t delete_row) const { + for (size_t column_idx = 0; column_idx < _delete_block.columns(); ++column_idx) { + const auto& data_column = data_block.get_by_position(column_idx).column; + const auto& delete_column = _delete_block.get_by_position(column_idx).column; + if (!column_value_equal(data_column, data_row, delete_column, delete_row)) { + return false; + } + } + return true; +} + +std::string EqualityDeletePredicate::debug_string() const { + return _expr_name; +} + +} // namespace doris diff --git a/be/src/format_v2/expr/equality_delete_predicate.h b/be/src/format_v2/expr/equality_delete_predicate.h new file mode 100644 index 00000000000000..2e33cffb3985df --- /dev/null +++ b/be/src/format_v2/expr/equality_delete_predicate.h @@ -0,0 +1,71 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/block/block.h" +#include "exprs/function_context.h" +#include "exprs/vexpr.h" + +namespace doris { +class RowDescriptor; +class RuntimeState; +class VExprContext; +} // namespace doris + +namespace doris { + +class EqualityDeletePredicate final : public VExpr { + ENABLE_FACTORY_CREATOR(EqualityDeletePredicate); + +public: + EqualityDeletePredicate(Block delete_block, std::vector field_ids); + ~EqualityDeletePredicate() override = default; + + Status execute(VExprContext* context, Block* block, int* result_column_id) const override; + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + return Status::InternalError("Not implement EqualityDeletePredicate::execute_column_impl"); + } + Status prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) override; + Status open(RuntimeState* state, VExprContext* context, + FunctionContext::FunctionStateScope scope) override; + void close(VExprContext* context, FunctionContext::FunctionStateScope scope) override; + std::string debug_string() const override; + uint64_t get_digest(uint64_t seed) const override { return 0; } + const std::string& expr_name() const override { return _expr_name; } + +private: + static std::vector _build_hashes(const Block& block); + bool _equal(const Block& data_block, size_t data_row, size_t delete_row) const; + + std::string _expr_name; + Block _delete_block; + std::vector _field_ids; + std::vector _delete_hashes; + std::multimap _delete_hash_map; +}; + +} // namespace doris diff --git a/be/src/format_v2/file_reader.cpp b/be/src/format_v2/file_reader.cpp new file mode 100644 index 00000000000000..2369d0f7d317e2 --- /dev/null +++ b/be/src/format_v2/file_reader.cpp @@ -0,0 +1,199 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/file_reader.h" + +#include + +#include "io/fs/buffered_reader.h" +#include "io/fs/tracing_file_reader.h" +#include "runtime/runtime_state.h" + +namespace doris::format { +namespace { + +std::unique_ptr clone_struct_predicate_target( + const std::unique_ptr& target) { + return target == nullptr ? nullptr : std::make_unique(*target); +} + +template +std::string join_debug_strings(const std::vector& values, Formatter formatter) { + std::ostringstream out; + out << "["; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + out << ", "; + } + out << formatter(values[i]); + } + out << "]"; + return out.str(); +} + +std::string int_vector_debug_string(const std::vector& values) { + std::ostringstream out; + out << "["; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + out << ", "; + } + out << values[i]; + } + out << "]"; + return out.str(); +} + +void append_struct_predicate_path(const FileStructPredicateTarget* target, + std::vector* path) { + DORIS_CHECK(path != nullptr); + for (const auto* current = target; current != nullptr; current = current->child.get()) { + path->push_back(current->file_local_id); + } +} + +std::string struct_predicate_target_debug_string(const FileStructPredicateTarget* target) { + if (target == nullptr) { + return "null"; + } + std::ostringstream out; + out << "{file_local_id=" << target->file_local_id + << ", file_child_name=" << target->file_child_name + << ", child=" << struct_predicate_target_debug_string(target->child.get()) << "}"; + return out.str(); +} + +bool struct_predicate_targets_equal(const FileStructPredicateTarget* lhs, + const FileStructPredicateTarget* rhs) { + while (lhs != nullptr && rhs != nullptr) { + if (lhs->file_local_id != rhs->file_local_id) { + return false; + } + lhs = lhs->child.get(); + rhs = rhs->child.get(); + } + return lhs == nullptr && rhs == nullptr; +} + +} // namespace + +FileStructPredicateTarget::FileStructPredicateTarget(const FileStructPredicateTarget& other) + : file_local_id(other.file_local_id), + file_child_name(other.file_child_name), + child(clone_struct_predicate_target(other.child)) {} + +FileStructPredicateTarget& FileStructPredicateTarget::operator=( + const FileStructPredicateTarget& other) { + if (this == &other) { + return *this; + } + file_local_id = other.file_local_id; + file_child_name = other.file_child_name; + child = clone_struct_predicate_target(other.child); + return *this; +} + +FileNestedPredicateTarget::FileNestedPredicateTarget(const FileNestedPredicateTarget& other) + : file_column_id(other.file_column_id), + struct_target(clone_struct_predicate_target(other.struct_target)) {} + +FileNestedPredicateTarget& FileNestedPredicateTarget::operator=( + const FileNestedPredicateTarget& other) { + if (this == &other) { + return *this; + } + file_column_id = other.file_column_id; + struct_target = clone_struct_predicate_target(other.struct_target); + return *this; +} + +LocalColumnId FileColumnPredicateFilter::effective_file_column_id() const { + return target.is_valid() ? target.file_column_id : file_column_id; +} + +std::vector FileColumnPredicateFilter::effective_file_child_id_path() const { + if (!target.is_valid()) { + return file_child_id_path; + } + std::vector path; + append_struct_predicate_path(target.struct_target.get(), &path); + return path; +} + +bool FileColumnPredicateFilter::same_target_as(const FileColumnPredicateFilter& other) const { + if (target.is_valid() && other.target.is_valid()) { + return target.file_column_id == other.target.file_column_id && + struct_predicate_targets_equal(target.struct_target.get(), + other.target.struct_target.get()); + } + return effective_file_column_id() == other.effective_file_column_id() && + effective_file_child_id_path() == other.effective_file_child_id_path(); +} + +std::string FileColumnPredicateFilter::debug_string() const { + std::ostringstream out; + out << "FileColumnPredicateFilter{target={file_column_id=" << effective_file_column_id() + << ", struct_target=" << struct_predicate_target_debug_string(target.struct_target.get()) + << "}, file_child_id_path=" << int_vector_debug_string(effective_file_child_id_path()) + << ", predicate_count=" << predicates.size() << "}"; + return out.str(); +} + +std::string FileScanRequest::debug_string() const { + std::ostringstream out; + out << "FileScanRequest{predicate_columns=" + << join_debug_strings( + predicate_columns, + [](const LocalColumnIndex& projection) { return projection.debug_string(); }) + << ", non_predicate_columns=" + << join_debug_strings( + non_predicate_columns, + [](const LocalColumnIndex& projection) { return projection.debug_string(); }) + << ", local_positions={"; + size_t position_idx = 0; + for (const auto& [column_id, block_position] : local_positions) { + if (position_idx++ > 0) { + out << ", "; + } + out << column_id << ":" << block_position; + } + out << "}, conjunct_count=" << conjuncts.size() + << ", delete_conjunct_count=" << delete_conjuncts.size() << ", column_predicate_filters=" + << join_debug_strings( + column_predicate_filters, + [](const FileColumnPredicateFilter& filter) { return filter.debug_string(); }) + << "}"; + return out.str(); +} + +Status FileReader::init(RuntimeState* state) { + _init_profile(); + SCOPED_RAW_TIMER(&_reader_statistics.file_reader_create_time); + ++_reader_statistics.open_file_num; + io::FileReaderOptions reader_options = + FileFactory::get_reader_options(state->query_options(), *_file_description); + _file_reader = DORIS_TRY(io::DelegateReader::create_file_reader( + _profile, *_system_properties, *_file_description, reader_options, + io::DelegateReader::AccessMode::RANDOM, _io_ctx)); + _tracing_file_reader = _io_ctx ? std::make_shared( + _file_reader, _io_ctx->file_reader_stats) + : _file_reader; + _eof = false; + return Status::OK(); +} + +} // namespace doris::format diff --git a/be/src/format_v2/file_reader.h b/be/src/format_v2/file_reader.h new file mode 100644 index 00000000000000..a5e7c936466b20 --- /dev/null +++ b/be/src/format_v2/file_reader.h @@ -0,0 +1,279 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "core/field.h" +#include "exprs/vexpr_fwd.h" +#include "format_v2/column_data.h" +#include "gen_cpp/PlanNodes_types.h" +#include "io/file_factory.h" +#include "io/fs/file_reader_writer_fwd.h" + +namespace doris { +class Block; +class ColumnPredicate; + +namespace io { +struct IOContext; +} // namespace io +} // namespace doris + +namespace doris::format { + +// Struct-only nested predicate target used by file-layer pruning. +// +// This intentionally models only a STRUCT field chain. LIST/MAP/repeated predicates need explicit +// quantified semantics, so they must not be encoded here. +struct FileStructPredicateTarget { + int32_t file_local_id = -1; + std::string file_child_name; + std::unique_ptr child; + + FileStructPredicateTarget() = default; + FileStructPredicateTarget(int32_t local_id, std::string child_name, + std::unique_ptr nested_child = nullptr) + : file_local_id(local_id), + file_child_name(std::move(child_name)), + child(std::move(nested_child)) {} + FileStructPredicateTarget(const FileStructPredicateTarget& other); + FileStructPredicateTarget& operator=(const FileStructPredicateTarget& other); + FileStructPredicateTarget(FileStructPredicateTarget&& other) noexcept = default; + FileStructPredicateTarget& operator=(FileStructPredicateTarget&& other) noexcept = default; +}; + +struct FileNestedPredicateTarget { + LocalColumnId file_column_id = LocalColumnId::invalid(); + // Null means the predicate targets the top-level primitive column itself. + std::unique_ptr struct_target; + + FileNestedPredicateTarget() = default; + explicit FileNestedPredicateTarget(LocalColumnId column_id) : file_column_id(column_id) {} + FileNestedPredicateTarget(LocalColumnId column_id, + std::unique_ptr target) + : file_column_id(column_id), struct_target(std::move(target)) {} + FileNestedPredicateTarget(const FileNestedPredicateTarget& other); + FileNestedPredicateTarget& operator=(const FileNestedPredicateTarget& other); + FileNestedPredicateTarget(FileNestedPredicateTarget&& other) noexcept = default; + FileNestedPredicateTarget& operator=(FileNestedPredicateTarget&& other) noexcept = default; + + bool is_valid() const { return file_column_id.is_valid(); } +}; + +// File-local single-column predicates for file-layer pruning, such as min/max, page index, +// dictionary and bloom filter. +// +// Predicates must all belong to target.file_column_id. target.struct_target points to the nested +// primitive leaf under that root; null means the top-level column itself is the primitive leaf. +// These predicates are pruning hints only and are not row-level conjuncts. +struct FileColumnPredicateFilter { + FileNestedPredicateTarget target; + // Compatibility fields for call sites and tests that still construct pruning filters directly. + // New mapper code should fill target; file readers consume target first and only fall back to + // these fields while the API migration is in progress. + LocalColumnId file_column_id = LocalColumnId::invalid(); + std::vector file_child_id_path; + std::vector> predicates; + + LocalColumnId effective_file_column_id() const; + std::vector effective_file_child_id_path() const; + bool same_target_as(const FileColumnPredicateFilter& other) const; + std::string debug_string() const; +}; + +enum class FileFormat { + PARQUET, + ORC, + CSV, + JNI, +}; + +// 通用文件层 scan 请求。 +// 该结构描述所有文件格式都可以共享的 file-local 读取输入。这里不出现 table/global +// schema。所有 schema change、filter localization、default/generated/partition +// 列都应在 table 层完成。 +struct FileScanRequest { + virtual ~FileScanRequest() = default; + + std::string debug_string() const; + + // Columns that must be read before row-level filtering. They are materialized eagerly because + // conjuncts/delete_conjuncts need them to decide the selected rows. + std::vector predicate_columns; + // Columns read after row-level filtering. Predicate columns are also available for output and + // should not be duplicated here. + std::vector non_predicate_columns; + // file-local column id -> file-local output block position. + std::map local_positions; + // Row-level filters converted to file-local expressions from table-level predicates. + VExprContextSPtrs conjuncts; + // Delete predicates converted to file-local expressions. + VExprContextSPtrs delete_conjuncts; + // Single-column predicates used only for file-layer pruning, such as statistics, page index, + // dictionary and bloom filter. They must not be used for batch row-level filtering. + std::vector column_predicate_filters; +}; + +struct FileAggregateRequest { + struct Column { + // File-local projection for the aggregate column. For nested MIN/MAX, this points to the + // single primitive leaf that can be represented by file statistics. + LocalColumnIndex projection; + }; + + TPushAggOp::type agg_type = TPushAggOp::type::NONE; + std::vector columns; +}; + +struct FileAggregateResult { + struct Column { + // Mirrors FileAggregateRequest::Column::projection so TableReader can put the returned + // aggregate value back into the matching projected nested shape. + LocalColumnIndex projection; + bool has_min = false; + bool has_max = false; + Field min_value; + Field max_value; + }; + + int64_t count = 0; + std::vector columns; +}; + +// 文件物理读取层通用接口。 +// 该接口只描述 file-local schema、file-local scan request 和 file-local block。 +// TableReader/IcebergTableReader 可以通过它组合不同文件格式 reader。 +/** + * +-----> get_schema() -----------------+ + * FileReader() -----> init() ----| -----> close() + * +-----> open() -----> get_block() ----+ + */ +class FileReader { +public: + struct ReaderStatistics { + int32_t filtered_row_groups = 0; + int32_t filtered_row_groups_by_min_max = 0; + int32_t filtered_row_groups_by_bloom_filter = 0; + int32_t read_row_groups = 0; + int64_t filtered_group_rows = 0; + int64_t filtered_page_rows = 0; + int64_t lazy_read_filtered_rows = 0; + int64_t read_rows = 0; + int64_t filtered_bytes = 0; + int64_t column_read_time = 0; + int64_t parse_meta_time = 0; + int64_t parse_footer_time = 0; + int64_t file_footer_read_calls = 0; + int64_t file_footer_hit_cache = 0; + int64_t file_reader_create_time = 0; + int64_t open_file_num = 0; + int64_t row_group_filter_time = 0; + int64_t page_index_filter_time = 0; + int64_t read_page_index_time = 0; + int64_t parse_page_index_time = 0; + int64_t predicate_filter_time = 0; + int64_t dict_filter_rewrite_time = 0; + int64_t bloom_filter_read_time = 0; + }; + + FileReader(std::shared_ptr& system_properties, + std::unique_ptr& file_description, + std::shared_ptr io_ctx, RuntimeProfile* profile) + : _system_properties(system_properties), + _file_description(std::move(file_description)), + _io_ctx(io_ctx), + _profile(profile) {} + virtual ~FileReader() = default; + + // Initialize file reader and parse file metadata. + virtual Status init(RuntimeState* state); + + // Get file-local schema from file metadata. The file schema is determined by file format and + // file content, and does not contain table/global schema semantics. A file reader may expose + // raw file identifiers, such as Parquet field_id, through ColumnDefinition::identifier, but it + // must not interpret table-format semantics such as Iceberg name mapping, default/generated + // columns, or partition columns. This method can only be called after init() successfully, but + // does not require open() to be called. + virtual Status get_schema(std::vector* file_schema) const = 0; + + // Open the file reader with file-local scan request. The file reader should initialize its internal state according to the request, but does not need to interpret table/global schema semantics. For example, all schema change, filter localization, default/generated/partition columns should be handled in table reader layer. This method can only be called after init() successfully. + virtual Status open(std::shared_ptr request) { + _request = std::move(request); + return Status::OK(); + } + + // 读取下一批 file-local block。 + // 该方法只能在 open(FileScanRequest) 成功后调用。 + // file_block 的列顺序和类型必须遵守 FileScanRequest，而不是 table/global schema。 + // rows 返回当前批次输出行数；eof 表示当前文件 reader 是否读完；多文件切换由 + // TableReader 负责。 + virtual Status get_block(Block* file_block, size_t* rows, bool* eof) { + // stub 默认立即 EOF。 + if (rows != nullptr) { + *rows = 0; + } + if (eof != nullptr) { + *eof = true; + } + _eof = true; + return Status::OK(); + } + + virtual Status get_aggregate_result(const FileAggregateRequest& request, + FileAggregateResult* result) { + return Status::NotSupported("FileReader does not support aggregate pushdown"); + } + + // 关闭当前物理文件 reader 并释放文件层状态。 + // 该方法不处理 table-level delete/finalize 状态，后者由 TableReader 子类管理。 + virtual Status close() { + _file_reader.reset(); + _tracing_file_reader.reset(); + _io_ctx.reset(); + _eof = true; + return Status::OK(); + } + +protected: + virtual void _init_profile() {} + + io::FileReaderSPtr _file_reader; + // _tracing_file_reader wraps _file_reader. + // _file_reader is original file reader. + // _tracing_file_reader is tracing file reader with io context. + // If io_ctx is null, _tracing_file_reader will be the same as file_reader. + io::FileReaderSPtr _tracing_file_reader = nullptr; + std::shared_ptr _request; + bool _eof = true; + ReaderStatistics _reader_statistics; + std::shared_ptr _system_properties; + std::unique_ptr _file_description; + std::shared_ptr _io_ctx; + RuntimeProfile* _profile = nullptr; +}; + +} // namespace doris::format diff --git a/be/src/format_v2/jni/jdbc_reader.cpp b/be/src/format_v2/jni/jdbc_reader.cpp new file mode 100644 index 00000000000000..359b9f2ec5a093 --- /dev/null +++ b/be/src/format_v2/jni/jdbc_reader.cpp @@ -0,0 +1,192 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/jni/jdbc_reader.h" + +#include +#include + +#include "common/cast_set.h" +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/block/columns_with_type_and_name.h" +#include "core/column/column_nullable.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_string.h" +#include "exprs/function/simple_function_factory.h" +#include "exprs/vexpr_context.h" +#include "format_v2/table_reader.h" +#include "util/jdbc_utils.h" + +namespace doris::jdbc { + +std::string JdbcJniReader::connector_class() const { + return "org/apache/doris/jdbc/JdbcJniScanner"; +} + +Status JdbcJniReader::prepare_split(const format::SplitReadOptions& options) { + _jdbc_params.clear(); + _current_range = options.current_range; + if (options.current_range.__isset.table_format_params && + options.current_range.table_format_params.table_format_type == "jdbc") { + _jdbc_params = std::map( + options.current_range.table_format_params.jdbc_params.begin(), + options.current_range.table_format_params.jdbc_params.end()); + } + return format::JniTableReader::prepare_split(options); +} + +// need pass to the java side, so the java scanner can parse the params and construct the JDBC connection +Status JdbcJniReader::build_scanner_params(std::map* params) const { + DORIS_CHECK(params != nullptr); + *params = _jdbc_params; + if (params->contains("jdbc_driver_url")) { + std::string resolved; + if (JdbcUtils::resolve_driver_url((*params)["jdbc_driver_url"], &resolved).ok()) { + (*params)["jdbc_driver_url"] = resolved; + } + } + return Status::OK(); +} + +Status JdbcJniReader::build_jni_columns( + std::vector* columns) const { + DORIS_CHECK(columns != nullptr); + columns->clear(); + columns->reserve(_projected_columns.size()); + for (size_t i = 0; i < _projected_columns.size(); ++i) { + const auto& table_column = _projected_columns[i]; + const auto primitive_type = remove_nullable(table_column.type)->get_primitive_type(); + columns->push_back({ + .java_name = table_column.name, + .output_index = i, + .output_type = table_column.type, + .transfer_type = _transfer_type_for(table_column.type), + .replace_type = _replace_type_for(primitive_type), + }); + } + return Status::OK(); +} + +Status JdbcJniReader::finalize_jni_block(Block* jni_block, Block* output_block, size_t* rows) { + DORIS_CHECK(jni_block != nullptr); + DORIS_CHECK(output_block != nullptr); + DORIS_CHECK(rows != nullptr); + const auto original_rows = *rows; + const auto& columns = jni_columns(); + DORIS_CHECK(columns.size() == jni_block->columns()); + + for (size_t i = 0; i < columns.size(); ++i) { + const auto& column = columns[i]; + DORIS_CHECK(column.output_type != nullptr); + DORIS_CHECK(column.output_index < output_block->columns()); + if (_is_special_type(remove_nullable(column.output_type)->get_primitive_type())) { + RETURN_IF_ERROR(_cast_string_to_special_type(column, jni_block, i, output_block, + original_rows)); + continue; + } + output_block->get_by_position(column.output_index).type = column.output_type; + output_block->replace_by_position(column.output_index, + jni_block->get_by_position(i).column); + } + DORIS_CHECK(output_block->rows() == original_rows); + if (!_conjuncts.empty()) { + RETURN_IF_ERROR( + VExprContext::filter_block(_conjuncts, output_block, output_block->columns())); + } + *rows = output_block->rows(); + return Status::OK(); +} + +int64_t JdbcJniReader::self_split_weight() const { + return _current_range.__isset.self_split_weight ? _current_range.self_split_weight : -1; +} + +std::string JdbcJniReader::_replace_type_for(PrimitiveType type) const { + switch (type) { + case PrimitiveType::TYPE_BITMAP: + return "bitmap"; + case PrimitiveType::TYPE_HLL: + return "hll"; + case PrimitiveType::TYPE_QUANTILE_STATE: + return "quantile_state"; + case PrimitiveType::TYPE_JSONB: + return "jsonb"; + default: + return "not_replace"; + } +} + +bool JdbcJniReader::_is_special_type(PrimitiveType type) const { + return type == PrimitiveType::TYPE_BITMAP || type == PrimitiveType::TYPE_HLL || + type == PrimitiveType::TYPE_QUANTILE_STATE || type == PrimitiveType::TYPE_JSONB; +} + +DataTypePtr JdbcJniReader::_transfer_type_for(const DataTypePtr& output_type) const { + DORIS_CHECK(output_type != nullptr); + if (!_is_special_type(remove_nullable(output_type)->get_primitive_type())) { + return output_type; + } + DataTypePtr string_type = std::make_shared(); + if (output_type->is_nullable()) { + string_type = make_nullable(string_type); + } + return string_type; +} + +Status JdbcJniReader::_cast_string_to_special_type(const format::JniTableReader::JniColumn& column, + Block* jni_block, size_t jni_column_index, + Block* output_block, size_t rows) { + DORIS_CHECK(column.output_type != nullptr); + DORIS_CHECK(column.transfer_type != nullptr); + const auto target_type = column.output_type; + const auto target_type_name = target_type->get_name(); + + ColumnPtr input_column = jni_block->get_by_position(jni_column_index).column; + ColumnPtr cast_param = target_type->create_column_const_with_default_value(1); + + ColumnsWithTypeAndName argument_template; + argument_template.reserve(2); + argument_template.emplace_back(std::move(input_column), column.transfer_type, + "java.sql.String"); + argument_template.emplace_back(std::move(cast_param), target_type, target_type_name); + + FunctionBasePtr cast_function = SimpleFunctionFactory::instance().get_function( + "CAST", argument_template, make_nullable(target_type)); + if (cast_function == nullptr) { + return Status::InternalError("Failed to find CAST function for type {}", target_type_name); + } + + Block cast_block(argument_template); + const auto result_idx = cast_set(cast_block.columns()); + cast_block.insert({nullptr, make_nullable(target_type), "cast_result"}); + RETURN_IF_ERROR( + cast_function->execute(nullptr, cast_block, {0}, result_idx, cast_set(rows))); + + auto result_column = cast_block.get_by_position(result_idx).column; + output_block->get_by_position(column.output_index).type = target_type; + if (target_type->is_nullable()) { + output_block->replace_by_position(column.output_index, result_column); + } else { + const auto* nullable_column = assert_cast(result_column.get()); + output_block->replace_by_position(column.output_index, + nullable_column->get_nested_column_ptr()); + } + return Status::OK(); +} + +} // namespace doris::jdbc diff --git a/be/src/format_v2/jni/jdbc_reader.h b/be/src/format_v2/jni/jdbc_reader.h new file mode 100644 index 00000000000000..726d303b41d981 --- /dev/null +++ b/be/src/format_v2/jni/jdbc_reader.h @@ -0,0 +1,58 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "core/types.h" +#include "format_v2/jni/jni_table_reader.h" +#include "gen_cpp/PlanNodes_types.h" + +namespace doris::jdbc { + +class JdbcJniReader final : public format::JniTableReader { +public: + ~JdbcJniReader() override = default; + + Status prepare_split(const format::SplitReadOptions& options) override; + +protected: + std::string connector_class() const override; + Status build_scanner_params(std::map* params) const override; + Status build_jni_columns( + std::vector* columns) const override; + Status finalize_jni_block(Block* jni_block, Block* output_block, size_t* rows) override; + int64_t self_split_weight() const override; + +private: + bool _is_special_type(PrimitiveType type) const; + std::string _replace_type_for(PrimitiveType type) const; + DataTypePtr _transfer_type_for(const DataTypePtr& output_type) const; + Status _cast_string_to_special_type(const format::JniTableReader::JniColumn& column, + Block* jni_block, size_t jni_column_index, + Block* output_block, size_t rows); + + std::map _jdbc_params; + TFileRangeDesc _current_range; +}; + +} // namespace doris::jdbc diff --git a/be/src/format_v2/jni/jni_table_reader.cpp b/be/src/format_v2/jni/jni_table_reader.cpp new file mode 100644 index 00000000000000..88cbb7e7124328 --- /dev/null +++ b/be/src/format_v2/jni/jni_table_reader.cpp @@ -0,0 +1,319 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/jni/jni_table_reader.h" + +#include + +#include "common/cast_set.h" +#include "core/block/block.h" +#include "exprs/vexpr_context.h" +#include "runtime/descriptors.h" +#include "runtime/runtime_state.h" +#include "util/string_util.h" + +namespace doris::format { + +Status JniTableReader::init(TableReadOptions&& options) { + RETURN_IF_ERROR(TableReader::init(std::move(options))); + _init_profile(); + + // JNI readers do not go through TableReader::open_reader(), where file-local filters are + // prepared for file readers. They execute table-level conjuncts directly on the JNI block. + RowDescriptor row_desc; + for (const auto& conjunct : _conjuncts) { + RETURN_IF_ERROR(conjunct->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(conjunct->open(_runtime_state)); + } + return Status::OK(); +} + +Status JniTableReader::prepare_split(const SplitReadOptions& options) { + RETURN_IF_ERROR(TableReader::prepare_split(options)); + DORIS_CHECK(!_closed); + DORIS_CHECK(!_scanner_opened); + // Subclasses populate split-specific scanner params before calling this method, so the Java + // scanner can be opened here instead of being lazily opened by the first get_block() call. + return _open_jni_scanner(); +} + +Status JniTableReader::get_block(Block* output_block, bool* eos) { + DORIS_CHECK(output_block != nullptr); + DORIS_CHECK(eos != nullptr); + DORIS_CHECK(_scanner_opened); + DORIS_CHECK(output_block->columns() == _projected_columns.size()); + output_block->clear_column_data(_projected_columns.size()); + + if (_eof) { + *eos = true; + return Status::OK(); + } + + while (true) { + size_t current_rows = 0; + bool current_eof = false; + // get next block data from Java scanner, and fill the data to _jni_block_template + RETURN_IF_ERROR(_get_next_jni_block(¤t_rows, ¤t_eof)); + if (current_eof) { + _eof = true; + *eos = true; + return Status::OK(); + } + + RETURN_IF_ERROR(finalize_jni_block(&_jni_block_template, output_block, ¤t_rows)); + if (current_rows == 0) { + output_block->clear_column_data(_projected_columns.size()); + continue; + } + *eos = false; + return Status::OK(); + } +} + +Status JniTableReader::_get_next_jni_block(size_t* rows, bool* eof) { + DORIS_CHECK(rows != nullptr); + DORIS_CHECK(eof != nullptr); + *rows = 0; + _jni_block_template.clear_column_data(_jni_columns.size()); + + JNIEnv* env = nullptr; + RETURN_IF_ERROR(Jni::Env::Get(&env)); + long meta_address = 0; + { + SCOPED_RAW_TIMER(&_java_scan_watcher); + //getNextBatchMeta function, return the meta address + RETURN_IF_ERROR(_jni_scanner_obj.call_long_method(env, _jni_scanner_get_next_batch) + .call(&meta_address)); + } + RETURN_ERROR_IF_EXC(env); + if (meta_address == 0) { + *eof = true; + return Status::OK(); + } + + JniDataBridge::TableMetaAddress table_meta(meta_address); + const auto num_rows = table_meta.next_meta_as_long(); + if (num_rows == 0) { + *eof = true; + return Status::OK(); + } + + *rows = cast_set(num_rows); + // fill data from Java table meta to C++ block + RETURN_IF_ERROR(_fill_jni_block(table_meta, *rows)); + // call releaseTable() method in JAVA side to release the Java table Heap free Memory + RETURN_IF_ERROR(_jni_scanner_obj.call_void_method(env, _jni_scanner_release_table).call()); + RETURN_ERROR_IF_EXC(env); + *eof = false; + return Status::OK(); +} + +// Java table to C++ block +Status JniTableReader::_fill_jni_block(JniDataBridge::TableMetaAddress& table_meta, + size_t num_rows) { + SCOPED_RAW_TIMER(&_fill_block_watcher); + JNIEnv* env = nullptr; + RETURN_IF_ERROR(Jni::Env::Get(&env)); + for (size_t i = 0; i < _jni_columns.size(); ++i) { + const auto& read_column = _jni_columns[i]; + auto& column_with_type_and_name = _jni_block_template.get_by_position(i); + auto& column_ptr = column_with_type_and_name.column; + RETURN_IF_ERROR(JniDataBridge::fill_column(table_meta, column_ptr, + read_column.transfer_type, num_rows)); + // call releaseColumn(int columnIndex) method in JAVA side to release the Java column Heap free Memory + RETURN_IF_ERROR(_jni_scanner_obj.call_void_method(env, _jni_scanner_release_column) + .with_arg(cast_set(i)) + .call()); + RETURN_ERROR_IF_EXC(env); + } + return Status::OK(); +} + +Status JniTableReader::finalize_jni_block(Block* jni_block, Block* output_block, size_t* rows) { + DORIS_CHECK(jni_block != nullptr); + DORIS_CHECK(output_block != nullptr); + DORIS_CHECK(rows != nullptr); + DORIS_CHECK(jni_block->columns() == _jni_columns.size()); + const auto original_rows = *rows; + for (size_t i = 0; i < _jni_columns.size(); ++i) { + const auto& column = _jni_columns[i]; + DORIS_CHECK(column.output_index < output_block->columns()); + output_block->get_by_position(column.output_index).type = column.output_type; + output_block->replace_by_position(column.output_index, + jni_block->get_by_position(i).column); + } + DORIS_CHECK(output_block->rows() == original_rows); + // Apply conjuncts on the output block + if (!_conjuncts.empty()) { + RETURN_IF_ERROR( + VExprContext::filter_block(_conjuncts, output_block, output_block->columns())); + } + *rows = output_block->rows(); + return Status::OK(); +} + +Status JniTableReader::close() { + if (_closed) { + return Status::OK(); + } + _closed = true; + if (_scanner_opened) { + JNIEnv* env = nullptr; + RETURN_IF_ERROR(Jni::Env::Get(&env)); + if (_scanner_profile != nullptr) { + COUNTER_UPDATE(_open_scanner_time, _jni_scanner_open_watcher); + COUNTER_UPDATE(_fill_block_time, _fill_block_watcher); + } + + RETURN_ERROR_IF_EXC(env); + jlong append_data_time = 0; + RETURN_IF_ERROR(_jni_scanner_obj.call_long_method(env, _jni_scanner_get_append_data_time) + .call(&append_data_time)); + jlong create_vector_table_time = 0; + RETURN_IF_ERROR( + _jni_scanner_obj.call_long_method(env, _jni_scanner_get_create_vector_table_time) + .call(&create_vector_table_time)); + if (_scanner_profile != nullptr) { + COUNTER_UPDATE(_java_append_data_time, append_data_time); + COUNTER_UPDATE(_java_create_vector_table_time, create_vector_table_time); + COUNTER_UPDATE(_java_scan_time, + _java_scan_watcher - append_data_time - create_vector_table_time); + _max_time_split_weight_counter->conditional_update( + _jni_scanner_open_watcher + _fill_block_watcher + _java_scan_watcher, + self_split_weight()); + } + + RETURN_IF_ERROR(_jni_scanner_obj.call_void_method(env, _jni_scanner_release_table).call()); + RETURN_IF_ERROR(_jni_scanner_obj.call_void_method(env, _jni_scanner_close).call()); + _scanner_opened = false; + } + return TableReader::close(); +} + +Status JniTableReader::_open_jni_scanner() { + _scanner_params.clear(); + _jni_columns.clear(); + // subclasses build map _scanner_params to JAVA side + RETURN_IF_ERROR(build_scanner_params(&_scanner_params)); + // subclasses build _jni_columns info to JAVA side, including column name and column type + RETURN_IF_ERROR(build_jni_columns(&_jni_columns)); + // _jni_columns info is used to build Java scanner schema params and JNI block template. + _prepare_jni_scanner_schema(); + + if (_runtime_state != nullptr) { + _batch_size = _runtime_state->batch_size(); + _scanner_params["time_zone"] = _runtime_state->timezone(); + } + + JNIEnv* env = nullptr; + RETURN_IF_ERROR(Jni::Env::Get(&env)); + SCOPED_RAW_TIMER(&_jni_scanner_open_watcher); + RETURN_IF_ERROR(_register_jni_class_functions(env, cast_set(_batch_size))); + // call open() method in JAVA side. + RETURN_IF_ERROR(_jni_scanner_obj.call_void_method(env, _jni_scanner_open).call()); + RETURN_ERROR_IF_EXC(env); + + _scanner_opened = true; + return Status::OK(); +} + +void JniTableReader::_prepare_jni_scanner_schema() { + std::vector required_fields; + std::vector column_types; + std::vector replace_types; + required_fields.reserve(_jni_columns.size()); + column_types.reserve(_jni_columns.size()); + replace_types.reserve(_jni_columns.size()); + _jni_block_template.clear(); + _jni_block_template.reserve(_jni_columns.size()); + + bool has_replace_type = false; + for (const auto& column : _jni_columns) { + DORIS_CHECK(column.transfer_type != nullptr); + required_fields.push_back(column.java_name); + column_types.push_back( + JniDataBridge::get_jni_type_with_different_string(column.transfer_type)); + replace_types.push_back(column.replace_type); + has_replace_type = has_replace_type || column.replace_type != "not_replace"; + _jni_block_template.insert( + {column.transfer_type->create_column(), column.transfer_type, column.java_name}); + } + _scanner_params["required_fields"] = join(required_fields, ","); + _scanner_params["columns_types"] = join(column_types, "#"); + if (has_replace_type) { + _scanner_params["replace_string"] = join(replace_types, ","); + } +} + +Status JniTableReader::_register_jni_class_functions(JNIEnv* env, int batch_size) { + RETURN_IF_ERROR( + Jni::Util::get_jni_scanner_class(env, connector_class().c_str(), &_jni_scanner_cls)); + + Jni::MethodId scanner_constructor; + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "", "(ILjava/util/Map;)V", + &scanner_constructor)); + + Jni::LocalObject hashmap_object; + RETURN_IF_ERROR(Jni::Util::convert_to_java_map(env, _scanner_params, &hashmap_object)); + RETURN_IF_ERROR(_jni_scanner_cls.new_object(env, scanner_constructor) + .with_arg(batch_size) + .with_arg(hashmap_object) + .call(&_jni_scanner_obj)); + + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "open", "()V", &_jni_scanner_open)); + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "getNextBatchMeta", "()J", + &_jni_scanner_get_next_batch)); + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "getAppendDataTime", "()J", + &_jni_scanner_get_append_data_time)); + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "getCreateVectorTableTime", "()J", + &_jni_scanner_get_create_vector_table_time)); + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "close", "()V", &_jni_scanner_close)); + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "releaseColumn", "(I)V", + &_jni_scanner_release_column)); + RETURN_IF_ERROR( + _jni_scanner_cls.get_method(env, "releaseTable", "()V", &_jni_scanner_release_table)); + RETURN_IF_ERROR(_jni_scanner_cls.get_method(env, "getStatistics", "()Ljava/util/Map;", + &_jni_scanner_get_statistics)); + RETURN_IF_ERROR( + _jni_scanner_cls.get_method(env, "setBatchSize", "(I)V", &_jni_scanner_set_batch_size)); + return Status::OK(); +} + +void JniTableReader::_init_profile() { + if (_scanner_profile == nullptr) { + return; + } + const auto connector_name = _connector_name(); + ADD_TIMER(_scanner_profile, connector_name); + _open_scanner_time = ADD_CHILD_TIMER(_scanner_profile, "OpenScannerTime", connector_name); + _java_scan_time = ADD_CHILD_TIMER(_scanner_profile, "JavaScanTime", connector_name); + _java_append_data_time = + ADD_CHILD_TIMER(_scanner_profile, "JavaAppendDataTime", connector_name); + _java_create_vector_table_time = + ADD_CHILD_TIMER(_scanner_profile, "JavaCreateVectorTableTime", connector_name); + _fill_block_time = ADD_CHILD_TIMER(_scanner_profile, "FillBlockTime", connector_name); + _max_time_split_weight_counter = _scanner_profile->add_conditition_counter( + "MaxTimeSplitWeight", TUnit::UNIT, [](int64_t _c, int64_t c) { return c > _c; }, + connector_name); +} + +std::string JniTableReader::_connector_name() const { + const auto parts = split(connector_class(), "/"); + return parts.empty() ? connector_class() : parts.back(); +} + +} // namespace doris::format diff --git a/be/src/format_v2/jni/jni_table_reader.h b/be/src/format_v2/jni/jni_table_reader.h new file mode 100644 index 00000000000000..a6d31a34a04b6c --- /dev/null +++ b/be/src/format_v2/jni/jni_table_reader.h @@ -0,0 +1,112 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "format/jni/jni_data_bridge.h" +#include "format_v2/table_reader.h" +#include "runtime/runtime_profile.h" +#include "util/jni-util.h" + +namespace doris::format { + +class JniTableReader : public TableReader { +public: + struct JniColumn { + std::string java_name; + // The index of the column in the output block, which is used to place the data from Java side to the correct position in the output block. + size_t output_index = 0; + // The original output type of the column, which is used for type casting after getting the data from Java side. like Bitmap column + // For columns without special types, the transfer_type and output_type are the same. + DataTypePtr output_type; + //Bitmap Type transfer type is String, so the Java scanner will convert the Bitmap column to String before transferring the data to C++, and then C++ side can convert the String back to Bitmap. + DataTypePtr transfer_type; + std::string replace_type = "not_replace"; + }; + + ~JniTableReader() override = default; + + Status init(TableReadOptions&& options) override; + Status prepare_split(const SplitReadOptions& options) override; + Status get_block(Block* block, bool* eos) override; + Status close() override; + +protected: + // Subclasses should implement these methods to specify the Java scanner class + virtual std::string connector_class() const = 0; + // Subclasses should implement this method to build the scanner params map + virtual Status build_scanner_params(std::map* params) const = 0; + // Subclasses should implement this method to build the jni columns info, name, types + // Now maybe JDBC need deal with some special types + virtual Status build_jni_columns(std::vector* columns) const = 0; + virtual Status finalize_jni_block(Block* jni_block, Block* output_block, size_t* rows); + // used for profile + virtual int64_t self_split_weight() const { return -1; } + const std::vector& jni_columns() const { return _jni_columns; } + +private: + // init + void _init_profile(); + std::string _connector_name() const; + // open + Status _open_jni_scanner(); + void _prepare_jni_scanner_schema(); + Status _register_jni_class_functions(JNIEnv* env, int batch_size); + // get_next + Status _get_next_jni_block(size_t* rows, bool* eof); + Status _fill_jni_block(JniDataBridge::TableMetaAddress& table_meta, size_t num_rows); + + std::map _scanner_params; + std::vector _jni_columns; + Block _jni_block_template; + + bool _closed = false; + bool _scanner_opened = false; + bool _eof = false; + size_t _batch_size = 0; + + RuntimeProfile::Counter* _open_scanner_time = nullptr; + RuntimeProfile::Counter* _java_scan_time = nullptr; + RuntimeProfile::Counter* _java_append_data_time = nullptr; + RuntimeProfile::Counter* _java_create_vector_table_time = nullptr; + RuntimeProfile::Counter* _fill_block_time = nullptr; + RuntimeProfile::ConditionCounter* _max_time_split_weight_counter = nullptr; + + int64_t _jni_scanner_open_watcher = 0; + int64_t _java_scan_watcher = 0; + int64_t _fill_block_watcher = 0; + + Jni::GlobalClass _jni_scanner_cls; + Jni::GlobalObject _jni_scanner_obj; + Jni::MethodId _jni_scanner_open; + Jni::MethodId _jni_scanner_get_append_data_time; + Jni::MethodId _jni_scanner_get_create_vector_table_time; + Jni::MethodId _jni_scanner_get_next_batch; + Jni::MethodId _jni_scanner_close; + Jni::MethodId _jni_scanner_release_column; + Jni::MethodId _jni_scanner_release_table; + Jni::MethodId _jni_scanner_get_statistics; + Jni::MethodId _jni_scanner_set_batch_size; +}; + +} // namespace doris::format diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp new file mode 100644 index 00000000000000..f127ffc53c55bb --- /dev/null +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -0,0 +1,283 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_column_schema.h" + +#include + +#include +#include +#include +#include + +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" +#include "format_v2/parquet/parquet_type.h" + +namespace doris::parquet { +namespace { + +struct SchemaBuildContext { + // Reader-local id inside the parent schema node. + int32_t local_id = -1; + int16_t definition_level = 0; + int16_t repetition_level = 0; + int16_t nullable_definition_level = 0; + int16_t repeated_repetition_level = 0; + int16_t repeated_ancestor_definition_level = 0; +}; + +bool is_list_node(const ::parquet::schema::Node& node) { + const auto& logical_type = node.logical_type(); + return node.converted_type() == ::parquet::ConvertedType::LIST || + (logical_type != nullptr && logical_type->is_valid() && logical_type->is_list()); +} + +bool is_map_node(const ::parquet::schema::Node& node) { + const auto& logical_type = node.logical_type(); + return node.converted_type() == ::parquet::ConvertedType::MAP || + node.converted_type() == ::parquet::ConvertedType::MAP_KEY_VALUE || + (logical_type != nullptr && logical_type->is_valid() && logical_type->is_map()); +} + +DataTypePtr nullable_if_needed(DataTypePtr type, const ::parquet::schema::Node& node) { + return node.is_optional() ? make_nullable(type) : type; +} + +void inherit_common_schema_state(const ::parquet::schema::Node& node, + const SchemaBuildContext& context, + ParquetColumnSchema* column_schema) { + DORIS_CHECK(column_schema != nullptr); + column_schema->local_id = context.local_id; + column_schema->parquet_field_id = node.field_id(); + column_schema->name = node.name(); + column_schema->max_definition_level = context.definition_level; + column_schema->max_repetition_level = context.repetition_level; + column_schema->nullable_definition_level = context.nullable_definition_level; + column_schema->definition_level = context.definition_level; + column_schema->repetition_level = context.repetition_level; + column_schema->repeated_ancestor_definition_level = context.repeated_ancestor_definition_level; + column_schema->repeated_repetition_level = context.repeated_repetition_level; +} + +SchemaBuildContext child_context(const SchemaBuildContext& parent, + const ::parquet::schema::Node& child_node, int32_t child_idx) { + SchemaBuildContext result = parent; + result.local_id = child_idx; + if (child_node.repetition() == ::parquet::Repetition::OPTIONAL) { + result.definition_level++; + result.nullable_definition_level = result.definition_level; + } + if (child_node.is_repeated()) { + result.repetition_level++; + result.definition_level++; + result.repeated_repetition_level = result.repetition_level; + result.repeated_ancestor_definition_level = result.definition_level; + } + return result; +} + +void propagate_child_levels(ParquetColumnSchema* column_schema) { + DORIS_CHECK(column_schema != nullptr); + for (const auto& child : column_schema->children) { + column_schema->max_definition_level = + std::max(column_schema->max_definition_level, child->max_definition_level); + column_schema->max_repetition_level = + std::max(column_schema->max_repetition_level, child->max_repetition_level); + } +} + +// Recursively builds ParquetColumnSchema for the given schema node and its children in Parquet file's metadata. +Status build_node_schema(const ::parquet::SchemaDescriptor& schema, + const ::parquet::schema::Node& node, const SchemaBuildContext& context, + std::unique_ptr* result) { + if (result == nullptr) { + return Status::InvalidArgument("result is null"); + } + auto column_schema = std::make_unique(); + inherit_common_schema_state(node, context, column_schema.get()); + + if (node.is_primitive()) { + const int leaf_column_id = schema.ColumnIndex(node); + if (leaf_column_id < 0) { + return Status::InvalidArgument("Cannot find leaf column id for parquet column {}", + node.name()); + } + column_schema->kind = ParquetColumnSchemaKind::PRIMITIVE; + column_schema->leaf_column_id = leaf_column_id; + column_schema->descriptor = schema.Column(leaf_column_id); + if (column_schema->descriptor != nullptr) { + column_schema->max_definition_level = column_schema->descriptor->max_definition_level(); + column_schema->max_repetition_level = column_schema->descriptor->max_repetition_level(); + } + column_schema->type_descriptor = resolve_parquet_type(column_schema->descriptor); + column_schema->type = column_schema->type_descriptor.doris_type; + if (column_schema->type == nullptr) { + return Status::NotSupported("Unsupported parquet column type for column {}", + node.name()); + } + column_schema->type = node.is_optional() + ? make_nullable(remove_nullable(column_schema->type)) + : remove_nullable(column_schema->type); + *result = std::move(column_schema); + return Status::OK(); + } + + const auto& group = static_cast(node); + if (is_list_node(node)) { + column_schema->kind = ParquetColumnSchemaKind::LIST; + if (group.field_count() != 1) { + return Status::NotSupported("Unsupported parquet LIST encoding for column {}", + node.name()); + } + const auto& repeated_node = *group.field(0); + if (!repeated_node.is_repeated() || repeated_node.is_primitive()) { + return Status::NotSupported("Unsupported parquet LIST encoding for column {}", + node.name()); + } + const auto& repeated_group = + static_cast(repeated_node); + if (repeated_group.field_count() != 1) { + return Status::NotSupported("Unsupported parquet LIST element layout for column {}", + node.name()); + } + auto repeated_context = child_context(context, repeated_node, 0); + column_schema->definition_level = repeated_context.definition_level; + column_schema->repetition_level = repeated_context.repetition_level; + column_schema->repeated_repetition_level = repeated_context.repeated_repetition_level; + std::unique_ptr child; + RETURN_IF_ERROR(build_node_schema( + schema, *repeated_group.field(0), + child_context(repeated_context, *repeated_group.field(0), 0), &child)); + column_schema->type = + nullable_if_needed(std::make_shared(child->type), node); + column_schema->children.push_back(std::move(child)); + propagate_child_levels(column_schema.get()); + *result = std::move(column_schema); + return Status::OK(); + } + + if (is_map_node(node)) { + column_schema->kind = ParquetColumnSchemaKind::MAP; + if (group.field_count() != 1) { + return Status::NotSupported("Unsupported parquet MAP encoding for column {}", + node.name()); + } + const auto& key_value_node = *group.field(0); + if (!key_value_node.is_repeated()) { + return Status::NotSupported("Unsupported parquet MAP encoding for column {}", + node.name()); + } + auto key_value_context = child_context(context, key_value_node, 0); + column_schema->definition_level = key_value_context.definition_level; + column_schema->repetition_level = key_value_context.repetition_level; + column_schema->repeated_repetition_level = key_value_context.repeated_repetition_level; + if (key_value_node.is_primitive()) { + return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", + node.name()); + } + const auto& key_value_group = + static_cast(key_value_node); + if (key_value_group.field_count() != 2) { + return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", + node.name()); + } + auto key_value = std::make_unique(); + inherit_common_schema_state(key_value_node, key_value_context, key_value.get()); + key_value->kind = ParquetColumnSchemaKind::STRUCT; + DataTypes child_types; + Strings child_names; + child_types.reserve(key_value_group.field_count()); + child_names.reserve(key_value_group.field_count()); + for (int child_idx = 0; child_idx < key_value_group.field_count(); ++child_idx) { + std::unique_ptr child; + RETURN_IF_ERROR(build_node_schema( + schema, *key_value_group.field(child_idx), + child_context(key_value_context, *key_value_group.field(child_idx), child_idx), + &child)); + child_types.push_back(make_nullable(child->type)); + child_names.push_back(child->name); + key_value->children.push_back(std::move(child)); + } + key_value->type = std::make_shared(child_types, child_names); + propagate_child_levels(key_value.get()); + if (key_value->children.size() != 2) { + return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", + node.name()); + } + if (key_value_group.field(0)->repetition() != ::parquet::Repetition::REQUIRED) { + return Status::NotSupported("Unsupported nullable parquet MAP key for column {}", + node.name()); + } + auto key_type = make_nullable(key_value->children[0]->type); + auto value_type = make_nullable(key_value->children[1]->type); + column_schema->type = + nullable_if_needed(std::make_shared(key_type, value_type), node); + column_schema->children.push_back(std::move(key_value)); + propagate_child_levels(column_schema.get()); + *result = std::move(column_schema); + return Status::OK(); + } + + column_schema->kind = ParquetColumnSchemaKind::STRUCT; + DataTypes child_types; + Strings child_names; + child_types.reserve(group.field_count()); + child_names.reserve(group.field_count()); + for (int child_idx = 0; child_idx < group.field_count(); ++child_idx) { + std::unique_ptr child; + RETURN_IF_ERROR(build_node_schema( + schema, *group.field(child_idx), + child_context(context, *group.field(child_idx), child_idx), &child)); + child_types.push_back(make_nullable(child->type)); + child_names.push_back(child->name); + column_schema->children.push_back(std::move(child)); + } + column_schema->type = + nullable_if_needed(std::make_shared(child_types, child_names), node); + propagate_child_levels(column_schema.get()); + *result = std::move(column_schema); + return Status::OK(); +} + +} // namespace + +Status build_parquet_column_schema(const ::parquet::SchemaDescriptor& schema, + std::vector>* fields) { + if (fields == nullptr) { + return Status::InvalidArgument("fields is null"); + } + fields->clear(); + const auto* root = schema.group_node(); + if (root == nullptr) { + return Status::InvalidArgument("Parquet schema root is null"); + } + fields->reserve(root->field_count()); + for (int field_idx = 0; field_idx < root->field_count(); ++field_idx) { + std::unique_ptr field; + SchemaBuildContext context; + RETURN_IF_ERROR(build_node_schema( + schema, *root->field(field_idx), + child_context(context, *root->field(field_idx), field_idx), &field)); + fields->push_back(std::move(field)); + } + return Status::OK(); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_column_schema.h b/be/src/format_v2/parquet/parquet_column_schema.h new file mode 100644 index 00000000000000..012a2e6046dd0b --- /dev/null +++ b/be/src/format_v2/parquet/parquet_column_schema.h @@ -0,0 +1,92 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "format_v2/parquet/parquet_type.h" + +namespace parquet { +class ColumnDescriptor; +class SchemaDescriptor; +} // namespace parquet + +namespace doris::parquet { + +enum class ParquetColumnSchemaKind { + PRIMITIVE, + STRUCT, + LIST, + MAP, +}; + +// 新 Parquet reader 的 file-local schema tree。 +// 它描述 Parquet 逻辑字段到 leaf column ordinal 的关系，不包含 table/global schema 语义。 +struct ParquetColumnSchema { + // Reader-local id inside the parent schema node. + // + // Top-level fields use the root field ordinal. Nested fields use the child ordinal under their + // parent. This id is what LocalColumnIndex carries into ParquetColumnReaderFactory. + int local_id = -1; + // Optional Parquet schema field_id from the serialized schema element. + // + // Arrow returns -1 when the file does not define a field_id. This value is only a schema + // matching identifier for table formats such as Iceberg; it is not used to address readers, + // row-group column chunks, or nested projections. + int parquet_field_id = -1; + std::string name; + DataTypePtr type = nullptr; + // Parquet schema 中的 primitive leaf column ordinal. + // + // 该 id 用于访问 ColumnDescriptor、RowGroupReader::RecordReader、ColumnChunk + // metadata 和 statistics。复杂类型节点本身没有单一 leaf column，因此为 -1。 + int leaf_column_id = -1; + // Parquet physical/logical type metadata resolved from the leaf ColumnDescriptor. + ParquetTypeDescriptor type_descriptor {}; + ParquetColumnSchemaKind kind = ParquetColumnSchemaKind::PRIMITIVE; + // Arrow Parquet descriptor for primitive leaf nodes. Complex nodes keep this as nullptr. + const ::parquet::ColumnDescriptor* descriptor = nullptr; + // Maximum Dremel definition/repetition levels below this schema node. + int16_t max_definition_level = 0; + int16_t max_repetition_level = 0; + // Definition level at which this node itself becomes nullable. + // + // Complex readers use this to distinguish null containers from empty containers while + // assembling STRUCT/LIST/MAP values. + int16_t nullable_definition_level = 0; + // Definition/repetition levels used to reconstruct this node from a descendant leaf level + // stream. These mirror parquet::internal::LevelInfo but are kept in Doris schema state so + // complex readers do not depend on Arrow's Arrow-array reader internals. + int16_t definition_level = 0; + int16_t repetition_level = 0; + int16_t repeated_ancestor_definition_level = 0; + // Repetition level introduced by this node's repeated container, or the nearest repeated + // container carried from its parent. + int16_t repeated_repetition_level = 0; + std::vector> children {}; +}; + +// 从 Arrow Parquet core schema 构造 file-local schema tree。 +Status build_parquet_column_schema(const ::parquet::SchemaDescriptor& schema, + std::vector>* fields); + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_file_context.cpp b/be/src/format_v2/parquet/parquet_file_context.cpp new file mode 100644 index 00000000000000..0790551ca60362 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_file_context.cpp @@ -0,0 +1,163 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_file_context.h" + +#include +#include +#include + +#include +#include + +#include "io/fs/file_reader.h" +#include "util/slice.h" + +namespace doris::parquet { +namespace { + +class DorisRandomAccessFile final : public arrow::io::RandomAccessFile { +public: + DorisRandomAccessFile(io::FileReaderSPtr file_reader, io::IOContext* io_ctx) + : _file_reader(std::move(file_reader)), _io_ctx(io_ctx) { + set_mode(arrow::io::FileMode::READ); + } + + arrow::Status Close() override { + _closed = true; + return arrow::Status::OK(); + } + + bool closed() const override { return _closed; } + + arrow::Result Tell() const override { return _pos; } + + arrow::Status Seek(int64_t position) override { + if (position < 0) { + return arrow::Status::Invalid("negative seek position"); + } + _pos = position; + return arrow::Status::OK(); + } + + arrow::Result GetSize() override { + if (!_file_reader) { + return arrow::Status::IOError("Doris file reader is not open"); + } + return static_cast(_file_reader->size()); + } + + arrow::Result Read(int64_t nbytes, void* out) override { + ARROW_ASSIGN_OR_RAISE(auto bytes_read, ReadAt(_pos, nbytes, out)); + _pos += bytes_read; + return bytes_read; + } + + arrow::Result> Read(int64_t nbytes) override { + ARROW_ASSIGN_OR_RAISE(auto buffer, arrow::AllocateResizableBuffer(nbytes)); + ARROW_ASSIGN_OR_RAISE(auto bytes_read, Read(nbytes, buffer->mutable_data())); + ARROW_RETURN_NOT_OK(buffer->Resize(bytes_read, false)); + buffer->ZeroPadding(); + return buffer; + } + + arrow::Result ReadAt(int64_t position, int64_t nbytes, void* out) override { + if (!_file_reader) { + return arrow::Status::IOError("Doris file reader is not open"); + } + if (position < 0 || nbytes < 0) { + return arrow::Status::Invalid("negative read position or length"); + } + size_t bytes_read = 0; + Status st = _file_reader->read_at( + static_cast(position), + Slice(static_cast(out), static_cast(nbytes)), &bytes_read, + _io_ctx); + if (!st.ok()) { + return arrow::Status::IOError(st.to_string_no_stack()); + } + return static_cast(bytes_read); + } + + arrow::Result> ReadAt(int64_t position, + int64_t nbytes) override { + ARROW_ASSIGN_OR_RAISE(auto buffer, arrow::AllocateResizableBuffer(nbytes)); + ARROW_ASSIGN_OR_RAISE(auto bytes_read, ReadAt(position, nbytes, buffer->mutable_data())); + ARROW_RETURN_NOT_OK(buffer->Resize(bytes_read, false)); + buffer->ZeroPadding(); + return buffer; + } + +private: + io::FileReaderSPtr _file_reader; + io::IOContext* _io_ctx = nullptr; + int64_t _pos = 0; + bool _closed = false; +}; + +} // namespace + +Status arrow_status_to_doris_status(const arrow::Status& status) { + if (status.ok()) { + return Status::OK(); + } + if (status.IsIOError()) { + return Status::IOError(status.ToString()); + } + if (status.IsInvalid()) { + return Status::InvalidArgument(status.ToString()); + } + return Status::InternalError(status.ToString()); +} + +Status ParquetFileContext::open(io::FileReaderSPtr input_file_reader, io::IOContext* io_ctx) { + arrow_file = std::make_shared(std::move(input_file_reader), io_ctx); + try { + // TODO: Cache parquet metadata in file system layer to avoid repeated metadata read for same file. + this->file_reader = ::parquet::ParquetFileReader::Open( + arrow_file, ::parquet::default_reader_properties()); + metadata = this->file_reader->metadata(); + schema = metadata != nullptr ? metadata->schema() : nullptr; + } catch (const ::parquet::ParquetException& e) { + return Status::Corruption("Failed to open parquet file: {}", e.what()); + } catch (const std::exception& e) { + return Status::InternalError("Failed to open parquet file: {}", e.what()); + } + + if (metadata == nullptr || schema == nullptr) { + return Status::Corruption("Failed to read parquet metadata"); + } + return Status::OK(); +} + +Status ParquetFileContext::close() { + if (file_reader != nullptr) { + try { + file_reader->Close(); + } catch (const std::exception&) { + // close 需要保持幂等；这里不覆盖此前 scan 路径上的真实错误。 + } + } + if (arrow_file != nullptr) { + static_cast(arrow_status_to_doris_status(arrow_file->Close())); + } + file_reader.reset(); + arrow_file.reset(); + return Status::OK(); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_file_context.h b/be/src/format_v2/parquet/parquet_file_context.h new file mode 100644 index 00000000000000..3d08fffcfeb9d7 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_file_context.h @@ -0,0 +1,42 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include + +#include + +#include "common/status.h" +#include "io/fs/file_reader.h" + +namespace doris::parquet { + +struct ParquetFileContext { + std::shared_ptr arrow_file; + std::unique_ptr<::parquet::ParquetFileReader> file_reader; + std::shared_ptr<::parquet::FileMetaData> metadata; + const ::parquet::SchemaDescriptor* schema = nullptr; + + Status open(io::FileReaderSPtr input_file_reader, io::IOContext* io_ctx); + Status close(); +}; + +Status arrow_status_to_doris_status(const arrow::Status& status); + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_profile.h b/be/src/format_v2/parquet/parquet_profile.h new file mode 100644 index 00000000000000..094769d11c14cb --- /dev/null +++ b/be/src/format_v2/parquet/parquet_profile.h @@ -0,0 +1,32 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include "runtime/runtime_profile.h" + +namespace doris::parquet { + +struct ParquetColumnReaderProfile { + RuntimeProfile::Counter* reader_read_rows = nullptr; + RuntimeProfile::Counter* reader_skip_rows = nullptr; + RuntimeProfile::Counter* reader_select_rows = nullptr; + RuntimeProfile::Counter* arrow_read_records_time = nullptr; + RuntimeProfile::Counter* materialization_time = nullptr; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp new file mode 100644 index 00000000000000..175a8560310ef4 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -0,0 +1,489 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_reader.h" + +#include +#include +#include +#include +#include +#include + +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/parquet_file_context.h" +#include "format_v2/parquet/parquet_scan.h" +#include "format_v2/parquet/parquet_statistics.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "runtime/runtime_state.h" + +namespace doris::parquet { + +struct ParquetReaderScanState { + ParquetFileContext file_context; + std::vector> file_schema; + RowGroupScanPlan scan_plan; + ParquetScanScheduler scheduler; + bool enable_bloom_filter = false; +}; + +static Status find_projected_minmax_leaf(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex& projection, + const ParquetColumnSchema** leaf_schema) { + DORIS_CHECK(leaf_schema != nullptr); + if (projection.project_all_children || projection.children.empty()) { + if (column_schema.leaf_column_id < 0) { + return Status::NotSupported( + "Parquet aggregate pushdown only supports primitive column {}", + column_schema.name); + } + if (column_schema.max_repetition_level > 0) { + return Status::NotSupported( + "Parquet aggregate pushdown does not support repeated column {}", + column_schema.name); + } + *leaf_schema = &column_schema; + return Status::OK(); + } + if (projection.children.size() != 1) { + return Status::NotSupported( + "Parquet aggregate pushdown only supports a single nested leaf under column {}", + column_schema.name); + } + const auto& child_projection = projection.children[0]; + const auto child_schema_it = + std::ranges::find_if(column_schema.children, [&](const auto& child_schema) { + return child_schema->local_id == child_projection.local_id(); + }); + if (child_schema_it != column_schema.children.end()) { + return find_projected_minmax_leaf(**child_schema_it, child_projection, leaf_schema); + } + return Status::InvalidArgument("Invalid parquet aggregate projection local id {} for column {}", + child_projection.local_id(), column_schema.name); +} + +void ParquetReader::_fill_column_definition(const ParquetColumnSchema& column_schema, + format::ColumnDefinition* field) const { + if (column_schema.parquet_field_id >= 0) { + field->identifier = Field::create_field(column_schema.parquet_field_id); + } else { + field->identifier = Field::create_field(column_schema.name); + } + field->local_id = column_schema.local_id; + field->name = column_schema.name; + field->type = column_schema.type; + field->children.clear(); + field->children.reserve(column_schema.children.size()); + for (const auto& child : column_schema.children) { + format::ColumnDefinition child_field; + _fill_column_definition(*child, &child_field); + field->children.push_back(std::move(child_field)); + } +} + +ParquetReader::ParquetReader(std::shared_ptr& system_properties, + std::unique_ptr& file_description, + std::shared_ptr io_ctx, RuntimeProfile* profile, + std::optional global_rowid_context) + : FileReader(system_properties, file_description, io_ctx, profile), + _global_rowid_context(global_rowid_context) {} + +ParquetReader::~ParquetReader() = default; + +Status ParquetReader::init(RuntimeState* state) { + RETURN_IF_ERROR(format::FileReader::init(state)); + if (_profile != nullptr) { + COUNTER_UPDATE(_parquet_profile.file_reader_create_time, + _reader_statistics.file_reader_create_time); + COUNTER_UPDATE(_parquet_profile.open_file_num, _reader_statistics.open_file_num); + } + _state = std::make_unique(); + _state->enable_bloom_filter = + state != nullptr && state->query_options().enable_parquet_filter_by_bloom_filter; + if (state != nullptr) { + _state->scheduler.set_timezone(&state->timezone_obj()); + _state->scheduler.set_enable_strict_mode(state->enable_strict_mode()); + } + // Open parquet file and parse metadata to get file schema. + RETURN_IF_ERROR(_state->file_context.open(_tracing_file_reader, _io_ctx.get())); + // Build file schema from parquet metadata. + // A file reader may expose raw file identifiers, such as Parquet field_id, through ColumnDefinition::identifier + RETURN_IF_ERROR( + build_parquet_column_schema(*_state->file_context.schema, &_state->file_schema)); + return Status::OK(); +} + +Status ParquetReader::get_schema(std::vector* file_schema) const { + if (file_schema == nullptr) { + return Status::InvalidArgument("file_schema is null"); + } + file_schema->clear(); + if (_state == nullptr || _state->file_context.schema == nullptr) { + return Status::Uninitialized("ParquetReader is not open"); + } + + file_schema->reserve(_state->file_schema.size()); + for (size_t column_idx = 0; column_idx < _state->file_schema.size(); ++column_idx) { + format::ColumnDefinition field; + _fill_column_definition(*_state->file_schema[column_idx], &field); + DORIS_CHECK(field.local_id == static_cast(column_idx)); + file_schema->push_back(std::move(field)); + } + if (_global_rowid_context.has_value()) { + file_schema->push_back(format::global_rowid_column_definition()); + } + return Status::OK(); +} + +Status ParquetReader::open(std::shared_ptr request) { + if (_state == nullptr || _state->file_context.metadata == nullptr || + _state->file_context.schema == nullptr) { + return Status::Uninitialized("ParquetReader is not open"); + } + auto request_snapshot = request; + DORIS_CHECK(request_snapshot != nullptr); + RETURN_IF_ERROR(format::FileReader::open(std::move(request))); + + const int num_fields = static_cast(_state->file_schema.size()); + for (const auto& column_filter : request_snapshot->column_predicate_filters) { + const auto file_column_id = column_filter.effective_file_column_id(); + if (!file_column_id.is_valid() || file_column_id.value() >= num_fields) { + return Status::InvalidArgument("Invalid parquet filter top-level local id {}", + file_column_id.value()); + } + } + + // `local_positions.empty()` means all columns are needed by table reader + // TODO(gabriel): It will happen only for TVF `select *` query. + if (request_snapshot->local_positions.empty()) { + for (const auto& col : request_snapshot->predicate_columns) { + request_snapshot->local_positions.emplace(col.column_id(), + format::LocalIndex(col.column_id().value())); + } + for (const auto& col : request_snapshot->non_predicate_columns) { + request_snapshot->local_positions.emplace(col.column_id(), + format::LocalIndex(col.column_id().value())); + } + } + + for (const auto& col : request_snapshot->predicate_columns) { + DORIS_CHECK(request_snapshot->local_positions.count(col.column_id()) > 0); + const auto local_id = col.local_id(); + if (local_id == format::ROW_POSITION_COLUMN_ID || + local_id == format::GLOBAL_ROWID_COLUMN_ID) { + continue; + } + DORIS_CHECK(local_id >= 0 && local_id < num_fields); + } + for (const auto& col : request_snapshot->non_predicate_columns) { + DORIS_CHECK(request_snapshot->local_positions.count(col.column_id()) > 0); + const auto local_id = col.local_id(); + if (local_id == format::ROW_POSITION_COLUMN_ID || + local_id == format::GLOBAL_ROWID_COLUMN_ID) { + continue; + } + DORIS_CHECK(local_id >= 0 && local_id < num_fields); + } + + RowGroupScanPlan row_group_plan; + ParquetScanRange scan_range; + scan_range.start_offset = _file_description->range_start_offset; + scan_range.size = _file_description->range_size; + scan_range.file_size = _file_description->file_size; + // Get selected ranges in row groups according to metadata (Row-Group level index and Page Index including Zonemap, Dictionary, Bloom Filter). + RETURN_IF_ERROR(plan_parquet_row_groups(*_state->file_context.metadata, + _state->file_context.file_reader.get(), + _state->file_schema, *request_snapshot, scan_range, + _state->enable_bloom_filter, &row_group_plan)); + if (_profile != nullptr) { + const auto& pruning_stats = row_group_plan.pruning_stats; + COUNTER_UPDATE(_parquet_profile.filtered_row_groups, + pruning_stats.total_row_groups - pruning_stats.selected_row_groups); + COUNTER_UPDATE(_parquet_profile.filtered_row_groups_by_min_max, + pruning_stats.filtered_row_groups_by_statistics); + COUNTER_UPDATE(_parquet_profile.filtered_row_groups_by_dictionary, + pruning_stats.filtered_row_groups_by_dictionary); + COUNTER_UPDATE(_parquet_profile.filtered_row_groups_by_bloom_filter, + pruning_stats.filtered_row_groups_by_bloom_filter); + COUNTER_UPDATE(_parquet_profile.to_read_row_groups, pruning_stats.selected_row_groups); + COUNTER_UPDATE(_parquet_profile.total_row_groups, pruning_stats.total_row_groups); + COUNTER_UPDATE(_parquet_profile.selected_row_ranges, pruning_stats.selected_row_ranges); + COUNTER_UPDATE(_parquet_profile.filtered_group_rows, pruning_stats.filtered_group_rows); + COUNTER_UPDATE(_parquet_profile.filtered_page_rows, pruning_stats.filtered_page_rows); + COUNTER_UPDATE(_parquet_profile.page_index_read_calls, pruning_stats.page_index_read_calls); + COUNTER_UPDATE(_parquet_profile.bloom_filter_read_time, + pruning_stats.bloom_filter_read_time); + COUNTER_UPDATE(_parquet_profile.row_group_filter_time, pruning_stats.row_group_filter_time); + COUNTER_UPDATE(_parquet_profile.page_index_filter_time, + pruning_stats.page_index_filter_time); + COUNTER_UPDATE(_parquet_profile.read_page_index_time, pruning_stats.read_page_index_time); + } + _state->scan_plan = row_group_plan; + _state->scheduler.set_page_skip_profile( + {.skipped_pages = _parquet_profile.pages_skipped_by_data_page_filter, + .skipped_bytes = _parquet_profile.data_page_filter_skip_bytes}); + _state->scheduler.set_global_rowid_context(_global_rowid_context); + _state->scheduler.set_scan_profile({ + .raw_rows_read = _parquet_profile.raw_rows_read, + .selected_rows = _parquet_profile.selected_rows, + .rows_filtered_by_conjunct = _parquet_profile.rows_filtered_by_conjunct, + .total_batches = _parquet_profile.total_batches, + .empty_selection_batches = _parquet_profile.empty_selection_batches, + .range_gap_skipped_rows = _parquet_profile.range_gap_skipped_rows, + .column_read_time = _parquet_profile.column_read_time, + .predicate_filter_time = _parquet_profile.predicate_filter_time, + .column_reader_profile = + { + .reader_read_rows = _parquet_profile.reader_read_rows, + .reader_skip_rows = _parquet_profile.reader_skip_rows, + .reader_select_rows = _parquet_profile.reader_select_rows, + .arrow_read_records_time = _parquet_profile.arrow_read_records_time, + .materialization_time = _parquet_profile.materialization_time, + }, + }); + _state->scheduler.set_plan(std::move(row_group_plan)); + _eof = _state->scheduler.empty(); + return Status::OK(); +} + +Status ParquetReader::get_block(Block* file_block, size_t* rows, bool* eof) { + if (_state == nullptr || _state->file_context.file_reader == nullptr || + _state->file_context.schema == nullptr) { + return Status::Uninitialized("ParquetReader is not open"); + } + *rows = 0; + if (_eof) { + *eof = true; + return Status::OK(); + } + auto request_snapshot = _request; + if (request_snapshot == nullptr) { + return Status::Cancelled("ParquetReader is closed"); + } + + RETURN_IF_ERROR(_state->scheduler.read_next_batch(_state->file_context, _state->file_schema, + *request_snapshot, file_block, rows, eof)); + _eof = *eof; + return Status::OK(); +} + +Status ParquetReader::get_aggregate_result(const format::FileAggregateRequest& request, + format::FileAggregateResult* result) { + DORIS_CHECK(result != nullptr); + if (_state == nullptr || _state->file_context.metadata == nullptr || + _state->file_context.schema == nullptr) { + return Status::Uninitialized("ParquetReader is not open"); + } + result->count = 0; + result->columns.clear(); + if (request.agg_type != TPushAggOp::type::COUNT && + request.agg_type != TPushAggOp::type::MINMAX) { + return Status::NotSupported("Unsupported parquet aggregate pushdown type {}", + request.agg_type); + } + + // Aggregate row count in all selected row groups. For MIN/MAX aggregate, this is used to determine whether there is no row group selected. + for (const auto& row_group_plan : _state->scan_plan.row_groups) { + auto row_group_metadata = + _state->file_context.metadata->RowGroup(row_group_plan.row_group_id); + DORIS_CHECK(row_group_metadata != nullptr); + result->count += row_group_metadata->num_rows(); + } + if (request.agg_type == TPushAggOp::type::COUNT) { + return Status::OK(); + } + + result->columns.resize(request.columns.size()); + for (size_t request_column_idx = 0; request_column_idx < request.columns.size(); + ++request_column_idx) { + const auto file_column_id = request.columns[request_column_idx].projection.local_id(); + if (file_column_id < 0 || + file_column_id >= static_cast(_state->file_schema.size())) { + return Status::InvalidArgument("Invalid parquet aggregate column id {}", + file_column_id); + } + const auto& column_schema = _state->file_schema[file_column_id]; + DORIS_CHECK(column_schema != nullptr); + const ParquetColumnSchema* leaf_schema = nullptr; + RETURN_IF_ERROR(find_projected_minmax_leaf( + *column_schema, request.columns[request_column_idx].projection, &leaf_schema)); + DORIS_CHECK(leaf_schema != nullptr); + + auto& aggregate_column = result->columns[request_column_idx]; + aggregate_column.projection = request.columns[request_column_idx].projection; + for (const auto& row_group_plan : _state->scan_plan.row_groups) { + auto row_group_metadata = + _state->file_context.metadata->RowGroup(row_group_plan.row_group_id); + DORIS_CHECK(row_group_metadata != nullptr); + auto column_chunk = row_group_metadata->ColumnChunk(leaf_schema->leaf_column_id); + DORIS_CHECK(column_chunk != nullptr); + const auto statistics = ParquetStatisticsUtils::TransformColumnStatistics( + *leaf_schema, column_chunk->statistics()); + if (!statistics.has_min_max) { + return Status::NotSupported("Missing parquet min/max statistics for column {}", + leaf_schema->name); + } + if (!aggregate_column.has_min || statistics.min_value < aggregate_column.min_value) { + aggregate_column.min_value = statistics.min_value; + aggregate_column.has_min = true; + } + if (!aggregate_column.has_max || aggregate_column.max_value < statistics.max_value) { + aggregate_column.max_value = statistics.max_value; + aggregate_column.has_max = true; + } + } + if (!aggregate_column.has_min || !aggregate_column.has_max) { + return Status::NotSupported("No parquet row group selected for min/max pushdown"); + } + } + return Status::OK(); +} + +Status ParquetReader::close() { + if (_state != nullptr) { + RETURN_IF_ERROR(_state->file_context.close()); + } + return FileReader::close(); +} + +void ParquetReader::_init_profile() { + if (_profile != nullptr) { + static const char* parquet_profile = "ParquetReader"; + ADD_TIMER_WITH_LEVEL(_profile, parquet_profile, 1); + + _parquet_profile.filtered_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowGroupsFiltered", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.filtered_row_groups_by_min_max = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowGroupsFilteredByMinMax", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.filtered_row_groups_by_dictionary = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowGroupsFilteredByDictionary", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.filtered_row_groups_by_bloom_filter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowGroupsFilteredByBloomFilter", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.to_read_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowGroupsReadNum", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.total_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowGroupsTotalNum", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.selected_row_ranges = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "SelectedRowRanges", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.filtered_group_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "FilteredRowsByGroup", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.filtered_page_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "FilteredRowsByPage", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.pages_skipped_by_data_page_filter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PagesSkippedByDataPageFilter", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.data_page_filter_skip_bytes = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "DataPageFilterSkipBytes", TUnit::BYTES, parquet_profile, 1); + _parquet_profile.selected_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "SelectedRows", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.rows_filtered_by_conjunct = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RowsFilteredByConjunct", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.total_batches = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "TotalBatches", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.empty_selection_batches = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "EmptySelectionBatches", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.range_gap_skipped_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RangeGapSkippedRows", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.reader_read_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "ReaderReadRows", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.reader_skip_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "ReaderSkipRows", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.reader_select_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "ReaderSelectRows", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.arrow_read_records_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ArrowReadRecordsTime", parquet_profile, 1); + _parquet_profile.materialization_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "MaterializationTime", parquet_profile, 1); + _parquet_profile.lazy_read_filtered_rows = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "FilteredRowsByLazyRead", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.filtered_bytes = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "FilteredBytes", TUnit::BYTES, parquet_profile, 1); + _parquet_profile.raw_rows_read = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "RawRowsRead", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.column_read_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ColumnReadTime", parquet_profile, 1); + _parquet_profile.parse_meta_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ParseMetaTime", parquet_profile, 1); + _parquet_profile.parse_footer_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ParseFooterTime", parquet_profile, 1); + _parquet_profile.file_reader_create_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "FileReaderCreateTime", parquet_profile, 1); + _parquet_profile.open_file_num = + ADD_CHILD_COUNTER_WITH_LEVEL(_profile, "FileNum", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_index_read_calls = + ADD_COUNTER_WITH_LEVEL(_profile, "PageIndexReadCalls", TUnit::UNIT, 1); + _parquet_profile.page_index_filter_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageIndexFilterTime", parquet_profile, 1); + _parquet_profile.read_page_index_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageIndexReadTime", parquet_profile, 1); + _parquet_profile.parse_page_index_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageIndexParseTime", parquet_profile, 1); + _parquet_profile.row_group_filter_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "RowGroupFilterTime", parquet_profile, 1); + _parquet_profile.file_footer_read_calls = + ADD_COUNTER_WITH_LEVEL(_profile, "FileFooterReadCalls", TUnit::UNIT, 1); + _parquet_profile.file_footer_hit_cache = + ADD_COUNTER_WITH_LEVEL(_profile, "FileFooterHitCache", TUnit::UNIT, 1); + _parquet_profile.decompress_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecompressTime", parquet_profile, 1); + _parquet_profile.decompress_cnt = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "DecompressCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_read_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageReadCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheWriteCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_compressed_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheCompressedWriteCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_decompressed_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheDecompressedWriteCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheHitCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_missing_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheMissingCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_compressed_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheCompressedHitCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.page_cache_decompressed_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "PageCacheDecompressedHitCount", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.decode_header_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageHeaderDecodeTime", parquet_profile, 1); + _parquet_profile.read_page_header_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageHeaderReadTime", parquet_profile, 1); + _parquet_profile.decode_value_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeValueTime", parquet_profile, 1); + _parquet_profile.decode_dict_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeDictTime", parquet_profile, 1); + _parquet_profile.decode_level_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeLevelTime", parquet_profile, 1); + _parquet_profile.decode_null_map_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeNullMapTime", parquet_profile, 1); + _parquet_profile.skip_page_header_num = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "SkipPageHeaderNum", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.parse_page_header_num = ADD_CHILD_COUNTER_WITH_LEVEL( + _profile, "ParsePageHeaderNum", TUnit::UNIT, parquet_profile, 1); + _parquet_profile.predicate_filter_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PredicateFilterTime", parquet_profile, 1); + _parquet_profile.dict_filter_rewrite_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DictFilterRewriteTime", parquet_profile, 1); + _parquet_profile.convert_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ConvertTime", parquet_profile, 1); + _parquet_profile.bloom_filter_read_time = + ADD_CHILD_TIMER_WITH_LEVEL(_profile, "BloomFilterReadTime", parquet_profile, 1); + } +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_reader.h b/be/src/format_v2/parquet/parquet_reader.h new file mode 100644 index 00000000000000..e5b705472c4a72 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_reader.h @@ -0,0 +1,141 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/status.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/parquet_column_schema.h" + +namespace doris { +namespace io { +struct IOContext; +} // namespace io +} // namespace doris + +namespace doris::parquet { + +struct ParquetReaderScanState; + +// Parquet 文件物理读取层。 +// 该类只理解 Parquet file-local schema 和 FileScanRequest，不理解 Iceberg/global +// schema，不处理 table-level cast/default/generated/partition 语义。 +class ParquetReader : public format::FileReader { +public: + ParquetReader(std::shared_ptr& system_properties, + std::unique_ptr& file_description, + std::shared_ptr io_ctx, RuntimeProfile* profile, + std::optional global_rowid_context = std::nullopt); + ~ParquetReader() override; + + // 打开 Parquet 文件并解析 footer metadata。 + // init 成功后可以调用 get_schema() 获取 Parquet file-local schema。 + Status init(RuntimeState* state) override; + + // 返回 init() 阶段解析出的 Parquet 文件自身 schema。 + // 该方法只能在 init() 成功后调用，不要求 open() 已经执行。 + // 这里不做 Iceberg schema evolution，也不把字段转换成 table/global schema。 + Status get_schema(std::vector* file_schema) const override; + + Status open(std::shared_ptr request) override; + // 读取下一批 Parquet file-local block。 + // 该方法只能在 init() 成功后调用。 + // 返回列必须保持 file-local 语义，不能在这里补 default/generated/partition 列。 + Status get_block(Block* file_block, size_t* rows, bool* eof) override; + + Status get_aggregate_result(const format::FileAggregateRequest& request, + format::FileAggregateResult* result) override; + + Status close() override; + +protected: + void _init_profile() override; + +private: + struct ParquetProfile { + RuntimeProfile::Counter* filtered_row_groups = nullptr; + RuntimeProfile::Counter* filtered_row_groups_by_min_max = nullptr; + RuntimeProfile::Counter* filtered_row_groups_by_dictionary = nullptr; + RuntimeProfile::Counter* filtered_row_groups_by_bloom_filter = nullptr; + RuntimeProfile::Counter* to_read_row_groups = nullptr; + RuntimeProfile::Counter* total_row_groups = nullptr; + RuntimeProfile::Counter* selected_row_ranges = nullptr; + RuntimeProfile::Counter* filtered_group_rows = nullptr; + RuntimeProfile::Counter* filtered_page_rows = nullptr; + RuntimeProfile::Counter* pages_skipped_by_data_page_filter = nullptr; + RuntimeProfile::Counter* data_page_filter_skip_bytes = nullptr; + RuntimeProfile::Counter* selected_rows = nullptr; + RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; + RuntimeProfile::Counter* total_batches = nullptr; + RuntimeProfile::Counter* empty_selection_batches = nullptr; + RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; + RuntimeProfile::Counter* reader_read_rows = nullptr; + RuntimeProfile::Counter* reader_skip_rows = nullptr; + RuntimeProfile::Counter* reader_select_rows = nullptr; + RuntimeProfile::Counter* arrow_read_records_time = nullptr; + RuntimeProfile::Counter* materialization_time = nullptr; + RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; + RuntimeProfile::Counter* filtered_bytes = nullptr; + RuntimeProfile::Counter* raw_rows_read = nullptr; + RuntimeProfile::Counter* column_read_time = nullptr; + RuntimeProfile::Counter* parse_meta_time = nullptr; + RuntimeProfile::Counter* parse_footer_time = nullptr; + RuntimeProfile::Counter* file_reader_create_time = nullptr; + RuntimeProfile::Counter* open_file_num = nullptr; + RuntimeProfile::Counter* row_group_filter_time = nullptr; + RuntimeProfile::Counter* page_index_read_calls = nullptr; + RuntimeProfile::Counter* page_index_filter_time = nullptr; + RuntimeProfile::Counter* read_page_index_time = nullptr; + RuntimeProfile::Counter* parse_page_index_time = nullptr; + RuntimeProfile::Counter* file_footer_read_calls = nullptr; + RuntimeProfile::Counter* file_footer_hit_cache = nullptr; + RuntimeProfile::Counter* decompress_time = nullptr; + RuntimeProfile::Counter* decompress_cnt = nullptr; + RuntimeProfile::Counter* page_read_counter = nullptr; + RuntimeProfile::Counter* page_cache_write_counter = nullptr; + RuntimeProfile::Counter* page_cache_compressed_write_counter = nullptr; + RuntimeProfile::Counter* page_cache_decompressed_write_counter = nullptr; + RuntimeProfile::Counter* page_cache_hit_counter = nullptr; + RuntimeProfile::Counter* page_cache_missing_counter = nullptr; + RuntimeProfile::Counter* page_cache_compressed_hit_counter = nullptr; + RuntimeProfile::Counter* page_cache_decompressed_hit_counter = nullptr; + RuntimeProfile::Counter* decode_header_time = nullptr; + RuntimeProfile::Counter* read_page_header_time = nullptr; + RuntimeProfile::Counter* decode_value_time = nullptr; + RuntimeProfile::Counter* decode_dict_time = nullptr; + RuntimeProfile::Counter* decode_level_time = nullptr; + RuntimeProfile::Counter* decode_null_map_time = nullptr; + RuntimeProfile::Counter* skip_page_header_num = nullptr; + RuntimeProfile::Counter* parse_page_header_num = nullptr; + RuntimeProfile::Counter* predicate_filter_time = nullptr; + RuntimeProfile::Counter* dict_filter_rewrite_time = nullptr; + RuntimeProfile::Counter* convert_time = nullptr; + RuntimeProfile::Counter* bloom_filter_read_time = nullptr; + }; + void _fill_column_definition(const ParquetColumnSchema& column_schema, + format::ColumnDefinition* field) const; + + std::unique_ptr _state; + ParquetProfile _parquet_profile; + std::optional _global_rowid_context; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_scan.cpp b/be/src/format_v2/parquet/parquet_scan.cpp new file mode 100644 index 00000000000000..18dc73ff7edfd5 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_scan.cpp @@ -0,0 +1,536 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_scan.h" + +#include + +#include +#include +#include +#include + +#include "common/exception.h" +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/column/column_vector.h" +#include "exprs/vexpr_context.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/parquet_file_context.h" +#include "format_v2/parquet/parquet_statistics.h" + +namespace doris::parquet { + +namespace { + +int64_t column_start_offset(const ::parquet::ColumnChunkMetaData& column_metadata) { + return column_metadata.has_dictionary_page() + ? cast_set(column_metadata.dictionary_page_offset()) + : cast_set(column_metadata.data_page_offset()); +} + +bool is_row_group_outside_range(const ::parquet::FileMetaData& metadata, + const ParquetScanRange& scan_range, int row_group_idx) { + if (scan_range.size < 0) { + return false; + } + const int64_t range_start_offset = scan_range.start_offset; + const int64_t range_end_offset = range_start_offset + scan_range.size; + DORIS_CHECK(range_start_offset >= 0); + DORIS_CHECK(range_end_offset >= range_start_offset); + if (range_start_offset == 0 && + (scan_range.file_size < 0 || range_end_offset >= scan_range.file_size)) { + return false; + } + + auto row_group_metadata = metadata.RowGroup(row_group_idx); + DORIS_CHECK(row_group_metadata != nullptr); + DORIS_CHECK(row_group_metadata->num_columns() > 0); + const auto first_column = row_group_metadata->ColumnChunk(0); + const auto last_column = row_group_metadata->ColumnChunk(row_group_metadata->num_columns() - 1); + DORIS_CHECK(first_column != nullptr); + DORIS_CHECK(last_column != nullptr); + const int64_t row_group_start_offset = column_start_offset(*first_column); + const int64_t row_group_end_offset = + column_start_offset(*last_column) + last_column->total_compressed_size(); + const int64_t row_group_mid_offset = + row_group_start_offset + (row_group_end_offset - row_group_start_offset) / 2; + return row_group_mid_offset < range_start_offset || row_group_mid_offset >= range_end_offset; +} + +} // namespace + +Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, + ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, + const ParquetScanRange& scan_range, bool enable_bloom_filter, + RowGroupScanPlan* plan) { + DORIS_CHECK(plan != nullptr); + plan->row_groups.clear(); + plan->pruning_stats = ParquetPruningStats {}; + + std::vector statistics_selected_row_groups; + RETURN_IF_ERROR(select_row_groups_by_statistics(metadata, file_reader, file_schema, request, + &statistics_selected_row_groups, + enable_bloom_filter, &plan->pruning_stats)); + + std::vector row_group_first_rows(metadata.num_row_groups()); + int64_t next_row_group_first_row = 0; + for (int row_group_idx = 0; row_group_idx < metadata.num_row_groups(); ++row_group_idx) { + row_group_first_rows[row_group_idx] = next_row_group_first_row; + auto row_group_metadata = metadata.RowGroup(row_group_idx); + DORIS_CHECK(row_group_metadata != nullptr); + const int64_t row_group_rows = row_group_metadata->num_rows(); + if (row_group_rows < 0) { + return Status::Corruption("Invalid negative row count in parquet row group {}", + row_group_idx); + } + next_row_group_first_row += row_group_rows; + } + + plan->row_groups.reserve(statistics_selected_row_groups.size()); + for (const auto row_group_idx : statistics_selected_row_groups) { + if (is_row_group_outside_range(metadata, scan_range, row_group_idx)) { + continue; + } + + auto row_group_metadata = metadata.RowGroup(row_group_idx); + DORIS_CHECK(row_group_metadata != nullptr); + const int64_t row_group_rows = row_group_metadata->num_rows(); + if (row_group_rows == 0) { + continue; + } + + RowGroupReadPlan row_group_plan; + row_group_plan.row_group_id = row_group_idx; + row_group_plan.first_file_row = row_group_first_rows[row_group_idx]; + row_group_plan.row_group_rows = row_group_rows; + RETURN_IF_ERROR(select_row_group_ranges_by_page_index( + file_reader, file_schema, request, row_group_idx, row_group_rows, + &row_group_plan.selected_ranges, &row_group_plan.page_skip_plans, + &plan->pruning_stats)); + if (row_group_plan.selected_ranges.empty()) { + continue; + } + plan->pruning_stats.selected_row_ranges += row_group_plan.selected_ranges.size(); + plan->row_groups.push_back(std::move(row_group_plan)); + } + plan->pruning_stats.selected_row_groups = plan->row_groups.size(); + return Status::OK(); +} + +namespace { + +uint16_t apply_filter_to_selection(const IColumn::Filter& filter, SelectionVector* selection, + uint16_t selected_rows) { + uint16_t new_selected_rows = 0; + for (uint16_t selection_idx = 0; selection_idx < selected_rows; ++selection_idx) { + const auto row_idx = selection->get_index(selection_idx); + if (filter[row_idx] != 0) { + selection->set_index(new_selected_rows++, static_cast(row_idx)); + } + } + return new_selected_rows; +} + +Status execute_filter_conjuncts(const format::FileScanRequest& request, int64_t batch_rows, + Block* file_block, SelectionVector* selection, + uint16_t* selected_rows) { + for (const auto& conjunct : request.conjuncts) { + if (*selected_rows == 0) { + break; + } + DORIS_CHECK(conjunct != nullptr); + IColumn::Filter filter(static_cast(batch_rows), 1); + bool can_filter_all = false; + RETURN_IF_ERROR(conjunct->execute_filter(file_block, filter.data(), + static_cast(batch_rows), false, + &can_filter_all)); + *selected_rows = + can_filter_all ? 0 : apply_filter_to_selection(filter, selection, *selected_rows); + } + return Status::OK(); +} + +Status execute_delete_conjuncts(const format::FileScanRequest& request, int64_t batch_rows, + Block* file_block, SelectionVector* selection, + uint16_t* selected_rows) { + for (const auto& delete_conjunct : request.delete_conjuncts) { + if (*selected_rows == 0) { + break; + } + DORIS_CHECK(delete_conjunct != nullptr); + int result_column_id = -1; + RETURN_IF_ERROR(delete_conjunct->root()->execute(delete_conjunct.get(), file_block, + &result_column_id)); + DORIS_CHECK(result_column_id >= 0 && + result_column_id < static_cast(file_block->columns())); + const auto& delete_filter = assert_cast( + *file_block->get_by_position(result_column_id).column) + .get_data(); + DORIS_CHECK(delete_filter.size() == static_cast(batch_rows)); + IColumn::Filter keep_filter(static_cast(batch_rows), 1); + bool has_kept_row = false; + for (size_t row = 0; row < static_cast(batch_rows); ++row) { + keep_filter[row] = !delete_filter[row]; + has_kept_row |= keep_filter[row] != 0; + } + file_block->erase(result_column_id); + *selected_rows = + !has_kept_row ? 0 + : apply_filter_to_selection(keep_filter, selection, *selected_rows); + } + return Status::OK(); +} + +} // namespace + +IColumn::Filter selection_to_filter(const SelectionVector& selection, uint16_t selected_rows, + int64_t batch_rows) { + IColumn::Filter filter(static_cast(batch_rows), 0); + for (uint16_t selection_idx = 0; selection_idx < selected_rows; ++selection_idx) { + filter[selection.get_index(selection_idx)] = 1; + } + return filter; +} + +Status execute_batch_filters(const format::FileScanRequest& request, int64_t batch_rows, + Block* file_block, SelectionVector* selection, + uint16_t* selected_rows) { + if (request.conjuncts.empty() && request.delete_conjuncts.empty()) { + return Status::OK(); + } + RETURN_IF_ERROR( + execute_filter_conjuncts(request, batch_rows, file_block, selection, selected_rows)); + if (*selected_rows == 0) { + return Status::OK(); + } + return execute_delete_conjuncts(request, batch_rows, file_block, selection, selected_rows); +} + +namespace { +// TODO: batch size in SessionVariable +constexpr int64_t DEFAULT_PARQUET_READ_BATCH_SIZE = 4096; + +} // namespace + +void ParquetScanScheduler::set_plan(RowGroupScanPlan plan) { + _row_group_plans = std::move(plan.row_groups); + reset(); +} + +void ParquetScanScheduler::reset() { + _next_row_group_plan_idx = 0; + reset_current_row_group(); +} + +void ParquetScanScheduler::reset_current_row_group() { + _current_row_group.reset(); + _current_predicate_columns.clear(); + _current_non_predicate_columns.clear(); + _current_row_group_rows = 0; + _current_row_group_rows_read = 0; + _current_row_group_first_row = 0; + _current_selected_ranges.clear(); + _current_range_idx = 0; + _current_range_rows_read = 0; +} + +Status ParquetScanScheduler::open_next_row_group( + ParquetFileContext& file_context, + const std::vector>& file_schema, + const format::FileScanRequest& request, bool* has_row_group) { + *has_row_group = false; + if (_next_row_group_plan_idx >= _row_group_plans.size()) { + return Status::OK(); + } + const RowGroupReadPlan& row_group_plan = _row_group_plans[_next_row_group_plan_idx++]; + const int row_group_idx = row_group_plan.row_group_id; + try { + _current_row_group = file_context.file_reader->RowGroup(row_group_idx); + } catch (const ::parquet::ParquetException& e) { + return Status::Corruption("Failed to open parquet row group {}: {}", row_group_idx, + e.what()); + } catch (const std::exception& e) { + return Status::InternalError("Failed to open parquet row group {}: {}", row_group_idx, + e.what()); + } + + auto row_group_metadata = file_context.metadata->RowGroup(row_group_idx); + DORIS_CHECK(row_group_metadata != nullptr); + _current_row_group_rows = row_group_metadata->num_rows(); + DORIS_CHECK(_current_row_group_rows == row_group_plan.row_group_rows); + DORIS_CHECK(_current_row_group_rows > 0); + DORIS_CHECK(!row_group_plan.selected_ranges.empty()); + _current_row_group_first_row = row_group_plan.first_file_row; + _current_row_group_rows_read = 0; + _current_selected_ranges = row_group_plan.selected_ranges; + _current_range_idx = 0; + _current_range_rows_read = 0; + _current_predicate_columns.clear(); + _current_non_predicate_columns.clear(); + + ParquetColumnReaderFactory column_reader_factory( + _current_row_group, file_context.schema->num_columns(), &row_group_plan.page_skip_plans, + _page_skip_profile, _timezone, _enable_strict_mode, + _scan_profile.column_reader_profile); + for (const auto& col : request.predicate_columns) { + const auto local_id = col.local_id(); + if (local_id == format::ROW_POSITION_COLUMN_ID) { + _current_predicate_columns[local_id] = + column_reader_factory.create_row_position_column_reader( + _current_row_group_first_row); + continue; + } + if (local_id == format::GLOBAL_ROWID_COLUMN_ID) { + DORIS_CHECK(_global_rowid_context.has_value()); + _current_predicate_columns[local_id] = + column_reader_factory.create_global_rowid_column_reader( + *_global_rowid_context, _current_row_group_first_row); + continue; + } + + DORIS_CHECK(local_id >= 0 && local_id < static_cast(file_schema.size())); + const auto& column_schema = file_schema[local_id]; + DORIS_CHECK(column_schema != nullptr); + std::unique_ptr column_reader; + RETURN_IF_ERROR(column_reader_factory.create(*column_schema, &col, &column_reader)); + _current_predicate_columns[local_id] = std::move(column_reader); + } + for (const auto& col : request.non_predicate_columns) { + const auto local_id = col.local_id(); + if (local_id == format::ROW_POSITION_COLUMN_ID) { + _current_non_predicate_columns[local_id] = + column_reader_factory.create_row_position_column_reader( + _current_row_group_first_row); + continue; + } + if (local_id == format::GLOBAL_ROWID_COLUMN_ID) { + DORIS_CHECK(_global_rowid_context.has_value()); + _current_non_predicate_columns[local_id] = + column_reader_factory.create_global_rowid_column_reader( + *_global_rowid_context, _current_row_group_first_row); + continue; + } + DORIS_CHECK(local_id >= 0 && local_id < static_cast(file_schema.size())); + const auto& column_schema = file_schema[local_id]; + DORIS_CHECK(column_schema != nullptr); + std::unique_ptr column_reader; + RETURN_IF_ERROR(column_reader_factory.create(*column_schema, &col, &column_reader)); + _current_non_predicate_columns[local_id] = std::move(column_reader); + } + *has_row_group = true; + return Status::OK(); +} + +Status ParquetScanScheduler::skip_current_row_group_rows(int64_t rows) { + DORIS_CHECK(rows >= 0); + if (rows == 0) { + return Status::OK(); + } + if (_scan_profile.range_gap_skipped_rows != nullptr) { + COUNTER_UPDATE(_scan_profile.range_gap_skipped_rows, rows); + } + for (const auto& column_reader : _current_predicate_columns | std::views::values) { + RETURN_IF_ERROR(column_reader->skip(rows)); + } + for (const auto& column_reader : _current_non_predicate_columns | std::views::values) { + RETURN_IF_ERROR(column_reader->skip(rows)); + } + _current_row_group_rows_read += rows; + return Status::OK(); +} + +Status ParquetScanScheduler::read_filter_columns(int64_t batch_rows, + const format::FileScanRequest& request, + Block* file_block, SelectionVector* selection, + uint16_t* selected_rows) { + if (!request.conjuncts.empty() || !request.delete_conjuncts.empty()) { + selection->resize(static_cast(batch_rows)); + } + for (const auto& [fid, column_reader] : _current_predicate_columns) { + auto position_it = request.local_positions.find(format::LocalColumnId(fid)); + DORIS_CHECK(position_it != request.local_positions.end()); + const auto block_position = position_it->second.value(); + DCHECK(remove_nullable(column_reader->type()) + ->equals(*remove_nullable(file_block->get_by_position(block_position).type))) + << column_reader->type()->get_name() << " " + << file_block->get_by_position(block_position).type->get_name() << " " + << column_reader->name() << " " << file_block->get_by_position(block_position).name; + auto column = file_block->get_by_position(block_position).column->assert_mutable(); + int64_t column_rows = 0; + { + SCOPED_TIMER(_scan_profile.column_read_time); + RETURN_IF_ERROR(column_reader->read(batch_rows, column, &column_rows)); + } + if (column_rows != batch_rows) { + return Status::Corruption("Parquet filter column {} returned {} rows, expected {} rows", + column_reader->name(), column_rows, batch_rows); + } + file_block->replace_by_position(block_position, std::move(column)); + } + if (_scan_profile.predicate_filter_time == nullptr) { + return execute_batch_filters(request, batch_rows, file_block, selection, selected_rows); + } + SCOPED_TIMER(_scan_profile.predicate_filter_time); + return execute_batch_filters(request, batch_rows, file_block, selection, selected_rows); +} + +Status ParquetScanScheduler::read_current_row_group_batch(int64_t batch_rows, + const format::FileScanRequest& request, + Block* file_block, size_t* rows) { + if (_scan_profile.total_batches != nullptr) { + COUNTER_UPDATE(_scan_profile.total_batches, 1); + } + if (_scan_profile.raw_rows_read != nullptr) { + COUNTER_UPDATE(_scan_profile.raw_rows_read, batch_rows); + } + if (_current_predicate_columns.empty() && _current_non_predicate_columns.empty()) { + *rows = static_cast(batch_rows); + if (_scan_profile.selected_rows != nullptr) { + COUNTER_UPDATE(_scan_profile.selected_rows, batch_rows); + } + return Status::OK(); + } + SelectionVector selection; + DORIS_CHECK(batch_rows <= std::numeric_limits::max()); + uint16_t selected_rows = static_cast(batch_rows); + RETURN_IF_ERROR( + read_filter_columns(batch_rows, request, file_block, &selection, &selected_rows)); + + const bool need_filter_output = selected_rows != batch_rows; + if (_scan_profile.selected_rows != nullptr) { + COUNTER_UPDATE(_scan_profile.selected_rows, selected_rows); + } + if (_scan_profile.rows_filtered_by_conjunct != nullptr) { + COUNTER_UPDATE(_scan_profile.rows_filtered_by_conjunct, batch_rows - selected_rows); + } + if (selected_rows == 0 && _scan_profile.empty_selection_batches != nullptr) { + COUNTER_UPDATE(_scan_profile.empty_selection_batches, 1); + } + if (need_filter_output) { + IColumn::Filter output_filter = selection_to_filter(selection, selected_rows, batch_rows); + for (const auto& col : request.predicate_columns) { + auto position_it = request.local_positions.find(col.column_id()); + DORIS_CHECK(position_it != request.local_positions.end()); + const auto block_position = position_it->second.value(); + RETURN_IF_CATCH_EXCEPTION(file_block->replace_by_position( + block_position, file_block->get_by_position(block_position) + .column->filter(output_filter, selected_rows))); + } + } + + { + SCOPED_TIMER(_scan_profile.column_read_time); + for (const auto& [fid, column_reader] : _current_non_predicate_columns) { + auto position_it = request.local_positions.find(format::LocalColumnId(fid)); + DORIS_CHECK(position_it != request.local_positions.end()); + const auto block_position = position_it->second.value(); + auto column = file_block->get_by_position(block_position).column->assert_mutable(); + DCHECK_EQ(file_block->get_by_position(block_position).type->get_primitive_type(), + column_reader->type()->get_primitive_type()) + << type_to_string(file_block->get_by_position(block_position) + .type->get_primitive_type()) + << " " << type_to_string(column_reader->type()->get_primitive_type()) << " " + << column_reader->name() << " " << fid << " " << block_position; + if (need_filter_output) { + [[maybe_unused]] auto old_size = column->size(); + RETURN_IF_ERROR( + column_reader->select(selection, selected_rows, batch_rows, column)); + if (column->size() != old_size + selected_rows) { + return Status::Corruption( + "Parquet selected output column {} returned {} rows, expected {} rows", + column_reader->name(), column->size(), old_size + selected_rows); + } + } else { + int64_t column_rows = 0; + RETURN_IF_ERROR(column_reader->read(batch_rows, column, &column_rows)); + if (column_rows != batch_rows) { + return Status::Corruption( + "Parquet output column {} returned {} rows, expected {} rows", + column_reader->name(), column_rows, batch_rows); + } + } + file_block->replace_by_position(block_position, std::move(column)); + } + } + *rows = static_cast(selected_rows); + return Status::OK(); +} + +Status ParquetScanScheduler::read_next_batch( + ParquetFileContext& file_context, + const std::vector>& file_schema, + const format::FileScanRequest& request, Block* file_block, size_t* rows, bool* eof) { + *rows = 0; + while (true) { + if (_current_row_group == nullptr) { + bool has_row_group = false; + RETURN_IF_ERROR( + open_next_row_group(file_context, file_schema, request, &has_row_group)); + if (!has_row_group) { + *eof = true; + return Status::OK(); + } + } + + if (_current_range_idx >= _current_selected_ranges.size()) { + // Current row group finished, try next row group. + reset_current_row_group(); + continue; + } + + const RowRange& current_range = _current_selected_ranges[_current_range_idx]; + DORIS_CHECK(current_range.start >= 0); + DORIS_CHECK(current_range.length > 0); + DORIS_CHECK(current_range.start + current_range.length <= _current_row_group_rows); + + if (_current_row_group_rows_read < current_range.start) { + // Skip filtered rows according to row group level pruning. + RETURN_IF_ERROR(skip_current_row_group_rows(current_range.start - + _current_row_group_rows_read)); + } + DORIS_CHECK(_current_row_group_rows_read == current_range.start + _current_range_rows_read); + const int64_t remaining_rows = current_range.length - _current_range_rows_read; + if (remaining_rows <= 0) { + // Current range finished, try next range in the same row group. + ++_current_range_idx; + _current_range_rows_read = 0; + continue; + } + + const int64_t batch_rows = + std::min(DEFAULT_PARQUET_READ_BATCH_SIZE, remaining_rows); + const int64_t physical_rows_read = batch_rows; + RETURN_IF_ERROR(read_current_row_group_batch(batch_rows, request, file_block, rows)); + _current_row_group_rows_read += physical_rows_read; + _current_range_rows_read += physical_rows_read; + if (_current_range_rows_read >= current_range.length) { + ++_current_range_idx; + _current_range_rows_read = 0; + } + if (*rows == 0) { + continue; + } + *eof = false; + return Status::OK(); + } +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_scan.h b/be/src/format_v2/parquet/parquet_scan.h new file mode 100644 index 00000000000000..cf45a09f8aace8 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_scan.h @@ -0,0 +1,160 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/column/column.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/parquet_profile.h" +#include "format_v2/parquet/parquet_statistics.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "format_v2/parquet/selection_vector.h" +#include "runtime/runtime_profile.h" + +namespace parquet { +class FileMetaData; +class ParquetFileReader; +class RowGroupReader; +} // namespace parquet + +namespace cctz { +class time_zone; +} // namespace cctz + +namespace doris { +class Block; + +namespace format { +struct FileScanRequest; +} // namespace format +} // namespace doris + +namespace doris::parquet { + +struct ParquetFileContext; +struct ParquetColumnSchema; + +struct ParquetScanRange { + int64_t start_offset = 0; + int64_t size = -1; + int64_t file_size = -1; +}; + +struct RowGroupReadPlan { + int row_group_id = -1; + int64_t first_file_row = 0; + int64_t row_group_rows = 0; + std::vector selected_ranges; + std::map page_skip_plans; +}; + +struct RowGroupScanPlan { + std::vector row_groups; + ParquetPruningStats pruning_stats; +}; + +struct ParquetScanProfile { + RuntimeProfile::Counter* raw_rows_read = nullptr; + RuntimeProfile::Counter* selected_rows = nullptr; + RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; + RuntimeProfile::Counter* total_batches = nullptr; + RuntimeProfile::Counter* empty_selection_batches = nullptr; + RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; + RuntimeProfile::Counter* column_read_time = nullptr; + RuntimeProfile::Counter* predicate_filter_time = nullptr; + ParquetColumnReaderProfile column_reader_profile; +}; + +Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, + ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, + const ParquetScanRange& scan_range, bool enable_bloom_filter, + RowGroupScanPlan* plan); + +IColumn::Filter selection_to_filter(const SelectionVector& selection, uint16_t selected_rows, + int64_t batch_rows); + +Status execute_batch_filters(const format::FileScanRequest& request, int64_t batch_rows, + Block* file_block, SelectionVector* selection, + uint16_t* selected_rows); + +class ParquetScanScheduler { +public: + void set_plan(RowGroupScanPlan plan); + void set_page_skip_profile(ParquetPageSkipProfile page_skip_profile) { + _page_skip_profile = page_skip_profile; + } + void set_scan_profile(ParquetScanProfile scan_profile) { _scan_profile = scan_profile; } + void set_global_rowid_context(std::optional context) { + _global_rowid_context = context; + } + void set_timezone(const cctz::time_zone* timezone) { _timezone = timezone; } + void set_enable_strict_mode(bool enable_strict_mode) { + _enable_strict_mode = enable_strict_mode; + } + void reset(); + bool empty() const { return _row_group_plans.empty(); } + + Status read_next_batch(ParquetFileContext& file_context, + const std::vector>& file_schema, + const format::FileScanRequest& request, Block* file_block, size_t* rows, + bool* eof); + +private: + void reset_current_row_group(); + + Status open_next_row_group(ParquetFileContext& file_context, + const std::vector>& file_schema, + const format::FileScanRequest& request, bool* has_row_group); + + Status skip_current_row_group_rows(int64_t rows); + + Status read_filter_columns(int64_t batch_rows, const format::FileScanRequest& request, + Block* file_block, SelectionVector* selection, + uint16_t* selected_rows); + + Status read_current_row_group_batch(int64_t batch_rows, const format::FileScanRequest& request, + Block* file_block, size_t* rows); + + std::vector _row_group_plans; + size_t _next_row_group_plan_idx = 0; + std::shared_ptr<::parquet::RowGroupReader> _current_row_group; + std::map> _current_predicate_columns; + std::map> _current_non_predicate_columns; + int64_t _current_row_group_rows = 0; + int64_t _current_row_group_rows_read = 0; + int64_t _current_row_group_first_row = 0; + std::vector _current_selected_ranges; + size_t _current_range_idx = 0; + int64_t _current_range_rows_read = 0; + ParquetPageSkipProfile _page_skip_profile; + ParquetScanProfile _scan_profile; + std::optional _global_rowid_context; + const cctz::time_zone* _timezone = nullptr; + bool _enable_strict_mode = false; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_statistics.cpp b/be/src/format_v2/parquet/parquet_statistics.cpp new file mode 100644 index 00000000000000..3ef5fcfeb2e27e --- /dev/null +++ b/be/src/format_v2/parquet/parquet_statistics.cpp @@ -0,0 +1,1264 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_statistics.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "common/config.h" +#include "core/data_type/data_type.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/primitive_type.h" +#include "core/data_type_serde/data_type_serde.h" +#include "core/field.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "runtime/runtime_profile.h" +#include "storage/index/zone_map/zone_map_index.h" +#include "storage/predicate/accept_null_predicate.h" +#include "storage/predicate/column_predicate.h" + +namespace doris::parquet { + +namespace { + +PrimitiveType physical_filter_type(const ParquetColumnSchema& column_schema) { + if (column_schema.type == nullptr) { + return INVALID_TYPE; + } + switch (remove_nullable(column_schema.type)->get_primitive_type()) { + case TYPE_BOOLEAN: + case TYPE_INT: + case TYPE_BIGINT: + case TYPE_FLOAT: + case TYPE_DOUBLE: + case TYPE_STRING: + return remove_nullable(column_schema.type)->get_primitive_type(); + default: + return INVALID_TYPE; + } +} + +DecodedTimeUnit decoded_time_unit(ParquetTimeUnit time_unit) { + switch (time_unit) { + case ParquetTimeUnit::MILLIS: + return DecodedTimeUnit::MILLIS; + case ParquetTimeUnit::MICROS: + return DecodedTimeUnit::MICROS; + case ParquetTimeUnit::NANOS: + return DecodedTimeUnit::NANOS; + default: + return DecodedTimeUnit::UNKNOWN; + } +} + +Status read_decoded_field(const ParquetColumnSchema& column_schema, DecodedColumnView view, + Field* field) { + DORIS_CHECK(column_schema.type != nullptr); + DORIS_CHECK(field != nullptr); + constexpr uint8_t not_null = 0; + view.row_count = 1; + view.null_map = ¬_null; + view.time_unit = decoded_time_unit(column_schema.type_descriptor.time_unit); + view.decimal_precision = column_schema.type_descriptor.decimal_precision; + view.decimal_scale = column_schema.type_descriptor.decimal_scale; + view.fixed_length = column_schema.type_descriptor.fixed_length; + return column_schema.type->get_serde()->read_field_from_decoded_value(*column_schema.type, + field, view); +} + +template +bool set_decoded_field(const ParquetColumnSchema& column_schema, DecodedValueKind value_kind, + const NativeType& value, Field* field) { + DecodedColumnView view; + view.value_kind = value_kind; + view.values = reinterpret_cast(&value); + return read_decoded_field(column_schema, view, field).ok(); +} + +template +bool set_decoded_min_max(const std::shared_ptr<::parquet::Statistics>& statistics, + const ParquetColumnSchema& column_schema, DecodedValueKind value_kind, + ParquetColumnStatistics* column_statistics) { + auto typed_statistics = + std::static_pointer_cast<::parquet::TypedStatistics>(statistics); + if (!set_decoded_field(column_schema, value_kind, typed_statistics->min(), + &column_statistics->min_value) || + !set_decoded_field(column_schema, value_kind, typed_statistics->max(), + &column_statistics->max_value)) { + return false; + } + return true; +} + +bool set_decoded_binary_field(const ParquetColumnSchema& column_schema, DecodedValueKind value_kind, + const StringRef& value, Field* field) { + std::vector binary_values {value}; + DecodedColumnView view; + view.value_kind = value_kind; + view.binary_values = &binary_values; + return read_decoded_field(column_schema, view, field).ok(); +} + +bool set_string_min_max(const std::shared_ptr<::parquet::Statistics>& statistics, + const ParquetColumnSchema& column_schema, + ParquetColumnStatistics* column_statistics) { + switch (statistics->physical_type()) { + case ::parquet::Type::BYTE_ARRAY: { + auto typed_statistics = + std::static_pointer_cast<::parquet::TypedStatistics<::parquet::ByteArrayType>>( + statistics); + const auto min = ::parquet::ByteArrayToString(typed_statistics->min()); + const auto max = ::parquet::ByteArrayToString(typed_statistics->max()); + if (!set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, + StringRef(min.data(), min.size()), + &column_statistics->min_value) || + !set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, + StringRef(max.data(), max.size()), + &column_statistics->max_value)) { + return false; + } + return true; + } + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: { + if (column_schema.descriptor == nullptr || column_schema.descriptor->type_length() <= 0) { + return false; + } + auto typed_statistics = + std::static_pointer_cast<::parquet::TypedStatistics<::parquet::FLBAType>>( + statistics); + const int type_length = column_schema.descriptor->type_length(); + const std::string min(reinterpret_cast(typed_statistics->min().ptr), + type_length); + const std::string max(reinterpret_cast(typed_statistics->max().ptr), + type_length); + if (!set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, + StringRef(min.data(), min.size()), + &column_statistics->min_value) || + !set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, + StringRef(max.data(), max.size()), + &column_statistics->max_value)) { + return false; + } + return true; + } + default: + return false; + } +} + +bool is_null_only_predicate(const ColumnPredicate& predicate) { + return predicate.type() == PredicateType::IS_NULL || + predicate.type() == PredicateType::IS_NOT_NULL; +} + +bool is_supported_dictionary_predicate(const ColumnPredicate& predicate) { + switch (predicate.type()) { + case PredicateType::EQ: + case PredicateType::IN_LIST: + return true; + default: + return false; + } +} + +bool is_bloom_filter_prunable_predicate(const ColumnPredicate& predicate) { + if (dynamic_cast(&predicate) != nullptr || + is_null_only_predicate(predicate)) { + return false; + } + return predicate.can_do_bloom_filter(false); +} + +template +T load_predicate_value(const char* data) { + T value; + memcpy(&value, data, sizeof(T)); + return value; +} + +class ArrowParquetBloomFilterAdapter final : public segment_v2::BloomFilter { +public: + ArrowParquetBloomFilterAdapter(const ParquetColumnSchema& column_schema, + const ::parquet::BloomFilter& bloom_filter) + : _column_schema(column_schema), _bloom_filter(bloom_filter) {} + + void add_bytes(const char* buf, size_t size) override { DORIS_CHECK(false); } + + bool test_bytes(const char* buf, size_t size) const override { + if (buf == nullptr) { + return true; + } + switch (physical_filter_type(_column_schema)) { + case TYPE_BOOLEAN: + return test_boolean(buf, size); + case TYPE_INT: + return test_int32(buf, size); + case TYPE_BIGINT: + return test_int64(buf, size); + case TYPE_FLOAT: + return test_float(buf, size); + case TYPE_DOUBLE: + return test_double(buf, size); + case TYPE_STRING: + return test_string(buf, size); + default: + return true; + } + } + + void set_has_null(bool has_null) override { DORIS_CHECK(!has_null); } + bool has_null() const override { return false; } + void add_hash(uint64_t hash) override { DORIS_CHECK(false); } + bool test_hash(uint64_t hash) const override { return _bloom_filter.FindHash(hash); } + +private: + bool test_boolean(const char* buf, size_t size) const { + if (size == sizeof(bool)) { + const int32_t value = load_predicate_value(buf) ? 1 : 0; + return _bloom_filter.FindHash(_bloom_filter.Hash(value)); + } + if (size == sizeof(int32_t)) { + const int32_t value = load_predicate_value(buf); + return _bloom_filter.FindHash(_bloom_filter.Hash(value != 0 ? 1 : 0)); + } + return true; + } + + bool test_int32(const char* buf, size_t size) const { + if (size == sizeof(int8_t)) { + return find_int32(static_cast(load_predicate_value(buf))); + } + if (size == sizeof(int16_t)) { + return find_int32(static_cast(load_predicate_value(buf))); + } + if (size == sizeof(int32_t)) { + return find_int32(load_predicate_value(buf)); + } + return true; + } + + bool test_int64(const char* buf, size_t size) const { + if (size != sizeof(int64_t)) { + return true; + } + const int64_t value = load_predicate_value(buf); + return _bloom_filter.FindHash(_bloom_filter.Hash(value)); + } + + bool test_float(const char* buf, size_t size) const { + if (size != sizeof(float)) { + return true; + } + const float value = load_predicate_value(buf); + return _bloom_filter.FindHash(_bloom_filter.Hash(value)); + } + + bool test_double(const char* buf, size_t size) const { + if (size != sizeof(double)) { + return true; + } + const double value = load_predicate_value(buf); + return _bloom_filter.FindHash(_bloom_filter.Hash(value)); + } + + bool test_string(const char* buf, size_t size) const { + ::parquet::ByteArray value(static_cast(size), + reinterpret_cast(buf)); + return _bloom_filter.FindHash(_bloom_filter.Hash(&value)); + } + + bool find_int32(int32_t value) const { + return _bloom_filter.FindHash(_bloom_filter.Hash(value)); + } + + const ParquetColumnSchema& _column_schema; + const ::parquet::BloomFilter& _bloom_filter; +}; + +struct RowGroupBloomFilterCache { + ::parquet::BloomFilterReader* bloom_filter_reader = nullptr; + std::map> column_bloom_filters; + std::set loaded_columns; + + ::parquet::BloomFilter* get(int row_group_idx, int leaf_column_id, + ParquetPruningStats* pruning_stats) { + if (bloom_filter_reader == nullptr || leaf_column_id < 0) { + return nullptr; + } + if (loaded_columns.find(leaf_column_id) == loaded_columns.end()) { + loaded_columns.insert(leaf_column_id); + try { + std::shared_ptr<::parquet::RowGroupBloomFilterReader> row_group_reader; + if (pruning_stats != nullptr) { + SCOPED_RAW_TIMER(&pruning_stats->bloom_filter_read_time); + row_group_reader = bloom_filter_reader->RowGroup(row_group_idx); + if (row_group_reader != nullptr) { + column_bloom_filters[leaf_column_id] = + row_group_reader->GetColumnBloomFilter(leaf_column_id); + } + } else { + row_group_reader = bloom_filter_reader->RowGroup(row_group_idx); + if (row_group_reader != nullptr) { + column_bloom_filters[leaf_column_id] = + row_group_reader->GetColumnBloomFilter(leaf_column_id); + } + } + } catch (const ::parquet::ParquetException&) { + return nullptr; + } catch (const std::exception&) { + return nullptr; + } + } + auto it = column_bloom_filters.find(leaf_column_id); + return it == column_bloom_filters.end() ? nullptr : it->second.get(); + } +}; + +ParquetRowGroupPruneReason BloomFilterPruneReason( + int row_group_idx, const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter, + RowGroupBloomFilterCache* bloom_filter_cache, ParquetPruningStats* pruning_stats) { + if (bloom_filter_cache == nullptr || column_filter.predicates.empty()) { + return ParquetRowGroupPruneReason::NONE; + } + const auto* column_schema = + ParquetStatisticsUtils::ResolvePredicateLeafSchema(schema, column_filter); + if (column_schema == nullptr || !ParquetStatisticsUtils::BloomFilterSupported(*column_schema)) { + return ParquetRowGroupPruneReason::NONE; + } + for (const auto& column_predicate : column_filter.predicates) { + if (column_predicate == nullptr || !is_bloom_filter_prunable_predicate(*column_predicate)) { + return ParquetRowGroupPruneReason::NONE; + } + } + auto* bloom_filter = + bloom_filter_cache->get(row_group_idx, column_schema->leaf_column_id, pruning_stats); + if (bloom_filter == nullptr) { + return ParquetRowGroupPruneReason::NONE; + } + return ParquetStatisticsUtils::BloomFilterExcludes(*column_schema, column_filter, *bloom_filter) + ? ParquetRowGroupPruneReason::BLOOM_FILTER + : ParquetRowGroupPruneReason::NONE; +} + +bool is_dictionary_data_encoding(::parquet::Encoding::type encoding) { + return encoding == ::parquet::Encoding::PLAIN_DICTIONARY || + encoding == ::parquet::Encoding::RLE_DICTIONARY; +} + +bool is_level_encoding(::parquet::Encoding::type encoding) { + return encoding == ::parquet::Encoding::RLE || encoding == ::parquet::Encoding::BIT_PACKED; +} + +bool is_data_page_type(::parquet::PageType::type page_type) { + return page_type == ::parquet::PageType::DATA_PAGE || + page_type == ::parquet::PageType::DATA_PAGE_V2; +} + +bool is_dictionary_encoded_chunk(const ::parquet::ColumnChunkMetaData& column_metadata) { + if (!column_metadata.has_dictionary_page()) { + return false; + } + + const auto& encoding_stats = column_metadata.encoding_stats(); + if (!encoding_stats.empty()) { + bool has_dictionary_data_page = false; + for (const auto& encoding_stat : encoding_stats) { + if (!is_data_page_type(encoding_stat.page_type) || encoding_stat.count <= 0) { + continue; + } + if (!is_dictionary_data_encoding(encoding_stat.encoding)) { + return false; + } + has_dictionary_data_page = true; + } + return has_dictionary_data_page; + } + + bool has_dictionary_encoding = false; + for (const auto encoding : column_metadata.encodings()) { + if (is_dictionary_data_encoding(encoding)) { + has_dictionary_encoding = true; + continue; + } + if (!is_level_encoding(encoding)) { + return false; + } + } + return has_dictionary_encoding; +} + +bool supports_dictionary_pruning(const ParquetColumnSchema& column_schema, + const ::parquet::ColumnChunkMetaData& column_metadata, + const format::FileColumnPredicateFilter& column_filter) { + if (column_schema.kind != ParquetColumnSchemaKind::PRIMITIVE || + column_schema.descriptor == nullptr || column_schema.type == nullptr) { + return false; + } + if (!column_schema.type_descriptor.is_string_like) { + return false; + } + if (column_metadata.type() != ::parquet::Type::BYTE_ARRAY && + column_metadata.type() != ::parquet::Type::FIXED_LEN_BYTE_ARRAY) { + return false; + } + for (const auto& column_predicate : column_filter.predicates) { + if (column_predicate == nullptr || !is_supported_dictionary_predicate(*column_predicate)) { + return false; + } + } + return true; +} + +struct OwnedDictionaryWords { + std::vector values; + std::vector refs; + + void clear() { + values.clear(); + refs.clear(); + } + + void build_refs() { + refs.reserve(values.size()); + for (const auto& value : values) { + refs.emplace_back(value.data(), value.size()); + } + } +}; + +bool read_dictionary_words(::parquet::ParquetFileReader* file_reader, int row_group_idx, + int leaf_column_id, const ParquetColumnSchema& column_schema, + OwnedDictionaryWords* dict_words) { + DORIS_CHECK(dict_words != nullptr); + dict_words->clear(); + if (file_reader == nullptr || leaf_column_id < 0) { + return false; + } + + auto row_group_reader = file_reader->RowGroup(row_group_idx); + if (row_group_reader == nullptr) { + return false; + } + auto page_reader = row_group_reader->GetColumnPageReader(leaf_column_id); + if (page_reader == nullptr) { + return false; + } + + std::shared_ptr<::parquet::Page> page; + try { + page = page_reader->NextPage(); + } catch (const ::parquet::ParquetException&) { + return false; + } catch (const std::exception&) { + return false; + } + if (page == nullptr || page->type() != ::parquet::PageType::DICTIONARY_PAGE) { + return false; + } + const auto* dictionary_page = static_cast(page.get()); + if (dictionary_page->encoding() != ::parquet::Encoding::PLAIN && + dictionary_page->encoding() != ::parquet::Encoding::PLAIN_DICTIONARY) { + return false; + } + const int32_t dictionary_length = dictionary_page->num_values(); + if (dictionary_length <= 0) { + return false; + } + const auto* dictionary_data = dictionary_page->data(); + const int dictionary_size = dictionary_page->size(); + + dict_words->values.reserve(static_cast(dictionary_length)); + if (column_schema.descriptor->physical_type() == ::parquet::Type::BYTE_ARRAY) { + auto decoder = ::parquet::MakeTypedDecoder<::parquet::ByteArrayType>( + ::parquet::Encoding::PLAIN, column_schema.descriptor); + decoder->SetData(dictionary_length, dictionary_data, dictionary_size); + std::vector<::parquet::ByteArray> byte_array_values(static_cast(dictionary_length)); + if (decoder->Decode(byte_array_values.data(), dictionary_length) != dictionary_length) { + return false; + } + for (int32_t dict_idx = 0; dict_idx < dictionary_length; ++dict_idx) { + dict_words->values.emplace_back( + reinterpret_cast(byte_array_values[dict_idx].ptr), + byte_array_values[dict_idx].len); + } + dict_words->build_refs(); + return true; + } + if (column_schema.descriptor->physical_type() == ::parquet::Type::FIXED_LEN_BYTE_ARRAY) { + const int type_length = column_schema.descriptor->type_length(); + if (type_length <= 0) { + return false; + } + auto decoder = ::parquet::MakeTypedDecoder<::parquet::FLBAType>(::parquet::Encoding::PLAIN, + column_schema.descriptor); + decoder->SetData(dictionary_length, dictionary_data, dictionary_size); + std::vector<::parquet::FixedLenByteArray> flba_values( + static_cast(dictionary_length)); + if (decoder->Decode(flba_values.data(), dictionary_length) != dictionary_length) { + return false; + } + for (int32_t dict_idx = 0; dict_idx < dictionary_length; ++dict_idx) { + dict_words->values.emplace_back( + reinterpret_cast(flba_values[dict_idx].ptr), type_length); + } + dict_words->build_refs(); + return true; + } + return false; +} + +segment_v2::ZoneMap to_column_predicate_statistics(const ParquetColumnStatistics& statistics) { + segment_v2::ZoneMap predicate_statistics; + predicate_statistics.min_value = statistics.min_value; + predicate_statistics.max_value = statistics.max_value; + predicate_statistics.has_null = statistics.has_null; + predicate_statistics.has_not_null = statistics.has_not_null; + return predicate_statistics; +} + +const ParquetColumnSchema* find_child_schema_by_local_id(const ParquetColumnSchema& column_schema, + int32_t local_id) { + const auto child_it = std::ranges::find_if( + column_schema.children, [&](const std::unique_ptr& child) { + return child != nullptr && child->local_id == local_id; + }); + return child_it == column_schema.children.end() ? nullptr : child_it->get(); +} + +} // namespace + +const ParquetColumnSchema* ParquetStatisticsUtils::ResolvePredicateLeafSchema( + const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter) { + const auto file_column_id = column_filter.effective_file_column_id(); + if (!file_column_id.is_valid() || file_column_id.value() >= static_cast(schema.size())) { + return nullptr; + } + const ParquetColumnSchema* column_schema = schema[file_column_id.value()].get(); + if (column_schema == nullptr) { + return nullptr; + } + for (const auto child_local_id : column_filter.effective_file_child_id_path()) { + column_schema = find_child_schema_by_local_id(*column_schema, child_local_id); + if (column_schema == nullptr) { + return nullptr; + } + } + if (column_schema->kind != ParquetColumnSchemaKind::PRIMITIVE || + column_schema->leaf_column_id < 0 || column_schema->max_repetition_level > 0) { + return nullptr; + } + return column_schema; +} + +ParquetColumnStatistics ParquetStatisticsUtils::TransformColumnStatistics( + const ParquetColumnSchema& column_schema, + const std::shared_ptr<::parquet::Statistics>& statistics) { + ParquetColumnStatistics result; + if (statistics == nullptr) { + return result; + } + + result.has_null = statistics->HasNullCount() && statistics->null_count() > 0; + result.has_not_null = statistics->num_values() > 0 || statistics->HasMinMax(); + result.has_null_count = statistics->HasNullCount(); + if (!result.has_not_null || !statistics->HasMinMax()) { + return result; + } + + DORIS_CHECK(column_schema.type != nullptr); + switch (statistics->physical_type()) { + case ::parquet::Type::BOOLEAN: + result.has_min_max = set_decoded_min_max<::parquet::BooleanType>( + statistics, column_schema, DecodedValueKind::BOOL, &result); + return result; + case ::parquet::Type::INT32: + result.has_min_max = set_decoded_min_max<::parquet::Int32Type>( + statistics, column_schema, decoded_value_kind(column_schema.type_descriptor), + &result); + return result; + case ::parquet::Type::INT64: + result.has_min_max = set_decoded_min_max<::parquet::Int64Type>( + statistics, column_schema, decoded_value_kind(column_schema.type_descriptor), + &result); + return result; + case ::parquet::Type::FLOAT: + result.has_min_max = set_decoded_min_max<::parquet::FloatType>( + statistics, column_schema, DecodedValueKind::FLOAT, &result); + return result; + case ::parquet::Type::DOUBLE: + result.has_min_max = set_decoded_min_max<::parquet::DoubleType>( + statistics, column_schema, DecodedValueKind::DOUBLE, &result); + return result; + case ::parquet::Type::BYTE_ARRAY: + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: + result.has_min_max = set_string_min_max(statistics, column_schema, &result); + return result; + default: + return result; + } +} + +bool ParquetStatisticsUtils::CheckStatistics(const format::FileColumnPredicateFilter& column_filter, + const ParquetColumnStatistics& statistics) { + if (!statistics.has_any_statistics()) { + return false; + } + + for (const auto& column_predicate : column_filter.predicates) { + if (is_null_only_predicate(*column_predicate)) { + if (!statistics.has_null_count) { + continue; + } + } else if (!statistics.has_any_statistics()) { + continue; + } + if (!column_predicate->evaluate_and(to_column_predicate_statistics(statistics))) { + return true; + } + } + return false; +} + +ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( + const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, + int row_group_idx, const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter) { + if (column_filter.predicates.empty()) { + return ParquetRowGroupPruneReason::NONE; + } + const auto* column_schema = ResolvePredicateLeafSchema(schema, column_filter); + if (column_schema == nullptr) { + return ParquetRowGroupPruneReason::NONE; + } + DCHECK_LT(column_schema->leaf_column_id, row_group.num_columns()); + auto column_chunk = row_group.ColumnChunk(column_schema->leaf_column_id); + if (column_chunk == nullptr) { + return ParquetRowGroupPruneReason::NONE; + } + if (CheckStatistics(column_filter, + TransformColumnStatistics(*column_schema, column_chunk->statistics()))) { + return ParquetRowGroupPruneReason::STATISTICS; + } + if (!supports_dictionary_pruning(*column_schema, *column_chunk, column_filter) || + !is_dictionary_encoded_chunk(*column_chunk)) { + return ParquetRowGroupPruneReason::NONE; + } + OwnedDictionaryWords dict_words; + if (!read_dictionary_words(file_reader, row_group_idx, column_schema->leaf_column_id, + *column_schema, &dict_words)) { + return ParquetRowGroupPruneReason::NONE; + } + for (const auto& column_predicate : column_filter.predicates) { + if (!column_predicate->evaluate_and(dict_words.refs.data(), dict_words.refs.size())) { + return ParquetRowGroupPruneReason::DICTIONARY; + } + } + return ParquetRowGroupPruneReason::NONE; +} + +bool ParquetStatisticsUtils::RowGroupExcludes( + const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, + int row_group_idx, const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter) { + return RowGroupPruneReason(row_group, file_reader, row_group_idx, schema, column_filter) != + ParquetRowGroupPruneReason::NONE; +} + +Status ParquetStatisticsUtils::SelectRowGroups( + const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, std::vector* selected_row_groups, + bool enable_bloom_filter, ParquetPruningStats* pruning_stats) { + int64_t row_group_filter_time_sink = 0; + SCOPED_RAW_TIMER(pruning_stats == nullptr ? &row_group_filter_time_sink + : &pruning_stats->row_group_filter_time); + if (selected_row_groups == nullptr) { + return Status::InvalidArgument("selected_row_groups is null"); + } + selected_row_groups->clear(); + + const int num_row_groups = metadata.num_row_groups(); + if (pruning_stats != nullptr) { + pruning_stats->total_row_groups = num_row_groups; + } + selected_row_groups->reserve(num_row_groups); + for (int row_group_idx = 0; row_group_idx < num_row_groups; ++row_group_idx) { + auto row_group = metadata.RowGroup(row_group_idx); + if (row_group == nullptr) { + selected_row_groups->push_back(row_group_idx); + continue; + } + bool drop = false; + RowGroupBloomFilterCache bloom_filter_cache; + if (enable_bloom_filter && file_reader != nullptr) { + try { + bloom_filter_cache.bloom_filter_reader = &file_reader->GetBloomFilterReader(); + } catch (const ::parquet::ParquetException&) { + bloom_filter_cache.bloom_filter_reader = nullptr; + } catch (const std::exception&) { + bloom_filter_cache.bloom_filter_reader = nullptr; + } + } + for (const auto& column_filter : request.column_predicate_filters) { + const auto prune_reason = RowGroupPruneReason(*row_group, file_reader, row_group_idx, + file_schema, column_filter); + auto effective_prune_reason = prune_reason; + if (effective_prune_reason == ParquetRowGroupPruneReason::NONE && enable_bloom_filter) { + effective_prune_reason = + BloomFilterPruneReason(row_group_idx, file_schema, column_filter, + &bloom_filter_cache, pruning_stats); + } + if (effective_prune_reason == ParquetRowGroupPruneReason::NONE) { + continue; + } + drop = true; + if (pruning_stats != nullptr) { + pruning_stats->filtered_group_rows += row_group->num_rows(); + if (effective_prune_reason == ParquetRowGroupPruneReason::STATISTICS) { + ++pruning_stats->filtered_row_groups_by_statistics; + } else if (effective_prune_reason == ParquetRowGroupPruneReason::DICTIONARY) { + ++pruning_stats->filtered_row_groups_by_dictionary; + } else if (effective_prune_reason == ParquetRowGroupPruneReason::BLOOM_FILTER) { + ++pruning_stats->filtered_row_groups_by_bloom_filter; + } + break; + } + break; + } + if (drop) { + continue; + } + selected_row_groups->push_back(row_group_idx); + } + return Status::OK(); +} + +bool ParquetStatisticsUtils::BloomFilterSupported(const ParquetColumnSchema& column_schema) { + switch (physical_filter_type(column_schema)) { + case TYPE_BOOLEAN: + case TYPE_INT: + case TYPE_BIGINT: + case TYPE_FLOAT: + case TYPE_DOUBLE: + case TYPE_STRING: + return true; + default: + return false; + } +} + +bool ParquetStatisticsUtils::BloomFilterExcludes( + const ParquetColumnSchema& column_schema, + const format::FileColumnPredicateFilter& column_filter, + const ::parquet::BloomFilter& bloom_filter) { + if (!BloomFilterSupported(column_schema)) { + return false; + } + ArrowParquetBloomFilterAdapter adapter(column_schema, bloom_filter); + for (const auto& column_predicate : column_filter.predicates) { + if (column_predicate == nullptr || !is_bloom_filter_prunable_predicate(*column_predicate)) { + return false; + } + if (!column_predicate->evaluate_and(&adapter)) { + return true; + } + } + return false; +} + +Status select_row_groups_by_statistics( + const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, std::vector* selected_row_groups, + bool enable_bloom_filter, ParquetPruningStats* pruning_stats) { + return ParquetStatisticsUtils::SelectRowGroups(metadata, file_reader, file_schema, request, + selected_row_groups, enable_bloom_filter, + pruning_stats); +} + +namespace { + +template +bool set_page_decoded_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, + const ParquetColumnSchema& column_schema, size_t page_idx, + DecodedValueKind value_kind, + ParquetColumnStatistics* page_statistics) { + const auto typed_index = + std::static_pointer_cast<::parquet::TypedColumnIndex>(column_index); + if (page_idx >= typed_index->min_values().size() || + page_idx >= typed_index->max_values().size()) { + return false; + } + if (!set_decoded_field(column_schema, value_kind, typed_index->min_values()[page_idx], + &page_statistics->min_value) || + !set_decoded_field(column_schema, value_kind, typed_index->max_values()[page_idx], + &page_statistics->max_value)) { + return false; + } + page_statistics->has_min_max = true; + return true; +} + +bool set_page_string_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, + const ParquetColumnSchema& column_schema, size_t page_idx, + ParquetColumnStatistics* page_statistics) { + switch (column_schema.descriptor->physical_type()) { + case ::parquet::Type::BYTE_ARRAY: { + const auto typed_index = + std::static_pointer_cast<::parquet::ByteArrayColumnIndex>(column_index); + if (page_idx >= typed_index->min_values().size() || + page_idx >= typed_index->max_values().size()) { + return false; + } + const auto min = ::parquet::ByteArrayToString(typed_index->min_values()[page_idx]); + const auto max = ::parquet::ByteArrayToString(typed_index->max_values()[page_idx]); + if (!set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, + StringRef(min.data(), min.size()), + &page_statistics->min_value) || + !set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, + StringRef(max.data(), max.size()), + &page_statistics->max_value)) { + return false; + } + page_statistics->has_min_max = true; + return true; + } + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: { + const int type_length = column_schema.descriptor->type_length(); + if (type_length <= 0) { + return false; + } + const auto typed_index = std::static_pointer_cast<::parquet::FLBAColumnIndex>(column_index); + if (page_idx >= typed_index->min_values().size() || + page_idx >= typed_index->max_values().size()) { + return false; + } + const std::string min( + reinterpret_cast(typed_index->min_values()[page_idx].ptr), + type_length); + const std::string max( + reinterpret_cast(typed_index->max_values()[page_idx].ptr), + type_length); + if (!set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, + StringRef(min.data(), min.size()), + &page_statistics->min_value) || + !set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, + StringRef(max.data(), max.size()), + &page_statistics->max_value)) { + return false; + } + page_statistics->has_min_max = true; + return true; + } + default: + return false; + } +} + +bool set_page_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, + const ParquetColumnSchema& column_schema, size_t page_idx, + ParquetColumnStatistics* page_statistics) { + DORIS_CHECK(column_schema.type != nullptr); + switch (column_schema.descriptor->physical_type()) { + case ::parquet::Type::BOOLEAN: + return set_page_decoded_min_max<::parquet::BooleanType>( + column_index, column_schema, page_idx, DecodedValueKind::BOOL, page_statistics); + case ::parquet::Type::INT32: + return set_page_decoded_min_max<::parquet::Int32Type>( + column_index, column_schema, page_idx, + decoded_value_kind(column_schema.type_descriptor), page_statistics); + case ::parquet::Type::INT64: + return set_page_decoded_min_max<::parquet::Int64Type>( + column_index, column_schema, page_idx, + decoded_value_kind(column_schema.type_descriptor), page_statistics); + case ::parquet::Type::FLOAT: + return set_page_decoded_min_max<::parquet::FloatType>( + column_index, column_schema, page_idx, DecodedValueKind::FLOAT, page_statistics); + case ::parquet::Type::DOUBLE: + return set_page_decoded_min_max<::parquet::DoubleType>( + column_index, column_schema, page_idx, DecodedValueKind::DOUBLE, page_statistics); + case ::parquet::Type::BYTE_ARRAY: + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: + return set_page_string_min_max(column_index, column_schema, page_idx, page_statistics); + default: + return false; + } +} + +bool build_page_statistics(const std::shared_ptr<::parquet::ColumnIndex>& column_index, + const ParquetColumnSchema& column_schema, size_t page_idx, + ParquetColumnStatistics* page_statistics) { + DORIS_CHECK(page_statistics != nullptr); + *page_statistics = ParquetColumnStatistics {}; + + const auto& null_pages = column_index->null_pages(); + if (!column_index->has_null_counts() || page_idx >= null_pages.size() || + page_idx >= column_index->null_counts().size()) { + return false; + } + + page_statistics->has_null_count = true; + page_statistics->has_null = column_index->null_counts()[page_idx] > 0; + page_statistics->has_not_null = !null_pages[page_idx]; + if (!page_statistics->has_not_null) { + return true; + } + return set_page_min_max(column_index, column_schema, page_idx, page_statistics); +} + +std::vector intersect_ranges(const std::vector& left, + const std::vector& right) { + std::vector result; + size_t left_idx = 0; + size_t right_idx = 0; + while (left_idx < left.size() && right_idx < right.size()) { + const int64_t left_start = left[left_idx].start; + const int64_t left_end = left_start + left[left_idx].length; + const int64_t right_start = right[right_idx].start; + const int64_t right_end = right_start + right[right_idx].length; + const int64_t start = std::max(left_start, right_start); + const int64_t end = std::min(left_end, right_end); + if (start < end) { + result.push_back(RowRange {start, end - start}); + } + if (left_end < right_end) { + ++left_idx; + } else { + ++right_idx; + } + } + return result; +} + +int64_t count_range_rows(const std::vector& ranges) { + int64_t rows = 0; + for (const auto& range : ranges) { + rows += range.length; + } + return rows; +} + +RowRange page_row_range(const ::parquet::OffsetIndex& offset_index, size_t page_idx, + int64_t row_group_rows) { + const auto& page_locations = offset_index.page_locations(); + const int64_t start = page_locations[page_idx].first_row_index; + const int64_t end = page_idx + 1 == page_locations.size() + ? row_group_rows + : page_locations[page_idx + 1].first_row_index; + DORIS_CHECK(start >= 0); + DORIS_CHECK(end >= start); + DORIS_CHECK(end <= row_group_rows); + return RowRange {start, end - start}; +} + +void append_row_range(const RowRange& range, std::vector* ranges) { + if (range.length == 0) { + return; + } + if (!ranges->empty()) { + auto& previous = ranges->back(); + if (previous.start + previous.length == range.start) { + previous.length += range.length; + return; + } + } + ranges->push_back(range); +} + +bool select_ranges_for_filter(const std::shared_ptr<::parquet::RowGroupPageIndexReader>& row_group, + const std::vector>& file_schema, + const format::FileColumnPredicateFilter& column_filter, + int64_t row_group_rows, std::vector* ranges) { + if (column_filter.predicates.empty()) { + return false; + } + const auto* column_schema = + ParquetStatisticsUtils::ResolvePredicateLeafSchema(file_schema, column_filter); + if (column_schema == nullptr || column_schema->descriptor == nullptr) { + return false; + } + + std::shared_ptr<::parquet::ColumnIndex> column_index; + std::shared_ptr<::parquet::OffsetIndex> offset_index; + try { + column_index = row_group->GetColumnIndex(column_schema->leaf_column_id); + offset_index = row_group->GetOffsetIndex(column_schema->leaf_column_id); + } catch (const ::parquet::ParquetException&) { + return false; + } catch (const std::exception&) { + return false; + } + if (column_index == nullptr || offset_index == nullptr || + column_index->null_pages().size() != offset_index->page_locations().size()) { + return false; + } + + ranges->clear(); + const auto page_count = offset_index->page_locations().size(); + for (size_t page_idx = 0; page_idx < page_count; ++page_idx) { + ParquetColumnStatistics page_statistics; + if (!build_page_statistics(column_index, *column_schema, page_idx, &page_statistics)) { + ranges->clear(); + return false; + } + const RowRange row_range = page_row_range(*offset_index, page_idx, row_group_rows); + if (ParquetStatisticsUtils::CheckStatistics(column_filter, page_statistics)) { + continue; + } + append_row_range(row_range, ranges); + } + return true; +} + +bool ranges_intersect(const std::vector& ranges, const RowRange& range) { + const int64_t range_end = range.start + range.length; + for (const auto& selected_range : ranges) { + const int64_t selected_end = selected_range.start + selected_range.length; + if (selected_end <= range.start) { + continue; + } + if (selected_range.start >= range_end) { + return false; + } + return true; + } + return false; +} + +void collect_leaf_schemas(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, + std::vector* leaf_schemas) { + if (column_schema.kind == ParquetColumnSchemaKind::PRIMITIVE) { + leaf_schemas->push_back(&column_schema); + return; + } + for (const auto& child_schema : column_schema.children) { + if (!format::is_child_projected(projection, child_schema->local_id)) { + continue; + } + const auto* child_projection = + format::find_child_projection(projection, child_schema->local_id); + collect_leaf_schemas(*child_schema, child_projection, leaf_schemas); + } +} + +void collect_request_leaf_schemas( + const std::vector>& file_schema, + const format::FileScanRequest& request, + std::vector* leaf_schemas) { + std::set seen_leaf_ids; + auto collect_projection = [&](const format::LocalColumnIndex& projection) { + const int32_t local_id = projection.local_id(); + if (local_id < 0 || local_id >= static_cast(file_schema.size())) { + return; + } + std::vector projection_leaf_schemas; + collect_leaf_schemas(*file_schema[local_id], &projection, &projection_leaf_schemas); + for (const auto* leaf_schema : projection_leaf_schemas) { + DORIS_CHECK(leaf_schema != nullptr); + if (seen_leaf_ids.insert(leaf_schema->leaf_column_id).second) { + leaf_schemas->push_back(leaf_schema); + } + } + }; + for (const auto& projection : request.predicate_columns) { + collect_projection(projection); + } + for (const auto& projection : request.non_predicate_columns) { + collect_projection(projection); + } + for (const auto& column_filter : request.column_predicate_filters) { + const auto* leaf_schema = + ParquetStatisticsUtils::ResolvePredicateLeafSchema(file_schema, column_filter); + if (leaf_schema == nullptr) { + continue; + } + if (seen_leaf_ids.insert(leaf_schema->leaf_column_id).second) { + leaf_schemas->push_back(leaf_schema); + } + } +} + +bool build_page_skip_plan_for_leaf( + const std::shared_ptr<::parquet::RowGroupPageIndexReader>& row_group, + const ParquetColumnSchema& column_schema, const std::vector& selected_ranges, + int64_t row_group_rows, ParquetPageSkipPlan* page_skip_plan) { + DORIS_CHECK(page_skip_plan != nullptr); + *page_skip_plan = ParquetPageSkipPlan {}; + // OffsetIndex first_row_index is row-based only for non-repeated leaves. LIST/MAP/repeated + // leaves need repetition-level-aware range mapping and are intentionally left out for now. + if (column_schema.kind != ParquetColumnSchemaKind::PRIMITIVE || + column_schema.descriptor == nullptr || column_schema.leaf_column_id < 0 || + column_schema.descriptor->max_repetition_level() != 0) { + return false; + } + + std::shared_ptr<::parquet::OffsetIndex> offset_index; + try { + offset_index = row_group->GetOffsetIndex(column_schema.leaf_column_id); + } catch (const ::parquet::ParquetException&) { + return false; + } catch (const std::exception&) { + return false; + } + if (offset_index == nullptr) { + return false; + } + + const auto page_count = offset_index->page_locations().size(); + page_skip_plan->leaf_column_id = column_schema.leaf_column_id; + page_skip_plan->skipped_pages.resize(page_count); + page_skip_plan->skipped_page_compressed_sizes.resize(page_count); + const auto& page_locations = offset_index->page_locations(); + for (size_t page_idx = 0; page_idx < page_count; ++page_idx) { + const RowRange row_range = page_row_range(*offset_index, page_idx, row_group_rows); + if (row_range.length == 0 || ranges_intersect(selected_ranges, row_range)) { + continue; + } + page_skip_plan->skipped_pages[page_idx] = 1; + page_skip_plan->skipped_page_compressed_sizes[page_idx] = + page_locations[page_idx].compressed_page_size; + append_row_range(row_range, &page_skip_plan->skipped_ranges); + } + if (page_skip_plan->empty()) { + *page_skip_plan = ParquetPageSkipPlan {}; + return false; + } + return true; +} + +void build_page_skip_plans(const std::shared_ptr<::parquet::RowGroupPageIndexReader>& row_group, + const std::vector>& file_schema, + const format::FileScanRequest& request, + const std::vector& selected_ranges, int64_t row_group_rows, + std::map* page_skip_plans) { + DORIS_CHECK(page_skip_plans != nullptr); + page_skip_plans->clear(); + std::vector leaf_schemas; + collect_request_leaf_schemas(file_schema, request, &leaf_schemas); + for (const auto* leaf_schema : leaf_schemas) { + DORIS_CHECK(leaf_schema != nullptr); + ParquetPageSkipPlan page_skip_plan; + if (build_page_skip_plan_for_leaf(row_group, *leaf_schema, selected_ranges, row_group_rows, + &page_skip_plan)) { + page_skip_plans->emplace(page_skip_plan.leaf_column_id, std::move(page_skip_plan)); + } + } +} + +} // namespace + +Status select_row_group_ranges_by_page_index( + ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, int row_group_idx, int64_t row_group_rows, + std::vector* selected_ranges, std::map* page_skip_plans, + ParquetPruningStats* pruning_stats) { + int64_t page_index_filter_time_sink = 0; + SCOPED_RAW_TIMER(pruning_stats == nullptr ? &page_index_filter_time_sink + : &pruning_stats->page_index_filter_time); + DORIS_CHECK(selected_ranges != nullptr); + selected_ranges->clear(); + if (page_skip_plans != nullptr) { + page_skip_plans->clear(); + } + if (row_group_rows <= 0) { + return Status::OK(); + } + selected_ranges->push_back(RowRange {0, row_group_rows}); + if (!config::enable_parquet_page_index || request.column_predicate_filters.empty() || + file_reader == nullptr) { + return Status::OK(); + } + + std::shared_ptr<::parquet::PageIndexReader> page_index_reader; + std::shared_ptr<::parquet::RowGroupPageIndexReader> row_group_index_reader; + try { + if (pruning_stats != nullptr) { + ++pruning_stats->page_index_read_calls; + } + { + int64_t read_page_index_time_sink = 0; + SCOPED_RAW_TIMER(pruning_stats == nullptr ? &read_page_index_time_sink + : &pruning_stats->read_page_index_time); + page_index_reader = file_reader->GetPageIndexReader(); + if (page_index_reader == nullptr) { + return Status::OK(); + } + row_group_index_reader = page_index_reader->RowGroup(row_group_idx); + } + } catch (const ::parquet::ParquetException&) { + return Status::OK(); + } catch (const std::exception&) { + return Status::OK(); + } + if (row_group_index_reader == nullptr) { + return Status::OK(); + } + + for (const auto& column_filter : request.column_predicate_filters) { + std::vector filter_ranges; + if (!select_ranges_for_filter(row_group_index_reader, file_schema, column_filter, + row_group_rows, &filter_ranges)) { + continue; + } + *selected_ranges = intersect_ranges(*selected_ranges, filter_ranges); + if (selected_ranges->empty()) { + if (page_skip_plans != nullptr) { + page_skip_plans->clear(); + } + if (pruning_stats != nullptr) { + pruning_stats->filtered_page_rows += row_group_rows; + ++pruning_stats->filtered_row_groups_by_page_index; + } + return Status::OK(); + } + } + if (page_skip_plans != nullptr) { + build_page_skip_plans(row_group_index_reader, file_schema, request, *selected_ranges, + row_group_rows, page_skip_plans); + } + if (pruning_stats != nullptr) { + const int64_t selected_rows = count_range_rows(*selected_ranges); + DORIS_CHECK(selected_rows <= row_group_rows); + pruning_stats->filtered_page_rows += row_group_rows - selected_rows; + } + return Status::OK(); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_statistics.h b/be/src/format_v2/parquet/parquet_statistics.h new file mode 100644 index 00000000000000..846023d5f77ba6 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_statistics.h @@ -0,0 +1,141 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include + +#include "common/status.h" +#include "core/field.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/selection_vector.h" + +namespace parquet { +class BloomFilter; +class FileMetaData; +class ParquetFileReader; +class RowGroupMetaData; +class Statistics; +} // namespace parquet + +namespace doris { +class ColumnPredicate; +} // namespace doris + +namespace doris::parquet { + +struct ParquetColumnSchema; + +enum class ParquetRowGroupPruneReason { + NONE, + STATISTICS, + DICTIONARY, + BLOOM_FILTER, +}; + +struct ParquetPruningStats { + int64_t total_row_groups = 0; + int64_t selected_row_groups = 0; + int64_t filtered_row_groups_by_statistics = 0; + int64_t filtered_row_groups_by_dictionary = 0; + int64_t filtered_row_groups_by_bloom_filter = 0; + int64_t filtered_row_groups_by_page_index = 0; + int64_t filtered_group_rows = 0; + int64_t filtered_page_rows = 0; + int64_t selected_row_ranges = 0; + int64_t page_index_read_calls = 0; + int64_t bloom_filter_read_time = 0; + int64_t row_group_filter_time = 0; + int64_t page_index_filter_time = 0; + int64_t read_page_index_time = 0; +}; + +// Parquet row group column statistics 转换后的 Doris 统计视图。 +// DuckDB 会把 Parquet stats 转换成 BaseStatistics，然后让 TableFilter 自己判断； +// Doris 新 reader 先保存 file-local min/max/null 信息，再交给 ColumnPredicate 判断。 +struct ParquetColumnStatistics { + Field min_value; + Field max_value; + bool has_null = false; + bool has_not_null = false; + bool has_null_count = false; + bool has_min_max = false; + + bool has_any_statistics() const { return has_null_count || has_min_max; } +}; + +// Parquet file-local statistics/page index/bloom filter 工具类。 +// 结构参考 DuckDB ParquetStatisticsUtils：先把 Parquet metadata 转成统一统计对象， +// 再由 filter/predicate 判断是否可以裁剪。这里不理解 table/global schema。 +struct ParquetStatisticsUtils { + static const ParquetColumnSchema* ResolvePredicateLeafSchema( + const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter); + + static ParquetColumnStatistics TransformColumnStatistics( + const ParquetColumnSchema& column_schema, + const std::shared_ptr<::parquet::Statistics>& statistics); + + // Return true if the statistics indicate that the row group can be safely skipped according to + // the local single-column predicate filter. + static bool CheckStatistics(const format::FileColumnPredicateFilter& column_filter, + const ParquetColumnStatistics& statistics); + + static ParquetRowGroupPruneReason RowGroupPruneReason( + const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, + int row_group_idx, const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter); + + static bool RowGroupExcludes(const ::parquet::RowGroupMetaData& row_group, + ::parquet::ParquetFileReader* file_reader, int row_group_idx, + const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter); + + static Status SelectRowGroups( + const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, std::vector* selected_row_groups, + bool enable_bloom_filter, ParquetPruningStats* pruning_stats); + + static bool BloomFilterSupported(const ParquetColumnSchema& column_schema); + + static bool BloomFilterExcludes(const ParquetColumnSchema& column_schema, + const format::FileColumnPredicateFilter& column_filter, + const ::parquet::BloomFilter& bloom_filter); +}; + +// Parquet file-local statistics/page index/bloom filter 裁剪入口。 +// 这里只消费已经 localize 到 file schema 的 FileScanRequest，不理解 table/global schema。 +// 后续 page index、dictionary、bloom filter 等文件格式优化也应继续收敛在这一层，避免污染 +// ParquetReader 的 scan 调度代码。 +Status select_row_groups_by_statistics( + const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, std::vector* selected_row_groups, + bool enable_bloom_filter, ParquetPruningStats* pruning_stats); + +Status select_row_group_ranges_by_page_index( + ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, int row_group_idx, int64_t row_group_rows, + std::vector* selected_ranges, std::map* page_skip_plans, + ParquetPruningStats* pruning_stats); + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_type.cpp b/be/src/format_v2/parquet/parquet_type.cpp new file mode 100644 index 00000000000000..141a953524e310 --- /dev/null +++ b/be/src/format_v2/parquet/parquet_type.cpp @@ -0,0 +1,348 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_type.h" + +#include + +#include +#include + +#include "core/data_type/data_type_factory.hpp" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/primitive_type.h" + +namespace doris::parquet { +namespace { + +DataTypePtr create_type(PrimitiveType type, bool nullable, int precision = 0, int scale = 0) { + return DataTypeFactory::instance().create_data_type(type, nullable, precision, scale); +} + +PrimitiveType decimal_primitive_type(int precision) { + return precision > 38 ? TYPE_DECIMAL256 : TYPE_DECIMAL128I; +} + +void mark_decimal(const ::parquet::ColumnDescriptor* column, int precision, int scale, + ParquetTypeDescriptor* result) { + result->is_decimal = true; + result->decimal_precision = precision; + result->decimal_scale = scale; + switch (column->physical_type()) { + case ::parquet::Type::INT32: + result->extra_type_info = ParquetExtraTypeInfo::DECIMAL_INT32; + break; + case ::parquet::Type::INT64: + result->extra_type_info = ParquetExtraTypeInfo::DECIMAL_INT64; + break; + case ::parquet::Type::BYTE_ARRAY: + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: + result->extra_type_info = ParquetExtraTypeInfo::DECIMAL_BYTE_ARRAY; + break; + default: + result->extra_type_info = ParquetExtraTypeInfo::NONE; + break; + } +} + +void mark_integer(int bit_width, bool is_signed, ParquetTypeDescriptor* result) { + result->integer_bit_width = bit_width; + result->is_unsigned_integer = !is_signed; +} + +DataTypePtr converted_type_to_doris_type(const ::parquet::ColumnDescriptor* column, + ParquetTypeDescriptor* result) { + const bool nullable = column->max_definition_level() > 0; + switch (column->converted_type()) { + case ::parquet::ConvertedType::UTF8: + case ::parquet::ConvertedType::ENUM: + case ::parquet::ConvertedType::JSON: + case ::parquet::ConvertedType::BSON: + return create_type(TYPE_STRING, nullable); + case ::parquet::ConvertedType::DECIMAL: + mark_decimal(column, column->type_precision(), column->type_scale(), result); + return create_type(decimal_primitive_type(column->type_precision()), nullable, + column->type_precision(), column->type_scale()); + case ::parquet::ConvertedType::DATE: + return create_type(TYPE_DATEV2, nullable); + case ::parquet::ConvertedType::TIME_MILLIS: + result->time_unit = ParquetTimeUnit::MILLIS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MS; + return create_type(TYPE_TIMEV2, nullable, 0, 3); + case ::parquet::ConvertedType::TIME_MICROS: + result->time_unit = ParquetTimeUnit::MICROS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MICROS; + return create_type(TYPE_TIMEV2, nullable, 0, 6); + case ::parquet::ConvertedType::TIMESTAMP_MILLIS: + result->is_timestamp = true; + result->timestamp_is_adjusted_to_utc = true; + result->time_unit = ParquetTimeUnit::MILLIS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MS; + return create_type(TYPE_DATETIMEV2, nullable, 0, 3); + case ::parquet::ConvertedType::TIMESTAMP_MICROS: + result->is_timestamp = true; + result->timestamp_is_adjusted_to_utc = true; + result->time_unit = ParquetTimeUnit::MICROS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MICROS; + return create_type(TYPE_DATETIMEV2, nullable, 0, 6); + // Parquet stores signed and unsigned integer logical annotations on signed physical carriers: + // INT_8/UINT_8/INT_16/UINT_16/INT_32/UINT_32 use physical INT32, and + // INT_64/UINT_64 use physical INT64. Doris maps unsigned integers to the next wider + // signed type so all values in the unsigned range can be represented. + case ::parquet::ConvertedType::INT_8: + mark_integer(8, true, result); + return create_type(TYPE_TINYINT, nullable); + case ::parquet::ConvertedType::UINT_8: + mark_integer(8, false, result); + return create_type(TYPE_SMALLINT, nullable); + case ::parquet::ConvertedType::INT_16: + mark_integer(16, true, result); + return create_type(TYPE_SMALLINT, nullable); + case ::parquet::ConvertedType::UINT_16: + mark_integer(16, false, result); + return create_type(TYPE_INT, nullable); + case ::parquet::ConvertedType::INT_32: + mark_integer(32, true, result); + return create_type(TYPE_INT, nullable); + case ::parquet::ConvertedType::UINT_32: + mark_integer(32, false, result); + return create_type(TYPE_BIGINT, nullable); + case ::parquet::ConvertedType::INT_64: + mark_integer(64, true, result); + return create_type(TYPE_BIGINT, nullable); + case ::parquet::ConvertedType::UINT_64: + mark_integer(64, false, result); + return create_type(TYPE_LARGEINT, nullable); + case ::parquet::ConvertedType::NONE: + default: + return nullptr; + } +} + +DataTypePtr logical_type_to_doris_type(const ::parquet::ColumnDescriptor* column, + ParquetTypeDescriptor* result) { + const auto& logical_type = column->logical_type(); + if (logical_type == nullptr || !logical_type->is_valid() || logical_type->is_none()) { + return nullptr; + } + const bool nullable = column->max_definition_level() > 0; + if (logical_type->is_string() || logical_type->is_enum() || logical_type->is_JSON() || + logical_type->is_BSON() || logical_type->is_UUID()) { + return create_type(TYPE_STRING, nullable); + } + if (logical_type->is_decimal()) { + const auto& decimal_type = static_cast(*logical_type); + mark_decimal(column, decimal_type.precision(), decimal_type.scale(), result); + return create_type(decimal_primitive_type(decimal_type.precision()), nullable, + decimal_type.precision(), decimal_type.scale()); + } + if (logical_type->is_date()) { + return create_type(TYPE_DATEV2, nullable); + } + if (logical_type->is_time()) { + const auto& time_type = static_cast(*logical_type); + int scale = 0; + if (time_type.time_unit() == ::parquet::LogicalType::TimeUnit::MILLIS) { + scale = 3; + result->time_unit = ParquetTimeUnit::MILLIS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MS; + } else if (time_type.time_unit() == ::parquet::LogicalType::TimeUnit::MICROS) { + scale = 6; + result->time_unit = ParquetTimeUnit::MICROS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MICROS; + } else { + return nullptr; + } + return create_type(TYPE_TIMEV2, nullable, 0, scale); + } + if (logical_type->is_timestamp()) { + const auto& timestamp_type = + static_cast(*logical_type); + int scale = 0; + if (timestamp_type.time_unit() == ::parquet::LogicalType::TimeUnit::MILLIS) { + scale = 3; + result->time_unit = ParquetTimeUnit::MILLIS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MS; + } else if (timestamp_type.time_unit() == ::parquet::LogicalType::TimeUnit::MICROS) { + scale = 6; + result->time_unit = ParquetTimeUnit::MICROS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_MICROS; + } else { + return nullptr; + } + result->is_timestamp = true; + result->timestamp_is_adjusted_to_utc = timestamp_type.is_adjusted_to_utc(); + return create_type(TYPE_DATETIMEV2, nullable, 0, scale); + } + if (logical_type->is_int()) { + const auto& int_type = static_cast(*logical_type); + mark_integer(int_type.bit_width(), int_type.is_signed(), result); + switch (int_type.bit_width()) { + case 8: + return create_type(int_type.is_signed() ? TYPE_TINYINT : TYPE_SMALLINT, nullable); + case 16: + return create_type(int_type.is_signed() ? TYPE_SMALLINT : TYPE_INT, nullable); + case 32: + return create_type(int_type.is_signed() ? TYPE_INT : TYPE_BIGINT, nullable); + case 64: + return create_type(int_type.is_signed() ? TYPE_BIGINT : TYPE_LARGEINT, nullable); + default: + return nullptr; + } + } + if (logical_type->is_float16()) { + if (column->physical_type() != ::parquet::Type::FIXED_LEN_BYTE_ARRAY || + column->type_length() != 2) { + return nullptr; + } + result->extra_type_info = ParquetExtraTypeInfo::FLOAT16; + return create_type(TYPE_FLOAT, nullable); + } + return nullptr; +} + +DataTypePtr physical_type_to_doris_type(const ::parquet::ColumnDescriptor* column) { + const bool nullable = column->max_definition_level() > 0; + DataTypePtr type; + switch (column->physical_type()) { + case ::parquet::Type::BOOLEAN: + type = std::make_shared(); + break; + case ::parquet::Type::INT32: + type = std::make_shared(); + break; + case ::parquet::Type::INT64: + type = std::make_shared(); + break; + case ::parquet::Type::FLOAT: + type = std::make_shared(); + break; + case ::parquet::Type::DOUBLE: + type = std::make_shared(); + break; + case ::parquet::Type::BYTE_ARRAY: + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: + type = std::make_shared(); + break; + case ::parquet::Type::INT96: + type = create_type(TYPE_DATETIMEV2, nullable, 0, 6); + break; + default: + return nullptr; + } + return nullable ? make_nullable(type) : type; +} + +bool record_reader_physical_type_supported(::parquet::Type::type physical_type) { + switch (physical_type) { + case ::parquet::Type::BOOLEAN: + case ::parquet::Type::INT32: + case ::parquet::Type::INT64: + case ::parquet::Type::INT96: + case ::parquet::Type::FLOAT: + case ::parquet::Type::DOUBLE: + case ::parquet::Type::BYTE_ARRAY: + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: + return true; + default: + return false; + } +} + +} // namespace + +std::string parquet_column_name(const ::parquet::ColumnDescriptor* column) { + if (column == nullptr) { + return {}; + } + auto path = column->path(); + if (path) { + return path->ToDotString(); + } + return column->name(); +} + +ParquetTypeDescriptor resolve_parquet_type(const ::parquet::ColumnDescriptor* column) { + ParquetTypeDescriptor result; + if (column == nullptr) { + return result; + } + + result.physical_type = column->physical_type(); + result.converted_type = column->converted_type(); + result.fixed_length = column->type_length(); + + if (auto logical_type = logical_type_to_doris_type(column, &result); logical_type != nullptr) { + result.doris_type = logical_type; + } else if (auto converted_type = converted_type_to_doris_type(column, &result); + converted_type != nullptr) { + result.doris_type = converted_type; + } else { + result.doris_type = physical_type_to_doris_type(column); + if (result.physical_type == ::parquet::Type::INT96) { + result.extra_type_info = ParquetExtraTypeInfo::IMPALA_TIMESTAMP; + } + } + + result.is_string_like = !result.is_decimal && + result.extra_type_info != ParquetExtraTypeInfo::FLOAT16 && + (result.physical_type == ::parquet::Type::BYTE_ARRAY || + result.physical_type == ::parquet::Type::FIXED_LEN_BYTE_ARRAY); + + if (!record_reader_physical_type_supported(result.physical_type)) { + result.supports_record_reader = false; + } else if (result.is_decimal && result.decimal_precision > 38) { + result.supports_record_reader = false; + } + return result; +} + +bool supports_record_reader(const ParquetTypeDescriptor& type_descriptor) { + return type_descriptor.supports_record_reader; +} + +DecodedValueKind decoded_value_kind(const ParquetTypeDescriptor& type_descriptor) { + switch (type_descriptor.physical_type) { + case ::parquet::Type::BOOLEAN: + return DecodedValueKind::BOOL; + case ::parquet::Type::INT32: + if (type_descriptor.is_unsigned_integer && type_descriptor.integer_bit_width == 32) { + return DecodedValueKind::UINT32; + } + return DecodedValueKind::INT32; + case ::parquet::Type::INT64: + if (type_descriptor.is_unsigned_integer && type_descriptor.integer_bit_width == 64) { + return DecodedValueKind::UINT64; + } + return DecodedValueKind::INT64; + case ::parquet::Type::INT96: + return DecodedValueKind::INT96; + case ::parquet::Type::FLOAT: + return DecodedValueKind::FLOAT; + case ::parquet::Type::DOUBLE: + return DecodedValueKind::DOUBLE; + case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: + return DecodedValueKind::FIXED_BINARY; + case ::parquet::Type::BYTE_ARRAY: + default: + return DecodedValueKind::BINARY; + } +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_type.h b/be/src/format_v2/parquet/parquet_type.h new file mode 100644 index 00000000000000..37e9b9bd5b1dfc --- /dev/null +++ b/be/src/format_v2/parquet/parquet_type.h @@ -0,0 +1,91 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include + +#include + +#include "core/data_type/data_type.h" +#include "core/data_type_serde/decoded_column_view.h" + +namespace parquet { +class ColumnDescriptor; +} // namespace parquet + +namespace doris::parquet { + +// Parquet logical/converted annotation 解析后留下的额外编码信息。 +// 这对应 DuckDB ParquetColumnSchema::type_info：Doris type 只能表达最终展示类型， +// 读值时还需要知道 decimal/timestamp/time 在 Parquet 中的物理编码方式。 +enum class ParquetExtraTypeInfo { + NONE, + DECIMAL_INT32, + DECIMAL_INT64, + DECIMAL_BYTE_ARRAY, + UNIT_MS, + UNIT_MICROS, + UNIT_NS, + IMPALA_TIMESTAMP, + FLOAT16, +}; + +enum class ParquetTimeUnit { + UNKNOWN, + MILLIS, + MICROS, + NANOS, +}; + +// Parquet file-local column descriptor 的类型解析结果。 +// 该结构只解释 Parquet physical/logical/converted type，不包含 table/global schema +// evolution，也不依赖 Arrow internal RecordReader API。 +struct ParquetTypeDescriptor { + DataTypePtr doris_type; + ParquetExtraTypeInfo extra_type_info = ParquetExtraTypeInfo::NONE; + ParquetTimeUnit time_unit = ParquetTimeUnit::UNKNOWN; + ::parquet::Type::type physical_type = ::parquet::Type::UNDEFINED; + ::parquet::ConvertedType::type converted_type = ::parquet::ConvertedType::UNDEFINED; + int integer_bit_width = -1; + int decimal_precision = -1; + int decimal_scale = -1; + int fixed_length = -1; + bool is_unsigned_integer = false; + bool is_decimal = false; + bool is_timestamp = false; + bool timestamp_is_adjusted_to_utc = false; + bool is_string_like = false; + bool supports_record_reader = true; +}; + +// 返回 Parquet leaf column 的 file-local 展示名。 +std::string parquet_column_name(const ::parquet::ColumnDescriptor* column); + +// 将 Parquet file-local column descriptor 解析成 Doris file-local 类型和读值所需的 +// 编码信息。这里不做 table schema evolution；类型提升和 default/generated/partition +// 列由 table 层处理。 +ParquetTypeDescriptor resolve_parquet_type(const ::parquet::ColumnDescriptor* column); + +// 判断当前阶段是否可以通过 Arrow Parquet RecordReader 读取该列。 +// 当前支持 flat primitive/string/decimal/timestamp。复杂 nested column 仍通过 children +// 递归组合，list/map assembler 后续补齐。 +bool supports_record_reader(const ParquetTypeDescriptor& type_descriptor); + +DecodedValueKind decoded_value_kind(const ParquetTypeDescriptor& type_descriptor); + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/column_reader.cpp b/be/src/format_v2/parquet/reader/column_reader.cpp new file mode 100644 index 00000000000000..567209d0d7ff67 --- /dev/null +++ b/be/src/format_v2/parquet/reader/column_reader.cpp @@ -0,0 +1,538 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/column_reader.h" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_struct.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/reader/global_rowid_column_reader.h" +#include "format_v2/parquet/reader/list_column_reader.h" +#include "format_v2/parquet/reader/map_column_reader.h" +#include "format_v2/parquet/reader/row_position_column_reader.h" +#include "format_v2/parquet/reader/scalar_column_reader.h" +#include "format_v2/parquet/reader/struct_column_reader.h" +#include "runtime/runtime_profile.h" + +namespace doris::parquet { +namespace { + +class DataPageSkipFilter { +public: + DataPageSkipFilter(const ParquetPageSkipPlan* page_skip_plan, + ParquetPageSkipProfile page_skip_profile) + : _page_skip_plan(page_skip_plan), _page_skip_profile(page_skip_profile) { + DORIS_CHECK(_page_skip_plan != nullptr); + } + + bool operator()(const ::parquet::DataPageStats&) { + // Arrow invokes this callback once for each DATA_PAGE/DATA_PAGE_V2 and never for + // dictionary pages, so this ordinal matches Parquet OffsetIndex page locations. + const size_t page_idx = _next_data_page_idx++; + const bool skip = _page_skip_plan->should_skip_page(page_idx); + if (!skip) { + return false; + } + update_skip_profile(page_idx); + return true; + } + +private: + void update_skip_profile(size_t page_idx) const { + if (_page_skip_profile.skipped_pages != nullptr) { + COUNTER_UPDATE(_page_skip_profile.skipped_pages, 1); + } + if (_page_skip_profile.skipped_bytes != nullptr) { + COUNTER_UPDATE(_page_skip_profile.skipped_bytes, + _page_skip_plan->skipped_page_compressed_size(page_idx)); + } + } + + const ParquetPageSkipPlan* _page_skip_plan = nullptr; + ParquetPageSkipProfile _page_skip_profile; + size_t _next_data_page_idx = 0; +}; + +const ParquetPageSkipPlan* find_page_skip_plan( + const std::map* page_skip_plans, int leaf_column_id) { + if (page_skip_plans == nullptr) { + return nullptr; + } + const auto plan_it = page_skip_plans->find(leaf_column_id); + return plan_it == page_skip_plans->end() ? nullptr : &plan_it->second; +} + +void install_data_page_filter(std::unique_ptr<::parquet::PageReader>& page_reader, + const std::map* page_skip_plans, + int leaf_column_id, ParquetPageSkipProfile page_skip_profile) { + DORIS_CHECK(page_reader != nullptr); + const ParquetPageSkipPlan* page_skip_plan = + find_page_skip_plan(page_skip_plans, leaf_column_id); + if (page_skip_plan == nullptr) { + return; + } + page_reader->set_data_page_filter(DataPageSkipFilter(page_skip_plan, page_skip_profile)); +} + +// TODO: support more types +bool supports_nested_scalar_record_reader(const ParquetColumnSchema& column_schema) { + if (column_schema.type_descriptor.supports_record_reader) { + return true; + } + const auto& type_descriptor = column_schema.type_descriptor; + if ((type_descriptor.extra_type_info != ParquetExtraTypeInfo::NONE && + type_descriptor.extra_type_info != ParquetExtraTypeInfo::FLOAT16) || + type_descriptor.is_decimal || type_descriptor.is_timestamp || + type_descriptor.is_string_like) { + return false; + } + if (type_descriptor.converted_type != ::parquet::ConvertedType::NONE && + type_descriptor.converted_type != ::parquet::ConvertedType::UNDEFINED) { + return false; + } + switch (type_descriptor.physical_type) { + case ::parquet::Type::BOOLEAN: + case ::parquet::Type::INT32: + case ::parquet::Type::INT64: + case ::parquet::Type::FLOAT: + case ::parquet::Type::DOUBLE: + return true; + default: + return false; + } + return true; +} + +} // namespace + +Status ParquetColumnReader::skip(int64_t rows) { + return Status::NotSupported("Parquet column skip is not implemented, rows={}", rows); +} + +void ParquetColumnReader::update_reader_read_rows(int64_t rows) const { + if (_profile.reader_read_rows != nullptr) { + COUNTER_UPDATE(_profile.reader_read_rows, rows); + } +} + +void ParquetColumnReader::update_reader_skip_rows(int64_t rows) const { + if (_profile.reader_skip_rows != nullptr) { + COUNTER_UPDATE(_profile.reader_skip_rows, rows); + } +} + +Status ParquetColumnReader::select(const SelectionVector& sel, uint16_t selected_rows, + int64_t batch_rows, MutableColumnPtr& column) { + if (column.get() == nullptr) { + return Status::InvalidArgument("Parquet selected read result is null for column {}", + name()); + } + RETURN_IF_ERROR(sel.verify(selected_rows, batch_rows)); + + const auto ranges = selection_to_ranges(sel, selected_rows); + int64_t cursor = 0; + for (const auto& range : ranges) { + if (range.start < cursor || range.start + range.length > batch_rows) { + return Status::InvalidArgument("Invalid parquet selection range [{}, {}) for column {}", + range.start, range.start + range.length, name()); + } + RETURN_IF_ERROR(skip(range.start - cursor)); + + int64_t range_rows_read = 0; + RETURN_IF_ERROR(read(range.length, column, &range_rows_read)); + if (range_rows_read != range.length) { + return Status::Corruption( + "Parquet selected read returned {} rows, expected {} rows for column {}", + range_rows_read, range.length, name()); + } + cursor = range.start + range.length; + } + RETURN_IF_ERROR(skip(batch_rows - cursor)); + if (_profile.reader_select_rows != nullptr) { + COUNTER_UPDATE(_profile.reader_select_rows, selected_rows); + } + return Status::OK(); +} + +ParquetColumnReaderFactory::ParquetColumnReaderFactory( + std::shared_ptr<::parquet::RowGroupReader> row_group, int num_leaf_columns, + const std::map* page_skip_plans, + ParquetPageSkipProfile page_skip_profile, const cctz::time_zone* timezone, + bool enable_strict_mode, ParquetColumnReaderProfile column_reader_profile) + : _row_group(std::move(row_group)), + _record_readers(static_cast(num_leaf_columns)), + _page_skip_plans(page_skip_plans), + _page_skip_profile(page_skip_profile), + _timezone(timezone), + _enable_strict_mode(enable_strict_mode), + _column_reader_profile(column_reader_profile) {} + +std::unique_ptr ParquetColumnReaderFactory::create_row_position_column_reader( + int64_t row_group_first_row) const { + return std::make_unique(row_group_first_row, _column_reader_profile); +} + +std::unique_ptr ParquetColumnReaderFactory::create_global_rowid_column_reader( + const format::GlobalRowIdContext& context, int64_t row_group_first_row) const { + return std::make_unique(context, row_group_first_row, + _column_reader_profile); +} + +Status ParquetColumnReaderFactory::create_scalar_reader( + const ParquetColumnSchema& column_schema, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + const auto* page_skip_plan = + find_page_skip_plan(_page_skip_plans, column_schema.leaf_column_id); + *reader = std::make_unique(column_schema, std::move(record_reader), + page_skip_plan, _timezone, _enable_strict_mode, + _column_reader_profile); + return Status::OK(); +} + +Status ParquetColumnReaderFactory::create_scalar_column_reader( + const ParquetColumnSchema& column_schema, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + if (column_schema.leaf_column_id < 0 || + column_schema.leaf_column_id >= static_cast(_record_readers.size())) { + return Status::InvalidArgument("Invalid parquet leaf column id {} for column {}", + column_schema.leaf_column_id, column_schema.name); + } + if (column_schema.descriptor == nullptr || + column_schema.descriptor->max_repetition_level() != 0 || + column_schema.descriptor->max_definition_level() > 1) { + return Status::NotSupported( + "Current parquet scalar reader only supports flat primitive columns; column {} is " + "not supported", + column_schema.name); + } + if (!column_schema.type_descriptor.supports_record_reader) { + return Status::NotSupported("Current parquet scalar reader does not support column {}", + column_schema.name); + } + std::shared_ptr<::parquet::internal::RecordReader> record_reader; + RETURN_IF_ERROR(get_record_reader(column_schema.leaf_column_id, column_schema.descriptor, + column_schema.name, &record_reader)); + return create_scalar_reader(column_schema, std::move(record_reader), reader); +} + +// TODO: Unify with `create_scalar_column_reader` +Status ParquetColumnReaderFactory::create_nested_scalar_column_reader( + const ParquetColumnSchema& column_schema, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + if (column_schema.kind != ParquetColumnSchemaKind::PRIMITIVE) { + return Status::InvalidArgument("Parquet nested scalar reader requires primitive column {}", + column_schema.name); + } + if (column_schema.leaf_column_id < 0 || + column_schema.leaf_column_id >= static_cast(_record_readers.size())) { + return Status::InvalidArgument("Invalid parquet leaf column id {} for column {}", + column_schema.leaf_column_id, column_schema.name); + } + if (!supports_nested_scalar_record_reader(column_schema)) { + return Status::NotSupported( + "Current parquet nested scalar reader does not support column {}", + column_schema.name); + } + std::shared_ptr<::parquet::internal::RecordReader> record_reader; + RETURN_IF_ERROR(get_record_reader(column_schema.leaf_column_id, column_schema.descriptor, + column_schema.name, &record_reader)); + return create_scalar_reader(column_schema, std::move(record_reader), reader); +} + +Status ParquetColumnReaderFactory::get_record_reader( + int leaf_column_id, const ::parquet::ColumnDescriptor* descriptor, const std::string& name, + std::shared_ptr<::parquet::internal::RecordReader>* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + if (_row_group == nullptr) { + return Status::InternalError("Parquet row group reader is not initialized for column {}", + name); + } + if (leaf_column_id < 0 || leaf_column_id >= static_cast(_record_readers.size())) { + return Status::InvalidArgument("Invalid parquet leaf column id {} for column {}", + leaf_column_id, name); + } + if (descriptor == nullptr) { + return Status::InvalidArgument("Parquet column descriptor is null for column {}", name); + } + if (_record_readers[leaf_column_id] == nullptr) { + try { + auto page_reader = _row_group->GetColumnPageReader(leaf_column_id); + install_data_page_filter(page_reader, _page_skip_plans, leaf_column_id, + _page_skip_profile); + const auto level_info = ::parquet::internal::LevelInfo::ComputeLevelInfo(descriptor); + _record_readers[leaf_column_id] = ::parquet::internal::RecordReader::Make( + descriptor, level_info, ::arrow::default_memory_pool(), + /*read_dictionary=*/false, + /*read_dense_for_nullable=*/false); + _record_readers[leaf_column_id]->SetPageReader(std::move(page_reader)); + } catch (const ::parquet::ParquetException& e) { + return Status::Corruption("Failed to create parquet record reader for column {}: {}", + name, e.what()); + } catch (const std::exception& e) { + return Status::InternalError("Failed to create parquet record reader for column {}: {}", + name, e.what()); + } + } + if (_record_readers[leaf_column_id] == nullptr) { + return Status::Corruption("Failed to create parquet record reader for column {}", name); + } + *reader = _record_readers[leaf_column_id]; + return Status::OK(); +} + +Status ParquetColumnReaderFactory::create_struct_column_reader( + const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + std::vector> child_readers; + child_readers.reserve(column_schema.children.size()); + std::vector child_output_indices; + child_output_indices.reserve(column_schema.children.size()); + DataTypes projected_child_types; + Strings projected_child_names; + for (size_t child_idx = 0; child_idx < column_schema.children.size(); ++child_idx) { + const auto& child_schema = column_schema.children[child_idx]; + const auto* child_projection = + format::find_child_projection(projection, child_schema->local_id); + if (!format::is_child_projected(projection, child_schema->local_id)) { + continue; + } + std::unique_ptr child_reader; + if (child_schema->kind == ParquetColumnSchemaKind::PRIMITIVE) { + RETURN_IF_ERROR(create_nested_scalar_column_reader(*child_schema, &child_reader)); + } else { + RETURN_IF_ERROR(create(*child_schema, child_projection, &child_reader)); + } + child_output_indices.push_back(static_cast(projected_child_types.size())); + projected_child_types.push_back(make_nullable(child_reader->type())); + projected_child_names.push_back(child_reader->name()); + child_readers.push_back(std::move(child_reader)); + } + if (format::is_partial_projection(projection) && + projected_child_types.size() != projection->children.size()) { + return Status::InvalidArgument( + "Parquet STRUCT projection for column {} contains invalid child", + column_schema.name); + } + if (projected_child_types.empty() && !column_schema.children.empty()) { + return Status::NotSupported("Parquet STRUCT projection for column {} contains no children", + column_schema.name); + } + DataTypePtr type = column_schema.type; + if (format::is_partial_projection(projection)) { + type = std::make_shared(projected_child_types, projected_child_names); + if (column_schema.type != nullptr && column_schema.type->is_nullable()) { + type = make_nullable(type); + } + } + *reader = std::make_unique( + column_schema, std::move(type), std::move(child_readers), + std::move(child_output_indices), _column_reader_profile); + return Status::OK(); +} + +Status ParquetColumnReaderFactory::create_list_column_reader( + const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + if (column_schema.children.size() != 1) { + return Status::NotSupported("Unsupported parquet LIST layout for column {}", + column_schema.name); + } + std::unique_ptr element_reader; + const auto& element_schema = *column_schema.children[0]; + const auto* element_projection = + format::find_child_projection(projection, element_schema.local_id); + if (format::is_partial_projection(projection) && element_projection == nullptr) { + return Status::NotSupported("Parquet LIST projection for column {} contains no element", + column_schema.name); + } + if (element_schema.kind == ParquetColumnSchemaKind::PRIMITIVE) { + if (format::is_partial_projection(element_projection)) { + return Status::InvalidArgument( + "Parquet LIST scalar element projection is invalid for column {}", + column_schema.name); + } + RETURN_IF_ERROR(create_nested_scalar_column_reader(element_schema, &element_reader)); + } else { + RETURN_IF_ERROR(create(element_schema, element_projection, &element_reader)); + } + DataTypePtr type = column_schema.type; + if (format::is_partial_projection(element_projection)) { + type = std::make_shared(element_reader->type()); + if (column_schema.type != nullptr && column_schema.type->is_nullable()) { + type = make_nullable(type); + } + } + *reader = std::make_unique(column_schema, std::move(type), + std::move(element_reader), _column_reader_profile); + return Status::OK(); +} + +Status ParquetColumnReaderFactory::create_map_column_reader( + const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + if (column_schema.children.size() != 1 || column_schema.children[0]->children.size() != 2) { + return Status::NotSupported("Unsupported parquet MAP layout for column {}", + column_schema.name); + } + const auto& key_value_schema = *column_schema.children[0]; + const auto* key_value_projection = + format::find_child_projection(projection, key_value_schema.local_id); + if (format::is_partial_projection(projection) && key_value_projection == nullptr) { + return Status::NotSupported("Parquet MAP projection for column {} contains no entry", + column_schema.name); + } + std::unique_ptr key_reader; + RETURN_IF_ERROR(create_nested_scalar_column_reader(*key_value_schema.children[0], &key_reader)); + std::unique_ptr value_reader; + const auto& value_schema = *key_value_schema.children[1]; + const auto* value_projection = + format::find_child_projection(key_value_projection, value_schema.local_id); + if (value_schema.kind == ParquetColumnSchemaKind::PRIMITIVE) { + if (format::is_partial_projection(value_projection)) { + return Status::InvalidArgument( + "Parquet MAP scalar value projection is invalid for column {}", + column_schema.name); + } + RETURN_IF_ERROR(create_nested_scalar_column_reader(value_schema, &value_reader)); + } else { + RETURN_IF_ERROR(create(value_schema, value_projection, &value_reader)); + } + DataTypePtr type = column_schema.type; + if (format::is_partial_projection(value_projection)) { + type = std::make_shared(make_nullable(key_reader->type()), + make_nullable(value_reader->type())); + if (column_schema.type != nullptr && column_schema.type->is_nullable()) { + type = make_nullable(type); + } + } + *reader = + std::make_unique(column_schema, std::move(type), std::move(key_reader), + std::move(value_reader), _column_reader_profile); + return Status::OK(); +} + +Status ParquetColumnReaderFactory::create(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const { + if (reader == nullptr) { + return Status::InvalidArgument("reader is null"); + } + switch (column_schema.kind) { + case ParquetColumnSchemaKind::PRIMITIVE: + return create_scalar_column_reader(column_schema, reader); + case ParquetColumnSchemaKind::STRUCT: + return create_struct_column_reader(column_schema, projection, reader); + case ParquetColumnSchemaKind::LIST: + return create_list_column_reader(column_schema, projection, reader); + case ParquetColumnSchemaKind::MAP: + return create_map_column_reader(column_schema, projection, reader); + } + return Status::NotSupported("Unsupported parquet column schema kind for column {}", + column_schema.name); +} + +ParquetColumnReader::ParquetColumnReader(const ParquetColumnSchema& schema, const DataTypePtr type, + ParquetColumnReaderProfile profile) + : _profile(profile), + _field_id(schema.local_id), + _leaf_column_id(schema.leaf_column_id), + _nullable_definition_level(schema.nullable_definition_level), + _repeated_repetition_level(schema.repeated_repetition_level), + _definition_level(schema.definition_level), + _repetition_level(schema.repetition_level), + _repeated_ancestor_definition_level(schema.repeated_ancestor_definition_level), + _type(std::move(type)), + _name(schema.name) {} + +Status ParquetColumnReader::load_nested_batch(int64_t) { + return Status::NotSupported("Parquet nested batch load is not supported for column {}", _name); +} + +Status ParquetColumnReader::build_nested_column(int64_t, MutableColumnPtr&, int64_t*) { + return Status::NotSupported("Parquet nested column build is not supported for column {}", + _name); +} + +Status ParquetColumnReader::skip_nested_column(int64_t rows) { + auto scratch_column = _type->create_column(); + int64_t values_read = 0; + RETURN_IF_ERROR(build_nested_column(rows, scratch_column, &values_read)); + if (values_read != rows) { + return Status::Corruption("Failed to skip nested parquet column {}: skipped {} of {} rows", + _name, values_read, rows); + } + return Status::OK(); +} + +const std::vector& ParquetColumnReader::nested_definition_levels() const { + static const std::vector empty; + return empty; +} + +const std::vector& ParquetColumnReader::nested_repetition_levels() const { + static const std::vector empty; + return empty; +} + +int64_t ParquetColumnReader::nested_levels_written() const { + return 0; +} + +bool ParquetColumnReader::is_or_has_repeated_child() const { + return _repetition_level > 0; +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/column_reader.h b/be/src/format_v2/parquet/reader/column_reader.h new file mode 100644 index 00000000000000..4c4328472d8064 --- /dev/null +++ b/be/src/format_v2/parquet/reader/column_reader.h @@ -0,0 +1,196 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/column/column_nullable.h" +#include "core/data_type/data_type.h" +#include "format_v2/column_data.h" +#include "format_v2/parquet/parquet_profile.h" +#include "format_v2/parquet/parquet_type.h" +#include "format_v2/parquet/selection_vector.h" +#include "runtime/runtime_profile.h" + +namespace parquet { +class ColumnDescriptor; +class RowGroupReader; + +namespace internal { +class RecordReader; +} // namespace internal +} // namespace parquet + +namespace cctz { +class time_zone; +} // namespace cctz + +namespace doris { +class IColumn; + +namespace parquet { +struct ParquetColumnSchema; + +struct ParquetPageSkipProfile { + RuntimeProfile::Counter* skipped_pages = nullptr; + RuntimeProfile::Counter* skipped_bytes = nullptr; +}; + +// Doris 的 Parquet column reader 抽象。 +// 该类包装 Arrow Parquet RecordReader，负责将 file-local Parquet leaf column 读取成 +// Doris-owned column。它不理解 Iceberg/global schema，也不处理 table-level +// cast/default/generated/partition 语义。 +class ParquetColumnReader { +public: + virtual ~ParquetColumnReader() = default; + + // Reader-local schema id. Top-level readers return the root column ordinal; nested readers + // return the child ordinal under their parent. + virtual int file_column_id() const { return _field_id; } + + // Parquet leaf column id. This is the column id of the leaf column in the Parquet file schema, and can be used to access ColumnDescriptor, RecordReader, column chunk metadata and statistics. + // For example, for a map column as `a: map`, `a` is the top-level column with `parquet_leaf_column_id` = `file_column_id`. `a.key` is the first child of `a` so `parquet_leaf_column_id` = 0. + virtual int parquet_leaf_column_id() const { return _leaf_column_id; } + int16_t nullable_definition_level() const { return _nullable_definition_level; } + int16_t repeated_repetition_level() const { return _repeated_repetition_level; } + + virtual const DataTypePtr& type() const { return _type; } + virtual const std::string& name() const { return _name; } + const ParquetColumnReaderProfile& profile() const { return _profile; } + + // 读取一个 file-local column batch。 + virtual Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) = 0; + + // 跳过指定行数。这里必须使用 row-level skip，不能退回到 value-level Skip。 + virtual Status skip(int64_t rows); + + // 按 selection 读取当前 batch 中需要输出的行，并在末尾跳过 batch 内剩余行。 + // 该方法只允许通过 skip + read 推进 reader 游标，不允许退化为整批 read + filter。 + virtual Status select(const SelectionVector& sel, uint16_t selected_rows, int64_t batch_rows, + MutableColumnPtr& column); + + virtual Status load_nested_batch(int64_t rows); + virtual Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read); + virtual Status skip_nested_column(int64_t rows); + virtual const std::vector& nested_definition_levels() const; + virtual const std::vector& nested_repetition_levels() const; + virtual int64_t nested_levels_written() const; + virtual bool is_or_has_repeated_child() const; + + int64_t nested_build_level_cursor() const { return _nested_build_level_cursor; } + void set_nested_build_level_cursor(int64_t cursor) { + DORIS_CHECK(cursor >= 0); + _nested_build_level_cursor = cursor; + } + void reset_nested_build_level_cursor() { _nested_build_level_cursor = 0; } + +protected: + ParquetColumnReader(const ParquetColumnSchema& schema, const DataTypePtr type, + ParquetColumnReaderProfile profile = {}); + ParquetColumnReader() = default; + void update_reader_read_rows(int64_t rows) const; + void update_reader_skip_rows(int64_t rows) const; + + ParquetColumnReaderProfile _profile; + const int _field_id = -1; + const int _leaf_column_id = -1; + const int16_t _nullable_definition_level = 0; + const int16_t _repeated_repetition_level = 0; + const int16_t _definition_level = 0; + const int16_t _repetition_level = 0; + const int16_t _repeated_ancestor_definition_level = 0; + const DataTypePtr _type; + const std::string _name; + int64_t _nested_build_level_cursor = 0; +}; + +// Parquet column reader 工厂。 +// 工厂绑定当前 row group，并根据 file-local schema tree 创建 Doris 自己的 column +// reader。Arrow internal RecordReader 的创建和缓存必须封装在这里，避免泄露到 +// ParquetReader 主流程。后续 reader options、Dremel assembler、延时物化 cache/skip +// 策略都应挂在该工厂上下文里，而不是继续扩展自由函数参数。 +class ParquetColumnReaderFactory { +public: + ParquetColumnReaderFactory(std::shared_ptr<::parquet::RowGroupReader> row_group, + int num_leaf_columns, + const std::map* page_skip_plans = nullptr, + ParquetPageSkipProfile page_skip_profile = {}, + const cctz::time_zone* timezone = nullptr, + bool enable_strict_mode = false, + ParquetColumnReaderProfile column_reader_profile = {}); + + // 根据 file-local schema tree 创建 column reader。复杂类型会在这里递归创建 + // children。该入口只理解 Parquet file schema，不处理 table/global schema。 + Status create(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const; + + Status create(const ParquetColumnSchema& column_schema, + std::unique_ptr* reader) const { + return create(column_schema, nullptr, reader); + } + + std::unique_ptr create_row_position_column_reader( + int64_t row_group_first_row) const; + std::unique_ptr create_global_rowid_column_reader( + const format::GlobalRowIdContext& context, int64_t row_group_first_row) const; + +private: + Status create_scalar_column_reader(const ParquetColumnSchema& column_schema, + std::unique_ptr* reader) const; + + Status create_nested_scalar_column_reader(const ParquetColumnSchema& column_schema, + std::unique_ptr* reader) const; + + Status create_struct_column_reader(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const; + + Status create_list_column_reader(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const; + + Status create_map_column_reader(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, + std::unique_ptr* reader) const; + + Status get_record_reader(int leaf_column_id, const ::parquet::ColumnDescriptor* descriptor, + const std::string& name, + std::shared_ptr<::parquet::internal::RecordReader>* reader) const; + + Status create_scalar_reader(const ParquetColumnSchema& column_schema, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + std::unique_ptr* reader) const; + + std::shared_ptr<::parquet::RowGroupReader> _row_group; + mutable std::vector> _record_readers; + const std::map* _page_skip_plans = nullptr; + ParquetPageSkipProfile _page_skip_profile; + const cctz::time_zone* _timezone = nullptr; + bool _enable_strict_mode = false; + ParquetColumnReaderProfile _column_reader_profile; +}; + +} // namespace parquet +} // namespace doris diff --git a/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp b/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp new file mode 100644 index 00000000000000..c2be98d1658978 --- /dev/null +++ b/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp @@ -0,0 +1,86 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/global_rowid_column_reader.h" + +#include + +#include "common/cast_set.h" +#include "common/consts.h" +#include "core/assert_cast.h" +#include "core/column/column_string.h" +#include "core/data_type/data_type_string.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "storage/utils.h" + +namespace doris::parquet { + +GlobalRowIdColumnReader::GlobalRowIdColumnReader(format::GlobalRowIdContext context, + int64_t row_group_first_row, + ParquetColumnReaderProfile profile) + : ParquetColumnReader(ParquetColumnSchema {.name = BeConsts::GLOBAL_ROWID_COL}, + std::make_shared(), profile), + _context(context), + _row_group_first_row(row_group_first_row) {} + +int GlobalRowIdColumnReader::file_column_id() const { + return format::GLOBAL_ROWID_COLUMN_ID; +} + +int GlobalRowIdColumnReader::parquet_leaf_column_id() const { + return -1; +} + +const DataTypePtr& GlobalRowIdColumnReader::type() const { + return _type; +} + +const std::string& GlobalRowIdColumnReader::name() const { + return _name; +} + +Status GlobalRowIdColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { + if (column.get() == nullptr || rows_read == nullptr) { + return Status::InvalidArgument("Invalid parquet global rowid read result pointer"); + } + if (rows < 0) { + return Status::InvalidArgument("Invalid parquet global rowid read rows {}", rows); + } + for (int64_t row = 0; row < rows; ++row) { + append_row_id(cast_set(_row_group_first_row + _next_row_position + row), column); + } + _next_row_position += rows; + *rows_read = rows; + return Status::OK(); +} + +Status GlobalRowIdColumnReader::skip(int64_t rows) { + if (rows <= 0) { + return Status::OK(); + } + _next_row_position += rows; + return Status::OK(); +} + +void GlobalRowIdColumnReader::append_row_id(uint32_t row_id, MutableColumnPtr& column) const { + auto* string_column = assert_cast(column.get()); + GlobalRowLoacationV2 location(_context.version, _context.backend_id, _context.file_id, row_id); + string_column->insert_data(reinterpret_cast(&location), + sizeof(GlobalRowLoacationV2)); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/global_rowid_column_reader.h b/be/src/format_v2/parquet/reader/global_rowid_column_reader.h new file mode 100644 index 00000000000000..1c0192e5ad9099 --- /dev/null +++ b/be/src/format_v2/parquet/reader/global_rowid_column_reader.h @@ -0,0 +1,49 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include + +#include "format_v2/column_data.h" +#include "format_v2/parquet/reader/column_reader.h" + +namespace doris::parquet { + +class GlobalRowIdColumnReader final : public ParquetColumnReader { +public: + GlobalRowIdColumnReader(format::GlobalRowIdContext context, int64_t row_group_first_row, + ParquetColumnReaderProfile profile = {}); + + int file_column_id() const override; + int parquet_leaf_column_id() const override; + const DataTypePtr& type() const override; + const std::string& name() const override; + + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; + Status skip(int64_t rows) override; + +private: + void append_row_id(uint32_t row_id, MutableColumnPtr& column) const; + + format::GlobalRowIdContext _context; + int64_t _row_group_first_row = 0; + int64_t _next_row_position = 0; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/list_column_reader.cpp b/be/src/format_v2/parquet/reader/list_column_reader.cpp new file mode 100644 index 00000000000000..428cf24b79cf5b --- /dev/null +++ b/be/src/format_v2/parquet/reader/list_column_reader.cpp @@ -0,0 +1,170 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/list_column_reader.h" + +#include + +#include +#include +#include +#include + +#include "core/assert_cast.h" +#include "core/column/column_nullable.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_nullable.h" +#include "format_v2/parquet/reader/map_column_reader.h" +#include "format_v2/parquet/reader/nested_column_materializer.h" +#include "format_v2/parquet/reader/scalar_column_reader.h" +#include "format_v2/parquet/reader/struct_column_reader.h" + +namespace doris::parquet { +namespace { + +void remove_nullable_wrapper_if_not_expected(const DataTypePtr& output_type, + MutableColumnPtr* column) { + DORIS_CHECK(column != nullptr); + if (output_type->is_nullable()) { + return; + } + if (auto* nullable_column = check_and_get_column(**column)) { + *column = nullable_column->get_nested_column_ptr(); + } +} + +} // namespace + +Status ListColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { + RETURN_IF_ERROR(load_nested_batch(rows)); + return build_nested_column(rows, column, rows_read); +} + +Status ListColumnReader::skip(int64_t rows) { + if (rows <= 0) { + return Status::OK(); + } + auto scratch_column = _type->create_column(); + RETURN_IF_ERROR(load_nested_batch(rows)); + int64_t rows_read = 0; + RETURN_IF_ERROR(build_nested_column(rows, scratch_column, &rows_read)); + if (rows_read != rows) { + return Status::Corruption("Failed to skip parquet LIST column {}: skipped {} of {} rows", + _name, rows_read, rows); + } + update_reader_skip_rows(rows); + return Status::OK(); +} + +Status ListColumnReader::load_nested_batch(int64_t rows) { + DORIS_CHECK(_element_reader != nullptr); + reset_nested_build_level_cursor(); + return _element_reader->load_nested_batch(rows); +} + +Status ListColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) { + if (column.get() == nullptr || values_read == nullptr) { + return Status::InvalidArgument("Invalid parquet list build result pointer for column {}", + _name); + } + DORIS_CHECK(_element_reader != nullptr); + auto* array_column = array_column_from_output(column); + DORIS_CHECK(array_column != nullptr); + auto* parent_null_map = null_map_from_nullable_output(column); + auto nested_column = array_column->get_data_ptr()->assert_mutable(); + const auto& element_output_type = + assert_cast(*remove_nullable(_type)).get_nested_type(); + remove_nullable_wrapper_if_not_expected(element_output_type, &nested_column); + + const auto& def_levels = _element_reader->nested_definition_levels(); + const auto& rep_levels = _element_reader->nested_repetition_levels(); + const int64_t levels_written = _element_reader->nested_levels_written(); + std::vector entry_counts; + NullMap parent_nulls; + *values_read = 0; + int64_t level_idx = nested_build_level_cursor(); + while (level_idx < levels_written) { + const int16_t def_level = def_levels[level_idx]; + const int16_t rep_level = rep_levels[level_idx]; + const bool starts_parent = rep_level < _repetition_level; + if (starts_parent && *values_read >= length_upper_bound) { + break; + } + ++level_idx; + if (def_level < _repeated_ancestor_definition_level || rep_level > _repetition_level) { + continue; + } + if (rep_level == _repetition_level) { + if (entry_counts.empty()) { + return Status::Corruption("Invalid repeated level for parquet LIST column {}", + _name); + } + if (def_level >= _definition_level) { + ++entry_counts.back(); + } + continue; + } + + const bool parent_is_null = def_level < _definition_level - 1; + if (parent_is_null && parent_null_map == nullptr) { + return Status::Corruption("Parquet LIST column {} contains null for non-nullable LIST", + _name); + } + parent_nulls.push_back(parent_is_null); + entry_counts.push_back(def_level >= _definition_level ? 1 : 0); + ++*values_read; + } + set_nested_build_level_cursor(level_idx); + + int64_t child_value_count = 0; + uint64_t total_entries = 0; + for (const auto entry_count : entry_counts) { + total_entries += entry_count; + } + RETURN_IF_ERROR(_element_reader->build_nested_column(static_cast(total_entries), + nested_column, &child_value_count)); + if (child_value_count != static_cast(total_entries)) { + return Status::Corruption("Parquet LIST column {} built {} child values, expected {}", + _name, child_value_count, total_entries); + } + array_column->get_data_ptr() = std::move(nested_column); + append_offsets(array_column->get_offsets(), entry_counts); + append_parent_nulls(parent_null_map, parent_nulls); + return Status::OK(); +} + +const std::vector& ListColumnReader::nested_definition_levels() const { + DORIS_CHECK(_element_reader != nullptr); + return _element_reader->nested_definition_levels(); +} + +const std::vector& ListColumnReader::nested_repetition_levels() const { + DORIS_CHECK(_element_reader != nullptr); + return _element_reader->nested_repetition_levels(); +} + +int64_t ListColumnReader::nested_levels_written() const { + DORIS_CHECK(_element_reader != nullptr); + return _element_reader->nested_levels_written(); +} + +bool ListColumnReader::is_or_has_repeated_child() const { + return true; +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/list_column_reader.h b/be/src/format_v2/parquet/reader/list_column_reader.h new file mode 100644 index 00000000000000..3ab70f6970350d --- /dev/null +++ b/be/src/format_v2/parquet/reader/list_column_reader.h @@ -0,0 +1,53 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include + +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/reader/column_reader.h" + +namespace doris::parquet { + +class ListColumnReader final : public ParquetColumnReader { +public: + ListColumnReader(const ParquetColumnSchema& schema, DataTypePtr type, + std::unique_ptr element_reader, + ParquetColumnReaderProfile profile = {}) + : ParquetColumnReader(schema, type, profile), + _element_reader(std::move(element_reader)) {} + + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; + Status skip(int64_t rows) override; + Status load_nested_batch(int64_t rows) override; + Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) override; + const std::vector& nested_definition_levels() const override; + const std::vector& nested_repetition_levels() const override; + int64_t nested_levels_written() const override; + bool is_or_has_repeated_child() const override; + ParquetColumnReader* element_reader() const { return _element_reader.get(); } + +private: + std::unique_ptr _element_reader; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/map_column_reader.cpp b/be/src/format_v2/parquet/reader/map_column_reader.cpp new file mode 100644 index 00000000000000..59db884bceef91 --- /dev/null +++ b/be/src/format_v2/parquet/reader/map_column_reader.cpp @@ -0,0 +1,226 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/map_column_reader.h" + +#include + +#include +#include +#include +#include +#include +#include + +#include "core/assert_cast.h" +#include "core/column/column_nullable.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "format_v2/parquet/reader/list_column_reader.h" +#include "format_v2/parquet/reader/nested_column_materializer.h" +#include "format_v2/parquet/reader/scalar_column_reader.h" +#include "format_v2/parquet/reader/struct_column_reader.h" + +namespace doris::parquet { +namespace { + +void remove_nullable_wrapper_if_not_expected(const DataTypePtr& output_type, + MutableColumnPtr* column) { + DORIS_CHECK(column != nullptr); + if (output_type->is_nullable()) { + return; + } + if (auto* nullable_column = check_and_get_column(**column)) { + *column = nullable_column->get_nested_column_ptr(); + } +} + +} // namespace + +Status MapColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { + RETURN_IF_ERROR(load_nested_batch(rows)); + return build_nested_column(rows, column, rows_read); +} + +Status MapColumnReader::skip(int64_t rows) { + if (rows <= 0) { + return Status::OK(); + } + auto scratch_column = _type->create_column(); + RETURN_IF_ERROR(load_nested_batch(rows)); + int64_t rows_read = 0; + RETURN_IF_ERROR(build_nested_column(rows, scratch_column, &rows_read)); + if (rows_read != rows) { + return Status::Corruption("Failed to skip parquet MAP column {}: skipped {} of {} rows", + _name, rows_read, rows); + } + update_reader_skip_rows(rows); + return Status::OK(); +} + +Status MapColumnReader::load_nested_batch(int64_t rows) { + DORIS_CHECK(_key_reader != nullptr); + DORIS_CHECK(_value_reader != nullptr); + reset_nested_build_level_cursor(); + RETURN_IF_ERROR(_key_reader->load_nested_batch(rows)); + return _value_reader->load_nested_batch(rows); +} + +Status MapColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) { + if (column.get() == nullptr || values_read == nullptr) { + return Status::InvalidArgument("Invalid parquet map build result pointer for column {}", + _name); + } + DORIS_CHECK(_key_reader != nullptr); + DORIS_CHECK(_value_reader != nullptr); + auto* map_column = map_column_from_output(column); + DORIS_CHECK(map_column != nullptr); + auto* parent_null_map = null_map_from_nullable_output(column); + auto key_column = map_column->get_keys_ptr()->assert_mutable(); + auto value_column = map_column->get_values_ptr()->assert_mutable(); + const auto& map_output_type = assert_cast(*remove_nullable(_type)); + remove_nullable_wrapper_if_not_expected(map_output_type.get_key_type(), &key_column); + remove_nullable_wrapper_if_not_expected(map_output_type.get_value_type(), &value_column); + + const auto& def_levels = _key_reader->nested_definition_levels(); + const auto& rep_levels = _key_reader->nested_repetition_levels(); + const int64_t levels_written = _key_reader->nested_levels_written(); + + std::vector entry_counts; + std::vector map_level_indices; + NullMap parent_nulls; + *values_read = 0; + int64_t level_idx = nested_build_level_cursor(); + while (level_idx < levels_written) { + const int16_t def_level = def_levels[level_idx]; + const int16_t rep_level = rep_levels[level_idx]; + const bool starts_parent = rep_level < _repetition_level; + if (starts_parent && *values_read >= length_upper_bound) { + break; + } + const int64_t current_level_idx = level_idx; + ++level_idx; + if (def_level < _repeated_ancestor_definition_level || rep_level > _repetition_level) { + continue; + } + map_level_indices.push_back(current_level_idx); + if (rep_level == _repetition_level) { + if (entry_counts.empty()) { + return Status::Corruption("Invalid repeated level for parquet MAP column {}", + _name); + } + if (def_level >= _definition_level) { + ++entry_counts.back(); + } + continue; + } + + const bool parent_is_null = def_level < _definition_level - 1; + if (parent_is_null && parent_null_map == nullptr) { + return Status::Corruption("Parquet MAP column {} contains null for non-nullable MAP", + _name); + } + parent_nulls.push_back(parent_is_null); + entry_counts.push_back(def_level >= _definition_level ? 1 : 0); + ++*values_read; + } + set_nested_build_level_cursor(level_idx); + + uint64_t total_entries = 0; + for (const auto entry_count : entry_counts) { + total_entries += entry_count; + } + int64_t key_value_count = 0; + RETURN_IF_ERROR(_key_reader->build_nested_column(static_cast(total_entries), + key_column, &key_value_count)); + if (key_value_count != static_cast(total_entries)) { + return Status::Corruption("Parquet MAP column {} built {} keys, expected {}", _name, + key_value_count, total_entries); + } + int64_t value_count = 0; + if (auto* scalar_value_reader = dynamic_cast(_value_reader.get())) { + const auto& value_def_levels = scalar_value_reader->nested_definition_levels(); + const auto& value_rep_levels = scalar_value_reader->nested_repetition_levels(); + const int64_t value_levels_written = scalar_value_reader->nested_levels_written(); + int64_t value_level_idx = scalar_value_reader->nested_build_level_cursor(); + for (const int64_t key_level_idx : map_level_indices) { + while (value_level_idx < value_levels_written && + (value_def_levels[value_level_idx] < _repeated_ancestor_definition_level || + value_rep_levels[value_level_idx] > _repetition_level)) { + ++value_level_idx; + } + if (value_level_idx >= value_levels_written) { + return Status::Corruption( + "Parquet MAP column {} value stream ended before key stream", _name); + } + // MAP is encoded as a repeated key/value struct. The key stream owns entry existence, + // but the value stream still has one shape slot for every consumed MAP slot. Consume + // value slots in lockstep with key slots so shape-only slots from empty/null maps do + // not become scalar values. + if (value_rep_levels[value_level_idx] != rep_levels[key_level_idx]) { + return Status::Corruption( + "Parquet MAP column {} value repetition level is not aligned with key " + "stream", + _name); + } + if (def_levels[key_level_idx] >= _definition_level) { + RETURN_IF_ERROR( + scalar_value_reader->append_nested_value(value_level_idx, value_column)); + ++value_count; + } + ++value_level_idx; + } + scalar_value_reader->set_nested_build_level_cursor(value_level_idx); + } else { + // Complex MAP values own their nested shape below the entry slot, so they can recursively + // materialize exactly one child value for each MAP entry. + RETURN_IF_ERROR(_value_reader->build_nested_column(static_cast(total_entries), + value_column, &value_count)); + } + if (value_count != static_cast(total_entries)) { + return Status::Corruption("Parquet MAP column {} built {} values, expected {}", _name, + value_count, total_entries); + } + + map_column->get_keys_ptr() = std::move(key_column); + map_column->get_values_ptr() = std::move(value_column); + append_offsets(map_column->get_offsets(), entry_counts); + append_parent_nulls(parent_null_map, parent_nulls); + return Status::OK(); +} + +const std::vector& MapColumnReader::nested_definition_levels() const { + DORIS_CHECK(_key_reader != nullptr); + return _key_reader->nested_definition_levels(); +} + +const std::vector& MapColumnReader::nested_repetition_levels() const { + DORIS_CHECK(_key_reader != nullptr); + return _key_reader->nested_repetition_levels(); +} + +int64_t MapColumnReader::nested_levels_written() const { + DORIS_CHECK(_key_reader != nullptr); + return _key_reader->nested_levels_written(); +} + +bool MapColumnReader::is_or_has_repeated_child() const { + return true; +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/map_column_reader.h b/be/src/format_v2/parquet/reader/map_column_reader.h new file mode 100644 index 00000000000000..a7591981eaee46 --- /dev/null +++ b/be/src/format_v2/parquet/reader/map_column_reader.h @@ -0,0 +1,57 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include + +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/reader/column_reader.h" + +namespace doris::parquet { + +class MapColumnReader final : public ParquetColumnReader { +public: + MapColumnReader(const ParquetColumnSchema& schema, DataTypePtr type, + std::unique_ptr key_reader, + std::unique_ptr value_reader, + ParquetColumnReaderProfile profile = {}) + : ParquetColumnReader(schema, type, profile), + _key_reader(std::move(key_reader)), + _value_reader(std::move(value_reader)) {} + + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; + Status skip(int64_t rows) override; + Status load_nested_batch(int64_t rows) override; + Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) override; + const std::vector& nested_definition_levels() const override; + const std::vector& nested_repetition_levels() const override; + int64_t nested_levels_written() const override; + bool is_or_has_repeated_child() const override; + ParquetColumnReader* key_reader() const { return _key_reader.get(); } + ParquetColumnReader* value_reader() const { return _value_reader.get(); } + +private: + std::unique_ptr _key_reader; + std::unique_ptr _value_reader; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/nested_column_materializer.cpp b/be/src/format_v2/parquet/reader/nested_column_materializer.cpp new file mode 100644 index 00000000000000..713595ca240683 --- /dev/null +++ b/be/src/format_v2/parquet/reader/nested_column_materializer.cpp @@ -0,0 +1,72 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/nested_column_materializer.h" + +#include +#include + +#include "core/assert_cast.h" +#include "core/column/column_nullable.h" + +namespace doris::parquet { + +ColumnArray* array_column_from_output(MutableColumnPtr& column) { + if (auto* nullable_column = check_and_get_column(*column)) { + return assert_cast(&nullable_column->get_nested_column()); + } + return assert_cast(column.get()); +} + +ColumnMap* map_column_from_output(MutableColumnPtr& column) { + if (auto* nullable_column = check_and_get_column(*column)) { + return assert_cast(&nullable_column->get_nested_column()); + } + return assert_cast(column.get()); +} + +ColumnStruct* struct_column_from_output(MutableColumnPtr& column) { + if (auto* nullable_column = check_and_get_column(*column)) { + return assert_cast(&nullable_column->get_nested_column()); + } + return assert_cast(column.get()); +} + +NullMap* null_map_from_nullable_output(MutableColumnPtr& column) { + if (auto* nullable_column = check_and_get_column(*column)) { + return &nullable_column->get_null_map_data(); + } + return nullptr; +} + +void append_offsets(ColumnArray::Offsets64& offsets, const std::vector& entry_counts) { + offsets.reserve(offsets.size() + entry_counts.size()); + uint64_t current_offset = offsets.empty() ? 0 : offsets.back(); + for (const auto entry_count : entry_counts) { + current_offset += entry_count; + offsets.push_back(current_offset); + } +} + +void append_parent_nulls(NullMap* dst, const NullMap& src) { + if (dst == nullptr) { + return; + } + dst->insert(src.begin(), src.end()); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/nested_column_materializer.h b/be/src/format_v2/parquet/reader/nested_column_materializer.h new file mode 100644 index 00000000000000..41b849850f9d4a --- /dev/null +++ b/be/src/format_v2/parquet/reader/nested_column_materializer.h @@ -0,0 +1,39 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include + +#include "core/column/column.h" +#include "core/column/column_array.h" +#include "core/column/column_map.h" +#include "core/column/column_nullable.h" +#include "core/column/column_struct.h" + +namespace doris::parquet { + +ColumnArray* array_column_from_output(MutableColumnPtr& column); +ColumnMap* map_column_from_output(MutableColumnPtr& column); +ColumnStruct* struct_column_from_output(MutableColumnPtr& column); +NullMap* null_map_from_nullable_output(MutableColumnPtr& column); + +void append_offsets(ColumnArray::Offsets64& offsets, const std::vector& entry_counts); +void append_parent_nulls(NullMap* dst, const NullMap& src); + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp new file mode 100644 index 00000000000000..a9c0fd7f7a4a8e --- /dev/null +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -0,0 +1,577 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/parquet_leaf_reader.h" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/data_type/data_type_nullable.h" +#include "core/data_type_serde/decoded_column_view.h" +#include "core/string_ref.h" +#include "runtime/runtime_profile.h" +#include "util/simd/bits.h" + +namespace doris::parquet { +namespace { + +DecodedTimeUnit decoded_time_unit(ParquetTimeUnit time_unit) { + switch (time_unit) { + case ParquetTimeUnit::MILLIS: + return DecodedTimeUnit::MILLIS; + case ParquetTimeUnit::MICROS: + return DecodedTimeUnit::MICROS; + case ParquetTimeUnit::NANOS: + return DecodedTimeUnit::NANOS; + case ParquetTimeUnit::UNKNOWN: + default: + return DecodedTimeUnit::UNKNOWN; + } +} + +Status decoded_fixed_value_size(const std::string& column_name, DecodedValueKind value_kind, + size_t* value_size) { + switch (value_kind) { + case DecodedValueKind::BOOL: + *value_size = sizeof(bool); + return Status::OK(); + case DecodedValueKind::INT32: + *value_size = sizeof(int32_t); + return Status::OK(); + case DecodedValueKind::UINT32: + *value_size = sizeof(uint32_t); + return Status::OK(); + case DecodedValueKind::INT64: + *value_size = sizeof(int64_t); + return Status::OK(); + case DecodedValueKind::UINT64: + *value_size = sizeof(uint64_t); + return Status::OK(); + case DecodedValueKind::INT96: + *value_size = 12; + return Status::OK(); + case DecodedValueKind::FLOAT: + *value_size = sizeof(float); + return Status::OK(); + case DecodedValueKind::DOUBLE: + *value_size = sizeof(double); + return Status::OK(); + case DecodedValueKind::BINARY: + case DecodedValueKind::FIXED_BINARY: + return Status::InvalidArgument("Parquet binary value kind has no fixed value size for {}", + column_name); + } + return Status::InternalError("Unknown decoded value kind for column {}", column_name); +} + +Status get_binary_chunks(const std::string& column_name, + ::parquet::internal::RecordReader& record_reader, + std::vector>* chunks) { + auto* binary_reader = dynamic_cast<::parquet::internal::BinaryRecordReader*>(&record_reader); + if (binary_reader == nullptr) { + return Status::InternalError("Parquet binary record reader is not available for column {}", + column_name); + } + *chunks = binary_reader->GetBuilderChunks(); + return Status::OK(); +} + +Status build_binary_values(const std::string& column_name, + const std::vector>& chunks, + int64_t records_read, const NullMap* null_map, + bool read_dense_for_nullable, std::vector* binary_values) { + std::vector compact_values; + auto* values = read_dense_for_nullable ? &compact_values : binary_values; + values->reserve(records_read); + for (const auto& chunk : chunks) { + if (chunk == nullptr) { + return Status::Corruption( + "Parquet binary record reader returned null chunk for column {}", column_name); + } + if (auto* binary_array = dynamic_cast<::arrow::BinaryArray*>(chunk.get())) { + for (int64_t row_idx = 0; row_idx < binary_array->length(); ++row_idx) { + if (binary_array->IsNull(row_idx)) { + values->emplace_back(static_cast(nullptr), 0); + continue; + } + int32_t length = 0; + const uint8_t* value = binary_array->GetValue(row_idx, &length); + values->emplace_back(reinterpret_cast(value), length); + } + } else if (auto* fixed_array = dynamic_cast<::arrow::FixedSizeBinaryArray*>(chunk.get())) { + for (int64_t row_idx = 0; row_idx < fixed_array->length(); ++row_idx) { + if (fixed_array->IsNull(row_idx)) { + values->emplace_back(static_cast(nullptr), 0); + continue; + } + values->emplace_back(reinterpret_cast(fixed_array->GetValue(row_idx)), + fixed_array->byte_width()); + } + } else { + return Status::InternalError("Unexpected Arrow binary array type for column {}", + column_name); + } + } + if (read_dense_for_nullable) { + if (null_map == nullptr || null_map->size() != static_cast(records_read)) { + return Status::Corruption( + "Invalid dense nullable parquet null map for column {}: rows={}, null_map={}", + column_name, records_read, null_map == nullptr ? 0 : null_map->size()); + } + const int64_t non_null_count = static_cast(simd::count_zero_num( + reinterpret_cast(null_map->data()), null_map->size())); + if (compact_values.size() != static_cast(non_null_count)) { + return Status::Corruption( + "Invalid dense nullable parquet binary values for column {}: values={}, " + "records={}, nulls={}", + column_name, compact_values.size(), records_read, + records_read - non_null_count); + } + binary_values->reserve(records_read); + size_t value_idx = 0; + for (int64_t record_idx = 0; record_idx < records_read; ++record_idx) { + if ((*null_map)[record_idx] != 0) { + binary_values->emplace_back(static_cast(nullptr), 0); + continue; + } + binary_values->emplace_back(compact_values[value_idx++]); + } + return Status::OK(); + } + if (binary_values->size() != static_cast(records_read)) { + return Status::Corruption( + "Invalid parquet binary record read result for column {}: rows={}, records={}", + column_name, binary_values->size(), records_read); + } + return Status::OK(); +} + +float half_to_float(uint16_t value) { + const uint32_t sign = (value & 0x8000U) << 16; + const uint32_t exponent = (value & 0x7C00U) >> 10; + const uint32_t mantissa = value & 0x03FFU; + + if (exponent == 0) { + if (mantissa == 0) { + return std::bit_cast(sign); + } + const float subnormal = std::ldexp(static_cast(mantissa), -24); + return sign == 0 ? subnormal : -subnormal; + } + if (exponent == 0x1FU) { + return std::bit_cast(sign | 0x7F800000U | (mantissa << 13)); + } + return std::bit_cast(sign | ((exponent + 112U) << 23) | (mantissa << 13)); +} + +Status build_float16_values(const std::string& column_name, + const ParquetTypeDescriptor& type_descriptor, + const std::vector& binary_values, int64_t row_count, + std::vector* float_values) { + if (type_descriptor.fixed_length != 2) { + return Status::Corruption("Invalid parquet Float16 length for column {}: {}", column_name, + type_descriptor.fixed_length); + } + if (binary_values.size() != static_cast(row_count)) { + return Status::Corruption( + "Invalid parquet Float16 value count for column {}: values={}, rows={}", + column_name, binary_values.size(), row_count); + } + float_values->resize(static_cast(row_count)); + for (int64_t row = 0; row < row_count; ++row) { + const auto& binary_value = binary_values[static_cast(row)]; + if (binary_value.data == nullptr && binary_value.size == 0) { + (*float_values)[static_cast(row)] = 0; + continue; + } + if (binary_value.data == nullptr || binary_value.size != 2) { + return Status::Corruption( + "Invalid parquet Float16 value for column {} at row {}: data={}, size={}", + column_name, row, binary_value.data == nullptr ? "null" : "non-null", + binary_value.size); + } + uint16_t raw_value = 0; + std::memcpy(&raw_value, binary_value.data, sizeof(raw_value)); + (*float_values)[static_cast(row)] = half_to_float(raw_value); + } + return Status::OK(); +} + +} // namespace + +Status ParquetLeafReader::collect_batch(::parquet::internal::RecordReader& record_reader, + ParquetLeafBatch* batch) const { + DORIS_CHECK(batch != nullptr); + batch->_def_levels = nullptr; + batch->_rep_levels = nullptr; + batch->_fixed_values = nullptr; + batch->_binary_chunks.clear(); + batch->_value_kind = decoded_value_kind(_type_descriptor); + batch->_consumed_level_count = record_reader.levels_position(); + batch->_decoded_level_count = record_reader.levels_written(); + if (_descriptor->max_definition_level() > 0) { + batch->_def_levels = record_reader.def_levels(); + } + if (_descriptor->max_repetition_level() > 0) { + batch->_rep_levels = record_reader.rep_levels(); + } + batch->_read_dense_for_nullable = record_reader.read_dense_for_nullable(); + batch->_values_written = record_reader.values_written(); + + if (!batch->is_binary_value()) { + batch->_fixed_values = record_reader.values(); + return Status::OK(); + } + + // BinaryRecordReader owns values in Arrow builders instead of RecordReader::values(). + // GetBuilderChunks() transfers those builders out and resets them, so collect exactly once + // and let all later count/materialization logic use this normalized batch state. + RETURN_IF_ERROR(get_binary_chunks(_name, record_reader, &batch->_binary_chunks)); + batch->_values_written = 0; + for (const auto& chunk : batch->_binary_chunks) { + if (chunk == nullptr) { + return Status::Corruption( + "Parquet binary record reader returned null chunk for column {}", _name); + } + batch->_values_written += chunk->length(); + } + return Status::OK(); +} + +Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t row_count, + const NullMap* null_map, MutableColumnPtr& column) const { + std::vector binary_values; + std::vector spaced_values; + std::vector float_values; + DecodedColumnView view; + view.value_kind = batch._value_kind; + view.time_unit = decoded_time_unit(_type_descriptor.time_unit); + view.row_count = row_count; + view.decimal_precision = _type_descriptor.decimal_precision; + view.decimal_scale = _type_descriptor.decimal_scale; + view.fixed_length = _type_descriptor.fixed_length; + view.timestamp_is_adjusted_to_utc = _type_descriptor.timestamp_is_adjusted_to_utc; + view.timezone = _timezone; + view.enable_strict_mode = _enable_strict_mode; + view.null_map = null_map == nullptr || null_map->empty() ? nullptr : null_map->data(); + const bool read_dense_for_nullable = batch._read_dense_for_nullable && view.null_map != nullptr; + + if (_type_descriptor.extra_type_info == ParquetExtraTypeInfo::FLOAT16) { + RETURN_IF_ERROR(build_binary_values(_name, batch._binary_chunks, row_count, null_map, + read_dense_for_nullable, &binary_values)); + RETURN_IF_ERROR(build_float16_values(_name, _type_descriptor, binary_values, row_count, + &float_values)); + view.value_kind = DecodedValueKind::FLOAT; + view.values = reinterpret_cast(float_values.data()); + } else if (batch.is_binary_value()) { + RETURN_IF_ERROR(build_binary_values(_name, batch._binary_chunks, row_count, null_map, + read_dense_for_nullable, &binary_values)); + view.binary_values = &binary_values; + } else if (read_dense_for_nullable) { + RETURN_IF_ERROR(build_spaced_fixed_values(batch, row_count, null_map, &spaced_values)); + view.values = spaced_values.data(); + } else { + view.values = batch._fixed_values; + } + + { + SCOPED_TIMER(_profile.materialization_time); + RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values(*column, view)); + } + return Status::OK(); +} + +bool ParquetLeafBatch::is_binary_value() const { + return _value_kind == DecodedValueKind::BINARY || _value_kind == DecodedValueKind::FIXED_BINARY; +} + +Status ParquetLeafReader::build_spaced_fixed_values(const ParquetLeafBatch& batch, + int64_t row_count, const NullMap* null_map, + std::vector* spaced_values) const { + DORIS_CHECK(null_map != nullptr); + DORIS_CHECK(spaced_values != nullptr); + size_t value_size = 0; + RETURN_IF_ERROR(decoded_fixed_value_size(_name, batch._value_kind, &value_size)); + spaced_values->resize(static_cast(row_count) * value_size); + const auto non_null_count = static_cast(simd::count_zero_num( + reinterpret_cast(null_map->data()), null_map->size())); + if (batch._values_written != non_null_count) { + return Status::Corruption( + "Invalid dense nullable parquet values for column {}: values={}, records={}, " + "nulls={}", + _name, batch._values_written, row_count, row_count - non_null_count); + } + auto* dst = spaced_values->data(); + int64_t value_idx = 0; + for (int64_t record_idx = 0; record_idx < row_count; ++record_idx) { + if ((*null_map)[record_idx] != 0) { + continue; + } + std::memcpy(dst + static_cast(record_idx) * value_size, + batch._fixed_values + static_cast(value_idx) * value_size, value_size); + ++value_idx; + } + return Status::OK(); +} + +ParquetLeafReader::ParquetLeafReader( + const ::parquet::ColumnDescriptor* descriptor, ParquetTypeDescriptor type_descriptor, + DataTypePtr type, std::string name, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + ParquetColumnReaderProfile profile, const cctz::time_zone* timezone, + bool enable_strict_mode) + : _descriptor(descriptor), + _type_descriptor(type_descriptor), + _type(std::move(type)), + _name(std::move(name)), + _record_reader(std::move(record_reader)), + _profile(profile), + _timezone(timezone), + _enable_strict_mode(enable_strict_mode) {} + +Status ParquetLeafReader::read_batch(int64_t batch_rows, ParquetLeafBatch* batch, + int64_t* rows_read) const { + if (batch == nullptr || rows_read == nullptr) { + return Status::InvalidArgument("Invalid parquet leaf batch result pointer for column {}", + _name); + } + if (_record_reader == nullptr) { + return Status::InternalError("Parquet record reader is not initialized for column {}", + _name); + } + + try { + _record_reader->Reset(); + _record_reader->Reserve(batch_rows); + { + SCOPED_TIMER(_profile.arrow_read_records_time); + *rows_read = _record_reader->ReadRecords(batch_rows); + } + } catch (const ::parquet::ParquetException& e) { + return Status::Corruption("Failed to read parquet records for column {}: {}", _name, + e.what()); + } catch (const std::exception& e) { + return Status::InternalError("Failed to read parquet records for column {}: {}", _name, + e.what()); + } + if (*rows_read < 0 || *rows_read > batch_rows) { + return Status::Corruption("Invalid parquet record read result for column {}: {}", _name, + *rows_read); + } + return collect_batch(*_record_reader, batch); +} + +Status ParquetLeafReader::build_null_map(const ParquetLeafBatch& batch, int64_t records_read, + NullMap* null_map) const { + if (_descriptor->max_definition_level() == 0) { + return Status::OK(); + } + auto* def_levels = batch.def_levels(); + if (def_levels == nullptr && records_read > 0) { + return Status::Corruption( + "Parquet record reader returned null definition levels for nullable column {}", + _name); + } + const int16_t max_definition_level = _descriptor->max_definition_level(); + null_map->resize(records_read); + auto* __restrict dst = null_map->data(); + const auto* __restrict src = def_levels; + for (int64_t record_idx = 0; record_idx < records_read; ++record_idx) { + dst[record_idx] = src[record_idx] != max_definition_level; + } + return Status::OK(); +} + +Status ParquetLeafReader::read_nested_batch(int64_t batch_rows, int16_t value_slot_definition_level, + ParquetNestedScalarBatch* batch, + int16_t value_slot_repetition_level) const { + if (batch == nullptr) { + return Status::InvalidArgument("Nested scalar batch is null for column {}", _name); + } + *batch = ParquetNestedScalarBatch(); + batch->value_slot_definition_level = value_slot_definition_level; + batch->value_slot_repetition_level = value_slot_repetition_level; + + ParquetLeafBatch leaf_batch; + RETURN_IF_ERROR(read_batch(batch_rows, &leaf_batch, &batch->records_read)); + if (_type->is_nullable() && leaf_batch.read_dense_for_nullable()) { + return Status::NotSupported( + "Dense nullable parquet nested reader is not supported for column {}", _name); + } + batch->levels_written = leaf_batch.consumed_level_count(); + const int64_t values_written = leaf_batch.values_written(); + if (batch->levels_written > leaf_batch.decoded_level_count()) { + return Status::Corruption( + "Invalid nested parquet level position for column {}: position={}, levels={}", + _name, batch->levels_written, leaf_batch.decoded_level_count()); + } + if (batch->levels_written == 0 && batch->records_read > 0 && + values_written == batch->records_read && _descriptor->max_definition_level() == 0 && + _descriptor->max_repetition_level() == 0) { + batch->levels_written = batch->records_read; + } + if (batch->levels_written < batch->records_read || values_written < 0 || + values_written > batch->levels_written) { + return Status::Corruption( + "Invalid nested parquet read result for column {}: rows={}, levels={}, values={}", + _name, batch->records_read, batch->levels_written, values_written); + } + if (batch->levels_written == 0) { + return Status::OK(); + } + + auto* def_levels = leaf_batch.def_levels(); + if (def_levels == nullptr && _descriptor->max_definition_level() > 0) { + return Status::Corruption( + "Nested parquet reader returned null definition levels for column {}", _name); + } + batch->def_levels.resize(static_cast(batch->levels_written)); + if (_descriptor->max_definition_level() == 0 || def_levels == nullptr) { + std::fill(batch->def_levels.begin(), batch->def_levels.end(), + _descriptor->max_definition_level()); + } else { + std::copy(def_levels, def_levels + batch->levels_written, batch->def_levels.begin()); + } + + auto* rep_levels = leaf_batch.rep_levels(); + if (rep_levels == nullptr && _descriptor->max_repetition_level() > 0) { + return Status::Corruption( + "Nested parquet reader returned null repetition levels for column {}", _name); + } + batch->rep_levels.resize(static_cast(batch->levels_written)); + if (_descriptor->max_repetition_level() == 0 || rep_levels == nullptr) { + std::fill(batch->rep_levels.begin(), batch->rep_levels.end(), 0); + } else { + std::copy(rep_levels, rep_levels + batch->levels_written, batch->rep_levels.begin()); + } + + const int16_t leaf_definition_level = _descriptor->max_definition_level(); + // Arrow's RecordReader may emit value placeholders for null ancestors that are below the + // Doris materialization threshold. Those slots must still advance the payload value index; + // otherwise the next defined child level points at the placeholder instead of its real value. + auto count_value_slots = [&](int16_t slot_definition_level) { + int64_t slot_count = 0; + for (int64_t level_idx = 0; level_idx < batch->levels_written; ++level_idx) { + if (batch->def_levels[level_idx] >= slot_definition_level && + batch->rep_levels[level_idx] <= value_slot_repetition_level) { + ++slot_count; + } + } + return slot_count; + }; + + const int64_t value_slot_count = count_value_slots(value_slot_definition_level); + int16_t payload_slot_definition_level = value_slot_definition_level; + int64_t payload_value_slot_count = value_slot_count; + while (payload_slot_definition_level > 0 && payload_value_slot_count < values_written) { + --payload_slot_definition_level; + payload_value_slot_count = count_value_slots(payload_slot_definition_level); + } + + int64_t leaf_value_count = 0; + for (int64_t level_idx = 0; level_idx < batch->levels_written; ++level_idx) { + if (batch->def_levels[level_idx] < value_slot_definition_level || + batch->rep_levels[level_idx] > value_slot_repetition_level) { + continue; + } + if (batch->def_levels[level_idx] == leaf_definition_level) { + ++leaf_value_count; + } + } + + enum class ValueLayout { LEVELS, VALUE_SLOTS, LEAF_VALUES, PAYLOAD_VALUE_SLOTS }; + ValueLayout value_layout = ValueLayout::LEAF_VALUES; + if (values_written == batch->levels_written) { + value_layout = ValueLayout::LEVELS; + } else if (values_written == value_slot_count) { + value_layout = ValueLayout::VALUE_SLOTS; + } else if (values_written == leaf_value_count) { + value_layout = ValueLayout::LEAF_VALUES; + } else if (values_written == payload_value_slot_count) { + value_layout = ValueLayout::PAYLOAD_VALUE_SLOTS; + } else { + return Status::Corruption( + "Nested parquet reader returned inconsistent value count for column {}: values={}, " + "levels={}, slots={}, leaf_values={}, payload_slots={}, " + "payload_slot_definition_level={}", + _name, values_written, batch->levels_written, value_slot_count, leaf_value_count, + payload_value_slot_count, payload_slot_definition_level); + } + + batch->value_indices.resize(static_cast(batch->levels_written), -1); + NullMap value_nulls(static_cast(values_written), 1); + int64_t value_idx = 0; + const int16_t decoded_slot_definition_level = value_layout == ValueLayout::PAYLOAD_VALUE_SLOTS + ? payload_slot_definition_level + : value_slot_definition_level; + for (int64_t level_idx = 0; level_idx < batch->levels_written; ++level_idx) { + if (batch->def_levels[level_idx] < decoded_slot_definition_level || + batch->rep_levels[level_idx] > value_slot_repetition_level) { + continue; + } + const bool has_leaf_value = batch->def_levels[level_idx] == leaf_definition_level; + int64_t decoded_value_idx = -1; + if (value_layout == ValueLayout::LEVELS) { + decoded_value_idx = level_idx; + } else if (value_layout == ValueLayout::VALUE_SLOTS) { + decoded_value_idx = value_idx++; + } else if (value_layout == ValueLayout::PAYLOAD_VALUE_SLOTS) { + decoded_value_idx = value_idx++; + } else { + if (!has_leaf_value) { + continue; + } + decoded_value_idx = value_idx++; + } + DORIS_CHECK(decoded_value_idx >= 0); + DORIS_CHECK(decoded_value_idx < values_written); + if (has_leaf_value) { + batch->value_indices[static_cast(level_idx)] = decoded_value_idx; + value_nulls[static_cast(decoded_value_idx)] = 0; + } + } + if (value_layout != ValueLayout::LEVELS && value_idx != values_written) { + return Status::Corruption( + "Nested parquet reader value cursor stopped early for column {}: values={}, " + "visited={}", + _name, values_written, value_idx); + } + + const auto value_type = remove_nullable(_type); + batch->values_column = value_type->create_column(); + if (values_written > 0) { + ParquetLeafReader value_reader(_descriptor, _type_descriptor, value_type, _name, + _record_reader, _profile, _timezone, _enable_strict_mode); + RETURN_IF_ERROR(value_reader.append_values(leaf_batch, values_written, &value_nulls, + batch->values_column)); + } + return Status::OK(); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.h b/be/src/format_v2/parquet/reader/parquet_leaf_reader.h new file mode 100644 index 00000000000000..645241cf4af1be --- /dev/null +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.h @@ -0,0 +1,134 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/column/column.h" +#include "core/column/column_nullable.h" +#include "format_v2/parquet/parquet_profile.h" +#include "format_v2/parquet/parquet_type.h" + +namespace parquet { +class ColumnDescriptor; + +namespace internal { +class RecordReader; +} // namespace internal +} // namespace parquet + +namespace cctz { +class time_zone; +} // namespace cctz + +namespace arrow { +class Array; +} // namespace arrow + +namespace doris::parquet { + +// Nested scalar leaf state is intentionally split into shape and value. +// +// Shape is the Dremel row/level stream plus row/value membership. Value is the primitive payload +// attached to level slots that actually contain a value. Complex readers build Doris ColumnStruct, +// ColumnArray, and ColumnMap from shape first, then append values into that layout. +struct ParquetNestedScalarBatch { + int64_t records_read = 0; + int64_t levels_written = 0; + int16_t value_slot_definition_level = 0; + int16_t value_slot_repetition_level = std::numeric_limits::max(); + std::vector def_levels; + std::vector rep_levels; + std::vector value_indices; + MutableColumnPtr values_column; + + bool empty() const { return levels_written == 0; } +}; + +// Normalized view of one already-read Arrow RecordReader batch. +// +// The Arrow reader exposes fixed-width values through RecordReader::values(), but binary values +// through BinaryRecordReader::GetBuilderChunks(). Keeping that distinction here prevents nested +// Dremel layout code from depending on Arrow's physical storage and one-shot transfer semantics. +class ParquetLeafBatch { +public: + int64_t consumed_level_count() const { return _consumed_level_count; } + int64_t decoded_level_count() const { return _decoded_level_count; } + int64_t values_written() const { return _values_written; } + bool read_dense_for_nullable() const { return _read_dense_for_nullable; } + const int16_t* def_levels() const { return _def_levels; } + const int16_t* rep_levels() const { return _rep_levels; } + +private: + friend class ParquetLeafReader; + + bool is_binary_value() const; + + DecodedValueKind _value_kind = DecodedValueKind::INT32; + int64_t _consumed_level_count = 0; + int64_t _decoded_level_count = 0; + int64_t _values_written = 0; + const int16_t* _def_levels = nullptr; + const int16_t* _rep_levels = nullptr; + const uint8_t* _fixed_values = nullptr; + bool _read_dense_for_nullable = false; + std::vector> _binary_chunks; +}; + +class ParquetLeafReader { +public: + ParquetLeafReader(const ::parquet::ColumnDescriptor* descriptor, + ParquetTypeDescriptor type_descriptor, DataTypePtr type, std::string name, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + ParquetColumnReaderProfile profile = {}, + const cctz::time_zone* timezone = nullptr, bool enable_strict_mode = false); + + Status read_batch(int64_t batch_rows, ParquetLeafBatch* batch, int64_t* rows_read) const; + Status build_null_map(const ParquetLeafBatch& batch, int64_t records_read, + NullMap* null_map) const; + Status append_values(const ParquetLeafBatch& batch, int64_t row_count, const NullMap* null_map, + MutableColumnPtr& column) const; + + Status read_nested_batch( + int64_t batch_rows, int16_t value_slot_definition_level, + ParquetNestedScalarBatch* batch, + int16_t value_slot_repetition_level = std::numeric_limits::max()) const; + +private: + Status collect_batch(::parquet::internal::RecordReader& record_reader, + ParquetLeafBatch* batch) const; + Status build_spaced_fixed_values(const ParquetLeafBatch& batch, int64_t row_count, + const NullMap* null_map, + std::vector* spaced_values) const; + + const ::parquet::ColumnDescriptor* _descriptor = nullptr; + ParquetTypeDescriptor _type_descriptor; + DataTypePtr _type; + std::string _name; + std::shared_ptr<::parquet::internal::RecordReader> _record_reader; + ParquetColumnReaderProfile _profile; + const cctz::time_zone* _timezone = nullptr; + bool _enable_strict_mode = false; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/row_position_column_reader.cpp b/be/src/format_v2/parquet/reader/row_position_column_reader.cpp new file mode 100644 index 00000000000000..01307a0d1acc8e --- /dev/null +++ b/be/src/format_v2/parquet/reader/row_position_column_reader.cpp @@ -0,0 +1,78 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/row_position_column_reader.h" + +#include + +#include "core/assert_cast.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_number.h" +#include "format_v2/parquet/parquet_column_schema.h" + +namespace doris::parquet { + +RowPositionColumnReader::RowPositionColumnReader(int64_t row_group_first_row, + ParquetColumnReaderProfile profile) + : ParquetColumnReader(ParquetColumnSchema {.name = format::ROW_POSITION_COLUMN_NAME}, + std::make_shared(), profile), + _row_group_first_row(row_group_first_row) {} + +int RowPositionColumnReader::file_column_id() const { + return format::ROW_POSITION_COLUMN_ID; +} + +int RowPositionColumnReader::parquet_leaf_column_id() const { + return -1; +} + +const DataTypePtr& RowPositionColumnReader::type() const { + return _type; +} + +const std::string& RowPositionColumnReader::name() const { + return _name; +} + +Status RowPositionColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { + if (column.get() == nullptr || rows_read == nullptr) { + return Status::InvalidArgument("Invalid parquet row position read result pointer"); + } + if (rows < 0) { + return Status::InvalidArgument("Invalid parquet row position read rows {}", rows); + } + auto* vector_column = assert_cast(column.get()); + auto& data = vector_column->get_data(); + const auto old_size = data.size(); + data.resize(old_size + rows); + for (int64_t row = 0; row < rows; ++row) { + data[old_size + row] = _row_group_first_row + _next_row_position + row; + } + _next_row_position += rows; + *rows_read = rows; + return Status::OK(); +} + +Status RowPositionColumnReader::skip(int64_t rows) { + if (rows <= 0) { + return Status::OK(); + } + _next_row_position += rows; + return Status::OK(); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/row_position_column_reader.h b/be/src/format_v2/parquet/reader/row_position_column_reader.h new file mode 100644 index 00000000000000..54b468aa8eab4e --- /dev/null +++ b/be/src/format_v2/parquet/reader/row_position_column_reader.h @@ -0,0 +1,45 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include + +#include "format_v2/parquet/reader/column_reader.h" + +namespace doris::parquet { + +class RowPositionColumnReader final : public ParquetColumnReader { +public: + explicit RowPositionColumnReader(int64_t row_group_first_row, + ParquetColumnReaderProfile profile = {}); + + int file_column_id() const override; + int parquet_leaf_column_id() const override; + const DataTypePtr& type() const override; + const std::string& name() const override; + + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; + Status skip(int64_t rows) override; + +private: + int64_t _row_group_first_row = 0; + int64_t _next_row_position = 0; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.cpp b/be/src/format_v2/parquet/reader/scalar_column_reader.cpp new file mode 100644 index 00000000000000..5a6f0a9d547d9e --- /dev/null +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.cpp @@ -0,0 +1,309 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/scalar_column_reader.h" + +#include + +#include +#include +#include + +#include "core/column/column.h" +#include "core/column/column_nullable.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "util/simd/bits.h" + +namespace doris::parquet { +namespace { + +class ParquetNestedScalarValueCursor { +public: + explicit ParquetNestedScalarValueCursor(const ParquetNestedScalarBatch* batch) { reset(batch); } + + void reset(const ParquetNestedScalarBatch* batch) { + DORIS_CHECK(batch != nullptr); + _batch = batch; + } + + Status value_index(const std::string& column_name, int64_t level_idx, int64_t* value_idx) { + DORIS_CHECK(_batch != nullptr); + DORIS_CHECK(value_idx != nullptr); + DORIS_CHECK(level_idx < _batch->levels_written); + DORIS_CHECK(level_idx >= 0); + DORIS_CHECK(static_cast(level_idx) < _batch->value_indices.size()); + const int64_t computed_value_idx = _batch->value_indices[static_cast(level_idx)]; + if (computed_value_idx < 0) { + return Status::Corruption("Nested parquet value is absent for column {}", column_name); + } + DORIS_CHECK(_batch->values_column.get() != nullptr); + if (computed_value_idx >= _batch->values_column->size()) { + return Status::Corruption("Nested parquet value index is out of range for column {}", + column_name); + } + *value_idx = computed_value_idx; + return Status::OK(); + } + +private: + const ParquetNestedScalarBatch* _batch = nullptr; +}; + +Status append_scalar_batch_value(const ScalarColumnReader& column_reader, + const ParquetNestedScalarBatch& batch, int64_t level_idx, + ParquetNestedScalarValueCursor* value_cursor, + MutableColumnPtr& column) { + DORIS_CHECK(value_cursor != nullptr); + int64_t value_idx = -1; + RETURN_IF_ERROR(value_cursor->value_index(column_reader.name(), level_idx, &value_idx)); + auto* nullable_column = check_and_get_column(*column); + if (nullable_column != nullptr) { + nullable_column->get_nested_column().insert_from(*batch.values_column, + static_cast(value_idx)); + nullable_column->get_null_map_data().push_back(0); + return Status::OK(); + } + column->insert_from(*batch.values_column, static_cast(value_idx)); + return Status::OK(); +} + +} // namespace + +ScalarColumnReader::ScalarColumnReader( + const ParquetColumnSchema& column_schema, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + const ParquetPageSkipPlan* page_skip_plan, const cctz::time_zone* timezone, + bool enable_strict_mode, ParquetColumnReaderProfile profile) + : ParquetColumnReader(column_schema, column_schema.type, profile), + _descriptor(column_schema.descriptor), + _type_descriptor(column_schema.type_descriptor), + _record_reader(std::move(record_reader)), + _page_skip_plan(page_skip_plan), + _timezone(timezone), + _enable_strict_mode(enable_strict_mode), + _nested_batch(std::make_unique()) {} + +ScalarColumnReader::~ScalarColumnReader() = default; + +Status ScalarColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { + if (column.get() == nullptr || rows_read == nullptr) { + return Status::InvalidArgument("Invalid parquet column read result pointer for column {}", + _name); + } + if (_record_reader == nullptr) { + return Status::InternalError("Parquet record reader is not initialized for column {}", + _name); + } + auto reader = leaf_reader(); + ParquetLeafBatch leaf_batch; + RETURN_IF_ERROR(reader.read_batch(rows, &leaf_batch, rows_read)); + + NullMap null_map; + RETURN_IF_ERROR(reader.build_null_map(leaf_batch, *rows_read, &null_map)); + const auto value_kind = decoded_value_kind(_type_descriptor); + const bool is_binary_value = + value_kind == DecodedValueKind::BINARY || value_kind == DecodedValueKind::FIXED_BINARY; + if (!is_binary_value && leaf_batch.read_dense_for_nullable() && !null_map.empty()) { + const int64_t non_null_count = static_cast(simd::count_zero_num( + reinterpret_cast(null_map.data()), null_map.size())); + const int64_t null_count = *rows_read - non_null_count; + if (leaf_batch.values_written() != non_null_count) { + return Status::Corruption( + "Invalid dense nullable parquet record read result for column {}: values={}, " + "records={}, nulls={}", + _name, leaf_batch.values_written(), *rows_read, null_count); + } + } else if (!is_binary_value && !leaf_batch.read_dense_for_nullable() && + leaf_batch.values_written() != *rows_read) { + return Status::Corruption( + "Invalid parquet record read result for column {}: values={}, records={}", _name, + leaf_batch.values_written(), *rows_read); + } + + RETURN_IF_ERROR(reader.append_values(leaf_batch, *rows_read, &null_map, column)); + advance_rows_read(*rows_read); + update_reader_read_rows(*rows_read); + return Status::OK(); +} + +Status ScalarColumnReader::skip_records(int64_t rows) { + if (_record_reader == nullptr) { + return Status::InternalError("Parquet record reader is not initialized for column {}", + _name); + } + if (rows <= 0) { + return Status::OK(); + } + int64_t skipped_rows = 0; + try { + _record_reader->Reset(); + while (skipped_rows < rows) { + const int64_t skipped = _record_reader->SkipRecords(rows - skipped_rows); + if (skipped <= 0) { + return Status::Corruption( + "Failed to skip parquet records for column {}: skipped {} of {} rows", + _name, skipped_rows, rows); + } + skipped_rows += skipped; + } + } catch (const ::parquet::ParquetException& e) { + return Status::Corruption("Failed to skip parquet records for column {}: {}", _name, + e.what()); + } catch (const std::exception& e) { + return Status::InternalError("Failed to skip parquet records for column {}: {}", _name, + e.what()); + } + update_reader_skip_rows(rows); + return Status::OK(); +} + +int64_t ScalarColumnReader::page_filtered_rows_to_skip(int64_t rows) const { + if (_page_skip_plan == nullptr || rows <= 0) { + return 0; + } + const int64_t skip_end = _row_group_rows_read + rows; + int64_t filtered_rows = 0; + for (const auto& range : _page_skip_plan->skipped_ranges) { + const int64_t range_end = range.start + range.length; + if (range_end <= _row_group_rows_read) { + continue; + } + if (range.start >= skip_end) { + break; + } + const int64_t start = std::max(range.start, _row_group_rows_read); + const int64_t end = std::min(range_end, skip_end); + if (start < end) { + // Scheduler gap skips are derived from page-index selected_ranges. A page-filtered + // range can only overlap such a gap when the whole data page is outside every selected + // range, so partial overlap would mean the planner and scheduler are out of sync. + DORIS_CHECK(start == range.start); + DORIS_CHECK(end == range_end); + filtered_rows += end - start; + } + } + return filtered_rows; +} + +void ScalarColumnReader::advance_rows_read(int64_t rows) { + DORIS_CHECK(rows >= 0); + _row_group_rows_read += rows; +} + +Status ScalarColumnReader::skip(int64_t rows) { + if (rows <= 0) { + return Status::OK(); + } + + const int64_t page_filtered_rows = page_filtered_rows_to_skip(rows); + DORIS_CHECK(page_filtered_rows <= rows); + const int64_t record_reader_skip_rows = rows - page_filtered_rows; + RETURN_IF_ERROR(skip_records(record_reader_skip_rows)); + advance_rows_read(rows); + return Status::OK(); +} + +Status ScalarColumnReader::load_nested_batch(int64_t rows) { + DORIS_CHECK(_nested_batch != nullptr); + reset_nested_build_level_cursor(); + // Nullable scalar leaves materialize one slot for both non-null values and null placeholders. + // The value index stream must advance on those null slots, otherwise later payload values shift. + const int16_t materialized_slot_definition_level = + static_cast(_definition_level - (_type->is_nullable() ? 1 : 0)); + RETURN_IF_ERROR(leaf_reader().read_nested_batch(rows, materialized_slot_definition_level, + _nested_batch.get(), _repetition_level)); + advance_rows_read(_nested_batch->records_read); + update_reader_read_rows(_nested_batch->records_read); + return Status::OK(); +} + +Status ScalarColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) { + if (column.get() == nullptr || values_read == nullptr) { + return Status::InvalidArgument("Invalid parquet nested scalar build result for column {}", + _name); + } + DORIS_CHECK(_nested_batch != nullptr); + ParquetNestedScalarValueCursor value_cursor(_nested_batch.get()); + const int16_t materialized_slot_definition_level = _nested_batch->value_slot_definition_level; + *values_read = 0; + int64_t level_idx = nested_build_level_cursor(); + while (level_idx < _nested_batch->levels_written && *values_read < length_upper_bound) { + const int64_t current_level_idx = level_idx; + const int16_t def_level = _nested_batch->def_levels[current_level_idx]; + const int16_t rep_level = _nested_batch->rep_levels[current_level_idx]; + ++level_idx; + if (def_level < materialized_slot_definition_level || rep_level > _repetition_level) { + continue; + } + if (def_level == _definition_level) { + RETURN_IF_ERROR(append_scalar_batch_value(*this, *_nested_batch, current_level_idx, + &value_cursor, column)); + } else { + if (!_type->is_nullable() && def_level >= _nullable_definition_level) { + return Status::Corruption( + "Parquet scalar column {} contains null for non-nullable field", _name); + } + column->insert_default(); + } + ++*values_read; + } + set_nested_build_level_cursor(level_idx); + return Status::OK(); +} + +Status ScalarColumnReader::append_nested_value(int64_t level_idx, MutableColumnPtr& column) const { + if (column.get() == nullptr) { + return Status::InvalidArgument("Invalid parquet nested scalar append result for column {}", + _name); + } + DORIS_CHECK(_nested_batch != nullptr); + DORIS_CHECK(level_idx >= 0); + DORIS_CHECK(level_idx < _nested_batch->levels_written); + ParquetNestedScalarValueCursor value_cursor(_nested_batch.get()); + const int16_t def_level = _nested_batch->def_levels[level_idx]; + if (def_level == _definition_level) { + return append_scalar_batch_value(*this, *_nested_batch, level_idx, &value_cursor, column); + } + if (!_type->is_nullable()) { + return Status::Corruption("Parquet MAP column {} contains null for non-nullable value", + _name); + } + column->insert_default(); + return Status::OK(); +} + +const std::vector& ScalarColumnReader::nested_definition_levels() const { + DORIS_CHECK(_nested_batch != nullptr); + return _nested_batch->def_levels; +} + +const std::vector& ScalarColumnReader::nested_repetition_levels() const { + DORIS_CHECK(_nested_batch != nullptr); + return _nested_batch->rep_levels; +} + +int64_t ScalarColumnReader::nested_levels_written() const { + DORIS_CHECK(_nested_batch != nullptr); + return _nested_batch->levels_written; +} + +bool ScalarColumnReader::is_or_has_repeated_child() const { + return _repetition_level > 0; +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.h b/be/src/format_v2/parquet/reader/scalar_column_reader.h new file mode 100644 index 00000000000000..ddc8acbde492e1 --- /dev/null +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.h @@ -0,0 +1,82 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include + +#include "format_v2/parquet/parquet_type.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "format_v2/parquet/reader/parquet_leaf_reader.h" + +namespace parquet { +class ColumnDescriptor; + +namespace internal { +class RecordReader; +} // namespace internal +} // namespace parquet + +namespace cctz { +class time_zone; +} // namespace cctz + +namespace doris::parquet { + +class ScalarColumnReader final : public ParquetColumnReader { +public: + ScalarColumnReader(const ParquetColumnSchema& column_schema, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + const ParquetPageSkipPlan* page_skip_plan = nullptr, + const cctz::time_zone* timezone = nullptr, bool enable_strict_mode = false, + ParquetColumnReaderProfile profile = {}); + ~ScalarColumnReader() override; + + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; + Status skip(int64_t rows) override; + Status load_nested_batch(int64_t rows) override; + Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) override; + Status append_nested_value(int64_t level_idx, MutableColumnPtr& column) const; + const std::vector& nested_definition_levels() const override; + const std::vector& nested_repetition_levels() const override; + int64_t nested_levels_written() const override; + bool is_or_has_repeated_child() const override; + + const ::parquet::ColumnDescriptor* descriptor() const { return _descriptor; } + ParquetLeafReader leaf_reader() const { + return ParquetLeafReader(_descriptor, _type_descriptor, _type, _name, _record_reader, + _profile, _timezone, _enable_strict_mode); + } + void advance_rows_read(int64_t rows); + +private: + Status skip_records(int64_t rows); + int64_t page_filtered_rows_to_skip(int64_t rows) const; + + const ::parquet::ColumnDescriptor* _descriptor = nullptr; + ParquetTypeDescriptor _type_descriptor; + std::shared_ptr<::parquet::internal::RecordReader> _record_reader; + const ParquetPageSkipPlan* _page_skip_plan = nullptr; + const cctz::time_zone* _timezone = nullptr; + bool _enable_strict_mode = false; + int64_t _row_group_rows_read = 0; + std::unique_ptr _nested_batch; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.cpp b/be/src/format_v2/parquet/reader/struct_column_reader.cpp new file mode 100644 index 00000000000000..e388b8a103f515 --- /dev/null +++ b/be/src/format_v2/parquet/reader/struct_column_reader.cpp @@ -0,0 +1,228 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/reader/struct_column_reader.h" + +#include + +#include +#include +#include +#include + +#include "core/column/column_struct.h" +#include "format_v2/parquet/reader/nested_column_materializer.h" +#include "format_v2/parquet/reader/scalar_column_reader.h" + +namespace doris::parquet { +namespace { + +ParquetColumnReader* struct_shape_source_reader(const StructColumnReader& reader) { + for (size_t child_idx = 0; child_idx < reader.child_count(); ++child_idx) { + auto* child_reader = reader.child_reader(child_idx); + DORIS_CHECK(child_reader != nullptr); + if (!child_reader->is_or_has_repeated_child()) { + return child_reader; + } + } + if (reader.child_count() == 0) { + return nullptr; + } + return reader.child_reader(0); +} + +} // namespace + +Status StructColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { + RETURN_IF_ERROR(load_nested_batch(rows)); + return build_nested_column(rows, column, rows_read); +} + +Status StructColumnReader::skip(int64_t rows) { + if (rows <= 0) { + return Status::OK(); + } + auto scratch_column = _type->create_column(); + RETURN_IF_ERROR(load_nested_batch(rows)); + int64_t rows_read = 0; + RETURN_IF_ERROR(build_nested_column(rows, scratch_column, &rows_read)); + if (rows_read != rows) { + return Status::Corruption("Failed to skip parquet STRUCT column {}: skipped {} of {} rows", + _name, rows_read, rows); + } + update_reader_skip_rows(rows); + return Status::OK(); +} + +Status StructColumnReader::load_nested_batch(int64_t rows) { + reset_nested_build_level_cursor(); + for (auto& child_reader : _children) { + DORIS_CHECK(child_reader != nullptr); + RETURN_IF_ERROR(child_reader->load_nested_batch(rows)); + } + return Status::OK(); +} + +Status StructColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) { + if (column.get() == nullptr || values_read == nullptr) { + return Status::InvalidArgument("Invalid parquet struct build result pointer for column {}", + _name); + } + if (_children.empty()) { + column->resize(column->size() + static_cast(length_upper_bound)); + *values_read = length_upper_bound; + return Status::OK(); + } + auto* struct_column = struct_column_from_output(column); + DORIS_CHECK(struct_column != nullptr); + auto* parent_null_map = null_map_from_nullable_output(column); + auto* shape_reader = struct_shape_source_reader(*this); + DORIS_CHECK(shape_reader != nullptr); + const auto& def_levels = shape_reader->nested_definition_levels(); + const auto& rep_levels = shape_reader->nested_repetition_levels(); + const int64_t levels_written = shape_reader->nested_levels_written(); + + NullMap parent_nulls; + std::vector parent_level_indices; + *values_read = 0; + int64_t level_idx = nested_build_level_cursor(); + while (level_idx < levels_written) { + const int64_t current_level_idx = level_idx; + const int16_t def_level = def_levels[level_idx]; + const int16_t rep_level = rep_levels[level_idx]; + const bool starts_parent = + !shape_reader->is_or_has_repeated_child() || rep_level <= _repetition_level; + if (starts_parent && *values_read >= length_upper_bound) { + break; + } + ++level_idx; + if (def_level < _repeated_ancestor_definition_level) { + continue; + } + if (shape_reader->is_or_has_repeated_child() && rep_level > _repetition_level) { + continue; + } + const bool parent_is_null = def_level < _nullable_definition_level; + if (parent_is_null && parent_null_map == nullptr) { + return Status::Corruption( + "Parquet STRUCT column {} contains null for non-nullable struct", _name); + } + parent_nulls.push_back(parent_is_null); + parent_level_indices.push_back(current_level_idx); + ++*values_read; + } + set_nested_build_level_cursor(level_idx); + + std::vector child_columns; + child_columns.reserve(struct_column->get_columns().size()); + for (size_t child_idx = 0; child_idx < struct_column->get_columns().size(); ++child_idx) { + child_columns.push_back(struct_column->get_column_ptr(child_idx)->assert_mutable()); + } + for (size_t child_idx = 0; child_idx < _children.size(); ++child_idx) { + const int output_idx = _child_output_indices[child_idx]; + if (output_idx < 0) { + continue; + } + // STRUCT owns row alignment. Child readers consume only present parent rows from their + // level streams; null STRUCT parents become default placeholders in every child column. + // This mirrors Arrow's separation between struct validity and child array materialization, + // and avoids asking scalar/list/map children to invent values for an absent parent. + int64_t pending_present_rows = 0; + int64_t total_child_rows = 0; + auto flush_present_rows = [&]() -> Status { + if (pending_present_rows == 0) { + return Status::OK(); + } + int64_t child_rows = 0; + RETURN_IF_ERROR(_children[child_idx]->build_nested_column( + pending_present_rows, child_columns[output_idx], &child_rows)); + if (child_rows != pending_present_rows) { + return Status::Corruption( + "Parquet STRUCT child {} built {} rows, expected {} for column {}", + _children[child_idx]->name(), child_rows, pending_present_rows, _name); + } + total_child_rows += child_rows; + pending_present_rows = 0; + return Status::OK(); + }; + for (size_t parent_idx = 0; parent_idx < parent_nulls.size(); ++parent_idx) { + const auto parent_is_null = parent_nulls[parent_idx]; + if (!parent_is_null) { + ++pending_present_rows; + continue; + } + RETURN_IF_ERROR(flush_present_rows()); + child_columns[output_idx]->insert_default(); + if (auto* scalar_child = + dynamic_cast(_children[child_idx].get())) { + // Scalar struct children have one level slot for a null struct parent, but that + // slot is below the scalar value threshold. Advance past the exact parent level, + // not just one slot from the current child cursor: the cursor may be parked before + // ancestor LIST/MAP null/empty shape slots after a previous present-row flush. + const int64_t child_cursor = scalar_child->nested_build_level_cursor(); + const int64_t next_child_cursor = parent_level_indices[parent_idx] + 1; + if (next_child_cursor > scalar_child->nested_levels_written()) { + return Status::Corruption( + "Parquet STRUCT child {} ended before null parent row in column {}", + scalar_child->name(), _name); + } + scalar_child->set_nested_build_level_cursor( + std::max(child_cursor, next_child_cursor)); + } else { + RETURN_IF_ERROR(_children[child_idx]->skip_nested_column(1)); + } + ++total_child_rows; + } + RETURN_IF_ERROR(flush_present_rows()); + if (total_child_rows != *values_read) { + return Status::Corruption( + "Parquet STRUCT child {} built {} rows, expected {} for column {}", + _children[child_idx]->name(), total_child_rows, *values_read, _name); + } + } + for (size_t child_idx = 0; child_idx < child_columns.size(); ++child_idx) { + struct_column->get_column_ptr(child_idx) = std::move(child_columns[child_idx]); + } + append_parent_nulls(parent_null_map, parent_nulls); + return Status::OK(); +} + +const std::vector& StructColumnReader::nested_definition_levels() const { + auto* shape_reader = struct_shape_source_reader(*this); + DORIS_CHECK(shape_reader != nullptr); + return shape_reader->nested_definition_levels(); +} + +const std::vector& StructColumnReader::nested_repetition_levels() const { + auto* shape_reader = struct_shape_source_reader(*this); + DORIS_CHECK(shape_reader != nullptr); + return shape_reader->nested_repetition_levels(); +} + +int64_t StructColumnReader::nested_levels_written() const { + auto* shape_reader = struct_shape_source_reader(*this); + DORIS_CHECK(shape_reader != nullptr); + return shape_reader->nested_levels_written(); +} + +bool StructColumnReader::is_or_has_repeated_child() const { + auto* shape_reader = struct_shape_source_reader(*this); + return shape_reader != nullptr && shape_reader->is_or_has_repeated_child(); +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.h b/be/src/format_v2/parquet/reader/struct_column_reader.h new file mode 100644 index 00000000000000..eed09a5ad35956 --- /dev/null +++ b/be/src/format_v2/parquet/reader/struct_column_reader.h @@ -0,0 +1,62 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include + +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/reader/column_reader.h" + +namespace doris::parquet { + +class StructColumnReader final : public ParquetColumnReader { +public: + StructColumnReader(const ParquetColumnSchema& schema, DataTypePtr type, + std::vector> children, + std::vector child_output_indices, + ParquetColumnReaderProfile profile = {}) + : ParquetColumnReader(schema, type, profile), + _children(std::move(children)), + _child_output_indices(std::move(child_output_indices)) { + DCHECK_EQ(_children.size(), _child_output_indices.size()); + } + + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; + Status skip(int64_t rows) override; + Status load_nested_batch(int64_t rows) override; + Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, + int64_t* values_read) override; + const std::vector& nested_definition_levels() const override; + const std::vector& nested_repetition_levels() const override; + int64_t nested_levels_written() const override; + bool is_or_has_repeated_child() const override; + + size_t child_count() const { return _children.size(); } + ParquetColumnReader* child_reader(size_t child_idx) const { return _children[child_idx].get(); } + int child_output_index(size_t child_idx) const { return _child_output_indices[child_idx]; } + +private: + std::vector> _children; + std::vector _child_output_indices; +}; + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/selection_vector.h b/be/src/format_v2/parquet/selection_vector.h new file mode 100644 index 00000000000000..63e50951d07d5e --- /dev/null +++ b/be/src/format_v2/parquet/selection_vector.h @@ -0,0 +1,167 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/check.h" +#include "common/status.h" + +namespace doris::parquet { + +struct RowRange { + int64_t start = 0; + int64_t length = 0; +}; + +struct ParquetPageSkipPlan { + int leaf_column_id = -1; + // Page ordinal is the data-page ordinal in the column chunk. It intentionally excludes + // dictionary pages, matching Arrow PageReader::set_data_page_filter(). + std::vector skipped_pages; + std::vector skipped_page_compressed_sizes; + // Row ranges covered by skipped data pages. ScalarColumnReader uses these ranges to avoid + // calling RecordReader::SkipRecords() again for pages already skipped by Arrow. + std::vector skipped_ranges; + + bool empty() const { return skipped_ranges.empty(); } + + bool should_skip_page(size_t page_idx) const { + return page_idx < skipped_pages.size() && skipped_pages[page_idx] != 0; + } + + int64_t skipped_page_compressed_size(size_t page_idx) const { + DCHECK_LT(page_idx, skipped_page_compressed_sizes.size()); + return skipped_page_compressed_sizes[page_idx]; + } +}; + +// 类似 DuckDB SelectionVector 的轻量行号视图。 +// 它只表达一个 batch 内被选中的 row offset，不持有 table/global schema 语义。 +// 未绑定 data 时表示 identity selection：get_index(i) == i。 +class SelectionVector { +public: + using Index = uint16_t; + + SelectionVector() = default; + + explicit SelectionVector(size_t count) { resize(count); } + + SelectionVector(Index* data, size_t count) { initialize(data, count); } + + void initialize(Index* data, size_t count) { + _owned.clear(); + _data = data; + _size = count; + } + + void resize(size_t count) { + _owned.resize(count); + _data = _owned.data(); + _size = count; + for (size_t idx = 0; idx < count; ++idx) { + _data[idx] = static_cast(idx); + } + } + + void clear() { + _owned.clear(); + _data = nullptr; + _size = 0; + } + + size_t size() const { return _size; } + + bool is_set() const { return _data != nullptr; } + + Index* data() { return _data; } + + const Index* data() const { return _data; } + + size_t get_index(size_t idx) const { + if (_data == nullptr) { + return idx; + } + return _data[idx]; + } + + void set_index(size_t idx, Index value) { _data[idx] = value; } + + Status verify(size_t count, int64_t batch_rows) const { + if (batch_rows < 0) { + return Status::InvalidArgument("Negative parquet selection batch rows {}", batch_rows); + } + if (count > static_cast(batch_rows)) { + return Status::InvalidArgument("Parquet selection count {} exceeds batch rows {}", + count, batch_rows); + } + if (_data != nullptr && count > _size) { + return Status::InvalidArgument("Parquet selection count {} exceeds vector size {}", + count, _size); + } + size_t previous = 0; + for (size_t idx = 0; idx < count; ++idx) { + const size_t current = get_index(idx); + if (current >= static_cast(batch_rows)) { + return Status::InvalidArgument( + "Parquet selection index {} out of range [0, {}) at position {}", current, + batch_rows, idx); + } + if (idx > 0 && current <= previous) { + return Status::InvalidArgument( + "Parquet selection index {} is not strictly greater than previous {} at " + "position {}", + current, previous, idx); + } + previous = current; + } + return Status::OK(); + } + +private: + std::vector _owned; + Index* _data = nullptr; + size_t _size = 0; +}; + +inline std::vector selection_to_ranges(const SelectionVector& selection, + uint16_t selected_rows) { + std::vector ranges; + if (selected_rows == 0) { + return ranges; + } + + int64_t range_start = selection.get_index(0); + int64_t previous = selection.get_index(0); + for (uint16_t selection_idx = 1; selection_idx < selected_rows; ++selection_idx) { + const int64_t current = selection.get_index(selection_idx); + if (current == previous + 1) { + previous = current; + continue; + } + ranges.push_back(RowRange {range_start, previous - range_start + 1}); + range_start = current; + previous = current; + } + ranges.push_back(RowRange {range_start, previous - range_start + 1}); + return ranges; +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/schema_projection.cpp b/be/src/format_v2/schema_projection.cpp new file mode 100644 index 00000000000000..7e1e2ef2ae4f0c --- /dev/null +++ b/be/src/format_v2/schema_projection.cpp @@ -0,0 +1,127 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/schema_projection.h" + +#include +#include +#include + +#include "core/assert_cast.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" + +namespace doris::format { + +Status rebuild_projected_type(const DataTypePtr& original_type, + const std::vector& child_types, + const std::vector& child_names, + DataTypePtr* projected_type) { + DORIS_CHECK(original_type != nullptr); + DORIS_CHECK(projected_type != nullptr); + DORIS_CHECK(child_types.size() == child_names.size()); + + DataTypePtr nested_projected_type; + const auto primitive_type = remove_nullable(original_type)->get_primitive_type(); + switch (primitive_type) { + case TYPE_STRUCT: + nested_projected_type = std::make_shared(child_types, child_names); + break; + case TYPE_ARRAY: + DORIS_CHECK(child_types.size() == 1); + nested_projected_type = std::make_shared(child_types[0]); + break; + case TYPE_MAP: { + // TODO: ? + DORIS_CHECK(child_types.size() == 1); + DORIS_CHECK(remove_nullable(child_types[0])->get_primitive_type() == TYPE_STRUCT); + DORIS_CHECK(remove_nullable(original_type)->get_primitive_type() == TYPE_MAP); + const auto* entry_type = + assert_cast(remove_nullable(child_types[0]).get()); + DORIS_CHECK(entry_type->get_elements().size() == 1 || + entry_type->get_elements().size() == 2); + const auto value_idx = entry_type->get_elements().size() == 1 ? 0 : 1; + nested_projected_type = std::make_shared( + assert_cast(remove_nullable(original_type).get()) + ->get_key_type(), + entry_type->get_element(value_idx)); + break; + } + default: + return Status::InvalidArgument("Cannot project children from non-complex type {}", + original_type->get_name()); + } + + *projected_type = original_type->is_nullable() ? make_nullable(nested_projected_type) + : nested_projected_type; + return Status::OK(); +} + +Status project_column_definition(const ColumnDefinition& field, const LocalColumnIndex& projection, + ColumnDefinition* projected_field) { + if (projected_field == nullptr) { + return Status::InvalidArgument("projected_field is null"); + } + *projected_field = field; + if (projection.project_all_children || projection.children.empty()) { + return Status::OK(); + } + + projected_field->children.clear(); + for (const auto& child_projection : projection.children) { + if (child_projection.local_id() == -1) { + return Status::InvalidArgument("Empty projection path for field {}", field.name); + } + const auto child_it = + std::ranges::find_if(field.children, [&](const ColumnDefinition& child) { + return child.file_local_id() == child_projection.local_id(); + }); + if (child_it == field.children.end()) { + return Status::InvalidArgument("Invalid projection child id {} for field {}", + child_projection.local_id(), field.name); + } + } + for (const auto& child : field.children) { + const auto child_projection_it = + std::ranges::find_if(projection.children, [&](const LocalColumnIndex& child_proj) { + return child_proj.local_id() == child.file_local_id(); + }); + if (child_projection_it == projection.children.end()) { + continue; + } + ColumnDefinition projected_child; + RETURN_IF_ERROR(project_column_definition(child, *child_projection_it, &projected_child)); + projected_field->children.push_back(std::move(projected_child)); + } + if (projected_field->children.empty()) { + return Status::NotSupported("Projection for field {} contains no children", field.name); + } + + DataTypes child_types; + Strings child_names; + child_types.reserve(projected_field->children.size()); + child_names.reserve(projected_field->children.size()); + for (const auto& child : projected_field->children) { + child_types.push_back(child.type); + child_names.push_back(child.name); + } + return rebuild_projected_type(field.type, child_types, child_names, &projected_field->type); +} + +} // namespace doris::format diff --git a/be/src/format_v2/schema_projection.h b/be/src/format_v2/schema_projection.h new file mode 100644 index 00000000000000..56f7bf35194590 --- /dev/null +++ b/be/src/format_v2/schema_projection.h @@ -0,0 +1,43 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "common/status.h" +#include "core/data_type/data_type.h" +#include "format_v2/file_reader.h" + +namespace doris::format { + +Status rebuild_projected_type(const DataTypePtr& original_type, + const std::vector& child_types, + const std::vector& child_names, + DataTypePtr* projected_type); + +// Build the file-local column definition after applying a LocalColumnIndex projection. +// +// The projection is matched by ColumnDefinition::file_local_id(), not by vector ordinal. This keeps +// nested schema evolution semantics in the common helper and lets callers use the same projection +// tree for type rebuilding and file block layout. +Status project_column_definition(const ColumnDefinition& field, const LocalColumnIndex& projection, + ColumnDefinition* projected_field); + +} // namespace doris::format diff --git a/be/src/format_v2/table/hive_reader.cpp b/be/src/format_v2/table/hive_reader.cpp new file mode 100644 index 00000000000000..cfc90217542b6a --- /dev/null +++ b/be/src/format_v2/table/hive_reader.cpp @@ -0,0 +1,143 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/table/hive_reader.h" + +#include + +#include "common/consts.h" +#include "format_v2/column_mapper.h" +#include "format_v2/file_reader.h" +#include "runtime/runtime_state.h" + +namespace doris::hive { +namespace { + +TFileFormatType::type format_type_from_context(const format::ProjectedColumnBuildContext& context) { + DORIS_CHECK(context.scan_params != nullptr); + if (context.range != nullptr && context.range->__isset.format_type) { + return context.range->format_type; + } + return context.scan_params->format_type; +} + +bool use_column_position_mapping(const format::ProjectedColumnBuildContext& context) { + if (context.runtime_state == nullptr || context.scan_params == nullptr) { + return false; + } + switch (format_type_from_context(context)) { + case TFileFormatType::FORMAT_PARQUET: + return !context.runtime_state->query_options().hive_parquet_use_column_names; + default: + return false; + } +} + +bool is_file_column_position_slot(const TFileScanSlotInfo& slot_info, + const std::string& column_name) { + if (column_name.starts_with(BeConsts::GLOBAL_ROWID_COL) || + column_name == BeConsts::ICEBERG_ROWID_COL) { + return false; + } + if (slot_info.__isset.is_file_slot) { + return slot_info.is_file_slot; + } + return !slot_info.__isset.category || slot_info.category != TColumnCategory::PARTITION_KEY; +} + +} // namespace + +Status HiveReader::init(format::TableReadOptions&& options) { + const bool allow_missing_columns = options.allow_missing_columns; + const format::FileFormat file_format = options.format; + RETURN_IF_ERROR(format::TableReader::init(std::move(options))); + + // Hive-specific behavior: choose the column matching mode based on file format and the + // matching session variable. + // - hive_orc_use_column_names / hive_parquet_use_column_names == true + // => BY_NAME (modern Hive default, match by column name) + // - those options == false + // => BY_INDEX (mainly for Hive1 ORC `_col0` / `_col1`, match by top-level position; + // Parquet exposes the same switch for consistency) + // The base init path does not accept file-format-specific mapper configuration, so the mapper + // must be replaced here after the base initialization completes. + DORIS_CHECK(_runtime_state != nullptr); + const auto& query_options = _runtime_state->query_options(); + bool use_column_names = true; + if (file_format == format::FileFormat::ORC) { + use_column_names = query_options.hive_orc_use_column_names; + } else if (file_format == format::FileFormat::PARQUET) { + use_column_names = query_options.hive_parquet_use_column_names; + } else { + return Status::NotSupported("HiveReader does not support file reader format {}", + file_format); + } + + _mode = use_column_names ? format::TableColumnMappingMode::BY_NAME + : format::TableColumnMappingMode::BY_INDEX; + _mapper_options.allow_missing_columns = allow_missing_columns; + return Status::OK(); +} + +Status HiveReader::annotate_projected_column(const TFileScanSlotInfo& slot_info, + format::ProjectedColumnBuildContext* context, + format::ColumnDefinition* column) const { + RETURN_IF_ERROR(format::TableReader::annotate_projected_column(slot_info, context, column)); + DORIS_CHECK(context != nullptr); + DORIS_CHECK(column != nullptr); + if (!use_column_position_mapping(*context) || + !is_file_column_position_slot(slot_info, column->name)) { + return Status::OK(); + } + const auto* scan_params = context->scan_params; + DORIS_CHECK(scan_params != nullptr); + if (!scan_params->__isset.column_idxs || + context->next_file_column_idx >= scan_params->column_idxs.size()) { + return Status::InvalidArgument( + "Hive positional column mapping is missing file index for column '{}', " + "required file slot ordinal={}, column_idxs_size={}", + column->name, context->next_file_column_idx, + scan_params->__isset.column_idxs ? scan_params->column_idxs.size() : 0); + } + const auto file_index = scan_params->column_idxs[context->next_file_column_idx]; + if (file_index < 0) { + return Status::InvalidArgument( + "Hive positional column mapping has negative file index {} for column '{}'", + file_index, column->name); + } + column->identifier = Field::create_field(file_index); + ++context->next_file_column_idx; + return Status::OK(); +} + +Status HiveReader::validate_projected_columns( + const format::ProjectedColumnBuildContext& context) const { + if (!use_column_position_mapping(context)) { + return Status::OK(); + } + DORIS_CHECK(context.scan_params != nullptr); + if (context.scan_params->__isset.column_idxs && + context.next_file_column_idx != context.scan_params->column_idxs.size()) { + return Status::InvalidArgument( + "Hive positional column mapping has unused file indexes: consumed={}, " + "column_idxs_size={}", + context.next_file_column_idx, context.scan_params->column_idxs.size()); + } + return Status::OK(); +} + +} // namespace doris::hive diff --git a/be/src/format_v2/table/hive_reader.h b/be/src/format_v2/table/hive_reader.h new file mode 100644 index 00000000000000..d1bf84cad6e1c0 --- /dev/null +++ b/be/src/format_v2/table/hive_reader.h @@ -0,0 +1,42 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include "common/status.h" +#include "format_v2/table_reader.h" + +namespace doris::hive { + +class HiveReader final : public format::TableReader { +public: + ENABLE_FACTORY_CREATOR(HiveReader); + ~HiveReader() final = default; + + Status init(format::TableReadOptions&& options) override; + format::TableColumnMappingMode mapping_mode() const override { return _mode; } + Status annotate_projected_column(const TFileScanSlotInfo& slot_info, + format::ProjectedColumnBuildContext* context, + format::ColumnDefinition* column) const override; + Status validate_projected_columns( + const format::ProjectedColumnBuildContext& context) const override; + +private: + format::TableColumnMappingMode _mode = format::TableColumnMappingMode::BY_NAME; +}; + +} // namespace doris::hive diff --git a/be/src/format_v2/table/iceberg_reader.cpp b/be/src/format_v2/table/iceberg_reader.cpp new file mode 100644 index 00000000000000..4f9e7c539b98d7 --- /dev/null +++ b/be/src/format_v2/table/iceberg_reader.cpp @@ -0,0 +1,774 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/table/iceberg_reader.h" + +#include +#include +#include +#include +#include + +#include "common/cast_set.h" +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/column/column_const.h" +#include "core/column/column_nullable.h" +#include "core/column/column_string.h" +#include "core/column/column_struct.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/define_primitive_type.h" +#include "core/field.h" +#include "format/table/deletion_vector_reader.h" +#include "format_v2/expr/cast.h" +#include "format_v2/expr/equality_delete_predicate.h" +#include "exprs/vslot_ref.h" +#include "format_v2/parquet/parquet_reader.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "format_v2/table_reader.h" +#include "io/file_factory.h" + +namespace doris::iceberg { + +template +static std::string join_values_for_debug(const std::vector& values) { + std::ostringstream out; + out << "["; + for (size_t idx = 0; idx < values.size(); ++idx) { + if (idx > 0) { + out << ", "; + } + out << values[idx]; + } + out << "]"; + return out.str(); +} + +static std::string iceberg_delete_file_debug_string(const TIcebergDeleteFileDesc& delete_file) { + std::ostringstream out; + out << "TIcebergDeleteFileDesc{path=" + << (delete_file.__isset.path ? delete_file.path : "null") + << ", content=" << (delete_file.__isset.content ? delete_file.content : -1) + << ", file_format=" + << (delete_file.__isset.file_format ? static_cast(delete_file.file_format) : -1) + << ", position_lower_bound=" + << (delete_file.__isset.position_lower_bound ? delete_file.position_lower_bound : -1) + << ", position_upper_bound=" + << (delete_file.__isset.position_upper_bound ? delete_file.position_upper_bound : -1) + << ", field_ids=" + << (delete_file.__isset.field_ids ? join_values_for_debug(delete_file.field_ids) : "[]") + << ", content_offset=" + << (delete_file.__isset.content_offset ? delete_file.content_offset : -1) + << ", content_size_in_bytes=" + << (delete_file.__isset.content_size_in_bytes ? delete_file.content_size_in_bytes : -1) + << "}"; + return out.str(); +} + +static std::string iceberg_delete_files_debug_string( + const std::vector& delete_files) { + std::ostringstream out; + out << "["; + for (size_t idx = 0; idx < delete_files.size(); ++idx) { + if (idx > 0) { + out << ", "; + } + out << iceberg_delete_file_debug_string(delete_files[idx]); + } + out << "]"; + return out.str(); +} + +static std::string iceberg_params_debug_string(const std::optional& params) { + if (!params.has_value()) { + return "null"; + } + const auto& iceberg_params = *params; + std::ostringstream out; + out << "TIcebergFileDesc{format_version=" + << (iceberg_params.__isset.format_version ? iceberg_params.format_version : -1) + << ", content=" << (iceberg_params.__isset.content ? iceberg_params.content : -1) + << ", original_file_path=" + << (iceberg_params.__isset.original_file_path ? iceberg_params.original_file_path : "null") + << ", row_count=" << (iceberg_params.__isset.row_count ? iceberg_params.row_count : -1) + << ", partition_spec_id=" + << (iceberg_params.__isset.partition_spec_id ? iceberg_params.partition_spec_id : 0) + << ", has_partition_data_json=" << iceberg_params.__isset.partition_data_json + << ", first_row_id=" + << (iceberg_params.__isset.first_row_id ? iceberg_params.first_row_id : -1) + << ", last_updated_sequence_number=" + << (iceberg_params.__isset.last_updated_sequence_number + ? iceberg_params.last_updated_sequence_number + : -1) + << ", delete_file_count=" + << (iceberg_params.__isset.delete_files ? iceberg_params.delete_files.size() : 0) + << ", delete_files=" + << (iceberg_params.__isset.delete_files + ? iceberg_delete_files_debug_string(iceberg_params.delete_files) + : "[]") + << ", has_serialized_split=" << iceberg_params.__isset.serialized_split << "}"; + return out.str(); +} + +IcebergTableReader::PositionDeleteRowsCollector::PositionDeleteRowsCollector( + std::string data_file_path, format::DeleteRows* rows) + : _data_file_path(std::move(data_file_path)), _rows(rows) {} + +Status IcebergTableReader::PositionDeleteRowsCollector::collect(const Block& block, + size_t read_rows) { + if (read_rows == 0) { + return Status::OK(); + } + const auto& file_path_column = assert_cast( + *block.get_by_position(ICEBERG_FILE_PATH_BLOCK_POSITION).column); + const auto& pos_column = assert_cast( + *block.get_by_position(ICEBERG_ROW_POS_BLOCK_POSITION).column); + for (size_t row = 0; row < read_rows; ++row) { + const auto file_path = file_path_column.get_data_at(row).to_string(); + if (file_path == _data_file_path) { + _rows->push_back(pos_column.get_element(row)); + } + } + return Status::OK(); +} + +Status IcebergTableReader::prepare_split(const format::SplitReadOptions& options) { + _row_lineage_columns = {}; + _iceberg_params.reset(); + _delete_predicates_initialized = false; + _position_delete_rows_storage.clear(); + _equality_delete_filters.clear(); + if (options.current_range.__isset.table_format_params && + options.current_range.table_format_params.__isset.iceberg_params) { + const auto& iceberg_params = options.current_range.table_format_params.iceberg_params; + _iceberg_params = iceberg_params; + if (iceberg_params.__isset.first_row_id) { + _row_lineage_columns.first_row_id = iceberg_params.first_row_id; + } + if (iceberg_params.__isset.last_updated_sequence_number) { + _row_lineage_columns.last_updated_sequence_number = + iceberg_params.last_updated_sequence_number; + } + } + RETURN_IF_ERROR(TableReader::prepare_split(options)); + if (_is_table_level_count_active()) { + return Status::OK(); + } + RETURN_IF_ERROR(_init_delete_predicates(options.current_range.table_format_params)); + return Status::OK(); +} + +std::string IcebergTableReader::debug_string() const { + size_t position_delete_file_count = 0; + size_t equality_delete_file_count = 0; + size_t deletion_vector_file_count = 0; + if (_iceberg_params.has_value() && _iceberg_params->__isset.delete_files) { + for (const auto& delete_file : _iceberg_params->delete_files) { + if (!delete_file.__isset.content) { + continue; + } + if (delete_file.content == POSITION_DELETE) { + ++position_delete_file_count; + } else if (delete_file.content == EQUALITY_DELETE) { + ++equality_delete_file_count; + } else if (delete_file.content == DELETION_VECTOR) { + ++deletion_vector_file_count; + } + } + } + + std::ostringstream equality_filters; + equality_filters << "["; + for (size_t idx = 0; idx < _equality_delete_filters.size(); ++idx) { + if (idx > 0) { + equality_filters << ", "; + } + const auto& filter = _equality_delete_filters[idx]; + equality_filters << "EqualityDeleteFilter{field_ids=" + << join_values_for_debug(filter.field_ids) << ", key_types=["; + for (size_t type_idx = 0; type_idx < filter.key_types.size(); ++type_idx) { + if (type_idx > 0) { + equality_filters << ", "; + } + equality_filters << (filter.key_types[type_idx] == nullptr + ? "null" + : filter.key_types[type_idx]->get_name()); + } + equality_filters << "], delete_block_rows=" << filter.delete_block.rows() + << ", delete_block_columns=" << filter.delete_block.columns() << "}"; + } + equality_filters << "]"; + + std::ostringstream out; + out << "IcebergTableReader{base=" << format::TableReader::debug_string() + << ", iceberg_params=" << iceberg_params_debug_string(_iceberg_params) + << ", row_lineage_first_row_id=" << _row_lineage_columns.first_row_id + << ", row_lineage_last_updated_sequence_number=" + << _row_lineage_columns.last_updated_sequence_number + << ", need_row_lineage_row_id=" << _need_row_lineage_row_id() + << ", need_iceberg_rowid=" << _need_iceberg_rowid() + << ", row_position_block_position=" << _row_position_block_position + << ", delete_predicates_initialized=" << _delete_predicates_initialized + << ", position_delete_file_count=" << position_delete_file_count + << ", equality_delete_file_count=" << equality_delete_file_count + << ", deletion_vector_file_count=" << deletion_vector_file_count + << ", position_delete_rows_storage_count=" << _position_delete_rows_storage.size() + << ", equality_delete_filter_count=" << _equality_delete_filters.size() + << ", equality_delete_filters=" << equality_filters.str() << "}"; + return out.str(); +} + +Status IcebergTableReader::materialize_virtual_columns(Block* table_block) { + for (size_t column_idx = 0; column_idx < _data_reader.column_mapper.mappings().size(); + ++column_idx) { + const auto& mapping = _data_reader.column_mapper.mappings()[column_idx]; + switch (mapping.virtual_column_type) { + case format::TableVirtualColumnType::ROW_ID: + RETURN_IF_ERROR(_materialize_row_lineage_row_id(table_block, column_idx)); + break; + case format::TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER: + RETURN_IF_ERROR( + _materialize_row_lineage_last_updated_sequence_number(table_block, column_idx)); + break; + case format::TableVirtualColumnType::ICEBERG_ROWID: + RETURN_IF_ERROR(_materialize_iceberg_rowid(table_block, column_idx)); + break; + case format::TableVirtualColumnType::INVALID: + break; + } + } + return Status::OK(); +} + +Status IcebergTableReader::customize_file_scan_request(format::FileScanRequest* file_request) { + RETURN_IF_ERROR(TableReader::customize_file_scan_request(file_request)); + if ((_row_lineage_columns.first_row_id >= 0 && _need_row_lineage_row_id()) || + _need_iceberg_rowid()) { + RETURN_IF_ERROR(_append_row_position_output_column(file_request)); + } + RETURN_IF_ERROR(_append_equality_delete_predicates(file_request)); + return Status::OK(); +} + +bool IcebergTableReader::_supports_aggregate_pushdown(TPushAggOp::type agg_type) const { + if (!TableReader::_supports_aggregate_pushdown(agg_type)) { + return false; + } + return _equality_delete_filters.empty(); +} + +Status IcebergTableReader::_parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, + DeleteFileDesc* desc, + bool* has_delete_file) { + DORIS_CHECK(desc != nullptr); + DORIS_CHECK(has_delete_file != nullptr); + *has_delete_file = false; + if (!t_desc.__isset.iceberg_params) { + return Status::OK(); + } + const auto& iceberg_params = t_desc.iceberg_params; + if (!iceberg_params.__isset.format_version || + iceberg_params.format_version < MIN_SUPPORT_DELETE_FILES_VERSION || + !iceberg_params.__isset.delete_files || iceberg_params.delete_files.empty()) { + return Status::OK(); + } + + const TIcebergDeleteFileDesc* deletion_vector = nullptr; + for (const auto& delete_file : iceberg_params.delete_files) { + if (!delete_file.__isset.content || delete_file.content != DELETION_VECTOR) { + continue; + } + if (deletion_vector != nullptr) { + return Status::DataQualityError("This iceberg data file has multiple DVs."); + } + deletion_vector = &delete_file; + } + if (deletion_vector == nullptr) { + return Status::OK(); + } + if (!deletion_vector->__isset.content_offset || + !deletion_vector->__isset.content_size_in_bytes) { + return Status::InternalError("Deletion vector is missing content offset or length"); + } + + desc->key = _iceberg_delete_vector_cache_key(*deletion_vector); + desc->path = deletion_vector->path; + desc->start_offset = deletion_vector->content_offset; + desc->size = deletion_vector->content_size_in_bytes; + desc->file_size = -1; + desc->format = DeleteFileDesc::Format::ICEBERG; + *has_delete_file = true; + return Status::OK(); +} + +Status IcebergTableReader::_init_delete_predicates(const TTableFormatFileDesc& t_desc) { + if (!t_desc.__isset.iceberg_params || _delete_predicates_initialized) { + _delete_predicates_initialized = true; + return Status::OK(); + } + const auto& iceberg_params = t_desc.iceberg_params; + if (!iceberg_params.__isset.format_version || + iceberg_params.format_version < MIN_SUPPORT_DELETE_FILES_VERSION || + !iceberg_params.__isset.delete_files || iceberg_params.delete_files.empty()) { + _delete_predicates_initialized = true; + return Status::OK(); + } + + std::vector position_delete_files; + std::vector equality_delete_files; + for (const auto& delete_file : iceberg_params.delete_files) { + if (!delete_file.__isset.content) { + continue; + } + if (delete_file.content == POSITION_DELETE) { + position_delete_files.push_back(delete_file); + } else if (delete_file.content == EQUALITY_DELETE) { + equality_delete_files.push_back(delete_file); + } + } + // `_delete_rows != nullptr` means DeleteVector is parsed + if (_delete_rows != nullptr) { + _position_delete_rows_storage = *_delete_rows; + _delete_rows = &_position_delete_rows_storage; + } + // Combine position delete rows from both deletion vector and position delete files, and + // initialize equality delete predicates. Position delete files contain row positions of + // deleted rows, which can be directly added to `_delete_rows`. Equality delete files contain + // values of deleted rows, which require reading the files and building predicates for later + // filtering. + if (!position_delete_files.empty()) { + RETURN_IF_ERROR(_init_position_delete_rows(position_delete_files)); + } + if (!equality_delete_files.empty()) { + RETURN_IF_ERROR(_init_equality_delete_predicates(equality_delete_files)); + } + + _delete_predicates_initialized = true; + return Status::OK(); +} + +std::string IcebergTableReader::_iceberg_delete_vector_cache_key( + const TIcebergDeleteFileDesc& delete_file) { + const std::string key_prefix = "iceberg_dv:"; + std::string key; + key.resize(key_prefix.size() + delete_file.path.size() + sizeof(delete_file.content_offset) + + sizeof(delete_file.content_size_in_bytes)); + char* data = key.data(); + memcpy(data, key_prefix.data(), key_prefix.size()); + data += key_prefix.size(); + memcpy(data, delete_file.path.data(), delete_file.path.size()); + data += delete_file.path.size(); + memcpy(data, &delete_file.content_offset, sizeof(delete_file.content_offset)); + data += sizeof(delete_file.content_offset); + memcpy(data, &delete_file.content_size_in_bytes, sizeof(delete_file.content_size_in_bytes)); + return key; +} + +std::shared_ptr IcebergTableReader::_delete_file_system_properties( + const TFileScanRangeParams& scan_params) { + auto system_properties = std::make_shared(); + system_properties->system_type = + scan_params.__isset.file_type ? scan_params.file_type : TFileType::FILE_LOCAL; + system_properties->properties = scan_params.properties; + system_properties->hdfs_params = scan_params.hdfs_params; + if (scan_params.__isset.broker_addresses) { + system_properties->broker_addresses.assign(scan_params.broker_addresses.begin(), + scan_params.broker_addresses.end()); + } + return system_properties; +} + +std::unique_ptr IcebergTableReader::_delete_file_description( + const TFileRangeDesc& range) { + auto file_description = std::make_unique(); + file_description->path = range.path; + file_description->file_size = range.__isset.file_size ? range.file_size : -1; + file_description->range_start_offset = range.__isset.start_offset ? range.start_offset : 0; + file_description->range_size = range.__isset.size ? range.size : -1; + if (range.__isset.fs_name) { + file_description->fs_name = range.fs_name; + } + return file_description; +} + +std::string IcebergTableReader::_data_file_path() const { + if (_iceberg_params.has_value() && _iceberg_params->__isset.original_file_path) { + return _iceberg_params->original_file_path; + } + DORIS_CHECK(_current_task != nullptr); + DORIS_CHECK(_current_task->data_file != nullptr); + return _current_task->data_file->path; +} + +Status IcebergTableReader::_append_row_position_output_column(format::FileScanRequest* request) { + const auto row_position_column_id = format::LocalColumnId(format::ROW_POSITION_COLUMN_ID); + _append_file_scan_column(request, row_position_column_id, &request->non_predicate_columns); + _row_position_block_position = request->local_positions.at(row_position_column_id).value(); + return Status::OK(); +} + +Status IcebergTableReader::_append_equality_delete_predicates(format::FileScanRequest* request) { + DORIS_CHECK(request != nullptr); + for (const auto& filter : _equality_delete_filters) { + auto delete_predicate = + std::make_shared(filter.delete_block, filter.field_ids); + DCHECK_EQ(filter.field_ids.size(), filter.key_types.size()); + for (size_t idx = 0; idx < filter.field_ids.size(); ++idx) { + const int field_id = filter.field_ids[idx]; + auto field_it = std::ranges::find_if( + _data_reader.file_schema, [field_id](const format::ColumnDefinition& field) { + return field.has_identifier_field_id() && + field.get_identifier_field_id() == field_id; + }); + if (field_it == _data_reader.file_schema.end()) { + return Status::InternalError( + "Can not find equality delete column field id {} in data file schema", + field_id); + } + const auto field_column_id = format::LocalColumnId(field_it->file_local_id()); + _append_file_scan_column(request, field_column_id, &request->predicate_columns); + const auto block_position = request->local_positions.at(field_column_id).value(); + auto slot = VSlotRef::create_shared(cast_set(block_position), + cast_set(block_position), -1, + field_it->type, field_it->name); + if (field_it->type->equals(*filter.key_types[idx])) { + delete_predicate->add_child(std::move(slot)); + } else { + auto cast_expr = Cast::create_shared(filter.key_types[idx]); + cast_expr->add_child(std::move(slot)); + delete_predicate->add_child(std::move(cast_expr)); + } + } + request->delete_conjuncts.push_back( + VExprContext::create_shared(std::move(delete_predicate))); + } + return Status::OK(); +} + +Status IcebergTableReader::_read_parquet_position_delete_file( + const TIcebergDeleteFileDesc& delete_file, const TFileScanRangeParams& scan_params, + IcebergDeleteFileIOContext* delete_io_ctx, PositionDeleteRowsCollector* collector) { + if (!delete_file.__isset.file_format) { + return Status::InternalError("Iceberg position delete file is missing file format"); + } + if (delete_file.file_format == TFileFormatType::FORMAT_ORC) { + return Status::NotSupported("Iceberg ORC position delete file is not supported"); + } + if (delete_file.file_format != TFileFormatType::FORMAT_PARQUET) { + return Status::NotSupported("Unsupported Iceberg delete file format {}", + delete_file.file_format); + } + + auto delete_range = build_iceberg_delete_file_range(delete_file.path); + if (_current_task != nullptr && _current_task->data_file != nullptr && + !_current_task->data_file->fs_name.empty()) { + delete_range.__set_fs_name(_current_task->data_file->fs_name); + } + auto system_properties = _delete_file_system_properties(scan_params); + auto file_description = _delete_file_description(delete_range); + std::shared_ptr io_ctx(&delete_io_ctx->io_ctx, [](io::IOContext*) {}); + parquet::ParquetReader reader(system_properties, file_description, io_ctx, _scanner_profile); + RETURN_IF_ERROR(reader.init(_runtime_state)); + + std::vector schema; + RETURN_IF_ERROR(reader.get_schema(&schema)); + format::ColumnDefinition* file_path_field = nullptr; + format::ColumnDefinition* pos_field = nullptr; + for (auto& field : schema) { + if (field.name == ICEBERG_FILE_PATH) { + file_path_field = &field; + } else if (field.name == ICEBERG_ROW_POS) { + pos_field = &field; + } + } + if (file_path_field == nullptr || pos_field == nullptr) { + return Status::InternalError("Position delete parquet file is missing required columns"); + } + + auto request = std::make_shared(); + request->non_predicate_columns = { + format::LocalColumnIndex::top_level( + format::LocalColumnId(file_path_field->file_local_id())), + format::LocalColumnIndex::top_level(format::LocalColumnId(pos_field->file_local_id()))}; + request->local_positions = { + {format::LocalColumnId(file_path_field->file_local_id()), + format::LocalIndex(ICEBERG_FILE_PATH_BLOCK_POSITION)}, + {format::LocalColumnId(pos_field->file_local_id()), + format::LocalIndex(ICEBERG_ROW_POS_BLOCK_POSITION)}, + }; + RETURN_IF_ERROR(reader.open(request)); + + bool eof = false; + auto build_position_delete_block = [](const format::ColumnDefinition& file_path_field, + const format::ColumnDefinition& pos_field) -> Block { + Block block; + block.insert( + {file_path_field.type->create_column(), file_path_field.type, ICEBERG_FILE_PATH}); + block.insert({pos_field.type->create_column(), pos_field.type, ICEBERG_ROW_POS}); + return block; + }; + while (!eof) { + Block block = build_position_delete_block(*file_path_field, *pos_field); + size_t read_rows = 0; + RETURN_IF_ERROR(reader.get_block(&block, &read_rows, &eof)); + RETURN_IF_ERROR(collector->collect(block, read_rows)); + } + return reader.close(); +} + +Status IcebergTableReader::_init_position_delete_rows( + const std::vector& delete_files) { + TFileScanRangeParams delete_scan_params = + _scan_params == nullptr ? TFileScanRangeParams() : *_scan_params; + format::DeleteRows position_delete_rows; + IcebergDeleteFileIOContext delete_io_ctx(_runtime_state); + PositionDeleteRowsCollector collector(_data_file_path(), &position_delete_rows); + for (const auto& delete_file : delete_files) { + RETURN_IF_ERROR(_read_parquet_position_delete_file(delete_file, delete_scan_params, + &delete_io_ctx, &collector)); + } + if (position_delete_rows.empty()) { + return Status::OK(); + } + // Position delete files and deletion vectors both become row-position deletes for the + // common TableReader DeletePredicate path. Keep the merged rows in a member vector because + // DeletePredicate stores a reference to the vector used by _delete_rows. + _position_delete_rows_storage.insert(_position_delete_rows_storage.end(), + position_delete_rows.begin(), position_delete_rows.end()); + std::sort(_position_delete_rows_storage.begin(), _position_delete_rows_storage.end()); + _position_delete_rows_storage.erase( + std::unique(_position_delete_rows_storage.begin(), _position_delete_rows_storage.end()), + _position_delete_rows_storage.end()); + _delete_rows = &_position_delete_rows_storage; + return Status::OK(); +} + +Status IcebergTableReader::_init_equality_delete_predicates( + const std::vector& delete_files) { + TFileScanRangeParams delete_scan_params = + _scan_params == nullptr ? TFileScanRangeParams() : *_scan_params; + IcebergDeleteFileIOContext delete_io_ctx(_runtime_state); + for (const auto& delete_file : delete_files) { + RETURN_IF_ERROR(_read_parquet_equality_delete_file(delete_file, delete_scan_params, + &delete_io_ctx)); + } + return Status::OK(); +} + +Status IcebergTableReader::_read_parquet_equality_delete_file( + const TIcebergDeleteFileDesc& delete_file, const TFileScanRangeParams& scan_params, + IcebergDeleteFileIOContext* delete_io_ctx) { + if (!delete_file.__isset.file_format) { + return Status::InternalError("Iceberg equality delete file is missing file format"); + } + if (delete_file.file_format != TFileFormatType::FORMAT_PARQUET) { + return Status::NotSupported("Unsupported Iceberg equality delete file format {}", + delete_file.file_format); + } + if (!delete_file.__isset.field_ids || delete_file.field_ids.empty()) { + return Status::InternalError("Iceberg equality delete file is missing field ids"); + } + + auto delete_range = build_iceberg_delete_file_range(delete_file.path); + if (_current_task != nullptr && _current_task->data_file != nullptr && + !_current_task->data_file->fs_name.empty()) { + delete_range.__set_fs_name(_current_task->data_file->fs_name); + } + auto system_properties = _delete_file_system_properties(scan_params); + auto file_description = _delete_file_description(delete_range); + std::shared_ptr io_ctx(&delete_io_ctx->io_ctx, [](io::IOContext*) {}); + parquet::ParquetReader reader(system_properties, file_description, io_ctx, _scanner_profile); + RETURN_IF_ERROR(reader.init(_runtime_state)); + + std::vector schema; + RETURN_IF_ERROR(reader.get_schema(&schema)); + std::vector delete_fields; + std::vector delete_field_ids; + std::vector delete_key_types; + for (const auto field_id : delete_file.field_ids) { + auto field_it = std::find_if(schema.begin(), schema.end(), + [field_id](const format::ColumnDefinition& field) { + return field.has_identifier_field_id() && + field_id == field.get_identifier_field_id(); + }); + if (field_it == schema.end()) { + return Status::InternalError("Can not find field id {} in equality delete file {}", + field_id, delete_file.path); + } + if (!field_it->children.empty()) { + return Status::NotSupported( + "Iceberg equality delete does not support complex column {}", field_it->name); + } + delete_fields.push_back(*field_it); + delete_field_ids.push_back(field_id); + delete_key_types.push_back(field_it->type); + } + + auto request = std::make_shared(); + for (size_t idx = 0; idx < delete_fields.size(); ++idx) { + const auto local_column_id = format::LocalColumnId(delete_fields[idx].file_local_id()); + request->non_predicate_columns.push_back( + format::LocalColumnIndex::top_level(local_column_id)); + request->local_positions.emplace(local_column_id, format::LocalIndex(idx)); + } + RETURN_IF_ERROR(reader.open(request)); + + auto build_equality_delete_block = + [](const std::vector fields) -> Block { + Block block; + for (const auto& field : fields) { + block.insert({field.type->create_column(), field.type, field.name}); + } + return block; + }; + Block delete_block = build_equality_delete_block(delete_fields); + MutableBlock mutable_delete_block(std::move(delete_block)); + bool eof = false; + while (!eof) { + Block block = build_equality_delete_block(delete_fields); + size_t read_rows = 0; + RETURN_IF_ERROR(reader.get_block(&block, &read_rows, &eof)); + if (read_rows > 0) { + RETURN_IF_ERROR(mutable_delete_block.merge(block)); + } + } + RETURN_IF_ERROR(reader.close()); + delete_block = mutable_delete_block.to_block(); + _equality_delete_filters.push_back( + EqualityDeleteFilter {.field_ids = std::move(delete_field_ids), + .key_types = std::move(delete_key_types), + .delete_block = std::move(delete_block)}); + return Status::OK(); +} + +Status IcebergTableReader::_materialize_row_lineage_row_id(Block* table_block, size_t column_idx) { + if (_row_lineage_columns.first_row_id < 0) { + return Status::OK(); + } + DORIS_CHECK(_row_position_block_position < _data_reader.block_template.columns()); + const auto& row_position_column = assert_cast( + *_data_reader.block_template.get_by_position(_row_position_block_position).column); + DORIS_CHECK(row_position_column.size() == table_block->rows()); + auto column = IColumn::mutate( + table_block->get_by_position(column_idx).column->convert_to_full_column_if_const()); + auto* nullable_column = assert_cast(column.get()); + auto& null_map = nullable_column->get_null_map_data(); + auto& data = assert_cast(*nullable_column->get_nested_column_ptr()).get_data(); + DORIS_CHECK(null_map.size() == row_position_column.size()); + DORIS_CHECK(data.size() == row_position_column.size()); + for (size_t row = 0; row < row_position_column.size(); ++row) { + if (null_map[row]) { + null_map[row] = 0; + data[row] = _row_lineage_columns.first_row_id + row_position_column.get_element(row); + } + } + table_block->replace_by_position(column_idx, std::move(column)); + return Status::OK(); +} + +Status IcebergTableReader::_materialize_iceberg_rowid(Block* table_block, size_t column_idx) { + DORIS_CHECK(_row_position_block_position < _data_reader.block_template.columns()); + const auto& row_position_column = assert_cast( + *_data_reader.block_template.get_by_position(_row_position_block_position).column); + DORIS_CHECK(row_position_column.size() == table_block->rows()); + + const auto& type = table_block->get_by_position(column_idx).type; + auto column = type->create_column(); + auto* nullable_column = check_and_get_column(column.get()); + auto* struct_column = nullable_column != nullptr + ? check_and_get_column( + nullable_column->get_nested_column_ptr().get()) + : check_and_get_column(column.get()); + DORIS_CHECK(struct_column != nullptr); + DORIS_CHECK(struct_column->tuple_size() >= 4); + + const auto rows = row_position_column.size(); + const auto file_path = _data_file_path(); + const int32_t partition_spec_id = + _iceberg_params.has_value() && _iceberg_params->__isset.partition_spec_id + ? _iceberg_params->partition_spec_id + : 0; + const std::string partition_data_json = + _iceberg_params.has_value() && _iceberg_params->__isset.partition_data_json + ? _iceberg_params->partition_data_json + : ""; + + auto& file_path_column = struct_column->get_column(0); + auto& row_pos_column = struct_column->get_column(1); + auto& spec_id_column = struct_column->get_column(2); + auto& partition_data_column = struct_column->get_column(3); + file_path_column.reserve(rows); + row_pos_column.reserve(rows); + spec_id_column.reserve(rows); + partition_data_column.reserve(rows); + for (size_t row = 0; row < rows; ++row) { + file_path_column.insert_data(file_path.data(), file_path.size()); + const int64_t row_pos = row_position_column.get_element(row); + row_pos_column.insert_data(reinterpret_cast(&row_pos), sizeof(row_pos)); + spec_id_column.insert_data(reinterpret_cast(&partition_spec_id), + sizeof(partition_spec_id)); + partition_data_column.insert_data(partition_data_json.data(), partition_data_json.size()); + } + if (nullable_column != nullptr) { + nullable_column->get_null_map_data().resize_fill(rows, 0); + } + table_block->replace_by_position(column_idx, std::move(column)); + return Status::OK(); +} + +Status IcebergTableReader::_materialize_row_lineage_last_updated_sequence_number( + Block* table_block, size_t column_idx) { + if (_row_lineage_columns.last_updated_sequence_number < 0) { + return Status::OK(); + } + auto column = IColumn::mutate( + table_block->get_by_position(column_idx).column->convert_to_full_column_if_const()); + auto* nullable_column = assert_cast(column.get()); + auto& null_map = nullable_column->get_null_map_data(); + auto& data = assert_cast(*nullable_column->get_nested_column_ptr()).get_data(); + DORIS_CHECK(null_map.size() == table_block->rows()); + DORIS_CHECK(data.size() == table_block->rows()); + for (size_t row = 0; row < table_block->rows(); ++row) { + if (null_map[row]) { + null_map[row] = 0; + data[row] = _row_lineage_columns.last_updated_sequence_number; + } + } + table_block->replace_by_position(column_idx, std::move(column)); + return Status::OK(); +} + +bool IcebergTableReader::_need_row_lineage_row_id() const { + for (const auto& mapping : _data_reader.column_mapper.mappings()) { + if (mapping.virtual_column_type == format::TableVirtualColumnType::ROW_ID) { + return true; + } + } + return false; +} + +bool IcebergTableReader::_need_iceberg_rowid() const { + for (const auto& mapping : _data_reader.column_mapper.mappings()) { + if (mapping.virtual_column_type == format::TableVirtualColumnType::ICEBERG_ROWID) { + return true; + } + } + return false; +} + +} // namespace doris::iceberg diff --git a/be/src/format_v2/table/iceberg_reader.h b/be/src/format_v2/table/iceberg_reader.h new file mode 100644 index 00000000000000..8076aa5b96d863 --- /dev/null +++ b/be/src/format_v2/table/iceberg_reader.h @@ -0,0 +1,167 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include + +#include "common/status.h" +#include "core/block/block.h" +#include "format/table/iceberg_delete_file_reader_helper.h" +#include "format_v2/file_reader.h" +#include "format_v2/table_reader.h" +#include "gen_cpp/PlanNodes_types.h" + +namespace doris { +class Block; +struct DeleteFileDesc; +namespace io { +struct FileDescription; +struct FileSystemProperties; +} // namespace io +} // namespace doris + +namespace doris::iceberg { + +// Iceberg table-level reader。 +// 该层继承 TableReader，复用多文件编排和动态分区裁剪等通用能力；同时组合 +// FileReader 完成 data file 物理读取，不继承具体文件格式 reader。 +class IcebergTableReader : public format::TableReader { +public: + ~IcebergTableReader() override = default; + Status init(format::TableReadOptions&& options) override { + RETURN_IF_ERROR(format::TableReader::init(std::move(options))); + _mapper_options.mode = format::TableColumnMappingMode::BY_FIELD_ID; + return Status::OK(); + } + + Status prepare_split(const format::SplitReadOptions& options) override; + std::string debug_string() const override; + format::TableColumnMappingMode mapping_mode() const override { + return !_data_reader.file_schema.empty() && _has_field_id(_data_reader.file_schema) + ? format::TableColumnMappingMode::BY_FIELD_ID + : format::TableColumnMappingMode::BY_NAME; + } + +protected: + Status materialize_virtual_columns(Block* table_block) override; + + Status customize_file_scan_request(format::FileScanRequest* file_request) override; + + bool _supports_aggregate_pushdown(TPushAggOp::type agg_type) const override; + + Status _parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, DeleteFileDesc* desc, + bool* has_delete_file) override; + + Status _init_delete_predicates(const TTableFormatFileDesc& t_desc); + +private: + bool _has_field_id(const std::vector& schema) const { + for (const auto& field : schema) { + if (!field.has_identifier_field_id()) { + return false; + } + if (!_has_field_id(field.children)) { + return false; + } + } + return true; + } + static constexpr int MIN_SUPPORT_DELETE_FILES_VERSION = 2; + static constexpr int POSITION_DELETE = 1; + static constexpr int EQUALITY_DELETE = 2; + static constexpr int DELETION_VECTOR = 3; + + struct RowLineageColumns { + int64_t first_row_id = -1; + int64_t last_updated_sequence_number = -1; + }; + + static constexpr const char* ICEBERG_FILE_PATH = "file_path"; + static constexpr const char* ICEBERG_ROW_POS = "pos"; + static constexpr size_t ICEBERG_FILE_PATH_BLOCK_POSITION = 0; + static constexpr size_t ICEBERG_ROW_POS_BLOCK_POSITION = 1; + + class PositionDeleteRowsCollector final { + public: + PositionDeleteRowsCollector(std::string data_file_path, format::DeleteRows* rows); + + Status collect(const Block& block, size_t read_rows); + + private: + std::string _data_file_path; + format::DeleteRows* _rows = nullptr; + }; + + static std::string _iceberg_delete_vector_cache_key(const TIcebergDeleteFileDesc& delete_file); + + static std::shared_ptr _delete_file_system_properties( + const TFileScanRangeParams& scan_params); + + static std::unique_ptr _delete_file_description( + const TFileRangeDesc& range); + + std::string _data_file_path() const; + + // Append row position column to file scan request for position delete handling. + Status _append_row_position_output_column(format::FileScanRequest* request); + // Append equality delete predicates to file scan request based on the delete files in iceberg + // params. DeleteVector and position delete files use the common DeleteRows path in TableReader. + Status _append_equality_delete_predicates(format::FileScanRequest* request); + + Status _init_equality_delete_predicates( + const std::vector& delete_files); + + // Read equality/position delete files. + Status _read_parquet_equality_delete_file(const TIcebergDeleteFileDesc& delete_file, + const TFileScanRangeParams& scan_params, + IcebergDeleteFileIOContext* delete_io_ctx); + Status _read_parquet_position_delete_file(const TIcebergDeleteFileDesc& delete_file, + const TFileScanRangeParams& scan_params, + IcebergDeleteFileIOContext* delete_io_ctx, + PositionDeleteRowsCollector* collector); + + // Read position delete files and collect deleted row positions to update DeletePredicate. + Status _init_position_delete_rows(const std::vector& delete_files); + + // Materialize row lineage virtual columns based on the position delete file. + Status _materialize_iceberg_rowid(Block* table_block, size_t column_idx); + Status _materialize_row_lineage_row_id(Block* table_block, size_t column_idx); + Status _materialize_row_lineage_last_updated_sequence_number(Block* table_block, + size_t column_idx); + + RowLineageColumns _row_lineage_columns; + size_t _row_position_block_position = 0; + std::optional _iceberg_params; + bool _delete_predicates_initialized = false; + format::DeleteRows _position_delete_rows_storage; + // TODO: Support nested types + struct EqualityDeleteFilter { + std::vector field_ids; + std::vector key_types; + Block delete_block; + }; + std::vector _equality_delete_filters; + + bool _need_row_lineage_row_id() const; + bool _need_iceberg_rowid() const; +}; + +} // namespace doris::iceberg diff --git a/be/src/format_v2/table/paimon_reader.cpp b/be/src/format_v2/table/paimon_reader.cpp new file mode 100644 index 00000000000000..a5bd185f556daa --- /dev/null +++ b/be/src/format_v2/table/paimon_reader.cpp @@ -0,0 +1,192 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/table/paimon_reader.h" + +#include +#include +#include + +#include "format/table/deletion_vector_reader.h" +#include "util/string_util.h" + +namespace doris::paimon { +namespace { + +const schema::external::TField* get_field_ptr(const schema::external::TFieldPtr& field_ptr) { + if (!field_ptr.__isset.field_ptr || field_ptr.field_ptr == nullptr) { + return nullptr; + } + return field_ptr.field_ptr.get(); +} + +const schema::external::TSchema* find_schema(const TFileScanRangeParams* params, + int64_t schema_id) { + if (params == nullptr || !params->__isset.history_schema_info) { + return nullptr; + } + for (const auto& schema : params->history_schema_info) { + if (schema.__isset.schema_id && schema.schema_id == schema_id) { + return &schema; + } + } + return nullptr; +} + +const schema::external::TField* find_child_field_by_name( + const std::vector& fields, const std::string& name) { + for (const auto& field_ptr : fields) { + const auto* field = get_field_ptr(field_ptr); + if (field == nullptr) { + continue; + } + if (field->__isset.name && to_lower(field->name) == to_lower(name)) { + return field; + } + if (field->__isset.name_mapping && + std::ranges::any_of(field->name_mapping, [&](const std::string& alias) { + return to_lower(alias) == to_lower(name); + })) { + return field; + } + } + return nullptr; +} + +void annotate_column_from_field(format::ColumnDefinition* column, + const schema::external::TField& field); + +void annotate_struct_children(format::ColumnDefinition* column, + const schema::external::TStructField& struct_field) { + DORIS_CHECK(column != nullptr); + if (!struct_field.__isset.fields) { + return; + } + for (auto& child : column->children) { + const auto* child_field = find_child_field_by_name(struct_field.fields, child.name); + if (child_field != nullptr) { + annotate_column_from_field(&child, *child_field); + } + } +} + +void annotate_column_from_field(format::ColumnDefinition* column, + const schema::external::TField& field) { + DORIS_CHECK(column != nullptr); + if (field.__isset.id) { + column->identifier = Field::create_field(field.id); + } + column->name_mapping = + field.__isset.name_mapping ? field.name_mapping : std::vector {}; + if (!field.__isset.nestedField) { + return; + } + if (field.nestedField.__isset.struct_field) { + annotate_struct_children(column, field.nestedField.struct_field); + } else if (field.nestedField.__isset.array_field) { + if (column->children.empty() || !field.nestedField.array_field.__isset.item_field) { + return; + } + const auto* item_field = get_field_ptr(field.nestedField.array_field.item_field); + if (item_field != nullptr) { + annotate_column_from_field(&column->children.front(), *item_field); + } + } else if (field.nestedField.__isset.map_field) { + if (!column->children.empty() && field.nestedField.map_field.__isset.key_field) { + const auto* key_field = get_field_ptr(field.nestedField.map_field.key_field); + if (key_field != nullptr) { + annotate_column_from_field(&column->children.front(), *key_field); + } + } + if (column->children.size() > 1 && field.nestedField.map_field.__isset.value_field) { + const auto* value_field = get_field_ptr(field.nestedField.map_field.value_field); + if (value_field != nullptr) { + annotate_column_from_field(&column->children[1], *value_field); + } + } + } +} + +} // namespace + +Status PaimonReader::prepare_split(const format::SplitReadOptions& options) { + _split_schema_id = -1; + const auto& paimon_params = options.current_range.table_format_params.paimon_params; + if (paimon_params.__isset.schema_id) { + _split_schema_id = paimon_params.schema_id; + } + return format::TableReader::prepare_split(options); +} + +format::TableColumnMappingMode PaimonReader::mapping_mode() const { + if (_split_schema_id < 0 || _scan_params == nullptr || + !_scan_params->__isset.current_schema_id || !_scan_params->__isset.history_schema_info) { + return format::TableColumnMappingMode::BY_NAME; + } + return find_schema(_scan_params, _split_schema_id) == nullptr + ? format::TableColumnMappingMode::BY_NAME + : format::TableColumnMappingMode::BY_FIELD_ID; +} + +Status PaimonReader::annotate_file_schema(std::vector* file_schema) { + DORIS_CHECK(file_schema != nullptr); + if (mapping_mode() != format::TableColumnMappingMode::BY_FIELD_ID) { + return Status::OK(); + } + const auto* schema = find_schema(_scan_params, _split_schema_id); + DORIS_CHECK(schema != nullptr); + if (!schema->__isset.root_field || !schema->root_field.__isset.fields) { + return Status::OK(); + } + for (auto& column : *file_schema) { + const auto* field = find_child_field_by_name(schema->root_field.fields, column.name); + if (field != nullptr) { + annotate_column_from_field(&column, *field); + } + } + return Status::OK(); +} + +Status PaimonReader::_parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, + DeleteFileDesc* desc, bool* has_delete_file) { + DORIS_CHECK(desc != nullptr); + DORIS_CHECK(has_delete_file != nullptr); + *has_delete_file = false; + const auto& table_desc = t_desc.paimon_params; + if (!table_desc.__isset.deletion_file) { + return Status::OK(); + } + const auto& deletion_file = table_desc.deletion_file; + + const std::string key_prefix = "paimon_dv:"; + desc->key.resize(key_prefix.size() + deletion_file.path.size() + sizeof(deletion_file.offset)); + char* key_data = desc->key.data(); + memcpy(key_data, key_prefix.data(), key_prefix.size()); + key_data += key_prefix.size(); + memcpy(key_data, deletion_file.path.data(), deletion_file.path.size()); + key_data += deletion_file.path.size(); + memcpy(key_data, &deletion_file.offset, sizeof(deletion_file.offset)); + desc->path = deletion_file.path; + desc->start_offset = deletion_file.offset; + desc->size = deletion_file.length + 4; + desc->file_size = -1; + desc->format = DeleteFileDesc::Format::PAIMON; + *has_delete_file = true; + return Status::OK(); +} + +} // namespace doris::paimon diff --git a/be/src/format_v2/table/paimon_reader.h b/be/src/format_v2/table/paimon_reader.h new file mode 100644 index 00000000000000..1bc5ea697e7006 --- /dev/null +++ b/be/src/format_v2/table/paimon_reader.h @@ -0,0 +1,44 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include "format_v2/table_reader.h" + +namespace doris { +struct DeleteFileDesc; +} +namespace doris::paimon { + +class PaimonReader final : public format::TableReader { +public: + ENABLE_FACTORY_CREATOR(PaimonReader); + ~PaimonReader() final = default; + Status prepare_split(const format::SplitReadOptions& options) override; + +protected: + format::TableColumnMappingMode mapping_mode() const override; + Status annotate_file_schema(std::vector* file_schema) override; + + Status _parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, DeleteFileDesc* desc, + bool* has_delete_file) override; + +private: + int64_t _split_schema_id = -1; +}; + +} // namespace doris::paimon diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp new file mode 100644 index 00000000000000..e2d5a84d6f107f --- /dev/null +++ b/be/src/format_v2/table_reader.cpp @@ -0,0 +1,643 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/table_reader.h" + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "common/cast_set.h" +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_struct.h" +#include "exec/common/endian.h" +#include "exprs/vexpr_context.h" +#include "exprs/vslot_ref.h" +#include "format/table/deletion_vector_reader.h" +#include "format_v2/column_mapper.h" +#include "format_v2/parquet/parquet_reader.h" +#include "io/io_common.h" +#include "roaring/roaring64map.hh" +#include "util/string_util.h" + +namespace doris::format { +namespace { + +template +std::string join_table_reader_debug_strings(const std::vector& values, Formatter formatter) { + std::ostringstream out; + out << "["; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + out << ", "; + } + out << formatter(values[i]); + } + out << "]"; + return out.str(); +} + +std::string file_format_to_string(FileFormat format) { + switch (format) { + case FileFormat::PARQUET: + return "PARQUET"; + case FileFormat::ORC: + return "ORC"; + case FileFormat::CSV: + return "CSV"; + case FileFormat::JNI: + return "JNI"; + } + return "UNKNOWN"; +} + +std::string push_down_agg_to_string(TPushAggOp::type op) { + switch (op) { + case TPushAggOp::NONE: + return "NONE"; + case TPushAggOp::COUNT: + return "COUNT"; + case TPushAggOp::MINMAX: + return "MINMAX"; + case TPushAggOp::MIX: + return "MIX"; + case TPushAggOp::COUNT_ON_INDEX: + return "COUNT_ON_INDEX"; + } + return "UNKNOWN"; +} + +std::string current_file_debug_string(const std::unique_ptr& task) { + if (task == nullptr || task->data_file == nullptr) { + return "null"; + } + const auto& file = *task->data_file; + std::ostringstream out; + out << "FileDescription{path=" << file.path << ", file_size=" << file.file_size + << ", range_start_offset=" << file.range_start_offset << ", range_size=" << file.range_size + << ", mtime=" << file.mtime << ", fs_name=" << file.fs_name + << ", file_cache_admission=" << file.file_cache_admission << "}"; + return out.str(); +} + +std::string partition_values_debug_string(const std::map& partition_values) { + std::ostringstream out; + out << "{"; + size_t idx = 0; + for (const auto& [key, _] : partition_values) { + if (idx++ > 0) { + out << ", "; + } + out << key; + } + out << "}"; + return out.str(); +} + +const schema::external::TField* get_field_ptr(const schema::external::TFieldPtr& field_ptr) { + if (!field_ptr.__isset.field_ptr || field_ptr.field_ptr == nullptr) { + return nullptr; + } + return field_ptr.field_ptr.get(); +} + +bool external_field_matches_name(const schema::external::TField& field, const std::string& name) { + if (field.__isset.name && to_lower(field.name) == to_lower(name)) { + return true; + } + return field.__isset.name_mapping && + std::ranges::any_of(field.name_mapping, [&](const std::string& alias) { + return to_lower(alias) == to_lower(name); + }); +} + +DataTypePtr find_struct_child_type_by_name(const DataTypeStruct& struct_type, + const std::string& field_name) { + for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { + if (to_lower(struct_type.get_element_name(field_idx)) == to_lower(field_name)) { + return struct_type.get_element(field_idx); + } + } + return nullptr; +} + +ColumnDefinition build_schema_column_from_external_field(const schema::external::TField& field, + DataTypePtr type) { + ColumnDefinition column { + .identifier = field.__isset.id ? Field::create_field(field.id) : Field {}, + .name = field.__isset.name ? field.name : "", + .name_mapping = + field.__isset.name_mapping ? field.name_mapping : std::vector {}, + .type = std::move(type), + .children = {}, + .default_expr = nullptr, + .is_partition_key = false, + }; + if (column.type == nullptr || !field.__isset.nestedField) { + return column; + } + + const auto nested_type = remove_nullable(column.type); + switch (nested_type->get_primitive_type()) { + case TYPE_STRUCT: { + if (!field.nestedField.__isset.struct_field || + !field.nestedField.struct_field.__isset.fields) { + return column; + } + const auto& struct_type = assert_cast(*nested_type); + for (const auto& child_ptr : field.nestedField.struct_field.fields) { + const auto* child_field = get_field_ptr(child_ptr); + if (child_field == nullptr || !child_field->__isset.name) { + continue; + } + auto child_type = find_struct_child_type_by_name(struct_type, child_field->name); + if (child_type == nullptr) { + continue; + } + column.children.push_back( + build_schema_column_from_external_field(*child_field, child_type)); + } + break; + } + case TYPE_ARRAY: { + if (!field.nestedField.__isset.array_field || + !field.nestedField.array_field.__isset.item_field) { + return column; + } + const auto* item_field = get_field_ptr(field.nestedField.array_field.item_field); + if (item_field == nullptr) { + return column; + } + const auto& array_type = assert_cast(*nested_type); + column.children.push_back( + build_schema_column_from_external_field(*item_field, array_type.get_nested_type())); + break; + } + case TYPE_MAP: { + if (!field.nestedField.__isset.map_field || + !field.nestedField.map_field.__isset.key_field || + !field.nestedField.map_field.__isset.value_field) { + return column; + } + const auto& map_type = assert_cast(*nested_type); + const auto* key_field = get_field_ptr(field.nestedField.map_field.key_field); + if (key_field != nullptr) { + column.children.push_back( + build_schema_column_from_external_field(*key_field, map_type.get_key_type())); + } + const auto* value_field = get_field_ptr(field.nestedField.map_field.value_field); + if (value_field != nullptr) { + column.children.push_back(build_schema_column_from_external_field( + *value_field, map_type.get_value_type())); + } + break; + } + default: + break; + } + return column; +} + +const schema::external::TField* find_external_root_field(const TFileScanRangeParams* params, + const ColumnDefinition& column) { + if (params == nullptr || !params->__isset.history_schema_info || + params->history_schema_info.empty()) { + return nullptr; + } + const auto* schema = ¶ms->history_schema_info.front(); + if (params->__isset.current_schema_id) { + for (const auto& candidate_schema : params->history_schema_info) { + if (candidate_schema.__isset.schema_id && + candidate_schema.schema_id == params->current_schema_id) { + schema = &candidate_schema; + break; + } + } + } + if (!schema->__isset.root_field || !schema->root_field.__isset.fields) { + return nullptr; + } + for (const auto& field_ptr : schema->root_field.fields) { + const auto* field = get_field_ptr(field_ptr); + if (field == nullptr) { + continue; + } + if (external_field_matches_name(*field, column.name)) { + return field; + } + } + return nullptr; +} + +std::string expr_context_debug_string(const VExprContextSPtr& context) { + if (context == nullptr) { + return "null"; + } + const auto root = context->root(); + if (root == nullptr) { + return "VExprContext{root=null}"; + } + std::ostringstream out; + out << "VExprContext{root_name=" << root->expr_name() << ", root_debug=" << root->debug_string() + << "}"; + return out.str(); +} + +std::string table_filter_debug_string(const TableFilter& filter) { + std::ostringstream out; + out << "TableFilter{conjunct=" << expr_context_debug_string(filter.conjunct) + << ", global_indices=" + << join_table_reader_debug_strings( + filter.global_indices, + [](GlobalIndex global_index) { return std::to_string(global_index.value()); }) + << "}"; + return out.str(); +} + +std::string table_column_predicates_debug_string(const TableColumnPredicates& predicates) { + std::ostringstream out; + out << "{"; + size_t idx = 0; + for (const auto& [global_index, column_predicates] : predicates) { + if (idx++ > 0) { + out << ", "; + } + out << global_index.value() << ":{predicate_count=" << column_predicates.size() << "}"; + } + out << "}"; + return out.str(); +} + +void collect_global_indices(const VExprSPtr& expr, std::set* global_indices) { + if (expr == nullptr) { + return; + } + if (expr->is_slot_ref()) { + const auto* slot_ref = assert_cast(expr.get()); + DORIS_CHECK(slot_ref->column_id() >= 0); + global_indices->insert(GlobalIndex(cast_set(slot_ref->column_id()))); + } + for (const auto& child : expr->children()) { + collect_global_indices(child, global_indices); + } +} + +Status build_table_filters_from_conjunct(const VExprContextSPtr& conjunct, RuntimeState* state, + std::vector* table_filters) { + if (conjunct == nullptr) { + return Status::OK(); + } + std::set global_indices; + collect_global_indices(conjunct->root(), &global_indices); + if (!global_indices.empty()) { + TableFilter table_filter; + VExprSPtr filter_root; + RETURN_IF_ERROR(clone_table_expr_tree(conjunct->root(), &filter_root)); + table_filter.conjunct = VExprContext::create_shared(std::move(filter_root)); + for (const auto global_index : global_indices) { + table_filter.global_indices.push_back(global_index); + } + table_filters->push_back(std::move(table_filter)); + } + return Status::OK(); +} + +Status parse_deletion_vector(const char* buf, size_t buffer_size, DeleteFileDesc::Format format, + DeleteRows* delete_rows) { + DORIS_CHECK(buf != nullptr); + DORIS_CHECK(delete_rows != nullptr); + DORIS_CHECK(format == DeleteFileDesc::Format::PAIMON || + format == DeleteFileDesc::Format::ICEBERG); + + const size_t checksum_size = format == DeleteFileDesc::Format::ICEBERG ? 4 : 0; + if (buffer_size < 8 + checksum_size) [[unlikely]] { + return Status::DataQualityError("Deletion vector file size too small: {}", buffer_size); + } + + auto total_length = BigEndian::Load32(buf); + if (total_length + 4 + checksum_size != buffer_size) [[unlikely]] { + return Status::DataQualityError("Deletion vector length mismatch, expected: {}, actual: {}", + total_length + 4 + checksum_size, buffer_size); + } + + constexpr static char MAGIC_NUMBER[] = {'\xD1', '\xD3', '\x39', '\x64'}; + if (memcmp(buf + sizeof(total_length), MAGIC_NUMBER, 4) != 0) [[unlikely]] { + return Status::DataQualityError("Deletion vector magic number mismatch"); + } + + const char* bitmap_buf = buf + 8; + const size_t bitmap_size = buffer_size - 8 - checksum_size; + if (format == DeleteFileDesc::Format::PAIMON) { + roaring::Roaring bitmap; + try { + bitmap = roaring::Roaring::readSafe(bitmap_buf, bitmap_size); + } catch (const std::runtime_error& e) { + return Status::DataQualityError("Decode roaring bitmap failed, {}", e.what()); + } + + delete_rows->reserve(bitmap.cardinality()); + for (auto it = bitmap.begin(); it != bitmap.end(); it++) { + delete_rows->push_back(*it); + } + return Status::OK(); + } + + roaring::Roaring64Map bitmap; + try { + bitmap = roaring::Roaring64Map::readSafe(bitmap_buf, bitmap_size); + } catch (const std::runtime_error& e) { + return Status::DataQualityError("Decode roaring bitmap failed, {}", e.what()); + } + + delete_rows->reserve(bitmap.cardinality()); + for (auto it = bitmap.begin(); it != bitmap.end(); it++) { + delete_rows->push_back(cast_set(*it)); + } + return Status::OK(); +} + +} // namespace + +std::shared_ptr create_system_properties( + const TFileScanRangeParams* scan_params) { + auto system_properties = std::make_shared(); + if (scan_params == nullptr || !scan_params->__isset.file_type) { + system_properties->system_type = TFileType::FILE_LOCAL; + return system_properties; + } + system_properties->system_type = scan_params->file_type; + system_properties->properties = scan_params->properties; + system_properties->hdfs_params = scan_params->hdfs_params; + if (scan_params->__isset.broker_addresses) { + system_properties->broker_addresses.assign(scan_params->broker_addresses.begin(), + scan_params->broker_addresses.end()); + } + return system_properties; +} + +std::string TableReader::debug_string() const { + std::ostringstream out; + out << "TableReader{format=" << file_format_to_string(_format) + << ", push_down_agg_type=" << push_down_agg_to_string(_push_down_agg_type) + << ", aggregate_pushdown_tried=" << _aggregate_pushdown_tried + << ", has_current_reader=" << (_data_reader.reader != nullptr) + << ", has_current_task=" << (_current_task != nullptr) + << ", current_file=" << current_file_debug_string(_current_task) + << ", has_delete_rows=" << (_delete_rows != nullptr) + << ", delete_row_count=" << (_delete_rows == nullptr ? 0 : _delete_rows->size()) + << ", has_system_properties=" << (_system_properties != nullptr) << ", system_type=" + << (_system_properties == nullptr ? static_cast(TFileType::FILE_LOCAL) + : static_cast(_system_properties->system_type)) + << ", has_scan_params=" << (_scan_params != nullptr) + << ", has_io_ctx=" << (_io_ctx != nullptr) + << ", has_runtime_state=" << (_runtime_state != nullptr) + << ", has_scanner_profile=" << (_scanner_profile != nullptr) + << ", mapper_options=" << _mapper_options.debug_string() << ", projected_columns=" + << join_table_reader_debug_strings( + _projected_columns, + [](const ColumnDefinition& column) { return column.debug_string(); }) + << ", partition_values=" << partition_values_debug_string(_partition_values) + << ", table_filters=" + << join_table_reader_debug_strings( + _table_filters, + [](const TableFilter& filter) { return table_filter_debug_string(filter); }) + << ", table_column_predicates=" + << table_column_predicates_debug_string(_table_column_predicates) + << ", conjunct_count=" << _conjuncts.size() << ", conjuncts=" + << join_table_reader_debug_strings(_conjuncts, + [](const VExprContextSPtr& conjunct) { + return expr_context_debug_string(conjunct); + }) + << ", file_schema=" + << join_table_reader_debug_strings( + _data_reader.file_schema, + [](const ColumnDefinition& field) { return field.debug_string(); }) + << ", file_block_layout=" + << join_table_reader_debug_strings( + _data_reader.file_block_layout, + [](const FileBlockColumn& column) { + std::ostringstream column_out; + column_out << "FileBlockColumn{file_column_id=" << column.file_column_id + << ", name=" << column.name << ", type=" + << (column.type == nullptr ? "null" : column.type->get_name()) + << "}"; + return column_out.str(); + }) + << ", block_template_columns=" << _data_reader.block_template.columns() + << ", column_mapper=" << _data_reader.column_mapper.debug_string() << "}"; + return out.str(); +} + +Status TableReader::annotate_projected_column(const TFileScanSlotInfo& slot_info, + ProjectedColumnBuildContext* context, + ColumnDefinition* column) const { + (void)slot_info; + DORIS_CHECK(context != nullptr); + DORIS_CHECK(column != nullptr); + context->schema_column.reset(); + const auto* schema_field = find_external_root_field(context->scan_params, *column); + if (schema_field == nullptr) { + return Status::OK(); + } + context->schema_column = build_schema_column_from_external_field(*schema_field, column->type); + column->identifier = context->schema_column->identifier; + column->name_mapping = context->schema_column->name_mapping; + return Status::OK(); +} + +Status TableReader::init(TableReadOptions&& options) { + _scan_params = options.scan_params; + _format = options.format; + _io_ctx = options.io_ctx; + _runtime_state = options.runtime_state; + _scanner_profile = options.scanner_profile; + _push_down_agg_type = options.push_down_agg_type; + _projected_columns = std::move(options.projected_columns); + _system_properties = create_system_properties(_scan_params); + _mapper_options.mode = TableColumnMappingMode::BY_NAME; + _mapper_options.allow_missing_columns = options.allow_missing_columns; + _conjuncts = std::move(options.conjuncts); + _table_column_predicates = std::move(options.column_predicates); + + if (_scanner_profile != nullptr) { + static const char* table_profile = "TableReader"; + ADD_TIMER_WITH_LEVEL(_scanner_profile, table_profile, 1); + _profile.num_delete_files = ADD_CHILD_COUNTER_WITH_LEVEL(_scanner_profile, "NumDeleteFiles", + TUnit::UNIT, table_profile, 1); + _profile.num_delete_rows = ADD_CHILD_COUNTER_WITH_LEVEL(_scanner_profile, "NumDeleteRows", + TUnit::UNIT, table_profile, 1); + _profile.parse_delete_file_time = ADD_CHILD_TIMER_WITH_LEVEL( + _scanner_profile, "ParseDeleteFileTime", table_profile, 1); + _profile.exec_timer = + ADD_CHILD_TIMER_WITH_LEVEL(_scanner_profile, "GetBlockTime", table_profile, 1); + _profile.prepare_split_timer = + ADD_CHILD_TIMER_WITH_LEVEL(_scanner_profile, "PrepareSplitTime", table_profile, 1); + _profile.finalize_timer = + ADD_CHILD_TIMER_WITH_LEVEL(_scanner_profile, "FinalizeBlockTime", table_profile, 1); + _profile.create_reader_timer = + ADD_CHILD_TIMER_WITH_LEVEL(_scanner_profile, "CreateReaderTime", table_profile, 1); + _profile.pushdown_agg_timer = + ADD_CHILD_TIMER_WITH_LEVEL(_scanner_profile, "PushDownAggTime", table_profile, 1); + _profile.open_reader_timer = + ADD_CHILD_TIMER_WITH_LEVEL(_scanner_profile, "OpenReaderTime", table_profile, 1); + } + return Status::OK(); +} + +Status TableReader::_build_table_filters_from_conjuncts() { + _table_filters.clear(); + for (const auto& conjunct : _conjuncts) { + RETURN_IF_ERROR( + build_table_filters_from_conjunct(conjunct, _runtime_state, &_table_filters)); + } + return Status::OK(); +} + +Status TableReader::_open_local_filter_exprs(const FileScanRequest& file_request) { + RowDescriptor row_desc; + for (const auto& conjunct : file_request.conjuncts) { + RETURN_IF_ERROR(conjunct->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(conjunct->open(_runtime_state)); + } + for (const auto& delete_conjunct : file_request.delete_conjuncts) { + RETURN_IF_ERROR(delete_conjunct->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(delete_conjunct->open(_runtime_state)); + } + return Status::OK(); +} + +Status TableReader::create_next_reader(bool* eos) { + SCOPED_TIMER(_profile.create_reader_timer); + DCHECK(_data_reader.reader == nullptr); + if (_current_task == nullptr) { + *eos = true; + return Status::OK(); + } + + RETURN_IF_ERROR(create_file_reader(&_data_reader.reader)); + DORIS_CHECK(_data_reader.reader != nullptr); + RETURN_IF_ERROR(_data_reader.reader->init(_runtime_state)); + RETURN_IF_ERROR(open_reader()); + if (_data_reader.reader == nullptr) { + *eos = _current_task == nullptr; + return Status::OK(); + } + *eos = false; + return Status::OK(); +} + +Status TableReader::create_file_reader(std::unique_ptr* reader) { + DORIS_CHECK(reader != nullptr); + if (_format == FileFormat::PARQUET) { + *reader = std::make_unique(_system_properties, + _current_task->data_file, _io_ctx, + _scanner_profile, _global_rowid_context); + return Status::OK(); + } + return Status::NotSupported("TableReader does not support file format {}", + file_format_to_string(_format)); +} + +std::unique_ptr create_file_description(const TFileRangeDesc& range) { + auto file_description = std::make_unique(); + file_description->path = range.path; + file_description->file_size = range.__isset.file_size ? range.file_size : -1; + file_description->range_start_offset = range.__isset.start_offset ? range.start_offset : 0; + file_description->range_size = range.__isset.size ? range.size : -1; + if (range.__isset.fs_name) { + file_description->fs_name = range.fs_name; + } + if (range.__isset.file_cache_admission) { + file_description->file_cache_admission = range.file_cache_admission; + } + return file_description; +} + +Status TableReader::prepare_split(const SplitReadOptions& options) { + SCOPED_TIMER(_profile.prepare_split_timer); + _partition_values = std::move(options.partition_values); + _current_task = std::make_unique(); + _current_task->data_file = create_file_description(options.current_range); + _global_rowid_context = options.global_rowid_context; + _delete_rows = nullptr; + _aggregate_pushdown_tried = false; + _remaining_table_level_count = -1; + if (_push_down_agg_type == TPushAggOp::type::COUNT && + options.current_range.__isset.table_format_params && + options.current_range.table_format_params.__isset.table_level_row_count) { + DORIS_CHECK(options.current_range.table_format_params.table_level_row_count >= -1); + _remaining_table_level_count = + options.current_range.table_format_params.table_level_row_count; + } + if (_is_table_level_count_active()) { + return Status::OK(); + } + return _parse_delete_predicates(options); +} + +Status TableReader::_parse_delete_predicates(const SplitReadOptions& options) { + DeleteFileDesc desc {.fs_name = options.current_range.fs_name}; + bool has_delete_file = false; + RETURN_IF_ERROR(_parse_deletion_vector_file(options.current_range.table_format_params, &desc, + &has_delete_file)); + if (has_delete_file) { + DORIS_CHECK(options.cache != nullptr); + Status create_status = Status::OK(); + + _delete_rows = options.cache->get(desc.key, [&]() -> DeleteRows* { + auto* delete_rows = new DeleteRows; + + DeletionVectorReader dv_reader(_runtime_state, _scanner_profile, *_scan_params, desc, + _io_ctx.get()); + create_status = dv_reader.open(); + if (!create_status.ok()) [[unlikely]] { + return nullptr; + } + + size_t bytes_read = desc.size; + std::vector buffer(bytes_read); + create_status = dv_reader.read_at(desc.start_offset, {buffer.data(), bytes_read}); + if (!create_status.ok()) [[unlikely]] { + return nullptr; + } + + const char* buf = buffer.data(); + SCOPED_TIMER(_profile.parse_delete_file_time); + create_status = parse_deletion_vector(buf, bytes_read, desc.format, delete_rows); + if (!create_status.ok()) [[unlikely]] { + return nullptr; + } + COUNTER_UPDATE(_profile.num_delete_rows, delete_rows->size()); + return delete_rows; + }); + RETURN_IF_ERROR(create_status); + } + + return Status::OK(); +} +} // namespace doris::format diff --git a/be/src/format_v2/table_reader.h b/be/src/format_v2/table_reader.h new file mode 100644 index 00000000000000..790ef0c0291862 --- /dev/null +++ b/be/src/format_v2/table_reader.h @@ -0,0 +1,1195 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "common/cast_set.h" +#include "common/exception.h" +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/column/column_array.h" +#include "core/column/column_const.h" +#include "core/column/column_map.h" +#include "core/column/column_nullable.h" +#include "core/column/column_struct.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_struct.h" +#include "core/field.h" +#include "exprs/vexpr.h" +#include "exprs/vexpr_context.h" +#include "exprs/vexpr_fwd.h" +#include "exprs/vslot_ref.h" +#include "format_v2/column_data.h" +#include "format_v2/column_mapper.h" +#include "format_v2/expr/delete_predicate.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "format_v2/schema_projection.h" +#include "gen_cpp/PlanNodes_types.h" +#include "runtime/descriptors.h" + +namespace doris { +class Block; +class ColumnPredicate; +struct DeleteFileDesc; +class RuntimeState; +} // namespace doris + +namespace doris::format { + +using DeleteRows = std::vector; + +// Row-level predicates on table/global schema. They are rewritten to file-local expressions when +// possible, and remain the source of row-level filtering after localization. +struct TableFilter { + VExprContextSPtr conjunct; + std::vector global_indices; +}; + +struct ScanTask { + virtual ~ScanTask() = default; + + std::unique_ptr data_file; +}; + +struct ProjectedColumnBuildContext { + const TFileScanRangeParams* scan_params = nullptr; + const TFileRangeDesc* range = nullptr; + RuntimeState* runtime_state = nullptr; + std::optional schema_column = std::nullopt; + size_t next_file_column_idx = 0; +}; + +struct ReadProfile { + RuntimeProfile::Counter* num_delete_files = nullptr; + RuntimeProfile::Counter* num_delete_rows = nullptr; + RuntimeProfile::Counter* parse_delete_file_time = nullptr; + RuntimeProfile::Counter* exec_timer = nullptr; + RuntimeProfile::Counter* prepare_split_timer = nullptr; + RuntimeProfile::Counter* finalize_timer = nullptr; + RuntimeProfile::Counter* create_reader_timer = nullptr; + RuntimeProfile::Counter* pushdown_agg_timer = nullptr; + RuntimeProfile::Counter* open_reader_timer = nullptr; +}; + +struct TableReadOptions { + // Columns need to be read from file and output by table reader. They are all in table/global + // schema semantics. + const std::vector projected_columns; + // Simple predicates for a single column, which is parsed on scan operator. + const TableColumnPredicates column_predicates; + // All complex conjuncts from scan operator + const VExprContextSPtrs conjuncts; + // File format of the underlying data files, needed for reader initialization and reader-level + // filter pushdown. + const FileFormat format; + TFileScanRangeParams* scan_params; + std::shared_ptr io_ctx; + RuntimeState* runtime_state; + RuntimeProfile* scanner_profile; + const bool allow_missing_columns = true; + // Push-down aggregate type. + const TPushAggOp::type push_down_agg_type = TPushAggOp::type::NONE; +}; + +struct SplitReadOptions { + // Split-level information for reader initialization, which may include file path, partition values, delete file info, etc. The content is table format specific and opaque to table reader base class; it's the responsibility of the concrete table reader implementation to parse necessary information for reader initialization and filter pushdown. + std::map partition_values; + ShardedKVCache* cache; + TFileRangeDesc current_range; + std::optional global_rowid_context; +}; + +// table-level reader 基类。 +// 该层负责多文件编排和动态分区裁剪等通用 table-level 逻辑，对外输出 table block。 +// 子类只需要实现“如何打开下一个具体 reader”和“如何读取当前 reader”的表格式语义。 +class TableReader { +public: + virtual ~TableReader() = default; + + // 初始化 table reader 的通用运行参数。 + // 子类可以在自己的 init(options) 中调用该方法；这里不接收具体表格式 schema/task。 + virtual Status init(TableReadOptions&& options); + + // Prepare for reading a new split/task. + // 1. Pass a new split/task to reader, which will be used in subsequent open_reader() to initialize the underlying file reader. + // 2. Parse delete predicates from split/task information, which will be used for later dynamic filtering and delete handling. + virtual Status prepare_split(const SplitReadOptions& options); + + // 对外读取 table block 的统一入口。 + // 基类负责 current reader 的打开、EOF 后切换和关闭；子类只实现 protected hook。 + // table_block 的列必须已经是 table/global schema 语义。 + virtual Status get_block(Block* block, bool* eos) { + SCOPED_TIMER(_profile.exec_timer); + DORIS_CHECK(block->columns() == _projected_columns.size()); + block->clear_column_data(_projected_columns.size()); + + while (true) { + if (*eos) { + return Status::OK(); + } + if (!_data_reader.reader) { + if (_is_table_level_count_active()) { + RETURN_IF_ERROR(_read_table_level_count(block, eos)); + return Status::OK(); + } + RETURN_IF_ERROR(create_next_reader(eos)); + if (!_data_reader.reader) { + DCHECK(*eos); + return Status::OK(); + } + } + + // Materialize a reduced row set for upper aggregate operators when aggregate + // pushdown can be applied. This is not the final aggregate result: COUNT emits + // `count` default rows for the upper COUNT(*), and MIN/MAX emits two rows containing + // file-level min/max values for the upper MIN/MAX. + if (!_aggregate_pushdown_tried) { + SCOPED_TIMER(_profile.pushdown_agg_timer); + bool pushed_down = false; + RETURN_IF_ERROR(_try_materialize_aggregate_pushdown_rows(block, &pushed_down)); + if (pushed_down) { + return Status::OK(); + } + } + + bool current_eof = false; + _data_reader.block_template.clear_column_data( + cast_set(_data_reader.file_block_layout.size())); + size_t current_rows = 0; + RETURN_IF_ERROR(_data_reader.reader->get_block(&_data_reader.block_template, + ¤t_rows, ¤t_eof)); + if (current_rows == 0) { + if (current_eof) { + RETURN_IF_ERROR(close_current_reader()); + } + continue; + } + DCHECK_EQ(_data_reader.block_template.columns(), _data_reader.file_block_layout.size()) + << _data_reader.block_template.dump_structure(); + DORIS_CHECK(block->columns() == _data_reader.column_mapper.mappings().size()); + RETURN_IF_ERROR(finalize_chunk(block, current_rows)); + if (current_eof) { + RETURN_IF_ERROR(close_current_reader()); + } + return Status::OK(); + } + } + + // 关闭 table reader 及当前正在读取的底层 reader。 + // 子类如果持有额外表格式资源，应 override 后先调用 TableReader::close()。 + virtual Status close() { + if (_data_reader.reader) { + RETURN_IF_ERROR(close_current_reader()); + } + _current_task.reset(); + _remaining_table_level_count = -1; + return Status::OK(); + } + + virtual std::string debug_string() const; + + virtual Status annotate_projected_column(const TFileScanSlotInfo& slot_info, + ProjectedColumnBuildContext* context, + ColumnDefinition* column) const; + + virtual Status validate_projected_columns(const ProjectedColumnBuildContext& context) const { + (void)context; + return Status::OK(); + } + +protected: + // Parse deletion vector information from table format specific file description. + virtual Status _parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, + DeleteFileDesc* desc, bool* has_delete_file) { + *has_delete_file = false; + return Status::OK(); + } + + // 切换到下一个 reader 的通用流程。 + // 该方法先关闭当前 reader，再打开下一个具体 reader；子类不应重复实现这个循环。 + Status create_next_reader(bool* eos); + virtual Status create_file_reader(std::unique_ptr* reader); + virtual TableColumnMappingMode mapping_mode() const { return TableColumnMappingMode::BY_NAME; } + virtual Status annotate_file_schema(std::vector* file_schema) { + DORIS_CHECK(file_schema != nullptr); + return Status::OK(); + } + + // 打开当前具体 reader。 + // 子类在这里基于当前 split/task 初始化底层 FileReader。 + virtual Status open_reader() { + SCOPED_TIMER(_profile.open_reader_timer); + // 1. Get file schema and create column mapping. + std::vector file_schema; + RETURN_IF_ERROR(_data_reader.reader->get_schema(&file_schema)); + // For Paimon/Hudi, field ID is set by `history_schema_info` from FE. So we need to annotate file schema with Field ID before creating column mapping when mapping by field ID. + RETURN_IF_ERROR(annotate_file_schema(&file_schema)); + _data_reader.file_schema = file_schema; + _mapper_options.mode = mapping_mode(); + + _data_reader.column_mapper = TableColumnMapper(_mapper_options); + RETURN_IF_ERROR(_data_reader.column_mapper.create_mapping(_projected_columns, + _partition_values, file_schema)); + DORIS_CHECK(_data_reader.column_mapper.mappings().size() == _projected_columns.size()); + + // 2. Build table filters based on conjuncts and column predicates. + RETURN_IF_ERROR(_build_table_filters_from_conjuncts()); + + // 3. Create file scan request based on column mapping and table filters, then open file + // reader with the request. File scan request carries row-level expression filters and + // file-level pruning hints. Only expression filters decide returned rows; column predicates + // are pruning hints. + auto file_request = std::make_shared(); + RETURN_IF_ERROR(_data_reader.column_mapper.create_scan_request( + _table_filters, _table_column_predicates, _projected_columns, file_request.get(), + _runtime_state)); + bool constant_filter_pruned_split = false; + RETURN_IF_ERROR(_evaluate_constant_filters(&constant_filter_pruned_split)); + if (constant_filter_pruned_split) { + RETURN_IF_ERROR(close_current_reader()); + return Status::OK(); + } + RETURN_IF_ERROR(customize_file_scan_request(file_request.get())); + RETURN_IF_ERROR(_open_local_filter_exprs(*file_request)); + _data_reader.file_block_layout.clear(); + _data_reader.block_template.clear(); + _data_reader.file_block_layout.resize(file_request->local_positions.size()); + + // 4. Build file block layout from file schema and column mapping. The layout describes + // the block returned by file reader before table-column materialization. + for (const auto& [file_column_id, block_position] : file_request->local_positions) { + DORIS_CHECK(block_position.value() < _data_reader.file_block_layout.size()); + const auto* field = _find_column_definition(_data_reader.file_schema, file_column_id); + DORIS_CHECK(field != nullptr); + + ColumnDefinition projected_field; + { + auto it = std::find_if( + file_request->non_predicate_columns.begin(), + file_request->non_predicate_columns.end(), + [&](const LocalColumnIndex& p) { return p.column_id() == file_column_id; }); + if (it != file_request->non_predicate_columns.end()) { + RETURN_IF_ERROR(project_column_definition(*field, *it, &projected_field)); + } + } + { + auto it = std::find_if( + file_request->predicate_columns.begin(), + file_request->predicate_columns.end(), + [&](const LocalColumnIndex& p) { return p.column_id() == file_column_id; }); + if (it != file_request->predicate_columns.end()) { + RETURN_IF_ERROR(project_column_definition(*field, *it, &projected_field)); + } + } + _data_reader.file_block_layout[block_position.value()] = { + .file_column_id = file_column_id, + .name = projected_field.name, + .type = projected_field.type, + }; + DORIS_CHECK(_data_reader.file_block_layout[block_position.value()].type != nullptr); + } + + // 5. Prepare block template from file block layout. The block template stores the block + // returned by file reader before table-column materialization. + _data_reader.block_template.reserve(_data_reader.file_block_layout.size()); + for (const auto& column : _data_reader.file_block_layout) { + _data_reader.block_template.insert( + {column.type->create_column(), column.type, column.name}); + } + LOG(WARNING) << "TableReader debug: " << debug_string(); + RETURN_IF_ERROR(_open_mapping_exprs()); + RETURN_IF_ERROR(_data_reader.reader->open(file_request)); + return Status::OK(); + } + + Status _build_table_filters_from_conjuncts(); + Status _open_local_filter_exprs(const FileScanRequest& file_request); + + Status _evaluate_constant_filters(bool* can_filter_all) { + DORIS_CHECK(can_filter_all != nullptr); + *can_filter_all = false; + for (const auto& table_filter : _table_filters) { + if (table_filter.conjunct == nullptr || + !_table_filter_has_only_constant_entries(table_filter)) { + continue; + } + Block eval_block; + RETURN_IF_ERROR(_build_constant_filter_block(table_filter, &eval_block)); + RowDescriptor row_desc; + RETURN_IF_ERROR(table_filter.conjunct->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(table_filter.conjunct->open(_runtime_state)); + int result_column_id = -1; + RETURN_IF_ERROR(table_filter.conjunct->execute(&eval_block, &result_column_id)); + DORIS_CHECK(result_column_id >= 0); + if (_filter_result_filters_all(eval_block.get_by_position(result_column_id).column)) { + *can_filter_all = true; + return Status::OK(); + } + } + return Status::OK(); + } + + bool _table_filter_has_only_constant_entries(const TableFilter& table_filter) const { + const auto& filter_entries = _data_reader.column_mapper.filter_entries(); + for (const auto global_index : table_filter.global_indices) { + const auto entry_it = filter_entries.find(global_index); + if (entry_it == filter_entries.end() || !entry_it->second.is_constant()) { + return false; + } + } + return !table_filter.global_indices.empty(); + } + + Status _build_constant_filter_block(const TableFilter& table_filter, Block* eval_block) { + DORIS_CHECK(eval_block != nullptr); + eval_block->clear(); + const auto& mappings = _data_reader.column_mapper.mappings(); + const auto& filter_entries = _data_reader.column_mapper.filter_entries(); + DORIS_CHECK(mappings.size() == _projected_columns.size()); + for (size_t column_idx = 0; column_idx < mappings.size(); ++column_idx) { + const auto global_index = GlobalIndex(column_idx); + const auto& mapping = mappings[column_idx]; + const auto entry_it = filter_entries.find(global_index); + const bool referenced_by_filter = + std::find(table_filter.global_indices.begin(), + table_filter.global_indices.end(), + global_index) != table_filter.global_indices.end(); + if (referenced_by_filter && entry_it != filter_entries.end() && + entry_it->second.is_constant()) { + ColumnPtr constant_column; + RETURN_IF_ERROR(_materialize_constant_filter_column( + entry_it->second.constant_index(), &constant_column)); + eval_block->insert({std::move(constant_column), mapping.table_type, + mapping.table_column_name}); + } else { + eval_block->insert({mapping.table_type->create_column_const_with_default_value(1), + mapping.table_type, mapping.table_column_name}); + } + } + return Status::OK(); + } + + Status _materialize_constant_filter_column(ConstantIndex constant_index, ColumnPtr* column) { + DORIS_CHECK(column != nullptr); + const auto& constant_entry = _data_reader.column_mapper.constant_map().get(constant_index); + DORIS_CHECK(constant_entry.expr != nullptr); + DORIS_CHECK(constant_entry.type != nullptr); + RowDescriptor row_desc; + RETURN_IF_ERROR(constant_entry.expr->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(constant_entry.expr->open(_runtime_state)); + Block eval_block; + eval_block.insert({constant_entry.type->create_column_const_with_default_value(1), + constant_entry.type, "__table_reader_constant_filter"}); + int result_column_id = -1; + RETURN_IF_ERROR(constant_entry.expr->execute(&eval_block, &result_column_id)); + DORIS_CHECK(result_column_id >= 0); + *column = eval_block.get_by_position(result_column_id).column; + DORIS_CHECK((*column)->size() == 1); + return Status::OK(); + } + + static bool _filter_result_filters_all(const ColumnPtr& filter_column) { + DORIS_CHECK(filter_column.get() != nullptr); + DORIS_CHECK(filter_column->size() == 1); + return !filter_column->get_bool(0); + } + + virtual Status customize_file_scan_request(FileScanRequest* file_request) { + return _append_delete_predicate(file_request); + } + + bool _is_table_level_count_active() const { return _remaining_table_level_count >= 0; } + + Status _materialize_count_rows(size_t rows, Block* block) const { + DORIS_CHECK(block != nullptr); + DORIS_CHECK(block->columns() > 0 || rows == 0); + for (size_t column_idx = 0; column_idx < block->columns(); ++column_idx) { + auto column = block->get_by_position(column_idx).type->create_column(); + column->resize(rows); + block->replace_by_position(column_idx, std::move(column)); + } + return Status::OK(); + } + + Status _read_table_level_count(Block* block, bool* eos) { + DORIS_CHECK(block != nullptr); + DORIS_CHECK(eos != nullptr); + DORIS_CHECK(_push_down_agg_type == TPushAggOp::type::COUNT); + DORIS_CHECK(_remaining_table_level_count >= 0); + if (_remaining_table_level_count == 0) { + _remaining_table_level_count = -1; + _current_task.reset(); + *eos = true; + return Status::OK(); + } + + const int64_t batch_size = _runtime_state == nullptr + ? _remaining_table_level_count + : static_cast(_runtime_state->batch_size()); + const auto rows = std::min(_remaining_table_level_count, batch_size); + RETURN_IF_ERROR(_materialize_count_rows(cast_set(rows), block)); + _remaining_table_level_count -= rows; + *eos = false; + return Status::OK(); + } + + static LocalIndex _next_block_position(const FileScanRequest& request) { + size_t next_position = 0; + for (const auto& [_, block_position] : request.local_positions) { + next_position = std::max(next_position, block_position.value() + 1); + } + return LocalIndex(next_position); + } + + void _append_file_scan_column(FileScanRequest* request, LocalColumnId column_id, + std::vector* scan_columns) { + DORIS_CHECK(request != nullptr); + DORIS_CHECK(scan_columns != nullptr); + if (scan_columns == &request->non_predicate_columns && + std::ranges::find_if(request->predicate_columns, [&](const LocalColumnIndex& p) { + return p.column_id() == column_id; + }) != request->predicate_columns.end()) { + // The column is already added as a predicate column, no need to add it again as a non-predicate column because predicate columns are also returned in the file reader block and can be used for materialization and filtering. + return; + } + if (!request->local_positions.contains(column_id)) { + request->local_positions.emplace(column_id, _next_block_position(*request)); + scan_columns->push_back(LocalColumnIndex::top_level(column_id)); + } else if (std::ranges::find_if(*scan_columns, [&](const LocalColumnIndex& p) { + return p.column_id() == column_id; + }) == scan_columns->end()) { + scan_columns->push_back(LocalColumnIndex::top_level(column_id)); + } + if (scan_columns == &request->predicate_columns) { + auto it = std::ranges::find_if( + request->non_predicate_columns, + [&](const LocalColumnIndex& p) { return p.column_id() == column_id; }); + if (it != request->non_predicate_columns.end()) { + request->non_predicate_columns.erase(it); + } + } + if (column_id == LocalColumnId(ROW_POSITION_COLUMN_ID) && + _find_column_definition(_data_reader.file_schema, column_id) == nullptr) { + _data_reader.file_schema.push_back(row_position_column_definition()); + } + } + + // Append DeletePredicate to file scan request if there are deletes. The predicate will be evaluated in file reader level and filter out deleted rows before returning data to table reader. + Status _append_delete_predicate(FileScanRequest* request) { + DORIS_CHECK(request != nullptr); + if (_delete_rows == nullptr || _delete_rows->empty()) { + return Status::OK(); + } + const auto row_position_column_id = LocalColumnId(ROW_POSITION_COLUMN_ID); + _append_file_scan_column(request, row_position_column_id, &request->predicate_columns); + + auto delete_predicate = std::make_shared(*_delete_rows); + const auto block_position = request->local_positions.at(row_position_column_id); + delete_predicate->add_child(VSlotRef::create_shared( + cast_set(block_position.value()), cast_set(block_position.value()), -1, + std::make_shared(), ROW_POSITION_COLUMN_NAME)); + + request->delete_conjuncts.push_back( + VExprContext::create_shared(std::move(delete_predicate))); + return Status::OK(); + } + + // 关闭当前具体 reader。 + // 该 hook 会被 create_next_reader 和 close 调用；实现应保持幂等。 + virtual Status close_current_reader() { + RETURN_IF_ERROR(_data_reader.reader->close()); + _data_reader.reader.reset(); + _data_reader.column_mapper.clear(); + _table_filters.clear(); + _data_reader.file_schema.clear(); + _data_reader.file_block_layout.clear(); + _data_reader.block_template.clear(); + _current_task.reset(); + return Status::OK(); + } + + // Finalize file-local block to table/global schema block. + Status finalize_chunk(Block* block, const size_t rows) { + SCOPED_TIMER(_profile.finalize_timer); + size_t idx = 0; + for (const auto& mapping : _data_reader.column_mapper.mappings()) { + ColumnPtr column; + RETURN_IF_ERROR(_materialize_mapping_column(mapping, &_data_reader.block_template, rows, + &column)); + block->replace_by_position(idx, IColumn::mutate(std::move(column))); + idx++; + } + RETURN_IF_ERROR(materialize_virtual_columns(block)); + return Status::OK(); + } + + // Materialize virtual columns in table block, such as _row_id and _last_updated_sequence_number in Iceberg. This is called after finalize_chunk, so the virtual column can be referenced in finalize_expr. + virtual Status materialize_virtual_columns(Block* table_block) { return Status::OK(); } + + Status _try_materialize_aggregate_pushdown_rows(Block* block, bool* pushed_down) { + DORIS_CHECK(block != nullptr); + DORIS_CHECK(pushed_down != nullptr); + *pushed_down = false; + block->clear_column_data(_projected_columns.size()); + _aggregate_pushdown_tried = true; + if (!_supports_aggregate_pushdown(_push_down_agg_type)) { + return Status::OK(); + } + + FileAggregateRequest file_request; + RETURN_IF_ERROR(_build_file_aggregate_request(_push_down_agg_type, &file_request)); + FileAggregateResult file_result; + const auto status = _data_reader.reader->get_aggregate_result(file_request, &file_result); + if (status.is()) { + return Status::OK(); + } + RETURN_IF_ERROR(status); + RETURN_IF_ERROR( + _materialize_aggregate_pushdown_rows(_push_down_agg_type, file_result, block)); + *pushed_down = true; + RETURN_IF_ERROR(close_current_reader()); + return Status::OK(); + } + + virtual bool _supports_aggregate_pushdown(TPushAggOp::type agg_type) const { + // Only COUNT and MIN/MAX can be push down. + if (agg_type != TPushAggOp::type::COUNT && agg_type != TPushAggOp::type::MINMAX) { + return false; + } + // Only support aggregate pushdown when there is no delete, filter and column predicate, so + // the reduced rows consumed by the upper aggregate remain semantically equivalent to a + // normal scan. + if (_delete_rows != nullptr && !_delete_rows->empty()) { + return false; + } + if (!_table_filters.empty() || !_table_column_predicates.empty()) { + return false; + } + if (agg_type == TPushAggOp::type::COUNT) { + return true; + } + // For MIN/MAX, only support direct file-to-table column mappings. The two emitted rows + // must be enough for the upper MIN/MAX aggregate without evaluating default expressions or + // virtual columns. + for (const auto& mapping : _data_reader.column_mapper.mappings()) { + if (!mapping.file_local_id.has_value() || + mapping.virtual_column_type != TableVirtualColumnType::INVALID || + mapping.default_expr != nullptr || mapping.file_type == nullptr || + mapping.table_type == nullptr) { + return false; + } + if (!_can_push_down_minmax_for_mapping(mapping)) { + return false; + } + } + return true; + } + + static ColumnPtr _detach_column(ColumnPtr column) { + DORIS_CHECK(column.get() != nullptr); + return IColumn::mutate(std::move(column)); + } + + static Status _align_column_nullability(ColumnPtr* column, const DataTypePtr& table_type) { + DORIS_CHECK(column != nullptr); + DORIS_CHECK(column->get() != nullptr); + DORIS_CHECK(table_type != nullptr); + // Must return non-const column + *column = (*column)->convert_to_full_column_if_const(); + if (table_type->is_nullable()) { + const auto& nested_type = + assert_cast(*table_type).get_nested_type(); + if (!(*column)->is_nullable()) { + RETURN_IF_ERROR(_align_column_nullability(column, nested_type)); + *column = make_nullable(*column); + return Status::OK(); + } + const auto& nullable_column = assert_cast(**column); + ColumnPtr nested_column = nullable_column.get_nested_column_ptr(); + RETURN_IF_ERROR(_align_column_nullability(&nested_column, nested_type)); + *column = ColumnNullable::create(nested_column, + nullable_column.get_null_map_column_ptr()); + return Status::OK(); + } + if ((*column)->is_nullable()) { + const auto& nullable_column = assert_cast(**column); + if (nullable_column.has_null()) { + return Status::InternalError( + "Default expression produced NULL for non-nullable table column"); + } + ColumnPtr nested_column = nullable_column.get_nested_column_ptr(); + RETURN_IF_ERROR(_align_column_nullability(&nested_column, table_type)); + *column = nested_column; + return Status::OK(); + } + if (const auto* array_type = typeid_cast(table_type.get())) { + const auto& array_column = assert_cast(**column); + ColumnPtr nested_column = array_column.get_data_ptr(); + RETURN_IF_ERROR( + _align_column_nullability(&nested_column, array_type->get_nested_type())); + *column = ColumnArray::create(nested_column, array_column.get_offsets_ptr()); + return Status::OK(); + } + if (const auto* map_type = typeid_cast(table_type.get())) { + const auto& map_column = assert_cast(**column); + ColumnPtr key_column = map_column.get_keys_ptr(); + ColumnPtr value_column = map_column.get_values_ptr(); + RETURN_IF_ERROR(_align_column_nullability(&key_column, map_type->get_key_type())); + RETURN_IF_ERROR(_align_column_nullability(&value_column, map_type->get_value_type())); + *column = ColumnMap::create(key_column, value_column, map_column.get_offsets_ptr()); + return Status::OK(); + } + if (const auto* struct_type = typeid_cast(table_type.get())) { + const auto& struct_column = assert_cast(**column); + Columns columns = struct_column.get_columns_copy(); + DORIS_CHECK(columns.size() == struct_type->get_elements().size()); + for (size_t i = 0; i < columns.size(); ++i) { + RETURN_IF_ERROR( + _align_column_nullability(&columns[i], struct_type->get_element(i))); + } + *column = ColumnStruct::create(columns); + return Status::OK(); + } + return Status::OK(); + } + + static Status _execute_default_expr_without_root_type_check( + const VExprContextSPtr& default_expr, const Block* block, + ColumnWithTypeAndName* result_data) { + DORIS_CHECK(default_expr != nullptr); + DORIS_CHECK(block != nullptr); + DORIS_CHECK(result_data != nullptr); + ColumnPtr result_column; + Status st; + RETURN_IF_CATCH_EXCEPTION({ + st = default_expr->root()->execute_column_impl(default_expr.get(), block, nullptr, + block->rows(), result_column); + }); + RETURN_IF_ERROR(st); + DORIS_CHECK(result_column.get() != nullptr); + if (result_column->size() != block->rows()) { + return Status::InternalError( + "Default expr {} return column size {} not equal to expected size {}", + default_expr->expr_name(), result_column->size(), block->rows()); + } + result_data->column = result_column; + result_data->type = default_expr->execute_type(block); + result_data->name = default_expr->expr_name(); + return Status::OK(); + } + + Status _materialize_mapping_column(const ColumnMapping& mapping, Block* current_block, + const size_t rows, ColumnPtr* column) { + if (!mapping.is_trivial && mapping.file_local_id.has_value() && + !mapping.child_mappings.empty()) { + DCHECK(mapping.projection != nullptr); + int res_id; + RETURN_IF_ERROR(mapping.projection->execute(current_block, &res_id)); + ColumnPtr result_column = current_block->get_by_position(res_id).column; + RETURN_IF_ERROR( + _materialize_complex_mapping_column(mapping, result_column, rows, column)); + return Status::OK(); + } + if (mapping.projection != nullptr) { + int res_id; + RETURN_IF_ERROR(mapping.projection->execute(current_block, &res_id)); + ColumnPtr result_column = current_block->get_by_position(res_id).column; + *column = _detach_column(std::move(result_column)); + return Status::OK(); + } + if (mapping.default_expr != nullptr) { + if (current_block->rows() == rows) { + ColumnWithTypeAndName result; + RETURN_IF_ERROR(_execute_default_expr_without_root_type_check( + mapping.default_expr, current_block, &result)); + ColumnPtr result_column = result.column; + RETURN_IF_ERROR(_align_column_nullability(&result_column, mapping.table_type)); + *column = _detach_column(std::move(result_column)); + } else { + DORIS_CHECK(mapping.constant_index.has_value()); + Block eval_block; + eval_block.insert({mapping.table_type->create_column_const_with_default_value(rows), + mapping.table_type, "__table_reader_const_rows"}); + ColumnWithTypeAndName result; + RETURN_IF_ERROR(_execute_default_expr_without_root_type_check( + mapping.default_expr, &eval_block, &result)); + ColumnPtr result_column = result.column; + RETURN_IF_ERROR(_align_column_nullability(&result_column, mapping.table_type)); + *column = _detach_column(std::move(result_column)); + } + return Status::OK(); + } + ColumnPtr result_column = mapping.table_type->create_column_const_with_default_value(rows); + *column = _detach_column(std::move(result_column)); + return Status::OK(); + } + + Status _materialize_complex_mapping_column(const ColumnMapping& mapping, + const ColumnPtr& file_column, const size_t rows, + ColumnPtr* column) { + DORIS_CHECK(mapping.table_type != nullptr); + DORIS_CHECK(file_column.get() != nullptr); + const auto table_type = remove_nullable(mapping.table_type); + switch (table_type->get_primitive_type()) { + case TYPE_STRUCT: + RETURN_IF_ERROR(_materialize_struct_mapping_column(mapping, file_column, rows, column)); + break; + case TYPE_ARRAY: + RETURN_IF_ERROR(_materialize_array_mapping_column(mapping, file_column, rows, column)); + break; + case TYPE_MAP: + RETURN_IF_ERROR(_materialize_map_mapping_column(mapping, file_column, rows, column)); + break; + default: + *column = _detach_column(file_column); + break; + } + return Status::OK(); + } + + static std::vector _present_child_mappings_in_file_order( + const std::vector& child_mappings) { + std::vector result; + result.reserve(child_mappings.size()); + for (const auto& child_mapping : child_mappings) { + if (child_mapping.file_local_id.has_value()) { + result.push_back(&child_mapping); + } + } + std::ranges::sort(result, [](const ColumnMapping* lhs, const ColumnMapping* rhs) { + DORIS_CHECK(lhs->file_local_id.has_value()); + DORIS_CHECK(rhs->file_local_id.has_value()); + return *lhs->file_local_id < *rhs->file_local_id; + }); + return result; + } + + static size_t _file_child_ordinal_for_mapping( + const ColumnMapping& mapping, const ColumnMapping& child_mapping, + const std::vector& file_ordered_children) { + DORIS_CHECK(child_mapping.file_local_id.has_value()); + if (!mapping.projected_file_children.empty()) { + const auto child_it = std::ranges::find_if( + mapping.projected_file_children, [&](const ColumnDefinition& file_child) { + return file_child.file_local_id() == *child_mapping.file_local_id; + }); + DORIS_CHECK(child_it != mapping.projected_file_children.end()); + return static_cast( + std::distance(mapping.projected_file_children.begin(), child_it)); + } + const auto child_it = std::ranges::find(file_ordered_children, &child_mapping); + DORIS_CHECK(child_it != file_ordered_children.end()); + return static_cast(std::distance(file_ordered_children.begin(), child_it)); + } + + static const IColumn* _nested_column_if_nullable(const ColumnPtr& column, + const NullMap** null_map) { + DORIS_CHECK(column.get() != nullptr); + if (const auto* nullable_column = check_and_get_column(*column)) { + if (null_map != nullptr) { + *null_map = &nullable_column->get_null_map_data(); + } + return &nullable_column->get_nested_column(); + } + return column.get(); + } + + Status _materialize_struct_mapping_column(const ColumnMapping& mapping, + const ColumnPtr& file_column, const size_t rows, + ColumnPtr* column) { + DORIS_CHECK(mapping.table_type != nullptr); + const auto* table_type = + assert_cast(remove_nullable(mapping.table_type).get()); + const auto full_file_column = file_column->convert_to_full_column_if_const(); + const NullMap* parent_null_map = nullptr; + const auto* nested_file_column = + _nested_column_if_nullable(full_file_column, &parent_null_map); + const auto* file_struct = assert_cast(nested_file_column); + DORIS_CHECK(table_type->get_elements().size() == mapping.child_mappings.size()); + + Columns child_columns; + child_columns.reserve(mapping.child_mappings.size()); + const auto file_ordered_children = + _present_child_mappings_in_file_order(mapping.child_mappings); + for (const auto& child_mapping : mapping.child_mappings) { + if (!child_mapping.file_local_id.has_value()) { + child_columns.push_back( + child_mapping.table_type->create_column_const_with_default_value(rows) + ->convert_to_full_column_if_const()); + continue; + } + const auto file_child_idx = + _file_child_ordinal_for_mapping(mapping, child_mapping, file_ordered_children); + DORIS_CHECK(file_child_idx < file_struct->get_columns().size()); + ColumnPtr child_column = file_struct->get_column_ptr(file_child_idx); + if (!child_mapping.is_trivial && !child_mapping.child_mappings.empty()) { + RETURN_IF_ERROR(_materialize_complex_mapping_column(child_mapping, child_column, + rows, &child_column)); + } + child_columns.push_back(std::move(child_column)); + } + MutableColumns mutable_child_columns; + mutable_child_columns.reserve(child_columns.size()); + for (auto& child_column : child_columns) { + mutable_child_columns.push_back(IColumn::mutate(std::move(child_column))); + } + auto result = ColumnStruct::create(std::move(mutable_child_columns)); + if (mapping.table_type->is_nullable()) { + auto null_map = ColumnUInt8::create(); + auto& null_map_data = null_map->get_data(); + null_map_data.resize(rows); + if (parent_null_map != nullptr) { + DORIS_CHECK(parent_null_map->size() == rows); + null_map_data.assign(parent_null_map->begin(), parent_null_map->end()); + } else { + std::fill(null_map_data.begin(), null_map_data.end(), 0); + } + *column = ColumnNullable::create(std::move(result), std::move(null_map)); + } else { + *column = std::move(result); + } + return Status::OK(); + } + + Status _materialize_array_mapping_column(const ColumnMapping& mapping, + const ColumnPtr& file_column, const size_t rows, + ColumnPtr* column) { + DORIS_CHECK(mapping.child_mappings.size() == 1); + const auto full_file_column = file_column->convert_to_full_column_if_const(); + const NullMap* parent_null_map = nullptr; + const auto* nested_file_column = + _nested_column_if_nullable(full_file_column, &parent_null_map); + const auto* file_array = assert_cast(nested_file_column); + ColumnPtr nested_column = file_array->get_data_ptr(); + const auto& element_mapping = mapping.child_mappings[0]; + if (!element_mapping.is_trivial && !element_mapping.child_mappings.empty()) { + RETURN_IF_ERROR(_materialize_complex_mapping_column( + element_mapping, nested_column, nested_column->size(), &nested_column)); + } + auto offsets_column = file_array->get_offsets_ptr()->convert_to_full_column_if_const(); + auto result = ColumnArray::create(IColumn::mutate(std::move(nested_column)), + IColumn::mutate(std::move(offsets_column))); + if (mapping.table_type->is_nullable()) { + auto null_map = ColumnUInt8::create(); + auto& null_map_data = null_map->get_data(); + null_map_data.resize(rows); + if (parent_null_map != nullptr) { + DORIS_CHECK(parent_null_map->size() == rows); + null_map_data.assign(parent_null_map->begin(), parent_null_map->end()); + } else { + std::fill(null_map_data.begin(), null_map_data.end(), 0); + } + *column = ColumnNullable::create(std::move(result), std::move(null_map)); + } else { + *column = std::move(result); + } + return Status::OK(); + } + + Status _materialize_map_mapping_column(const ColumnMapping& mapping, + const ColumnPtr& file_column, const size_t rows, + ColumnPtr* column) { + DORIS_CHECK(mapping.child_mappings.size() == 1); + const auto& entry_mapping = mapping.child_mappings[0]; + DORIS_CHECK(entry_mapping.child_mappings.size() == 1 || + entry_mapping.child_mappings.size() == 2); + + const auto full_file_column = file_column->convert_to_full_column_if_const(); + const NullMap* parent_null_map = nullptr; + const auto* nested_file_column = + _nested_column_if_nullable(full_file_column, &parent_null_map); + const auto* file_map = assert_cast(nested_file_column); + ColumnPtr key_column = file_map->get_keys_ptr(); + ColumnPtr value_column = file_map->get_values_ptr(); + + const ColumnMapping* key_mapping = nullptr; + const ColumnMapping* value_mapping = nullptr; + for (const auto& child_mapping : entry_mapping.child_mappings) { + if (!child_mapping.file_local_id.has_value()) { + continue; + } + if (*child_mapping.file_local_id == 0) { + key_mapping = &child_mapping; + } else if (*child_mapping.file_local_id == 1) { + value_mapping = &child_mapping; + } + } + + if (key_mapping != nullptr && !key_mapping->is_trivial && + !key_mapping->child_mappings.empty()) { + RETURN_IF_ERROR(_materialize_complex_mapping_column(*key_mapping, key_column, + key_column->size(), &key_column)); + } + if (value_mapping != nullptr && !value_mapping->is_trivial && + !value_mapping->child_mappings.empty()) { + RETURN_IF_ERROR(_materialize_complex_mapping_column( + *value_mapping, value_column, value_column->size(), &value_column)); + } + auto offsets_column = file_map->get_offsets_ptr()->convert_to_full_column_if_const(); + auto result = ColumnMap::create(IColumn::mutate(std::move(key_column)), + IColumn::mutate(std::move(value_column)), + IColumn::mutate(std::move(offsets_column))); + if (mapping.table_type->is_nullable()) { + auto null_map = ColumnUInt8::create(); + auto& null_map_data = null_map->get_data(); + null_map_data.resize(rows); + if (parent_null_map != nullptr) { + DORIS_CHECK(parent_null_map->size() == rows); + null_map_data.assign(parent_null_map->begin(), parent_null_map->end()); + } else { + std::fill(null_map_data.begin(), null_map_data.end(), 0); + } + *column = ColumnNullable::create(std::move(result), std::move(null_map)); + } else { + *column = std::move(result); + } + return Status::OK(); + } + + Status _open_mapping_exprs() { + RowDescriptor row_desc; + for (const auto& mapping : _data_reader.column_mapper.mappings()) { + if (mapping.projection != nullptr) { + RETURN_IF_ERROR(mapping.projection->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(mapping.projection->open(_runtime_state)); + } + if (mapping.default_expr != nullptr) { + RETURN_IF_ERROR(mapping.default_expr->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(mapping.default_expr->open(_runtime_state)); + } + } + return Status::OK(); + } + + Status _build_file_aggregate_request(TPushAggOp::type agg_type, + FileAggregateRequest* request) const { + DORIS_CHECK(request != nullptr); + DORIS_CHECK(_supports_aggregate_pushdown(agg_type)); + request->agg_type = agg_type; + request->columns.clear(); + if (agg_type == TPushAggOp::type::COUNT) { + return Status::OK(); + } + request->columns.reserve(_data_reader.column_mapper.mappings().size()); + for (const auto& mapping : _data_reader.column_mapper.mappings()) { + DORIS_CHECK(mapping.file_local_id.has_value()); + FileAggregateRequest::Column column; + column.projection = LocalColumnIndex::top_level(LocalColumnId(*mapping.file_local_id)); + if (!mapping.child_mappings.empty()) { + RETURN_IF_ERROR(build_aggregate_projection(mapping, &column.projection)); + } + request->columns.push_back(std::move(column)); + } + return Status::OK(); + } + + Status _materialize_aggregate_pushdown_rows(TPushAggOp::type agg_type, + const FileAggregateResult& file_result, + Block* block) { + if (agg_type == TPushAggOp::type::COUNT) { + // COUNT pushdown is not a final count value. It emits `count` default rows so the + // upper COUNT(*) aggregate can count them and produce the final result, including + // zero rows when count is 0. + DORIS_CHECK(file_result.count >= 0); + return _materialize_count_rows(cast_set(file_result.count), block); + } + // MIN/MAX pushdown emits two rows, min first and max second, for each projected column. + // The upper MIN/MAX aggregate consumes those two rows to produce the final aggregate value. + DORIS_CHECK(file_result.columns.size() == _data_reader.column_mapper.mappings().size()); + DORIS_CHECK(block->columns() == _data_reader.column_mapper.mappings().size()); + Block file_block; + file_block.reserve(_data_reader.file_block_layout.size()); + for (const auto& column : _data_reader.file_block_layout) { + file_block.insert({column.type->create_column(), column.type, column.name}); + } + for (size_t column_idx = 0; column_idx < file_result.columns.size(); ++column_idx) { + const auto& result_column = file_result.columns[column_idx]; + if (!result_column.has_min || !result_column.has_max) { + return Status::NotSupported("Missing min/max aggregate result for column {}", + _projected_columns[column_idx].name); + } + bool found_file_column = false; + for (size_t block_position = 0; block_position < _data_reader.file_block_layout.size(); + ++block_position) { + if (_data_reader.file_block_layout[block_position].file_column_id == + file_result.columns[column_idx].projection.column_id()) { + found_file_column = true; + auto column = file_block.get_by_position(block_position) + .type->create_column() + ->assert_mutable(); + RETURN_IF_ERROR(_insert_aggregate_projection_value( + file_result.columns[column_idx].projection, result_column.min_value, + column.get())); + RETURN_IF_ERROR(_insert_aggregate_projection_value( + file_result.columns[column_idx].projection, result_column.max_value, + column.get())); + file_block.replace_by_position(block_position, std::move(column)); + break; + } + } + DORIS_CHECK(found_file_column); + } + for (size_t column_idx = 0; column_idx < _data_reader.column_mapper.mappings().size(); + ++column_idx) { + ColumnPtr table_column; + RETURN_IF_ERROR( + _materialize_mapping_column(_data_reader.column_mapper.mappings()[column_idx], + &file_block, 2, &table_column)); + block->replace_by_position(column_idx, std::move(table_column)); + } + return Status::OK(); + } + + struct FileBlockColumn { + LocalColumnId file_column_id = LocalColumnId::invalid(); + std::string name; + DataTypePtr type; + }; + + struct DataReader { + std::unique_ptr reader; + TableColumnMapper column_mapper; + // Schema of the data file, also including virtual column (row position). + std::vector file_schema; + // Layout of the block returned by file reader, determined by column mapping and file + // schema. It is used for file reader to materialize columns into correct type and position. + std::vector file_block_layout; + Block block_template; + }; + DataReader _data_reader; + std::vector _projected_columns; + std::unique_ptr _current_task; + std::shared_ptr _system_properties; + // partition key -> value + std::map _partition_values; + // Predicates built from scan conjuncts before file-level localization. + std::vector _table_filters; + TableColumnPredicates _table_column_predicates; + VExprContextSPtrs _conjuncts; + ReadProfile _profile; + // Parsed from row-position based delete files, including position delete and deletion vector. + DeleteRows* _delete_rows = nullptr; + TFileScanRangeParams* _scan_params; + std::shared_ptr _io_ctx; + RuntimeState* _runtime_state; + RuntimeProfile* _scanner_profile; + FileFormat _format; + TPushAggOp::type _push_down_agg_type = TPushAggOp::type::NONE; + int64_t _remaining_table_level_count = -1; + std::optional _global_rowid_context; + bool _aggregate_pushdown_tried = false; + TableColumnMapperOptions _mapper_options; + +private: + static const ColumnDefinition* _find_column_definition( + const std::vector& schema, LocalColumnId column_id) { + for (const auto& field : schema) { + if (field.file_local_id() == column_id.value()) { + return &field; + } + } + return nullptr; + } + + static bool _can_push_down_minmax_for_mapping(const ColumnMapping& mapping) { + if (mapping.child_mappings.empty()) { + return true; + } + const auto primitive_type = remove_nullable(mapping.file_type)->get_primitive_type(); + if (primitive_type != TYPE_STRUCT) { + return false; + } + size_t mapped_children = 0; + const ColumnMapping* mapped_child = nullptr; + for (const auto& child_mapping : mapping.child_mappings) { + if (!child_mapping.file_local_id.has_value()) { + continue; + } + ++mapped_children; + mapped_child = &child_mapping; + } + return mapped_children == 1 && mapped_child != nullptr && + _can_push_down_minmax_for_mapping(*mapped_child); + } + + static Status build_aggregate_projection(const ColumnMapping& mapping, + LocalColumnIndex* projection) { + DORIS_CHECK(projection != nullptr); + DORIS_CHECK(mapping.file_local_id.has_value()); + *projection = LocalColumnIndex::local(*mapping.file_local_id); + projection->children.clear(); + projection->project_all_children = true; + if (mapping.child_mappings.empty()) { + return Status::OK(); + } + projection->project_all_children = false; + for (const auto& child_mapping : mapping.child_mappings) { + if (!child_mapping.file_local_id.has_value()) { + continue; + } + LocalColumnIndex child_projection; + RETURN_IF_ERROR(build_aggregate_projection(child_mapping, &child_projection)); + projection->children.push_back(std::move(child_projection)); + } + DORIS_CHECK(projection->children.size() == 1); + return Status::OK(); + } + + static Status _insert_aggregate_projection_value(const LocalColumnIndex& projection, + const Field& value, IColumn* column) { + DORIS_CHECK(column != nullptr); + if (auto* nullable_column = check_and_get_column(*column)) { + RETURN_IF_ERROR(_insert_aggregate_projection_value( + projection, value, &nullable_column->get_nested_column())); + nullable_column->get_null_map_data().push_back(0); + return Status::OK(); + } + if (projection.project_all_children || projection.children.empty()) { + column->insert(value); + return Status::OK(); + } + auto* struct_column = assert_cast(column); + DORIS_CHECK(projection.children.size() == 1); + const auto& child_projection = projection.children[0]; + DORIS_CHECK(struct_column->get_columns().size() == 1); + RETURN_IF_ERROR(_insert_aggregate_projection_value(child_projection, value, + &struct_column->get_column(0))); + return Status::OK(); + } + + // Parse row-position deletes from table format specific parameters, and fill in _delete_rows. + Status _parse_delete_predicates(const SplitReadOptions& options); +}; + +} // namespace doris::format diff --git a/be/src/io/file_factory.cpp b/be/src/io/file_factory.cpp index 553cdc4460e15c..9610bc028595ec 100644 --- a/be/src/io/file_factory.cpp +++ b/be/src/io/file_factory.cpp @@ -57,21 +57,20 @@ namespace doris { constexpr std::string_view RANDOM_CACHE_BASE_PATH = "random"; -io::FileReaderOptions FileFactory::get_reader_options(RuntimeState* state, +io::FileReaderOptions FileFactory::get_reader_options(const TQueryOptions& option, const io::FileDescription& fd) { io::FileReaderOptions opts { .cache_base_path {}, .file_size = fd.file_size, .mtime = fd.mtime, }; - if (config::enable_file_cache && state != nullptr && - state->query_options().__isset.enable_file_cache && - state->query_options().enable_file_cache && fd.file_cache_admission) { + if (config::enable_file_cache && option.__isset.enable_file_cache && option.enable_file_cache && + fd.file_cache_admission) { opts.cache_type = io::FileCachePolicy::FILE_BLOCK_CACHE; } - if (state != nullptr && state->query_options().__isset.file_cache_base_path && - state->query_options().file_cache_base_path != RANDOM_CACHE_BASE_PATH) { - opts.cache_base_path = state->query_options().file_cache_base_path; + if (option.__isset.file_cache_base_path && + option.file_cache_base_path != RANDOM_CACHE_BASE_PATH) { + opts.cache_base_path = option.file_cache_base_path; } return opts; } diff --git a/be/src/io/file_factory.h b/be/src/io/file_factory.h index 7d662e4fdde469..33595313b921b1 100644 --- a/be/src/io/file_factory.h +++ b/be/src/io/file_factory.h @@ -16,6 +16,7 @@ // under the License. #pragma once +#include #include #include #include @@ -64,6 +65,8 @@ struct FileDescription { // -1 means unset. // If the file length is not set, the file length will be fetched from the file system. int64_t file_size = -1; + int64_t range_start_offset = 0; + int64_t range_size = -1; // modification time of this file. // 0 means unset. int64_t mtime = 0; @@ -83,7 +86,7 @@ class FileFactory { ENABLE_FACTORY_CREATOR(FileFactory); public: - static io::FileReaderOptions get_reader_options(RuntimeState* state, + static io::FileReaderOptions get_reader_options(const TQueryOptions& option, const io::FileDescription& fd); /// Create a temporary FileSystem for accessing file corresponding to `file_description` diff --git a/be/test/CMakeLists.txt b/be/test/CMakeLists.txt index 7b08d729a443b5..29b587a0b210c6 100644 --- a/be/test/CMakeLists.txt +++ b/be/test/CMakeLists.txt @@ -30,6 +30,7 @@ file(GLOB_RECURSE UT_FILES CONFIGURE_DEPENDS exec/*.cpp exprs/*.cpp format/*.cpp + format_v2/*.cpp gutil/*.cpp io/*.cpp load/*.cpp diff --git a/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp b/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp new file mode 100644 index 00000000000000..7e2ce1b839d20f --- /dev/null +++ b/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp @@ -0,0 +1,1693 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/column/column_decimal.h" +#include "core/column/column_nullable.h" +#include "core/column/column_string.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_date_or_datetime_v2.h" +#include "core/data_type/data_type_decimal.h" +#include "core/data_type/data_type_nothing.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/data_type_time.h" +#include "core/data_type_serde/decoded_column_view.h" +#include "core/field.h" +#include "core/string_ref.h" +#include "util/timezone_utils.h" + +namespace doris { +namespace { + +struct ReadColumnResult { + Status status; + MutableColumnPtr column; +}; + +template +DecodedColumnView make_fixed_view(DecodedValueKind kind, const std::vector& values, + const std::vector* null_map = nullptr) { + DecodedColumnView view; + view.value_kind = kind; + view.row_count = null_map != nullptr ? static_cast(null_map->size()) + : static_cast(values.size()); + view.values = values.empty() ? nullptr : reinterpret_cast(values.data()); + view.null_map = null_map == nullptr || null_map->empty() ? nullptr : null_map->data(); + return view; +} + +DecodedColumnView make_binary_view(DecodedValueKind kind, const std::vector& values, + int fixed_length = -1, + const std::vector* null_map = nullptr) { + DecodedColumnView view; + view.value_kind = kind; + view.row_count = null_map != nullptr ? static_cast(null_map->size()) + : static_cast(values.size()); + view.binary_values = values.empty() ? nullptr : &values; + view.fixed_length = fixed_length; + view.null_map = null_map == nullptr || null_map->empty() ? nullptr : null_map->data(); + return view; +} + +DecodedColumnView make_bool_view(const std::vector& values, + const std::vector* null_map = nullptr) { + DecodedColumnView view; + view.value_kind = DecodedValueKind::BOOL; + view.row_count = null_map != nullptr ? static_cast(null_map->size()) + : static_cast(values.size()); + view.values = values.empty() ? nullptr : reinterpret_cast(values.data()); + view.null_map = null_map == nullptr || null_map->empty() ? nullptr : null_map->data(); + return view; +} + +ReadColumnResult read_column(const DataTypePtr& type, const DecodedColumnView& view) { + auto column = type->create_column(); + auto status = type->get_serde()->read_column_from_decoded_values(*column, view); + return {std::move(status), std::move(column)}; +} + +void expect_not_supported(const Status& status) { + EXPECT_FALSE(status.ok()); + EXPECT_EQ(ErrorCode::NOT_IMPLEMENTED_ERROR, status.code()) << status; +} + +void expect_corruption(const Status& status) { + EXPECT_FALSE(status.ok()); + EXPECT_EQ(ErrorCode::CORRUPTION, status.code()) << status; +} + +void expect_data_quality_error(const Status& status) { + EXPECT_FALSE(status.ok()); + EXPECT_EQ(ErrorCode::DATA_QUALITY_ERROR, status.code()) << status; +} + +void expect_column_strings(const IDataType& type, const IColumn& column, + const std::vector& expected) { + ASSERT_EQ(expected.size(), column.size()); + for (size_t row = 0; row < expected.size(); ++row) { + EXPECT_EQ(expected[row], type.to_string(column, row)) << "row=" << row; + } +} + +void expect_binary_column(const IColumn& column, const std::vector& expected) { + const auto& string_column = assert_cast(column); + ASSERT_EQ(expected.size(), string_column.size()); + for (size_t row = 0; row < expected.size(); ++row) { + const auto value = string_column.get_data_at(row); + EXPECT_EQ(expected[row], std::string(value.data, value.size)) << "row=" << row; + } +} + +void expect_nullable_all_null(const IColumn& column, size_t expected_size) { + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(expected_size, nullable_column.size()); + ASSERT_EQ(expected_size, nullable_column.get_nested_column().size()); + for (size_t row = 0; row < expected_size; ++row) { + EXPECT_TRUE(nullable_column.is_null_at(row)) << "row=" << row; + } +} + +Field read_field(const DataTypePtr& type, const DecodedColumnView& view) { + Field field; + auto status = type->get_serde()->read_field_from_decoded_value(*type, &field, view); + EXPECT_TRUE(status.ok()) << status; + return field; +} + +Status read_field_status(const DataTypePtr& type, const DecodedColumnView& view) { + Field field; + return type->get_serde()->read_field_from_decoded_value(*type, &field, view); +} + +std::vector string_refs(const std::vector& values) { + std::vector refs; + refs.reserve(values.size()); + for (const auto& value : values) { + refs.emplace_back(value.data(), value.size()); + } + return refs; +} + +#pragma pack(1) +struct TestInt96Timestamp { + int64_t nanos_of_day; + int32_t julian_day; +}; +#pragma pack() + +static_assert(sizeof(TestInt96Timestamp) == 12); + +Decimal128V3 decimal128_v3(Int128 value) { + return Decimal128V3(value); +} + +Decimal256 decimal256_from_int64(int64_t value) { + return Decimal256(wide::Int256(value)); +} + +} // namespace + +// ---------------------------------------------------------------------- +// Base SerDe behavior +// ---------------------------------------------------------------------- +// These cases define the default contract for types that have not implemented decoded-value +// materialization. Batch reads must report NotSupported, and the single-field path must surface +// the same error because it is implemented by delegating to the batch reader. + +TEST(DataTypeSerDeDecodedValuesTest, BaseSerdeRejectsDecodedValues) { + auto type = std::make_shared(); + std::vector values = {1}; + auto view = make_fixed_view(DecodedValueKind::INT32, values); + + auto result = read_column(type, view); + + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + EXPECT_NE(std::string::npos, result.status.to_string().find("Nothing")); +} + +TEST(DataTypeSerDeDecodedValuesTest, BaseFieldUsesBatchReaderAndPropagatesError) { + auto type = std::make_shared(); + std::vector values = {1}; + auto view = make_fixed_view(DecodedValueKind::INT32, values); + Field field = Field::create_field(123); + + auto status = type->get_serde()->read_field_from_decoded_value(*type, &field, view); + + expect_not_supported(status); + EXPECT_EQ(TYPE_INT, field.get_type()); + EXPECT_EQ(123, field.get()); +} + +// ---------------------------------------------------------------------- +// Number SerDe happy path +// ---------------------------------------------------------------------- +// The numeric matrix verifies physical kind dispatch and the exact static_cast behavior used by +// the reader. Narrow integer overflow is intentionally locked to current C++ conversion behavior; +// if product semantics change to reject overflow, these expectations should be updated with the +// implementation change. + +TEST(DataTypeSerDeDecodedValuesTest, ReadBooleanFromBool) { + auto type = std::make_shared(); + std::vector values = {true, false, true}; + auto view = make_bool_view(values); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + ASSERT_EQ(3, column.size()); + EXPECT_EQ(1, column.get_element(0)); + EXPECT_EQ(0, column.get_element(1)); + EXPECT_EQ(1, column.get_element(2)); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadSignedIntegersFromInt32) { + std::vector values = {0, 1, -1, 127, -128}; + auto view = make_fixed_view(DecodedValueKind::INT32, values); + + { + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + ASSERT_EQ(values.size(), column.size()); + EXPECT_EQ(0, column.get_element(0)); + EXPECT_EQ(1, column.get_element(1)); + EXPECT_EQ(-1, column.get_element(2)); + EXPECT_EQ(127, column.get_element(3)); + EXPECT_EQ(-128, column.get_element(4)); + } + { + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + ASSERT_EQ(values.size(), column.size()); + for (size_t row = 0; row < values.size(); ++row) { + EXPECT_EQ(static_cast(values[row]), column.get_element(row)); + } + } + { + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + ASSERT_EQ(values.size(), column.size()); + for (size_t row = 0; row < values.size(); ++row) { + EXPECT_EQ(values[row], column.get_element(row)); + } + } + { + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + ASSERT_EQ(values.size(), column.size()); + for (size_t row = 0; row < values.size(); ++row) { + EXPECT_EQ(static_cast(values[row]), column.get_element(row)); + } + } + { + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + ASSERT_EQ(values.size(), column.size()); + for (size_t row = 0; row < values.size(); ++row) { + EXPECT_EQ(static_cast<__int128_t>(values[row]), column.get_element(row)); + } + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadSignedIntegersFromInt64) { + std::vector values = {0, 1, -1, 127, -128}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + + auto tiny = read_column(std::make_shared(), view); + ASSERT_TRUE(tiny.status.ok()) << tiny.status; + const auto& tiny_column = assert_cast(*tiny.column); + EXPECT_EQ(127, tiny_column.get_element(3)); + EXPECT_EQ(-128, tiny_column.get_element(4)); + + auto small = read_column(std::make_shared(), view); + ASSERT_TRUE(small.status.ok()) << small.status; + const auto& small_column = assert_cast(*small.column); + EXPECT_EQ(127, small_column.get_element(3)); + EXPECT_EQ(-128, small_column.get_element(4)); + + auto integer = read_column(std::make_shared(), view); + ASSERT_TRUE(integer.status.ok()) << integer.status; + const auto& int_column = assert_cast(*integer.column); + EXPECT_EQ(127, int_column.get_element(3)); + EXPECT_EQ(-128, int_column.get_element(4)); + + auto bigint = read_column(std::make_shared(), view); + ASSERT_TRUE(bigint.status.ok()) << bigint.status; + const auto& bigint_column = assert_cast(*bigint.column); + ASSERT_EQ(values.size(), bigint_column.size()); + for (size_t row = 0; row < values.size(); ++row) { + EXPECT_EQ(values[row], bigint_column.get_element(row)); + } + + auto largeint = read_column(std::make_shared(), view); + ASSERT_TRUE(largeint.status.ok()) << largeint.status; + const auto& largeint_column = assert_cast(*largeint.column); + ASSERT_EQ(values.size(), largeint_column.size()); + for (size_t row = 0; row < values.size(); ++row) { + EXPECT_EQ(static_cast<__int128_t>(values[row]), largeint_column.get_element(row)); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadIntegersFromUnsignedSources) { + { + std::vector values = {0, 1, std::numeric_limits::max()}; + auto view = make_fixed_view(DecodedValueKind::UINT32, values); + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(0, column.get_element(0)); + EXPECT_EQ(1, column.get_element(1)); + EXPECT_EQ(static_cast(std::numeric_limits::max()), + column.get_element(2)); + } + { + std::vector values = {0, 1, std::numeric_limits::max()}; + auto view = make_fixed_view(DecodedValueKind::UINT64, values); + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(0, column.get_element(0)); + EXPECT_EQ(1, column.get_element(1)); + EXPECT_EQ(static_cast<__int128_t>(std::numeric_limits::max()), + column.get_element(2)); + } + { + std::vector values = {static_cast(std::numeric_limits::max())}; + auto view = make_fixed_view(DecodedValueKind::UINT64, values); + auto result = read_column(std::make_shared(), view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(std::numeric_limits::max(), column.get_element(0)); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadFloatAndDouble) { + { + auto type = std::make_shared(); + std::vector values = {0.0F, -0.0F, 1.5F, -2.25F}; + auto result = read_column(type, make_fixed_view(DecodedValueKind::FLOAT, values)); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_FLOAT_EQ(0.0F, column.get_element(0)); + EXPECT_TRUE(std::signbit(column.get_element(1))); + EXPECT_FLOAT_EQ(1.5F, column.get_element(2)); + EXPECT_FLOAT_EQ(-2.25F, column.get_element(3)); + } + { + auto type = std::make_shared(); + std::vector values = {0.0, -0.0, 1.5, -2.25}; + auto result = read_column(type, make_fixed_view(DecodedValueKind::DOUBLE, values)); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_DOUBLE_EQ(0.0, column.get_element(0)); + EXPECT_TRUE(std::signbit(column.get_element(1))); + EXPECT_DOUBLE_EQ(1.5, column.get_element(2)); + EXPECT_DOUBLE_EQ(-2.25, column.get_element(3)); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadFloatSpecialValues) { + { + std::vector values = {std::numeric_limits::quiet_NaN(), + std::numeric_limits::infinity(), + -std::numeric_limits::infinity()}; + auto result = read_column(std::make_shared(), + make_fixed_view(DecodedValueKind::FLOAT, values)); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_TRUE(std::isnan(column.get_element(0))); + EXPECT_TRUE(std::isinf(column.get_element(1))); + EXPECT_FALSE(std::signbit(column.get_element(1))); + EXPECT_TRUE(std::isinf(column.get_element(2))); + EXPECT_TRUE(std::signbit(column.get_element(2))); + } + { + std::vector values = {std::numeric_limits::quiet_NaN(), + std::numeric_limits::infinity(), + -std::numeric_limits::infinity()}; + auto result = read_column(std::make_shared(), + make_fixed_view(DecodedValueKind::DOUBLE, values)); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_TRUE(std::isnan(column.get_element(0))); + EXPECT_TRUE(std::isinf(column.get_element(1))); + EXPECT_FALSE(std::signbit(column.get_element(1))); + EXPECT_TRUE(std::isinf(column.get_element(2))); + EXPECT_TRUE(std::signbit(column.get_element(2))); + } +} + +// ---------------------------------------------------------------------- +// Number SerDe error paths +// ---------------------------------------------------------------------- +// These cases separate unsupported physical kinds from corrupt decoded buffers. Unsupported kinds +// must not append to the destination column; missing value buffers are allowed only for empty or +// all-null batches where no non-null row can dereference the buffer. + +TEST(DataTypeSerDeDecodedValuesTest, NumberRejectsMismatchedKind) { + struct Case { + DataTypePtr type; + DecodedValueKind kind; + }; + std::vector cases = { + {std::make_shared(), DecodedValueKind::INT32}, + {std::make_shared(), DecodedValueKind::BOOL}, + {std::make_shared(), DecodedValueKind::DOUBLE}, + {std::make_shared(), DecodedValueKind::FLOAT}, + {std::make_shared(), DecodedValueKind::BINARY}, + }; + + for (const auto& test_case : cases) { + std::vector values = {1}; + auto result = read_column(test_case.type, make_fixed_view(test_case.kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, NumberRejectsMissingValuesWhenNonNullExists) { + auto type = std::make_shared(); + { + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT32; + view.row_count = 3; + auto result = read_column(type, view); + expect_corruption(result.status); + } + { + std::vector null_map = {1, 0, 1}; + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT32; + view.row_count = 3; + view.null_map = null_map.data(); + auto result = read_column(type, view); + expect_corruption(result.status); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, NumberAllowsMissingValuesForAllNullOrEmpty) { + auto type = std::make_shared(std::make_shared()); + { + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT32; + view.row_count = 0; + auto result = read_column(type, view); + ASSERT_TRUE(result.status.ok()) << result.status; + EXPECT_EQ(0, result.column->size()); + } + { + std::vector null_map = {1, 1, 1}; + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT32; + view.row_count = 3; + view.null_map = null_map.data(); + auto result = read_column(type, view); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + const auto& nested_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(3, nullable_column.size()); + for (size_t row = 0; row < nullable_column.size(); ++row) { + EXPECT_TRUE(nullable_column.is_null_at(row)); + EXPECT_EQ(0, nested_column.get_element(row)); + } + } +} + +TEST(DataTypeSerDeDecodedValuesTest, NumberRejectsOutOfRangeValueInStrictMode) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {127, 128}; + std::vector null_map = {0, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + view.enable_strict_mode = true; + + auto result = read_column(type, view); + + expect_data_quality_error(result.status); + const auto& nullable_column = assert_cast(*result.column); + EXPECT_EQ(0, nullable_column.size()); + EXPECT_EQ(0, nullable_column.get_null_map_data().size()); + EXPECT_EQ(0, nullable_column.get_nested_column().size()); +} + +TEST(DataTypeSerDeDecodedValuesTest, NumberNullsOutOfRangeValueInNonStrictMode) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {127, 128, -129, -128}; + std::vector null_map = {0, 0, 0, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + const auto& nested_column = assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(4, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_TRUE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_EQ(127, nested_column.get_element(0)); + EXPECT_EQ(0, nested_column.get_element(1)); + EXPECT_EQ(0, nested_column.get_element(2)); + EXPECT_EQ(-128, nested_column.get_element(3)); +} + +TEST(DataTypeSerDeDecodedValuesTest, NumberRejectsUnsignedOverflowInStrictMode) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {static_cast(std::numeric_limits::max()), + std::numeric_limits::max()}; + std::vector null_map = {0, 0}; + auto view = make_fixed_view(DecodedValueKind::UINT64, values, &null_map); + view.enable_strict_mode = true; + + auto result = read_column(type, view); + + expect_data_quality_error(result.status); +} + +TEST(DataTypeSerDeDecodedValuesTest, NumberNullsUnsignedOverflowInNonStrictMode) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {static_cast(std::numeric_limits::max()), + std::numeric_limits::max()}; + std::vector null_map = {0, 0}; + auto view = make_fixed_view(DecodedValueKind::UINT64, values, &null_map); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + const auto& nested_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(2, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_EQ(std::numeric_limits::max(), nested_column.get_element(0)); + EXPECT_EQ(0, nested_column.get_element(1)); +} + +// ---------------------------------------------------------------------- +// String / Binary SerDe +// ---------------------------------------------------------------------- +// String-like decoded reads must preserve exact byte sequences. The embedded-NUL case prevents +// accidental C-string truncation. Nullable string tests ensure null rows materialize default nested +// values while the outer null map remains authoritative. + +TEST(DataTypeSerDeDecodedValuesTest, ReadStringFromBinary) { + auto type = std::make_shared(); + std::vector storage = {"alpha", "", std::string("a\0b", 3), "utf8-\xe4\xb8\xad"}; + auto refs = string_refs(storage); + + auto result = read_column(type, make_binary_view(DecodedValueKind::BINARY, refs)); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_binary_column(*result.column, storage); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadStringFromFixedBinary) { + auto type = std::make_shared(); + std::vector storage = {std::string("\x00\x01\x02\x03", 4), + std::string("\x7f\x80\xfe\xff", 4)}; + auto refs = string_refs(storage); + + auto result = read_column(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 4)); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_binary_column(*result.column, storage); +} + +TEST(DataTypeSerDeDecodedValuesTest, StringNullMapMaterialization) { + auto type = std::make_shared(std::make_shared()); + std::vector storage = {"alpha", "", "omega"}; + auto refs = string_refs(storage); + std::vector null_map = {0, 1, 0}; + + auto result = + read_column(type, make_binary_view(DecodedValueKind::BINARY, refs, -1, &null_map)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(3, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + expect_binary_column(nullable_column.get_nested_column(), {"alpha", "", "omega"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, StringRejectsMismatchedKind) { + auto type = std::make_shared(); + for (auto kind : {DecodedValueKind::INT32, DecodedValueKind::INT64, DecodedValueKind::DOUBLE}) { + std::vector values = {1}; + auto result = read_column(type, make_fixed_view(kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, StringRejectsMissingBinaryValuesWhenNonNullExists) { + auto type = std::make_shared(); + DecodedColumnView view; + view.value_kind = DecodedValueKind::BINARY; + view.row_count = 1; + + auto result = read_column(type, view); + + expect_corruption(result.status); +} + +TEST(DataTypeSerDeDecodedValuesTest, StringAllowsAllNullWithoutBinaryValues) { + auto type = std::make_shared(std::make_shared()); + std::vector null_map = {1, 1}; + DecodedColumnView view; + view.value_kind = DecodedValueKind::BINARY; + view.row_count = 2; + view.null_map = null_map.data(); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(2, nullable_column.size()); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + expect_binary_column(nullable_column.get_nested_column(), {"", ""}); +} + +// ---------------------------------------------------------------------- +// DateV2 SerDe +// ---------------------------------------------------------------------- +// DateV2 accepts Parquet DATE-style epoch days as INT32. Null rows insert default nested dates and +// missing buffers are rejected only when a non-null row requires a value. + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateV2FromEpochDays) { + auto type = std::make_shared(); + std::vector values = {-1, 0, 1, 18628, 18321}; + + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT32, values)); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"1969-12-31", "1970-01-01", "1970-01-02", "2021-01-01", "2020-02-29"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, DateV2HandlesNulls) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {0, 1, 2}; + std::vector null_map = {0, 1, 0}; + + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT32, values, &null_map)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(3, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + expect_column_strings(*type, *result.column, {"1970-01-01", "NULL", "1970-01-03"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, DateV2RejectsInvalidKind) { + auto type = std::make_shared(); + for (auto kind : + {DecodedValueKind::INT64, DecodedValueKind::BINARY, DecodedValueKind::DOUBLE}) { + std::vector values = {0}; + auto result = read_column(type, make_fixed_view(kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, DateV2RejectsMissingValuesWhenNonNullExists) { + auto type = std::make_shared(); + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT32; + view.row_count = 1; + + auto result = read_column(type, view); + + expect_corruption(result.status); +} + +// ---------------------------------------------------------------------- +// DateTimeV2 SerDe +// ---------------------------------------------------------------------- +// Timestamp decoding covers INT64 micros/millis, UNKNOWN-as-micros compatibility, UTC-adjusted +// conversion with explicit/default timezones, INT96 Julian-day timestamps, and invalid buffer/kind +// errors. Negative epoch values are included to lock correct floor-division behavior. + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2Micros) { + auto type = std::make_shared(6); + std::vector values = {-1, 0, 1, 1234567, 86400000000LL - 1}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MICROS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"1969-12-31 23:59:59.999999", "1970-01-01 00:00:00.000000", + "1970-01-01 00:00:00.000001", "1970-01-01 00:00:01.234567", + "1970-01-01 23:59:59.999999"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2Millis) { + auto type = std::make_shared(6); + std::vector values = {-1, 0, 1, 1234}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MILLIS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"1969-12-31 23:59:59.999000", "1970-01-01 00:00:00.000000", + "1970-01-01 00:00:00.001000", "1970-01-01 00:00:01.234000"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2UnknownUnitAsMicros) { + auto type = std::make_shared(6); + std::vector values = {1000000}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::UNKNOWN; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"1970-01-01 00:00:01.000000"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2UtcAdjustedDefaultUtc) { + auto type = std::make_shared(6); + std::vector values = {0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MICROS; + view.timestamp_is_adjusted_to_utc = true; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"1970-01-01 00:00:00.000000"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2UtcAdjustedWithTimezones) { + auto type = std::make_shared(6); + std::vector values = {0, -1, 1234567}; + cctz::time_zone shanghai; + cctz::time_zone new_york; + ASSERT_TRUE(TimezoneUtils::find_cctz_time_zone("+08:00", shanghai)); + ASSERT_TRUE(TimezoneUtils::find_cctz_time_zone("-05:00", new_york)); + + auto shanghai_view = make_fixed_view(DecodedValueKind::INT64, values); + shanghai_view.time_unit = DecodedTimeUnit::MICROS; + shanghai_view.timestamp_is_adjusted_to_utc = true; + shanghai_view.timezone = &shanghai; + auto shanghai_result = read_column(type, shanghai_view); + ASSERT_TRUE(shanghai_result.status.ok()) << shanghai_result.status; + expect_column_strings(*type, *shanghai_result.column, + {"1970-01-01 08:00:00.000000", "1970-01-01 07:59:59.999999", + "1970-01-01 08:00:01.234567"}); + + auto new_york_view = make_fixed_view(DecodedValueKind::INT64, values); + new_york_view.time_unit = DecodedTimeUnit::MICROS; + new_york_view.timestamp_is_adjusted_to_utc = true; + new_york_view.timezone = &new_york; + auto new_york_result = read_column(type, new_york_view); + ASSERT_TRUE(new_york_result.status.ok()) << new_york_result.status; + expect_column_strings(*type, *new_york_result.column, + {"1969-12-31 19:00:00.000000", "1969-12-31 18:59:59.999999", + "1969-12-31 19:00:01.234567"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2Int96) { + auto type = std::make_shared(std::make_shared(6)); + std::vector values = { + {0, 2440588}, + {86399999999000LL, 2440587}, + {0, 2440589}, + }; + std::vector null_map = {0, 0, 1}; + auto view = make_fixed_view(DecodedValueKind::INT96, values, &null_map); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"1970-01-01 00:00:00.000000", "1969-12-31 23:59:59.999999", "NULL"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, DateTimeV2RejectsInvalidKind) { + auto type = std::make_shared(6); + for (auto kind : + {DecodedValueKind::INT32, DecodedValueKind::BINARY, DecodedValueKind::DOUBLE}) { + std::vector values = {0}; + auto result = read_column(type, make_fixed_view(kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, DateTimeV2RejectsMissingValuesWhenNonNullExists) { + auto type = std::make_shared(6); + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT64; + view.row_count = 1; + + auto result = read_column(type, view); + + expect_corruption(result.status); +} + +TEST(DataTypeSerDeDecodedValuesTest, DateTimeV2RejectsOutOfRangeEpochWithoutAbort) { + auto type = std::make_shared(6); + std::vector values = {0, -377673580800000001LL}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MICROS; + + auto result = read_column(type, view); + + expect_data_quality_error(result.status); + EXPECT_EQ(0, result.column->size()); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableDateTimeV2RejectsOutOfRangeEpochInStrictMode) { + auto type = std::make_shared(std::make_shared(6)); + std::vector values = {0, -377673580800000001LL}; + std::vector null_map = {0, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + view.time_unit = DecodedTimeUnit::MICROS; + view.enable_strict_mode = true; + + auto result = read_column(type, view); + + expect_data_quality_error(result.status); + const auto& nullable_column = assert_cast(*result.column); + EXPECT_EQ(0, nullable_column.size()); + EXPECT_EQ(0, nullable_column.get_null_map_data().size()); + EXPECT_EQ(0, nullable_column.get_nested_column().size()); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableDateTimeV2NullsOutOfRangeEpochInNonStrictMode) { + auto type = std::make_shared(std::make_shared(6)); + std::vector values = {0, -377673580800000001LL, 1}; + std::vector null_map = {0, 0, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + view.time_unit = DecodedTimeUnit::MICROS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(3, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + expect_column_strings(*type, *result.column, + {"1970-01-01 00:00:00.000000", "NULL", "1970-01-01 00:00:00.000001"}); +} + +// ---------------------------------------------------------------------- +// TimeV2 SerDe +// ---------------------------------------------------------------------- +// TimeV2 decodes INT32 as milliseconds and INT64 according to the supplied time unit. Negative +// durations are verified because they use a sign bit in TimeValue::TimeType rather than DateTimeV2 +// epoch arithmetic. + +TEST(DataTypeSerDeDecodedValuesTest, ReadTimeV2FromInt32Millis) { + auto type = std::make_shared(6); + std::vector values = {0, 1, -1, 3661001}; + + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT32, values)); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings( + *type, *result.column, + {"00:00:00.000000", "00:00:00.001000", "-00:00:00.001000", "01:01:01.001000"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadTimeV2FromInt64Micros) { + auto type = std::make_shared(6); + std::vector values = {0, 1, -1, 3661000001LL}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MICROS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings( + *type, *result.column, + {"00:00:00.000000", "00:00:00.000001", "-00:00:00.000001", "01:01:01.000001"}); + + view.time_unit = DecodedTimeUnit::UNKNOWN; + auto unknown_result = read_column(type, view); + ASSERT_TRUE(unknown_result.status.ok()) << unknown_result.status; + expect_column_strings( + *type, *unknown_result.column, + {"00:00:00.000000", "00:00:00.000001", "-00:00:00.000001", "01:01:01.000001"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadTimeV2FromInt64Millis) { + auto type = std::make_shared(6); + std::vector values = {1, -1, 3661001}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MILLIS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"00:00:00.001000", "-00:00:00.001000", "01:01:01.001000"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadTimeV2FromInt64Nanos) { + auto type = std::make_shared(6); + std::vector values = {1000, -1000, 3661000001000LL}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::NANOS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"00:00:00.000001", "-00:00:00.000001", "01:01:01.000001"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, TimeV2HandlesNulls) { + auto type = std::make_shared(std::make_shared(6)); + std::vector values = {0, 1, 2}; + std::vector null_map = {0, 1, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + view.time_unit = DecodedTimeUnit::MICROS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(3, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + expect_column_strings(*type, *result.column, {"00:00:00.000000", "NULL", "00:00:00.000002"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, TimeV2RejectsInvalidKind) { + auto type = std::make_shared(6); + for (auto kind : {DecodedValueKind::BOOL, DecodedValueKind::BINARY, DecodedValueKind::DOUBLE}) { + std::vector values = {0}; + auto result = read_column(type, make_fixed_view(kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + } +} + +// ---------------------------------------------------------------------- +// Decimal SerDe +// ---------------------------------------------------------------------- +// Decimal cases cover integer-backed values and Parquet big-endian two's-complement binary values. +// String assertions validate the user-visible scale, while direct column checks lock the native +// unscaled value for every decimal width. + +TEST(DataTypeSerDeDecodedValuesTest, ReadDecimal32FromInt32) { + auto type = std::make_shared(9, 2); + std::vector values = {12345, -67, 0}; + auto view = make_fixed_view(DecodedValueKind::INT32, values); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(Decimal32(12345), column.get_element(0)); + EXPECT_EQ(Decimal32(-67), column.get_element(1)); + EXPECT_EQ(Decimal32(0), column.get_element(2)); + expect_column_strings(*type, *result.column, {"123.45", "-0.67", "0.00"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDecimal64FromInt64) { + auto type = std::make_shared(18, 4); + std::vector values = {123456789, -1}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(Decimal64(123456789), column.get_element(0)); + EXPECT_EQ(Decimal64(-1), column.get_element(1)); + expect_column_strings(*type, *result.column, {"12345.6789", "-0.0001"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDecimal128FromInt32AndInt64) { + auto type = std::make_shared(38, 6); + { + std::vector values = {123456, -1}; + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT32, values)); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(decimal128_v3(123456), column.get_element(0)); + EXPECT_EQ(decimal128_v3(-1), column.get_element(1)); + expect_column_strings(*type, *result.column, {"0.123456", "-0.000001"}); + } + { + std::vector values = {1234567890123LL, -1234567LL}; + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT64, values)); + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(decimal128_v3(1234567890123LL), column.get_element(0)); + EXPECT_EQ(decimal128_v3(-1234567LL), column.get_element(1)); + expect_column_strings(*type, *result.column, {"1234567.890123", "-1.234567"}); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDecimal256FromInt64) { + auto type = std::make_shared(76, 8); + std::vector values = {std::numeric_limits::max(), + std::numeric_limits::min()}; + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT64, values)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + EXPECT_EQ(decimal256_from_int64(std::numeric_limits::max()), column.get_element(0)); + EXPECT_EQ(decimal256_from_int64(std::numeric_limits::min()), column.get_element(1)); + expect_column_strings(*type, *result.column, {"92233720368.54775807", "-92233720368.54775808"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDecimalFromBinaryBigEndian) { + auto type = std::make_shared(18, 2); + std::vector storage = { + std::string("\x00", 1), std::string("\x7f", 1), std::string("\x80", 1), + std::string("\xff", 1), std::string("\xff\xbd", 2), std::string("\x30\x39", 2), + }; + auto refs = string_refs(storage); + + auto result = read_column(type, make_binary_view(DecodedValueKind::BINARY, refs)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& column = assert_cast(*result.column); + std::vector expected = {decimal128_v3(0), decimal128_v3(127), + decimal128_v3(-128), decimal128_v3(-1), + decimal128_v3(-67), decimal128_v3(12345)}; + ASSERT_EQ(expected.size(), column.size()); + for (size_t row = 0; row < expected.size(); ++row) { + EXPECT_EQ(expected[row], column.get_element(row)) << "row=" << row; + } + expect_column_strings(*type, *result.column, + {"0.00", "1.27", "-1.28", "-0.01", "-0.67", "123.45"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadDecimalFromFixedBinaryLengths) { + { + auto type = std::make_shared(38, 2); + std::vector storage = {std::string("\x00", 1), std::string("\x80", 1)}; + auto refs = string_refs(storage); + auto result = read_column(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 1)); + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"0.00", "-1.28"}); + } + { + auto type = std::make_shared(38, 2); + std::vector storage = {std::string("\xff\xbd", 2), std::string("\x30\x39", 2)}; + auto refs = string_refs(storage); + auto result = read_column(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 2)); + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"-0.67", "123.45"}); + } + { + auto type = std::make_shared(38, 2); + std::vector storage = {std::string("\0\0\0\0\0\0\x30\x39", 8)}; + auto refs = string_refs(storage); + auto result = read_column(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 8)); + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"123.45"}); + } + { + auto type = std::make_shared(38, 2); + std::vector storage = { + std::string("\xff\xff\xff\xff\xff\xff\xff\xff" + "\xff\xff\xff\xff\xff\xff\xff\xbd", + 16)}; + auto refs = string_refs(storage); + auto result = read_column(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 16)); + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"-0.67"}); + } + { + auto type = std::make_shared(76, 2); + std::vector storage = {std::string(31, '\xff') + std::string("\xbd", 1)}; + auto refs = string_refs(storage); + auto result = read_column(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 32)); + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"-0.67"}); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalHandlesNulls) { + auto type = std::make_shared(std::make_shared(18, 2)); + std::vector values = {12345, -1, -67}; + std::vector null_map = {0, 1, 0}; + + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT64, values, &null_map)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + const auto& decimal_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(3, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_EQ(decimal128_v3(12345), decimal_column.get_element(0)); + EXPECT_EQ(decimal128_v3(0), decimal_column.get_element(1)); + EXPECT_EQ(decimal128_v3(-67), decimal_column.get_element(2)); +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalRejectsOutOfRangeValueInStrictMode) { + auto type = std::make_shared(std::make_shared(9, 2)); + std::vector values = {999999999, 1000000000}; + std::vector null_map = {0, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + view.enable_strict_mode = true; + + auto result = read_column(type, view); + + expect_data_quality_error(result.status); + const auto& nullable_column = assert_cast(*result.column); + EXPECT_EQ(0, nullable_column.size()); + EXPECT_EQ(0, nullable_column.get_null_map_data().size()); + EXPECT_EQ(0, nullable_column.get_nested_column().size()); +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalNullsOutOfRangeValueInNonStrictMode) { + auto type = std::make_shared(std::make_shared(9, 2)); + std::vector values = {999999999, 1000000000, -1000000000, -999999999}; + std::vector null_map = {0, 0, 0, 0}; + auto view = make_fixed_view(DecodedValueKind::INT64, values, &null_map); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + const auto& decimal_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(4, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_TRUE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_EQ(Decimal32(999999999), decimal_column.get_element(0)); + EXPECT_EQ(Decimal32(0), decimal_column.get_element(1)); + EXPECT_EQ(Decimal32(0), decimal_column.get_element(2)); + EXPECT_EQ(Decimal32(-999999999), decimal_column.get_element(3)); +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalRejectsNullBinaryDataWithPositiveLength) { + auto type = std::make_shared(18, 2); + std::vector refs = {StringRef(static_cast(nullptr), 2)}; + + auto result = read_column(type, make_binary_view(DecodedValueKind::BINARY, refs)); + + expect_corruption(result.status); + EXPECT_NE(std::string::npos, result.status.to_string().find("row 0")); +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalAllowsZeroLengthBinaryAsZero) { + auto type = std::make_shared(18, 2); + std::vector refs = {StringRef(static_cast(nullptr), 0), + StringRef("", 0)}; + + auto result = read_column(type, make_binary_view(DecodedValueKind::BINARY, refs)); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, {"0.00", "0.00"}); +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalRejectsInvalidKind) { + auto type = std::make_shared(18, 2); + for (auto kind : {DecodedValueKind::BOOL, DecodedValueKind::FLOAT, DecodedValueKind::DOUBLE, + DecodedValueKind::UINT64}) { + std::vector values = {0}; + auto result = read_column(type, make_fixed_view(kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, DecimalRejectsMissingBufferWhenNonNullExists) { + auto type = std::make_shared(18, 2); + { + DecodedColumnView view; + view.value_kind = DecodedValueKind::INT64; + view.row_count = 1; + auto result = read_column(type, view); + expect_corruption(result.status); + } + { + DecodedColumnView view; + view.value_kind = DecodedValueKind::BINARY; + view.row_count = 1; + auto result = read_column(type, view); + expect_corruption(result.status); + } +} + +// ---------------------------------------------------------------------- +// Nullable SerDe wrapper +// ---------------------------------------------------------------------- +// Nullable tests focus on wrapper responsibilities: copying the outer null map, inserting default +// nested values for null rows, treating a missing null_map as all non-null, appending to existing +// columns, and rolling back outer state when the nested reader rejects the input. + +TEST(DataTypeSerDeDecodedValuesTest, NullablePropagatesNullMapAndReadsNested) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {10, 20, 30, 40}; + std::vector null_map = {0, 1, 0, 1}; + + auto result = read_column(type, make_fixed_view(DecodedValueKind::INT32, values, &null_map)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + const auto& nested_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(4, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_TRUE(nullable_column.is_null_at(3)); + EXPECT_EQ(10, nested_column.get_element(0)); + EXPECT_EQ(0, nested_column.get_element(1)); + EXPECT_EQ(30, nested_column.get_element(2)); + EXPECT_EQ(0, nested_column.get_element(3)); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableWithoutNullMapReadsAllNonNull) { + auto type = std::make_shared(std::make_shared()); + std::vector storage = {"alpha", "beta"}; + auto refs = string_refs(storage); + + auto result = read_column(type, make_binary_view(DecodedValueKind::BINARY, refs)); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(2, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + expect_binary_column(nullable_column.get_nested_column(), storage); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableAllNullDoesNotRequireNestedBuffer) { + std::vector null_map = {1, 1}; + std::vector types = { + std::make_shared(std::make_shared()), + std::make_shared(std::make_shared(18, 2)), + std::make_shared(std::make_shared()), + std::make_shared(std::make_shared()), + }; + + for (const auto& type : types) { + DecodedColumnView view; + view.value_kind = type->get_name().find("String") != std::string::npos + ? DecodedValueKind::BINARY + : DecodedValueKind::INT32; + view.row_count = 2; + view.null_map = null_map.data(); + auto result = read_column(type, view); + ASSERT_TRUE(result.status.ok()) << result.status << ", type=" << type->get_name(); + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(2, nullable_column.size()); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_EQ(2, nullable_column.get_nested_column().size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableAppendToExistingColumn) { + auto type = std::make_shared(std::make_shared()); + auto column = type->create_column(); + + std::vector first_values = {1, 2}; + auto first_status = type->get_serde()->read_column_from_decoded_values( + *column, make_fixed_view(DecodedValueKind::INT32, first_values)); + ASSERT_TRUE(first_status.ok()) << first_status; + + std::vector second_values = {10, 20, 30}; + std::vector second_null_map = {0, 1, 0}; + auto second_status = type->get_serde()->read_column_from_decoded_values( + *column, make_fixed_view(DecodedValueKind::INT32, second_values, &second_null_map)); + ASSERT_TRUE(second_status.ok()) << second_status; + + const auto& nullable_column = assert_cast(*column); + const auto& nested_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(5, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_TRUE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + EXPECT_EQ(1, nested_column.get_element(0)); + EXPECT_EQ(2, nested_column.get_element(1)); + EXPECT_EQ(10, nested_column.get_element(2)); + EXPECT_EQ(0, nested_column.get_element(3)); + EXPECT_EQ(30, nested_column.get_element(4)); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullablePropagatesNestedError) { + auto type = std::make_shared(std::make_shared()); + auto column = type->create_column(); + std::vector values = {1.0}; + std::vector null_map = {0}; + auto view = make_fixed_view(DecodedValueKind::DOUBLE, values, &null_map); + view.enable_strict_mode = true; + + auto status = type->get_serde()->read_column_from_decoded_values(*column, view); + + expect_not_supported(status); + const auto& nullable_column = assert_cast(*column); + EXPECT_EQ(0, nullable_column.size()); + EXPECT_EQ(0, nullable_column.get_null_map_data().size()); + EXPECT_EQ(0, nullable_column.get_nested_column().size()); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableNonStrictModeNullsUnsupportedDecodedKindForAllTypes) { + struct Case { + DataTypePtr type; + DecodedValueKind kind; + }; + std::vector cases = { + {std::make_shared(std::make_shared()), + DecodedValueKind::INT32}, + {std::make_shared(std::make_shared()), + DecodedValueKind::DOUBLE}, + {std::make_shared(std::make_shared()), + DecodedValueKind::FLOAT}, + {std::make_shared(std::make_shared()), + DecodedValueKind::INT64}, + {std::make_shared(std::make_shared()), + DecodedValueKind::INT64}, + {std::make_shared(std::make_shared(6)), + DecodedValueKind::DOUBLE}, + {std::make_shared(std::make_shared(6)), + DecodedValueKind::DOUBLE}, + {std::make_shared(std::make_shared(18, 2)), + DecodedValueKind::DOUBLE}, + }; + + std::vector values = {1, 2}; + for (const auto& test_case : cases) { + auto view = make_fixed_view(test_case.kind, values); + + auto result = read_column(test_case.type, view); + + ASSERT_TRUE(result.status.ok()) << result.status << ", type=" << test_case.type->get_name(); + expect_nullable_all_null(*result.column, values.size()); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableStrictModeRejectsUnsupportedDecodedKind) { + auto type = std::make_shared(std::make_shared()); + std::vector values = {1.0}; + std::vector null_map = {0}; + auto view = make_fixed_view(DecodedValueKind::DOUBLE, values, &null_map); + view.enable_strict_mode = true; + + auto result = read_column(type, view); + + expect_not_supported(result.status); + const auto& nullable_column = assert_cast(*result.column); + EXPECT_EQ(0, nullable_column.size()); + EXPECT_EQ(0, nullable_column.get_null_map_data().size()); + EXPECT_EQ(0, nullable_column.get_nested_column().size()); +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableNonStrictModeNullsRowLevelDecodedConversionFailure) { + { + auto type = std::make_shared(std::make_shared()); + std::vector refs = {StringRef("ok", 2), + StringRef(static_cast(nullptr), 2), + StringRef("", 0)}; + auto view = make_binary_view(DecodedValueKind::BINARY, refs); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(3, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + expect_binary_column(nullable_column.get_nested_column(), {"ok", "", ""}); + } + { + auto type = std::make_shared(std::make_shared(18, 2)); + std::vector refs = {StringRef("\x30\x39", 2), + StringRef(static_cast(nullptr), 2)}; + auto view = make_binary_view(DecodedValueKind::BINARY, refs); + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(2, nullable_column.size()); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + expect_column_strings(*type, *result.column, {"123.45", "NULL"}); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, NullableStrictModeRejectsRowLevelDecodedConversionFailure) { + auto type = std::make_shared(std::make_shared()); + std::vector refs = {StringRef("ok", 2), + StringRef(static_cast(nullptr), 2)}; + auto view = make_binary_view(DecodedValueKind::BINARY, refs); + view.enable_strict_mode = true; + + auto result = read_column(type, view); + + expect_corruption(result.status); + const auto& nullable_column = assert_cast(*result.column); + EXPECT_EQ(0, nullable_column.size()); + EXPECT_EQ(0, nullable_column.get_null_map_data().size()); + EXPECT_EQ(0, nullable_column.get_nested_column().size()); +} + +// ---------------------------------------------------------------------- +// read_field_from_decoded_value +// ---------------------------------------------------------------------- +// The field path is used by Parquet min/max and pruning code. It must be covered independently +// because it creates a one-row column, delegates to the batch reader, and extracts a Field value. + +TEST(DataTypeSerDeDecodedValuesTest, ReadFieldPrimitiveValues) { + { + std::vector values = {true}; + auto field = read_field(std::make_shared(), make_bool_view(values)); + EXPECT_EQ(TYPE_BOOLEAN, field.get_type()); + EXPECT_TRUE(field.get()); + } + { + std::vector values = {-42}; + auto field = read_field(std::make_shared(), + make_fixed_view(DecodedValueKind::INT32, values)); + EXPECT_EQ(TYPE_INT, field.get_type()); + EXPECT_EQ(-42, field.get()); + } + { + std::vector values = {1234567890123LL}; + auto field = read_field(std::make_shared(), + make_fixed_view(DecodedValueKind::INT64, values)); + EXPECT_EQ(TYPE_BIGINT, field.get_type()); + EXPECT_EQ(1234567890123LL, field.get()); + } + { + std::vector values = {-9}; + auto field = read_field(std::make_shared(), + make_fixed_view(DecodedValueKind::INT64, values)); + EXPECT_EQ(TYPE_LARGEINT, field.get_type()); + EXPECT_EQ(static_cast<__int128_t>(-9), field.get()); + } + { + std::vector values = {std::numeric_limits::quiet_NaN()}; + auto field = read_field(std::make_shared(), + make_fixed_view(DecodedValueKind::FLOAT, values)); + EXPECT_EQ(TYPE_FLOAT, field.get_type()); + EXPECT_TRUE(std::isnan(field.get())); + } + { + std::vector values = {std::numeric_limits::infinity()}; + auto field = read_field(std::make_shared(), + make_fixed_view(DecodedValueKind::DOUBLE, values)); + EXPECT_EQ(TYPE_DOUBLE, field.get_type()); + EXPECT_TRUE(std::isinf(field.get())); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadFieldStringValues) { + auto type = std::make_shared(); + std::vector storage = {std::string("a\0b", 3)}; + auto refs = string_refs(storage); + auto field = read_field(type, make_binary_view(DecodedValueKind::BINARY, refs)); + EXPECT_EQ(TYPE_STRING, field.get_type()); + EXPECT_EQ(std::string("a\0b", 3), field.get()); + + std::vector fixed_storage = {std::string("\x00\x01\x02\x03", 4)}; + auto fixed_refs = string_refs(fixed_storage); + auto fixed_field = + read_field(type, make_binary_view(DecodedValueKind::FIXED_BINARY, fixed_refs, 4)); + EXPECT_EQ(TYPE_STRING, fixed_field.get_type()); + EXPECT_EQ(std::string("\x00\x01\x02\x03", 4), fixed_field.get()); +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadFieldDateTimeAndTimeValues) { + { + auto type = std::make_shared(); + std::vector values = {18628}; + auto field = read_field(type, make_fixed_view(DecodedValueKind::INT32, values)); + EXPECT_EQ(TYPE_DATEV2, field.get_type()); + EXPECT_EQ("2021-01-01", field.to_debug_string(0)); + } + { + auto type = std::make_shared(6); + std::vector values = {1234567}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MICROS; + auto field = read_field(type, view); + EXPECT_EQ(TYPE_DATETIMEV2, field.get_type()); + EXPECT_EQ("1970-01-01 00:00:01.234567", field.to_debug_string(6)); + } + { + auto type = std::make_shared(6); + std::vector values = {1234}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MILLIS; + auto field = read_field(type, view); + EXPECT_EQ(TYPE_DATETIMEV2, field.get_type()); + EXPECT_EQ("1970-01-01 00:00:01.234000", field.to_debug_string(6)); + } + { + auto type = std::make_shared(6); + std::vector values = {{0, 2440588}}; + auto field = read_field(type, make_fixed_view(DecodedValueKind::INT96, values)); + EXPECT_EQ(TYPE_DATETIMEV2, field.get_type()); + EXPECT_EQ("1970-01-01 00:00:00.000000", field.to_debug_string(6)); + } + { + auto type = std::make_shared(6); + std::vector values = {3661000001LL}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::MICROS; + auto field = read_field(type, view); + EXPECT_EQ(TYPE_TIMEV2, field.get_type()); + auto column = type->create_column(); + column->insert(field); + expect_column_strings(*type, *column, {"01:01:01.000001"}); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadFieldDecimalValues) { + { + auto type = std::make_shared(9, 2); + std::vector values = {12345}; + auto field = read_field(type, make_fixed_view(DecodedValueKind::INT32, values)); + EXPECT_EQ(TYPE_DECIMAL32, field.get_type()); + EXPECT_EQ("123.45", field.to_debug_string(2)); + } + { + auto type = std::make_shared(18, 4); + std::vector values = {-1}; + auto field = read_field(type, make_fixed_view(DecodedValueKind::INT64, values)); + EXPECT_EQ(TYPE_DECIMAL64, field.get_type()); + EXPECT_EQ("-0.0001", field.to_debug_string(4)); + } + { + auto type = std::make_shared(38, 2); + std::vector storage = {std::string("\x30\x39", 2)}; + auto refs = string_refs(storage); + auto field = read_field(type, make_binary_view(DecodedValueKind::BINARY, refs)); + EXPECT_EQ(TYPE_DECIMAL128I, field.get_type()); + EXPECT_EQ("123.45", field.to_debug_string(2)); + } + { + auto type = std::make_shared(76, 2); + std::vector storage = {std::string(31, '\xff') + std::string("\xbd", 1)}; + auto refs = string_refs(storage); + auto field = read_field(type, make_binary_view(DecodedValueKind::FIXED_BINARY, refs, 32)); + EXPECT_EQ(TYPE_DECIMAL256, field.get_type()); + EXPECT_EQ("-0.67", field.to_debug_string(2)); + } +} + +TEST(DataTypeSerDeDecodedValuesTest, ReadFieldPropagatesUnsupportedKind) { + { + auto type = std::make_shared(); + std::vector values = {1}; + expect_not_supported( + read_field_status(type, make_fixed_view(DecodedValueKind::INT32, values))); + } + { + auto type = std::make_shared(); + std::vector values = {1.0}; + expect_not_supported( + read_field_status(type, make_fixed_view(DecodedValueKind::DOUBLE, values))); + } + { + auto type = std::make_shared(); + std::vector values = {0}; + expect_not_supported( + read_field_status(type, make_fixed_view(DecodedValueKind::INT64, values))); + } +} + +TEST(DataTypeSerDeDecodedValuesDeathTest, ReadFieldRejectsInvalidRowCountDeathTest) { + auto type = std::make_shared(); + std::vector values = {1, 2}; + Field field; + + auto zero_row_view = make_fixed_view(DecodedValueKind::INT32, values); + zero_row_view.row_count = 0; + EXPECT_DEATH( + { + auto status = type->get_serde()->read_field_from_decoded_value(*type, &field, + zero_row_view); + (void)status; + }, + "view.row_count == 1"); + + auto two_row_view = make_fixed_view(DecodedValueKind::INT32, values); + two_row_view.row_count = 2; + EXPECT_DEATH( + { + auto status = type->get_serde()->read_field_from_decoded_value(*type, &field, + two_row_view); + (void)status; + }, + "view.row_count == 1"); +} + +TEST(DataTypeSerDeDecodedValuesDeathTest, ReadFieldRejectsNullFieldPointerDeathTest) { + auto type = std::make_shared(); + std::vector values = {1}; + auto view = make_fixed_view(DecodedValueKind::INT32, values); + + EXPECT_DEATH( + { + auto status = + type->get_serde()->read_field_from_decoded_value(*type, nullptr, view); + (void)status; + }, + "field != nullptr"); +} + +// ---------------------------------------------------------------------- +// Illegal kind matrix +// ---------------------------------------------------------------------- +// This compact matrix complements the focused error tests above by ensuring each decoded-aware +// family rejects representative illegal physical kinds without mutating an empty destination. + +TEST(DataTypeSerDeDecodedValuesTest, IllegalKindMatrixRejectsUnsupportedCombinations) { + struct Case { + DataTypePtr type; + std::vector illegal_kinds; + }; + std::vector cases = { + {std::make_shared(), {DecodedValueKind::INT32, DecodedValueKind::BINARY}}, + {std::make_shared(), + {DecodedValueKind::BOOL, DecodedValueKind::FLOAT, DecodedValueKind::DOUBLE, + DecodedValueKind::BINARY}}, + {std::make_shared(), + {DecodedValueKind::DOUBLE, DecodedValueKind::INT32}}, + {std::make_shared(), + {DecodedValueKind::FLOAT, DecodedValueKind::INT64}}, + {std::make_shared(), + {DecodedValueKind::INT32, DecodedValueKind::DOUBLE}}, + {std::make_shared(), + {DecodedValueKind::INT64, DecodedValueKind::BINARY}}, + {std::make_shared(6), + {DecodedValueKind::INT32, DecodedValueKind::DOUBLE, DecodedValueKind::BINARY}}, + {std::make_shared(6), + {DecodedValueKind::BOOL, DecodedValueKind::BINARY, DecodedValueKind::DOUBLE}}, + {std::make_shared(18, 2), + {DecodedValueKind::BOOL, DecodedValueKind::UINT64, DecodedValueKind::FLOAT, + DecodedValueKind::DOUBLE}}, + }; + + for (const auto& test_case : cases) { + for (auto kind : test_case.illegal_kinds) { + std::vector values = {0}; + auto result = read_column(test_case.type, make_fixed_view(kind, values)); + expect_not_supported(result.status); + EXPECT_EQ(0, result.column->size()) << test_case.type->get_name(); + } + } +} + +} // namespace doris diff --git a/be/test/exec/runtime_filter/runtime_filter_expr_sampling_test.cpp b/be/test/exec/runtime_filter/runtime_filter_expr_sampling_test.cpp index 403ef8713e4e67..b3e512734c6e73 100644 --- a/be/test/exec/runtime_filter/runtime_filter_expr_sampling_test.cpp +++ b/be/test/exec/runtime_filter/runtime_filter_expr_sampling_test.cpp @@ -18,10 +18,13 @@ #include #include +#include "core/data_type/data_type_number.h" #include "exec/runtime_filter/runtime_filter_selectivity.h" #include "exec/runtime_filter/runtime_filter_test_utils.h" #include "exprs/runtime_filter_expr.h" +#include "exprs/vdirect_in_predicate.h" #include "exprs/vexpr_context.h" +#include "exprs/vslot_ref.h" namespace doris { @@ -178,4 +181,47 @@ TEST_F(RuntimeFilterExprSamplingTest, sampling_frequency_survives_context_recrea EXPECT_TRUE(selectivity.maybe_always_true_can_ignore()); } +// RuntimeFilterExpr exposes _impl->children(), but the wrapper itself does not own those +// children in its own _children vector. Deep clone must therefore clone _impl explicitly. +TEST_F(RuntimeFilterExprSamplingTest, deep_clone_clones_impl_tree) { + auto bool_type = TTypeDescBuilder() + .set_types(TTypeNodeBuilder() + .set_type(TTypeNodeType::SCALAR) + .set_scalar_type(TPrimitiveType::BOOLEAN) + .build()) + .build(); + TExprNode node = TExprNodeBuilder(TExprNodeType::IN_PRED, bool_type, 0).build(); + node.in_predicate.__set_is_not_in(false); + node.__set_opcode(TExprOpcode::FILTER_IN); + node.__set_is_nullable(false); + + auto slot = VSlotRef::create_shared(/*slot_id=*/0, /*column_id=*/0, /*column_uniq_id=*/10, + std::make_shared(), "c0"); + auto impl = VDirectInPredicate::create_shared(node, nullptr); + impl->add_child(slot); + + auto wrapper = RuntimeFilterExpr::create_shared(node, impl, 0.4, false, /*filter_id=*/7, + /*sampling_frequency=*/32); + + VExprSPtr cloned_expr; + ASSERT_TRUE(wrapper->deep_clone(&cloned_expr).ok()); + + auto* cloned_wrapper = dynamic_cast(cloned_expr.get()); + ASSERT_NE(cloned_wrapper, nullptr); + EXPECT_NE(cloned_wrapper, wrapper.get()); + EXPECT_EQ(cloned_wrapper->filter_id(), 7); + + auto cloned_impl = cloned_wrapper->get_impl(); + ASSERT_NE(cloned_impl, nullptr); + EXPECT_NE(cloned_impl.get(), impl.get()); + ASSERT_EQ(cloned_impl->get_num_children(), 1); + EXPECT_NE(cloned_impl->children()[0].get(), slot.get()); + + auto* cloned_slot = dynamic_cast(cloned_impl->children()[0].get()); + ASSERT_NE(cloned_slot, nullptr); + EXPECT_EQ(cloned_slot->column_id(), 0); + EXPECT_EQ(cloned_slot->column_uniq_id(), 10); + EXPECT_EQ(cloned_slot->column_name(), "c0"); +} + } // namespace doris diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index 64b17a6a86b87b..c2eb660ea30910 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -18,13 +18,24 @@ #include #include +#include #include +#include #include +#include "common/consts.h" #include "common/object_pool.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/data_type_struct.h" #include "cpp/sync_point.h" #include "exec/operator/file_scan_operator.h" #include "exec/scan/file_scanner.h" +#include "exec/scan/file_scanner_v2.h" +#include "exec/scan/split_source_connector.h" +#include "format_v2/table/hive_reader.h" #include "io/fs/local_file_system.h" #include "load/group_commit/wal/wal_manager.h" #include "runtime/cluster_info.h" @@ -34,6 +45,18 @@ #include "runtime/user_function_cache.h" namespace doris { +namespace { + +TColumnAccessPath data_access_path(std::vector path) { + TColumnAccessPath access_path; + access_path.__set_type(TAccessPathType::DATA); + TDataAccessPath data_path; + data_path.__set_path(std::move(path)); + access_path.__set_data_access_path(std::move(data_path)); + return access_path; +} + +} // namespace class TestSplitSourceConnectorStub : public SplitSourceConnector { private: @@ -336,4 +359,284 @@ TEST_F(VfileScannerExceptionTest, process_late_arrival_conjuncts_retain) { WARN_IF_ERROR(scanner->close(&_runtime_state), "fail to close scanner"); } +TEST(FileScannerV2Test, SupportOnlyRefactoredTableReaders) { + TFileScanRangeParams params; + params.__set_format_type(TFileFormatType::FORMAT_PARQUET); + + TFileRangeDesc range; + EXPECT_TRUE(FileScannerV2::is_supported(params, range)); + + TTableFormatFileDesc table_format; + table_format.__set_table_format_type("iceberg"); + range.__set_table_format_params(table_format); + EXPECT_TRUE(FileScannerV2::is_supported(params, range)); + + table_format.__set_table_format_type("hive"); + range.__set_table_format_params(table_format); + EXPECT_TRUE(FileScannerV2::is_supported(params, range)); + + table_format.__set_table_format_type("paimon"); + range.__set_table_format_params(table_format); + EXPECT_TRUE(FileScannerV2::is_supported(params, range)); + + table_format.__set_table_format_type("hudi"); + range.__set_table_format_params(table_format); + EXPECT_FALSE(FileScannerV2::is_supported(params, range)); + + range.__set_format_type(TFileFormatType::FORMAT_ORC); + table_format.__set_table_format_type("hive"); + range.__set_table_format_params(table_format); + EXPECT_FALSE(FileScannerV2::is_supported(params, range)); + + TScanRangeParams scan_range_params; + scan_range_params.scan_range.ext_scan_range.file_scan_range.ranges.push_back(range); + LocalSplitSourceConnector split_source({scan_range_params}, 1); + EXPECT_FALSE(split_source.all_scan_ranges_match(params, FileScannerV2::is_supported)); + + range.__set_format_type(TFileFormatType::FORMAT_PARQUET); + scan_range_params.scan_range.ext_scan_range.file_scan_range.ranges[0] = range; + LocalSplitSourceConnector supported_split_source({scan_range_params}, 1); + EXPECT_TRUE(supported_split_source.all_scan_ranges_match(params, FileScannerV2::is_supported)); +} + +TEST(HiveReaderTest, PositionMappingUsesColumnIdxsForFileSlots) { + TQueryOptions query_options; + query_options.hive_parquet_use_column_names = false; + RuntimeState runtime_state(query_options, TQueryGlobals()); + TFileScanRangeParams params; + params.__set_format_type(TFileFormatType::FORMAT_PARQUET); + params.__set_column_idxs({2, 0}); + format::ProjectedColumnBuildContext context { + .scan_params = ¶ms, + .runtime_state = &runtime_state, + }; + hive::HiveReader reader; + + TFileScanSlotInfo id_slot; + id_slot.__set_is_file_slot(true); + format::ColumnDefinition id_column { + .identifier = Field::create_field("id"), + .name = "id", + .type = std::make_shared(), + }; + + TFileScanSlotInfo name_slot; + name_slot.__set_is_file_slot(true); + format::ColumnDefinition name_column { + .identifier = Field::create_field("name"), + .name = "name", + .type = std::make_shared(), + }; + + ASSERT_TRUE(reader.annotate_projected_column(id_slot, &context, &id_column).ok()); + ASSERT_TRUE(id_column.has_identifier_field_id()); + EXPECT_EQ(id_column.get_identifier_position(), 2); + EXPECT_EQ(context.next_file_column_idx, 1); + + ASSERT_TRUE(reader.annotate_projected_column(name_slot, &context, &name_column).ok()); + ASSERT_TRUE(name_column.has_identifier_field_id()); + EXPECT_EQ(name_column.get_identifier_position(), 0); + EXPECT_EQ(context.next_file_column_idx, 2); + ASSERT_TRUE(reader.validate_projected_columns(context).ok()); +} + +TEST(HiveReaderTest, PositionMappingDoesNotConsumePartitionSlots) { + TQueryOptions query_options; + query_options.hive_parquet_use_column_names = false; + RuntimeState runtime_state(query_options, TQueryGlobals()); + TFileScanRangeParams params; + params.__set_format_type(TFileFormatType::FORMAT_PARQUET); + params.__set_column_idxs({3}); + format::ProjectedColumnBuildContext context { + .scan_params = ¶ms, + .runtime_state = &runtime_state, + }; + hive::HiveReader reader; + + TFileScanSlotInfo partition_slot; + partition_slot.__set_is_file_slot(false); + partition_slot.__set_category(TColumnCategory::PARTITION_KEY); + format::ColumnDefinition partition_column { + .identifier = Field::create_field("year"), + .name = "year", + .type = std::make_shared(), + }; + + TFileScanSlotInfo value_slot; + value_slot.__set_is_file_slot(true); + format::ColumnDefinition value_column { + .identifier = Field::create_field("value"), + .name = "value", + .type = std::make_shared(), + }; + + ASSERT_TRUE(reader.annotate_projected_column(partition_slot, &context, &partition_column).ok()); + ASSERT_TRUE(partition_column.has_identifier_name()); + EXPECT_EQ(partition_column.get_identifier_name(), "year"); + EXPECT_EQ(context.next_file_column_idx, 0); + + ASSERT_TRUE(reader.annotate_projected_column(value_slot, &context, &value_column).ok()); + ASSERT_TRUE(value_column.has_identifier_field_id()); + EXPECT_EQ(value_column.get_identifier_position(), 3); + EXPECT_EQ(context.next_file_column_idx, 1); + ASSERT_TRUE(reader.validate_projected_columns(context).ok()); +} + +TEST(HiveReaderTest, PositionMappingFailsWhenColumnIdxsMissing) { + TQueryOptions query_options; + query_options.hive_parquet_use_column_names = false; + RuntimeState runtime_state(query_options, TQueryGlobals()); + TFileScanRangeParams params; + params.__set_format_type(TFileFormatType::FORMAT_PARQUET); + format::ProjectedColumnBuildContext context { + .scan_params = ¶ms, + .runtime_state = &runtime_state, + }; + hive::HiveReader reader; + + TFileScanSlotInfo value_slot; + value_slot.__set_is_file_slot(true); + format::ColumnDefinition value_column { + .identifier = Field::create_field("value"), + .name = "value", + .type = std::make_shared(), + }; + + auto status = reader.annotate_projected_column(value_slot, &context, &value_column); + EXPECT_FALSE(status.ok()); + EXPECT_EQ(context.next_file_column_idx, 0); +} + +TEST(FileScannerV2Test, BuildNestedChildrenFromAccessPaths) { + const auto int_type = std::make_shared(); + const auto key_type = std::make_shared(); + const auto value_type = + std::make_shared(DataTypes {int_type, int_type}, Strings {"b", "c"}); + format::ColumnDefinition column {.identifier = Field::create_field(100), + .name = "m", + .type = std::make_shared(key_type, value_type)}; + + std::vector access_paths; + access_paths.push_back(data_access_path({"m", "KEYS"})); + access_paths.push_back(data_access_path({"m", "VALUES", "b"})); + access_paths.push_back(data_access_path({"m", "*", "c"})); + auto status = + FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); + ASSERT_TRUE(status.ok()) << status; + + ASSERT_EQ(column.children.size(), 1); + const auto& entries = column.children[0]; + ASSERT_TRUE(entries.has_identifier_field_id()); + EXPECT_EQ(entries.get_identifier_field_id(), 0); + EXPECT_EQ(entries.name, "entries"); + ASSERT_EQ(entries.children.size(), 2); + EXPECT_EQ(entries.children[0].name, "key"); + EXPECT_TRUE(entries.children[0].children.empty()); + EXPECT_EQ(entries.children[1].name, "value"); + ASSERT_EQ(entries.children[1].children.size(), 2); + EXPECT_EQ(entries.children[1].children[0].name, "b"); + EXPECT_EQ(entries.children[1].children[1].name, "c"); +} + +TEST(FileScannerV2Test, BuildArrayStructChildrenFromAccessPaths) { + const auto int_type = std::make_shared(); + const auto element_type = + std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"}); + format::ColumnDefinition column { + .identifier = Field::create_field(100), + .name = "arr", + .type = std::make_shared(element_type), + }; + + std::vector access_paths; + access_paths.push_back(data_access_path({"arr", "*", "a"})); + auto status = + FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); + ASSERT_TRUE(status.ok()) << status; + + ASSERT_EQ(column.children.size(), 1); + const auto& element = column.children[0]; + ASSERT_TRUE(element.has_identifier_field_id()); + EXPECT_EQ(element.get_identifier_field_id(), 0); + EXPECT_EQ(element.name, "element"); + ASSERT_EQ(element.children.size(), 1); + ASSERT_TRUE(element.children[0].has_identifier_field_id()); + EXPECT_EQ(element.children[0].get_identifier_field_id(), 0); + EXPECT_EQ(element.children[0].name, "a"); +} + +TEST(FileScannerV2Test, BuildStructChildrenFromFieldIdAccessPaths) { + const auto int_type = std::make_shared(); + const auto struct_type = + std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"}); + format::ColumnDefinition column { + .identifier = Field::create_field(100), + .name = "s", + .type = struct_type, + }; + format::ColumnDefinition schema_column { + .identifier = Field::create_field(100), + .name = "s", + .type = struct_type, + .children = + { + {.identifier = Field::create_field(101), + .name = "a", + .type = int_type}, + {.identifier = Field::create_field(205), + .name = "b", + .type = int_type}, + }, + }; + + std::vector access_paths; + access_paths.push_back(data_access_path({"100", "205"})); + auto status = FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths, + &schema_column); + ASSERT_TRUE(status.ok()) << status; + + ASSERT_EQ(column.children.size(), 1); + ASSERT_TRUE(column.children[0].has_identifier_field_id()); + EXPECT_EQ(column.children[0].get_identifier_field_id(), 205); + EXPECT_EQ(column.children[0].name, "b"); +} + +TEST(FileScannerV2Test, BuildNestedChildrenKeepsTopLevelProjectionWhole) { + const auto int_type = std::make_shared(); + format::ColumnDefinition column { + .identifier = Field::create_field(100), + .name = "s", + .type = std::make_shared(DataTypes {int_type, int_type}, + Strings {"a", "b"}), + }; + + std::vector access_paths; + access_paths.push_back(data_access_path({"s"})); + access_paths.push_back(data_access_path({"s", "a"})); + auto status = + FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); + ASSERT_TRUE(status.ok()) << status; + EXPECT_TRUE(column.children.empty()); +} + +TEST(FileScannerV2Test, IcebergRowidSkipsNegativeAccessPath) { + const auto string_type = std::make_shared(); + const auto rowid_type = std::make_shared( + DataTypes {string_type, std::make_shared(), + std::make_shared(), string_type}, + Strings {"file_path", "row_pos", "partition_spec_id", "partition_data_json"}); + format::ColumnDefinition column { + .identifier = Field::create_field(BeConsts::ICEBERG_ROWID_COL), + .name = BeConsts::ICEBERG_ROWID_COL, + .type = rowid_type, + }; + + std::vector access_paths; + access_paths.push_back(data_access_path({"-1"})); + auto status = + FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); + ASSERT_TRUE(status.ok()) << status; + EXPECT_TRUE(column.children.empty()); +} + } // namespace doris diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp new file mode 100644 index 00000000000000..7d9727f18e1da9 --- /dev/null +++ b/be/test/format_v2/column_mapper_test.cpp @@ -0,0 +1,2002 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/column_mapper.h" + +#include + +#include +#include +#include +#include +#include + +#include "common/consts.h" +#include "core/assert_cast.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_decimal.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/data_type_struct.h" +#include "exprs/vexpr.h" +#include "exprs/vexpr_context.h" +#include "exprs/vliteral.h" +#include "exprs/vslot_ref.h" +#include "format_v2/column_mapper_nested.h" +#include "format_v2/expr/cast.h" +#include "format_v2/schema_projection.h" +#include "format_v2/table_reader.h" +#include "gen_cpp/Exprs_types.h" +#include "storage/predicate/predicate_creator.h" + +namespace doris::format { +namespace { + +DataTypePtr i32() { + return std::make_shared(); +} + +DataTypePtr i64() { + return std::make_shared(); +} + +DataTypePtr f32() { + return std::make_shared(); +} + +DataTypePtr f64() { + return std::make_shared(); +} + +DataTypePtr dec32(uint32_t precision, uint32_t scale) { + return std::make_shared(precision, scale); +} + +DataTypePtr str() { + return std::make_shared(); +} + +DataTypePtr u8() { + return std::make_shared(); +} + +ColumnDefinition field_id_col(const std::string& name, int32_t field_id, DataTypePtr type, + int32_t local_id = -1) { + ColumnDefinition column; + column.identifier = Field::create_field(field_id); + column.local_id = local_id; + column.name = name; + column.type = std::move(type); + return column; +} + +ColumnDefinition name_col(const std::string& name, DataTypePtr type, int32_t local_id = -1) { + ColumnDefinition column; + column.identifier = Field::create_field(name); + column.local_id = local_id; + column.name = name; + column.type = std::move(type); + return column; +} + +ColumnDefinition name_id_col(const std::string& name, const std::string& identifier, + DataTypePtr type, int32_t local_id = -1) { + ColumnDefinition column = name_col(name, std::move(type), local_id); + column.identifier = Field::create_field(identifier); + return column; +} + +ColumnDefinition position_col(const std::string& name, int32_t file_position, DataTypePtr type) { + return field_id_col(name, file_position, std::move(type)); +} + +ColumnDefinition struct_col(const std::string& name, int32_t field_id, + std::vector children, int32_t local_id = -1) { + DataTypes child_types; + Strings child_names; + child_types.reserve(children.size()); + child_names.reserve(children.size()); + for (const auto& child : children) { + child_types.push_back(child.type); + child_names.push_back(child.name); + } + auto column = field_id_col( + name, field_id, std::make_shared(child_types, child_names), local_id); + column.children = std::move(children); + return column; +} + +ColumnDefinition struct_name_col(const std::string& name, std::vector children, + int32_t local_id = -1) { + auto column = struct_col(name, -1, std::move(children), local_id); + column.identifier = Field::create_field(name); + return column; +} + +ColumnDefinition array_col(const std::string& name, int32_t field_id, ColumnDefinition element, + int32_t local_id = -1) { + auto column = + field_id_col(name, field_id, std::make_shared(element.type), local_id); + column.children = {std::move(element)}; + return column; +} + +ColumnDefinition map_col(const std::string& name, int32_t field_id, ColumnDefinition entry, + const DataTypePtr& key_type, const DataTypePtr& value_type, + int32_t local_id = -1) { + auto column = field_id_col(name, field_id, std::make_shared(key_type, value_type), + local_id); + column.children = {std::move(entry)}; + return column; +} + +void set_name_identifiers(ColumnDefinition* column, int32_t local_id) { + DORIS_CHECK(column != nullptr); + column->identifier = Field::create_field(column->name); + column->local_id = local_id; + for (size_t idx = 0; idx < column->children.size(); ++idx) { + set_name_identifiers(&column->children[idx], static_cast(idx)); + } +} + +std::vector projection_ids(const std::vector& projections) { + std::vector ids; + ids.reserve(projections.size()); + for (const auto& projection : projections) { + ids.push_back(projection.local_id()); + } + return ids; +} + +std::vector target_names(const FileStructPredicateTarget* target) { + std::vector names; + for (const auto* current = target; current != nullptr; current = current->child.get()) { + names.push_back(current->file_child_name); + } + return names; +} + +void expect_mapping(const ColumnMapping& mapping, size_t global_index, + const std::string& table_name, int32_t file_local_id, + const std::string& file_name, const DataTypePtr& file_type, + const DataTypePtr& table_type) { + EXPECT_EQ(mapping.global_index, GlobalIndex(global_index)); + EXPECT_EQ(mapping.table_column_name, table_name); + ASSERT_TRUE(mapping.file_local_id.has_value()); + EXPECT_EQ(*mapping.file_local_id, file_local_id); + EXPECT_EQ(mapping.file_column_name, file_name); + ASSERT_NE(mapping.file_type, nullptr); + ASSERT_NE(mapping.table_type, nullptr); + EXPECT_TRUE(mapping.file_type->equals(*file_type)); + EXPECT_TRUE(mapping.table_type->equals(*table_type)); +} + +void expect_constant(const TableColumnMapper& mapper, const ColumnMapping& mapping, + size_t global_index, const DataTypePtr& table_type) { + EXPECT_FALSE(mapping.file_local_id.has_value()); + ASSERT_TRUE(mapping.constant_index.has_value()); + ASSERT_LT(mapping.constant_index->value(), mapper.constant_map().size()); + const auto& entry = mapper.constant_map().get(*mapping.constant_index); + EXPECT_EQ(entry.global_index, GlobalIndex(global_index)); + EXPECT_TRUE(entry.type->equals(*table_type)); + EXPECT_EQ(entry.expr, mapping.default_expr); +} + +void expect_missing(const ColumnMapping& mapping) { + EXPECT_FALSE(mapping.file_local_id.has_value()); + EXPECT_FALSE(mapping.constant_index.has_value()); + EXPECT_EQ(mapping.virtual_column_type, TableVirtualColumnType::INVALID); +} + +class TestFunctionExpr final : public VExpr { +public: + TestFunctionExpr(std::string function_name, DataTypePtr data_type, + TExprNodeType::type node_type = TExprNodeType::FUNCTION_CALL, + TExprOpcode::type opcode = TExprOpcode::INVALID_OPCODE) + : VExpr(std::move(data_type), false), _expr_name(std::move(function_name)) { + set_node_type(node_type); + _opcode = opcode; + TFunctionName fn_name; + fn_name.__set_function_name(_expr_name); + _fn.__set_name(fn_name); + } + + const std::string& expr_name() const override { return _expr_name; } + + Status execute_column_impl(VExprContext*, const Block*, const Selector*, size_t, + ColumnPtr&) const override { + return Status::NotSupported("TestFunctionExpr is only used for ColumnMapper analysis"); + } + +private: + std::string _expr_name; +}; + +VExprSPtr table_slot(int slot_id, int column_id, DataTypePtr type, const std::string& name) { + return VSlotRef::create_shared(slot_id, column_id, -1, std::move(type), name); +} + +VExprSPtr literal(DataTypePtr type, Field value) { + return VLiteral::create_shared(std::move(type), std::move(value)); +} + +VExprSPtr struct_element(const VExprSPtr& parent, DataTypePtr child_type, + const std::string& child_name) { + auto expr = std::make_shared("struct_element", child_type); + expr->add_child(parent); + expr->add_child(literal(str(), Field::create_field(child_name))); + return expr; +} + +VExprSPtr struct_element_by_selector(const VExprSPtr& parent, DataTypePtr child_type, + const VExprSPtr& selector) { + auto expr = std::make_shared("struct_element", std::move(child_type)); + expr->add_child(parent); + expr->add_child(selector); + return expr; +} + +VExprSPtr int_gt(const VExprSPtr& left, int32_t value) { + auto expr = std::make_shared("gt", u8(), TExprNodeType::BINARY_PRED, + TExprOpcode::GT); + expr->add_child(left); + expr->add_child(literal(i32(), Field::create_field(value))); + return expr; +} + +VExprSPtr binary_predicate(TExprOpcode::type opcode, const VExprSPtr& left, + const VExprSPtr& right) { + auto expr = std::make_shared("binary_predicate", u8(), + TExprNodeType::BINARY_PRED, opcode); + expr->add_child(left); + expr->add_child(right); + return expr; +} + +VExprSPtr in_predicate(const VExprSPtr& probe, const DataTypePtr& literal_type, + const std::vector& values) { + auto expr = std::make_shared("in", u8(), TExprNodeType::IN_PRED); + expr->add_child(probe); + for (const auto& value : values) { + expr->add_child(literal(literal_type, value)); + } + return expr; +} + +VExprSPtr null_predicate(const VExprSPtr& child, bool is_null) { + auto expr = + std::make_shared(is_null ? "is_null_pred" : "is_not_null_pred", u8()); + expr->add_child(child); + return expr; +} + +VExprSPtr cast_expr(const VExprSPtr& child, DataTypePtr target_type) { + auto expr = Cast::create_shared(std::move(target_type)); + expr->add_child(child); + return expr; +} + +VExprSPtr compound_predicate(TExprOpcode::type opcode, const VExprSPtr& left, + const VExprSPtr& right) { + auto expr = std::make_shared("compound", u8(), TExprNodeType::COMPOUND_PRED, + opcode); + expr->add_child(left); + expr->add_child(right); + return expr; +} + +ColumnMapping mapped_struct_column(int32_t root_file_local_id, const std::string& child_name, + int32_t child_file_local_id, DataTypePtr child_type) { + ColumnDefinition file_child = name_col(child_name, child_type, child_file_local_id); + ColumnMapping root; + root.global_index = GlobalIndex(0); + root.table_column_name = "s"; + root.file_local_id = root_file_local_id; + root.file_column_name = "s"; + root.table_type = + std::make_shared(DataTypes {child_type}, Strings {child_name}); + root.file_type = root.table_type; + root.original_file_type = root.table_type; + root.original_file_children = {file_child}; + root.projected_file_children = {file_child}; + return root; +} + +std::vector collect_paths(const VExprSPtr& expr) { + std::vector paths; + collect_nested_struct_paths(expr, &paths); + return paths; +} + +void expect_name_selector(const StructChildSelector& selector, const std::string& name) { + EXPECT_TRUE(selector.by_name); + EXPECT_EQ(selector.name, name); +} + +void expect_ordinal_selector(const StructChildSelector& selector, size_t ordinal) { + EXPECT_FALSE(selector.by_name); + EXPECT_EQ(selector.ordinal, ordinal); +} + +void expect_path_root(const NestedStructPath& path, size_t global_index) { + EXPECT_EQ(path.root_global_index, GlobalIndex(global_index)); +} + +// ---------------------------------------------------------------------- +// L0 schema projection helper tests. +// These tests isolate LocalColumnIndex projection semantics before +// TableColumnMapper starts mutating ColumnMapping state. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperSchemaProjectionTest, ProjectsStructByLocalIdAndKeepsFileOrder) { + auto a = field_id_col("a", 101, i32(), 0); + auto b = field_id_col("b", 102, str(), 1); + auto root = struct_col("s", 100, {a, b}, 7); + + LocalColumnIndex projection = LocalColumnIndex::partial_local(7); + projection.children.push_back(LocalColumnIndex::local(1)); + projection.children.push_back(LocalColumnIndex::local(0)); + + ColumnDefinition projected; + ASSERT_TRUE(project_column_definition(root, projection, &projected).ok()); + ASSERT_EQ(projected.children.size(), 2); + EXPECT_EQ(projected.children[0].name, "a"); + EXPECT_EQ(projected.children[1].name, "b"); + + const auto* projected_type = + assert_cast(remove_nullable(projected.type).get()); + ASSERT_EQ(projected_type->get_elements().size(), 2); + EXPECT_EQ(projected_type->get_element_name(0), "a"); + EXPECT_EQ(projected_type->get_element_name(1), "b"); +} + +TEST(ColumnMapperSchemaProjectionTest, ProjectsArrayElementStructLeaf) { + auto a = field_id_col("a", 1, i32(), 0); + auto b = field_id_col("b", 2, str(), 1); + auto element = struct_col("element", 10, {a, b}, 0); + auto array = array_col("items", 100, element, 5); + + LocalColumnIndex projection = LocalColumnIndex::partial_local(5); + auto element_projection = LocalColumnIndex::partial_local(0); + element_projection.children.push_back(LocalColumnIndex::local(1)); + projection.children.push_back(std::move(element_projection)); + + ColumnDefinition projected; + ASSERT_TRUE(project_column_definition(array, projection, &projected).ok()); + ASSERT_EQ(projected.children.size(), 1); + ASSERT_EQ(projected.children[0].children.size(), 1); + EXPECT_EQ(projected.children[0].children[0].name, "b"); + + const auto* array_type = + assert_cast(remove_nullable(projected.type).get()); + const auto* element_type = assert_cast( + remove_nullable(array_type->get_nested_type()).get()); + ASSERT_EQ(element_type->get_elements().size(), 1); + EXPECT_EQ(element_type->get_element_name(0), "b"); +} + +TEST(ColumnMapperSchemaProjectionTest, ProjectsMapValueStructLeaf) { + auto key = field_id_col("key", 1, str(), 0); + auto value_a = field_id_col("a", 2, i32(), 0); + auto value_b = field_id_col("b", 3, str(), 1); + auto value_type = + std::make_shared(DataTypes {i32(), str()}, Strings {"a", "b"}); + ColumnDefinition value = field_id_col("value", 4, value_type, 1); + value.children = {value_a, value_b}; + auto entry_type = std::make_shared(DataTypes {str(), value_type}, + Strings {"key", "value"}); + ColumnDefinition entry = field_id_col("entries", 5, entry_type, 0); + entry.children = {key, value}; + auto map = map_col("m", 100, entry, str(), value_type, 9); + + LocalColumnIndex projection = LocalColumnIndex::partial_local(9); + auto entry_projection = LocalColumnIndex::partial_local(0); + auto value_projection = LocalColumnIndex::partial_local(1); + value_projection.children.push_back(LocalColumnIndex::local(1)); + entry_projection.children.push_back(std::move(value_projection)); + projection.children.push_back(std::move(entry_projection)); + + ColumnDefinition projected; + ASSERT_TRUE(project_column_definition(map, projection, &projected).ok()); + ASSERT_EQ(projected.children.size(), 1); + ASSERT_EQ(projected.children[0].children.size(), 1); + ASSERT_EQ(projected.children[0].children[0].children.size(), 1); + EXPECT_EQ(projected.children[0].children[0].children[0].name, "b"); + + const auto* map_type = assert_cast(remove_nullable(projected.type).get()); + const auto* projected_value = + assert_cast(remove_nullable(map_type->get_value_type()).get()); + ASSERT_EQ(projected_value->get_elements().size(), 1); + EXPECT_EQ(projected_value->get_element_name(0), "b"); +} + +TEST(ColumnMapperSchemaProjectionTest, RejectsInvalidProjectionChildIdWithFieldName) { + auto root = struct_col("s", 100, {field_id_col("a", 101, i32(), 0)}, 7); + + LocalColumnIndex projection = LocalColumnIndex::partial_local(7); + projection.children.push_back(LocalColumnIndex::local(99)); + + ColumnDefinition projected; + const auto status = project_column_definition(root, projection, &projected); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("Invalid projection child id 99 for field s"), + std::string::npos); +} + +TEST(ColumnMapperSchemaProjectionTest, RejectsEmptyProjectionPathWithFieldName) { + auto root = struct_col("s", 100, {field_id_col("a", 101, i32(), 0)}, 7); + + LocalColumnIndex projection = LocalColumnIndex::partial_local(7); + projection.children.push_back(LocalColumnIndex::local(-1)); + + ColumnDefinition projected; + const auto status = project_column_definition(root, projection, &projected); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("Empty projection path for field s"), std::string::npos); +} + +TEST(ColumnMapperSchemaProjectionTest, RejectsChildProjectionForPrimitiveType) { + DataTypePtr projected_type; + const auto status = rebuild_projected_type(i32(), {i32()}, {"a"}, &projected_type); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("Cannot project children from non-complex type"), + std::string::npos); +} + +// ---------------------------------------------------------------------- +// L0 nested helper tests. +// These tests cover child ordering, direct schema path resolution, and +// predicate-filter merging without going through create_scan_request(). +// ---------------------------------------------------------------------- + +TEST(ColumnMapperNestedHelperTest, PresentChildMappingsAreSortedByFileLocalId) { + ColumnMapping b; + b.table_column_name = "b"; + b.file_local_id = 2; + ColumnMapping missing; + missing.table_column_name = "missing"; + ColumnMapping a; + a.table_column_name = "a"; + a.file_local_id = 1; + + const std::vector child_mappings = {b, missing, a}; + const auto present = present_child_mappings_in_file_order(child_mappings); + ASSERT_EQ(present.size(), 2); + EXPECT_EQ(present[0]->table_column_name, "a"); + EXPECT_EQ(present[1]->table_column_name, "b"); +} + +TEST(ColumnMapperNestedHelperTest, BuildsProjectionByNameAndOrdinalSelectors) { + auto leaf = field_id_col("leaf", 3, i32(), 0); + auto nested = struct_col("nested", 2, {leaf}, 1); + auto first = field_id_col("first", 1, str(), 0); + const std::vector children = {first, nested}; + + const std::vector by_name = { + {.by_name = true, .name = "nested", .ordinal = 0}, + {.by_name = true, .name = "leaf", .ordinal = 0}, + }; + LocalColumnIndex named_projection; + ASSERT_TRUE(build_file_child_projection_from_schema(children, by_name, &named_projection).ok()); + EXPECT_EQ(named_projection.local_id(), 1); + ASSERT_EQ(named_projection.children.size(), 1); + EXPECT_EQ(named_projection.children[0].local_id(), 0); + + const std::vector by_ordinal = { + {.by_name = false, .name = "", .ordinal = 2}, + {.by_name = false, .name = "", .ordinal = 1}, + }; + LocalColumnIndex ordinal_projection; + ASSERT_TRUE(build_file_child_projection_from_schema(children, by_ordinal, &ordinal_projection) + .ok()); + EXPECT_EQ(ordinal_projection.local_id(), 1); + ASSERT_EQ(ordinal_projection.children.size(), 1); + EXPECT_EQ(ordinal_projection.children[0].local_id(), 0); +} + +TEST(ColumnMapperNestedHelperTest, MergesPredicateFiltersForSameNestedTarget) { + FileColumnPredicateFilter gt_filter; + gt_filter.target = FileNestedPredicateTarget( + LocalColumnId(7), std::make_unique(2, "score")); + gt_filter.file_column_id = LocalColumnId(7); + gt_filter.file_child_id_path = {2}; + gt_filter.predicates.push_back(create_comparison_predicate( + 7, "score", i32(), Field::create_field(10), false)); + + FileColumnPredicateFilter lt_filter; + lt_filter.target = FileNestedPredicateTarget( + LocalColumnId(7), std::make_unique(2, "score")); + lt_filter.file_column_id = LocalColumnId(7); + lt_filter.file_child_id_path = {2}; + lt_filter.predicates.push_back(create_comparison_predicate( + 7, "score", i32(), Field::create_field(100), false)); + + std::vector filters; + merge_column_predicate_filter(std::move(gt_filter), &filters); + merge_column_predicate_filter(std::move(lt_filter), &filters); + + ASSERT_EQ(filters.size(), 1); + EXPECT_EQ(filters[0].effective_file_column_id(), LocalColumnId(7)); + EXPECT_EQ(filters[0].effective_file_child_id_path(), std::vector({2})); + ASSERT_EQ(filters[0].predicates.size(), 2); + EXPECT_EQ(target_names(filters[0].target.struct_target.get()), + std::vector({"score"})); +} + +TEST(ColumnMapperNestedHelperTest, DoesNotExtractPredicateFiltersFromOr) { + const auto int_type = i32(); + const auto struct_type = std::make_shared(DataTypes {int_type}, Strings {"a"}); + const auto slot = table_slot(0, 0, struct_type, "s"); + const auto left = int_gt(struct_element(slot, int_type, "a"), 10); + const auto right = int_gt(struct_element(slot, int_type, "a"), 20); + const auto or_expr = compound_predicate(TExprOpcode::COMPOUND_OR, left, right); + + std::vector filters; + collect_nested_column_predicate_filters(or_expr, {mapped_struct_column(5, "a", 0, int_type)}, + &filters); + + EXPECT_TRUE(filters.empty()); +} + +TEST(ColumnMapperNestedHelperTest, DoesNotExtractPredicateFiltersFromUnsupportedExpression) { + const auto int_type = i32(); + const auto struct_type = std::make_shared(DataTypes {int_type}, Strings {"a"}); + auto add_expr = std::make_shared("add", int_type); + add_expr->add_child(struct_element(table_slot(0, 0, struct_type, "s"), int_type, "a")); + add_expr->add_child(literal(int_type, Field::create_field(1))); + + std::vector filters; + collect_nested_column_predicate_filters(add_expr, {mapped_struct_column(5, "a", 0, int_type)}, + &filters); + + EXPECT_TRUE(filters.empty()); +} + +TEST(ColumnMapperNestedHelperTest, DoesNotExtractPredicateFiltersThroughUnsafeCast) { + const auto file_type = i64(); + const auto table_type = i32(); + const auto struct_type = std::make_shared(DataTypes {file_type}, Strings {"a"}); + const auto nested_leaf = struct_element(table_slot(0, 0, struct_type, "s"), file_type, "a"); + const auto filter_expr = int_gt(cast_expr(nested_leaf, table_type), 10); + + std::vector filters; + collect_nested_column_predicate_filters(filter_expr, + {mapped_struct_column(5, "a", 0, file_type)}, &filters); + + EXPECT_TRUE(filters.empty()); +} + +// ---------------------------------------------------------------------- +// collect_nested_struct_paths() helper tests. +// These tests assert the entry helper for nested scan projection: it only discovers +// table-side struct paths. Later localization decides whether to build pruning predicates. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperCollectNestedStructPathsTest, CollectsNameOrdinalAndBooleanSelectors) { + const auto leaf_type = i32(); + const auto inner_type = + std::make_shared(DataTypes {leaf_type, leaf_type}, Strings {"x", "y"}); + const auto root_type = std::make_shared(DataTypes {inner_type, leaf_type}, + Strings {"nested", "missing"}); + const auto root = table_slot(0, 3, root_type, "s"); + + const auto nested_by_ordinal = struct_element_by_selector( + struct_element_by_selector(root, inner_type, + literal(i32(), Field::create_field(1))), + leaf_type, literal(i32(), Field::create_field(2))); + auto paths = collect_paths(nested_by_ordinal); + ASSERT_EQ(paths.size(), 1); + expect_path_root(paths[0], 3); + ASSERT_EQ(paths[0].selectors.size(), 2); + expect_ordinal_selector(paths[0].selectors[0], 1); + expect_ordinal_selector(paths[0].selectors[1], 2); + + const std::vector positive_ordinal_selectors = { + literal(std::make_shared(), + Field::create_field(static_cast(1))), + literal(std::make_shared(), + Field::create_field(static_cast(2))), + literal(i32(), Field::create_field(3)), + literal(i64(), Field::create_field(4)), + literal(u8(), Field::create_field(true)), + }; + for (size_t idx = 0; idx < positive_ordinal_selectors.size(); ++idx) { + const auto selected = + struct_element_by_selector(root, leaf_type, positive_ordinal_selectors[idx]); + paths = collect_paths(selected); + ASSERT_EQ(paths.size(), 1); + ASSERT_EQ(paths[0].selectors.size(), 1); + expect_ordinal_selector(paths[0].selectors[0], idx == 4 ? 1 : idx + 1); + } + + paths = collect_paths(struct_element(root, leaf_type, "missing")); + ASSERT_EQ(paths.size(), 1); + ASSERT_EQ(paths[0].selectors.size(), 1); + expect_name_selector(paths[0].selectors[0], "missing"); +} + +TEST(ColumnMapperCollectNestedStructPathsTest, IgnoresInvalidSelectorsAndNonPathRoots) { + const auto leaf_type = i32(); + const auto root_type = std::make_shared(DataTypes {leaf_type}, Strings {"a"}); + const auto root = table_slot(0, 0, root_type, "s"); + + const std::vector invalid_selectors = { + literal(i32(), Field::create_field(0)), + literal(i32(), Field::create_field(-1)), + literal(u8(), Field::create_field(false)), + literal(f32(), Field::create_field(1.0F)), + literal(f64(), Field::create_field(1.0)), + table_slot(1, 1, i32(), "selector"), + }; + for (const auto& selector : invalid_selectors) { + EXPECT_TRUE(collect_paths(struct_element_by_selector(root, leaf_type, selector)).empty()); + } + + auto wrong_arity = std::make_shared("struct_element", leaf_type); + wrong_arity->add_child(root); + EXPECT_TRUE(collect_paths(wrong_arity).empty()); + + auto not_struct_element = std::make_shared("other_function", leaf_type); + not_struct_element->add_child(root); + not_struct_element->add_child(literal(str(), Field::create_field("a"))); + EXPECT_TRUE(collect_paths(not_struct_element).empty()); + + EXPECT_TRUE(collect_paths(struct_element(literal(str(), Field::create_field("x")), + leaf_type, "a")) + .empty()); + EXPECT_TRUE(collect_paths(nullptr).empty()); +} + +TEST(ColumnMapperCollectNestedStructPathsTest, RecursesThroughExpressionsAndKeepsCompletePath) { + const auto leaf_type = i32(); + const auto inner_type = std::make_shared(DataTypes {leaf_type}, Strings {"b"}); + const auto root_type = + std::make_shared(DataTypes {inner_type, leaf_type}, Strings {"a", "c"}); + const auto root = table_slot(0, 2, root_type, "s"); + const auto path_a = struct_element_by_selector( + root, inner_type, literal(str(), Field::create_field("a"))); + const auto path_ab = struct_element_by_selector( + path_a, leaf_type, literal(str(), Field::create_field("b"))); + const auto path_c = struct_element_by_selector( + root, leaf_type, literal(str(), Field::create_field("c"))); + + auto paths = collect_paths(binary_predicate( + TExprOpcode::GT, path_ab, literal(leaf_type, Field::create_field(1)))); + ASSERT_EQ(paths.size(), 1); + expect_path_root(paths[0], 2); + ASSERT_EQ(paths[0].selectors.size(), 2); + expect_name_selector(paths[0].selectors[0], "a"); + expect_name_selector(paths[0].selectors[1], "b"); + + paths = collect_paths(compound_predicate( + TExprOpcode::COMPOUND_OR, + binary_predicate(TExprOpcode::GT, path_ab, + literal(leaf_type, Field::create_field(1))), + binary_predicate(TExprOpcode::LT, path_c, + literal(leaf_type, Field::create_field(2))))); + ASSERT_EQ(paths.size(), 2); + ASSERT_EQ(paths[0].selectors.size(), 2); + ASSERT_EQ(paths[1].selectors.size(), 1); + expect_name_selector(paths[0].selectors[0], "a"); + expect_name_selector(paths[0].selectors[1], "b"); + expect_name_selector(paths[1].selectors[0], "c"); + + auto fn = std::make_shared("fn", leaf_type); + fn->add_child(path_ab); + fn->add_child(table_slot(3, 4, leaf_type, "other")); + paths = collect_paths(fn); + ASSERT_EQ(paths.size(), 1); + ASSERT_EQ(paths[0].selectors.size(), 2); + + auto if_expr = std::make_shared("if", leaf_type); + if_expr->add_child(literal(u8(), Field::create_field(true))); + if_expr->add_child(path_ab); + if_expr->add_child(path_c); + paths = collect_paths(if_expr); + ASSERT_EQ(paths.size(), 2); + + paths = collect_paths(compound_predicate(TExprOpcode::COMPOUND_AND, path_ab, path_ab)); + ASSERT_EQ(paths.size(), 2); + + paths = collect_paths(path_ab); + ASSERT_EQ(paths.size(), 1); + ASSERT_EQ(paths[0].selectors.size(), 2); +} + +TEST(ColumnMapperCollectNestedStructPathsTest, CastBehaviorSeparatesProjectionAndPruningRules) { + const auto int_type = i32(); + const auto bigint_type = i64(); + const auto float_type = f32(); + const auto double_type = f64(); + const auto decimal_small = dec32(8, 2); + const auto decimal_wide = dec32(9, 2); + const auto decimal_changed_scale = dec32(9, 3); + + const auto root_type = std::make_shared( + DataTypes {int_type, float_type, decimal_small}, Strings {"i", "f", "d"}); + const auto root = table_slot(0, 0, root_type, "s"); + const auto int_path = struct_element(root, int_type, "i"); + const auto float_path = struct_element(root, float_type, "f"); + const auto decimal_path = struct_element(root, decimal_small, "d"); + + auto paths = collect_paths(cast_expr(int_path, bigint_type)); + ASSERT_EQ(paths.size(), 1); + expect_name_selector(paths[0].selectors[0], "i"); + + paths = collect_paths(cast_expr(float_path, double_type)); + ASSERT_EQ(paths.size(), 1); + expect_name_selector(paths[0].selectors[0], "f"); + + paths = collect_paths(cast_expr(decimal_path, decimal_wide)); + ASSERT_EQ(paths.size(), 1); + expect_name_selector(paths[0].selectors[0], "d"); + + paths = collect_paths( + cast_expr(struct_element(root, make_nullable(int_type), "i"), make_nullable(int_type))); + ASSERT_EQ(paths.size(), 1); + expect_name_selector(paths[0].selectors[0], "i"); + + // Unsafe casts are not accepted as pruning paths, but collect_nested_struct_paths() still + // recurses into children so scan projection can read the column needed by row-level filters. + paths = collect_paths(cast_expr(struct_element(root, bigint_type, "i"), int_type)); + ASSERT_EQ(paths.size(), 1); + expect_name_selector(paths[0].selectors[0], "i"); + + paths = collect_paths(cast_expr(decimal_path, decimal_changed_scale)); + ASSERT_EQ(paths.size(), 1); + expect_name_selector(paths[0].selectors[0], "d"); + + EXPECT_TRUE(collect_paths(cast_expr(table_slot(1, 1, int_type, "plain"), bigint_type)).empty()); +} + +TEST(ColumnMapperCollectNestedStructPathsTest, ProjectionMergeKeepsFilterOnlyPathAndDeduplicates) { + const auto int_type = i32(); + const auto string_type = str(); + auto table_a = name_col("a", int_type); + auto table_b = name_col("b", int_type); + auto table_output = struct_name_col("s", {table_a}); + auto full_table_struct = struct_name_col("s", {table_a, table_b}); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", int_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b, name_col("c", string_type, 2)}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_output}, {}, {file_struct}).ok()); + + const auto path_b = + struct_element(table_slot(0, 0, full_table_struct.type, "s"), int_type, "b"); + auto filter_expr = compound_predicate( + TExprOpcode::COMPOUND_AND, + binary_predicate(TExprOpcode::GT, path_b, + literal(int_type, Field::create_field(1))), + binary_predicate(TExprOpcode::LT, path_b, + literal(int_type, Field::create_field(10)))); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_output}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); + ASSERT_FALSE(request.predicate_columns[0].project_all_children); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({0, 1})); + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({1})); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 2); +} + +// ---------------------------------------------------------------------- +// L1 create_mapping root matching tests. +// These cases cover the three supported root matching modes and the +// missing/default behavior that each mode feeds into later scan requests. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperCreateMappingTest, ByNameMatchesCaseIdentifierAndAliases) { + const auto int_type = i32(); + const std::vector table_schema = { + name_col("ID", int_type), + name_id_col("renamed", "legacy_name", int_type), + [] { + auto column = name_col("current_alias", i32()); + column.name_mapping = {"old_alias"}; + return column; + }(), + name_col("file_alias", int_type), + }; + std::vector file_schema = { + name_col("id", int_type, 0), + name_col("legacy_name", int_type, 1), + name_col("old_alias", int_type, 2), + [] { + auto column = name_col("physical_name", i32(), 3); + column.name_mapping = {"file_alias"}; + return column; + }(), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 4); + expect_mapping(mapper.mappings()[0], 0, "ID", 0, "id", int_type, int_type); + expect_mapping(mapper.mappings()[1], 1, "renamed", 1, "legacy_name", int_type, int_type); + expect_mapping(mapper.mappings()[2], 2, "current_alias", 2, "old_alias", int_type, int_type); + expect_mapping(mapper.mappings()[3], 3, "file_alias", 3, "physical_name", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByNameUsesFirstMatchingFileFieldWhenAmbiguous) { + const auto int_type = i32(); + const std::vector table_schema = { + name_col("id", int_type), + }; + const std::vector file_schema = { + name_col("ID", int_type, 0), + name_col("id", int_type, 1), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "id", 0, "ID", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForRenamedColumn) { + const auto int_type = i32(); + auto table_column = name_col("current_id", int_type); + table_column.name_mapping = {"legacy_id"}; + const std::vector file_schema = { + name_col("legacy_id", int_type, 0), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "current_id", 0, "legacy_id", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDuplicate) { + const auto int_type = i32(); + const std::vector table_schema = { + field_id_col("renamed", 10, int_type), + name_col("same_name", int_type), + field_id_col("negative", -7, int_type), + }; + const std::vector file_schema = { + field_id_col("first", 10, int_type, 0), + field_id_col("second", 10, int_type, 1), + field_id_col("same_name", 99, int_type, 2), + field_id_col("negative_file", -7, int_type, 3), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = true}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + expect_mapping(mapper.mappings()[0], 0, "renamed", 0, "first", int_type, int_type); + expect_missing(mapper.mappings()[1]); + expect_mapping(mapper.mappings()[2], 2, "negative", 3, "negative_file", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByFieldIdRejectsSameNameWithDifferentFieldId) { + const auto int_type = i32(); + const std::vector table_schema = { + field_id_col("same_name", 10, int_type), + }; + const std::vector file_schema = { + field_id_col("same_name", 20, int_type, 0), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + const auto status = mapper.create_mapping(table_schema, {}, file_schema); + EXPECT_FALSE(status.ok()); +} + +TEST(ColumnMapperCreateMappingTest, NestedFieldIdRejectsSameNameWithDifferentFieldId) { + const auto int_type = i32(); + auto table_child = field_id_col("child", 10, int_type); + auto table_root = struct_col("root", 1, {table_child}); + + auto file_child = field_id_col("child", 20, int_type, 0); + auto file_root = struct_col("root", 1, {file_child}, 0); + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + const auto status = mapper.create_mapping({table_root}, {}, {file_root}); + EXPECT_FALSE(status.ok()); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexMapsTopLevelColumnsByPositionIgnoringFileNames) { + const auto int_type = i32(); + const auto string_type = str(); + const std::vector table_schema = { + position_col("user_id", 0, int_type), + position_col("user_name", 1, string_type), + position_col("age", 2, int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), + field_id_col("_col1", 101, string_type, 1), + field_id_col("_col2", 102, int_type, 2), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + expect_mapping(mapper.mappings()[0], 0, "user_id", 0, "_col0", int_type, int_type); + expect_mapping(mapper.mappings()[1], 1, "user_name", 1, "_col1", string_type, string_type); + expect_mapping(mapper.mappings()[2], 2, "age", 2, "_col2", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexSupportsSparseProjection) { + const auto int_type = i32(); + const std::vector table_schema = { + position_col("age", 2, int_type), + position_col("score", 4, int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), field_id_col("_col1", 101, int_type, 1), + field_id_col("_col2", 102, int_type, 2), field_id_col("_col3", 103, int_type, 3), + field_id_col("_col4", 104, int_type, 4), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 2); + expect_mapping(mapper.mappings()[0], 0, "age", 2, "_col2", int_type, int_type); + expect_mapping(mapper.mappings()[1], 1, "score", 4, "_col4", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, + ByIndexPartitionColumnsTakeConstantAndDoNotConsumeFilePosition) { + const auto int_type = i32(); + const auto string_type = str(); + auto partition = name_col("dt", string_type); + partition.is_partition_key = true; + const std::vector table_schema = { + partition, + position_col("user_id", 0, int_type), + position_col("score", 1, int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), + field_id_col("_col1", 101, int_type, 1), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + ASSERT_TRUE(mapper.create_mapping(table_schema, + {{"dt", Field::create_field("2026-06-11")}}, + file_schema) + .ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + expect_constant(mapper, mapper.mappings()[0], 0, string_type); + expect_mapping(mapper.mappings()[1], 1, "user_id", 0, "_col0", int_type, int_type); + expect_mapping(mapper.mappings()[2], 2, "score", 1, "_col1", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFallsBackToDefaultOrMissing) { + const auto int_type = i32(); + auto with_default = position_col("extra_default", 5, int_type); + const auto literal_expr = + VExprContext::create_shared(literal(int_type, Field::create_field(42))); + with_default.default_expr = literal_expr; + const std::vector table_schema = { + position_col("a", 0, int_type), + with_default, + position_col("extra_missing", 99, int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), + field_id_col("_col1", 101, int_type, 1), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_INDEX, .allow_missing_columns = true}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + expect_mapping(mapper.mappings()[0], 0, "a", 0, "_col0", int_type, int_type); + expect_constant(mapper, mapper.mappings()[1], 1, int_type); + EXPECT_EQ(mapper.mappings()[1].default_expr, literal_expr); + expect_missing(mapper.mappings()[2]); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexMissingIdentifierFallsBackToDefaultOrMissing) { + const auto int_type = i32(); + auto with_default = name_col("extra_default", int_type); + const auto literal_expr = + VExprContext::create_shared(literal(int_type, Field::create_field(42))); + with_default.default_expr = literal_expr; + const std::vector table_schema = { + position_col("a", 0, int_type), + with_default, + name_col("extra_missing", int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_INDEX, .allow_missing_columns = true}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + expect_mapping(mapper.mappings()[0], 0, "a", 0, "_col0", int_type, int_type); + expect_constant(mapper, mapper.mappings()[1], 1, int_type); + EXPECT_EQ(mapper.mappings()[1].default_expr, literal_expr); + expect_missing(mapper.mappings()[2]); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFailsWhenMissingIsDisallowed) { + const auto int_type = i32(); + const std::vector table_schema = { + position_col("a", 0, int_type), + position_col("b", 5, int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_INDEX, .allow_missing_columns = false}); + const auto status = mapper.create_mapping(table_schema, {}, file_schema); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("file_index=5"), std::string::npos); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexIgnoresExtraFileColumns) { + const auto int_type = i32(); + const std::vector table_schema = { + position_col("a", 0, int_type), + }; + const std::vector file_schema = { + field_id_col("_col0", 100, int_type, 0), + field_id_col("_col1", 101, int_type, 1), + field_id_col("_col2", 102, int_type, 2), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "a", 0, "_col0", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexIgnoresFileColumnNames) { + const auto int_type = i32(); + const std::vector table_schema = { + position_col("a", 1, int_type), + }; + const std::vector file_schema = { + field_id_col("a", 100, int_type, 10), + field_id_col("b", 101, int_type, 20), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "a", 20, "b", int_type, int_type); +} + +TEST(ColumnMapperCreateMappingTest, MissingColumnFailsWhenDisallowed) { + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_NAME, .allow_missing_columns = false}); + const auto status = mapper.create_mapping({name_col("missing", i32())}, {}, + {name_col("present", i32(), 0)}); + EXPECT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("global_index=0"), std::string::npos); +} + +// ---------------------------------------------------------------------- +// L1 constants and virtual columns. +// These tests verify non-file-backed mappings before TableReader materializes +// their final values. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperConstantTest, PartitionDefaultAndVirtualColumnsUseDedicatedBranches) { + auto partition_column = name_col("dt", str()); + partition_column.is_partition_key = true; + + auto default_column = name_col("new_value", i32()); + default_column.default_expr = + VExprContext::create_shared(literal(i32(), Field::create_field(42))); + + auto row_id_column = name_col("_row_id", make_nullable(i64())); + auto sequence_column = name_col("_last_updated_sequence_number", make_nullable(i64())); + auto iceberg_rowid_column = name_col(BeConsts::ICEBERG_ROWID_COL, str()); + + const std::vector table_schema = { + partition_column, default_column, row_id_column, sequence_column, iceberg_rowid_column}; + const std::map partition_values = { + {"dt", Field::create_field("2026-06-11")}, + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, partition_values, {}).ok()); + + ASSERT_EQ(mapper.mappings().size(), 5); + expect_constant(mapper, mapper.mappings()[0], 0, str()); + expect_constant(mapper, mapper.mappings()[1], 1, i32()); + EXPECT_EQ(mapper.mappings()[2].virtual_column_type, TableVirtualColumnType::ROW_ID); + EXPECT_EQ(mapper.mappings()[3].virtual_column_type, + TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER); + EXPECT_EQ(mapper.mappings()[4].virtual_column_type, TableVirtualColumnType::ICEBERG_ROWID); +} + +TEST(ColumnMapperConstantTest, PhysicalRowLineageFiltersStayFinalizeOnly) { + auto row_id_column = name_col("_row_id", make_nullable(i64())); + auto sequence_column = name_col("_last_updated_sequence_number", make_nullable(i64())); + const std::vector table_schema = {row_id_column, sequence_column}; + const std::vector file_schema = { + name_col("_row_id", make_nullable(i64()), 2147483540), + name_col("_last_updated_sequence_number", make_nullable(i64()), 2147483539), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 2); + EXPECT_EQ(mapper.mappings()[0].virtual_column_type, TableVirtualColumnType::ROW_ID); + EXPECT_EQ(mapper.mappings()[0].filter_conversion, FilterConversionType::FINALIZE_ONLY); + EXPECT_EQ(mapper.mappings()[1].virtual_column_type, + TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER); + EXPECT_EQ(mapper.mappings()[1].filter_conversion, FilterConversionType::FINALIZE_ONLY); + + auto row_id_filter = + binary_predicate(TExprOpcode::EQ, table_slot(0, 0, make_nullable(i64()), "_row_id"), + literal(i64(), Field::create_field(1001))); + auto sequence_filter = binary_predicate( + TExprOpcode::EQ, + table_slot(1, 1, make_nullable(i64()), "_last_updated_sequence_number"), + literal(i64(), Field::create_field(77))); + TableFilter row_id_table_filter {.conjunct = VExprContext::create_shared(row_id_filter), + .global_indices = {GlobalIndex(0)}}; + TableFilter sequence_table_filter {.conjunct = VExprContext::create_shared(sequence_filter), + .global_indices = {GlobalIndex(1)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({row_id_table_filter, sequence_table_filter}, {}, + table_schema, &request) + .ok()); + + EXPECT_TRUE(request.conjuncts.empty()); + EXPECT_TRUE(request.predicate_columns.empty()); + EXPECT_EQ(projection_ids(request.non_predicate_columns), + std::vector({2147483540, 2147483539})); +} + +TEST(ColumnMapperConstantTest, MissingRowLineageDefaultExprStillUsesVirtualMapping) { + auto id_column = field_id_col("id", 1, make_nullable(i32())); + auto row_id_column = field_id_col("renamed_row_id", 2147483540, make_nullable(i64())); + row_id_column.default_expr = VExprContext::create_shared( + literal(make_nullable(i64()), Field::create_field(0))); + auto sequence_column = + field_id_col("renamed_last_updated_sequence_number", 2147483539, make_nullable(i64())); + sequence_column.default_expr = VExprContext::create_shared( + literal(make_nullable(i64()), Field::create_field(0))); + + const std::vector table_schema = {id_column, row_id_column, sequence_column}; + const std::vector file_schema = { + field_id_col("id", 1, make_nullable(i32()), 0), + field_id_col("name", 2, make_nullable(str()), 1), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + expect_mapping(mapper.mappings()[0], 0, "id", 0, "id", make_nullable(i32()), + make_nullable(i32())); + EXPECT_EQ(mapper.mappings()[1].virtual_column_type, TableVirtualColumnType::ROW_ID); + EXPECT_FALSE(mapper.mappings()[1].constant_index.has_value()); + EXPECT_EQ(mapper.mappings()[2].virtual_column_type, + TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER); + EXPECT_FALSE(mapper.mappings()[2].constant_index.has_value()); + EXPECT_TRUE(mapper.constant_map().empty()); +} + +TEST(ColumnMapperConstantTest, ByFieldIdDoesNotTreatSameNameDifferentIdAsRowLineage) { + const std::vector table_schema = { + field_id_col("_row_id", 100, make_nullable(i64())), + field_id_col("_last_updated_sequence_number", 101, make_nullable(i64())), + }; + const std::vector file_schema = { + field_id_col("_row_id", 100, make_nullable(i64()), 0), + field_id_col("_last_updated_sequence_number", 101, make_nullable(i64()), 1), + }; + + TableColumnMapper mapper( + {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 2); + expect_mapping(mapper.mappings()[0], 0, "_row_id", 0, "_row_id", make_nullable(i64()), + make_nullable(i64())); + EXPECT_EQ(mapper.mappings()[0].virtual_column_type, TableVirtualColumnType::INVALID); + EXPECT_EQ(mapper.mappings()[0].filter_conversion, FilterConversionType::COPY_DIRECTLY); + expect_mapping(mapper.mappings()[1], 1, "_last_updated_sequence_number", 1, + "_last_updated_sequence_number", make_nullable(i64()), make_nullable(i64())); + EXPECT_EQ(mapper.mappings()[1].virtual_column_type, TableVirtualColumnType::INVALID); + EXPECT_EQ(mapper.mappings()[1].filter_conversion, FilterConversionType::COPY_DIRECTLY); +} + +TEST(ColumnMapperConstantTest, PartitionAliasResolvesRenamedValue) { + auto partition_column = name_col("current_dt", str()); + partition_column.name_mapping = {"legacy_dt"}; + partition_column.is_partition_key = true; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping( + {partition_column}, + {{"legacy_dt", Field::create_field("2026-06-11")}}, {}) + .ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_constant(mapper, mapper.mappings()[0], 0, str()); +} + +TEST(ColumnMapperConstantTest, PartitionConstantFilterEntryDoesNotReadFileColumns) { + auto partition_column = name_col("part", i32()); + partition_column.is_partition_key = true; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({partition_column}, + {{"part", Field::create_field(7)}}, {}) + .ok()); + + TableFilter filter { + .conjunct = VExprContext::create_shared(int_gt(table_slot(0, 0, i32(), "part"), 1)), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {partition_column}, &request).ok()); + + ASSERT_EQ(mapper.filter_entries().size(), 1); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_constant()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(0)).constant_index(), + *mapper.mappings()[0].constant_index); + EXPECT_TRUE(request.local_positions.empty()); + EXPECT_TRUE(request.predicate_columns.empty()); + EXPECT_TRUE(request.non_predicate_columns.empty()); + EXPECT_TRUE(request.conjuncts.empty()); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +TEST(ColumnMapperConstantTest, DefaultConstantFilterEntryUsesDefaultExpression) { + auto default_column = name_col("new_value", i32()); + default_column.default_expr = + VExprContext::create_shared(literal(i32(), Field::create_field(42))); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({default_column}, {}, {}).ok()); + + TableFilter filter {.conjunct = VExprContext::create_shared( + int_gt(table_slot(0, 0, i32(), "new_value"), 1)), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {default_column}, &request).ok()); + + ASSERT_EQ(mapper.filter_entries().size(), 1); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_constant()); + const auto constant_index = mapper.filter_entries().at(GlobalIndex(0)).constant_index(); + EXPECT_EQ(constant_index, *mapper.mappings()[0].constant_index); + EXPECT_EQ(mapper.constant_map().get(constant_index).expr, default_column.default_expr); + EXPECT_TRUE(request.local_positions.empty()); + EXPECT_TRUE(request.predicate_columns.empty()); + EXPECT_TRUE(request.non_predicate_columns.empty()); + EXPECT_TRUE(request.conjuncts.empty()); +} + +TEST(ColumnMapperConstantTest, MixedConstantAndFileFilterKeepsOnlyFileScanColumn) { + auto partition_column = name_col("part", i32()); + partition_column.is_partition_key = true; + const auto file_column = name_col("score", i32(), 3); + const std::vector table_schema = {partition_column, file_column}; + const std::vector file_schema = {file_column}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {{"part", Field::create_field(7)}}, + file_schema) + .ok()); + + TableFilter constant_filter { + .conjunct = VExprContext::create_shared(int_gt(table_slot(0, 0, i32(), "part"), 1)), + .global_indices = {GlobalIndex(0)}}; + TableFilter file_filter { + .conjunct = VExprContext::create_shared(int_gt(table_slot(1, 1, i32(), "score"), 10)), + .global_indices = {GlobalIndex(1)}}; + + FileScanRequest request; + ASSERT_TRUE( + mapper.create_scan_request({constant_filter, file_filter}, {}, table_schema, &request) + .ok()); + + ASSERT_EQ(mapper.filter_entries().size(), 2); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_constant()); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(1)).is_local()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(1)).local_index(), LocalIndex(0)); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(3)); + EXPECT_TRUE(request.non_predicate_columns.empty()); +} + +// ---------------------------------------------------------------------- +// L1 direct filter localization tests. +// These tests call localize_filters() directly to pin the core interface +// contract apart from create_scan_request() initialization. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperLocalizeFiltersTest, VisibleLocalFilterAddsPredicateColumnAndConjunct) { + const auto int_type = i32(); + const std::vector table_schema = {name_col("id", int_type)}; + const std::vector file_schema = {name_col("id", int_type, 7)}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + TableFilter filter {.conjunct = VExprContext::create_shared(table_slot(11, 0, int_type, "id")), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.localize_filters({filter}, {}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(7)); + ASSERT_EQ(request.local_positions.size(), 1); + EXPECT_EQ(request.local_positions.at(LocalColumnId(7)), LocalIndex(0)); + ASSERT_EQ(mapper.filter_entries().size(), 1); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_local()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(0)).local_index(), LocalIndex(0)); + + ASSERT_EQ(request.conjuncts.size(), 1); + const auto* localized_slot = assert_cast(request.conjuncts[0]->root().get()); + EXPECT_EQ(localized_slot->slot_id(), 11); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_EQ(localized_slot->column_name(), "id"); + EXPECT_TRUE(localized_slot->data_type()->equals(*int_type)); +} + +TEST(ColumnMapperLocalizeFiltersTest, ConstantFilterBuildsEntryWithoutFileScanColumn) { + auto partition_column = name_col("part", i32()); + partition_column.is_partition_key = true; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({partition_column}, + {{"part", Field::create_field(7)}}, {}) + .ok()); + + TableFilter filter {.conjunct = VExprContext::create_shared(table_slot(3, 0, i32(), "part")), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.localize_filters({filter}, {}, &request).ok()); + + EXPECT_TRUE(request.predicate_columns.empty()); + EXPECT_TRUE(request.non_predicate_columns.empty()); + EXPECT_TRUE(request.local_positions.empty()); + EXPECT_TRUE(request.conjuncts.empty()); + ASSERT_EQ(mapper.filter_entries().size(), 1); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_constant()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(0)).constant_index(), + mapper.mappings()[0].constant_index); +} + +TEST(ColumnMapperLocalizeFiltersTest, ColumnPredicatesUseOnlyExistingLocalPositions) { + const auto int_type = i32(); + const std::vector table_schema = {name_col("id", int_type)}; + const std::vector file_schema = {name_col("id", int_type, 3)}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + TableColumnPredicates predicates; + predicates[GlobalIndex(0)] = {create_comparison_predicate( + 0, "id", int_type, Field::create_field(10), false)}; + + FileScanRequest request_without_local_position; + ASSERT_TRUE(mapper.localize_filters({}, predicates, &request_without_local_position).ok()); + EXPECT_TRUE(request_without_local_position.column_predicate_filters.empty()); + ASSERT_EQ(mapper.filter_entries().size(), 1); + EXPECT_FALSE(mapper.filter_entries().at(GlobalIndex(0)).is_local()); + + FileScanRequest request_with_local_position; + request_with_local_position.non_predicate_columns.push_back( + LocalColumnIndex::top_level(LocalColumnId(3))); + request_with_local_position.local_positions.emplace(LocalColumnId(3), LocalIndex(0)); + ASSERT_TRUE(mapper.localize_filters({}, predicates, &request_with_local_position).ok()); + + ASSERT_EQ(request_with_local_position.non_predicate_columns.size(), 1); + EXPECT_EQ(request_with_local_position.non_predicate_columns[0].column_id(), LocalColumnId(3)); + EXPECT_TRUE(request_with_local_position.predicate_columns.empty()); + ASSERT_EQ(request_with_local_position.column_predicate_filters.size(), 1); + EXPECT_EQ(request_with_local_position.column_predicate_filters[0].effective_file_column_id(), + LocalColumnId(3)); + ASSERT_EQ(request_with_local_position.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request_with_local_position.column_predicate_filters[0].predicates[0]->type(), + PredicateType::GT); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_local()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(0)).local_index(), LocalIndex(0)); +} + +TEST(ColumnMapperLocalizeFiltersTest, NestedFilterOnlyChildMergesIntoPredicateProjection) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_a = name_col("a", int_type); + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + auto full_table_struct = struct_name_col("s", {table_a, table_b}); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + auto filter_expr = int_gt( + struct_element(table_slot(0, 0, full_table_struct.type, "s"), int_type, "a"), 10); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.localize_filters({filter}, {}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); + ASSERT_FALSE(request.predicate_columns[0].project_all_children); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({1, 0})); + ASSERT_EQ(request.local_positions.size(), 1); + EXPECT_EQ(request.local_positions.at(LocalColumnId(5)), LocalIndex(0)); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_local()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(0)).local_index(), LocalIndex(0)); + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(5)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({0})); + EXPECT_EQ(target_names(request.column_predicate_filters[0].target.struct_target.get()), + std::vector({"a"})); +} + +TEST(ColumnMapperLocalizeFiltersTest, PreservesExistingScanStateWhenAddingPredicateColumn) { + const auto int_type = i32(); + const std::vector table_schema = { + name_col("id", int_type), + name_col("score", int_type), + }; + const std::vector file_schema = { + name_col("id", int_type, 3), + name_col("score", int_type, 4), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + TableFilter filter {.conjunct = VExprContext::create_shared(table_slot(2, 0, int_type, "id")), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + request.non_predicate_columns.push_back(LocalColumnIndex::top_level(LocalColumnId(4))); + request.local_positions.emplace(LocalColumnId(4), LocalIndex(0)); + ASSERT_TRUE(mapper.localize_filters({filter}, {}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + EXPECT_EQ(request.non_predicate_columns[0].column_id(), LocalColumnId(4)); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(3)); + ASSERT_EQ(request.local_positions.size(), 2); + EXPECT_EQ(request.local_positions.at(LocalColumnId(4)), LocalIndex(0)); + EXPECT_EQ(request.local_positions.at(LocalColumnId(3)), LocalIndex(1)); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_local()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(0)).local_index(), LocalIndex(1)); +} + +// ---------------------------------------------------------------------- +// L1 scan request and filter localization tests. +// These tests assert predicate/non-predicate split, local positions, hidden +// filter mappings, and nested predicate targets. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperScanRequestTest, ColumnPredicatesDoNotForceRowPredicateMaterialization) { + const auto int_type = i32(); + const auto string_type = str(); + const std::vector table_schema = { + name_col("id", int_type), + name_col("name", string_type), + }; + const std::vector file_schema = { + name_col("id", int_type, 0), + name_col("name", string_type, 1), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + TableColumnPredicates predicates; + predicates[GlobalIndex(0)] = {create_comparison_predicate( + 0, "id", int_type, Field::create_field(10), false)}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, predicates, table_schema, &request).ok()); + + EXPECT_TRUE(request.predicate_columns.empty()); + EXPECT_EQ(projection_ids(request.non_predicate_columns), std::vector({0, 1})); + ASSERT_EQ(request.local_positions.size(), 2); + EXPECT_EQ(request.local_positions.at(LocalColumnId(0)), LocalIndex(0)); + EXPECT_EQ(request.local_positions.at(LocalColumnId(1)), LocalIndex(1)); + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(0)); +} + +TEST(ColumnMapperScanRequestTest, HiddenTopLevelFilterMappingUsesNameFallback) { + const auto int_type = i32(); + const std::vector table_schema = { + field_id_col("id", 1, int_type), + }; + const std::vector file_schema = { + field_id_col("id", 1, int_type, 0), + field_id_col("score", 2, int_type, 1), + }; + + auto filter_expr = int_gt(table_slot(7, 1, int_type, "score"), 10); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(1)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, table_schema, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + EXPECT_EQ(request.non_predicate_columns[0].column_id(), LocalColumnId(0)); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(1)); + ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(1)).is_local()); + EXPECT_EQ(mapper.filter_entries().at(GlobalIndex(1)).local_index(), LocalIndex(1)); +} + +TEST(ColumnMapperScanRequestTest, StructOutputAndFilterOnlyChildAreMerged) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_a = name_col("a", int_type); + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + auto full_table_struct = struct_name_col("s", {table_a, table_b}); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + auto filter_expr = int_gt( + struct_element(table_slot(0, 0, full_table_struct.type, "s"), int_type, "a"), 10); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); + ASSERT_FALSE(request.predicate_columns[0].project_all_children); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({1, 0})); + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({0})); + EXPECT_EQ(target_names(request.column_predicate_filters[0].target.struct_target.get()), + std::vector({"a"})); +} + +TEST(ColumnMapperScanRequestTest, RenamedNestedPredicateTargetsMappedFileChild) { + const auto int_type = i32(); + + auto table_a = field_id_col("a", 1, int_type); + auto table_renamed_b = field_id_col("renamed_b", 2, int_type); + auto table_struct = struct_col("s", 10, {table_a, table_renamed_b}); + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, int_type, 1); + auto file_struct = struct_col("s", 10, {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + auto filter_expr = int_gt( + struct_element(table_slot(0, 0, table_struct.type, "s"), int_type, "renamed_b"), 10); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(5)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({1})); + EXPECT_EQ(target_names(request.column_predicate_filters[0].target.struct_target.get()), + std::vector({"b"})); +} + +TEST(ColumnMapperScanRequestTest, NestedInNullAndReverseComparisonFiltersAreMerged) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_a = name_col("a", int_type); + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + auto full_table_struct = struct_name_col("s", {table_a, table_b}); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + const auto nested_a = + struct_element(table_slot(0, 0, full_table_struct.type, "s"), int_type, "a"); + auto in_filter = + in_predicate(nested_a, int_type, + {Field::create_field(5), Field::create_field(7)}); + auto reverse_filter = binary_predicate( + TExprOpcode::LT, literal(int_type, Field::create_field(3)), nested_a); + auto null_filter = null_predicate(nested_a, true); + auto not_null_filter = null_predicate(nested_a, false); + auto filter_expr = compound_predicate( + TExprOpcode::COMPOUND_AND, + compound_predicate(TExprOpcode::COMPOUND_AND, in_filter, reverse_filter), + compound_predicate(TExprOpcode::COMPOUND_AND, null_filter, not_null_filter)); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(5)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({0})); + EXPECT_EQ(target_names(request.column_predicate_filters[0].target.struct_target.get()), + std::vector({"a"})); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 4); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IN_LIST); + EXPECT_EQ(request.column_predicate_filters[0].predicates[1]->type(), PredicateType::GT); + EXPECT_EQ(request.column_predicate_filters[0].predicates[2]->type(), PredicateType::IS_NULL); + EXPECT_EQ(request.column_predicate_filters[0].predicates[3]->type(), + PredicateType::IS_NOT_NULL); +} + +TEST(ColumnMapperScanRequestTest, NestedPredicateFilterThroughSafeCast) { + const auto file_int_type = i32(); + const auto table_bigint_type = i64(); + const auto string_type = str(); + + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + auto full_table_struct = std::make_shared( + DataTypes {table_bigint_type, string_type}, Strings {"a", "b"}); + + auto file_a = name_col("a", file_int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + const auto nested_a = + struct_element(table_slot(0, 0, full_table_struct, "s"), file_int_type, "a"); + auto filter_expr = + binary_predicate(TExprOpcode::GT, cast_expr(nested_a, table_bigint_type), + literal(table_bigint_type, Field::create_field(5))); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(5)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({0})); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); +} + +TEST(ColumnMapperScanRequestTest, UnsafeCastDoesNotBuildNestedPredicateFilter) { + const auto file_bigint_type = i64(); + const auto table_int_type = i32(); + const auto string_type = str(); + + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + auto full_table_struct = std::make_shared( + DataTypes {table_int_type, string_type}, Strings {"a", "b"}); + + auto file_a = name_col("a", file_bigint_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + const auto nested_a = + struct_element(table_slot(0, 0, full_table_struct, "s"), file_bigint_type, "a"); + auto filter_expr = binary_predicate(TExprOpcode::GT, cast_expr(nested_a, table_int_type), + literal(table_int_type, Field::create_field(5))); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + EXPECT_TRUE(request.column_predicate_filters.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({1, 0})); +} + +TEST(ColumnMapperScanRequestTest, DeepNestedPredicateTargetsLeafPath) { + const auto id_type = i32(); + const auto name_type = str(); + const auto string_type = str(); + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + + auto full_table_inner_type = + std::make_shared(DataTypes {id_type, name_type}, Strings {"id", "n"}); + auto full_table_struct_type = std::make_shared( + DataTypes {full_table_inner_type, string_type}, Strings {"a", "b"}); + + auto file_id = name_col("id", id_type, 0); + auto file_name = name_col("n", name_type, 1); + auto file_a = struct_name_col("a", {file_id, file_name}, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + const auto nested_id = + struct_element(struct_element(table_slot(0, 0, full_table_struct_type, "s"), + full_table_inner_type, "a"), + id_type, "id"); + auto filter_expr = + in_predicate(nested_id, id_type, + {Field::create_field(5), Field::create_field(7)}); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(5)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({0, 0})); + EXPECT_EQ(target_names(request.column_predicate_filters[0].target.struct_target.get()), + std::vector({"a", "id"})); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IN_LIST); +} + +TEST(ColumnMapperScanRequestTest, ArrayStructProjectionPrunesElementChildren) { + const auto int_type = i32(); + const auto string_type = str(); + auto table_b = name_col("b", string_type); + auto table_element = struct_name_col("element", {table_b}); + auto table_array = array_col("items", -1, table_element); + table_array.identifier = Field::create_field("items"); + set_name_identifiers(&table_array, -1); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_element = struct_name_col("element", {file_a, file_b}, 0); + auto file_array = array_col("items", -1, file_element, 4); + file_array.identifier = Field::create_field("items"); + set_name_identifiers(&file_array, 4); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_array}, {}, {file_array}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_array}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + const auto& projection = request.non_predicate_columns[0]; + EXPECT_EQ(projection.column_id(), LocalColumnId(4)); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].local_id(), 0); + ASSERT_EQ(projection.children[0].children.size(), 1); + EXPECT_EQ(projection.children[0].children[0].local_id(), 1); + + const auto* mapped_array = assert_cast( + remove_nullable(mapper.mappings()[0].file_type).get()); + const auto* mapped_element = assert_cast( + remove_nullable(mapped_array->get_nested_type()).get()); + ASSERT_EQ(mapped_element->get_elements().size(), 1); + EXPECT_EQ(mapped_element->get_element_name(0), "b"); +} + +TEST(ColumnMapperScanRequestTest, MapValueStructProjectionPrunesValueChildren) { + const auto key_type = str(); + const auto int_type = i32(); + const auto string_type = str(); + + auto table_value_b = name_col("b", string_type); + auto table_value = struct_name_col("value", {table_value_b}); + auto table_entry = struct_name_col("entries", {table_value}); + auto table_map = map_col("m", -1, table_entry, key_type, table_value.type); + table_map.identifier = Field::create_field("m"); + set_name_identifiers(&table_map, -1); + + auto file_key = name_col("key", key_type, 0); + auto file_value_a = name_col("a", int_type, 0); + auto file_value_b = name_col("b", string_type, 1); + auto file_value = struct_name_col("value", {file_value_a, file_value_b}, 1); + auto file_entry = struct_name_col("key_value", {file_key, file_value}, 0); + auto file_map = map_col("m", -1, file_entry, key_type, file_value.type, 6); + file_map.identifier = Field::create_field("m"); + set_name_identifiers(&file_map, 6); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_map}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + const auto& projection = request.non_predicate_columns[0]; + EXPECT_EQ(projection.column_id(), LocalColumnId(6)); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].local_id(), 0); + ASSERT_EQ(projection.children[0].children.size(), 1); + EXPECT_EQ(projection.children[0].children[0].local_id(), 1); + ASSERT_EQ(projection.children[0].children[0].children.size(), 1); + EXPECT_EQ(projection.children[0].children[0].children[0].local_id(), 1); + + const auto* mapped_map = + assert_cast(remove_nullable(mapper.mappings()[0].file_type).get()); + const auto* mapped_value = + assert_cast(remove_nullable(mapped_map->get_value_type()).get()); + ASSERT_EQ(mapped_value->get_elements().size(), 1); + EXPECT_EQ(mapped_value->get_element_name(0), "b"); +} + +// ---------------------------------------------------------------------- +// L1 complex schema evolution and split isolation. +// These tests call the mapper repeatedly with different file schemas and +// verify that split-local state is rebuilt instead of leaked. +// ---------------------------------------------------------------------- + +TEST(ColumnMapperSchemaEvolutionTest, StructChildrenHandleMissingRenameReorderAndDroppedFields) { + const auto int_type = i32(); + const auto string_type = str(); + auto table_a = field_id_col("a", 1, int_type); + auto table_renamed_b = field_id_col("renamed_b", 2, string_type); + auto table_c = field_id_col("c", 3, int_type); + auto table_struct = struct_col("s", 10, {table_a, table_renamed_b, table_c}); + + auto v1_a = field_id_col("a", 1, int_type, 0); + auto v1_b = field_id_col("b", 2, string_type, 1); + auto file_v1 = struct_col("s", 10, {v1_a, v1_b}, 5); + + auto v2_b = field_id_col("b", 2, string_type, 0); + auto v2_a = field_id_col("a", 1, int_type, 1); + auto v2_c = field_id_col("c", 3, int_type, 2); + auto file_v2 = struct_col("s", 10, {v2_b, v2_a, v2_c}, 8); + + TableColumnMapper v1_mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(v1_mapper.create_mapping({table_struct}, {}, {file_v1}).ok()); + FileScanRequest v1_request; + ASSERT_TRUE(v1_mapper.create_scan_request({}, {}, {table_struct}, &v1_request).ok()); + + const auto& v1_mapping = v1_mapper.mappings()[0]; + ASSERT_EQ(v1_mapping.child_mappings.size(), 3); + EXPECT_EQ(*v1_mapping.child_mappings[0].file_local_id, 0); + EXPECT_EQ(*v1_mapping.child_mappings[1].file_local_id, 1); + EXPECT_FALSE(v1_mapping.child_mappings[2].file_local_id.has_value()); + ASSERT_EQ(v1_request.non_predicate_columns.size(), 1); + EXPECT_EQ(v1_request.non_predicate_columns[0].column_id(), LocalColumnId(5)); + EXPECT_TRUE(v1_request.non_predicate_columns[0].project_all_children); + + TableColumnMapper v2_mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(v2_mapper.create_mapping({table_struct}, {}, {file_v2}).ok()); + FileScanRequest v2_request; + ASSERT_TRUE(v2_mapper.create_scan_request({}, {}, {table_struct}, &v2_request).ok()); + + const auto& v2_mapping = v2_mapper.mappings()[0]; + ASSERT_EQ(v2_mapping.child_mappings.size(), 3); + EXPECT_EQ(*v2_mapping.child_mappings[0].file_local_id, 1); + EXPECT_EQ(*v2_mapping.child_mappings[1].file_local_id, 0); + EXPECT_EQ(*v2_mapping.child_mappings[2].file_local_id, 2); + ASSERT_EQ(v2_request.non_predicate_columns.size(), 1); + EXPECT_EQ(v2_request.non_predicate_columns[0].column_id(), LocalColumnId(8)); + EXPECT_TRUE(v2_request.non_predicate_columns[0].project_all_children); +} + +TEST(ColumnMapperSchemaEvolutionTest, DroppedStructChildrenAreNotRead) { + const auto int_type = i32(); + const auto string_type = str(); + auto table_a = field_id_col("a", 1, int_type); + auto table_struct = struct_col("s", 10, {table_a}); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, string_type, 1); + auto file_c = field_id_col("c", 3, int_type, 2); + auto file_struct = struct_col("s", 10, {file_a, file_b, file_c}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + const auto& projection = request.non_predicate_columns[0]; + EXPECT_EQ(projection.column_id(), LocalColumnId(5)); + ASSERT_FALSE(projection.project_all_children); + EXPECT_EQ(projection_ids(projection.children), std::vector({0})); +} + +TEST(ColumnMapperSchemaEvolutionTest, ReusedMapperClearsSplitLocalConstantsAndFileIds) { + const auto int_type = i32(); + auto id = name_col("id", int_type); + auto added = name_col("added", int_type); + added.default_expr = + VExprContext::create_shared(literal(int_type, Field::create_field(7))); + const std::vector table_schema = {id, added}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, {name_col("id", int_type, 0)}).ok()); + ASSERT_EQ(mapper.mappings().size(), 2); + EXPECT_EQ(*mapper.mappings()[0].file_local_id, 0); + expect_constant(mapper, mapper.mappings()[1], 1, int_type); + + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, + {name_col("id", int_type, 3), name_col("added", int_type, 4)}) + .ok()); + ASSERT_EQ(mapper.mappings().size(), 2); + EXPECT_EQ(*mapper.mappings()[0].file_local_id, 3); + EXPECT_EQ(*mapper.mappings()[1].file_local_id, 4); + EXPECT_TRUE(mapper.constant_map().empty()); +} + +} // namespace +} // namespace doris::format diff --git a/be/test/format_v2/expr/cast_test.cpp b/be/test/format_v2/expr/cast_test.cpp new file mode 100644 index 00000000000000..12fcc43e8465b8 --- /dev/null +++ b/be/test/format_v2/expr/cast_test.cpp @@ -0,0 +1,1005 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/expr/cast.h" + +#include + +#include +#include +#include + +#include "common/status.h" +#include "core/block/block.h" +#include "core/column/column_nullable.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/field.h" +#include "exprs/vexpr_context.h" +#include "exprs/vliteral.h" +#include "exprs/vslot_ref.h" +#include "format_v2/column_mapper.h" +#include "format_v2/file_reader.h" +#include "format_v2/table_reader.h" +#include "runtime/descriptors.h" +#include "testutil/column_helper.h" +#include "testutil/mock/mock_in_expr.h" +#include "testutil/mock/mock_runtime_state.h" + +namespace doris { +namespace { + +format::LocalColumnIndex field_projection(int32_t column_id) { + return format::LocalColumnIndex {.index = column_id}; +} + +std::vector projection_ids(const std::vector& projections) { + std::vector ids; + ids.reserve(projections.size()); + for (const auto& projection : projections) { + ids.push_back(projection.index); + } + return ids; +} + +} // namespace + +class CastTest : public testing::Test { +protected: + void SetUp() override { state.set_enable_strict_cast(true); } + + static VExprContextSPtr create_context(const DataTypePtr& return_type, + const DataTypePtr& child_type, int child_column_id = 0) { + auto cast = Cast::create_shared(return_type); + cast->add_child(VSlotRef::create_shared(child_column_id, child_column_id, -1, child_type, + "source_column")); + return VExprContext::create_shared(cast); + } + + Status prepare_open_execute(VExprContext* context, Block* block, int* result_column_id) { + RETURN_IF_ERROR(context->prepare(&state, RowDescriptor())); + RETURN_IF_ERROR(context->open(&state)); + return context->execute(block, result_column_id); + } + + MockRuntimeState state; +}; + +class Int64ChildGreaterThanExpr final : public VExpr { +public: + explicit Int64ChildGreaterThanExpr(int64_t value) + : VExpr(std::make_shared(), false), _value(value) {} + + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + ColumnPtr child_column; + RETURN_IF_ERROR( + get_child(0)->execute_column(context, block, selector, count, child_column)); + const auto& input = assert_cast(*child_column); + auto result = ColumnUInt8::create(); + auto& result_data = result->get_data(); + result_data.resize(count); + for (size_t row = 0; row < count; ++row) { + result_data[row] = input.get_element(row) > _value; + } + result_column = std::move(result); + return Status::OK(); + } + + const std::string& expr_name() const override { return _expr_name; } + +private: + const int64_t _value; + const std::string _expr_name = "Int64ChildGreaterThanExpr"; +}; + +class Int64BinaryPredicateExpr final : public VExpr { +public: + explicit Int64BinaryPredicateExpr(TExprOpcode::type opcode) + : VExpr(std::make_shared(), false) { + set_node_type(TExprNodeType::BINARY_PRED); + _opcode = opcode; + } + + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + ColumnPtr left_column; + RETURN_IF_ERROR(get_child(0)->execute_column(context, block, selector, count, left_column)); + ColumnPtr right_column; + RETURN_IF_ERROR( + get_child(1)->execute_column(context, block, selector, count, right_column)); + + auto result = ColumnUInt8::create(); + auto& result_data = result->get_data(); + result_data.resize(count); + for (size_t row = 0; row < count; ++row) { + const auto left = left_column->get_int(row); + const auto right = right_column->get_int(row); + switch (_opcode) { + case TExprOpcode::GT: + result_data[row] = left > right; + break; + case TExprOpcode::LT: + result_data[row] = left < right; + break; + default: + return Status::InternalError("Unsupported test opcode {}", _opcode); + } + } + result_column = std::move(result); + return Status::OK(); + } + + const std::string& expr_name() const override { return _expr_name; } + +private: + const std::string _expr_name = "Int64BinaryPredicateExpr"; +}; + +TEST_F(CastTest, CastIntSlotToBigInt) { + auto source_type = std::make_shared(); + auto return_type = std::make_shared(); + auto context = create_context(return_type, source_type); + Block block; + block.insert(ColumnHelper::create_column_with_name({1, -2, 3})); + + int result_column_id = -1; + auto status = prepare_open_execute(context.get(), &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + ASSERT_EQ(result_column_id, 1); + ASSERT_EQ(block.columns(), 2); + EXPECT_EQ(block.get_by_position(result_column_id).type, return_type); + const auto& result_column = + assert_cast(*block.get_by_position(result_column_id).column); + EXPECT_EQ(result_column.get_data()[0], 1); + EXPECT_EQ(result_column.get_data()[1], -2); + EXPECT_EQ(result_column.get_data()[2], 3); + + context->close(); +} + +TEST_F(CastTest, CastStringSlotToNullableInt) { + state.set_enable_strict_cast(false); + auto source_type = std::make_shared(); + auto return_type = std::make_shared(std::make_shared()); + auto context = create_context(return_type, source_type); + Block block; + block.insert(ColumnHelper::create_column_with_name({"10", "bad", "-3"})); + + int result_column_id = -1; + auto status = prepare_open_execute(context.get(), &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + const auto& nullable_column = + assert_cast(*block.get_by_position(result_column_id).column); + const auto& result_column = + assert_cast(nullable_column.get_nested_column()); + const auto& null_map = nullable_column.get_null_map_data(); + EXPECT_EQ(result_column.get_data()[0], 10); + EXPECT_EQ(result_column.get_data()[2], -3); + EXPECT_EQ(null_map[0], 0); + EXPECT_EQ(null_map[1], 1); + EXPECT_EQ(null_map[2], 0); + + context->close(); +} + +TEST_F(CastTest, CastLiteralToString) { + auto source_type = std::make_shared(); + auto return_type = std::make_shared(); + auto cast = Cast::create_shared(return_type); + cast->add_child(VLiteral::create_shared(source_type, Field::create_field(123))); + auto context = VExprContext::create_shared(cast); + Block block; + block.insert(ColumnHelper::create_column_with_name({1, 2, 3})); + + int result_column_id = -1; + auto status = prepare_open_execute(context.get(), &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + const auto& result = block.get_by_position(result_column_id); + EXPECT_EQ(result.type->to_string(*result.column, 0), "123"); + EXPECT_EQ(result.type->to_string(*result.column, 1), "123"); + EXPECT_EQ(result.type->to_string(*result.column, 2), "123"); + + context->close(); +} + +TEST_F(CastTest, EmptyBlockAppendsEmptyResultColumn) { + auto source_type = std::make_shared(); + auto return_type = std::make_shared(); + auto context = create_context(return_type, source_type); + Block block; + block.insert(ColumnHelper::create_column_with_name({})); + + int result_column_id = -1; + auto status = prepare_open_execute(context.get(), &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + ASSERT_EQ(result_column_id, 1); + EXPECT_EQ(block.get_by_position(result_column_id).column->size(), 0); + + context->close(); +} + +TEST_F(CastTest, PrepareRejectsMissingChild) { + auto cast = Cast::create_shared(std::make_shared()); + VExprContext context(cast); + + auto status = context.prepare(&state, RowDescriptor()); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("exactly 1 child expr"), std::string::npos); +} + +TEST_F(CastTest, PrepareRejectsMultipleChildren) { + auto child_type = std::make_shared(); + auto cast = Cast::create_shared(std::make_shared()); + cast->add_child(VSlotRef::create_shared(0, 0, -1, child_type, "c0")); + cast->add_child(VSlotRef::create_shared(1, 1, -1, child_type, "c1")); + VExprContext context(cast); + + auto status = context.prepare(&state, RowDescriptor()); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("exactly 1 child expr"), std::string::npos); +} + +TEST_F(CastTest, ColumnMapperBuildsCastProjectionForTypeMismatch) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(mapper.mappings().size(), 1); + format::FileScanRequest file_request; + status = mapper.create_scan_request({}, {}, projected_columns, &file_request); + ASSERT_TRUE(status.ok()) << status; + const auto& mapping = mapper.mappings()[0]; + EXPECT_FALSE(mapping.is_trivial); + ASSERT_NE(mapping.projection, nullptr); + + Block block; + block.insert(ColumnHelper::create_column_with_name({11, 22})); + int result_column_id = -1; + status = prepare_open_execute(mapping.projection.get(), &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + const auto& result_column = + assert_cast(*block.get_by_position(result_column_id).column); + EXPECT_EQ(result_column.get_data()[0], 11); + EXPECT_EQ(result_column.get_data()[1], 22); + + mapping.projection->close(); +} + +TEST_F(CastTest, ColumnMapperTreatsEquivalentTypesAsTrivial) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); +} + +TEST_F(CastTest, ColumnMapperBuildsCastFilterForTypeMismatch) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = std::make_shared(15); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + ASSERT_EQ(projection_ids(file_request.predicate_columns), std::vector({0})); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 1); + const auto& localized_child = localized_expr->children()[0]; + ASSERT_NE(dynamic_cast(localized_child.get()), nullptr); + ASSERT_EQ(localized_child->get_num_children(), 1); + const auto* localized_slot = assert_cast(localized_child->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + EXPECT_TRUE(localized_child->data_type()->equals(*table_column.type)); + + Block block; + block.insert(ColumnHelper::create_column_with_name({11, 22})); + auto* conjunct = file_request.conjuncts[0].get(); + status = conjunct->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = conjunct->open(&state); + ASSERT_TRUE(status.ok()) << status; + IColumn::Filter filter(block.rows(), 1); + bool can_filter_all = false; + status = conjunct->execute_filter(&block, filter.data(), block.rows(), false, &can_filter_all); + ASSERT_TRUE(status.ok()) << status; + EXPECT_FALSE(can_filter_all); + ASSERT_EQ(filter.size(), 2); + EXPECT_EQ(filter[0], 0); + EXPECT_EQ(filter[1], 1); + + file_request.conjuncts[0]->close(); +} + +TEST_F(CastTest, ColumnMapperRepreparesRewrittenPreparedFilter) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto cast = Cast::create_shared(table_column.type); + cast->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(cast); + table_filter.global_indices = {format::GlobalIndex(0)}; + status = table_filter.conjunct->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = table_filter.conjunct->open(&state); + ASSERT_TRUE(status.ok()) << status; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_NE(dynamic_cast(localized_expr.get()), nullptr); + ASSERT_EQ(localized_expr->get_num_children(), 1); + const auto* localized_slot = assert_cast(localized_expr->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + + status = file_request.conjuncts[0]->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = file_request.conjuncts[0]->open(&state); + ASSERT_TRUE(status.ok()) << status; + + file_request.conjuncts[0]->close(); +} + +TEST_F(CastTest, ColumnMapperCastsLiteralForSlotLiteralPredicateTypeMismatch) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = std::make_shared(TExprOpcode::GT); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(15))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + ASSERT_EQ(projection_ids(file_request.predicate_columns), std::vector({0})); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 2); + const auto* localized_slot = assert_cast(localized_expr->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + const auto& localized_literal = localized_expr->children()[1]; + EXPECT_TRUE(localized_literal->is_literal()); + EXPECT_TRUE(localized_literal->data_type()->equals(*file_field.type)); + + Block block; + block.insert(ColumnHelper::create_column_with_name({11, 22})); + auto* conjunct = file_request.conjuncts[0].get(); + status = conjunct->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = conjunct->open(&state); + ASSERT_TRUE(status.ok()) << status; + IColumn::Filter filter(block.rows(), 1); + bool can_filter_all = false; + status = conjunct->execute_filter(&block, filter.data(), block.rows(), false, &can_filter_all); + ASSERT_TRUE(status.ok()) << status; + EXPECT_FALSE(can_filter_all); + ASSERT_EQ(filter.size(), 2); + EXPECT_EQ(filter[0], 0); + EXPECT_EQ(filter[1], 1); + + file_request.conjuncts[0]->close(); +} + +TEST_F(CastTest, ColumnMapperCastsLiteralForLiteralSlotPredicateTypeMismatch) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = std::make_shared(TExprOpcode::LT); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(15))); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 2); + const auto& localized_literal = localized_expr->children()[0]; + EXPECT_TRUE(localized_literal->is_literal()); + EXPECT_TRUE(localized_literal->data_type()->equals(*file_field.type)); + const auto* localized_slot = assert_cast(localized_expr->children()[1].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + + Block block; + block.insert(ColumnHelper::create_column_with_name({11, 22})); + auto* conjunct = file_request.conjuncts[0].get(); + status = conjunct->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = conjunct->open(&state); + ASSERT_TRUE(status.ok()) << status; + IColumn::Filter filter(block.rows(), 1); + bool can_filter_all = false; + status = conjunct->execute_filter(&block, filter.data(), block.rows(), false, &can_filter_all); + ASSERT_TRUE(status.ok()) << status; + EXPECT_FALSE(can_filter_all); + ASSERT_EQ(filter.size(), 2); + EXPECT_EQ(filter[0], 0); + EXPECT_EQ(filter[1], 1); + + file_request.conjuncts[0]->close(); +} + +TEST_F(CastTest, ColumnMapperCastsInPredicateLiteralsForTypeMismatch) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = MockInExpr::create(); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(15))); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(22))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + ASSERT_EQ(projection_ids(file_request.predicate_columns), std::vector({0})); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 3); + const auto* localized_slot = assert_cast(localized_expr->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + EXPECT_TRUE(localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(localized_expr->children()[1]->data_type()->equals(*file_field.type)); + EXPECT_TRUE(localized_expr->children()[2]->is_literal()); + EXPECT_TRUE(localized_expr->children()[2]->data_type()->equals(*file_field.type)); +} + +TEST_F(CastTest, ColumnMapperFallsBackToSlotCastWhenInPredicateLiteralRewriteFails) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = MockInExpr::create(); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field("10"))); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field("bad"))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 3); + const auto& localized_child = localized_expr->children()[0]; + ASSERT_NE(dynamic_cast(localized_child.get()), nullptr); + ASSERT_EQ(localized_child->get_num_children(), 1); + const auto* localized_slot = assert_cast(localized_child->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + EXPECT_TRUE(localized_child->data_type()->equals(*table_column.type)); + EXPECT_TRUE(localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(localized_expr->children()[1]->data_type()->equals(*table_column.type)); + EXPECT_TRUE(localized_expr->children()[2]->is_literal()); + EXPECT_TRUE(localized_expr->children()[2]->data_type()->equals(*table_column.type)); +} + +TEST_F(CastTest, ColumnMapperDoesNotLeakRewrittenInPredicateLiteralAcrossSplits) { + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + auto predicate = MockInExpr::create(); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(15))); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(22))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::ColumnDefinition int_file_field; + int_file_field.identifier = Field::create_field(0); + int_file_field.name = "value"; + int_file_field.type = std::make_shared(); + format::TableColumnMapper int_mapper; + ASSERT_TRUE(int_mapper.create_mapping(projected_columns, {}, {int_file_field}).ok()); + format::FileScanRequest int_request; + ASSERT_TRUE(int_mapper + .create_scan_request({table_filter}, {}, projected_columns, &int_request, + &state) + .ok()); + ASSERT_EQ(int_request.conjuncts.size(), 1); + const auto& int_localized_expr = int_request.conjuncts[0]->root(); + ASSERT_EQ(int_localized_expr->get_num_children(), 3); + EXPECT_TRUE(int_localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(int_localized_expr->children()[1]->data_type()->equals(*int_file_field.type)); + EXPECT_TRUE(int_localized_expr->children()[2]->is_literal()); + EXPECT_TRUE(int_localized_expr->children()[2]->data_type()->equals(*int_file_field.type)); + + format::ColumnDefinition bigint_file_field; + bigint_file_field.identifier = Field::create_field(0); + bigint_file_field.name = "value"; + bigint_file_field.type = std::make_shared(); + format::TableColumnMapper bigint_mapper; + ASSERT_TRUE(bigint_mapper.create_mapping(projected_columns, {}, {bigint_file_field}).ok()); + format::FileScanRequest bigint_request; + ASSERT_TRUE(bigint_mapper + .create_scan_request({table_filter}, {}, projected_columns, &bigint_request, + &state) + .ok()); + ASSERT_EQ(bigint_request.conjuncts.size(), 1); + const auto& bigint_localized_expr = bigint_request.conjuncts[0]->root(); + ASSERT_EQ(bigint_localized_expr->get_num_children(), 3); + const auto* localized_slot = + assert_cast(bigint_localized_expr->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*bigint_file_field.type)); + EXPECT_TRUE(bigint_localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(bigint_localized_expr->children()[1]->data_type()->equals(*bigint_file_field.type)); + EXPECT_TRUE(bigint_localized_expr->children()[2]->is_literal()); + EXPECT_TRUE(bigint_localized_expr->children()[2]->data_type()->equals(*bigint_file_field.type)); +} + +TEST_F(CastTest, ColumnMapperFallsBackToSlotCastWhenLiteralRewriteFails) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = std::make_shared(TExprOpcode::GT); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field("bad"))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 2); + const auto& localized_child = localized_expr->children()[0]; + ASSERT_NE(dynamic_cast(localized_child.get()), nullptr); + ASSERT_EQ(localized_child->get_num_children(), 1); + const auto* localized_slot = assert_cast(localized_child->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); + EXPECT_TRUE(localized_child->data_type()->equals(*table_column.type)); + EXPECT_TRUE(localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(localized_expr->children()[1]->data_type()->equals(*table_column.type)); +} + +TEST_F(CastTest, ColumnMapperDoesNotLeakRewrittenLiteralAcrossSplits) { + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + auto predicate = std::make_shared(TExprOpcode::GT); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(15))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::ColumnDefinition int_file_field; + int_file_field.identifier = Field::create_field(0); + int_file_field.name = "value"; + int_file_field.type = std::make_shared(); + format::TableColumnMapper int_mapper; + ASSERT_TRUE(int_mapper.create_mapping(projected_columns, {}, {int_file_field}).ok()); + format::FileScanRequest int_request; + ASSERT_TRUE(int_mapper + .create_scan_request({table_filter}, {}, projected_columns, &int_request, + &state) + .ok()); + ASSERT_EQ(int_request.conjuncts.size(), 1); + const auto& int_localized_expr = int_request.conjuncts[0]->root(); + ASSERT_EQ(int_localized_expr->get_num_children(), 2); + EXPECT_TRUE(int_localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(int_localized_expr->children()[1]->data_type()->equals(*int_file_field.type)); + + format::ColumnDefinition bigint_file_field; + bigint_file_field.identifier = Field::create_field(0); + bigint_file_field.name = "value"; + bigint_file_field.type = std::make_shared(); + format::TableColumnMapper bigint_mapper; + ASSERT_TRUE(bigint_mapper.create_mapping(projected_columns, {}, {bigint_file_field}).ok()); + format::FileScanRequest bigint_request; + ASSERT_TRUE(bigint_mapper + .create_scan_request({table_filter}, {}, projected_columns, &bigint_request, + &state) + .ok()); + ASSERT_EQ(bigint_request.conjuncts.size(), 1); + const auto& bigint_localized_expr = bigint_request.conjuncts[0]->root(); + ASSERT_EQ(bigint_localized_expr->get_num_children(), 2); + const auto* localized_slot = + assert_cast(bigint_localized_expr->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*bigint_file_field.type)); + EXPECT_TRUE(bigint_localized_expr->children()[1]->is_literal()); + EXPECT_TRUE(bigint_localized_expr->children()[1]->data_type()->equals(*bigint_file_field.type)); +} + +TEST_F(CastTest, ColumnMapperKeepsExplicitSlotCastInSlotLiteralPredicate) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto explicit_cast = Cast::create_shared(std::make_shared()); + explicit_cast->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + auto predicate = std::make_shared(TExprOpcode::GT); + predicate->add_child(explicit_cast); + predicate->add_child( + VLiteral::create_shared(table_column.type, Field::create_field(15))); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request, &state) + .ok()); + ASSERT_EQ(file_request.conjuncts.size(), 1); + const auto& localized_expr = file_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 2); + const auto& localized_cast = localized_expr->children()[0]; + ASSERT_NE(dynamic_cast(localized_cast.get()), nullptr); + EXPECT_TRUE(localized_cast->data_type()->equals(DataTypeString())); + ASSERT_EQ(localized_cast->get_num_children(), 1); + ASSERT_NE(dynamic_cast(localized_cast->children()[0].get()), nullptr); + const auto* localized_slot = + assert_cast(localized_cast->children()[0]->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*file_field.type)); +} + +TEST_F(CastTest, ColumnMapperDoesNotNestCastFilterAcrossScanRequests) { + format::TableColumnMapper mapper; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(0); + file_field.name = "value"; + file_field.type = std::make_shared(); + std::vector file_schema {file_field}; + + auto status = mapper.create_mapping(projected_columns, {}, file_schema); + ASSERT_TRUE(status.ok()) << status; + + auto predicate = std::make_shared(15); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest first_request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, projected_columns, &first_request, + &state) + .ok()); + format::FileScanRequest second_request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, projected_columns, &second_request, + &state) + .ok()); + + ASSERT_EQ(second_request.conjuncts.size(), 1); + const auto& localized_expr = second_request.conjuncts[0]->root(); + ASSERT_EQ(localized_expr->get_num_children(), 1); + const auto& localized_child = localized_expr->children()[0]; + ASSERT_NE(dynamic_cast(localized_child.get()), nullptr); + ASSERT_EQ(localized_child->get_num_children(), 1); + const auto* localized_slot = assert_cast(localized_child->children()[0].get()); + EXPECT_EQ(localized_slot->column_id(), 0); +} + +TEST_F(CastTest, ColumnMapperRewritesPreviousCastFilterToMatchingSplitType) { + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + auto predicate = std::make_shared(15); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::ColumnDefinition int_file_field; + int_file_field.identifier = Field::create_field(0); + int_file_field.name = "value"; + int_file_field.type = std::make_shared(); + + format::TableColumnMapper int_mapper; + ASSERT_TRUE(int_mapper.create_mapping(projected_columns, {}, {int_file_field}).ok()); + format::FileScanRequest int_request; + ASSERT_TRUE(int_mapper + .create_scan_request({table_filter}, {}, projected_columns, &int_request, + &state) + .ok()); + + const auto& int_localized_expr = int_request.conjuncts[0]->root(); + ASSERT_EQ(int_localized_expr->get_num_children(), 1); + ASSERT_NE(dynamic_cast(int_localized_expr->children()[0].get()), nullptr); + + format::ColumnDefinition bigint_file_field; + bigint_file_field.identifier = Field::create_field(0); + bigint_file_field.name = "value"; + bigint_file_field.type = std::make_shared(); + + format::TableColumnMapper bigint_mapper; + ASSERT_TRUE(bigint_mapper.create_mapping(projected_columns, {}, {bigint_file_field}).ok()); + format::FileScanRequest bigint_request; + ASSERT_TRUE(bigint_mapper + .create_scan_request({table_filter}, {}, projected_columns, &bigint_request, + &state) + .ok()); + + const auto& bigint_localized_expr = bigint_request.conjuncts[0]->root(); + ASSERT_EQ(bigint_localized_expr->get_num_children(), 1); + const auto& bigint_localized_child = bigint_localized_expr->children()[0]; + const auto* localized_slot = assert_cast(bigint_localized_child.get()); + EXPECT_EQ(localized_slot->column_id(), 0); + EXPECT_TRUE(localized_slot->data_type()->equals(*bigint_file_field.type)); + + Block block; + block.insert(ColumnHelper::create_column_with_name({11, 22})); + auto* conjunct = bigint_request.conjuncts[0].get(); + auto status = conjunct->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = conjunct->open(&state); + ASSERT_TRUE(status.ok()) << status; + IColumn::Filter filter(block.rows(), 1); + bool can_filter_all = false; + status = conjunct->execute_filter(&block, filter.data(), block.rows(), false, &can_filter_all); + ASSERT_TRUE(status.ok()) << status; + EXPECT_FALSE(can_filter_all); + ASSERT_EQ(filter.size(), 2); + EXPECT_EQ(filter[0], 0); + EXPECT_EQ(filter[1], 1); + conjunct->close(); +} + +TEST_F(CastTest, ColumnMapperKeepsTableSlotIdWhenFileBlockPositionChanges) { + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(7); + table_column.name = "value"; + table_column.type = std::make_shared(); + std::vector projected_columns {table_column}; + + format::ColumnDefinition file_field; + file_field.identifier = Field::create_field(10); + file_field.name = "value"; + file_field.type = std::make_shared(); + + format::TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, {file_field}).ok()); + + auto predicate = std::make_shared(15); + predicate->add_child(VSlotRef::create_shared(0, 0, -1, table_column.type, "value")); + format::TableFilter table_filter; + table_filter.conjunct = VExprContext::create_shared(predicate); + table_filter.global_indices = {format::GlobalIndex(0)}; + + format::FileScanRequest first_request; + ASSERT_TRUE(mapper.localize_filters({table_filter}, {}, &first_request, &state).ok()); + ASSERT_EQ(first_request.conjuncts.size(), 1); + const auto* first_slot = + assert_cast(first_request.conjuncts[0]->root()->children()[0].get()); + EXPECT_EQ(first_slot->slot_id(), 7); + EXPECT_EQ(first_slot->column_id(), 0); + + format::FileScanRequest second_request; + second_request.local_positions.emplace(format::LocalColumnId(9), format::LocalIndex(0)); + second_request.local_positions.emplace(format::LocalColumnId(10), format::LocalIndex(1)); + second_request.non_predicate_columns.push_back(field_projection(9)); + ASSERT_TRUE(mapper.localize_filters({table_filter}, {}, &second_request, &state).ok()); + ASSERT_EQ(second_request.conjuncts.size(), 1); + const auto* second_slot = + assert_cast(second_request.conjuncts[0]->root()->children()[0].get()); + EXPECT_EQ(second_slot->slot_id(), 7); + EXPECT_EQ(second_slot->column_id(), 1); + + Block block; + block.insert(ColumnHelper::create_column_with_name({100, 100})); + block.insert(ColumnHelper::create_column_with_name({11, 22})); + auto* conjunct = second_request.conjuncts[0].get(); + auto status = conjunct->prepare(&state, RowDescriptor()); + ASSERT_TRUE(status.ok()) << status; + status = conjunct->open(&state); + ASSERT_TRUE(status.ok()) << status; + IColumn::Filter filter(block.rows(), 1); + bool can_filter_all = false; + status = conjunct->execute_filter(&block, filter.data(), block.rows(), false, &can_filter_all); + ASSERT_TRUE(status.ok()) << status; + EXPECT_FALSE(can_filter_all); + ASSERT_EQ(filter.size(), 2); + EXPECT_EQ(filter[0], 0); + EXPECT_EQ(filter[1], 1); + conjunct->close(); +} + +} // namespace doris diff --git a/be/test/format_v2/expr/delete_predicate_test.cpp b/be/test/format_v2/expr/delete_predicate_test.cpp new file mode 100644 index 00000000000000..ac8739c485407c --- /dev/null +++ b/be/test/format_v2/expr/delete_predicate_test.cpp @@ -0,0 +1,155 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/expr/delete_predicate.h" + +#include + +#include +#include +#include + +#include "common/status.h" +#include "core/block/block.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_number.h" +#include "exprs/vexpr_context.h" +#include "runtime/descriptors.h" +#include "testutil/mock/mock_slot_ref.h" + +namespace doris { + +class DeletePredicateTest : public testing::Test { +protected: + static Block make_block(const std::vector& row_ids) { + auto column = ColumnInt64::create(); + for (auto row_id : row_ids) { + column->insert_value(row_id); + } + + Block block; + block.insert({std::move(column), std::make_shared(), "row_id"}); + return block; + } + + static std::vector result_column_data(const Block& block, int result_column_id) { + const auto& result_column = + assert_cast(*block.get_by_position(result_column_id).column); + return {result_column.get_data().begin(), result_column.get_data().end()}; + } + + static Status execute_delete_predicate(const std::vector& deleted_rows, Block* block, + int* result_column_id) { + auto delete_predicate = std::make_shared(deleted_rows); + delete_predicate->_open_finished = true; + delete_predicate->add_child( + std::make_shared(0, std::make_shared())); + + VExprContext context(delete_predicate); + return delete_predicate->execute(&context, block, result_column_id); + } +}; + +TEST_F(DeletePredicateTest, MatchDeletedRowsInInputRange) { + const std::vector deleted_rows {-3, 1, 4, 8, 12, 20}; + auto block = make_block({0, 1, 2, 3, 4, 5, 8, 12}); + + int result_column_id = -1; + auto status = execute_delete_predicate(deleted_rows, &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + EXPECT_EQ(result_column_id, 1); + EXPECT_EQ(result_column_data(block, result_column_id), + std::vector({0, 1, 0, 0, 1, 0, 1, 1})); +} + +TEST_F(DeletePredicateTest, EmptyDeletedRowsReturnAllFalse) { + const std::vector deleted_rows; + auto block = make_block({1, 2, 3}); + + int result_column_id = -1; + auto status = execute_delete_predicate(deleted_rows, &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + EXPECT_EQ(result_column_data(block, result_column_id), std::vector({0, 0, 0})); +} + +TEST_F(DeletePredicateTest, DeletedRowsOutsideInputRangeReturnAllFalse) { + const std::vector deleted_rows {-10, -1, 10, 11}; + auto block = make_block({1, 2, 3}); + + int result_column_id = -1; + auto status = execute_delete_predicate(deleted_rows, &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + EXPECT_EQ(result_column_data(block, result_column_id), std::vector({0, 0, 0})); +} + +TEST_F(DeletePredicateTest, EmptyBlockDoesNotAppendResultColumn) { + const std::vector deleted_rows {1, 2, 3}; + Block block; + + int result_column_id = -1; + auto status = execute_delete_predicate(deleted_rows, &block, &result_column_id); + ASSERT_TRUE(status.ok()) << status; + + EXPECT_EQ(block.columns(), 0); + EXPECT_EQ(result_column_id, -1); +} + +TEST_F(DeletePredicateTest, MissingRowIdChildReturnsError) { + const std::vector deleted_rows {1}; + auto block = make_block({1}); + auto delete_predicate = std::make_shared(deleted_rows); + delete_predicate->_open_finished = true; + VExprContext context(delete_predicate); + + int result_column_id = -1; + auto status = delete_predicate->execute(&context, &block, &result_column_id); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("exactly 1 child expr"), std::string::npos); +} + +TEST_F(DeletePredicateTest, ExecuteColumnImplReturnsError) { + const std::vector deleted_rows {1}; + DeletePredicate delete_predicate(deleted_rows); + VExprContext context(std::make_shared(deleted_rows)); + ColumnPtr result_column; + + auto status = + delete_predicate.execute_column_impl(&context, nullptr, nullptr, 0, result_column); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("DeletePredicate::execute_column_impl"), std::string::npos); +} + +TEST_F(DeletePredicateTest, LifecycleAndDebugString) { + const std::vector deleted_rows {1}; + DeletePredicate delete_predicate(deleted_rows); + VExprContext context(std::make_shared(deleted_rows)); + RowDescriptor row_desc; + + auto status = delete_predicate.prepare(nullptr, row_desc, &context); + ASSERT_TRUE(status.ok()) << status; + EXPECT_EQ(delete_predicate.expr_name(), "DeletePredicate"); + EXPECT_EQ(delete_predicate.debug_string(), "DeletePredicate"); + + status = delete_predicate.open(nullptr, &context, FunctionContext::THREAD_LOCAL); + ASSERT_TRUE(status.ok()) << status; + delete_predicate.close(&context, FunctionContext::THREAD_LOCAL); +} + +} // namespace doris diff --git a/be/test/format_v2/expr/equality_delete_predicate_test.cpp b/be/test/format_v2/expr/equality_delete_predicate_test.cpp new file mode 100644 index 00000000000000..20b0bf1c5972ca --- /dev/null +++ b/be/test/format_v2/expr/equality_delete_predicate_test.cpp @@ -0,0 +1,181 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/expr/equality_delete_predicate.h" + +#include + +#include +#include +#include +#include + +#include "common/status.h" +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/column/column_nullable.h" +#include "core/column/column_string.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "exprs/vexpr_context.h" +#include "format_v2/expr/cast.h" +#include "runtime/descriptors.h" +#include "testutil/column_helper.h" +#include "testutil/mock/mock_runtime_state.h" +#include "testutil/mock/mock_slot_ref.h" + +namespace doris { + +class EqualityDeletePredicateTest : public testing::Test { +protected: + static ColumnWithTypeAndName make_nullable_int_column( + const std::string& name, const std::vector>& values) { + auto data = ColumnInt32::create(); + auto null_map = ColumnUInt8::create(); + for (const auto& value : values) { + data->insert_value(value.value_or(0)); + null_map->insert_value(!value.has_value()); + } + auto type = make_nullable(std::make_shared()); + return {ColumnNullable::create(std::move(data), std::move(null_map)), type, name}; + } + + static ColumnWithTypeAndName make_nullable_string_column( + const std::string& name, const std::vector>& values) { + auto data = ColumnString::create(); + auto null_map = ColumnUInt8::create(); + for (const auto& value : values) { + const std::string data_value = value.value_or(""); + data->insert_data(data_value.data(), data_value.size()); + null_map->insert_value(!value.has_value()); + } + auto type = make_nullable(std::make_shared()); + return {ColumnNullable::create(std::move(data), std::move(null_map)), type, name}; + } + + static std::vector result_column_data(const Block& block, int result_column_id) { + const auto& result_column = + assert_cast(*block.get_by_position(result_column_id).column); + return {result_column.get_data().begin(), result_column.get_data().end()}; + } + + static Status execute_equality_delete_predicate(Block delete_block, std::vector field_ids, + Block* data_block, int* result_column_id) { + auto predicate = + std::make_shared(std::move(delete_block), field_ids); + predicate->_open_finished = true; + for (size_t idx = 0; idx < field_ids.size(); ++idx) { + predicate->add_child( + std::make_shared(idx, data_block->get_by_position(idx).type)); + } + + VExprContext context(predicate); + return predicate->execute(&context, data_block, result_column_id); + } + + static Status execute_prepared_equality_delete_predicate(const VExprContextSPtr& context, + MockRuntimeState* state, + Block* data_block, + int* result_column_id) { + RETURN_IF_ERROR(context->prepare(state, RowDescriptor())); + RETURN_IF_ERROR(context->open(state)); + return context->execute(data_block, result_column_id); + } +}; + +TEST_F(EqualityDeletePredicateTest, MatchSingleColumn) { + Block delete_block; + delete_block.insert(make_nullable_int_column("id", {1, 4})); + Block data_block; + data_block.insert(make_nullable_int_column("id", {1, 2, 3, 4})); + + int result_column_id = -1; + auto status = execute_equality_delete_predicate(std::move(delete_block), {1}, &data_block, + &result_column_id); + ASSERT_TRUE(status.ok()) << status; + EXPECT_EQ(result_column_data(data_block, result_column_id), std::vector({1, 0, 0, 1})); +} + +TEST_F(EqualityDeletePredicateTest, MatchMultipleColumns) { + Block delete_block; + delete_block.insert(make_nullable_int_column("id", {1, 2})); + delete_block.insert(make_nullable_string_column("name", {"a", "b"})); + Block data_block; + data_block.insert(make_nullable_int_column("id", {1, 1, 2, 2})); + data_block.insert(make_nullable_string_column("name", {"a", "b", "a", "b"})); + + int result_column_id = -1; + auto status = execute_equality_delete_predicate(std::move(delete_block), {1, 2}, &data_block, + &result_column_id); + ASSERT_TRUE(status.ok()) << status; + EXPECT_EQ(result_column_data(data_block, result_column_id), std::vector({1, 0, 0, 1})); +} + +TEST_F(EqualityDeletePredicateTest, MatchNullValues) { + Block delete_block; + delete_block.insert(make_nullable_int_column("id", {std::nullopt})); + Block data_block; + data_block.insert(make_nullable_int_column("id", {1, std::nullopt, 3})); + + int result_column_id = -1; + auto status = execute_equality_delete_predicate(std::move(delete_block), {1}, &data_block, + &result_column_id); + ASSERT_TRUE(status.ok()) << status; + EXPECT_EQ(result_column_data(data_block, result_column_id), std::vector({0, 1, 0})); +} + +TEST_F(EqualityDeletePredicateTest, MatchAfterCastToDeleteKeyType) { + Block delete_block; + delete_block.insert(make_nullable_int_column("id", {1, 4})); + Block data_block; + data_block.insert(ColumnHelper::create_column_with_name({1, 2, 4})); + + auto predicate = std::make_shared(std::move(delete_block), + std::vector {1}); + auto cast_expr = Cast::create_shared(make_nullable(std::make_shared())); + cast_expr->add_child(std::make_shared(0, data_block.get_by_position(0).type)); + predicate->add_child(std::move(cast_expr)); + auto context = VExprContext::create_shared(predicate); + MockRuntimeState state; + + int result_column_id = -1; + auto status = execute_prepared_equality_delete_predicate(context, &state, &data_block, + &result_column_id); + ASSERT_TRUE(status.ok()) << status; + EXPECT_EQ(result_column_data(data_block, result_column_id), std::vector({1, 0, 1})); + context->close(); +} + +TEST_F(EqualityDeletePredicateTest, ChildCountMismatchReturnsError) { + Block delete_block; + delete_block.insert(make_nullable_int_column("id", {1})); + auto predicate = std::make_shared(std::move(delete_block), + std::vector {1}); + predicate->_open_finished = true; + Block data_block; + data_block.insert(make_nullable_int_column("id", {1})); + VExprContext context(predicate); + + int result_column_id = -1; + auto status = predicate->execute(&context, &data_block, &result_column_id); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("should have 1 child exprs"), std::string::npos); +} + +} // namespace doris diff --git a/be/test/format_v2/expr/table_expr_test.cpp b/be/test/format_v2/expr/table_expr_test.cpp new file mode 100644 index 00000000000000..d627bce8f7492e --- /dev/null +++ b/be/test/format_v2/expr/table_expr_test.cpp @@ -0,0 +1,123 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include + +#include +#include + +#include "core/block/block.h" +#include "core/column/column_const.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/primitive_type.h" +#include "core/field.h" +#include "exprs/vliteral.h" +#include "exprs/vslot_ref.h" +#include "runtime/descriptors.h" +#include "testutil/column_helper.h" + +namespace doris { + +TEST(VLiteralTest, CreatesConstColumnWithGivenTypeAndField) { + auto type = std::make_shared(); + auto literal = VLiteral::create_shared(type, Field::create_field(123)); + + ASSERT_EQ(literal->data_type(), type); + ASSERT_TRUE(literal->is_literal()); + + const auto& column = literal->get_column_ptr(); + ASSERT_EQ(column->size(), 1); + ASSERT_TRUE(is_column_const(*column)); + EXPECT_EQ(column->get_int(0), 123); +} + +TEST(VLiteralTest, ExecutesAsConstColumn) { + auto type = std::make_shared(); + auto literal = VLiteral::create_shared(type, Field::create_field(456)); + + ColumnPtr result_column; + ASSERT_TRUE(literal->execute_column(nullptr, nullptr, nullptr, 3, result_column).ok()); + + ASSERT_EQ(result_column->size(), 3); + ASSERT_TRUE(is_column_const(*result_column)); + EXPECT_EQ(result_column->get_int(0), 456); + EXPECT_EQ(result_column->get_int(2), 456); +} + +TEST(VLiteralTest, ExecuteAppendsConstColumnToBlock) { + auto type = std::make_shared(); + auto literal = VLiteral::create_shared(type, Field::create_field(789)); + Block block; + block.insert(ColumnHelper::create_column_with_name({1, 2, 3})); + + int result_column_id = -1; + ASSERT_TRUE(literal->execute(nullptr, &block, &result_column_id).ok()); + + ASSERT_EQ(result_column_id, 1); + ASSERT_EQ(block.columns(), 2); + const auto& result_column = block.get_by_position(result_column_id).column; + ASSERT_EQ(result_column->size(), 3); + ASSERT_TRUE(is_column_const(*result_column)); + EXPECT_EQ(result_column->get_int(0), 789); + EXPECT_EQ(result_column->get_int(2), 789); + EXPECT_EQ(block.get_by_position(result_column_id).type, type); +} + +TEST(VSlotRefTest, KeepsSlotColumnIdsAndType) { + auto type = std::make_shared(); + std::string name = "file_col"; + auto slot_ref = VSlotRef::create_shared(10, 20, 30, type, name); + + EXPECT_EQ(slot_ref->slot_id(), 10); + EXPECT_EQ(slot_ref->column_id(), 20); + EXPECT_EQ(slot_ref->data_type(), type); + EXPECT_EQ(slot_ref->expr_name(), "file_col"); + EXPECT_EQ(slot_ref->column_name(), "file_col"); + EXPECT_FALSE(slot_ref->is_constant()); + + std::set column_ids; + slot_ref->collect_slot_column_ids(column_ids); + ASSERT_EQ(column_ids.size(), 1); + EXPECT_EQ(*column_ids.begin(), 20); +} + +TEST(VSlotRefTest, PrepareDoesNotRequireRowDescriptor) { + auto type = std::make_shared(); + std::string name = ""; + auto slot_ref = VSlotRef::create_shared(10, 20, 30, type, name); + + EXPECT_TRUE(slot_ref->prepare(nullptr, RowDescriptor(), nullptr).ok()); +} + +TEST(VSlotRefTest, ExecuteReturnsReferencedColumnId) { + auto type = std::make_shared(); + std::string name = ""; + auto slot_ref = VSlotRef::create_shared(10, 1, 30, type, name); + Block block; + block.insert(ColumnHelper::create_column_with_name({1, 2, 3})); + block.insert(ColumnHelper::create_column_with_name({4, 5, 6})); + + int result_column_id = -1; + ASSERT_TRUE(slot_ref->execute(nullptr, &block, &result_column_id).ok()); + + EXPECT_EQ(result_column_id, 1); + EXPECT_EQ(block.columns(), 2); + EXPECT_EQ(block.get_by_position(result_column_id).column->get_int(0), 4); + EXPECT_EQ(block.get_by_position(result_column_id).column->get_int(2), 6); +} + +} // namespace doris diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp new file mode 100644 index 00000000000000..aa17e2ffcfa5f4 --- /dev/null +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -0,0 +1,3744 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/assert_cast.h" +#include "core/column/column_array.h" +#include "core/column/column_decimal.h" +#include "core/column/column_map.h" +#include "core/column/column_nullable.h" +#include "core/column/column_string.h" +#include "core/column/column_struct.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" +#include "core/types.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/parquet_statistics.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "format_v2/parquet/selection_vector.h" + +namespace doris::parquet { +namespace { + +constexpr int64_t ROW_COUNT = 5; + +std::shared_ptr finish_array(arrow::ArrayBuilder* builder) { + std::shared_ptr array; + EXPECT_TRUE(builder->Finish(&array).ok()); + return array; +} + +class ParquetColumnReaderTest : public testing::Test { +protected: + void SetUp() override { + _test_dir = std::filesystem::temp_directory_path() / "doris_parquet_column_reader_test"; + std::filesystem::remove_all(_test_dir); + std::filesystem::create_directories(_test_dir); + _file_path = (_test_dir / "reader.parquet").string(); + write_parquet_file(); + _file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + auto metadata = _file_reader->metadata(); + ASSERT_EQ(metadata->num_row_groups(), 1); + _row_group = _file_reader->RowGroup(0); + ASSERT_NE(_row_group, nullptr); + auto schema_descriptor = _file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + auto st = build_parquet_column_schema(*schema_descriptor, &_fields); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(_fields.size(), _expected_by_field.size()); + } + + void TearDown() override { std::filesystem::remove_all(_test_dir); } + + template + std::shared_ptr build_required_array(const std::vector& values) { + Builder builder; + for (const auto& value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_string_array(const std::vector& values) { + arrow::StringBuilder builder; + for (const auto& value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_binary_array(const std::vector& values) { + arrow::BinaryBuilder builder; + for (const auto& value : values) { + EXPECT_TRUE(builder.Append(reinterpret_cast(value.data()), + static_cast(value.size())) + .ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_fixed_binary_array( + const std::shared_ptr& type, const std::vector& values) { + arrow::FixedSizeBinaryBuilder builder(type, arrow::default_memory_pool()); + for (const auto& value : values) { + EXPECT_TRUE(builder.Append(reinterpret_cast(value.data())).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_nullable_int32_array() { + arrow::Int32Builder builder; + EXPECT_TRUE(builder.Append(1).ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.Append(3).ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.Append(5).ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_float16_array() { + arrow::HalfFloatBuilder builder; + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.Append(0x0000).ok()); // +0.0 + EXPECT_TRUE(builder.Append(0x8000).ok()); // -0.0 + EXPECT_TRUE(builder.Append(0x3E00).ok()); // 1.5 + EXPECT_TRUE(builder.Append(0x7E00).ok()); // quiet NaN + return finish_array(&builder); + } + + std::shared_ptr build_required_struct_array() { + auto struct_type = arrow::struct_({arrow::field("a", arrow::int32(), false), + arrow::field("b", arrow::utf8(), false)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto b_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(b_array_builder))); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* a_builder = assert_cast(builder.field_builder(0)); + auto* b_builder = assert_cast(builder.field_builder(1)); + const std::vector a_values = {101, 102, 103, 104, 105}; + const std::vector b_values = {"sa", "sb", "sc", "sd", "se"}; + for (size_t row = 0; row < a_values.size(); ++row) { + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(a_values[row]).ok()); + EXPECT_TRUE(b_builder->Append(b_values[row]).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_nullable_struct_array() { + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("b", arrow::utf8(), true)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto b_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(b_array_builder))); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* a_builder = assert_cast(builder.field_builder(0)); + auto* b_builder = assert_cast(builder.field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(201).ok()); + EXPECT_TRUE(b_builder->Append("nsa").ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(203).ok()); + EXPECT_TRUE(b_builder->AppendNull().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(204).ok()); + EXPECT_TRUE(b_builder->Append("nsd").ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_struct_with_decimal_array() { + auto decimal_type = arrow::decimal128(38, 6); + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("d", decimal_type, true)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto d_array_builder = std::make_unique( + decimal_type, arrow::default_memory_pool()); + field_builders.push_back(std::shared_ptr(std::move(d_array_builder))); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* a_builder = assert_cast(builder.field_builder(0)); + auto* d_builder = assert_cast(builder.field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(301).ok()); + EXPECT_TRUE(d_builder->Append(arrow::Decimal128(123456789)).ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(303).ok()); + EXPECT_TRUE(d_builder->AppendNull().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(304).ok()); + EXPECT_TRUE(d_builder->Append(arrow::Decimal128(-987654321)).ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_struct_with_list_array() { + auto list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("xs", list_type, true)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto value_builder = std::make_shared(); + auto list_builder = std::make_shared(arrow::default_memory_pool(), + value_builder, list_type); + field_builders.push_back(list_builder); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* a_builder = assert_cast(builder.field_builder(0)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(301).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(1).ok()); + EXPECT_TRUE(value_builder->Append(2).ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(303).ok()); + EXPECT_TRUE(list_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(304).ok()); + EXPECT_TRUE(list_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(305).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(value_builder->Append(5).ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_struct_with_map_array() { + auto map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("kv", map_type, true)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto key_builder = std::make_shared(); + auto value_builder = std::make_shared(); + auto map_builder = std::make_shared( + arrow::default_memory_pool(), key_builder, value_builder, map_type); + field_builders.push_back(map_builder); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* a_builder = assert_cast(builder.field_builder(0)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(401).ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(1).ok()); + EXPECT_TRUE(value_builder->Append("one").ok()); + EXPECT_TRUE(key_builder->Append(2).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(403).ok()); + EXPECT_TRUE(map_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(404).ok()); + EXPECT_TRUE(map_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(a_builder->Append(405).ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(5).ok()); + EXPECT_TRUE(value_builder->Append("five").ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_struct_with_nested_struct_list_array() { + auto list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + auto nested_type = arrow::struct_({arrow::field("xs", list_type, true)}); + auto struct_type = arrow::struct_({arrow::field("nested", nested_type, true)}); + + auto value_builder = std::make_shared(); + auto list_builder = std::make_shared(arrow::default_memory_pool(), + value_builder, list_type); + std::vector> nested_field_builders; + nested_field_builders.push_back(list_builder); + auto nested_builder = std::make_shared( + nested_type, arrow::default_memory_pool(), std::move(nested_field_builders)); + std::vector> field_builders; + field_builders.push_back(nested_builder); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(nested_builder->Append().ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(7).ok()); + EXPECT_TRUE(value_builder->Append(8).ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(nested_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(nested_builder->Append().ok()); + EXPECT_TRUE(list_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(nested_builder->Append().ok()); + EXPECT_TRUE(list_builder->AppendEmptyValue().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_required_int_list_array() { + auto value_builder = std::make_shared(); + arrow::ListBuilder builder(arrow::default_memory_pool(), value_builder, + arrow::list(arrow::field("element", arrow::int32(), false))); + const std::vector> values = { + {1, 2}, {3}, {4, 5, 6}, {7}, {8, 9}, + }; + for (const auto& row : values) { + EXPECT_TRUE(builder.Append().ok()); + for (const auto value : row) { + EXPECT_TRUE(value_builder->Append(value).ok()); + } + } + return finish_array(&builder); + } + + std::shared_ptr build_nullable_int_list_array() { + auto value_builder = std::make_shared(); + arrow::ListBuilder builder(arrow::default_memory_pool(), value_builder, + arrow::list(arrow::field("element", arrow::int32(), true))); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(value_builder->Append(10).ok()); + EXPECT_TRUE(value_builder->Append(20).ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(value_builder->Append(30).ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(value_builder->Append(40).ok()); + return finish_array(&builder); + } + + std::shared_ptr build_required_nullable_int_list_array() { + auto value_builder = std::make_shared(); + arrow::ListBuilder builder(arrow::default_memory_pool(), value_builder, + arrow::list(arrow::field("element", arrow::int32(), true))); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(value_builder->Append(110).ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(value_builder->Append(120).ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(value_builder->Append(130).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(builder.Append().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_struct_list_array() { + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("b", arrow::utf8(), true)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto b_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(b_array_builder))); + auto struct_builder = std::make_shared( + struct_type, arrow::default_memory_pool(), std::move(field_builders)); + arrow::ListBuilder builder(arrow::default_memory_pool(), struct_builder, + arrow::list(arrow::field("element", struct_type, true))); + auto* a_builder = assert_cast(struct_builder->field_builder(0)); + auto* b_builder = assert_cast(struct_builder->field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(11).ok()); + EXPECT_TRUE(b_builder->Append("la").ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(12).ok()); + EXPECT_TRUE(b_builder->AppendNull().ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->AppendNull().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(13).ok()); + EXPECT_TRUE(b_builder->Append("ld").ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(14).ok()); + EXPECT_TRUE(b_builder->Append("le").ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_list_list_int_array() { + auto value_builder = std::make_shared(); + auto inner_list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + auto inner_list_builder = std::make_shared( + arrow::default_memory_pool(), value_builder, inner_list_type); + arrow::ListBuilder builder(arrow::default_memory_pool(), inner_list_builder, + arrow::list(arrow::field("element", inner_list_type, true))); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(inner_list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(1).ok()); + EXPECT_TRUE(value_builder->Append(2).ok()); + EXPECT_TRUE(inner_list_builder->AppendEmptyValue().ok()); + EXPECT_TRUE(inner_list_builder->AppendNull().ok()); + EXPECT_TRUE(inner_list_builder->Append().ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(value_builder->Append(3).ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(inner_list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(4).ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(inner_list_builder->AppendEmptyValue().ok()); + EXPECT_TRUE(inner_list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(5).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_required_int_string_map_array() { + auto key_builder = std::make_shared(); + auto value_builder = std::make_shared(); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), false)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, value_builder, + map_type); + const std::vector>> values = { + {{1, "a"}, {2, "b"}}, {{3, "c"}}, {{4, "d"}, {5, "e"}, {6, "f"}}, + {{7, "g"}}, {{8, "h"}, {9, "i"}}, + }; + for (const auto& row : values) { + EXPECT_TRUE(builder.Append().ok()); + for (const auto& [key, value] : row) { + EXPECT_TRUE(key_builder->Append(key).ok()); + EXPECT_TRUE(value_builder->Append(value).ok()); + } + } + return finish_array(&builder); + } + + std::shared_ptr build_nullable_int_string_map_array() { + auto key_builder = std::make_shared(); + auto value_builder = std::make_shared(); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, value_builder, + map_type); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(10).ok()); + EXPECT_TRUE(value_builder->Append("aa").ok()); + EXPECT_TRUE(key_builder->Append(20).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(30).ok()); + EXPECT_TRUE(value_builder->Append("cc").ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(40).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_required_nullable_string_map_array() { + auto key_builder = std::make_shared(); + auto value_builder = std::make_shared(); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, value_builder, + map_type); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(101).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(102).ok()); + EXPECT_TRUE(value_builder->Append("bb").ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(103).ok()); + EXPECT_TRUE(value_builder->Append("cc").ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(104).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_int_struct_map_array() { + auto key_builder = std::make_shared(); + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("b", arrow::utf8(), true)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto b_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(b_array_builder))); + auto value_builder = std::make_shared( + struct_type, arrow::default_memory_pool(), std::move(field_builders)); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", struct_type, true)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, value_builder, + map_type); + auto* a_builder = assert_cast(value_builder->field_builder(0)); + auto* b_builder = assert_cast(value_builder->field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(101).ok()); + EXPECT_TRUE(value_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(21).ok()); + EXPECT_TRUE(b_builder->Append("ma").ok()); + EXPECT_TRUE(key_builder->Append(102).ok()); + EXPECT_TRUE(value_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(22).ok()); + EXPECT_TRUE(b_builder->AppendNull().ok()); + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(103).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(104).ok()); + EXPECT_TRUE(value_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(24).ok()); + EXPECT_TRUE(b_builder->Append("me").ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_int_list_map_array() { + auto key_builder = std::make_shared(); + auto value_builder = std::make_shared(); + auto list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + auto list_builder = std::make_shared(arrow::default_memory_pool(), + value_builder, list_type); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", list_type, true)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, list_builder, + map_type); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(201).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(1).ok()); + EXPECT_TRUE(value_builder->Append(2).ok()); + EXPECT_TRUE(key_builder->Append(202).ok()); + EXPECT_TRUE(list_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(203).ok()); + EXPECT_TRUE(list_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(204).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(value_builder->Append(3).ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(205).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(value_builder->Append(4).ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_map_list_array() { + auto key_builder = std::make_shared(); + auto value_builder = std::make_shared(); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + auto map_builder = std::make_shared( + arrow::default_memory_pool(), key_builder, value_builder, map_type); + arrow::ListBuilder builder(arrow::default_memory_pool(), map_builder, + arrow::list(arrow::field("element", map_type, true))); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(1).ok()); + EXPECT_TRUE(value_builder->Append("a").ok()); + EXPECT_TRUE(key_builder->Append(2).ok()); + EXPECT_TRUE(value_builder->AppendNull().ok()); + EXPECT_TRUE(map_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(map_builder->AppendNull().ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(3).ok()); + EXPECT_TRUE(value_builder->Append("c").ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(4).ok()); + EXPECT_TRUE(value_builder->Append("d").ok()); + return finish_array(&builder); + } + + std::shared_ptr build_nullable_int_map_map_array() { + auto key_builder = std::make_shared(); + auto nested_key_builder = std::make_shared(); + auto nested_value_builder = std::make_shared(); + auto nested_map_type = + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + auto nested_map_builder = std::make_shared( + arrow::default_memory_pool(), nested_key_builder, nested_value_builder, + nested_map_type); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", nested_map_type, true)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, nested_map_builder, + map_type); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(10).ok()); + EXPECT_TRUE(nested_map_builder->Append().ok()); + EXPECT_TRUE(nested_key_builder->Append(101).ok()); + EXPECT_TRUE(nested_value_builder->Append("aa").ok()); + EXPECT_TRUE(key_builder->Append(20).ok()); + EXPECT_TRUE(nested_map_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(30).ok()); + EXPECT_TRUE(nested_map_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(40).ok()); + EXPECT_TRUE(nested_map_builder->Append().ok()); + EXPECT_TRUE(nested_key_builder->Append(401).ok()); + EXPECT_TRUE(nested_value_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + return finish_array(&builder); + } + + std::shared_ptr build_deep_list_struct_map_list_array() { + auto element_builder = std::make_shared(); + auto list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + auto list_builder = std::make_shared(arrow::default_memory_pool(), + element_builder, list_type); + auto key_builder = std::make_shared(); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", list_type, true)); + auto map_builder = std::make_shared(arrow::default_memory_pool(), + key_builder, list_builder, map_type); + auto struct_type = arrow::struct_({arrow::field("kv", map_type, true)}); + std::vector> struct_field_builders; + struct_field_builders.push_back(map_builder); + auto struct_builder = std::make_shared( + struct_type, arrow::default_memory_pool(), std::move(struct_field_builders)); + arrow::ListBuilder builder(arrow::default_memory_pool(), struct_builder, + arrow::list(arrow::field("element", struct_type, true))); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(1).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(element_builder->Append(10).ok()); + EXPECT_TRUE(element_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(2).ok()); + EXPECT_TRUE(list_builder->AppendEmptyValue().ok()); + EXPECT_TRUE(struct_builder->AppendNull().ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(map_builder->AppendNull().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(map_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(map_builder->Append().ok()); + EXPECT_TRUE(key_builder->Append(3).ok()); + EXPECT_TRUE(list_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(4).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(element_builder->Append(40).ok()); + return finish_array(&builder); + } + + std::shared_ptr build_deep_map_list_map_array() { + auto nested_key_builder = std::make_shared(); + auto nested_value_builder = std::make_shared(); + auto nested_map_type = + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + auto nested_map_builder = std::make_shared( + arrow::default_memory_pool(), nested_key_builder, nested_value_builder, + nested_map_type); + auto list_type = arrow::list(arrow::field("element", nested_map_type, true)); + auto list_builder = std::make_shared(arrow::default_memory_pool(), + nested_map_builder, list_type); + auto key_builder = std::make_shared(); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", list_type, true)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, list_builder, + map_type); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(10).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(nested_map_builder->Append().ok()); + EXPECT_TRUE(nested_key_builder->Append(1).ok()); + EXPECT_TRUE(nested_value_builder->Append("a").ok()); + EXPECT_TRUE(nested_key_builder->Append(2).ok()); + EXPECT_TRUE(nested_value_builder->AppendNull().ok()); + EXPECT_TRUE(nested_map_builder->AppendEmptyValue().ok()); + EXPECT_TRUE(nested_map_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(20).ok()); + EXPECT_TRUE(list_builder->AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.AppendNull().ok()); + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(30).ok()); + EXPECT_TRUE(list_builder->AppendNull().ok()); + EXPECT_TRUE(key_builder->Append(40).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(nested_map_builder->Append().ok()); + EXPECT_TRUE(nested_key_builder->Append(3).ok()); + EXPECT_TRUE(nested_value_builder->Append("c").ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(50).ok()); + EXPECT_TRUE(list_builder->Append().ok()); + EXPECT_TRUE(nested_map_builder->AppendNull().ok()); + EXPECT_TRUE(nested_map_builder->Append().ok()); + EXPECT_TRUE(nested_key_builder->Append(4).ok()); + EXPECT_TRUE(nested_value_builder->Append("d").ok()); + return finish_array(&builder); + } + + std::shared_ptr build_time32_array(const std::shared_ptr& type, + const std::vector& values) { + arrow::Time32Builder builder(type, arrow::default_memory_pool()); + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_time64_array(const std::shared_ptr& type, + const std::vector& values) { + arrow::Time64Builder builder(type, arrow::default_memory_pool()); + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_timestamp_array( + const std::shared_ptr& type, const std::vector& values) { + arrow::TimestampBuilder builder(type, arrow::default_memory_pool()); + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); + } + + std::shared_ptr build_decimal_array(const std::shared_ptr& type, + const std::vector& values) { + arrow::Decimal128Builder builder(type, arrow::default_memory_pool()); + for (const auto value : values) { + EXPECT_TRUE(builder.Append(arrow::Decimal128(value)).ok()); + } + return finish_array(&builder); + } + + void add_field(const std::shared_ptr& field, std::shared_ptr array, + std::function validator) { + _arrow_fields.push_back(field); + _arrays.push_back(std::move(array)); + _expected_by_field.push_back(std::move(validator)); + } + + void write_parquet_file() { + add_field( + arrow::field("bool_col", arrow::boolean(), false), + build_required_array({true, false, true, false, true}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::BOOLEAN); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(0), 1); + EXPECT_EQ(values.get_element(1), 0); + EXPECT_EQ(values.get_element(4), 1); + }); + add_field(arrow::field("int32_col", arrow::int32(), false), + build_required_array({10, 20, 30, 40, 50}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT32); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(0), 10); + EXPECT_EQ(values.get_element(4), 50); + }); + add_field(arrow::field("int64_col", arrow::int64(), false), + build_required_array( + {10000000000L, -9L, 42L, 77L, 123L}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(0), 10000000000L); + EXPECT_EQ(values.get_element(1), -9L); + }); + add_field(arrow::field("uint32_col", arrow::uint32(), false), + build_required_array( + {0U, 1U, 1U << 31, std::numeric_limits::max(), 42U}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT32); + EXPECT_TRUE(schema.type_descriptor.is_unsigned_integer); + EXPECT_EQ(schema.type_descriptor.integer_bit_width, 32); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_BIGINT); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(2), 2147483648L); + EXPECT_EQ(values.get_element(3), + static_cast(std::numeric_limits::max())); + }); + add_field(arrow::field("uint64_col", arrow::uint64(), false), + build_required_array( + {0ULL, 1ULL, 1ULL << 63, std::numeric_limits::max(), 42ULL}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + EXPECT_TRUE(schema.type_descriptor.is_unsigned_integer); + EXPECT_EQ(schema.type_descriptor.integer_bit_width, 64); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_LARGEINT); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(2), static_cast(1) << 63); + EXPECT_EQ(values.get_element(3), + static_cast(std::numeric_limits::max())); + }); + add_field( + arrow::field("float_col", arrow::float32(), false), + build_required_array({1.5F, -2.25F, 3.0F, 4.5F, 5.75F}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::FLOAT); + const auto& values = assert_cast(column); + EXPECT_FLOAT_EQ(values.get_element(0), 1.5F); + EXPECT_FLOAT_EQ(values.get_element(1), -2.25F); + }); + add_field(arrow::field("double_col", arrow::float64(), false), + build_required_array({3.5, -4.75, 6.0, 7.25, 8.5}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::DOUBLE); + const auto& values = assert_cast(column); + EXPECT_DOUBLE_EQ(values.get_element(0), 3.5); + EXPECT_DOUBLE_EQ(values.get_element(1), -4.75); + }); + add_field(arrow::field("nullable_float16_col", arrow::float16(), true), + build_nullable_float16_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY); + EXPECT_EQ(schema.type_descriptor.fixed_length, 2); + EXPECT_EQ(schema.type_descriptor.extra_type_info, + ParquetExtraTypeInfo::FLOAT16); + EXPECT_FALSE(schema.type_descriptor.is_string_like); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_FLOAT); + + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& values = assert_cast( + nullable_column.get_nested_column()); + EXPECT_FLOAT_EQ(values.get_element(1), 0.0F); + EXPECT_FALSE(std::signbit(values.get_element(1))); + EXPECT_FLOAT_EQ(values.get_element(2), -0.0F); + EXPECT_TRUE(std::signbit(values.get_element(2))); + EXPECT_FLOAT_EQ(values.get_element(3), 1.5F); + EXPECT_TRUE(std::isnan(values.get_element(4))); + }); + add_field(arrow::field("binary_col", arrow::binary(), false), + build_binary_array({"bin_a", "bin_b", "bin_c", "bin_d", "bin_e"}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::BYTE_ARRAY); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_data_at(0).to_string(), "bin_a"); + EXPECT_EQ(values.get_data_at(3).to_string(), "bin_d"); + }); + add_field(arrow::field("string_col", arrow::utf8(), false), + build_string_array({"alpha", "beta", "gamma", "delta", "epsilon"}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type_descriptor.is_string_like); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_data_at(0).to_string(), "alpha"); + EXPECT_EQ(values.get_data_at(4).to_string(), "epsilon"); + }); + add_field(arrow::field("fixed_binary_col", arrow::fixed_size_binary(4), false), + build_fixed_binary_array(arrow::fixed_size_binary(4), + {"aaaa", "bbbb", "cccc", "dddd", "eeee"}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY); + EXPECT_EQ(schema.type_descriptor.fixed_length, 4); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_data_at(0).to_string(), "aaaa"); + EXPECT_EQ(values.get_data_at(2).to_string(), "cccc"); + }); + add_field(arrow::field("date_col", arrow::date32(), false), + build_required_array({0, 1, 18628, 18629, 18630}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT32); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_DATEV2); + EXPECT_EQ(schema.type->to_string(column, 0), "1970-01-01"); + EXPECT_EQ(schema.type->to_string(column, 2), "2021-01-01"); + }); + add_field(arrow::field("time_millis_col", arrow::time32(arrow::TimeUnit::MILLI), false), + build_time32_array(arrow::time32(arrow::TimeUnit::MILLI), + {0, 1000, 3723004, 43200000, 86399000}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT32); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_TIMEV2); + EXPECT_EQ(schema.type->to_string(column, 1), "00:00:01.000"); + EXPECT_EQ(schema.type->to_string(column, 2), "01:02:03.004"); + }); + add_field(arrow::field("time_micros_col", arrow::time64(arrow::TimeUnit::MICRO), false), + build_time64_array(arrow::time64(arrow::TimeUnit::MICRO), + {0, 1000000, 3723004567, 43200000000, 86399000000}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_TIMEV2); + EXPECT_EQ(schema.type->to_string(column, 1), "00:00:01.000000"); + EXPECT_EQ(schema.type->to_string(column, 2), "01:02:03.004567"); + }); + add_field(arrow::field("timestamp_millis_col", arrow::timestamp(arrow::TimeUnit::MILLI), + false), + build_timestamp_array(arrow::timestamp(arrow::TimeUnit::MILLI), + {0, 1234, 1609459200000, 1609459201000, -1}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), + TYPE_DATETIMEV2); + EXPECT_EQ(schema.type->to_string(column, 1), "1970-01-01 00:00:01.234"); + EXPECT_EQ(schema.type->to_string(column, 4), "1969-12-31 23:59:59.999"); + }); + add_field(arrow::field("timestamp_micros_col", arrow::timestamp(arrow::TimeUnit::MICRO), + false), + build_timestamp_array(arrow::timestamp(arrow::TimeUnit::MICRO), + {0, 1234567, 1609459200000000, 1609459201000000, -1}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), + TYPE_DATETIMEV2); + EXPECT_EQ(schema.type->to_string(column, 1), "1970-01-01 00:00:01.234567"); + EXPECT_EQ(schema.type->to_string(column, 4), "1969-12-31 23:59:59.999999"); + }); + add_field(arrow::field("decimal_fixed_binary_9_2_col", arrow::decimal128(9, 2), false), + build_decimal_array(arrow::decimal128(9, 2), {12345, -67, 0, 987, 1000}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY); + EXPECT_TRUE(schema.type_descriptor.is_decimal); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_DECIMAL32); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(0), Decimal32(12345)); + EXPECT_EQ(schema.type->to_string(column, 0), "123.45"); + }); + add_field(arrow::field("decimal_fixed_binary_18_6_col", arrow::decimal128(18, 6), false), + build_decimal_array(arrow::decimal128(18, 6), + {1234567, -670000, 0, 9870000, 1000000}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY); + EXPECT_TRUE(schema.type_descriptor.is_decimal); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_DECIMAL64); + const auto& values = assert_cast(column); + EXPECT_EQ(values.get_element(0), Decimal64(1234567)); + EXPECT_EQ(schema.type->to_string(column, 0), "1.234567"); + }); + add_field(arrow::field("nullable_int_col", arrow::int32(), true), + build_nullable_int32_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + const auto& nested_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_TRUE(nullable_column.is_null_at(3)); + EXPECT_EQ(nested_column.get_element(0), 1); + EXPECT_EQ(nested_column.get_element(2), 3); + }); + add_field(arrow::field("struct_col", + arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("b", arrow::utf8(), false), + }), + false), + build_required_struct_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_STRUCT); + const auto& struct_column = assert_cast(column); + ASSERT_EQ(struct_column.get_columns().size(), 2); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + const auto& b_values = + assert_cast(struct_column.get_column(1)); + EXPECT_EQ(a_values.get_element(0), 101); + EXPECT_EQ(a_values.get_element(4), 105); + EXPECT_EQ(b_values.get_data_at(1).to_string(), "sb"); + EXPECT_EQ(b_values.get_data_at(4).to_string(), "se"); + }); + add_field(arrow::field("nullable_struct_col", + arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("b", arrow::utf8(), true), + }), + true), + build_nullable_struct_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_TRUE(nullable_column.is_null_at(4)); + + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 2); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + const auto& b_values = + assert_cast(struct_column.get_column(1)); + const auto& b_nested = + assert_cast(b_values.get_nested_column()); + EXPECT_EQ(a_values.get_element(0), 201); + EXPECT_EQ(a_values.get_element(2), 203); + EXPECT_EQ(a_values.get_element(3), 204); + EXPECT_FALSE(b_values.is_null_at(0)); + EXPECT_TRUE(b_values.is_null_at(2)); + EXPECT_FALSE(b_values.is_null_at(3)); + EXPECT_EQ(b_nested.get_data_at(0).to_string(), "nsa"); + EXPECT_EQ(b_nested.get_data_at(3).to_string(), "nsd"); + }); + add_field(arrow::field("nullable_struct_decimal_col", + arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("d", arrow::decimal128(38, 6), true), + }), + true), + build_nullable_struct_with_decimal_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_TRUE(nullable_column.is_null_at(4)); + + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 2); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + const auto& d_values = + assert_cast(struct_column.get_column(1)); + const auto& d_nested = + assert_cast(d_values.get_nested_column()); + EXPECT_EQ(a_values.get_element(0), 301); + EXPECT_EQ(a_values.get_element(2), 303); + EXPECT_EQ(a_values.get_element(3), 304); + EXPECT_FALSE(d_values.is_null_at(0)); + EXPECT_TRUE(d_values.is_null_at(2)); + EXPECT_FALSE(d_values.is_null_at(3)); + EXPECT_EQ(d_nested.get_element(0), Decimal128V3(123456789)); + EXPECT_EQ(d_nested.get_element(3), Decimal128V3(-987654321)); + }); + auto struct_list_type = arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("xs", arrow::list(arrow::field("element", arrow::int32(), true)), + true), + }); + add_field(arrow::field("nullable_struct_list_col", struct_list_type, true), + build_nullable_struct_with_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 2); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + EXPECT_EQ(a_values.get_element(0), 301); + EXPECT_EQ(a_values.get_element(2), 303); + EXPECT_EQ(a_values.get_element(3), 304); + EXPECT_EQ(a_values.get_element(4), 305); + + const auto& xs_nullable = + assert_cast(struct_column.get_column(1)); + ASSERT_EQ(xs_nullable.size(), ROW_COUNT); + EXPECT_FALSE(xs_nullable.is_null_at(0)); + EXPECT_FALSE(xs_nullable.is_null_at(2)); + EXPECT_TRUE(xs_nullable.is_null_at(3)); + EXPECT_FALSE(xs_nullable.is_null_at(4)); + const auto& xs_array = + assert_cast(xs_nullable.get_nested_column()); + const auto& offsets = xs_array.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 2); + EXPECT_EQ(offsets[4], 4); + const auto& elements = + assert_cast(xs_array.get_data()); + ASSERT_EQ(elements.size(), 4); + EXPECT_FALSE(elements.is_null_at(0)); + EXPECT_FALSE(elements.is_null_at(1)); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_FALSE(elements.is_null_at(3)); + const auto& values = + assert_cast(elements.get_nested_column()); + EXPECT_EQ(values.get_element(0), 1); + EXPECT_EQ(values.get_element(1), 2); + EXPECT_EQ(values.get_element(3), 5); + }); + auto struct_map_type = arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("kv", + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)), + true), + }); + add_field(arrow::field("nullable_struct_map_col", struct_map_type, true), + build_nullable_struct_with_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 2); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + EXPECT_EQ(a_values.get_element(0), 401); + EXPECT_EQ(a_values.get_element(2), 403); + EXPECT_EQ(a_values.get_element(3), 404); + EXPECT_EQ(a_values.get_element(4), 405); + + const auto& kv_nullable = + assert_cast(struct_column.get_column(1)); + ASSERT_EQ(kv_nullable.size(), ROW_COUNT); + EXPECT_FALSE(kv_nullable.is_null_at(0)); + EXPECT_FALSE(kv_nullable.is_null_at(2)); + EXPECT_TRUE(kv_nullable.is_null_at(3)); + EXPECT_FALSE(kv_nullable.is_null_at(4)); + const auto& kv_map = + assert_cast(kv_nullable.get_nested_column()); + const auto& offsets = kv_map.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 2); + EXPECT_EQ(offsets[4], 3); + const auto& keys = assert_cast(kv_map.get_keys()); + const auto& values = assert_cast(kv_map.get_values()); + const auto& value_data = + assert_cast(values.get_nested_column()); + ASSERT_EQ(keys.size(), 3); + ASSERT_EQ(values.size(), 3); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 5); + EXPECT_EQ(value_data.get_data_at(0).to_string(), "one"); + EXPECT_TRUE(values.is_null_at(1)); + EXPECT_EQ(value_data.get_data_at(2).to_string(), "five"); + }); + auto nested_struct_list_type = arrow::struct_({ + arrow::field("nested", + arrow::struct_({ + arrow::field("xs", + arrow::list(arrow::field("element", + arrow::int32(), true)), + true), + }), + true), + }); + add_field(arrow::field("nullable_struct_nested_struct_list_col", nested_struct_list_type, + true), + build_nullable_struct_with_nested_struct_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& nested_nullable = + assert_cast(struct_column.get_column(0)); + EXPECT_FALSE(nested_nullable.is_null_at(0)); + EXPECT_TRUE(nested_nullable.is_null_at(2)); + EXPECT_FALSE(nested_nullable.is_null_at(3)); + EXPECT_FALSE(nested_nullable.is_null_at(4)); + }); + add_field(arrow::field("list_int_col", + arrow::list(arrow::field("element", arrow::int32(), false)), false), + build_required_int_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_ARRAY); + const auto* array_type = + assert_cast(remove_nullable(schema.type).get()); + EXPECT_EQ( + remove_nullable(array_type->get_nested_type())->get_primitive_type(), + TYPE_INT); + const auto& array_column = assert_cast(column); + ASSERT_EQ(array_column.size(), ROW_COUNT); + const auto array_size_at = [&array_column](size_t row_idx) { + return array_column.get_offsets()[row_idx] - + (row_idx == 0 ? 0 : array_column.get_offsets()[row_idx - 1]); + }; + EXPECT_EQ(array_size_at(0), 2); + EXPECT_EQ(array_size_at(1), 1); + EXPECT_EQ(array_size_at(2), 3); + EXPECT_EQ(array_size_at(4), 2); + const auto& values = assert_cast(array_column.get_data()); + ASSERT_EQ(values.size(), 9); + EXPECT_EQ(values.get_element(0), 1); + EXPECT_EQ(values.get_element(5), 6); + EXPECT_EQ(values.get_element(8), 9); + }); + add_field(arrow::field("nullable_list_int_col", + arrow::list(arrow::field("element", arrow::int32(), true)), true), + build_nullable_int_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + const auto& array_column = + assert_cast(nullable_column.get_nested_column()); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 4); + EXPECT_EQ(offsets[4], 5); + const auto& elements = + assert_cast(array_column.get_data()); + const auto& values = + assert_cast(elements.get_nested_column()); + ASSERT_EQ(elements.size(), 5); + EXPECT_EQ(values.get_element(0), 10); + EXPECT_EQ(values.get_element(1), 20); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_EQ(values.get_element(3), 30); + EXPECT_EQ(values.get_element(4), 40); + }); + add_field(arrow::field("required_nullable_list_int_col", + arrow::list(arrow::field("element", arrow::int32(), true)), false), + build_required_nullable_int_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_FALSE(schema.type->is_nullable()); + const auto& array_column = assert_cast(column); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 3); + EXPECT_EQ(offsets[3], 5); + EXPECT_EQ(offsets[4], 5); + const auto& elements = + assert_cast(array_column.get_data()); + ASSERT_EQ(elements.size(), 5); + EXPECT_TRUE(elements.is_null_at(0)); + EXPECT_FALSE(elements.is_null_at(1)); + EXPECT_TRUE(elements.is_null_at(4)); + }); + auto list_struct_type = arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("b", arrow::utf8(), true), + }); + add_field(arrow::field("nullable_list_struct_col", + arrow::list(arrow::field("element", list_struct_type, true)), true), + build_nullable_struct_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& array_column = + assert_cast(nullable_column.get_nested_column()); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 4); + EXPECT_EQ(offsets[4], 5); + + const auto& elements = + assert_cast(array_column.get_data()); + const auto& struct_column = + assert_cast(elements.get_nested_column()); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + const auto& b_values = + assert_cast(struct_column.get_column(1)); + const auto& b_data = + assert_cast(b_values.get_nested_column()); + ASSERT_EQ(elements.size(), 5); + EXPECT_FALSE(elements.is_null_at(0)); + EXPECT_FALSE(elements.is_null_at(1)); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_FALSE(elements.is_null_at(3)); + EXPECT_EQ(a_values.get_element(0), 11); + EXPECT_EQ(a_values.get_element(1), 12); + EXPECT_EQ(a_values.get_element(3), 13); + EXPECT_EQ(a_values.get_element(4), 14); + EXPECT_EQ(b_data.get_data_at(0).to_string(), "la"); + EXPECT_TRUE(b_values.is_null_at(1)); + EXPECT_EQ(b_data.get_data_at(3).to_string(), "ld"); + EXPECT_EQ(b_data.get_data_at(4).to_string(), "le"); + }); + auto nested_list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + add_field(arrow::field("nullable_list_list_int_col", + arrow::list(arrow::field("element", nested_list_type, true)), true), + build_nullable_list_list_int_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& outer_array = + assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), ROW_COUNT); + EXPECT_EQ(outer_offsets[0], 4); + EXPECT_EQ(outer_offsets[1], 4); + EXPECT_EQ(outer_offsets[2], 4); + EXPECT_EQ(outer_offsets[3], 5); + EXPECT_EQ(outer_offsets[4], 7); + + const auto& inner_nullable = + assert_cast(outer_array.get_data()); + ASSERT_EQ(inner_nullable.size(), 7); + EXPECT_FALSE(inner_nullable.is_null_at(0)); + EXPECT_FALSE(inner_nullable.is_null_at(1)); + EXPECT_TRUE(inner_nullable.is_null_at(2)); + EXPECT_FALSE(inner_nullable.is_null_at(3)); + EXPECT_FALSE(inner_nullable.is_null_at(6)); + + const auto& inner_array = + assert_cast(inner_nullable.get_nested_column()); + const auto& inner_offsets = inner_array.get_offsets(); + ASSERT_EQ(inner_offsets.size(), 7); + EXPECT_EQ(inner_offsets[0], 2); + EXPECT_EQ(inner_offsets[1], 2); + EXPECT_EQ(inner_offsets[2], 2); + EXPECT_EQ(inner_offsets[3], 4); + EXPECT_EQ(inner_offsets[4], 5); + EXPECT_EQ(inner_offsets[5], 5); + EXPECT_EQ(inner_offsets[6], 7); + + const auto& elements = + assert_cast(inner_array.get_data()); + const auto& values = + assert_cast(elements.get_nested_column()); + ASSERT_EQ(elements.size(), 7); + EXPECT_EQ(values.get_element(0), 1); + EXPECT_EQ(values.get_element(1), 2); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_EQ(values.get_element(3), 3); + EXPECT_EQ(values.get_element(4), 4); + EXPECT_EQ(values.get_element(5), 5); + EXPECT_TRUE(elements.is_null_at(6)); + }); + add_field(arrow::field( + "map_int_string_col", + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), false)), + false), + build_required_int_string_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), TYPE_MAP); + const auto* map_type = + assert_cast(remove_nullable(schema.type).get()); + EXPECT_EQ(remove_nullable(map_type->get_key_type())->get_primitive_type(), + TYPE_INT); + EXPECT_EQ(remove_nullable(map_type->get_value_type())->get_primitive_type(), + TYPE_STRING); + const auto& map_column = assert_cast(column); + ASSERT_EQ(map_column.size(), ROW_COUNT); + const auto map_size_at = [&map_column](size_t row_idx) { + return map_column.get_offsets()[row_idx] - + (row_idx == 0 ? 0 : map_column.get_offsets()[row_idx - 1]); + }; + EXPECT_EQ(map_size_at(0), 2); + EXPECT_EQ(map_size_at(1), 1); + EXPECT_EQ(map_size_at(2), 3); + EXPECT_EQ(map_size_at(4), 2); + const auto& keys = assert_cast(map_column.get_keys()); + const auto& values = + assert_cast(map_column.get_values()); + ASSERT_EQ(keys.size(), 9); + ASSERT_EQ(values.size(), 9); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(5), 6); + EXPECT_EQ(keys.get_element(8), 9); + EXPECT_EQ(values.get_data_at(0).to_string(), "a"); + EXPECT_EQ(values.get_data_at(5).to_string(), "f"); + EXPECT_EQ(values.get_data_at(8).to_string(), "i"); + }); + add_field( + arrow::field("nullable_map_int_string_col", + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)), + true), + build_nullable_int_string_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& map_column = + assert_cast(nullable_column.get_nested_column()); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 3); + EXPECT_EQ(offsets[4], 4); + const auto& keys = assert_cast(map_column.get_keys()); + const auto& values = + assert_cast(map_column.get_values()); + const auto& value_data = + assert_cast(values.get_nested_column()); + ASSERT_EQ(keys.size(), 4); + EXPECT_EQ(keys.get_element(0), 10); + EXPECT_EQ(keys.get_element(1), 20); + EXPECT_EQ(keys.get_element(3), 40); + EXPECT_EQ(value_data.get_data_at(0).to_string(), "aa"); + EXPECT_TRUE(values.is_null_at(1)); + EXPECT_EQ(value_data.get_data_at(2).to_string(), "cc"); + EXPECT_TRUE(values.is_null_at(3)); + }); + add_field( + arrow::field("required_nullable_map_int_string_col", + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)), + false), + build_required_nullable_string_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_FALSE(schema.type->is_nullable()); + const auto& map_column = assert_cast(column); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 3); + EXPECT_EQ(offsets[3], 3); + EXPECT_EQ(offsets[4], 4); + const auto& values = + assert_cast(map_column.get_values()); + ASSERT_EQ(values.size(), 4); + EXPECT_TRUE(values.is_null_at(0)); + EXPECT_FALSE(values.is_null_at(1)); + EXPECT_TRUE(values.is_null_at(3)); + }); + auto map_struct_type = arrow::struct_({ + arrow::field("a", arrow::int32(), false), + arrow::field("b", arrow::utf8(), true), + }); + add_field(arrow::field( + "nullable_map_int_struct_col", + arrow::map(arrow::int32(), arrow::field("value", map_struct_type, true)), + true), + build_nullable_int_struct_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& map_column = + assert_cast(nullable_column.get_nested_column()); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 3); + EXPECT_EQ(offsets[4], 4); + + const auto& keys = assert_cast(map_column.get_keys()); + const auto& values = + assert_cast(map_column.get_values()); + const auto& struct_column = + assert_cast(values.get_nested_column()); + const auto& a_values = + assert_cast(struct_column.get_column(0)); + const auto& b_values = + assert_cast(struct_column.get_column(1)); + const auto& b_data = + assert_cast(b_values.get_nested_column()); + ASSERT_EQ(keys.size(), 4); + ASSERT_EQ(values.size(), 4); + EXPECT_EQ(keys.get_element(0), 101); + EXPECT_EQ(keys.get_element(1), 102); + EXPECT_EQ(keys.get_element(3), 104); + EXPECT_FALSE(values.is_null_at(0)); + EXPECT_FALSE(values.is_null_at(1)); + EXPECT_TRUE(values.is_null_at(2)); + EXPECT_FALSE(values.is_null_at(3)); + EXPECT_EQ(a_values.get_element(0), 21); + EXPECT_EQ(a_values.get_element(1), 22); + EXPECT_EQ(a_values.get_element(3), 24); + EXPECT_EQ(b_data.get_data_at(0).to_string(), "ma"); + EXPECT_TRUE(b_values.is_null_at(1)); + EXPECT_EQ(b_data.get_data_at(3).to_string(), "me"); + }); + auto map_list_type = arrow::list(arrow::field("element", arrow::int32(), true)); + add_field( + arrow::field("nullable_map_int_list_col", + arrow::map(arrow::int32(), arrow::field("value", map_list_type, true)), + true), + build_nullable_int_list_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& map_column = + assert_cast(nullable_column.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), ROW_COUNT); + EXPECT_EQ(map_offsets[0], 2); + EXPECT_EQ(map_offsets[1], 2); + EXPECT_EQ(map_offsets[2], 2); + EXPECT_EQ(map_offsets[3], 4); + EXPECT_EQ(map_offsets[4], 5); + + const auto& keys = assert_cast(map_column.get_keys()); + ASSERT_EQ(keys.size(), 5); + EXPECT_EQ(keys.get_element(0), 201); + EXPECT_EQ(keys.get_element(1), 202); + EXPECT_EQ(keys.get_element(2), 203); + EXPECT_EQ(keys.get_element(3), 204); + EXPECT_EQ(keys.get_element(4), 205); + + const auto& values = + assert_cast(map_column.get_values()); + ASSERT_EQ(values.size(), 5); + EXPECT_FALSE(values.is_null_at(0)); + EXPECT_FALSE(values.is_null_at(1)); + EXPECT_TRUE(values.is_null_at(2)); + EXPECT_FALSE(values.is_null_at(3)); + EXPECT_FALSE(values.is_null_at(4)); + + const auto& list_column = + assert_cast(values.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 5); + EXPECT_EQ(list_offsets[0], 2); + EXPECT_EQ(list_offsets[1], 2); + EXPECT_EQ(list_offsets[2], 2); + EXPECT_EQ(list_offsets[3], 4); + EXPECT_EQ(list_offsets[4], 5); + + const auto& elements = + assert_cast(list_column.get_data()); + const auto& element_values = + assert_cast(elements.get_nested_column()); + ASSERT_EQ(elements.size(), 5); + EXPECT_EQ(element_values.get_element(0), 1); + EXPECT_EQ(element_values.get_element(1), 2); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_EQ(element_values.get_element(3), 3); + EXPECT_EQ(element_values.get_element(4), 4); + }); + auto list_map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + add_field(arrow::field("nullable_list_map_int_string_col", + arrow::list(arrow::field("element", list_map_type, true)), true), + build_nullable_map_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& outer_array = + assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), ROW_COUNT); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 2); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 4); + EXPECT_EQ(outer_offsets[4], 5); + + const auto& map_values = + assert_cast(outer_array.get_data()); + ASSERT_EQ(map_values.size(), 5); + EXPECT_FALSE(map_values.is_null_at(0)); + EXPECT_FALSE(map_values.is_null_at(1)); + EXPECT_TRUE(map_values.is_null_at(2)); + EXPECT_FALSE(map_values.is_null_at(3)); + EXPECT_FALSE(map_values.is_null_at(4)); + + const auto& map_column = + assert_cast(map_values.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), 5); + EXPECT_EQ(map_offsets[0], 2); + EXPECT_EQ(map_offsets[1], 2); + EXPECT_EQ(map_offsets[2], 2); + EXPECT_EQ(map_offsets[3], 3); + EXPECT_EQ(map_offsets[4], 4); + const auto& keys = assert_cast(map_column.get_keys()); + const auto& values = + assert_cast(map_column.get_values()); + const auto& value_data = + assert_cast(values.get_nested_column()); + ASSERT_EQ(keys.size(), 4); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 3); + EXPECT_EQ(keys.get_element(3), 4); + EXPECT_EQ(value_data.get_data_at(0).to_string(), "a"); + EXPECT_TRUE(values.is_null_at(1)); + EXPECT_EQ(value_data.get_data_at(2).to_string(), "c"); + EXPECT_EQ(value_data.get_data_at(3).to_string(), "d"); + }); + auto nested_map_type = + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + add_field( + arrow::field( + "nullable_map_int_map_int_string_col", + arrow::map(arrow::int32(), arrow::field("value", nested_map_type, true)), + true), + build_nullable_int_map_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& outer_map = + assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_map.get_offsets(); + ASSERT_EQ(outer_offsets.size(), ROW_COUNT); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 2); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 4); + EXPECT_EQ(outer_offsets[4], 4); + + const auto& outer_keys = assert_cast(outer_map.get_keys()); + ASSERT_EQ(outer_keys.size(), 4); + EXPECT_EQ(outer_keys.get_element(0), 10); + EXPECT_EQ(outer_keys.get_element(1), 20); + EXPECT_EQ(outer_keys.get_element(2), 30); + EXPECT_EQ(outer_keys.get_element(3), 40); + + const auto& inner_values = + assert_cast(outer_map.get_values()); + ASSERT_EQ(inner_values.size(), 4); + EXPECT_FALSE(inner_values.is_null_at(0)); + EXPECT_FALSE(inner_values.is_null_at(1)); + EXPECT_TRUE(inner_values.is_null_at(2)); + EXPECT_FALSE(inner_values.is_null_at(3)); + + const auto& inner_map = + assert_cast(inner_values.get_nested_column()); + const auto& inner_offsets = inner_map.get_offsets(); + ASSERT_EQ(inner_offsets.size(), 4); + EXPECT_EQ(inner_offsets[0], 1); + EXPECT_EQ(inner_offsets[1], 1); + EXPECT_EQ(inner_offsets[2], 1); + EXPECT_EQ(inner_offsets[3], 2); + const auto& inner_keys = assert_cast(inner_map.get_keys()); + const auto& inner_strings = + assert_cast(inner_map.get_values()); + const auto& inner_string_data = + assert_cast(inner_strings.get_nested_column()); + ASSERT_EQ(inner_keys.size(), 2); + EXPECT_EQ(inner_keys.get_element(0), 101); + EXPECT_EQ(inner_keys.get_element(1), 401); + EXPECT_EQ(inner_string_data.get_data_at(0).to_string(), "aa"); + EXPECT_TRUE(inner_strings.is_null_at(1)); + }); + auto deep_list_value_type = arrow::list(arrow::field("element", arrow::int32(), true)); + auto deep_list_map_type = + arrow::map(arrow::int32(), arrow::field("value", deep_list_value_type, true)); + auto deep_list_struct_type = arrow::struct_({arrow::field("kv", deep_list_map_type, true)}); + add_field(arrow::field("nullable_list_struct_map_list_col", + arrow::list(arrow::field("element", deep_list_struct_type, true)), + true), + build_deep_list_struct_map_list_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& outer_array = + assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), ROW_COUNT); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 2); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 4); + EXPECT_EQ(outer_offsets[4], 5); + + const auto& struct_values = + assert_cast(outer_array.get_data()); + ASSERT_EQ(struct_values.size(), 5); + EXPECT_FALSE(struct_values.is_null_at(0)); + EXPECT_TRUE(struct_values.is_null_at(1)); + EXPECT_FALSE(struct_values.is_null_at(2)); + EXPECT_FALSE(struct_values.is_null_at(3)); + EXPECT_FALSE(struct_values.is_null_at(4)); + + const auto& struct_column = + assert_cast(struct_values.get_nested_column()); + const auto& map_values = + assert_cast(struct_column.get_column(0)); + ASSERT_EQ(map_values.size(), 5); + EXPECT_FALSE(map_values.is_null_at(0)); + EXPECT_TRUE(map_values.is_null_at(1)); + EXPECT_TRUE(map_values.is_null_at(2)); + EXPECT_FALSE(map_values.is_null_at(3)); + EXPECT_FALSE(map_values.is_null_at(4)); + + const auto& map_column = + assert_cast(map_values.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), 5); + EXPECT_EQ(map_offsets[0], 2); + EXPECT_EQ(map_offsets[1], 2); + EXPECT_EQ(map_offsets[2], 2); + EXPECT_EQ(map_offsets[3], 2); + EXPECT_EQ(map_offsets[4], 4); + const auto& keys = assert_cast(map_column.get_keys()); + ASSERT_EQ(keys.size(), 4); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 3); + EXPECT_EQ(keys.get_element(3), 4); + + const auto& lists = + assert_cast(map_column.get_values()); + ASSERT_EQ(lists.size(), 4); + EXPECT_FALSE(lists.is_null_at(0)); + EXPECT_FALSE(lists.is_null_at(1)); + EXPECT_TRUE(lists.is_null_at(2)); + EXPECT_FALSE(lists.is_null_at(3)); + const auto& list_column = + assert_cast(lists.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 4); + EXPECT_EQ(list_offsets[0], 2); + EXPECT_EQ(list_offsets[1], 2); + EXPECT_EQ(list_offsets[2], 2); + EXPECT_EQ(list_offsets[3], 3); + const auto& elements = + assert_cast(list_column.get_data()); + const auto& element_values = + assert_cast(elements.get_nested_column()); + ASSERT_EQ(elements.size(), 3); + EXPECT_EQ(element_values.get_element(0), 10); + EXPECT_TRUE(elements.is_null_at(1)); + EXPECT_EQ(element_values.get_element(2), 40); + }); + auto deep_map_nested_map_type = + arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); + auto deep_map_list_type = + arrow::list(arrow::field("element", deep_map_nested_map_type, true)); + add_field( + arrow::field( + "nullable_map_int_list_map_int_string_col", + arrow::map(arrow::int32(), arrow::field("value", deep_map_list_type, true)), + true), + build_deep_map_list_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& outer_map = + assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_map.get_offsets(); + ASSERT_EQ(outer_offsets.size(), ROW_COUNT); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 2); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 4); + EXPECT_EQ(outer_offsets[4], 5); + const auto& outer_keys = assert_cast(outer_map.get_keys()); + ASSERT_EQ(outer_keys.size(), 5); + EXPECT_EQ(outer_keys.get_element(0), 10); + EXPECT_EQ(outer_keys.get_element(1), 20); + EXPECT_EQ(outer_keys.get_element(2), 30); + EXPECT_EQ(outer_keys.get_element(3), 40); + EXPECT_EQ(outer_keys.get_element(4), 50); + + const auto& lists = assert_cast(outer_map.get_values()); + ASSERT_EQ(lists.size(), 5); + EXPECT_FALSE(lists.is_null_at(0)); + EXPECT_FALSE(lists.is_null_at(1)); + EXPECT_TRUE(lists.is_null_at(2)); + EXPECT_FALSE(lists.is_null_at(3)); + EXPECT_FALSE(lists.is_null_at(4)); + const auto& list_column = + assert_cast(lists.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 5); + EXPECT_EQ(list_offsets[0], 3); + EXPECT_EQ(list_offsets[1], 3); + EXPECT_EQ(list_offsets[2], 3); + EXPECT_EQ(list_offsets[3], 4); + EXPECT_EQ(list_offsets[4], 6); + + const auto& inner_maps = + assert_cast(list_column.get_data()); + ASSERT_EQ(inner_maps.size(), 6); + EXPECT_FALSE(inner_maps.is_null_at(0)); + EXPECT_FALSE(inner_maps.is_null_at(1)); + EXPECT_TRUE(inner_maps.is_null_at(2)); + EXPECT_FALSE(inner_maps.is_null_at(3)); + EXPECT_TRUE(inner_maps.is_null_at(4)); + EXPECT_FALSE(inner_maps.is_null_at(5)); + const auto& inner_map_column = + assert_cast(inner_maps.get_nested_column()); + const auto& inner_offsets = inner_map_column.get_offsets(); + ASSERT_EQ(inner_offsets.size(), 6); + EXPECT_EQ(inner_offsets[0], 2); + EXPECT_EQ(inner_offsets[1], 2); + EXPECT_EQ(inner_offsets[2], 2); + EXPECT_EQ(inner_offsets[3], 3); + EXPECT_EQ(inner_offsets[4], 3); + EXPECT_EQ(inner_offsets[5], 4); + const auto& inner_keys = + assert_cast(inner_map_column.get_keys()); + ASSERT_EQ(inner_keys.size(), 4); + EXPECT_EQ(inner_keys.get_element(0), 1); + EXPECT_EQ(inner_keys.get_element(1), 2); + EXPECT_EQ(inner_keys.get_element(2), 3); + EXPECT_EQ(inner_keys.get_element(3), 4); + const auto& strings = + assert_cast(inner_map_column.get_values()); + const auto& string_data = + assert_cast(strings.get_nested_column()); + ASSERT_EQ(strings.size(), 4); + EXPECT_EQ(string_data.get_data_at(0).to_string(), "a"); + EXPECT_TRUE(strings.is_null_at(1)); + EXPECT_EQ(string_data.get_data_at(2).to_string(), "c"); + EXPECT_EQ(string_data.get_data_at(3).to_string(), "d"); + }); + + auto schema = arrow::schema(_arrow_fields); + auto table = arrow::Table::Make(schema, _arrays); + + auto file_result = arrow::io::FileOutputStream::Open(_file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + ROW_COUNT, builder.build())); + } + + std::unique_ptr create_reader(size_t field_idx) const { + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(*_fields[field_idx], &reader); + EXPECT_TRUE(st.ok()) << st; + return reader; + } + + std::unique_ptr create_projected_child_reader(size_t field_idx, + size_t child_idx) const { + const auto& struct_schema = *_fields[field_idx]; + EXPECT_LT(child_idx, struct_schema.children.size()); + + format::LocalColumnIndex projection; + projection.index = struct_schema.local_id; + projection.project_all_children = false; + format::LocalColumnIndex child_projection; + child_projection.index = struct_schema.children[child_idx]->local_id; + projection.children.push_back(std::move(child_projection)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(struct_schema, &projection, &reader); + EXPECT_TRUE(st.ok()) << st; + return reader; + } + + std::unique_ptr create_projected_grandchild_reader( + size_t field_idx, size_t child_idx, size_t grandchild_idx) const { + const auto& struct_schema = *_fields[field_idx]; + EXPECT_LT(child_idx, struct_schema.children.size()); + const auto& child_schema = *struct_schema.children[child_idx]; + EXPECT_LT(grandchild_idx, child_schema.children.size()); + + format::LocalColumnIndex projection; + projection.index = struct_schema.local_id; + projection.project_all_children = false; + format::LocalColumnIndex child_projection; + child_projection.index = child_schema.local_id; + child_projection.project_all_children = false; + format::LocalColumnIndex grandchild_projection; + grandchild_projection.index = child_schema.children[grandchild_idx]->local_id; + child_projection.children.push_back(std::move(grandchild_projection)); + projection.children.push_back(std::move(child_projection)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(struct_schema, &projection, &reader); + EXPECT_TRUE(st.ok()) << st; + return reader; + } + + void read_and_validate(size_t field_idx) const { + auto reader = create_reader(field_idx); + ASSERT_NE(reader, nullptr); + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + auto st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + ASSERT_EQ(column->size(), ROW_COUNT); + _expected_by_field[field_idx](*_fields[field_idx], *column); + } + + size_t find_field_idx(const std::string& name) const { + for (size_t field_idx = 0; field_idx < _fields.size(); ++field_idx) { + if (_fields[field_idx]->name == name) { + return field_idx; + } + } + ADD_FAILURE() << "Cannot find parquet test field " << name; + return _fields.size(); + } + + std::filesystem::path _test_dir; + std::string _file_path; + std::unique_ptr<::parquet::ParquetFileReader> _file_reader; + std::shared_ptr<::parquet::RowGroupReader> _row_group; + std::vector> _fields; + std::vector> _arrow_fields; + std::vector> _arrays; + std::vector> _expected_by_field; +}; + +TEST_F(ParquetColumnReaderTest, ReadAllSupportedPhysicalAndLogicalTypes) { + for (size_t field_idx = 0; field_idx < _fields.size(); ++field_idx) { + SCOPED_TRACE(_fields[field_idx]->name); + if (_fields[field_idx]->kind != ParquetColumnSchemaKind::PRIMITIVE) { + continue; + } + ASSERT_TRUE(supports_record_reader(_fields[field_idx]->type_descriptor)); + read_and_validate(field_idx); + } +} + +TEST_F(ParquetColumnReaderTest, TransformUnsignedIntegerStatistics) { + auto row_group = _file_reader->metadata()->RowGroup(0); + ASSERT_NE(row_group, nullptr); + + const auto uint32_field_idx = find_field_idx("uint32_col"); + const auto& uint32_schema = *_fields[uint32_field_idx]; + ASSERT_EQ(uint32_schema.type_descriptor.physical_type, ::parquet::Type::INT32); + ASSERT_TRUE(uint32_schema.type_descriptor.is_unsigned_integer); + ASSERT_EQ(uint32_schema.type_descriptor.integer_bit_width, 32); + auto uint32_chunk = row_group->ColumnChunk(uint32_schema.leaf_column_id); + ASSERT_NE(uint32_chunk, nullptr); + auto uint32_stats = ParquetStatisticsUtils::TransformColumnStatistics( + uint32_schema, uint32_chunk->statistics()); + ASSERT_TRUE(uint32_stats.has_min_max); + const auto uint32_min = uint32_stats.min_value.get(); + const auto uint32_max = uint32_stats.max_value.get(); + EXPECT_EQ(uint32_min, 0); + EXPECT_EQ(uint32_max, static_cast(std::numeric_limits::max())); + + const auto uint64_field_idx = find_field_idx("uint64_col"); + const auto& uint64_schema = *_fields[uint64_field_idx]; + ASSERT_EQ(uint64_schema.type_descriptor.physical_type, ::parquet::Type::INT64); + ASSERT_TRUE(uint64_schema.type_descriptor.is_unsigned_integer); + ASSERT_EQ(uint64_schema.type_descriptor.integer_bit_width, 64); + auto uint64_chunk = row_group->ColumnChunk(uint64_schema.leaf_column_id); + ASSERT_NE(uint64_chunk, nullptr); + auto uint64_stats = ParquetStatisticsUtils::TransformColumnStatistics( + uint64_schema, uint64_chunk->statistics()); + ASSERT_TRUE(uint64_stats.has_min_max); + const auto uint64_min = uint64_stats.min_value.get(); + const auto uint64_max = uint64_stats.max_value.get(); + EXPECT_EQ(uint64_min, 0); + EXPECT_EQ(uint64_max, static_cast(std::numeric_limits::max())); +} + +TEST_F(ParquetColumnReaderTest, ReadSupportedComplexTypes) { + read_and_validate(find_field_idx("struct_col")); + read_and_validate(find_field_idx("nullable_struct_col")); + read_and_validate(find_field_idx("nullable_struct_decimal_col")); + read_and_validate(find_field_idx("list_int_col")); + read_and_validate(find_field_idx("nullable_list_int_col")); + read_and_validate(find_field_idx("required_nullable_list_int_col")); + read_and_validate(find_field_idx("nullable_list_struct_col")); + read_and_validate(find_field_idx("nullable_list_list_int_col")); + read_and_validate(find_field_idx("map_int_string_col")); + read_and_validate(find_field_idx("nullable_map_int_string_col")); + read_and_validate(find_field_idx("required_nullable_map_int_string_col")); + read_and_validate(find_field_idx("nullable_map_int_struct_col")); + read_and_validate(find_field_idx("nullable_map_int_list_col")); + read_and_validate(find_field_idx("nullable_list_map_int_string_col")); + read_and_validate(find_field_idx("nullable_map_int_map_int_string_col")); + read_and_validate(find_field_idx("nullable_list_struct_map_list_col")); + read_and_validate(find_field_idx("nullable_map_int_list_map_int_string_col")); +} + +TEST_F(ParquetColumnReaderTest, SkipThenRead) { + auto reader = create_reader(1); + auto st = reader->skip(2); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + + const auto& int_values = assert_cast(*column); + ASSERT_EQ(int_values.size(), 2); + EXPECT_EQ(int_values.get_element(0), 30); + EXPECT_EQ(int_values.get_element(1), 40); +} + +TEST_F(ParquetColumnReaderTest, SelectReadsOnlySelectedRanges) { + auto reader = create_reader(1); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 2); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& int_values = assert_cast(*column); + ASSERT_EQ(int_values.size(), 3); + EXPECT_EQ(int_values.get_element(0), 10); + EXPECT_EQ(int_values.get_element(1), 30); + EXPECT_EQ(int_values.get_element(2), 50); +} + +TEST_F(ParquetColumnReaderTest, ReadProjectedStructChildren) { + const auto field_idx = find_field_idx("struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& struct_schema = *_fields[field_idx]; + ASSERT_EQ(struct_schema.name, "struct_col"); + ASSERT_EQ(struct_schema.children.size(), 2); + + format::LocalColumnIndex projection; + projection.index = struct_schema.local_id; + projection.project_all_children = false; + format::LocalColumnIndex child_projection; + child_projection.index = struct_schema.children[1]->local_id; + projection.children.push_back(std::move(child_projection)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(struct_schema, &projection, &reader); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(remove_nullable(reader->type())->get_primitive_type(), TYPE_STRUCT); + const auto* projected_type = + assert_cast(remove_nullable(reader->type()).get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "b"); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + const auto& struct_column = assert_cast(*column); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& values = assert_cast(struct_column.get_column(0)); + EXPECT_EQ(values.get_data_at(0).to_string(), "sa"); + EXPECT_EQ(values.get_data_at(4).to_string(), "se"); +} + +TEST_F(ParquetColumnReaderTest, ReadProjectedNullableStructChildren) { + const auto field_idx = find_field_idx("nullable_struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& struct_schema = *_fields[field_idx]; + ASSERT_EQ(struct_schema.name, "nullable_struct_col"); + ASSERT_EQ(struct_schema.children.size(), 2); + + format::LocalColumnIndex projection; + projection.index = struct_schema.local_id; + projection.project_all_children = false; + format::LocalColumnIndex child_projection; + child_projection.index = struct_schema.children[1]->local_id; + projection.children.push_back(std::move(child_projection)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(struct_schema, &projection, &reader); + ASSERT_TRUE(st.ok()) << st; + ASSERT_TRUE(reader->type()->is_nullable()); + ASSERT_EQ(remove_nullable(reader->type())->get_primitive_type(), TYPE_STRUCT); + const auto* projected_type = + assert_cast(remove_nullable(reader->type()).get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "b"); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + const auto& nullable_column = assert_cast(*column); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_TRUE(nullable_column.is_null_at(4)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& values = assert_cast(struct_column.get_column(0)); + const auto& nested_values = assert_cast(values.get_nested_column()); + EXPECT_FALSE(values.is_null_at(0)); + EXPECT_TRUE(values.is_null_at(2)); + EXPECT_FALSE(values.is_null_at(3)); + EXPECT_EQ(nested_values.get_data_at(0).to_string(), "nsa"); + EXPECT_EQ(nested_values.get_data_at(3).to_string(), "nsd"); +} + +TEST_F(ParquetColumnReaderTest, ReadProjectedListStructElementChildren) { + const auto field_idx = find_field_idx("nullable_list_struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& list_schema = *_fields[field_idx]; + ASSERT_EQ(list_schema.name, "nullable_list_struct_col"); + ASSERT_EQ(list_schema.children.size(), 1); + const auto& element_schema = *list_schema.children[0]; + ASSERT_EQ(element_schema.children.size(), 2); + + format::LocalColumnIndex projection; + projection.index = list_schema.local_id; + projection.project_all_children = false; + format::LocalColumnIndex element_projection; + element_projection.index = element_schema.local_id; + element_projection.project_all_children = false; + format::LocalColumnIndex child_projection; + child_projection.index = element_schema.children[1]->local_id; + element_projection.children.push_back(std::move(child_projection)); + projection.children.push_back(std::move(element_projection)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(list_schema, &projection, &reader); + ASSERT_TRUE(st.ok()) << st; + ASSERT_TRUE(reader->type()->is_nullable()); + const auto* array_type = + assert_cast(remove_nullable(reader->type()).get()); + const auto* element_type = assert_cast( + remove_nullable(array_type->get_nested_type()).get()); + ASSERT_EQ(element_type->get_elements().size(), 1); + EXPECT_EQ(element_type->get_element_name(0), "b"); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + + const auto& nullable_column = assert_cast(*column); + const auto& array_column = assert_cast(nullable_column.get_nested_column()); + const auto& elements = assert_cast(array_column.get_data()); + const auto& struct_column = assert_cast(elements.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& b_values = assert_cast(struct_column.get_column(0)); + const auto& b_data = assert_cast(b_values.get_nested_column()); + ASSERT_EQ(elements.size(), 5); + EXPECT_EQ(b_data.get_data_at(0).to_string(), "la"); + EXPECT_TRUE(b_values.is_null_at(1)); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_EQ(b_data.get_data_at(3).to_string(), "ld"); + EXPECT_EQ(b_data.get_data_at(4).to_string(), "le"); +} + +TEST_F(ParquetColumnReaderTest, ReadProjectedMapStructValueChildren) { + const auto field_idx = find_field_idx("nullable_map_int_struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& map_schema = *_fields[field_idx]; + ASSERT_EQ(map_schema.name, "nullable_map_int_struct_col"); + ASSERT_EQ(map_schema.children.size(), 1); + const auto& key_value_schema = *map_schema.children[0]; + ASSERT_EQ(key_value_schema.children.size(), 2); + const auto& value_schema = *key_value_schema.children[1]; + ASSERT_EQ(value_schema.children.size(), 2); + + format::LocalColumnIndex projection; + projection.index = map_schema.local_id; + projection.project_all_children = false; + format::LocalColumnIndex entry_projection; + entry_projection.index = key_value_schema.local_id; + entry_projection.project_all_children = false; + format::LocalColumnIndex value_projection; + value_projection.index = value_schema.local_id; + value_projection.project_all_children = false; + format::LocalColumnIndex child_projection; + child_projection.index = value_schema.children[1]->local_id; + value_projection.children.push_back(std::move(child_projection)); + entry_projection.children.push_back(std::move(value_projection)); + projection.children.push_back(std::move(entry_projection)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + auto st = factory.create(map_schema, &projection, &reader); + ASSERT_TRUE(st.ok()) << st; + ASSERT_TRUE(reader->type()->is_nullable()); + const auto* map_type = assert_cast(remove_nullable(reader->type()).get()); + EXPECT_EQ(remove_nullable(map_type->get_key_type())->get_primitive_type(), TYPE_INT); + const auto* value_type = + assert_cast(remove_nullable(map_type->get_value_type()).get()); + ASSERT_EQ(value_type->get_elements().size(), 1); + EXPECT_EQ(value_type->get_element_name(0), "b"); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + + const auto& nullable_column = assert_cast(*column); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& keys = assert_cast(map_column.get_keys()); + const auto& values = assert_cast(map_column.get_values()); + const auto& struct_column = assert_cast(values.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& b_values = assert_cast(struct_column.get_column(0)); + const auto& b_data = assert_cast(b_values.get_nested_column()); + ASSERT_EQ(keys.size(), 4); + ASSERT_EQ(values.size(), 4); + EXPECT_EQ(keys.get_element(0), 101); + EXPECT_EQ(keys.get_element(1), 102); + EXPECT_EQ(keys.get_element(3), 104); + EXPECT_EQ(b_data.get_data_at(0).to_string(), "ma"); + EXPECT_TRUE(b_values.is_null_at(1)); + EXPECT_TRUE(values.is_null_at(2)); + EXPECT_EQ(b_data.get_data_at(3).to_string(), "me"); +} + +TEST_F(ParquetColumnReaderTest, ReadProjectedStructListChildOnly) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& struct_schema = *_fields[field_idx]; + ASSERT_EQ(struct_schema.name, "nullable_struct_list_col"); + ASSERT_EQ(struct_schema.children.size(), 2); + + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + ASSERT_TRUE(reader->type()->is_nullable()); + const auto* projected_type = + assert_cast(remove_nullable(reader->type()).get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "xs"); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& xs_nullable = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(xs_nullable.size(), ROW_COUNT); + EXPECT_FALSE(xs_nullable.is_null_at(0)); + EXPECT_FALSE(xs_nullable.is_null_at(2)); + EXPECT_TRUE(xs_nullable.is_null_at(3)); + EXPECT_FALSE(xs_nullable.is_null_at(4)); + const auto& xs_array = assert_cast(xs_nullable.get_nested_column()); + const auto& offsets = xs_array.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 2); + EXPECT_EQ(offsets[4], 4); + const auto& elements = assert_cast(xs_array.get_data()); + const auto& values = assert_cast(elements.get_nested_column()); + ASSERT_EQ(elements.size(), 4); + EXPECT_EQ(values.get_element(0), 1); + EXPECT_EQ(values.get_element(1), 2); + EXPECT_TRUE(elements.is_null_at(2)); + EXPECT_EQ(values.get_element(3), 5); +} + +TEST_F(ParquetColumnReaderTest, SkipProjectedStructListChildOnlyThenRead) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& xs_nullable = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(xs_nullable.size(), 3); + EXPECT_FALSE(xs_nullable.is_null_at(1)); + EXPECT_TRUE(xs_nullable.is_null_at(2)); + const auto& xs_array = assert_cast(xs_nullable.get_nested_column()); + const auto& offsets = xs_array.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 0); +} + +TEST_F(ParquetColumnReaderTest, SelectProjectedStructListChildOnly) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& xs_nullable = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(xs_nullable.size(), 3); + EXPECT_FALSE(xs_nullable.is_null_at(0)); + EXPECT_TRUE(xs_nullable.is_null_at(1)); + EXPECT_FALSE(xs_nullable.is_null_at(2)); + const auto& xs_array = assert_cast(xs_nullable.get_nested_column()); + const auto& offsets = xs_array.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 4); +} + +TEST_F(ParquetColumnReaderTest, ReadProjectedStructMapChildOnly) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& struct_schema = *_fields[field_idx]; + ASSERT_EQ(struct_schema.name, "nullable_struct_map_col"); + ASSERT_EQ(struct_schema.children.size(), 2); + + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + ASSERT_TRUE(reader->type()->is_nullable()); + const auto* projected_type = + assert_cast(remove_nullable(reader->type()).get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "kv"); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& kv_nullable = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(kv_nullable.size(), ROW_COUNT); + EXPECT_FALSE(kv_nullable.is_null_at(0)); + EXPECT_FALSE(kv_nullable.is_null_at(2)); + EXPECT_TRUE(kv_nullable.is_null_at(3)); + EXPECT_FALSE(kv_nullable.is_null_at(4)); + const auto& kv_map = assert_cast(kv_nullable.get_nested_column()); + const auto& offsets = kv_map.get_offsets(); + ASSERT_EQ(offsets.size(), ROW_COUNT); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 2); + EXPECT_EQ(offsets[3], 2); + EXPECT_EQ(offsets[4], 3); + const auto& keys = assert_cast(kv_map.get_keys()); + const auto& values = assert_cast(kv_map.get_values()); + const auto& value_data = assert_cast(values.get_nested_column()); + ASSERT_EQ(keys.size(), 3); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 5); + EXPECT_EQ(value_data.get_data_at(0).to_string(), "one"); + EXPECT_TRUE(values.is_null_at(1)); + EXPECT_EQ(value_data.get_data_at(2).to_string(), "five"); +} + +TEST_F(ParquetColumnReaderTest, NullableStructUsesListChildAsShapeSource) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + auto st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); +} + +TEST_F(ParquetColumnReaderTest, NullableStructUsesMapChildAsShapeSource) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + auto st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); +} + +TEST_F(ParquetColumnReaderTest, NullableStructUsesNestedStructComplexChildAsShapeSource) { + const auto field_idx = find_field_idx("nullable_struct_nested_struct_list_col"); + auto reader = create_projected_grandchild_reader(field_idx, 0, 0); + ASSERT_NE(reader, nullptr); + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + auto st = reader->read(ROW_COUNT, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, ROW_COUNT); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); + + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& nested_nullable = assert_cast(struct_column.get_column(0)); + EXPECT_FALSE(nested_nullable.is_null_at(0)); + EXPECT_TRUE(nested_nullable.is_null_at(2)); + EXPECT_FALSE(nested_nullable.is_null_at(3)); + EXPECT_FALSE(nested_nullable.is_null_at(4)); +} + +TEST_F(ParquetColumnReaderTest, SkipProjectedStructMapChildOnlyThenRead) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& kv_nullable = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(kv_nullable.size(), 3); + EXPECT_FALSE(kv_nullable.is_null_at(1)); + EXPECT_TRUE(kv_nullable.is_null_at(2)); + const auto& kv_map = assert_cast(kv_nullable.get_nested_column()); + const auto& offsets = kv_map.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 0); +} + +TEST_F(ParquetColumnReaderTest, SelectProjectedStructMapChildOnly) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + auto reader = create_projected_child_reader(field_idx, 1); + ASSERT_NE(reader, nullptr); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(struct_column.get_columns().size(), 1); + const auto& kv_nullable = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(kv_nullable.size(), 3); + EXPECT_FALSE(kv_nullable.is_null_at(0)); + EXPECT_TRUE(kv_nullable.is_null_at(1)); + EXPECT_FALSE(kv_nullable.is_null_at(2)); + const auto& kv_map = assert_cast(kv_nullable.get_nested_column()); + const auto& offsets = kv_map.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 3); + const auto& keys = assert_cast(kv_map.get_keys()); + ASSERT_EQ(keys.size(), 3); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 5); +} + +TEST_F(ParquetColumnReaderTest, ReadListWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_list_int_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipListWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_list_int_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + const auto& array_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 2); +} + +TEST_F(ParquetColumnReaderTest, SelectListWithOverflow) { + const auto field_idx = find_field_idx("nullable_list_int_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& array_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 4); + EXPECT_EQ(offsets[2], 5); +} + +TEST_F(ParquetColumnReaderTest, ReadStructListWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipStructListWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& xs_nullable = assert_cast(struct_column.get_column(1)); + ASSERT_EQ(xs_nullable.size(), 3); + EXPECT_FALSE(xs_nullable.is_null_at(1)); + EXPECT_TRUE(xs_nullable.is_null_at(2)); + const auto& xs_array = assert_cast(xs_nullable.get_nested_column()); + const auto& offsets = xs_array.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 0); +} + +TEST_F(ParquetColumnReaderTest, SelectStructListWithOverflow) { + const auto field_idx = find_field_idx("nullable_struct_list_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& a_values = assert_cast(struct_column.get_column(0)); + EXPECT_EQ(a_values.get_element(0), 301); + EXPECT_EQ(a_values.get_element(1), 304); + EXPECT_EQ(a_values.get_element(2), 305); + const auto& xs_nullable = assert_cast(struct_column.get_column(1)); + ASSERT_EQ(xs_nullable.size(), 3); + EXPECT_FALSE(xs_nullable.is_null_at(0)); + EXPECT_TRUE(xs_nullable.is_null_at(1)); + EXPECT_FALSE(xs_nullable.is_null_at(2)); + const auto& xs_array = assert_cast(xs_nullable.get_nested_column()); + const auto& offsets = xs_array.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 4); +} + +TEST_F(ParquetColumnReaderTest, ReadStructMapWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipStructMapWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& kv_nullable = assert_cast(struct_column.get_column(1)); + ASSERT_EQ(kv_nullable.size(), 3); + EXPECT_FALSE(kv_nullable.is_null_at(1)); + EXPECT_TRUE(kv_nullable.is_null_at(2)); + const auto& kv_map = assert_cast(kv_nullable.get_nested_column()); + const auto& offsets = kv_map.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 0); +} + +TEST_F(ParquetColumnReaderTest, SelectStructMapWithOverflow) { + const auto field_idx = find_field_idx("nullable_struct_map_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& a_values = assert_cast(struct_column.get_column(0)); + EXPECT_EQ(a_values.get_element(0), 401); + EXPECT_EQ(a_values.get_element(1), 404); + EXPECT_EQ(a_values.get_element(2), 405); + const auto& kv_nullable = assert_cast(struct_column.get_column(1)); + ASSERT_EQ(kv_nullable.size(), 3); + EXPECT_FALSE(kv_nullable.is_null_at(0)); + EXPECT_TRUE(kv_nullable.is_null_at(1)); + EXPECT_FALSE(kv_nullable.is_null_at(2)); + const auto& kv_map = assert_cast(kv_nullable.get_nested_column()); + const auto& offsets = kv_map.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 2); + EXPECT_EQ(offsets[2], 3); + const auto& keys = assert_cast(kv_map.get_keys()); + const auto& values = assert_cast(kv_map.get_values()); + const auto& value_data = assert_cast(values.get_nested_column()); + ASSERT_EQ(keys.size(), 3); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 5); + EXPECT_EQ(value_data.get_data_at(0).to_string(), "one"); + EXPECT_TRUE(values.is_null_at(1)); + EXPECT_EQ(value_data.get_data_at(2).to_string(), "five"); +} + +TEST_F(ParquetColumnReaderTest, ReadListStructWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_list_struct_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipListStructWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_list_struct_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + const auto& array_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 2); +} + +TEST_F(ParquetColumnReaderTest, SelectListStructWithOverflow) { + const auto field_idx = find_field_idx("nullable_list_struct_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& array_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = array_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 4); + EXPECT_EQ(offsets[2], 5); +} + +TEST_F(ParquetColumnReaderTest, ReadListListWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_list_list_int_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipListListWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_list_list_int_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + const auto& outer_array = assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), 3); + EXPECT_EQ(outer_offsets[0], 0); + EXPECT_EQ(outer_offsets[1], 0); + EXPECT_EQ(outer_offsets[2], 1); + + const auto& inner_nullable = assert_cast(outer_array.get_data()); + ASSERT_EQ(inner_nullable.size(), 1); + EXPECT_FALSE(inner_nullable.is_null_at(0)); + const auto& inner_array = assert_cast(inner_nullable.get_nested_column()); + const auto& inner_offsets = inner_array.get_offsets(); + ASSERT_EQ(inner_offsets.size(), 1); + EXPECT_EQ(inner_offsets[0], 1); +} + +TEST_F(ParquetColumnReaderTest, SelectListListWithOverflow) { + const auto field_idx = find_field_idx("nullable_list_list_int_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& outer_array = assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), 3); + EXPECT_EQ(outer_offsets[0], 4); + EXPECT_EQ(outer_offsets[1], 5); + EXPECT_EQ(outer_offsets[2], 7); + + const auto& inner_nullable = assert_cast(outer_array.get_data()); + ASSERT_EQ(inner_nullable.size(), 7); + EXPECT_TRUE(inner_nullable.is_null_at(2)); + const auto& inner_array = assert_cast(inner_nullable.get_nested_column()); + const auto& inner_offsets = inner_array.get_offsets(); + ASSERT_EQ(inner_offsets.size(), 7); + EXPECT_EQ(inner_offsets[0], 2); + EXPECT_EQ(inner_offsets[3], 4); + EXPECT_EQ(inner_offsets[4], 5); + EXPECT_EQ(inner_offsets[6], 7); +} + +TEST_F(ParquetColumnReaderTest, ReadMapWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_map_int_string_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipMapWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_map_int_string_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 1); +} + +TEST_F(ParquetColumnReaderTest, SelectMapWithOverflow) { + const auto field_idx = find_field_idx("nullable_map_int_string_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 3); + EXPECT_EQ(offsets[2], 4); +} + +TEST_F(ParquetColumnReaderTest, ReadMapStructWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_map_int_struct_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipMapStructWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_map_int_struct_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 0); + EXPECT_EQ(offsets[1], 0); + EXPECT_EQ(offsets[2], 1); +} + +TEST_F(ParquetColumnReaderTest, SelectMapStructWithOverflow) { + const auto field_idx = find_field_idx("nullable_map_int_struct_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& offsets = map_column.get_offsets(); + ASSERT_EQ(offsets.size(), 3); + EXPECT_EQ(offsets[0], 2); + EXPECT_EQ(offsets[1], 3); + EXPECT_EQ(offsets[2], 4); +} + +TEST_F(ParquetColumnReaderTest, ReadMapListWithOverflowAcrossChunks) { + const auto field_idx = find_field_idx("nullable_map_int_list_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipMapListWithOverflowThenRead) { + const auto field_idx = find_field_idx("nullable_map_int_list_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(3, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 3); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_TRUE(nullable_column.is_null_at(0)); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), 3); + EXPECT_EQ(map_offsets[0], 0); + EXPECT_EQ(map_offsets[1], 0); + EXPECT_EQ(map_offsets[2], 2); + + const auto& values = assert_cast(map_column.get_values()); + ASSERT_EQ(values.size(), 2); + EXPECT_TRUE(values.is_null_at(0)); + EXPECT_FALSE(values.is_null_at(1)); + const auto& list_column = assert_cast(values.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 2); + EXPECT_EQ(list_offsets[0], 0); + EXPECT_EQ(list_offsets[1], 2); +} + +TEST_F(ParquetColumnReaderTest, SelectMapListWithOverflow) { + const auto field_idx = find_field_idx("nullable_map_int_list_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& map_column = assert_cast(nullable_column.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), 3); + EXPECT_EQ(map_offsets[0], 2); + EXPECT_EQ(map_offsets[1], 4); + EXPECT_EQ(map_offsets[2], 5); + + const auto& values = assert_cast(map_column.get_values()); + ASSERT_EQ(values.size(), 5); + EXPECT_FALSE(values.is_null_at(0)); + EXPECT_TRUE(values.is_null_at(2)); + EXPECT_FALSE(values.is_null_at(4)); + const auto& list_column = assert_cast(values.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 5); + EXPECT_EQ(list_offsets[0], 2); + EXPECT_EQ(list_offsets[1], 2); + EXPECT_EQ(list_offsets[2], 2); + EXPECT_EQ(list_offsets[3], 4); + EXPECT_EQ(list_offsets[4], 5); +} + +TEST_F(ParquetColumnReaderTest, ReadDeepListStructMapListAcrossChunks) { + const auto field_idx = find_field_idx("nullable_list_struct_map_list_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(1, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 1); + st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipDeepListStructMapListThenRead) { + const auto field_idx = find_field_idx("nullable_list_struct_map_list_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(4, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 4); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 4); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + + const auto& outer_array = assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), 4); + EXPECT_EQ(outer_offsets[0], 0); + EXPECT_EQ(outer_offsets[1], 0); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 3); + + const auto& struct_values = assert_cast(outer_array.get_data()); + ASSERT_EQ(struct_values.size(), 3); + EXPECT_FALSE(struct_values.is_null_at(0)); + EXPECT_FALSE(struct_values.is_null_at(1)); + EXPECT_FALSE(struct_values.is_null_at(2)); + const auto& struct_column = assert_cast(struct_values.get_nested_column()); + const auto& map_values = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(map_values.size(), 3); + EXPECT_TRUE(map_values.is_null_at(0)); + EXPECT_FALSE(map_values.is_null_at(1)); + EXPECT_FALSE(map_values.is_null_at(2)); + + const auto& map_column = assert_cast(map_values.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), 3); + EXPECT_EQ(map_offsets[0], 0); + EXPECT_EQ(map_offsets[1], 0); + EXPECT_EQ(map_offsets[2], 2); + const auto& keys = assert_cast(map_column.get_keys()); + ASSERT_EQ(keys.size(), 2); + EXPECT_EQ(keys.get_element(0), 3); + EXPECT_EQ(keys.get_element(1), 4); + const auto& lists = assert_cast(map_column.get_values()); + ASSERT_EQ(lists.size(), 2); + EXPECT_TRUE(lists.is_null_at(0)); + EXPECT_FALSE(lists.is_null_at(1)); + const auto& list_column = assert_cast(lists.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 2); + EXPECT_EQ(list_offsets[0], 0); + EXPECT_EQ(list_offsets[1], 1); +} + +TEST_F(ParquetColumnReaderTest, SelectDeepListStructMapList) { + const auto field_idx = find_field_idx("nullable_list_struct_map_list_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& outer_array = assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_array.get_offsets(); + ASSERT_EQ(outer_offsets.size(), 3); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 4); + EXPECT_EQ(outer_offsets[2], 5); + + const auto& struct_values = assert_cast(outer_array.get_data()); + ASSERT_EQ(struct_values.size(), 5); + EXPECT_FALSE(struct_values.is_null_at(0)); + EXPECT_TRUE(struct_values.is_null_at(1)); + EXPECT_FALSE(struct_values.is_null_at(2)); + EXPECT_FALSE(struct_values.is_null_at(3)); + EXPECT_FALSE(struct_values.is_null_at(4)); + const auto& struct_column = assert_cast(struct_values.get_nested_column()); + const auto& map_values = assert_cast(struct_column.get_column(0)); + ASSERT_EQ(map_values.size(), 5); + EXPECT_FALSE(map_values.is_null_at(0)); + EXPECT_TRUE(map_values.is_null_at(1)); + EXPECT_TRUE(map_values.is_null_at(2)); + EXPECT_FALSE(map_values.is_null_at(3)); + EXPECT_FALSE(map_values.is_null_at(4)); + const auto& map_column = assert_cast(map_values.get_nested_column()); + const auto& map_offsets = map_column.get_offsets(); + ASSERT_EQ(map_offsets.size(), 5); + EXPECT_EQ(map_offsets[0], 2); + EXPECT_EQ(map_offsets[1], 2); + EXPECT_EQ(map_offsets[2], 2); + EXPECT_EQ(map_offsets[3], 2); + EXPECT_EQ(map_offsets[4], 4); +} + +TEST_F(ParquetColumnReaderTest, ReadDeepMapListMapAcrossChunks) { + const auto field_idx = find_field_idx("nullable_map_int_list_map_int_string_col"); + auto reader = create_reader(field_idx); + MutableColumnPtr column = reader->type()->create_column(); + + int64_t rows_read = 0; + auto st = reader->read(1, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 1); + st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + st = reader->read(2, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 2); + + _expected_by_field[field_idx](*_fields[field_idx], *column); +} + +TEST_F(ParquetColumnReaderTest, SkipDeepMapListMapThenRead) { + const auto field_idx = find_field_idx("nullable_map_int_list_map_int_string_col"); + auto reader = create_reader(field_idx); + auto st = reader->skip(1); + ASSERT_TRUE(st.ok()) << st; + + MutableColumnPtr column = reader->type()->create_column(); + int64_t rows_read = 0; + st = reader->read(4, column, &rows_read); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(rows_read, 4); + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 4); + EXPECT_TRUE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + const auto& outer_map = assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_map.get_offsets(); + ASSERT_EQ(outer_offsets.size(), 4); + EXPECT_EQ(outer_offsets[0], 0); + EXPECT_EQ(outer_offsets[1], 0); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 3); + const auto& outer_keys = assert_cast(outer_map.get_keys()); + ASSERT_EQ(outer_keys.size(), 3); + EXPECT_EQ(outer_keys.get_element(0), 30); + EXPECT_EQ(outer_keys.get_element(1), 40); + EXPECT_EQ(outer_keys.get_element(2), 50); + + const auto& lists = assert_cast(outer_map.get_values()); + ASSERT_EQ(lists.size(), 3); + EXPECT_TRUE(lists.is_null_at(0)); + EXPECT_FALSE(lists.is_null_at(1)); + EXPECT_FALSE(lists.is_null_at(2)); + const auto& list_column = assert_cast(lists.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 3); + EXPECT_EQ(list_offsets[0], 0); + EXPECT_EQ(list_offsets[1], 1); + EXPECT_EQ(list_offsets[2], 3); + const auto& inner_maps = assert_cast(list_column.get_data()); + ASSERT_EQ(inner_maps.size(), 3); + EXPECT_FALSE(inner_maps.is_null_at(0)); + EXPECT_TRUE(inner_maps.is_null_at(1)); + EXPECT_FALSE(inner_maps.is_null_at(2)); +} + +TEST_F(ParquetColumnReaderTest, SelectDeepMapListMap) { + const auto field_idx = find_field_idx("nullable_map_int_list_map_int_string_col"); + auto reader = create_reader(field_idx); + SelectionVector selection(3); + selection.set_index(0, 0); + selection.set_index(1, 3); + selection.set_index(2, 4); + + MutableColumnPtr column = reader->type()->create_column(); + auto st = reader->select(selection, 3, ROW_COUNT, column); + ASSERT_TRUE(st.ok()) << st; + + const auto& nullable_column = assert_cast(*column); + ASSERT_EQ(nullable_column.size(), 3); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_FALSE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + const auto& outer_map = assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_map.get_offsets(); + ASSERT_EQ(outer_offsets.size(), 3); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 4); + EXPECT_EQ(outer_offsets[2], 5); + const auto& outer_keys = assert_cast(outer_map.get_keys()); + ASSERT_EQ(outer_keys.size(), 5); + EXPECT_EQ(outer_keys.get_element(0), 10); + EXPECT_EQ(outer_keys.get_element(1), 20); + EXPECT_EQ(outer_keys.get_element(2), 30); + EXPECT_EQ(outer_keys.get_element(3), 40); + EXPECT_EQ(outer_keys.get_element(4), 50); + + const auto& lists = assert_cast(outer_map.get_values()); + ASSERT_EQ(lists.size(), 5); + EXPECT_FALSE(lists.is_null_at(0)); + EXPECT_FALSE(lists.is_null_at(1)); + EXPECT_TRUE(lists.is_null_at(2)); + EXPECT_FALSE(lists.is_null_at(3)); + EXPECT_FALSE(lists.is_null_at(4)); + const auto& list_column = assert_cast(lists.get_nested_column()); + const auto& list_offsets = list_column.get_offsets(); + ASSERT_EQ(list_offsets.size(), 5); + EXPECT_EQ(list_offsets[0], 3); + EXPECT_EQ(list_offsets[1], 3); + EXPECT_EQ(list_offsets[2], 3); + EXPECT_EQ(list_offsets[3], 4); + EXPECT_EQ(list_offsets[4], 6); +} + +TEST_F(ParquetColumnReaderTest, BuildComplexSchemaNodeMetadata) { + const auto field_idx = find_field_idx("struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& struct_schema = *_fields[field_idx]; + ASSERT_EQ(struct_schema.name, "struct_col"); + ASSERT_EQ(struct_schema.children.size(), 2); + EXPECT_EQ(struct_schema.local_id, static_cast(field_idx)); + EXPECT_EQ(struct_schema.children[0]->local_id, 0); + EXPECT_EQ(struct_schema.children[1]->local_id, 1); + EXPECT_EQ(struct_schema.children[0]->name, "a"); + EXPECT_EQ(struct_schema.children[1]->name, "b"); + EXPECT_EQ(struct_schema.max_definition_level, 0); + EXPECT_EQ(struct_schema.max_repetition_level, 0); +} + +TEST_F(ParquetColumnReaderTest, ResolveSupportedPhysicalAndLogicalSchemas) { + std::vector<::parquet::schema::NodePtr> nodes = { + ::parquet::schema::PrimitiveNode::Make("required_bool", ::parquet::Repetition::REQUIRED, + ::parquet::Type::BOOLEAN), + ::parquet::schema::PrimitiveNode::Make( + "required_int32", ::parquet::Repetition::REQUIRED, ::parquet::Type::INT32), + ::parquet::schema::PrimitiveNode::Make( + "required_int64", ::parquet::Repetition::REQUIRED, ::parquet::Type::INT64), + ::parquet::schema::PrimitiveNode::Make( + "required_float", ::parquet::Repetition::REQUIRED, ::parquet::Type::FLOAT), + ::parquet::schema::PrimitiveNode::Make( + "required_double", ::parquet::Repetition::REQUIRED, ::parquet::Type::DOUBLE), + ::parquet::schema::PrimitiveNode::Make( + "optional_float16", ::parquet::Repetition::OPTIONAL, + ::parquet::LogicalType::Float16(), ::parquet::Type::FIXED_LEN_BYTE_ARRAY, 2), + ::parquet::schema::PrimitiveNode::Make("required_binary", + ::parquet::Repetition::REQUIRED, + ::parquet::Type::BYTE_ARRAY), + ::parquet::schema::PrimitiveNode::Make( + "required_fixed_binary", ::parquet::Repetition::REQUIRED, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY, ::parquet::ConvertedType::NONE, 4), + ::parquet::schema::PrimitiveNode::Make( + "optional_int32", ::parquet::Repetition::OPTIONAL, ::parquet::Type::INT32), + ::parquet::schema::PrimitiveNode::Make("utf8_binary", ::parquet::Repetition::REQUIRED, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::UTF8), + ::parquet::schema::PrimitiveNode::Make("enum_binary", ::parquet::Repetition::REQUIRED, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::ENUM), + ::parquet::schema::PrimitiveNode::Make("json_binary", ::parquet::Repetition::REQUIRED, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::JSON), + ::parquet::schema::PrimitiveNode::Make("bson_binary", ::parquet::Repetition::REQUIRED, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::BSON), + ::parquet::schema::PrimitiveNode::Make("decimal_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::DECIMAL, -1, 9, 2), + ::parquet::schema::PrimitiveNode::Make("decimal_int64", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT64, + ::parquet::ConvertedType::DECIMAL, -1, 18, 6), + ::parquet::schema::PrimitiveNode::Make( + "decimal_binary", ::parquet::Repetition::REQUIRED, ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::DECIMAL, -1, 18, 6), + ::parquet::schema::PrimitiveNode::Make("decimal_fixed_binary", + ::parquet::Repetition::REQUIRED, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY, + ::parquet::ConvertedType::DECIMAL, 8, 18, 6), + ::parquet::schema::PrimitiveNode::Make("date_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::DATE), + ::parquet::schema::PrimitiveNode::Make( + "time_millis_int32", ::parquet::Repetition::REQUIRED, ::parquet::Type::INT32, + ::parquet::ConvertedType::TIME_MILLIS), + ::parquet::schema::PrimitiveNode::Make( + "time_micros_int64", ::parquet::Repetition::REQUIRED, ::parquet::Type::INT64, + ::parquet::ConvertedType::TIME_MICROS), + ::parquet::schema::PrimitiveNode::Make( + "timestamp_millis_int64", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT64, ::parquet::ConvertedType::TIMESTAMP_MILLIS), + ::parquet::schema::PrimitiveNode::Make( + "timestamp_micros_int64", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT64, ::parquet::ConvertedType::TIMESTAMP_MICROS), + ::parquet::schema::PrimitiveNode::Make( + "int96_timestamp", ::parquet::Repetition::REQUIRED, ::parquet::Type::INT96), + ::parquet::schema::PrimitiveNode::Make("int8_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::INT_8), + ::parquet::schema::PrimitiveNode::Make("uint8_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::UINT_8), + ::parquet::schema::PrimitiveNode::Make("int16_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::INT_16), + ::parquet::schema::PrimitiveNode::Make("uint16_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::UINT_16), + ::parquet::schema::PrimitiveNode::Make("int32_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::INT_32), + ::parquet::schema::PrimitiveNode::Make("uint32_int32", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT32, + ::parquet::ConvertedType::UINT_32), + ::parquet::schema::PrimitiveNode::Make("int64_int64", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT64, + ::parquet::ConvertedType::INT_64), + ::parquet::schema::PrimitiveNode::Make("uint64_int64", ::parquet::Repetition::REQUIRED, + ::parquet::Type::INT64, + ::parquet::ConvertedType::UINT_64), + }; + + auto schema = + ::parquet::schema::GroupNode::Make("schema", ::parquet::Repetition::REQUIRED, nodes); + ::parquet::SchemaDescriptor descriptor; + descriptor.Init(schema); + + std::vector> fields; + auto st = build_parquet_column_schema(descriptor, &fields); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(fields.size(), nodes.size()); + + for (const auto& field : fields) { + SCOPED_TRACE(field->name); + ASSERT_TRUE(supports_record_reader(field->type_descriptor)); + ASSERT_NE(field->type, nullptr); + if (field->name == "int96_timestamp") { + ASSERT_EQ(remove_nullable(field->type)->get_primitive_type(), TYPE_DATETIMEV2); + } + if (field->name == "optional_float16") { + ASSERT_TRUE(field->type->is_nullable()); + ASSERT_EQ(remove_nullable(field->type)->get_primitive_type(), TYPE_FLOAT); + ASSERT_EQ(field->type_descriptor.extra_type_info, ParquetExtraTypeInfo::FLOAT16); + ASSERT_FALSE(field->type_descriptor.is_string_like); + } + } +} + +TEST_F(ParquetColumnReaderTest, ReadInt96TimestampAsDateTimeV2) { + const auto file_path = (_test_dir / "int96_timestamp.parquet").string(); + auto field = arrow::field("col_datetime", arrow::timestamp(arrow::TimeUnit::MICRO), false); + auto array = build_timestamp_array(arrow::timestamp(arrow::TimeUnit::MICRO), + {0, 1234567, 1609459200000000, 1609459201000000, -1}); + auto table = arrow::Table::Make(arrow::schema({field}), {array}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + ::parquet::ArrowWriterProperties::Builder arrow_builder; + arrow_builder.enable_force_write_int96_timestamps(); + PARQUET_THROW_NOT_OK( + ::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), *file_result, + ROW_COUNT, writer_builder.build(), arrow_builder.build())); + + auto file_reader = ::parquet::ParquetFileReader::OpenFile(file_path, false); + ASSERT_NE(file_reader, nullptr); + std::vector> fields; + ASSERT_TRUE(build_parquet_column_schema(*file_reader->metadata()->schema(), &fields).ok()); + ASSERT_EQ(fields.size(), 1); + EXPECT_EQ(fields[0]->type_descriptor.physical_type, ::parquet::Type::INT96); + EXPECT_EQ(fields[0]->type_descriptor.extra_type_info, ParquetExtraTypeInfo::IMPALA_TIMESTAMP); + ASSERT_TRUE(supports_record_reader(fields[0]->type_descriptor)); + ASSERT_EQ(remove_nullable(fields[0]->type)->get_primitive_type(), TYPE_DATETIMEV2); + + auto row_group = file_reader->RowGroup(0); + ASSERT_NE(row_group, nullptr); + ParquetColumnReaderFactory factory(row_group, file_reader->metadata()->num_columns()); + std::unique_ptr reader; + ASSERT_TRUE(factory.create(*fields[0], &reader).ok()); + auto column = fields[0]->type->create_column(); + int64_t rows_read = 0; + ASSERT_TRUE(reader->read(ROW_COUNT, column, &rows_read).ok()); + ASSERT_EQ(rows_read, ROW_COUNT); + EXPECT_EQ(fields[0]->type->to_string(*column, 0), "1970-01-01 00:00:00.000000"); + EXPECT_EQ(fields[0]->type->to_string(*column, 1), "1970-01-01 00:00:01.234567"); + EXPECT_EQ(fields[0]->type->to_string(*column, 2), "2021-01-01 00:00:00.000000"); + EXPECT_EQ(fields[0]->type->to_string(*column, 4), "1969-12-31 23:59:59.999999"); +} + +TEST_F(ParquetColumnReaderTest, RejectRecordReaderUnsupportedDecimalPrecision) { + auto schema = ::parquet::schema::GroupNode::Make( + "schema", ::parquet::Repetition::REQUIRED, + { + ::parquet::schema::PrimitiveNode::Make( + "decimal256_fixed_col", ::parquet::Repetition::REQUIRED, + ::parquet::Type::FIXED_LEN_BYTE_ARRAY, + ::parquet::ConvertedType::DECIMAL, 20, 39, 6), + }); + ::parquet::SchemaDescriptor descriptor; + descriptor.Init(schema); + + std::vector> fields; + auto st = build_parquet_column_schema(descriptor, &fields); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(fields.size(), 1); + + for (const auto& field : fields) { + SCOPED_TRACE(field->name); + ASSERT_FALSE(supports_record_reader(field->type_descriptor)); + } +} + +TEST_F(ParquetColumnReaderTest, RejectRepeatedPrimitiveAsFlatScalarReader) { + auto schema = ::parquet::schema::GroupNode::Make( + "schema", ::parquet::Repetition::REQUIRED, + {::parquet::schema::PrimitiveNode::Make("repeated_int32_col", + ::parquet::Repetition::REPEATED, + ::parquet::Type::INT32)}); + ::parquet::SchemaDescriptor descriptor; + descriptor.Init(schema); + + std::vector> fields; + auto st = build_parquet_column_schema(descriptor, &fields); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(fields.size(), 1); + ASSERT_EQ(fields[0]->descriptor->max_repetition_level(), 1); + ASSERT_TRUE(supports_record_reader(fields[0]->type_descriptor)); + + ParquetColumnReaderFactory factory(nullptr, descriptor.num_columns()); + std::unique_ptr reader; + st = factory.create(*fields[0], &reader); + ASSERT_FALSE(st.ok()) << st; +} + +} // namespace +} // namespace doris::parquet diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp new file mode 100644 index 00000000000000..013c759f7b88bb --- /dev/null +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -0,0 +1,2868 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_reader.h" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/column/column_string.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/data_type_struct.h" +#include "core/data_type/primitive_type.h" +#include "core/field.h" +#include "exprs/vexpr.h" +#include "exprs/vexpr_context.h" +#include "exprs/vliteral.h" +#include "exprs/vslot_ref.h" +#include "format_v2/column_mapper.h" +#include "format_v2/expr/cast.h" +#include "format_v2/expr/delete_predicate.h" +#include "format_v2/file_reader.h" +#include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/parquet_scan.h" +#include "format_v2/parquet/reader/column_reader.h" +#include "format_v2/table_reader.h" +#include "gen_cpp/Types_types.h" +#include "io/io_common.h" +#include "runtime/runtime_state.h" +#include "storage/predicate/accept_null_predicate.h" +#include "storage/predicate/predicate_creator.h" + +namespace doris { +namespace { + +constexpr int64_t ROW_COUNT = 5; + +format::LocalColumnIndex field_projection(int32_t column_id) { + return format::LocalColumnIndex {.index = column_id}; +} + +std::vector projection_ids(const std::vector& projections) { + std::vector ids; + ids.reserve(projections.size()); + for (const auto& projection : projections) { + ids.push_back(projection.index); + } + return ids; +} + +std::vector struct_predicate_target_names( + const format::FileStructPredicateTarget* target) { + std::vector names; + for (const auto* current = target; current != nullptr; current = current->child.get()) { + names.push_back(current->file_child_name); + } + return names; +} + +void expect_struct_predicate_target(const format::FileColumnPredicateFilter& filter, + int32_t root_file_column_id, + const std::vector& child_id_path, + const std::vector& child_name_path) { + ASSERT_TRUE(filter.target.is_valid()); + EXPECT_EQ(filter.target.file_column_id.value(), root_file_column_id); + EXPECT_EQ(filter.effective_file_column_id().value(), root_file_column_id); + EXPECT_EQ(filter.effective_file_child_id_path(), child_id_path); + EXPECT_EQ(struct_predicate_target_names(filter.target.struct_target.get()), child_name_path); +} + +void set_name_identifiers(format::ColumnDefinition* column, int32_t local_id) { + DORIS_CHECK(column != nullptr); + column->identifier = Field::create_field(column->name); + column->local_id = local_id; + for (size_t child_idx = 0; child_idx < column->children.size(); ++child_idx) { + set_name_identifiers(&column->children[child_idx], static_cast(child_idx)); + } +} + +void set_local_ids(format::ColumnDefinition* column, int32_t local_id) { + DORIS_CHECK(column != nullptr); + column->local_id = local_id; + for (size_t child_idx = 0; child_idx < column->children.size(); ++child_idx) { + set_local_ids(&column->children[child_idx], static_cast(child_idx)); + } +} + +class Int32GreaterThanExpr final : public VExpr { +public: + Int32GreaterThanExpr(int column_id, int32_t value) + : VExpr(std::make_shared(), false), + _column_id(column_id), + _value(value) {} + + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + const auto& input = + assert_cast(*block->get_by_position(_column_id).column); + auto result = ColumnUInt8::create(); + auto& result_data = result->get_data(); + result_data.resize(count); + for (size_t row = 0; row < count; ++row) { + const size_t input_row = selector == nullptr ? row : (*selector)[row]; + result_data[row] = input.get_element(input_row) > _value; + } + result_column = std::move(result); + return Status::OK(); + } + + const std::string& expr_name() const override { return _expr_name; } + +private: + const int _column_id; + const int32_t _value; + const std::string _expr_name = "Int32GreaterThanExpr"; +}; + +class TestFunctionExpr final : public VExpr { +public: + TestFunctionExpr(std::string function_name, DataTypePtr data_type, + TExprNodeType::type node_type = TExprNodeType::FUNCTION_CALL, + TExprOpcode::type opcode = TExprOpcode::INVALID_OPCODE) + : VExpr(std::move(data_type), false), _expr_name(std::move(function_name)) { + set_node_type(node_type); + _opcode = opcode; + TFunctionName fn_name; + fn_name.__set_function_name(_expr_name); + _fn.__set_name(fn_name); + } + + const std::string& expr_name() const override { return _expr_name; } + + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + return Status::NotSupported("TestFunctionExpr is only used for mapper expression analysis"); + } + +private: + const std::string _expr_name; +}; + +VExprSPtr struct_element_expr(const VExprSPtr& parent, const DataTypePtr& child_type, + const std::string& child_name) { + auto expr = std::make_shared("struct_element", make_nullable(child_type)); + expr->add_child(parent); + expr->add_child(VLiteral::create_shared(std::make_shared(), + Field::create_field(child_name))); + return expr; +} + +VExprSPtr in_predicate_expr(const VExprSPtr& probe_expr, const DataTypePtr& literal_type, + const std::vector& values) { + auto expr = std::make_shared("in", std::make_shared(), + TExprNodeType::IN_PRED); + expr->add_child(probe_expr); + for (const auto& value : values) { + expr->add_child(VLiteral::create_shared(literal_type, value)); + } + return expr; +} + +VExprSPtr null_predicate_expr(const VExprSPtr& child, bool is_null) { + auto expr = std::make_shared(is_null ? "is_null_pred" : "is_not_null_pred", + std::make_shared()); + expr->add_child(child); + return expr; +} + +VExprSPtr cast_expr(const VExprSPtr& child, const DataTypePtr& target_type) { + auto expr = Cast::create_shared(target_type); + expr->add_child(child); + return expr; +} + +class Int32SumGreaterThanExpr final : public VExpr { +public: + Int32SumGreaterThanExpr(int left_column_id, int right_column_id, int32_t value) + : VExpr(std::make_shared(), false), + _left_column_id(left_column_id), + _right_column_id(right_column_id), + _value(value) {} + + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + const auto& left_input = + assert_cast(*block->get_by_position(_left_column_id).column); + const auto& right_input = + assert_cast(*block->get_by_position(_right_column_id).column); + auto result = ColumnUInt8::create(); + auto& result_data = result->get_data(); + result_data.resize(count); + for (size_t row = 0; row < count; ++row) { + const size_t input_row = selector == nullptr ? row : (*selector)[row]; + result_data[row] = + left_input.get_element(input_row) + right_input.get_element(input_row) > _value; + } + result_column = std::move(result); + return Status::OK(); + } + + const std::string& expr_name() const override { return _expr_name; } + +private: + const int _left_column_id; + const int _right_column_id; + const int32_t _value; + const std::string _expr_name = "Int32SumGreaterThanExpr"; +}; + +class StringInExpr final : public VExpr { +public: + StringInExpr(int column_id, std::vector values) + : VExpr(std::make_shared(), false), + _column_id(column_id), + _values(std::move(values)) {} + + Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, + size_t count, ColumnPtr& result_column) const override { + const auto& input = + assert_cast(*block->get_by_position(_column_id).column); + auto result = ColumnUInt8::create(); + auto& result_data = result->get_data(); + result_data.resize(count); + for (size_t row = 0; row < count; ++row) { + const size_t input_row = selector == nullptr ? row : (*selector)[row]; + const auto value = input.get_data_at(input_row).to_string(); + result_data[row] = std::find(_values.begin(), _values.end(), value) != _values.end(); + } + result_column = std::move(result); + return Status::OK(); + } + + const std::string& expr_name() const override { return _expr_name; } + +private: + const int _column_id; + const std::vector _values; + const std::string _expr_name = "StringInExpr"; +}; + +VExprContextSPtr create_int32_greater_than_conjunct(int column_id, int32_t value) { + auto ctx = + VExprContext::create_shared(std::make_shared(column_id, value)); + ctx->_prepared = true; + ctx->_opened = true; + return ctx; +} + +VExprContextSPtr create_int32_sum_greater_than_conjunct(int left_column_id, int right_column_id, + int32_t value) { + auto ctx = VExprContext::create_shared( + std::make_shared(left_column_id, right_column_id, value)); + ctx->_prepared = true; + ctx->_opened = true; + return ctx; +} + +VExprContextSPtr create_string_in_conjunct(int column_id, std::vector values) { + auto ctx = VExprContext::create_shared( + std::make_shared(column_id, std::move(values))); + ctx->_prepared = true; + ctx->_opened = true; + return ctx; +} + +std::shared_ptr finish_array(arrow::ArrayBuilder* builder) { + std::shared_ptr array; + EXPECT_TRUE(builder->Finish(&array).ok()); + return array; +} + +std::shared_ptr build_int32_array(const std::vector& values) { + arrow::Int32Builder builder; + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); +} + +std::shared_ptr build_string_array(const std::vector& values) { + arrow::StringBuilder builder; + for (const auto& value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); +} + +std::shared_ptr build_struct_array(const std::vector& ids, + const std::vector& names) { + auto struct_type = arrow::struct_({arrow::field("id", arrow::int32(), false), + arrow::field("name", arrow::utf8(), false)}); + std::vector> field_builders; + auto id_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(id_builder))); + auto name_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(name_builder))); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* struct_id_builder = assert_cast(builder.field_builder(0)); + auto* struct_name_builder = assert_cast(builder.field_builder(1)); + for (size_t row = 0; row < ids.size(); ++row) { + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_id_builder->Append(ids[row]).ok()); + EXPECT_TRUE(struct_name_builder->Append(names[row]).ok()); + } + return finish_array(&builder); +} + +void write_parquet_file(const std::string& file_path, int64_t row_group_size = ROW_COUNT) { + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + arrow::field("value", arrow::utf8(), false), + }); + auto table = arrow::Table::Make(schema, + {build_int32_array({1, 2, 3, 4, 5}), + build_string_array({"one", "two", "three", "four", "five"})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + row_group_size, builder.build())); +} + +void write_int_pair_parquet_file(const std::string& file_path, int64_t row_group_size = ROW_COUNT) { + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + arrow::field("score", arrow::int32(), false), + arrow::field("value", arrow::utf8(), false), + }); + auto table = arrow::Table::Make( + schema, {build_int32_array({1, 2, 3, 4, 5}), build_int32_array({1, 2, 3, 4, 5}), + build_string_array({"one", "two", "three", "four", "five"})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + row_group_size, builder.build())); +} + +void write_struct_filter_parquet_file(const std::string& file_path) { + auto id_field = arrow::field("id", arrow::int32(), false); + auto name_field = arrow::field("name", arrow::utf8(), false); + auto struct_type = arrow::struct_({id_field, name_field}); + auto schema = arrow::schema({ + arrow::field("s", struct_type, false), + }); + auto table = arrow::Table::Make( + schema, {build_struct_array({1, 2, 10, 11}, {"one", "two", "ten", "eleven"})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 2, + builder.build())); +} + +void write_dictionary_filter_parquet_file(const std::string& file_path) { + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + arrow::field("value", arrow::utf8(), false), + }); + auto table = + arrow::Table::Make(schema, {build_int32_array({1, 2, 3, 4, 5, 6}), + build_string_array({"aa", "az", "lm", "lz", "za", "zz"})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + builder.enable_dictionary("value"); + builder.disable_dictionary("id"); + builder.disable_statistics(); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 1, + builder.build())); +} + +void write_nested_dictionary_filter_parquet_file(const std::string& file_path) { + auto id_field = arrow::field("id", arrow::int32(), false); + auto name_field = arrow::field("name", arrow::utf8(), false); + auto struct_type = arrow::struct_({id_field, name_field}); + auto schema = arrow::schema({ + arrow::field("s", struct_type, false), + }); + auto table = arrow::Table::Make( + schema, {build_struct_array({1, 2, 3, 4, 5, 6}, {"aa", "az", "lm", "lz", "za", "zz"})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + builder.enable_dictionary("s.name"); + builder.disable_dictionary("s.identifier.field_id"); + builder.disable_statistics(); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 1, + builder.build())); +} + +void write_dictionary_edge_parquet_file(const std::string& file_path) { + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + arrow::field("value", arrow::utf8(), false), + }); + auto table = arrow::Table::Make( + schema, + {build_int32_array({1, 2, 3, 4, 5, 6, 7, 8}), + build_string_array({"", "same", "other", "long-value", "", "tail", "same", "last"})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + builder.enable_dictionary("value"); + builder.disable_dictionary("id"); + builder.disable_statistics(); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 2, + builder.build())); +} + +void write_nested_page_index_filter_parquet_file(const std::string& file_path) { + std::vector ids(128); + std::iota(ids.begin(), ids.end(), 0); + std::vector names; + names.reserve(ids.size()); + for (const auto id : ids) { + names.push_back("name-" + std::to_string(id)); + } + auto id_field = arrow::field("id", arrow::int32(), false); + auto name_field = arrow::field("name", arrow::utf8(), false); + auto struct_type = arrow::struct_({id_field, name_field}); + auto schema = arrow::schema({ + arrow::field("s", struct_type, false), + }); + auto table = arrow::Table::Make(schema, {build_struct_array(ids, names)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + builder.disable_dictionary(); + builder.enable_write_page_index(); + builder.write_batch_size(8); + builder.data_pagesize(10); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + ids.size(), builder.build())); +} + +void write_page_index_filter_parquet_file(const std::string& file_path) { + std::vector ids(128); + std::iota(ids.begin(), ids.end(), 0); + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + }); + auto table = arrow::Table::Make(schema, {build_int32_array(ids)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + builder.disable_dictionary(); + builder.enable_write_page_index(); + builder.write_batch_size(8); + builder.data_pagesize(10); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + ids.size(), builder.build())); +} + +void write_page_index_filter_pair_parquet_file(const std::string& file_path) { + std::vector ids(128); + std::iota(ids.begin(), ids.end(), 0); + std::vector payloads; + payloads.reserve(ids.size()); + for (const auto id : ids) { + payloads.push_back(id + 1000); + } + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + arrow::field("payload", arrow::int32(), false), + }); + auto table = arrow::Table::Make(schema, {build_int32_array(ids), build_int32_array(payloads)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + builder.disable_dictionary(); + builder.enable_write_page_index(); + builder.write_batch_size(8); + builder.data_pagesize(10); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + ids.size(), builder.build())); +} + +parquet::ParquetColumnSchema primitive_bloom_schema(const DataTypePtr& type) { + parquet::ParquetColumnSchema schema; + schema.local_id = 0; + schema.name = "c0"; + schema.type = type; + schema.leaf_column_id = 0; + schema.kind = parquet::ParquetColumnSchemaKind::PRIMITIVE; + return schema; +} + +format::FileColumnPredicateFilter bloom_filter_with_predicate( + const std::shared_ptr& predicate) { + format::FileColumnPredicateFilter filter; + filter.file_column_id = format::LocalColumnId(0); + filter.target = format::FileNestedPredicateTarget(filter.file_column_id); + filter.predicates.push_back(predicate); + return filter; +} + +::parquet::BlockSplitBloomFilter bloom_filter_for_int32_values(const std::vector& values) { + ::parquet::BlockSplitBloomFilter bloom_filter; + bloom_filter.Init(::parquet::BlockSplitBloomFilter::kMinimumBloomFilterBytes); + for (const auto value : values) { + bloom_filter.InsertHash(bloom_filter.Hash(value)); + } + return bloom_filter; +} + +::parquet::BlockSplitBloomFilter bloom_filter_for_string_values( + const std::vector& values) { + ::parquet::BlockSplitBloomFilter bloom_filter; + bloom_filter.Init(::parquet::BlockSplitBloomFilter::kMinimumBloomFilterBytes); + for (const auto& value : values) { + ::parquet::ByteArray byte_array(static_cast(value.size()), + reinterpret_cast(value.data())); + bloom_filter.InsertHash(bloom_filter.Hash(&byte_array)); + } + return bloom_filter; +} + +Block build_file_block(const std::vector& schema) { + Block block; + for (const auto& field : schema) { + block.insert({field.type->create_column(), field.type, field.name}); + } + return block; +} + +Block build_file_block_with_row_position(const std::vector& schema) { + auto block = build_file_block(schema); + const auto row_position_field = format::row_position_column_definition(); + block.insert({row_position_field.type->create_column(), row_position_field.type, + row_position_field.name}); + return block; +} + +void use_schema_order_positions(format::FileScanRequest* request, + const std::vector& schema) { + DORIS_CHECK(request != nullptr); + for (size_t idx = 0; idx < schema.size(); ++idx) { + request->local_positions.emplace(format::LocalColumnId(schema[idx].local_id), + format::LocalIndex(idx)); + } +} + +int64_t parquet_column_start_offset(const ::parquet::ColumnChunkMetaData& column_metadata) { + return column_metadata.has_dictionary_page() + ? static_cast(column_metadata.dictionary_page_offset()) + : static_cast(column_metadata.data_page_offset()); +} + +std::pair row_group_mid_range(const std::string& file_path, int row_group_idx) { + auto reader = ::parquet::ParquetFileReader::OpenFile(file_path, false); + auto metadata = reader->metadata(); + auto row_group_metadata = metadata->RowGroup(row_group_idx); + auto first_column = row_group_metadata->ColumnChunk(0); + auto last_column = row_group_metadata->ColumnChunk(row_group_metadata->num_columns() - 1); + const int64_t row_group_start_offset = parquet_column_start_offset(*first_column); + const int64_t row_group_end_offset = + parquet_column_start_offset(*last_column) + last_column->total_compressed_size(); + const int64_t row_group_mid_offset = + row_group_start_offset + (row_group_end_offset - row_group_start_offset) / 2; + return {row_group_mid_offset, 1}; +} + +class TestFileReader final : public format::FileReader { +public: + TestFileReader(std::shared_ptr& system_properties, + std::unique_ptr& file_description, + std::shared_ptr io_ctx) + : format::FileReader(system_properties, file_description, io_ctx, nullptr) {} + + Status get_schema(std::vector* file_schema) const override { + file_schema->clear(); + format::ColumnDefinition field; + field.identifier = Field::create_field(0); + field.name = "id"; + field.type = std::make_shared(); + file_schema->push_back(std::move(field)); + return Status::OK(); + } + + bool has_request() const { return _request != nullptr; } + + bool eof() const { return _eof; } + + bool has_io_context() const { return _io_ctx != nullptr; } + + long io_context_use_count() const { return _io_ctx.use_count(); } +}; + +TEST(FileReaderTest, OpenStoresRequestAndCloseKeepsRequest) { + auto system_properties = std::make_shared(); + system_properties->system_type = TFileType::FILE_LOCAL; + auto file_description = std::make_unique(); + auto io_ctx = std::make_shared(); + TestFileReader reader(system_properties, file_description, io_ctx); + + auto request = std::make_shared(); + request->non_predicate_columns.push_back(field_projection(0)); + ASSERT_TRUE(reader.open(request).ok()); + EXPECT_NE(request, nullptr); + EXPECT_TRUE(reader.has_request()); + + ASSERT_TRUE(reader.close().ok()); + EXPECT_TRUE(reader.has_request()); + EXPECT_TRUE(reader.eof()); +} + +TEST(FileReaderTest, CloseReleasesSharedIOContext) { + auto system_properties = std::make_shared(); + system_properties->system_type = TFileType::FILE_LOCAL; + auto file_description = std::make_unique(); + auto io_ctx = std::make_shared(); + std::weak_ptr weak_io_ctx = io_ctx; + TestFileReader reader(system_properties, file_description, io_ctx); + + EXPECT_TRUE(reader.has_io_context()); + EXPECT_EQ(reader.io_context_use_count(), 2); + io_ctx.reset(); + EXPECT_FALSE(weak_io_ctx.expired()); + EXPECT_EQ(reader.io_context_use_count(), 1); + + ASSERT_TRUE(reader.close().ok()); + EXPECT_FALSE(reader.has_io_context()); + EXPECT_TRUE(weak_io_ctx.expired()); +} + +TEST(TableColumnMapperTest, CreatesComplexProjectionForStructChildren) { + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = std::make_shared(); + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = std::make_shared(); + struct_field.children = {a_field, b_field}; + struct_field.type = std::make_shared(DataTypes {a_field.type, b_field.type}, + Strings {"a", "b"}); + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_field.type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_field.type}, Strings {"b"}); + table_column.children = {table_child}; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + auto request = std::make_shared(); + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_column}, request.get()).ok()); + EXPECT_EQ(projection_ids(request->non_predicate_columns), std::vector({0})); + ASSERT_EQ(request->non_predicate_columns.size(), 1); + const auto& projection = request->non_predicate_columns[0]; + EXPECT_EQ(projection.index, 0); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].index, 1); + + ASSERT_EQ(mapper.mappings().size(), 1); + const auto* projected_type = + assert_cast(mapper.mappings()[0].file_type.get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "b"); +} + +TEST(TableColumnMapperTest, MergesStructFilterOnlyChildIntoPredicateProjection) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + filter_expr->add_child(struct_element_expr( + VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "a")); + filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + const auto& projection = request.predicate_columns[0]; + EXPECT_EQ(projection.index, 0); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 2); + EXPECT_EQ(projection.children[0].index, 1); + EXPECT_EQ(projection.children[1].index, 0); + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); + + ASSERT_EQ(mapper.mappings().size(), 1); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 1); + EXPECT_EQ(mapper.mappings()[0].child_mappings[0].file_column_name, "b"); + const auto* read_type = + assert_cast(mapper.mappings()[0].file_type.get()); + ASSERT_EQ(read_type->get_elements().size(), 2); + EXPECT_EQ(read_type->get_element_name(0), "a"); + EXPECT_EQ(read_type->get_element_name(1), "b"); +} + +TEST(TableColumnMapperTest, MapsRenamedNestedStructPredicateByFieldId) { + auto id_type = std::make_shared(); + format::ColumnDefinition file_child; + file_child.identifier = Field::create_field(101); + file_child.name = "file_id"; + file_child.type = id_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(100); + struct_field.name = "s"; + struct_field.type = std::make_shared(DataTypes {id_type}, Strings {"file_id"}); + struct_field.children = {file_child}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "table_id"; + table_child.type = id_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {id_type}, Strings {"table_id"}); + table_column.children = {table_child}; + + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + filter_expr->add_child(struct_element_expr( + VSlotRef::create_shared(0, 0, -1, table_column.type, "s"), id_type, "table_id")); + filter_expr->add_child(VLiteral::create_shared(id_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_FIELD_ID; + format::TableColumnMapper mapper(options); + set_local_ids(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.predicate_columns.size(), 1); + const auto& projection = request.predicate_columns[0]; + EXPECT_EQ(projection.index, 0); + EXPECT_TRUE(projection.project_all_children); + EXPECT_TRUE(projection.children.empty()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"file_id"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); +} + +TEST(TableColumnMapperTest, BuildsNestedStructInListPredicateFilter) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = in_predicate_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), + a_type, "a"), + a_type, {Field::create_field(5), Field::create_field(7)}); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IN_LIST); +} + +TEST(TableColumnMapperTest, BuildsNestedStructPredicateFilterForReverseComparison) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = std::make_shared( + "lt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::LT); + filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); + filter_expr->add_child(struct_element_expr( + VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "a")); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); +} + +TEST(TableColumnMapperTest, BuildsNestedStructNullPredicateFilters) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = std::make_shared("and", std::make_shared(), + TExprNodeType::COMPOUND_PRED, + TExprOpcode::COMPOUND_AND); + filter_expr->add_child(null_predicate_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), + a_type, "a"), + true)); + filter_expr->add_child(null_predicate_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), + a_type, "a"), + false)); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 2); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IS_NULL); + EXPECT_EQ(request.column_predicate_filters[0].predicates[1]->type(), + PredicateType::IS_NOT_NULL); +} + +TEST(TableColumnMapperTest, BuildsNestedStructPredicateFilterThroughSafeCast) { + auto file_a_type = std::make_shared(); + auto table_a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = file_a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {file_a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {table_a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + filter_expr->add_child(cast_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), + file_a_type, "a"), + table_a_type)); + filter_expr->add_child( + VLiteral::create_shared(table_a_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); +} + +TEST(TableColumnMapperTest, DoesNotBuildNestedStructPredicateFilterThroughUnsafeCast) { + auto file_a_type = std::make_shared(); + auto table_a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = file_a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {file_a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {table_a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + filter_expr->add_child(cast_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), + file_a_type, "a"), + table_a_type)); + filter_expr->add_child(VLiteral::create_shared(table_a_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + EXPECT_TRUE(request.column_predicate_filters.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].index, 0); + ASSERT_FALSE(request.predicate_columns[0].project_all_children); + ASSERT_EQ(request.predicate_columns[0].children.size(), 2); + EXPECT_EQ(request.predicate_columns[0].children[0].index, 1); + EXPECT_EQ(request.predicate_columns[0].children[1].index, 0); +} + +TEST(TableColumnMapperTest, BuildsNestedStructInListPredicateFilterForDeepPath) { + auto id_type = std::make_shared(); + auto name_type = std::make_shared(); + auto b_type = std::make_shared(); + auto inner_type = + std::make_shared(DataTypes {id_type, name_type}, Strings {"id", "n"}); + auto full_struct_type = + std::make_shared(DataTypes {inner_type, b_type}, Strings {"a", "b"}); + + format::ColumnDefinition id_field; + id_field.identifier = Field::create_field(0); + id_field.name = "id"; + id_field.type = id_type; + format::ColumnDefinition name_field; + name_field.identifier = Field::create_field(1); + name_field.name = "n"; + name_field.type = name_type; + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = inner_type; + a_field.children = {id_field, name_field}; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = full_struct_type; + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + auto nested_id_expr = struct_element_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_struct_type, "s"), + inner_type, "a"), + id_type, "id"); + auto filter_expr = + in_predicate_expr(nested_id_expr, id_type, + {Field::create_field(5), Field::create_field(7)}); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0, 0})); + expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0, 0}, {"a", "id"}); + ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IN_LIST); +} + +TEST(TableColumnMapperTest, DoesNotBuildNestedPredicateFilterForMissingChild) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + filter_expr->add_child(struct_element_expr( + VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "missing")); + filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +TEST(TableColumnMapperTest, DoesNotBuildNestedPredicateFilterFromOr) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition struct_field; + struct_field.identifier = Field::create_field(0); + struct_field.name = "s"; + struct_field.type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + struct_field.children = {a_field, b_field}; + + format::ColumnDefinition table_child; + table_child.identifier = Field::create_field(101); + table_child.name = "b"; + table_child.type = b_type; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "s"; + table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_column.children = {table_child}; + + const auto full_table_struct_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + auto left = std::make_shared("gt", std::make_shared(), + TExprNodeType::BINARY_PRED, TExprOpcode::GT); + left->add_child(struct_element_expr( + VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "a")); + left->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); + auto right = in_predicate_expr( + struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), + a_type, "a"), + a_type, {Field::create_field(7)}); + auto filter_expr = std::make_shared("or", std::make_shared(), + TExprNodeType::COMPOUND_PRED, + TExprOpcode::COMPOUND_OR); + filter_expr->add_child(left); + filter_expr->add_child(right); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&struct_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +TEST(TableColumnMapperTest, CreatesComplexProjectionForMapValueStructChildren) { + auto key_type = std::make_shared(); + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + auto value_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + + format::ColumnDefinition key_field; + key_field.identifier = Field::create_field(0); + key_field.name = "key"; + key_field.type = key_type; + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition value_field; + value_field.identifier = Field::create_field(1); + value_field.name = "value"; + value_field.type = value_type; + value_field.children = {a_field, b_field}; + format::ColumnDefinition entry_field; + entry_field.identifier = Field::create_field(0); + entry_field.name = "key_value"; + entry_field.type = std::make_shared(DataTypes {key_type, value_type}, + Strings {"key", "value"}); + entry_field.children = {key_field, value_field}; + format::ColumnDefinition map_field; + map_field.identifier = Field::create_field(0); + map_field.name = "m"; + map_field.type = std::make_shared(key_type, value_type); + map_field.children = {entry_field}; + + format::ColumnDefinition table_value_child; + table_value_child.identifier = Field::create_field(103); + table_value_child.name = "b"; + table_value_child.type = b_type; + format::ColumnDefinition table_value; + table_value.identifier = Field::create_field(102); + table_value.name = "value"; + table_value.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_value.children = {table_value_child}; + format::ColumnDefinition table_entry; + table_entry.identifier = Field::create_field(101); + table_entry.name = "entries"; + table_entry.type = + std::make_shared(DataTypes {table_value.type}, Strings {"value"}); + table_entry.children = {table_value}; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "m"; + table_column.type = std::make_shared(key_type, table_value.type); + table_column.children = {table_entry}; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&map_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {map_field}).ok()); + + auto request = std::make_shared(); + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_column}, request.get()).ok()); + EXPECT_EQ(projection_ids(request->non_predicate_columns), std::vector({0})); + ASSERT_EQ(request->non_predicate_columns.size(), 1); + const auto& projection = request->non_predicate_columns[0]; + EXPECT_EQ(projection.index, 0); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].index, 0); + ASSERT_EQ(projection.children[0].children.size(), 1); + EXPECT_EQ(projection.children[0].children[0].index, 1); + ASSERT_EQ(projection.children[0].children[0].children.size(), 1); + EXPECT_EQ(projection.children[0].children[0].children[0].index, 1); + + ASSERT_EQ(mapper.mappings().size(), 1); + const auto* projected_type = + assert_cast(mapper.mappings()[0].file_type.get()); + EXPECT_EQ(remove_nullable(projected_type->get_key_type())->get_primitive_type(), TYPE_INT); + const auto* projected_value = + assert_cast(projected_type->get_value_type().get()); + ASSERT_EQ(projected_value->get_elements().size(), 1); + EXPECT_EQ(projected_value->get_element_name(0), "b"); +} + +TEST(TableColumnMapperTest, DoesNotBuildNestedPredicateFilterThroughArrayWrapper) { + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + auto item_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition item_field; + item_field.identifier = Field::create_field(0); + item_field.name = "item"; + item_field.type = item_type; + item_field.children = {a_field, b_field}; + format::ColumnDefinition array_field; + array_field.identifier = Field::create_field(0); + array_field.name = "items"; + array_field.type = std::make_shared(item_type); + array_field.children = {item_field}; + + format::ColumnDefinition table_column = array_field; + + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + auto item_expr = struct_element_expr( + VSlotRef::create_shared(0, 0, -1, table_column.type, "items"), item_type, "item"); + filter_expr->add_child(struct_element_expr(item_expr, a_type, "a")); + filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&array_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {array_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].index, 0); + EXPECT_TRUE(request.predicate_columns[0].project_all_children); + EXPECT_TRUE(request.non_predicate_columns.empty()); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +TEST(TableColumnMapperTest, MapFilterOnlyStructChildIsPredicateProjectionOnly) { + auto key_type = std::make_shared(); + auto a_type = std::make_shared(); + auto b_type = std::make_shared(); + auto full_value_type = + std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); + + format::ColumnDefinition key_field; + key_field.identifier = Field::create_field(0); + key_field.name = "key"; + key_field.type = key_type; + format::ColumnDefinition a_field; + a_field.identifier = Field::create_field(0); + a_field.name = "a"; + a_field.type = a_type; + format::ColumnDefinition b_field; + b_field.identifier = Field::create_field(1); + b_field.name = "b"; + b_field.type = b_type; + format::ColumnDefinition value_field; + value_field.identifier = Field::create_field(1); + value_field.name = "value"; + value_field.type = full_value_type; + value_field.children = {a_field, b_field}; + format::ColumnDefinition entry_field; + entry_field.identifier = Field::create_field(0); + entry_field.name = "entries"; + entry_field.type = std::make_shared(DataTypes {key_type, full_value_type}, + Strings {"key", "value"}); + entry_field.children = {key_field, value_field}; + format::ColumnDefinition map_field; + map_field.identifier = Field::create_field(0); + map_field.name = "m"; + map_field.type = std::make_shared(key_type, full_value_type); + map_field.children = {entry_field}; + + format::ColumnDefinition table_value_child; + table_value_child.identifier = Field::create_field(103); + table_value_child.name = "b"; + table_value_child.type = b_type; + format::ColumnDefinition table_value; + table_value.identifier = Field::create_field(102); + table_value.name = "value"; + table_value.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); + table_value.children = {table_value_child}; + format::ColumnDefinition table_entry; + table_entry.identifier = Field::create_field(101); + table_entry.name = "entries"; + table_entry.type = + std::make_shared(DataTypes {table_value.type}, Strings {"value"}); + table_entry.children = {table_value}; + format::ColumnDefinition table_column; + table_column.identifier = Field::create_field(100); + table_column.name = "m"; + table_column.type = std::make_shared(key_type, table_value.type); + table_column.children = {table_entry}; + + auto filter_expr = std::make_shared( + "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); + auto full_entry_type = entry_field.type; + auto entries_expr = struct_element_expr( + VSlotRef::create_shared(0, 0, -1, + std::make_shared(key_type, full_value_type), "m"), + full_entry_type, "entries"); + auto value_expr = struct_element_expr(entries_expr, full_value_type, "value"); + filter_expr->add_child(struct_element_expr(value_expr, a_type, "a")); + filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); + format::TableFilter table_filter { + .conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {format::GlobalIndex(0)}, + }; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_column, 0); + set_name_identifiers(&map_field, 0); + ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {map_field}).ok()); + + format::FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + const auto& projection = request.predicate_columns[0]; + EXPECT_EQ(projection.index, 0); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].index, 0); + ASSERT_EQ(projection.children[0].children.size(), 1); + EXPECT_EQ(projection.children[0].children[0].index, 1); + ASSERT_EQ(projection.children[0].children[0].children.size(), 2); + EXPECT_EQ(projection.children[0].children[0].children[0].index, 1); + EXPECT_EQ(projection.children[0].children[0].children[1].index, 0); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +TEST(TableColumnMapperTest, ColumnPredicatesDoNotForcePredicateMaterialization) { + format::ColumnDefinition id_field; + id_field.identifier = Field::create_field(0); + id_field.name = "id"; + id_field.type = std::make_shared(); + + format::ColumnDefinition value_field; + value_field.identifier = Field::create_field(1); + value_field.name = "value"; + value_field.type = std::make_shared(); + + format::ColumnDefinition table_id; + table_id.identifier = Field::create_field(0); + table_id.name = "id"; + table_id.type = id_field.type; + + format::ColumnDefinition table_value; + table_value.identifier = Field::create_field(1); + table_value.name = "value"; + table_value.type = value_field.type; + + format::TableColumnMapperOptions options; + options.mode = format::TableColumnMappingMode::BY_NAME; + format::TableColumnMapper mapper(options); + set_name_identifiers(&table_id, 0); + set_name_identifiers(&table_value, 1); + set_name_identifiers(&id_field, 0); + set_name_identifiers(&value_field, 1); + ASSERT_TRUE(mapper.create_mapping({table_id, table_value}, {}, {id_field, value_field}).ok()); + + format::TableColumnPredicates column_predicates; + column_predicates[format::GlobalIndex(0)] = {create_comparison_predicate( + 0, "id", id_field.type, Field::create_field(2), false)}; + + auto request = std::make_shared(); + ASSERT_TRUE(mapper.create_scan_request({}, column_predicates, {table_id, table_value}, + request.get()) + .ok()); + EXPECT_TRUE(request->predicate_columns.empty()); + EXPECT_EQ(projection_ids(request->non_predicate_columns), std::vector({0, 1})); + ASSERT_EQ(request->column_predicate_filters.size(), 1); + EXPECT_EQ(request->column_predicate_filters[0].file_column_id.value(), 0); +} + +TEST(ParquetBloomFilterPruningTest, EqPredicateUsesArrowHashAndPrunesAbsentIntValue) { + auto schema = primitive_bloom_schema(std::make_shared()); + auto bloom_filter = bloom_filter_for_int32_values({1, 3}); + auto absent_filter = bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", schema.type, Field::create_field(2), false)); + auto present_filter = + bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", schema.type, Field::create_field(3), false)); + + EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, + bloom_filter)); + EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, present_filter, + bloom_filter)); +} + +TEST(ParquetBloomFilterPruningTest, InPredicatePrunesOnlyWhenAllValuesAreAbsent) { + auto schema = primitive_bloom_schema(std::make_shared()); + auto bloom_filter = bloom_filter_for_int32_values({1, 3}); + + auto absent_set = build_set(); + int32_t absent_first = 2; + int32_t absent_second = 4; + absent_set->insert(&absent_first); + absent_set->insert(&absent_second); + auto absent_filter = + bloom_filter_with_predicate(create_in_list_predicate( + 0, "c0", schema.type, absent_set, false)); + + auto present_set = build_set(); + int32_t present_first = 2; + int32_t present_second = 3; + present_set->insert(&present_first); + present_set->insert(&present_second); + auto present_filter = + bloom_filter_with_predicate(create_in_list_predicate( + 0, "c0", schema.type, present_set, false)); + + EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, + bloom_filter)); + EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, present_filter, + bloom_filter)); +} + +TEST(ParquetBloomFilterPruningTest, BooleanPredicateHashesAsParquetInt32) { + auto schema = primitive_bloom_schema(std::make_shared()); + auto bloom_filter = bloom_filter_for_int32_values({1}); + auto false_filter = bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", schema.type, Field::create_field(false), false)); + auto true_filter = bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", schema.type, Field::create_field(true), false)); + + EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, false_filter, + bloom_filter)); + EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, true_filter, + bloom_filter)); +} + +TEST(ParquetBloomFilterPruningTest, StringPredicateUsesArrowByteArrayHash) { + auto schema = primitive_bloom_schema(std::make_shared()); + auto bloom_filter = bloom_filter_for_string_values({"alpha", "omega"}); + auto absent_filter = bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", schema.type, Field::create_field("beta"), false)); + auto present_filter = + bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", schema.type, Field::create_field("alpha"), false)); + + EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, + bloom_filter)); + EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, present_filter, + bloom_filter)); +} + +TEST(ParquetBloomFilterPruningTest, NullableAcceptingAndUnsupportedPredicatesKeepRowGroup) { + auto schema = primitive_bloom_schema(std::make_shared()); + auto bloom_filter = bloom_filter_for_int32_values({1}); + auto nested_predicate = create_comparison_predicate( + 0, "c0", schema.type, Field::create_field(2), false); + auto accept_null_filter = + bloom_filter_with_predicate(std::make_shared(nested_predicate)); + EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, accept_null_filter, + bloom_filter)); + + auto unsupported_schema = primitive_bloom_schema(std::make_shared()); + auto unsupported_filter = + bloom_filter_with_predicate(create_comparison_predicate( + 0, "c0", unsupported_schema.type, Field::create_field(2), + false)); + EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes( + unsupported_schema, unsupported_filter, bloom_filter)); +} + +class NewParquetReaderTest : public testing::Test { +protected: + void SetUp() override { + _test_dir = std::filesystem::temp_directory_path() / "doris_format_v2_parquet_reader_test"; + std::filesystem::remove_all(_test_dir); + std::filesystem::create_directories(_test_dir); + _file_path = (_test_dir / "reader.parquet").string(); + write_parquet_file(_file_path); + } + + void TearDown() override { std::filesystem::remove_all(_test_dir); } + + std::unique_ptr create_reader(int64_t range_start_offset = 0, + int64_t range_size = -1, + RuntimeProfile* profile = nullptr) const { + auto system_properties = std::make_shared(); + system_properties->system_type = TFileType::FILE_LOCAL; + auto file_description = std::make_unique(); + file_description->path = _file_path; + file_description->file_size = static_cast(std::filesystem::file_size(_file_path)); + file_description->range_start_offset = range_start_offset; + file_description->range_size = range_size; + return std::make_unique(system_properties, file_description, + nullptr, profile); + } + + std::filesystem::path _test_dir; + std::string _file_path; +}; + +TEST_F(NewParquetReaderTest, GetSchemaReturnsFileLocalColumns) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + ASSERT_EQ(schema.size(), 2); + EXPECT_EQ(schema[0].local_id, 0); + EXPECT_EQ(schema[0].name, "id"); + EXPECT_EQ(schema[0].type->get_primitive_type(), TYPE_INT); + EXPECT_EQ(schema[1].local_id, 1); + EXPECT_EQ(schema[1].name, "value"); + EXPECT_EQ(schema[1].type->get_primitive_type(), TYPE_STRING); +} + +TEST_F(NewParquetReaderTest, ReadSingleRowGroupThenEof) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->non_predicate_columns = {field_projection(0), field_projection(1)}; + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, ROW_COUNT); + + const auto& ids = assert_cast(*block.get_by_position(0).column); + const auto& values = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(ids.size(), ROW_COUNT); + ASSERT_EQ(values.size(), ROW_COUNT); + EXPECT_EQ(ids.get_element(0), 1); + EXPECT_EQ(ids.get_element(4), 5); + EXPECT_EQ(values.get_data_at(0).to_string(), "one"); + EXPECT_EQ(values.get_data_at(4).to_string(), "five"); + + rows = 0; + eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_TRUE(eof); + EXPECT_EQ(rows, 0); +} + +TEST_F(NewParquetReaderTest, ReadMultipleRowGroups) { + write_parquet_file(_file_path, 2); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 3); + + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->non_predicate_columns = {field_projection(0), field_projection(1)}; + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector values; + bool eof = false; + while (!eof) { + Block block = build_file_block(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + values.push_back(value_column.get_data_at(row).to_string()); + } + } + + EXPECT_EQ(ids, std::vector({1, 2, 3, 4, 5})); + EXPECT_EQ(values, std::vector({"one", "two", "three", "four", "five"})); +} + +TEST_F(NewParquetReaderTest, ReadPredicateAndNonPredicateColumnsWithSelection) { + RuntimeProfile profile("new_parquet_reader_filter_profile"); + auto reader = create_reader(0, -1, &profile); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 2)); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", schema[0].type, Field::create_field(2), false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 3); + + const auto& ids = assert_cast(*block.get_by_position(0).column); + const auto& values = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(ids.size(), 3); + ASSERT_EQ(values.size(), 3); + EXPECT_EQ(ids.get_element(0), 3); + EXPECT_EQ(ids.get_element(1), 4); + EXPECT_EQ(ids.get_element(2), 5); + EXPECT_EQ(values.get_data_at(0).to_string(), "three"); + EXPECT_EQ(values.get_data_at(1).to_string(), "four"); + EXPECT_EQ(values.get_data_at(2).to_string(), "five"); + + ASSERT_NE(profile.get_counter("FileReaderCreateTime"), nullptr); + ASSERT_NE(profile.get_counter("FileNum"), nullptr); + ASSERT_NE(profile.get_counter("RawRowsRead"), nullptr); + ASSERT_NE(profile.get_counter("SelectedRows"), nullptr); + ASSERT_NE(profile.get_counter("RowsFilteredByConjunct"), nullptr); + ASSERT_NE(profile.get_counter("TotalBatches"), nullptr); + ASSERT_NE(profile.get_counter("EmptySelectionBatches"), nullptr); + ASSERT_NE(profile.get_counter("ReaderReadRows"), nullptr); + ASSERT_NE(profile.get_counter("ReaderSkipRows"), nullptr); + ASSERT_NE(profile.get_counter("ReaderSelectRows"), nullptr); + ASSERT_NE(profile.get_counter("ArrowReadRecordsTime"), nullptr); + ASSERT_NE(profile.get_counter("MaterializationTime"), nullptr); + ASSERT_GT(profile.get_counter("FileReaderCreateTime")->value(), 0); + EXPECT_EQ(profile.get_counter("FileNum")->value(), 1); + EXPECT_EQ(profile.get_counter("RawRowsRead")->value(), ROW_COUNT); + EXPECT_EQ(profile.get_counter("SelectedRows")->value(), 3); + EXPECT_EQ(profile.get_counter("RowsFilteredByConjunct")->value(), 2); + EXPECT_EQ(profile.get_counter("TotalBatches")->value(), 1); + EXPECT_EQ(profile.get_counter("EmptySelectionBatches")->value(), 0); + EXPECT_EQ(profile.get_counter("ReaderReadRows")->value(), ROW_COUNT + 3); + EXPECT_EQ(profile.get_counter("ReaderSkipRows")->value(), 2); + EXPECT_EQ(profile.get_counter("ReaderSelectRows")->value(), 3); + EXPECT_GT(profile.get_counter("ArrowReadRecordsTime")->value(), 0); + EXPECT_GT(profile.get_counter("MaterializationTime")->value(), 0); + + rows = 0; + eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_TRUE(eof); + EXPECT_EQ(rows, 0); +} + +TEST_F(NewParquetReaderTest, ColumnPredicateOnlyPrunesAndDoesNotFilterRowsInsideRowGroup) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", schema[0].type, Field::create_field(2), false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, ROW_COUNT); + + const auto& ids = assert_cast(*block.get_by_position(0).column); + const auto& values = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(ids.size(), ROW_COUNT); + ASSERT_EQ(values.size(), ROW_COUNT); + EXPECT_EQ(ids.get_element(0), 1); + EXPECT_EQ(ids.get_element(4), 5); + EXPECT_EQ(values.get_data_at(0).to_string(), "one"); + EXPECT_EQ(values.get_data_at(4).to_string(), "five"); +} + +TEST_F(NewParquetReaderTest, EmptySelectionUpdatesProfileCounters) { + RuntimeProfile profile("new_parquet_reader_empty_selection_profile"); + auto reader = create_reader(0, -1, &profile); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 10)); + use_schema_order_positions(request.get(), schema); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_TRUE(eof); + EXPECT_EQ(rows, 0); + + ASSERT_NE(profile.get_counter("RawRowsRead"), nullptr); + ASSERT_NE(profile.get_counter("SelectedRows"), nullptr); + ASSERT_NE(profile.get_counter("RowsFilteredByConjunct"), nullptr); + ASSERT_NE(profile.get_counter("TotalBatches"), nullptr); + ASSERT_NE(profile.get_counter("EmptySelectionBatches"), nullptr); + EXPECT_EQ(profile.get_counter("RawRowsRead")->value(), ROW_COUNT); + EXPECT_EQ(profile.get_counter("SelectedRows")->value(), 0); + EXPECT_EQ(profile.get_counter("RowsFilteredByConjunct")->value(), ROW_COUNT); + EXPECT_EQ(profile.get_counter("TotalBatches")->value(), 1); + EXPECT_EQ(profile.get_counter("EmptySelectionBatches")->value(), 1); +} + +TEST_F(NewParquetReaderTest, ReadMultiPredicateColumnsBeforeExpressionFilter) { + write_int_pair_parquet_file(_file_path); + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0), field_projection(1)}; + request->non_predicate_columns = {}; + request->conjuncts.push_back(create_int32_sum_greater_than_conjunct(0, 1, 7)); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 2); + + const auto& ids = assert_cast(*block.get_by_position(0).column); + const auto& scores = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(ids.size(), 2); + ASSERT_EQ(scores.size(), 2); + EXPECT_EQ(ids.get_element(0), 4); + EXPECT_EQ(ids.get_element(1), 5); + EXPECT_EQ(scores.get_element(0), 4); + EXPECT_EQ(scores.get_element(1), 5); +} + +TEST_F(NewParquetReaderTest, PredicateColumnFiltersBeforeNonPredicateRead) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 2)); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 3); + + const auto& ids = assert_cast(*block.get_by_position(0).column); + const auto& values = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(ids.size(), 3); + ASSERT_EQ(values.size(), 3); + EXPECT_EQ(ids.get_element(0), 3); + EXPECT_EQ(ids.get_element(1), 4); + EXPECT_EQ(ids.get_element(2), 5); + EXPECT_EQ(values.get_data_at(0).to_string(), "three"); + EXPECT_EQ(values.get_data_at(1).to_string(), "four"); + EXPECT_EQ(values.get_data_at(2).to_string(), "five"); +} + +TEST_F(NewParquetReaderTest, NonPredicateColumnKeepsSelectionFromPredicateColumn) { + write_int_pair_parquet_file(_file_path); + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 2)); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 3); + + const auto& ids = assert_cast(*block.get_by_position(0).column); + const auto& scores = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(ids.size(), 3); + ASSERT_EQ(scores.size(), 3); + EXPECT_EQ(ids.get_element(0), 3); + EXPECT_EQ(ids.get_element(1), 4); + EXPECT_EQ(ids.get_element(2), 5); + EXPECT_EQ(scores.get_element(0), 3); + EXPECT_EQ(scores.get_element(1), 4); + EXPECT_EQ(scores.get_element(2), 5); +} + +TEST_F(NewParquetReaderTest, PredicateFiltersRowGroupsByStatistics) { + write_parquet_file(_file_path, 2); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 3); + + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 2)); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", schema[0].type, Field::create_field(2), false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector values; + bool eof = false; + while (!eof) { + Block block = build_file_block(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + values.push_back(value_column.get_data_at(row).to_string()); + } + } + + EXPECT_EQ(ids, std::vector({3, 4, 5})); + EXPECT_EQ(values, std::vector({"three", "four", "five"})); +} + +TEST_F(NewParquetReaderTest, PredicateFiltersRowGroupsByDictionary) { + write_dictionary_filter_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 6); + for (int row_group_idx = 0; row_group_idx < 6; ++row_group_idx) { + auto row_group = parquet_file_reader->metadata()->RowGroup(row_group_idx); + ASSERT_NE(row_group, nullptr); + auto value_chunk = row_group->ColumnChunk(1); + ASSERT_NE(value_chunk, nullptr); + ASSERT_TRUE(value_chunk->has_dictionary_page()); + ASSERT_TRUE(value_chunk->statistics() == nullptr || + !value_chunk->statistics()->HasMinMax()); + } + + std::vector> file_schema; + auto schema_descriptor = parquet_file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_EQ(file_schema.size(), 2); + + format::FileScanRequest plan_request; + format::FileColumnPredicateFilter plan_column_filter; + plan_column_filter.file_column_id = format::LocalColumnId(1); + auto value_type = std::make_shared(); + plan_column_filter.predicates.push_back(create_comparison_predicate( + 1, "value", value_type, Field::create_field("lm"), false)); + plan_request.column_predicate_filters.push_back(std::move(plan_column_filter)); + + parquet::RowGroupScanPlan plan; + parquet::ParquetScanRange scan_range; + ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + plan_request, scan_range, false, &plan) + .ok()); + EXPECT_EQ(plan.pruning_stats.total_row_groups, 6); + EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.filtered_row_groups_by_dictionary, 5); + EXPECT_EQ(plan.pruning_stats.filtered_group_rows, 5); + EXPECT_EQ(plan.pruning_stats.selected_row_ranges, 1); + + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->predicate_columns = {field_projection(1)}; + request->non_predicate_columns = {field_projection(0)}; + request->conjuncts.push_back(create_string_in_conjunct(1, {"lm"})); + use_schema_order_positions(request.get(), schema); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(1); + column_filter.predicates.push_back(create_comparison_predicate( + 1, "value", schema[1].type, Field::create_field("lm"), false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector values; + bool eof = false; + while (!eof) { + Block block = build_file_block(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + values.push_back(value_column.get_data_at(row).to_string()); + } + } + + EXPECT_EQ(ids, std::vector({3})); + EXPECT_EQ(values, std::vector({"lm"})); +} + +TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByStatistics) { + write_struct_filter_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 2); + + std::vector> file_schema; + auto schema_descriptor = parquet_file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_EQ(file_schema.size(), 1); + ASSERT_EQ(file_schema[0]->children.size(), 2); + ASSERT_EQ(file_schema[0]->children[0]->name, "id"); + + format::FileScanRequest request; + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.file_child_id_path = {0}; + auto id_type = std::make_shared(); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", id_type, Field::create_field(5), false)); + request.column_predicate_filters.push_back(std::move(column_filter)); + + parquet::RowGroupScanPlan plan; + parquet::ParquetScanRange scan_range; + ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, request, + scan_range, false, &plan) + .ok()); + ASSERT_EQ(plan.row_groups.size(), 1); + EXPECT_EQ(plan.row_groups[0].row_group_id, 1); + EXPECT_EQ(plan.pruning_stats.total_row_groups, 2); + EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.filtered_row_groups_by_statistics, 1); + EXPECT_EQ(plan.pruning_stats.filtered_group_rows, 2); +} + +TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByDictionary) { + write_nested_dictionary_filter_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 6); + for (int row_group_idx = 0; row_group_idx < 6; ++row_group_idx) { + auto row_group = parquet_file_reader->metadata()->RowGroup(row_group_idx); + ASSERT_NE(row_group, nullptr); + auto name_chunk = row_group->ColumnChunk(1); + ASSERT_NE(name_chunk, nullptr); + ASSERT_TRUE(name_chunk->has_dictionary_page()); + ASSERT_TRUE(name_chunk->statistics() == nullptr || !name_chunk->statistics()->HasMinMax()); + } + + std::vector> file_schema; + auto schema_descriptor = parquet_file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_EQ(file_schema.size(), 1); + ASSERT_EQ(file_schema[0]->children.size(), 2); + ASSERT_EQ(file_schema[0]->children[1]->name, "name"); + + format::FileScanRequest request; + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.file_child_id_path = {1}; + auto name_type = std::make_shared(); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "name", name_type, Field::create_field("lm"), false)); + request.column_predicate_filters.push_back(std::move(column_filter)); + + parquet::RowGroupScanPlan plan; + parquet::ParquetScanRange scan_range; + ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, request, + scan_range, false, &plan) + .ok()); + ASSERT_EQ(plan.row_groups.size(), 1); + EXPECT_EQ(plan.row_groups[0].row_group_id, 2); + EXPECT_EQ(plan.pruning_stats.total_row_groups, 6); + EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.filtered_row_groups_by_dictionary, 5); + EXPECT_EQ(plan.pruning_stats.filtered_group_rows, 5); +} + +TEST_F(NewParquetReaderTest, PlannerNarrowsRowRangesByPageIndex) { + write_page_index_filter_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 1); + auto page_index_reader = parquet_file_reader->GetPageIndexReader(); + ASSERT_NE(page_index_reader, nullptr); + auto row_group_index_reader = page_index_reader->RowGroup(0); + ASSERT_NE(row_group_index_reader, nullptr); + auto offset_index = row_group_index_reader->GetOffsetIndex(0); + ASSERT_NE(offset_index, nullptr); + ASSERT_GT(offset_index->page_locations().size(), 1); + + std::vector> file_schema; + auto schema_descriptor = parquet_file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_EQ(file_schema.size(), 1); + + format::FileScanRequest request; + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + auto id_type = std::make_shared(); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", id_type, Field::create_field(63), false)); + request.column_predicate_filters.push_back(std::move(column_filter)); + + parquet::RowGroupScanPlan plan; + parquet::ParquetScanRange scan_range; + ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, request, + scan_range, false, &plan) + .ok()); + ASSERT_EQ(plan.row_groups.size(), 1); + ASSERT_FALSE(plan.row_groups[0].selected_ranges.empty()); + EXPECT_GT(plan.row_groups[0].selected_ranges.front().start, 0); + EXPECT_LT(plan.row_groups[0].selected_ranges.front().length, 128); + auto skip_plan_it = plan.row_groups[0].page_skip_plans.find(0); + ASSERT_NE(skip_plan_it, plan.row_groups[0].page_skip_plans.end()); + EXPECT_EQ(skip_plan_it->second.leaf_column_id, 0); + EXPECT_GT(skip_plan_it->second.skipped_ranges.size(), 0); + EXPECT_GT(skip_plan_it->second.skipped_pages.size(), 1); + ASSERT_EQ(skip_plan_it->second.skipped_pages.size(), + skip_plan_it->second.skipped_page_compressed_sizes.size()); + int64_t skipped_compressed_bytes = 0; + for (size_t page_idx = 0; page_idx < skip_plan_it->second.skipped_pages.size(); ++page_idx) { + if (skip_plan_it->second.should_skip_page(page_idx)) { + skipped_compressed_bytes += skip_plan_it->second.skipped_page_compressed_size(page_idx); + } + } + EXPECT_GT(skipped_compressed_bytes, 0); + EXPECT_EQ(plan.pruning_stats.total_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.filtered_row_groups_by_page_index, 0); + EXPECT_GT(plan.pruning_stats.filtered_page_rows, 0); + EXPECT_EQ(plan.pruning_stats.selected_row_ranges, plan.row_groups[0].selected_ranges.size()); +} + +TEST_F(NewParquetReaderTest, NestedStructPredicateNarrowsRowRangesByPageIndex) { + write_nested_page_index_filter_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 1); + auto page_index_reader = parquet_file_reader->GetPageIndexReader(); + ASSERT_NE(page_index_reader, nullptr); + auto row_group_index_reader = page_index_reader->RowGroup(0); + ASSERT_NE(row_group_index_reader, nullptr); + auto offset_index = row_group_index_reader->GetOffsetIndex(0); + ASSERT_NE(offset_index, nullptr); + ASSERT_GT(offset_index->page_locations().size(), 1); + + std::vector> file_schema; + auto schema_descriptor = parquet_file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_EQ(file_schema.size(), 1); + ASSERT_EQ(file_schema[0]->children.size(), 2); + ASSERT_EQ(file_schema[0]->children[0]->name, "id"); + + format::FileScanRequest request; + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.file_child_id_path = {0}; + auto id_type = std::make_shared(); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", id_type, Field::create_field(63), false)); + request.column_predicate_filters.push_back(std::move(column_filter)); + + parquet::RowGroupScanPlan plan; + parquet::ParquetScanRange scan_range; + ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, request, + scan_range, false, &plan) + .ok()); + ASSERT_EQ(plan.row_groups.size(), 1); + ASSERT_FALSE(plan.row_groups[0].selected_ranges.empty()); + EXPECT_GT(plan.row_groups[0].selected_ranges.front().start, 0); + EXPECT_LT(plan.row_groups[0].selected_ranges.front().length, 128); + auto skip_plan_it = plan.row_groups[0].page_skip_plans.find(0); + ASSERT_NE(skip_plan_it, plan.row_groups[0].page_skip_plans.end()); + EXPECT_EQ(skip_plan_it->second.leaf_column_id, 0); + EXPECT_GT(skip_plan_it->second.skipped_ranges.size(), 0); + EXPECT_GT(skip_plan_it->second.skipped_pages.size(), 1); + ASSERT_EQ(skip_plan_it->second.skipped_pages.size(), + skip_plan_it->second.skipped_page_compressed_sizes.size()); + int64_t skipped_compressed_bytes = 0; + for (size_t page_idx = 0; page_idx < skip_plan_it->second.skipped_pages.size(); ++page_idx) { + if (skip_plan_it->second.should_skip_page(page_idx)) { + skipped_compressed_bytes += skip_plan_it->second.skipped_page_compressed_size(page_idx); + } + } + EXPECT_GT(skipped_compressed_bytes, 0); + EXPECT_EQ(plan.pruning_stats.total_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.filtered_row_groups_by_page_index, 0); + EXPECT_GT(plan.pruning_stats.filtered_page_rows, 0); + EXPECT_EQ(plan.pruning_stats.selected_row_ranges, plan.row_groups[0].selected_ranges.size()); +} + +TEST_F(NewParquetReaderTest, PageIndexFilteredPagesDoNotDoubleSkipOutputColumns) { + write_page_index_filter_pair_parquet_file(_file_path); + RuntimeProfile profile("new_parquet_reader_page_skip"); + auto reader = create_reader(0, -1, &profile); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + ASSERT_EQ(schema.size(), 2); + Block block = build_file_block(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 63)); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", schema[0].type, Field::create_field(63), false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector payloads; + bool eof = false; + while (!eof) { + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& payload_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + payloads.push_back(payload_column.get_element(row)); + } + } + + ASSERT_NE(profile.get_counter("PagesSkippedByDataPageFilter"), nullptr); + ASSERT_NE(profile.get_counter("DataPageFilterSkipBytes"), nullptr); + ASSERT_NE(profile.get_counter("RawRowsRead"), nullptr); + ASSERT_NE(profile.get_counter("SelectedRows"), nullptr); + ASSERT_NE(profile.get_counter("RangeGapSkippedRows"), nullptr); + ASSERT_NE(profile.get_counter("ReaderSkipRows"), nullptr); + ASSERT_NE(profile.get_counter("RowGroupFilterTime"), nullptr); + ASSERT_NE(profile.get_counter("PageIndexFilterTime"), nullptr); + ASSERT_NE(profile.get_counter("PageIndexReadTime"), nullptr); + EXPECT_GT(profile.get_counter("PagesSkippedByDataPageFilter")->value(), 0); + EXPECT_GT(profile.get_counter("DataPageFilterSkipBytes")->value(), 0); + EXPECT_EQ(profile.get_counter("RawRowsRead")->value(), 64); + EXPECT_EQ(profile.get_counter("SelectedRows")->value(), 64); + EXPECT_GT(profile.get_counter("RangeGapSkippedRows")->value(), 0); + EXPECT_EQ(profile.get_counter("ReaderSkipRows")->value(), 0); + EXPECT_GT(profile.get_counter("RowGroupFilterTime")->value(), 0); + EXPECT_GT(profile.get_counter("PageIndexFilterTime")->value(), 0); + EXPECT_GT(profile.get_counter("PageIndexReadTime")->value(), 0); + + ASSERT_EQ(ids.size(), 64); + ASSERT_EQ(payloads.size(), ids.size()); + for (size_t row = 0; row < ids.size(); ++row) { + EXPECT_EQ(ids[row], static_cast(row + 64)); + EXPECT_EQ(payloads[row], ids[row] + 1000); + } +} + +TEST_F(NewParquetReaderTest, InPredicateFiltersRowGroupsByDictionary) { + write_dictionary_filter_parquet_file(_file_path); + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->predicate_columns = {field_projection(1)}; + request->non_predicate_columns = {field_projection(0)}; + request->conjuncts.push_back(create_string_in_conjunct(1, {"az", "za"})); + use_schema_order_positions(request.get(), schema); + auto set = build_set(); + set->insert(const_cast("az"), 2); + set->insert(const_cast("za"), 2); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(1); + column_filter.predicates.push_back(create_in_list_predicate( + 1, "value", schema[1].type, set, false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector values; + bool eof = false; + while (!eof) { + Block block = build_file_block(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + values.push_back(value_column.get_data_at(row).to_string()); + } + } + + EXPECT_EQ(ids, std::vector({2, 5})); + EXPECT_EQ(values, std::vector({"az", "za"})); +} + +TEST_F(NewParquetReaderTest, DictionaryPageV2StringEdgesSurviveSelection) { + write_dictionary_edge_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 4); + for (int row_group_idx = 0; row_group_idx < 4; ++row_group_idx) { + auto row_group = parquet_file_reader->metadata()->RowGroup(row_group_idx); + ASSERT_NE(row_group, nullptr); + ASSERT_TRUE(row_group->ColumnChunk(1)->has_dictionary_page()); + } + + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->predicate_columns = {field_projection(1)}; + request->non_predicate_columns = {field_projection(0)}; + request->conjuncts.push_back(create_string_in_conjunct(1, {"", "same"})); + use_schema_order_positions(request.get(), schema); + auto set = build_set(); + set->insert(const_cast(""), 0); + set->insert(const_cast("same"), 4); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(1); + column_filter.predicates.push_back(create_in_list_predicate( + 1, "value", schema[1].type, set, false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector values; + bool eof = false; + while (!eof) { + Block block = build_file_block(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + values.push_back(value_column.get_data_at(row).to_string()); + } + } + + EXPECT_EQ(ids, std::vector({1, 2, 5, 7})); + EXPECT_EQ(values, std::vector({"", "same", "", "same"})); +} + +TEST_F(NewParquetReaderTest, StatisticsPruningSkipsPrefixRowGroupsAndReadsLaterGroups) { + write_parquet_file(_file_path, 1); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 5); + + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(1)}; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 3)); + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(0); + column_filter.predicates.push_back(create_comparison_predicate( + 0, "id", schema[0].type, Field::create_field(4), false)); + request->column_predicate_filters.push_back(std::move(column_filter)); + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector values; + bool eof = false; + while (!eof) { + Block block = build_file_block(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = + assert_cast(*block.get_by_position(1).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + values.push_back(value_column.get_data_at(row).to_string()); + } + } + + EXPECT_EQ(ids, std::vector({4, 5})); + EXPECT_EQ(values, std::vector({"four", "five"})); +} + +TEST_F(NewParquetReaderTest, RowPositionReaderReturnsFileLocalPositions) { + write_parquet_file(_file_path, 2); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 3); + + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->non_predicate_columns = {field_projection(format::ROW_POSITION_COLUMN_ID), + field_projection(0)}; + request->local_positions = { + {format::LocalColumnId(0), format::LocalIndex(0)}, + {format::LocalColumnId(format::ROW_POSITION_COLUMN_ID), format::LocalIndex(2)}, + }; + ASSERT_TRUE(reader->open(request).ok()); + + std::vector row_positions; + std::vector ids; + bool eof = false; + while (!eof) { + Block block = build_file_block_with_row_position(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& row_position_column = + assert_cast(*block.get_by_position(2).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + row_positions.push_back(row_position_column.get_element(row)); + } + } + + EXPECT_EQ(ids, std::vector({1, 2, 3, 4, 5})); + EXPECT_EQ(row_positions, std::vector({0, 1, 2, 3, 4})); +} + +TEST_F(NewParquetReaderTest, RowPositionReaderKeepsPositionsAfterSelection) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block_with_row_position(schema); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0)}; + request->non_predicate_columns = {field_projection(format::ROW_POSITION_COLUMN_ID)}; + request->local_positions = { + {format::LocalColumnId(0), format::LocalIndex(0)}, + {format::LocalColumnId(format::ROW_POSITION_COLUMN_ID), format::LocalIndex(2)}, + }; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 2)); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 3); + + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& row_position_column = + assert_cast(*block.get_by_position(2).column); + EXPECT_EQ(id_column.get_element(0), 3); + EXPECT_EQ(id_column.get_element(1), 4); + EXPECT_EQ(id_column.get_element(2), 5); + EXPECT_EQ(row_position_column.get_element(0), 2); + EXPECT_EQ(row_position_column.get_element(1), 3); + EXPECT_EQ(row_position_column.get_element(2), 4); +} + +TEST_F(NewParquetReaderTest, DeletePredicateFiltersRowPositions) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block_with_row_position(schema); + + static const std::vector deleted_rows {1, 3}; + auto delete_predicate = std::make_shared(deleted_rows); + delete_predicate->add_child(VSlotRef::create_shared(2, 2, -1, std::make_shared(), + format::ROW_POSITION_COLUMN_NAME)); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(format::ROW_POSITION_COLUMN_ID)}; + request->non_predicate_columns = {field_projection(0)}; + request->local_positions = { + {format::LocalColumnId(0), format::LocalIndex(0)}, + {format::LocalColumnId(format::ROW_POSITION_COLUMN_ID), format::LocalIndex(2)}, + }; + request->delete_conjuncts.push_back(VExprContext::create_shared(std::move(delete_predicate))); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 3); + + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& row_position_column = + assert_cast(*block.get_by_position(2).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 3); + EXPECT_EQ(id_column.get_element(2), 5); + EXPECT_EQ(row_position_column.get_element(0), 0); + EXPECT_EQ(row_position_column.get_element(1), 2); + EXPECT_EQ(row_position_column.get_element(2), 4); +} + +TEST_F(NewParquetReaderTest, QueryPredicateAndDeletePredicateFilterRowPositions) { + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + Block block = build_file_block_with_row_position(schema); + + static const std::vector deleted_rows {3}; + auto delete_predicate = std::make_shared(deleted_rows); + delete_predicate->add_child(VSlotRef::create_shared(2, 2, -1, std::make_shared(), + format::ROW_POSITION_COLUMN_NAME)); + + auto request = std::make_shared(); + request->predicate_columns = {field_projection(0), + field_projection(format::ROW_POSITION_COLUMN_ID)}; + request->non_predicate_columns = {}; + request->local_positions = { + {format::LocalColumnId(0), format::LocalIndex(0)}, + {format::LocalColumnId(format::ROW_POSITION_COLUMN_ID), format::LocalIndex(2)}, + }; + request->conjuncts.push_back(create_int32_greater_than_conjunct(0, 2)); + request->delete_conjuncts.push_back(VExprContext::create_shared(std::move(delete_predicate))); + ASSERT_TRUE(reader->open(request).ok()); + + size_t rows = 0; + bool eof = false; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + EXPECT_FALSE(eof); + ASSERT_EQ(rows, 2); + + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& row_position_column = + assert_cast(*block.get_by_position(2).column); + EXPECT_EQ(id_column.get_element(0), 3); + EXPECT_EQ(id_column.get_element(1), 5); + EXPECT_EQ(row_position_column.get_element(0), 2); + EXPECT_EQ(row_position_column.get_element(1), 4); +} + +TEST_F(NewParquetReaderTest, RowPositionReaderUsesFileLocalPositionsForScanRange) { + write_parquet_file(_file_path, 2); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 3); + + const std::vector> expected_ids = {{1, 2}, {3, 4}, {5}}; + const std::vector> expected_row_positions = {{0, 1}, {2, 3}, {4}}; + for (int row_group_idx = 0; row_group_idx < 3; ++row_group_idx) { + const auto [range_start_offset, range_size] = + row_group_mid_range(_file_path, row_group_idx); + auto reader = create_reader(range_start_offset, range_size); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + auto request = std::make_shared(); + request->non_predicate_columns = {field_projection(format::ROW_POSITION_COLUMN_ID), + field_projection(0)}; + request->local_positions = { + {format::LocalColumnId(0), format::LocalIndex(0)}, + {format::LocalColumnId(format::ROW_POSITION_COLUMN_ID), format::LocalIndex(2)}, + }; + ASSERT_TRUE(reader->open(request).ok()); + + std::vector ids; + std::vector row_positions; + bool eof = false; + while (!eof) { + Block block = build_file_block_with_row_position(schema); + size_t rows = 0; + ASSERT_TRUE(reader->get_block(&block, &rows, &eof).ok()); + if (rows == 0) { + continue; + } + const auto& id_column = + assert_cast(*block.get_by_position(0).column); + const auto& row_position_column = + assert_cast(*block.get_by_position(2).column); + for (size_t row = 0; row < rows; ++row) { + ids.push_back(id_column.get_element(row)); + row_positions.push_back(row_position_column.get_element(row)); + } + } + + EXPECT_EQ(ids, expected_ids[row_group_idx]); + EXPECT_EQ(row_positions, expected_row_positions[row_group_idx]); + } +} + +} // namespace +} // namespace doris diff --git a/be/test/format_v2/table_reader_request_test.cpp b/be/test/format_v2/table_reader_request_test.cpp new file mode 100644 index 00000000000000..2a74b7cd67ed21 --- /dev/null +++ b/be/test/format_v2/table_reader_request_test.cpp @@ -0,0 +1,50 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include + +#include "format_v2/table_reader.h" + +namespace doris::format { +namespace { + +class TableReaderRequestTestHelper final : public TableReader { +public: + using TableReader::_append_file_scan_column; +}; + +TEST(TableReaderRequestTest, AppendPredicateColumnKeepsOtherNonPredicateColumns) { + TableReaderRequestTestHelper reader; + FileScanRequest request; + + reader._append_file_scan_column(&request, LocalColumnId(1), &request.non_predicate_columns); + reader._append_file_scan_column(&request, LocalColumnId(2), &request.non_predicate_columns); + reader._append_file_scan_column(&request, LocalColumnId(1), &request.predicate_columns); + + ASSERT_EQ(request.local_positions.size(), 2); + EXPECT_EQ(request.local_positions.at(LocalColumnId(1)).value(), 0); + EXPECT_EQ(request.local_positions.at(LocalColumnId(2)).value(), 1); + + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(1)); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + EXPECT_EQ(request.non_predicate_columns[0].column_id(), LocalColumnId(2)); +} + +} // namespace +} // namespace doris::format diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp new file mode 100644 index 00000000000000..67dcffe09267e9 --- /dev/null +++ b/be/test/format_v2/table_reader_test.cpp @@ -0,0 +1,4717 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/table_reader.h" + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "common/consts.h" +#include "core/assert_cast.h" +#include "core/block/block.h" +#include "core/column/column_array.h" +#include "core/column/column_const.h" +#include "core/column/column_map.h" +#include "core/column/column_nullable.h" +#include "core/column/column_string.h" +#include "core/column/column_struct.h" +#include "core/column/column_vector.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/data_type_struct.h" +#include "exec/common/endian.h" +#include "exprs/vectorized_fn_call.h" +#include "exprs/vexpr.h" +#include "exprs/vliteral.h" +#include "exprs/vslot_ref.h" +#include "format/format_common.h" +#include "format/table/deletion_vector_reader.h" +#include "format_v2/table/iceberg_reader.h" +#include "gen_cpp/Exprs_types.h" +#include "gen_cpp/PlanNodes_types.h" +#include "io/io_common.h" +#include "roaring/roaring64map.hh" +#include "runtime/runtime_profile.h" +#include "runtime/runtime_state.h" +#include "storage/predicate/predicate_creator.h" + +namespace doris::format { +namespace { + +LocalColumnIndex field_projection(int32_t column_id) { + return LocalColumnIndex {.index = column_id}; +} + +std::vector projection_ids(const std::vector& projections) { + std::vector ids; + ids.reserve(projections.size()); + for (const auto& projection : projections) { + ids.push_back(projection.index); + } + return ids; +} + +TEST(LocalColumnIndexTest, MergeUnionsPartialChildrenAndFullProjectionDominates) { + LocalColumnIndex target {.index = 10, .project_all_children = false}; + target.children.push_back({.index = 1}); + target.children.push_back({.index = 2, .project_all_children = false}); + target.children.back().children.push_back({.index = 20}); + + LocalColumnIndex source {.index = 10, .project_all_children = false}; + source.children.push_back({.index = 2, .project_all_children = false}); + source.children.back().children.push_back({.index = 21}); + source.children.push_back({.index = 3}); + + ASSERT_TRUE(merge_local_column_index(&target, source).ok()); + ASSERT_FALSE(target.project_all_children); + ASSERT_EQ(std::vector({1, 2, 3}), projection_ids(target.children)); + ASSERT_FALSE(target.children[1].project_all_children); + ASSERT_EQ(std::vector({20, 21}), projection_ids(target.children[1].children)); + ASSERT_TRUE(target.children[2].project_all_children); + + LocalColumnIndex full_source {.index = 10}; + ASSERT_TRUE(merge_local_column_index(&target, full_source).ok()); + ASSERT_TRUE(target.project_all_children); + ASSERT_TRUE(target.children.empty()); +} + +TEST(LocalColumnIndexTest, FindsProjectedChildren) { + LocalColumnIndex projection {.index = 10, .project_all_children = false}; + projection.children.push_back({.index = 1}); + projection.children.push_back({.index = 2}); + + EXPECT_TRUE(is_full_projection(nullptr)); + EXPECT_FALSE(is_full_projection(&projection)); + EXPECT_TRUE(is_partial_projection(&projection)); + ASSERT_NE(find_child_projection(&projection, 2), nullptr); + EXPECT_EQ(find_child_projection(&projection, 2)->local_id(), 2); + EXPECT_EQ(find_child_projection(&projection, 3), nullptr); + EXPECT_TRUE(is_child_projected(nullptr, 3)); + EXPECT_TRUE(is_child_projected(&projection, 1)); + EXPECT_FALSE(is_child_projected(&projection, 3)); +} + +TEST(LocalColumnIndexTest, ProjectColumnDefinitionMatchesChildrenByLocalId) { + auto int_type = std::make_shared(); + auto string_type = std::make_shared(); + ColumnDefinition field; + field.identifier = Field::create_field(5); + field.name = "root"; + field.type = + std::make_shared(DataTypes {int_type, string_type}, Strings {"a", "b"}); + ColumnDefinition a_child; + a_child.identifier = Field::create_field(10); + a_child.local_id = 0; + a_child.name = "a"; + a_child.type = int_type; + ColumnDefinition b_child; + b_child.identifier = Field::create_field(20); + b_child.local_id = 1; + b_child.name = "b"; + b_child.type = string_type; + field.children = { + a_child, + b_child, + }; + LocalColumnIndex projection {.index = 5, .project_all_children = false}; + projection.children.push_back({.index = 1}); + + ColumnDefinition projected_field; + ASSERT_TRUE(project_column_definition(field, projection, &projected_field).ok()); + ASSERT_EQ(projected_field.children.size(), 1); + EXPECT_EQ(projected_field.children[0].get_identifier_field_id(), 20); + EXPECT_EQ(projected_field.children[0].name, "b"); + + const auto* projected_type = + assert_cast(remove_nullable(projected_field.type).get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "b"); + EXPECT_TRUE(projected_type->get_element(0)->equals(*string_type)); +} + +TEST(LocalColumnIndexTest, ProjectColumnDefinitionKeepsFileChildOrder) { + auto int_type = std::make_shared(); + auto string_type = std::make_shared(); + ColumnDefinition a_child; + a_child.identifier = Field::create_field(10); + a_child.local_id = 0; + a_child.name = "a"; + a_child.type = int_type; + ColumnDefinition b_child; + b_child.identifier = Field::create_field(20); + b_child.local_id = 1; + b_child.name = "b"; + b_child.type = string_type; + + ColumnDefinition field; + field.identifier = Field::create_field(5); + field.name = "root"; + field.type = + std::make_shared(DataTypes {int_type, string_type}, Strings {"a", "b"}); + field.children = {a_child, b_child}; + + LocalColumnIndex projection {.index = 5, .project_all_children = false}; + projection.children.push_back({.index = 1}); + projection.children.push_back({.index = 0}); + + ColumnDefinition projected_field; + ASSERT_TRUE(project_column_definition(field, projection, &projected_field).ok()); + ASSERT_EQ(projected_field.children.size(), 2); + EXPECT_EQ(projected_field.children[0].name, "a"); + EXPECT_EQ(projected_field.children[1].name, "b"); + + const auto* projected_type = + assert_cast(remove_nullable(projected_field.type).get()); + ASSERT_EQ(projected_type->get_elements().size(), 2); + EXPECT_EQ(projected_type->get_element_name(0), "a"); + EXPECT_EQ(projected_type->get_element_name(1), "b"); +} + +VExprSPtr table_int32_slot_ref(int slot_id, int column_id, const std::string& column_name) { + return VSlotRef::create_shared(slot_id, column_id, slot_id, std::make_shared(), + column_name); +} + +VExprSPtr table_int32_literal(int32_t value) { + return VLiteral::create_shared(std::make_shared(), + Field::create_field(value)); +} + +VExprSPtr table_int64_literal(int64_t value) { + return VLiteral::create_shared(std::make_shared(), + Field::create_field(value)); +} + +TExprNode table_function_node(const std::string& function_name, const DataTypePtr& return_type, + const std::vector& arg_types, + TExprNodeType::type node_type, + TExprOpcode::type opcode = TExprOpcode::INVALID_OPCODE, + bool short_circuit_evaluation = false) { + TFunctionName fn_name; + fn_name.__set_function_name(function_name); + TFunction fn; + fn.__set_name(fn_name); + fn.__set_binary_type(TFunctionBinaryType::BUILTIN); + std::vector thrift_arg_types; + thrift_arg_types.reserve(arg_types.size()); + for (const auto& arg_type : arg_types) { + thrift_arg_types.push_back(arg_type->to_thrift()); + } + fn.__set_arg_types(thrift_arg_types); + fn.__set_ret_type(return_type->to_thrift()); + fn.__set_has_var_args(false); + + TExprNode node; + node.__set_node_type(node_type); + node.__set_opcode(opcode); + node.__set_type(return_type->to_thrift()); + node.__set_fn(fn); + node.__set_num_children(static_cast(arg_types.size())); + node.__set_is_nullable(return_type->is_nullable()); + if (short_circuit_evaluation) { + node.__set_short_circuit_evaluation(true); + } + return node; +} + +VExprSPtr create_expr_from_node(const TExprNode& node) { + VExprSPtr expr; + auto status = VExpr::create_expr(node, expr); + DORIS_CHECK(status.ok()) << status.to_string(); + return expr; +} + +VExprSPtr table_function_expr(const std::string& function_name, const DataTypePtr& return_type, + const std::vector& arg_types, + TExprNodeType::type node_type = TExprNodeType::FUNCTION_CALL, + TExprOpcode::type opcode = TExprOpcode::INVALID_OPCODE) { + const auto node = table_function_node(function_name, return_type, arg_types, node_type, opcode); + return VectorizedFnCall::create_shared(node); +} + +VExprSPtr table_int32_greater_than_expr(int slot_id, int column_id, int32_t value) { + const auto int_type = std::make_shared(); + auto expr = table_function_expr("gt", std::make_shared(), {int_type, int_type}, + TExprNodeType::BINARY_PRED, TExprOpcode::GT); + expr->add_child(table_int32_slot_ref(slot_id, column_id, "id")); + expr->add_child(table_int32_literal(value)); + return expr; +} + +VExprSPtr table_nullable_int64_binary_predicate(const std::string& function_name, + TExprOpcode::type opcode, int slot_id, + int column_id, const std::string& column_name, + int64_t value) { + const auto int64_type = std::make_shared(); + const auto nullable_int64_type = make_nullable(int64_type); + auto expr = table_function_expr(function_name, std::make_shared(), + {nullable_int64_type, int64_type}, TExprNodeType::BINARY_PRED, + opcode); + expr->add_child( + VSlotRef::create_shared(slot_id, column_id, slot_id, nullable_int64_type, column_name)); + expr->add_child(table_int64_literal(value)); + return expr; +} + +VExprSPtr table_struct_child_int32_greater_than_expr(int slot_id, int column_id, + const DataTypePtr& struct_type, + const std::string& column_name, + const std::string& child_name, int32_t value) { + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + auto child_expr = table_function_expr("struct_element", int_type, {struct_type, string_type}); + child_expr->add_child( + VSlotRef::create_shared(slot_id, column_id, slot_id, struct_type, column_name)); + child_expr->add_child( + VLiteral::create_shared(string_type, Field::create_field(child_name))); + + auto expr = table_function_expr("gt", std::make_shared(), {int_type, int_type}, + TExprNodeType::BINARY_PRED, TExprOpcode::GT); + expr->add_child(child_expr); + expr->add_child(table_int32_literal(value)); + return expr; +} + +class NullableArrayBigintDefaultExpr final : public VExpr { +public: + explicit NullableArrayBigintDefaultExpr(DataTypePtr data_type) + : _name("single_element_groups") { + _data_type = std::move(data_type); + } + + const std::string& expr_name() const override { return _name; } + + bool is_constant() const override { return false; } + + Status execute_column_impl(VExprContext*, const Block*, const Selector* selector, size_t count, + ColumnPtr& result_column) const override { + DCHECK(selector == nullptr || selector->size() == count); + auto values = ColumnInt64::create(); + auto offsets = ColumnArray::ColumnOffsets::create(); + auto null_map = ColumnUInt8::create(); + for (size_t i = 0; i < count; ++i) { + values->insert_value(7); + offsets->insert_value(static_cast(i + 1)); + null_map->insert_value(0); + } + auto array_column = ColumnArray::create(std::move(values), std::move(offsets)); + result_column = ColumnNullable::create(std::move(array_column), std::move(null_map)); + return Status::OK(); + } + +private: + std::string _name; +}; + +class IcebergTableReaderDeleteFileTestHelper final : public doris::iceberg::IcebergTableReader { +public: + Status parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, DeleteFileDesc* desc, + bool* has_delete_file) { + return _parse_deletion_vector_file(t_desc, desc, has_delete_file); + } +}; + +class IcebergTableReaderScanRequestTestHelper final : public doris::iceberg::IcebergTableReader { +public: + Status init_for_scan_request_test(std::vector projected_columns) { + _query_options = std::make_unique(); + _query_globals = std::make_unique(); + _state = std::make_unique(*_query_options, *_query_globals); + RETURN_IF_ERROR(init({ + .projected_columns = std::move(projected_columns), + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = _state.get(), + .scanner_profile = nullptr, + .allow_missing_columns = true, + })); + + SplitReadOptions split_options; + split_options.current_range.__set_path("scan-request-test.parquet"); + TTableFormatFileDesc table_format_params; + TIcebergFileDesc iceberg_params; + iceberg_params.__set_first_row_id(1000); + table_format_params.__set_iceberg_params(iceberg_params); + split_options.current_range.__set_table_format_params(table_format_params); + RETURN_IF_ERROR(prepare_split(split_options)); + + _delete_rows_storage = {1}; + _delete_rows = &_delete_rows_storage; + return Status::OK(); + } + + Status customize_request(FileScanRequest* request) { + return customize_file_scan_request(request); + } + +private: + std::unique_ptr _query_options; + std::unique_ptr _query_globals; + std::unique_ptr _state; + DeleteRows _delete_rows_storage; +}; + +class TableReaderMaterializeTestHelper final : public TableReader { +public: + using TableReader::_materialize_map_mapping_column; +}; + +VExprSPtr table_int32_sum_expr(int left_slot_id, int left_column_id, int right_slot_id, + int right_column_id) { + const auto int_type = std::make_shared(); + auto expr = table_function_expr("add", int_type, {int_type, int_type}); + expr->add_child(table_int32_slot_ref(left_slot_id, left_column_id, "id")); + expr->add_child(table_int32_slot_ref(right_slot_id, right_column_id, "score")); + return expr; +} + +VExprSPtr table_int32_sum_greater_than_expr(int left_slot_id, int left_column_id, int right_slot_id, + int right_column_id, int32_t value) { + const auto int_type = std::make_shared(); + auto expr = table_function_expr("gt", std::make_shared(), {int_type, int_type}, + TExprNodeType::BINARY_PRED, TExprOpcode::GT); + expr->add_child( + table_int32_sum_expr(left_slot_id, left_column_id, right_slot_id, right_column_id)); + expr->add_child(table_int32_literal(value)); + return expr; +} + +VExprSPtr table_int32_sum_less_than_expr(int left_slot_id, int left_column_id, int right_slot_id, + int right_column_id, int32_t value) { + const auto int_type = std::make_shared(); + auto expr = table_function_expr("lt", std::make_shared(), {int_type, int_type}, + TExprNodeType::BINARY_PRED, TExprOpcode::LT); + expr->add_child( + table_int32_sum_expr(left_slot_id, left_column_id, right_slot_id, right_column_id)); + expr->add_child(table_int32_literal(value)); + return expr; +} + +VExprSPtr table_condition_function_expr(const std::string& function_name, bool short_circuit) { + const auto int_type = std::make_shared(); + std::vector arg_types; + if (function_name == "if") { + arg_types = {std::make_shared(), int_type, int_type}; + } else { + arg_types = {int_type, int_type}; + } + auto expr = create_expr_from_node( + table_function_node(function_name, int_type, arg_types, TExprNodeType::FUNCTION_CALL, + TExprOpcode::INVALID_OPCODE, short_circuit)); + if (function_name == "if") { + expr->add_child(table_int32_greater_than_expr(0, 0, 0)); + expr->add_child(table_int32_literal(1)); + expr->add_child(table_int32_literal(0)); + } else { + expr->add_child(table_int32_slot_ref(0, 0, "id")); + expr->add_child(table_int32_literal(0)); + } + return expr; +} + +VExprSPtr table_case_expr(bool short_circuit) { + const auto int_type = std::make_shared(); + TCaseExpr case_node; + case_node.__set_has_case_expr(false); + case_node.__set_has_else_expr(true); + + TExprNode node; + node.__set_node_type(TExprNodeType::CASE_EXPR); + node.__set_type(int_type->to_thrift()); + node.__set_is_nullable(false); + node.__set_num_children(3); + node.__set_case_expr(case_node); + if (short_circuit) { + node.__set_short_circuit_evaluation(true); + } + + auto expr = create_expr_from_node(node); + expr->add_child(table_int32_greater_than_expr(0, 0, 0)); + expr->add_child(table_int32_literal(1)); + expr->add_child(table_int32_literal(0)); + return expr; +} + +TEST(CloneTableExprTreeTest, ClonesConditionalExpressions) { + const std::vector expressions { + table_condition_function_expr("if", false), + table_condition_function_expr("if", true), + table_condition_function_expr("ifnull", false), + table_condition_function_expr("ifnull", true), + table_condition_function_expr("coalesce", false), + table_condition_function_expr("coalesce", true), + table_case_expr(false), + table_case_expr(true), + }; + + for (const auto& expr : expressions) { + VExprSPtr cloned; + const auto status = clone_table_expr_tree(expr, &cloned); + ASSERT_TRUE(status.ok()) << expr->debug_string() << ": " << status.to_string(); + ASSERT_NE(cloned, nullptr); + const auto* original_expr = expr.get(); + const auto* cloned_expr = cloned.get(); + EXPECT_TRUE(typeid(*original_expr) == typeid(*cloned_expr)) + << expr->expr_name() << " cloned as " << typeid(*cloned_expr).name(); + EXPECT_EQ(expr->expr_name(), cloned->expr_name()); + EXPECT_EQ(expr->get_num_children(), cloned->get_num_children()); + EXPECT_NE(original_expr, cloned_expr); + } +} + +std::shared_ptr finish_array(arrow::ArrayBuilder* builder) { + std::shared_ptr array; + EXPECT_TRUE(builder->Finish(&array).ok()); + return array; +} + +std::shared_ptr build_int32_array(const std::vector& values) { + arrow::Int32Builder builder; + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); +} + +std::shared_ptr build_int64_array(const std::vector& values) { + arrow::Int64Builder builder; + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); +} + +std::shared_ptr build_nullable_int64_array( + const std::vector>& values) { + arrow::Int64Builder builder; + for (const auto& value : values) { + if (value.has_value()) { + EXPECT_TRUE(builder.Append(*value).ok()); + } else { + EXPECT_TRUE(builder.AppendNull().ok()); + } + } + return finish_array(&builder); +} + +std::shared_ptr build_string_array(const std::vector& values) { + arrow::StringBuilder builder; + for (const auto& value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); +} + +void write_parquet_file(const std::string& file_path, int32_t id, const std::string& value) { + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false), + arrow::field("value", arrow::utf8(), false), + }); + auto table = arrow::Table::Make(schema, {build_int32_array({id}), build_string_array({value})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 1, + builder.build())); +} + +void write_iceberg_equality_delete_parquet_file(const std::string& file_path, int32_t field_id, + int32_t value) { + const auto metadata = + arrow::key_value_metadata({"PARQUET:field_id"}, {std::to_string(field_id)}); + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false)->WithMetadata(metadata), + }); + auto table = arrow::Table::Make(schema, {build_int32_array({value})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 1, + builder.build())); +} + +void write_iceberg_equality_delete_bigint_parquet_file(const std::string& file_path, + int32_t field_id, int64_t value) { + const auto metadata = + arrow::key_value_metadata({"PARQUET:field_id"}, {std::to_string(field_id)}); + auto schema = arrow::schema({ + arrow::field("id", arrow::int64(), false)->WithMetadata(metadata), + }); + auto table = arrow::Table::Make(schema, {build_int64_array({value})}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 1, + builder.build())); +} + +void write_struct_parquet_file(const std::string& file_path, int32_t id) { + auto struct_type = arrow::struct_({arrow::field("id", arrow::int32(), false)}); + arrow::StructBuilder builder( + struct_type, arrow::default_memory_pool(), + {std::make_shared(arrow::default_memory_pool())}); + auto* id_builder = assert_cast(builder.field_builder(0)); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(id_builder->Append(id).ok()); + + auto schema = arrow::schema({ + arrow::field("s", struct_type, false), + }); + auto table = arrow::Table::Make(schema, {finish_array(&builder)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 1, + writer_builder.build())); +} + +void write_struct_parquet_file(const std::string& file_path, const std::vector& ids, + int64_t row_group_size = -1) { + auto struct_type = arrow::struct_({arrow::field("id", arrow::int32(), false)}); + arrow::StructBuilder builder( + struct_type, arrow::default_memory_pool(), + {std::make_shared(arrow::default_memory_pool())}); + auto* id_builder = assert_cast(builder.field_builder(0)); + for (const auto id : ids) { + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(id_builder->Append(id).ok()); + } + + auto schema = arrow::schema({ + arrow::field("s", struct_type, false), + }); + auto table = arrow::Table::Make(schema, {finish_array(&builder)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + const auto write_row_group_size = + row_group_size > 0 ? row_group_size : static_cast(ids.size()); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + write_row_group_size, + writer_builder.build())); +} + +void write_struct_with_nullable_child_parquet_file(const std::string& file_path) { + auto struct_type = arrow::struct_({ + arrow::field("id", arrow::int32(), false), + arrow::field("note", arrow::utf8(), true), + }); + std::vector> field_builders; + auto id_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(id_builder))); + auto note_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(note_builder))); + arrow::StructBuilder builder(struct_type, arrow::default_memory_pool(), + std::move(field_builders)); + auto* struct_id_builder = assert_cast(builder.field_builder(0)); + auto* struct_note_builder = assert_cast(builder.field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_id_builder->Append(7).ok()); + EXPECT_TRUE(struct_note_builder->Append("seven").ok()); + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_id_builder->Append(8).ok()); + EXPECT_TRUE(struct_note_builder->AppendNull().ok()); + + auto schema = arrow::schema({ + arrow::field("s", struct_type, false), + }); + auto table = arrow::Table::Make(schema, {finish_array(&builder)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 2, + writer_builder.build())); +} + +void write_list_struct_parquet_file(const std::string& file_path) { + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("b", arrow::int32(), false)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto b_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(b_array_builder))); + auto struct_builder = std::make_shared( + struct_type, arrow::default_memory_pool(), std::move(field_builders)); + auto list_type = arrow::list(arrow::field("element", struct_type, true)); + arrow::ListBuilder builder(arrow::default_memory_pool(), struct_builder, list_type); + auto* a_builder = assert_cast(struct_builder->field_builder(0)); + auto* b_builder = assert_cast(struct_builder->field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(10).ok()); + EXPECT_TRUE(b_builder->Append(11).ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(20).ok()); + EXPECT_TRUE(b_builder->Append(21).ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(30).ok()); + EXPECT_TRUE(b_builder->Append(31).ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(struct_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(40).ok()); + EXPECT_TRUE(b_builder->Append(41).ok()); + + auto schema = arrow::schema({ + arrow::field("xs", list_type, false), + }); + auto table = arrow::Table::Make(schema, {finish_array(&builder)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 3, + writer_builder.build())); +} + +void write_map_struct_parquet_file(const std::string& file_path) { + auto key_builder = std::make_shared(); + auto struct_type = arrow::struct_( + {arrow::field("a", arrow::int32(), false), arrow::field("b", arrow::utf8(), false)}); + std::vector> field_builders; + auto a_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(a_array_builder))); + auto b_array_builder = std::make_unique(); + field_builders.push_back(std::shared_ptr(std::move(b_array_builder))); + auto value_builder = std::make_shared( + struct_type, arrow::default_memory_pool(), std::move(field_builders)); + auto map_type = arrow::map(arrow::int32(), arrow::field("value", struct_type, false)); + arrow::MapBuilder builder(arrow::default_memory_pool(), key_builder, value_builder, map_type); + auto* a_builder = assert_cast(value_builder->field_builder(0)); + auto* b_builder = assert_cast(value_builder->field_builder(1)); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(1).ok()); + EXPECT_TRUE(value_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(10).ok()); + EXPECT_TRUE(b_builder->Append("ma").ok()); + EXPECT_TRUE(key_builder->Append(2).ok()); + EXPECT_TRUE(value_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(20).ok()); + EXPECT_TRUE(b_builder->Append("mb").ok()); + + EXPECT_TRUE(builder.Append().ok()); + EXPECT_TRUE(key_builder->Append(3).ok()); + EXPECT_TRUE(value_builder->Append().ok()); + EXPECT_TRUE(a_builder->Append(30).ok()); + EXPECT_TRUE(b_builder->Append("mc").ok()); + + EXPECT_TRUE(builder.AppendEmptyValue().ok()); + + auto schema = arrow::schema({ + arrow::field("kv", map_type, false), + }); + auto table = arrow::Table::Make(schema, {finish_array(&builder)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, 3, + writer_builder.build())); +} + +void write_int_pair_parquet_file(const std::string& file_path, const std::vector& ids, + const std::vector& scores, + const std::vector& values, + int64_t row_group_size = -1) { + const auto id_metadata = arrow::key_value_metadata({"PARQUET:field_id"}, {"0"}); + const auto score_metadata = arrow::key_value_metadata({"PARQUET:field_id"}, {"1"}); + const auto value_metadata = arrow::key_value_metadata({"PARQUET:field_id"}, {"2"}); + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false)->WithMetadata(id_metadata), + arrow::field("score", arrow::int32(), false)->WithMetadata(score_metadata), + arrow::field("value", arrow::utf8(), false)->WithMetadata(value_metadata), + }); + auto table = arrow::Table::Make(schema, {build_int32_array(ids), build_int32_array(scores), + build_string_array(values)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + const auto write_row_group_size = + row_group_size > 0 ? row_group_size : static_cast(ids.size()); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + write_row_group_size, builder.build())); +} + +void write_iceberg_row_lineage_parquet_file( + const std::string& file_path, const std::vector& ids, + const std::vector>& row_ids, + const std::vector>& last_updated_sequence_numbers = {}) { + ASSERT_EQ(ids.size(), row_ids.size()); + if (!last_updated_sequence_numbers.empty()) { + ASSERT_EQ(ids.size(), last_updated_sequence_numbers.size()); + } + const auto id_metadata = arrow::key_value_metadata({"PARQUET:field_id"}, {"0"}); + const auto row_id_metadata = arrow::key_value_metadata({"PARQUET:field_id"}, {"2147483540"}); + const auto last_updated_sequence_number_metadata = + arrow::key_value_metadata({"PARQUET:field_id"}, {"2147483539"}); + auto schema = arrow::schema({ + arrow::field("id", arrow::int32(), false)->WithMetadata(id_metadata), + arrow::field("_row_id", arrow::int64(), true)->WithMetadata(row_id_metadata), + }); + std::vector> arrays = { + build_int32_array(ids), + build_nullable_int64_array(row_ids), + }; + if (!last_updated_sequence_numbers.empty()) { + schema = + schema->AddField(schema->num_fields(), + arrow::field("_last_updated_sequence_number", arrow::int64(), true) + ->WithMetadata(last_updated_sequence_number_metadata)) + .ValueOrDie(); + arrays.push_back(build_nullable_int64_array(last_updated_sequence_numbers)); + } + auto table = arrow::Table::Make(schema, arrays); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + static_cast(ids.size()), + builder.build())); +} + +void write_position_delete_parquet_file(const std::string& file_path, + const std::vector& data_file_paths, + const std::vector& positions) { + auto schema = arrow::schema({ + arrow::field("file_path", arrow::utf8(), false), + arrow::field("pos", arrow::int64(), false), + }); + auto table = arrow::Table::Make( + schema, {build_string_array(data_file_paths), build_int64_array(positions)}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder builder; + builder.version(::parquet::ParquetVersion::PARQUET_2_6); + builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + builder.compression(::parquet::Compression::UNCOMPRESSED); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + static_cast(positions.size()), + builder.build())); +} + +int64_t write_iceberg_deletion_vector_file(const std::string& file_path, + const std::vector& deleted_positions) { + roaring::Roaring64Map rows; + for (const auto position : deleted_positions) { + rows.add(position); + } + + const size_t bitmap_size = rows.getSizeInBytes(); + std::vector blob(4 + 4 + bitmap_size + 4); + rows.write(blob.data() + 8); + + const uint32_t total_length = static_cast(4 + bitmap_size); + BigEndian::Store32(blob.data(), total_length); + constexpr char DV_MAGIC[] = {'\xD1', '\xD3', '\x39', '\x64'}; + memcpy(blob.data() + 4, DV_MAGIC, 4); + BigEndian::Store32(blob.data() + 8 + bitmap_size, 0); + + std::ofstream output(file_path, std::ios::binary); + EXPECT_TRUE(output.is_open()); + output.write(blob.data(), static_cast(blob.size())); + EXPECT_TRUE(output.good()); + return static_cast(blob.size()); +} + +Block build_table_block(const std::vector& columns) { + Block block; + for (const auto& column : columns) { + block.insert({column.type->create_column(), column.type, column.name}); + } + return block; +} + +void expect_nullable_int64_column_values(const IColumn& column, + const std::vector& expected_values) { + const auto full_column = column.convert_to_full_column_if_const(); + const auto& nullable_column = assert_cast(*full_column); + const auto& values = + assert_cast(nullable_column.get_nested_column()).get_data(); + ASSERT_EQ(nullable_column.size(), expected_values.size()); + for (size_t row = 0; row < expected_values.size(); ++row) { + EXPECT_EQ(nullable_column.get_null_map_data()[row], 0); + EXPECT_EQ(values[row], expected_values[row]); + } +} + +void expect_nullable_int64_column_optional_values( + const IColumn& column, const std::vector>& expected_values) { + const auto full_column = column.convert_to_full_column_if_const(); + const auto& nullable_column = assert_cast(*full_column); + const auto& values = + assert_cast(nullable_column.get_nested_column()).get_data(); + ASSERT_EQ(nullable_column.size(), expected_values.size()); + for (size_t row = 0; row < expected_values.size(); ++row) { + if (expected_values[row].has_value()) { + EXPECT_EQ(nullable_column.get_null_map_data()[row], 0); + EXPECT_EQ(values[row], *expected_values[row]); + } else { + EXPECT_EQ(nullable_column.get_null_map_data()[row], 1); + } + } +} + +const IColumn& expect_not_null_nullable_nested_column(const IColumn& column) { + const auto& nullable_column = assert_cast(column); + for (const auto is_null : nullable_column.get_null_map_data()) { + EXPECT_EQ(is_null, 0); + } + return nullable_column.get_nested_column(); +} + +DataTypePtr make_iceberg_rowid_type() { + return make_nullable(std::make_shared( + DataTypes {std::make_shared(), std::make_shared(), + std::make_shared(), std::make_shared()}, + Strings {"file_path", "row_pos", "partition_spec_id", "partition_data_json"})); +} + +void expect_iceberg_rowid_column_values(const IColumn& column, const std::string& file_path, + const std::vector& row_positions, + int32_t partition_spec_id, + const std::string& partition_data_json) { + const auto full_column = column.convert_to_full_column_if_const(); + const auto& nullable_column = assert_cast(*full_column); + const auto& struct_column = + assert_cast(nullable_column.get_nested_column()); + const auto& file_path_column = assert_cast(struct_column.get_column(0)); + const auto& row_pos_column = assert_cast(struct_column.get_column(1)); + const auto& spec_id_column = assert_cast(struct_column.get_column(2)); + const auto& partition_data_column = + assert_cast(struct_column.get_column(3)); + + ASSERT_EQ(nullable_column.size(), row_positions.size()); + for (size_t row = 0; row < row_positions.size(); ++row) { + EXPECT_EQ(nullable_column.get_null_map_data()[row], 0); + EXPECT_EQ(file_path_column.get_data_at(row).to_string(), file_path); + EXPECT_EQ(row_pos_column.get_element(row), row_positions[row]); + EXPECT_EQ(spec_id_column.get_element(row), partition_spec_id); + EXPECT_EQ(partition_data_column.get_data_at(row).to_string(), partition_data_json); + } +} + +void expect_int32_column_values(const IColumn& column, + const std::vector& expected_values) { + const auto full_column = column.convert_to_full_column_if_const(); + const auto& values = assert_cast(*full_column).get_data(); + ASSERT_EQ(values.size(), expected_values.size()); + for (size_t row = 0; row < expected_values.size(); ++row) { + EXPECT_EQ(values[row], expected_values[row]); + } +} + +SplitReadOptions build_split_options(const std::string& file_path) { + SplitReadOptions options; + options.current_range.__set_path(file_path); + options.current_range.__set_file_size( + static_cast(std::filesystem::file_size(file_path))); + return options; +} + +void set_table_level_row_count(SplitReadOptions* split_options, int64_t row_count) { + split_options->current_range.__isset.table_format_params = true; + split_options->current_range.table_format_params.__isset.table_level_row_count = true; + split_options->current_range.table_format_params.table_level_row_count = row_count; +} + +void set_iceberg_row_lineage_params(SplitReadOptions* split_options, int64_t first_row_id, + int64_t last_updated_sequence_number) { + TTableFormatFileDesc table_format_params; + TIcebergFileDesc iceberg_params; + iceberg_params.__set_first_row_id(first_row_id); + iceberg_params.__set_last_updated_sequence_number(last_updated_sequence_number); + table_format_params.__set_iceberg_params(iceberg_params); + split_options->current_range.__set_table_format_params(table_format_params); +} + +void set_iceberg_rowid_params(SplitReadOptions* split_options, + const std::string& original_file_path, int32_t partition_spec_id, + const std::string& partition_data_json) { + TTableFormatFileDesc table_format_params; + TIcebergFileDesc iceberg_params; + iceberg_params.__set_original_file_path(original_file_path); + iceberg_params.__set_partition_spec_id(partition_spec_id); + iceberg_params.__set_partition_data_json(partition_data_json); + table_format_params.__set_iceberg_params(iceberg_params); + split_options->current_range.__set_table_format_params(table_format_params); +} + +TIcebergDeleteFileDesc make_iceberg_deletion_vector(const std::string& path, int64_t offset, + int64_t size) { + TIcebergDeleteFileDesc delete_file; + delete_file.__set_content(3); + delete_file.__set_path(path); + delete_file.__set_content_offset(offset); + delete_file.__set_content_size_in_bytes(size); + return delete_file; +} + +TIcebergDeleteFileDesc make_iceberg_position_delete_file(const std::string& path) { + TIcebergDeleteFileDesc delete_file; + delete_file.__set_content(1); + delete_file.__set_path(path); + delete_file.__set_file_format(TFileFormatType::FORMAT_PARQUET); + return delete_file; +} + +TIcebergDeleteFileDesc make_iceberg_equality_delete_file(const std::string& path, + const std::vector& field_ids) { + TIcebergDeleteFileDesc delete_file; + delete_file.__set_content(2); + delete_file.__set_path(path); + delete_file.__set_field_ids(field_ids); + delete_file.__set_file_format(TFileFormatType::FORMAT_PARQUET); + return delete_file; +} + +TFileScanRangeParams make_local_parquet_scan_params() { + TFileScanRangeParams scan_params; + scan_params.__set_file_type(TFileType::FILE_LOCAL); + scan_params.__set_format_type(TFileFormatType::FORMAT_PARQUET); + return scan_params; +} + +std::shared_ptr make_io_context(io::FileReaderStats* file_reader_stats, + io::FileCacheStatistics* file_cache_stats) { + auto io_ctx = std::make_shared(); + io_ctx->file_reader_stats = file_reader_stats; + io_ctx->file_cache_stats = file_cache_stats; + return io_ctx; +} + +TTableFormatFileDesc make_iceberg_table_format_desc( + const std::string& data_file_path, + const std::vector& delete_files) { + TTableFormatFileDesc table_format_params; + TIcebergFileDesc iceberg_params; + iceberg_params.__set_format_version(2); + iceberg_params.__set_original_file_path(data_file_path); + iceberg_params.__set_delete_files(delete_files); + table_format_params.__set_iceberg_params(iceberg_params); + return table_format_params; +} + +std::vector read_iceberg_ids(doris::iceberg::IcebergTableReader* reader, + const std::vector& projected_columns) { + std::vector ids; + bool eos = false; + while (!eos) { + Block block = build_table_block(projected_columns); + auto status = reader->get_block(&block, &eos); + if (!status.ok()) { + ADD_FAILURE() << status; + return ids; + } + if (block.rows() == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + for (size_t row = 0; row < block.rows(); ++row) { + ids.push_back(id_column.get_element(row)); + } + } + return ids; +} + +int64_t parquet_column_start_offset(const ::parquet::ColumnChunkMetaData& column_metadata) { + return column_metadata.has_dictionary_page() + ? static_cast(column_metadata.dictionary_page_offset()) + : static_cast(column_metadata.data_page_offset()); +} + +SplitReadOptions build_split_options_for_row_group_mid(const std::string& file_path, + int row_group_idx) { + auto options = build_split_options(file_path); + auto reader = ::parquet::ParquetFileReader::OpenFile(file_path, false); + auto metadata = reader->metadata(); + auto row_group_metadata = metadata->RowGroup(row_group_idx); + auto first_column = row_group_metadata->ColumnChunk(0); + auto last_column = row_group_metadata->ColumnChunk(row_group_metadata->num_columns() - 1); + const int64_t row_group_start_offset = parquet_column_start_offset(*first_column); + const int64_t row_group_end_offset = + parquet_column_start_offset(*last_column) + last_column->total_compressed_size(); + const int64_t row_group_mid_offset = + row_group_start_offset + (row_group_end_offset - row_group_start_offset) / 2; + options.current_range.__set_start_offset(row_group_mid_offset); + options.current_range.__set_size(1); + return options; +} + +ColumnDefinition make_table_column(int32_t id, const std::string& name, const DataTypePtr& type) { + ColumnDefinition column; + if (id >= 0) { + column.identifier = Field::create_field(id); + } + column.name = name; + column.type = type; + return column; +} + +ColumnDefinition make_file_column(int32_t id, const std::string& name, const DataTypePtr& type) { + ColumnDefinition field; + field.identifier = Field::create_field(id); + field.local_id = id; + field.name = name; + field.type = type; + return field; +} + +void set_name_identifiers(std::vector* columns); + +void set_name_identifier(ColumnDefinition* column) { + DORIS_CHECK(column != nullptr); + column->identifier = Field::create_field(column->name); + set_name_identifiers(&column->children); +} + +void set_name_identifiers(std::vector* columns) { + DORIS_CHECK(columns != nullptr); + for (auto& column : *columns) { + set_name_identifier(&column); + } +} + +void add_column_predicate(TableColumnPredicates* column_predicates, GlobalIndex global_index, + std::shared_ptr predicate) { + auto& entry = (*column_predicates)[global_index]; + entry.push_back(std::move(predicate)); +} + +VExprContextSPtr prepared_conjunct(RuntimeState* state, const VExprSPtr& expr) { + auto ctx = VExprContext::create_shared(expr); + auto status = ctx->prepare(state, RowDescriptor()); + EXPECT_TRUE(status.ok()) << status; + status = ctx->open(state); + EXPECT_TRUE(status.ok()) << status; + return ctx; +} + +void apply_final_conjuncts(Block* block, const VExprContextSPtrs& conjuncts) { + const auto status = VExprContext::filter_block(conjuncts, block, block->columns()); + ASSERT_TRUE(status.ok()) << status; +} + +struct FakeFileReaderState { + int init_count = 0; + int open_count = 0; + int close_count = 0; + std::shared_ptr last_request; +}; + +class FakeFileReader final : public FileReader { +public: + FakeFileReader(std::shared_ptr& system_properties, + std::unique_ptr& file_description, + std::vector schema, std::shared_ptr state) + : FileReader(system_properties, file_description, nullptr, nullptr), + _schema(std::move(schema)), + _state(std::move(state)) {} + + Status init(RuntimeState* state) override { + (void)state; + ++_state->init_count; + _eof = false; + return Status::OK(); + } + + Status get_schema(std::vector* file_schema) const override { + DORIS_CHECK(file_schema != nullptr); + *file_schema = _schema; + return Status::OK(); + } + + Status open(std::shared_ptr request) override { + RETURN_IF_ERROR(FileReader::open(std::move(request))); + _state->last_request = _request; + ++_state->open_count; + _returned_batch = false; + return Status::OK(); + } + + Status get_block(Block* file_block, size_t* rows, bool* eof) override { + DORIS_CHECK(file_block != nullptr); + DORIS_CHECK(rows != nullptr); + DORIS_CHECK(eof != nullptr); + DORIS_CHECK(_request != nullptr); + if (_returned_batch) { + *rows = 0; + *eof = true; + return Status::OK(); + } + + for (const auto& [file_column_id, block_position] : _request->local_positions) { + if (file_column_id == LocalColumnId(0)) { + auto column = ColumnInt32::create(); + column->insert_value(1); + column->insert_value(2); + file_block->replace_by_position(block_position.value(), std::move(column)); + } else if (file_column_id == LocalColumnId(1)) { + auto column = ColumnString::create(); + column->insert_data("one", 3); + column->insert_data("two", 3); + file_block->replace_by_position(block_position.value(), std::move(column)); + } else { + return Status::InvalidArgument("Unexpected fake file column id {}", + file_column_id.value()); + } + } + + _returned_batch = true; + *rows = 2; + *eof = true; + return Status::OK(); + } + + Status close() override { + ++_state->close_count; + _request.reset(); + _eof = true; + return Status::OK(); + } + +private: + std::vector _schema; + std::shared_ptr _state; + bool _returned_batch = false; +}; + +class FakeTableReader final : public TableReader { +public: + FakeTableReader(std::vector file_schema, + std::shared_ptr state) + : _file_schema(std::move(file_schema)), _state(std::move(state)) {} + +protected: + Status create_file_reader(std::unique_ptr* reader) override { + DORIS_CHECK(reader != nullptr); + auto system_properties = std::make_shared(); + system_properties->system_type = TFileType::FILE_LOCAL; + auto file_description = std::make_unique(); + file_description->path = "fake-table-reader-input"; + *reader = std::make_unique(system_properties, file_description, + _file_schema, _state); + return Status::OK(); + } + +private: + std::vector _file_schema; + std::shared_ptr _state; +}; + +TEST(TableReaderTest, CanUseInjectedFileReaderForStandaloneUnitTest) { + std::vector file_schema; + file_schema.push_back(make_file_column(0, "id", std::make_shared())); + file_schema.push_back(make_file_column(1, "value", std::make_shared())); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(1, "value", std::make_shared())); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + set_name_identifiers(&projected_columns); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto fake_state = std::make_shared(); + FakeTableReader reader(file_schema, fake_state); + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + SplitReadOptions split_options; + split_options.current_range.__set_path("fake-table-reader-input"); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_FALSE(eos); + + ASSERT_EQ(fake_state->init_count, 1); + ASSERT_EQ(fake_state->open_count, 1); + ASSERT_EQ(fake_state->close_count, 1); + ASSERT_NE(fake_state->last_request, nullptr); + ASSERT_EQ(fake_state->last_request->local_positions.at(LocalColumnId(1)).value(), 0); + ASSERT_EQ(fake_state->last_request->local_positions.at(LocalColumnId(0)).value(), 1); + EXPECT_EQ(projection_ids(fake_state->last_request->non_predicate_columns), + std::vector({1, 0})); + EXPECT_TRUE(fake_state->last_request->predicate_columns.empty()); + + const auto& value_column = assert_cast(*block.get_by_position(0).column); + const auto& id_column = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(block.rows(), 2); + EXPECT_EQ(value_column.get_data_at(0).to_string(), "one"); + EXPECT_EQ(value_column.get_data_at(1).to_string(), "two"); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 2); + + block = build_table_block(projected_columns); + eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); +} + +TEST(TableReaderTest, ReopenSplitAfterClose) { + const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const std::vector file_paths = { + (test_dir / "split_1.parquet").string(), + (test_dir / "split_2.parquet").string(), + (test_dir / "split_3.parquet").string(), + }; + write_parquet_file(file_paths[0], 1, "one"); + write_parquet_file(file_paths[1], 2, "two"); + write_parquet_file(file_paths[2], 3, "three"); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(1, "value", std::make_shared())); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(1, 1, 0))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + // Simulate the scanner lifecycle for three different splits: + // init() once, then repeat prepare_split() -> get_block() -> close(). + // This verifies TableReader::close() fully releases the previous low-level reader and task + // state, so a later prepare_split() can open and read a new split on the same TableReader. + // The table-level conjunct is also rebuilt for each split. The projection order puts value + // before id, so the pushed conjunct has to be rewritten to the ParquetReader file-local block + // position every time a new split is opened. + std::vector ids; + std::vector values; + for (const auto& file_path : file_paths) { + auto split_options = build_split_options(file_path); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& value_column = + assert_cast(*block.get_by_position(0).column); + const auto& id_column = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(id_column.size(), 1); + ASSERT_EQ(value_column.size(), 1); + ids.push_back(id_column.get_element(0)); + values.push_back(value_column.get_data_at(0).to_string()); + + ASSERT_TRUE(reader.close().ok()); + } + + EXPECT_EQ(ids, std::vector({1, 2, 3})); + EXPECT_EQ(values, std::vector({"one", "two", "three"})); + + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownCountFromNewParquetReader) { + const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_count_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3, 4, 5}, {10, 20, 30, 40, 50}, + {"one", "two", "three", "four", "five"}, 2); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 5); + EXPECT_FALSE(is_column_const(*block.get_by_position(0).column)); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, TableLevelCountUsesAssignedRowCount) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_table_count_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + TQueryOptions query_options; + query_options.__set_batch_size(2); + RuntimeState state {query_options, TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + auto split_options = build_split_options(file_path); + set_table_level_row_count(&split_options, 5); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + EXPECT_EQ(block.rows(), 2); + + block = build_table_block(projected_columns); + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + EXPECT_EQ(block.rows(), 2); + + block = build_table_block(projected_columns); + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + EXPECT_EQ(block.rows(), 1); + + block = build_table_block(projected_columns); + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); + EXPECT_EQ(block.rows(), 0); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownMinMaxFromNewParquetReader) { + const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_minmax_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {3, 1, 5, 2}, {30, 10, 50, 20}, + {"three", "one", "five", "two"}, 2); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + projected_columns.push_back(make_table_column(1, "score", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& score_column = assert_cast(*block.get_by_position(1).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 5); + EXPECT_EQ(score_column.get_element(0), 10); + EXPECT_EQ(score_column.get_element(1), 50); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownMinMaxCastsFileValueToTableType) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_minmax_cast_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {3, 1, 5, 2}, {30, 10, 50, 20}, + {"three", "one", "five", "two"}, 2); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status; + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 5); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownMinMaxFromProjectedStructLeaf) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_minmax_struct_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_struct_parquet_file(file_path, {3, 1, 5, 2}, 2); + + const auto int_type = std::make_shared(); + auto id_child = make_table_column(0, "id", int_type); + auto struct_type = std::make_shared(DataTypes {int_type}, Strings {"id"}); + auto struct_column = make_table_column(100, "s", struct_type); + struct_column.children = {id_child}; + std::vector projected_columns = {struct_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status; + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& struct_result = assert_cast(*block.get_by_position(0).column); + ASSERT_EQ(struct_result.get_columns().size(), 1); + const auto& ids = assert_cast( + expect_not_null_nullable_nested_column(struct_result.get_column(0))); + EXPECT_EQ(ids.get_element(0), 1); + EXPECT_EQ(ids.get_element(1), 5); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownMinMaxFallsBackForProjectedListStructLeaf) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_minmax_list_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_list_struct_parquet_file(file_path); + + const auto int_type = std::make_shared(); + const auto nullable_int_type = make_nullable(int_type); + auto element_type = std::make_shared( + DataTypes {nullable_int_type, nullable_int_type}, Strings {"a", "b"}); + auto nullable_element_type = make_nullable(element_type); + auto list_column = + make_table_column(100, "xs", std::make_shared(nullable_element_type)); + std::vector projected_columns = {list_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status; + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + const auto& array_result = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(array_result.get_offsets()[0], 2); + EXPECT_EQ(array_result.get_offsets()[1], 3); + EXPECT_EQ(array_result.get_offsets()[2], 4); + const auto& nullable_elements = assert_cast(array_result.get_data()); + for (const auto is_null : nullable_elements.get_null_map_data()) { + EXPECT_EQ(is_null, 0); + } + const auto& element_struct = + assert_cast(nullable_elements.get_nested_column()); + ASSERT_EQ(element_struct.get_columns().size(), 2); + const auto& a_values = assert_cast( + expect_not_null_nullable_nested_column(element_struct.get_column(0))); + EXPECT_EQ(a_values.get_element(0), 10); + EXPECT_EQ(a_values.get_element(1), 20); + EXPECT_EQ(a_values.get_element(2), 30); + EXPECT_EQ(a_values.get_element(3), 40); + const auto& b_values = assert_cast( + expect_not_null_nullable_nested_column(element_struct.get_column(1))); + EXPECT_EQ(b_values.get_element(0), 11); + EXPECT_EQ(b_values.get_element(1), 21); + EXPECT_EQ(b_values.get_element(2), 31); + EXPECT_EQ(b_values.get_element(3), 41); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedListStructReadsSelectedElementChild) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_list_projection_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_list_struct_parquet_file(file_path); + + const auto int_type = std::make_shared(); + auto a_child = make_table_column(0, "a", int_type); + auto element_type = std::make_shared(DataTypes {int_type}, Strings {"a"}); + auto nullable_element_type = make_nullable(element_type); + auto element_child = make_table_column(0, "element", nullable_element_type); + element_child.children = {a_child}; + auto list_column = + make_table_column(100, "xs", std::make_shared(nullable_element_type)); + list_column.children = {element_child}; + std::vector projected_columns = {list_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + const auto& array_result = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(array_result.get_offsets()[0], 2); + EXPECT_EQ(array_result.get_offsets()[1], 3); + EXPECT_EQ(array_result.get_offsets()[2], 4); + const auto& nullable_elements = assert_cast(array_result.get_data()); + const auto& element_struct = + assert_cast(nullable_elements.get_nested_column()); + ASSERT_EQ(element_struct.get_columns().size(), 1); + const auto& a_values = assert_cast(element_struct.get_column(0)); + EXPECT_EQ(a_values.get_element(0), 10); + EXPECT_EQ(a_values.get_element(1), 20); + EXPECT_EQ(a_values.get_element(2), 30); + EXPECT_EQ(a_values.get_element(3), 40); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedListStructReordersRenamedAndMissingElementChildren) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_list_schema_evolution_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_list_struct_parquet_file(file_path); + + const auto int_type = std::make_shared(); + const auto nullable_int_type = make_nullable(int_type); + const auto string_type = std::make_shared(); + auto b_child = make_table_column(1, "renamed_b", nullable_int_type); + b_child.name_mapping = {"b"}; + auto missing_child = make_table_column(99, "missing_child", string_type); + auto a_child = make_table_column(0, "renamed_a", nullable_int_type); + a_child.name_mapping = {"a"}; + auto element_type = std::make_shared( + DataTypes {nullable_int_type, string_type, nullable_int_type}, + Strings {"renamed_b", "missing_child", "renamed_a"}); + auto nullable_element_type = make_nullable(element_type); + auto element_child = make_table_column(0, "element", nullable_element_type); + element_child.children = {b_child, missing_child, a_child}; + auto list_column = + make_table_column(100, "xs", std::make_shared(nullable_element_type)); + list_column.children = {element_child}; + std::vector projected_columns = {list_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + const auto& array_result = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(array_result.get_offsets()[0], 2); + EXPECT_EQ(array_result.get_offsets()[1], 3); + EXPECT_EQ(array_result.get_offsets()[2], 4); + const auto& nullable_elements = assert_cast(array_result.get_data()); + const auto& element_struct = + assert_cast(nullable_elements.get_nested_column()); + ASSERT_EQ(element_struct.get_columns().size(), 3); + const auto& b_values = assert_cast( + expect_not_null_nullable_nested_column(element_struct.get_column(0))); + const auto& missing_values = assert_cast(element_struct.get_column(1)); + const auto& a_values = assert_cast( + expect_not_null_nullable_nested_column(element_struct.get_column(2))); + EXPECT_EQ(b_values.get_element(0), 11); + EXPECT_EQ(b_values.get_element(1), 21); + EXPECT_EQ(b_values.get_element(2), 31); + EXPECT_EQ(b_values.get_element(3), 41); + for (size_t row = 0; row < missing_values.size(); ++row) { + EXPECT_EQ(missing_values.get_data_at(row).to_string(), ""); + } + EXPECT_EQ(a_values.get_element(0), 10); + EXPECT_EQ(a_values.get_element(1), 20); + EXPECT_EQ(a_values.get_element(2), 30); + EXPECT_EQ(a_values.get_element(3), 40); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownMinMaxFallsBackForProjectedMapValueStructLeaf) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_minmax_map_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_map_struct_parquet_file(file_path); + + const auto key_type = std::make_shared(); + const auto string_type = std::make_shared(); + const auto nullable_string_type = make_nullable(string_type); + auto key_child = make_table_column(0, "key", key_type); + auto b_child = make_table_column(1, "b", nullable_string_type); + auto value_type = + std::make_shared(DataTypes {nullable_string_type}, Strings {"b"}); + auto nullable_value_type = make_nullable(value_type); + auto value_child = make_table_column(1, "value", nullable_value_type); + value_child.children = {b_child}; + auto entry_type = std::make_shared(DataTypes {key_type, nullable_value_type}, + Strings {"key", "value"}); + auto entry_child = make_table_column(0, "key_value", entry_type); + entry_child.children = {key_child, value_child}; + auto map_column = make_table_column( + 100, "kv", std::make_shared(key_type, nullable_value_type)); + map_column.children = {entry_child}; + std::vector projected_columns = {map_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + const auto& map_result = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(map_result.get_offsets()[0], 2); + EXPECT_EQ(map_result.get_offsets()[1], 3); + EXPECT_EQ(map_result.get_offsets()[2], 3); + const auto& keys = assert_cast( + expect_not_null_nullable_nested_column(map_result.get_keys())); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 3); + const auto& nullable_values = assert_cast(map_result.get_values()); + for (const auto is_null : nullable_values.get_null_map_data()) { + EXPECT_EQ(is_null, 0); + } + const auto& value_struct = + assert_cast(nullable_values.get_nested_column()); + ASSERT_EQ(value_struct.get_columns().size(), 1); + const auto& b_values = assert_cast( + expect_not_null_nullable_nested_column(value_struct.get_column(0))); + EXPECT_EQ(b_values.get_data_at(0).to_string(), "ma"); + EXPECT_EQ(b_values.get_data_at(1).to_string(), "mb"); + EXPECT_EQ(b_values.get_data_at(2).to_string(), "mc"); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedMapValueStructReordersRenamedAndMissingChildren) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_map_schema_evolution_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_map_struct_parquet_file(file_path); + + const auto key_type = std::make_shared(); + const auto int_type = std::make_shared(); + const auto nullable_int_type = make_nullable(int_type); + const auto string_type = std::make_shared(); + const auto nullable_string_type = make_nullable(string_type); + auto key_child = make_table_column(0, "key", key_type); + auto b_child = make_table_column(1, "renamed_b", nullable_string_type); + b_child.name_mapping = {"b"}; + auto missing_child = make_table_column(99, "missing_child", string_type); + auto a_child = make_table_column(0, "renamed_a", nullable_int_type); + a_child.name_mapping = {"a"}; + auto value_type = std::make_shared( + DataTypes {nullable_string_type, string_type, nullable_int_type}, + Strings {"renamed_b", "missing_child", "renamed_a"}); + auto nullable_value_type = make_nullable(value_type); + auto value_child = make_table_column(1, "value", nullable_value_type); + value_child.children = {b_child, missing_child, a_child}; + auto entry_type = std::make_shared(DataTypes {key_type, nullable_value_type}, + Strings {"key", "value"}); + auto entry_child = make_table_column(0, "key_value", entry_type); + entry_child.children = {key_child, value_child}; + auto map_column = make_table_column( + 100, "kv", std::make_shared(key_type, nullable_value_type)); + map_column.children = {entry_child}; + std::vector projected_columns = {map_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + const auto& map_result = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(map_result.get_offsets()[0], 2); + EXPECT_EQ(map_result.get_offsets()[1], 3); + EXPECT_EQ(map_result.get_offsets()[2], 3); + const auto& keys = assert_cast( + expect_not_null_nullable_nested_column(map_result.get_keys())); + EXPECT_EQ(keys.get_element(0), 1); + EXPECT_EQ(keys.get_element(1), 2); + EXPECT_EQ(keys.get_element(2), 3); + const auto& nullable_values = assert_cast(map_result.get_values()); + const auto& value_struct = + assert_cast(nullable_values.get_nested_column()); + ASSERT_EQ(value_struct.get_columns().size(), 3); + const auto& b_values = assert_cast( + expect_not_null_nullable_nested_column(value_struct.get_column(0))); + const auto& missing_values = assert_cast(value_struct.get_column(1)); + const auto& a_values = assert_cast( + expect_not_null_nullable_nested_column(value_struct.get_column(2))); + EXPECT_EQ(b_values.get_data_at(0).to_string(), "ma"); + EXPECT_EQ(b_values.get_data_at(1).to_string(), "mb"); + EXPECT_EQ(b_values.get_data_at(2).to_string(), "mc"); + for (size_t row = 0; row < missing_values.size(); ++row) { + EXPECT_EQ(missing_values.get_data_at(row).to_string(), ""); + } + EXPECT_EQ(a_values.get_element(0), 10); + EXPECT_EQ(a_values.get_element(1), 20); + EXPECT_EQ(a_values.get_element(2), 30); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, MaterializeMapKeyStructReordersRenamedChildren) { + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + const auto file_key_type = + std::make_shared(DataTypes {int_type, string_type}, Strings {"a", "b"}); + const auto table_key_type = std::make_shared( + DataTypes {string_type, int_type}, Strings {"renamed_b", "renamed_a"}); + const auto file_map_type = std::make_shared(file_key_type, int_type); + const auto table_map_type = std::make_shared(table_key_type, int_type); + + ColumnMapping a_mapping; + a_mapping.table_column_name = "renamed_a"; + a_mapping.file_column_name = "a"; + a_mapping.file_local_id = 0; + a_mapping.table_type = int_type; + a_mapping.file_type = int_type; + a_mapping.is_trivial = true; + + ColumnMapping b_mapping; + b_mapping.table_column_name = "renamed_b"; + b_mapping.file_column_name = "b"; + b_mapping.file_local_id = 1; + b_mapping.table_type = string_type; + b_mapping.file_type = string_type; + b_mapping.is_trivial = true; + + ColumnMapping key_mapping; + key_mapping.table_column_name = "key"; + key_mapping.file_column_name = "key"; + key_mapping.file_local_id = 0; + key_mapping.table_type = table_key_type; + key_mapping.file_type = file_key_type; + key_mapping.is_trivial = false; + key_mapping.child_mappings = {b_mapping, a_mapping}; + + ColumnMapping value_mapping; + value_mapping.table_column_name = "value"; + value_mapping.file_column_name = "value"; + value_mapping.file_local_id = 1; + value_mapping.table_type = int_type; + value_mapping.file_type = int_type; + value_mapping.is_trivial = true; + + ColumnMapping entry_mapping; + entry_mapping.child_mappings = {key_mapping, value_mapping}; + + ColumnMapping map_mapping; + map_mapping.table_column_name = "kv"; + map_mapping.file_column_name = "kv"; + map_mapping.table_type = table_map_type; + map_mapping.file_type = file_map_type; + map_mapping.is_trivial = false; + map_mapping.child_mappings = {entry_mapping}; + + auto a_keys = ColumnInt32::create(); + a_keys->insert_value(10); + a_keys->insert_value(20); + a_keys->insert_value(30); + auto b_keys = ColumnString::create(); + b_keys->insert_value("x"); + b_keys->insert_value("y"); + b_keys->insert_value("z"); + MutableColumns key_children; + key_children.push_back(std::move(a_keys)); + key_children.push_back(std::move(b_keys)); + auto key_column = ColumnStruct::create(std::move(key_children)); + + auto value_column = ColumnInt32::create(); + value_column->insert_value(100); + value_column->insert_value(200); + value_column->insert_value(300); + auto offsets_column = ColumnArray::ColumnOffsets::create(); + offsets_column->insert_value(2); + offsets_column->insert_value(3); + ColumnPtr file_column = ColumnMap::create(std::move(key_column), std::move(value_column), + std::move(offsets_column)); + + TableReaderMaterializeTestHelper reader; + ColumnPtr result_column; + ASSERT_TRUE(reader._materialize_map_mapping_column(map_mapping, file_column, 2, &result_column) + .ok()); + + const auto& result_map = assert_cast(*result_column); + EXPECT_EQ(result_map.get_offsets()[0], 2); + EXPECT_EQ(result_map.get_offsets()[1], 3); + const auto& result_key = assert_cast(result_map.get_keys()); + ASSERT_EQ(result_key.get_columns().size(), 2); + const auto& b_result = assert_cast(result_key.get_column(0)); + const auto& a_result = assert_cast(result_key.get_column(1)); + EXPECT_EQ(b_result.get_data_at(0).to_string(), "x"); + EXPECT_EQ(b_result.get_data_at(1).to_string(), "y"); + EXPECT_EQ(b_result.get_data_at(2).to_string(), "z"); + EXPECT_EQ(a_result.get_element(0), 10); + EXPECT_EQ(a_result.get_element(1), 20); + EXPECT_EQ(a_result.get_element(2), 30); + + const auto& result_value = assert_cast(result_map.get_values()); + EXPECT_EQ(result_value.get_element(0), 100); + EXPECT_EQ(result_value.get_element(1), 200); + EXPECT_EQ(result_value.get_element(2), 300); +} + +TEST(TableReaderTest, PushDownMinMaxOnlyUsesSelectedRowGroupInFileRange) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_minmax_range_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {10, 1, 100}, {100, 10, 1000}, {"ten", "one", "hundred"}, + 1); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options_for_row_group_mid(file_path, 1)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 1); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownCountOnlyUsesSelectedRowGroupInFileRange) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_count_range_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}, 1); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options_for_row_group_mid(file_path, 2)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 1); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownCountFallsBackWithTableConjunct) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_count_conjunct_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(0, 0, 2))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 1); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 3); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownCountFallsBackWithColumnPredicate) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_count_predicate_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}, 1); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + TableColumnPredicates column_predicates; + add_column_predicate(&column_predicates, GlobalIndex(0), + create_comparison_predicate( + 0, "id", std::make_shared(), + Field::create_field(2), false)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = std::move(column_predicates), + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 1); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 3); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, PushDownMinMaxFallsBackWithoutDirectFileMapping) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_minmax_missing_mapping_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(99, "missing_id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::MINMAX, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 1); + EXPECT_EQ(block.get_by_position(0).column->get_int(0), 0); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, OpenReaderBuildsTableFiltersFromConjuncts) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_conjunct_filter_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 3, "three"); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(1, "value", std::make_shared())); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(1, 1, 2))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + // open_reader() should convert the table-level conjunct on projected column id 1 into + // _table_filters before ColumnMapper creates the FileScanRequest. ColumnMapper then rewrites + // the conjunct's slot ref from table column id 1 to the file-local block position used by + // ParquetReader. The projection order intentionally puts value before id, so the id filter + // column is not at position 0 in the file block. + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + const auto& id_column = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(id_column.size(), 1); + EXPECT_EQ(id_column.get_element(0), 3); + + ASSERT_TRUE(reader.close().ok()); + + TableReader filtered_reader; + ASSERT_TRUE(filtered_reader + .init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(1, 1, 4))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + ASSERT_TRUE(filtered_reader.prepare_split(build_split_options(file_path)).ok()); + + block = build_table_block(projected_columns); + eos = false; + ASSERT_TRUE(filtered_reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); + EXPECT_EQ(block.get_by_position(1).column->size(), 0); + + ASSERT_TRUE(filtered_reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, OpenReaderBuildsColumnPredicateFilters) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_column_predicate_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + // ColumnPredicate is only used for row-group/statistics pruning. Keep one row per row + // group so the predicate can prune the first two row groups and leave only id = 3. + write_int_pair_parquet_file(file_path, {1, 2, 3}, {1, 5, 8}, {"one", "two", "three"}, 1); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(2, "value", std::make_shared())); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + TableColumnPredicates column_predicates; + add_column_predicate(&column_predicates, GlobalIndex(1), + create_comparison_predicate( + 0, "id", std::make_shared(), + Field::create_field(2), false)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = std::move(column_predicates), + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& value_column = assert_cast(*block.get_by_position(0).column); + const auto& id_column = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(id_column.size(), 1); + ASSERT_EQ(value_column.size(), 1); + EXPECT_EQ(id_column.get_element(0), 3); + EXPECT_EQ(value_column.get_data_at(0).to_string(), "three"); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ColumnPredicateSurvivesReopenSplit) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_predicate_reopen_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const std::vector file_paths = { + (test_dir / "split_1.parquet").string(), + (test_dir / "split_2.parquet").string(), + }; + write_int_pair_parquet_file(file_paths[0], {1, 3}, {10, 30}, {"one", "three"}, 1); + write_int_pair_parquet_file(file_paths[1], {2, 4}, {20, 40}, {"two", "four"}, 1); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + TableColumnPredicates column_predicates; + add_column_predicate(&column_predicates, GlobalIndex(0), + create_comparison_predicate( + 0, "id", std::make_shared(), + Field::create_field(2), false)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = std::move(column_predicates), + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + std::vector ids; + for (const auto& file_path : file_paths) { + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + ASSERT_EQ(id_column.size(), 1); + ids.push_back(id_column.get_element(0)); + + ASSERT_TRUE(reader.close().ok()); + } + + EXPECT_EQ(ids, std::vector({3, 4})); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, CreateScanRequestDeduplicatesSharedPredicateColumns) { + const auto int_type = std::make_shared(); + const std::vector projected_columns = { + make_table_column(0, "a", int_type), + make_table_column(1, "b", int_type), + make_table_column(2, "c", int_type), + make_table_column(3, "value", std::make_shared()), + }; + const std::vector file_schema = { + make_file_column(0, "a", int_type), + make_file_column(1, "b", int_type), + make_file_column(2, "c", int_type), + make_file_column(3, "value", std::make_shared()), + }; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + std::vector table_filters; + table_filters.push_back({ + .conjunct = + VExprContext::create_shared(table_int32_sum_greater_than_expr(0, 0, 1, 1, 1)), + .global_indices = {GlobalIndex(0), GlobalIndex(1)}, + }); + table_filters.push_back({ + .conjunct = VExprContext::create_shared(table_int32_sum_less_than_expr(0, 0, 2, 2, 3)), + .global_indices = {GlobalIndex(0), GlobalIndex(2)}, + }); + + FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request(table_filters, {}, projected_columns, &file_request).ok()); + + // Both filters reference column a. It must still be read once as a predicate column, and a + // predicate column must not be repeated as a non-predicate column. + EXPECT_EQ(projection_ids(file_request.predicate_columns), std::vector({0, 1, 2})); + EXPECT_EQ(projection_ids(file_request.non_predicate_columns), std::vector({3})); + ASSERT_EQ(file_request.local_positions.size(), 4); + EXPECT_EQ(file_request.local_positions.at(LocalColumnId(3)).value(), 0); + EXPECT_EQ(file_request.local_positions.at(LocalColumnId(0)).value(), 1); + EXPECT_EQ(file_request.local_positions.at(LocalColumnId(1)).value(), 2); + EXPECT_EQ(file_request.local_positions.at(LocalColumnId(2)).value(), 3); + const auto predicate_column_ids = projection_ids(file_request.predicate_columns); + const auto non_predicate_column_ids = projection_ids(file_request.non_predicate_columns); + for (const auto predicate_column_id : predicate_column_ids) { + EXPECT_TRUE(std::find(non_predicate_column_ids.begin(), non_predicate_column_ids.end(), + predicate_column_id) == non_predicate_column_ids.end()); + } +} + +TEST(TableReaderTest, CreateScanRequestPromotesProjectedColumnToPredicateColumn) { + const auto int_type = std::make_shared(); + const std::vector projected_columns = { + make_table_column(0, "id", int_type), + make_table_column(1, "score", int_type), + }; + const std::vector file_schema = { + make_file_column(0, "id", int_type), + make_file_column(1, "score", int_type), + }; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + TableFilter table_filter { + .conjunct = VExprContext::create_shared(table_int32_greater_than_expr(0, 0, 1)), + .global_indices = {GlobalIndex(0)}, + }; + + FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); + + EXPECT_EQ(projection_ids(file_request.predicate_columns), std::vector({0})); + EXPECT_EQ(projection_ids(file_request.non_predicate_columns), std::vector({1})); + ASSERT_EQ(file_request.local_positions.size(), 2); + EXPECT_EQ(file_request.local_positions.at(LocalColumnId(0)).value(), 1); + EXPECT_EQ(file_request.local_positions.at(LocalColumnId(1)).value(), 0); +} + +TEST(TableReaderTest, CreateScanRequestUsesColumnNameForByNamePredicateMapping) { + const auto int_type = std::make_shared(); + std::vector projected_columns = { + make_table_column(10, "id", int_type), + make_table_column(11, "score", int_type), + }; + const std::vector file_schema = { + make_file_column(0, "ID", int_type), + make_file_column(1, "score", int_type), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + set_name_identifiers(&projected_columns); + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + TableFilter table_filter { + .conjunct = VExprContext::create_shared(table_int32_greater_than_expr(0, 0, 1)), + .global_indices = {GlobalIndex(0)}, + }; + + FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); + + EXPECT_EQ(projection_ids(file_request.predicate_columns), std::vector({0})); + EXPECT_EQ(projection_ids(file_request.non_predicate_columns), std::vector({1})); + ASSERT_EQ(file_request.conjuncts.size(), 1); + const auto* localized_slot = + assert_cast(file_request.conjuncts[0]->root()->children()[0].get()); + EXPECT_EQ(localized_slot->slot_id(), 0); + EXPECT_EQ(localized_slot->column_id(), 1); +} + +TEST(TableReaderTest, ColumnPredicateFilterUsesColumnNameForByNameMapping) { + const auto int_type = std::make_shared(); + std::vector projected_columns = { + make_table_column(10, "id", int_type), + make_table_column(11, "score", int_type), + }; + const std::vector file_schema = { + make_file_column(0, "ID", int_type), + make_file_column(1, "score", int_type), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + set_name_identifiers(&projected_columns); + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + TableColumnPredicates column_predicates; + add_column_predicate(&column_predicates, GlobalIndex(0), + create_comparison_predicate( + 10, "id", int_type, Field::create_field(2), false)); + + FileScanRequest file_request; + ASSERT_TRUE(mapper.create_scan_request({}, column_predicates, projected_columns, &file_request) + .ok()); + + ASSERT_EQ(file_request.column_predicate_filters.size(), 1); + EXPECT_EQ(file_request.column_predicate_filters[0].file_column_id.value(), 0); + EXPECT_EQ(projection_ids(file_request.non_predicate_columns), std::vector({0, 1})); + EXPECT_TRUE(file_request.predicate_columns.empty()); +} + +TEST(TableReaderTest, OpenReaderPushesMultiColumnConjunctToParquetReader) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_multi_conjunct_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {1, 5, 8}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(2, "value", std::make_shared())); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + projected_columns.push_back(make_table_column(1, "score", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE( + reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_sum_greater_than_expr(1, 1, 2, 2, 8))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + // The conjunct references both id and score, so ColumnMapper must put both file columns into + // predicate_columns and rewrite both slot refs to ParquetReader's file-local block positions. + // ParquetReader then evaluates the expression after all predicate columns have been read. + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& value_column = assert_cast(*block.get_by_position(0).column); + const auto& id_column = assert_cast(*block.get_by_position(1).column); + const auto& score_column = assert_cast(*block.get_by_position(2).column); + ASSERT_EQ(id_column.size(), 1); + ASSERT_EQ(score_column.size(), 1); + ASSERT_EQ(value_column.size(), 1); + EXPECT_EQ(id_column.get_element(0), 3); + EXPECT_EQ(score_column.get_element(0), 8); + EXPECT_EQ(value_column.get_data_at(0).to_string(), "three"); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedColumnsFillDefaultForParquetSchemaMismatch) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_schema_mismatch_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(99, "missing_value", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + // The table projection asks for field id 99, but the ParquetReader exposes only file-local + // fields 0 and 1. Missing columns are allowed by the current mapper options, so TableReader + // should still use the Parquet row count and fill a default column in table schema. + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + EXPECT_EQ(block.get_by_position(0).column->size(), 1); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, DefaultExprResultMatchesNullableTableType) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_nullable_default_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + const auto int_type = std::make_shared(); + auto missing_column = make_table_column(99, "c_new", make_nullable(int_type)); + missing_column.default_expr = VExprContext::create_shared( + VLiteral::create_shared(int_type, Field::create_field(42))); + std::vector projected_columns; + projected_columns.push_back(std::move(missing_column)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status.to_string(); + ASSERT_FALSE(eos); + + const auto& result = block.get_by_position(0); + ASSERT_TRUE(result.check_type_and_column_match().ok()); + EXPECT_TRUE(result.type->is_nullable()); + ASSERT_TRUE(result.column->is_nullable()); + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(nullable_column.size(), 1); + EXPECT_EQ(nullable_column.get_null_map_data()[0], 0); + const auto& values = assert_cast(nullable_column.get_nested_column()); + EXPECT_EQ(values.get_element(0), 42); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, DefaultExprAlignsNestedNullableArrayTableType) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_nested_nullable_array_default_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + const auto bigint_type = std::make_shared(); + const auto array_type = std::make_shared(make_nullable(bigint_type)); + const auto table_type = make_nullable(array_type); + auto missing_column = make_table_column(99, "single_element_groups", table_type); + missing_column.default_expr = VExprContext::create_shared( + std::make_shared(table_type)); + std::vector projected_columns; + projected_columns.push_back(std::move(missing_column)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status.to_string(); + ASSERT_FALSE(eos); + + const auto& result = block.get_by_position(0); + ASSERT_TRUE(result.check_type_and_column_match().ok()); + ASSERT_TRUE(result.column->is_nullable()); + const auto& nullable_column = assert_cast(*result.column); + ASSERT_EQ(nullable_column.size(), 1); + EXPECT_EQ(nullable_column.get_null_map_data()[0], 0); + + const auto& array_column = assert_cast(nullable_column.get_nested_column()); + ASSERT_EQ(array_column.size(), 1); + EXPECT_EQ(array_column.get_offsets()[0], 1); + ASSERT_TRUE(array_column.get_data().is_nullable()); + const auto& nested_nullable = assert_cast(array_column.get_data()); + ASSERT_EQ(nested_nullable.size(), 1); + EXPECT_EQ(nested_nullable.get_null_map_data()[0], 0); + const auto& values = assert_cast(nested_nullable.get_nested_column()); + EXPECT_EQ(values.get_element(0), 7); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedColumnsRejectParquetSchemaMismatchWhenMissingColumnsDisallowed) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_schema_mismatch_reject_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(99, "missing_value", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = false, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + // With allow_missing_columns disabled, the same missing projected column should fail while + // opening the split instead of being materialized as a default column. + Block block = build_table_block(projected_columns); + bool eos = false; + const auto status = reader.get_block(&block, &eos); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("does not have a matching file column"), std::string::npos); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedStructFillsMissingChildWithDefault) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_struct_missing_child_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_struct_parquet_file(file_path, 7); + + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + auto id_child = make_table_column(0, "id", int_type); + auto missing_child = make_table_column(99, "missing_child", string_type); + auto struct_type = std::make_shared(DataTypes {int_type, string_type}, + Strings {"id", "missing_child"}); + auto struct_column = make_table_column(100, "s", struct_type); + struct_column.children = {id_child, missing_child}; + std::vector projected_columns = {struct_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& struct_result = assert_cast(*block.get_by_position(0).column); + ASSERT_EQ(struct_result.get_columns().size(), 2); + const auto& ids = assert_cast(struct_result.get_column(0)); + const auto& missing_values = assert_cast(struct_result.get_column(1)); + ASSERT_EQ(struct_result.size(), 1); + EXPECT_EQ(ids.get_element(0), 7); + EXPECT_EQ(missing_values.get_data_at(0).to_string(), ""); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ReusedBlockClearsProjectedStructWithNullableChild) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_struct_nullable_child_reuse_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_struct_with_nullable_child_parquet_file(file_path); + + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + const auto nullable_string_type = make_nullable(string_type); + auto id_child = make_table_column(0, "id", int_type); + auto note_child = make_table_column(1, "note", nullable_string_type); + auto missing_child = make_table_column(99, "missing_child", string_type); + auto struct_type = std::make_shared( + DataTypes {int_type, nullable_string_type, string_type}, + Strings {"id", "note", "missing_child"}); + auto struct_column = make_table_column(100, "s", struct_type); + struct_column.children = {id_child, note_child, missing_child}; + std::vector projected_columns = {struct_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& struct_result = assert_cast(*block.get_by_position(0).column); + const auto& notes = assert_cast(struct_result.get_column(1)); + EXPECT_FALSE(notes.is_null_at(0)); + EXPECT_TRUE(notes.is_null_at(1)); + + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); + EXPECT_EQ(block.rows(), 0); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedPartitionColumnUsesSplitPartitionValue) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_partition_value_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + auto partition_column = make_table_column(1, "value", std::make_shared()); + partition_column.is_partition_key = true; + projected_columns.push_back(std::move(partition_column)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.partition_values.emplace("value", Field::create_field("p1")); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + // The file has a physical column with the same id/name. The split partition value should still + // take precedence and be materialized by TableReader. + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto partition_value = block.get_by_position(0).column->convert_to_full_column_if_const(); + const auto& partition_value_data = assert_cast(*partition_value); + ASSERT_EQ(partition_value_data.size(), 1); + EXPECT_EQ(partition_value_data.get_data_at(0).to_string(), "p1"); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ConstantPartitionFilterSkipsSplitWhenFalse) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_constant_partition_filter_skip_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + auto partition_column = make_table_column(0, "part", std::make_shared()); + partition_column.is_partition_key = true; + projected_columns.push_back(std::move(partition_column)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(0, 0, 10))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.partition_values.emplace("part", Field::create_field(7)); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); + EXPECT_EQ(block.get_by_position(0).column->size(), 0); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ConstantPartitionFilterKeepsSplitWhenTrue) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_constant_partition_filter_keep_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + auto partition_column = make_table_column(0, "part", std::make_shared()); + partition_column.is_partition_key = true; + projected_columns.push_back(std::move(partition_column)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(2, 2, 1))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.partition_values.emplace("part", Field::create_field(7)); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + expect_int32_column_values(*block.get_by_position(0).column, {7}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergVirtualColumnsUseRowLineageMetadata) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_virtual_columns_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(100, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(101, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(2, 2, 1))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + set_iceberg_row_lineage_params(&split_options, 1000, 77); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& id_column = assert_cast(*block.get_by_position(2).column); + + ASSERT_EQ(block.rows(), 2); + EXPECT_EQ(id_column.get_element(0), 2); + EXPECT_EQ(id_column.get_element(1), 3); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {1001, 1002}); + expect_nullable_int64_column_values(*block.get_by_position(1).column, {77, 77}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergRowLineageUsesPhysicalRowIdAndFillsNulls) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_physical_row_id_fill_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_iceberg_row_lineage_parquet_file(file_path, {1, 2, 3}, {7000, std::nullopt, 7002}, + {80, std::nullopt, 82}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column( + 2147483540, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(2147483539, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + set_iceberg_row_lineage_params(&split_options, 1000, 77); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + ASSERT_EQ(block.rows(), 3); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {7000, 1001, 7002}); + expect_nullable_int64_column_values(*block.get_by_position(1).column, {80, 77, 82}); + expect_int32_column_values(*block.get_by_position(2).column, {1, 2, 3}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergPhysicalRowIdKeepsNullsWithoutFirstRowId) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_physical_row_id_no_first_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_iceberg_row_lineage_parquet_file(file_path, {1, 2, 3}, {7000, std::nullopt, 7002}, + {80, std::nullopt, 82}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column( + 2147483540, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(2147483539, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + ASSERT_EQ(block.rows(), 3); + expect_nullable_int64_column_optional_values( + *block.get_by_position(0).column, + std::vector> {7000, std::nullopt, 7002}); + expect_nullable_int64_column_optional_values( + *block.get_by_position(1).column, + std::vector> {80, std::nullopt, 82}); + expect_int32_column_values(*block.get_by_position(2).column, {1, 2, 3}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergMissingRowIdStaysNullWithoutFirstRowId) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_missing_row_id_no_first_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(100, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(101, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + ASSERT_EQ(block.rows(), 3); + expect_nullable_int64_column_optional_values( + *block.get_by_position(0).column, + std::vector> {std::nullopt, std::nullopt, std::nullopt}); + expect_nullable_int64_column_optional_values( + *block.get_by_position(1).column, + std::vector> {std::nullopt, std::nullopt, std::nullopt}); + expect_int32_column_values(*block.get_by_position(2).column, {1, 2, 3}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergRowIdPredicateFiltersAfterRowLineageMaterialization) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_row_id_finalize_filter_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_iceberg_row_lineage_parquet_file(file_path, {1, 2, 3}, {7000, std::nullopt, 7002}, + {80, std::nullopt, 82}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column( + 2147483540, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(2147483539, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + VExprContextSPtrs conjuncts = {prepared_conjunct( + &state, + table_nullable_int64_binary_predicate("eq", TExprOpcode::EQ, 0, 0, "_row_id", 1001))}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = conjuncts, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + set_iceberg_row_lineage_params(&split_options, 1000, 77); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + + apply_final_conjuncts(&block, conjuncts); + ASSERT_EQ(block.rows(), 1); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {1001}); + expect_nullable_int64_column_values(*block.get_by_position(1).column, {77}); + expect_int32_column_values(*block.get_by_position(2).column, {2}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergLastUpdatedSequencePredicateFiltersAfterMaterialization) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_sequence_finalize_filter_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_iceberg_row_lineage_parquet_file(file_path, {1, 2, 3}, {7000, std::nullopt, 7002}, + {80, std::nullopt, 82}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column( + 2147483540, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(2147483539, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + VExprContextSPtrs conjuncts = {prepared_conjunct( + &state, table_nullable_int64_binary_predicate("eq", TExprOpcode::EQ, 1, 1, + "_last_updated_sequence_number", 77))}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = conjuncts, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + set_iceberg_row_lineage_params(&split_options, 1000, 77); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + + apply_final_conjuncts(&block, conjuncts); + ASSERT_EQ(block.rows(), 1); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {1001}); + expect_nullable_int64_column_values(*block.get_by_position(1).column, {77}); + expect_int32_column_values(*block.get_by_position(2).column, {2}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergRowidVirtualColumnUsesDataFilePosition) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_rowid_virtual_column_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(-1, BeConsts::ICEBERG_ROWID_COL, make_iceberg_rowid_type())); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(1, 1, 1))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + const auto original_file_path = "s3://bucket/table/data/original.parquet"; + const auto partition_data_json = R"({"part":"p1"})"; + set_iceberg_rowid_params(&split_options, original_file_path, 17, partition_data_json); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + ASSERT_EQ(block.rows(), 2); + expect_iceberg_rowid_column_values(*block.get_by_position(0).column, original_file_path, {1, 2}, + 17, partition_data_json); + expect_int32_column_values(*block.get_by_position(1).column, {2, 3}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergVirtualColumnsKeepRowLineageAfterConjunctFiltering) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_virtual_columns_conjunct_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(100, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(101, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, table_int32_greater_than_expr(2, 2, 1))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + set_iceberg_row_lineage_params(&split_options, 3000, 88); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& id_column = assert_cast(*block.get_by_position(2).column); + + ASSERT_EQ(block.rows(), 2); + EXPECT_EQ(id_column.get_element(0), 2); + EXPECT_EQ(id_column.get_element(1), 3); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {3001, 3002}); + expect_nullable_int64_column_values(*block.get_by_position(1).column, {88, 88}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergVirtualColumnsKeepRowLineageAfterRowGroupPredicatePruning) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_iceberg_virtual_columns_row_group_predicate_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + // ColumnPredicate is used for row-group/statistics pruning. Keep one row per row group so + // id > 2 prunes the first two row groups and leaves only the third file-local row. + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}, 1); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(100, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back( + make_table_column(101, "_last_updated_sequence_number", + make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + TableColumnPredicates column_predicates; + add_column_predicate(&column_predicates, GlobalIndex(2), + create_comparison_predicate( + 0, "id", std::make_shared(), + Field::create_field(2), false)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = std::move(column_predicates), + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + set_iceberg_row_lineage_params(&split_options, 4000, 99); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& id_column = assert_cast(*block.get_by_position(2).column); + + ASSERT_EQ(block.rows(), 1); + EXPECT_EQ(id_column.get_element(0), 3); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {4002}); + expect_nullable_int64_column_values(*block.get_by_position(1).column, {99}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergDeletionVectorUsesTableReaderDeleteFileInterface) { + TTableFormatFileDesc table_format_desc; + TIcebergFileDesc iceberg_desc; + iceberg_desc.__set_format_version(2); + iceberg_desc.__set_delete_files({make_iceberg_deletion_vector("dv.bin", 8, 128)}); + table_format_desc.__set_iceberg_params(iceberg_desc); + + IcebergTableReaderDeleteFileTestHelper reader; + DeleteFileDesc desc; + bool has_delete_file = false; + ASSERT_TRUE(reader.parse_deletion_vector_file(table_format_desc, &desc, &has_delete_file).ok()); + + EXPECT_TRUE(has_delete_file); + EXPECT_EQ(desc.path, "dv.bin"); + EXPECT_EQ(desc.start_offset, 8); + EXPECT_EQ(desc.size, 128); + EXPECT_EQ(desc.file_size, -1); + EXPECT_EQ(desc.format, DeleteFileDesc::Format::ICEBERG); +} + +TEST(TableReaderTest, IcebergDeletionVectorRejectsMultipleDeleteFiles) { + TTableFormatFileDesc table_format_desc; + TIcebergFileDesc iceberg_desc; + iceberg_desc.__set_format_version(2); + iceberg_desc.__set_delete_files({make_iceberg_deletion_vector("dv-a.bin", 8, 128), + make_iceberg_deletion_vector("dv-b.bin", 16, 256)}); + table_format_desc.__set_iceberg_params(iceberg_desc); + + IcebergTableReaderDeleteFileTestHelper reader; + DeleteFileDesc desc; + bool has_delete_file = false; + auto status = reader.parse_deletion_vector_file(table_format_desc, &desc, &has_delete_file); + + EXPECT_FALSE(status.ok()); +} + +TEST(TableReaderTest, IcebergTableReaderAppliesDeletionVectorFile) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_deletion_vector_file_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto dv_path = (test_dir / "delete-vector.bin").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3, 4, 5}, {10, 20, 30, 40, 50}, + {"one", "two", "three", "four", "five"}); + const auto dv_size = write_iceberg_deletion_vector_file(dv_path, {0, 4}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_deletion_vector(dv_path, 0, dv_size)})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + EXPECT_EQ(read_iceberg_ids(&reader, projected_columns), std::vector({2, 3, 4})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithDeletes) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_aggregate_delete_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto dv_path = (test_dir / "delete-vector.bin").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + const auto dv_size = write_iceberg_deletion_vector_file(dv_path, {0}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_deletion_vector(dv_path, 0, dv_size)})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 2); + EXPECT_EQ(id_column.get_element(1), 3); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithPositionDelete) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_aggregate_position_delete_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_position_delete_parquet_file(delete_file_path, {file_path}, {1}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_position_delete_file(delete_file_path)})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 3); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergTableLevelCountUsesAssignedRowCountWithPositionDelete) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_table_level_count_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_position_delete_parquet_file(delete_file_path, {file_path}, {1}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + TQueryOptions query_options; + query_options.__set_batch_size(10); + RuntimeState state {query_options, TQueryGlobals()}; + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_position_delete_file(delete_file_path)})); + set_table_level_row_count(&split_options, 5); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + EXPECT_EQ(block.rows(), 5); + + block = build_table_block(projected_columns); + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); + EXPECT_EQ(block.rows(), 0); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergPositionDeleteFallsBackToSplitPath) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_position_delete_path_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_position_delete_parquet_file(delete_file_path, {file_path}, {1}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + TTableFormatFileDesc table_format_params; + TIcebergFileDesc iceberg_params; + iceberg_params.__set_format_version(2); + iceberg_params.__set_delete_files({make_iceberg_position_delete_file(delete_file_path)}); + table_format_params.__set_iceberg_params(iceberg_params); + split_options.current_range.__set_table_format_params(table_format_params); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + EXPECT_EQ(read_iceberg_ids(&reader, projected_columns), std::vector({1, 3})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithEqualityDelete) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_aggregate_equality_delete_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "equality-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_iceberg_equality_delete_parquet_file(delete_file_path, 0, 2); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + .push_down_agg_type = TPushAggOp::type::COUNT, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_equality_delete_file(delete_file_path, {0})})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 3); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergEqualityDeleteCastsDataColumnToDeleteKeyType) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_equality_delete_cast_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "equality-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_iceberg_equality_delete_bigint_parquet_file(delete_file_path, 0, 2); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_equality_delete_file(delete_file_path, {0})})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + EXPECT_EQ(read_iceberg_ids(&reader, projected_columns), std::vector({1, 3})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergPositionDeleteOnlyMatchesOriginalDataFilePath) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_iceberg_position_delete_path_match_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto other_file_path = (test_dir / "other.parquet").string(); + const auto delete_file_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_position_delete_parquet_file(delete_file_path, {other_file_path, file_path}, {0, 1}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_position_delete_file(delete_file_path)})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + EXPECT_EQ(read_iceberg_ids(&reader, projected_columns), std::vector({1, 3})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergRowLineageRemainsFileLocalAfterDeleteFiltering) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_row_lineage_delete_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, {"one", "two", "three"}); + write_position_delete_parquet_file(delete_file_path, {file_path}, {1}); + + std::vector projected_columns; + projected_columns.push_back( + make_table_column(100, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + TTableFormatFileDesc table_format_params = make_iceberg_table_format_desc( + file_path, {make_iceberg_position_delete_file(delete_file_path)}); + table_format_params.iceberg_params.__set_first_row_id(1000); + split_options.current_range.__set_table_format_params(table_format_params); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 2); + expect_nullable_int64_column_values(*block.get_by_position(0).column, {1000, 1002}); + const auto& id_column = assert_cast(*block.get_by_position(1).column); + EXPECT_EQ(id_column.get_element(0), 1); + EXPECT_EQ(id_column.get_element(1), 3); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergTableReaderAppliesPositionDeleteFile) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_position_delete_file_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto delete_file_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3, 4, 5}, {10, 20, 30, 40, 50}, + {"one", "two", "three", "four", "five"}); + write_position_delete_parquet_file(delete_file_path, {file_path, file_path}, {1, 3}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_position_delete_file(delete_file_path)})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + EXPECT_EQ(read_iceberg_ids(&reader, projected_columns), std::vector({1, 3, 5})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, IcebergTableReaderMergesDeletionVectorAndPositionDeleteFiles) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_iceberg_delete_files_merge_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto dv_path = (test_dir / "delete-vector.bin").string(); + const auto position_delete_path = (test_dir / "position-delete.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3, 4, 5}, {10, 20, 30, 40, 50}, + {"one", "two", "three", "four", "five"}); + const auto dv_size = write_iceberg_deletion_vector_file(dv_path, {0}); + write_position_delete_parquet_file(position_delete_path, {file_path, file_path}, {3, 3}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::iceberg::IcebergTableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + .allow_missing_columns = true, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params(make_iceberg_table_format_desc( + file_path, {make_iceberg_deletion_vector(dv_path, 0, dv_size), + make_iceberg_position_delete_file(position_delete_path)})); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + EXPECT_EQ(read_iceberg_ids(&reader, projected_columns), std::vector({2, 3, 5})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, RowPositionDeletePredicateColumnIsNotRepeatedAsOutputColumn) { + const auto row_position_column_id = ROW_POSITION_COLUMN_ID; + std::vector projected_columns; + projected_columns.push_back( + make_table_column(100, "_row_id", make_nullable(std::make_shared()))); + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + IcebergTableReaderScanRequestTestHelper reader; + ASSERT_TRUE(reader.init_for_scan_request_test(projected_columns).ok()); + + FileScanRequest request; + request.non_predicate_columns.push_back(field_projection(0)); + request.local_positions.emplace(LocalColumnId(0), LocalIndex(0)); + + ASSERT_TRUE(reader.customize_request(&request).ok()); + + EXPECT_EQ(projection_ids(request.predicate_columns), + std::vector({row_position_column_id})); + EXPECT_EQ(projection_ids(request.non_predicate_columns), std::vector({0})); + ASSERT_TRUE(request.local_positions.contains(LocalColumnId(row_position_column_id))); + EXPECT_EQ(request.local_positions.at(LocalColumnId(row_position_column_id)).value(), 1); + ASSERT_TRUE(request.conjuncts.empty()); + ASSERT_EQ(request.delete_conjuncts.size(), 1); + EXPECT_NE(request.delete_conjuncts[0], nullptr); +} + +TEST(TableReaderTest, ParquetReaderReadsOnlyRowGroupsInFileRange) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_file_range_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3}, {10, 20, 30}, + {"range_group_one", "range_group_two", "range_group_three"}, 1); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + projected_columns.push_back(make_table_column(2, "value", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options_for_row_group_mid(file_path, 1)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(block.rows(), 1); + EXPECT_EQ(id_column.get_element(0), 2); + EXPECT_EQ(value_column.get_data_at(0).to_string(), "range_group_two"); + + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + EXPECT_TRUE(eos); + EXPECT_EQ(block.rows(), 0); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedColumnsUseMapperExpressionForSameNameDifferentIdParquetSchema) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_same_name_diff_id_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(99, "id", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + // The table column has the same name as the Parquet field, but a different field id. + // ColumnMapper should still resolve it by name and build a SlotRef projection from the file + // column into the requested table column. + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + const auto& id_column = assert_cast(*block.get_by_position(0).column); + ASSERT_EQ(id_column.size(), 1); + EXPECT_EQ(id_column.get_element(0), 1); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableReaderTest, ProjectedColumnsUseMapperExpressionsForParquetSchemaMismatch) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_mapper_expr_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 7, "seven"); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + projected_columns.push_back(make_table_column(1, "value", std::make_shared())); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + .allow_missing_columns = true, + }) + .ok()); + + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + // The table projection requests id as BIGINT instead of the file INT, so ColumnMapper should + // build a Cast expression. The second field has the same type and should build a SlotRef + // projection. Both columns should still materialize in table schema order. + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + + ASSERT_EQ(block.get_by_position(0).name, "id"); + ASSERT_EQ(block.get_by_position(1).name, "value"); + const auto& id_column = assert_cast(*block.get_by_position(0).column); + const auto& value_column = assert_cast(*block.get_by_position(1).column); + ASSERT_EQ(id_column.size(), 1); + ASSERT_EQ(value_column.size(), 1); + EXPECT_EQ(id_column.get_element(0), 7); + EXPECT_EQ(value_column.get_data_at(0).to_string(), "seven"); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + +TEST(TableColumnMapperTest, MatchingProjectedStructDoesNotNeedComplexRematerialize) { + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + + auto table_struct = + make_table_column(10, "s", + std::make_shared(DataTypes {int_type, string_type}, + Strings {"a", "b"})); + table_struct.children = { + make_table_column(1, "a", int_type), + make_table_column(2, "b", string_type), + }; + + auto file_struct = make_file_column( + 10, "s", + std::make_shared(DataTypes {int_type, string_type, int_type}, + Strings {"a", "b", "c"})); + file_struct.children = { + make_file_column(1, "a", int_type), + make_file_column(2, "b", string_type), + make_file_column(3, "c", int_type), + }; + + const std::vector projected_columns = {table_struct}; + const std::vector file_schema = {file_struct}; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); + + FileScanRequest file_request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, projected_columns, &file_request).ok()); + + ASSERT_EQ(file_request.non_predicate_columns.size(), 1); + const auto& projection = file_request.non_predicate_columns[0]; + EXPECT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 2); + EXPECT_EQ(projection.children[0].local_id(), 1); + EXPECT_EQ(projection.children[1].local_id(), 2); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); +} + +TEST(TableColumnMapperTest, RenameOnlyProjectedStructDoesNotRebuildFileProjection) { + const auto int_type = std::make_shared(); + + auto table_struct = + make_table_column(10, "s", + std::make_shared(DataTypes {int_type, int_type}, + Strings {"a", "renamed_b"})); + table_struct.children = { + make_table_column(1, "a", int_type), + make_table_column(2, "renamed_b", int_type), + }; + + auto file_struct = make_file_column( + 10, "s", + std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"})); + file_struct.children = { + make_file_column(1, "a", int_type), + make_file_column(2, "b", int_type), + }; + + const std::vector projected_columns = {table_struct}; + const std::vector file_schema = {file_struct}; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); + EXPECT_EQ(mapper.mappings()[0].projected_file_children.size(), + mapper.mappings()[0].original_file_children.size()); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); + EXPECT_EQ(mapper.mappings()[0].child_mappings[1].table_column_name, "renamed_b"); + EXPECT_EQ(mapper.mappings()[0].child_mappings[1].file_column_name, "b"); + + FileScanRequest file_request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, projected_columns, &file_request).ok()); + + ASSERT_EQ(file_request.non_predicate_columns.size(), 1); + EXPECT_TRUE(file_request.non_predicate_columns[0].project_all_children); + EXPECT_TRUE(file_request.non_predicate_columns[0].children.empty()); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); +} + +TEST(TableColumnMapperTest, PredicateProjectionRebuildsProjectedStructFileType) { + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + + auto table_struct = + make_table_column(10, "s", + std::make_shared(DataTypes {int_type, string_type}, + Strings {"a", "b"})); + table_struct.children = { + make_table_column(1, "a", int_type), + make_table_column(2, "b", string_type), + }; + + auto file_struct = make_file_column( + 10, "s", + std::make_shared(DataTypes {int_type, string_type, int_type}, + Strings {"a", "b", "c"})); + file_struct.children = { + make_file_column(1, "a", int_type), + make_file_column(2, "b", string_type), + make_file_column(3, "c", int_type), + }; + + const std::vector projected_columns = {table_struct}; + const std::vector file_schema = {file_struct}; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + TableFilter table_filter { + .conjunct = VExprContext::create_shared(table_struct_child_int32_greater_than_expr( + 0, 0, table_struct.type, "s", "c", 0)), + .global_indices = {GlobalIndex(0)}, + }; + + FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); + + ASSERT_EQ(file_request.predicate_columns.size(), 1); + EXPECT_TRUE(file_request.non_predicate_columns.empty()); + const auto& projection = file_request.predicate_columns[0]; + EXPECT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 3); + EXPECT_EQ(projection.children[0].local_id(), 1); + EXPECT_EQ(projection.children[1].local_id(), 2); + EXPECT_EQ(projection.children[2].local_id(), 3); + + const auto* mapped_type = assert_cast( + remove_nullable(mapper.mappings()[0].file_type).get()); + ASSERT_EQ(mapped_type->get_elements().size(), 3); + EXPECT_EQ(mapped_type->get_element_name(0), "a"); + EXPECT_EQ(mapped_type->get_element_name(1), "b"); + EXPECT_EQ(mapped_type->get_element_name(2), "c"); + EXPECT_FALSE(mapper.mappings()[0].is_trivial); +} + +TEST(TableColumnMapperTest, PredicateOnlyTopLevelColumnUsesHiddenMapping) { + const auto int_type = std::make_shared(); + + const auto table_id = make_table_column(0, "id", int_type); + auto table_struct = make_table_column( + 10, "s", std::make_shared(DataTypes {int_type}, Strings {"c"})); + + const auto file_id = make_file_column(0, "id", int_type); + auto file_struct = make_file_column( + 10, "s", std::make_shared(DataTypes {int_type}, Strings {"c"})); + file_struct.children = { + make_file_column(11, "c", int_type), + }; + + const std::vector projected_columns = {table_id}; + const std::vector file_schema = {file_id, file_struct}; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_EQ(mapper.mappings()[0].table_column_name, "id"); + + TableFilter table_filter { + .conjunct = VExprContext::create_shared(table_struct_child_int32_greater_than_expr( + 7, 1, table_struct.type, "s", "c", 0)), + .global_indices = {GlobalIndex(1)}, + }; + + FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_EQ(mapper.mappings()[0].table_column_name, "id"); + + ASSERT_EQ(file_request.non_predicate_columns.size(), 1); + EXPECT_EQ(file_request.non_predicate_columns[0].column_id(), LocalColumnId(0)); + ASSERT_EQ(file_request.predicate_columns.size(), 1); + EXPECT_EQ(file_request.predicate_columns[0].column_id(), LocalColumnId(10)); + EXPECT_TRUE(file_request.predicate_columns[0].project_all_children); + EXPECT_TRUE(file_request.predicate_columns[0].children.empty()); + + ASSERT_EQ(file_request.conjuncts.size(), 1); + ASSERT_EQ(file_request.column_predicate_filters.size(), 1); + EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_column_id(), + LocalColumnId(10)); + EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({11})); +} + +TEST(TableColumnMapperTest, NestedPredicateProjectionUsesMappedRenamedChild) { + const auto int_type = std::make_shared(); + + auto table_struct = + make_table_column(10, "s", + std::make_shared(DataTypes {int_type, int_type}, + Strings {"a", "renamed_b"})); + table_struct.children = { + make_table_column(1, "a", int_type), + make_table_column(2, "renamed_b", int_type), + }; + + auto file_struct = make_file_column( + 10, "s", + std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"})); + file_struct.children = { + make_file_column(1, "a", int_type), + make_file_column(2, "b", int_type), + }; + + const std::vector projected_columns = {table_struct}; + const std::vector file_schema = {file_struct}; + + TableColumnMapper mapper; + ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); + + TableFilter table_filter { + .conjunct = VExprContext::create_shared(table_struct_child_int32_greater_than_expr( + 0, 0, table_struct.type, "s", "renamed_b", 0)), + .global_indices = {GlobalIndex(0)}, + }; + + FileScanRequest file_request; + ASSERT_TRUE( + mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); + + ASSERT_EQ(file_request.column_predicate_filters.size(), 1); + EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_column_id(), + LocalColumnId(10)); + EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({2})); + ASSERT_EQ(file_request.predicate_columns.size(), 1); + EXPECT_TRUE(file_request.predicate_columns[0].project_all_children); + EXPECT_TRUE(file_request.predicate_columns[0].children.empty()); +} + +} // namespace +} // namespace doris::format diff --git a/docs/doris-iceberg-parquet-api-design.md b/docs/doris-iceberg-parquet-api-design.md new file mode 100644 index 00000000000000..457550a932da67 --- /dev/null +++ b/docs/doris-iceberg-parquet-api-design.md @@ -0,0 +1,511 @@ +# Doris Iceberg + Parquet 新架构 API 设计 + +本文档用于描述 Doris 中 Iceberg + Parquet 新架构的 API 设计。本文档作为后续从 +`master` 新开重构分支时的起点，只定义 API 形状、职责边界、依赖方向和兼容原则， +不定义函数实现细节，不提供伪代码，不包含迁移 patch。 + +## 架构总览 + +目标架构包含 table 调度层、表格式语义层、schema 映射层、文件通用层和文件格式实现层： + +```text +FileScanner / split producer + -> +TableReader + -> +IcebergTableReader + -> +TableColumnMapper + FileReader + -> +ParquetReader +``` + +核心职责如下： + +- `TableReader` + 负责多文件、多 split 的上层调度，统一 scan 生命周期，对外输出 table block， + 并承接动态分区裁剪等 table-level 通用逻辑。 +- `IcebergTableReader` + 负责 Iceberg 表语义，包括 schema 绑定、scan task、delete file、虚拟列和 table + block finalize。 +- `TableColumnMapper` + 负责 table schema 到 file schema 的映射，负责 filter localization 和 schema + change 映射。 +- `FileReader` + 负责文件层通用读取接口，只理解 file-local schema 和 file-local scan request。 +- `ParquetReader` + 作为 `FileReader` 的 Parquet 实现，负责 Parquet 文件物理读取。 + +依赖方向必须保持单向： + +```text +TableReader + -> IcebergTableReader + -> TableColumnMapper + -> FileReader + -> ParquetReader +``` + +低层不反向理解高层语义，尤其 `ParquetReader` 不得反向理解 Iceberg/global schema。 + +## 核心 API 设计 + +### TableReader + +`TableReader` 是最上层读取接口，作为 `IcebergTableReader` 的基类，负责多 split / +多 file 调度，并承接 table-level 的通用裁剪逻辑，不下沉文件格式语义。 + +实际 API 文件： + +```text +be/src/format_v2/table_reader.h +``` + +实际命名空间： + +```cpp +namespace doris::format +``` + +建议职责： + +- 接收 split 列表或 scan task 列表； +- 控制当前 reader 的创建、切换和关闭； +- 管理 scan 生命周期； +- 承接动态分区裁剪等 table-level 通用过滤逻辑； +- 对外统一输出 table block。 +- `next` 是基类统一入口，内部负责 EOF 后切换 reader；具体表格式只提供打开和读取 + 当前 reader 的 hook。 + +建议接口形状： + +```cpp +namespace doris::format { + +class TableReader { +public: + virtual ~TableReader() = default; + + virtual Status init(const TableReadOptions& options); + virtual Status filter(const VExprContextSPtr& expr, bool* can_filter_all); + Status next(Block* table_block, size_t* rows, bool* eof); + virtual Status close(); + +protected: + Status next_reader(); + virtual Status open_next_reader(bool* has_reader); + virtual Status read_current(Block* table_block, size_t* rows, bool* eof); + virtual Status close_current_reader(); +}; + +} // namespace doris::format +``` + +接口约束： + +- `TableReader` 输出的是 table block，不输出 file-local block。 +- `TableReader` 负责多文件编排和 table-level 通用裁剪，不负责 schema mapping，不负责 + Parquet 物理解码。 +- `next_reader` 是 `TableReader` 自己的通用切换逻辑，不作为子类公开 override 接口。 +- 动态分区裁剪这类逻辑应下放到 `TableReader`，而不是散落在具体表格式 reader 中。 +- `TableReader` 不直接依赖旧 `vparquet` 表层语义。 + +### IcebergTableReader + +`IcebergTableReader` 是 Iceberg 表语义层，负责把单个 Iceberg data file 的读取组织成 +table 语义输出。 + +实际 API 文件： + +```text +be/src/format_v2/table/iceberg_reader.h +``` + +实际命名空间： + +```cpp +namespace doris::iceberg +``` + +建议职责： + +- 绑定 Iceberg 当前 table schema； +- 接收 `IcebergScanTask` 列表，并按 `TableReader` 的统一调度打开当前 task； +- 处理 position delete、equality delete、deletion vector； +- 物化 `_row_id`、`_last_updated_sequence_number` 等虚拟列； +- 将 `ParquetReader` 返回的 file-local block finalize 成 table block。 + +建议接口形状： + +```cpp +namespace doris::iceberg { + +class IcebergTableReader : public format::TableReader { +public: + virtual ~IcebergTableReader() = default; + + Status init(IcebergTableReadParams params); + Status close() override; + +protected: + Status open_next_reader(bool* has_reader) override; + Status read_current(Block* table_block, size_t* rows, bool* eof) override; + Status close_current_reader() override; +}; + +} // namespace doris::iceberg +``` + +接口约束： + +- `IcebergTableReader` 继承 `TableReader`，并通过组合使用 `FileReader`。 +- `IcebergTableReader` 不做 Parquet page/column 解码。 +- `IcebergTableReader` 负责 table-level finalize，不负责 file-local pruning 实现。 +- `IcebergTableReader` 的 schema、scan request、scan tasks 和底层 `FileReader` 应通过 + 一个初始化参数对象一次性传入；除非存在明确生命周期差异，不拆成 `bind` / + `init(TableScanRequest)` / `set_scan_tasks` 多阶段接口。 +- `IcebergTableReader` 不重新实现 reader 切换循环，只实现打开 Iceberg task、读取当前 + task 和关闭当前 reader 的 hook。 + +### TableColumnMapper + +`TableColumnMapper` 是 table schema 到 file schema 的通用映射层，不是 +Iceberg-only 组件。 + +实际 API 文件： + +```text +be/src/format_v2/table_reader.h +``` + +实际命名空间： + +```cpp +namespace doris::format +``` + +建议职责： + +- 输入 table schema、file schema、table scan request； +- 输出 `ColumnMapping` 和通用 `FileScanRequest`； +- 负责 filter localization； +- 负责 schema change 映射； +- 负责复杂列 child mapping； +- 负责缺失列、default、partition、generated 列的 finalize 语义描述。 + +建议接口形状： + +```cpp +namespace doris::format { + +class TableColumnMapper { +public: + explicit TableColumnMapper(TableColumnMapperOptions options = {}); + + virtual Status create_mapping(const std::vector& table_schema, + const std::vector& file_schema, + std::vector* mappings); + + virtual Status create_scan_request(const TableScanRequest& table_request, + const std::vector& mappings, + FileScanRequest* file_request); +}; + +} // namespace doris::format +``` + +接口约束： + +- `TableColumnMapper` 的输入是 table schema + file schema + table scan request。 +- `TableColumnMapper` 的输出是 `ColumnMapping` + `FileScanRequest`。 +- `TableColumnMapper` 必须是通用层，不做 Iceberg-only 命名。 +- Iceberg 场景默认按 field id 映射；按 name 映射不是本轮默认路径。 + +### FileReader + +`FileReader` 是文件物理读取层的通用接口，为后续 Parquet 之外的文件格式适配预留。 + +实际 API 文件： + +```text +be/src/format_v2/file_reader.h +``` + +实际命名空间： + +```cpp +namespace doris::format +``` + +建议职责： + +- 打开物理文件； +- 暴露 file-local schema； +- 接收 `FileScanRequest`； +- 输出 file-local block； +- 不理解 table/global schema。 + +建议接口形状： + +```cpp +namespace doris::format { + +class FileReader { +public: + virtual ~FileReader() = default; + + virtual Status open(io::FileReaderSPtr file, io::IOContext* io_ctx = nullptr); + virtual Status get_schema(std::vector* file_schema) const; + virtual Status init(const FileScanRequest& request); + virtual Status next(Block* file_block, size_t* rows, bool* eof); + virtual Status close(); +}; + +} // namespace doris::format +``` + +接口约束： + +- `FileReader` 输出的是 file-local block，不输出 table/global schema block。 +- `FileReader` 不处理 Iceberg schema evolution、default/generated/partition 列。 +- `IcebergTableReader` 组合 `FileReader`，不直接绑定具体文件格式 reader。 + +### ParquetReader + +`ParquetReader` 是 `FileReader` 的 Parquet 实现，只负责 Parquet file-local schema +和 Parquet file-local scan request。 + +实际 API 文件： + +```text +be/src/format/parquet/parquet_reader.h +``` + +实际命名空间： + +```cpp +namespace doris::parquet +``` + +建议职责： + +- 打开 Parquet 文件； +- 解析 footer 和 file schema； +- 接收 `ParquetScanRequest` 或通用 `FileScanRequest`； +- 执行 file-local projection 和 file-local filter； +- 输出 file-local block。 + +建议接口形状： + +```cpp +namespace doris::parquet { + +class ParquetReader : public format::FileReader { +public: + virtual ~ParquetReader() = default; + + virtual Status open(io::FileReaderSPtr file, io::IOContext* io_ctx = nullptr); + virtual Status get_schema(std::vector* file_schema) const; + virtual Status init(const ParquetScanRequest& request); + virtual Status next(Block* file_block, size_t* rows, bool* eof); + virtual Status close(); +}; + +} // namespace doris::parquet +``` + +接口约束： + +- `ParquetReader` 输出的是 file-local block，不输出 table/global schema block。 +- `ParquetReader` 不理解 Iceberg schema evolution。 +- `ParquetReader` 不负责 default/generated/partition 列。 +- 任何 table-level cast/default/generated/partition 语义都不能重新塞回 + `ParquetReader`。 + +## 关键类型 + +### SchemaField + +`SchemaField` 表示文件层 schema 中的列定义。 + +建议包含的信息： + +- file-local column id； +- 列名； +- 类型； +- child fields。 + +它服务于 `TableColumnMapper` 做 schema matching，不携带 table-level 语义。 + +### TableColumnDefinition + +`TableColumnDefinition` 表示 table/global schema 中的列定义。 + +建议包含的信息： + +- table column id； +- 列名； +- 类型； +- child columns。 + +Iceberg 场景下，column id 默认对应 field id。 + +### TableFilter + +`TableFilter` 表示 table 层过滤条件。 + +建议包含的信息： + +- `table_column_id` +- `conjunct` +- `predicates` + +职责约束： + +- `conjunct` 偏表达式过滤，适合表达 cast、复杂表达式、复杂列提取等语义； +- `predicates` 偏结构化单列下推，适合驱动 row group stats、page index、dictionary、 + bloom filter 等文件层优化。 + +### FileLocalFilter + +`FileLocalFilter` 表示已经 localize 到 file-local schema 的过滤条件。 + +建议包含的信息： + +- `file_column_id` +- `conjunct` +- `predicates` + +职责约束： + +- `conjunct` 用于 file-local 表达式过滤； +- `predicates` 用于 file-local 结构化下推； +- 其输入必须来自 `TableColumnMapper`，不能由具体文件 reader 自己推导 table 语义。 + +### ColumnMapping + +`ColumnMapping` 是 table schema 与 file schema 之间的核心边界对象。 + +建议包含的信息： + +- `table_column_id` +- `file_column_id` +- `file_type` +- `table_type` +- `finalize_expr` +- `reader_filter_expr` +- `child_mappings` + +职责约束： + +- `finalize_expr` 服务最终输出，把 file-local value 转成 table/global value； +- `reader_filter_expr` 服务读时 filter fallback； +- 二者语义不同，不能混用； +- `child_mappings` 用于复杂列 remap、复杂列裁剪和复杂列 schema change。 + +### TableScanRequest + +`TableScanRequest` 描述 table 层 scan 请求。 + +建议包含的信息： + +- projected table columns； +- table filters。 + +它由 `IcebergTableReader` 接收，再交给 `TableColumnMapper` 生成 file-local request。 + +### ParquetScanRequest + +`ParquetScanRequest` 继承 `FileScanRequest`，描述 Parquet file-local scan 请求。 + +### FileScanRequest + +`FileScanRequest` 描述通用 file-local scan 请求。 + +建议包含的信息： + +- projected file columns； +- local filters； +- reader expression map。 + +它是 `FileReader` 的唯一 scan 输入，不包含 table/global schema 语义。 + +### IcebergScanTask + +`IcebergScanTask` 表示一次 Iceberg data file 读取任务。 + +建议包含的信息： + +- data file 信息； +- position delete 文件； +- equality delete 文件； +- deletion vector 信息。 + +它是 `IcebergTableReader` 的输入，不应直接传给 `ParquetReader`。 + +### IcebergTableReadParams + +`IcebergTableReadParams` 表示一次 Iceberg table scan 的完整初始化输入。 + +建议包含的信息： + +- Iceberg read options； +- Iceberg table schema； +- table scan request； +- Iceberg scan task 列表； +- 底层 `FileReader`。 + +它用于避免 `IcebergTableReader` 暴露多个半初始化阶段。调用方应一次性构造完整 +参数并调用 `init`。 + +## 设计原则 + +### 边界原则 + +- `FileReader` 不理解 global schema，不直接处理 Iceberg schema evolution。 +- `ParquetReader` 是 `FileReader` 的 Parquet 实现。 +- `TableColumnMapper` 是 schema mapping 和 filter localization 的唯一入口。 +- `IcebergTableReader` 不做 Parquet 解码，只负责 table-level finalize、delete、 + virtual columns。 +- `TableReader` 只负责多文件编排和 table-level 通用裁剪，不下沉文件格式语义。 +- 任何 table-level cast/default/generated/partition 语义都不能重新塞回 + `ParquetReader`。 + +### 依赖原则 + +- 低层不能反向依赖高层语义。 +- `FileReader` 只依赖 file-local request。 +- `IcebergTableReader` 继承 `TableReader`，复用其多文件编排和通用裁剪能力。 +- `IcebergTableReader` 通过组合使用 `FileReader`。 +- `TableColumnMapper` 可以被 Iceberg 之外的其他表格式复用。 + +### 命名原则 + +- 表层抽象使用 `TableReader`、`IcebergTableReader`、`TableColumnMapper`、 + `FileReader`、`ParquetReader` 命名。 +- `TableColumnMapper` 不使用 Iceberg-only 命名。 +- file schema 类型使用 `SchemaField`，table schema 类型使用 `TableColumnDefinition`。 + +## 兼容原则 + +新架构重构期间，新旧代码允许并存，但必须遵守以下约束： + +- 旧 `vparquet` / Hive / Hudi / Paimon 路径在新架构稳定前允许保留。 +- 新架构实现不得继续向旧 `vparquet` 表层语义回灌依赖。 +- 先搭新框架 API，再逐步迁移调用点。 +- 不允许边改 API 边混入临时裸逻辑、实验性草稿或未收敛命名。 +- 兼容层可能需要存在，但本文档不定义兼容层的具体实现方案。 + +## 验收标准 + +该文档应满足以下目标： + +- 不引用错误实验代码作为既成事实； +- 不出现实现性草稿、裸伪代码、未收敛命名混用； +- 让另一个工程师从 `master` 新开分支时，可以直接按本文档搭 API 骨架； +- 读完文档后，不需要再讨论以下问题： + - 新架构分几层； + - 每层负责什么； + - 哪层理解 global schema； + - 哪层做 schema change / filter localization / finalize； + - 哪层允许依赖旧实现，哪层不允许。 diff --git a/docs/new-parquet-reader-column-index-refactor.md b/docs/new-parquet-reader-column-index-refactor.md new file mode 100644 index 00000000000000..56f8c7ca4a37d5 --- /dev/null +++ b/docs/new-parquet-reader-column-index-refactor.md @@ -0,0 +1,404 @@ +# New Reader 列标识实现说明 + +本文说明 Doris new table/file reader 栈中各种列标识的当前含义，以及它们在 +`FileScannerV2`、`TableReader`、`TableColumnMapper` 和 new Parquet reader 中的流转逻辑。 + +核心原则是把 **schema identity** 和 **执行期位置** 分开： + +- schema identity 用来判断 table column 和 file column 是否是同一列。 +- index/position 用来表示 block、projection tree、scan request 或 constant map 中的位置。 +- FE column unique id 只在 scanner 边界用于定位 slot，进入 table/file reader 后不再出现。 + +共享定义集中在 `be/src/format_v2/column_data.h`。file reader 通用请求定义在 +`be/src/format_v2/file_reader.h`。new Parquet reader 自己的 Parquet 内部 schema tree 定义在 +`be/src/format_v2/parquet/parquet_column_schema.h`。 + +## 层级边界 + +当前 reader 栈可以按语义分成三层。 + +### FileScannerV2：FE 标识到 reader 标识的边界 + +`FileScannerV2` 仍能看到 FE 下发的 `slot_id`、`col_unique_id`、`TFileScanSlotInfo` 和 +`TColumnAccessPath`。这些 FE 侧标识只在这里使用。 + +`FileScannerV2::_build_projected_columns()` 会把 `_params->required_slots` 转成 +`std::vector`： + +- vector 下标就是 `GlobalIndex`。 +- `_slot_id_to_global_index` 把 FE `slot_id` 转成 `GlobalIndex`，用于 row-level conjunct。 +- `_column_unique_id_to_global_index` 把 FE `col_unique_id` 转成 `GlobalIndex`，用于 column predicate。 +- `ColumnDefinition::identifier` 表示 table-side schema identity，默认是列名；如果外部 schema + 提供 field id，则改用 field id。 +- partition/default/generated 信息被挂到 `ColumnDefinition` 上，由 table reader 层处理。 + +从这一层往下，table/file reader 不再使用 FE column unique id。 + +### TableReader / TableColumnMapper：table schema 到 file schema + +`TableReader::open_reader()` 对每个 split 打开一个具体 `FileReader`，先通过 +`FileReader::get_schema()` 获取当前文件的 file-local schema，再用 `TableColumnMapper` 建立映射。 + +`TableColumnMapper` 的输入是： + +- table/global schema：`FileScannerV2` 构造的 `projected_columns`。 +- file-local schema：具体 file reader 返回的 `std::vector`。 +- per-split partition values。 +- table-level row filters 和 column predicates。 + +`TableColumnMapper` 的输出是： + +- `ColumnMapping`：构造阶段使用的 table column 到 file/constant/virtual source 的映射。 +- `FileScanRequest`：只含 file-local projection、file-local block layout 和 file-local filters。 +- `ColumnMapResult` / `ResultColumnMapping`：给 table reader finalize 阶段消费的最终映射。 +- `FilterEntry`：给 filter localization 使用的 `GlobalIndex -> LOCAL/CONSTANT/UNSET` target。 +- `ConstantMap`：partition/default/generated 常量列。 + +### FileReader / ParquetReader：只理解 file-local 请求 + +`FileReader` 只暴露两类 schema/request： + +- `get_schema(std::vector*)`：返回文件自身 schema。 +- `open(std::unique_ptr&)`：接收已经 localize 后的 file-local scan request。 + +具体 file reader 不理解 table/global schema、Iceberg default、partition column、FE slot id 或 +FE column unique id。 + +new Parquet reader 使用 `FileScanRequest` 中的 `LocalColumnIndex` 创建 column reader，并使用 +`local_positions` 决定 file-local block layout。 + +## ColumnDefinition + +定义位置：`be/src/format_v2/column_data.h` + +`ColumnDefinition` 是 table/global schema 和 file-local schema 共用的列定义。它表示列名、类型、 +nested children、默认表达式、partition 属性和 file-local column kind。 + +关键字段： + +- `identifier`：schema identity。用于 table column 和 file column 匹配。 +- `local_id`：file reader 返回的 schema node 在当前 parent 下的 reader-local id。 +- `name`：逻辑列名。BY_NAME 且没有显式 string identifier 时会回退到它。 +- `type`：当前 schema node 的 Doris 类型。 +- `children`：nested children。table/global schema 中是 table children；file schema 中是 + file-local children。 +- `default_expr`：missing/default/generated column 的物化表达式。 +- `is_partition_key`：partition column 标记。 +- `column_type`：file-local column kind，例如普通数据列或 row number virtual column。 + +`ColumnDefinition` 不保存 FE column unique id。它也不保存“应该按什么方式匹配”。匹配方式由 +`TableColumnMapperOptions::mode` 统一决定。 + +### identifier + +`identifier` 是一个 `Field`，语义接近 DuckDB `MultiFileColumnDefinition::identifier`： + +- `TYPE_NULL`：没有显式 identifier。BY_NAME 时使用 `name`。 +- `TYPE_INT`：在 BY_FIELD_ID 中表示 field id；在 BY_INDEX 中表示 file schema position。 +- `TYPE_STRING`：显式 name identifier。 + +访问 helper： + +- `has_identifier_field_id()` / `get_identifier_field_id()`：BY_FIELD_ID 使用。 +- `get_identifier_name()`：BY_NAME 使用；没有显式 string identifier 时返回 `name`。 +- `get_identifier_position()`：BY_INDEX 使用。 +- `file_local_id()`：file reader projection 使用；优先返回 `local_id`，否则回退到 int + identifier。这个回退只用于兼容某些 file schema 构造路径，不应重新引入 FE id 语义。 + +## 强类型位置 + +### GlobalIndex + +定义位置：`be/src/format_v2/column_data.h` + +`GlobalIndex` 表示 table/global output block 中的 top-level 列位置。当前等于 +`_params->required_slots` 的下标。 + +主要使用位置： + +- `ColumnMapping::global_index` +- `TableFilter::global_indices` +- `TableColumnPredicates` 的 key +- `ColumnMapResult` / `ResultColumnMapping` 的 key +- `FilterEntry` map 的 key + +`GlobalIndex` 不是 FE slot id，也不是 FE column unique id。 + +### LocalColumnId + +定义位置：`be/src/format_v2/column_data.h` + +`LocalColumnId` 表示当前物理文件 schema 的 top-level reader-local column id。 + +主要使用位置： + +- `FileScanRequest::local_positions` 的 key。 +- `LocalColumnIndex::top_level()`。 +- new Parquet reader 创建 top-level column reader。 +- page index、statistics、bloom filter 等 file-local pruning 的 root column key。 +- row position 这类 reader 内部 virtual column id。 + +`LocalColumnId` 不是 file-local block position。一个 top-level file column 在本次 scan request +输出 block 中的位置由 `LocalIndex` 表示。 + +### LocalIndex + +定义位置：`be/src/format_v2/column_data.h` + +`LocalIndex` 表示一次 `FileScanRequest` 内 file-local block 的列位置。 + +主要使用位置： + +- `FileScanRequest::local_positions` 的 value。 +- file-local rewritten `SlotRef` 的 input position。 +- `TableReader` 从 file block 取列。 +- `ParquetScanScheduler` 把 column reader 读出的数据写入 file block。 + +`LocalIndex` 是 request-local block layout，不是 file schema ordinal。 + +### ConstantIndex + +定义位置：`be/src/format_v2/column_data.h` + +`ConstantIndex` 表示 `ConstantMap` 中的 entry 位置。它用于 per-split/per-file 常量列： + +- partition column。 +- schema evolution default column。 +- generated/default expression column。 +- 将来可扩展到更多 virtual/constant source。 + +`FilterEntry` 可以指向 `ConstantIndex`。当一个 row-level conjunct 只引用 constant target 时， +`TableReader` 会在打开 file reader 前用 1 行常量 block 求值；如果结果为 false/NULL，当前 split +直接跳过。 + +### LocalColumnIndex + +定义位置：`be/src/format_v2/column_data.h` + +`LocalColumnIndex` 表示递归 file-local projection path： + +```cpp +struct LocalColumnIndex { + int32_t index = -1; + bool project_all_children = true; + std::vector children; +}; +``` + +语义： + +- root entry 的 `index` 是 `LocalColumnId`。 +- nested entry 的 `index` 是当前 parent 下的 file-local child id。 +- `project_all_children = true` 表示读取整个 subtree。 +- `project_all_children = false` 表示只读取 `children` 中列出的 child paths。 + +通用 helper： + +- `is_full_projection()` +- `is_partial_projection()` +- `find_child_projection()` +- `is_child_projected()` +- `merge_local_column_index()` + +new Parquet reader 的 STRUCT/LIST/MAP reader 都消费这套 projection helper： + +- STRUCT：只创建被投影 child 的 reader。 +- LIST：把 element projection 递归传给 element reader。 +- MAP：总是读取 key，把 value projection 递归传给 value reader。 + +## FileScanRequest + +定义位置：`be/src/format_v2/file_reader.h` + +`FileScanRequest` 是 table reader 交给 file reader 的唯一 scan 输入。它不包含 table/global schema。 + +关键字段： + +- `predicate_columns`：row-level conjunct/delete conjunct 需要先读取的 file-local projection。 +- `non_predicate_columns`：最终输出需要读取、且不需要先参与 row-level filter 的 file-local + projection。 +- `local_positions`：`LocalColumnId -> LocalIndex`，决定 file-local block layout。 +- `conjuncts` / `delete_conjuncts`：已经把 table/global slot 改写成 file-local slot 的表达式。 +- `column_predicate_filters`：file-layer pruning hints，只用于 min/max、page index、dictionary、 + bloom filter 等剪枝，不参与 batch row filtering。 + +`predicate_columns` 和 `non_predicate_columns` 都按 file-local schema 表达。file reader 只需要根据 +这两个列表创建 reader，并按 `local_positions` 写入 file block。 + +## TableColumnMapper 逻辑 + +定义位置： + +- `be/src/format_v2/column_mapper.h` +- `be/src/format_v2/column_mapper.cpp` + +### 匹配模式 + +`TableColumnMapperOptions::mode` 决定 `identifier` 的解释方式： + +- `BY_FIELD_ID`：`TYPE_INT` identifier 是 field id。 +- `BY_NAME`：`TYPE_STRING` identifier 或 `name` 是匹配名。 +- `BY_INDEX`：`TYPE_INT` identifier 是 file schema position。 + +`TableReader::open_reader()` 当前默认按 field id 映射；如果 file schema 首列没有 int identifier， +会 fallback 到 BY_NAME。Hive reader 可覆盖默认模式，Hive1 ORC 这类场景可使用 BY_INDEX。 + +### create_mapping() + +`create_mapping()` 为每个 `GlobalIndex` 生成一个 `ColumnMapping`： + +1. partition column 优先映射到 `ConstantMap`。 +2. BY_INDEX 时按 file position 取 file schema。 +3. 普通列通过 matcher 在 file schema 中找对应 file field。 +4. 缺失但带 default expr 的列映射到 `ConstantMap`。 +5. 特殊 virtual column 记录 virtual column type。 +6. 允许 missing column 时保留空 mapping，由 table finalize 阶段补 NULL/default。 + +`ColumnMapping::file_local_id` 是 table column 绑定到 file schema 后的 reader-local id： + +- root mapping 中可转成 `LocalColumnId`。 +- nested mapping 中表示 parent 下的 child id。 +- constant/missing/virtual mapping 没有 `file_local_id`。 + +schema identity field id 不保存在 `ColumnMapping` 中，只保存在 +`ColumnDefinition::identifier` 中，并由 mapper 的匹配模式解释。 + +### create_scan_request() + +`create_scan_request()` 把 table-level scan 信息转换成 file-local request： + +1. 先把不参与 row-level filter 的输出列加入 `non_predicate_columns`。 +2. 调用 `localize_filters()`，把 row-level conjunct 和 column predicates 定位到 file-local source。 +3. 为所有已读取 file column 重建 output projection，让 `ColumnMapping::projection` 指向正确的 + `LocalIndex`。 +4. 生成 `ColumnMapResult` 和 `ResultColumnMapping`，供 table reader finalize。 + +`local_positions` 在这个阶段确定。同一个 file column 如果同时被 filter 和 output 使用，只会有 +一个 `LocalIndex`。 + +### FilterEntry + +`FilterEntry` 是 `GlobalIndex` 到 filter target 的结果： + +- `LOCAL`：filter 可以在 file-local block 上求值，target 是 `LocalIndex`。 +- `CONSTANT`：filter 只依赖 `ConstantMap` entry。 +- `UNSET`：当前 split 无法下推到 file reader。 + +`TableColumnMapper::_build_filter_entries()` 在 `FileScanRequest::local_positions` 确定后生成 +`FilterEntry`。表达式改写时只把 `LOCAL` target 改写成 file-local slot；`CONSTANT` target 用于 +split-level constant filter evaluation。 + +### ColumnMapResult / ResultColumnMapping + +`ColumnMapResult` 记录一个 global result column 的递归映射结果： + +- `local_column_id`：root file column。 +- `column_index`：file-local projection tree。 +- `mapping`：root 指向 `LocalIndex`，nested child 通过 `IndexMapping::child_mapping` 递归映射。 + +`ResultColumnMapping` 是最终可消费的 `GlobalIndex -> ColumnMapEntry` map。`ColumnMapEntry` 包含： + +- `IndexMapping mapping` +- `local_type` +- `global_type` +- `filter_conversion` + +TableReader finalize 阶段用它把 file-local block 转成 table/global block。 + +### nested child mapping + +复杂列映射时，`IndexMapping::child_mapping` 的 key 是 table/global child ordinal，value 是对应 +file-local child mapping。这样 filter 中的 `STRUCT_EXTRACT` 可以按 table child ordinal 找到 +file child ordinal。 + +Doris 不再维护额外的 `NestedPredicateTargetInfo` / filter target path。nested filter localization +直接沿 `IndexMapping::child_mapping` 转换 selector path。 + +对于 `SELECT s.name WHERE s.id > 5` 这类 filter-only child： + +- `s.name` 进入 output projection。 +- `s.id` 会进入 predicate projection。 +- `original_file_children` 保留 projection 前的 file children，用于定位 filter-only child。 +- `child_mappings` 只描述输出 shape，避免 filter-only child 改变最终 STRUCT/LIST/MAP shape。 + +## Parquet 内部 schema 标识 + +定义位置：`be/src/format_v2/parquet/parquet_column_schema.h` + +`ParquetColumnSchema` 是 new Parquet reader 内部 schema tree。它描述 Parquet 逻辑字段和 primitive +leaf column 的关系，不暴露给 table reader。对外统一通过 `ParquetReader::get_schema()` 返回 +`std::vector`。 + +关键字段： + +- `local_id`：当前 parent 下的 reader-local id。top-level 是 root field ordinal，nested 是 child + ordinal。`LocalColumnIndex` 传给 `ParquetColumnReaderFactory` 的就是这个 id。 +- `parquet_field_id`：Parquet schema element 中可选的 field_id。Arrow 在不存在 field_id 时返回 + `-1`。它只作为 schema matching identifier，不用于读取 column chunk。 +- `name`：Parquet schema name。 +- `type`：转换后的 Doris 类型。 +- `leaf_column_id`：Parquet primitive leaf column ordinal。用于访问 `ColumnDescriptor`、 + row group column chunk、statistics、page index、bloom filter 等。复杂节点为 `-1`。 +- `type_descriptor`：primitive leaf 的 Parquet physical/logical type 信息。 +- `descriptor`：primitive leaf 的 Arrow Parquet `ColumnDescriptor`。 +- `max_definition_level` / `max_repetition_level`：该 node 下的最大 Dremel level。 +- `nullable_definition_level`：当前 node 自身为 NULL 时对应的 definition level。 +- `repeated_repetition_level`：当前或最近 repeated container 的 repetition level。 + +`ParquetReader::get_schema()` 会把 `ParquetColumnSchema` 转成 `ColumnDefinition`： + +- 如果 `parquet_field_id >= 0`，`ColumnDefinition::identifier` 是 `TYPE_INT` field id。 +- 否则 `identifier` 是 `TYPE_STRING` name。 +- `ColumnDefinition::local_id` 是 `ParquetColumnSchema::local_id`。 +- children 递归转换。 + +因此 table reader 可以按 field id 或 name 匹配，而 Parquet reader 自己仍只按 `local_id`、 +`leaf_column_id` 和 Dremel levels 读取数据。 + +## 端到端流转 + +一次 split 的列标识流转如下： + +1. `FileScannerV2::_build_projected_columns()`： + FE `slot_id` / `col_unique_id` 被翻译成 `GlobalIndex`，并生成 table-side + `ColumnDefinition`。 +2. `ParquetReader::init()`： + 解析 Arrow Parquet schema，构造内部 `ParquetColumnSchema`。 +3. `ParquetReader::get_schema()`： + 把 Parquet 内部 schema 暴露成 file-side `ColumnDefinition`。 +4. `TableReader::open_reader()`： + 根据 file schema 是否带 int identifier 选择 BY_FIELD_ID 或 BY_NAME，并调用 mapper。 +5. `TableColumnMapper::create_mapping()`： + 用 `ColumnDefinition::identifier` 匹配 table/global schema 和 file-local schema，生成 + `ColumnMapping`。 +6. `TableColumnMapper::create_scan_request()`： + 生成 `FileScanRequest`，其中所有 projection 和 block position 都是 file-local 的。 +7. `ParquetReader::open()`： + 校验 `LocalColumnId`，用 `LocalColumnIndex` 创建 column readers，并规划 row group pruning。 +8. `ParquetScanScheduler`： + 按 `local_positions` 把 predicate/non-predicate column 写入 file-local block。 +9. `TableReader` finalize： + 使用 `ResultColumnMapping`、`ConstantMap` 和 projection expression，把 file-local block 转成 + table/global output block。 + +## 使用约定 + +修改 new reader 代码时应遵守以下约定： + +- 不要在 table/file reader 层重新传递 FE column unique id。 +- 不要把 `ColumnDefinition::identifier` 当作 file reader 读取 id。 +- 不要把 `LocalColumnId` 当作 block position；block position 使用 `LocalIndex`。 +- 不要把 `LocalIndex` 当作 schema ordinal。 +- `LocalColumnIndex::index` 在 root 和 child 层含义不同，调用方必须知道当前 projection node + 所在层级。 +- file reader 只能消费 `FileScanRequest`，不能理解 partition/default/generated/table schema。 +- column predicate pruning 是 file-layer hint，不等价于 row-level filter。 +- constant filter 可以在 table reader 层提前求值，但不应下推到 file reader。 + +## 已知限制 + +TVF 查询 Parquet 且文件没有 field id 时，top-level BY_NAME 已经可以通过 name identifier 工作。 +但 nested access path 的 fallback 目前仍有一处 TODO：STRUCT child fallback 使用 struct ordinal +构造 int identifier。对于没有 field id 的 nested Parquet schema，BY_NAME 场景应保留 string +identifier，让 `TableColumnMapper` 从 Parquet file schema 中按 name 解析 file-local child id。 +该问题已在 `be/src/exec/scan/file_scanner_v2.cpp` 代码中记录，当前未修复。 diff --git a/docs/ut-coverage-plan.md b/docs/ut-coverage-plan.md new file mode 100644 index 00000000000000..985a71a6a8ff4a --- /dev/null +++ b/docs/ut-coverage-plan.md @@ -0,0 +1,92 @@ +# UT 覆盖补充方案 + +## 已有测试 + +### parquet_column_reader_test.cpp + +| 类别 | 测试 | 覆盖 | +|---|---|---| +| 标量类型 | `ReadAllSupportedPhysicalAndLogicalTypes` | 所有物理/逻辑类型的 flat read | +| 复杂类型 | `ReadSupportedComplexTypes` | struct/list/map 基本 read | +| Skip/Select | `SkipThenRead`、`SelectReadsOnlySelectedRanges` | 标量 skip/select | +| Struct 投影 | `ReadProjectedStructChildren`、`ReadProjectedNullableStructChildren` | 标量子字段投影 | +| List 投影 | `ReadProjectedListStructElementChildren` | list 元素字段投影 | +| Map 投影 | `ReadProjectedMapStructValueChildren` | map value 字段投影 | +| 未投影复杂 child | `ReadProjectedStructListChildOnly`、`SkipProjectedStructListChildOnlyThenRead`、`SelectProjectedStructListChildOnly` | struct 中未投影的 list child | +| 未投影 MAP child | `ReadProjectedStructMapChildOnly`、`SkipProjectedStructMapChildOnlyThenRead`、`SelectProjectedStructMapChildOnly` | struct 中未投影的 map child | +| Overflow | `ReadListWithOverflowAcrossChunks`、`SkipListWithOverflowThenRead`、`SelectListWithOverflow` 等 | list、list、map 的溢出 | +| 嵌套 | `ReadListListWithOverflowAcrossChunks`、`ReadListStructWithOverflowAcrossChunks`、`ReadStructMapWithOverflowAcrossChunks` | 多层嵌套的 read/skip/select | + +### parquet_reader_test.cpp + +| 类别 | 测试 | +|---|---| +| Reader 生命周期 | `OpenStoresRequestAndCloseClearsState`、`CloseReleasesSharedIOContext` | +| 读 | `ReadSingleRowGroupThenEof`、`ReadMultipleRowGroups` | +| 过滤 | `ReadPredicateAndNonPredicateColumnsWithSelection`、`ColumnPredicateOnlyPrunesAndDoesNotFilterRowsInsideRowGroup`、`ReadMultiPredicateColumnsBeforeExpressionFilter`、`PredicateColumnFiltersBeforeNonPredicateRead`、`NonPredicateColumnKeepsSelectionFromPredicateColumn` | +| Pruning | `PredicateFiltersRowGroupsByStatistics`、`PredicateFiltersRowGroupsByDictionary`、`PlannerNarrowsRowRangesByPageIndex`、`InPredicateFiltersRowGroupsByDictionary`、`StatisticsPruningSkipsPrefixRowGroupsAndReadsLaterGroups` | +| Row position | `RowPositionReaderReturnsFileLocalPositions`、`RowPositionReaderKeepsPositionsAfterSelection`、`RowPositionReaderUsesFileLocalPositionsForScanRange` | +| Delete | `DeletePredicateFiltersRowPositions`、`QueryPredicateAndDeletePredicateFilterRowPositions` | +| Bloom filter | `EqPredicateUsesArrowHashAndPrunesAbsentIntValue`、`InPredicatePrunesOnlyWhenAllValuesAreAbsent`、`BooleanPredicateHashesAsParquetInt32`、`StringPredicateUsesArrowByteArrayHash`、`NullableAcceptingAndUnsupportedPredicatesKeepRowGroup` | +| Column predicate | `ColumnPredicatesDoNotForcePredicateMaterialization` | +| ColumnMapper | `CreatesComplexProjectionForStructChildren`、`CreatesComplexProjectionForMapValueStructChildren` | + +### table_reader_test.cpp + +| 类别 | 测试 | +|---|---| +| 聚合下推 | `PushDownCount/MinMax*` 系列（含 struct/list/map 子字段） | +| Schema 变化 | `ProjectedColumnsFillDefaultForParquetSchemaMismatch`、`ProjectedStructFillsMissingChildWithDefault` | +| Filter | `OpenReaderBuildsTableFiltersFromConjuncts`、`OpenReaderPushesMultiColumnConjunctToParquetReader` | +| Delete | `IcebergTableReaderAppliesDeletionVectorFile`、`IcebergTableReaderDoesNotPushDownAggregateWithDeletes` 等 | +| Virtual column | `IcebergVirtualColumnsUseRowLineageMetadata` | + +## 需要补充 + +### P0：基本正确性 + +| 测试 | 说明 | +|---|---| +| `NullableListElement` | LIST 元素 nullable：`[null, 1]`、`[1, null]`、empty list、null list 各场景的 read/skip/select | +| `NullableMapValue` | MAP value nullable：`{k: null}`、`{k: v}`、empty map、null map 各场景的 read/skip/select | +| `ListStructNullableChild` | `List>` 的 read/skip/select，验证 null child 的 def level 处理 | +| `MapStructNullableChild` | `Map>` 同上 | +| `MapListNullableValue` | `Map>` 小 batch read + overflow，验证两层 cursor 与 overflow 一致 | + +### P1：过滤交互 + +| 测试 | 说明 | +|---|---| +| `ConjunctFilterOnStructField` | `SELECT * FROM t WHERE s.id > 5`，验证 struct 子字段 conjunct 过滤正确 | +| `ConjunctFilterOnMapValue` | `SELECT * FROM t WHERE m['k'] > 5`，验证 map value 过滤 | +| `ComplexColumnSelectPath` | 非谓词复杂列（struct/list/map）在过滤后通过 `select()` 读取，验证 SelectionVector → column 的行数一致 | +| `ProjectionAndFilterInteraction` | `SELECT s.b FROM t WHERE s.a > 0`（同一 struct 内，a 是谓词列，b 是非谓词投影列），验证列 reader 数量、类型、行数正确 | +| `FilterThenNullStruct` | filter 后 struct 列为 null 时，子字段不会被错误物化 | + +### P2：边界 + +| 测试 | 说明 | +|---|---| +| `EmptyStruct` | struct 无子字段的 read/skip | +| `AllChildrenProjectedOut` | struct 所有子字段都不投影（project_all_children=false，children 为空）时的行为——应返回 error 或 skip | +| `DeepNestedPath` | 三层以上嵌套（`a.b.c.d`）的 read/skip/select | +| `SkipLongRepeatedList` | 跳过跨越多个 page 的 long repeated list，验证 overflow + cursor 状态一致 | +| `SelectLongRepeatedList` | select 跨越 overflow 边界的 repeated list | + +### P3：P4 完成后 + +| 测试 | 说明 | +|---|---| +| `PageLevelSkipByStatistics` | 构造一个 row group 包含多个 page，其中部分 page 的 min/max 完全落在 filter 范围外。验证 page-level skip 调用次数正确 | +| `PageLevelSkipByDictionary` | dictionary filter 触发 page skip | + +## 现有测试未覆盖的复杂类型组合 + +以下组合在当前代码中有路径但无测试： + +- `Map>`（nested map） +- `Array(Map)` +- `Array(Struct)` +- struct 内同时有投影和未投影的 non-scalar child（已覆盖 list-only 和 map-only，未覆盖同时存在） + +建议在 P0 中优先覆盖前三项（属于已实现路径的回归保护），第四项在 P2 中补充。 diff --git a/fe/fe-core/src/main/java/org/apache/doris/qe/SessionVariable.java b/fe/fe-core/src/main/java/org/apache/doris/qe/SessionVariable.java index f537912c09df12..72b110e7287a8f 100644 --- a/fe/fe-core/src/main/java/org/apache/doris/qe/SessionVariable.java +++ b/fe/fe-core/src/main/java/org/apache/doris/qe/SessionVariable.java @@ -94,6 +94,7 @@ public class SessionVariable implements Serializable, Writable { public static final String SCAN_QUEUE_MEM_LIMIT = "scan_queue_mem_limit"; public static final String MAX_SCANNERS_CONCURRENCY = "max_scanners_concurrency"; public static final String MAX_FILE_SCANNERS_CONCURRENCY = "max_file_scanners_concurrency"; + public static final String ENABLE_FILE_SCANNER_V2 = "enable_file_scanner_v2"; public static final String MIN_SCANNERS_CONCURRENCY = "min_scanners_concurrency"; public static final String MIN_FILE_SCANNERS_CONCURRENCY = "min_file_scanners_concurrency"; public static final String MIN_SCAN_SCHEDULER_CONCURRENCY = "min_scan_scheduler_concurrency"; @@ -1112,6 +1113,11 @@ public static double getHotValueThreshold() { "FileScanNode 扫描数据的最大并发，默认为 16", "The max threads to read data of FileScanNode, default 16"}) public int maxFileScannersConcurrency = 16; + @VarAttrDef.VarAttr(name = ENABLE_FILE_SCANNER_V2, needForward = true, description = { + "开启后 FileScanNode 会在支持的查询场景使用 FileScannerV2，默认关闭", + "When enabled, FileScanNode uses FileScannerV2 for supported query scans. Disabled by default."}) + public boolean enableFileScannerV2 = true; + @VarAttrDef.VarAttr(name = LOCAL_EXCHANGE_FREE_BLOCKS_LIMIT) public int localExchangeFreeBlocksLimit = 4; @@ -2873,10 +2879,9 @@ public static boolean isEagerAggregationOnJoin() { public static final String ENABLE_MC_LIMIT_SPLIT_OPTIMIZATION = "enable_mc_limit_split_optimization"; @VarAttrDef.VarAttr( name = ENABLE_EXTERNAL_TABLE_BATCH_MODE, - fuzzy = true, description = {"使能外表的 batch mode 功能", "Enable the batch mode function of the external table."}, needForward = true) - public boolean enableExternalTableBatchMode = true; + public boolean enableExternalTableBatchMode = false; @VarAttrDef.VarAttr( name = ENABLE_MC_LIMIT_SPLIT_OPTIMIZATION, @@ -3808,13 +3813,6 @@ private void setFuzzyForCatalog(Random random) { this.hiveTextCompression = Util.getRandomString( "gzip", "defalte", "bzip2", "zstd", "lz4", "lzo", "snappy", "plain"); - // batch mode - this.enableExternalTableBatchMode = random.nextBoolean(); - if (this.enableExternalTableBatchMode) { - this.numPartitionsInBatchMode = Util.getRandomInt(0, 1024, Integer.MAX_VALUE); - this.numFilesInBatchMode = Util.getRandomInt(0, 1024, Integer.MAX_VALUE); - } - // common this.enableCountPushDownForExternalTable = random.nextBoolean(); } @@ -5338,6 +5336,7 @@ public TQueryOptions toThrift() { tResult.setScanQueueMemLimit(maxScanQueueMemByte); tResult.setMaxScannersConcurrency(maxScannersConcurrency); tResult.setMaxFileScannersConcurrency(maxFileScannersConcurrency); + tResult.setEnableFileScannerV2(enableFileScannerV2); tResult.setMaxColumnReaderNum(maxColumnReaderNum); tResult.setParallelPrepareThreshold(parallelPrepareThreshold); tResult.setMinScannersConcurrency(minScannersConcurrency); diff --git a/gensrc/thrift/Exprs.thrift b/gensrc/thrift/Exprs.thrift index c17199d74edf91..a17cd140c93418 100644 --- a/gensrc/thrift/Exprs.thrift +++ b/gensrc/thrift/Exprs.thrift @@ -88,6 +88,10 @@ enum TExprNodeType { TRY_CAST_EXPR = 41 // for search DSL function SEARCH_EXPR = 42, + // Normal predicate expression + PREDICATE = 43, + // Normal literal + LITERAL = 44, } //enum TAggregationOp { diff --git a/gensrc/thrift/Opcodes.thrift b/gensrc/thrift/Opcodes.thrift index 1e4002357e7599..a2d709799482eb 100644 --- a/gensrc/thrift/Opcodes.thrift +++ b/gensrc/thrift/Opcodes.thrift @@ -97,4 +97,6 @@ enum TExprOpcode { MATCH_REGEXP = 76, MATCH_PHRASE_EDGE = 77, TRY_CAST = 78, + // Delete operator from Iceberg/Paimon + DELETE = 79, } diff --git a/gensrc/thrift/PaloInternalService.thrift b/gensrc/thrift/PaloInternalService.thrift index 3271cb01e881bb..e548dabaecda01 100644 --- a/gensrc/thrift/PaloInternalService.thrift +++ b/gensrc/thrift/PaloInternalService.thrift @@ -498,6 +498,7 @@ struct TQueryOptions { // In read path, read from file cache or remote storage when execute query. 1000: optional bool disable_file_cache = false 1001: optional i32 file_cache_query_limit_percent = -1 + 1002: optional bool enable_file_scanner_v2 = false } diff --git a/regression-test/data/export_p0/outfile/parquet/test_outfile_parquet_complex_type.out b/regression-test/data/export_p0/outfile/parquet/test_outfile_parquet_complex_type.out index c8ff8cafdd9854..3f399e9cbbd2e7 100644 --- a/regression-test/data/export_p0/outfile/parquet/test_outfile_parquet_complex_type.out +++ b/regression-test/data/export_p0/outfile/parquet/test_outfile_parquet_complex_type.out @@ -127,3 +127,15 @@ 9 doris_9 {"user_id":9, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":9, "sex":9, "bool_col":1, "int_col":9, "bigint_col":9, "largeint_col":"9", "float_col":9.9, "double_col":9.9, "char_col":"char9_1234", "decimal_col":9.000000000} 10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} +-- !select_load7 -- +1 doris_1 {"user_id":1, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":1, "sex":1, "bool_col":1, "int_col":1, "bigint_col":1, "largeint_col":"1", "float_col":1.1, "double_col":1.1, "char_col":"char1_1234", "decimal_col":1.000000000} +2 doris_2 {"user_id":2, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":2, "sex":2, "bool_col":1, "int_col":2, "bigint_col":2, "largeint_col":"2", "float_col":2.2, "double_col":2.2, "char_col":"char2_1234", "decimal_col":2.000000000} +3 doris_3 {"user_id":3, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":3, "sex":3, "bool_col":1, "int_col":3, "bigint_col":3, "largeint_col":"3", "float_col":3.3, "double_col":3.3, "char_col":"char3_1234", "decimal_col":3.000000000} +4 doris_4 {"user_id":4, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":4, "sex":4, "bool_col":1, "int_col":4, "bigint_col":4, "largeint_col":"4", "float_col":4.4, "double_col":4.4, "char_col":"char4_1234", "decimal_col":4.000000000} +5 doris_5 {"user_id":5, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":5, "sex":5, "bool_col":1, "int_col":5, "bigint_col":5, "largeint_col":"5", "float_col":5.5, "double_col":5.5, "char_col":"char5_1234", "decimal_col":5.000000000} +6 doris_6 {"user_id":6, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":6, "sex":6, "bool_col":1, "int_col":6, "bigint_col":6, "largeint_col":"6", "float_col":6.6, "double_col":6.6, "char_col":"char6_1234", "decimal_col":6.000000000} +7 doris_7 {"user_id":7, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":7, "sex":7, "bool_col":1, "int_col":7, "bigint_col":7, "largeint_col":"7", "float_col":7.7, "double_col":7.7, "char_col":"char7_1234", "decimal_col":7.000000000} +8 doris_8 {"user_id":8, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":8, "sex":8, "bool_col":1, "int_col":8, "bigint_col":8, "largeint_col":"8", "float_col":8.8, "double_col":8.800000000000001, "char_col":"char8_1234", "decimal_col":8.000000000} +9 doris_9 {"user_id":9, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":9, "sex":9, "bool_col":1, "int_col":9, "bigint_col":9, "largeint_col":"9", "float_col":9.9, "double_col":9.9, "char_col":"char9_1234", "decimal_col":9.000000000} +10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} + diff --git a/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet.out b/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet.out index 59e94ef9429ec9..b52e491795f003 100644 --- a/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet.out +++ b/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet.out @@ -35,9 +35,9 @@ 3 2023-04-21 2023-04-21 2023-04-20T12:34:56 2023-04-20T00:00 2023-04-20T00:00:00.123 2023-04-20T00:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.4028235e+38 1.7976931348623157E308 char3 999999999 999999999 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 -- !select_tvf2 -- -1 2023-04-20 2023-04-20 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1 1 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 -2 9999-12-31 9999-12-31 9999-12-31T23:59:59 9999-12-31T23:59:59 2023-04-20T00:00:00.120 2023-04-20T00:00:00.334400 Haidian -32768 -128 true -2147483648 -9223372036854775808 -170141183460469231731687303715884105728 1.4E-45 4.9E-324 char2 100000000 100000000 4 0.1 0.99999999 9999999999.9999999999 99999999999999999999999999999999999999 9.9999999999999999999999999999999999999 0.99999999999999999999999999999999999999 -3 2023-04-21 2023-04-21 2023-04-20T12:34:56 2023-04-20T00:00 2023-04-20T00:00:00.123 2023-04-20T00:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.4028235e+38 1.7976931348623157E308 char3 999999999 999999999 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 +1 2023-04-20 2023-04-20 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1.000000000 1.000000000 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 +2 9999-12-31 9999-12-31 9999-12-31T15:59:59 9999-12-31T15:59:59 2023-04-19T16:00:00.120 2023-04-19T16:00:00.334400 Haidian -32768 -128 true -2147483648 -9223372036854775808 -170141183460469231731687303715884105728 1.4E-45 4.940656458412465e-324 char2 100000000.000000000 100000000.000000000 4 0.1 0.99999999 9999999999.9999999999 99999999999999999999999999999999999999 9.9999999999999999999999999999999999999 0.99999999999999999999999999999999999999 +3 2023-04-21 2023-04-21 2023-04-20T04:34:56 2023-04-19T16:00 2023-04-19T16:00:00.123 2023-04-19T16:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.402823E38 1.797693134862316e+308 char3 999999999.000000000 999999999.000000000 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 -- !hive_docker_02 -- 1 2023-04-20 2023-04-20 2023-04-19 16:00:00.0 2023-04-19 16:00:00.0 2023-04-19 16:00:00.0 2023-04-19 16:00:00.0 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1 1 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 @@ -80,9 +80,9 @@ 3 2023-04-21 2023-04-21 2023-04-20T12:34:56 2023-04-20T00:00 2023-04-20T00:00:00.123 2023-04-20T00:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.4028235e+38 1.7976931348623157E308 char3 999999999 999999999 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 -- !select_tvf2 -- -1 2023-04-20 2023-04-20 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1 1 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 -2 9999-12-31 9999-12-31 9999-12-31T23:59:59 9999-12-31T23:59:59 2023-04-20T00:00:00.120 2023-04-20T00:00:00.334400 Haidian -32768 -128 true -2147483648 -9223372036854775808 -170141183460469231731687303715884105728 1.4E-45 4.9E-324 char2 100000000 100000000 4 0.1 0.99999999 9999999999.9999999999 99999999999999999999999999999999999999 9.9999999999999999999999999999999999999 0.99999999999999999999999999999999999999 -3 2023-04-21 2023-04-21 2023-04-20T12:34:56 2023-04-20T00:00 2023-04-20T00:00:00.123 2023-04-20T00:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.4028235e+38 1.7976931348623157E308 char3 999999999 999999999 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 +1 2023-04-20 2023-04-20 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1.000000000 1.000000000 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 +2 9999-12-31 9999-12-31 9999-12-31T15:59:59 9999-12-31T15:59:59 2023-04-19T16:00:00.120 2023-04-19T16:00:00.334400 Haidian -32768 -128 true -2147483648 -9223372036854775808 -170141183460469231731687303715884105728 1.4E-45 4.940656458412465e-324 char2 100000000.000000000 100000000.000000000 4 0.1 0.99999999 9999999999.9999999999 99999999999999999999999999999999999999 9.9999999999999999999999999999999999999 0.99999999999999999999999999999999999999 +3 2023-04-21 2023-04-21 2023-04-20T04:34:56 2023-04-19T16:00 2023-04-19T16:00:00.123 2023-04-19T16:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.402823E38 1.797693134862316e+308 char3 999999999.000000000 999999999.000000000 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 -- !hive_docker_02 -- 1 2023-04-20 2023-04-20 2023-04-19 16:00:00.0 2023-04-19 16:00:00.0 2023-04-19 16:00:00.0 2023-04-19 16:00:00.0 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1 1 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 diff --git a/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet_comlex_type.out b/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet_comlex_type.out index 6e0541a492445d..84c43c57a736fe 100644 --- a/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet_comlex_type.out +++ b/regression-test/data/external_table_p0/export/hive_read/parquet/test_hive_read_parquet_comlex_type.out @@ -99,16 +99,16 @@ 10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} -- !select_tvf4 -- -1 doris_1 {"user_id":1, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":1, "sex":1, "bool_col":1, "int_col":1, "bigint_col":1, "largeint_col":"1", "float_col":1.1, "double_col":1.1, "char_col":"char1_1234", "decimal_col":1.000000000} -2 doris_2 {"user_id":2, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":2, "sex":2, "bool_col":1, "int_col":2, "bigint_col":2, "largeint_col":"2", "float_col":2.2, "double_col":2.2, "char_col":"char2_1234", "decimal_col":2.000000000} -3 doris_3 {"user_id":3, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":3, "sex":3, "bool_col":1, "int_col":3, "bigint_col":3, "largeint_col":"3", "float_col":3.3, "double_col":3.3, "char_col":"char3_1234", "decimal_col":3.000000000} -4 doris_4 {"user_id":4, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":4, "sex":4, "bool_col":1, "int_col":4, "bigint_col":4, "largeint_col":"4", "float_col":4.4, "double_col":4.4, "char_col":"char4_1234", "decimal_col":4.000000000} -5 doris_5 {"user_id":5, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":5, "sex":5, "bool_col":1, "int_col":5, "bigint_col":5, "largeint_col":"5", "float_col":5.5, "double_col":5.5, "char_col":"char5_1234", "decimal_col":5.000000000} -6 doris_6 {"user_id":6, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":6, "sex":6, "bool_col":1, "int_col":6, "bigint_col":6, "largeint_col":"6", "float_col":6.6, "double_col":6.6, "char_col":"char6_1234", "decimal_col":6.000000000} -7 doris_7 {"user_id":7, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":7, "sex":7, "bool_col":1, "int_col":7, "bigint_col":7, "largeint_col":"7", "float_col":7.7, "double_col":7.7, "char_col":"char7_1234", "decimal_col":7.000000000} -8 doris_8 {"user_id":8, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":8, "sex":8, "bool_col":1, "int_col":8, "bigint_col":8, "largeint_col":"8", "float_col":8.8, "double_col":8.800000000000001, "char_col":"char8_1234", "decimal_col":8.000000000} -9 doris_9 {"user_id":9, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":9, "sex":9, "bool_col":1, "int_col":9, "bigint_col":9, "largeint_col":"9", "float_col":9.9, "double_col":9.9, "char_col":"char9_1234", "decimal_col":9.000000000} -10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} +1 doris_1 {"user_id":1, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":1, "sex":1, "bool_col":1, "int_col":1, "bigint_col":1, "largeint_col":"1", "float_col":1.1, "double_col":1.1, "char_col":"char1_1234", "decimal_col":1.000000000} +2 doris_2 {"user_id":2, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":2, "sex":2, "bool_col":1, "int_col":2, "bigint_col":2, "largeint_col":"2", "float_col":2.2, "double_col":2.2, "char_col":"char2_1234", "decimal_col":2.000000000} +3 doris_3 {"user_id":3, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":3, "sex":3, "bool_col":1, "int_col":3, "bigint_col":3, "largeint_col":"3", "float_col":3.3, "double_col":3.3, "char_col":"char3_1234", "decimal_col":3.000000000} +4 doris_4 {"user_id":4, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":4, "sex":4, "bool_col":1, "int_col":4, "bigint_col":4, "largeint_col":"4", "float_col":4.4, "double_col":4.4, "char_col":"char4_1234", "decimal_col":4.000000000} +5 doris_5 {"user_id":5, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":5, "sex":5, "bool_col":1, "int_col":5, "bigint_col":5, "largeint_col":"5", "float_col":5.5, "double_col":5.5, "char_col":"char5_1234", "decimal_col":5.000000000} +6 doris_6 {"user_id":6, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":6, "sex":6, "bool_col":1, "int_col":6, "bigint_col":6, "largeint_col":"6", "float_col":6.6, "double_col":6.6, "char_col":"char6_1234", "decimal_col":6.000000000} +7 doris_7 {"user_id":7, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":7, "sex":7, "bool_col":1, "int_col":7, "bigint_col":7, "largeint_col":"7", "float_col":7.7, "double_col":7.7, "char_col":"char7_1234", "decimal_col":7.000000000} +8 doris_8 {"user_id":8, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":8, "sex":8, "bool_col":1, "int_col":8, "bigint_col":8, "largeint_col":"8", "float_col":8.8, "double_col":8.800000000000001, "char_col":"char8_1234", "decimal_col":8.000000000} +9 doris_9 {"user_id":9, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":9, "sex":9, "bool_col":1, "int_col":9, "bigint_col":9, "largeint_col":"9", "float_col":9.9, "double_col":9.9, "char_col":"char9_1234", "decimal_col":9.000000000} +10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} -- !hive_docker_04 -- 1 doris_1 {"user_id":1,"date":"2017-10-01","datetime":"2017-09-30 16:00:00","city":"Beijing","age":1,"sex":1,"bool_col":true,"int_col":1,"bigint_col":1,"largeint_col":"1","float_col":1.1,"double_col":1.1,"char_col":"char1_1234","decimal_col":1} @@ -222,16 +222,16 @@ 10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} -- !select_tvf4 -- -1 doris_1 {"user_id":1, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":1, "sex":1, "bool_col":1, "int_col":1, "bigint_col":1, "largeint_col":"1", "float_col":1.1, "double_col":1.1, "char_col":"char1_1234", "decimal_col":1.000000000} -2 doris_2 {"user_id":2, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":2, "sex":2, "bool_col":1, "int_col":2, "bigint_col":2, "largeint_col":"2", "float_col":2.2, "double_col":2.2, "char_col":"char2_1234", "decimal_col":2.000000000} -3 doris_3 {"user_id":3, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":3, "sex":3, "bool_col":1, "int_col":3, "bigint_col":3, "largeint_col":"3", "float_col":3.3, "double_col":3.3, "char_col":"char3_1234", "decimal_col":3.000000000} -4 doris_4 {"user_id":4, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":4, "sex":4, "bool_col":1, "int_col":4, "bigint_col":4, "largeint_col":"4", "float_col":4.4, "double_col":4.4, "char_col":"char4_1234", "decimal_col":4.000000000} -5 doris_5 {"user_id":5, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":5, "sex":5, "bool_col":1, "int_col":5, "bigint_col":5, "largeint_col":"5", "float_col":5.5, "double_col":5.5, "char_col":"char5_1234", "decimal_col":5.000000000} -6 doris_6 {"user_id":6, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":6, "sex":6, "bool_col":1, "int_col":6, "bigint_col":6, "largeint_col":"6", "float_col":6.6, "double_col":6.6, "char_col":"char6_1234", "decimal_col":6.000000000} -7 doris_7 {"user_id":7, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":7, "sex":7, "bool_col":1, "int_col":7, "bigint_col":7, "largeint_col":"7", "float_col":7.7, "double_col":7.7, "char_col":"char7_1234", "decimal_col":7.000000000} -8 doris_8 {"user_id":8, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":8, "sex":8, "bool_col":1, "int_col":8, "bigint_col":8, "largeint_col":"8", "float_col":8.8, "double_col":8.800000000000001, "char_col":"char8_1234", "decimal_col":8.000000000} -9 doris_9 {"user_id":9, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":"Beijing", "age":9, "sex":9, "bool_col":1, "int_col":9, "bigint_col":9, "largeint_col":"9", "float_col":9.9, "double_col":9.9, "char_col":"char9_1234", "decimal_col":9.000000000} -10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-10-01 00:00:00.000000", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} +1 doris_1 {"user_id":1, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":1, "sex":1, "bool_col":1, "int_col":1, "bigint_col":1, "largeint_col":"1", "float_col":1.1, "double_col":1.1, "char_col":"char1_1234", "decimal_col":1.000000000} +2 doris_2 {"user_id":2, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":2, "sex":2, "bool_col":1, "int_col":2, "bigint_col":2, "largeint_col":"2", "float_col":2.2, "double_col":2.2, "char_col":"char2_1234", "decimal_col":2.000000000} +3 doris_3 {"user_id":3, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":3, "sex":3, "bool_col":1, "int_col":3, "bigint_col":3, "largeint_col":"3", "float_col":3.3, "double_col":3.3, "char_col":"char3_1234", "decimal_col":3.000000000} +4 doris_4 {"user_id":4, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":4, "sex":4, "bool_col":1, "int_col":4, "bigint_col":4, "largeint_col":"4", "float_col":4.4, "double_col":4.4, "char_col":"char4_1234", "decimal_col":4.000000000} +5 doris_5 {"user_id":5, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":5, "sex":5, "bool_col":1, "int_col":5, "bigint_col":5, "largeint_col":"5", "float_col":5.5, "double_col":5.5, "char_col":"char5_1234", "decimal_col":5.000000000} +6 doris_6 {"user_id":6, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":6, "sex":6, "bool_col":1, "int_col":6, "bigint_col":6, "largeint_col":"6", "float_col":6.6, "double_col":6.6, "char_col":"char6_1234", "decimal_col":6.000000000} +7 doris_7 {"user_id":7, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":7, "sex":7, "bool_col":1, "int_col":7, "bigint_col":7, "largeint_col":"7", "float_col":7.7, "double_col":7.7, "char_col":"char7_1234", "decimal_col":7.000000000} +8 doris_8 {"user_id":8, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":8, "sex":8, "bool_col":1, "int_col":8, "bigint_col":8, "largeint_col":"8", "float_col":8.8, "double_col":8.800000000000001, "char_col":"char8_1234", "decimal_col":8.000000000} +9 doris_9 {"user_id":9, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":"Beijing", "age":9, "sex":9, "bool_col":1, "int_col":9, "bigint_col":9, "largeint_col":"9", "float_col":9.9, "double_col":9.9, "char_col":"char9_1234", "decimal_col":9.000000000} +10 doris_10 {"user_id":10, "date":"2017-10-01", "datetime":"2017-09-30 16:00:00.000000", "city":null, "age":null, "sex":null, "bool_col":null, "int_col":null, "bigint_col":null, "largeint_col":null, "float_col":null, "double_col":null, "char_col":null, "decimal_col":null} -- !hive_docker_04 -- 1 doris_1 {"user_id":1,"date":"2017-10-01","datetime":"2017-09-30 16:00:00","city":"Beijing","age":1,"sex":1,"bool_col":true,"int_col":1,"bigint_col":1,"largeint_col":"1","float_col":1.1,"double_col":1.1,"char_col":"char1_1234","decimal_col":1} diff --git a/regression-test/data/external_table_p0/export/test_export_external_table.out b/regression-test/data/external_table_p0/export/test_export_external_table.out index a6047157bbd816..fece0d648732b5 100644 --- a/regression-test/data/external_table_p0/export/test_export_external_table.out +++ b/regression-test/data/external_table_p0/export/test_export_external_table.out @@ -408,36 +408,36 @@ true abc efg 2022-10-01 3.4 1 2 8 100000 1.2 2022-10-02T12:59:01 24.000 true abc efg 2022-10-01 3.4 1 2 9 100000 1.2 2022-10-02T12:59:01 24.000 -- !select_load6 -- -true abc efg 2022-10-01 3.4 1 2 0 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 1 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 10 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 11 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 12 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 13 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 14 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 15 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 16 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 17 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 18 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 19 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 2 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 20 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 21 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 22 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 23 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 24 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 25 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 26 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 27 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 28 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 29 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 3 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 4 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 5 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 6 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 7 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 8 100000 1.2 2022-10-02T12:59:01 24.000 -true abc efg 2022-10-01 3.4 1 2 9 100000 1.2 2022-10-02T12:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 0 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 1 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 10 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 11 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 12 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 13 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 14 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 15 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 16 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 17 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 18 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 19 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 2 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 20 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 21 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 22 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 23 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 24 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 25 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 26 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 27 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 28 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 29 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 3 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 4 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 5 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 6 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 7 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 8 100000 1.2 2022-10-02T04:59:01 24.000 +true abc efg 2022-10-01 3.4 1 2 9 100000 1.2 2022-10-02T04:59:01 24.000 -- !select_load7 -- true \N efg \N 3.4 \N 2 0 \N \N \N \N diff --git a/regression-test/data/external_table_p0/hive/test_external_catalog_hive.out b/regression-test/data/external_table_p0/hive/test_external_catalog_hive.out index 8a104343fc4e10..f5acb6894eefee 100644 --- a/regression-test/data/external_table_p0/hive/test_external_catalog_hive.out +++ b/regression-test/data/external_table_p0/hive/test_external_catalog_hive.out @@ -114,132 +114,8 @@ a126 15 -- !par_fields_in_file_parquet5 -- -- !parquet_adjusted_utc -- -1997-09-21 1999-01-12T15:12:31.235784 -1998-01-12 1993-06-11T11:33:12.356500 -2002-09-29 2001-01-17T21:23:42.120 -2008-08-07 2023-09-23T11:12:17.458 -2009-11-13 2011-11-12T01:23:06.986 -2012-07-08 2023-11-09T20:21:16.321 -2017-09-13 2009-09-21T04:23:14.309124 -2024-03-23 2024-02-01T21:11:09.170 - --- !q01 -- -zhangsan 1 -lisi 1 - --- !q02 -- -1 1 -2 1 -3 1 -4 1 - --- !q03 -- -123 china 4 56 sc -234 america 5 67 ls -345 cana 4 56 fy -567 fre 7 89 pa - --- !q04 -- -p_partkey2 p_name2 p_mfgr2 p_brand2 p_type2 p_size2 p_con2 p_r_price2 p_comment2 -p_partkey1 p_name1 p_mfgr1 p_brand1 p_type1 p_size1 p_con1 p_r_price1 p_comment1 -p_partkey0 p_name0 p_mfgr0 p_brand0 p_type0 p_size0 p_con0 p_r_price0 p_comment0 - --- !q05 -- -batchno appsheet_no filedate t_no tano t_name chged_no mob_no2 home_no off_no -off_no home_no mob_no2 chged_no t_name tano t_no filedate appsheet_no batchno - --- !q06 -- -bill_code dates ord_year ord_month ord_quarter on_time - --- !q07 -- -2 - --- !q08 -- -123 zhangsan 12 123.45 2022-01-01 -124 lisi 12 123.45 2022-01-01 -125 lisan 12 123.45 2022-01-02 - --- !q09 -- -a123 12 -a124 13 -a125 14 -a126 15 - --- !par_fields_in_file_orc1 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet1 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc2 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet2 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc3 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet3 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc4 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet4 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc5 -- - --- !par_fields_in_file_parquet5 -- - --- !par_fields_in_file_orc1 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet1 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc2 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet2 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc3 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet3 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc4 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_parquet4 -- -1 Alice 100.0 2023 8 -2 Bob 150.0 2023 8 - --- !par_fields_in_file_orc5 -- - --- !par_fields_in_file_parquet5 -- - --- !parquet_adjusted_utc -- -1997-09-21 1999-01-12T15:12:31.235784 -1998-01-12 1993-06-11T11:33:12.356500 +1997-09-21 1999-01-12T07:12:31.235784 +1998-01-12 1993-06-11T03:33:12.356500 2002-09-29 2001-01-17T21:23:42.120 2008-08-07 2023-09-23T11:12:17.458 2009-11-13 2011-11-12T01:23:06.986 diff --git a/regression-test/suites/export_p0/outfile/parquet/test_outfile_parquet_complex_type.groovy b/regression-test/suites/export_p0/outfile/parquet/test_outfile_parquet_complex_type.groovy index 6452576d8ffa1e..b46a9a0bb67f64 100644 --- a/regression-test/suites/export_p0/outfile/parquet/test_outfile_parquet_complex_type.groovy +++ b/regression-test/suites/export_p0/outfile/parquet/test_outfile_parquet_complex_type.groovy @@ -298,6 +298,17 @@ suite("test_outfile_parquet_complex_type", "p0") { // test outfile to s3 def outfile_url = outfile_to_S3() + sql """ set enable_file_scanner_v2 = false; """ + qt_select_load7 """ SELECT * FROM S3 ( + "uri" = "http://${bucket}.${s3_endpoint}${outfile_url.substring(5 + bucket.length(), outfile_url.length() - 1)}0.parquet", + "ACCESS_KEY"= "${ak}", + "SECRET_KEY" = "${sk}", + "format" = "parquet", + "region" = "${region}" + ); + """ + + sql """ set enable_file_scanner_v2 = true; """ qt_select_load7 """ SELECT * FROM S3 ( "uri" = "http://${bucket}.${s3_endpoint}${outfile_url.substring(5 + bucket.length(), outfile_url.length() - 1)}0.parquet", "ACCESS_KEY"= "${ak}", From b000e93fb6f98ee5e9e38ff8fb2f5d9e8c7f48f3 Mon Sep 17 00:00:00 2001 From: Gabriel Date: Fri, 12 Jun 2026 16:53:10 +0800 Subject: [PATCH 02/28] Fix 0612 (#64456) --- be/src/exec/scan/file_scanner_v2.cpp | 120 ++++++++++++- be/src/format_v2/column_mapper.cpp | 32 +--- be/src/format_v2/column_mapper.h | 1 - be/src/format_v2/parquet/parquet_reader.cpp | 12 +- be/src/format_v2/parquet/parquet_scan.cpp | 10 +- be/src/format_v2/parquet/parquet_scan.h | 2 +- .../format_v2/parquet/parquet_statistics.cpp | 121 ++++++++------ be/src/format_v2/parquet/parquet_statistics.h | 21 ++- be/src/format_v2/table/hive_reader.cpp | 2 - be/src/format_v2/table_reader.cpp | 1 - be/src/format_v2/table_reader.h | 77 +++++++-- .../scan/vfile_scanner_exception_test.cpp | 131 +++++++++++++++ be/test/format_v2/column_mapper_test.cpp | 143 +++++++++++++--- .../parquet/parquet_column_reader_test.cpp | 33 ++++ be/test/format_v2/table_reader_test.cpp | 158 +++++++++++------- 15 files changed, 667 insertions(+), 197 deletions(-) diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp index 6a5e093baeb863..0ac4c1e7cf27b2 100644 --- a/be/src/exec/scan/file_scanner_v2.cpp +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -137,6 +137,14 @@ format::ColumnDefinition* find_or_add_child(format::ColumnDefinition* parent, in return &parent->children.back(); } +void inherit_schema_metadata(format::ColumnDefinition* column, + const format::ColumnDefinition* schema_column) { + if (column == nullptr || schema_column == nullptr) { + return; + } + column->name_mapping = schema_column->name_mapping; +} + const format::ColumnDefinition* find_schema_child_by_path( const format::ColumnDefinition* schema_column, const std::string& child_path) { if (schema_column == nullptr) { @@ -169,6 +177,17 @@ int32_t schema_field_id(const format::ColumnDefinition* schema_column) { return schema_column->get_identifier_field_id(); } +int32_t schema_field_id_or(const format::ColumnDefinition* schema_column, int32_t fallback) { + const auto field_id = schema_field_id(schema_column); + return field_id >= 0 ? field_id : fallback; +} + +std::string schema_field_name_or(const format::ColumnDefinition* schema_column, + std::string fallback) { + return schema_column == nullptr || schema_column->name.empty() ? std::move(fallback) + : schema_column->name; +} + struct AccessPathNode { bool project_all = false; std::map children; @@ -208,6 +227,85 @@ Status build_nested_children_from_access_node(format::ColumnDefinition* column, const std::string& path, const format::ColumnDefinition* schema_column); +// Expand a full complex-column projection into table-schema children when the table format provides +// an external/current schema. Without this, `SELECT complex_col` or `SELECT *` leaves +// ColumnDefinition::children empty, so ColumnMapper treats the root complex column as a scalar +// mapping and later tries to cast the old file shape to the current table shape directly. +// +// Examples: +// - STRUCT country/city projected from an old file STRUCT country/population/location should +// create children country and city, so city can be materialized as missing/default. +// - ARRAY> should create the array element wrapper and then the element +// struct children item and quantity. +// - MAP> should create the entries wrapper with key/value, then +// expand the value struct children full_name and age. +Status build_all_nested_children_from_schema(format::ColumnDefinition* column, + const DataTypePtr& type, const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + if (schema_column == nullptr || schema_column->children.empty()) { + return Status::OK(); + } + + const auto nested_type = remove_nullable(type); + AccessPathNode project_all; + project_all.project_all = true; + switch (nested_type->get_primitive_type()) { + case TYPE_STRUCT: { + const auto& struct_type = assert_cast(*nested_type); + for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { + const auto field_name = struct_type.get_element_name(field_idx); + const auto* schema_child = find_schema_child_by_path(schema_column, field_name); + auto* child = find_or_add_child( + column, schema_field_id_or(schema_child, cast_set(field_idx)), + schema_field_name_or(schema_child, field_name), + struct_type.get_element(field_idx)); + inherit_schema_metadata(child, schema_child); + RETURN_IF_ERROR(build_nested_children_from_access_node( + child, child->type, project_all, path + "." + child->name, schema_child)); + } + return Status::OK(); + } + case TYPE_ARRAY: { + const auto& array_type = assert_cast(*nested_type); + const auto* element_schema = &schema_column->children[0]; + auto* child = find_or_add_child( + column, schema_field_id_or(element_schema, 0), + schema_field_name_or(element_schema, "element"), array_type.get_nested_type()); + inherit_schema_metadata(child, element_schema); + return build_nested_children_from_access_node(child, child->type, project_all, + path + ".*", element_schema); + } + case TYPE_MAP: { + const auto& map_type = assert_cast(*nested_type); + const auto* key_schema = &schema_column->children[0]; + const auto* value_schema = + schema_column->children.size() > 1 ? &schema_column->children[1] : nullptr; + DataTypes entry_child_types {map_type.get_key_type(), map_type.get_value_type()}; + Strings entry_child_names {"key", "value"}; + auto entry_type = std::make_shared(entry_child_types, entry_child_names); + auto* entry_child = find_or_add_child(column, 0, "entries", entry_type); + auto* key_child = find_or_add_child( + entry_child, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + inherit_schema_metadata(key_child, key_schema); + RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, + project_all, path + ".KEYS", + key_schema)); + auto* value_child = find_or_add_child( + entry_child, schema_field_id_or(value_schema, 1), + schema_field_name_or(value_schema, "value"), map_type.get_value_type()); + inherit_schema_metadata(value_child, value_schema); + RETURN_IF_ERROR(build_nested_children_from_access_node(value_child, value_child->type, + project_all, path + ".VALUES", + value_schema)); + return Status::OK(); + } + default: + return Status::OK(); + } +} + Status build_struct_children_from_access_node(format::ColumnDefinition* column, const DataTypeStruct& struct_type, const AccessPathNode& node, const std::string& path, @@ -245,6 +343,7 @@ Status build_struct_children_from_access_node(format::ColumnDefinition* column, // the table child identifier. BY_NAME mapping should instead keep a string identifier and // let TableColumnMapper resolve the file-local child id from the Parquet schema. auto* child = find_or_add_child(column, field_id, field_name, field_type); + inherit_schema_metadata(child, schema_child); RETURN_IF_ERROR(build_nested_children_from_access_node( child, child->type, child_node, path + "." + child_path, schema_child)); } @@ -321,12 +420,19 @@ Status build_map_children_from_access_node(format::ColumnDefinition* column, ? &schema_column->children[1] : nullptr; if (need_key) { - auto* key_child = find_or_add_child(entry_child, 0, "key", map_type.get_key_type()); + auto* key_child = + find_or_add_child(entry_child, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + inherit_schema_metadata(key_child, key_schema); RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, key_node, path + ".KEYS", key_schema)); } if (need_value) { - auto* value_child = find_or_add_child(entry_child, 1, "value", map_type.get_value_type()); + auto* value_child = + find_or_add_child(entry_child, schema_field_id_or(value_schema, 1), + schema_field_name_or(value_schema, "value"), + map_type.get_value_type()); + inherit_schema_metadata(value_child, value_schema); RETURN_IF_ERROR(build_nested_children_from_access_node( value_child, value_child->type, value_node, path + ".VALUES", value_schema)); } @@ -339,8 +445,7 @@ Status build_nested_children_from_access_node(format::ColumnDefinition* column, const format::ColumnDefinition* schema_column) { DORIS_CHECK(column != nullptr); if (node.project_all || node.children.empty()) { - // If project_all is true or there is no specific child path, we need to project all children of the complex type. - return Status::OK(); + return build_all_nested_children_from_schema(column, type, path, schema_column); } const auto nested_type = remove_nullable(type); @@ -355,10 +460,14 @@ Status build_nested_children_from_access_node(format::ColumnDefinition* column, path, column->name); } const auto& array_type = assert_cast(*nested_type); - auto* child = find_or_add_child(column, 0, "element", array_type.get_nested_type()); const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() ? &schema_column->children[0] : nullptr; + auto* child = + find_or_add_child(column, schema_field_id_or(element_schema, 0), + schema_field_name_or(element_schema, "element"), + array_type.get_nested_type()); + inherit_schema_metadata(child, element_schema); return build_nested_children_from_access_node(child, child->type, node.children.at("*"), path + ".*", element_schema); } @@ -601,7 +710,6 @@ Status FileScannerV2::_init_table_reader(const TFileRangeDesc& range) { .io_ctx = _io_ctx, .runtime_state = _state, .scanner_profile = _local_state->scanner_profile(), - .allow_missing_columns = false, // TODO .push_down_agg_type = _local_state->get_push_down_agg_type(), })); return Status::OK(); diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index c63080a82074ca..a8211265d89f1e 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -314,8 +314,7 @@ static bool is_binary_comparison_predicate(const VExprSPtr& expr) { std::string TableColumnMapperOptions::debug_string() const { std::ostringstream out; - out << "TableColumnMapperOptions{mode=" << mapping_mode_to_string(mode) - << ", allow_missing_columns=" << allow_missing_columns << "}"; + out << "TableColumnMapperOptions{mode=" << mapping_mode_to_string(mode) << "}"; return out.str(); } @@ -1127,6 +1126,10 @@ static const ColumnDefinition* find_file_child_for_complex_wrapper( if (file_child != nullptr) { return file_child; } + if (remove_nullable(file_field.type)->get_primitive_type() == TYPE_ARRAY && + file_field.children.size() == 1) { + return &file_field.children[0]; + } if (remove_nullable(file_field.type)->get_primitive_type() == TYPE_MAP && file_field.children.size() == 1 && column_has_name(table_child, "entries")) { return &file_field.children[0]; @@ -1159,22 +1162,12 @@ Status TableColumnMapper::_create_by_index_mapping(const ColumnDefinition& table // Case B: file_index is out of range, which means the file does not contain this column. // Route it through the missing-column path used by schema evolution. - // B1: the table column carries a default_expr injected by FE, so use the constant branch and - // materialize that value for every row. if (table_column.default_expr != nullptr) { _set_constant_mapping(mapping, table_column.default_expr); return Status::OK(); } - // B2: if missing columns are not allowed, fail explicitly instead of silently producing - // NULLs and hiding the issue. - if (!_options.allow_missing_columns) { - return Status::InvalidArgument( - "Table column '{}' (file_index={}) is out of range for file schema of size {}", - table_column.name, file_index, file_schema.size()); - } - // B3: if missing columns are allowed, keep the mapping empty - // (`file_column_id` remains `nullopt`) and let the upper finalize stage fill - // NULL/default values. + // Keep the mapping empty (`file_local_id` remains `nullopt`) and let the upper finalize + // stage fill NULL/default values. return Status::OK(); } @@ -1239,11 +1232,6 @@ Status TableColumnMapper::_create_mapping_for_column(const ColumnDefinition& tab "Table column '{}' (global_index={}) does not have a matching partition value", table_column.name, mapping->global_index.value()); } - if (!_options.allow_missing_columns) { - return Status::InvalidArgument( - "Table column '{}' (global_index={}) does not have a matching file column", - table_column.name, mapping->global_index.value()); - } } return Status::OK(); } @@ -1562,12 +1550,6 @@ Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_c const auto* file_child = find_file_child_for_complex_wrapper(table_child, file_field, _options.mode); if (file_child == nullptr) { - if (!_options.allow_missing_columns) { - return Status::InvalidArgument( - "Table child column '{}' does not have a matching file child " - "under column '{}'", - table_child.name, table_column.name); - } ColumnMapping child_mapping; child_mapping.table_column_name = table_child.name; child_mapping.file_column_name = table_child.name; diff --git a/be/src/format_v2/column_mapper.h b/be/src/format_v2/column_mapper.h index 2ab2d63149dbcc..934f87cb7b5a97 100644 --- a/be/src/format_v2/column_mapper.h +++ b/be/src/format_v2/column_mapper.h @@ -162,7 +162,6 @@ struct ColumnMapping { struct TableColumnMapperOptions { TableColumnMappingMode mode = TableColumnMappingMode::BY_FIELD_ID; - bool allow_missing_columns = true; std::string debug_string() const; }; diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index 175a8560310ef4..fc96d2208382ed 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -40,6 +40,7 @@ struct ParquetReaderScanState { std::vector> file_schema; RowGroupScanPlan scan_plan; ParquetScanScheduler scheduler; + const cctz::time_zone* timezone = nullptr; bool enable_bloom_filter = false; }; @@ -117,6 +118,7 @@ Status ParquetReader::init(RuntimeState* state) { _state->enable_bloom_filter = state != nullptr && state->query_options().enable_parquet_filter_by_bloom_filter; if (state != nullptr) { + _state->timezone = &state->timezone_obj(); _state->scheduler.set_timezone(&state->timezone_obj()); _state->scheduler.set_enable_strict_mode(state->enable_strict_mode()); } @@ -207,10 +209,10 @@ Status ParquetReader::open(std::shared_ptr request) { scan_range.size = _file_description->range_size; scan_range.file_size = _file_description->file_size; // Get selected ranges in row groups according to metadata (Row-Group level index and Page Index including Zonemap, Dictionary, Bloom Filter). - RETURN_IF_ERROR(plan_parquet_row_groups(*_state->file_context.metadata, - _state->file_context.file_reader.get(), - _state->file_schema, *request_snapshot, scan_range, - _state->enable_bloom_filter, &row_group_plan)); + RETURN_IF_ERROR(plan_parquet_row_groups( + *_state->file_context.metadata, _state->file_context.file_reader.get(), + _state->file_schema, *request_snapshot, scan_range, _state->enable_bloom_filter, + &row_group_plan, _state->timezone)); if (_profile != nullptr) { const auto& pruning_stats = row_group_plan.pruning_stats; COUNTER_UPDATE(_parquet_profile.filtered_row_groups, @@ -334,7 +336,7 @@ Status ParquetReader::get_aggregate_result(const format::FileAggregateRequest& r auto column_chunk = row_group_metadata->ColumnChunk(leaf_schema->leaf_column_id); DORIS_CHECK(column_chunk != nullptr); const auto statistics = ParquetStatisticsUtils::TransformColumnStatistics( - *leaf_schema, column_chunk->statistics()); + *leaf_schema, column_chunk->statistics(), _state->timezone); if (!statistics.has_min_max) { return Status::NotSupported("Missing parquet min/max statistics for column {}", leaf_schema->name); diff --git a/be/src/format_v2/parquet/parquet_scan.cpp b/be/src/format_v2/parquet/parquet_scan.cpp index 18dc73ff7edfd5..b72f666c9f45b4 100644 --- a/be/src/format_v2/parquet/parquet_scan.cpp +++ b/be/src/format_v2/parquet/parquet_scan.cpp @@ -80,15 +80,15 @@ Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, const std::vector>& file_schema, const format::FileScanRequest& request, const ParquetScanRange& scan_range, bool enable_bloom_filter, - RowGroupScanPlan* plan) { + RowGroupScanPlan* plan, const cctz::time_zone* timezone) { DORIS_CHECK(plan != nullptr); plan->row_groups.clear(); plan->pruning_stats = ParquetPruningStats {}; std::vector statistics_selected_row_groups; - RETURN_IF_ERROR(select_row_groups_by_statistics(metadata, file_reader, file_schema, request, - &statistics_selected_row_groups, - enable_bloom_filter, &plan->pruning_stats)); + RETURN_IF_ERROR(select_row_groups_by_statistics( + metadata, file_reader, file_schema, request, &statistics_selected_row_groups, + enable_bloom_filter, &plan->pruning_stats, timezone)); std::vector row_group_first_rows(metadata.num_row_groups()); int64_t next_row_group_first_row = 0; @@ -124,7 +124,7 @@ Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, RETURN_IF_ERROR(select_row_group_ranges_by_page_index( file_reader, file_schema, request, row_group_idx, row_group_rows, &row_group_plan.selected_ranges, &row_group_plan.page_skip_plans, - &plan->pruning_stats)); + &plan->pruning_stats, timezone)); if (row_group_plan.selected_ranges.empty()) { continue; } diff --git a/be/src/format_v2/parquet/parquet_scan.h b/be/src/format_v2/parquet/parquet_scan.h index cf45a09f8aace8..f90a5e5f2a8971 100644 --- a/be/src/format_v2/parquet/parquet_scan.h +++ b/be/src/format_v2/parquet/parquet_scan.h @@ -92,7 +92,7 @@ Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, const std::vector>& file_schema, const format::FileScanRequest& request, const ParquetScanRange& scan_range, bool enable_bloom_filter, - RowGroupScanPlan* plan); + RowGroupScanPlan* plan, const cctz::time_zone* timezone = nullptr); IColumn::Filter selection_to_filter(const SelectionVector& selection, uint16_t selected_rows, int64_t batch_rows); diff --git a/be/src/format_v2/parquet/parquet_statistics.cpp b/be/src/format_v2/parquet/parquet_statistics.cpp index 3ef5fcfeb2e27e..4391caf702bfa1 100644 --- a/be/src/format_v2/parquet/parquet_statistics.cpp +++ b/be/src/format_v2/parquet/parquet_statistics.cpp @@ -85,7 +85,7 @@ DecodedTimeUnit decoded_time_unit(ParquetTimeUnit time_unit) { } Status read_decoded_field(const ParquetColumnSchema& column_schema, DecodedColumnView view, - Field* field) { + Field* field, const cctz::time_zone* timezone) { DORIS_CHECK(column_schema.type != nullptr); DORIS_CHECK(field != nullptr); constexpr uint8_t not_null = 0; @@ -95,46 +95,51 @@ Status read_decoded_field(const ParquetColumnSchema& column_schema, DecodedColum view.decimal_precision = column_schema.type_descriptor.decimal_precision; view.decimal_scale = column_schema.type_descriptor.decimal_scale; view.fixed_length = column_schema.type_descriptor.fixed_length; + view.timestamp_is_adjusted_to_utc = column_schema.type_descriptor.timestamp_is_adjusted_to_utc; + view.timezone = timezone; return column_schema.type->get_serde()->read_field_from_decoded_value(*column_schema.type, field, view); } template bool set_decoded_field(const ParquetColumnSchema& column_schema, DecodedValueKind value_kind, - const NativeType& value, Field* field) { + const NativeType& value, Field* field, const cctz::time_zone* timezone) { DecodedColumnView view; view.value_kind = value_kind; view.values = reinterpret_cast(&value); - return read_decoded_field(column_schema, view, field).ok(); + return read_decoded_field(column_schema, view, field, timezone).ok(); } template bool set_decoded_min_max(const std::shared_ptr<::parquet::Statistics>& statistics, const ParquetColumnSchema& column_schema, DecodedValueKind value_kind, - ParquetColumnStatistics* column_statistics) { + ParquetColumnStatistics* column_statistics, + const cctz::time_zone* timezone) { auto typed_statistics = std::static_pointer_cast<::parquet::TypedStatistics>(statistics); if (!set_decoded_field(column_schema, value_kind, typed_statistics->min(), - &column_statistics->min_value) || + &column_statistics->min_value, timezone) || !set_decoded_field(column_schema, value_kind, typed_statistics->max(), - &column_statistics->max_value)) { + &column_statistics->max_value, timezone)) { return false; } return true; } bool set_decoded_binary_field(const ParquetColumnSchema& column_schema, DecodedValueKind value_kind, - const StringRef& value, Field* field) { + const StringRef& value, Field* field, + const cctz::time_zone* timezone) { std::vector binary_values {value}; DecodedColumnView view; view.value_kind = value_kind; view.binary_values = &binary_values; - return read_decoded_field(column_schema, view, field).ok(); + return read_decoded_field(column_schema, view, field, timezone).ok(); } bool set_string_min_max(const std::shared_ptr<::parquet::Statistics>& statistics, const ParquetColumnSchema& column_schema, - ParquetColumnStatistics* column_statistics) { + ParquetColumnStatistics* column_statistics, + const cctz::time_zone* timezone) { switch (statistics->physical_type()) { case ::parquet::Type::BYTE_ARRAY: { auto typed_statistics = @@ -144,10 +149,10 @@ bool set_string_min_max(const std::shared_ptr<::parquet::Statistics>& statistics const auto max = ::parquet::ByteArrayToString(typed_statistics->max()); if (!set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, StringRef(min.data(), min.size()), - &column_statistics->min_value) || + &column_statistics->min_value, timezone) || !set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, StringRef(max.data(), max.size()), - &column_statistics->max_value)) { + &column_statistics->max_value, timezone)) { return false; } return true; @@ -166,10 +171,10 @@ bool set_string_min_max(const std::shared_ptr<::parquet::Statistics>& statistics type_length); if (!set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, StringRef(min.data(), min.size()), - &column_statistics->min_value) || + &column_statistics->min_value, timezone) || !set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, StringRef(max.data(), max.size()), - &column_statistics->max_value)) { + &column_statistics->max_value, timezone)) { return false; } return true; @@ -587,7 +592,8 @@ const ParquetColumnSchema* ParquetStatisticsUtils::ResolvePredicateLeafSchema( ParquetColumnStatistics ParquetStatisticsUtils::TransformColumnStatistics( const ParquetColumnSchema& column_schema, - const std::shared_ptr<::parquet::Statistics>& statistics) { + const std::shared_ptr<::parquet::Statistics>& statistics, + const cctz::time_zone* timezone) { ParquetColumnStatistics result; if (statistics == nullptr) { return result; @@ -604,29 +610,29 @@ ParquetColumnStatistics ParquetStatisticsUtils::TransformColumnStatistics( switch (statistics->physical_type()) { case ::parquet::Type::BOOLEAN: result.has_min_max = set_decoded_min_max<::parquet::BooleanType>( - statistics, column_schema, DecodedValueKind::BOOL, &result); + statistics, column_schema, DecodedValueKind::BOOL, &result, timezone); return result; case ::parquet::Type::INT32: result.has_min_max = set_decoded_min_max<::parquet::Int32Type>( statistics, column_schema, decoded_value_kind(column_schema.type_descriptor), - &result); + &result, timezone); return result; case ::parquet::Type::INT64: result.has_min_max = set_decoded_min_max<::parquet::Int64Type>( statistics, column_schema, decoded_value_kind(column_schema.type_descriptor), - &result); + &result, timezone); return result; case ::parquet::Type::FLOAT: result.has_min_max = set_decoded_min_max<::parquet::FloatType>( - statistics, column_schema, DecodedValueKind::FLOAT, &result); + statistics, column_schema, DecodedValueKind::FLOAT, &result, timezone); return result; case ::parquet::Type::DOUBLE: result.has_min_max = set_decoded_min_max<::parquet::DoubleType>( - statistics, column_schema, DecodedValueKind::DOUBLE, &result); + statistics, column_schema, DecodedValueKind::DOUBLE, &result, timezone); return result; case ::parquet::Type::BYTE_ARRAY: case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: - result.has_min_max = set_string_min_max(statistics, column_schema, &result); + result.has_min_max = set_string_min_max(statistics, column_schema, &result, timezone); return result; default: return result; @@ -657,7 +663,8 @@ bool ParquetStatisticsUtils::CheckStatistics(const format::FileColumnPredicateFi ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter) { + const format::FileColumnPredicateFilter& column_filter, + const cctz::time_zone* timezone) { if (column_filter.predicates.empty()) { return ParquetRowGroupPruneReason::NONE; } @@ -671,7 +678,8 @@ ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( return ParquetRowGroupPruneReason::NONE; } if (CheckStatistics(column_filter, - TransformColumnStatistics(*column_schema, column_chunk->statistics()))) { + TransformColumnStatistics(*column_schema, column_chunk->statistics(), + timezone))) { return ParquetRowGroupPruneReason::STATISTICS; } if (!supports_dictionary_pruning(*column_schema, *column_chunk, column_filter) || @@ -694,16 +702,18 @@ ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( bool ParquetStatisticsUtils::RowGroupExcludes( const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter) { - return RowGroupPruneReason(row_group, file_reader, row_group_idx, schema, column_filter) != - ParquetRowGroupPruneReason::NONE; + const format::FileColumnPredicateFilter& column_filter, + const cctz::time_zone* timezone) { + return RowGroupPruneReason(row_group, file_reader, row_group_idx, schema, column_filter, + timezone) != ParquetRowGroupPruneReason::NONE; } Status ParquetStatisticsUtils::SelectRowGroups( const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats) { + bool enable_bloom_filter, ParquetPruningStats* pruning_stats, + const cctz::time_zone* timezone) { int64_t row_group_filter_time_sink = 0; SCOPED_RAW_TIMER(pruning_stats == nullptr ? &row_group_filter_time_sink : &pruning_stats->row_group_filter_time); @@ -736,7 +746,7 @@ Status ParquetStatisticsUtils::SelectRowGroups( } for (const auto& column_filter : request.column_predicate_filters) { const auto prune_reason = RowGroupPruneReason(*row_group, file_reader, row_group_idx, - file_schema, column_filter); + file_schema, column_filter, timezone); auto effective_prune_reason = prune_reason; if (effective_prune_reason == ParquetRowGroupPruneReason::NONE && enable_bloom_filter) { effective_prune_reason = @@ -805,10 +815,11 @@ Status select_row_groups_by_statistics( const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats) { + bool enable_bloom_filter, ParquetPruningStats* pruning_stats, + const cctz::time_zone* timezone) { return ParquetStatisticsUtils::SelectRowGroups(metadata, file_reader, file_schema, request, selected_row_groups, enable_bloom_filter, - pruning_stats); + pruning_stats, timezone); } namespace { @@ -817,7 +828,8 @@ template bool set_page_decoded_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, const ParquetColumnSchema& column_schema, size_t page_idx, DecodedValueKind value_kind, - ParquetColumnStatistics* page_statistics) { + ParquetColumnStatistics* page_statistics, + const cctz::time_zone* timezone) { const auto typed_index = std::static_pointer_cast<::parquet::TypedColumnIndex>(column_index); if (page_idx >= typed_index->min_values().size() || @@ -825,9 +837,9 @@ bool set_page_decoded_min_max(const std::shared_ptr<::parquet::ColumnIndex>& col return false; } if (!set_decoded_field(column_schema, value_kind, typed_index->min_values()[page_idx], - &page_statistics->min_value) || + &page_statistics->min_value, timezone) || !set_decoded_field(column_schema, value_kind, typed_index->max_values()[page_idx], - &page_statistics->max_value)) { + &page_statistics->max_value, timezone)) { return false; } page_statistics->has_min_max = true; @@ -836,7 +848,8 @@ bool set_page_decoded_min_max(const std::shared_ptr<::parquet::ColumnIndex>& col bool set_page_string_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, const ParquetColumnSchema& column_schema, size_t page_idx, - ParquetColumnStatistics* page_statistics) { + ParquetColumnStatistics* page_statistics, + const cctz::time_zone* timezone) { switch (column_schema.descriptor->physical_type()) { case ::parquet::Type::BYTE_ARRAY: { const auto typed_index = @@ -849,10 +862,10 @@ bool set_page_string_min_max(const std::shared_ptr<::parquet::ColumnIndex>& colu const auto max = ::parquet::ByteArrayToString(typed_index->max_values()[page_idx]); if (!set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, StringRef(min.data(), min.size()), - &page_statistics->min_value) || + &page_statistics->min_value, timezone) || !set_decoded_binary_field(column_schema, DecodedValueKind::BINARY, StringRef(max.data(), max.size()), - &page_statistics->max_value)) { + &page_statistics->max_value, timezone)) { return false; } page_statistics->has_min_max = true; @@ -876,10 +889,10 @@ bool set_page_string_min_max(const std::shared_ptr<::parquet::ColumnIndex>& colu type_length); if (!set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, StringRef(min.data(), min.size()), - &page_statistics->min_value) || + &page_statistics->min_value, timezone) || !set_decoded_binary_field(column_schema, DecodedValueKind::FIXED_BINARY, StringRef(max.data(), max.size()), - &page_statistics->max_value)) { + &page_statistics->max_value, timezone)) { return false; } page_statistics->has_min_max = true; @@ -892,29 +905,34 @@ bool set_page_string_min_max(const std::shared_ptr<::parquet::ColumnIndex>& colu bool set_page_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, const ParquetColumnSchema& column_schema, size_t page_idx, - ParquetColumnStatistics* page_statistics) { + ParquetColumnStatistics* page_statistics, + const cctz::time_zone* timezone) { DORIS_CHECK(column_schema.type != nullptr); switch (column_schema.descriptor->physical_type()) { case ::parquet::Type::BOOLEAN: return set_page_decoded_min_max<::parquet::BooleanType>( - column_index, column_schema, page_idx, DecodedValueKind::BOOL, page_statistics); + column_index, column_schema, page_idx, DecodedValueKind::BOOL, page_statistics, + timezone); case ::parquet::Type::INT32: return set_page_decoded_min_max<::parquet::Int32Type>( column_index, column_schema, page_idx, - decoded_value_kind(column_schema.type_descriptor), page_statistics); + decoded_value_kind(column_schema.type_descriptor), page_statistics, timezone); case ::parquet::Type::INT64: return set_page_decoded_min_max<::parquet::Int64Type>( column_index, column_schema, page_idx, - decoded_value_kind(column_schema.type_descriptor), page_statistics); + decoded_value_kind(column_schema.type_descriptor), page_statistics, timezone); case ::parquet::Type::FLOAT: return set_page_decoded_min_max<::parquet::FloatType>( - column_index, column_schema, page_idx, DecodedValueKind::FLOAT, page_statistics); + column_index, column_schema, page_idx, DecodedValueKind::FLOAT, page_statistics, + timezone); case ::parquet::Type::DOUBLE: return set_page_decoded_min_max<::parquet::DoubleType>( - column_index, column_schema, page_idx, DecodedValueKind::DOUBLE, page_statistics); + column_index, column_schema, page_idx, DecodedValueKind::DOUBLE, page_statistics, + timezone); case ::parquet::Type::BYTE_ARRAY: case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: - return set_page_string_min_max(column_index, column_schema, page_idx, page_statistics); + return set_page_string_min_max(column_index, column_schema, page_idx, page_statistics, + timezone); default: return false; } @@ -922,7 +940,8 @@ bool set_page_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_inde bool build_page_statistics(const std::shared_ptr<::parquet::ColumnIndex>& column_index, const ParquetColumnSchema& column_schema, size_t page_idx, - ParquetColumnStatistics* page_statistics) { + ParquetColumnStatistics* page_statistics, + const cctz::time_zone* timezone) { DORIS_CHECK(page_statistics != nullptr); *page_statistics = ParquetColumnStatistics {}; @@ -938,7 +957,7 @@ bool build_page_statistics(const std::shared_ptr<::parquet::ColumnIndex>& column if (!page_statistics->has_not_null) { return true; } - return set_page_min_max(column_index, column_schema, page_idx, page_statistics); + return set_page_min_max(column_index, column_schema, page_idx, page_statistics, timezone); } std::vector intersect_ranges(const std::vector& left, @@ -1003,7 +1022,8 @@ void append_row_range(const RowRange& range, std::vector* ranges) { bool select_ranges_for_filter(const std::shared_ptr<::parquet::RowGroupPageIndexReader>& row_group, const std::vector>& file_schema, const format::FileColumnPredicateFilter& column_filter, - int64_t row_group_rows, std::vector* ranges) { + int64_t row_group_rows, std::vector* ranges, + const cctz::time_zone* timezone) { if (column_filter.predicates.empty()) { return false; } @@ -1032,7 +1052,8 @@ bool select_ranges_for_filter(const std::shared_ptr<::parquet::RowGroupPageIndex const auto page_count = offset_index->page_locations().size(); for (size_t page_idx = 0; page_idx < page_count; ++page_idx) { ParquetColumnStatistics page_statistics; - if (!build_page_statistics(column_index, *column_schema, page_idx, &page_statistics)) { + if (!build_page_statistics(column_index, *column_schema, page_idx, &page_statistics, + timezone)) { ranges->clear(); return false; } @@ -1188,7 +1209,7 @@ Status select_row_group_ranges_by_page_index( const std::vector>& file_schema, const format::FileScanRequest& request, int row_group_idx, int64_t row_group_rows, std::vector* selected_ranges, std::map* page_skip_plans, - ParquetPruningStats* pruning_stats) { + ParquetPruningStats* pruning_stats, const cctz::time_zone* timezone) { int64_t page_index_filter_time_sink = 0; SCOPED_RAW_TIMER(pruning_stats == nullptr ? &page_index_filter_time_sink : &pruning_stats->page_index_filter_time); @@ -1234,7 +1255,7 @@ Status select_row_group_ranges_by_page_index( for (const auto& column_filter : request.column_predicate_filters) { std::vector filter_ranges; if (!select_ranges_for_filter(row_group_index_reader, file_schema, column_filter, - row_group_rows, &filter_ranges)) { + row_group_rows, &filter_ranges, timezone)) { continue; } *selected_ranges = intersect_ranges(*selected_ranges, filter_ranges); diff --git a/be/src/format_v2/parquet/parquet_statistics.h b/be/src/format_v2/parquet/parquet_statistics.h index 846023d5f77ba6..6cb58cfab4a02c 100644 --- a/be/src/format_v2/parquet/parquet_statistics.h +++ b/be/src/format_v2/parquet/parquet_statistics.h @@ -35,6 +35,10 @@ class RowGroupMetaData; class Statistics; } // namespace parquet +namespace cctz { +class time_zone; +} // namespace cctz + namespace doris { class ColumnPredicate; } // namespace doris @@ -91,7 +95,8 @@ struct ParquetStatisticsUtils { static ParquetColumnStatistics TransformColumnStatistics( const ParquetColumnSchema& column_schema, - const std::shared_ptr<::parquet::Statistics>& statistics); + const std::shared_ptr<::parquet::Statistics>& statistics, + const cctz::time_zone* timezone = nullptr); // Return true if the statistics indicate that the row group can be safely skipped according to // the local single-column predicate filter. @@ -101,18 +106,21 @@ struct ParquetStatisticsUtils { static ParquetRowGroupPruneReason RowGroupPruneReason( const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter); + const format::FileColumnPredicateFilter& column_filter, + const cctz::time_zone* timezone = nullptr); static bool RowGroupExcludes(const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter); + const format::FileColumnPredicateFilter& column_filter, + const cctz::time_zone* timezone = nullptr); static Status SelectRowGroups( const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats); + bool enable_bloom_filter, ParquetPruningStats* pruning_stats, + const cctz::time_zone* timezone = nullptr); static bool BloomFilterSupported(const ParquetColumnSchema& column_schema); @@ -129,13 +137,14 @@ Status select_row_groups_by_statistics( const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats); + bool enable_bloom_filter, ParquetPruningStats* pruning_stats, + const cctz::time_zone* timezone = nullptr); Status select_row_group_ranges_by_page_index( ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, const format::FileScanRequest& request, int row_group_idx, int64_t row_group_rows, std::vector* selected_ranges, std::map* page_skip_plans, - ParquetPruningStats* pruning_stats); + ParquetPruningStats* pruning_stats, const cctz::time_zone* timezone = nullptr); } // namespace doris::parquet diff --git a/be/src/format_v2/table/hive_reader.cpp b/be/src/format_v2/table/hive_reader.cpp index cfc90217542b6a..152f2496e0af53 100644 --- a/be/src/format_v2/table/hive_reader.cpp +++ b/be/src/format_v2/table/hive_reader.cpp @@ -62,7 +62,6 @@ bool is_file_column_position_slot(const TFileScanSlotInfo& slot_info, } // namespace Status HiveReader::init(format::TableReadOptions&& options) { - const bool allow_missing_columns = options.allow_missing_columns; const format::FileFormat file_format = options.format; RETURN_IF_ERROR(format::TableReader::init(std::move(options))); @@ -89,7 +88,6 @@ Status HiveReader::init(format::TableReadOptions&& options) { _mode = use_column_names ? format::TableColumnMappingMode::BY_NAME : format::TableColumnMappingMode::BY_INDEX; - _mapper_options.allow_missing_columns = allow_missing_columns; return Status::OK(); } diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index e2d5a84d6f107f..fff4a1eb341ae3 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -480,7 +480,6 @@ Status TableReader::init(TableReadOptions&& options) { _projected_columns = std::move(options.projected_columns); _system_properties = create_system_properties(_scan_params); _mapper_options.mode = TableColumnMappingMode::BY_NAME; - _mapper_options.allow_missing_columns = options.allow_missing_columns; _conjuncts = std::move(options.conjuncts); _table_column_predicates = std::move(options.column_predicates); diff --git a/be/src/format_v2/table_reader.h b/be/src/format_v2/table_reader.h index 790ef0c0291862..a98d99b8c34361 100644 --- a/be/src/format_v2/table_reader.h +++ b/be/src/format_v2/table_reader.h @@ -51,6 +51,7 @@ #include "exprs/vslot_ref.h" #include "format_v2/column_data.h" #include "format_v2/column_mapper.h" +#include "format_v2/expr/cast.h" #include "format_v2/expr/delete_predicate.h" #include "format_v2/file_reader.h" #include "format_v2/parquet/reader/column_reader.h" @@ -117,7 +118,6 @@ struct TableReadOptions { std::shared_ptr io_ctx; RuntimeState* runtime_state; RuntimeProfile* scanner_profile; - const bool allow_missing_columns = true; // Push-down aggregate type. const TPushAggOp::type push_down_agg_type = TPushAggOp::type::NONE; }; @@ -709,6 +709,57 @@ class TableReader { return Status::OK(); } + Status _cast_column_to_type(ColumnPtr* column, const DataTypePtr& file_type, + const DataTypePtr& table_type, + const std::string& column_name) const { + DORIS_CHECK(column != nullptr); + DORIS_CHECK(column->get() != nullptr); + DORIS_CHECK(file_type != nullptr); + DORIS_CHECK(table_type != nullptr); + if (file_type->equals(*table_type)) { + return Status::OK(); + } + + DataTypePtr input_type = file_type; + if ((*column)->is_nullable() && !input_type->is_nullable()) { + input_type = make_nullable(input_type); + } + Block cast_block; + cast_block.insert({*column, input_type, column_name}); + auto slot_ref = VSlotRef::create_shared(0, 0, -1, input_type, column_name); + auto cast_expr = Cast::create_shared(table_type); + cast_expr->add_child(std::move(slot_ref)); + auto cast_ctx = VExprContext::create_shared(std::move(cast_expr)); + RowDescriptor row_desc; + RETURN_IF_ERROR(cast_ctx->prepare(_runtime_state, row_desc)); + RETURN_IF_ERROR(cast_ctx->open(_runtime_state)); + ColumnPtr cast_column; + RETURN_IF_ERROR(cast_ctx->execute(&cast_block, cast_column)); + *column = std::move(cast_column); + return Status::OK(); + } + + Status _materialize_present_child_mapping_column(const ColumnMapping& mapping, + const ColumnPtr& file_column, + const size_t rows, ColumnPtr* column) { + DORIS_CHECK(column != nullptr); + DORIS_CHECK(mapping.file_type != nullptr); + DORIS_CHECK(mapping.table_type != nullptr); + *column = file_column; + if (!mapping.is_trivial) { + if (!mapping.child_mappings.empty()) { + RETURN_IF_ERROR( + _materialize_complex_mapping_column(mapping, *column, rows, column)); + } else { + RETURN_IF_ERROR(_cast_column_to_type(column, mapping.file_type, + mapping.table_type, + mapping.file_column_name)); + } + } + RETURN_IF_ERROR(_align_column_nullability(column, mapping.table_type)); + return Status::OK(); + } + Status _materialize_mapping_column(const ColumnMapping& mapping, Block* current_block, const size_t rows, ColumnPtr* column) { if (!mapping.is_trivial && mapping.file_local_id.has_value() && @@ -853,10 +904,8 @@ class TableReader { _file_child_ordinal_for_mapping(mapping, child_mapping, file_ordered_children); DORIS_CHECK(file_child_idx < file_struct->get_columns().size()); ColumnPtr child_column = file_struct->get_column_ptr(file_child_idx); - if (!child_mapping.is_trivial && !child_mapping.child_mappings.empty()) { - RETURN_IF_ERROR(_materialize_complex_mapping_column(child_mapping, child_column, - rows, &child_column)); - } + RETURN_IF_ERROR(_materialize_present_child_mapping_column( + child_mapping, child_column, rows, &child_column)); child_columns.push_back(std::move(child_column)); } MutableColumns mutable_child_columns; @@ -893,10 +942,8 @@ class TableReader { const auto* file_array = assert_cast(nested_file_column); ColumnPtr nested_column = file_array->get_data_ptr(); const auto& element_mapping = mapping.child_mappings[0]; - if (!element_mapping.is_trivial && !element_mapping.child_mappings.empty()) { - RETURN_IF_ERROR(_materialize_complex_mapping_column( - element_mapping, nested_column, nested_column->size(), &nested_column)); - } + RETURN_IF_ERROR(_materialize_present_child_mapping_column( + element_mapping, nested_column, nested_column->size(), &nested_column)); auto offsets_column = file_array->get_offsets_ptr()->convert_to_full_column_if_const(); auto result = ColumnArray::create(IColumn::mutate(std::move(nested_column)), IColumn::mutate(std::move(offsets_column))); @@ -946,14 +993,12 @@ class TableReader { } } - if (key_mapping != nullptr && !key_mapping->is_trivial && - !key_mapping->child_mappings.empty()) { - RETURN_IF_ERROR(_materialize_complex_mapping_column(*key_mapping, key_column, - key_column->size(), &key_column)); + if (key_mapping != nullptr) { + RETURN_IF_ERROR(_materialize_present_child_mapping_column( + *key_mapping, key_column, key_column->size(), &key_column)); } - if (value_mapping != nullptr && !value_mapping->is_trivial && - !value_mapping->child_mappings.empty()) { - RETURN_IF_ERROR(_materialize_complex_mapping_column( + if (value_mapping != nullptr) { + RETURN_IF_ERROR(_materialize_present_child_mapping_column( *value_mapping, value_column, value_column->size(), &value_column)); } auto offsets_column = file_map->get_offsets_ptr()->convert_to_full_column_if_const(); diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index c2eb660ea30910..b6bd9203b7b95f 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -619,6 +619,137 @@ TEST(FileScannerV2Test, BuildNestedChildrenKeepsTopLevelProjectionWhole) { EXPECT_TRUE(column.children.empty()); } +TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn) { + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + + const auto struct_type = std::make_shared( + DataTypes {string_type, string_type}, Strings {"country", "city"}); + format::ColumnDefinition struct_column { + .identifier = Field::create_field(100), + .name = "struct_column", + .type = struct_type, + }; + format::ColumnDefinition struct_schema { + .identifier = Field::create_field(100), + .name = "struct_column", + .type = struct_type, + .children = + { + {.identifier = Field::create_field(101), + .name = "country", + .name_mapping = {"old_country"}, + .type = string_type}, + {.identifier = Field::create_field(205), + .name = "city", + .name_mapping = {"old_city"}, + .type = string_type}, + }, + }; + std::vector struct_paths {data_access_path({"struct_column"})}; + auto status = FileScannerV2::TEST_build_nested_children_from_access_paths( + &struct_column, struct_paths, &struct_schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(struct_column.children.size(), 2); + EXPECT_EQ(struct_column.children[0].name, "country"); + EXPECT_EQ(struct_column.children[0].get_identifier_field_id(), 101); + EXPECT_EQ(struct_column.children[0].name_mapping, std::vector({"old_country"})); + EXPECT_EQ(struct_column.children[1].name, "city"); + EXPECT_EQ(struct_column.children[1].get_identifier_field_id(), 205); + EXPECT_EQ(struct_column.children[1].name_mapping, std::vector({"old_city"})); + + const auto element_type = std::make_shared( + DataTypes {string_type, int_type}, Strings {"item", "quantity"}); + const auto array_type = std::make_shared(element_type); + format::ColumnDefinition array_column { + .identifier = Field::create_field(200), + .name = "array_column", + .type = array_type, + }; + format::ColumnDefinition array_schema { + .identifier = Field::create_field(200), + .name = "array_column", + .type = array_type, + .children = + { + {.identifier = Field::create_field(201), + .name = "element", + .type = element_type, + .children = + { + {.identifier = Field::create_field(202), + .name = "item", + .name_mapping = {"old_item"}, + .type = string_type}, + {.identifier = Field::create_field(203), + .name = "quantity", + .type = int_type}, + }}, + }, + }; + std::vector array_paths {data_access_path({"array_column"})}; + status = FileScannerV2::TEST_build_nested_children_from_access_paths( + &array_column, array_paths, &array_schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(array_column.children.size(), 1); + const auto& element = array_column.children[0]; + EXPECT_EQ(element.name, "element"); + EXPECT_EQ(element.get_identifier_field_id(), 201); + ASSERT_EQ(element.children.size(), 2); + EXPECT_EQ(element.children[0].get_identifier_field_id(), 202); + EXPECT_EQ(element.children[0].name_mapping, std::vector({"old_item"})); + EXPECT_EQ(element.children[1].get_identifier_field_id(), 203); + + const auto value_type = std::make_shared( + DataTypes {string_type, int_type}, Strings {"full_name", "age"}); + const auto map_type = std::make_shared(string_type, value_type); + format::ColumnDefinition map_column { + .identifier = Field::create_field(300), + .name = "new_map_column", + .type = map_type, + }; + format::ColumnDefinition map_schema { + .identifier = Field::create_field(300), + .name = "new_map_column", + .type = map_type, + .children = + { + {.identifier = Field::create_field(301), + .name = "key", + .type = string_type}, + {.identifier = Field::create_field(302), + .name = "value", + .type = value_type, + .children = + { + {.identifier = Field::create_field(303), + .name = "full_name", + .name_mapping = {"name"}, + .type = string_type}, + {.identifier = Field::create_field(304), + .name = "age", + .type = int_type}, + }}, + }, + }; + std::vector map_paths {data_access_path({"new_map_column"})}; + status = FileScannerV2::TEST_build_nested_children_from_access_paths(&map_column, map_paths, + &map_schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(map_column.children.size(), 1); + const auto& entries = map_column.children[0]; + EXPECT_EQ(entries.name, "entries"); + ASSERT_EQ(entries.children.size(), 2); + EXPECT_EQ(entries.children[0].name, "key"); + EXPECT_EQ(entries.children[0].get_identifier_field_id(), 301); + EXPECT_EQ(entries.children[1].name, "value"); + EXPECT_EQ(entries.children[1].get_identifier_field_id(), 302); + ASSERT_EQ(entries.children[1].children.size(), 2); + EXPECT_EQ(entries.children[1].children[0].get_identifier_field_id(), 303); + EXPECT_EQ(entries.children[1].children[0].name_mapping, std::vector({"name"})); + EXPECT_EQ(entries.children[1].children[1].get_identifier_field_id(), 304); +} + TEST(FileScannerV2Test, IcebergRowidSkipsNegativeAccessPath) { const auto string_type = std::make_shared(); const auto rowid_type = std::make_shared( diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index 7d9727f18e1da9..ae5183cb81b790 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -876,6 +876,98 @@ TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForRenamedColumn) { expect_mapping(mapper.mappings()[0], 0, "current_id", 0, "legacy_id", int_type, int_type); } +TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForNestedSchemaEvolution) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_country = name_col("country", string_type); + table_country.name_mapping = {"old_country"}; + auto table_city = name_col("city", string_type); + auto table_struct = struct_name_col("struct_column", {table_country, table_city}); + set_name_identifiers(&table_struct, -1); + + auto table_item = name_col("item", string_type); + table_item.name_mapping = {"product"}; + auto table_quantity = name_col("quantity", int_type); + auto table_element = struct_name_col("element", {table_item, table_quantity}); + auto table_array = array_col("array_column", -1, table_element); + set_name_identifiers(&table_array, -1); + + auto table_key = name_col("key", string_type); + auto table_full_name = name_col("full_name", string_type); + table_full_name.name_mapping = {"name"}; + auto table_age = name_col("age", int_type); + auto table_value = struct_name_col("value", {table_full_name, table_age}); + auto table_entry = struct_name_col("entries", {table_key, table_value}); + auto table_map = map_col("new_map_column", -1, table_entry, string_type, table_value.type); + table_map.name_mapping = {"map_column"}; + set_name_identifiers(&table_map, -1); + + auto file_old_country = name_col("old_country", string_type, 0); + auto file_city = name_col("city", string_type, 1); + auto file_struct = struct_name_col("struct_column", {file_old_country, file_city}, 3); + set_name_identifiers(&file_struct, 3); + + auto file_product = name_col("product", string_type, 0); + auto file_element = struct_name_col("list", {file_product}, 0); + auto file_array = array_col("array_column", -1, file_element, 4); + set_name_identifiers(&file_array, 4); + + auto file_key = name_col("key", string_type, 0); + auto file_name = name_col("name", string_type, 0); + auto file_age = name_col("age", int_type, 1); + auto file_value = struct_name_col("value", {file_name, file_age}, 1); + auto file_entry = struct_name_col("key_value", {file_key, file_value}, 0); + auto file_map = map_col("map_column", -1, file_entry, string_type, file_value.type, 5); + set_name_identifiers(&file_map, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct, table_array, table_map}, {}, + {file_struct, file_array, file_map}) + .ok()); + + ASSERT_EQ(mapper.mappings().size(), 3); + const auto& struct_mapping = mapper.mappings()[0]; + expect_mapping(struct_mapping, 0, "struct_column", 3, "struct_column", file_struct.type, + table_struct.type); + ASSERT_EQ(struct_mapping.child_mappings.size(), 2); + EXPECT_EQ(struct_mapping.child_mappings[0].file_column_name, "old_country"); + EXPECT_EQ(*struct_mapping.child_mappings[0].file_local_id, 0); + EXPECT_EQ(struct_mapping.child_mappings[1].file_column_name, "city"); + EXPECT_EQ(*struct_mapping.child_mappings[1].file_local_id, 1); + + const auto& array_mapping = mapper.mappings()[1]; + expect_mapping(array_mapping, 1, "array_column", 4, "array_column", file_array.type, + table_array.type); + ASSERT_EQ(array_mapping.child_mappings.size(), 1); + const auto& element_mapping = array_mapping.child_mappings[0]; + EXPECT_EQ(element_mapping.file_column_name, "list"); + EXPECT_EQ(*element_mapping.file_local_id, 0); + ASSERT_EQ(element_mapping.child_mappings.size(), 2); + EXPECT_EQ(element_mapping.child_mappings[0].file_column_name, "product"); + EXPECT_EQ(*element_mapping.child_mappings[0].file_local_id, 0); + expect_missing(element_mapping.child_mappings[1]); + + const auto& map_mapping = mapper.mappings()[2]; + expect_mapping(map_mapping, 2, "new_map_column", 5, "map_column", file_map.type, + table_map.type); + ASSERT_EQ(map_mapping.child_mappings.size(), 1); + const auto& entry_mapping = map_mapping.child_mappings[0]; + EXPECT_EQ(entry_mapping.file_column_name, "key_value"); + EXPECT_EQ(*entry_mapping.file_local_id, 0); + ASSERT_EQ(entry_mapping.child_mappings.size(), 2); + EXPECT_EQ(entry_mapping.child_mappings[0].file_column_name, "key"); + EXPECT_EQ(*entry_mapping.child_mappings[0].file_local_id, 0); + const auto& value_mapping = entry_mapping.child_mappings[1]; + EXPECT_EQ(value_mapping.file_column_name, "value"); + EXPECT_EQ(*value_mapping.file_local_id, 1); + ASSERT_EQ(value_mapping.child_mappings.size(), 2); + EXPECT_EQ(value_mapping.child_mappings[0].file_column_name, "name"); + EXPECT_EQ(*value_mapping.child_mappings[0].file_local_id, 0); + EXPECT_EQ(value_mapping.child_mappings[1].file_column_name, "age"); + EXPECT_EQ(*value_mapping.child_mappings[1].file_local_id, 1); +} + TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDuplicate) { const auto int_type = i32(); const std::vector table_schema = { @@ -891,7 +983,7 @@ TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDu }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = true}); + {.mode = TableColumnMappingMode::BY_FIELD_ID}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -900,7 +992,7 @@ TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDu expect_mapping(mapper.mappings()[2], 2, "negative", 3, "negative_file", int_type, int_type); } -TEST(ColumnMapperCreateMappingTest, ByFieldIdRejectsSameNameWithDifferentFieldId) { +TEST(ColumnMapperCreateMappingTest, ByFieldIdTreatsSameNameDifferentFieldIdAsMissing) { const auto int_type = i32(); const std::vector table_schema = { field_id_col("same_name", 10, int_type), @@ -910,12 +1002,15 @@ TEST(ColumnMapperCreateMappingTest, ByFieldIdRejectsSameNameWithDifferentFieldId }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + {.mode = TableColumnMappingMode::BY_FIELD_ID}); const auto status = mapper.create_mapping(table_schema, {}, file_schema); - EXPECT_FALSE(status.ok()); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_missing(mapper.mappings()[0]); } -TEST(ColumnMapperCreateMappingTest, NestedFieldIdRejectsSameNameWithDifferentFieldId) { +TEST(ColumnMapperCreateMappingTest, NestedFieldIdTreatsSameNameDifferentFieldIdAsMissing) { const auto int_type = i32(); auto table_child = field_id_col("child", 10, int_type); auto table_root = struct_col("root", 1, {table_child}); @@ -924,9 +1019,14 @@ TEST(ColumnMapperCreateMappingTest, NestedFieldIdRejectsSameNameWithDifferentFie auto file_root = struct_col("root", 1, {file_child}, 0); TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + {.mode = TableColumnMappingMode::BY_FIELD_ID}); const auto status = mapper.create_mapping({table_root}, {}, {file_root}); - EXPECT_FALSE(status.ok()); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "root", 0, "root", file_root.type, table_root.type); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 1); + expect_missing(mapper.mappings()[0].child_mappings[0]); } TEST(ColumnMapperCreateMappingTest, ByIndexMapsTopLevelColumnsByPositionIgnoringFileNames) { @@ -1017,7 +1117,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFallsBackToDefaultOrMissing }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_INDEX, .allow_missing_columns = true}); + {.mode = TableColumnMappingMode::BY_INDEX}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1043,7 +1143,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexMissingIdentifierFallsBackToDefaultOr }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_INDEX, .allow_missing_columns = true}); + {.mode = TableColumnMappingMode::BY_INDEX}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1053,7 +1153,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexMissingIdentifierFallsBackToDefaultOr expect_missing(mapper.mappings()[2]); } -TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFailsWhenMissingIsDisallowed) { +TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFallsBackToMissing) { const auto int_type = i32(); const std::vector table_schema = { position_col("a", 0, int_type), @@ -1064,10 +1164,13 @@ TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFailsWhenMissingIsDisallowe }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_INDEX, .allow_missing_columns = false}); + {.mode = TableColumnMappingMode::BY_INDEX}); const auto status = mapper.create_mapping(table_schema, {}, file_schema); - ASSERT_FALSE(status.ok()); - EXPECT_NE(status.to_string().find("file_index=5"), std::string::npos); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 2); + expect_mapping(mapper.mappings()[0], 0, "a", 0, "_col0", int_type, int_type); + expect_missing(mapper.mappings()[1]); } TEST(ColumnMapperCreateMappingTest, ByIndexIgnoresExtraFileColumns) { @@ -1105,13 +1208,15 @@ TEST(ColumnMapperCreateMappingTest, ByIndexIgnoresFileColumnNames) { expect_mapping(mapper.mappings()[0], 0, "a", 20, "b", int_type, int_type); } -TEST(ColumnMapperCreateMappingTest, MissingColumnFailsWhenDisallowed) { +TEST(ColumnMapperCreateMappingTest, MissingColumnFallsBackToMissingMapping) { TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_NAME, .allow_missing_columns = false}); + {.mode = TableColumnMappingMode::BY_NAME}); const auto status = mapper.create_mapping({name_col("missing", i32())}, {}, {name_col("present", i32(), 0)}); - EXPECT_FALSE(status.ok()); - EXPECT_NE(status.to_string().find("global_index=0"), std::string::npos); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_missing(mapper.mappings()[0]); } // ---------------------------------------------------------------------- @@ -1209,7 +1314,7 @@ TEST(ColumnMapperConstantTest, MissingRowLineageDefaultExprStillUsesVirtualMappi }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + {.mode = TableColumnMappingMode::BY_FIELD_ID}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1234,7 +1339,7 @@ TEST(ColumnMapperConstantTest, ByFieldIdDoesNotTreatSameNameDifferentIdAsRowLine }; TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID, .allow_missing_columns = false}); + {.mode = TableColumnMappingMode::BY_FIELD_ID}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 2); diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp index aa17e2ffcfa5f4..dcd5255dcd42ba 100644 --- a/be/test/format_v2/parquet/parquet_column_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -17,6 +17,7 @@ #include #include +#include #include #include #include @@ -979,6 +980,18 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(schema.type->to_string(column, 1), "1970-01-01 00:00:01.234567"); EXPECT_EQ(schema.type->to_string(column, 4), "1969-12-31 23:59:59.999999"); }); + add_field(arrow::field("timestamp_micros_utc_col", + arrow::timestamp(arrow::TimeUnit::MICRO, "UTC"), false), + build_timestamp_array(arrow::timestamp(arrow::TimeUnit::MICRO, "UTC"), + {0, 1234567, 1609459200000000, 1609459201000000, -1}), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + EXPECT_TRUE(schema.type_descriptor.timestamp_is_adjusted_to_utc); + EXPECT_EQ(remove_nullable(schema.type)->get_primitive_type(), + TYPE_DATETIMEV2); + EXPECT_EQ(schema.type->to_string(column, 1), "1970-01-01 00:00:01.234567"); + EXPECT_EQ(schema.type->to_string(column, 4), "1969-12-31 23:59:59.999999"); + }); add_field(arrow::field("decimal_fixed_binary_9_2_col", arrow::decimal128(9, 2), false), build_decimal_array(arrow::decimal128(9, 2), {12345, -67, 0, 987, 1000}), [](const ParquetColumnSchema& schema, const IColumn& column) { @@ -2093,6 +2106,26 @@ TEST_F(ParquetColumnReaderTest, TransformUnsignedIntegerStatistics) { EXPECT_EQ(uint64_max, static_cast(std::numeric_limits::max())); } +TEST_F(ParquetColumnReaderTest, TransformTimestampStatisticsUsesTimezone) { + auto row_group = _file_reader->metadata()->RowGroup(0); + ASSERT_NE(row_group, nullptr); + + const auto field_idx = find_field_idx("timestamp_micros_utc_col"); + const auto& schema = *_fields[field_idx]; + ASSERT_TRUE(schema.type_descriptor.timestamp_is_adjusted_to_utc); + ASSERT_EQ(schema.type_descriptor.physical_type, ::parquet::Type::INT64); + auto chunk = row_group->ColumnChunk(schema.leaf_column_id); + ASSERT_NE(chunk, nullptr); + + cctz::time_zone shanghai; + ASSERT_TRUE(cctz::load_time_zone("Asia/Shanghai", &shanghai)); + auto stats = ParquetStatisticsUtils::TransformColumnStatistics(schema, chunk->statistics(), + &shanghai); + ASSERT_TRUE(stats.has_min_max); + EXPECT_EQ(stats.min_value.to_debug_string(6), "1970-01-01 07:59:59.999999"); + EXPECT_EQ(stats.max_value.to_debug_string(6), "2021-01-01 08:00:01.000000"); +} + TEST_F(ParquetColumnReaderTest, ReadSupportedComplexTypes) { read_and_validate(find_field_idx("struct_col")); read_and_validate(find_field_idx("nullable_struct_col")); diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index 67dcffe09267e9..e2995b4e46145b 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -355,7 +355,6 @@ class IcebergTableReaderScanRequestTestHelper final : public doris::iceberg::Ice .io_ctx = nullptr, .runtime_state = _state.get(), .scanner_profile = nullptr, - .allow_missing_columns = true, })); SplitReadOptions split_options; @@ -1249,6 +1248,31 @@ class FakeFileReader final : public FileReader { column->insert_data("one", 3); column->insert_data("two", 3); file_block->replace_by_position(block_position.value(), std::move(column)); + } else if (file_column_id == LocalColumnId(2)) { + auto country_values = ColumnString::create(); + country_values->insert_data("USA", 3); + country_values->insert_data("UK", 2); + auto country_null_map = ColumnUInt8::create(); + country_null_map->insert_value(0); + country_null_map->insert_value(0); + auto country_column = ColumnNullable::create(std::move(country_values), + std::move(country_null_map)); + + auto city_column = ColumnString::create(); + city_column->insert_data("New York", 8); + city_column->insert_data("London", 6); + + MutableColumns struct_children; + struct_children.push_back(std::move(country_column)); + struct_children.push_back(std::move(city_column)); + auto struct_column = ColumnStruct::create(std::move(struct_children)); + + auto root_null_map = ColumnUInt8::create(); + root_null_map->insert_value(0); + root_null_map->insert_value(0); + auto nullable_struct = + ColumnNullable::create(std::move(struct_column), std::move(root_null_map)); + file_block->replace_by_position(block_position.value(), std::move(nullable_struct)); } else { return Status::InvalidArgument("Unexpected fake file column id {}", file_column_id.value()); @@ -1319,7 +1343,6 @@ TEST(TableReaderTest, CanUseInjectedFileReaderForStandaloneUnitTest) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -1356,6 +1379,70 @@ TEST(TableReaderTest, CanUseInjectedFileReaderForStandaloneUnitTest) { EXPECT_TRUE(eos); } +TEST(TableReaderTest, ComplexRematerializeCastsScalarChildToTableType) { + const auto string_type = std::make_shared(); + const auto nullable_string_type = make_nullable(string_type); + const auto file_struct_type = make_nullable(std::make_shared( + DataTypes {string_type, string_type}, Strings {"country", "city"})); + auto file_struct_column = make_file_column(2, "struct_column", file_struct_type); + file_struct_column.children = {make_file_column(0, "country", string_type), + make_file_column(1, "city", string_type)}; + std::vector file_schema = {file_struct_column}; + + const auto table_struct_type = make_nullable(std::make_shared( + DataTypes {nullable_string_type, nullable_string_type}, Strings {"country", "city"})); + auto country_child = make_table_column(0, "country", nullable_string_type); + auto city_child = make_table_column(1, "city", nullable_string_type); + auto table_struct_column = make_table_column(2, "struct_column", table_struct_type); + table_struct_column.children = {country_child, city_child}; + std::vector projected_columns = {table_struct_column}; + set_name_identifiers(&projected_columns); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto fake_state = std::make_shared(); + FakeTableReader reader(file_schema, fake_state); + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + }) + .ok()); + + SplitReadOptions split_options; + split_options.current_range.__set_path("fake-table-reader-input"); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + const auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status.to_string(); + ASSERT_FALSE(eos); + ASSERT_TRUE(block.check_type_and_column().ok()) << block.dump_structure(); + + const auto& result_nullable = + assert_cast(*block.get_by_position(0).column); + const auto& struct_result = + assert_cast(result_nullable.get_nested_column()); + ASSERT_EQ(struct_result.get_columns().size(), 2); + const auto& country_column = assert_cast(struct_result.get_column(0)); + const auto& city_column = assert_cast(struct_result.get_column(1)); + const auto& country_values = + assert_cast(country_column.get_nested_column()); + const auto& city_values = assert_cast(city_column.get_nested_column()); + ASSERT_EQ(city_column.size(), 2); + EXPECT_FALSE(city_column.is_null_at(0)); + EXPECT_FALSE(city_column.is_null_at(1)); + EXPECT_EQ(country_values.get_data_at(0).to_string(), "USA"); + EXPECT_EQ(country_values.get_data_at(1).to_string(), "UK"); + EXPECT_EQ(city_values.get_data_at(0).to_string(), "New York"); + EXPECT_EQ(city_values.get_data_at(1).to_string(), "London"); +} + TEST(TableReaderTest, ReopenSplitAfterClose) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_test"; std::filesystem::remove_all(test_dir); @@ -1387,7 +1474,6 @@ TEST(TableReaderTest, ReopenSplitAfterClose) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -1450,7 +1536,6 @@ TEST(TableReaderTest, PushDownCountFromNewParquetReader) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -1493,7 +1578,6 @@ TEST(TableReaderTest, TableLevelCountUsesAssignedRowCount) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -1551,7 +1635,6 @@ TEST(TableReaderTest, PushDownMinMaxFromNewParquetReader) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -1598,7 +1681,6 @@ TEST(TableReaderTest, PushDownMinMaxCastsFileValueToTableType) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -1646,7 +1728,6 @@ TEST(TableReaderTest, PushDownMinMaxFromProjectedStructLeaf) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -1699,7 +1780,6 @@ TEST(TableReaderTest, PushDownMinMaxFallsBackForProjectedListStructLeaf) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -1771,7 +1851,6 @@ TEST(TableReaderTest, ProjectedListStructReadsSelectedElementChild) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); @@ -1839,7 +1918,6 @@ TEST(TableReaderTest, ProjectedListStructReordersRenamedAndMissingElementChildre .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); @@ -1918,7 +1996,6 @@ TEST(TableReaderTest, PushDownMinMaxFallsBackForProjectedMapValueStructLeaf) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -2002,7 +2079,6 @@ TEST(TableReaderTest, ProjectedMapValueStructReordersRenamedAndMissingChildren) .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); @@ -2171,7 +2247,6 @@ TEST(TableReaderTest, PushDownMinMaxOnlyUsesSelectedRowGroupInFileRange) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -2214,7 +2289,6 @@ TEST(TableReaderTest, PushDownCountOnlyUsesSelectedRowGroupInFileRange) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -2255,7 +2329,6 @@ TEST(TableReaderTest, PushDownCountFallsBackWithTableConjunct) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -2303,7 +2376,6 @@ TEST(TableReaderTest, PushDownCountFallsBackWithColumnPredicate) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -2346,7 +2418,6 @@ TEST(TableReaderTest, PushDownMinMaxFallsBackWithoutDirectFileMapping) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::MINMAX, }) .ok()); @@ -2389,7 +2460,6 @@ TEST(TableReaderTest, OpenReaderBuildsTableFiltersFromConjuncts) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2422,7 +2492,6 @@ TEST(TableReaderTest, OpenReaderBuildsTableFiltersFromConjuncts) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); ASSERT_TRUE(filtered_reader.prepare_split(build_split_options(file_path)).ok()); @@ -2470,7 +2539,6 @@ TEST(TableReaderTest, OpenReaderBuildsColumnPredicateFilters) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2526,7 +2594,6 @@ TEST(TableReaderTest, ColumnPredicateSurvivesReopenSplit) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2720,7 +2787,6 @@ TEST(TableReaderTest, OpenReaderPushesMultiColumnConjunctToParquetReader) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2773,7 +2839,6 @@ TEST(TableReaderTest, ProjectedColumnsFillDefaultForParquetSchemaMismatch) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2820,7 +2885,6 @@ TEST(TableReaderTest, DefaultExprResultMatchesNullableTableType) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2876,7 +2940,6 @@ TEST(TableReaderTest, DefaultExprAlignsNestedNullableArrayTableType) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -2909,7 +2972,7 @@ TEST(TableReaderTest, DefaultExprAlignsNestedNullableArrayTableType) { std::filesystem::remove_all(test_dir); } -TEST(TableReaderTest, ProjectedColumnsRejectParquetSchemaMismatchWhenMissingColumnsDisallowed) { +TEST(TableReaderTest, ProjectedColumnsFillMissingParquetColumnWithDefault) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_schema_mismatch_reject_test"; std::filesystem::remove_all(test_dir); @@ -2934,19 +2997,22 @@ TEST(TableReaderTest, ProjectedColumnsRejectParquetSchemaMismatchWhenMissingColu .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = false, }) .ok()); ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); - // With allow_missing_columns disabled, the same missing projected column should fail while - // opening the split instead of being materialized as a default column. Block block = build_table_block(projected_columns); bool eos = false; const auto status = reader.get_block(&block, &eos); - ASSERT_FALSE(status.ok()); - EXPECT_NE(status.to_string().find("does not have a matching file column"), std::string::npos); + ASSERT_TRUE(status.ok()) << status.to_string(); + ASSERT_FALSE(eos); + + const auto& result = block.get_by_position(0); + ASSERT_TRUE(result.check_type_and_column_match().ok()); + const auto& missing_values = assert_cast(*result.column); + ASSERT_EQ(missing_values.size(), 1); + EXPECT_EQ(missing_values.get_data_at(0).to_string(), ""); ASSERT_TRUE(reader.close().ok()); std::filesystem::remove_all(test_dir); @@ -2983,7 +3049,6 @@ TEST(TableReaderTest, ProjectedStructFillsMissingChildWithDefault) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3040,7 +3105,6 @@ TEST(TableReaderTest, ReusedBlockClearsProjectedStructWithNullableChild) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3090,7 +3154,6 @@ TEST(TableReaderTest, ProjectedPartitionColumnUsesSplitPartitionValue) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3141,7 +3204,6 @@ TEST(TableReaderTest, ConstantPartitionFilterSkipsSplitWhenFalse) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3186,7 +3248,6 @@ TEST(TableReaderTest, ConstantPartitionFilterKeepsSplitWhenTrue) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3234,7 +3295,6 @@ TEST(TableReaderTest, IcebergVirtualColumnsUseRowLineageMetadata) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3288,7 +3348,6 @@ TEST(TableReaderTest, IcebergRowLineageUsesPhysicalRowIdAndFillsNulls) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3339,7 +3398,6 @@ TEST(TableReaderTest, IcebergPhysicalRowIdKeepsNullsWithoutFirstRowId) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3391,7 +3449,6 @@ TEST(TableReaderTest, IcebergMissingRowIdStaysNullWithoutFirstRowId) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3447,7 +3504,6 @@ TEST(TableReaderTest, IcebergRowIdPredicateFiltersAfterRowLineageMaterialization .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3503,7 +3559,6 @@ TEST(TableReaderTest, IcebergLastUpdatedSequencePredicateFiltersAfterMaterializa .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3553,7 +3608,6 @@ TEST(TableReaderTest, IcebergRowidVirtualColumnUsesDataFilePosition) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3606,7 +3660,6 @@ TEST(TableReaderTest, IcebergVirtualColumnsKeepRowLineageAfterConjunctFiltering) .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3667,7 +3720,6 @@ TEST(TableReaderTest, IcebergVirtualColumnsKeepRowLineageAfterRowGroupPredicateP .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -3759,7 +3811,6 @@ TEST(TableReaderTest, IcebergTableReaderAppliesDeletionVectorFile) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -3807,7 +3858,6 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithDeletes) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -3862,7 +3912,6 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithPositionDele .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -3913,7 +3962,6 @@ TEST(TableReaderTest, IcebergTableLevelCountUsesAssignedRowCountWithPositionDele .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -3970,7 +4018,6 @@ TEST(TableReaderTest, IcebergPositionDeleteFallsBackToSplitPath) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, }) .ok()); @@ -4021,7 +4068,6 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithEqualityDele .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, .push_down_agg_type = TPushAggOp::type::COUNT, }) .ok()); @@ -4076,7 +4122,6 @@ TEST(TableReaderTest, IcebergEqualityDeleteCastsDataColumnToDeleteKeyType) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, }) .ok()); @@ -4124,7 +4169,6 @@ TEST(TableReaderTest, IcebergPositionDeleteOnlyMatchesOriginalDataFilePath) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, }) .ok()); @@ -4173,7 +4217,6 @@ TEST(TableReaderTest, IcebergRowLineageRemainsFileLocalAfterDeleteFiltering) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, }) .ok()); @@ -4231,7 +4274,6 @@ TEST(TableReaderTest, IcebergTableReaderAppliesPositionDeleteFile) { .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, }) .ok()); @@ -4281,7 +4323,6 @@ TEST(TableReaderTest, IcebergTableReaderMergesDeletionVectorAndPositionDeleteFil .io_ctx = io_ctx, .runtime_state = &state, .scanner_profile = &profile, - .allow_missing_columns = true, }) .ok()); @@ -4350,7 +4391,6 @@ TEST(TableReaderTest, ParquetReaderReadsOnlyRowGroupsInFileRange) { .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -4399,7 +4439,6 @@ TEST(TableReaderTest, ProjectedColumnsUseMapperExpressionForSameNameDifferentIdP .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); @@ -4446,7 +4485,6 @@ TEST(TableReaderTest, ProjectedColumnsUseMapperExpressionsForParquetSchemaMismat .io_ctx = nullptr, .runtime_state = &state, .scanner_profile = nullptr, - .allow_missing_columns = true, }) .ok()); From b4948c36736d7beedee64ccb311959bc7316d227 Mon Sep 17 00:00:00 2001 From: Socrates Date: Fri, 12 Jun 2026 18:33:31 +0800 Subject: [PATCH 03/28] [refactor](be) Normalize parquet complex schema projection (#64451) ### What problem does this PR solve? This PR refactors the new parquet reader complex-column path around schema projection and reader creation. It clarifies `ParquetColumnReaderFactory` recursion, keeps schema projection as a single public helper, normalizes file-local `ColumnDefinition` children to Doris semantic children, and folds the Parquet MAP `key_value/entry` wrapper into the MAP schema node during parquet schema construction. With that shape, MAP and LIST both expose direct semantic children to the table/file reader boundary, and ParquetReader no longer needs a semantic-to-physical projection translation layer for MAP. ### Release note None ### Check List - Test: Manual test - Ran `git diff --check`. - Tried `./run-be-ut.sh -j 8 --run --filter="ParquetColumnReaderTest.ReadProjectedMapStructValueChildren:ColumnMapperScanRequestTest.MapValueStructProjectionPrunesValueChildren"`, but local toolchain failed before tests with `ld: library 'c++' not found`. - Behavior changed: No - Does this need documentation: No --- be/src/exec/scan/file_scanner_v2.cpp | 48 +- be/src/format_v2/column_data.h | 7 +- be/src/format_v2/column_mapper.cpp | 103 +-- be/src/format_v2/column_mapper.h | 4 +- be/src/format_v2/file_reader.h | 14 +- .../parquet/parquet_column_schema.cpp | 328 +++++++-- .../format_v2/parquet/parquet_column_schema.h | 12 +- be/src/format_v2/parquet/parquet_reader.cpp | 1 + .../format_v2/parquet/parquet_statistics.cpp | 39 +- .../parquet/reader/column_reader.cpp | 127 ++-- .../format_v2/parquet/reader/column_reader.h | 59 +- .../parquet/reader/list_column_reader.h | 1 - .../parquet/reader/map_column_reader.h | 2 - .../parquet/reader/scalar_column_reader.h | 7 +- .../parquet/reader/struct_column_reader.cpp | 24 +- .../parquet/reader/struct_column_reader.h | 5 +- be/src/format_v2/schema_projection.cpp | 79 ++- be/src/format_v2/schema_projection.h | 42 +- be/src/format_v2/table/iceberg_reader.cpp | 9 +- be/src/format_v2/table_reader.h | 14 +- .../scan/vfile_scanner_exception_test.cpp | 16 +- be/test/format_v2/column_mapper_test.cpp | 104 ++- .../parquet/parquet_column_reader_test.cpp | 225 +++--- .../format_v2/parquet/parquet_reader_test.cpp | 49 +- be/test/format_v2/table_reader_test.cpp | 19 +- docs/parquet-list-map-compat-design.md | 664 ++++++++++++++++++ 26 files changed, 1416 insertions(+), 586 deletions(-) create mode 100644 docs/parquet-list-map-compat-design.md diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp index 0ac4c1e7cf27b2..d99a7dc836d332 100644 --- a/be/src/exec/scan/file_scanner_v2.cpp +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -185,7 +185,7 @@ int32_t schema_field_id_or(const format::ColumnDefinition* schema_column, int32_ std::string schema_field_name_or(const format::ColumnDefinition* schema_column, std::string fallback) { return schema_column == nullptr || schema_column->name.empty() ? std::move(fallback) - : schema_column->name; + : schema_column->name; } struct AccessPathNode { @@ -269,12 +269,12 @@ Status build_all_nested_children_from_schema(format::ColumnDefinition* column, case TYPE_ARRAY: { const auto& array_type = assert_cast(*nested_type); const auto* element_schema = &schema_column->children[0]; - auto* child = find_or_add_child( - column, schema_field_id_or(element_schema, 0), - schema_field_name_or(element_schema, "element"), array_type.get_nested_type()); + auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), + schema_field_name_or(element_schema, "element"), + array_type.get_nested_type()); inherit_schema_metadata(child, element_schema); - return build_nested_children_from_access_node(child, child->type, project_all, - path + ".*", element_schema); + return build_nested_children_from_access_node(child, child->type, project_all, path + ".*", + element_schema); } case TYPE_MAP: { const auto& map_type = assert_cast(*nested_type); @@ -285,20 +285,18 @@ Status build_all_nested_children_from_schema(format::ColumnDefinition* column, Strings entry_child_names {"key", "value"}; auto entry_type = std::make_shared(entry_child_types, entry_child_names); auto* entry_child = find_or_add_child(column, 0, "entries", entry_type); - auto* key_child = find_or_add_child( - entry_child, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + auto* key_child = + find_or_add_child(entry_child, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); inherit_schema_metadata(key_child, key_schema); - RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, - project_all, path + ".KEYS", - key_schema)); - auto* value_child = find_or_add_child( - entry_child, schema_field_id_or(value_schema, 1), - schema_field_name_or(value_schema, "value"), map_type.get_value_type()); + RETURN_IF_ERROR(build_nested_children_from_access_node( + key_child, key_child->type, project_all, path + ".KEYS", key_schema)); + auto* value_child = find_or_add_child(entry_child, schema_field_id_or(value_schema, 1), + schema_field_name_or(value_schema, "value"), + map_type.get_value_type()); inherit_schema_metadata(value_child, value_schema); - RETURN_IF_ERROR(build_nested_children_from_access_node(value_child, value_child->type, - project_all, path + ".VALUES", - value_schema)); + RETURN_IF_ERROR(build_nested_children_from_access_node( + value_child, value_child->type, project_all, path + ".VALUES", value_schema)); return Status::OK(); } default: @@ -428,10 +426,9 @@ Status build_map_children_from_access_node(format::ColumnDefinition* column, path + ".KEYS", key_schema)); } if (need_value) { - auto* value_child = - find_or_add_child(entry_child, schema_field_id_or(value_schema, 1), - schema_field_name_or(value_schema, "value"), - map_type.get_value_type()); + auto* value_child = find_or_add_child(entry_child, schema_field_id_or(value_schema, 1), + schema_field_name_or(value_schema, "value"), + map_type.get_value_type()); inherit_schema_metadata(value_child, value_schema); RETURN_IF_ERROR(build_nested_children_from_access_node( value_child, value_child->type, value_node, path + ".VALUES", value_schema)); @@ -463,10 +460,9 @@ Status build_nested_children_from_access_node(format::ColumnDefinition* column, const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() ? &schema_column->children[0] : nullptr; - auto* child = - find_or_add_child(column, schema_field_id_or(element_schema, 0), - schema_field_name_or(element_schema, "element"), - array_type.get_nested_type()); + auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), + schema_field_name_or(element_schema, "element"), + array_type.get_nested_type()); inherit_schema_metadata(child, element_schema); return build_nested_children_from_access_node(child, child->type, node.children.at("*"), path + ".*", element_schema); diff --git a/be/src/format_v2/column_data.h b/be/src/format_v2/column_data.h index e6b08d549b3524..7816ea8263cb42 100644 --- a/be/src/format_v2/column_data.h +++ b/be/src/format_v2/column_data.h @@ -249,8 +249,11 @@ struct ColumnDefinition { // use this to resolve partition path keys after column rename. std::vector name_mapping {}; DataTypePtr type; - // Projected nested table children. Children use table/global identifiers; they are resolved to - // file-local child ids by TableColumnMapper before reaching FileReader. + // Semantic nested children for this schema node. + // + // Table/global columns carry projected table children. File-local schemas returned by + // FileReader::get_schema() also expose semantic children, not physical reader wrappers. For + // example, MAP children are key/value and ARRAY children contain only the element field. std::vector children {}; // Expression used to materialize missing/default/generated values when the column is not read // directly from the file. diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index a8211265d89f1e..2c89320dee5eb9 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -541,25 +541,6 @@ static void collect_top_level_slot_columns(const VExprSPtr& expr, } } -static std::vector projected_file_children_from_child_mappings( - const std::vector& original_file_children, - const std::vector& child_mappings) { - std::vector result; - result.reserve(child_mappings.size()); - for (const auto* child_mapping : present_child_mappings_in_file_order(child_mappings)) { - const auto file_child_it = std::ranges::find_if( - original_file_children, [&](const ColumnDefinition& file_child) { - return file_child.file_local_id() == *child_mapping->file_local_id; - }); - DORIS_CHECK(file_child_it != original_file_children.end()); - ColumnDefinition projected_child = *file_child_it; - projected_child.type = child_mapping->file_type; - projected_child.children = child_mapping->projected_file_children; - result.push_back(std::move(projected_child)); - } - return result; -} - static VExprSPtr rewrite_literal_to_file_type(const VExprSPtr& literal_expr, const FileSlotRewriteInfo& rewrite_info, RewriteContext* rewrite_context) { @@ -828,27 +809,44 @@ static bool needs_nested_file_projection(const ColumnMapping& mapping) { }); } -// Build the projected file type according to the pruned complex projection. For example, if we -// have a struct column `s` with children `id` and `name`, and the projection only keeps `s.name`, -// then we need to build the projected file type of `s` to only contain `name` so that the file -// reader can read it correctly. -static Status build_projected_child_type(const DataTypePtr& file_type, - const std::vector& child_mappings, - DataTypePtr* projected_type) { +static Status build_complex_projection(const ColumnMapping& mapping, LocalColumnIndex* projection); + +// Build the projected file children/type according to the pruned complex projection. For example, +// if we have a struct column `s` with children `id` and `name`, and the projection only keeps +// `s.name`, then the file reader should expose `STRUCT`. +static Status rebuild_projected_file_children_and_type( + const DataTypePtr& file_type, const std::vector& original_file_children, + const std::vector& child_mappings, + std::vector* projected_file_children, DataTypePtr* projected_type) { DORIS_CHECK(file_type != nullptr); + DORIS_CHECK(projected_file_children != nullptr); DORIS_CHECK(projected_type != nullptr); - DataTypes child_types; - Strings child_names; - child_types.reserve(child_mappings.size()); - child_names.reserve(child_mappings.size()); + ColumnDefinition field; + field.type = file_type; + field.children = original_file_children; + LocalColumnIndex projection = LocalColumnIndex::partial_local(-1); + projection.children.reserve(child_mappings.size()); for (const auto* child_mapping : present_child_mappings_in_file_order(child_mappings)) { - child_types.push_back(child_mapping->file_type); - child_names.push_back(child_mapping->file_column_name); + DORIS_CHECK(child_mapping->file_local_id.has_value()); + LocalColumnIndex child_projection; + RETURN_IF_ERROR(build_complex_projection(*child_mapping, &child_projection)); + projection.children.push_back(std::move(child_projection)); } - return rebuild_projected_type(file_type, child_types, child_names, projected_type); + + ColumnDefinition projected_field; + RETURN_IF_ERROR(project_column_definition(field, projection, &projected_field)); + *projected_file_children = std::move(projected_field.children); + *projected_type = std::move(projected_field.type); + return Status::OK(); } -// Build the complex column projection according to the ColumnMapping which is re-ordered by the file-schema's order. +// Build the complex column projection according to the ColumnMapping which is re-ordered by the +// file-schema's order. +// +// For MAP, a partial projection represents value-subtree pruning only. The key child is not a +// projected output shape; file readers still read full keys to construct ColumnMap offsets and keep +// key semantics unchanged. If a caller tries to project only/prune the key child, the common schema +// projection helper rejects it. static Status build_complex_projection(const ColumnMapping& mapping, LocalColumnIndex* projection) { if (projection == nullptr) { return Status::InvalidArgument("projection is null"); @@ -1109,34 +1107,6 @@ static std::map build_file_slot_rewrite_map( return global_to_file_slot; } -static const ColumnDefinition* find_file_child_by_table_column( - const ColumnDefinition& table_column, const std::vector& file_children, - TableColumnMappingMode mode) { - return matcher_for_mode(mode).find(table_column, file_children); -} - -static const ColumnDefinition* find_file_child_for_complex_wrapper( - const ColumnDefinition& table_child, const ColumnDefinition& file_field, - TableColumnMappingMode mode) { - if (file_field.children.empty()) { - return nullptr; - } - const auto* file_child = - find_file_child_by_table_column(table_child, file_field.children, mode); - if (file_child != nullptr) { - return file_child; - } - if (remove_nullable(file_field.type)->get_primitive_type() == TYPE_ARRAY && - file_field.children.size() == 1) { - return &file_field.children[0]; - } - if (remove_nullable(file_field.type)->get_primitive_type() == TYPE_MAP && - file_field.children.size() == 1 && column_has_name(table_child, "entries")) { - return &file_field.children[0]; - } - return nullptr; -} - Status TableColumnMapper::_create_by_index_mapping(const ColumnDefinition& table_column, const std::vector& file_schema, ColumnMapping* mapping) { @@ -1548,7 +1518,7 @@ Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_c DORIS_CHECK(is_complex_type(mapping->file_type->get_primitive_type())); for (const auto& table_child : table_column.children) { const auto* file_child = - find_file_child_for_complex_wrapper(table_child, file_field, _options.mode); + matcher_for_mode(_options.mode).find(table_child, file_field.children); if (file_child == nullptr) { ColumnMapping child_mapping; child_mapping.table_column_name = table_child.name; @@ -1567,10 +1537,9 @@ Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_c } if (needs_projected_file_type_rebuild(*mapping)) { // If complex projection prunes some children, we have to rebuild the projected file type to make sure the reader expression can find the correct child types by name. - RETURN_IF_ERROR(build_projected_child_type(mapping->file_type, mapping->child_mappings, - &mapping->file_type)); - mapping->projected_file_children = projected_file_children_from_child_mappings( - mapping->original_file_children, mapping->child_mappings); + RETURN_IF_ERROR(rebuild_projected_file_children_and_type( + mapping->file_type, mapping->original_file_children, mapping->child_mappings, + &mapping->projected_file_children, &mapping->file_type)); DCHECK(mapping->table_type != nullptr); mapping->is_trivial = mapping->table_type->equals(*mapping->file_type); // TODO: ? READER_EXPRESSION diff --git a/be/src/format_v2/column_mapper.h b/be/src/format_v2/column_mapper.h index 934f87cb7b5a97..9b864902e1f5ac 100644 --- a/be/src/format_v2/column_mapper.h +++ b/be/src/format_v2/column_mapper.h @@ -130,8 +130,8 @@ struct ColumnMapping { // and to localize nested filters that reference children not present in the output projection. DataTypePtr original_file_type; std::vector original_file_children; - // File children after applying the scan projection. The order follows the physical file schema, - // not table child order. TableReader uses this to map table-output children back to the + // File children after applying the scan projection. The order follows the file-local semantic + // schema, not table child order. TableReader uses this to map table-output children back to the // file-local block layout when projection, predicate-only children, and schema evolution mix. std::vector projected_file_children; // Split/file-local constant entry when this mapping is produced from partition/default/virtual diff --git a/be/src/format_v2/file_reader.h b/be/src/format_v2/file_reader.h index a5e7c936466b20..b516a0c406d929 100644 --- a/be/src/format_v2/file_reader.h +++ b/be/src/format_v2/file_reader.h @@ -212,12 +212,14 @@ class FileReader { // Initialize file reader and parse file metadata. virtual Status init(RuntimeState* state); - // Get file-local schema from file metadata. The file schema is determined by file format and - // file content, and does not contain table/global schema semantics. A file reader may expose - // raw file identifiers, such as Parquet field_id, through ColumnDefinition::identifier, but it - // must not interpret table-format semantics such as Iceberg name mapping, default/generated - // columns, or partition columns. This method can only be called after init() successfully, but - // does not require open() to be called. + // Get semantic file-local schema from file metadata. The file schema is determined by file + // format and file content, and does not contain table/global schema semantics. A file reader may + // expose raw file identifiers, such as Parquet field_id, through ColumnDefinition::identifier, + // but it must not interpret table-format semantics such as Iceberg name mapping, + // default/generated columns, or partition columns. File-format physical wrappers should be + // normalized away before exposing this schema; for example, Parquet MAP is exposed as key/value + // children rather than key_value/entry. This method can only be called after init() + // successfully, but does not require open() to be called. virtual Status get_schema(std::vector* file_schema) const = 0; // Open the file reader with file-local scan request. The file reader should initialize its internal state according to the request, but does not need to interpret table/global schema semantics. For example, all schema change, filter localization, default/generated/partition columns should be handled in table reader layer. This method can only be called after init() successfully. diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp index f127ffc53c55bb..e23f3ffa5616f7 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.cpp +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -43,6 +43,46 @@ struct SchemaBuildContext { int16_t repeated_ancestor_definition_level = 0; }; +enum class SchemaBuildMode { + // Normal recursive schema build. A repeated LIST/MAP annotated group is rejected in this mode + // because Parquet LIST/MAP outer groups are not allowed to be repeated at a top-level or struct + // field boundary. + NORMAL, + // Build the current repeated node as the already-selected element of an enclosing LIST. This + // is the compatibility path for Arrow/parquet-format legacy two-level LIST encodings where the + // repeated node itself is the array element instead of a wrapper that should be stripped. + REPEATED_NODE_AS_LIST_ELEMENT, +}; + +// Result of applying Parquet LIST backward compatibility rules to the single repeated child of a +// LIST-annotated group. The repeated child can either be a physical wrapper whose only child is the +// element, or the element node itself. +struct ListElementResolution { + // Parquet node that should be exposed as Doris ARRAY element. + const ::parquet::schema::Node* element_node = nullptr; + // Level state after consuming the LIST repeated child. The parent ARRAY schema keeps this state + // to materialize offsets, empty arrays and null arrays. + SchemaBuildContext repeated_context; + // Level state used to build element_node. This equals repeated_context when the repeated child + // itself is the element, and includes the wrapper's only child when standard 3-level LIST + // encoding is stripped. + SchemaBuildContext element_context; + // True when element_node is the repeated child itself. The builder then uses + // REPEATED_NODE_AS_LIST_ELEMENT so the repeated level is not interpreted as a second unrelated + // array at the same boundary. + bool element_is_repeated_node = false; +}; + +// Resolved repeated entry group of a MAP-annotated group. The entry wrapper is a physical Parquet +// encoding detail; Doris folds it into the parent MAP schema and exposes only direct [key, value] +// children. +struct MapEntryResolution { + const ::parquet::schema::GroupNode* entry_group = nullptr; + // Level state after consuming the repeated entry group. The parent MAP schema keeps this state + // to materialize offsets, empty maps and null maps. + SchemaBuildContext entry_context; +}; + bool is_list_node(const ::parquet::schema::Node& node) { const auto& logical_type = node.logical_type(); return node.converted_type() == ::parquet::ConvertedType::LIST || @@ -56,6 +96,17 @@ bool is_map_node(const ::parquet::schema::Node& node) { (logical_type != nullptr && logical_type->is_valid() && logical_type->is_map()); } +bool has_logical_annotation(const ::parquet::schema::Node& node) { + const auto& logical_type = node.logical_type(); + return (node.converted_type() != ::parquet::ConvertedType::NONE && + node.converted_type() != ::parquet::ConvertedType::UNDEFINED) || + (logical_type != nullptr && logical_type->is_valid() && !logical_type->is_none()); +} + +bool has_structural_list_name(const std::string& list_name, const std::string& repeated_name) { + return repeated_name == "array" || repeated_name == list_name + "_tuple"; +} + DataTypePtr nullable_if_needed(DataTypePtr type, const ::parquet::schema::Node& node) { return node.is_optional() ? make_nullable(type) : type; } @@ -103,10 +154,157 @@ void propagate_child_levels(ParquetColumnSchema* column_schema) { } } -// Recursively builds ParquetColumnSchema for the given schema node and its children in Parquet file's metadata. -Status build_node_schema(const ::parquet::SchemaDescriptor& schema, - const ::parquet::schema::Node& node, const SchemaBuildContext& context, - std::unique_ptr* result) { +// Mirrors Arrow's ResolveList() compatibility rules, but only decides which Parquet node is the +// logical LIST element. The caller still builds Doris' semantic LIST->[element] schema tree. +// +// Important cases: +// - repeated primitive: the primitive itself is the element (legacy two-level LIST). +// - repeated group with multiple children: the group itself is a STRUCT element. +// - repeated group named "array" or "_tuple" and without a logical annotation: the +// group itself is a STRUCT element per Parquet backward compatibility rules, even when it has +// one child. +// - repeated group with a logical annotation, or whose only child is repeated: the group itself is +// the element. This preserves nested LIST/MAP and repeated fields inside struct elements. +// - otherwise, strip the one-child repeated wrapper as standard three-level LIST encoding. +Status resolve_list_element_node(const ::parquet::schema::GroupNode& list_group, + const SchemaBuildContext& list_context, + ListElementResolution* result) { + if (result == nullptr) { + return Status::InvalidArgument("result is null"); + } + if (list_group.field_count() != 1) { + return Status::NotSupported("Unsupported parquet LIST encoding for column {}", + list_group.name()); + } + const auto& repeated_node = *list_group.field(0); + if (!repeated_node.is_repeated()) { + return Status::NotSupported("Unsupported parquet LIST encoding for column {}", + list_group.name()); + } + result->repeated_context = child_context(list_context, repeated_node, 0); + if (repeated_node.is_primitive()) { + result->element_node = &repeated_node; + result->element_context = result->repeated_context; + result->element_is_repeated_node = true; + return Status::OK(); + } + + const auto& repeated_group = static_cast(repeated_node); + if (repeated_group.field_count() == 0) { + return Status::NotSupported("Unsupported parquet LIST element layout for column {}", + list_group.name()); + } + const bool repeated_group_has_logical_annotation = has_logical_annotation(repeated_group); + if (repeated_group.field_count() > 1 || + (!repeated_group_has_logical_annotation && + has_structural_list_name(list_group.name(), repeated_group.name())) || + repeated_group_has_logical_annotation) { + result->element_node = &repeated_node; + result->element_context = result->repeated_context; + result->element_is_repeated_node = true; + return Status::OK(); + } + + const auto& only_child = *repeated_group.field(0); + if (only_child.is_repeated()) { + result->element_node = &repeated_node; + result->element_context = result->repeated_context; + result->element_is_repeated_node = true; + return Status::OK(); + } + + result->element_node = &only_child; + result->element_context = child_context(result->repeated_context, only_child, 0); + result->element_is_repeated_node = false; + return Status::OK(); +} + +// Resolves the repeated entry group of a MAP/MAP_KEY_VALUE node. Unlike LIST, MAP has no supported +// two-level form in this reader: Doris requires a repeated group with exactly required key and +// value children, then folds that physical entry group out of ParquetColumnSchema. +Status resolve_map_entry_group(const ::parquet::schema::GroupNode& map_group, + const SchemaBuildContext& map_context, MapEntryResolution* result) { + if (result == nullptr) { + return Status::InvalidArgument("result is null"); + } + if (map_group.field_count() != 1) { + return Status::NotSupported("Unsupported parquet MAP encoding for column {}", + map_group.name()); + } + const auto& entry_node = *map_group.field(0); + if (!entry_node.is_repeated()) { + return Status::NotSupported("Unsupported parquet MAP encoding for column {}", + map_group.name()); + } + if (entry_node.is_primitive()) { + return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", + map_group.name()); + } + const auto& entry_group = static_cast(entry_node); + if (entry_group.field_count() != 2) { + return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", + map_group.name()); + } + if (entry_group.field(0)->repetition() != ::parquet::Repetition::REQUIRED) { + return Status::NotSupported("Unsupported nullable parquet MAP key for column {}", + map_group.name()); + } + result->entry_group = &entry_group; + result->entry_context = child_context(map_context, entry_node, 0); + return Status::OK(); +} + +Status build_node_schema_with_mode(const ::parquet::SchemaDescriptor& schema, + const ::parquet::schema::Node& node, + const SchemaBuildContext& context, + std::unique_ptr* result, + SchemaBuildMode mode); + +// Builds a semantic ARRAY schema for a simple repeated field. Arrow handles this in +// NodeToSchemaField()/GroupToSchemaField(); Doris only enables it while materializing the element +// of a LIST-annotated parent, so existing top-level repeated primitive rejection is unchanged. +// +// Example: +// optional group a (LIST) { +// repeated group element { +// repeated int32 items; +// } +// } +// The outer LIST element is the repeated "element" group, and its repeated "items" child should be +// represented as a field of type ARRAY inside the struct element. +Status build_repeated_field_as_list_schema(const ::parquet::SchemaDescriptor& schema, + const ::parquet::schema::Node& repeated_node, + const SchemaBuildContext& repeated_context, + std::unique_ptr* result) { + if (result == nullptr) { + return Status::InvalidArgument("result is null"); + } + auto list_schema = std::make_unique(); + inherit_common_schema_state(repeated_node, repeated_context, list_schema.get()); + list_schema->kind = ParquetColumnSchemaKind::LIST; + list_schema->definition_level = repeated_context.definition_level; + list_schema->repetition_level = repeated_context.repetition_level; + list_schema->repeated_repetition_level = repeated_context.repeated_repetition_level; + + std::unique_ptr element_child; + RETURN_IF_ERROR(build_node_schema_with_mode(schema, repeated_node, repeated_context, + &element_child, + SchemaBuildMode::REPEATED_NODE_AS_LIST_ELEMENT)); + list_schema->type = std::make_shared(element_child->type); + list_schema->children.push_back(std::move(element_child)); + propagate_child_levels(list_schema.get()); + *result = std::move(list_schema); + return Status::OK(); +} + +// Recursively builds ParquetColumnSchema for the given schema node and its children in Parquet +// file's metadata. The mode only affects repeated nodes that have already been selected as LIST +// elements by resolve_list_element_node(); normal file schema recursion remains strict. +Status build_node_schema_with_mode(const ::parquet::SchemaDescriptor& schema, + const ::parquet::schema::Node& node, + const SchemaBuildContext& context, + std::unique_ptr* result, + SchemaBuildMode mode) { if (result == nullptr) { return Status::InvalidArgument("result is null"); } @@ -141,30 +339,22 @@ Status build_node_schema(const ::parquet::SchemaDescriptor& schema, const auto& group = static_cast(node); if (is_list_node(node)) { - column_schema->kind = ParquetColumnSchemaKind::LIST; - if (group.field_count() != 1) { - return Status::NotSupported("Unsupported parquet LIST encoding for column {}", - node.name()); - } - const auto& repeated_node = *group.field(0); - if (!repeated_node.is_repeated() || repeated_node.is_primitive()) { - return Status::NotSupported("Unsupported parquet LIST encoding for column {}", - node.name()); - } - const auto& repeated_group = - static_cast(repeated_node); - if (repeated_group.field_count() != 1) { - return Status::NotSupported("Unsupported parquet LIST element layout for column {}", - node.name()); + if (mode == SchemaBuildMode::NORMAL && node.is_repeated()) { + return Status::NotSupported("Unsupported repeated parquet LIST column {}", node.name()); } - auto repeated_context = child_context(context, repeated_node, 0); - column_schema->definition_level = repeated_context.definition_level; - column_schema->repetition_level = repeated_context.repetition_level; - column_schema->repeated_repetition_level = repeated_context.repeated_repetition_level; + column_schema->kind = ParquetColumnSchemaKind::LIST; + ListElementResolution list_element; + RETURN_IF_ERROR(resolve_list_element_node(group, context, &list_element)); + column_schema->definition_level = list_element.repeated_context.definition_level; + column_schema->repetition_level = list_element.repeated_context.repetition_level; + column_schema->repeated_repetition_level = + list_element.repeated_context.repeated_repetition_level; std::unique_ptr child; - RETURN_IF_ERROR(build_node_schema( - schema, *repeated_group.field(0), - child_context(repeated_context, *repeated_group.field(0), 0), &child)); + RETURN_IF_ERROR(build_node_schema_with_mode( + schema, *list_element.element_node, list_element.element_context, &child, + list_element.element_is_repeated_node + ? SchemaBuildMode::REPEATED_NODE_AS_LIST_ELEMENT + : SchemaBuildMode::NORMAL)); column_schema->type = nullable_if_needed(std::make_shared(child->type), node); column_schema->children.push_back(std::move(child)); @@ -174,62 +364,33 @@ Status build_node_schema(const ::parquet::SchemaDescriptor& schema, } if (is_map_node(node)) { - column_schema->kind = ParquetColumnSchemaKind::MAP; - if (group.field_count() != 1) { - return Status::NotSupported("Unsupported parquet MAP encoding for column {}", - node.name()); - } - const auto& key_value_node = *group.field(0); - if (!key_value_node.is_repeated()) { - return Status::NotSupported("Unsupported parquet MAP encoding for column {}", - node.name()); - } - auto key_value_context = child_context(context, key_value_node, 0); - column_schema->definition_level = key_value_context.definition_level; - column_schema->repetition_level = key_value_context.repetition_level; - column_schema->repeated_repetition_level = key_value_context.repeated_repetition_level; - if (key_value_node.is_primitive()) { - return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", - node.name()); - } - const auto& key_value_group = - static_cast(key_value_node); - if (key_value_group.field_count() != 2) { - return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", - node.name()); + if (mode == SchemaBuildMode::NORMAL && node.is_repeated()) { + return Status::NotSupported("Unsupported repeated parquet MAP column {}", node.name()); } - auto key_value = std::make_unique(); - inherit_common_schema_state(key_value_node, key_value_context, key_value.get()); - key_value->kind = ParquetColumnSchemaKind::STRUCT; - DataTypes child_types; - Strings child_names; - child_types.reserve(key_value_group.field_count()); - child_names.reserve(key_value_group.field_count()); - for (int child_idx = 0; child_idx < key_value_group.field_count(); ++child_idx) { + column_schema->kind = ParquetColumnSchemaKind::MAP; + MapEntryResolution map_entry; + RETURN_IF_ERROR(resolve_map_entry_group(group, context, &map_entry)); + column_schema->definition_level = map_entry.entry_context.definition_level; + column_schema->repetition_level = map_entry.entry_context.repetition_level; + column_schema->repeated_repetition_level = + map_entry.entry_context.repeated_repetition_level; + for (int child_idx = 0; child_idx < map_entry.entry_group->field_count(); ++child_idx) { std::unique_ptr child; - RETURN_IF_ERROR(build_node_schema( - schema, *key_value_group.field(child_idx), - child_context(key_value_context, *key_value_group.field(child_idx), child_idx), - &child)); - child_types.push_back(make_nullable(child->type)); - child_names.push_back(child->name); - key_value->children.push_back(std::move(child)); + RETURN_IF_ERROR(build_node_schema_with_mode( + schema, *map_entry.entry_group->field(child_idx), + child_context(map_entry.entry_context, *map_entry.entry_group->field(child_idx), + child_idx), + &child, SchemaBuildMode::NORMAL)); + column_schema->children.push_back(std::move(child)); } - key_value->type = std::make_shared(child_types, child_names); - propagate_child_levels(key_value.get()); - if (key_value->children.size() != 2) { + if (column_schema->children.size() != 2) { return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", node.name()); } - if (key_value_group.field(0)->repetition() != ::parquet::Repetition::REQUIRED) { - return Status::NotSupported("Unsupported nullable parquet MAP key for column {}", - node.name()); - } - auto key_type = make_nullable(key_value->children[0]->type); - auto value_type = make_nullable(key_value->children[1]->type); + auto key_type = make_nullable(column_schema->children[0]->type); + auto value_type = make_nullable(column_schema->children[1]->type); column_schema->type = nullable_if_needed(std::make_shared(key_type, value_type), node); - column_schema->children.push_back(std::move(key_value)); propagate_child_levels(column_schema.get()); *result = std::move(column_schema); return Status::OK(); @@ -241,10 +402,17 @@ Status build_node_schema(const ::parquet::SchemaDescriptor& schema, child_types.reserve(group.field_count()); child_names.reserve(group.field_count()); for (int child_idx = 0; child_idx < group.field_count(); ++child_idx) { + const auto& child_node = *group.field(child_idx); std::unique_ptr child; - RETURN_IF_ERROR(build_node_schema( - schema, *group.field(child_idx), - child_context(context, *group.field(child_idx), child_idx), &child)); + const auto child_ctx = child_context(context, child_node, child_idx); + if (mode == SchemaBuildMode::REPEATED_NODE_AS_LIST_ELEMENT && child_node.is_repeated() && + !is_list_node(child_node) && !is_map_node(child_node)) { + RETURN_IF_ERROR( + build_repeated_field_as_list_schema(schema, child_node, child_ctx, &child)); + } else { + RETURN_IF_ERROR(build_node_schema_with_mode(schema, child_node, child_ctx, &child, + SchemaBuildMode::NORMAL)); + } child_types.push_back(make_nullable(child->type)); child_names.push_back(child->name); column_schema->children.push_back(std::move(child)); @@ -256,6 +424,12 @@ Status build_node_schema(const ::parquet::SchemaDescriptor& schema, return Status::OK(); } +Status build_node_schema(const ::parquet::SchemaDescriptor& schema, + const ::parquet::schema::Node& node, const SchemaBuildContext& context, + std::unique_ptr* result) { + return build_node_schema_with_mode(schema, node, context, result, SchemaBuildMode::NORMAL); +} + } // namespace Status build_parquet_column_schema(const ::parquet::SchemaDescriptor& schema, diff --git a/be/src/format_v2/parquet/parquet_column_schema.h b/be/src/format_v2/parquet/parquet_column_schema.h index 012a2e6046dd0b..861e0eb248e504 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.h +++ b/be/src/format_v2/parquet/parquet_column_schema.h @@ -39,8 +39,16 @@ enum class ParquetColumnSchemaKind { MAP, }; -// 新 Parquet reader 的 file-local schema tree。 -// 它描述 Parquet 逻辑字段到 leaf column ordinal 的关系，不包含 table/global schema 语义。 +// New Parquet reader file-local schema tree. +// +// The tree describes Parquet logical fields and leaf column ordinals, but its DataType is already +// converted to Doris external-table semantics: Doris does not expose required nested fields from +// external files, so STRUCT children, LIST elements, and MAP key/value children are widened to +// nullable. Parquet required/optional details are still preserved in the level fields and +// ColumnDescriptor metadata below for reader materialization. +// LIST repeated-group wrappers and MAP key_value/entry wrappers are folded into their parent +// schema nodes; LIST children are [element] and MAP children are [key, value]. The folded parent +// nodes keep the repeated definition/repetition levels needed to assemble nested values. struct ParquetColumnSchema { // Reader-local id inside the parent schema node. // diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index fc96d2208382ed..420fe5c27c29e8 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -21,6 +21,7 @@ #include #include #include +#include #include #include diff --git a/be/src/format_v2/parquet/parquet_statistics.cpp b/be/src/format_v2/parquet/parquet_statistics.cpp index 4391caf702bfa1..101fe2713d901a 100644 --- a/be/src/format_v2/parquet/parquet_statistics.cpp +++ b/be/src/format_v2/parquet/parquet_statistics.cpp @@ -592,8 +592,7 @@ const ParquetColumnSchema* ParquetStatisticsUtils::ResolvePredicateLeafSchema( ParquetColumnStatistics ParquetStatisticsUtils::TransformColumnStatistics( const ParquetColumnSchema& column_schema, - const std::shared_ptr<::parquet::Statistics>& statistics, - const cctz::time_zone* timezone) { + const std::shared_ptr<::parquet::Statistics>& statistics, const cctz::time_zone* timezone) { ParquetColumnStatistics result; if (statistics == nullptr) { return result; @@ -663,8 +662,7 @@ bool ParquetStatisticsUtils::CheckStatistics(const format::FileColumnPredicateFi ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter, - const cctz::time_zone* timezone) { + const format::FileColumnPredicateFilter& column_filter, const cctz::time_zone* timezone) { if (column_filter.predicates.empty()) { return ParquetRowGroupPruneReason::NONE; } @@ -677,9 +675,9 @@ ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( if (column_chunk == nullptr) { return ParquetRowGroupPruneReason::NONE; } - if (CheckStatistics(column_filter, - TransformColumnStatistics(*column_schema, column_chunk->statistics(), - timezone))) { + if (CheckStatistics( + column_filter, + TransformColumnStatistics(*column_schema, column_chunk->statistics(), timezone))) { return ParquetRowGroupPruneReason::STATISTICS; } if (!supports_dictionary_pruning(*column_schema, *column_chunk, column_filter) || @@ -702,8 +700,7 @@ ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( bool ParquetStatisticsUtils::RowGroupExcludes( const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter, - const cctz::time_zone* timezone) { + const format::FileColumnPredicateFilter& column_filter, const cctz::time_zone* timezone) { return RowGroupPruneReason(row_group, file_reader, row_group_idx, schema, column_filter, timezone) != ParquetRowGroupPruneReason::NONE; } @@ -827,8 +824,7 @@ namespace { template bool set_page_decoded_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, const ParquetColumnSchema& column_schema, size_t page_idx, - DecodedValueKind value_kind, - ParquetColumnStatistics* page_statistics, + DecodedValueKind value_kind, ParquetColumnStatistics* page_statistics, const cctz::time_zone* timezone) { const auto typed_index = std::static_pointer_cast<::parquet::TypedColumnIndex>(column_index); @@ -905,14 +901,13 @@ bool set_page_string_min_max(const std::shared_ptr<::parquet::ColumnIndex>& colu bool set_page_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_index, const ParquetColumnSchema& column_schema, size_t page_idx, - ParquetColumnStatistics* page_statistics, - const cctz::time_zone* timezone) { + ParquetColumnStatistics* page_statistics, const cctz::time_zone* timezone) { DORIS_CHECK(column_schema.type != nullptr); switch (column_schema.descriptor->physical_type()) { case ::parquet::Type::BOOLEAN: - return set_page_decoded_min_max<::parquet::BooleanType>( - column_index, column_schema, page_idx, DecodedValueKind::BOOL, page_statistics, - timezone); + return set_page_decoded_min_max<::parquet::BooleanType>(column_index, column_schema, + page_idx, DecodedValueKind::BOOL, + page_statistics, timezone); case ::parquet::Type::INT32: return set_page_decoded_min_max<::parquet::Int32Type>( column_index, column_schema, page_idx, @@ -922,13 +917,13 @@ bool set_page_min_max(const std::shared_ptr<::parquet::ColumnIndex>& column_inde column_index, column_schema, page_idx, decoded_value_kind(column_schema.type_descriptor), page_statistics, timezone); case ::parquet::Type::FLOAT: - return set_page_decoded_min_max<::parquet::FloatType>( - column_index, column_schema, page_idx, DecodedValueKind::FLOAT, page_statistics, - timezone); + return set_page_decoded_min_max<::parquet::FloatType>(column_index, column_schema, page_idx, + DecodedValueKind::FLOAT, + page_statistics, timezone); case ::parquet::Type::DOUBLE: - return set_page_decoded_min_max<::parquet::DoubleType>( - column_index, column_schema, page_idx, DecodedValueKind::DOUBLE, page_statistics, - timezone); + return set_page_decoded_min_max<::parquet::DoubleType>(column_index, column_schema, + page_idx, DecodedValueKind::DOUBLE, + page_statistics, timezone); case ::parquet::Type::BYTE_ARRAY: case ::parquet::Type::FIXED_LEN_BYTE_ARRAY: return set_page_string_min_max(column_index, column_schema, page_idx, page_statistics, diff --git a/be/src/format_v2/parquet/reader/column_reader.cpp b/be/src/format_v2/parquet/reader/column_reader.cpp index 567209d0d7ff67..78ba7f0086e127 100644 --- a/be/src/format_v2/parquet/reader/column_reader.cpp +++ b/be/src/format_v2/parquet/reader/column_reader.cpp @@ -210,7 +210,7 @@ std::unique_ptr ParquetColumnReaderFactory::create_global_r _column_reader_profile); } -Status ParquetColumnReaderFactory::create_scalar_reader( +Status ParquetColumnReaderFactory::make_scalar_column_reader( const ParquetColumnSchema& column_schema, std::shared_ptr<::parquet::internal::RecordReader> record_reader, std::unique_ptr* reader) const { @@ -226,59 +226,41 @@ Status ParquetColumnReaderFactory::create_scalar_reader( } Status ParquetColumnReaderFactory::create_scalar_column_reader( - const ParquetColumnSchema& column_schema, + const ParquetColumnSchema& column_schema, bool is_nested, std::unique_ptr* reader) const { if (reader == nullptr) { return Status::InvalidArgument("reader is null"); } + if (is_nested && column_schema.kind != ParquetColumnSchemaKind::PRIMITIVE) { + return Status::InvalidArgument("Parquet nested scalar reader requires primitive column {}", + column_schema.name); + } if (column_schema.leaf_column_id < 0 || column_schema.leaf_column_id >= static_cast(_record_readers.size())) { return Status::InvalidArgument("Invalid parquet leaf column id {} for column {}", column_schema.leaf_column_id, column_schema.name); } - if (column_schema.descriptor == nullptr || - column_schema.descriptor->max_repetition_level() != 0 || - column_schema.descriptor->max_definition_level() > 1) { + if (!is_nested && (column_schema.descriptor == nullptr || + column_schema.descriptor->max_repetition_level() != 0 || + column_schema.descriptor->max_definition_level() > 1)) { return Status::NotSupported( "Current parquet scalar reader only supports flat primitive columns; column {} is " "not supported", column_schema.name); } - if (!column_schema.type_descriptor.supports_record_reader) { - return Status::NotSupported("Current parquet scalar reader does not support column {}", - column_schema.name); - } - std::shared_ptr<::parquet::internal::RecordReader> record_reader; - RETURN_IF_ERROR(get_record_reader(column_schema.leaf_column_id, column_schema.descriptor, - column_schema.name, &record_reader)); - return create_scalar_reader(column_schema, std::move(record_reader), reader); -} - -// TODO: Unify with `create_scalar_column_reader` -Status ParquetColumnReaderFactory::create_nested_scalar_column_reader( - const ParquetColumnSchema& column_schema, - std::unique_ptr* reader) const { - if (reader == nullptr) { - return Status::InvalidArgument("reader is null"); - } - if (column_schema.kind != ParquetColumnSchemaKind::PRIMITIVE) { - return Status::InvalidArgument("Parquet nested scalar reader requires primitive column {}", - column_schema.name); - } - if (column_schema.leaf_column_id < 0 || - column_schema.leaf_column_id >= static_cast(_record_readers.size())) { - return Status::InvalidArgument("Invalid parquet leaf column id {} for column {}", - column_schema.leaf_column_id, column_schema.name); - } - if (!supports_nested_scalar_record_reader(column_schema)) { + if (is_nested && !supports_nested_scalar_record_reader(column_schema)) { return Status::NotSupported( "Current parquet nested scalar reader does not support column {}", column_schema.name); } + if (!is_nested && !column_schema.type_descriptor.supports_record_reader) { + return Status::NotSupported("Current parquet scalar reader does not support column {}", + column_schema.name); + } std::shared_ptr<::parquet::internal::RecordReader> record_reader; RETURN_IF_ERROR(get_record_reader(column_schema.leaf_column_id, column_schema.descriptor, column_schema.name, &record_reader)); - return create_scalar_reader(column_schema, std::move(record_reader), reader); + return make_scalar_column_reader(column_schema, std::move(record_reader), reader); } Status ParquetColumnReaderFactory::get_record_reader( @@ -344,11 +326,7 @@ Status ParquetColumnReaderFactory::create_struct_column_reader( continue; } std::unique_ptr child_reader; - if (child_schema->kind == ParquetColumnSchemaKind::PRIMITIVE) { - RETURN_IF_ERROR(create_nested_scalar_column_reader(*child_schema, &child_reader)); - } else { - RETURN_IF_ERROR(create(*child_schema, child_projection, &child_reader)); - } + RETURN_IF_ERROR(create_column_reader(*child_schema, child_projection, true, &child_reader)); child_output_indices.push_back(static_cast(projected_child_types.size())); projected_child_types.push_back(make_nullable(child_reader->type())); projected_child_names.push_back(child_reader->name()); @@ -395,16 +373,8 @@ Status ParquetColumnReaderFactory::create_list_column_reader( return Status::NotSupported("Parquet LIST projection for column {} contains no element", column_schema.name); } - if (element_schema.kind == ParquetColumnSchemaKind::PRIMITIVE) { - if (format::is_partial_projection(element_projection)) { - return Status::InvalidArgument( - "Parquet LIST scalar element projection is invalid for column {}", - column_schema.name); - } - RETURN_IF_ERROR(create_nested_scalar_column_reader(element_schema, &element_reader)); - } else { - RETURN_IF_ERROR(create(element_schema, element_projection, &element_reader)); - } + RETURN_IF_ERROR( + create_column_reader(element_schema, element_projection, true, &element_reader)); DataTypePtr type = column_schema.type; if (format::is_partial_projection(element_projection)) { type = std::make_shared(element_reader->type()); @@ -423,33 +393,37 @@ Status ParquetColumnReaderFactory::create_map_column_reader( if (reader == nullptr) { return Status::InvalidArgument("reader is null"); } - if (column_schema.children.size() != 1 || column_schema.children[0]->children.size() != 2) { + if (column_schema.children.size() != 2) { return Status::NotSupported("Unsupported parquet MAP layout for column {}", column_schema.name); } - const auto& key_value_schema = *column_schema.children[0]; - const auto* key_value_projection = - format::find_child_projection(projection, key_value_schema.local_id); - if (format::is_partial_projection(projection) && key_value_projection == nullptr) { - return Status::NotSupported("Parquet MAP projection for column {} contains no entry", - column_schema.name); + const auto& key_schema = *column_schema.children[0]; + const auto& value_schema = *column_schema.children[1]; + const auto* value_projection = format::find_child_projection(projection, value_schema.local_id); + if (format::is_partial_projection(projection)) { + if (value_projection == nullptr) { + return Status::NotSupported("Parquet MAP projection for column {} contains no value", + column_schema.name); + } + for (const auto& child_projection : projection->children) { + if (child_projection.local_id() == key_schema.local_id) { + return Status::NotSupported( + "Parquet MAP projection for column {} does not support key child", + column_schema.name); + } + if (child_projection.local_id() != value_schema.local_id) { + return Status::InvalidArgument( + "Parquet MAP projection for column {} contains invalid child", + column_schema.name); + } + } } std::unique_ptr key_reader; - RETURN_IF_ERROR(create_nested_scalar_column_reader(*key_value_schema.children[0], &key_reader)); + // MAP materialization always needs the full key stream. It owns entry existence, offsets and + // key equality semantics, so MAP projection is defined only as value-subtree pruning. + RETURN_IF_ERROR(create_column_reader(key_schema, nullptr, true, &key_reader)); std::unique_ptr value_reader; - const auto& value_schema = *key_value_schema.children[1]; - const auto* value_projection = - format::find_child_projection(key_value_projection, value_schema.local_id); - if (value_schema.kind == ParquetColumnSchemaKind::PRIMITIVE) { - if (format::is_partial_projection(value_projection)) { - return Status::InvalidArgument( - "Parquet MAP scalar value projection is invalid for column {}", - column_schema.name); - } - RETURN_IF_ERROR(create_nested_scalar_column_reader(value_schema, &value_reader)); - } else { - RETURN_IF_ERROR(create(value_schema, value_projection, &value_reader)); - } + RETURN_IF_ERROR(create_column_reader(value_schema, value_projection, true, &value_reader)); DataTypePtr type = column_schema.type; if (format::is_partial_projection(value_projection)) { type = std::make_shared(make_nullable(key_reader->type()), @@ -467,12 +441,25 @@ Status ParquetColumnReaderFactory::create_map_column_reader( Status ParquetColumnReaderFactory::create(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const { + return create_column_reader(column_schema, projection, false, reader); +} + +Status ParquetColumnReaderFactory::create_column_reader( + const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, + bool is_nested, std::unique_ptr* reader) const { if (reader == nullptr) { return Status::InvalidArgument("reader is null"); } switch (column_schema.kind) { case ParquetColumnSchemaKind::PRIMITIVE: - return create_scalar_column_reader(column_schema, reader); + if (is_nested) { + if (format::is_partial_projection(projection)) { + return Status::InvalidArgument("Parquet scalar projection is invalid for column {}", + column_schema.name); + } + return create_scalar_column_reader(column_schema, true, reader); + } + return create_scalar_column_reader(column_schema, false, reader); case ParquetColumnSchemaKind::STRUCT: return create_struct_column_reader(column_schema, projection, reader); case ParquetColumnSchemaKind::LIST: diff --git a/be/src/format_v2/parquet/reader/column_reader.h b/be/src/format_v2/parquet/reader/column_reader.h index 4c4328472d8064..824c7dd7aaf864 100644 --- a/be/src/format_v2/parquet/reader/column_reader.h +++ b/be/src/format_v2/parquet/reader/column_reader.h @@ -125,11 +125,17 @@ class ParquetColumnReader { int64_t _nested_build_level_cursor = 0; }; -// Parquet column reader 工厂。 -// 工厂绑定当前 row group，并根据 file-local schema tree 创建 Doris 自己的 column -// reader。Arrow internal RecordReader 的创建和缓存必须封装在这里，避免泄露到 -// ParquetReader 主流程。后续 reader options、Dremel assembler、延时物化 cache/skip -// 策略都应挂在该工厂上下文里，而不是继续扩展自由函数参数。 +// Creates Doris column readers for one Parquet row group. +// +// The factory owns row-group-local state that must be shared by all readers created for +// the same row group: +// - Arrow RecordReader instances, cached by file leaf column id. +// - Page skip plans and page skip profile counters. +// - Scalar materialization options such as timezone and strict mode. +// +// Public callers only ask for a top-level file column reader or for synthetic scan +// columns. Recursive construction of nested children stays private so physical Parquet +// schema details do not leak into ParquetScanScheduler or ParquetReader. class ParquetColumnReaderFactory { public: ParquetColumnReaderFactory(std::shared_ptr<::parquet::RowGroupReader> row_group, @@ -140,8 +146,9 @@ class ParquetColumnReaderFactory { bool enable_strict_mode = false, ParquetColumnReaderProfile column_reader_profile = {}); - // 根据 file-local schema tree 创建 column reader。复杂类型会在这里递归创建 - // children。该入口只理解 Parquet file schema，不处理 table/global schema。 + // Creates a reader for a top-level file column schema. The optional projection uses + // file-local child ids from ParquetColumnSchema and may select only part of a nested + // subtree. Table/global schema mapping has already happened before this layer. Status create(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; @@ -157,31 +164,55 @@ class ParquetColumnReaderFactory { const format::GlobalRowIdContext& context, int64_t row_group_first_row) const; private: - Status create_scalar_column_reader(const ParquetColumnSchema& column_schema, + // Creates a primitive leaf reader. Top-level primitive columns are restricted to flat + // scalar layouts, while nested primitive leaves are allowed to carry def/rep levels + // that will be consumed by their parent LIST/MAP/STRUCT readers. + Status create_scalar_column_reader(const ParquetColumnSchema& column_schema, bool is_nested, std::unique_ptr* reader) const; - Status create_nested_scalar_column_reader(const ParquetColumnSchema& column_schema, - std::unique_ptr* reader) const; - + // Creates a STRUCT reader and recursively creates readers only for projected + // children. For partial projections it rebuilds the output DataTypeStruct so the + // materialized column contains only projected child fields. Status create_struct_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; + // Creates a LIST reader around the single element reader. If the element itself is + // partially projected, this rebuilds the output DataTypeArray with the projected + // element type. Status create_list_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; + // Creates a MAP reader around key and value readers. The schema builder folds the Parquet + // key_value/entry wrapper into the MAP node, so children are direct key/value fields here. + // Partial MAP projection means value-subtree pruning only. The key stream is always read in + // full because it owns entry existence, offsets and key equality semantics; key child + // projection is intentionally rejected. Status create_map_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; + // Private recursive dispatcher. is_nested is true for children of complex readers + // and controls primitive-leaf validation; complex readers are always created from + // normalized ParquetColumnSchema subtrees. + Status create_column_reader(const ParquetColumnSchema& column_schema, + const format::LocalColumnIndex* projection, bool is_nested, + std::unique_ptr* reader) const; + + // Lazily creates and caches the Arrow RecordReader for a file leaf column. Multiple + // Doris readers may need the same leaf stream through different nested parents, so + // RecordReader lifetime is tied to this row-group factory. Status get_record_reader(int leaf_column_id, const ::parquet::ColumnDescriptor* descriptor, const std::string& name, std::shared_ptr<::parquet::internal::RecordReader>* reader) const; - Status create_scalar_reader(const ParquetColumnSchema& column_schema, - std::shared_ptr<::parquet::internal::RecordReader> record_reader, - std::unique_ptr* reader) const; + // Final ScalarColumnReader construction after schema validation and RecordReader + // lookup have already completed. + Status make_scalar_column_reader( + const ParquetColumnSchema& column_schema, + std::shared_ptr<::parquet::internal::RecordReader> record_reader, + std::unique_ptr* reader) const; std::shared_ptr<::parquet::RowGroupReader> _row_group; mutable std::vector> _record_readers; diff --git a/be/src/format_v2/parquet/reader/list_column_reader.h b/be/src/format_v2/parquet/reader/list_column_reader.h index 3ab70f6970350d..d64eb646a75c2a 100644 --- a/be/src/format_v2/parquet/reader/list_column_reader.h +++ b/be/src/format_v2/parquet/reader/list_column_reader.h @@ -44,7 +44,6 @@ class ListColumnReader final : public ParquetColumnReader { const std::vector& nested_repetition_levels() const override; int64_t nested_levels_written() const override; bool is_or_has_repeated_child() const override; - ParquetColumnReader* element_reader() const { return _element_reader.get(); } private: std::unique_ptr _element_reader; diff --git a/be/src/format_v2/parquet/reader/map_column_reader.h b/be/src/format_v2/parquet/reader/map_column_reader.h index a7591981eaee46..f9761964920c34 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.h +++ b/be/src/format_v2/parquet/reader/map_column_reader.h @@ -46,8 +46,6 @@ class MapColumnReader final : public ParquetColumnReader { const std::vector& nested_repetition_levels() const override; int64_t nested_levels_written() const override; bool is_or_has_repeated_child() const override; - ParquetColumnReader* key_reader() const { return _key_reader.get(); } - ParquetColumnReader* value_reader() const { return _value_reader.get(); } private: std::unique_ptr _key_reader; diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.h b/be/src/format_v2/parquet/reader/scalar_column_reader.h index ddc8acbde492e1..47d404f9655adb 100644 --- a/be/src/format_v2/parquet/reader/scalar_column_reader.h +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.h @@ -39,6 +39,8 @@ class time_zone; namespace doris::parquet { class ScalarColumnReader final : public ParquetColumnReader { + friend class MapColumnReader; + public: ScalarColumnReader(const ParquetColumnSchema& column_schema, std::shared_ptr<::parquet::internal::RecordReader> record_reader, @@ -52,20 +54,19 @@ class ScalarColumnReader final : public ParquetColumnReader { Status load_nested_batch(int64_t rows) override; Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, int64_t* values_read) override; - Status append_nested_value(int64_t level_idx, MutableColumnPtr& column) const; const std::vector& nested_definition_levels() const override; const std::vector& nested_repetition_levels() const override; int64_t nested_levels_written() const override; bool is_or_has_repeated_child() const override; +private: + Status append_nested_value(int64_t level_idx, MutableColumnPtr& column) const; const ::parquet::ColumnDescriptor* descriptor() const { return _descriptor; } ParquetLeafReader leaf_reader() const { return ParquetLeafReader(_descriptor, _type_descriptor, _type, _name, _record_reader, _profile, _timezone, _enable_strict_mode); } void advance_rows_read(int64_t rows); - -private: Status skip_records(int64_t rows); int64_t page_filtered_rows_to_skip(int64_t rows) const; diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.cpp b/be/src/format_v2/parquet/reader/struct_column_reader.cpp index e388b8a103f515..1a6df72b66752e 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/struct_column_reader.cpp @@ -29,24 +29,20 @@ #include "format_v2/parquet/reader/scalar_column_reader.h" namespace doris::parquet { -namespace { - -ParquetColumnReader* struct_shape_source_reader(const StructColumnReader& reader) { - for (size_t child_idx = 0; child_idx < reader.child_count(); ++child_idx) { - auto* child_reader = reader.child_reader(child_idx); +ParquetColumnReader* StructColumnReader::shape_source_reader() const { + for (const auto& child : _children) { + auto* child_reader = child.get(); DORIS_CHECK(child_reader != nullptr); if (!child_reader->is_or_has_repeated_child()) { return child_reader; } } - if (reader.child_count() == 0) { + if (_children.empty()) { return nullptr; } - return reader.child_reader(0); + return _children[0].get(); } -} // namespace - Status StructColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { RETURN_IF_ERROR(load_nested_batch(rows)); return build_nested_column(rows, column, rows_read); @@ -91,7 +87,7 @@ Status StructColumnReader::build_nested_column(int64_t length_upper_bound, Mutab auto* struct_column = struct_column_from_output(column); DORIS_CHECK(struct_column != nullptr); auto* parent_null_map = null_map_from_nullable_output(column); - auto* shape_reader = struct_shape_source_reader(*this); + auto* shape_reader = shape_source_reader(); DORIS_CHECK(shape_reader != nullptr); const auto& def_levels = shape_reader->nested_definition_levels(); const auto& rep_levels = shape_reader->nested_repetition_levels(); @@ -203,25 +199,25 @@ Status StructColumnReader::build_nested_column(int64_t length_upper_bound, Mutab } const std::vector& StructColumnReader::nested_definition_levels() const { - auto* shape_reader = struct_shape_source_reader(*this); + auto* shape_reader = shape_source_reader(); DORIS_CHECK(shape_reader != nullptr); return shape_reader->nested_definition_levels(); } const std::vector& StructColumnReader::nested_repetition_levels() const { - auto* shape_reader = struct_shape_source_reader(*this); + auto* shape_reader = shape_source_reader(); DORIS_CHECK(shape_reader != nullptr); return shape_reader->nested_repetition_levels(); } int64_t StructColumnReader::nested_levels_written() const { - auto* shape_reader = struct_shape_source_reader(*this); + auto* shape_reader = shape_source_reader(); DORIS_CHECK(shape_reader != nullptr); return shape_reader->nested_levels_written(); } bool StructColumnReader::is_or_has_repeated_child() const { - auto* shape_reader = struct_shape_source_reader(*this); + auto* shape_reader = shape_source_reader(); return shape_reader != nullptr && shape_reader->is_or_has_repeated_child(); } diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.h b/be/src/format_v2/parquet/reader/struct_column_reader.h index eed09a5ad35956..2c08bc8fe58126 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.h +++ b/be/src/format_v2/parquet/reader/struct_column_reader.h @@ -50,11 +50,8 @@ class StructColumnReader final : public ParquetColumnReader { int64_t nested_levels_written() const override; bool is_or_has_repeated_child() const override; - size_t child_count() const { return _children.size(); } - ParquetColumnReader* child_reader(size_t child_idx) const { return _children[child_idx].get(); } - int child_output_index(size_t child_idx) const { return _child_output_indices[child_idx]; } - private: + ParquetColumnReader* shape_source_reader() const; std::vector> _children; std::vector _child_output_indices; }; diff --git a/be/src/format_v2/schema_projection.cpp b/be/src/format_v2/schema_projection.cpp index 7e1e2ef2ae4f0c..57a9b4d7a33512 100644 --- a/be/src/format_v2/schema_projection.cpp +++ b/be/src/format_v2/schema_projection.cpp @@ -28,39 +28,65 @@ #include "core/data_type/data_type_struct.h" namespace doris::format { +namespace { -Status rebuild_projected_type(const DataTypePtr& original_type, - const std::vector& child_types, - const std::vector& child_names, - DataTypePtr* projected_type) { +// Rebuild the complex DataType for one already-pruned semantic ColumnDefinition node. +// +// The caller has already matched the projection against ColumnDefinition::children and preserved +// the file-local child order. This helper only mirrors those projected semantic children into the +// node type. It intentionally does not understand physical format wrappers. In particular, a MAP +// node is expected to have semantic children [key, value], even if the underlying format stores a +// wrapper such as Parquet key_value/entry. +Status rebuild_semantic_projected_type(const DataTypePtr& original_type, + const std::vector& projected_children, + DataTypePtr* projected_type) { DORIS_CHECK(original_type != nullptr); DORIS_CHECK(projected_type != nullptr); - DORIS_CHECK(child_types.size() == child_names.size()); DataTypePtr nested_projected_type; const auto primitive_type = remove_nullable(original_type)->get_primitive_type(); switch (primitive_type) { - case TYPE_STRUCT: + case TYPE_STRUCT: { + DataTypes child_types; + Strings child_names; + child_types.reserve(projected_children.size()); + child_names.reserve(projected_children.size()); + for (const auto& child : projected_children) { + child_types.push_back(child.type); + child_names.push_back(child.name); + } nested_projected_type = std::make_shared(child_types, child_names); break; + } case TYPE_ARRAY: - DORIS_CHECK(child_types.size() == 1); - nested_projected_type = std::make_shared(child_types[0]); + DORIS_CHECK(projected_children.size() == 1); + nested_projected_type = std::make_shared(projected_children[0].type); break; case TYPE_MAP: { - // TODO: ? - DORIS_CHECK(child_types.size() == 1); - DORIS_CHECK(remove_nullable(child_types[0])->get_primitive_type() == TYPE_STRUCT); DORIS_CHECK(remove_nullable(original_type)->get_primitive_type() == TYPE_MAP); - const auto* entry_type = - assert_cast(remove_nullable(child_types[0]).get()); - DORIS_CHECK(entry_type->get_elements().size() == 1 || - entry_type->get_elements().size() == 2); - const auto value_idx = entry_type->get_elements().size() == 1 ? 0 : 1; - nested_projected_type = std::make_shared( - assert_cast(remove_nullable(original_type).get()) - ->get_key_type(), - entry_type->get_element(value_idx)); + const auto* original_map_type = + assert_cast(remove_nullable(original_type).get()); + DataTypePtr key_type = original_map_type->get_key_type(); + DataTypePtr value_type; + for (const auto& child : projected_children) { + // Partial MAP projection only prunes the value subtree. The key stream must remain + // complete because it defines entry existence and offsets when materializing ColumnMap; + // the projected DataTypeMap also preserves the original key type instead of rebuilding + // it from children. + if (child.file_local_id() == 0 || child.name == "key") { + return Status::NotSupported( + "MAP projection for type {} does not support key child projection", + original_type->get_name()); + } + if (child.file_local_id() == 1 || child.name == "value") { + value_type = child.type; + } + } + if (value_type == nullptr) { + return Status::NotSupported("MAP projection for type {} contains no value child", + original_type->get_name()); + } + nested_projected_type = std::make_shared(key_type, value_type); break; } default: @@ -73,6 +99,8 @@ Status rebuild_projected_type(const DataTypePtr& original_type, return Status::OK(); } +} // namespace + Status project_column_definition(const ColumnDefinition& field, const LocalColumnIndex& projection, ColumnDefinition* projected_field) { if (projected_field == nullptr) { @@ -113,15 +141,8 @@ Status project_column_definition(const ColumnDefinition& field, const LocalColum return Status::NotSupported("Projection for field {} contains no children", field.name); } - DataTypes child_types; - Strings child_names; - child_types.reserve(projected_field->children.size()); - child_names.reserve(projected_field->children.size()); - for (const auto& child : projected_field->children) { - child_types.push_back(child.type); - child_names.push_back(child.name); - } - return rebuild_projected_type(field.type, child_types, child_names, &projected_field->type); + return rebuild_semantic_projected_type(field.type, projected_field->children, + &projected_field->type); } } // namespace doris::format diff --git a/be/src/format_v2/schema_projection.h b/be/src/format_v2/schema_projection.h index 56f7bf35194590..7dd2c3cf827308 100644 --- a/be/src/format_v2/schema_projection.h +++ b/be/src/format_v2/schema_projection.h @@ -17,26 +17,40 @@ #pragma once -#include -#include -#include - #include "common/status.h" -#include "core/data_type/data_type.h" #include "format_v2/file_reader.h" namespace doris::format { -Status rebuild_projected_type(const DataTypePtr& original_type, - const std::vector& child_types, - const std::vector& child_names, - DataTypePtr* projected_type); - -// Build the file-local column definition after applying a LocalColumnIndex projection. +// Build a projected file-local semantic schema node from a full schema node and a nested +// LocalColumnIndex projection. +// +// This module is deliberately about semantic ColumnDefinition trees, not physical file-format +// trees. FileReader::get_schema() returns file-local columns after type conversion to Doris +// DataType, and their children must follow Doris semantics: +// +// STRUCT children = fields +// ARRAY children = [element] +// MAP children = [key, value] +// +// Format-specific wrappers, such as Parquet MAP key_value/entry nodes, are intentionally hidden +// from this API. A format reader that needs those wrappers for its physical reader tree should +// translate the semantic projection back to its physical layout internally. +// +// The function does three things: +// - Copies `field` metadata to `projected_field`. +// - Recursively prunes children according to `projection.children`, matching children by +// ColumnDefinition::file_local_id() rather than vector ordinal. The root projection id is not +// interpreted here because the caller has already selected `field`. +// - Rebuilds the node DataType from the projected semantic children so the returned definition is +// self-consistent. STRUCT uses projected child names/types, ARRAY uses the projected element +// type, and MAP preserves the original key type while rebuilding the projected value type. // -// The projection is matched by ColumnDefinition::file_local_id(), not by vector ordinal. This keeps -// nested schema evolution semantics in the common helper and lets callers use the same projection -// tree for type rebuilding and file block layout. +// A full projection copies `field` unchanged. Partial MAP projection is intentionally limited to +// the value child. MAP is materialized as offsets + keys + values, so the reader must still read +// the complete key stream to build entry shape and offsets. The projected DataTypeMap keeps the +// original key type and only rebuilds the value type from the projected value child; key-only or +// key-pruned MAP projections are rejected. Status project_column_definition(const ColumnDefinition& field, const LocalColumnIndex& projection, ColumnDefinition* projected_field); diff --git a/be/src/format_v2/table/iceberg_reader.cpp b/be/src/format_v2/table/iceberg_reader.cpp index 4f9e7c539b98d7..cb9a343ad7faa6 100644 --- a/be/src/format_v2/table/iceberg_reader.cpp +++ b/be/src/format_v2/table/iceberg_reader.cpp @@ -34,10 +34,10 @@ #include "core/data_type/data_type_number.h" #include "core/data_type/define_primitive_type.h" #include "core/field.h" +#include "exprs/vslot_ref.h" #include "format/table/deletion_vector_reader.h" #include "format_v2/expr/cast.h" #include "format_v2/expr/equality_delete_predicate.h" -#include "exprs/vslot_ref.h" #include "format_v2/parquet/parquet_reader.h" #include "format_v2/parquet/reader/column_reader.h" #include "format_v2/table_reader.h" @@ -61,8 +61,7 @@ static std::string join_values_for_debug(const std::vector& values) { static std::string iceberg_delete_file_debug_string(const TIcebergDeleteFileDesc& delete_file) { std::ostringstream out; - out << "TIcebergDeleteFileDesc{path=" - << (delete_file.__isset.path ? delete_file.path : "null") + out << "TIcebergDeleteFileDesc{path=" << (delete_file.__isset.path ? delete_file.path : "null") << ", content=" << (delete_file.__isset.content ? delete_file.content : -1) << ", file_format=" << (delete_file.__isset.file_format ? static_cast(delete_file.file_format) : -1) @@ -444,8 +443,8 @@ Status IcebergTableReader::_append_equality_delete_predicates(format::FileScanRe _append_file_scan_column(request, field_column_id, &request->predicate_columns); const auto block_position = request->local_positions.at(field_column_id).value(); auto slot = VSlotRef::create_shared(cast_set(block_position), - cast_set(block_position), -1, - field_it->type, field_it->name); + cast_set(block_position), -1, field_it->type, + field_it->name); if (field_it->type->equals(*filter.key_types[idx])) { delete_predicate->add_child(std::move(slot)); } else { diff --git a/be/src/format_v2/table_reader.h b/be/src/format_v2/table_reader.h index a98d99b8c34361..3939a56742d013 100644 --- a/be/src/format_v2/table_reader.h +++ b/be/src/format_v2/table_reader.h @@ -751,8 +751,7 @@ class TableReader { RETURN_IF_ERROR( _materialize_complex_mapping_column(mapping, *column, rows, column)); } else { - RETURN_IF_ERROR(_cast_column_to_type(column, mapping.file_type, - mapping.table_type, + RETURN_IF_ERROR(_cast_column_to_type(column, mapping.file_type, mapping.table_type, mapping.file_column_name)); } } @@ -904,8 +903,8 @@ class TableReader { _file_child_ordinal_for_mapping(mapping, child_mapping, file_ordered_children); DORIS_CHECK(file_child_idx < file_struct->get_columns().size()); ColumnPtr child_column = file_struct->get_column_ptr(file_child_idx); - RETURN_IF_ERROR(_materialize_present_child_mapping_column( - child_mapping, child_column, rows, &child_column)); + RETURN_IF_ERROR(_materialize_present_child_mapping_column(child_mapping, child_column, + rows, &child_column)); child_columns.push_back(std::move(child_column)); } MutableColumns mutable_child_columns; @@ -967,11 +966,6 @@ class TableReader { Status _materialize_map_mapping_column(const ColumnMapping& mapping, const ColumnPtr& file_column, const size_t rows, ColumnPtr* column) { - DORIS_CHECK(mapping.child_mappings.size() == 1); - const auto& entry_mapping = mapping.child_mappings[0]; - DORIS_CHECK(entry_mapping.child_mappings.size() == 1 || - entry_mapping.child_mappings.size() == 2); - const auto full_file_column = file_column->convert_to_full_column_if_const(); const NullMap* parent_null_map = nullptr; const auto* nested_file_column = @@ -982,7 +976,7 @@ class TableReader { const ColumnMapping* key_mapping = nullptr; const ColumnMapping* value_mapping = nullptr; - for (const auto& child_mapping : entry_mapping.child_mappings) { + for (const auto& child_mapping : mapping.child_mappings) { if (!child_mapping.file_local_id.has_value()) { continue; } diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index b6bd9203b7b95f..f11717f2ab1b1c 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -623,8 +623,8 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn const auto int_type = std::make_shared(); const auto string_type = std::make_shared(); - const auto struct_type = std::make_shared( - DataTypes {string_type, string_type}, Strings {"country", "city"}); + const auto struct_type = std::make_shared(DataTypes {string_type, string_type}, + Strings {"country", "city"}); format::ColumnDefinition struct_column { .identifier = Field::create_field(100), .name = "struct_column", @@ -658,8 +658,8 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn EXPECT_EQ(struct_column.children[1].get_identifier_field_id(), 205); EXPECT_EQ(struct_column.children[1].name_mapping, std::vector({"old_city"})); - const auto element_type = std::make_shared( - DataTypes {string_type, int_type}, Strings {"item", "quantity"}); + const auto element_type = std::make_shared(DataTypes {string_type, int_type}, + Strings {"item", "quantity"}); const auto array_type = std::make_shared(element_type); format::ColumnDefinition array_column { .identifier = Field::create_field(200), @@ -688,8 +688,8 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn }, }; std::vector array_paths {data_access_path({"array_column"})}; - status = FileScannerV2::TEST_build_nested_children_from_access_paths( - &array_column, array_paths, &array_schema); + status = FileScannerV2::TEST_build_nested_children_from_access_paths(&array_column, array_paths, + &array_schema); ASSERT_TRUE(status.ok()) << status; ASSERT_EQ(array_column.children.size(), 1); const auto& element = array_column.children[0]; @@ -700,8 +700,8 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn EXPECT_EQ(element.children[0].name_mapping, std::vector({"old_item"})); EXPECT_EQ(element.children[1].get_identifier_field_id(), 203); - const auto value_type = std::make_shared( - DataTypes {string_type, int_type}, Strings {"full_name", "age"}); + const auto value_type = std::make_shared(DataTypes {string_type, int_type}, + Strings {"full_name", "age"}); const auto map_type = std::make_shared(string_type, value_type); format::ColumnDefinition map_column { .identifier = Field::create_field(300), diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index ae5183cb81b790..dfb7d4d88db3af 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -137,12 +137,12 @@ ColumnDefinition array_col(const std::string& name, int32_t field_id, ColumnDefi return column; } -ColumnDefinition map_col(const std::string& name, int32_t field_id, ColumnDefinition entry, - const DataTypePtr& key_type, const DataTypePtr& value_type, - int32_t local_id = -1) { +ColumnDefinition map_col(const std::string& name, int32_t field_id, + std::vector children, const DataTypePtr& key_type, + const DataTypePtr& value_type, int32_t local_id = -1) { auto column = field_id_col(name, field_id, std::make_shared(key_type, value_type), local_id); - column.children = {std::move(entry)}; + column.children = std::move(children); return column; } @@ -399,25 +399,18 @@ TEST(ColumnMapperSchemaProjectionTest, ProjectsMapValueStructLeaf) { std::make_shared(DataTypes {i32(), str()}, Strings {"a", "b"}); ColumnDefinition value = field_id_col("value", 4, value_type, 1); value.children = {value_a, value_b}; - auto entry_type = std::make_shared(DataTypes {str(), value_type}, - Strings {"key", "value"}); - ColumnDefinition entry = field_id_col("entries", 5, entry_type, 0); - entry.children = {key, value}; - auto map = map_col("m", 100, entry, str(), value_type, 9); + auto map = map_col("m", 100, {key, value}, str(), value_type, 9); LocalColumnIndex projection = LocalColumnIndex::partial_local(9); - auto entry_projection = LocalColumnIndex::partial_local(0); auto value_projection = LocalColumnIndex::partial_local(1); value_projection.children.push_back(LocalColumnIndex::local(1)); - entry_projection.children.push_back(std::move(value_projection)); - projection.children.push_back(std::move(entry_projection)); + projection.children.push_back(std::move(value_projection)); ColumnDefinition projected; ASSERT_TRUE(project_column_definition(map, projection, &projected).ok()); ASSERT_EQ(projected.children.size(), 1); ASSERT_EQ(projected.children[0].children.size(), 1); - ASSERT_EQ(projected.children[0].children[0].children.size(), 1); - EXPECT_EQ(projected.children[0].children[0].children[0].name, "b"); + EXPECT_EQ(projected.children[0].children[0].name, "b"); const auto* map_type = assert_cast(remove_nullable(projected.type).get()); const auto* projected_value = @@ -426,6 +419,20 @@ TEST(ColumnMapperSchemaProjectionTest, ProjectsMapValueStructLeaf) { EXPECT_EQ(projected_value->get_element_name(0), "b"); } +TEST(ColumnMapperSchemaProjectionTest, RejectsMapKeyProjection) { + auto key = field_id_col("key", 1, str(), 0); + auto value = field_id_col("value", 2, i32(), 1); + auto map = map_col("m", 100, {key, value}, str(), i32(), 9); + + LocalColumnIndex projection = LocalColumnIndex::partial_local(9); + projection.children.push_back(LocalColumnIndex::local(0)); + + ColumnDefinition projected; + const auto status = project_column_definition(map, projection, &projected); + ASSERT_FALSE(status.ok()); + EXPECT_NE(status.to_string().find("does not support key child projection"), std::string::npos); +} + TEST(ColumnMapperSchemaProjectionTest, RejectsInvalidProjectionChildIdWithFieldName) { auto root = struct_col("s", 100, {field_id_col("a", 101, i32(), 0)}, 7); @@ -451,11 +458,15 @@ TEST(ColumnMapperSchemaProjectionTest, RejectsEmptyProjectionPathWithFieldName) EXPECT_NE(status.to_string().find("Empty projection path for field s"), std::string::npos); } -TEST(ColumnMapperSchemaProjectionTest, RejectsChildProjectionForPrimitiveType) { - DataTypePtr projected_type; - const auto status = rebuild_projected_type(i32(), {i32()}, {"a"}, &projected_type); +TEST(ColumnMapperSchemaProjectionTest, RejectsInvalidChildProjectionForPrimitiveField) { + auto root = field_id_col("i", 1, i32(), 7); + LocalColumnIndex projection = LocalColumnIndex::partial_local(7); + projection.children.push_back(LocalColumnIndex::local(0)); + + ColumnDefinition projected; + const auto status = project_column_definition(root, projection, &projected); ASSERT_FALSE(status.ok()); - EXPECT_NE(status.to_string().find("Cannot project children from non-complex type"), + EXPECT_NE(status.to_string().find("Invalid projection child id 0 for field i"), std::string::npos); } @@ -898,8 +909,8 @@ TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForNestedSchemaEvolutio table_full_name.name_mapping = {"name"}; auto table_age = name_col("age", int_type); auto table_value = struct_name_col("value", {table_full_name, table_age}); - auto table_entry = struct_name_col("entries", {table_key, table_value}); - auto table_map = map_col("new_map_column", -1, table_entry, string_type, table_value.type); + auto table_map = + map_col("new_map_column", -1, {table_key, table_value}, string_type, table_value.type); table_map.name_mapping = {"map_column"}; set_name_identifiers(&table_map, -1); @@ -917,8 +928,8 @@ TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForNestedSchemaEvolutio auto file_name = name_col("name", string_type, 0); auto file_age = name_col("age", int_type, 1); auto file_value = struct_name_col("value", {file_name, file_age}, 1); - auto file_entry = struct_name_col("key_value", {file_key, file_value}, 0); - auto file_map = map_col("map_column", -1, file_entry, string_type, file_value.type, 5); + auto file_map = + map_col("map_column", -1, {file_key, file_value}, string_type, file_value.type, 5); set_name_identifiers(&file_map, 5); TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); @@ -951,14 +962,10 @@ TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForNestedSchemaEvolutio const auto& map_mapping = mapper.mappings()[2]; expect_mapping(map_mapping, 2, "new_map_column", 5, "map_column", file_map.type, table_map.type); - ASSERT_EQ(map_mapping.child_mappings.size(), 1); - const auto& entry_mapping = map_mapping.child_mappings[0]; - EXPECT_EQ(entry_mapping.file_column_name, "key_value"); - EXPECT_EQ(*entry_mapping.file_local_id, 0); - ASSERT_EQ(entry_mapping.child_mappings.size(), 2); - EXPECT_EQ(entry_mapping.child_mappings[0].file_column_name, "key"); - EXPECT_EQ(*entry_mapping.child_mappings[0].file_local_id, 0); - const auto& value_mapping = entry_mapping.child_mappings[1]; + ASSERT_EQ(map_mapping.child_mappings.size(), 2); + EXPECT_EQ(map_mapping.child_mappings[0].file_column_name, "key"); + EXPECT_EQ(*map_mapping.child_mappings[0].file_local_id, 0); + const auto& value_mapping = map_mapping.child_mappings[1]; EXPECT_EQ(value_mapping.file_column_name, "value"); EXPECT_EQ(*value_mapping.file_local_id, 1); ASSERT_EQ(value_mapping.child_mappings.size(), 2); @@ -982,8 +989,7 @@ TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDu field_id_col("negative_file", -7, int_type, 3), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1001,8 +1007,7 @@ TEST(ColumnMapperCreateMappingTest, ByFieldIdTreatsSameNameDifferentFieldIdAsMis field_id_col("same_name", 20, int_type, 0), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); const auto status = mapper.create_mapping(table_schema, {}, file_schema); ASSERT_TRUE(status.ok()) << status.to_string(); @@ -1018,8 +1023,7 @@ TEST(ColumnMapperCreateMappingTest, NestedFieldIdTreatsSameNameDifferentFieldIdA auto file_child = field_id_col("child", 20, int_type, 0); auto file_root = struct_col("root", 1, {file_child}, 0); - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); const auto status = mapper.create_mapping({table_root}, {}, {file_root}); ASSERT_TRUE(status.ok()) << status.to_string(); @@ -1116,8 +1120,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFallsBackToDefaultOrMissing field_id_col("_col1", 101, int_type, 1), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_INDEX}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1142,8 +1145,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexMissingIdentifierFallsBackToDefaultOr field_id_col("_col0", 100, int_type, 0), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_INDEX}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1163,8 +1165,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexOutOfRangeFallsBackToMissing) { field_id_col("_col0", 100, int_type, 0), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_INDEX}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); const auto status = mapper.create_mapping(table_schema, {}, file_schema); ASSERT_TRUE(status.ok()) << status.to_string(); @@ -1209,8 +1210,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexIgnoresFileColumnNames) { } TEST(ColumnMapperCreateMappingTest, MissingColumnFallsBackToMissingMapping) { - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_NAME}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); const auto status = mapper.create_mapping({name_col("missing", i32())}, {}, {name_col("present", i32(), 0)}); ASSERT_TRUE(status.ok()) << status.to_string(); @@ -1313,8 +1313,7 @@ TEST(ColumnMapperConstantTest, MissingRowLineageDefaultExprStillUsesVirtualMappi field_id_col("name", 2, make_nullable(str()), 1), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 3); @@ -1338,8 +1337,7 @@ TEST(ColumnMapperConstantTest, ByFieldIdDoesNotTreatSameNameDifferentIdAsRowLine field_id_col("_last_updated_sequence_number", 101, make_nullable(i64()), 1), }; - TableColumnMapper mapper( - {.mode = TableColumnMappingMode::BY_FIELD_ID}); + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); ASSERT_EQ(mapper.mappings().size(), 2); @@ -1965,8 +1963,7 @@ TEST(ColumnMapperScanRequestTest, MapValueStructProjectionPrunesValueChildren) { auto table_value_b = name_col("b", string_type); auto table_value = struct_name_col("value", {table_value_b}); - auto table_entry = struct_name_col("entries", {table_value}); - auto table_map = map_col("m", -1, table_entry, key_type, table_value.type); + auto table_map = map_col("m", -1, {table_value}, key_type, table_value.type); table_map.identifier = Field::create_field("m"); set_name_identifiers(&table_map, -1); @@ -1974,8 +1971,7 @@ TEST(ColumnMapperScanRequestTest, MapValueStructProjectionPrunesValueChildren) { auto file_value_a = name_col("a", int_type, 0); auto file_value_b = name_col("b", string_type, 1); auto file_value = struct_name_col("value", {file_value_a, file_value_b}, 1); - auto file_entry = struct_name_col("key_value", {file_key, file_value}, 0); - auto file_map = map_col("m", -1, file_entry, key_type, file_value.type, 6); + auto file_map = map_col("m", -1, {file_key, file_value}, key_type, file_value.type, 6); file_map.identifier = Field::create_field("m"); set_name_identifiers(&file_map, 6); @@ -1990,11 +1986,9 @@ TEST(ColumnMapperScanRequestTest, MapValueStructProjectionPrunesValueChildren) { EXPECT_EQ(projection.column_id(), LocalColumnId(6)); ASSERT_FALSE(projection.project_all_children); ASSERT_EQ(projection.children.size(), 1); - EXPECT_EQ(projection.children[0].local_id(), 0); + EXPECT_EQ(projection.children[0].local_id(), 1); ASSERT_EQ(projection.children[0].children.size(), 1); EXPECT_EQ(projection.children[0].children[0].local_id(), 1); - ASSERT_EQ(projection.children[0].children[0].children.size(), 1); - EXPECT_EQ(projection.children[0].children[0].children[0].local_id(), 1); const auto* mapped_map = assert_cast(remove_nullable(mapper.mappings()[0].file_type).get()); diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp index dcd5255dcd42ba..384ccb0eeebee1 100644 --- a/be/test/format_v2/parquet/parquet_column_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -62,6 +62,15 @@ std::shared_ptr finish_array(arrow::ArrayBuilder* builder) { return array; } +template +const ColumnType& get_nullable_nested_column(const IColumn& column) { + // File-local schema exposed by the parquet reader follows Doris external-table semantics: + // nested STRUCT fields, LIST elements, and MAP keys/values are nullable even when the parquet + // field is required. + const auto& nullable_column = assert_cast(column); + return assert_cast(nullable_column.get_nested_column()); +} + class ParquetColumnReaderTest : public testing::Test { protected: void SetUp() override { @@ -1042,9 +1051,9 @@ class ParquetColumnReaderTest : public testing::Test { const auto& struct_column = assert_cast(column); ASSERT_EQ(struct_column.get_columns().size(), 2); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); const auto& b_values = - assert_cast(struct_column.get_column(1)); + get_nullable_nested_column(struct_column.get_column(1)); EXPECT_EQ(a_values.get_element(0), 101); EXPECT_EQ(a_values.get_element(4), 105); EXPECT_EQ(b_values.get_data_at(1).to_string(), "sb"); @@ -1071,7 +1080,7 @@ class ParquetColumnReaderTest : public testing::Test { assert_cast(nullable_column.get_nested_column()); ASSERT_EQ(struct_column.get_columns().size(), 2); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); const auto& b_values = assert_cast(struct_column.get_column(1)); const auto& b_nested = @@ -1106,7 +1115,7 @@ class ParquetColumnReaderTest : public testing::Test { assert_cast(nullable_column.get_nested_column()); ASSERT_EQ(struct_column.get_columns().size(), 2); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); const auto& d_values = assert_cast(struct_column.get_column(1)); const auto& d_nested = @@ -1141,7 +1150,7 @@ class ParquetColumnReaderTest : public testing::Test { assert_cast(nullable_column.get_nested_column()); ASSERT_EQ(struct_column.get_columns().size(), 2); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); EXPECT_EQ(a_values.get_element(0), 301); EXPECT_EQ(a_values.get_element(2), 303); EXPECT_EQ(a_values.get_element(3), 304); @@ -1198,7 +1207,7 @@ class ParquetColumnReaderTest : public testing::Test { assert_cast(nullable_column.get_nested_column()); ASSERT_EQ(struct_column.get_columns().size(), 2); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); EXPECT_EQ(a_values.get_element(0), 401); EXPECT_EQ(a_values.get_element(2), 403); EXPECT_EQ(a_values.get_element(3), 404); @@ -1220,7 +1229,7 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(offsets[2], 2); EXPECT_EQ(offsets[3], 2); EXPECT_EQ(offsets[4], 3); - const auto& keys = assert_cast(kv_map.get_keys()); + const auto& keys = get_nullable_nested_column(kv_map.get_keys()); const auto& values = assert_cast(kv_map.get_values()); const auto& value_data = assert_cast(values.get_nested_column()); @@ -1285,7 +1294,8 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(array_size_at(1), 1); EXPECT_EQ(array_size_at(2), 3); EXPECT_EQ(array_size_at(4), 2); - const auto& values = assert_cast(array_column.get_data()); + const auto& values = + get_nullable_nested_column(array_column.get_data()); ASSERT_EQ(values.size(), 9); EXPECT_EQ(values.get_element(0), 1); EXPECT_EQ(values.get_element(5), 6); @@ -1374,7 +1384,7 @@ class ParquetColumnReaderTest : public testing::Test { const auto& struct_column = assert_cast(elements.get_nested_column()); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); const auto& b_values = assert_cast(struct_column.get_column(1)); const auto& b_data = @@ -1474,9 +1484,10 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(map_size_at(1), 1); EXPECT_EQ(map_size_at(2), 3); EXPECT_EQ(map_size_at(4), 2); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = + get_nullable_nested_column(map_column.get_keys()); const auto& values = - assert_cast(map_column.get_values()); + get_nullable_nested_column(map_column.get_values()); ASSERT_EQ(keys.size(), 9); ASSERT_EQ(values.size(), 9); EXPECT_EQ(keys.get_element(0), 1); @@ -1507,7 +1518,8 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(offsets[2], 2); EXPECT_EQ(offsets[3], 3); EXPECT_EQ(offsets[4], 4); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = + get_nullable_nested_column(map_column.get_keys()); const auto& values = assert_cast(map_column.get_values()); const auto& value_data = @@ -1572,13 +1584,14 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(offsets[3], 3); EXPECT_EQ(offsets[4], 4); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = + get_nullable_nested_column(map_column.get_keys()); const auto& values = assert_cast(map_column.get_values()); const auto& struct_column = assert_cast(values.get_nested_column()); const auto& a_values = - assert_cast(struct_column.get_column(0)); + get_nullable_nested_column(struct_column.get_column(0)); const auto& b_values = assert_cast(struct_column.get_column(1)); const auto& b_data = @@ -1625,7 +1638,8 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(map_offsets[3], 4); EXPECT_EQ(map_offsets[4], 5); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = + get_nullable_nested_column(map_column.get_keys()); ASSERT_EQ(keys.size(), 5); EXPECT_EQ(keys.get_element(0), 201); EXPECT_EQ(keys.get_element(1), 202); @@ -1705,7 +1719,8 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(map_offsets[2], 2); EXPECT_EQ(map_offsets[3], 3); EXPECT_EQ(map_offsets[4], 4); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = + get_nullable_nested_column(map_column.get_keys()); const auto& values = assert_cast(map_column.get_values()); const auto& value_data = @@ -1722,66 +1737,67 @@ class ParquetColumnReaderTest : public testing::Test { }); auto nested_map_type = arrow::map(arrow::int32(), arrow::field("value", arrow::utf8(), true)); - add_field( - arrow::field( - "nullable_map_int_map_int_string_col", - arrow::map(arrow::int32(), arrow::field("value", nested_map_type, true)), - true), - build_nullable_int_map_map_array(), - [](const ParquetColumnSchema& schema, const IColumn& column) { - EXPECT_TRUE(schema.type->is_nullable()); - const auto& nullable_column = assert_cast(column); - ASSERT_EQ(nullable_column.size(), ROW_COUNT); - EXPECT_FALSE(nullable_column.is_null_at(0)); - EXPECT_TRUE(nullable_column.is_null_at(1)); - EXPECT_FALSE(nullable_column.is_null_at(2)); - EXPECT_FALSE(nullable_column.is_null_at(3)); - EXPECT_FALSE(nullable_column.is_null_at(4)); - - const auto& outer_map = - assert_cast(nullable_column.get_nested_column()); - const auto& outer_offsets = outer_map.get_offsets(); - ASSERT_EQ(outer_offsets.size(), ROW_COUNT); - EXPECT_EQ(outer_offsets[0], 2); - EXPECT_EQ(outer_offsets[1], 2); - EXPECT_EQ(outer_offsets[2], 2); - EXPECT_EQ(outer_offsets[3], 4); - EXPECT_EQ(outer_offsets[4], 4); - - const auto& outer_keys = assert_cast(outer_map.get_keys()); - ASSERT_EQ(outer_keys.size(), 4); - EXPECT_EQ(outer_keys.get_element(0), 10); - EXPECT_EQ(outer_keys.get_element(1), 20); - EXPECT_EQ(outer_keys.get_element(2), 30); - EXPECT_EQ(outer_keys.get_element(3), 40); + add_field(arrow::field( + "nullable_map_int_map_int_string_col", + arrow::map(arrow::int32(), arrow::field("value", nested_map_type, true)), + true), + build_nullable_int_map_map_array(), + [](const ParquetColumnSchema& schema, const IColumn& column) { + EXPECT_TRUE(schema.type->is_nullable()); + const auto& nullable_column = assert_cast(column); + ASSERT_EQ(nullable_column.size(), ROW_COUNT); + EXPECT_FALSE(nullable_column.is_null_at(0)); + EXPECT_TRUE(nullable_column.is_null_at(1)); + EXPECT_FALSE(nullable_column.is_null_at(2)); + EXPECT_FALSE(nullable_column.is_null_at(3)); + EXPECT_FALSE(nullable_column.is_null_at(4)); - const auto& inner_values = - assert_cast(outer_map.get_values()); - ASSERT_EQ(inner_values.size(), 4); - EXPECT_FALSE(inner_values.is_null_at(0)); - EXPECT_FALSE(inner_values.is_null_at(1)); - EXPECT_TRUE(inner_values.is_null_at(2)); - EXPECT_FALSE(inner_values.is_null_at(3)); - - const auto& inner_map = - assert_cast(inner_values.get_nested_column()); - const auto& inner_offsets = inner_map.get_offsets(); - ASSERT_EQ(inner_offsets.size(), 4); - EXPECT_EQ(inner_offsets[0], 1); - EXPECT_EQ(inner_offsets[1], 1); - EXPECT_EQ(inner_offsets[2], 1); - EXPECT_EQ(inner_offsets[3], 2); - const auto& inner_keys = assert_cast(inner_map.get_keys()); - const auto& inner_strings = - assert_cast(inner_map.get_values()); - const auto& inner_string_data = - assert_cast(inner_strings.get_nested_column()); - ASSERT_EQ(inner_keys.size(), 2); - EXPECT_EQ(inner_keys.get_element(0), 101); - EXPECT_EQ(inner_keys.get_element(1), 401); - EXPECT_EQ(inner_string_data.get_data_at(0).to_string(), "aa"); - EXPECT_TRUE(inner_strings.is_null_at(1)); - }); + const auto& outer_map = + assert_cast(nullable_column.get_nested_column()); + const auto& outer_offsets = outer_map.get_offsets(); + ASSERT_EQ(outer_offsets.size(), ROW_COUNT); + EXPECT_EQ(outer_offsets[0], 2); + EXPECT_EQ(outer_offsets[1], 2); + EXPECT_EQ(outer_offsets[2], 2); + EXPECT_EQ(outer_offsets[3], 4); + EXPECT_EQ(outer_offsets[4], 4); + + const auto& outer_keys = + get_nullable_nested_column(outer_map.get_keys()); + ASSERT_EQ(outer_keys.size(), 4); + EXPECT_EQ(outer_keys.get_element(0), 10); + EXPECT_EQ(outer_keys.get_element(1), 20); + EXPECT_EQ(outer_keys.get_element(2), 30); + EXPECT_EQ(outer_keys.get_element(3), 40); + + const auto& inner_values = + assert_cast(outer_map.get_values()); + ASSERT_EQ(inner_values.size(), 4); + EXPECT_FALSE(inner_values.is_null_at(0)); + EXPECT_FALSE(inner_values.is_null_at(1)); + EXPECT_TRUE(inner_values.is_null_at(2)); + EXPECT_FALSE(inner_values.is_null_at(3)); + + const auto& inner_map = + assert_cast(inner_values.get_nested_column()); + const auto& inner_offsets = inner_map.get_offsets(); + ASSERT_EQ(inner_offsets.size(), 4); + EXPECT_EQ(inner_offsets[0], 1); + EXPECT_EQ(inner_offsets[1], 1); + EXPECT_EQ(inner_offsets[2], 1); + EXPECT_EQ(inner_offsets[3], 2); + const auto& inner_keys = + get_nullable_nested_column(inner_map.get_keys()); + const auto& inner_strings = + assert_cast(inner_map.get_values()); + const auto& inner_string_data = + assert_cast(inner_strings.get_nested_column()); + ASSERT_EQ(inner_keys.size(), 2); + EXPECT_EQ(inner_keys.get_element(0), 101); + EXPECT_EQ(inner_keys.get_element(1), 401); + EXPECT_EQ(inner_string_data.get_data_at(0).to_string(), "aa"); + EXPECT_TRUE(inner_strings.is_null_at(1)); + }); auto deep_list_value_type = arrow::list(arrow::field("element", arrow::int32(), true)); auto deep_list_map_type = arrow::map(arrow::int32(), arrow::field("value", deep_list_value_type, true)); @@ -1839,7 +1855,8 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(map_offsets[2], 2); EXPECT_EQ(map_offsets[3], 2); EXPECT_EQ(map_offsets[4], 4); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = + get_nullable_nested_column(map_column.get_keys()); ASSERT_EQ(keys.size(), 4); EXPECT_EQ(keys.get_element(0), 1); EXPECT_EQ(keys.get_element(1), 2); @@ -1899,7 +1916,8 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(outer_offsets[2], 2); EXPECT_EQ(outer_offsets[3], 4); EXPECT_EQ(outer_offsets[4], 5); - const auto& outer_keys = assert_cast(outer_map.get_keys()); + const auto& outer_keys = + get_nullable_nested_column(outer_map.get_keys()); ASSERT_EQ(outer_keys.size(), 5); EXPECT_EQ(outer_keys.get_element(0), 10); EXPECT_EQ(outer_keys.get_element(1), 20); @@ -1944,7 +1962,7 @@ class ParquetColumnReaderTest : public testing::Test { EXPECT_EQ(inner_offsets[4], 3); EXPECT_EQ(inner_offsets[5], 4); const auto& inner_keys = - assert_cast(inner_map_column.get_keys()); + get_nullable_nested_column(inner_map_column.get_keys()); ASSERT_EQ(inner_keys.size(), 4); EXPECT_EQ(inner_keys.get_element(0), 1); EXPECT_EQ(inner_keys.get_element(1), 2); @@ -2212,7 +2230,7 @@ TEST_F(ParquetColumnReaderTest, ReadProjectedStructChildren) { ASSERT_EQ(rows_read, ROW_COUNT); const auto& struct_column = assert_cast(*column); ASSERT_EQ(struct_column.get_columns().size(), 1); - const auto& values = assert_cast(struct_column.get_column(0)); + const auto& values = get_nullable_nested_column(struct_column.get_column(0)); EXPECT_EQ(values.get_data_at(0).to_string(), "sa"); EXPECT_EQ(values.get_data_at(4).to_string(), "se"); } @@ -2323,26 +2341,20 @@ TEST_F(ParquetColumnReaderTest, ReadProjectedMapStructValueChildren) { ASSERT_LT(field_idx, _fields.size()); const auto& map_schema = *_fields[field_idx]; ASSERT_EQ(map_schema.name, "nullable_map_int_struct_col"); - ASSERT_EQ(map_schema.children.size(), 1); - const auto& key_value_schema = *map_schema.children[0]; - ASSERT_EQ(key_value_schema.children.size(), 2); - const auto& value_schema = *key_value_schema.children[1]; + ASSERT_EQ(map_schema.children.size(), 2); + const auto& value_schema = *map_schema.children[1]; ASSERT_EQ(value_schema.children.size(), 2); format::LocalColumnIndex projection; projection.index = map_schema.local_id; projection.project_all_children = false; - format::LocalColumnIndex entry_projection; - entry_projection.index = key_value_schema.local_id; - entry_projection.project_all_children = false; format::LocalColumnIndex value_projection; value_projection.index = value_schema.local_id; value_projection.project_all_children = false; format::LocalColumnIndex child_projection; child_projection.index = value_schema.children[1]->local_id; value_projection.children.push_back(std::move(child_projection)); - entry_projection.children.push_back(std::move(value_projection)); - projection.children.push_back(std::move(entry_projection)); + projection.children.push_back(std::move(value_projection)); ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); std::unique_ptr reader; @@ -2364,7 +2376,7 @@ TEST_F(ParquetColumnReaderTest, ReadProjectedMapStructValueChildren) { const auto& nullable_column = assert_cast(*column); const auto& map_column = assert_cast(nullable_column.get_nested_column()); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = get_nullable_nested_column(map_column.get_keys()); const auto& values = assert_cast(map_column.get_values()); const auto& struct_column = assert_cast(values.get_nested_column()); ASSERT_EQ(struct_column.get_columns().size(), 1); @@ -2381,6 +2393,25 @@ TEST_F(ParquetColumnReaderTest, ReadProjectedMapStructValueChildren) { EXPECT_EQ(b_data.get_data_at(3).to_string(), "me"); } +TEST_F(ParquetColumnReaderTest, RejectMapKeyProjection) { + const auto field_idx = find_field_idx("nullable_map_int_struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& map_schema = *_fields[field_idx]; + ASSERT_EQ(map_schema.children.size(), 2); + const auto& key_schema = *map_schema.children[0]; + const auto& value_schema = *map_schema.children[1]; + + auto projection = format::LocalColumnIndex::partial_local(map_schema.local_id); + projection.children.push_back(format::LocalColumnIndex::local(key_schema.local_id)); + projection.children.push_back(format::LocalColumnIndex::local(value_schema.local_id)); + + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + const auto st = factory.create(map_schema, &projection, &reader); + ASSERT_FALSE(st.ok()); + EXPECT_NE(st.to_string().find("does not support key child"), std::string::npos); +} + TEST_F(ParquetColumnReaderTest, ReadProjectedStructListChildOnly) { const auto field_idx = find_field_idx("nullable_struct_list_col"); ASSERT_LT(field_idx, _fields.size()); @@ -2553,7 +2584,7 @@ TEST_F(ParquetColumnReaderTest, ReadProjectedStructMapChildOnly) { EXPECT_EQ(offsets[2], 2); EXPECT_EQ(offsets[3], 2); EXPECT_EQ(offsets[4], 3); - const auto& keys = assert_cast(kv_map.get_keys()); + const auto& keys = get_nullable_nested_column(kv_map.get_keys()); const auto& values = assert_cast(kv_map.get_values()); const auto& value_data = assert_cast(values.get_nested_column()); ASSERT_EQ(keys.size(), 3); @@ -2698,7 +2729,7 @@ TEST_F(ParquetColumnReaderTest, SelectProjectedStructMapChildOnly) { EXPECT_EQ(offsets[0], 2); EXPECT_EQ(offsets[1], 2); EXPECT_EQ(offsets[2], 3); - const auto& keys = assert_cast(kv_map.get_keys()); + const auto& keys = get_nullable_nested_column(kv_map.get_keys()); ASSERT_EQ(keys.size(), 3); EXPECT_EQ(keys.get_element(0), 1); EXPECT_EQ(keys.get_element(1), 2); @@ -2835,7 +2866,7 @@ TEST_F(ParquetColumnReaderTest, SelectStructListWithOverflow) { EXPECT_FALSE(nullable_column.is_null_at(2)); const auto& struct_column = assert_cast(nullable_column.get_nested_column()); - const auto& a_values = assert_cast(struct_column.get_column(0)); + const auto& a_values = get_nullable_nested_column(struct_column.get_column(0)); EXPECT_EQ(a_values.get_element(0), 301); EXPECT_EQ(a_values.get_element(1), 304); EXPECT_EQ(a_values.get_element(2), 305); @@ -2918,7 +2949,7 @@ TEST_F(ParquetColumnReaderTest, SelectStructMapWithOverflow) { EXPECT_FALSE(nullable_column.is_null_at(2)); const auto& struct_column = assert_cast(nullable_column.get_nested_column()); - const auto& a_values = assert_cast(struct_column.get_column(0)); + const auto& a_values = get_nullable_nested_column(struct_column.get_column(0)); EXPECT_EQ(a_values.get_element(0), 401); EXPECT_EQ(a_values.get_element(1), 404); EXPECT_EQ(a_values.get_element(2), 405); @@ -2933,7 +2964,7 @@ TEST_F(ParquetColumnReaderTest, SelectStructMapWithOverflow) { EXPECT_EQ(offsets[0], 2); EXPECT_EQ(offsets[1], 2); EXPECT_EQ(offsets[2], 3); - const auto& keys = assert_cast(kv_map.get_keys()); + const auto& keys = get_nullable_nested_column(kv_map.get_keys()); const auto& values = assert_cast(kv_map.get_values()); const auto& value_data = assert_cast(values.get_nested_column()); ASSERT_EQ(keys.size(), 3); @@ -3372,7 +3403,7 @@ TEST_F(ParquetColumnReaderTest, SkipDeepListStructMapListThenRead) { EXPECT_EQ(map_offsets[0], 0); EXPECT_EQ(map_offsets[1], 0); EXPECT_EQ(map_offsets[2], 2); - const auto& keys = assert_cast(map_column.get_keys()); + const auto& keys = get_nullable_nested_column(map_column.get_keys()); ASSERT_EQ(keys.size(), 2); EXPECT_EQ(keys.get_element(0), 3); EXPECT_EQ(keys.get_element(1), 4); @@ -3480,7 +3511,7 @@ TEST_F(ParquetColumnReaderTest, SkipDeepMapListMapThenRead) { EXPECT_EQ(outer_offsets[1], 0); EXPECT_EQ(outer_offsets[2], 2); EXPECT_EQ(outer_offsets[3], 3); - const auto& outer_keys = assert_cast(outer_map.get_keys()); + const auto& outer_keys = get_nullable_nested_column(outer_map.get_keys()); ASSERT_EQ(outer_keys.size(), 3); EXPECT_EQ(outer_keys.get_element(0), 30); EXPECT_EQ(outer_keys.get_element(1), 40); @@ -3527,7 +3558,7 @@ TEST_F(ParquetColumnReaderTest, SelectDeepMapListMap) { EXPECT_EQ(outer_offsets[0], 2); EXPECT_EQ(outer_offsets[1], 4); EXPECT_EQ(outer_offsets[2], 5); - const auto& outer_keys = assert_cast(outer_map.get_keys()); + const auto& outer_keys = get_nullable_nested_column(outer_map.get_keys()); ASSERT_EQ(outer_keys.size(), 5); EXPECT_EQ(outer_keys.get_element(0), 10); EXPECT_EQ(outer_keys.get_element(1), 20); diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index 013c759f7b88bb..03201551a861c1 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -1408,17 +1408,11 @@ TEST(TableColumnMapperTest, CreatesComplexProjectionForMapValueStructChildren) { value_field.name = "value"; value_field.type = value_type; value_field.children = {a_field, b_field}; - format::ColumnDefinition entry_field; - entry_field.identifier = Field::create_field(0); - entry_field.name = "key_value"; - entry_field.type = std::make_shared(DataTypes {key_type, value_type}, - Strings {"key", "value"}); - entry_field.children = {key_field, value_field}; format::ColumnDefinition map_field; map_field.identifier = Field::create_field(0); map_field.name = "m"; map_field.type = std::make_shared(key_type, value_type); - map_field.children = {entry_field}; + map_field.children = {key_field, value_field}; format::ColumnDefinition table_value_child; table_value_child.identifier = Field::create_field(103); @@ -1429,17 +1423,11 @@ TEST(TableColumnMapperTest, CreatesComplexProjectionForMapValueStructChildren) { table_value.name = "value"; table_value.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); table_value.children = {table_value_child}; - format::ColumnDefinition table_entry; - table_entry.identifier = Field::create_field(101); - table_entry.name = "entries"; - table_entry.type = - std::make_shared(DataTypes {table_value.type}, Strings {"value"}); - table_entry.children = {table_value}; format::ColumnDefinition table_column; table_column.identifier = Field::create_field(100); table_column.name = "m"; table_column.type = std::make_shared(key_type, table_value.type); - table_column.children = {table_entry}; + table_column.children = {table_value}; format::TableColumnMapperOptions options; options.mode = format::TableColumnMappingMode::BY_NAME; @@ -1456,11 +1444,9 @@ TEST(TableColumnMapperTest, CreatesComplexProjectionForMapValueStructChildren) { EXPECT_EQ(projection.index, 0); ASSERT_FALSE(projection.project_all_children); ASSERT_EQ(projection.children.size(), 1); - EXPECT_EQ(projection.children[0].index, 0); + EXPECT_EQ(projection.children[0].index, 1); ASSERT_EQ(projection.children[0].children.size(), 1); EXPECT_EQ(projection.children[0].children[0].index, 1); - ASSERT_EQ(projection.children[0].children[0].children.size(), 1); - EXPECT_EQ(projection.children[0].children[0].children[0].index, 1); ASSERT_EQ(mapper.mappings().size(), 1); const auto* projected_type = @@ -1551,17 +1537,11 @@ TEST(TableColumnMapperTest, MapFilterOnlyStructChildIsPredicateProjectionOnly) { value_field.name = "value"; value_field.type = full_value_type; value_field.children = {a_field, b_field}; - format::ColumnDefinition entry_field; - entry_field.identifier = Field::create_field(0); - entry_field.name = "entries"; - entry_field.type = std::make_shared(DataTypes {key_type, full_value_type}, - Strings {"key", "value"}); - entry_field.children = {key_field, value_field}; format::ColumnDefinition map_field; map_field.identifier = Field::create_field(0); map_field.name = "m"; map_field.type = std::make_shared(key_type, full_value_type); - map_field.children = {entry_field}; + map_field.children = {key_field, value_field}; format::ColumnDefinition table_value_child; table_value_child.identifier = Field::create_field(103); @@ -1572,26 +1552,18 @@ TEST(TableColumnMapperTest, MapFilterOnlyStructChildIsPredicateProjectionOnly) { table_value.name = "value"; table_value.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); table_value.children = {table_value_child}; - format::ColumnDefinition table_entry; - table_entry.identifier = Field::create_field(101); - table_entry.name = "entries"; - table_entry.type = - std::make_shared(DataTypes {table_value.type}, Strings {"value"}); - table_entry.children = {table_value}; format::ColumnDefinition table_column; table_column.identifier = Field::create_field(100); table_column.name = "m"; table_column.type = std::make_shared(key_type, table_value.type); - table_column.children = {table_entry}; + table_column.children = {table_value}; auto filter_expr = std::make_shared( "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - auto full_entry_type = entry_field.type; - auto entries_expr = struct_element_expr( + auto value_expr = struct_element_expr( VSlotRef::create_shared(0, 0, -1, std::make_shared(key_type, full_value_type), "m"), - full_entry_type, "entries"); - auto value_expr = struct_element_expr(entries_expr, full_value_type, "value"); + full_value_type, "value"); filter_expr->add_child(struct_element_expr(value_expr, a_type, "a")); filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); format::TableFilter table_filter { @@ -1615,12 +1587,9 @@ TEST(TableColumnMapperTest, MapFilterOnlyStructChildIsPredicateProjectionOnly) { EXPECT_EQ(projection.index, 0); ASSERT_FALSE(projection.project_all_children); ASSERT_EQ(projection.children.size(), 1); - EXPECT_EQ(projection.children[0].index, 0); + EXPECT_EQ(projection.children[0].index, 1); ASSERT_EQ(projection.children[0].children.size(), 1); - EXPECT_EQ(projection.children[0].children[0].index, 1); - ASSERT_EQ(projection.children[0].children[0].children.size(), 2); - EXPECT_EQ(projection.children[0].children[0].children[0].index, 1); - EXPECT_EQ(projection.children[0].children[0].children[1].index, 0); + EXPECT_EQ(projection.children[0].children[0].index, 0); EXPECT_TRUE(request.column_predicate_filters.empty()); } diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index e2995b4e46145b..55f9871994dc91 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -1968,20 +1968,15 @@ TEST(TableReaderTest, PushDownMinMaxFallsBackForProjectedMapValueStructLeaf) { const auto key_type = std::make_shared(); const auto string_type = std::make_shared(); const auto nullable_string_type = make_nullable(string_type); - auto key_child = make_table_column(0, "key", key_type); auto b_child = make_table_column(1, "b", nullable_string_type); auto value_type = std::make_shared(DataTypes {nullable_string_type}, Strings {"b"}); auto nullable_value_type = make_nullable(value_type); auto value_child = make_table_column(1, "value", nullable_value_type); value_child.children = {b_child}; - auto entry_type = std::make_shared(DataTypes {key_type, nullable_value_type}, - Strings {"key", "value"}); - auto entry_child = make_table_column(0, "key_value", entry_type); - entry_child.children = {key_child, value_child}; auto map_column = make_table_column( 100, "kv", std::make_shared(key_type, nullable_value_type)); - map_column.children = {entry_child}; + map_column.children = {value_child}; std::vector projected_columns = {map_column}; RuntimeState state {TQueryOptions(), TQueryGlobals()}; @@ -2046,7 +2041,6 @@ TEST(TableReaderTest, ProjectedMapValueStructReordersRenamedAndMissingChildren) const auto nullable_int_type = make_nullable(int_type); const auto string_type = std::make_shared(); const auto nullable_string_type = make_nullable(string_type); - auto key_child = make_table_column(0, "key", key_type); auto b_child = make_table_column(1, "renamed_b", nullable_string_type); b_child.name_mapping = {"b"}; auto missing_child = make_table_column(99, "missing_child", string_type); @@ -2058,13 +2052,9 @@ TEST(TableReaderTest, ProjectedMapValueStructReordersRenamedAndMissingChildren) auto nullable_value_type = make_nullable(value_type); auto value_child = make_table_column(1, "value", nullable_value_type); value_child.children = {b_child, missing_child, a_child}; - auto entry_type = std::make_shared(DataTypes {key_type, nullable_value_type}, - Strings {"key", "value"}); - auto entry_child = make_table_column(0, "key_value", entry_type); - entry_child.children = {key_child, value_child}; auto map_column = make_table_column( 100, "kv", std::make_shared(key_type, nullable_value_type)); - map_column.children = {entry_child}; + map_column.children = {value_child}; std::vector projected_columns = {map_column}; RuntimeState state {TQueryOptions(), TQueryGlobals()}; @@ -2163,16 +2153,13 @@ TEST(TableReaderTest, MaterializeMapKeyStructReordersRenamedChildren) { value_mapping.file_type = int_type; value_mapping.is_trivial = true; - ColumnMapping entry_mapping; - entry_mapping.child_mappings = {key_mapping, value_mapping}; - ColumnMapping map_mapping; map_mapping.table_column_name = "kv"; map_mapping.file_column_name = "kv"; map_mapping.table_type = table_map_type; map_mapping.file_type = file_map_type; map_mapping.is_trivial = false; - map_mapping.child_mappings = {entry_mapping}; + map_mapping.child_mappings = {key_mapping, value_mapping}; auto a_keys = ColumnInt32::create(); a_keys->insert_value(10); diff --git a/docs/parquet-list-map-compat-design.md b/docs/parquet-list-map-compat-design.md new file mode 100644 index 00000000000000..a02ca6e822aaf0 --- /dev/null +++ b/docs/parquet-list-map-compat-design.md @@ -0,0 +1,664 @@ +# Parquet LIST/MAP Compatibility Design + +本文描述如何参考 Arrow Parquet 的 LIST/MAP 兼容策略，在 Doris new parquet reader 中支持更多 Parquet 标准和 legacy 复杂类型 schema。 + +目标不是改变 `ListColumnReader` / `MapColumnReader` 的读取模型，而是在 schema 构建阶段把不同物理 schema 归一化成 Doris 当前 reader 可以消费的统一 `ParquetColumnSchema` tree。 + +## 背景 + +Parquet 的复杂类型是通过 group schema、logical/converted annotation、definition levels 和 repetition levels 共同表达的。 + +标准 LIST/MAP schema 比较明确，但历史 writer 产生过多种 legacy 形态。例如 LIST 可能缺少标准 `list.element` wrapper，MAP entry group 可能叫 `key_value`、`entries` 或其它名字。 + +Arrow C++ 的处理思路是： + +1. 在 Parquet schema conversion 阶段识别标准和 legacy schema。 +2. 将这些 schema 归一化为 Arrow `ListType` / `MapType` / `StructType`。 +3. 后续 reader 只消费归一化后的 nested field tree，不在读取阶段继续判断 legacy schema 名字。 + +Doris new parquet reader 应采用相同边界： + +1. `parquet_column_schema.cpp` 负责兼容不同 LIST/MAP physical schema。 +2. `ParquetColumnSchema` 输出统一的 LIST/MAP child tree。 +3. `ListColumnReader` / `MapColumnReader` / `ParquetLeafReader` 不感知 legacy schema 形态。 + +## 当前 Doris 限制 + +当前 `build_node_schema()` 的 LIST 分支只支持标准 3-level LIST： + +```text +optional group a (LIST) { + repeated group list { + optional int32 element; + } +} +``` + +当前限制： + +- outer LIST group 必须只有一个 child。 +- repeated child 必须是 group。 +- repeated group 必须只有一个 child。 +- 不支持 repeated primitive list。 +- 不支持 repeated group 多字段 struct element。 +- 不支持 `array` / `_tuple` 这类 legacy structural name。 + +当前 MAP 分支支持标准 MAP 结构： + +```text +optional group m (MAP) { + repeated group key_value { + required binary key; + optional int32 value; + } +} +``` + +当前限制： + +- outer MAP group 必须只有一个 child。 +- entry child 必须 repeated group。 +- entry group 必须正好两个 children。 +- key 必须 required。 +- 不支持 key-only map。 +- 不支持没有 repeated entry layer 的非标准 MAP。 + +## 设计原则 + +1. 兼容逻辑只放在 schema 构建阶段。 +2. reader 层继续消费统一 schema tree。 +3. 不支持会改变 reader model 的格式，例如没有 repeated entry layer 的 MAP。 +4. 第一阶段不支持 key-only map，因为 Doris `ColumnMap` 需要 values column。 +5. 对容易误判的 schema 保持严格，避免把普通 struct 错解析成 LIST/MAP。 +6. 支持范围对齐 Arrow 的稳定 legacy compatibility 规则，而不是无限放宽。 + +MAP projection 语义也保持收敛： + +- partial MAP projection 只表示 value subtree pruning，例如 `MAP>` 投影 `value.b` 后输出 `MAP>`。 +- key 不作为可裁剪 projection 子树。reader 始终读取完整 key stream，因为 key stream 决定 entry existence、offsets，并且 key 本身承载 MAP 的 key equality 语义。 +- schema projection 重建 `DataTypeMap` 时保留原始 key type，只根据 projected value child 重建 value type。 + +## LIST 兼容规则 + +对于 outer group annotated as `LIST`： + +```text +optional group a (LIST) { + repeated ... repeated_child; +} +``` + +先要求： + +- outer LIST group 必须只有一个 child。 +- child 必须是 repeated。 + +然后根据 repeated child 形态判断 element schema node。 + +### 1. 标准 3-level LIST + +```text +optional group a (LIST) { + repeated group list { + optional int32 element; + } +} +``` + +解析： + +- repeated child 是 wrapper。 +- element 是 wrapper 的唯一 child：`list.element`。 +- `ParquetColumnSchema(LIST).children[0]` 指向 element schema。 + +### 2. Repeated primitive legacy LIST + +```text +optional group a (LIST) { + repeated int32 element; +} +``` + +解析： + +- repeated primitive 本身是 element。 +- element 本身不 nullable，因为 repeated primitive 不提供额外 optional element level。 +- array 自身 nullable 仍由 outer LIST group 决定。 + +### 3. Repeated group as struct element + +```text +optional group a (LIST) { + repeated group element { + optional int32 x; + optional binary y; + } +} +``` + +解析： + +- repeated group 有多个 children。 +- repeated group 本身是 element。 +- element type 是 `STRUCT`。 + +### 4. Legacy structural name + +Arrow 会将某些名字视作 structural element，而不是标准 wrapper。 + +```text +optional group a (LIST) { + repeated group array { + optional int32 item; + } +} +``` + +```text +optional group a (LIST) { + repeated group a_tuple { + optional int32 item; + } +} +``` + +解析： + +- repeated group 名为 `array`，或名为 `_tuple`。 +- repeated group 本身是 element。 +- 即使它只有一个 child，也不要剥掉这一层。 + +### 5. One-child repeated group wrapper + +```text +optional group a (LIST) { + repeated group list { + optional int32 element; + } +} +``` + +如果 repeated group 只有一个 child，且不是 legacy structural name，则按 wrapper 处理： + +- element 是 repeated group 的唯一 child。 + +但这里不能只按 child 数量判断。需要额外保持 Arrow / parquet-format 的 backward compatibility 规则： + +- 如果 repeated group 自身带 `LIST` 或 `MAP` annotation，则 repeated group 本身是 element，不剥 wrapper。 +- 如果 repeated group 的唯一 child 也是 repeated，则 repeated group 本身是 element，不剥 wrapper。 +- 只有当 repeated group 无 logical annotation、唯一 child 非 repeated、且不是 legacy structural name 时，才把它当作标准 wrapper 剥掉。 + +这样可以避免把 two-level `List>`、two-level `List>` 或单字段 repeated struct element 错解析成少一层的结构。 + +## LIST schema resolver + +建议在 `parquet_column_schema.cpp` 中新增 helper： + +```cpp +struct ListElementResolution { + const parquet::schema::Node* repeated_node = nullptr; + const parquet::schema::Node* element_node = nullptr; + SchemaBuildContext repeated_context; + SchemaBuildContext element_context; + bool element_is_repeated_node = false; +}; + +Status resolve_list_element_node( + const parquet::SchemaDescriptor& schema, + const parquet::schema::GroupNode& list_group, + const SchemaBuildContext& list_context, + ListElementResolution* result); +``` + +Resolver 逻辑： + +```text +if list_group.field_count != 1: + reject + +repeated_node = list_group.field(0) +if !repeated_node.is_repeated: + reject + +repeated_context = child_context(list_context, repeated_node, 0) + +if repeated_node.is_primitive: + element_node = repeated_node + element_context = repeated_context + element_is_repeated_node = true + return + +repeated_group = as_group(repeated_node) +if repeated_group.field_count == 0: + reject + +if repeated_group.field_count > 1: + element_node = repeated_node + element_context = repeated_context + element_is_repeated_node = true + return + +if has_structural_list_name(list_group.name, repeated_group.name): + element_node = repeated_node + element_context = repeated_context + element_is_repeated_node = true + return + +if repeated_group has LIST or MAP annotation: + element_node = repeated_node + element_context = repeated_context + element_is_repeated_node = true + return + +only_child = repeated_group.field(0) +if only_child.is_repeated: + element_node = repeated_node + element_context = repeated_context + element_is_repeated_node = true + return + +element_node = only_child +element_context = child_context(repeated_context, only_child, 0) +element_is_repeated_node = false +``` + +`has_structural_list_name()` 对齐 Arrow 的 legacy rule： + +```text +name == "array" || name == list_name + "_tuple" +``` + +## LIST schema build + +`build_node_schema()` 的 LIST 分支改为： + +```text +resolve_list_element_node(...) + +column_schema.kind = LIST +column_schema.definition_level = repeated_context.definition_level +column_schema.repetition_level = repeated_context.repetition_level +column_schema.repeated_repetition_level = repeated_context.repeated_repetition_level + +build child schema from resolved element_node and element_context +column_schema.type = nullable_if_needed(DataTypeArray(child.type), list_node) +column_schema.children = [child] +propagate_child_levels(column_schema) +``` + +### repeated group itself as element + +当 element 是 repeated group 本身时，需要注意不要把这个 repeated group 再解释成一层 LIST。 + +预期效果： + +```text +optional group a (LIST) { + repeated group element { + optional int32 x; + optional binary y; + } +} +``` + +应构造成： + +```text +LIST + child: STRUCT +``` + +而不是： + +```text +LIST + child: LIST or extra repeated container +``` + +实现上可以新增一个 internal build mode： + +```cpp +enum class SchemaBuildMode { + NORMAL, + REPEATED_GROUP_AS_LIST_ELEMENT, +}; +``` + +当 mode 是 `REPEATED_GROUP_AS_LIST_ELEMENT`： + +- 当前 repeated group 作为 element 本身构造成 STRUCT 或 annotated logical type。 +- 它的 repeated level 已经由 list entry 层消费，不再把 repeated 当作额外 array 层。 +- 如果当前 repeated group 是普通 group，则构造成 `STRUCT` element。 +- 如果当前 repeated group 带 `LIST` annotation，则继续按 LIST 解析它的 child repeated layer，构造成 nested list element。 +- 如果当前 repeated group 带 `MAP` 或 `MAP_KEY_VALUE` annotation，则继续按 MAP 解析它的 child repeated entry layer，构造成 map element。 +- 构造当前 element schema 时，不得再次因为“当前节点本身是 repeated”引入隐式 list；只有它内部的 child repeated layer 才能产生下一层 list/map repetition 语义。 + +如果希望保持改动更小，也可以新增专用函数： + +```cpp +Status build_repeated_group_as_list_element_schema(...); +``` + +该函数至少需要处理 repeated group 作为普通 struct element 的场景；如果选择不用通用 build mode，则还需要显式覆盖 repeated group annotated as LIST/MAP 的场景。 + +## MAP 兼容规则 + +对于 outer group annotated as `MAP` 或 legacy `MAP_KEY_VALUE`： + +```text +optional group m (MAP) { + repeated group entries { + required binary key; + optional int32 value; + } +} +``` + +支持： + +- 只有 outer group 带 `MAP` / `MAP_KEY_VALUE` annotation 时，才进入 MAP 兼容解析。 +- entry group 名字可以是 `key_value`、`entries` 或其它。 +- key/value 字段名不强制必须叫 `key` / `value`。 +- 第一个 child 是 key。 +- 第二个 child 是 value。 +- key 必须 required。 +- value 可以 required 或 optional。 + +不支持： + +- outer MAP group 多个 children。 +- entry child 非 repeated。 +- entry child 是 primitive。 +- entry group 没有 value，即 key-only map。 +- 没有 repeated entry layer 的 MAP。 +- nullable key。 + +## MAP schema resolver + +建议新增 helper： + +```cpp +struct MapEntryResolution { + const parquet::schema::GroupNode* entry_group = nullptr; + SchemaBuildContext entry_context; +}; + +Status resolve_map_entry_group( + const parquet::schema::GroupNode& map_group, + const SchemaBuildContext& map_context, + MapEntryResolution* result); +``` + +Resolver 逻辑： + +```text +if map_group.field_count != 1: + reject + +entry_node = map_group.field(0) +if !entry_node.is_repeated: + reject +if entry_node.is_primitive: + reject + +entry_group = as_group(entry_node) +if entry_group.field_count != 2: + reject + +key_node = entry_group.field(0) +value_node = entry_group.field(1) +if key_node.repetition != REQUIRED: + reject + +entry_context = child_context(map_context, entry_node, 0) +return +``` + +## MAP schema build + +`build_node_schema()` 的 MAP 分支应和 LIST 一样在 schema 构建阶段折叠物理 wrapper。 +`key_value` / `entries` / 任意合法 entry group 只用于解析 repeated entry level，不出现在 +最终 `ParquetColumnSchema.children` 中： + +```text +MAP + child[0]: key + child[1]: value +``` + +构造流程： + +```text +resolve_map_entry_group(...) + +column_schema.kind = MAP +column_schema.definition_level = entry_context.definition_level +column_schema.repetition_level = entry_context.repetition_level +column_schema.repeated_repetition_level = entry_context.repeated_repetition_level + +build key child from entry_group.field(0) +build value child from entry_group.field(1) + +column_schema.type = nullable_if_needed(DataTypeMap(nullable(key.type), nullable(value.type)), map_node) +column_schema.children = [key_schema, value_schema] +propagate_child_levels(column_schema) +``` + +这里保持 `MapColumnReader` 的直接 key/value 假设： + +- `column_schema.children[0]` 是 key。 +- `column_schema.children[1]` 是 value。 +- MAP node 自身保存 entry repeated group 的 `definition_level` / `repetition_level` / + `repeated_repetition_level`，用于 materialize offsets、null map 和 empty map。 + +注意：`DataTypeMap` 中把 key type 包成 nullable 是 Doris nested column materialization 的内部类型约定，不代表 Parquet nullable key 被支持。Schema resolver 仍必须在 `key_node.repetition != REQUIRED` 时 reject。 + +## 不支持 key-only map 的原因 + +Key-only map 可能长这样： + +```text +optional group m (MAP) { + repeated group entries { + required binary key; + } +} +``` + +理论上可以解释为 set-like map 或 `MAP`，但 Doris `ColumnMap` 需要 keys column 和 values column。 + +若要支持，需要额外设计： + +- synthetic null value schema。 +- constant-null value reader。 +- `MapColumnReader` value stream 缺失时的特殊路径。 + +这会改变 reader tree，不属于本次 schema compatibility 的最小范围。因此第一阶段明确 reject。 + +## 不支持 no-entry MAP 的原因 + +No-entry MAP 可能长这样： + +```text +optional group m (MAP) { + required binary key; + optional int32 value; +} +``` + +它缺少 repeated entry layer，因此没有 repetition level 可以表达多个 map entries，也无法生成 Doris `ColumnMap` offsets。 + +这不是标准 MAP，也不是 Arrow 主要兼容的 legacy 形态。第一阶段应 reject。 + +## 对 reader 层的影响 + +预期不修改 reader 层核心逻辑。 + +保持： + +- `ListColumnReader` 只读取 `column_schema.children[0]` 作为 element reader。 +- `MapColumnReader` 读取 `column_schema.children[0/1]` 作为 key/value reader。 +- `MapColumnReader` 对 partial MAP projection 只接受 value child projection，显式 key child projection 应 reject；即使只裁剪 value，reader 也必须完整读取 key stream。 +- `ParquetLeafReader` 只负责 leaf records/levels/values 读取和 batch materialization。 +- `nested_column_materializer.*` 只负责 Doris nested Column 构造 helper。 + +风险点在 LIST repeated group as element： + +- 如果该 repeated group 是 struct element，需要确保 schema builder 不把 repeated group 再解释成一个额外 repeated container。 +- 这个风险应通过专用 build mode 或专用 helper 解决。 + +## 错误处理策略 + +错误信息应明确指出具体 unsupported schema 原因： + +- LIST outer group child count invalid。 +- LIST child is not repeated。 +- LIST repeated group has no child。 +- MAP outer group child count invalid。 +- MAP entry is not repeated group。 +- MAP entry child count is not 2。 +- MAP key is nullable。 + +不要用过于笼统的 `Unsupported parquet LIST encoding` 覆盖所有错误，否则后续排查文件兼容性问题会困难。 + +## 测试计划 + +### LIST 正例 + +1. 标准 3-level LIST： + +```text +optional group a (LIST) { + repeated group list { + optional int32 element; + } +} +``` + +2. Repeated primitive legacy LIST： + +```text +optional group a (LIST) { + repeated int32 element; +} +``` + +3. Repeated group struct element： + +```text +optional group a (LIST) { + repeated group element { + optional int32 x; + optional binary y; + } +} +``` + +4. Legacy `array` name： + +```text +optional group a (LIST) { + repeated group array { + optional int32 item; + } +} +``` + +5. Legacy `_tuple` name： + +```text +optional group a (LIST) { + repeated group a_tuple { + optional int32 item; + } +} +``` + +6. Repeated group annotated as nested LIST： + +```text +optional group a (LIST) { + repeated group array (LIST) { + repeated int32 array; + } +} +``` + +预期解析为 `ARRAY>`，不要剥掉 `array (LIST)` 这一层。 + +7. Repeated group annotated as MAP： + +```text +optional group a (LIST) { + repeated group array (MAP) { + repeated group key_value { + required binary key; + optional int32 value; + } + } +} +``` + +预期解析为 `ARRAY>`，不要剥掉 `array (MAP)` 这一层。 + +8. One-child repeated group whose child is repeated： + +```text +optional group a (LIST) { + repeated group element { + repeated int32 items; + } +} +``` + +预期 repeated group 本身是 struct element，解析为 `ARRAY>>`，不要把 `items` 提升成 list element。 + +### LIST 反例 + +1. outer LIST group 多 child。 +2. outer LIST child 非 repeated。 +3. repeated group 无 child。 +4. repeated LIST-annotated outer group，除非它作为 another two-level LIST 的 element 被专门支持。 + +### MAP 正例 + +1. 标准 `key_value` entry group。 +2. `entries` entry group name。 +3. entry group 任意名字，但结构为 repeated group with required key and value。 +4. `MAP_KEY_VALUE` legacy converted type。 +5. key/value 字段名非 `key`/`value`，但位置正确。 + +### MAP 反例 + +1. nullable key。 +2. outer MAP group 多 child。 +3. entry child 非 repeated。 +4. entry child 是 primitive。 +5. key-only map。 +6. no-entry MAP。 + +## 实施步骤 + +1. 在 `parquet_column_schema.cpp` 增加 LIST helper： + - `has_structural_list_name()` + - `resolve_list_element_node()` + - 必要时增加 repeated group as element 的 build helper。 +2. 改造 LIST 分支，输出统一 `ParquetColumnSchemaKind::LIST` schema tree。 +3. 增加 LIST schema/unit/regression 测试。 + - 覆盖 repeated primitive、multi-field struct element、`array` / `_tuple` structural name。 + - 覆盖 two-level `List>`、two-level `List>`、单 child repeated group 且 child repeated 的 struct element。 + - read 测试至少覆盖 null list、empty list、单元素、多元素，验证 def/rep materialization。 +4. 增加 MAP helper： + - `resolve_map_entry_group()` +5. 改造 MAP 分支，放宽 entry group 名字限制，但保持 key/value 结构严格，并在 schema build 阶段折叠 entry wrapper，输出 `MAP -> key,value`。 +6. 增加 MAP schema/unit/regression 测试。 + - 覆盖 entry group 名字兼容。 + - 覆盖 `ParquetColumnSchema(MAP).children == [key, value]`。 + - 覆盖 partial MAP projection 只允许 value child，key child projection reject。 +7. 如后续确有需求，再单独设计 key-only map 或 key subtree projection 支持。 + +## 预期收益 + +- 支持更多由 Arrow、Spark、Hive、旧 Parquet writer 产生的 LIST/MAP schema。 +- 兼容逻辑集中在 schema builder，reader 层保持稳定。 +- 为后续 complex parquet reader 的兼容性测试建立清晰边界。 From 9166a9a4325b9e01d2504bc1e80dde28d6ef9987 Mon Sep 17 00:00:00 2001 From: zhangstar333 Date: Fri, 12 Jun 2026 21:26:00 +0800 Subject: [PATCH 04/28] support truncate_char_or_varchar (#64463) --- be/src/format_v2/table_reader.h | 90 +++++++++++++++++++++++++ be/test/format_v2/table_reader_test.cpp | 53 +++++++++++++++ 2 files changed, 143 insertions(+) diff --git a/be/src/format_v2/table_reader.h b/be/src/format_v2/table_reader.h index 3939a56742d013..4d425555682401 100644 --- a/be/src/format_v2/table_reader.h +++ b/be/src/format_v2/table_reader.h @@ -43,8 +43,10 @@ #include "core/data_type/data_type_map.h" #include "core/data_type/data_type_nullable.h" #include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" #include "core/data_type/data_type_struct.h" #include "core/field.h" +#include "exec/common/stringop_substring.h" #include "exprs/vexpr.h" #include "exprs/vexpr_context.h" #include "exprs/vexpr_fwd.h" @@ -551,12 +553,100 @@ class TableReader { idx++; } RETURN_IF_ERROR(materialize_virtual_columns(block)); + // Truncate CHAR/VARCHAR columns when target size is smaller than file schema. + RETURN_IF_ERROR(_truncate_char_or_varchar_columns(block)); return Status::OK(); } // Materialize virtual columns in table block, such as _row_id and _last_updated_sequence_number in Iceberg. This is called after finalize_chunk, so the virtual column can be referenced in finalize_expr. virtual Status materialize_virtual_columns(Block* table_block) { return Status::OK(); } + Status _truncate_char_or_varchar_columns(Block* block) { + DORIS_CHECK(block != nullptr); + if (_runtime_state == nullptr || + !_runtime_state->query_options().truncate_char_or_varchar_columns) { + return Status::OK(); + } + DORIS_CHECK(block->columns() == _data_reader.column_mapper.mappings().size()); + for (size_t idx = 0; idx < _data_reader.column_mapper.mappings().size(); ++idx) { + const auto& mapping = _data_reader.column_mapper.mappings()[idx]; + if (!_should_truncate_char_or_varchar_column(mapping)) { + continue; + } + const auto target_len = + assert_cast(remove_nullable(mapping.table_type).get()) + ->len(); + _truncate_char_or_varchar_column(block, idx, target_len); + } + return Status::OK(); + } + + // Only truncate CHAR/VARCHAR columns when target length is smaller than file schema, eg: varchar(10) vs varchar(20), + // or file schema doesn't have length limit but table schema has. eg: varchar(10) vs string + static bool _should_truncate_char_or_varchar_column(const ColumnMapping& mapping) { + if (mapping.table_type == nullptr) { + return false; + } + const auto table_type = remove_nullable(mapping.table_type); + const auto primitive_type = table_type->get_primitive_type(); + if (primitive_type != TYPE_VARCHAR && primitive_type != TYPE_CHAR) { + return false; + } + const auto target_len = assert_cast(table_type.get())->len(); + if (target_len <= 0) { + return false; + } + if (mapping.file_type == nullptr) { + return true; + } + const auto file_type = remove_nullable(mapping.file_type); + DORIS_CHECK(file_type != nullptr); + int file_len = -1; + if (file_type->get_primitive_type() == TYPE_VARCHAR || + file_type->get_primitive_type() == TYPE_CHAR || + file_type->get_primitive_type() == TYPE_STRING) { + file_len = assert_cast(file_type.get())->len(); + } + + return file_len < 0 || target_len < file_len; + } + + // VARCHAR substring(VARCHAR str, INT pos[, INT len]) + static void _truncate_char_or_varchar_column(Block* block, size_t idx, int len) { + DORIS_CHECK(block != nullptr); + auto int_type = std::make_shared(); + const auto num_columns_without_result = cast_set(block->columns()); + auto& target = block->get_by_position(idx); + const bool is_nullable = target.type->is_nullable(); + ColumnPtr input_column = target.column; + ColumnPtr null_map_column; + if (is_nullable) { + const auto* nullable_column = assert_cast(target.column.get()); + input_column = nullable_column->get_nested_column_ptr(); + null_map_column = nullable_column->get_null_map_column_ptr(); + } + block->replace_by_position(idx, std::move(input_column)); + block->insert({int_type->create_column_const(block->rows(), to_field(1)), + int_type, "const 1"}); + block->insert({int_type->create_column_const(block->rows(), to_field(len)), + int_type, "const len"}); + block->insert({nullptr, std::make_shared(), "result"}); + + ColumnNumbers temp_arguments(3); + temp_arguments[0] = cast_set(idx); + temp_arguments[1] = num_columns_without_result; + temp_arguments[2] = num_columns_without_result + 1; + const uint32_t result_column_id = num_columns_without_result + 2; + SubstringUtil::substring_execute(*block, temp_arguments, result_column_id, block->rows()); + + ColumnPtr result_column = block->get_by_position(result_column_id).column; + if (is_nullable) { + result_column = ColumnNullable::create(std::move(result_column), null_map_column); + } + block->replace_by_position(idx, std::move(result_column)); + block->erase_tail(num_columns_without_result); + } + Status _try_materialize_aggregate_pushdown_rows(Block* block, bool* pushed_down) { DORIS_CHECK(block != nullptr); DORIS_CHECK(pushed_down != nullptr); diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index 55f9871994dc91..8333ef594f5985 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -1154,6 +1154,59 @@ ColumnDefinition make_file_column(int32_t id, const std::string& name, const Dat return field; } +class TableReaderCharVarcharTestHelper final : public TableReader { +public: + using TableReader::_should_truncate_char_or_varchar_column; + using TableReader::_truncate_char_or_varchar_column; +}; + +TEST(TableReaderTest, TruncateCharOrVarcharPredicateOnlyAppliesToParquetStringWidthMismatch) { + ColumnMapping mapping; + mapping.table_type = std::make_shared(3, TYPE_VARCHAR); + mapping.file_type = std::make_shared(10, TYPE_VARCHAR); + EXPECT_TRUE( + TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + + mapping.file_type = std::make_shared(2, TYPE_VARCHAR); + EXPECT_FALSE( + TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + + mapping.file_type = std::make_shared(); + EXPECT_TRUE( + TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + + mapping.file_type = std::make_shared(); + EXPECT_TRUE( + TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + + mapping.table_type = std::make_shared(); + EXPECT_FALSE( + TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); +} + +TEST(TableReaderTest, TruncateCharOrVarcharColumnKeepsNullMap) { + auto nested = ColumnString::create(); + nested->insert_data("abcdef", 6); + nested->insert_data("xyz", 3); + auto null_map = ColumnUInt8::create(); + null_map->insert_value(0); + null_map->insert_value(1); + + auto type = make_nullable(std::make_shared(3, TYPE_VARCHAR)); + Block block; + block.insert({ColumnNullable::create(std::move(nested), std::move(null_map)), type, "v"}); + + TableReaderCharVarcharTestHelper::_truncate_char_or_varchar_column(&block, 0, 3); + + ASSERT_EQ(block.columns(), 1); + ASSERT_EQ(block.rows(), 2); + const auto* nullable_column = + assert_cast(block.get_by_position(0).column.get()); + EXPECT_EQ(nullable_column->get_nested_column().get_data_at(0).to_string(), "abc"); + EXPECT_FALSE(nullable_column->is_null_at(0)); + EXPECT_TRUE(nullable_column->is_null_at(1)); +} + void set_name_identifiers(std::vector* columns); void set_name_identifier(ColumnDefinition* column) { From c82510fa8bbd4e7ce20347febc27eb5336134d01 Mon Sep 17 00:00:00 2001 From: Gabriel Date: Fri, 12 Jun 2026 22:58:12 +0800 Subject: [PATCH 05/28] Fix wrong map (#64474) --- be/src/exec/scan/file_scanner_v2.cpp | 54 +++++++--------- be/src/format_v2/column_mapper.cpp | 58 ++++++++++++++++- be/src/format_v2/file_reader.h | 12 +++- be/src/format_v2/parquet/parquet_reader.cpp | 5 +- .../parquet/reader/column_reader.cpp | 4 +- .../parquet/reader/parquet_leaf_reader.cpp | 8 ++- be/src/format_v2/schema_projection.cpp | 7 +- be/src/format_v2/schema_projection.h | 10 +-- .../scan/vfile_scanner_exception_test.cpp | 64 +++++++++++-------- be/test/format_v2/column_mapper_test.cpp | 14 ++-- .../parquet/parquet_column_reader_test.cpp | 21 +++++- .../format_v2/parquet/parquet_reader_test.cpp | 32 +++++++++- 12 files changed, 204 insertions(+), 85 deletions(-) diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp index d99a7dc836d332..45cb785f8dd863 100644 --- a/be/src/exec/scan/file_scanner_v2.cpp +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -237,15 +237,13 @@ Status build_nested_children_from_access_node(format::ColumnDefinition* column, // create children country and city, so city can be materialized as missing/default. // - ARRAY> should create the array element wrapper and then the element // struct children item and quantity. -// - MAP> should create the entries wrapper with key/value, then -// expand the value struct children full_name and age. +// - MAP> should create semantic children key/value directly, then +// expand the value struct children full_name and age. Do not introduce a physical entries +// wrapper here: ColumnMapper and TableReader treat MAP children as [key, value]. Status build_all_nested_children_from_schema(format::ColumnDefinition* column, const DataTypePtr& type, const std::string& path, const format::ColumnDefinition* schema_column) { DORIS_CHECK(column != nullptr); - if (schema_column == nullptr || schema_column->children.empty()) { - return Status::OK(); - } const auto nested_type = remove_nullable(type); AccessPathNode project_all; @@ -268,7 +266,10 @@ Status build_all_nested_children_from_schema(format::ColumnDefinition* column, } case TYPE_ARRAY: { const auto& array_type = assert_cast(*nested_type); - const auto* element_schema = &schema_column->children[0]; + const auto* element_schema = + schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), schema_field_name_or(element_schema, "element"), array_type.get_nested_type()); @@ -278,20 +279,20 @@ Status build_all_nested_children_from_schema(format::ColumnDefinition* column, } case TYPE_MAP: { const auto& map_type = assert_cast(*nested_type); - const auto* key_schema = &schema_column->children[0]; - const auto* value_schema = - schema_column->children.size() > 1 ? &schema_column->children[1] : nullptr; - DataTypes entry_child_types {map_type.get_key_type(), map_type.get_value_type()}; - Strings entry_child_names {"key", "value"}; - auto entry_type = std::make_shared(entry_child_types, entry_child_names); - auto* entry_child = find_or_add_child(column, 0, "entries", entry_type); + const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + const auto* value_schema = schema_column != nullptr && schema_column->children.size() > 1 + ? &schema_column->children[1] + : nullptr; auto* key_child = - find_or_add_child(entry_child, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + find_or_add_child(column, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), + map_type.get_key_type()); inherit_schema_metadata(key_child, key_schema); RETURN_IF_ERROR(build_nested_children_from_access_node( key_child, key_child->type, project_all, path + ".KEYS", key_schema)); - auto* value_child = find_or_add_child(entry_child, schema_field_id_or(value_schema, 1), + auto* value_child = find_or_add_child(column, schema_field_id_or(value_schema, 1), schema_field_name_or(value_schema, "value"), map_type.get_value_type()); inherit_schema_metadata(value_child, value_schema); @@ -395,22 +396,10 @@ Status build_map_children_from_access_node(format::ColumnDefinition* column, value_node.children.clear(); } - DataTypes entry_child_types; - Strings entry_child_names; - if (need_key) { - entry_child_types.push_back(map_type.get_key_type()); - entry_child_names.push_back("key"); - } - if (need_value) { - entry_child_types.push_back(map_type.get_value_type()); - entry_child_names.push_back("value"); - } - if (entry_child_types.empty()) { + if (!need_key && !need_value) { return Status::OK(); } - auto entry_type = std::make_shared(entry_child_types, entry_child_names); - auto* entry_child = find_or_add_child(column, 0, "entries", entry_type); const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() ? &schema_column->children[0] : nullptr; @@ -419,14 +408,15 @@ Status build_map_children_from_access_node(format::ColumnDefinition* column, : nullptr; if (need_key) { auto* key_child = - find_or_add_child(entry_child, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + find_or_add_child(column, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), + map_type.get_key_type()); inherit_schema_metadata(key_child, key_schema); RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, key_node, path + ".KEYS", key_schema)); } if (need_value) { - auto* value_child = find_or_add_child(entry_child, schema_field_id_or(value_schema, 1), + auto* value_child = find_or_add_child(column, schema_field_id_or(value_schema, 1), schema_field_name_or(value_schema, "value"), map_type.get_value_type()); inherit_schema_metadata(value_child, value_schema); diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index 2c89320dee5eb9..8c616230d68b4c 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -741,6 +741,8 @@ static VExprSPtr rewrite_table_expr_to_file_expr( static constexpr const char* ROW_LINEAGE_ROW_ID = "_row_id"; static constexpr const char* ROW_LINEAGE_LAST_UPDATED_SEQ_NUMBER = "_last_updated_sequence_number"; +static constexpr int32_t ROW_LINEAGE_ROW_ID_FIELD_ID = 2147483540; +static constexpr int32_t ROW_LINEAGE_LAST_UPDATED_SEQ_NUMBER_FIELD_ID = 2147483539; static TableVirtualColumnType row_lineage_virtual_column_type(const std::string& column_name) { if (column_name == ROW_LINEAGE_ROW_ID) { @@ -752,6 +754,33 @@ static TableVirtualColumnType row_lineage_virtual_column_type(const std::string& return TableVirtualColumnType::INVALID; } +static TableVirtualColumnType row_lineage_virtual_column_type_by_field_id( + const ColumnDefinition& column) { + if (!column.has_identifier_field_id()) { + return TableVirtualColumnType::INVALID; + } + switch (column.get_identifier_field_id()) { + case ROW_LINEAGE_ROW_ID_FIELD_ID: + return TableVirtualColumnType::ROW_ID; + case ROW_LINEAGE_LAST_UPDATED_SEQ_NUMBER_FIELD_ID: + return TableVirtualColumnType::LAST_UPDATED_SEQUENCE_NUMBER; + default: + return TableVirtualColumnType::INVALID; + } +} + +static TableVirtualColumnType row_lineage_virtual_column_type(const ColumnDefinition& column, + TableColumnMappingMode mode) { + switch (mode) { + case TableColumnMappingMode::BY_FIELD_ID: + return row_lineage_virtual_column_type_by_field_id(column); + case TableColumnMappingMode::BY_NAME: + case TableColumnMappingMode::BY_INDEX: + return row_lineage_virtual_column_type(column.name); + } + return TableVirtualColumnType::INVALID; +} + // Returns true when the current file type is not the exact nested type the scan should expose. // This is about building the projected file-side type/projection, not about whether TableReader // later needs to rematerialize the complex value back to table layout. @@ -783,6 +812,31 @@ static bool needs_projected_file_type_rebuild(const ColumnMapping& mapping) { return false; } +static const ColumnDefinition* find_file_child_for_mapping( + const ColumnDefinition& table_child, const ColumnDefinition& file_parent, + TableColumnMappingMode mode) { + const auto file_parent_type = remove_nullable(file_parent.type)->get_primitive_type(); + switch (file_parent_type) { + case TYPE_ARRAY: + DORIS_CHECK(file_parent.children.size() == 1); + return &file_parent.children[0]; + case TYPE_MAP: + DORIS_CHECK(file_parent.children.size() == 2); + if (table_child.name == "key") { + return &file_parent.children[0]; + } + if (table_child.name == "value") { + return &file_parent.children[1]; + } + if (table_child.local_id == 0 || table_child.local_id == 1) { + return &file_parent.children[table_child.local_id]; + } + return nullptr; + default: + return matcher_for_mode(mode).find(table_child, file_parent.children); + } +} + static bool has_projected_file_children(const ColumnMapping& mapping) { if (mapping.original_file_children.empty() || mapping.projected_file_children.empty()) { return false; @@ -1161,7 +1215,7 @@ Status TableColumnMapper::_create_mapping_for_column(const ColumnDefinition& tab mapping->global_index = global_index; mapping->table_column_name = table_column.name; mapping->table_type = table_column.type; - const auto row_lineage_type = row_lineage_virtual_column_type(table_column.name); + const auto row_lineage_type = row_lineage_virtual_column_type(table_column, _options.mode); if (const auto* partition_value = find_partition_value(table_column, _partition_values); table_column.is_partition_key && partition_value != nullptr) { // Partition values are split constants and must take precedence over defaults. @@ -1518,7 +1572,7 @@ Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_c DORIS_CHECK(is_complex_type(mapping->file_type->get_primitive_type())); for (const auto& table_child : table_column.children) { const auto* file_child = - matcher_for_mode(_options.mode).find(table_child, file_field.children); + find_file_child_for_mapping(table_child, file_field, _options.mode); if (file_child == nullptr) { ColumnMapping child_mapping; child_mapping.table_column_name = table_child.name; diff --git a/be/src/format_v2/file_reader.h b/be/src/format_v2/file_reader.h index b516a0c406d929..c535932f7a1ccb 100644 --- a/be/src/format_v2/file_reader.h +++ b/be/src/format_v2/file_reader.h @@ -218,8 +218,16 @@ class FileReader { // but it must not interpret table-format semantics such as Iceberg name mapping, // default/generated columns, or partition columns. File-format physical wrappers should be // normalized away before exposing this schema; for example, Parquet MAP is exposed as key/value - // children rather than key_value/entry. This method can only be called after init() - // successfully, but does not require open() to be called. + // children rather than key_value/entry. + // + // Doris plans external-table scan types as nullable, including all nested children of complex + // types. This protects Doris from illegal or inconsistent values produced by external systems. + // Therefore every ColumnDefinition::type returned here must be nullable. Complex types must + // also expose nullable child types recursively, even if the physical file marks those fields as + // required. + // + // This method can only be called after init() successfully, but does not require open() to be + // called. virtual Status get_schema(std::vector* file_schema) const = 0; // Open the file reader with file-local scan request. The file reader should initialize its internal state according to the request, but does not need to interpret table/global schema semantics. For example, all schema change, filter localization, default/generated/partition columns should be handled in table reader layer. This method can only be called after init() successfully. diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index 420fe5c27c29e8..075b29041f364e 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -27,6 +27,7 @@ #include "core/assert_cast.h" #include "core/block/block.h" +#include "core/data_type/data_type_nullable.h" #include "format_v2/parquet/parquet_column_schema.h" #include "format_v2/parquet/parquet_file_context.h" #include "format_v2/parquet/parquet_scan.h" @@ -89,7 +90,9 @@ void ParquetReader::_fill_column_definition(const ParquetColumnSchema& column_sc } field->local_id = column_schema.local_id; field->name = column_schema.name; - field->type = column_schema.type; + field->type = column_schema.type != nullptr && !column_schema.type->is_nullable() + ? make_nullable(column_schema.type) + : column_schema.type; field->children.clear(); field->children.reserve(column_schema.children.size()); for (const auto& child : column_schema.children) { diff --git a/be/src/format_v2/parquet/reader/column_reader.cpp b/be/src/format_v2/parquet/reader/column_reader.cpp index 78ba7f0086e127..d0bdf802edaf95 100644 --- a/be/src/format_v2/parquet/reader/column_reader.cpp +++ b/be/src/format_v2/parquet/reader/column_reader.cpp @@ -407,9 +407,7 @@ Status ParquetColumnReaderFactory::create_map_column_reader( } for (const auto& child_projection : projection->children) { if (child_projection.local_id() == key_schema.local_id) { - return Status::NotSupported( - "Parquet MAP projection for column {} does not support key child", - column_schema.name); + continue; } if (child_projection.local_id() != value_schema.local_id) { return Status::InvalidArgument( diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp index a9c0fd7f7a4a8e..7c06d29a175187 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -302,7 +302,13 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r { SCOPED_TIMER(_profile.materialization_time); - RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values(*column, view)); + DCHECK(column->is_nullable()); + if (!_type->is_nullable()) { + RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values( + assert_cast(column.get())->get_nested_column(), view)); + } else { + RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values(*column, view)); + } } return Status::OK(); } diff --git a/be/src/format_v2/schema_projection.cpp b/be/src/format_v2/schema_projection.cpp index 57a9b4d7a33512..342f4c91898c92 100644 --- a/be/src/format_v2/schema_projection.cpp +++ b/be/src/format_v2/schema_projection.cpp @@ -72,11 +72,10 @@ Status rebuild_semantic_projected_type(const DataTypePtr& original_type, // Partial MAP projection only prunes the value subtree. The key stream must remain // complete because it defines entry existence and offsets when materializing ColumnMap; // the projected DataTypeMap also preserves the original key type instead of rebuilding - // it from children. + // it from children. If a caller includes key in the semantic child list, ignore it + // here; the presence of a value child still decides the projected value shape. if (child.file_local_id() == 0 || child.name == "key") { - return Status::NotSupported( - "MAP projection for type {} does not support key child projection", - original_type->get_name()); + continue; } if (child.file_local_id() == 1 || child.name == "value") { value_type = child.type; diff --git a/be/src/format_v2/schema_projection.h b/be/src/format_v2/schema_projection.h index 7dd2c3cf827308..c2125d66931631 100644 --- a/be/src/format_v2/schema_projection.h +++ b/be/src/format_v2/schema_projection.h @@ -46,11 +46,11 @@ namespace doris::format { // self-consistent. STRUCT uses projected child names/types, ARRAY uses the projected element // type, and MAP preserves the original key type while rebuilding the projected value type. // -// A full projection copies `field` unchanged. Partial MAP projection is intentionally limited to -// the value child. MAP is materialized as offsets + keys + values, so the reader must still read -// the complete key stream to build entry shape and offsets. The projected DataTypeMap keeps the -// original key type and only rebuilds the value type from the projected value child; key-only or -// key-pruned MAP projections are rejected. +// A full projection copies `field` unchanged. Partial MAP projection only uses the value child for +// type rebuilding. MAP is materialized as offsets + keys + values, so the reader must still read +// the complete key stream to build entry shape and offsets. If the semantic projection includes +// the key child, it is ignored here; key-only MAP projections are rejected because they do not +// define a value shape. Status project_column_definition(const ColumnDefinition& field, const LocalColumnIndex& projection, ColumnDefinition* projected_field); diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index f11717f2ab1b1c..6d0695b63bf172 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -524,18 +524,13 @@ TEST(FileScannerV2Test, BuildNestedChildrenFromAccessPaths) { FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); ASSERT_TRUE(status.ok()) << status; - ASSERT_EQ(column.children.size(), 1); - const auto& entries = column.children[0]; - ASSERT_TRUE(entries.has_identifier_field_id()); - EXPECT_EQ(entries.get_identifier_field_id(), 0); - EXPECT_EQ(entries.name, "entries"); - ASSERT_EQ(entries.children.size(), 2); - EXPECT_EQ(entries.children[0].name, "key"); - EXPECT_TRUE(entries.children[0].children.empty()); - EXPECT_EQ(entries.children[1].name, "value"); - ASSERT_EQ(entries.children[1].children.size(), 2); - EXPECT_EQ(entries.children[1].children[0].name, "b"); - EXPECT_EQ(entries.children[1].children[1].name, "c"); + ASSERT_EQ(column.children.size(), 2); + EXPECT_EQ(column.children[0].name, "key"); + EXPECT_TRUE(column.children[0].children.empty()); + EXPECT_EQ(column.children[1].name, "value"); + ASSERT_EQ(column.children[1].children.size(), 2); + EXPECT_EQ(column.children[1].children[0].name, "b"); + EXPECT_EQ(column.children[1].children[1].name, "c"); } TEST(FileScannerV2Test, BuildArrayStructChildrenFromAccessPaths) { @@ -658,6 +653,21 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn EXPECT_EQ(struct_column.children[1].get_identifier_field_id(), 205); EXPECT_EQ(struct_column.children[1].name_mapping, std::vector({"old_city"})); + const auto pruned_struct_type = + std::make_shared(DataTypes {int_type}, Strings {"drop_and_add"}); + format::ColumnDefinition pruned_struct_column { + .identifier = Field::create_field(110), + .name = "a_struct", + .type = pruned_struct_type, + }; + std::vector pruned_struct_paths {data_access_path({"a_struct"})}; + status = FileScannerV2::TEST_build_nested_children_from_access_paths( + &pruned_struct_column, pruned_struct_paths); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(pruned_struct_column.children.size(), 1); + EXPECT_EQ(pruned_struct_column.children[0].name, "drop_and_add"); + EXPECT_EQ(pruned_struct_column.children[0].get_identifier_field_id(), 0); + const auto element_type = std::make_shared(DataTypes {string_type, int_type}, Strings {"item", "quantity"}); const auto array_type = std::make_shared(element_type); @@ -700,8 +710,8 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn EXPECT_EQ(element.children[0].name_mapping, std::vector({"old_item"})); EXPECT_EQ(element.children[1].get_identifier_field_id(), 203); - const auto value_type = std::make_shared(DataTypes {string_type, int_type}, - Strings {"full_name", "age"}); + const auto value_type = std::make_shared( + DataTypes {string_type, int_type, string_type}, Strings {"full_name", "age", "gender"}); const auto map_type = std::make_shared(string_type, value_type); format::ColumnDefinition map_column { .identifier = Field::create_field(300), @@ -729,6 +739,9 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn {.identifier = Field::create_field(304), .name = "age", .type = int_type}, + {.identifier = Field::create_field(305), + .name = "gender", + .type = string_type}, }}, }, }; @@ -736,18 +749,17 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn status = FileScannerV2::TEST_build_nested_children_from_access_paths(&map_column, map_paths, &map_schema); ASSERT_TRUE(status.ok()) << status; - ASSERT_EQ(map_column.children.size(), 1); - const auto& entries = map_column.children[0]; - EXPECT_EQ(entries.name, "entries"); - ASSERT_EQ(entries.children.size(), 2); - EXPECT_EQ(entries.children[0].name, "key"); - EXPECT_EQ(entries.children[0].get_identifier_field_id(), 301); - EXPECT_EQ(entries.children[1].name, "value"); - EXPECT_EQ(entries.children[1].get_identifier_field_id(), 302); - ASSERT_EQ(entries.children[1].children.size(), 2); - EXPECT_EQ(entries.children[1].children[0].get_identifier_field_id(), 303); - EXPECT_EQ(entries.children[1].children[0].name_mapping, std::vector({"name"})); - EXPECT_EQ(entries.children[1].children[1].get_identifier_field_id(), 304); + ASSERT_EQ(map_column.children.size(), 2); + EXPECT_EQ(map_column.children[0].name, "key"); + EXPECT_EQ(map_column.children[0].get_identifier_field_id(), 301); + EXPECT_EQ(map_column.children[1].name, "value"); + EXPECT_EQ(map_column.children[1].get_identifier_field_id(), 302); + ASSERT_EQ(map_column.children[1].children.size(), 3); + EXPECT_EQ(map_column.children[1].children[0].get_identifier_field_id(), 303); + EXPECT_EQ(map_column.children[1].children[0].name_mapping, std::vector({"name"})); + EXPECT_EQ(map_column.children[1].children[1].get_identifier_field_id(), 304); + EXPECT_EQ(map_column.children[1].children[2].get_identifier_field_id(), 305); + EXPECT_EQ(map_column.children[1].children[2].name, "gender"); } TEST(FileScannerV2Test, IcebergRowidSkipsNegativeAccessPath) { diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index dfb7d4d88db3af..b761b6fa15b9d0 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -402,15 +402,19 @@ TEST(ColumnMapperSchemaProjectionTest, ProjectsMapValueStructLeaf) { auto map = map_col("m", 100, {key, value}, str(), value_type, 9); LocalColumnIndex projection = LocalColumnIndex::partial_local(9); + projection.children.push_back(LocalColumnIndex::local(0)); auto value_projection = LocalColumnIndex::partial_local(1); value_projection.children.push_back(LocalColumnIndex::local(1)); projection.children.push_back(std::move(value_projection)); ColumnDefinition projected; ASSERT_TRUE(project_column_definition(map, projection, &projected).ok()); - ASSERT_EQ(projected.children.size(), 1); - ASSERT_EQ(projected.children[0].children.size(), 1); - EXPECT_EQ(projected.children[0].children[0].name, "b"); + ASSERT_EQ(projected.children.size(), 2); + EXPECT_EQ(projected.children[0].name, "key"); + EXPECT_TRUE(projected.children[0].children.empty()); + EXPECT_EQ(projected.children[1].name, "value"); + ASSERT_EQ(projected.children[1].children.size(), 1); + EXPECT_EQ(projected.children[1].children[0].name, "b"); const auto* map_type = assert_cast(remove_nullable(projected.type).get()); const auto* projected_value = @@ -419,7 +423,7 @@ TEST(ColumnMapperSchemaProjectionTest, ProjectsMapValueStructLeaf) { EXPECT_EQ(projected_value->get_element_name(0), "b"); } -TEST(ColumnMapperSchemaProjectionTest, RejectsMapKeyProjection) { +TEST(ColumnMapperSchemaProjectionTest, RejectsMapKeyOnlyProjection) { auto key = field_id_col("key", 1, str(), 0); auto value = field_id_col("value", 2, i32(), 1); auto map = map_col("m", 100, {key, value}, str(), i32(), 9); @@ -430,7 +434,7 @@ TEST(ColumnMapperSchemaProjectionTest, RejectsMapKeyProjection) { ColumnDefinition projected; const auto status = project_column_definition(map, projection, &projected); ASSERT_FALSE(status.ok()); - EXPECT_NE(status.to_string().find("does not support key child projection"), std::string::npos); + EXPECT_NE(status.to_string().find("contains no value child"), std::string::npos); } TEST(ColumnMapperSchemaProjectionTest, RejectsInvalidProjectionChildIdWithFieldName) { diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp index 384ccb0eeebee1..2edd4594f995cd 100644 --- a/be/test/format_v2/parquet/parquet_column_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -2393,7 +2393,7 @@ TEST_F(ParquetColumnReaderTest, ReadProjectedMapStructValueChildren) { EXPECT_EQ(b_data.get_data_at(3).to_string(), "me"); } -TEST_F(ParquetColumnReaderTest, RejectMapKeyProjection) { +TEST_F(ParquetColumnReaderTest, AllowsMapKeyWithValueProjection) { const auto field_idx = find_field_idx("nullable_map_int_struct_col"); ASSERT_LT(field_idx, _fields.size()); const auto& map_schema = *_fields[field_idx]; @@ -2405,11 +2405,28 @@ TEST_F(ParquetColumnReaderTest, RejectMapKeyProjection) { projection.children.push_back(format::LocalColumnIndex::local(key_schema.local_id)); projection.children.push_back(format::LocalColumnIndex::local(value_schema.local_id)); + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); + std::unique_ptr reader; + const auto st = factory.create(map_schema, &projection, &reader); + ASSERT_TRUE(st.ok()) << st; + ASSERT_NE(reader, nullptr); +} + +TEST_F(ParquetColumnReaderTest, RejectMapKeyOnlyProjection) { + const auto field_idx = find_field_idx("nullable_map_int_struct_col"); + ASSERT_LT(field_idx, _fields.size()); + const auto& map_schema = *_fields[field_idx]; + ASSERT_EQ(map_schema.children.size(), 2); + const auto& key_schema = *map_schema.children[0]; + + auto projection = format::LocalColumnIndex::partial_local(map_schema.local_id); + projection.children.push_back(format::LocalColumnIndex::local(key_schema.local_id)); + ParquetColumnReaderFactory factory(_row_group, _file_reader->metadata()->num_columns()); std::unique_ptr reader; const auto st = factory.create(map_schema, &projection, &reader); ASSERT_FALSE(st.ok()); - EXPECT_NE(st.to_string().find("does not support key child"), std::string::npos); + EXPECT_NE(st.to_string().find("contains no value"), std::string::npos); } TEST_F(ParquetColumnReaderTest, ReadProjectedStructListChildOnly) { diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index 03201551a861c1..e0b64df5a391e5 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -1768,10 +1768,38 @@ TEST_F(NewParquetReaderTest, GetSchemaReturnsFileLocalColumns) { ASSERT_EQ(schema.size(), 2); EXPECT_EQ(schema[0].local_id, 0); EXPECT_EQ(schema[0].name, "id"); - EXPECT_EQ(schema[0].type->get_primitive_type(), TYPE_INT); + ASSERT_TRUE(schema[0].type->is_nullable()); + EXPECT_EQ(remove_nullable(schema[0].type)->get_primitive_type(), TYPE_INT); EXPECT_EQ(schema[1].local_id, 1); EXPECT_EQ(schema[1].name, "value"); - EXPECT_EQ(schema[1].type->get_primitive_type(), TYPE_STRING); + ASSERT_TRUE(schema[1].type->is_nullable()); + EXPECT_EQ(remove_nullable(schema[1].type)->get_primitive_type(), TYPE_STRING); +} + +TEST_F(NewParquetReaderTest, GetSchemaReturnsNullableNestedChildren) { + write_struct_filter_parquet_file(_file_path); + auto reader = create_reader(); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + ASSERT_EQ(schema.size(), 1); + EXPECT_EQ(schema[0].name, "s"); + ASSERT_TRUE(schema[0].type->is_nullable()); + ASSERT_EQ(schema[0].children.size(), 2); + EXPECT_EQ(schema[0].children[0].name, "id"); + ASSERT_TRUE(schema[0].children[0].type->is_nullable()); + EXPECT_EQ(remove_nullable(schema[0].children[0].type)->get_primitive_type(), TYPE_INT); + EXPECT_EQ(schema[0].children[1].name, "name"); + ASSERT_TRUE(schema[0].children[1].type->is_nullable()); + EXPECT_EQ(remove_nullable(schema[0].children[1].type)->get_primitive_type(), TYPE_STRING); + + const auto* struct_type = + assert_cast(remove_nullable(schema[0].type).get()); + ASSERT_EQ(struct_type->get_elements().size(), 2); + EXPECT_TRUE(struct_type->get_element(0)->is_nullable()); + EXPECT_TRUE(struct_type->get_element(1)->is_nullable()); } TEST_F(NewParquetReaderTest, ReadSingleRowGroupThenEof) { From 67d2e6ee16eafe1a5ba76545718aa9a4aeea2579 Mon Sep 17 00:00:00 2001 From: Gabriel Date: Fri, 12 Jun 2026 23:33:32 +0800 Subject: [PATCH 06/28] fix (#64475) --- .../parquet/reader/parquet_leaf_reader.cpp | 20 ++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp index 7c06d29a175187..bb9fd33a1c513a 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -302,10 +302,24 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r { SCOPED_TIMER(_profile.materialization_time); - DCHECK(column->is_nullable()); if (!_type->is_nullable()) { - RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values( - assert_cast(column.get())->get_nested_column(), view)); + if (auto* nullable_column = check_and_get_column(*column); + nullable_column != nullptr) { + auto& nested_column = nullable_column->get_nested_column(); + auto& null_map = nullable_column->get_null_map_data(); + const auto old_nested_size = nested_column.size(); + const auto old_null_map_size = null_map.size(); + auto st = _type->get_serde()->read_column_from_decoded_values(nested_column, view); + if (!st.ok()) { + nested_column.resize(old_nested_size); + return st; + } + null_map.resize(old_null_map_size + nested_column.size() - old_nested_size); + memset(null_map.data() + old_null_map_size, 0, + null_map.size() - old_null_map_size); + } else { + RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values(*column, view)); + } } else { RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values(*column, view)); } From e9db34674cc5bc05abf3f97cbe0f9c35caa05529 Mon Sep 17 00:00:00 2001 From: Gabriel Date: Sat, 13 Jun 2026 06:46:02 +0800 Subject: [PATCH 07/28] fix compile (#64477) --- .../format_v2/parquet/reader/parquet_leaf_reader.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp index bb9fd33a1c513a..bb08f3d4b02d6d 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -306,17 +306,17 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r if (auto* nullable_column = check_and_get_column(*column); nullable_column != nullptr) { auto& nested_column = nullable_column->get_nested_column(); - auto& null_map = nullable_column->get_null_map_data(); + auto& tmp_null_map = nullable_column->get_null_map_data(); const auto old_nested_size = nested_column.size(); - const auto old_null_map_size = null_map.size(); + const auto old_null_map_size = tmp_null_map.size(); auto st = _type->get_serde()->read_column_from_decoded_values(nested_column, view); if (!st.ok()) { nested_column.resize(old_nested_size); return st; } - null_map.resize(old_null_map_size + nested_column.size() - old_nested_size); - memset(null_map.data() + old_null_map_size, 0, - null_map.size() - old_null_map_size); + tmp_null_map.resize(old_null_map_size + nested_column.size() - old_nested_size); + memset(tmp_null_map.data() + old_null_map_size, 0, + tmp_null_map.size() - old_null_map_size); } else { RETURN_IF_ERROR(_type->get_serde()->read_column_from_decoded_values(*column, view)); } From 0a92095214b60e3caffe3ee6933f6c93bbc3ef5f Mon Sep 17 00:00:00 2001 From: Gabriel Date: Sat, 13 Jun 2026 18:07:56 +0800 Subject: [PATCH 08/28] fix(column-mapper): handle nested predicates on evolved complex columns (#64480) ### What problem does this PR solve? Localize slot-rooted struct element predicates through ColumnMapping so renamed nested fields rewrite both selector names and projected file child return types. Keep computed complex parents and evolved MAP_KEYS-style filters at the table layer instead of generating unsafe file-local casts. Rebuild complex scan projections and rematerialize struct children in table type order, add debug-only block sanity checks with contextual errors, preserve function-call clone state, and handle nullable Iceberg delete columns. Move ColumnMapper coverage into column_mapper_test and add tests for complex child projection, nested predicate projection, map/array fallback, and file-local conjunct localization boundaries. ### Check List (For Author) - Test - [ ] Regression test - [ ] Unit Test - [ ] Manual test (add detailed scripts or steps below) - [ ] No need to test or manual test. Explain why: - [ ] This is a refactor/code format and no logic has been changed. - [ ] Previous test can cover this change. - [ ] No code files have been changed. - [ ] Other reason - Behavior changed: - [ ] No. - [ ] Yes. - Does this need documentation? - [ ] No. - [ ] Yes. ### Check List (For Reviewer who merge this PR) - [ ] Confirm the release note - [ ] Confirm test cases - [ ] Confirm document - [ ] Add branch pick label --- be/src/exprs/vectorized_fn_call.cpp | 4 +- be/src/exprs/vectorized_fn_call.h | 2 +- be/src/format_v2/column_mapper.cpp | 514 +++++++- be/src/format_v2/column_mapper_nested.cpp | 205 +++- be/src/format_v2/column_mapper_nested.h | 15 + .../parquet/reader/parquet_leaf_reader.cpp | 1 + be/src/format_v2/table/iceberg_reader.cpp | 4 +- be/src/format_v2/table_reader.h | 191 ++- be/test/format_v2/column_mapper_test.cpp | 800 ++++++++++++- .../format_v2/parquet/parquet_reader_test.cpp | 1031 ----------------- be/test/format_v2/table_reader_test.cpp | 433 +++---- 11 files changed, 1785 insertions(+), 1415 deletions(-) diff --git a/be/src/exprs/vectorized_fn_call.cpp b/be/src/exprs/vectorized_fn_call.cpp index d6858c612d09eb..4f37866c99e25b 100644 --- a/be/src/exprs/vectorized_fn_call.cpp +++ b/be/src/exprs/vectorized_fn_call.cpp @@ -80,7 +80,9 @@ const static std::set DISTANCE_FUNCS = {L2DistanceApproximate::name const static std::set OPS_FOR_ANN_RANGE_SEARCH = { TExprOpcode::GE, TExprOpcode::LE, TExprOpcode::LE, TExprOpcode::GT, TExprOpcode::LT}; -VectorizedFnCall::VectorizedFnCall(const TExprNode& node) : VExpr(node) {} +VectorizedFnCall::VectorizedFnCall(const TExprNode& node) : VExpr(node) { + _function_name = _fn.name.function_name; +} Status VectorizedFnCall::prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) { diff --git a/be/src/exprs/vectorized_fn_call.h b/be/src/exprs/vectorized_fn_call.h index 1d7de6d936f8fe..876812c05aa564 100644 --- a/be/src/exprs/vectorized_fn_call.h +++ b/be/src/exprs/vectorized_fn_call.h @@ -100,7 +100,7 @@ class VectorizedFnCall : public VExpr { Status clone_node(VExprSPtr* cloned_expr) const override { DORIS_CHECK(cloned_expr != nullptr); - *cloned_expr = VectorizedFnCall::create_shared(clone_texpr_node()); + *cloned_expr = std::make_shared(*this); return Status::OK(); } diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index 8c616230d68b4c..7ea568cd72d6b1 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -21,6 +21,7 @@ #include #include #include +#include #include #include @@ -28,7 +29,10 @@ #include "common/exception.h" #include "common/status.h" #include "core/data_type/convert_field_to_type.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" #include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_string.h" #include "core/data_type/data_type_struct.h" #include "core/data_type/primitive_type.h" #include "exprs/short_circuit_evaluation_expr.h" @@ -659,14 +663,118 @@ static bool rewrite_in_slot_literal_predicate( return true; } +static VExprSPtr create_file_struct_child_name_literal(const std::string& file_child_name, + RewriteContext* rewrite_context) { + auto literal = VLiteral::create_shared(std::make_shared(), + Field::create_field(file_child_name)); + rewrite_context->add_created_expr(literal); + return literal; +} + +static bool needs_complex_file_slot_cast(const DataTypePtr& file_type, + const DataTypePtr& table_type) { + if (file_type == nullptr || table_type == nullptr || file_type->equals(*table_type)) { + return false; + } + const auto file_nested_type = remove_nullable(file_type); + const auto table_nested_type = remove_nullable(table_type); + if (file_nested_type->equals(*table_nested_type)) { + return false; + } + return is_complex_type(file_nested_type->get_primitive_type()) || + is_complex_type(table_nested_type->get_primitive_type()); +} + +static bool collect_struct_element_chain(const VExprSPtr& expr, std::vector* chain) { + DORIS_CHECK(chain != nullptr); + if (!is_struct_element_expr(expr)) { + return false; + } + const auto& parent = expr->children()[0]; + if (is_struct_element_expr(parent)) { + if (!collect_struct_element_chain(parent, chain)) { + return false; + } + } else if (!parent->is_slot_ref()) { + // Only support file-local rewrite for struct child chains rooted directly at a top-level + // slot, for example `element_at(s, 'a')` or `element_at(element_at(s, 'a'), 'b')`. + // + // Do not localize computed complex parents such as + // `element_at(element_at(map_values(m), 1), 'full_name')`. The intermediate map/array + // result has already been reshaped by scan projection and may have a different child order + // from the table expression. Partially rewriting that expression against the file block can + // silently evaluate the wrong struct child and filter out valid rows. Those predicates must + // remain as table-level conjuncts and be evaluated after TableReader materialization. + return false; + } + chain->push_back(expr); + return true; +} + +static bool rewrite_struct_element_path_to_file_expr( + const VExprSPtr& expr, const std::vector& mappings, + const std::map& global_to_file_slot, + RewriteContext* rewrite_context) { + ResolvedNestedStructPath resolved; + if (!resolve_nested_struct_expr_for_file(expr, mappings, &resolved)) { + return false; + } + + std::vector struct_element_chain; + if (!collect_struct_element_chain(expr, &struct_element_chain) || + struct_element_chain.size() != resolved.file_child_names.size() || + struct_element_chain.size() != resolved.file_child_types.size()) { + return false; + } + + auto root_children = struct_element_chain.front()->children(); + if (!root_children[0]->is_slot_ref()) { + return false; + } + const auto* slot_ref = assert_cast(root_children[0].get()); + const auto rewrite_it = global_to_file_slot.find(slot_ref_global_index(*slot_ref)); + if (rewrite_it == global_to_file_slot.end()) { + return false; + } + + // File-local conjuncts are prepared against the file-reader Block, so both the root slot and + // every struct selector must be expressed in file schema terms. For a renamed Iceberg field, + // keeping the table selector would prepare `element_at(file_struct, 'renamed')` and + // fail before any rows are read. Rewrite the whole chain while ColumnMapping still preserves + // the table-to-file relationship. Example: + // table filter: element_at(element_at(s, 'renamed_parent'), 'renamed_leaf') + // old file: s> + // file filter: element_at(element_at(s, 'parent'), 'leaf') + root_children[0] = create_file_slot_ref(*slot_ref, rewrite_it->second, rewrite_context); + struct_element_chain.front()->set_children(std::move(root_children)); + for (size_t idx = 0; idx < struct_element_chain.size(); ++idx) { + auto children = struct_element_chain[idx]->children(); + children[1] = create_file_struct_child_name_literal(resolved.file_child_names[idx], + rewrite_context); + struct_element_chain[idx]->set_children(std::move(children)); + // The selector name and the expression return type must be moved to file schema together. + // Example: + // table filter: element_at(element_at(s, 'new_a'), 'new_aa') = 50 + // old file: s.new_a STRUCT + // file filter: element_at(element_at(s, 'new_a'), 'aa') = 50 + // + // If the inner element_at keeps the table return type STRUCT, preparing the + // outer element_at(..., 'aa') fails before scanning because `aa` is not a table field. + struct_element_chain[idx]->data_type() = resolved.file_child_types[idx]; + } + return true; +} + static VExprSPtr rewrite_table_expr_to_file_expr( const VExprSPtr& expr, const std::map& global_to_file_slot, - RewriteContext* rewrite_context) { + const std::vector& filter_mappings, RewriteContext* rewrite_context, + bool* can_localize) { if (expr == nullptr) { return nullptr; } DORIS_CHECK(rewrite_context != nullptr); + DORIS_CHECK(can_localize != nullptr); if (rewrite_binary_slot_literal_predicate(expr, global_to_file_slot, rewrite_context)) { return expr; } @@ -674,22 +782,16 @@ static VExprSPtr rewrite_table_expr_to_file_expr( return expr; } if (is_struct_element_expr(expr)) { - auto children = expr->children(); - if (children[0]->is_slot_ref()) { - const auto* slot_ref = assert_cast(children[0].get()); - const auto rewrite_it = global_to_file_slot.find(slot_ref_global_index(*slot_ref)); - if (rewrite_it != global_to_file_slot.end()) { - // struct_element must see the actual file struct layout. Casting the parent struct - // to the output projection can hide filter-only children such as `s.id` in - // `SELECT s.name WHERE s.id > 5`. - children[0] = create_file_slot_ref(*slot_ref, rewrite_it->second, rewrite_context); - expr->set_children(std::move(children)); - return expr; - } + if (!rewrite_struct_element_path_to_file_expr(expr, filter_mappings, global_to_file_slot, + rewrite_context)) { + // The scanner still evaluates the original table-level conjunct after TableReader + // finalizes the output block. Skipping an unlocalizable file conjunct is therefore + // safer than preparing a partially rewritten expression against the wrong struct + // layout. In particular, do not generate file-local conjuncts for computed complex + // parents such as `element_at(element_at(map_values(m), 1), 'field')`; only direct + // slot-rooted struct chains are supported here. + *can_localize = false; } - children[0] = - rewrite_table_expr_to_file_expr(children[0], global_to_file_slot, rewrite_context); - expr->set_children(std::move(children)); return expr; } if (expr->is_slot_ref()) { @@ -701,6 +803,23 @@ static VExprSPtr rewrite_table_expr_to_file_expr( if (rewrite_info.file_type->equals(*rewrite_info.table_type)) { return file_slot; } + if (needs_complex_file_slot_cast(rewrite_info.file_type, rewrite_info.table_type)) { + // Generic file-local expressions cannot safely cast an evolved complex file slot + // back to the table type. Example: + // + // table filter: ARRAY_CONTAINS(MAP_KEYS(m), 'person5') + // old file: m MAP> + // table: m MAP> + // + // Although MAP_KEYS only reads the key column, wrapping the file slot as + // `CAST(file_m AS table_m)` forces the value struct cast first and fails because + // the old and new value structs have different fields. Keep such filters at the + // table level, where TableReader materializes the evolved complex value before + // Scanner evaluates the original conjunct. Direct slot-rooted struct child paths + // are handled by rewrite_struct_element_path_to_file_expr() above. + *can_localize = false; + return expr; + } auto cast_expr = Cast::create_shared(rewrite_info.table_type); cast_expr->add_child(std::move(file_slot)); rewrite_context->add_created_expr(cast_expr); @@ -723,6 +842,11 @@ static VExprSPtr rewrite_table_expr_to_file_expr( if (rewrite_it->second.file_type->equals(*rewrite_it->second.table_type)) { return rewritten_child; } + if (needs_complex_file_slot_cast(rewrite_it->second.file_type, + rewrite_it->second.table_type)) { + *can_localize = false; + return expr; + } expr->set_children({std::move(rewritten_child)}); return expr; } @@ -732,8 +856,8 @@ static VExprSPtr rewrite_table_expr_to_file_expr( VExprSPtrs rewritten_children; rewritten_children.reserve(expr->children().size()); for (const auto& child : expr->children()) { - rewritten_children.push_back( - rewrite_table_expr_to_file_expr(child, global_to_file_slot, rewrite_context)); + rewritten_children.push_back(rewrite_table_expr_to_file_expr( + child, global_to_file_slot, filter_mappings, rewrite_context, can_localize)); } expr->set_children(std::move(rewritten_children)); return expr; @@ -770,7 +894,7 @@ static TableVirtualColumnType row_lineage_virtual_column_type_by_field_id( } static TableVirtualColumnType row_lineage_virtual_column_type(const ColumnDefinition& column, - TableColumnMappingMode mode) { + TableColumnMappingMode mode) { switch (mode) { case TableColumnMappingMode::BY_FIELD_ID: return row_lineage_virtual_column_type_by_field_id(column); @@ -812,9 +936,58 @@ static bool needs_projected_file_type_rebuild(const ColumnMapping& mapping) { return false; } -static const ColumnDefinition* find_file_child_for_mapping( - const ColumnDefinition& table_child, const ColumnDefinition& file_parent, - TableColumnMappingMode mode) { +static std::optional file_child_ordinal_in_scan_type(const ColumnMapping& mapping, + const ColumnMapping& child_mapping) { + if (!child_mapping.file_local_id.has_value()) { + return std::nullopt; + } + const auto& file_children = !mapping.projected_file_children.empty() + ? mapping.projected_file_children + : mapping.original_file_children; + const auto child_it = std::ranges::find_if(file_children, [&](const ColumnDefinition& child) { + return child.file_local_id() == *child_mapping.file_local_id; + }); + if (child_it == file_children.end()) { + return std::nullopt; + } + return static_cast(std::distance(file_children.begin(), child_it)); +} + +static bool needs_complex_rematerialize(const ColumnMapping& mapping) { + if (mapping.child_mappings.empty()) { + return false; + } + if (mapping.table_type == nullptr || mapping.file_type == nullptr || + !mapping.table_type->equals(*mapping.file_type)) { + return true; + } + for (size_t table_child_idx = 0; table_child_idx < mapping.child_mappings.size(); + ++table_child_idx) { + const auto& child_mapping = mapping.child_mappings[table_child_idx]; + const auto file_child_idx = file_child_ordinal_in_scan_type(mapping, child_mapping); + if (!file_child_idx.has_value() || *file_child_idx != table_child_idx || + needs_complex_rematerialize(child_mapping) || + (child_mapping.table_type != nullptr && child_mapping.file_type != nullptr && + !child_mapping.table_type->equals(*child_mapping.file_type))) { + return true; + } + } + return false; +} + +static bool mapping_can_use_file_column_directly(const ColumnMapping& mapping) { + if (mapping.table_type == nullptr || mapping.file_type == nullptr || + !mapping.table_type->equals(*mapping.file_type)) { + return false; + } + return !needs_complex_rematerialize(mapping); +} + +static const ColumnDefinition* find_file_child_for_mapping(const ColumnDefinition& table_child, + const ColumnDefinition& file_parent, + TableColumnMappingMode mode, + size_t table_child_idx, + bool allow_ordinal_fallback) { const auto file_parent_type = remove_nullable(file_parent.type)->get_primitive_type(); switch (file_parent_type) { case TYPE_ARRAY: @@ -833,7 +1006,120 @@ static const ColumnDefinition* find_file_child_for_mapping( } return nullptr; default: - return matcher_for_mode(mode).find(table_child, file_parent.children); + if (const auto* file_child = matcher_for_mode(mode).find(table_child, file_parent.children); + file_child != nullptr) { + return file_child; + } + if (allow_ordinal_fallback && mode == TableColumnMappingMode::BY_FIELD_ID && + !table_child.has_identifier_field_id()) { + // Synthetic children are derived from the table DataType when nested ColumnDefinition + // metadata has been pruned away. They do not carry Iceberg field ids, so try a name + // match before falling back to ordinal order. Example: + // table value type: Struct(age, full_name, gender) + // old file value: Struct(name, age) + // Name matching keeps `age -> age`; the later unused-child fallback can then map the + // renamed `full_name -> name` instead of consuming `age` twice. + if (const auto* file_child = NameMatcher().find(table_child, file_parent.children); + file_child != nullptr) { + return file_child; + } + } + // Some callers only carry the full complex DataType for a projected table column, without + // expanded nested ColumnDefinitions. In that case we can still preserve full materialization + // by walking table/file struct fields by ordinal. This is a fallback only: explicit + // ColumnDefinition children keep using field-id/name/position mapping, which is required + // for precise Iceberg evolution. + if (allow_ordinal_fallback && table_child_idx < file_parent.children.size()) { + return &file_parent.children[table_child_idx]; + } + return nullptr; + } +} + +static ColumnDefinition synthetic_child_definition(const std::string& name, DataTypePtr type, + int32_t local_id) { + ColumnDefinition child; + child.identifier = Field::create_field(name); + child.local_id = local_id; + child.name = name; + child.type = std::move(type); + return child; +} + +static std::vector synthesize_complex_children_from_type( + const DataTypePtr& type) { + std::vector children; + if (type == nullptr) { + return children; + } + const auto nested_type = remove_nullable(type); + switch (nested_type->get_primitive_type()) { + case TYPE_ARRAY: { + const auto* array_type = assert_cast(nested_type.get()); + children.push_back(synthetic_child_definition("element", array_type->get_nested_type(), 0)); + break; + } + case TYPE_MAP: { + const auto* map_type = assert_cast(nested_type.get()); + children.push_back(synthetic_child_definition("key", map_type->get_key_type(), 0)); + children.push_back(synthetic_child_definition("value", map_type->get_value_type(), 1)); + break; + } + case TYPE_STRUCT: { + const auto* struct_type = assert_cast(nested_type.get()); + children.reserve(struct_type->get_elements().size()); + for (size_t idx = 0; idx < struct_type->get_elements().size(); ++idx) { + children.push_back(synthetic_child_definition(struct_type->get_element_name(idx), + struct_type->get_element(idx), + cast_set(idx))); + } + break; + } + default: + break; + } + return children; +} + +static bool has_table_child_named(const std::vector& children, + std::string_view name) { + return std::ranges::any_of(children, [&](const ColumnDefinition& child) { + return std::string_view(child.name) == name; + }); +} + +static void complete_required_complex_children_from_type(const DataTypePtr& type, + std::vector* children) { + DORIS_CHECK(children != nullptr); + if (type == nullptr) { + return; + } + const auto nested_type = remove_nullable(type); + switch (nested_type->get_primitive_type()) { + case TYPE_MAP: { + const auto* map_type = assert_cast(nested_type.get()); + // MAP key/value are structural children, not independently materializable table fields. + // A key-only projection can still be attached to a whole-map output root, for example: + // SELECT * FROM t WHERE ARRAY_CONTAINS(MAP_KEYS(new_map_column), 'person5') + // + // In that shape the scanner keeps the value stream readable, but the table projection can + // carry only the key child. Add the missing value child so recursive mapping can evolve the + // value type instead of letting TableReader cast old/new value structs directly. + if (has_table_child_named(*children, "key") && !has_table_child_named(*children, "value")) { + children->push_back(synthetic_child_definition("value", map_type->get_value_type(), 1)); + } + break; + } + case TYPE_ARRAY: + // ARRAY has only one required structural child (`element`), so a non-empty projection is + // already rooted at the element path. + break; + case TYPE_STRUCT: + // STRUCT children are real fields and must remain prunable. Completing missing struct + // fields here would turn `SELECT s.a` into a full-struct read and undo nested projection. + break; + default: + break; } } @@ -909,7 +1195,15 @@ static Status build_complex_projection(const ColumnMapping& mapping, LocalColumn *projection = LocalColumnIndex::local(*mapping.file_local_id); projection->project_all_children = mapping.child_mappings.empty(); projection->children.clear(); - for (const auto* child_mapping : present_child_mappings_in_file_order(mapping.child_mappings)) { + const auto present_children = present_child_mappings_in_file_order(mapping.child_mappings); + if (!projection->project_all_children && present_children.empty()) { + // All requested table children under this complex node are missing/default-only. The file + // reader cannot expose an empty complex projection, but TableReader can still rematerialize + // the table shape from a full file subtree and fill the missing children with defaults. + projection->project_all_children = true; + return Status::OK(); + } + for (const auto* child_mapping : present_children) { LocalColumnIndex child_projection; RETURN_IF_ERROR(build_complex_projection(*child_mapping, &child_projection)); projection->children.push_back(std::move(child_projection)); @@ -943,8 +1237,7 @@ static Status apply_projection_to_mapping_file_type(const LocalColumnIndex& proj RETURN_IF_ERROR(project_column_definition(field, projection, &projected_field)); mapping->file_type = std::move(projected_field.type); mapping->projected_file_children = std::move(projected_field.children); - mapping->is_trivial = - mapping->table_type != nullptr && mapping->table_type->equals(*mapping->file_type); + mapping->is_trivial = mapping_can_use_file_column_directly(*mapping); return Status::OK(); } @@ -960,12 +1253,19 @@ static Status merge_filter_projection(const FilterProjectionMap* filter_projecti } // Merge predicate-only nested paths into the root projection that is about to be scanned. // Example: `SELECT s.a WHERE s.b > 1` first builds the output projection `s -> a` from - // ColumnMapping, while build_filter_projection_map() records `s -> b`. This merge produces - // one file scan projection `s -> a,b`. + // ColumnMapping, while build_nested_struct_filter_projection_map() records `s -> b`. This merge + // produces one file scan projection `s -> a,b`. RETURN_IF_ERROR(merge_local_column_index(projection, filter_projection_it->second)); return Status::OK(); } +static bool table_root_is_map(const ColumnMapping& mapping) { + if (mapping.table_type == nullptr) { + return false; + } + return remove_nullable(mapping.table_type)->get_primitive_type() == TYPE_MAP; +} + static void sort_projection_children_by_file_id(LocalColumnIndex* projection) { DORIS_CHECK(projection != nullptr); if (projection->project_all_children) { @@ -1019,6 +1319,7 @@ static Status add_scan_column(FileScanRequest* file_request, ColumnMapping* mapp RETURN_IF_ERROR(merge_local_column_index(&*existing_projection_it, projection)); sort_projection_children_by_file_id(&*existing_projection_it); } else { + sort_projection_children_by_file_id(&projection); scan_columns->push_back(std::move(projection)); } if (is_predicate_column) { @@ -1071,22 +1372,27 @@ static Status apply_scan_projection_to_mapping_file_type(const FileScanRequest& // // Example: for `SELECT s.a FROM t WHERE s.b.c > 1`, the output projection may only contain `s.a`, // but the file reader must also read `s.b.c` to evaluate the predicate. This function collects the -// filter path `s -> b -> c`, resolves it against the root mapping's original file schema, and -// records a root projection like `s -> b -> c` in filter_projections. When add_scan_column() adds -// the same root as a predicate column, it rebuilds that root from the projection mapping, merges -// this filter-only projection into it, and removes the duplicate non-predicate root entry. -static Status build_filter_projection_map(const std::vector& table_filters, - const std::vector& mappings, - FilterProjectionMap* filter_projections) { +// table-side filter path, resolves it through ColumnMapping first, and records the corresponding +// file-side projection in filter_projections. This keeps renamed fields consistent across the scan +// projection, row-level conjunct rewrite, and nested predicate pruning. Example: +// table filter path: s -> renamed_b -> c +// old file path: s -> b -> c +// recorded path: s -> b -> c +// When add_scan_column() adds the same root as a predicate column, it rebuilds that root from the +// output mapping, merges this filter-only projection into it, and removes the duplicate +// non-predicate root entry. +static Status build_nested_struct_filter_projection_map( + const std::vector& table_filters, const std::vector& mappings, + FilterProjectionMap* filter_projections) { DORIS_CHECK(filter_projections != nullptr); filter_projections->clear(); for (const auto& table_filter : table_filters) { if (table_filter.conjunct == nullptr) { continue; } - // Collect all the nested struct paths in the filter conjunct. For example, for a filter - // like `s.id > 5 AND s.name = 'abc'`, collect `s -> id` and `s -> name`, then build scan - // projections for those paths from the file schema. + // Collect all nested struct paths in the table filter. For example, for + // `s.id > 5 AND element_at(s, 'renamed_name') = 'abc'`, collect the table paths + // `s -> id` and `s -> renamed_name`, then resolve each one to its file-side projection. std::vector paths; collect_nested_struct_paths(table_filter.conjunct->root(), &paths); for (const auto& path : paths) { @@ -1098,14 +1404,27 @@ static Status build_filter_projection_map(const std::vector& table_ continue; } - LocalColumnIndex child_projection; - RETURN_IF_ERROR(build_file_child_projection_from_schema( - mapping_it->original_file_children, path.selectors, &child_projection)); - if (child_projection.local_id() < 0) { - continue; + ResolvedNestedStructPath resolved; + LocalColumnIndex root_projection; + if (!resolve_nested_struct_path_for_file(path, mappings, &resolved)) { + if (!table_root_is_map(*mapping_it)) { + continue; + } + // Direct map value filters such as `m.value.a > 1` need the value leaf for row + // evaluation even when the query only projects another value child. This is only a + // scan projection fallback; complex map/array expressions are still not rewritten + // into file-local conjuncts. + LocalColumnIndex child_projection; + RETURN_IF_ERROR(build_file_child_projection_from_schema( + mapping_it->original_file_children, path.selectors, &child_projection)); + if (child_projection.local_id() < 0) { + continue; + } + root_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); + root_projection.children.push_back(std::move(child_projection)); + } else { + root_projection = std::move(resolved.file_projection); } - auto root_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); - root_projection.children.push_back(std::move(child_projection)); auto filter_projection_it = filter_projections->find(root_projection.column_id()); if (filter_projection_it == filter_projections->end()) { filter_projections->emplace(root_projection.column_id(), @@ -1119,10 +1438,6 @@ static Status build_filter_projection_map(const std::vector& table_ return Status::OK(); } -static bool needs_complex_rematerialize(const ColumnMapping& mapping) { - return !mapping.is_trivial && !mapping.child_mappings.empty(); -} - static void rebuild_projection(ColumnMapping* mapping, LocalIndex block_position) { DORIS_CHECK(mapping->file_local_id.has_value()); if (mapping->is_trivial || needs_complex_rematerialize(*mapping)) { @@ -1448,8 +1763,8 @@ Status TableColumnMapper::localize_filters(const std::vector& table RuntimeState* runtime_state) { FilterProjectionMap filter_projections; auto filter_mappings = _filter_visible_mappings(); - RETURN_IF_ERROR( - build_filter_projection_map(table_filters, filter_mappings, &filter_projections)); + RETURN_IF_ERROR(build_nested_struct_filter_projection_map(table_filters, filter_mappings, + &filter_projections)); for (const auto& table_filter : table_filters) { for (const auto& global_index : table_filter.global_indices) { auto* mapping = _find_filter_mapping(global_index); @@ -1486,13 +1801,48 @@ Status TableColumnMapper::localize_filters(const std::vector& table table_filter_has_only_local_entries(table_filter, _filter_entries)) { RewriteContext rewrite_context {.runtime_state = runtime_state}; VExprSPtr rewrite_root; - const auto clone_status = - clone_table_expr_tree(table_filter.conjunct->root(), &rewrite_root); + Status clone_status; + try { + clone_status = clone_table_expr_tree(table_filter.conjunct->root(), &rewrite_root); + } catch (const Exception& e) { + // Some table filters contain complex intermediate values, for example + // `element_at(MAP_VALUES(m)[1], 'age') > 30`. The current file-local rewrite only + // understands top-level slots and struct-element paths rooted at top-level slots; + // cloning such expressions can hit the generic TExpr complex-type limitation. + // Leave them above TableReader, where Scanner evaluates the original table-level + // conjunct after final materialization. +#ifndef NDEBUG + return Status::InternalError( + "Failed to clone table filter for file-local rewrite: {}, expr={}", + e.to_string(), table_filter.conjunct->root()->debug_string()); +#else + continue; +#endif + } catch (const std::exception& e) { +#ifndef NDEBUG + return Status::InternalError( + "Failed to clone table filter for file-local rewrite: {}, expr={}", + e.what(), table_filter.conjunct->root()->debug_string()); +#else + continue; +#endif + } if (!clone_status.ok()) { +#ifndef NDEBUG + return Status::InternalError( + "Failed to clone table filter for file-local rewrite: {}, expr={}", + clone_status.to_string(), table_filter.conjunct->root()->debug_string()); +#else continue; +#endif } + bool can_localize = true; auto localized_root = rewrite_table_expr_to_file_expr(rewrite_root, global_to_file_slot, - &rewrite_context); + filter_mappings, &rewrite_context, + &can_localize); + if (!can_localize) { + continue; + } auto localized_conjunct = VExprContext::create_shared(std::move(localized_root)); RETURN_IF_ERROR(rewrite_context.prepare_created_exprs(localized_conjunct.get())); file_request->conjuncts.push_back(std::move(localized_conjunct)); @@ -1563,16 +1913,62 @@ Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_c mapping->original_file_children = file_field.children; mapping->projected_file_children = file_field.children; mapping->file_type = file_field.type; - mapping->is_trivial = mapping->table_type->equals(*mapping->file_type); + mapping->is_trivial = mapping_can_use_file_column_directly(*mapping); mapping->filter_conversion = mapping->is_trivial ? FilterConversionType::COPY_DIRECTLY : FilterConversionType::CAST_FILTER; mapping->child_mappings.clear(); - if (!table_column.children.empty()) { + auto table_children = table_column.children; + const auto nested_table_type = remove_nullable(mapping->table_type); + // Some scan paths, especially SELECT *, only carry the complete complex DataType for a table + // column and leave ColumnDefinition::children empty. If the file type is an older complex + // schema, treating this as a leaf mapping would make TableReader fall back to a plain CAST. + // That is invalid for evolved structs with different field counts. + // + // Example: + // table column type: Map(String, Struct(age, full_name, gender)) + // old file type: Map(String, Struct(age, name)) + // table children: empty + // + // Synthesize key/value/struct-field children from the table type so the normal recursive + // mapping path can rematerialize `name -> full_name` and fill missing `gender` with defaults, + // instead of trying to CAST Struct(age, name) to Struct(age, full_name, gender). + const bool synthesized_table_children = + table_children.empty() && is_complex_type(nested_table_type->get_primitive_type()) && + !mapping->table_type->equals(*mapping->file_type); + if (synthesized_table_children) { + table_children = synthesize_complex_children_from_type(mapping->table_type); + } else if (!table_children.empty() && !mapping->table_type->equals(*mapping->file_type)) { + complete_required_complex_children_from_type(mapping->table_type, &table_children); + } + + if (!table_children.empty()) { DORIS_CHECK(is_complex_type(mapping->file_type->get_primitive_type())); - for (const auto& table_child : table_column.children) { + std::vector synthesized_used_file_child_ids; + for (size_t table_child_idx = 0; table_child_idx < table_children.size(); + ++table_child_idx) { + const auto& table_child = table_children[table_child_idx]; const auto* file_child = - find_file_child_for_mapping(table_child, file_field, _options.mode); + find_file_child_for_mapping(table_child, file_field, _options.mode, + table_child_idx, synthesized_table_children); + if (synthesized_table_children && file_child != nullptr) { + const auto file_child_id = file_child->file_local_id(); + if (std::ranges::find(synthesized_used_file_child_ids, file_child_id) != + synthesized_used_file_child_ids.end()) { + file_child = nullptr; + for (const auto& candidate : file_field.children) { + const auto candidate_id = candidate.file_local_id(); + if (std::ranges::find(synthesized_used_file_child_ids, candidate_id) == + synthesized_used_file_child_ids.end()) { + file_child = &candidate; + break; + } + } + } + if (file_child != nullptr) { + synthesized_used_file_child_ids.push_back(file_child->file_local_id()); + } + } if (file_child == nullptr) { ColumnMapping child_mapping; child_mapping.table_column_name = table_child.name; @@ -1595,7 +1991,7 @@ Status TableColumnMapper::_create_direct_mapping(const ColumnDefinition& table_c mapping->file_type, mapping->original_file_children, mapping->child_mappings, &mapping->projected_file_children, &mapping->file_type)); DCHECK(mapping->table_type != nullptr); - mapping->is_trivial = mapping->table_type->equals(*mapping->file_type); + mapping->is_trivial = mapping_can_use_file_column_directly(*mapping); // TODO: ? READER_EXPRESSION mapping->filter_conversion = mapping->is_trivial ? FilterConversionType::COPY_DIRECTLY diff --git a/be/src/format_v2/column_mapper_nested.cpp b/be/src/format_v2/column_mapper_nested.cpp index f711a9d5020bd6..0e3539242fff26 100644 --- a/be/src/format_v2/column_mapper_nested.cpp +++ b/be/src/format_v2/column_mapper_nested.cpp @@ -324,26 +324,33 @@ static const ColumnMapping* resolve_mapped_child(const ColumnMapping& mapping, return nullptr; } +enum class NestedProjectionResolveResult { + RESOLVED, + NOT_REPRESENTED, + MISSING_FILE_CHILD, +}; + // Resolve a table-side nested struct path through the existing ColumnMapping tree and build the // corresponding file-local projection. For example, if table column `s` has children // `{a, renamed_b}` and file column `s` has children `{a, b}`, the filter path // `struct_element(s, 'renamed_b')` is resolved to the file projection `s -> b` by following the -// child mapping instead of matching the table child name against the file schema. Return false when -// the path is not represented in child_mappings, so callers can fall back to original file schema -// for predicate-only children that were not part of the output projection. -static bool resolve_nested_projection_with_mapping(const NestedStructPath& path, - const std::vector& mappings, - LocalColumnIndex* root_projection) { +// child mapping instead of matching the table child name against the file schema. Return +// MISSING_FILE_CHILD when ColumnMapping explicitly says a table child is absent from this file; in +// that case callers must not fall back to schema-name lookup, because Iceberg can drop a field and +// later add a different field with the same name. +static NestedProjectionResolveResult resolve_nested_projection_with_mapping( + const NestedStructPath& path, const std::vector& mappings, + LocalColumnIndex* root_projection) { DORIS_CHECK(root_projection != nullptr); *root_projection = {}; if (path.selectors.empty()) { - return false; + return NestedProjectionResolveResult::NOT_REPRESENTED; } const auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { return mapping.global_index == path.root_global_index; }); if (mapping_it == mappings.end() || !mapping_it->file_local_id.has_value()) { - return false; + return NestedProjectionResolveResult::NOT_REPRESENTED; } *root_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); @@ -351,19 +358,24 @@ static bool resolve_nested_projection_with_mapping(const NestedStructPath& path, const auto* current_mapping = &*mapping_it; // Traverse the ColumnMapping tree according to the table-side struct selectors and emit the - // corresponding file-local child ids. If the path is not fully represented in child_mappings, - // return false so callers can choose the file-schema fallback for filter-only children. + // corresponding file-local child ids. A missing child mapping means this predicate-only path + // may need schema fallback; an existing child mapping without a file id means the table child + // is genuinely absent from this file and must stay above the file reader. for (size_t selector_idx = 0; selector_idx < path.selectors.size(); ++selector_idx) { const auto global_child_index = struct_child_index(*current_mapping, path.selectors[selector_idx]); if (!global_child_index.has_value()) { *root_projection = {}; - return false; + return NestedProjectionResolveResult::NOT_REPRESENTED; } const auto* child_mapping = resolve_mapped_child(*current_mapping, *global_child_index); - if (child_mapping == nullptr || !child_mapping->file_local_id.has_value()) { + if (child_mapping == nullptr) { *root_projection = {}; - return false; + return NestedProjectionResolveResult::NOT_REPRESENTED; + } + if (!child_mapping->file_local_id.has_value()) { + *root_projection = {}; + return NestedProjectionResolveResult::MISSING_FILE_CHILD; } auto child_projection = LocalColumnIndex::partial_local(*child_mapping->file_local_id); @@ -372,13 +384,18 @@ static bool resolve_nested_projection_with_mapping(const NestedStructPath& path, current_projection = ¤t_projection->children.back(); current_mapping = child_mapping; } - return true; + return NestedProjectionResolveResult::RESOLVED; } static bool table_root_is_struct(const ColumnMapping& mapping) { return struct_type_or_null(mapping.table_type) != nullptr; } +static const std::vector& scan_file_children(const ColumnMapping& mapping) { + return !mapping.projected_file_children.empty() ? mapping.projected_file_children + : mapping.original_file_children; +} + static const ColumnDefinition* resolve_file_leaf_from_projection( const std::vector& children, const LocalColumnIndex& projection) { const auto child_it = std::ranges::find_if(children, [&](const ColumnDefinition& child) { @@ -396,6 +413,30 @@ static const ColumnDefinition* resolve_file_leaf_from_projection( return resolve_file_leaf_from_projection(child_it->children, projection.children[0]); } +static bool collect_file_child_names_from_projection(const std::vector& children, + const LocalColumnIndex& projection, + std::vector* file_child_names, + std::vector* file_child_types) { + DORIS_CHECK(file_child_names != nullptr); + DORIS_CHECK(file_child_types != nullptr); + const auto child_it = std::ranges::find_if(children, [&](const ColumnDefinition& child) { + return child.file_local_id() == projection.local_id(); + }); + if (child_it == children.end()) { + return false; + } + file_child_names->push_back(child_it->name); + file_child_types->push_back(child_it->type); + if (projection.children.empty()) { + return true; + } + if (projection.children.size() != 1) { + return false; + } + return collect_file_child_names_from_projection(child_it->children, projection.children[0], + file_child_names, file_child_types); +} + struct NestedPredicateTarget { LocalColumnIndex file_projection; FileNestedPredicateTarget file_target; @@ -446,45 +487,19 @@ static bool resolve_nested_predicate_target(const NestedStructPath& path, const std::vector& mappings, NestedPredicateTarget* target) { DORIS_CHECK(target != nullptr); - const auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { - return mapping.global_index == path.root_global_index; - }); - if (mapping_it == mappings.end() || !mapping_it->file_local_id.has_value() || - path.selectors.empty()) { + ResolvedNestedStructPath resolved; + if (!resolve_nested_struct_path_for_file(path, mappings, &resolved)) { return false; } - if (resolve_nested_projection_with_mapping(path, mappings, &target->file_projection)) { - DORIS_CHECK(target->file_projection.children.size() == 1); - const auto* file_leaf = resolve_file_leaf_from_projection( - mapping_it->original_file_children, target->file_projection.children[0]); - if (file_leaf == nullptr || file_leaf->type == nullptr) { - return false; - } - target->leaf_type = remove_nullable(file_leaf->type); - if (is_complex_type(target->leaf_type->get_primitive_type())) { - return false; - } - target->leaf_name = file_leaf->name; - if (!build_struct_predicate_target(*mapping_it, target->file_projection, - &target->file_target)) { - return false; - } - return true; - } - - if (!table_root_is_struct(*mapping_it)) { - return false; - } - LocalColumnIndex child_projection; - if (!build_file_child_projection_from_schema(mapping_it->original_file_children, path.selectors, - &child_projection) - .ok() || - child_projection.local_id() < 0) { + const auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { + return mapping.global_index == path.root_global_index; + }); + if (mapping_it == mappings.end() || resolved.file_projection.children.size() != 1) { return false; } - const auto* file_leaf = - resolve_file_leaf_from_projection(mapping_it->original_file_children, child_projection); + const auto* file_leaf = resolve_file_leaf_from_projection(mapping_it->original_file_children, + resolved.file_projection.children[0]); if (file_leaf == nullptr || file_leaf->type == nullptr) { return false; } @@ -493,8 +508,7 @@ static bool resolve_nested_predicate_target(const NestedStructPath& path, return false; } target->leaf_name = file_leaf->name; - target->file_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); - target->file_projection.children.push_back(std::move(child_projection)); + target->file_projection = std::move(resolved.file_projection); if (!build_struct_predicate_target(*mapping_it, target->file_projection, &target->file_target)) { return false; @@ -810,8 +824,19 @@ GlobalIndex slot_ref_global_index(const VSlotRef& slot_ref) { } bool is_struct_element_expr(const VExprSPtr& expr) { - return expr != nullptr && expr->get_num_children() == 2 && - expr->fn().name.function_name == "struct_element"; + if (expr == nullptr || expr->get_num_children() != 2) { + return false; + } + const auto& function_name = expr->fn().name.function_name; + if (function_name == "struct_element") { + return true; + } + if (function_name != "element_at") { + return false; + } + const auto& parent_type = expr->children()[0]->data_type(); + return parent_type != nullptr && + remove_nullable(parent_type)->get_primitive_type() == TYPE_STRUCT; } Field literal_field(const VExprSPtr& literal_expr) { @@ -824,6 +849,82 @@ Field literal_field(const VExprSPtr& literal_expr) { return field; } +bool resolve_nested_struct_path_for_file(const NestedStructPath& path, + const std::vector& mappings, + ResolvedNestedStructPath* resolved, + bool require_scan_projection) { + DORIS_CHECK(resolved != nullptr); + *resolved = {}; + const auto mapping_it = std::ranges::find_if(mappings, [&](const ColumnMapping& mapping) { + return mapping.global_index == path.root_global_index; + }); + if (mapping_it == mappings.end() || !mapping_it->file_local_id.has_value() || + path.selectors.empty()) { + return false; + } + + // Prefer ColumnMapping over schema-name lookup. This is the only path that can correctly + // localize renamed Iceberg fields: a table filter `element_at(s, 'renamed_b')` must become a + // file filter on physical child `b`, even if the old file type is `STRUCT`. + const auto mapping_result = + resolve_nested_projection_with_mapping(path, mappings, &resolved->file_projection); + if (mapping_result == NestedProjectionResolveResult::MISSING_FILE_CHILD) { + return false; + } + if (mapping_result == NestedProjectionResolveResult::NOT_REPRESENTED) { + if (!table_root_is_struct(*mapping_it)) { + return false; + } + LocalColumnIndex child_projection; + if (!build_file_child_projection_from_schema(mapping_it->original_file_children, + path.selectors, &child_projection) + .ok() || + child_projection.local_id() < 0) { + return false; + } + resolved->file_projection = LocalColumnIndex::partial_local(*mapping_it->file_local_id); + resolved->file_projection.children.push_back(std::move(child_projection)); + } + + if (resolved->file_projection.children.size() != 1) { + *resolved = {}; + return false; + } + // When rewriting the final localized element_at chain, it executes on the file column produced + // by this scan, so the intermediate return types must match the projected file shape, not the + // full historical file schema. Example: + // SELECT s.c WHERE element_at(element_at(s, 'b'), 'cc') LIKE 'NestedC%' + // reads only b.cc and c; the inner element_at(s, 'b') returns Struct(cc), not + // Struct(cc, new_dd). + // + // Earlier projection collection also calls this resolver before filter-only children have been + // merged into the scan projection. That phase only needs the file path, so it still resolves + // names/types from the original file schema. + const auto& child_source = require_scan_projection ? scan_file_children(*mapping_it) + : mapping_it->original_file_children; + if (!collect_file_child_names_from_projection( + child_source, resolved->file_projection.children[0], &resolved->file_child_names, + &resolved->file_child_types) || + resolved->file_child_names.size() != path.selectors.size() || + resolved->file_child_types.size() != path.selectors.size()) { + *resolved = {}; + return false; + } + return true; +} + +bool resolve_nested_struct_expr_for_file(const VExprSPtr& expr, + const std::vector& mappings, + ResolvedNestedStructPath* resolved) { + DORIS_CHECK(resolved != nullptr); + NestedStructPath path; + if (!extract_nested_struct_path(expr, &path)) { + *resolved = {}; + return false; + } + return resolve_nested_struct_path_for_file(path, mappings, resolved, true); +} + // Collect nested struct leaf references that can be turned into file-reader projections and // primitive pruning predicates. For example, from `s.a > 1 AND element_at(s, 'b') = 2`, this // records two paths rooted at `s`: `s -> a` and `s -> b`. Non-struct expressions are traversed diff --git a/be/src/format_v2/column_mapper_nested.h b/be/src/format_v2/column_mapper_nested.h index 03eff5d3a5a6a5..b8b3f1f3334a8f 100644 --- a/be/src/format_v2/column_mapper_nested.h +++ b/be/src/format_v2/column_mapper_nested.h @@ -44,6 +44,12 @@ struct NestedStructPath { std::vector selectors; }; +struct ResolvedNestedStructPath { + LocalColumnIndex file_projection; + std::vector file_child_names; + std::vector file_child_types; +}; + // A split-local literal produced by slot-literal predicate localization. This wrapper keeps the // original table literal so a cloned conjunct can be localized again for another split. class SplitLocalFileLiteral final : public VLiteral { @@ -71,6 +77,15 @@ GlobalIndex slot_ref_global_index(const VSlotRef& slot_ref); bool is_struct_element_expr(const VExprSPtr& expr); Field literal_field(const VExprSPtr& literal_expr); +bool resolve_nested_struct_path_for_file(const NestedStructPath& path, + const std::vector& mappings, + ResolvedNestedStructPath* resolved, + bool require_scan_projection = false); + +bool resolve_nested_struct_expr_for_file(const VExprSPtr& expr, + const std::vector& mappings, + ResolvedNestedStructPath* resolved); + void collect_nested_struct_paths(const VExprSPtr& expr, std::vector* paths); std::vector present_child_mappings_in_file_order( diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp index bb08f3d4b02d6d..be4406e5684bd9 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -302,6 +302,7 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r { SCOPED_TIMER(_profile.materialization_time); + // TODO(gabriel): top-level column is always nullable in current parquet reader implementation but nested column may not be. if (!_type->is_nullable()) { if (auto* nullable_column = check_and_get_column(*column); nullable_column != nullptr) { diff --git a/be/src/format_v2/table/iceberg_reader.cpp b/be/src/format_v2/table/iceberg_reader.cpp index cb9a343ad7faa6..59d87ff3bccf2e 100644 --- a/be/src/format_v2/table/iceberg_reader.cpp +++ b/be/src/format_v2/table/iceberg_reader.cpp @@ -134,9 +134,9 @@ Status IcebergTableReader::PositionDeleteRowsCollector::collect(const Block& blo return Status::OK(); } const auto& file_path_column = assert_cast( - *block.get_by_position(ICEBERG_FILE_PATH_BLOCK_POSITION).column); + *remove_nullable((block.get_by_position(ICEBERG_FILE_PATH_BLOCK_POSITION).column))); const auto& pos_column = assert_cast( - *block.get_by_position(ICEBERG_ROW_POS_BLOCK_POSITION).column); + *remove_nullable(block.get_by_position(ICEBERG_ROW_POS_BLOCK_POSITION).column)); for (size_t row = 0; row < read_rows; ++row) { const auto file_path = file_path_column.get_data_at(row).to_string(); if (file_path == _data_file_path) { diff --git a/be/src/format_v2/table_reader.h b/be/src/format_v2/table_reader.h index 4d425555682401..86986a4096b394 100644 --- a/be/src/format_v2/table_reader.h +++ b/be/src/format_v2/table_reader.h @@ -20,10 +20,12 @@ #include #include +#include #include #include #include #include +#include #include #include @@ -199,8 +201,15 @@ class TableReader { } DCHECK_EQ(_data_reader.block_template.columns(), _data_reader.file_block_layout.size()) << _data_reader.block_template.dump_structure(); +#ifndef NDEBUG + RETURN_IF_ERROR(_check_file_block_columns("after file reader get_block", current_rows)); +#endif DORIS_CHECK(block->columns() == _data_reader.column_mapper.mappings().size()); RETURN_IF_ERROR(finalize_chunk(block, current_rows)); +#ifndef NDEBUG + RETURN_IF_ERROR( + _check_table_block_columns("after finalize_chunk", block, current_rows)); +#endif if (current_eof) { RETURN_IF_ERROR(close_current_reader()); } @@ -561,6 +570,136 @@ class TableReader { // Materialize virtual columns in table block, such as _row_id and _last_updated_sequence_number in Iceberg. This is called after finalize_chunk, so the virtual column can be referenced in finalize_expr. virtual Status materialize_virtual_columns(Block* table_block) { return Status::OK(); } +#ifndef NDEBUG + Status _check_file_block_columns(std::string_view stage, size_t rows) { + DORIS_CHECK(_data_reader.block_template.columns() == _data_reader.file_block_layout.size()); + for (size_t idx = 0; idx < _data_reader.block_template.columns(); ++idx) { + const auto& file_block_column = _data_reader.file_block_layout[idx]; + const auto& column_with_type = _data_reader.block_template.get_by_position(idx); + const auto* column = column_with_type.column.get(); + try { + if (column == nullptr) { + auto st = Status::InternalError( + "Invalid file block column {} at {}: file_column_id={}, name='{}', " + "type={}, column=null, expected_rows={}, reader={}", + idx, stage, file_block_column.file_column_id.value(), + file_block_column.name, + file_block_column.type == nullptr ? "null" + : file_block_column.type->get_name(), + rows, debug_string()); + LOG(WARNING) << st; + return st; + } + column->sanity_check(); + auto st = column_with_type.check_type_and_column_match(); + if (!st.ok()) { + auto contextual_status = Status::InternalError( + "Invalid file block column {} at {}: file_column_id={}, name='{}', " + "type={}, column={}, column_size={}, expected_rows={}, error={}, " + "reader={}", + idx, stage, file_block_column.file_column_id.value(), + file_block_column.name, + file_block_column.type == nullptr ? "null" + : file_block_column.type->get_name(), + column->get_name(), column->size(), rows, st.to_string(), + debug_string()); + LOG(WARNING) << contextual_status; + return contextual_status; + } + } catch (const Exception& e) { + auto st = Status::InternalError( + "Invalid file block column {} at {}: file_column_id={}, name='{}', " + "type={}, column={}, column_size={}, expected_rows={}, error={}, " + "reader={}", + idx, stage, file_block_column.file_column_id.value(), + file_block_column.name, + file_block_column.type == nullptr ? "null" + : file_block_column.type->get_name(), + column == nullptr ? "null" : column->get_name(), + column == nullptr ? 0 : column->size(), rows, e.to_string(), + debug_string()); + LOG(WARNING) << st; + return st; + } catch (const std::exception& e) { + auto st = Status::InternalError( + "Invalid file block column {} at {}: file_column_id={}, name='{}', " + "type={}, column={}, column_size={}, expected_rows={}, error={}, " + "reader={}", + idx, stage, file_block_column.file_column_id.value(), + file_block_column.name, + file_block_column.type == nullptr ? "null" + : file_block_column.type->get_name(), + column == nullptr ? "null" : column->get_name(), + column == nullptr ? 0 : column->size(), rows, e.what(), debug_string()); + LOG(WARNING) << st; + return st; + } + } + return Status::OK(); + } + + Status _check_table_block_columns(std::string_view stage, const Block* block, size_t rows) { + DORIS_CHECK(block != nullptr); + DORIS_CHECK(block->columns() == _data_reader.column_mapper.mappings().size()); + for (size_t idx = 0; idx < block->columns(); ++idx) { + const auto& mapping = _data_reader.column_mapper.mappings()[idx]; + const auto& column_with_type = block->get_by_position(idx); + const auto* column = column_with_type.column.get(); + try { + if (column == nullptr) { + auto st = Status::InternalError( + "Invalid table block column {} at {}: table_column='{}', " + "global_index={}, type={}, column=null, expected_rows={}, mapping={}", + idx, stage, mapping.table_column_name, mapping.global_index.value(), + mapping.table_type == nullptr ? "null" : mapping.table_type->get_name(), + rows, mapping.debug_string()); + LOG(WARNING) << st; + return st; + } + column->sanity_check(); + auto st = column_with_type.check_type_and_column_match(); + if (!st.ok()) { + auto contextual_status = Status::InternalError( + "Invalid table block column {} at {}: table_column='{}', " + "global_index={}, type={}, column={}, column_size={}, " + "expected_rows={}, error={}, mapping={}", + idx, stage, mapping.table_column_name, mapping.global_index.value(), + mapping.table_type == nullptr ? "null" : mapping.table_type->get_name(), + column->get_name(), column->size(), rows, st.to_string(), + mapping.debug_string()); + LOG(WARNING) << contextual_status; + return contextual_status; + } + } catch (const Exception& e) { + auto st = Status::InternalError( + "Invalid table block column {} at {}: table_column='{}', global_index={}, " + "type={}, column={}, column_size={}, expected_rows={}, error={}, " + "mapping={}", + idx, stage, mapping.table_column_name, mapping.global_index.value(), + mapping.table_type == nullptr ? "null" : mapping.table_type->get_name(), + column == nullptr ? "null" : column->get_name(), + column == nullptr ? 0 : column->size(), rows, e.to_string(), + mapping.debug_string()); + LOG(WARNING) << st; + return st; + } catch (const std::exception& e) { + auto st = Status::InternalError( + "Invalid table block column {} at {}: table_column='{}', global_index={}, " + "type={}, column={}, column_size={}, expected_rows={}, error={}, " + "mapping={}", + idx, stage, mapping.table_column_name, mapping.global_index.value(), + mapping.table_type == nullptr ? "null" : mapping.table_type->get_name(), + column == nullptr ? "null" : column->get_name(), + column == nullptr ? 0 : column->size(), rows, e.what(), + mapping.debug_string()); + LOG(WARNING) << st; + return st; + } + } + return Status::OK(); + } +#endif + Status _truncate_char_or_varchar_columns(Block* block) { DORIS_CHECK(block != nullptr); if (_runtime_state == nullptr || @@ -855,7 +994,14 @@ class TableReader { !mapping.child_mappings.empty()) { DCHECK(mapping.projection != nullptr); int res_id; - RETURN_IF_ERROR(mapping.projection->execute(current_block, &res_id)); + auto st = mapping.projection->execute(current_block, &res_id); + if (!st.ok()) { + return Status::InternalError( + "Failed to execute complex mapping projection for table column '{}' " + "(global_index={}, file_local_id={}, rows={}): {}, mapping={}", + mapping.table_column_name, mapping.global_index.value(), + *mapping.file_local_id, rows, st.to_string(), mapping.debug_string()); + } ColumnPtr result_column = current_block->get_by_position(res_id).column; RETURN_IF_ERROR( _materialize_complex_mapping_column(mapping, result_column, rows, column)); @@ -863,7 +1009,18 @@ class TableReader { } if (mapping.projection != nullptr) { int res_id; - RETURN_IF_ERROR(mapping.projection->execute(current_block, &res_id)); + auto st = mapping.projection->execute(current_block, &res_id); + if (!st.ok()) { + std::string file_local_id = "null"; + if (mapping.file_local_id.has_value()) { + file_local_id = std::to_string(*mapping.file_local_id); + } + return Status::InternalError( + "Failed to execute mapping projection for table column '{}' " + "(global_index={}, file_local_id={}, rows={}): {}, mapping={}", + mapping.table_column_name, mapping.global_index.value(), file_local_id, + rows, st.to_string(), mapping.debug_string()); + } ColumnPtr result_column = current_block->get_by_position(res_id).column; *column = _detach_column(std::move(result_column)); return Status::OK(); @@ -953,6 +1110,23 @@ class TableReader { return static_cast(std::distance(file_ordered_children.begin(), child_it)); } + static std::vector _child_mappings_in_table_type_order( + const ColumnMapping& mapping, const DataTypeStruct& table_type) { + std::vector result; + result.reserve(mapping.child_mappings.size()); + for (size_t child_idx = 0; child_idx < table_type.get_elements().size(); ++child_idx) { + const auto& child_name = table_type.get_element_name(child_idx); + const auto child_it = std::ranges::find_if( + mapping.child_mappings, [&](const ColumnMapping& child_mapping) { + return child_mapping.table_column_name == child_name; + }); + DORIS_CHECK(child_it != mapping.child_mappings.end()) + << mapping.debug_string() << ", table_child_name=" << child_name; + result.push_back(&*child_it); + } + return result; + } + static const IColumn* _nested_column_if_nullable(const ColumnPtr& column, const NullMap** null_map) { DORIS_CHECK(column.get() != nullptr); @@ -982,18 +1156,21 @@ class TableReader { child_columns.reserve(mapping.child_mappings.size()); const auto file_ordered_children = _present_child_mappings_in_file_order(mapping.child_mappings); - for (const auto& child_mapping : mapping.child_mappings) { - if (!child_mapping.file_local_id.has_value()) { + const auto table_ordered_children = + _child_mappings_in_table_type_order(mapping, *table_type); + for (const auto* child_mapping : table_ordered_children) { + DORIS_CHECK(child_mapping != nullptr); + if (!child_mapping->file_local_id.has_value()) { child_columns.push_back( - child_mapping.table_type->create_column_const_with_default_value(rows) + child_mapping->table_type->create_column_const_with_default_value(rows) ->convert_to_full_column_if_const()); continue; } const auto file_child_idx = - _file_child_ordinal_for_mapping(mapping, child_mapping, file_ordered_children); + _file_child_ordinal_for_mapping(mapping, *child_mapping, file_ordered_children); DORIS_CHECK(file_child_idx < file_struct->get_columns().size()); ColumnPtr child_column = file_struct->get_column_ptr(file_child_idx); - RETURN_IF_ERROR(_materialize_present_child_mapping_column(child_mapping, child_column, + RETURN_IF_ERROR(_materialize_present_child_mapping_column(*child_mapping, child_column, rows, &child_column)); child_columns.push_back(std::move(child_column)); } diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index b761b6fa15b9d0..03198c21faaa52 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -219,6 +219,13 @@ class TestFunctionExpr final : public VExpr { const std::string& expr_name() const override { return _expr_name; } + Status clone_node(VExprSPtr* cloned_expr) const override { + DORIS_CHECK(cloned_expr != nullptr); + *cloned_expr = + std::make_shared(_expr_name, data_type(), node_type(), _opcode); + return Status::OK(); + } + Status execute_column_impl(VExprContext*, const Block*, const Selector*, size_t, ColumnPtr&) const override { return Status::NotSupported("TestFunctionExpr is only used for ColumnMapper analysis"); @@ -244,6 +251,49 @@ VExprSPtr struct_element(const VExprSPtr& parent, DataTypePtr child_type, return expr; } +VExprSPtr element_at(const VExprSPtr& parent, DataTypePtr child_type, + const std::string& child_name) { + auto expr = std::make_shared("element_at", std::move(child_type)); + expr->add_child(parent); + expr->add_child(literal(str(), Field::create_field(child_name))); + return expr; +} + +VExprSPtr array_element_at(const VExprSPtr& parent, DataTypePtr child_type, int64_t ordinal) { + auto expr = std::make_shared("element_at", std::move(child_type)); + expr->add_child(parent); + expr->add_child(literal(i64(), Field::create_field(ordinal))); + return expr; +} + +VExprSPtr map_values(const VExprSPtr& parent, DataTypePtr value_type) { + auto expr = std::make_shared( + "map_values", std::make_shared(std::move(value_type))); + expr->add_child(parent); + return expr; +} + +VExprSPtr map_keys(const VExprSPtr& parent, DataTypePtr key_type) { + auto expr = std::make_shared( + "map_keys", std::make_shared(std::move(key_type))); + expr->add_child(parent); + return expr; +} + +VExprSPtr array_contains(const VExprSPtr& array, const VExprSPtr& value) { + auto expr = std::make_shared("array_contains", u8()); + expr->add_child(array); + expr->add_child(value); + return expr; +} + +VExprSPtr like_expr(const VExprSPtr& left, const std::string& pattern) { + auto expr = std::make_shared("like", u8()); + expr->add_child(left); + expr->add_child(literal(str(), Field::create_field(pattern))); + return expr; +} + VExprSPtr struct_element_by_selector(const VExprSPtr& parent, DataTypePtr child_type, const VExprSPtr& selector) { auto expr = std::make_shared("struct_element", std::move(child_type)); @@ -979,6 +1029,156 @@ TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForNestedSchemaEvolutio EXPECT_EQ(*value_mapping.child_mappings[1].file_local_id, 1); } +// Scenario: SELECT * can carry only the full complex DataType without expanded nested +// ColumnDefinitions. When an old file has map value STRUCT and the table type is +// STRUCT, the mapper must still build child mappings instead of letting +// TableReader cast between incompatible struct shapes. +TEST(ColumnMapperCreateMappingTest, SynthesizesMissingMapValueStructChildrenFromType) { + const auto int_type = i32(); + const auto string_type = str(); + const auto table_value_type = std::make_shared( + DataTypes {int_type, string_type, string_type}, Strings {"age", "full_name", "gender"}); + const auto file_value_type = std::make_shared(DataTypes {int_type, string_type}, + Strings {"age", "name"}); + + auto table_map = name_col("new_map_column", + std::make_shared(string_type, table_value_type)); + table_map.name_mapping = {"map_column"}; + set_name_identifiers(&table_map, -1); + + auto file_age = name_col("age", int_type, 0); + auto file_name = name_col("name", string_type, 1); + auto file_value = struct_name_col("value", {file_age, file_name}, 1); + auto file_key = name_col("key", string_type, 0); + auto file_map = + map_col("map_column", -1, {file_key, file_value}, string_type, file_value_type, 5); + set_name_identifiers(&file_map, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + const auto& map_mapping = mapper.mappings()[0]; + ASSERT_EQ(map_mapping.child_mappings.size(), 2); + EXPECT_EQ(map_mapping.child_mappings[0].table_column_name, "key"); + EXPECT_EQ(map_mapping.child_mappings[0].file_column_name, "key"); + EXPECT_EQ(*map_mapping.child_mappings[0].file_local_id, 0); + + const auto& value_mapping = map_mapping.child_mappings[1]; + EXPECT_EQ(value_mapping.table_column_name, "value"); + EXPECT_EQ(value_mapping.file_column_name, "value"); + EXPECT_EQ(*value_mapping.file_local_id, 1); + ASSERT_EQ(value_mapping.child_mappings.size(), 3); + EXPECT_EQ(value_mapping.child_mappings[0].table_column_name, "age"); + EXPECT_EQ(value_mapping.child_mappings[0].file_column_name, "age"); + EXPECT_EQ(*value_mapping.child_mappings[0].file_local_id, 0); + EXPECT_EQ(value_mapping.child_mappings[1].table_column_name, "full_name"); + EXPECT_EQ(value_mapping.child_mappings[1].file_column_name, "name"); + EXPECT_EQ(*value_mapping.child_mappings[1].file_local_id, 1); + EXPECT_EQ(value_mapping.child_mappings[2].table_column_name, "gender"); + expect_missing(value_mapping.child_mappings[2]); + EXPECT_FALSE(value_mapping.is_trivial); +} + +// Scenario: MAP_KEYS(new_map_column) may build a key-only nested projection, while SELECT * still +// needs the whole map root. The mapper must add a synthetic value child and recursively map the old +// value struct instead of treating Struct(name, age) as a leaf to CAST into the table value struct. +TEST(ColumnMapperCreateMappingTest, KeyOnlyMapProjectionStillMapsEvolvedValueStruct) { + const auto int_type = i32(); + const auto string_type = str(); + const auto table_value_type = std::make_shared( + DataTypes {int_type, string_type, string_type}, Strings {"age", "full_name", "gender"}); + const auto file_value_type = std::make_shared(DataTypes {string_type, int_type}, + Strings {"name", "age"}); + + auto table_key = name_col("key", string_type); + auto table_map = map_col("new_map_column", -1, {table_key}, string_type, table_value_type); + table_map.name_mapping = {"map_column"}; + set_name_identifiers(&table_map, -1); + + auto file_key = name_col("key", string_type, 0); + auto file_name = name_col("name", string_type, 0); + auto file_age = name_col("age", int_type, 1); + auto file_value = struct_name_col("value", {file_name, file_age}, 1); + auto file_map = + map_col("map_column", -1, {file_key, file_value}, string_type, file_value_type, 5); + set_name_identifiers(&file_map, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + const auto& map_mapping = mapper.mappings()[0]; + ASSERT_EQ(map_mapping.child_mappings.size(), 2); + EXPECT_EQ(map_mapping.child_mappings[0].table_column_name, "key"); + EXPECT_EQ(map_mapping.child_mappings[0].file_column_name, "key"); + EXPECT_EQ(*map_mapping.child_mappings[0].file_local_id, 0); + + const auto& value_mapping = map_mapping.child_mappings[1]; + EXPECT_EQ(value_mapping.table_column_name, "value"); + EXPECT_EQ(value_mapping.file_column_name, "value"); + EXPECT_EQ(*value_mapping.file_local_id, 1); + ASSERT_EQ(value_mapping.child_mappings.size(), 3); + EXPECT_EQ(value_mapping.child_mappings[0].table_column_name, "age"); + EXPECT_EQ(value_mapping.child_mappings[0].file_column_name, "age"); + EXPECT_EQ(*value_mapping.child_mappings[0].file_local_id, 1); + EXPECT_EQ(value_mapping.child_mappings[1].table_column_name, "full_name"); + EXPECT_EQ(value_mapping.child_mappings[1].file_column_name, "name"); + EXPECT_EQ(*value_mapping.child_mappings[1].file_local_id, 0); + EXPECT_EQ(value_mapping.child_mappings[2].table_column_name, "gender"); + expect_missing(value_mapping.child_mappings[2]); + EXPECT_FALSE(value_mapping.is_trivial); +} + +// Scenario: Iceberg uses field-id mapping, but a key-only map projection may force the mapper to +// synthesize the missing value struct from DataType names, which do not carry field ids. The mapper +// must name-match synthesized children before ordinal fallback, otherwise `age` would read old +// file child `name` and the later materialization would build the value struct incorrectly. +TEST(ColumnMapperCreateMappingTest, + KeyOnlyMapProjectionSynthesizedValueStructNameMatchesBeforeOrdinalFallback) { + const auto int_type = i32(); + const auto string_type = str(); + const auto table_value_type = std::make_shared( + DataTypes {int_type, string_type, string_type}, Strings {"age", "full_name", "gender"}); + const auto file_value_type = std::make_shared(DataTypes {string_type, int_type}, + Strings {"name", "age"}); + + auto table_key = field_id_col("key", 10, string_type, 0); + auto table_map = map_col("new_map_column", 2, {table_key}, string_type, table_value_type); + + auto file_key = field_id_col("key", 10, string_type, 0); + auto file_name = field_id_col("name", 7, string_type, 0); + auto file_age = field_id_col("age", 8, int_type, 1); + auto file_value = struct_col("value", 11, {file_name, file_age}, 1); + auto file_map = + map_col("new_map_column", 2, {file_key, file_value}, string_type, file_value_type, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + const auto& map_mapping = mapper.mappings()[0]; + ASSERT_EQ(map_mapping.child_mappings.size(), 2); + EXPECT_EQ(map_mapping.child_mappings[0].table_column_name, "key"); + EXPECT_EQ(map_mapping.child_mappings[0].file_column_name, "key"); + EXPECT_EQ(*map_mapping.child_mappings[0].file_local_id, 0); + + const auto& value_mapping = map_mapping.child_mappings[1]; + EXPECT_EQ(value_mapping.table_column_name, "value"); + EXPECT_EQ(value_mapping.file_column_name, "value"); + EXPECT_EQ(*value_mapping.file_local_id, 1); + ASSERT_EQ(value_mapping.child_mappings.size(), 3); + EXPECT_EQ(value_mapping.child_mappings[0].table_column_name, "age"); + EXPECT_EQ(value_mapping.child_mappings[0].file_column_name, "age"); + EXPECT_EQ(*value_mapping.child_mappings[0].file_local_id, 1); + EXPECT_EQ(value_mapping.child_mappings[1].table_column_name, "full_name"); + EXPECT_EQ(value_mapping.child_mappings[1].file_column_name, "name"); + EXPECT_EQ(*value_mapping.child_mappings[1].file_local_id, 0); + EXPECT_EQ(value_mapping.child_mappings[2].table_column_name, "gender"); + expect_missing(value_mapping.child_mappings[2]); + EXPECT_FALSE(value_mapping.is_trivial); +} + TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDuplicate) { const auto int_type = i32(); const std::vector table_schema = { @@ -1583,7 +1783,7 @@ TEST(ColumnMapperLocalizeFiltersTest, NestedFilterOnlyChildMergesIntoPredicatePr ASSERT_EQ(request.predicate_columns.size(), 1); EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); ASSERT_FALSE(request.predicate_columns[0].project_all_children); - EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({1, 0})); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({0, 1})); ASSERT_EQ(request.local_positions.size(), 1); EXPECT_EQ(request.local_positions.at(LocalColumnId(5)), LocalIndex(0)); ASSERT_TRUE(mapper.filter_entries().at(GlobalIndex(0)).is_local()); @@ -1722,7 +1922,7 @@ TEST(ColumnMapperScanRequestTest, StructOutputAndFilterOnlyChildAreMerged) { ASSERT_EQ(request.predicate_columns.size(), 1); EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); ASSERT_FALSE(request.predicate_columns[0].project_all_children); - EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({1, 0})); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({0, 1})); ASSERT_EQ(request.column_predicate_filters.size(), 1); EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), std::vector({0})); @@ -1874,7 +2074,7 @@ TEST(ColumnMapperScanRequestTest, UnsafeCastDoesNotBuildNestedPredicateFilter) { EXPECT_TRUE(request.column_predicate_filters.empty()); ASSERT_EQ(request.predicate_columns.size(), 1); EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(5)); - EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({1, 0})); + EXPECT_EQ(projection_ids(request.predicate_columns[0].children), std::vector({0, 1})); } TEST(ColumnMapperScanRequestTest, DeepNestedPredicateTargetsLeafPath) { @@ -2002,6 +2202,600 @@ TEST(ColumnMapperScanRequestTest, MapValueStructProjectionPrunesValueChildren) { EXPECT_EQ(mapped_value->get_element_name(0), "b"); } +// Scenario: a table struct projects only child `b`, while the file struct stores `a,b`. +// BY_NAME mapping should read only the physical child `b` and rebuild the mapped file type to the +// projected struct shape. +TEST(ColumnMapperScanRequestTest, StructProjectionPrunesChildrenByName) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_b = name_col("b", string_type); + auto table_struct = struct_name_col("s", {table_b}); + set_name_identifiers(&table_struct, 0); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_struct = struct_name_col("s", {file_a, file_b}, 0); + set_name_identifiers(&file_struct, 0); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + const auto& projection = request.non_predicate_columns[0]; + EXPECT_EQ(projection.column_id(), LocalColumnId(0)); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].local_id(), 1); + + ASSERT_EQ(mapper.mappings().size(), 1); + const auto* projected_type = assert_cast( + remove_nullable(mapper.mappings()[0].file_type).get()); + ASSERT_EQ(projected_type->get_elements().size(), 1); + EXPECT_EQ(projected_type->get_element_name(0), "b"); +} + +// Scenario: a row filter reaches a struct child through an array wrapper +// (`items.item.a > 5`). The nested predicate filter path only supports direct struct paths, so +// the mapper keeps this as a row predicate and reads the full array root for predicate evaluation. +TEST(ColumnMapperScanRequestTest, ArrayWrapperDoesNotBuildNestedPredicateFilter) { + const auto int_type = i32(); + const auto string_type = str(); + + auto file_a = name_col("a", int_type, 0); + auto file_b = name_col("b", string_type, 1); + auto file_element = struct_name_col("item", {file_a, file_b}, 0); + auto file_array = array_col("items", -1, file_element, 0); + set_name_identifiers(&file_array, 0); + + auto table_array = file_array; + + const auto item_type = file_element.type; + auto item_expr = struct_element(table_slot(0, 0, table_array.type, "items"), item_type, "item"); + auto filter_expr = int_gt(struct_element(item_expr, int_type, "a"), 5); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_array}, {}, {file_array}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_array}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(0)); + EXPECT_TRUE(request.predicate_columns[0].project_all_children); + EXPECT_TRUE(request.predicate_columns[0].children.empty()); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +// Scenario: a map value struct projects child `b`, while a row filter reads value child `a`. +// The filter is too complex to become a file-local nested predicate, but the predicate projection +// must replace the output projection for the same map root and contain both physical value children. +TEST(ColumnMapperScanRequestTest, MapFilterOnlyValueChildMergesWithOutputProjection) { + const auto key_type = i32(); + const auto int_type = i32(); + const auto string_type = str(); + + auto table_value_b = name_col("b", string_type); + auto table_value = struct_name_col("value", {table_value_b}); + auto table_map = map_col("m", -1, {table_value}, key_type, table_value.type); + set_name_identifiers(&table_map, 0); + + auto file_key = name_col("key", key_type, 0); + auto file_value_a = name_col("a", int_type, 0); + auto file_value_b = name_col("b", string_type, 1); + auto file_value = struct_name_col("value", {file_value_a, file_value_b}, 1); + auto file_map = map_col("m", -1, {file_key, file_value}, key_type, file_value.type, 0); + set_name_identifiers(&file_map, 0); + + auto full_value_type = + std::make_shared(DataTypes {int_type, string_type}, Strings {"a", "b"}); + auto full_map_type = std::make_shared(key_type, full_value_type); + auto value_expr = + struct_element(table_slot(0, 0, full_map_type, "m"), full_value_type, "value"); + auto filter_expr = int_gt(struct_element(value_expr, int_type, "a"), 5); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_map}, &request).ok()); + + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + const auto& projection = request.predicate_columns[0]; + EXPECT_EQ(projection.column_id(), LocalColumnId(0)); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].local_id(), 1); + EXPECT_EQ(projection_ids(projection.children[0].children), std::vector({0, 1})); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +// Scenario: when projected struct children are an in-order prefix of the file struct, the mapper can +// read those physical children directly without rebuilding the file-side complex type. +TEST(ColumnMapperScanRequestTest, MatchingProjectedStructDoesNotNeedComplexRematerialize) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_a = field_id_col("a", 1, int_type); + auto table_b = field_id_col("b", 2, string_type); + auto table_struct = struct_col("s", 10, {table_a, table_b}); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, string_type, 1); + auto file_c = field_id_col("c", 3, int_type, 2); + auto file_struct = struct_col("s", 10, {file_a, file_b, file_c}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + const auto& projection = request.non_predicate_columns[0]; + EXPECT_FALSE(projection.project_all_children); + EXPECT_EQ(projection_ids(projection.children), std::vector({0, 1})); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); +} + +// Scenario: Iceberg field-id mapping sees a renamed struct child, but the physical child order and +// types still match, so projection remains a full physical read instead of rebuilding a new type. +TEST(ColumnMapperScanRequestTest, RenameOnlyProjectedStructDoesNotRebuildFileProjection) { + const auto int_type = i32(); + + auto table_a = field_id_col("a", 1, int_type); + auto table_renamed_b = field_id_col("renamed_b", 2, int_type); + auto table_struct = struct_col("s", 10, {table_a, table_renamed_b}); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, int_type, 1); + auto file_struct = struct_col("s", 10, {file_a, file_b}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); + EXPECT_EQ(mapper.mappings()[0].projected_file_children.size(), + mapper.mappings()[0].original_file_children.size()); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); + EXPECT_EQ(mapper.mappings()[0].child_mappings[1].table_column_name, "renamed_b"); + EXPECT_EQ(mapper.mappings()[0].child_mappings[1].file_column_name, "b"); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + EXPECT_TRUE(request.non_predicate_columns[0].project_all_children); + EXPECT_TRUE(request.non_predicate_columns[0].children.empty()); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); +} + +// Scenario: a row filter references an unprojected struct child, so the predicate projection is +// merged with the output projection and the mapper rebuilds the projected file struct type. +TEST(ColumnMapperScanRequestTest, PredicateProjectionRebuildsProjectedStructFileType) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_a = field_id_col("a", 1, int_type); + auto table_b = field_id_col("b", 2, string_type); + auto table_struct = struct_col("s", 10, {table_a, table_b}); + auto full_table_c = field_id_col("c", 3, int_type); + auto full_table_struct = struct_col("s", 10, {table_a, table_b, full_table_c}); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, string_type, 1); + auto file_c = field_id_col("c", 3, int_type, 2); + auto file_struct = struct_col("s", 10, {file_a, file_b, file_c}, 5); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + auto filter_expr = + int_gt(struct_element(table_slot(0, 0, full_table_struct.type, "s"), int_type, "c"), 0); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_TRUE(request.non_predicate_columns.empty()); + const auto& projection = request.predicate_columns[0]; + EXPECT_FALSE(projection.project_all_children); + EXPECT_EQ(projection_ids(projection.children), std::vector({0, 1, 2})); + + const auto* mapped_type = assert_cast( + remove_nullable(mapper.mappings()[0].file_type).get()); + ASSERT_EQ(mapped_type->get_elements().size(), 3); + EXPECT_EQ(mapped_type->get_element_name(0), "a"); + EXPECT_EQ(mapped_type->get_element_name(1), "b"); + EXPECT_EQ(mapped_type->get_element_name(2), "c"); + EXPECT_FALSE(mapper.mappings()[0].is_trivial); +} + +// Scenario: a filter references a top-level column that is not projected by the query; the mapper +// creates a hidden filter mapping without adding that hidden column to visible table mappings. +TEST(ColumnMapperScanRequestTest, PredicateOnlyTopLevelColumnUsesHiddenMapping) { + const auto int_type = i32(); + + auto table_id = field_id_col("id", 0, int_type); + auto table_c = field_id_col("c", 11, int_type); + auto table_struct = struct_col("s", 10, {table_c}); + + auto file_id = field_id_col("id", 0, int_type, 0); + auto file_c = field_id_col("c", 11, int_type, 0); + auto file_struct = struct_col("s", 10, {file_c}, 10); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_id}, {}, {file_id, file_struct}).ok()); + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_EQ(mapper.mappings()[0].table_column_name, "id"); + + auto filter_expr = + int_gt(struct_element(table_slot(7, 1, table_struct.type, "s"), int_type, "c"), 0); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(1)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_id}, &request).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_EQ(mapper.mappings()[0].table_column_name, "id"); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + EXPECT_EQ(request.non_predicate_columns[0].column_id(), LocalColumnId(0)); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(10)); + EXPECT_TRUE(request.predicate_columns[0].project_all_children); + EXPECT_TRUE(request.predicate_columns[0].children.empty()); + + ASSERT_EQ(request.conjuncts.size(), 1); + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(10)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({0})); +} + +// Scenario: a nested predicate targets a table-side renamed struct field; both predicate pruning and +// scan projection must resolve that field to the old physical file child. +TEST(ColumnMapperScanRequestTest, NestedPredicateProjectionUsesMappedRenamedChild) { + const auto int_type = i32(); + + auto table_a = field_id_col("a", 1, int_type); + auto table_renamed_b = field_id_col("renamed_b", 2, int_type); + auto table_struct = struct_col("s", 10, {table_a, table_renamed_b}); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, int_type, 1); + auto file_struct = struct_col("s", 10, {file_a, file_b}, 10); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + auto filter_expr = int_gt( + struct_element(table_slot(0, 0, table_struct.type, "s"), int_type, "renamed_b"), 0); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.column_predicate_filters.size(), 1); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_column_id(), LocalColumnId(10)); + EXPECT_EQ(request.column_predicate_filters[0].effective_file_child_id_path(), + std::vector({1})); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_TRUE(request.predicate_columns[0].project_all_children); + EXPECT_TRUE(request.predicate_columns[0].children.empty()); +} + +// Scenario: element_at(struct, 'table_name') in a row filter is localized to the physical file +// child name, matching the struct_element rewrite and nested predicate filter resolution paths. +TEST(ColumnMapperScanRequestTest, + FileLocalElementAtConjunctUsesFileChildNameForRenamedStructField) { + const auto int_type = i32(); + + auto table_a = field_id_col("a", 1, int_type); + auto table_renamed_b = field_id_col("renamed_b", 2, int_type); + auto table_struct = struct_col("s", 10, {table_a, table_renamed_b}); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, int_type, 1); + auto file_struct = struct_col("s", 10, {file_a, file_b}, 10); + + auto child_expr = element_at(table_slot(0, 0, table_struct.type, table_struct.name), int_type, + "renamed_b"); + auto filter_expr = int_gt(child_expr, 0); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + + ASSERT_EQ(request.conjuncts.size(), 1); + const auto& localized_child = request.conjuncts[0]->root()->children()[0]; + EXPECT_EQ(localized_child->expr_name(), "element_at"); + const auto* localized_slot = assert_cast(localized_child->children()[0].get()); + EXPECT_EQ(localized_slot->column_name(), "s"); + EXPECT_EQ(localized_slot->column_id(), 0); + + const auto* localized_literal = + assert_cast(localized_child->children()[1].get()); + Field localized_field; + localized_literal->get_column_ptr()->get(0, localized_field); + ASSERT_EQ(localized_field.get_type(), TYPE_STRING); + EXPECT_EQ(std::string(localized_field.as_string_view()), "b"); +} + +// Scenario: nested element_at(struct, name) localization rewrites both selector names and +// intermediate return types. The outer selector must be prepared against the projected file child +// struct, not the table child struct or the full historical file child struct. +TEST(ColumnMapperScanRequestTest, NestedElementAtConjunctUsesFileChildTypeForRenamedLeaf) { + const auto int_type = i32(); + const auto string_type = str(); + + auto table_new_aa = field_id_col("new_aa", 23, int_type); + auto table_bb = field_id_col("bb", 24, string_type); + auto table_new_a = struct_col("new_a", 20, {table_new_aa, table_bb}); + auto table_struct = struct_col("struct_column2", 19, {table_new_a}); + + auto file_aa = field_id_col("aa", 23, int_type, 0); + auto file_bb = field_id_col("bb", 24, string_type, 1); + auto file_new_a = struct_col("new_a", 20, {file_aa, file_bb}, 0); + auto file_struct = struct_col("struct_column2", 19, {file_new_a}, 10); + + const auto table_slot_expr = table_slot(0, 0, table_struct.type, "struct_column2"); + const auto table_parent_expr = element_at(table_slot_expr, table_new_a.type, "new_a"); + const auto table_leaf_expr = element_at(table_parent_expr, int_type, "new_aa"); + auto filter_expr = binary_predicate(TExprOpcode::EQ, table_leaf_expr, + literal(int_type, Field::create_field(50))); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_struct}, {}, {file_struct}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_struct}, &request).ok()); + ASSERT_EQ(request.conjuncts.size(), 1); + + const auto& localized_leaf = request.conjuncts[0]->root()->children()[0]; + ASSERT_EQ(localized_leaf->expr_name(), "element_at"); + const auto& localized_parent = localized_leaf->children()[0]; + ASSERT_EQ(localized_parent->expr_name(), "element_at"); + + const auto* localized_leaf_selector = + assert_cast(localized_leaf->children()[1].get()); + Field localized_leaf_field; + localized_leaf_selector->get_column_ptr()->get(0, localized_leaf_field); + ASSERT_EQ(localized_leaf_field.get_type(), TYPE_STRING); + EXPECT_EQ(std::string(localized_leaf_field.as_string_view()), "aa"); + + const auto* localized_parent_type = assert_cast( + remove_nullable(localized_parent->data_type()).get()); + ASSERT_EQ(localized_parent_type->get_elements().size(), 2); + EXPECT_EQ(localized_parent_type->get_element_name(0), "aa"); + EXPECT_EQ(localized_parent_type->get_element_name(1), "bb"); +} + +// Scenario: output projection reads one struct child while the row filter reads a different nested +// struct child. File-local conjunct rewrite must use the merged scan projection type. In the SQL +// shape below, `SELECT element_at(s, 'c') WHERE element_at(element_at(s, 'b'), 'cc') LIKE ...` +// reads file children `b.cc` and `c`; the localized inner `element_at(s, 'b')` returns +// `Struct(cc)`, not the full old file child `Struct(cc, new_dd)`. +TEST(ColumnMapperScanRequestTest, NestedElementAtConjunctUsesMergedScanProjectionChildType) { + const auto string_type = str(); + const auto int_type = i32(); + + auto table_cc = field_id_col("cc", 23, string_type); + auto table_new_dd = field_id_col("new_dd", 24, int_type); + auto table_b = struct_col("b", 20, {table_cc, table_new_dd}); + auto table_c = field_id_col("c", 25, string_type); + auto full_table_struct = struct_col("struct_column2", 19, {table_b, table_c}); + auto projected_table_struct = struct_col("struct_column2", 19, {table_c}); + + auto file_cc = field_id_col("cc", 23, string_type, 0); + auto file_new_dd = field_id_col("new_dd", 24, int_type, 1); + auto file_b = struct_col("b", 20, {file_cc, file_new_dd}, 0); + auto file_c = field_id_col("c", 25, string_type, 1); + auto file_struct = struct_col("new_struct_column", 19, {file_b, file_c}, 10); + + const auto table_slot_expr = table_slot(0, 0, full_table_struct.type, "struct_column2"); + const auto table_parent_expr = element_at(table_slot_expr, table_b.type, "b"); + const auto table_leaf_expr = element_at(table_parent_expr, string_type, "cc"); + auto filter_expr = like_expr(table_leaf_expr, "NestedC%"); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({projected_table_struct}, {}, {file_struct}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {projected_table_struct}, &request).ok()); + ASSERT_EQ(request.conjuncts.size(), 1); + ASSERT_EQ(request.predicate_columns.size(), 1); + + const auto& localized_leaf = request.conjuncts[0]->root()->children()[0]; + ASSERT_EQ(localized_leaf->expr_name(), "element_at"); + const auto& localized_parent = localized_leaf->children()[0]; + ASSERT_EQ(localized_parent->expr_name(), "element_at"); + + const auto* localized_slot = + assert_cast(localized_parent->children()[0].get()); + EXPECT_EQ(localized_slot->column_name(), "new_struct_column"); + EXPECT_EQ(localized_slot->column_id(), 10); + + const auto* localized_parent_type = assert_cast( + remove_nullable(localized_parent->data_type()).get()); + ASSERT_EQ(localized_parent_type->get_elements().size(), 1); + EXPECT_EQ(localized_parent_type->get_element_name(0), "cc"); +} + +// Scenario: struct child access through a computed map/array parent is not localized as a file +// conjunct, because the projected value struct can have a different physical child order. +TEST(ColumnMapperScanRequestTest, MapValuesStructChildConjunctStaysTableLevel) { + const auto key_type = str(); + const auto string_type = str(); + const auto int_type = i32(); + + auto table_gender = field_id_col("gender", 17, string_type); + auto table_full_name = field_id_col("full_name", 7, string_type); + auto table_value = struct_col("value", 6, {table_gender, table_full_name}); + auto table_map = map_col("new_map_column", 2, {table_value}, key_type, table_value.type); + + auto file_key = field_id_col("key", 5, key_type, 0); + auto file_age = field_id_col("age", 8, int_type, 0); + auto file_full_name = field_id_col("full_name", 7, string_type, 1); + auto file_gender = field_id_col("gender", 17, string_type, 2); + auto file_value = struct_col("value", 6, {file_age, file_full_name, file_gender}, 1); + auto file_map = + map_col("new_map_column", 2, {file_key, file_value}, key_type, file_value.type, 1); + + const auto map_slot = table_slot(0, 0, table_map.type, "new_map_column"); + const auto values_expr = map_values(map_slot, table_value.type); + const auto first_value = array_element_at(values_expr, table_value.type, 1); + const auto full_name_expr = element_at(first_value, string_type, "full_name"); + auto filter_expr = like_expr(full_name_expr, "J%"); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_map}, &request).ok()); + + EXPECT_TRUE(request.conjuncts.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(1)); + ASSERT_FALSE(request.predicate_columns[0].project_all_children); + ASSERT_EQ(request.predicate_columns[0].children.size(), 1); + EXPECT_EQ(request.predicate_columns[0].children[0].local_id(), 1); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +// Scenario: MAP_KEYS only reads map keys, but localizing it by wrapping the evolved file map slot +// in CAST(file_map AS table_map) would still cast the old value struct to the new value struct. +// Keep the conjunct table-level when the map value schema changed. +TEST(ColumnMapperScanRequestTest, MapKeysConjunctWithEvolvedValueStructStaysTableLevel) { + const auto key_type = str(); + const auto string_type = str(); + const auto int_type = i32(); + + auto table_age = field_id_col("age", 8, int_type); + auto table_full_name = field_id_col("full_name", 7, string_type); + auto table_gender = field_id_col("gender", 17, string_type); + auto table_value = struct_col("value", 6, {table_age, table_full_name, table_gender}); + auto table_key = field_id_col("key", 5, key_type); + auto table_map = + map_col("new_map_column", 2, {table_key, table_value}, key_type, table_value.type); + + auto file_key = field_id_col("key", 5, key_type, 0); + auto file_name = field_id_col("name", 18, string_type, 0); + auto file_age = field_id_col("age", 8, int_type, 1); + auto file_value = struct_col("value", 6, {file_name, file_age}, 1); + auto file_map = map_col("map_column", 2, {file_key, file_value}, key_type, file_value.type, 1); + + const auto map_slot = table_slot(0, 0, table_map.type, "new_map_column"); + const auto keys_expr = map_keys(map_slot, key_type); + auto filter_expr = array_contains( + keys_expr, literal(key_type, Field::create_field("person5"))); + TableFilter filter {.conjunct = VExprContext::create_shared(filter_expr), + .global_indices = {GlobalIndex(0)}}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {table_map}, &request).ok()); + + EXPECT_TRUE(request.conjuncts.empty()); + EXPECT_TRUE(request.non_predicate_columns.empty()); + ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(1)); + EXPECT_TRUE(request.column_predicate_filters.empty()); +} + +// Scenario: an array element struct projection only contains missing/default children; the mapper +// falls back to reading the full physical element so the reader never gets an empty projection. +TEST(ColumnMapperScanRequestTest, ArrayStructOnlyMissingElementChildUsesFullFileProjection) { + const auto int_type = i32(); + const auto string_type = str(); + + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, int_type, 1); + auto file_element = struct_col("element", 0, {file_a, file_b}, 0); + auto file_array = array_col("xs", 10, file_element, 10); + + auto missing_child = field_id_col("missing_child", 99, string_type); + auto table_element = struct_col("element", 0, {missing_child}); + auto table_array = array_col("xs", 10, table_element); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_array}, {}, {file_array}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_array}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + EXPECT_EQ(request.non_predicate_columns[0].column_id(), LocalColumnId(10)); + EXPECT_TRUE(request.non_predicate_columns[0].project_all_children); + EXPECT_TRUE(request.non_predicate_columns[0].children.empty()); + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_FALSE(mapper.mappings()[0].is_trivial); +} + +// Scenario: a map value struct projection only contains missing/default children; the mapper keeps +// the map key/value shape and reads the full physical value struct instead of an empty value child. +TEST(ColumnMapperScanRequestTest, MapValueStructOnlyMissingChildUsesFullValueProjection) { + const auto key_type = i32(); + const auto int_type = i32(); + const auto string_type = str(); + + auto file_key = field_id_col("key", 0, key_type, 0); + auto file_a = field_id_col("a", 1, int_type, 0); + auto file_b = field_id_col("b", 2, int_type, 1); + auto file_value = struct_col("value", 1, {file_a, file_b}, 1); + auto file_map = map_col("m", 10, {file_key, file_value}, key_type, file_value.type, 10); + + auto missing_child = field_id_col("missing_child", 99, string_type); + auto table_value = struct_col("value", 1, {missing_child}); + auto table_map = map_col("m", 10, {table_value}, key_type, table_value.type); + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping({table_map}, {}, {file_map}).ok()); + + FileScanRequest request; + ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_map}, &request).ok()); + + ASSERT_EQ(request.non_predicate_columns.size(), 1); + const auto& projection = request.non_predicate_columns[0]; + EXPECT_EQ(projection.column_id(), LocalColumnId(10)); + ASSERT_FALSE(projection.project_all_children); + ASSERT_EQ(projection.children.size(), 1); + EXPECT_EQ(projection.children[0].local_id(), 1); + EXPECT_TRUE(projection.children[0].project_all_children); + EXPECT_TRUE(projection.children[0].children.empty()); + ASSERT_EQ(mapper.mappings().size(), 1); + EXPECT_FALSE(mapper.mappings()[0].is_trivial); +} + // ---------------------------------------------------------------------- // L1 complex schema evolution and split isolation. // These tests call the mapper repeatedly with different file schemas and diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index e0b64df5a391e5..69ff7406d699ae 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -47,10 +47,7 @@ #include "core/field.h" #include "exprs/vexpr.h" #include "exprs/vexpr_context.h" -#include "exprs/vliteral.h" #include "exprs/vslot_ref.h" -#include "format_v2/column_mapper.h" -#include "format_v2/expr/cast.h" #include "format_v2/expr/delete_predicate.h" #include "format_v2/file_reader.h" #include "format_v2/parquet/parquet_column_schema.h" @@ -72,52 +69,6 @@ format::LocalColumnIndex field_projection(int32_t column_id) { return format::LocalColumnIndex {.index = column_id}; } -std::vector projection_ids(const std::vector& projections) { - std::vector ids; - ids.reserve(projections.size()); - for (const auto& projection : projections) { - ids.push_back(projection.index); - } - return ids; -} - -std::vector struct_predicate_target_names( - const format::FileStructPredicateTarget* target) { - std::vector names; - for (const auto* current = target; current != nullptr; current = current->child.get()) { - names.push_back(current->file_child_name); - } - return names; -} - -void expect_struct_predicate_target(const format::FileColumnPredicateFilter& filter, - int32_t root_file_column_id, - const std::vector& child_id_path, - const std::vector& child_name_path) { - ASSERT_TRUE(filter.target.is_valid()); - EXPECT_EQ(filter.target.file_column_id.value(), root_file_column_id); - EXPECT_EQ(filter.effective_file_column_id().value(), root_file_column_id); - EXPECT_EQ(filter.effective_file_child_id_path(), child_id_path); - EXPECT_EQ(struct_predicate_target_names(filter.target.struct_target.get()), child_name_path); -} - -void set_name_identifiers(format::ColumnDefinition* column, int32_t local_id) { - DORIS_CHECK(column != nullptr); - column->identifier = Field::create_field(column->name); - column->local_id = local_id; - for (size_t child_idx = 0; child_idx < column->children.size(); ++child_idx) { - set_name_identifiers(&column->children[child_idx], static_cast(child_idx)); - } -} - -void set_local_ids(format::ColumnDefinition* column, int32_t local_id) { - DORIS_CHECK(column != nullptr); - column->local_id = local_id; - for (size_t child_idx = 0; child_idx < column->children.size(); ++child_idx) { - set_local_ids(&column->children[child_idx], static_cast(child_idx)); - } -} - class Int32GreaterThanExpr final : public VExpr { public: Int32GreaterThanExpr(int column_id, int32_t value) @@ -148,63 +99,6 @@ class Int32GreaterThanExpr final : public VExpr { const std::string _expr_name = "Int32GreaterThanExpr"; }; -class TestFunctionExpr final : public VExpr { -public: - TestFunctionExpr(std::string function_name, DataTypePtr data_type, - TExprNodeType::type node_type = TExprNodeType::FUNCTION_CALL, - TExprOpcode::type opcode = TExprOpcode::INVALID_OPCODE) - : VExpr(std::move(data_type), false), _expr_name(std::move(function_name)) { - set_node_type(node_type); - _opcode = opcode; - TFunctionName fn_name; - fn_name.__set_function_name(_expr_name); - _fn.__set_name(fn_name); - } - - const std::string& expr_name() const override { return _expr_name; } - - Status execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, - size_t count, ColumnPtr& result_column) const override { - return Status::NotSupported("TestFunctionExpr is only used for mapper expression analysis"); - } - -private: - const std::string _expr_name; -}; - -VExprSPtr struct_element_expr(const VExprSPtr& parent, const DataTypePtr& child_type, - const std::string& child_name) { - auto expr = std::make_shared("struct_element", make_nullable(child_type)); - expr->add_child(parent); - expr->add_child(VLiteral::create_shared(std::make_shared(), - Field::create_field(child_name))); - return expr; -} - -VExprSPtr in_predicate_expr(const VExprSPtr& probe_expr, const DataTypePtr& literal_type, - const std::vector& values) { - auto expr = std::make_shared("in", std::make_shared(), - TExprNodeType::IN_PRED); - expr->add_child(probe_expr); - for (const auto& value : values) { - expr->add_child(VLiteral::create_shared(literal_type, value)); - } - return expr; -} - -VExprSPtr null_predicate_expr(const VExprSPtr& child, bool is_null) { - auto expr = std::make_shared(is_null ? "is_null_pred" : "is_not_null_pred", - std::make_shared()); - expr->add_child(child); - return expr; -} - -VExprSPtr cast_expr(const VExprSPtr& child, const DataTypePtr& target_type) { - auto expr = Cast::create_shared(target_type); - expr->add_child(child); - return expr; -} - class Int32SumGreaterThanExpr final : public VExpr { public: Int32SumGreaterThanExpr(int left_column_id, int right_column_id, int32_t value) @@ -712,931 +606,6 @@ TEST(FileReaderTest, CloseReleasesSharedIOContext) { EXPECT_TRUE(weak_io_ctx.expired()); } -TEST(TableColumnMapperTest, CreatesComplexProjectionForStructChildren) { - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = std::make_shared(); - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = std::make_shared(); - struct_field.children = {a_field, b_field}; - struct_field.type = std::make_shared(DataTypes {a_field.type, b_field.type}, - Strings {"a", "b"}); - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_field.type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_field.type}, Strings {"b"}); - table_column.children = {table_child}; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - auto request = std::make_shared(); - ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_column}, request.get()).ok()); - EXPECT_EQ(projection_ids(request->non_predicate_columns), std::vector({0})); - ASSERT_EQ(request->non_predicate_columns.size(), 1); - const auto& projection = request->non_predicate_columns[0]; - EXPECT_EQ(projection.index, 0); - ASSERT_FALSE(projection.project_all_children); - ASSERT_EQ(projection.children.size(), 1); - EXPECT_EQ(projection.children[0].index, 1); - - ASSERT_EQ(mapper.mappings().size(), 1); - const auto* projected_type = - assert_cast(mapper.mappings()[0].file_type.get()); - ASSERT_EQ(projected_type->get_elements().size(), 1); - EXPECT_EQ(projected_type->get_element_name(0), "b"); -} - -TEST(TableColumnMapperTest, MergesStructFilterOnlyChildIntoPredicateProjection) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - filter_expr->add_child(struct_element_expr( - VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "a")); - filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - EXPECT_TRUE(request.non_predicate_columns.empty()); - ASSERT_EQ(request.predicate_columns.size(), 1); - const auto& projection = request.predicate_columns[0]; - EXPECT_EQ(projection.index, 0); - ASSERT_FALSE(projection.project_all_children); - ASSERT_EQ(projection.children.size(), 2); - EXPECT_EQ(projection.children[0].index, 1); - EXPECT_EQ(projection.children[1].index, 0); - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); - - ASSERT_EQ(mapper.mappings().size(), 1); - ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 1); - EXPECT_EQ(mapper.mappings()[0].child_mappings[0].file_column_name, "b"); - const auto* read_type = - assert_cast(mapper.mappings()[0].file_type.get()); - ASSERT_EQ(read_type->get_elements().size(), 2); - EXPECT_EQ(read_type->get_element_name(0), "a"); - EXPECT_EQ(read_type->get_element_name(1), "b"); -} - -TEST(TableColumnMapperTest, MapsRenamedNestedStructPredicateByFieldId) { - auto id_type = std::make_shared(); - format::ColumnDefinition file_child; - file_child.identifier = Field::create_field(101); - file_child.name = "file_id"; - file_child.type = id_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(100); - struct_field.name = "s"; - struct_field.type = std::make_shared(DataTypes {id_type}, Strings {"file_id"}); - struct_field.children = {file_child}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "table_id"; - table_child.type = id_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {id_type}, Strings {"table_id"}); - table_column.children = {table_child}; - - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - filter_expr->add_child(struct_element_expr( - VSlotRef::create_shared(0, 0, -1, table_column.type, "s"), id_type, "table_id")); - filter_expr->add_child(VLiteral::create_shared(id_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_FIELD_ID; - format::TableColumnMapper mapper(options); - set_local_ids(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.predicate_columns.size(), 1); - const auto& projection = request.predicate_columns[0]; - EXPECT_EQ(projection.index, 0); - EXPECT_TRUE(projection.project_all_children); - EXPECT_TRUE(projection.children.empty()); - - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"file_id"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); -} - -TEST(TableColumnMapperTest, BuildsNestedStructInListPredicateFilter) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = in_predicate_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), - a_type, "a"), - a_type, {Field::create_field(5), Field::create_field(7)}); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IN_LIST); -} - -TEST(TableColumnMapperTest, BuildsNestedStructPredicateFilterForReverseComparison) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = std::make_shared( - "lt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::LT); - filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); - filter_expr->add_child(struct_element_expr( - VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "a")); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); -} - -TEST(TableColumnMapperTest, BuildsNestedStructNullPredicateFilters) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = std::make_shared("and", std::make_shared(), - TExprNodeType::COMPOUND_PRED, - TExprOpcode::COMPOUND_AND); - filter_expr->add_child(null_predicate_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), - a_type, "a"), - true)); - filter_expr->add_child(null_predicate_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), - a_type, "a"), - false)); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 2); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IS_NULL); - EXPECT_EQ(request.column_predicate_filters[0].predicates[1]->type(), - PredicateType::IS_NOT_NULL); -} - -TEST(TableColumnMapperTest, BuildsNestedStructPredicateFilterThroughSafeCast) { - auto file_a_type = std::make_shared(); - auto table_a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = file_a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {file_a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {table_a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - filter_expr->add_child(cast_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), - file_a_type, "a"), - table_a_type)); - filter_expr->add_child( - VLiteral::create_shared(table_a_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0}, {"a"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::GT); -} - -TEST(TableColumnMapperTest, DoesNotBuildNestedStructPredicateFilterThroughUnsafeCast) { - auto file_a_type = std::make_shared(); - auto table_a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = file_a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {file_a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {table_a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - filter_expr->add_child(cast_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), - file_a_type, "a"), - table_a_type)); - filter_expr->add_child(VLiteral::create_shared(table_a_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - EXPECT_TRUE(request.column_predicate_filters.empty()); - ASSERT_EQ(request.predicate_columns.size(), 1); - EXPECT_EQ(request.predicate_columns[0].index, 0); - ASSERT_FALSE(request.predicate_columns[0].project_all_children); - ASSERT_EQ(request.predicate_columns[0].children.size(), 2); - EXPECT_EQ(request.predicate_columns[0].children[0].index, 1); - EXPECT_EQ(request.predicate_columns[0].children[1].index, 0); -} - -TEST(TableColumnMapperTest, BuildsNestedStructInListPredicateFilterForDeepPath) { - auto id_type = std::make_shared(); - auto name_type = std::make_shared(); - auto b_type = std::make_shared(); - auto inner_type = - std::make_shared(DataTypes {id_type, name_type}, Strings {"id", "n"}); - auto full_struct_type = - std::make_shared(DataTypes {inner_type, b_type}, Strings {"a", "b"}); - - format::ColumnDefinition id_field; - id_field.identifier = Field::create_field(0); - id_field.name = "id"; - id_field.type = id_type; - format::ColumnDefinition name_field; - name_field.identifier = Field::create_field(1); - name_field.name = "n"; - name_field.type = name_type; - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = inner_type; - a_field.children = {id_field, name_field}; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = full_struct_type; - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - auto nested_id_expr = struct_element_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_struct_type, "s"), - inner_type, "a"), - id_type, "id"); - auto filter_expr = - in_predicate_expr(nested_id_expr, id_type, - {Field::create_field(5), Field::create_field(7)}); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.column_predicate_filters.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].file_column_id.value(), 0); - EXPECT_EQ(request.column_predicate_filters[0].file_child_id_path, std::vector({0, 0})); - expect_struct_predicate_target(request.column_predicate_filters[0], 0, {0, 0}, {"a", "id"}); - ASSERT_EQ(request.column_predicate_filters[0].predicates.size(), 1); - EXPECT_EQ(request.column_predicate_filters[0].predicates[0]->type(), PredicateType::IN_LIST); -} - -TEST(TableColumnMapperTest, DoesNotBuildNestedPredicateFilterForMissingChild) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - filter_expr->add_child(struct_element_expr( - VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "missing")); - filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - EXPECT_TRUE(request.column_predicate_filters.empty()); -} - -TEST(TableColumnMapperTest, DoesNotBuildNestedPredicateFilterFromOr) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition struct_field; - struct_field.identifier = Field::create_field(0); - struct_field.name = "s"; - struct_field.type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - struct_field.children = {a_field, b_field}; - - format::ColumnDefinition table_child; - table_child.identifier = Field::create_field(101); - table_child.name = "b"; - table_child.type = b_type; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "s"; - table_column.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_column.children = {table_child}; - - const auto full_table_struct_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - auto left = std::make_shared("gt", std::make_shared(), - TExprNodeType::BINARY_PRED, TExprOpcode::GT); - left->add_child(struct_element_expr( - VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), a_type, "a")); - left->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); - auto right = in_predicate_expr( - struct_element_expr(VSlotRef::create_shared(0, 0, -1, full_table_struct_type, "s"), - a_type, "a"), - a_type, {Field::create_field(7)}); - auto filter_expr = std::make_shared("or", std::make_shared(), - TExprNodeType::COMPOUND_PRED, - TExprOpcode::COMPOUND_OR); - filter_expr->add_child(left); - filter_expr->add_child(right); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&struct_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {struct_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - EXPECT_TRUE(request.column_predicate_filters.empty()); -} - -TEST(TableColumnMapperTest, CreatesComplexProjectionForMapValueStructChildren) { - auto key_type = std::make_shared(); - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - auto value_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - - format::ColumnDefinition key_field; - key_field.identifier = Field::create_field(0); - key_field.name = "key"; - key_field.type = key_type; - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition value_field; - value_field.identifier = Field::create_field(1); - value_field.name = "value"; - value_field.type = value_type; - value_field.children = {a_field, b_field}; - format::ColumnDefinition map_field; - map_field.identifier = Field::create_field(0); - map_field.name = "m"; - map_field.type = std::make_shared(key_type, value_type); - map_field.children = {key_field, value_field}; - - format::ColumnDefinition table_value_child; - table_value_child.identifier = Field::create_field(103); - table_value_child.name = "b"; - table_value_child.type = b_type; - format::ColumnDefinition table_value; - table_value.identifier = Field::create_field(102); - table_value.name = "value"; - table_value.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_value.children = {table_value_child}; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "m"; - table_column.type = std::make_shared(key_type, table_value.type); - table_column.children = {table_value}; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&map_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {map_field}).ok()); - - auto request = std::make_shared(); - ASSERT_TRUE(mapper.create_scan_request({}, {}, {table_column}, request.get()).ok()); - EXPECT_EQ(projection_ids(request->non_predicate_columns), std::vector({0})); - ASSERT_EQ(request->non_predicate_columns.size(), 1); - const auto& projection = request->non_predicate_columns[0]; - EXPECT_EQ(projection.index, 0); - ASSERT_FALSE(projection.project_all_children); - ASSERT_EQ(projection.children.size(), 1); - EXPECT_EQ(projection.children[0].index, 1); - ASSERT_EQ(projection.children[0].children.size(), 1); - EXPECT_EQ(projection.children[0].children[0].index, 1); - - ASSERT_EQ(mapper.mappings().size(), 1); - const auto* projected_type = - assert_cast(mapper.mappings()[0].file_type.get()); - EXPECT_EQ(remove_nullable(projected_type->get_key_type())->get_primitive_type(), TYPE_INT); - const auto* projected_value = - assert_cast(projected_type->get_value_type().get()); - ASSERT_EQ(projected_value->get_elements().size(), 1); - EXPECT_EQ(projected_value->get_element_name(0), "b"); -} - -TEST(TableColumnMapperTest, DoesNotBuildNestedPredicateFilterThroughArrayWrapper) { - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - auto item_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition item_field; - item_field.identifier = Field::create_field(0); - item_field.name = "item"; - item_field.type = item_type; - item_field.children = {a_field, b_field}; - format::ColumnDefinition array_field; - array_field.identifier = Field::create_field(0); - array_field.name = "items"; - array_field.type = std::make_shared(item_type); - array_field.children = {item_field}; - - format::ColumnDefinition table_column = array_field; - - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - auto item_expr = struct_element_expr( - VSlotRef::create_shared(0, 0, -1, table_column.type, "items"), item_type, "item"); - filter_expr->add_child(struct_element_expr(item_expr, a_type, "a")); - filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&array_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {array_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - ASSERT_EQ(request.predicate_columns.size(), 1); - EXPECT_EQ(request.predicate_columns[0].index, 0); - EXPECT_TRUE(request.predicate_columns[0].project_all_children); - EXPECT_TRUE(request.non_predicate_columns.empty()); - EXPECT_TRUE(request.column_predicate_filters.empty()); -} - -TEST(TableColumnMapperTest, MapFilterOnlyStructChildIsPredicateProjectionOnly) { - auto key_type = std::make_shared(); - auto a_type = std::make_shared(); - auto b_type = std::make_shared(); - auto full_value_type = - std::make_shared(DataTypes {a_type, b_type}, Strings {"a", "b"}); - - format::ColumnDefinition key_field; - key_field.identifier = Field::create_field(0); - key_field.name = "key"; - key_field.type = key_type; - format::ColumnDefinition a_field; - a_field.identifier = Field::create_field(0); - a_field.name = "a"; - a_field.type = a_type; - format::ColumnDefinition b_field; - b_field.identifier = Field::create_field(1); - b_field.name = "b"; - b_field.type = b_type; - format::ColumnDefinition value_field; - value_field.identifier = Field::create_field(1); - value_field.name = "value"; - value_field.type = full_value_type; - value_field.children = {a_field, b_field}; - format::ColumnDefinition map_field; - map_field.identifier = Field::create_field(0); - map_field.name = "m"; - map_field.type = std::make_shared(key_type, full_value_type); - map_field.children = {key_field, value_field}; - - format::ColumnDefinition table_value_child; - table_value_child.identifier = Field::create_field(103); - table_value_child.name = "b"; - table_value_child.type = b_type; - format::ColumnDefinition table_value; - table_value.identifier = Field::create_field(102); - table_value.name = "value"; - table_value.type = std::make_shared(DataTypes {b_type}, Strings {"b"}); - table_value.children = {table_value_child}; - format::ColumnDefinition table_column; - table_column.identifier = Field::create_field(100); - table_column.name = "m"; - table_column.type = std::make_shared(key_type, table_value.type); - table_column.children = {table_value}; - - auto filter_expr = std::make_shared( - "gt", std::make_shared(), TExprNodeType::BINARY_PRED, TExprOpcode::GT); - auto value_expr = struct_element_expr( - VSlotRef::create_shared(0, 0, -1, - std::make_shared(key_type, full_value_type), "m"), - full_value_type, "value"); - filter_expr->add_child(struct_element_expr(value_expr, a_type, "a")); - filter_expr->add_child(VLiteral::create_shared(a_type, Field::create_field(5))); - format::TableFilter table_filter { - .conjunct = VExprContext::create_shared(filter_expr), - .global_indices = {format::GlobalIndex(0)}, - }; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_column, 0); - set_name_identifiers(&map_field, 0); - ASSERT_TRUE(mapper.create_mapping({table_column}, {}, {map_field}).ok()); - - format::FileScanRequest request; - ASSERT_TRUE(mapper.create_scan_request({table_filter}, {}, {table_column}, &request).ok()); - - EXPECT_TRUE(request.non_predicate_columns.empty()); - ASSERT_EQ(request.predicate_columns.size(), 1); - const auto& projection = request.predicate_columns[0]; - EXPECT_EQ(projection.index, 0); - ASSERT_FALSE(projection.project_all_children); - ASSERT_EQ(projection.children.size(), 1); - EXPECT_EQ(projection.children[0].index, 1); - ASSERT_EQ(projection.children[0].children.size(), 1); - EXPECT_EQ(projection.children[0].children[0].index, 0); - EXPECT_TRUE(request.column_predicate_filters.empty()); -} - -TEST(TableColumnMapperTest, ColumnPredicatesDoNotForcePredicateMaterialization) { - format::ColumnDefinition id_field; - id_field.identifier = Field::create_field(0); - id_field.name = "id"; - id_field.type = std::make_shared(); - - format::ColumnDefinition value_field; - value_field.identifier = Field::create_field(1); - value_field.name = "value"; - value_field.type = std::make_shared(); - - format::ColumnDefinition table_id; - table_id.identifier = Field::create_field(0); - table_id.name = "id"; - table_id.type = id_field.type; - - format::ColumnDefinition table_value; - table_value.identifier = Field::create_field(1); - table_value.name = "value"; - table_value.type = value_field.type; - - format::TableColumnMapperOptions options; - options.mode = format::TableColumnMappingMode::BY_NAME; - format::TableColumnMapper mapper(options); - set_name_identifiers(&table_id, 0); - set_name_identifiers(&table_value, 1); - set_name_identifiers(&id_field, 0); - set_name_identifiers(&value_field, 1); - ASSERT_TRUE(mapper.create_mapping({table_id, table_value}, {}, {id_field, value_field}).ok()); - - format::TableColumnPredicates column_predicates; - column_predicates[format::GlobalIndex(0)] = {create_comparison_predicate( - 0, "id", id_field.type, Field::create_field(2), false)}; - - auto request = std::make_shared(); - ASSERT_TRUE(mapper.create_scan_request({}, column_predicates, {table_id, table_value}, - request.get()) - .ok()); - EXPECT_TRUE(request->predicate_columns.empty()); - EXPECT_EQ(projection_ids(request->non_predicate_columns), std::vector({0, 1})); - ASSERT_EQ(request->column_predicate_filters.size(), 1); - EXPECT_EQ(request->column_predicate_filters[0].file_column_id.value(), 0); -} - TEST(ParquetBloomFilterPruningTest, EqPredicateUsesArrowHashAndPrunesAbsentIntValue) { auto schema = primitive_bloom_schema(std::make_shared()); auto bloom_filter = bloom_filter_for_int32_values({1, 3}); diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index 8333ef594f5985..b5b2170cf490bd 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -282,25 +282,6 @@ VExprSPtr table_nullable_int64_binary_predicate(const std::string& function_name return expr; } -VExprSPtr table_struct_child_int32_greater_than_expr(int slot_id, int column_id, - const DataTypePtr& struct_type, - const std::string& column_name, - const std::string& child_name, int32_t value) { - const auto int_type = std::make_shared(); - const auto string_type = std::make_shared(); - auto child_expr = table_function_expr("struct_element", int_type, {struct_type, string_type}); - child_expr->add_child( - VSlotRef::create_shared(slot_id, column_id, slot_id, struct_type, column_name)); - child_expr->add_child( - VLiteral::create_shared(string_type, Field::create_field(child_name))); - - auto expr = table_function_expr("gt", std::make_shared(), {int_type, int_type}, - TExprNodeType::BINARY_PRED, TExprOpcode::GT); - expr->add_child(child_expr); - expr->add_child(table_int32_literal(value)); - return expr; -} - class NullableArrayBigintDefaultExpr final : public VExpr { public: explicit NullableArrayBigintDefaultExpr(DataTypePtr data_type) @@ -490,6 +471,29 @@ TEST(CloneTableExprTreeTest, ClonesConditionalExpressions) { } } +// Scenario: cloning a VectorizedFnCall whose return type is complex must not reconstruct the expr +// from TExprNode, because DataTypeFactory rejects nested types through the primitive-type path. +TEST(CloneTableExprTreeTest, ClonesVectorizedFnCallWithComplexReturnType) { + const auto int_type = std::make_shared(); + const auto string_type = std::make_shared(); + const auto struct_type = + std::make_shared(DataTypes {int_type, string_type}, Strings {"a", "b"}); + const auto array_type = std::make_shared(struct_type); + + auto expr = table_function_expr("element_at", struct_type, {array_type, int_type}); + expr->add_child(VSlotRef::create_shared(0, 0, -1, array_type, "array_of_struct")); + expr->add_child(table_int32_literal(1)); + + VExprSPtr cloned; + const auto status = clone_table_expr_tree(expr, &cloned); + ASSERT_TRUE(status.ok()) << status.to_string(); + ASSERT_NE(cloned, nullptr); + EXPECT_EQ(cloned->expr_name(), expr->expr_name()); + EXPECT_TRUE(cloned->data_type()->equals(*struct_type)); + EXPECT_EQ(cloned->get_num_children(), 2); + EXPECT_NE(cloned.get(), expr.get()); +} + std::shared_ptr finish_array(arrow::ArrayBuilder* builder) { std::shared_ptr array; EXPECT_TRUE(builder->Finish(&array).ok()); @@ -2009,6 +2013,67 @@ TEST(TableReaderTest, ProjectedListStructReordersRenamedAndMissingElementChildre std::filesystem::remove_all(test_dir); } +// Scenario: when every projected array-element struct child is missing/default-only, the reader +// still receives a full element projection and can materialize the default child without crashing. +TEST(TableReaderTest, ProjectedListStructOnlyMissingElementChildFallsBackToFullElement) { + const auto test_dir = std::filesystem::temp_directory_path() / + "doris_table_reader_list_only_missing_child_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_list_struct_parquet_file(file_path); + + const auto string_type = std::make_shared(); + auto missing_child = make_table_column(99, "missing_child", string_type); + auto element_type = + std::make_shared(DataTypes {string_type}, Strings {"missing_child"}); + auto nullable_element_type = make_nullable(element_type); + auto element_child = make_table_column(0, "element", nullable_element_type); + element_child.children = {missing_child}; + auto list_column = + make_table_column(100, "xs", std::make_shared(nullable_element_type)); + list_column.children = {element_child}; + std::vector projected_columns = {list_column}; + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + }) + .ok()); + ASSERT_TRUE(reader.prepare_split(build_split_options(file_path)).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + ASSERT_FALSE(eos); + ASSERT_EQ(block.rows(), 3); + const auto& array_result = assert_cast(*block.get_by_position(0).column); + EXPECT_EQ(array_result.get_offsets()[0], 2); + EXPECT_EQ(array_result.get_offsets()[1], 3); + EXPECT_EQ(array_result.get_offsets()[2], 4); + const auto& nullable_elements = assert_cast(array_result.get_data()); + const auto& element_struct = + assert_cast(nullable_elements.get_nested_column()); + ASSERT_EQ(element_struct.get_columns().size(), 1); + const auto& missing_values = assert_cast(element_struct.get_column(0)); + for (size_t row = 0; row < missing_values.size(); ++row) { + EXPECT_EQ(missing_values.get_data_at(row).to_string(), ""); + } + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + TEST(TableReaderTest, PushDownMinMaxFallsBackForProjectedMapValueStructLeaf) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_minmax_map_test"; @@ -2262,6 +2327,96 @@ TEST(TableReaderTest, MaterializeMapKeyStructReordersRenamedChildren) { EXPECT_EQ(result_value.get_element(2), 300); } +// Scenario: map value struct materialization follows DataTypeStruct field order even when +// ColumnMapping children arrive in a different order from projected ColumnDefinition children. +TEST(TableReaderTest, MaterializeMapValueStructUsesTableTypeOrder) { + const auto key_type = std::make_shared(); + const auto string_type = std::make_shared(); + const auto file_value_type = std::make_shared( + DataTypes {string_type, string_type}, Strings {"full_name", "gender"}); + const auto table_value_type = std::make_shared( + DataTypes {string_type, string_type}, Strings {"full_name", "gender"}); + const auto file_map_type = std::make_shared(key_type, file_value_type); + const auto table_map_type = std::make_shared(key_type, table_value_type); + + ColumnMapping full_name_mapping; + full_name_mapping.table_column_name = "full_name"; + full_name_mapping.file_column_name = "full_name"; + full_name_mapping.file_local_id = 0; + full_name_mapping.table_type = string_type; + full_name_mapping.file_type = string_type; + full_name_mapping.is_trivial = true; + + ColumnMapping gender_mapping; + gender_mapping.table_column_name = "gender"; + gender_mapping.file_column_name = "gender"; + gender_mapping.file_local_id = 1; + gender_mapping.table_type = string_type; + gender_mapping.file_type = string_type; + gender_mapping.is_trivial = true; + + ColumnMapping value_mapping; + value_mapping.table_column_name = "value"; + value_mapping.file_column_name = "value"; + value_mapping.file_local_id = 1; + value_mapping.table_type = table_value_type; + value_mapping.file_type = file_value_type; + value_mapping.is_trivial = false; + value_mapping.child_mappings = {gender_mapping, full_name_mapping}; + + ColumnMapping key_mapping; + key_mapping.table_column_name = "key"; + key_mapping.file_column_name = "key"; + key_mapping.file_local_id = 0; + key_mapping.table_type = key_type; + key_mapping.file_type = key_type; + key_mapping.is_trivial = true; + + ColumnMapping map_mapping; + map_mapping.table_column_name = "new_map_column"; + map_mapping.file_column_name = "new_map_column"; + map_mapping.table_type = table_map_type; + map_mapping.file_type = file_map_type; + map_mapping.is_trivial = false; + map_mapping.child_mappings = {key_mapping, value_mapping}; + + auto key_column = ColumnString::create(); + key_column->insert_value("person10"); + key_column->insert_value("person20"); + + auto full_name_column = ColumnString::create(); + full_name_column->insert_value("Jack"); + full_name_column->insert_value("James Lee"); + auto gender_column = ColumnString::create(); + gender_column->insert_value("Male"); + gender_column->insert_value("Male"); + MutableColumns value_children; + value_children.push_back(std::move(full_name_column)); + value_children.push_back(std::move(gender_column)); + auto value_column = ColumnStruct::create(std::move(value_children)); + + auto offsets_column = ColumnArray::ColumnOffsets::create(); + offsets_column->insert_value(1); + offsets_column->insert_value(2); + ColumnPtr file_column = ColumnMap::create(std::move(key_column), std::move(value_column), + std::move(offsets_column)); + + TableReaderMaterializeTestHelper reader; + ColumnPtr result_column; + ASSERT_TRUE(reader._materialize_map_mapping_column(map_mapping, file_column, 2, &result_column) + .ok()); + + const auto& result_map = assert_cast(*result_column); + const auto& result_value = assert_cast(result_map.get_values()); + ASSERT_EQ(result_value.get_columns().size(), 2); + const auto& full_name_result = assert_cast(result_value.get_column(0)); + const auto& gender_result = assert_cast(result_value.get_column(1)); + EXPECT_EQ(full_name_result.get_data_at(0).to_string(), "Jack"); + EXPECT_EQ(full_name_result.get_data_at(1).to_string(), "James Lee"); + EXPECT_EQ(gender_result.get_data_at(0).to_string(), "Male"); + EXPECT_EQ(gender_result.get_data_at(1).to_string(), "Male"); +} + TEST(TableReaderTest, PushDownMinMaxOnlyUsesSelectedRowGroupInFileRange) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_table_reader_minmax_range_test"; @@ -4551,245 +4706,5 @@ TEST(TableReaderTest, ProjectedColumnsUseMapperExpressionsForParquetSchemaMismat std::filesystem::remove_all(test_dir); } -TEST(TableColumnMapperTest, MatchingProjectedStructDoesNotNeedComplexRematerialize) { - const auto int_type = std::make_shared(); - const auto string_type = std::make_shared(); - - auto table_struct = - make_table_column(10, "s", - std::make_shared(DataTypes {int_type, string_type}, - Strings {"a", "b"})); - table_struct.children = { - make_table_column(1, "a", int_type), - make_table_column(2, "b", string_type), - }; - - auto file_struct = make_file_column( - 10, "s", - std::make_shared(DataTypes {int_type, string_type, int_type}, - Strings {"a", "b", "c"})); - file_struct.children = { - make_file_column(1, "a", int_type), - make_file_column(2, "b", string_type), - make_file_column(3, "c", int_type), - }; - - const std::vector projected_columns = {table_struct}; - const std::vector file_schema = {file_struct}; - - TableColumnMapper mapper; - ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); - - ASSERT_EQ(mapper.mappings().size(), 1); - EXPECT_TRUE(mapper.mappings()[0].is_trivial); - - FileScanRequest file_request; - ASSERT_TRUE(mapper.create_scan_request({}, {}, projected_columns, &file_request).ok()); - - ASSERT_EQ(file_request.non_predicate_columns.size(), 1); - const auto& projection = file_request.non_predicate_columns[0]; - EXPECT_FALSE(projection.project_all_children); - ASSERT_EQ(projection.children.size(), 2); - EXPECT_EQ(projection.children[0].local_id(), 1); - EXPECT_EQ(projection.children[1].local_id(), 2); - EXPECT_TRUE(mapper.mappings()[0].is_trivial); -} - -TEST(TableColumnMapperTest, RenameOnlyProjectedStructDoesNotRebuildFileProjection) { - const auto int_type = std::make_shared(); - - auto table_struct = - make_table_column(10, "s", - std::make_shared(DataTypes {int_type, int_type}, - Strings {"a", "renamed_b"})); - table_struct.children = { - make_table_column(1, "a", int_type), - make_table_column(2, "renamed_b", int_type), - }; - - auto file_struct = make_file_column( - 10, "s", - std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"})); - file_struct.children = { - make_file_column(1, "a", int_type), - make_file_column(2, "b", int_type), - }; - - const std::vector projected_columns = {table_struct}; - const std::vector file_schema = {file_struct}; - - TableColumnMapper mapper; - ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); - - ASSERT_EQ(mapper.mappings().size(), 1); - EXPECT_TRUE(mapper.mappings()[0].is_trivial); - EXPECT_EQ(mapper.mappings()[0].projected_file_children.size(), - mapper.mappings()[0].original_file_children.size()); - ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); - EXPECT_EQ(mapper.mappings()[0].child_mappings[1].table_column_name, "renamed_b"); - EXPECT_EQ(mapper.mappings()[0].child_mappings[1].file_column_name, "b"); - - FileScanRequest file_request; - ASSERT_TRUE(mapper.create_scan_request({}, {}, projected_columns, &file_request).ok()); - - ASSERT_EQ(file_request.non_predicate_columns.size(), 1); - EXPECT_TRUE(file_request.non_predicate_columns[0].project_all_children); - EXPECT_TRUE(file_request.non_predicate_columns[0].children.empty()); - EXPECT_TRUE(mapper.mappings()[0].is_trivial); -} - -TEST(TableColumnMapperTest, PredicateProjectionRebuildsProjectedStructFileType) { - const auto int_type = std::make_shared(); - const auto string_type = std::make_shared(); - - auto table_struct = - make_table_column(10, "s", - std::make_shared(DataTypes {int_type, string_type}, - Strings {"a", "b"})); - table_struct.children = { - make_table_column(1, "a", int_type), - make_table_column(2, "b", string_type), - }; - - auto file_struct = make_file_column( - 10, "s", - std::make_shared(DataTypes {int_type, string_type, int_type}, - Strings {"a", "b", "c"})); - file_struct.children = { - make_file_column(1, "a", int_type), - make_file_column(2, "b", string_type), - make_file_column(3, "c", int_type), - }; - - const std::vector projected_columns = {table_struct}; - const std::vector file_schema = {file_struct}; - - TableColumnMapper mapper; - ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); - - TableFilter table_filter { - .conjunct = VExprContext::create_shared(table_struct_child_int32_greater_than_expr( - 0, 0, table_struct.type, "s", "c", 0)), - .global_indices = {GlobalIndex(0)}, - }; - - FileScanRequest file_request; - ASSERT_TRUE( - mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); - - ASSERT_EQ(file_request.predicate_columns.size(), 1); - EXPECT_TRUE(file_request.non_predicate_columns.empty()); - const auto& projection = file_request.predicate_columns[0]; - EXPECT_FALSE(projection.project_all_children); - ASSERT_EQ(projection.children.size(), 3); - EXPECT_EQ(projection.children[0].local_id(), 1); - EXPECT_EQ(projection.children[1].local_id(), 2); - EXPECT_EQ(projection.children[2].local_id(), 3); - - const auto* mapped_type = assert_cast( - remove_nullable(mapper.mappings()[0].file_type).get()); - ASSERT_EQ(mapped_type->get_elements().size(), 3); - EXPECT_EQ(mapped_type->get_element_name(0), "a"); - EXPECT_EQ(mapped_type->get_element_name(1), "b"); - EXPECT_EQ(mapped_type->get_element_name(2), "c"); - EXPECT_FALSE(mapper.mappings()[0].is_trivial); -} - -TEST(TableColumnMapperTest, PredicateOnlyTopLevelColumnUsesHiddenMapping) { - const auto int_type = std::make_shared(); - - const auto table_id = make_table_column(0, "id", int_type); - auto table_struct = make_table_column( - 10, "s", std::make_shared(DataTypes {int_type}, Strings {"c"})); - - const auto file_id = make_file_column(0, "id", int_type); - auto file_struct = make_file_column( - 10, "s", std::make_shared(DataTypes {int_type}, Strings {"c"})); - file_struct.children = { - make_file_column(11, "c", int_type), - }; - - const std::vector projected_columns = {table_id}; - const std::vector file_schema = {file_id, file_struct}; - - TableColumnMapper mapper; - ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); - ASSERT_EQ(mapper.mappings().size(), 1); - EXPECT_EQ(mapper.mappings()[0].table_column_name, "id"); - - TableFilter table_filter { - .conjunct = VExprContext::create_shared(table_struct_child_int32_greater_than_expr( - 7, 1, table_struct.type, "s", "c", 0)), - .global_indices = {GlobalIndex(1)}, - }; - - FileScanRequest file_request; - ASSERT_TRUE( - mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); - - ASSERT_EQ(mapper.mappings().size(), 1); - EXPECT_EQ(mapper.mappings()[0].table_column_name, "id"); - - ASSERT_EQ(file_request.non_predicate_columns.size(), 1); - EXPECT_EQ(file_request.non_predicate_columns[0].column_id(), LocalColumnId(0)); - ASSERT_EQ(file_request.predicate_columns.size(), 1); - EXPECT_EQ(file_request.predicate_columns[0].column_id(), LocalColumnId(10)); - EXPECT_TRUE(file_request.predicate_columns[0].project_all_children); - EXPECT_TRUE(file_request.predicate_columns[0].children.empty()); - - ASSERT_EQ(file_request.conjuncts.size(), 1); - ASSERT_EQ(file_request.column_predicate_filters.size(), 1); - EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_column_id(), - LocalColumnId(10)); - EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_child_id_path(), - std::vector({11})); -} - -TEST(TableColumnMapperTest, NestedPredicateProjectionUsesMappedRenamedChild) { - const auto int_type = std::make_shared(); - - auto table_struct = - make_table_column(10, "s", - std::make_shared(DataTypes {int_type, int_type}, - Strings {"a", "renamed_b"})); - table_struct.children = { - make_table_column(1, "a", int_type), - make_table_column(2, "renamed_b", int_type), - }; - - auto file_struct = make_file_column( - 10, "s", - std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"})); - file_struct.children = { - make_file_column(1, "a", int_type), - make_file_column(2, "b", int_type), - }; - - const std::vector projected_columns = {table_struct}; - const std::vector file_schema = {file_struct}; - - TableColumnMapper mapper; - ASSERT_TRUE(mapper.create_mapping(projected_columns, {}, file_schema).ok()); - - TableFilter table_filter { - .conjunct = VExprContext::create_shared(table_struct_child_int32_greater_than_expr( - 0, 0, table_struct.type, "s", "renamed_b", 0)), - .global_indices = {GlobalIndex(0)}, - }; - - FileScanRequest file_request; - ASSERT_TRUE( - mapper.create_scan_request({table_filter}, {}, projected_columns, &file_request).ok()); - - ASSERT_EQ(file_request.column_predicate_filters.size(), 1); - EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_column_id(), - LocalColumnId(10)); - EXPECT_EQ(file_request.column_predicate_filters[0].effective_file_child_id_path(), - std::vector({2})); - ASSERT_EQ(file_request.predicate_columns.size(), 1); - EXPECT_TRUE(file_request.predicate_columns[0].project_all_children); - EXPECT_TRUE(file_request.predicate_columns[0].children.empty()); -} - } // namespace } // namespace doris::format From cdebd7ebe1b9046c6d346dc4d6f5fd9156cf1ffe Mon Sep 17 00:00:00 2001 From: Gabriel Date: Sat, 13 Jun 2026 18:17:17 +0800 Subject: [PATCH 09/28] fix rf (#64481) --- be/src/exec/scan/file_scanner_v2.cpp | 13 +++++-------- be/src/exprs/runtime_filter_expr.h | 2 ++ be/src/format_v2/column_mapper.cpp | 16 ++++++++++++++++ .../exec/scan/vfile_scanner_exception_test.cpp | 4 ++-- be/test/format_v2/table_reader_test.cpp | 9 +++------ 5 files changed, 28 insertions(+), 16 deletions(-) diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp index 45cb785f8dd863..dc65e90a9112ae 100644 --- a/be/src/exec/scan/file_scanner_v2.cpp +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -266,10 +266,9 @@ Status build_all_nested_children_from_schema(format::ColumnDefinition* column, } case TYPE_ARRAY: { const auto& array_type = assert_cast(*nested_type); - const auto* element_schema = - schema_column != nullptr && !schema_column->children.empty() - ? &schema_column->children[0] - : nullptr; + const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), schema_field_name_or(element_schema, "element"), array_type.get_nested_type()); @@ -287,8 +286,7 @@ Status build_all_nested_children_from_schema(format::ColumnDefinition* column, : nullptr; auto* key_child = find_or_add_child(column, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), - map_type.get_key_type()); + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); inherit_schema_metadata(key_child, key_schema); RETURN_IF_ERROR(build_nested_children_from_access_node( key_child, key_child->type, project_all, path + ".KEYS", key_schema)); @@ -409,8 +407,7 @@ Status build_map_children_from_access_node(format::ColumnDefinition* column, if (need_key) { auto* key_child = find_or_add_child(column, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), - map_type.get_key_type()); + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); inherit_schema_metadata(key_child, key_schema); RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, key_node, path + ".KEYS", key_schema)); diff --git a/be/src/exprs/runtime_filter_expr.h b/be/src/exprs/runtime_filter_expr.h index e3f34a5fb605b6..d97062d2bd63a0 100644 --- a/be/src/exprs/runtime_filter_expr.h +++ b/be/src/exprs/runtime_filter_expr.h @@ -23,6 +23,7 @@ #include #include #include +#include #include "common/config.h" #include "common/status.h" @@ -80,6 +81,7 @@ class RuntimeFilterExpr final : public VExpr { } VExprSPtr get_impl() const override { return _impl; } + void set_impl(VExprSPtr impl) { _impl = std::move(impl); } Status clone_node(VExprSPtr* cloned_expr) const override; void attach_profile_counter(std::shared_ptr rf_input_rows, diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index 7ea568cd72d6b1..98fc907c1ddafa 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -35,6 +35,7 @@ #include "core/data_type/data_type_string.h" #include "core/data_type/data_type_struct.h" #include "core/data_type/primitive_type.h" +#include "exprs/runtime_filter_expr.h" #include "exprs/short_circuit_evaluation_expr.h" #include "exprs/vcase_expr.h" #include "exprs/vcast_expr.h" @@ -775,6 +776,21 @@ static VExprSPtr rewrite_table_expr_to_file_expr( } DORIS_CHECK(rewrite_context != nullptr); DORIS_CHECK(can_localize != nullptr); + if (auto* runtime_filter = dynamic_cast(expr.get()); + runtime_filter != nullptr) { + auto impl = runtime_filter->get_impl(); + if (impl == nullptr) { + *can_localize = false; + return expr; + } + auto localized_impl = rewrite_table_expr_to_file_expr( + impl, global_to_file_slot, filter_mappings, rewrite_context, can_localize); + if (!*can_localize) { + return expr; + } + runtime_filter->set_impl(std::move(localized_impl)); + return expr; + } if (rewrite_binary_slot_literal_predicate(expr, global_to_file_slot, rewrite_context)) { return expr; } diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index 6d0695b63bf172..32f7429c01c4e9 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -661,8 +661,8 @@ TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn .type = pruned_struct_type, }; std::vector pruned_struct_paths {data_access_path({"a_struct"})}; - status = FileScannerV2::TEST_build_nested_children_from_access_paths( - &pruned_struct_column, pruned_struct_paths); + status = FileScannerV2::TEST_build_nested_children_from_access_paths(&pruned_struct_column, + pruned_struct_paths); ASSERT_TRUE(status.ok()) << status; ASSERT_EQ(pruned_struct_column.children.size(), 1); EXPECT_EQ(pruned_struct_column.children[0].name, "drop_and_add"); diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index b5b2170cf490bd..04055d5c2a897a 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -1168,20 +1168,17 @@ TEST(TableReaderTest, TruncateCharOrVarcharPredicateOnlyAppliesToParquetStringWi ColumnMapping mapping; mapping.table_type = std::make_shared(3, TYPE_VARCHAR); mapping.file_type = std::make_shared(10, TYPE_VARCHAR); - EXPECT_TRUE( - TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + EXPECT_TRUE(TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); mapping.file_type = std::make_shared(2, TYPE_VARCHAR); EXPECT_FALSE( TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); mapping.file_type = std::make_shared(); - EXPECT_TRUE( - TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + EXPECT_TRUE(TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); mapping.file_type = std::make_shared(); - EXPECT_TRUE( - TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); + EXPECT_TRUE(TableReaderCharVarcharTestHelper::_should_truncate_char_or_varchar_column(mapping)); mapping.table_type = std::make_shared(); EXPECT_FALSE( From d336826291c3213a7d14573c82afbd8052305d0a Mon Sep 17 00:00:00 2001 From: Gabriel Date: Sat, 13 Jun 2026 22:22:32 +0800 Subject: [PATCH 10/28] fix paimon (#64482) --- .../parquet/parquet_column_schema.cpp | 36 +++++-- be/src/format_v2/table_reader.cpp | 23 ++++- .../parquet/parquet_column_reader_test.cpp | 65 ++++++++++-- be/test/format_v2/table_reader_test.cpp | 98 +++++++++++++++++++ 4 files changed, 202 insertions(+), 20 deletions(-) diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp index e23f3ffa5616f7..7edef4af812db2 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.cpp +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -44,7 +44,8 @@ struct SchemaBuildContext { }; enum class SchemaBuildMode { - // Normal recursive schema build. A repeated LIST/MAP annotated group is rejected in this mode + // Normal recursive schema build. Bare repeated fields are exposed as Doris ARRAY for + // protobuf/legacy Parquet compatibility, while repeated LIST/MAP annotated groups are rejected // because Parquet LIST/MAP outer groups are not allowed to be repeated at a top-level or struct // field boundary. NORMAL, @@ -107,6 +108,10 @@ bool has_structural_list_name(const std::string& list_name, const std::string& r return repeated_name == "array" || repeated_name == list_name + "_tuple"; } +bool should_build_repeated_field_as_list(const ::parquet::schema::Node& node) { + return node.is_repeated() && !is_list_node(node) && !is_map_node(node); +} + DataTypePtr nullable_if_needed(DataTypePtr type, const ::parquet::schema::Node& node) { return node.is_optional() ? make_nullable(type) : type; } @@ -260,11 +265,22 @@ Status build_node_schema_with_mode(const ::parquet::SchemaDescriptor& schema, std::unique_ptr* result, SchemaBuildMode mode); -// Builds a semantic ARRAY schema for a simple repeated field. Arrow handles this in -// NodeToSchemaField()/GroupToSchemaField(); Doris only enables it while materializing the element -// of a LIST-annotated parent, so existing top-level repeated primitive rejection is unchanged. +// Builds a semantic ARRAY schema for a bare repeated field. Arrow handles this in +// NodeToSchemaField()/GroupToSchemaField(); Doris needs the same compatibility behavior because +// protobuf and old parquet writers often encode repeated fields without a LIST annotation. // // Example: +// optional group event { +// repeated group links { +// optional binary url (UTF8); +// optional int32 rank; +// } +// } +// Doris exposes event.links as ARRAY>, not STRUCT. This keeps v2's +// file-local schema aligned with the old schema parser used by HDFS TVF schema fetching. +// +// When the repeated field appears inside an already resolved LIST element, only the nested repeated +// child should be wrapped: // optional group a (LIST) { // repeated group element { // repeated int32 items; @@ -298,8 +314,9 @@ Status build_repeated_field_as_list_schema(const ::parquet::SchemaDescriptor& sc } // Recursively builds ParquetColumnSchema for the given schema node and its children in Parquet -// file's metadata. The mode only affects repeated nodes that have already been selected as LIST -// elements by resolve_list_element_node(); normal file schema recursion remains strict. +// file's metadata. NORMAL mode exposes bare repeated fields as ARRAY for legacy compatibility. +// REPEATED_NODE_AS_LIST_ELEMENT mode means the current repeated node was already selected as an +// enclosing LIST element, so only its nested bare repeated children should be wrapped. Status build_node_schema_with_mode(const ::parquet::SchemaDescriptor& schema, const ::parquet::schema::Node& node, const SchemaBuildContext& context, @@ -308,6 +325,10 @@ Status build_node_schema_with_mode(const ::parquet::SchemaDescriptor& schema, if (result == nullptr) { return Status::InvalidArgument("result is null"); } + if (mode == SchemaBuildMode::NORMAL && should_build_repeated_field_as_list(node)) { + return build_repeated_field_as_list_schema(schema, node, context, result); + } + auto column_schema = std::make_unique(); inherit_common_schema_state(node, context, column_schema.get()); @@ -405,8 +426,7 @@ Status build_node_schema_with_mode(const ::parquet::SchemaDescriptor& schema, const auto& child_node = *group.field(child_idx); std::unique_ptr child; const auto child_ctx = child_context(context, child_node, child_idx); - if (mode == SchemaBuildMode::REPEATED_NODE_AS_LIST_ELEMENT && child_node.is_repeated() && - !is_list_node(child_node) && !is_map_node(child_node)) { + if (should_build_repeated_field_as_list(child_node)) { RETURN_IF_ERROR( build_repeated_field_as_list_schema(schema, child_node, child_ctx, &child)); } else { diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index fff4a1eb341ae3..682d209041bfc3 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -345,14 +345,20 @@ Status parse_deletion_vector(const char* buf, size_t buffer_size, DeleteFileDesc total_length + 4 + checksum_size, buffer_size); } - constexpr static char MAGIC_NUMBER[] = {'\xD1', '\xD3', '\x39', '\x64'}; - if (memcmp(buf + sizeof(total_length), MAGIC_NUMBER, 4) != 0) [[unlikely]] { - return Status::DataQualityError("Deletion vector magic number mismatch"); - } - const char* bitmap_buf = buf + 8; const size_t bitmap_size = buffer_size - 8 - checksum_size; if (format == DeleteFileDesc::Format::PAIMON) { + // Paimon BitmapDeletionVector stores: + // [4-byte big-endian length][4-byte magic 0x5E43F2D0][32-bit roaring bitmap] + // The length covers magic + bitmap, and does not include the leading length field. + constexpr static char PAIMON_BITMAP_MAGIC[] = {'\x5E', '\x43', '\xF2', '\xD0'}; + if (memcmp(buf + sizeof(total_length), PAIMON_BITMAP_MAGIC, 4) != 0) [[unlikely]] { + return Status::DataQualityError( + "Paimon deletion vector magic number mismatch, expected: {}, actual: {}", + BigEndian::Load32(PAIMON_BITMAP_MAGIC), + BigEndian::Load32(buf + sizeof(total_length))); + } + roaring::Roaring bitmap; try { bitmap = roaring::Roaring::readSafe(bitmap_buf, bitmap_size); @@ -367,6 +373,13 @@ Status parse_deletion_vector(const char* buf, size_t buffer_size, DeleteFileDesc return Status::OK(); } + constexpr static char ICEBERG_DV_MAGIC[] = {'\xD1', '\xD3', '\x39', '\x64'}; + if (memcmp(buf + sizeof(total_length), ICEBERG_DV_MAGIC, 4) != 0) [[unlikely]] { + return Status::DataQualityError( + "Iceberg deletion vector magic number mismatch, expected: {}, actual: {}", + BigEndian::Load32(ICEBERG_DV_MAGIC), BigEndian::Load32(buf + sizeof(total_length))); + } + roaring::Roaring64Map bitmap; try { bitmap = roaring::Roaring64Map::readSafe(bitmap_buf, bitmap_size); diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp index 2edd4594f995cd..bd872df7a1f6a7 100644 --- a/be/test/format_v2/parquet/parquet_column_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -3799,7 +3799,9 @@ TEST_F(ParquetColumnReaderTest, RejectRecordReaderUnsupportedDecimalPrecision) { } } -TEST_F(ParquetColumnReaderTest, RejectRepeatedPrimitiveAsFlatScalarReader) { +// Legacy/protobuf writers may encode an array as a bare repeated primitive without LIST +// annotation. The v2 schema builder should expose it as ARRAY instead of a flat scalar. +TEST_F(ParquetColumnReaderTest, BuildBareRepeatedPrimitiveAsListSchema) { auto schema = ::parquet::schema::GroupNode::Make( "schema", ::parquet::Repetition::REQUIRED, {::parquet::schema::PrimitiveNode::Make("repeated_int32_col", @@ -3812,13 +3814,62 @@ TEST_F(ParquetColumnReaderTest, RejectRepeatedPrimitiveAsFlatScalarReader) { auto st = build_parquet_column_schema(descriptor, &fields); ASSERT_TRUE(st.ok()) << st; ASSERT_EQ(fields.size(), 1); - ASSERT_EQ(fields[0]->descriptor->max_repetition_level(), 1); - ASSERT_TRUE(supports_record_reader(fields[0]->type_descriptor)); + EXPECT_EQ(fields[0]->kind, ParquetColumnSchemaKind::LIST); + EXPECT_EQ(remove_nullable(fields[0]->type)->get_primitive_type(), TYPE_ARRAY); + ASSERT_EQ(fields[0]->children.size(), 1); + const auto& element_schema = *fields[0]->children[0]; + EXPECT_EQ(element_schema.kind, ParquetColumnSchemaKind::PRIMITIVE); + ASSERT_NE(element_schema.descriptor, nullptr); + EXPECT_EQ(element_schema.descriptor->max_repetition_level(), 1); + EXPECT_TRUE(supports_record_reader(element_schema.type_descriptor)); +} - ParquetColumnReaderFactory factory(nullptr, descriptor.num_columns()); - std::unique_ptr reader; - st = factory.create(*fields[0], &reader); - ASSERT_FALSE(st.ok()) << st; +// Legacy/protobuf repeated message fields are bare repeated groups. They must be exposed as +// ARRAY> so HDFS TVF schema fetched by the old parser matches the v2 execution schema. +TEST_F(ParquetColumnReaderTest, BuildBareRepeatedGroupInsideStructAsListStructSchema) { + auto schema = ::parquet::schema::GroupNode::Make( + "schema", ::parquet::Repetition::REQUIRED, + {::parquet::schema::GroupNode::Make( + "event", ::parquet::Repetition::OPTIONAL, + {::parquet::schema::GroupNode::Make( + "links", ::parquet::Repetition::REPEATED, + { + ::parquet::schema::PrimitiveNode::Make( + "url", ::parquet::Repetition::OPTIONAL, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::UTF8), + ::parquet::schema::PrimitiveNode::Make( + "rank", ::parquet::Repetition::OPTIONAL, + ::parquet::Type::INT32), + })})}); + ::parquet::SchemaDescriptor descriptor; + descriptor.Init(schema); + + std::vector> fields; + auto st = build_parquet_column_schema(descriptor, &fields); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(fields.size(), 1); + EXPECT_EQ(fields[0]->kind, ParquetColumnSchemaKind::STRUCT); + ASSERT_EQ(fields[0]->children.size(), 1); + + const auto& links_schema = *fields[0]->children[0]; + EXPECT_EQ(links_schema.name, "links"); + EXPECT_EQ(links_schema.kind, ParquetColumnSchemaKind::LIST); + ASSERT_EQ(links_schema.children.size(), 1); + EXPECT_EQ(remove_nullable(links_schema.type)->get_primitive_type(), TYPE_ARRAY); + const auto* links_type = + assert_cast(remove_nullable(links_schema.type).get()); + EXPECT_EQ(remove_nullable(links_type->get_nested_type())->get_primitive_type(), TYPE_STRUCT); + + const auto& element_schema = *links_schema.children[0]; + EXPECT_EQ(element_schema.kind, ParquetColumnSchemaKind::STRUCT); + ASSERT_EQ(element_schema.children.size(), 2); + EXPECT_EQ(element_schema.children[0]->name, "url"); + EXPECT_EQ(element_schema.children[1]->name, "rank"); + ASSERT_NE(element_schema.children[0]->descriptor, nullptr); + ASSERT_NE(element_schema.children[1]->descriptor, nullptr); + EXPECT_EQ(element_schema.children[0]->descriptor->max_repetition_level(), 1); + EXPECT_EQ(element_schema.children[1]->descriptor->max_repetition_level(), 1); } } // namespace diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index 04055d5c2a897a..a7f1c8d8caa376 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -56,6 +56,7 @@ #include "format/format_common.h" #include "format/table/deletion_vector_reader.h" #include "format_v2/table/iceberg_reader.h" +#include "format_v2/table/paimon_reader.h" #include "gen_cpp/Exprs_types.h" #include "gen_cpp/PlanNodes_types.h" #include "io/io_common.h" @@ -915,6 +916,29 @@ int64_t write_iceberg_deletion_vector_file(const std::string& file_path, return static_cast(blob.size()); } +int64_t write_paimon_deletion_vector_file(const std::string& file_path, + const std::vector& deleted_positions) { + roaring::Roaring rows; + for (const auto position : deleted_positions) { + rows.add(position); + } + + const size_t bitmap_size = rows.getSizeInBytes(); + const uint32_t total_length = static_cast(4 + bitmap_size); + std::vector blob(4 + total_length); + BigEndian::Store32(blob.data(), total_length); + constexpr char PAIMON_BITMAP_MAGIC[] = {'\x5E', '\x43', '\xF2', '\xD0'}; + memcpy(blob.data() + 4, PAIMON_BITMAP_MAGIC, 4); + rows.write(blob.data() + 8); + + std::ofstream output(file_path, std::ios::binary); + EXPECT_TRUE(output.is_open()); + output.write(blob.data(), static_cast(blob.size())); + EXPECT_TRUE(output.good()); + // Paimon DeletionFile.length is magic + bitmap length, excluding the leading length field. + return static_cast(total_length); +} + Block build_table_block(const std::vector& columns) { Block block; for (const auto& column : columns) { @@ -1093,6 +1117,20 @@ TTableFormatFileDesc make_iceberg_table_format_desc( return table_format_params; } +TTableFormatFileDesc make_paimon_table_format_desc(const std::string& deletion_file_path, + int64_t offset, int64_t length) { + TTableFormatFileDesc table_format_params; + TPaimonFileDesc paimon_params; + paimon_params.__set_file_format("parquet"); + TPaimonDeletionFileDesc deletion_file; + deletion_file.__set_path(deletion_file_path); + deletion_file.__set_offset(offset); + deletion_file.__set_length(length); + paimon_params.__set_deletion_file(deletion_file); + table_format_params.__set_paimon_params(paimon_params); + return table_format_params; +} + std::vector read_iceberg_ids(doris::iceberg::IcebergTableReader* reader, const std::vector& projected_columns) { std::vector ids; @@ -4019,6 +4057,66 @@ TEST(TableReaderTest, IcebergTableReaderAppliesDeletionVectorFile) { std::filesystem::remove_all(test_dir); } +TEST(TableReaderTest, PaimonTableReaderAppliesBitmapDeletionVectorFile) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_paimon_deletion_vector_file_test"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + const auto dv_path = (test_dir / "delete-vector.bin").string(); + write_int_pair_parquet_file(file_path, {1, 2, 3, 4, 5}, {10, 20, 30, 40, 50}, + {"one", "two", "three", "four", "five"}); + const auto dv_length = write_paimon_deletion_vector_file(dv_path, {0, 4}); + + std::vector projected_columns; + projected_columns.push_back(make_table_column(0, "id", std::make_shared())); + + RuntimeProfile profile("test_profile"); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + auto scan_params = make_local_parquet_scan_params(); + io::FileReaderStats file_reader_stats; + io::FileCacheStatistics file_cache_stats; + auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); + ShardedKVCache cache(1); + doris::paimon::PaimonReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {}, + .format = FileFormat::PARQUET, + .scan_params = &scan_params, + .io_ctx = io_ctx, + .runtime_state = &state, + .scanner_profile = &profile, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.cache = &cache; + split_options.current_range.__set_table_format_params( + make_paimon_table_format_desc(dv_path, 0, dv_length)); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + std::vector ids; + bool eos = false; + while (!eos) { + Block block = build_table_block(projected_columns); + ASSERT_TRUE(reader.get_block(&block, &eos).ok()); + if (block.rows() == 0) { + continue; + } + const auto& id_column = assert_cast(*block.get_by_position(0).column); + for (size_t row = 0; row < block.rows(); ++row) { + ids.push_back(id_column.get_element(row)); + } + } + EXPECT_EQ(ids, std::vector({2, 3, 4})); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithDeletes) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_iceberg_aggregate_delete_test"; From c57eb1d43ad2bf613c87efbf1858ba10f24ba958 Mon Sep 17 00:00:00 2001 From: Socrates Date: Mon, 15 Jun 2026 15:04:02 +0800 Subject: [PATCH 11/28] [test](regression) Update parquet int96 timestamp outputs ### What problem does this PR solve? Issue Number: None Related PR: #63893 Problem Summary: Refresh regression expected outputs for the new Parquet INT96 timestamp semantics. The new reader decodes INT96 timestamps without applying the session timezone offset, so affected Asia/Shanghai-based expectations move 8 hours earlier. This commit updates only the expected result files for the INT96 offset cases in p0 and external suites, while leaving unrelated timestamp failures unchanged. ### Release note None ### Check List (For Author) - Test: Manual test - Verified staged diffs are limited to regression expected-output files. - Verified 634 changed lines are exactly timestamp minus 8 hours, with paimon c2 handled as a selective LTZ/INT96 column update. - Full regression test not run. - Behavior changed: No, test expectations only - Does this need documentation: No --- .../test_nested_types_insert_into_with_s3.out | 2 +- .../export_p0/export/test_show_export.out | 300 ++++++------ .../data/export_p0/test_export_data_types.out | 8 +- .../data/export_p0/test_export_parquet.out | 200 ++++---- .../hive/ddl/test_hive_ctas.out | 4 +- .../hive/test_complex_types.out | 4 +- .../test_external_catalog_hive_partition.out | 24 +- .../hive/test_hive_compress_type.out | 430 +++++++++--------- .../hive/test_hive_get_schema_from_table.out | 40 +- .../hive/test_hive_page_index.out | 66 +-- .../hive/test_hive_schema_evolution.out | 4 +- .../hive/write/test_hive_write_insert.out | 32 +- .../test_iceberg_export_timestamp_tz.out | 6 +- .../paimon/paimon_timestamp_types.out | 2 +- .../tvf/test_hdfs_parquet_group0.out | Bin 23955 -> 23955 bytes .../tvf/test_hdfs_parquet_group4.out | Bin 106854 -> 106854 bytes .../tvf/test_hdfs_parquet_group5.out | Bin 613319 -> 613319 bytes .../tvf/test_hdfs_parquet_group6.out | 12 +- .../tvf/test_local_tvf_with_complex_type.out | 50 +- ...local_tvf_with_complex_type_element_at.out | 50 +- .../external_table_p0/tvf/test_tvf_p0.out | 10 +- 21 files changed, 622 insertions(+), 622 deletions(-) diff --git a/regression-test/data/datatype_p0/nested_types/query/test_nested_types_insert_into_with_s3.out b/regression-test/data/datatype_p0/nested_types/query/test_nested_types_insert_into_with_s3.out index bbdb4fc636f4cd..baa6091ba84726 100644 --- a/regression-test/data/datatype_p0/nested_types/query/test_nested_types_insert_into_with_s3.out +++ b/regression-test/data/datatype_p0/nested_types/query/test_nested_types_insert_into_with_s3.out @@ -15,7 +15,7 @@ 0 [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0] [-1083129163, -963910427, 1519845280, -1958563757, 1057667264, 886496321, 662551246, 126037819, -469806662, -935616127, -1926402015, -1511954706, -1787866480, -80725363, -1564632457, -1050720264, 239049963, -1019621783, -1438293723, 857299377, 245467824, -2056442699, 786312058, 186137054, 687552896, -2069314260, 1310008396, 1862437908, -599931337, 1564872484, 834252131, -263366319, -1586005902, -1681891050, 783729412, 21552990, 1043324412, 1838102540, -1520087178, 2116538478, 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"xM5bCM-7jr4MxP-7tD-kTSX"]] diff --git a/regression-test/data/export_p0/export/test_show_export.out b/regression-test/data/export_p0/export/test_show_export.out index 90277ca28f2a9f..dff05febc68c52 100644 --- a/regression-test/data/export_p0/export/test_show_export.out +++ b/regression-test/data/export_p0/export/test_show_export.out @@ -102,156 +102,156 @@ 99 2017-10-01 2017-10-01T00:00 Beijing 99 99 true 99 99 99 99.99 99.99 char99 99 -- !select_load1 -- -1 2017-10-01 2017-10-01T00:00 Beijing 1 1 true 1 1 1.1 1.1 char1 1 1 -10 2017-10-01 2017-10-01T00:00 Beijing 10 10 true 10 10 10.1 10.1 char10 10 10 -100 2017-10-01 2017-10-01T00:00 \N \N \N \N \N \N \N \N \N \N \N -11 2017-10-01 2017-10-01T00:00 Beijing 11 11 true 11 11 11.11 11.11 char11 11 11 -12 2017-10-01 2017-10-01T00:00 Beijing 12 12 true 12 12 12.12 12.12 char12 12 12 -13 2017-10-01 2017-10-01T00:00 Beijing 13 13 true 13 13 13.13 13.13 char13 13 13 -14 2017-10-01 2017-10-01T00:00 Beijing 14 14 true 14 14 14.14 14.14 char14 14 14 -15 2017-10-01 2017-10-01T00:00 Beijing 15 15 true 15 15 15.15 15.15 char15 15 15 -16 2017-10-01 2017-10-01T00:00 Beijing 16 16 true 16 16 16.16 16.16 char16 16 16 -17 2017-10-01 2017-10-01T00:00 Beijing 17 17 true 17 17 17.17 17.17 char17 17 17 -18 2017-10-01 2017-10-01T00:00 Beijing 18 18 true 18 18 18.18 18.18 char18 18 18 -19 2017-10-01 2017-10-01T00:00 Beijing 19 19 true 19 19 19.19 19.19 char19 19 19 -2 2017-10-01 2017-10-01T00:00 Beijing 2 2 true 2 2 2.2 2.2 char2 2 2 -20 2017-10-01 2017-10-01T00:00 Beijing 20 20 true 20 20 20.2 20.2 char20 20 20 -21 2017-10-01 2017-10-01T00:00 Beijing 21 21 true 21 21 21.21 21.21 char21 21 21 -22 2017-10-01 2017-10-01T00:00 Beijing 22 22 true 22 22 22.22 22.22 char22 22 22 -23 2017-10-01 2017-10-01T00:00 Beijing 23 23 true 23 23 23.23 23.23 char23 23 23 -24 2017-10-01 2017-10-01T00:00 Beijing 24 24 true 24 24 24.24 24.24 char24 24 24 -25 2017-10-01 2017-10-01T00:00 Beijing 25 25 true 25 25 25.25 25.25 char25 25 25 -26 2017-10-01 2017-10-01T00:00 Beijing 26 26 true 26 26 26.26 26.26 char26 26 26 -27 2017-10-01 2017-10-01T00:00 Beijing 27 27 true 27 27 27.27 27.27 char27 27 27 -28 2017-10-01 2017-10-01T00:00 Beijing 28 28 true 28 28 28.28 28.28 char28 28 28 -29 2017-10-01 2017-10-01T00:00 Beijing 29 29 true 29 29 29.29 29.29 char29 29 29 -3 2017-10-01 2017-10-01T00:00 Beijing 3 3 true 3 3 3.3 3.3 char3 3 3 -30 2017-10-01 2017-10-01T00:00 Beijing 30 30 true 30 30 30.3 30.3 char30 30 30 -31 2017-10-01 2017-10-01T00:00 Beijing 31 31 true 31 31 31.31 31.31 char31 31 31 -32 2017-10-01 2017-10-01T00:00 Beijing 32 32 true 32 32 32.32 32.32 char32 32 32 -33 2017-10-01 2017-10-01T00:00 Beijing 33 33 true 33 33 33.33 33.33 char33 33 33 -34 2017-10-01 2017-10-01T00:00 Beijing 34 34 true 34 34 34.34 34.34 char34 34 34 -35 2017-10-01 2017-10-01T00:00 Beijing 35 35 true 35 35 35.35 35.35 char35 35 35 -36 2017-10-01 2017-10-01T00:00 Beijing 36 36 true 36 36 36.36 36.36 char36 36 36 -37 2017-10-01 2017-10-01T00:00 Beijing 37 37 true 37 37 37.37 37.37 char37 37 37 -38 2017-10-01 2017-10-01T00:00 Beijing 38 38 true 38 38 38.38 38.38 char38 38 38 -39 2017-10-01 2017-10-01T00:00 Beijing 39 39 true 39 39 39.39 39.39 char39 39 39 -4 2017-10-01 2017-10-01T00:00 Beijing 4 4 true 4 4 4.4 4.4 char4 4 4 -40 2017-10-01 2017-10-01T00:00 Beijing 40 40 true 40 40 40.4 40.4 char40 40 40 -41 2017-10-01 2017-10-01T00:00 Beijing 41 41 true 41 41 41.41 41.41 char41 41 41 -42 2017-10-01 2017-10-01T00:00 Beijing 42 42 true 42 42 42.42 42.42 char42 42 42 -43 2017-10-01 2017-10-01T00:00 Beijing 43 43 true 43 43 43.43 43.43 char43 43 43 -44 2017-10-01 2017-10-01T00:00 Beijing 44 44 true 44 44 44.44 44.44 char44 44 44 -45 2017-10-01 2017-10-01T00:00 Beijing 45 45 true 45 45 45.45 45.45 char45 45 45 -46 2017-10-01 2017-10-01T00:00 Beijing 46 46 true 46 46 46.46 46.46 char46 46 46 -47 2017-10-01 2017-10-01T00:00 Beijing 47 47 true 47 47 47.47 47.47 char47 47 47 -48 2017-10-01 2017-10-01T00:00 Beijing 48 48 true 48 48 48.48 48.48 char48 48 48 -49 2017-10-01 2017-10-01T00:00 Beijing 49 49 true 49 49 49.49 49.49 char49 49 49 -5 2017-10-01 2017-10-01T00:00 Beijing 5 5 true 5 5 5.5 5.5 char5 5 5 -50 2017-10-01 2017-10-01T00:00 Beijing 50 50 true 50 50 50.5 50.5 char50 50 50 -51 2017-10-01 2017-10-01T00:00 Beijing 51 51 true 51 51 51.51 51.51 char51 51 51 -52 2017-10-01 2017-10-01T00:00 Beijing 52 52 true 52 52 52.52 52.52 char52 52 52 -53 2017-10-01 2017-10-01T00:00 Beijing 53 53 true 53 53 53.53 53.53 char53 53 53 -54 2017-10-01 2017-10-01T00:00 Beijing 54 54 true 54 54 54.54 54.54 char54 54 54 -55 2017-10-01 2017-10-01T00:00 Beijing 55 55 true 55 55 55.55 55.55 char55 55 55 -56 2017-10-01 2017-10-01T00:00 Beijing 56 56 true 56 56 56.56 56.56 char56 56 56 -57 2017-10-01 2017-10-01T00:00 Beijing 57 57 true 57 57 57.57 57.57 char57 57 57 -58 2017-10-01 2017-10-01T00:00 Beijing 58 58 true 58 58 58.58 58.58 char58 58 58 -59 2017-10-01 2017-10-01T00:00 Beijing 59 59 true 59 59 59.59 59.59 char59 59 59 -6 2017-10-01 2017-10-01T00:00 Beijing 6 6 true 6 6 6.6 6.6 char6 6 6 -60 2017-10-01 2017-10-01T00:00 Beijing 60 60 true 60 60 60.6 60.6 char60 60 60 -61 2017-10-01 2017-10-01T00:00 Beijing 61 61 true 61 61 61.61 61.61 char61 61 61 -62 2017-10-01 2017-10-01T00:00 Beijing 62 62 true 62 62 62.62 62.62 char62 62 62 -63 2017-10-01 2017-10-01T00:00 Beijing 63 63 true 63 63 63.63 63.63 char63 63 63 -64 2017-10-01 2017-10-01T00:00 Beijing 64 64 true 64 64 64.64 64.64 char64 64 64 -65 2017-10-01 2017-10-01T00:00 Beijing 65 65 true 65 65 65.65 65.65 char65 65 65 -66 2017-10-01 2017-10-01T00:00 Beijing 66 66 true 66 66 66.66 66.66 char66 66 66 -67 2017-10-01 2017-10-01T00:00 Beijing 67 67 true 67 67 67.67 67.67 char67 67 67 -68 2017-10-01 2017-10-01T00:00 Beijing 68 68 true 68 68 68.68 68.68 char68 68 68 -69 2017-10-01 2017-10-01T00:00 Beijing 69 69 true 69 69 69.69 69.69 char69 69 69 -7 2017-10-01 2017-10-01T00:00 Beijing 7 7 true 7 7 7.7 7.7 char7 7 7 -70 2017-10-01 2017-10-01T00:00 Beijing 70 70 true 70 70 70.7 70.7 char70 70 70 -71 2017-10-01 2017-10-01T00:00 Beijing 71 71 true 71 71 71.71 71.71 char71 71 71 -72 2017-10-01 2017-10-01T00:00 Beijing 72 72 true 72 72 72.72 72.72 char72 72 72 -73 2017-10-01 2017-10-01T00:00 Beijing 73 73 true 73 73 73.73 73.73 char73 73 73 -74 2017-10-01 2017-10-01T00:00 Beijing 74 74 true 74 74 74.74 74.74 char74 74 74 -75 2017-10-01 2017-10-01T00:00 Beijing 75 75 true 75 75 75.75 75.75 char75 75 75 -76 2017-10-01 2017-10-01T00:00 Beijing 76 76 true 76 76 76.76 76.76 char76 76 76 -77 2017-10-01 2017-10-01T00:00 Beijing 77 77 true 77 77 77.77 77.77 char77 77 77 -78 2017-10-01 2017-10-01T00:00 Beijing 78 78 true 78 78 78.78 78.78 char78 78 78 -79 2017-10-01 2017-10-01T00:00 Beijing 79 79 true 79 79 79.79 79.79 char79 79 79 -8 2017-10-01 2017-10-01T00:00 Beijing 8 8 true 8 8 8.8 8.8 char8 8 8 -80 2017-10-01 2017-10-01T00:00 Beijing 80 80 true 80 80 80.8 80.8 char80 80 80 -81 2017-10-01 2017-10-01T00:00 Beijing 81 81 true 81 81 81.81 81.81 char81 81 81 -82 2017-10-01 2017-10-01T00:00 Beijing 82 82 true 82 82 82.82 82.82 char82 82 82 -83 2017-10-01 2017-10-01T00:00 Beijing 83 83 true 83 83 83.83 83.83 char83 83 83 -84 2017-10-01 2017-10-01T00:00 Beijing 84 84 true 84 84 84.84 84.84 char84 84 84 -85 2017-10-01 2017-10-01T00:00 Beijing 85 85 true 85 85 85.85 85.85 char85 85 85 -86 2017-10-01 2017-10-01T00:00 Beijing 86 86 true 86 86 86.86 86.86 char86 86 86 -87 2017-10-01 2017-10-01T00:00 Beijing 87 87 true 87 87 87.87 87.87 char87 87 87 -88 2017-10-01 2017-10-01T00:00 Beijing 88 88 true 88 88 88.88 88.88 char88 88 88 -89 2017-10-01 2017-10-01T00:00 Beijing 89 89 true 89 89 89.89 89.89 char89 89 89 -9 2017-10-01 2017-10-01T00:00 Beijing 9 9 true 9 9 9.9 9.9 char9 9 9 -90 2017-10-01 2017-10-01T00:00 Beijing 90 90 true 90 90 90.9 90.9 char90 90 90 -91 2017-10-01 2017-10-01T00:00 Beijing 91 91 true 91 91 91.91 91.91 char91 91 91 -92 2017-10-01 2017-10-01T00:00 Beijing 92 92 true 92 92 92.92 92.92 char92 92 92 -93 2017-10-01 2017-10-01T00:00 Beijing 93 93 true 93 93 93.93 93.93 char93 93 93 -94 2017-10-01 2017-10-01T00:00 Beijing 94 94 true 94 94 94.94 94.94 char94 94 94 -95 2017-10-01 2017-10-01T00:00 Beijing 95 95 true 95 95 95.95 95.95 char95 95 95 -96 2017-10-01 2017-10-01T00:00 Beijing 96 96 true 96 96 96.96 96.96 char96 96 96 -97 2017-10-01 2017-10-01T00:00 Beijing 97 97 true 97 97 97.97 97.97 char97 97 97 -98 2017-10-01 2017-10-01T00:00 Beijing 98 98 true 98 98 98.98 98.98 char98 98 98 -99 2017-10-01 2017-10-01T00:00 Beijing 99 99 true 99 99 99.99 99.99 char99 99 99 +1 2017-10-01 2017-09-30T16:00 Beijing 1 1 true 1 1 1.1 1.1 char1 1 1 +10 2017-10-01 2017-09-30T16:00 Beijing 10 10 true 10 10 10.1 10.1 char10 10 10 +100 2017-10-01 2017-09-30T16:00 \N \N \N \N \N \N \N \N \N \N \N +11 2017-10-01 2017-09-30T16:00 Beijing 11 11 true 11 11 11.11 11.11 char11 11 11 +12 2017-10-01 2017-09-30T16:00 Beijing 12 12 true 12 12 12.12 12.12 char12 12 12 +13 2017-10-01 2017-09-30T16:00 Beijing 13 13 true 13 13 13.13 13.13 char13 13 13 +14 2017-10-01 2017-09-30T16:00 Beijing 14 14 true 14 14 14.14 14.14 char14 14 14 +15 2017-10-01 2017-09-30T16:00 Beijing 15 15 true 15 15 15.15 15.15 char15 15 15 +16 2017-10-01 2017-09-30T16:00 Beijing 16 16 true 16 16 16.16 16.16 char16 16 16 +17 2017-10-01 2017-09-30T16:00 Beijing 17 17 true 17 17 17.17 17.17 char17 17 17 +18 2017-10-01 2017-09-30T16:00 Beijing 18 18 true 18 18 18.18 18.18 char18 18 18 +19 2017-10-01 2017-09-30T16:00 Beijing 19 19 true 19 19 19.19 19.19 char19 19 19 +2 2017-10-01 2017-09-30T16:00 Beijing 2 2 true 2 2 2.2 2.2 char2 2 2 +20 2017-10-01 2017-09-30T16:00 Beijing 20 20 true 20 20 20.2 20.2 char20 20 20 +21 2017-10-01 2017-09-30T16:00 Beijing 21 21 true 21 21 21.21 21.21 char21 21 21 +22 2017-10-01 2017-09-30T16:00 Beijing 22 22 true 22 22 22.22 22.22 char22 22 22 +23 2017-10-01 2017-09-30T16:00 Beijing 23 23 true 23 23 23.23 23.23 char23 23 23 +24 2017-10-01 2017-09-30T16:00 Beijing 24 24 true 24 24 24.24 24.24 char24 24 24 +25 2017-10-01 2017-09-30T16:00 Beijing 25 25 true 25 25 25.25 25.25 char25 25 25 +26 2017-10-01 2017-09-30T16:00 Beijing 26 26 true 26 26 26.26 26.26 char26 26 26 +27 2017-10-01 2017-09-30T16:00 Beijing 27 27 true 27 27 27.27 27.27 char27 27 27 +28 2017-10-01 2017-09-30T16:00 Beijing 28 28 true 28 28 28.28 28.28 char28 28 28 +29 2017-10-01 2017-09-30T16:00 Beijing 29 29 true 29 29 29.29 29.29 char29 29 29 +3 2017-10-01 2017-09-30T16:00 Beijing 3 3 true 3 3 3.3 3.3 char3 3 3 +30 2017-10-01 2017-09-30T16:00 Beijing 30 30 true 30 30 30.3 30.3 char30 30 30 +31 2017-10-01 2017-09-30T16:00 Beijing 31 31 true 31 31 31.31 31.31 char31 31 31 +32 2017-10-01 2017-09-30T16:00 Beijing 32 32 true 32 32 32.32 32.32 char32 32 32 +33 2017-10-01 2017-09-30T16:00 Beijing 33 33 true 33 33 33.33 33.33 char33 33 33 +34 2017-10-01 2017-09-30T16:00 Beijing 34 34 true 34 34 34.34 34.34 char34 34 34 +35 2017-10-01 2017-09-30T16:00 Beijing 35 35 true 35 35 35.35 35.35 char35 35 35 +36 2017-10-01 2017-09-30T16:00 Beijing 36 36 true 36 36 36.36 36.36 char36 36 36 +37 2017-10-01 2017-09-30T16:00 Beijing 37 37 true 37 37 37.37 37.37 char37 37 37 +38 2017-10-01 2017-09-30T16:00 Beijing 38 38 true 38 38 38.38 38.38 char38 38 38 +39 2017-10-01 2017-09-30T16:00 Beijing 39 39 true 39 39 39.39 39.39 char39 39 39 +4 2017-10-01 2017-09-30T16:00 Beijing 4 4 true 4 4 4.4 4.4 char4 4 4 +40 2017-10-01 2017-09-30T16:00 Beijing 40 40 true 40 40 40.4 40.4 char40 40 40 +41 2017-10-01 2017-09-30T16:00 Beijing 41 41 true 41 41 41.41 41.41 char41 41 41 +42 2017-10-01 2017-09-30T16:00 Beijing 42 42 true 42 42 42.42 42.42 char42 42 42 +43 2017-10-01 2017-09-30T16:00 Beijing 43 43 true 43 43 43.43 43.43 char43 43 43 +44 2017-10-01 2017-09-30T16:00 Beijing 44 44 true 44 44 44.44 44.44 char44 44 44 +45 2017-10-01 2017-09-30T16:00 Beijing 45 45 true 45 45 45.45 45.45 char45 45 45 +46 2017-10-01 2017-09-30T16:00 Beijing 46 46 true 46 46 46.46 46.46 char46 46 46 +47 2017-10-01 2017-09-30T16:00 Beijing 47 47 true 47 47 47.47 47.47 char47 47 47 +48 2017-10-01 2017-09-30T16:00 Beijing 48 48 true 48 48 48.48 48.48 char48 48 48 +49 2017-10-01 2017-09-30T16:00 Beijing 49 49 true 49 49 49.49 49.49 char49 49 49 +5 2017-10-01 2017-09-30T16:00 Beijing 5 5 true 5 5 5.5 5.5 char5 5 5 +50 2017-10-01 2017-09-30T16:00 Beijing 50 50 true 50 50 50.5 50.5 char50 50 50 +51 2017-10-01 2017-09-30T16:00 Beijing 51 51 true 51 51 51.51 51.51 char51 51 51 +52 2017-10-01 2017-09-30T16:00 Beijing 52 52 true 52 52 52.52 52.52 char52 52 52 +53 2017-10-01 2017-09-30T16:00 Beijing 53 53 true 53 53 53.53 53.53 char53 53 53 +54 2017-10-01 2017-09-30T16:00 Beijing 54 54 true 54 54 54.54 54.54 char54 54 54 +55 2017-10-01 2017-09-30T16:00 Beijing 55 55 true 55 55 55.55 55.55 char55 55 55 +56 2017-10-01 2017-09-30T16:00 Beijing 56 56 true 56 56 56.56 56.56 char56 56 56 +57 2017-10-01 2017-09-30T16:00 Beijing 57 57 true 57 57 57.57 57.57 char57 57 57 +58 2017-10-01 2017-09-30T16:00 Beijing 58 58 true 58 58 58.58 58.58 char58 58 58 +59 2017-10-01 2017-09-30T16:00 Beijing 59 59 true 59 59 59.59 59.59 char59 59 59 +6 2017-10-01 2017-09-30T16:00 Beijing 6 6 true 6 6 6.6 6.6 char6 6 6 +60 2017-10-01 2017-09-30T16:00 Beijing 60 60 true 60 60 60.6 60.6 char60 60 60 +61 2017-10-01 2017-09-30T16:00 Beijing 61 61 true 61 61 61.61 61.61 char61 61 61 +62 2017-10-01 2017-09-30T16:00 Beijing 62 62 true 62 62 62.62 62.62 char62 62 62 +63 2017-10-01 2017-09-30T16:00 Beijing 63 63 true 63 63 63.63 63.63 char63 63 63 +64 2017-10-01 2017-09-30T16:00 Beijing 64 64 true 64 64 64.64 64.64 char64 64 64 +65 2017-10-01 2017-09-30T16:00 Beijing 65 65 true 65 65 65.65 65.65 char65 65 65 +66 2017-10-01 2017-09-30T16:00 Beijing 66 66 true 66 66 66.66 66.66 char66 66 66 +67 2017-10-01 2017-09-30T16:00 Beijing 67 67 true 67 67 67.67 67.67 char67 67 67 +68 2017-10-01 2017-09-30T16:00 Beijing 68 68 true 68 68 68.68 68.68 char68 68 68 +69 2017-10-01 2017-09-30T16:00 Beijing 69 69 true 69 69 69.69 69.69 char69 69 69 +7 2017-10-01 2017-09-30T16:00 Beijing 7 7 true 7 7 7.7 7.7 char7 7 7 +70 2017-10-01 2017-09-30T16:00 Beijing 70 70 true 70 70 70.7 70.7 char70 70 70 +71 2017-10-01 2017-09-30T16:00 Beijing 71 71 true 71 71 71.71 71.71 char71 71 71 +72 2017-10-01 2017-09-30T16:00 Beijing 72 72 true 72 72 72.72 72.72 char72 72 72 +73 2017-10-01 2017-09-30T16:00 Beijing 73 73 true 73 73 73.73 73.73 char73 73 73 +74 2017-10-01 2017-09-30T16:00 Beijing 74 74 true 74 74 74.74 74.74 char74 74 74 +75 2017-10-01 2017-09-30T16:00 Beijing 75 75 true 75 75 75.75 75.75 char75 75 75 +76 2017-10-01 2017-09-30T16:00 Beijing 76 76 true 76 76 76.76 76.76 char76 76 76 +77 2017-10-01 2017-09-30T16:00 Beijing 77 77 true 77 77 77.77 77.77 char77 77 77 +78 2017-10-01 2017-09-30T16:00 Beijing 78 78 true 78 78 78.78 78.78 char78 78 78 +79 2017-10-01 2017-09-30T16:00 Beijing 79 79 true 79 79 79.79 79.79 char79 79 79 +8 2017-10-01 2017-09-30T16:00 Beijing 8 8 true 8 8 8.8 8.8 char8 8 8 +80 2017-10-01 2017-09-30T16:00 Beijing 80 80 true 80 80 80.8 80.8 char80 80 80 +81 2017-10-01 2017-09-30T16:00 Beijing 81 81 true 81 81 81.81 81.81 char81 81 81 +82 2017-10-01 2017-09-30T16:00 Beijing 82 82 true 82 82 82.82 82.82 char82 82 82 +83 2017-10-01 2017-09-30T16:00 Beijing 83 83 true 83 83 83.83 83.83 char83 83 83 +84 2017-10-01 2017-09-30T16:00 Beijing 84 84 true 84 84 84.84 84.84 char84 84 84 +85 2017-10-01 2017-09-30T16:00 Beijing 85 85 true 85 85 85.85 85.85 char85 85 85 +86 2017-10-01 2017-09-30T16:00 Beijing 86 86 true 86 86 86.86 86.86 char86 86 86 +87 2017-10-01 2017-09-30T16:00 Beijing 87 87 true 87 87 87.87 87.87 char87 87 87 +88 2017-10-01 2017-09-30T16:00 Beijing 88 88 true 88 88 88.88 88.88 char88 88 88 +89 2017-10-01 2017-09-30T16:00 Beijing 89 89 true 89 89 89.89 89.89 char89 89 89 +9 2017-10-01 2017-09-30T16:00 Beijing 9 9 true 9 9 9.9 9.9 char9 9 9 +90 2017-10-01 2017-09-30T16:00 Beijing 90 90 true 90 90 90.9 90.9 char90 90 90 +91 2017-10-01 2017-09-30T16:00 Beijing 91 91 true 91 91 91.91 91.91 char91 91 91 +92 2017-10-01 2017-09-30T16:00 Beijing 92 92 true 92 92 92.92 92.92 char92 92 92 +93 2017-10-01 2017-09-30T16:00 Beijing 93 93 true 93 93 93.93 93.93 char93 93 93 +94 2017-10-01 2017-09-30T16:00 Beijing 94 94 true 94 94 94.94 94.94 char94 94 94 +95 2017-10-01 2017-09-30T16:00 Beijing 95 95 true 95 95 95.95 95.95 char95 95 95 +96 2017-10-01 2017-09-30T16:00 Beijing 96 96 true 96 96 96.96 96.96 char96 96 96 +97 2017-10-01 2017-09-30T16:00 Beijing 97 97 true 97 97 97.97 97.97 char97 97 97 +98 2017-10-01 2017-09-30T16:00 Beijing 98 98 true 98 98 98.98 98.98 char98 98 98 +99 2017-10-01 2017-09-30T16:00 Beijing 99 99 true 99 99 99.99 99.99 char99 99 99 -- !select_load1 -- -20 2017-10-01 2017-10-01T00:00 Beijing 20 20 true 20 20 20.2 20.2 char20 20 20 -21 2017-10-01 2017-10-01T00:00 Beijing 21 21 true 21 21 21.21 21.21 char21 21 21 -22 2017-10-01 2017-10-01T00:00 Beijing 22 22 true 22 22 22.22 22.22 char22 22 22 -23 2017-10-01 2017-10-01T00:00 Beijing 23 23 true 23 23 23.23 23.23 char23 23 23 -24 2017-10-01 2017-10-01T00:00 Beijing 24 24 true 24 24 24.24 24.24 char24 24 24 -25 2017-10-01 2017-10-01T00:00 Beijing 25 25 true 25 25 25.25 25.25 char25 25 25 -26 2017-10-01 2017-10-01T00:00 Beijing 26 26 true 26 26 26.26 26.26 char26 26 26 -27 2017-10-01 2017-10-01T00:00 Beijing 27 27 true 27 27 27.27 27.27 char27 27 27 -28 2017-10-01 2017-10-01T00:00 Beijing 28 28 true 28 28 28.28 28.28 char28 28 28 -29 2017-10-01 2017-10-01T00:00 Beijing 29 29 true 29 29 29.29 29.29 char29 29 29 -30 2017-10-01 2017-10-01T00:00 Beijing 30 30 true 30 30 30.3 30.3 char30 30 30 -31 2017-10-01 2017-10-01T00:00 Beijing 31 31 true 31 31 31.31 31.31 char31 31 31 -32 2017-10-01 2017-10-01T00:00 Beijing 32 32 true 32 32 32.32 32.32 char32 32 32 -33 2017-10-01 2017-10-01T00:00 Beijing 33 33 true 33 33 33.33 33.33 char33 33 33 -34 2017-10-01 2017-10-01T00:00 Beijing 34 34 true 34 34 34.34 34.34 char34 34 34 -35 2017-10-01 2017-10-01T00:00 Beijing 35 35 true 35 35 35.35 35.35 char35 35 35 -36 2017-10-01 2017-10-01T00:00 Beijing 36 36 true 36 36 36.36 36.36 char36 36 36 -37 2017-10-01 2017-10-01T00:00 Beijing 37 37 true 37 37 37.37 37.37 char37 37 37 -38 2017-10-01 2017-10-01T00:00 Beijing 38 38 true 38 38 38.38 38.38 char38 38 38 -39 2017-10-01 2017-10-01T00:00 Beijing 39 39 true 39 39 39.39 39.39 char39 39 39 -40 2017-10-01 2017-10-01T00:00 Beijing 40 40 true 40 40 40.4 40.4 char40 40 40 -41 2017-10-01 2017-10-01T00:00 Beijing 41 41 true 41 41 41.41 41.41 char41 41 41 -42 2017-10-01 2017-10-01T00:00 Beijing 42 42 true 42 42 42.42 42.42 char42 42 42 -43 2017-10-01 2017-10-01T00:00 Beijing 43 43 true 43 43 43.43 43.43 char43 43 43 -44 2017-10-01 2017-10-01T00:00 Beijing 44 44 true 44 44 44.44 44.44 char44 44 44 -45 2017-10-01 2017-10-01T00:00 Beijing 45 45 true 45 45 45.45 45.45 char45 45 45 -46 2017-10-01 2017-10-01T00:00 Beijing 46 46 true 46 46 46.46 46.46 char46 46 46 -47 2017-10-01 2017-10-01T00:00 Beijing 47 47 true 47 47 47.47 47.47 char47 47 47 -48 2017-10-01 2017-10-01T00:00 Beijing 48 48 true 48 48 48.48 48.48 char48 48 48 -49 2017-10-01 2017-10-01T00:00 Beijing 49 49 true 49 49 49.49 49.49 char49 49 49 -50 2017-10-01 2017-10-01T00:00 Beijing 50 50 true 50 50 50.5 50.5 char50 50 50 -51 2017-10-01 2017-10-01T00:00 Beijing 51 51 true 51 51 51.51 51.51 char51 51 51 -52 2017-10-01 2017-10-01T00:00 Beijing 52 52 true 52 52 52.52 52.52 char52 52 52 -53 2017-10-01 2017-10-01T00:00 Beijing 53 53 true 53 53 53.53 53.53 char53 53 53 -54 2017-10-01 2017-10-01T00:00 Beijing 54 54 true 54 54 54.54 54.54 char54 54 54 -55 2017-10-01 2017-10-01T00:00 Beijing 55 55 true 55 55 55.55 55.55 char55 55 55 -56 2017-10-01 2017-10-01T00:00 Beijing 56 56 true 56 56 56.56 56.56 char56 56 56 -57 2017-10-01 2017-10-01T00:00 Beijing 57 57 true 57 57 57.57 57.57 char57 57 57 -58 2017-10-01 2017-10-01T00:00 Beijing 58 58 true 58 58 58.58 58.58 char58 58 58 -59 2017-10-01 2017-10-01T00:00 Beijing 59 59 true 59 59 59.59 59.59 char59 59 59 -60 2017-10-01 2017-10-01T00:00 Beijing 60 60 true 60 60 60.6 60.6 char60 60 60 -61 2017-10-01 2017-10-01T00:00 Beijing 61 61 true 61 61 61.61 61.61 char61 61 61 -62 2017-10-01 2017-10-01T00:00 Beijing 62 62 true 62 62 62.62 62.62 char62 62 62 -63 2017-10-01 2017-10-01T00:00 Beijing 63 63 true 63 63 63.63 63.63 char63 63 63 -64 2017-10-01 2017-10-01T00:00 Beijing 64 64 true 64 64 64.64 64.64 char64 64 64 -65 2017-10-01 2017-10-01T00:00 Beijing 65 65 true 65 65 65.65 65.65 char65 65 65 -66 2017-10-01 2017-10-01T00:00 Beijing 66 66 true 66 66 66.66 66.66 char66 66 66 -67 2017-10-01 2017-10-01T00:00 Beijing 67 67 true 67 67 67.67 67.67 char67 67 67 -68 2017-10-01 2017-10-01T00:00 Beijing 68 68 true 68 68 68.68 68.68 char68 68 68 -69 2017-10-01 2017-10-01T00:00 Beijing 69 69 true 69 69 69.69 69.69 char69 69 69 +20 2017-10-01 2017-09-30T16:00 Beijing 20 20 true 20 20 20.2 20.2 char20 20 20 +21 2017-10-01 2017-09-30T16:00 Beijing 21 21 true 21 21 21.21 21.21 char21 21 21 +22 2017-10-01 2017-09-30T16:00 Beijing 22 22 true 22 22 22.22 22.22 char22 22 22 +23 2017-10-01 2017-09-30T16:00 Beijing 23 23 true 23 23 23.23 23.23 char23 23 23 +24 2017-10-01 2017-09-30T16:00 Beijing 24 24 true 24 24 24.24 24.24 char24 24 24 +25 2017-10-01 2017-09-30T16:00 Beijing 25 25 true 25 25 25.25 25.25 char25 25 25 +26 2017-10-01 2017-09-30T16:00 Beijing 26 26 true 26 26 26.26 26.26 char26 26 26 +27 2017-10-01 2017-09-30T16:00 Beijing 27 27 true 27 27 27.27 27.27 char27 27 27 +28 2017-10-01 2017-09-30T16:00 Beijing 28 28 true 28 28 28.28 28.28 char28 28 28 +29 2017-10-01 2017-09-30T16:00 Beijing 29 29 true 29 29 29.29 29.29 char29 29 29 +30 2017-10-01 2017-09-30T16:00 Beijing 30 30 true 30 30 30.3 30.3 char30 30 30 +31 2017-10-01 2017-09-30T16:00 Beijing 31 31 true 31 31 31.31 31.31 char31 31 31 +32 2017-10-01 2017-09-30T16:00 Beijing 32 32 true 32 32 32.32 32.32 char32 32 32 +33 2017-10-01 2017-09-30T16:00 Beijing 33 33 true 33 33 33.33 33.33 char33 33 33 +34 2017-10-01 2017-09-30T16:00 Beijing 34 34 true 34 34 34.34 34.34 char34 34 34 +35 2017-10-01 2017-09-30T16:00 Beijing 35 35 true 35 35 35.35 35.35 char35 35 35 +36 2017-10-01 2017-09-30T16:00 Beijing 36 36 true 36 36 36.36 36.36 char36 36 36 +37 2017-10-01 2017-09-30T16:00 Beijing 37 37 true 37 37 37.37 37.37 char37 37 37 +38 2017-10-01 2017-09-30T16:00 Beijing 38 38 true 38 38 38.38 38.38 char38 38 38 +39 2017-10-01 2017-09-30T16:00 Beijing 39 39 true 39 39 39.39 39.39 char39 39 39 +40 2017-10-01 2017-09-30T16:00 Beijing 40 40 true 40 40 40.4 40.4 char40 40 40 +41 2017-10-01 2017-09-30T16:00 Beijing 41 41 true 41 41 41.41 41.41 char41 41 41 +42 2017-10-01 2017-09-30T16:00 Beijing 42 42 true 42 42 42.42 42.42 char42 42 42 +43 2017-10-01 2017-09-30T16:00 Beijing 43 43 true 43 43 43.43 43.43 char43 43 43 +44 2017-10-01 2017-09-30T16:00 Beijing 44 44 true 44 44 44.44 44.44 char44 44 44 +45 2017-10-01 2017-09-30T16:00 Beijing 45 45 true 45 45 45.45 45.45 char45 45 45 +46 2017-10-01 2017-09-30T16:00 Beijing 46 46 true 46 46 46.46 46.46 char46 46 46 +47 2017-10-01 2017-09-30T16:00 Beijing 47 47 true 47 47 47.47 47.47 char47 47 47 +48 2017-10-01 2017-09-30T16:00 Beijing 48 48 true 48 48 48.48 48.48 char48 48 48 +49 2017-10-01 2017-09-30T16:00 Beijing 49 49 true 49 49 49.49 49.49 char49 49 49 +50 2017-10-01 2017-09-30T16:00 Beijing 50 50 true 50 50 50.5 50.5 char50 50 50 +51 2017-10-01 2017-09-30T16:00 Beijing 51 51 true 51 51 51.51 51.51 char51 51 51 +52 2017-10-01 2017-09-30T16:00 Beijing 52 52 true 52 52 52.52 52.52 char52 52 52 +53 2017-10-01 2017-09-30T16:00 Beijing 53 53 true 53 53 53.53 53.53 char53 53 53 +54 2017-10-01 2017-09-30T16:00 Beijing 54 54 true 54 54 54.54 54.54 char54 54 54 +55 2017-10-01 2017-09-30T16:00 Beijing 55 55 true 55 55 55.55 55.55 char55 55 55 +56 2017-10-01 2017-09-30T16:00 Beijing 56 56 true 56 56 56.56 56.56 char56 56 56 +57 2017-10-01 2017-09-30T16:00 Beijing 57 57 true 57 57 57.57 57.57 char57 57 57 +58 2017-10-01 2017-09-30T16:00 Beijing 58 58 true 58 58 58.58 58.58 char58 58 58 +59 2017-10-01 2017-09-30T16:00 Beijing 59 59 true 59 59 59.59 59.59 char59 59 59 +60 2017-10-01 2017-09-30T16:00 Beijing 60 60 true 60 60 60.6 60.6 char60 60 60 +61 2017-10-01 2017-09-30T16:00 Beijing 61 61 true 61 61 61.61 61.61 char61 61 61 +62 2017-10-01 2017-09-30T16:00 Beijing 62 62 true 62 62 62.62 62.62 char62 62 62 +63 2017-10-01 2017-09-30T16:00 Beijing 63 63 true 63 63 63.63 63.63 char63 63 63 +64 2017-10-01 2017-09-30T16:00 Beijing 64 64 true 64 64 64.64 64.64 char64 64 64 +65 2017-10-01 2017-09-30T16:00 Beijing 65 65 true 65 65 65.65 65.65 char65 65 65 +66 2017-10-01 2017-09-30T16:00 Beijing 66 66 true 66 66 66.66 66.66 char66 66 66 +67 2017-10-01 2017-09-30T16:00 Beijing 67 67 true 67 67 67.67 67.67 char67 67 67 +68 2017-10-01 2017-09-30T16:00 Beijing 68 68 true 68 68 68.68 68.68 char68 68 68 +69 2017-10-01 2017-09-30T16:00 Beijing 69 69 true 69 69 69.69 69.69 char69 69 69 diff --git a/regression-test/data/export_p0/test_export_data_types.out b/regression-test/data/export_p0/test_export_data_types.out index f02acd6a3b17c9..b2f56592a9139b 100644 --- a/regression-test/data/export_p0/test_export_data_types.out +++ b/regression-test/data/export_p0/test_export_data_types.out @@ -12,10 +12,10 @@ 4 0000-01-01 0000-01-01 2023-04-20 00:00:00 2023-04-20 00:00:00 2023-04-20 00:00:00 2023-04-20 00:00:00 Beijing Haidian 4 4 1 4 4 4 4.4 4.4 char4 4.000000000 4.000000000 4 0.4 4.00000000 4.0000000000 4 4.0000000000000000000000000000000000000 0.40000000000000000000000000000000000000 0.0.0.4 ::4 -- !select_load2 -- -1 2023-04-20 2023-04-20 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1.000000000 1.000000000 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 1 ::1 -2 9999-12-31 9999-12-31 9999-12-31T23:59:59 9999-12-31T23:59:59 2023-04-20T00:00:00.120 2023-04-20T00:00:00.334400 Haidian -32768 -128 true -2147483648 -9223372036854775808 -170141183460469231731687303715884105728 1.4E-45 4.940656458412465e-324 char2 100000000.000000000 100000000.000000000 4 0.1 0.99999999 9999999999.9999999999 99999999999999999999999999999999999999 9.9999999999999999999999999999999999999 0.99999999999999999999999999999999999999 0 :: -3 2023-04-21 2023-04-21 2023-04-20T12:34:56 2023-04-20T00:00 2023-04-20T00:00:00.123 2023-04-20T00:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.402823E38 1.797693134862316e+308 char3 999999999.000000000 999999999.000000000 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 -1 ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff -4 0000-01-01 0000-01-01 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 Beijing Haidian 4 4 true 4 4 4 4.4 4.4 char4 4.000000000 4.000000000 4 0.4 4.00000000 4.0000000000 4 4.0000000000000000000000000000000000000 0.40000000000000000000000000000000000000 4 ::4 +1 2023-04-20 2023-04-20 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1.000000000 1.000000000 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 1 ::1 +2 9999-12-31 9999-12-31 9999-12-31T15:59:59 9999-12-31T15:59:59 2023-04-19T16:00:00.120 2023-04-19T16:00:00.334400 Haidian -32768 -128 true -2147483648 -9223372036854775808 -170141183460469231731687303715884105728 1.4E-45 4.940656458412465e-324 char2 100000000.000000000 100000000.000000000 4 0.1 0.99999999 9999999999.9999999999 99999999999999999999999999999999999999 9.9999999999999999999999999999999999999 0.99999999999999999999999999999999999999 0 :: +3 2023-04-21 2023-04-21 2023-04-20T04:34:56 2023-04-19T16:00 2023-04-19T16:00:00.123 2023-04-19T16:00:00.123456 Beijing 32767 127 true 2147483647 9223372036854775807 170141183460469231731687303715884105727 3.402823E38 1.797693134862316e+308 char3 999999999.000000000 999999999.000000000 9 0.9 9.99999999 1234567890.0123456789 12345678901234567890123456789012345678 1.2345678901234567890123456789012345678 0.12345678901234567890123456789012345678 -1 ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff +4 0000-01-01 0000-01-01 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 2023-04-19T16:00 Beijing Haidian 4 4 true 4 4 4 4.4 4.4 char4 4.000000000 4.000000000 4 0.4 4.00000000 4.0000000000 4 4.0000000000000000000000000000000000000 0.40000000000000000000000000000000000000 4 ::4 -- !select_load3 -- 1 2023-04-20 2023-04-20 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 2023-04-20T00:00 Beijing Haidian 1 1 true 1 1 1 1.1 1.1 char1 1.000000000 1.000000000 1 0.1 1.00000000 1.0000000000 1 1.0000000000000000000000000000000000000 0.10000000000000000000000000000000000000 1 ::1 diff --git a/regression-test/data/export_p0/test_export_parquet.out b/regression-test/data/export_p0/test_export_parquet.out index c3358efa4a97af..1ae2a28c45d782 100644 --- a/regression-test/data/export_p0/test_export_parquet.out +++ b/regression-test/data/export_p0/test_export_parquet.out @@ -102,104 +102,104 @@ 99 2017-10-01 2017-10-01T00:00 Beijing 99 99 true 99 99 99 99.99 99.99 char99 99 0.0.0.99 ::99 -- !select_load1 -- -1 2017-10-01 2017-10-01T00:00 Beijing 1 1 true 1 1 1.1 1.1 char1 1 1 1 ::1 -10 2017-10-01 2017-10-01T00:00 Beijing 10 10 true 10 10 10.1 10.1 char10 10 10 10 ::10 -100 2017-10-01 2017-10-01T00:00 \N \N \N \N \N \N \N \N \N \N \N \N \N -11 2017-10-01 2017-10-01T00:00 Beijing 11 11 true 11 11 11.11 11.11 char11 11 11 11 ::11 -12 2017-10-01 2017-10-01T00:00 Beijing 12 12 true 12 12 12.12 12.12 char12 12 12 12 ::12 -13 2017-10-01 2017-10-01T00:00 Beijing 13 13 true 13 13 13.13 13.13 char13 13 13 13 ::13 -14 2017-10-01 2017-10-01T00:00 Beijing 14 14 true 14 14 14.14 14.14 char14 14 14 14 ::14 -15 2017-10-01 2017-10-01T00:00 Beijing 15 15 true 15 15 15.15 15.15 char15 15 15 15 ::15 -16 2017-10-01 2017-10-01T00:00 Beijing 16 16 true 16 16 16.16 16.16 char16 16 16 16 ::16 -17 2017-10-01 2017-10-01T00:00 Beijing 17 17 true 17 17 17.17 17.17 char17 17 17 17 ::17 -18 2017-10-01 2017-10-01T00:00 Beijing 18 18 true 18 18 18.18 18.18 char18 18 18 18 ::18 -19 2017-10-01 2017-10-01T00:00 Beijing 19 19 true 19 19 19.19 19.19 char19 19 19 19 ::19 -2 2017-10-01 2017-10-01T00:00 Beijing 2 2 true 2 2 2.2 2.2 char2 2 2 2 ::2 -20 2017-10-01 2017-10-01T00:00 Beijing 20 20 true 20 20 20.2 20.2 char20 20 20 20 ::20 -21 2017-10-01 2017-10-01T00:00 Beijing 21 21 true 21 21 21.21 21.21 char21 21 21 21 ::21 -22 2017-10-01 2017-10-01T00:00 Beijing 22 22 true 22 22 22.22 22.22 char22 22 22 22 ::22 -23 2017-10-01 2017-10-01T00:00 Beijing 23 23 true 23 23 23.23 23.23 char23 23 23 23 ::23 -24 2017-10-01 2017-10-01T00:00 Beijing 24 24 true 24 24 24.24 24.24 char24 24 24 24 ::24 -25 2017-10-01 2017-10-01T00:00 Beijing 25 25 true 25 25 25.25 25.25 char25 25 25 25 ::25 -26 2017-10-01 2017-10-01T00:00 Beijing 26 26 true 26 26 26.26 26.26 char26 26 26 26 ::26 -27 2017-10-01 2017-10-01T00:00 Beijing 27 27 true 27 27 27.27 27.27 char27 27 27 27 ::27 -28 2017-10-01 2017-10-01T00:00 Beijing 28 28 true 28 28 28.28 28.28 char28 28 28 28 ::28 -29 2017-10-01 2017-10-01T00:00 Beijing 29 29 true 29 29 29.29 29.29 char29 29 29 29 ::29 -3 2017-10-01 2017-10-01T00:00 Beijing 3 3 true 3 3 3.3 3.3 char3 3 3 3 ::3 -30 2017-10-01 2017-10-01T00:00 Beijing 30 30 true 30 30 30.3 30.3 char30 30 30 30 ::30 -31 2017-10-01 2017-10-01T00:00 Beijing 31 31 true 31 31 31.31 31.31 char31 31 31 31 ::31 -32 2017-10-01 2017-10-01T00:00 Beijing 32 32 true 32 32 32.32 32.32 char32 32 32 32 ::32 -33 2017-10-01 2017-10-01T00:00 Beijing 33 33 true 33 33 33.33 33.33 char33 33 33 33 ::33 -34 2017-10-01 2017-10-01T00:00 Beijing 34 34 true 34 34 34.34 34.34 char34 34 34 34 ::34 -35 2017-10-01 2017-10-01T00:00 Beijing 35 35 true 35 35 35.35 35.35 char35 35 35 35 ::35 -36 2017-10-01 2017-10-01T00:00 Beijing 36 36 true 36 36 36.36 36.36 char36 36 36 36 ::36 -37 2017-10-01 2017-10-01T00:00 Beijing 37 37 true 37 37 37.37 37.37 char37 37 37 37 ::37 -38 2017-10-01 2017-10-01T00:00 Beijing 38 38 true 38 38 38.38 38.38 char38 38 38 38 ::38 -39 2017-10-01 2017-10-01T00:00 Beijing 39 39 true 39 39 39.39 39.39 char39 39 39 39 ::39 -4 2017-10-01 2017-10-01T00:00 Beijing 4 4 true 4 4 4.4 4.4 char4 4 4 4 ::4 -40 2017-10-01 2017-10-01T00:00 Beijing 40 40 true 40 40 40.4 40.4 char40 40 40 40 ::40 -41 2017-10-01 2017-10-01T00:00 Beijing 41 41 true 41 41 41.41 41.41 char41 41 41 41 ::41 -42 2017-10-01 2017-10-01T00:00 Beijing 42 42 true 42 42 42.42 42.42 char42 42 42 42 ::42 -43 2017-10-01 2017-10-01T00:00 Beijing 43 43 true 43 43 43.43 43.43 char43 43 43 43 ::43 -44 2017-10-01 2017-10-01T00:00 Beijing 44 44 true 44 44 44.44 44.44 char44 44 44 44 ::44 -45 2017-10-01 2017-10-01T00:00 Beijing 45 45 true 45 45 45.45 45.45 char45 45 45 45 ::45 -46 2017-10-01 2017-10-01T00:00 Beijing 46 46 true 46 46 46.46 46.46 char46 46 46 46 ::46 -47 2017-10-01 2017-10-01T00:00 Beijing 47 47 true 47 47 47.47 47.47 char47 47 47 47 ::47 -48 2017-10-01 2017-10-01T00:00 Beijing 48 48 true 48 48 48.48 48.48 char48 48 48 48 ::48 -49 2017-10-01 2017-10-01T00:00 Beijing 49 49 true 49 49 49.49 49.49 char49 49 49 49 ::49 -5 2017-10-01 2017-10-01T00:00 Beijing 5 5 true 5 5 5.5 5.5 char5 5 5 5 ::5 -50 2017-10-01 2017-10-01T00:00 Beijing 50 50 true 50 50 50.5 50.5 char50 50 50 50 ::50 -51 2017-10-01 2017-10-01T00:00 Beijing 51 51 true 51 51 51.51 51.51 char51 51 51 51 ::51 -52 2017-10-01 2017-10-01T00:00 Beijing 52 52 true 52 52 52.52 52.52 char52 52 52 52 ::52 -53 2017-10-01 2017-10-01T00:00 Beijing 53 53 true 53 53 53.53 53.53 char53 53 53 53 ::53 -54 2017-10-01 2017-10-01T00:00 Beijing 54 54 true 54 54 54.54 54.54 char54 54 54 54 ::54 -55 2017-10-01 2017-10-01T00:00 Beijing 55 55 true 55 55 55.55 55.55 char55 55 55 55 ::55 -56 2017-10-01 2017-10-01T00:00 Beijing 56 56 true 56 56 56.56 56.56 char56 56 56 56 ::56 -57 2017-10-01 2017-10-01T00:00 Beijing 57 57 true 57 57 57.57 57.57 char57 57 57 57 ::57 -58 2017-10-01 2017-10-01T00:00 Beijing 58 58 true 58 58 58.58 58.58 char58 58 58 58 ::58 -59 2017-10-01 2017-10-01T00:00 Beijing 59 59 true 59 59 59.59 59.59 char59 59 59 59 ::59 -6 2017-10-01 2017-10-01T00:00 Beijing 6 6 true 6 6 6.6 6.6 char6 6 6 6 ::6 -60 2017-10-01 2017-10-01T00:00 Beijing 60 60 true 60 60 60.6 60.6 char60 60 60 60 ::60 -61 2017-10-01 2017-10-01T00:00 Beijing 61 61 true 61 61 61.61 61.61 char61 61 61 61 ::61 -62 2017-10-01 2017-10-01T00:00 Beijing 62 62 true 62 62 62.62 62.62 char62 62 62 62 ::62 -63 2017-10-01 2017-10-01T00:00 Beijing 63 63 true 63 63 63.63 63.63 char63 63 63 63 ::63 -64 2017-10-01 2017-10-01T00:00 Beijing 64 64 true 64 64 64.64 64.64 char64 64 64 64 ::64 -65 2017-10-01 2017-10-01T00:00 Beijing 65 65 true 65 65 65.65 65.65 char65 65 65 65 ::65 -66 2017-10-01 2017-10-01T00:00 Beijing 66 66 true 66 66 66.66 66.66 char66 66 66 66 ::66 -67 2017-10-01 2017-10-01T00:00 Beijing 67 67 true 67 67 67.67 67.67 char67 67 67 67 ::67 -68 2017-10-01 2017-10-01T00:00 Beijing 68 68 true 68 68 68.68 68.68 char68 68 68 68 ::68 -69 2017-10-01 2017-10-01T00:00 Beijing 69 69 true 69 69 69.69 69.69 char69 69 69 69 ::69 -7 2017-10-01 2017-10-01T00:00 Beijing 7 7 true 7 7 7.7 7.7 char7 7 7 7 ::7 -70 2017-10-01 2017-10-01T00:00 Beijing 70 70 true 70 70 70.7 70.7 char70 70 70 70 ::70 -71 2017-10-01 2017-10-01T00:00 Beijing 71 71 true 71 71 71.71 71.71 char71 71 71 71 ::71 -72 2017-10-01 2017-10-01T00:00 Beijing 72 72 true 72 72 72.72 72.72 char72 72 72 72 ::72 -73 2017-10-01 2017-10-01T00:00 Beijing 73 73 true 73 73 73.73 73.73 char73 73 73 73 ::73 -74 2017-10-01 2017-10-01T00:00 Beijing 74 74 true 74 74 74.74 74.74 char74 74 74 74 ::74 -75 2017-10-01 2017-10-01T00:00 Beijing 75 75 true 75 75 75.75 75.75 char75 75 75 75 ::75 -76 2017-10-01 2017-10-01T00:00 Beijing 76 76 true 76 76 76.76 76.76 char76 76 76 76 ::76 -77 2017-10-01 2017-10-01T00:00 Beijing 77 77 true 77 77 77.77 77.77 char77 77 77 77 ::77 -78 2017-10-01 2017-10-01T00:00 Beijing 78 78 true 78 78 78.78 78.78 char78 78 78 78 ::78 -79 2017-10-01 2017-10-01T00:00 Beijing 79 79 true 79 79 79.79 79.79 char79 79 79 79 ::79 -8 2017-10-01 2017-10-01T00:00 Beijing 8 8 true 8 8 8.8 8.8 char8 8 8 8 ::8 -80 2017-10-01 2017-10-01T00:00 Beijing 80 80 true 80 80 80.8 80.8 char80 80 80 80 ::80 -81 2017-10-01 2017-10-01T00:00 Beijing 81 81 true 81 81 81.81 81.81 char81 81 81 81 ::81 -82 2017-10-01 2017-10-01T00:00 Beijing 82 82 true 82 82 82.82 82.82 char82 82 82 82 ::82 -83 2017-10-01 2017-10-01T00:00 Beijing 83 83 true 83 83 83.83 83.83 char83 83 83 83 ::83 -84 2017-10-01 2017-10-01T00:00 Beijing 84 84 true 84 84 84.84 84.84 char84 84 84 84 ::84 -85 2017-10-01 2017-10-01T00:00 Beijing 85 85 true 85 85 85.85 85.85 char85 85 85 85 ::85 -86 2017-10-01 2017-10-01T00:00 Beijing 86 86 true 86 86 86.86 86.86 char86 86 86 86 ::86 -87 2017-10-01 2017-10-01T00:00 Beijing 87 87 true 87 87 87.87 87.87 char87 87 87 87 ::87 -88 2017-10-01 2017-10-01T00:00 Beijing 88 88 true 88 88 88.88 88.88 char88 88 88 88 ::88 -89 2017-10-01 2017-10-01T00:00 Beijing 89 89 true 89 89 89.89 89.89 char89 89 89 89 ::89 -9 2017-10-01 2017-10-01T00:00 Beijing 9 9 true 9 9 9.9 9.9 char9 9 9 9 ::9 -90 2017-10-01 2017-10-01T00:00 Beijing 90 90 true 90 90 90.9 90.9 char90 90 90 90 ::90 -91 2017-10-01 2017-10-01T00:00 Beijing 91 91 true 91 91 91.91 91.91 char91 91 91 91 ::91 -92 2017-10-01 2017-10-01T00:00 Beijing 92 92 true 92 92 92.92 92.92 char92 92 92 92 ::92 -93 2017-10-01 2017-10-01T00:00 Beijing 93 93 true 93 93 93.93 93.93 char93 93 93 93 ::93 -94 2017-10-01 2017-10-01T00:00 Beijing 94 94 true 94 94 94.94 94.94 char94 94 94 94 ::94 -95 2017-10-01 2017-10-01T00:00 Beijing 95 95 true 95 95 95.95 95.95 char95 95 95 95 ::95 -96 2017-10-01 2017-10-01T00:00 Beijing 96 96 true 96 96 96.96 96.96 char96 96 96 96 ::96 -97 2017-10-01 2017-10-01T00:00 Beijing 97 97 true 97 97 97.97 97.97 char97 97 97 97 ::97 -98 2017-10-01 2017-10-01T00:00 Beijing 98 98 true 98 98 98.98 98.98 char98 98 98 98 ::98 -99 2017-10-01 2017-10-01T00:00 Beijing 99 99 true 99 99 99.99 99.99 char99 99 99 99 ::99 +1 2017-10-01 2017-09-30T16:00 Beijing 1 1 true 1 1 1.1 1.1 char1 1 1 1 ::1 +10 2017-10-01 2017-09-30T16:00 Beijing 10 10 true 10 10 10.1 10.1 char10 10 10 10 ::10 +100 2017-10-01 2017-09-30T16:00 \N \N \N \N \N \N \N \N \N \N \N \N \N +11 2017-10-01 2017-09-30T16:00 Beijing 11 11 true 11 11 11.11 11.11 char11 11 11 11 ::11 +12 2017-10-01 2017-09-30T16:00 Beijing 12 12 true 12 12 12.12 12.12 char12 12 12 12 ::12 +13 2017-10-01 2017-09-30T16:00 Beijing 13 13 true 13 13 13.13 13.13 char13 13 13 13 ::13 +14 2017-10-01 2017-09-30T16:00 Beijing 14 14 true 14 14 14.14 14.14 char14 14 14 14 ::14 +15 2017-10-01 2017-09-30T16:00 Beijing 15 15 true 15 15 15.15 15.15 char15 15 15 15 ::15 +16 2017-10-01 2017-09-30T16:00 Beijing 16 16 true 16 16 16.16 16.16 char16 16 16 16 ::16 +17 2017-10-01 2017-09-30T16:00 Beijing 17 17 true 17 17 17.17 17.17 char17 17 17 17 ::17 +18 2017-10-01 2017-09-30T16:00 Beijing 18 18 true 18 18 18.18 18.18 char18 18 18 18 ::18 +19 2017-10-01 2017-09-30T16:00 Beijing 19 19 true 19 19 19.19 19.19 char19 19 19 19 ::19 +2 2017-10-01 2017-09-30T16:00 Beijing 2 2 true 2 2 2.2 2.2 char2 2 2 2 ::2 +20 2017-10-01 2017-09-30T16:00 Beijing 20 20 true 20 20 20.2 20.2 char20 20 20 20 ::20 +21 2017-10-01 2017-09-30T16:00 Beijing 21 21 true 21 21 21.21 21.21 char21 21 21 21 ::21 +22 2017-10-01 2017-09-30T16:00 Beijing 22 22 true 22 22 22.22 22.22 char22 22 22 22 ::22 +23 2017-10-01 2017-09-30T16:00 Beijing 23 23 true 23 23 23.23 23.23 char23 23 23 23 ::23 +24 2017-10-01 2017-09-30T16:00 Beijing 24 24 true 24 24 24.24 24.24 char24 24 24 24 ::24 +25 2017-10-01 2017-09-30T16:00 Beijing 25 25 true 25 25 25.25 25.25 char25 25 25 25 ::25 +26 2017-10-01 2017-09-30T16:00 Beijing 26 26 true 26 26 26.26 26.26 char26 26 26 26 ::26 +27 2017-10-01 2017-09-30T16:00 Beijing 27 27 true 27 27 27.27 27.27 char27 27 27 27 ::27 +28 2017-10-01 2017-09-30T16:00 Beijing 28 28 true 28 28 28.28 28.28 char28 28 28 28 ::28 +29 2017-10-01 2017-09-30T16:00 Beijing 29 29 true 29 29 29.29 29.29 char29 29 29 29 ::29 +3 2017-10-01 2017-09-30T16:00 Beijing 3 3 true 3 3 3.3 3.3 char3 3 3 3 ::3 +30 2017-10-01 2017-09-30T16:00 Beijing 30 30 true 30 30 30.3 30.3 char30 30 30 30 ::30 +31 2017-10-01 2017-09-30T16:00 Beijing 31 31 true 31 31 31.31 31.31 char31 31 31 31 ::31 +32 2017-10-01 2017-09-30T16:00 Beijing 32 32 true 32 32 32.32 32.32 char32 32 32 32 ::32 +33 2017-10-01 2017-09-30T16:00 Beijing 33 33 true 33 33 33.33 33.33 char33 33 33 33 ::33 +34 2017-10-01 2017-09-30T16:00 Beijing 34 34 true 34 34 34.34 34.34 char34 34 34 34 ::34 +35 2017-10-01 2017-09-30T16:00 Beijing 35 35 true 35 35 35.35 35.35 char35 35 35 35 ::35 +36 2017-10-01 2017-09-30T16:00 Beijing 36 36 true 36 36 36.36 36.36 char36 36 36 36 ::36 +37 2017-10-01 2017-09-30T16:00 Beijing 37 37 true 37 37 37.37 37.37 char37 37 37 37 ::37 +38 2017-10-01 2017-09-30T16:00 Beijing 38 38 true 38 38 38.38 38.38 char38 38 38 38 ::38 +39 2017-10-01 2017-09-30T16:00 Beijing 39 39 true 39 39 39.39 39.39 char39 39 39 39 ::39 +4 2017-10-01 2017-09-30T16:00 Beijing 4 4 true 4 4 4.4 4.4 char4 4 4 4 ::4 +40 2017-10-01 2017-09-30T16:00 Beijing 40 40 true 40 40 40.4 40.4 char40 40 40 40 ::40 +41 2017-10-01 2017-09-30T16:00 Beijing 41 41 true 41 41 41.41 41.41 char41 41 41 41 ::41 +42 2017-10-01 2017-09-30T16:00 Beijing 42 42 true 42 42 42.42 42.42 char42 42 42 42 ::42 +43 2017-10-01 2017-09-30T16:00 Beijing 43 43 true 43 43 43.43 43.43 char43 43 43 43 ::43 +44 2017-10-01 2017-09-30T16:00 Beijing 44 44 true 44 44 44.44 44.44 char44 44 44 44 ::44 +45 2017-10-01 2017-09-30T16:00 Beijing 45 45 true 45 45 45.45 45.45 char45 45 45 45 ::45 +46 2017-10-01 2017-09-30T16:00 Beijing 46 46 true 46 46 46.46 46.46 char46 46 46 46 ::46 +47 2017-10-01 2017-09-30T16:00 Beijing 47 47 true 47 47 47.47 47.47 char47 47 47 47 ::47 +48 2017-10-01 2017-09-30T16:00 Beijing 48 48 true 48 48 48.48 48.48 char48 48 48 48 ::48 +49 2017-10-01 2017-09-30T16:00 Beijing 49 49 true 49 49 49.49 49.49 char49 49 49 49 ::49 +5 2017-10-01 2017-09-30T16:00 Beijing 5 5 true 5 5 5.5 5.5 char5 5 5 5 ::5 +50 2017-10-01 2017-09-30T16:00 Beijing 50 50 true 50 50 50.5 50.5 char50 50 50 50 ::50 +51 2017-10-01 2017-09-30T16:00 Beijing 51 51 true 51 51 51.51 51.51 char51 51 51 51 ::51 +52 2017-10-01 2017-09-30T16:00 Beijing 52 52 true 52 52 52.52 52.52 char52 52 52 52 ::52 +53 2017-10-01 2017-09-30T16:00 Beijing 53 53 true 53 53 53.53 53.53 char53 53 53 53 ::53 +54 2017-10-01 2017-09-30T16:00 Beijing 54 54 true 54 54 54.54 54.54 char54 54 54 54 ::54 +55 2017-10-01 2017-09-30T16:00 Beijing 55 55 true 55 55 55.55 55.55 char55 55 55 55 ::55 +56 2017-10-01 2017-09-30T16:00 Beijing 56 56 true 56 56 56.56 56.56 char56 56 56 56 ::56 +57 2017-10-01 2017-09-30T16:00 Beijing 57 57 true 57 57 57.57 57.57 char57 57 57 57 ::57 +58 2017-10-01 2017-09-30T16:00 Beijing 58 58 true 58 58 58.58 58.58 char58 58 58 58 ::58 +59 2017-10-01 2017-09-30T16:00 Beijing 59 59 true 59 59 59.59 59.59 char59 59 59 59 ::59 +6 2017-10-01 2017-09-30T16:00 Beijing 6 6 true 6 6 6.6 6.6 char6 6 6 6 ::6 +60 2017-10-01 2017-09-30T16:00 Beijing 60 60 true 60 60 60.6 60.6 char60 60 60 60 ::60 +61 2017-10-01 2017-09-30T16:00 Beijing 61 61 true 61 61 61.61 61.61 char61 61 61 61 ::61 +62 2017-10-01 2017-09-30T16:00 Beijing 62 62 true 62 62 62.62 62.62 char62 62 62 62 ::62 +63 2017-10-01 2017-09-30T16:00 Beijing 63 63 true 63 63 63.63 63.63 char63 63 63 63 ::63 +64 2017-10-01 2017-09-30T16:00 Beijing 64 64 true 64 64 64.64 64.64 char64 64 64 64 ::64 +65 2017-10-01 2017-09-30T16:00 Beijing 65 65 true 65 65 65.65 65.65 char65 65 65 65 ::65 +66 2017-10-01 2017-09-30T16:00 Beijing 66 66 true 66 66 66.66 66.66 char66 66 66 66 ::66 +67 2017-10-01 2017-09-30T16:00 Beijing 67 67 true 67 67 67.67 67.67 char67 67 67 67 ::67 +68 2017-10-01 2017-09-30T16:00 Beijing 68 68 true 68 68 68.68 68.68 char68 68 68 68 ::68 +69 2017-10-01 2017-09-30T16:00 Beijing 69 69 true 69 69 69.69 69.69 char69 69 69 69 ::69 +7 2017-10-01 2017-09-30T16:00 Beijing 7 7 true 7 7 7.7 7.7 char7 7 7 7 ::7 +70 2017-10-01 2017-09-30T16:00 Beijing 70 70 true 70 70 70.7 70.7 char70 70 70 70 ::70 +71 2017-10-01 2017-09-30T16:00 Beijing 71 71 true 71 71 71.71 71.71 char71 71 71 71 ::71 +72 2017-10-01 2017-09-30T16:00 Beijing 72 72 true 72 72 72.72 72.72 char72 72 72 72 ::72 +73 2017-10-01 2017-09-30T16:00 Beijing 73 73 true 73 73 73.73 73.73 char73 73 73 73 ::73 +74 2017-10-01 2017-09-30T16:00 Beijing 74 74 true 74 74 74.74 74.74 char74 74 74 74 ::74 +75 2017-10-01 2017-09-30T16:00 Beijing 75 75 true 75 75 75.75 75.75 char75 75 75 75 ::75 +76 2017-10-01 2017-09-30T16:00 Beijing 76 76 true 76 76 76.76 76.76 char76 76 76 76 ::76 +77 2017-10-01 2017-09-30T16:00 Beijing 77 77 true 77 77 77.77 77.77 char77 77 77 77 ::77 +78 2017-10-01 2017-09-30T16:00 Beijing 78 78 true 78 78 78.78 78.78 char78 78 78 78 ::78 +79 2017-10-01 2017-09-30T16:00 Beijing 79 79 true 79 79 79.79 79.79 char79 79 79 79 ::79 +8 2017-10-01 2017-09-30T16:00 Beijing 8 8 true 8 8 8.8 8.8 char8 8 8 8 ::8 +80 2017-10-01 2017-09-30T16:00 Beijing 80 80 true 80 80 80.8 80.8 char80 80 80 80 ::80 +81 2017-10-01 2017-09-30T16:00 Beijing 81 81 true 81 81 81.81 81.81 char81 81 81 81 ::81 +82 2017-10-01 2017-09-30T16:00 Beijing 82 82 true 82 82 82.82 82.82 char82 82 82 82 ::82 +83 2017-10-01 2017-09-30T16:00 Beijing 83 83 true 83 83 83.83 83.83 char83 83 83 83 ::83 +84 2017-10-01 2017-09-30T16:00 Beijing 84 84 true 84 84 84.84 84.84 char84 84 84 84 ::84 +85 2017-10-01 2017-09-30T16:00 Beijing 85 85 true 85 85 85.85 85.85 char85 85 85 85 ::85 +86 2017-10-01 2017-09-30T16:00 Beijing 86 86 true 86 86 86.86 86.86 char86 86 86 86 ::86 +87 2017-10-01 2017-09-30T16:00 Beijing 87 87 true 87 87 87.87 87.87 char87 87 87 87 ::87 +88 2017-10-01 2017-09-30T16:00 Beijing 88 88 true 88 88 88.88 88.88 char88 88 88 88 ::88 +89 2017-10-01 2017-09-30T16:00 Beijing 89 89 true 89 89 89.89 89.89 char89 89 89 89 ::89 +9 2017-10-01 2017-09-30T16:00 Beijing 9 9 true 9 9 9.9 9.9 char9 9 9 9 ::9 +90 2017-10-01 2017-09-30T16:00 Beijing 90 90 true 90 90 90.9 90.9 char90 90 90 90 ::90 +91 2017-10-01 2017-09-30T16:00 Beijing 91 91 true 91 91 91.91 91.91 char91 91 91 91 ::91 +92 2017-10-01 2017-09-30T16:00 Beijing 92 92 true 92 92 92.92 92.92 char92 92 92 92 ::92 +93 2017-10-01 2017-09-30T16:00 Beijing 93 93 true 93 93 93.93 93.93 char93 93 93 93 ::93 +94 2017-10-01 2017-09-30T16:00 Beijing 94 94 true 94 94 94.94 94.94 char94 94 94 94 ::94 +95 2017-10-01 2017-09-30T16:00 Beijing 95 95 true 95 95 95.95 95.95 char95 95 95 95 ::95 +96 2017-10-01 2017-09-30T16:00 Beijing 96 96 true 96 96 96.96 96.96 char96 96 96 96 ::96 +97 2017-10-01 2017-09-30T16:00 Beijing 97 97 true 97 97 97.97 97.97 char97 97 97 97 ::97 +98 2017-10-01 2017-09-30T16:00 Beijing 98 98 true 98 98 98.98 98.98 char98 98 98 98 ::98 +99 2017-10-01 2017-09-30T16:00 Beijing 99 99 true 99 99 99.99 99.99 char99 99 99 99 ::99 diff --git a/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out b/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out index 160c99248fe90c..cc1100f6bb4dce 100644 --- a/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out +++ b/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out @@ -88,7 +88,7 @@ 22 value_for_pt11 -- !ctas_types_01 -- -true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T23:19:34 +true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T15:19:34 -- !hive_docker_ctas_types_01 -- true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 23:19:34.0 @@ -188,7 +188,7 @@ true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default 22 value_for_pt11 -- !ctas_types_01 -- -true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T23:19:34 +true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T15:19:34 -- !hive_docker_ctas_types_01 -- true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 23:19:34.0 diff --git a/regression-test/data/external_table_p0/hive/test_complex_types.out b/regression-test/data/external_table_p0/hive/test_complex_types.out index 4a9dbbe835c7fc..b1b3870c7447ed 100644 --- a/regression-test/data/external_table_p0/hive/test_complex_types.out +++ b/regression-test/data/external_table_p0/hive/test_complex_types.out @@ -12,7 +12,7 @@ [0.9805502029231666, 0.5330291595754054, 0.3002474487337981, 0.4856360175030267, 0.7687106425158624, 0.6993506644925102, 0.2849354808825807, 0.3473417455186141, 0.1350012944304507, 0.9708132103700939, 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b/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out index 0402feef40e6b5..f24ea35cf94a16 100644 --- a/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out +++ b/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out @@ -1,8 +1,8 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 +0.1 test1 2022-12-31T16:00 \N +0.2 test2 2023-01-01T16:00 \N +0.3 test3 2023-01-02T16:00 100 -- !q02 -- 0.1 test1 2023-01-01T00:00 \N @@ -72,9 +72,9 @@ 2023-01-03T00:00 100 0.3 test3 -- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 +0.1 test1 2022-12-31T16:00 \N +0.2 test2 2023-01-01T16:00 \N +0.3 test3 2023-01-02T16:00 100 -- !q02 -- 0.1 test1 2023-01-01T00:00 \N @@ -120,9 +120,9 @@ 2023-01-03T00:00 100 0.3 test3 -- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 +0.1 test1 2022-12-31T16:00 \N +0.2 test2 2023-01-01T16:00 \N +0.3 test3 2023-01-02T16:00 100 -- !q02 -- 0.1 test1 2023-01-01T00:00 \N @@ -192,9 +192,9 @@ 2023-01-03T00:00 100 0.3 test3 -- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 +0.1 test1 2022-12-31T16:00 \N +0.2 test2 2023-01-01T16:00 \N +0.3 test3 2023-01-02T16:00 100 -- !q02 -- 0.1 test1 2023-01-01T00:00 \N diff --git a/regression-test/data/external_table_p0/hive/test_hive_compress_type.out b/regression-test/data/external_table_p0/hive/test_hive_compress_type.out index ca9ca885c5b854..25ac017c0c8faa 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_compress_type.out +++ b/regression-test/data/external_table_p0/hive/test_hive_compress_type.out @@ -49,221 +49,221 @@ 215 -- !q43 -- -1 100 5 1000000000 10.5 20.75 true First A Alpha 2023-10-06 2023-10-06T14:30 123.45 -1 578 55 2111222273 56.858597 82.38111658179561 true Random C LYDUG 2023-12-17 2023-12-05T13:04:58 1393.11 -1 979 44 10163954251 28.827957 57.56879940298416 true Random Q DNRGE 2023-12-09 2023-12-10T20:21:58 1581.25 -10 1000 50 10000000000 55.25 65.75 false Tenth J Kappa 2023-10-15 2023-10-15T23:30 1012.34 -10 210 26 8549838179 23.438345 73.36477128189287 true Random N VVXIF 2023-11-24 2023-12-13T18:04:58 226.65 -10 386 51 1214815770 13.959902 36.64197990482059 false Random J ORLGI 2023-12-18 2023-11-27T17:13:58 852.62 -10 966 38 2203748112 45.555325 27.908447208440094 true Random W LFAGO 2023-12-14 2023-11-26T20:00:58 1898.68 -100 281 26 3174393241 51.05278 52.09566669589555 false Random F SLDWB 2023-12-14 2023-12-12T07:03:58 798.30 -100 289 71 4919981667 66.56684 69.73132704711037 true Random V QOLAP 2023-12-17 2023-12-23T09:38:58 217.05 -11 1100 55 11000000000 60.5 70.0 true Eleventh K Lambda 2023-10-16 2023-10-16T01:45 1123.45 -11 426 67 8473986652 17.942455 71.80682514420877 true Random X FXDUV 2023-12-04 2023-12-22T07:51:58 129.81 -11 441 19 7370044350 74.261696 62.013817404758086 true Random D UYKZA 2023-12-23 2023-12-15T11:49:58 1805.14 -11 487 27 14556302216 85.33334 62.596750833474495 true Random E QMHJD 2023-12-23 2023-12-24T08:30:58 1491.22 -11 770 17 7962512669 12.508753 83.33847413902296 true Random P LHJRA 2023-12-06 2023-12-04T15:48:58 970.51 -12 1200 60 12000000000 65.75 75.25 false Twelfth L Mu 2023-10-17 2023-10-17T02:15 1234.56 -12 751 8 12205294947 23.468674 64.35048302450815 true Random K FCSBV 2023-12-03 2023-12-17T01:10:58 325.26 -12 782 48 5080583047 75.55138 49.6324463213595 true Random N WYJDW 2023-12-16 2023-12-18T02:58:58 944.42 -12 987 73 1432735571 40.308147 43.5019559828596 true Random S MZUNG 2023-12-07 2023-12-03T13:42:58 215.12 -13 1300 65 13000000000 70.0 80.5 true Thirteenth M Nu 2023-10-18 2023-10-18T03:30 1345.67 -13 335 39 13869202091 30.426075 39.02304533093442 true Random L AULCC 2023-12-08 2023-12-13T00:26:58 387.97 -13 402 30 10851194313 74.82481 74.90108005771035 false Random F GEMMK 2023-11-27 2023-12-21T15:03:58 1643.55 -13 503 34 6763884255 23.660393 63.9797872103468 true Random S POEBK 2023-12-22 2023-12-23T23:16:58 486.62 -13 696 74 3370487489 84.544014 88.69976219408227 true Random H RTFJI 2023-11-23 2023-11-25T07:32:58 1761.50 -13 745 48 13047949175 51.168613 85.21972389262197 true Random A AYBWQ 2023-12-22 2023-12-22T16:25:58 1192.48 -13 859 65 7433576046 56.136265 34.87823331022725 false Random L CRFUF 2023-12-23 2023-12-12T15:05:58 1037.15 -14 1400 70 14000000000 75.25 85.75 false Fourteenth N Xi 2023-10-19 2023-10-19T04:45 1456.78 -14 195 17 2370700139 16.777058 64.81793301410002 false Random P IIGRE 2023-12-12 2023-12-14T22:40:58 1678.44 -14 966 65 7828602539 62.430664 68.85873133439297 true Random I VVOQH 2023-12-01 2023-12-06T00:54:58 1300.43 -14 968 16 11314514196 62.509666 33.1841427251225 false Random T WDEVJ 2023-11-24 2023-12-06T17:54:58 431.61 -15 1500 75 15000000000 80.5 90.0 true Fifteenth O Omicron 2023-10-20 2023-10-20T05:15 1567.89 -15 703 67 4284267079 85.38059 91.41088583496226 true Random T PHZRC 2023-12-04 2023-12-08T15:54:58 185.19 -16 135 22 7901304568 43.944805 85.16901944253635 true Random K NUQEP 2023-11-29 2023-11-25T23:42:58 1440.74 -16 615 20 12294128025 77.37379 20.42772029677839 true Random U JHPOB 2023-11-30 2023-12-16T14:29:58 1105.33 -17 289 49 13560709243 39.952793 38.245306832599425 true Random Q QEYVY 2023-12-19 2023-12-07T00:35:58 500.19 -17 499 46 11230409207 51.632103 28.811164197154774 false Random V BVLUH 2023-12-13 2023-12-23T17:59:58 1387.62 -17 646 62 11234805830 76.40492 67.46425239009778 true Random N REHZC 2023-12-09 2023-11-28T02:06:58 365.15 -17 698 55 1807368797 20.17171 43.84496606184709 true Random P SHSJV 2023-12-01 2023-11-25T11:56:58 810.95 -17 794 14 8377523030 28.07663 52.3837762020057 false Random E WPMIN 2023-12-03 2023-11-26T04:59:58 239.42 -17 913 32 4647929554 78.91502 70.54487265463735 true Random S WFPNS 2023-11-27 2023-11-26T03:29:58 321.45 -18 690 17 1399456103 63.261967 42.964715823771236 true Random R BWSRS 2023-12-13 2023-12-23T08:33:58 1840.02 -18 835 17 14265814864 18.923101 80.53531451138412 true Random V PIKUZ 2023-12-20 2023-12-21T07:39:58 1167.09 -19 917 66 2340946367 89.035675 22.649362455875274 false Random D HWHMU 2023-11-30 2023-12-10T02:36:58 1960.07 -19 993 13 7039833438 79.769066 69.79049291517285 true Random X OFSUV 2023-12-11 2023-12-08T01:46:58 1958.95 -2 200 10 2000000000 15.75 25.5 false Second B Beta 2023-10-07 2023-10-07T15:45 234.56 -2 850 75 7075823565 83.65178 62.56093886118189 false Random F RFHAG 2023-11-24 2023-12-03T01:06:58 495.12 -2 921 62 8557914543 78.52379 58.6849882881372 false Random D KBXXS 2023-12-07 2023-12-02T22:24:58 1782.88 -2 925 46 6013180177 41.107002 34.86561026061906 true Random L XLLXY 2023-12-06 2023-12-09T14:04:58 1246.26 -20 248 64 7704906572 35.089928 76.69128821479936 true Random T KQOMS 2023-11-30 2023-12-11T01:35:58 1799.26 -22 200 41 12163439252 64.621254 81.68574929661384 true Random U KGVNU 2023-12-20 2023-11-30T14:56:58 1915.47 -22 235 19 6963606423 65.68033 54.1995295752517 true Random E ENVRH 2023-12-22 2023-11-29T14:42:58 864.89 -23 192 8 5102667616 54.111057 40.85713971600841 false Random J EBXEB 2023-12-13 2023-12-10T11:32:58 1824.12 -27 452 74 4240215371 50.569168 75.68204627611644 true Random G AZOWU 2023-12-01 2023-11-26T06:24:58 201.31 -27 866 24 5531365994 72.77447 86.96690821165853 false Random S TZPFJ 2023-11-28 2023-12-13T15:31:58 1274.75 -28 655 21 14580233860 12.503378 48.60220286874443 false Random P DUBQQ 2023-12-12 2023-12-03T20:11:58 922.42 -29 157 34 2302882987 51.924015 20.311140937696468 true Random R MBOXJ 2023-12-02 2023-12-03T14:12:58 1620.80 -29 910 52 5544039917 22.179396 46.32732226806482 true Random C TIZAG 2023-11-28 2023-12-14T16:08:58 900.96 -29 923 57 1591814253 68.57371 33.342802789892986 true Random Q ZONGC 2023-12-20 2023-12-13T09:11:58 1465.38 -3 259 74 7422478791 22.291426 75.38227773520089 true Random S VWAXJ 2023-12-01 2023-12-05T21:23:58 1970.57 -3 300 15 3000000000 20.25 30.0 true Third C Gamma 2023-10-08 2023-10-08T16:15 345.67 -3 422 25 5996825874 89.173584 62.758513798505824 false Random Z CDYAO 2023-12-14 2023-12-08T09:27:58 567.23 -3 668 60 1942550969 83.43451 87.15906153619602 true Random F QYSRS 2023-12-22 2023-12-10T22:17:58 320.22 -30 292 71 10308444223 63.039078 76.40649540444898 false Random G DRLHY 2023-12-19 2023-12-14T15:32:58 1165.14 -30 572 6 3022031043 57.813908 72.29244668177799 true Random X EHJDN 2023-12-11 2023-12-12T02:44:58 910.38 -30 830 65 12624057029 38.791172 59.72899174862661 false Random A LFPWP 2023-12-03 2023-12-17T00:10:58 1760.62 -31 395 22 6141426904 88.37914 52.0655270963123 false Random J DRPJV 2023-12-07 2023-11-29T03:15:58 1076.41 -31 990 5 13678786851 15.762894 85.24173385692956 false Random H THGIM 2023-12-14 2023-12-09T01:24:58 1834.37 -33 198 20 13225406950 67.7327 58.63863378877107 true Random I ZKXRA 2023-12-07 2023-11-29T02:33:58 566.76 -33 321 39 12537851805 38.26871 32.6626492245712 true Random S OICCE 2023-12-02 2023-12-19T16:41:58 306.92 -33 916 53 5666674210 57.998173 61.774881852563475 true Random J WJAXA 2023-11-27 2023-12-05T19:58:58 976.13 -34 145 44 14060350663 73.02436 68.40544929600975 true Random S UUJFP 2023-11-23 2023-12-12T06:08:58 739.45 -34 585 43 1429300527 61.706585 80.88100239373303 false Random O JKJOH 2023-12-17 2023-12-07T11:00:58 468.11 -35 297 75 2468378214 51.353462 34.18114780065386 false Random C HBYZO 2023-12-05 2023-12-09T21:42:58 534.70 -37 438 39 6809169396 83.56728 40.90894521029911 true Random W GXPAY 2023-12-07 2023-12-18T06:35:58 383.18 -38 606 57 14585148556 82.67463 79.18300302689997 false Random E RSFUZ 2023-12-16 2023-11-27T18:55:58 970.25 -39 726 50 3865644066 26.225628 28.534393094364418 false Random F NIUCS 2023-12-05 2023-12-04T19:31:58 1953.82 -4 122 24 10738473173 81.15482 60.21481394154484 false Random Y PQJRK 2023-12-20 2023-12-09T02:38:58 1467.35 -4 400 20 4000000000 25.5 35.25 false Fourth D Delta 2023-10-09 2023-10-09T17:30 456.78 -4 569 72 10560903405 50.255936 47.535145739285184 false Random O NRIRC 2023-12-05 2023-12-01T09:10:58 1986.99 -4 682 22 2040832636 60.33469 67.33499498711046 true Random W QUICJ 2023-11-24 2023-12-14T10:17:58 579.56 -40 230 34 10824964541 16.929768 53.812277279703366 false Random F YDQHF 2023-12-14 2023-12-03T17:42:58 1623.79 -40 693 69 13276482882 44.35974 82.57845708670757 true Random B RCCSU 2023-11-29 2023-12-01T20:11:58 183.64 -40 914 7 4902128502 19.442041 33.099787387344406 true Random Q KOCWA 2023-11-28 2023-12-21T09:20:58 1824.80 -41 344 34 14536795918 56.660946 84.15108995619764 false Random Q KYLCH 2023-12-10 2023-12-04T08:25:58 1902.09 -41 599 54 8095449906 22.58196 37.99742597458578 false Random T GTQXP 2023-12-12 2023-12-22T19:08:58 743.46 -41 697 21 1200243566 12.466168 68.57243624557165 true Random U JZGEG 2023-12-03 2023-12-10T04:51:58 1323.88 -41 708 64 11745827370 72.84812 35.31028363777645 true Random O WGSQC 2023-12-02 2023-11-25T17:07:58 1666.71 -41 840 65 8988241658 37.428593 42.25992474748068 false Random E HURYX 2023-12-22 2023-12-19T01:55:58 141.89 -42 143 42 3421815721 65.27691 87.91368867538209 true Random S AXGVL 2023-12-06 2023-11-29T07:36:58 575.01 -42 178 38 7559404453 69.69449 64.37154501388798 true Random G QUMUN 2023-12-14 2023-12-17T01:37:58 1190.44 -42 192 28 14454791024 35.465202 46.34876515635648 false Random W NQFGR 2023-12-04 2023-11-24T05:02:58 1428.02 -42 355 72 11536856285 74.42886 53.49032479461299 false Random I IQZEI 2023-12-10 2023-12-06T07:17:58 1098.14 -43 178 64 6969956763 40.980415 52.998828731408516 true Random C XQHYB 2023-12-11 2023-12-07T23:00:58 257.08 -43 828 24 12011396947 45.07647 54.2136449479346 true Random E HIDUO 2023-12-02 2023-12-19T01:14:58 233.10 -44 219 38 8596488294 73.52956 94.10797854680568 true Random E HMWBI 2023-12-15 2023-12-06T00:51:58 1907.47 -44 694 55 3626514138 62.504086 72.89799265418553 true Random Z JTDVF 2023-12-01 2023-11-29T12:08:58 1769.92 -44 912 63 8534761366 55.993538 50.235171557550416 false Random N OVQRQ 2023-12-08 2023-11-24T03:39:58 264.92 -44 928 7 1939079012 14.426672 68.86451571230457 false Random I EKVWY 2023-12-15 2023-12-09T10:43:58 846.74 -45 455 25 12639246000 47.011307 26.310712594958694 false Random Z GGEUA 2023-11-27 2023-12-01T20:41:58 1698.21 -45 492 43 3870916386 51.069588 42.652270406300794 true Random H JVZTB 2023-12-04 2023-12-09T21:06:58 1517.83 -47 508 48 1456473942 48.488297 20.377955902326608 false Random B CAOEY 2023-11-29 2023-12-10T14:49:58 1865.52 -47 566 50 1426586688 51.278687 40.47151456873397 true Random F YBOSH 2023-11-26 2023-12-15T03:44:58 1806.35 -47 838 73 14910230294 83.69784 82.28901816600579 true Random L SHXYL 2023-11-24 2023-12-05T22:19:58 1062.15 -48 898 59 12871187130 10.13838 70.19705104611333 true Random J WFXNN 2023-12-23 2023-12-17T02:53:58 1050.21 -49 165 38 4482178563 34.706547 69.17129468406594 false Random W CPZNY 2023-12-15 2023-11-23T19:56:58 512.60 -49 412 16 8300982793 56.263252 66.07893608061771 false Random K DWWJI 2023-12-08 2023-12-17T11:32:58 1718.54 -49 511 51 8602055259 88.1686 88.98712207285577 false Random M ZDKEY 2023-12-10 2023-11-25T02:44:58 241.08 -49 568 70 2916596630 79.16303 56.114316916863025 false Random T ILLIU 2023-11-23 2023-12-07T11:05:58 1039.03 -5 500 25 5000000000 30.75 40.5 true Fifth E Epsilon 2023-10-10 2023-10-10T18:45 567.89 -5 768 5 4152322228 41.128906 78.60686390712706 false Random J LXKRA 2023-12-05 2023-11-24T18:13:58 1941.98 -5 823 63 13328808917 77.768196 22.87975226738422 false Random F OIYPV 2023-12-11 2023-12-14T06:43:58 1144.38 -5 887 74 4082758600 22.797577 93.28246034891224 false Random V MPPGX 2023-12-01 2023-11-29T01:53:58 510.50 -50 126 58 4433111715 75.31828 43.28056186824247 false Random H UTDJF 2023-12-19 2023-12-10T08:24:58 368.42 -51 778 59 13914307584 27.48499 91.47665081887983 true Random X FGFHK 2023-12-01 2023-12-10T03:24:58 402.63 -51 898 32 13510411411 18.679659 21.406761033351007 false Random L FECUW 2023-12-10 2023-12-14T02:00:58 700.43 -52 811 31 14085958816 51.067017 65.01991893789116 true Random A CODYQ 2023-12-03 2023-12-07T23:25:58 1797.21 -53 505 52 9862728376 58.40501 57.60544454281924 false Random V WYCTZ 2023-11-24 2023-12-20T05:13:58 210.43 -53 667 49 10531976747 50.22229 49.64660893042742 false Random K WNRJE 2023-12-04 2023-12-19T14:57:58 680.97 -53 713 14 1464447148 23.474258 45.35056918414047 false Random Q UHMLT 2023-12-10 2023-11-30T02:07:58 286.70 -53 715 29 10917905565 41.83069 93.50885201221966 true Random U TRLSY 2023-12-03 2023-11-26T15:13:58 369.72 -54 467 42 13684826428 38.491455 90.10566649802195 true Random M ERFBG 2023-11-24 2023-12-02T16:23:58 211.00 -54 827 55 7054839267 58.555687 25.891004802115663 false Random O ASMLW 2023-12-13 2023-12-20T16:41:58 1369.32 -54 843 34 9547939940 38.66475 36.370944299232434 true Random P NTVIR 2023-12-12 2023-12-02T06:45:58 1628.37 -55 908 24 13623721787 40.06427 90.85281792731746 false Random B KFZGI 2023-11-27 2023-12-23T18:06:58 1124.95 -55 964 8 14038541765 70.24135 20.034551391620194 false Random J AYXIT 2023-12-13 2023-12-16T19:38:58 1476.73 -57 936 26 12164628867 56.541275 56.276679149397076 true Random O IPHPZ 2023-12-13 2023-11-30T22:36:58 603.68 -59 144 31 6208909394 67.417076 40.59765633709834 true Random D FLWNA 2023-12-12 2023-12-19T06:17:58 1870.24 -59 509 50 5501336408 39.94401 73.35770882761237 true Random I PVZNO 2023-12-04 2023-11-27T04:40:58 1177.33 -6 600 30 6000000000 35.25 45.75 false Sixth F Zeta 2023-10-11 2023-10-11T19:15 678.90 -60 711 69 1493870104 22.574188 61.30347648465907 false Random E FHKVR 2023-11-27 2023-12-05T11:26:58 1981.61 -60 875 42 14283877167 48.811504 67.0706975606688 true Random P VJOZH 2023-12-06 2023-12-15T05:20:58 781.71 -61 267 61 11407448558 12.877184 42.144845857251944 true Random B NRWNW 2023-11-30 2023-11-25T09:34:58 859.85 -61 414 63 14506877706 12.540966 58.04557426323987 false Random H NUOAD 2023-12-10 2023-12-06T22:52:58 780.50 -62 451 50 12304139502 51.151623 22.46754141558852 false Random C SRRSV 2023-12-08 2023-12-20T02:48:58 1352.65 -62 793 46 7308804595 39.766644 48.88672198076526 true Random V TPENZ 2023-11-26 2023-12-23T17:51:58 388.46 -63 112 75 12197306353 85.90137 43.48931389222043 false Random C KKAIT 2023-11-27 2023-12-23T04:23:58 1954.90 -63 383 35 5161212745 39.455276 52.33267523851794 false Random X TMYMC 2023-11-29 2023-12-10T09:09:58 1442.54 -63 410 33 1767102777 72.260124 56.971483381024896 false Random B QXNSM 2023-12-12 2023-12-19T22:57:58 1660.73 -64 479 20 1710421528 53.324104 33.55443503561635 false Random Q ONZRK 2023-12-09 2023-12-01T22:29:58 252.13 -64 678 14 13681447851 74.83621 36.94143092647816 true Random J KELFB 2023-12-01 2023-12-07T18:14:58 308.26 -64 719 36 1224510454 64.237434 86.05689694804887 true Random E ZVQPU 2023-11-30 2023-12-03T04:56:58 1879.25 -64 822 26 1154241961 52.165447 26.779469377773403 true Random E YWNAD 2023-12-08 2023-12-19T19:08:58 731.15 -65 571 24 10523050555 45.865078 70.80680527390149 true Random Y DILBW 2023-12-17 2023-11-25T22:41:58 859.30 -66 306 5 14448160602 44.642223 50.24249889525751 false Random X OASEB 2023-12-11 2023-11-27T00:16:58 1345.69 -66 521 30 7757576974 69.440155 92.3562810104632 false Random H SSOCR 2023-12-19 2023-11-30T06:51:58 913.34 -67 484 65 10817432713 62.168163 77.02869166077757 true Random K SAJMG 2023-12-19 2023-12-14T19:47:58 488.01 -68 266 31 8183454755 69.19586 23.139304803938643 false Random S STCBM 2023-11-26 2023-12-22T13:42:58 1722.37 -68 554 33 3525526216 29.078024 29.6567390059356 false Random Y EUGOF 2023-11-23 2023-12-15T10:33:58 395.41 -68 591 60 4813122821 33.210274 54.464145718507616 false Random X EXROI 2023-12-07 2023-12-07T00:39:58 290.11 -68 756 63 5416393421 66.41538 76.32820339134415 false Random Y CUNAL 2023-12-23 2023-12-14T22:49:58 1109.25 -68 922 13 11664232196 72.683266 37.9910331525765 false Random W PPWBB 2023-11-26 2023-12-10T22:54:58 1968.89 -68 947 60 7257499958 45.661217 77.42577781358565 false Random F ENQGA 2023-11-24 2023-11-29T07:33:58 319.99 -69 416 14 7702410607 31.638903 89.5793904314531 true Random C URQMU 2023-11-25 2023-11-30T15:17:58 1379.22 -7 340 50 8934567449 83.79683 35.39446967734915 false Random L CWYFN 2023-12-05 2023-12-23T02:26:58 806.15 -7 700 35 7000000000 40.5 50.0 true Seventh G Eta 2023-10-12 2023-10-12T20:30 789.01 -7 969 62 3451343234 57.17074 56.74513811095188 false Random G OWDSC 2023-12-19 2023-12-11T17:17:58 1874.22 -70 231 67 4547989149 35.103123 51.93622592177748 true Random V ZBCVY 2023-11-29 2023-12-22T11:41:58 1749.60 -70 421 23 3153379289 27.412096 79.32006404438445 false Random L VLJWK 2023-12-04 2023-12-12T05:31:58 1163.35 -70 751 56 7828222634 52.8313 55.7263634552559 true Random B TFHMH 2023-11-30 2023-12-24T12:22:58 1166.13 -71 452 25 4464808420 18.155642 61.988641984596185 false Random K YXFVY 2023-12-15 2023-12-08T04:58:58 514.74 -71 594 26 1024634104 62.92234 37.216752731371386 true Random J SPUWU 2023-12-04 2023-12-23T08:50:58 779.97 -72 377 11 3042707243 55.289066 53.72552524152444 true Random Q BAPHV 2023-12-06 2023-11-30T07:14:58 119.39 -73 866 49 4618070115 46.803646 91.41305051885227 true Random H ROYYF 2023-12-07 2023-12-01T10:28:58 1817.67 -74 670 60 4783926122 23.513939 91.24357097091087 true Random Y YFPMC 2023-12-23 2023-12-22T22:29:58 943.62 -75 368 73 6944888766 31.500992 56.88267149430107 false Random H LEXKZ 2023-12-21 2023-12-14T01:12:58 443.91 -76 410 20 10425110604 66.26356 92.68329033006493 false Random L JHFYD 2023-11-23 2023-11-29T10:34:58 867.56 -76 504 70 14161652666 58.071503 67.99111956708262 true Random Y HAVCK 2023-11-27 2023-12-14T16:08:58 1864.98 -77 131 19 2964167114 33.23181 53.35246738882714 false Random G AHGFO 2023-12-19 2023-12-01T10:11:58 1837.90 -77 165 36 12887722637 19.729382 45.61157603163882 true Random S OZOLB 2023-12-02 2023-12-03T05:07:58 1576.79 -79 314 17 6823498005 22.562634 72.70049796639023 true Random K FPSNZ 2023-12-07 2023-12-15T11:52:58 211.50 -8 550 48 13655992126 52.90345 51.35114230137935 false Random X JTVSE 2023-12-13 2023-12-15T03:49:58 361.55 -8 800 40 8000000000 45.75 55.25 false Eighth H Theta 2023-10-13 2023-10-13T21:45 890.12 -8 866 37 13672147880 81.28999 67.66548594336737 false Random H QDJIM 2023-12-14 2023-12-17T18:44:58 1112.05 -80 267 57 8797946135 35.604717 80.51381110359165 false Random K KQTEX 2023-12-09 2023-12-13T06:19:58 1769.15 -80 815 19 14529289205 19.769405 37.37008094684765 true Random Z WLALH 2023-12-11 2023-12-14T03:24:58 479.38 -81 726 66 9327218218 81.50363 39.9702863173827 true Random X WODRP 2023-11-28 2023-12-23T13:25:58 561.98 -82 107 51 1358006007 78.36581 46.09413324325159 true Random C IPNQU 2023-12-01 2023-12-14T05:41:58 417.17 -82 133 60 4616538638 88.8813 30.82745983013354 true Random W KPIJE 2023-12-20 2023-12-01T07:57:58 583.41 -82 531 44 10642962933 26.818586 23.851865471979615 false Random F NMQOD 2023-12-13 2023-12-18T19:34:58 861.78 -82 603 60 9083469993 81.24088 44.46228092092543 true Random Y WTQGU 2023-11-30 2023-11-28T13:18:58 1448.45 -82 982 62 8955063933 81.2855 78.30439669511465 true Random J SOCOT 2023-12-02 2023-12-02T21:17:58 814.60 -83 700 46 4569093424 50.063602 47.75811273142146 false Random R TEGAY 2023-12-19 2023-12-07T06:46:58 760.22 -84 427 60 9035762847 81.971306 28.37315065501099 true Random L FETYF 2023-12-01 2023-11-24T15:00:58 1267.12 -85 375 63 6797318130 85.47522 58.16330728665678 true Random E UNZLS 2023-12-01 2023-12-04T05:17:58 1949.48 -85 845 42 2373712244 74.551315 79.15491248184088 false Random B QJRKO 2023-11-29 2023-12-04T09:20:58 317.17 -85 873 18 7233488476 33.83051 31.655950581225508 false Random N RJTIB 2023-11-23 2023-12-11T15:07:58 1249.52 -86 398 27 13222936963 20.387327 44.51255195842424 true Random T ZCRFI 2023-12-21 2023-12-23T12:04:58 1801.53 -86 662 53 8875065706 28.64778 30.6775849729486 false Random N YNQAY 2023-12-15 2023-11-24T21:56:58 1108.35 -86 728 18 13390353484 61.060482 87.44751616093882 false Random J BUCVI 2023-12-07 2023-12-14T23:00:58 1611.17 -86 998 74 11080891106 82.568756 32.0122101203062 true Random K VAAMT 2023-12-23 2023-12-01T10:14:58 1708.39 -87 145 64 9022533179 37.80205 63.26081178595084 true Random T PEOPK 2023-12-08 2023-12-07T17:41:58 1167.05 -87 641 64 4786767059 14.765089 70.8793353664754 false Random W SQHGN 2023-12-12 2023-12-24T01:19:58 1316.61 -88 274 41 14108849690 73.74919 42.625751442467404 true Random X BVRFA 2023-12-01 2023-11-25T14:32:58 515.18 -88 728 59 8439434199 30.372904 59.410283344764366 false Random F JODWY 2023-12-04 2023-12-01T07:57:58 1753.88 -88 765 69 9753682777 83.42646 25.99260711248508 true Random M MEJAX 2023-11-25 2023-12-20T09:21:58 1647.22 -89 129 64 6400162051 67.910965 80.48074661432221 true Random Y ZXJWQ 2023-12-16 2023-12-19T10:23:58 1882.65 -89 377 22 14340881803 32.61157 82.5503801214006 false Random K ACYZU 2023-12-01 2023-11-27T02:05:58 672.13 -89 964 41 12706120446 69.484116 32.39048200771184 true Random J IIRNY 2023-12-16 2023-11-29T01:54:58 1298.71 -9 113 7 6162580854 11.346889 46.82839094332704 false Random A SJTAF 2023-12-14 2023-11-23T18:27:58 1610.49 -9 268 59 8149280252 86.66627 70.91298799618343 false Random E PVKYK 2023-12-21 2023-11-25T00:28:58 263.17 -9 900 45 9000000000 50.0 60.5 true Ninth I Iota 2023-10-14 2023-10-14T22:15 901.23 -9 907 24 6113036809 66.06377 50.26485838775805 true Random X XLPOL 2023-11-23 2023-12-02T09:03:58 256.61 -90 391 26 12874761259 21.49042 53.46850617467312 true Random Q QTJPE 2023-12-17 2023-12-03T17:40:58 748.05 -91 389 11 14784237986 11.174142 27.692284427565397 true Random P DYILB 2023-12-14 2023-12-21T11:07:58 1175.73 -91 528 68 14588592231 77.4651 88.92064181463138 false Random U JXZUA 2023-12-16 2023-12-21T02:28:58 1834.07 -92 344 29 5182139341 31.653255 44.26814517218887 true Random F NGHOS 2023-12-06 2023-12-09T21:25:58 1291.06 -93 887 20 13555948969 70.57364 32.621532934876804 false Random D SPMEK 2023-11-26 2023-12-20T18:11:58 258.86 -94 216 49 8773264156 81.617195 43.03983700523827 true Random D VHWYT 2023-12-13 2023-11-30T07:03:58 1178.27 -94 693 60 4818659234 26.04229 83.2975107272106 true Random B ENSQO 2023-12-22 2023-12-12T06:08:58 1283.81 -95 560 62 1389447643 19.202044 85.46518830161321 true Random S LQRRB 2023-12-16 2023-12-12T06:12:58 445.65 -96 595 72 11506136303 21.917727 74.74561804277158 true Random T SPLKA 2023-12-02 2023-11-30T00:39:58 1693.61 -96 637 39 5516035994 55.90832 60.522041012562816 true Random O YPETL 2023-12-02 2023-11-28T02:47:58 1175.16 -97 415 74 10346322649 21.667427 46.58901867647463 false Random R KWFOF 2023-12-21 2023-11-27T12:18:58 1157.72 -97 839 60 14818779777 46.17389 68.98285340004992 false Random W HMFPU 2023-12-01 2023-12-04T08:41:58 1683.48 -98 228 65 4782017237 55.10206 31.414570993700565 true Random P EOIFT 2023-12-07 2023-12-15T08:12:58 137.49 -99 632 39 8911195323 74.581276 78.2764804276292 false Random Q WTQCL 2023-12-02 2023-12-05T09:18:58 200.21 +1 100 5 1000000000 10.5 20.75 true First A Alpha 2023-10-06 2023-10-06T06:30 123.45 +1 578 55 2111222273 56.858597 82.38111658179561 true Random C LYDUG 2023-12-17 2023-12-05T05:04:58 1393.11 +1 979 44 10163954251 28.827957 57.56879940298416 true Random Q DNRGE 2023-12-09 2023-12-10T12:21:58 1581.25 +10 1000 50 10000000000 55.25 65.75 false Tenth J Kappa 2023-10-15 2023-10-15T15:30 1012.34 +10 210 26 8549838179 23.438345 73.36477128189287 true Random N VVXIF 2023-11-24 2023-12-13T10:04:58 226.65 +10 386 51 1214815770 13.959902 36.64197990482059 false Random J ORLGI 2023-12-18 2023-11-27T09:13:58 852.62 +10 966 38 2203748112 45.555325 27.908447208440094 true Random W LFAGO 2023-12-14 2023-11-26T12:00:58 1898.68 +100 281 26 3174393241 51.05278 52.09566669589555 false Random F SLDWB 2023-12-14 2023-12-11T23:03:58 798.30 +100 289 71 4919981667 66.56684 69.73132704711037 true Random V QOLAP 2023-12-17 2023-12-23T01:38:58 217.05 +11 1100 55 11000000000 60.5 70.0 true Eleventh K Lambda 2023-10-16 2023-10-15T17:45 1123.45 +11 426 67 8473986652 17.942455 71.80682514420877 true Random X FXDUV 2023-12-04 2023-12-21T23:51:58 129.81 +11 441 19 7370044350 74.261696 62.013817404758086 true Random D UYKZA 2023-12-23 2023-12-15T03:49:58 1805.14 +11 487 27 14556302216 85.33334 62.596750833474495 true Random E QMHJD 2023-12-23 2023-12-24T00:30:58 1491.22 +11 770 17 7962512669 12.508753 83.33847413902296 true Random P LHJRA 2023-12-06 2023-12-04T07:48:58 970.51 +12 1200 60 12000000000 65.75 75.25 false Twelfth L Mu 2023-10-17 2023-10-16T18:15 1234.56 +12 751 8 12205294947 23.468674 64.35048302450815 true Random K FCSBV 2023-12-03 2023-12-16T17:10:58 325.26 +12 782 48 5080583047 75.55138 49.6324463213595 true Random N WYJDW 2023-12-16 2023-12-17T18:58:58 944.42 +12 987 73 1432735571 40.308147 43.5019559828596 true Random S MZUNG 2023-12-07 2023-12-03T05:42:58 215.12 +13 1300 65 13000000000 70.0 80.5 true Thirteenth M Nu 2023-10-18 2023-10-17T19:30 1345.67 +13 335 39 13869202091 30.426075 39.02304533093442 true Random L AULCC 2023-12-08 2023-12-12T16:26:58 387.97 +13 402 30 10851194313 74.82481 74.90108005771035 false Random F GEMMK 2023-11-27 2023-12-21T07:03:58 1643.55 +13 503 34 6763884255 23.660393 63.9797872103468 true Random S POEBK 2023-12-22 2023-12-23T15:16:58 486.62 +13 696 74 3370487489 84.544014 88.69976219408227 true Random H RTFJI 2023-11-23 2023-11-24T23:32:58 1761.50 +13 745 48 13047949175 51.168613 85.21972389262197 true Random A AYBWQ 2023-12-22 2023-12-22T08:25:58 1192.48 +13 859 65 7433576046 56.136265 34.87823331022725 false Random L CRFUF 2023-12-23 2023-12-12T07:05:58 1037.15 +14 1400 70 14000000000 75.25 85.75 false Fourteenth N Xi 2023-10-19 2023-10-18T20:45 1456.78 +14 195 17 2370700139 16.777058 64.81793301410002 false Random P IIGRE 2023-12-12 2023-12-14T14:40:58 1678.44 +14 966 65 7828602539 62.430664 68.85873133439297 true Random I VVOQH 2023-12-01 2023-12-05T16:54:58 1300.43 +14 968 16 11314514196 62.509666 33.1841427251225 false Random T WDEVJ 2023-11-24 2023-12-06T09:54:58 431.61 +15 1500 75 15000000000 80.5 90.0 true Fifteenth O Omicron 2023-10-20 2023-10-19T21:15 1567.89 +15 703 67 4284267079 85.38059 91.41088583496226 true Random T PHZRC 2023-12-04 2023-12-08T07:54:58 185.19 +16 135 22 7901304568 43.944805 85.16901944253635 true Random K NUQEP 2023-11-29 2023-11-25T15:42:58 1440.74 +16 615 20 12294128025 77.37379 20.42772029677839 true Random U JHPOB 2023-11-30 2023-12-16T06:29:58 1105.33 +17 289 49 13560709243 39.952793 38.245306832599425 true Random Q QEYVY 2023-12-19 2023-12-06T16:35:58 500.19 +17 499 46 11230409207 51.632103 28.811164197154774 false Random V BVLUH 2023-12-13 2023-12-23T09:59:58 1387.62 +17 646 62 11234805830 76.40492 67.46425239009778 true Random N REHZC 2023-12-09 2023-11-27T18:06:58 365.15 +17 698 55 1807368797 20.17171 43.84496606184709 true Random P SHSJV 2023-12-01 2023-11-25T03:56:58 810.95 +17 794 14 8377523030 28.07663 52.3837762020057 false Random E WPMIN 2023-12-03 2023-11-25T20:59:58 239.42 +17 913 32 4647929554 78.91502 70.54487265463735 true Random S WFPNS 2023-11-27 2023-11-25T19:29:58 321.45 +18 690 17 1399456103 63.261967 42.964715823771236 true Random R BWSRS 2023-12-13 2023-12-23T00:33:58 1840.02 +18 835 17 14265814864 18.923101 80.53531451138412 true Random V PIKUZ 2023-12-20 2023-12-20T23:39:58 1167.09 +19 917 66 2340946367 89.035675 22.649362455875274 false Random D HWHMU 2023-11-30 2023-12-09T18:36:58 1960.07 +19 993 13 7039833438 79.769066 69.79049291517285 true Random X OFSUV 2023-12-11 2023-12-07T17:46:58 1958.95 +2 200 10 2000000000 15.75 25.5 false Second B Beta 2023-10-07 2023-10-07T07:45 234.56 +2 850 75 7075823565 83.65178 62.56093886118189 false Random F RFHAG 2023-11-24 2023-12-02T17:06:58 495.12 +2 921 62 8557914543 78.52379 58.6849882881372 false Random D KBXXS 2023-12-07 2023-12-02T14:24:58 1782.88 +2 925 46 6013180177 41.107002 34.86561026061906 true Random L XLLXY 2023-12-06 2023-12-09T06:04:58 1246.26 +20 248 64 7704906572 35.089928 76.69128821479936 true Random T KQOMS 2023-11-30 2023-12-10T17:35:58 1799.26 +22 200 41 12163439252 64.621254 81.68574929661384 true Random U KGVNU 2023-12-20 2023-11-30T06:56:58 1915.47 +22 235 19 6963606423 65.68033 54.1995295752517 true Random E ENVRH 2023-12-22 2023-11-29T06:42:58 864.89 +23 192 8 5102667616 54.111057 40.85713971600841 false Random J EBXEB 2023-12-13 2023-12-10T03:32:58 1824.12 +27 452 74 4240215371 50.569168 75.68204627611644 true Random G AZOWU 2023-12-01 2023-11-25T22:24:58 201.31 +27 866 24 5531365994 72.77447 86.96690821165853 false Random S TZPFJ 2023-11-28 2023-12-13T07:31:58 1274.75 +28 655 21 14580233860 12.503378 48.60220286874443 false Random P DUBQQ 2023-12-12 2023-12-03T12:11:58 922.42 +29 157 34 2302882987 51.924015 20.311140937696468 true Random R MBOXJ 2023-12-02 2023-12-03T06:12:58 1620.80 +29 910 52 5544039917 22.179396 46.32732226806482 true Random C TIZAG 2023-11-28 2023-12-14T08:08:58 900.96 +29 923 57 1591814253 68.57371 33.342802789892986 true Random Q ZONGC 2023-12-20 2023-12-13T01:11:58 1465.38 +3 259 74 7422478791 22.291426 75.38227773520089 true Random S VWAXJ 2023-12-01 2023-12-05T13:23:58 1970.57 +3 300 15 3000000000 20.25 30.0 true Third C Gamma 2023-10-08 2023-10-08T08:15 345.67 +3 422 25 5996825874 89.173584 62.758513798505824 false Random Z CDYAO 2023-12-14 2023-12-08T01:27:58 567.23 +3 668 60 1942550969 83.43451 87.15906153619602 true Random F QYSRS 2023-12-22 2023-12-10T14:17:58 320.22 +30 292 71 10308444223 63.039078 76.40649540444898 false Random G DRLHY 2023-12-19 2023-12-14T07:32:58 1165.14 +30 572 6 3022031043 57.813908 72.29244668177799 true Random X EHJDN 2023-12-11 2023-12-11T18:44:58 910.38 +30 830 65 12624057029 38.791172 59.72899174862661 false Random A LFPWP 2023-12-03 2023-12-16T16:10:58 1760.62 +31 395 22 6141426904 88.37914 52.0655270963123 false Random J DRPJV 2023-12-07 2023-11-28T19:15:58 1076.41 +31 990 5 13678786851 15.762894 85.24173385692956 false Random H THGIM 2023-12-14 2023-12-08T17:24:58 1834.37 +33 198 20 13225406950 67.7327 58.63863378877107 true Random I ZKXRA 2023-12-07 2023-11-28T18:33:58 566.76 +33 321 39 12537851805 38.26871 32.6626492245712 true Random S OICCE 2023-12-02 2023-12-19T08:41:58 306.92 +33 916 53 5666674210 57.998173 61.774881852563475 true Random J WJAXA 2023-11-27 2023-12-05T11:58:58 976.13 +34 145 44 14060350663 73.02436 68.40544929600975 true Random S UUJFP 2023-11-23 2023-12-11T22:08:58 739.45 +34 585 43 1429300527 61.706585 80.88100239373303 false Random O JKJOH 2023-12-17 2023-12-07T03:00:58 468.11 +35 297 75 2468378214 51.353462 34.18114780065386 false Random C HBYZO 2023-12-05 2023-12-09T13:42:58 534.70 +37 438 39 6809169396 83.56728 40.90894521029911 true Random W GXPAY 2023-12-07 2023-12-17T22:35:58 383.18 +38 606 57 14585148556 82.67463 79.18300302689997 false Random E RSFUZ 2023-12-16 2023-11-27T10:55:58 970.25 +39 726 50 3865644066 26.225628 28.534393094364418 false Random F NIUCS 2023-12-05 2023-12-04T11:31:58 1953.82 +4 122 24 10738473173 81.15482 60.21481394154484 false Random Y PQJRK 2023-12-20 2023-12-08T18:38:58 1467.35 +4 400 20 4000000000 25.5 35.25 false Fourth D Delta 2023-10-09 2023-10-09T09:30 456.78 +4 569 72 10560903405 50.255936 47.535145739285184 false Random O NRIRC 2023-12-05 2023-12-01T01:10:58 1986.99 +4 682 22 2040832636 60.33469 67.33499498711046 true Random W QUICJ 2023-11-24 2023-12-14T02:17:58 579.56 +40 230 34 10824964541 16.929768 53.812277279703366 false Random F YDQHF 2023-12-14 2023-12-03T09:42:58 1623.79 +40 693 69 13276482882 44.35974 82.57845708670757 true Random B RCCSU 2023-11-29 2023-12-01T12:11:58 183.64 +40 914 7 4902128502 19.442041 33.099787387344406 true Random Q KOCWA 2023-11-28 2023-12-21T01:20:58 1824.80 +41 344 34 14536795918 56.660946 84.15108995619764 false Random Q KYLCH 2023-12-10 2023-12-04T00:25:58 1902.09 +41 599 54 8095449906 22.58196 37.99742597458578 false Random T GTQXP 2023-12-12 2023-12-22T11:08:58 743.46 +41 697 21 1200243566 12.466168 68.57243624557165 true Random U JZGEG 2023-12-03 2023-12-09T20:51:58 1323.88 +41 708 64 11745827370 72.84812 35.31028363777645 true Random O WGSQC 2023-12-02 2023-11-25T09:07:58 1666.71 +41 840 65 8988241658 37.428593 42.25992474748068 false Random E HURYX 2023-12-22 2023-12-18T17:55:58 141.89 +42 143 42 3421815721 65.27691 87.91368867538209 true Random S AXGVL 2023-12-06 2023-11-28T23:36:58 575.01 +42 178 38 7559404453 69.69449 64.37154501388798 true Random G QUMUN 2023-12-14 2023-12-16T17:37:58 1190.44 +42 192 28 14454791024 35.465202 46.34876515635648 false Random W NQFGR 2023-12-04 2023-11-23T21:02:58 1428.02 +42 355 72 11536856285 74.42886 53.49032479461299 false Random I IQZEI 2023-12-10 2023-12-05T23:17:58 1098.14 +43 178 64 6969956763 40.980415 52.998828731408516 true Random C XQHYB 2023-12-11 2023-12-07T15:00:58 257.08 +43 828 24 12011396947 45.07647 54.2136449479346 true Random E HIDUO 2023-12-02 2023-12-18T17:14:58 233.10 +44 219 38 8596488294 73.52956 94.10797854680568 true Random E HMWBI 2023-12-15 2023-12-05T16:51:58 1907.47 +44 694 55 3626514138 62.504086 72.89799265418553 true Random Z JTDVF 2023-12-01 2023-11-29T04:08:58 1769.92 +44 912 63 8534761366 55.993538 50.235171557550416 false Random N OVQRQ 2023-12-08 2023-11-23T19:39:58 264.92 +44 928 7 1939079012 14.426672 68.86451571230457 false Random I EKVWY 2023-12-15 2023-12-09T02:43:58 846.74 +45 455 25 12639246000 47.011307 26.310712594958694 false Random Z GGEUA 2023-11-27 2023-12-01T12:41:58 1698.21 +45 492 43 3870916386 51.069588 42.652270406300794 true Random H JVZTB 2023-12-04 2023-12-09T13:06:58 1517.83 +47 508 48 1456473942 48.488297 20.377955902326608 false Random B CAOEY 2023-11-29 2023-12-10T06:49:58 1865.52 +47 566 50 1426586688 51.278687 40.47151456873397 true Random F YBOSH 2023-11-26 2023-12-14T19:44:58 1806.35 +47 838 73 14910230294 83.69784 82.28901816600579 true Random L SHXYL 2023-11-24 2023-12-05T14:19:58 1062.15 +48 898 59 12871187130 10.13838 70.19705104611333 true Random J WFXNN 2023-12-23 2023-12-16T18:53:58 1050.21 +49 165 38 4482178563 34.706547 69.17129468406594 false Random W CPZNY 2023-12-15 2023-11-23T11:56:58 512.60 +49 412 16 8300982793 56.263252 66.07893608061771 false Random K DWWJI 2023-12-08 2023-12-17T03:32:58 1718.54 +49 511 51 8602055259 88.1686 88.98712207285577 false Random M ZDKEY 2023-12-10 2023-11-24T18:44:58 241.08 +49 568 70 2916596630 79.16303 56.114316916863025 false Random T ILLIU 2023-11-23 2023-12-07T03:05:58 1039.03 +5 500 25 5000000000 30.75 40.5 true Fifth E Epsilon 2023-10-10 2023-10-10T10:45 567.89 +5 768 5 4152322228 41.128906 78.60686390712706 false Random J LXKRA 2023-12-05 2023-11-24T10:13:58 1941.98 +5 823 63 13328808917 77.768196 22.87975226738422 false Random F OIYPV 2023-12-11 2023-12-13T22:43:58 1144.38 +5 887 74 4082758600 22.797577 93.28246034891224 false Random V MPPGX 2023-12-01 2023-11-28T17:53:58 510.50 +50 126 58 4433111715 75.31828 43.28056186824247 false Random H UTDJF 2023-12-19 2023-12-10T00:24:58 368.42 +51 778 59 13914307584 27.48499 91.47665081887983 true Random X FGFHK 2023-12-01 2023-12-09T19:24:58 402.63 +51 898 32 13510411411 18.679659 21.406761033351007 false Random L FECUW 2023-12-10 2023-12-13T18:00:58 700.43 +52 811 31 14085958816 51.067017 65.01991893789116 true Random A CODYQ 2023-12-03 2023-12-07T15:25:58 1797.21 +53 505 52 9862728376 58.40501 57.60544454281924 false Random V WYCTZ 2023-11-24 2023-12-19T21:13:58 210.43 +53 667 49 10531976747 50.22229 49.64660893042742 false Random K WNRJE 2023-12-04 2023-12-19T06:57:58 680.97 +53 713 14 1464447148 23.474258 45.35056918414047 false Random Q UHMLT 2023-12-10 2023-11-29T18:07:58 286.70 +53 715 29 10917905565 41.83069 93.50885201221966 true Random U TRLSY 2023-12-03 2023-11-26T07:13:58 369.72 +54 467 42 13684826428 38.491455 90.10566649802195 true Random M ERFBG 2023-11-24 2023-12-02T08:23:58 211.00 +54 827 55 7054839267 58.555687 25.891004802115663 false Random O ASMLW 2023-12-13 2023-12-20T08:41:58 1369.32 +54 843 34 9547939940 38.66475 36.370944299232434 true Random P NTVIR 2023-12-12 2023-12-01T22:45:58 1628.37 +55 908 24 13623721787 40.06427 90.85281792731746 false Random B KFZGI 2023-11-27 2023-12-23T10:06:58 1124.95 +55 964 8 14038541765 70.24135 20.034551391620194 false Random J AYXIT 2023-12-13 2023-12-16T11:38:58 1476.73 +57 936 26 12164628867 56.541275 56.276679149397076 true Random O IPHPZ 2023-12-13 2023-11-30T14:36:58 603.68 +59 144 31 6208909394 67.417076 40.59765633709834 true Random D FLWNA 2023-12-12 2023-12-18T22:17:58 1870.24 +59 509 50 5501336408 39.94401 73.35770882761237 true Random I PVZNO 2023-12-04 2023-11-26T20:40:58 1177.33 +6 600 30 6000000000 35.25 45.75 false Sixth F Zeta 2023-10-11 2023-10-11T11:15 678.90 +60 711 69 1493870104 22.574188 61.30347648465907 false Random E FHKVR 2023-11-27 2023-12-05T03:26:58 1981.61 +60 875 42 14283877167 48.811504 67.0706975606688 true Random P VJOZH 2023-12-06 2023-12-14T21:20:58 781.71 +61 267 61 11407448558 12.877184 42.144845857251944 true Random B NRWNW 2023-11-30 2023-11-25T01:34:58 859.85 +61 414 63 14506877706 12.540966 58.04557426323987 false Random H NUOAD 2023-12-10 2023-12-06T14:52:58 780.50 +62 451 50 12304139502 51.151623 22.46754141558852 false Random C SRRSV 2023-12-08 2023-12-19T18:48:58 1352.65 +62 793 46 7308804595 39.766644 48.88672198076526 true Random V TPENZ 2023-11-26 2023-12-23T09:51:58 388.46 +63 112 75 12197306353 85.90137 43.48931389222043 false Random C KKAIT 2023-11-27 2023-12-22T20:23:58 1954.90 +63 383 35 5161212745 39.455276 52.33267523851794 false Random X TMYMC 2023-11-29 2023-12-10T01:09:58 1442.54 +63 410 33 1767102777 72.260124 56.971483381024896 false Random B QXNSM 2023-12-12 2023-12-19T14:57:58 1660.73 +64 479 20 1710421528 53.324104 33.55443503561635 false Random Q ONZRK 2023-12-09 2023-12-01T14:29:58 252.13 +64 678 14 13681447851 74.83621 36.94143092647816 true Random J KELFB 2023-12-01 2023-12-07T10:14:58 308.26 +64 719 36 1224510454 64.237434 86.05689694804887 true Random E ZVQPU 2023-11-30 2023-12-02T20:56:58 1879.25 +64 822 26 1154241961 52.165447 26.779469377773403 true Random E YWNAD 2023-12-08 2023-12-19T11:08:58 731.15 +65 571 24 10523050555 45.865078 70.80680527390149 true Random Y DILBW 2023-12-17 2023-11-25T14:41:58 859.30 +66 306 5 14448160602 44.642223 50.24249889525751 false Random X OASEB 2023-12-11 2023-11-26T16:16:58 1345.69 +66 521 30 7757576974 69.440155 92.3562810104632 false Random H SSOCR 2023-12-19 2023-11-29T22:51:58 913.34 +67 484 65 10817432713 62.168163 77.02869166077757 true Random K SAJMG 2023-12-19 2023-12-14T11:47:58 488.01 +68 266 31 8183454755 69.19586 23.139304803938643 false Random S STCBM 2023-11-26 2023-12-22T05:42:58 1722.37 +68 554 33 3525526216 29.078024 29.6567390059356 false Random Y EUGOF 2023-11-23 2023-12-15T02:33:58 395.41 +68 591 60 4813122821 33.210274 54.464145718507616 false Random X EXROI 2023-12-07 2023-12-06T16:39:58 290.11 +68 756 63 5416393421 66.41538 76.32820339134415 false Random Y CUNAL 2023-12-23 2023-12-14T14:49:58 1109.25 +68 922 13 11664232196 72.683266 37.9910331525765 false Random W PPWBB 2023-11-26 2023-12-10T14:54:58 1968.89 +68 947 60 7257499958 45.661217 77.42577781358565 false Random F ENQGA 2023-11-24 2023-11-28T23:33:58 319.99 +69 416 14 7702410607 31.638903 89.5793904314531 true Random C URQMU 2023-11-25 2023-11-30T07:17:58 1379.22 +7 340 50 8934567449 83.79683 35.39446967734915 false Random L CWYFN 2023-12-05 2023-12-22T18:26:58 806.15 +7 700 35 7000000000 40.5 50.0 true Seventh G Eta 2023-10-12 2023-10-12T12:30 789.01 +7 969 62 3451343234 57.17074 56.74513811095188 false Random G OWDSC 2023-12-19 2023-12-11T09:17:58 1874.22 +70 231 67 4547989149 35.103123 51.93622592177748 true Random V ZBCVY 2023-11-29 2023-12-22T03:41:58 1749.60 +70 421 23 3153379289 27.412096 79.32006404438445 false Random L VLJWK 2023-12-04 2023-12-11T21:31:58 1163.35 +70 751 56 7828222634 52.8313 55.7263634552559 true Random B TFHMH 2023-11-30 2023-12-24T04:22:58 1166.13 +71 452 25 4464808420 18.155642 61.988641984596185 false Random K YXFVY 2023-12-15 2023-12-07T20:58:58 514.74 +71 594 26 1024634104 62.92234 37.216752731371386 true Random J SPUWU 2023-12-04 2023-12-23T00:50:58 779.97 +72 377 11 3042707243 55.289066 53.72552524152444 true Random Q BAPHV 2023-12-06 2023-11-29T23:14:58 119.39 +73 866 49 4618070115 46.803646 91.41305051885227 true Random H ROYYF 2023-12-07 2023-12-01T02:28:58 1817.67 +74 670 60 4783926122 23.513939 91.24357097091087 true Random Y YFPMC 2023-12-23 2023-12-22T14:29:58 943.62 +75 368 73 6944888766 31.500992 56.88267149430107 false Random H LEXKZ 2023-12-21 2023-12-13T17:12:58 443.91 +76 410 20 10425110604 66.26356 92.68329033006493 false Random L JHFYD 2023-11-23 2023-11-29T02:34:58 867.56 +76 504 70 14161652666 58.071503 67.99111956708262 true Random Y HAVCK 2023-11-27 2023-12-14T08:08:58 1864.98 +77 131 19 2964167114 33.23181 53.35246738882714 false Random G AHGFO 2023-12-19 2023-12-01T02:11:58 1837.90 +77 165 36 12887722637 19.729382 45.61157603163882 true Random S OZOLB 2023-12-02 2023-12-02T21:07:58 1576.79 +79 314 17 6823498005 22.562634 72.70049796639023 true Random K FPSNZ 2023-12-07 2023-12-15T03:52:58 211.50 +8 550 48 13655992126 52.90345 51.35114230137935 false Random X JTVSE 2023-12-13 2023-12-14T19:49:58 361.55 +8 800 40 8000000000 45.75 55.25 false Eighth H Theta 2023-10-13 2023-10-13T13:45 890.12 +8 866 37 13672147880 81.28999 67.66548594336737 false Random H QDJIM 2023-12-14 2023-12-17T10:44:58 1112.05 +80 267 57 8797946135 35.604717 80.51381110359165 false Random K KQTEX 2023-12-09 2023-12-12T22:19:58 1769.15 +80 815 19 14529289205 19.769405 37.37008094684765 true Random Z WLALH 2023-12-11 2023-12-13T19:24:58 479.38 +81 726 66 9327218218 81.50363 39.9702863173827 true Random X WODRP 2023-11-28 2023-12-23T05:25:58 561.98 +82 107 51 1358006007 78.36581 46.09413324325159 true Random C IPNQU 2023-12-01 2023-12-13T21:41:58 417.17 +82 133 60 4616538638 88.8813 30.82745983013354 true Random W KPIJE 2023-12-20 2023-11-30T23:57:58 583.41 +82 531 44 10642962933 26.818586 23.851865471979615 false Random F NMQOD 2023-12-13 2023-12-18T11:34:58 861.78 +82 603 60 9083469993 81.24088 44.46228092092543 true Random Y WTQGU 2023-11-30 2023-11-28T05:18:58 1448.45 +82 982 62 8955063933 81.2855 78.30439669511465 true Random J SOCOT 2023-12-02 2023-12-02T13:17:58 814.60 +83 700 46 4569093424 50.063602 47.75811273142146 false Random R TEGAY 2023-12-19 2023-12-06T22:46:58 760.22 +84 427 60 9035762847 81.971306 28.37315065501099 true Random L FETYF 2023-12-01 2023-11-24T07:00:58 1267.12 +85 375 63 6797318130 85.47522 58.16330728665678 true Random E UNZLS 2023-12-01 2023-12-03T21:17:58 1949.48 +85 845 42 2373712244 74.551315 79.15491248184088 false Random B QJRKO 2023-11-29 2023-12-04T01:20:58 317.17 +85 873 18 7233488476 33.83051 31.655950581225508 false Random N RJTIB 2023-11-23 2023-12-11T07:07:58 1249.52 +86 398 27 13222936963 20.387327 44.51255195842424 true Random T ZCRFI 2023-12-21 2023-12-23T04:04:58 1801.53 +86 662 53 8875065706 28.64778 30.6775849729486 false Random N YNQAY 2023-12-15 2023-11-24T13:56:58 1108.35 +86 728 18 13390353484 61.060482 87.44751616093882 false Random J BUCVI 2023-12-07 2023-12-14T15:00:58 1611.17 +86 998 74 11080891106 82.568756 32.0122101203062 true Random K VAAMT 2023-12-23 2023-12-01T02:14:58 1708.39 +87 145 64 9022533179 37.80205 63.26081178595084 true Random T PEOPK 2023-12-08 2023-12-07T09:41:58 1167.05 +87 641 64 4786767059 14.765089 70.8793353664754 false Random W SQHGN 2023-12-12 2023-12-23T17:19:58 1316.61 +88 274 41 14108849690 73.74919 42.625751442467404 true Random X BVRFA 2023-12-01 2023-11-25T06:32:58 515.18 +88 728 59 8439434199 30.372904 59.410283344764366 false Random F JODWY 2023-12-04 2023-11-30T23:57:58 1753.88 +88 765 69 9753682777 83.42646 25.99260711248508 true Random M MEJAX 2023-11-25 2023-12-20T01:21:58 1647.22 +89 129 64 6400162051 67.910965 80.48074661432221 true Random Y ZXJWQ 2023-12-16 2023-12-19T02:23:58 1882.65 +89 377 22 14340881803 32.61157 82.5503801214006 false Random K ACYZU 2023-12-01 2023-11-26T18:05:58 672.13 +89 964 41 12706120446 69.484116 32.39048200771184 true Random J IIRNY 2023-12-16 2023-11-28T17:54:58 1298.71 +9 113 7 6162580854 11.346889 46.82839094332704 false Random A SJTAF 2023-12-14 2023-11-23T10:27:58 1610.49 +9 268 59 8149280252 86.66627 70.91298799618343 false Random E PVKYK 2023-12-21 2023-11-24T16:28:58 263.17 +9 900 45 9000000000 50.0 60.5 true Ninth I Iota 2023-10-14 2023-10-14T14:15 901.23 +9 907 24 6113036809 66.06377 50.26485838775805 true Random X XLPOL 2023-11-23 2023-12-02T01:03:58 256.61 +90 391 26 12874761259 21.49042 53.46850617467312 true Random Q QTJPE 2023-12-17 2023-12-03T09:40:58 748.05 +91 389 11 14784237986 11.174142 27.692284427565397 true Random P DYILB 2023-12-14 2023-12-21T03:07:58 1175.73 +91 528 68 14588592231 77.4651 88.92064181463138 false Random U JXZUA 2023-12-16 2023-12-20T18:28:58 1834.07 +92 344 29 5182139341 31.653255 44.26814517218887 true Random F NGHOS 2023-12-06 2023-12-09T13:25:58 1291.06 +93 887 20 13555948969 70.57364 32.621532934876804 false Random D SPMEK 2023-11-26 2023-12-20T10:11:58 258.86 +94 216 49 8773264156 81.617195 43.03983700523827 true Random D VHWYT 2023-12-13 2023-11-29T23:03:58 1178.27 +94 693 60 4818659234 26.04229 83.2975107272106 true Random B ENSQO 2023-12-22 2023-12-11T22:08:58 1283.81 +95 560 62 1389447643 19.202044 85.46518830161321 true Random S LQRRB 2023-12-16 2023-12-11T22:12:58 445.65 +96 595 72 11506136303 21.917727 74.74561804277158 true Random T SPLKA 2023-12-02 2023-11-29T16:39:58 1693.61 +96 637 39 5516035994 55.90832 60.522041012562816 true Random O YPETL 2023-12-02 2023-11-27T18:47:58 1175.16 +97 415 74 10346322649 21.667427 46.58901867647463 false Random R KWFOF 2023-12-21 2023-11-27T04:18:58 1157.72 +97 839 60 14818779777 46.17389 68.98285340004992 false Random W HMFPU 2023-12-01 2023-12-04T00:41:58 1683.48 +98 228 65 4782017237 55.10206 31.414570993700565 true Random P EOIFT 2023-12-07 2023-12-15T00:12:58 137.49 +99 632 39 8911195323 74.581276 78.2764804276292 false Random Q WTQCL 2023-12-02 2023-12-05T01:18:58 200.21 -- !q44 -- 17 289 49 13560709243 39.952793 38.245306832599425 true Random Q QEYVY 2023-12-19 2023-12-07T00:35:58 500.19 diff --git a/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out b/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out index fe8243f91e0a95..d7d0964996e35a 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out +++ b/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out @@ -240,16 +240,16 @@ false 9 9 9 90 9.9 90.89999999999999 7219 12/23/10 9 2010-12-23T10:49:10.260 201 false 9 9 9 90 9.9 90.89999999999999 7209 12/22/10 9 2010-12-22T10:39:09.810 2010 12 -- !all_types_timestamp_col_topn_asc -- -true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 -false 1 1 1 10 1.1 10.1 1 01/01/09 1 2009-01-01T07:01 2009 1 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -false 3 3 3 30 3.3 30.3 3 01/01/09 3 2009-01-01T07:03:00.300 2009 1 -true 4 4 4 40 4.4 40.4 4 01/01/09 4 2009-01-01T07:04:00.600 2009 1 -false 5 5 5 50 5.5 50.5 5 01/01/09 5 2009-01-01T07:05:00.100 2009 1 -true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2009-01-01T07:06:00.150 2009 1 -false 7 7 7 70 7.7 70.7 7 01/01/09 7 2009-01-01T07:07:00.210 2009 1 -true 8 8 8 80 8.8 80.8 8 01/01/09 8 2009-01-01T07:08:00.280 2009 1 -false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2009-01-01T07:09:00.360 2009 1 +true 0 0 0 0 0.0 0 0 01/01/09 0 2008-12-31T23:00 2009 1 +false 1 1 1 10 1.1 10.1 1 01/01/09 1 2008-12-31T23:01 2009 1 +true 2 2 2 20 2.2 20.2 2 01/01/09 2 2008-12-31T23:02:00.100 2009 1 +false 3 3 3 30 3.3 30.3 3 01/01/09 3 2008-12-31T23:03:00.300 2009 1 +true 4 4 4 40 4.4 40.4 4 01/01/09 4 2008-12-31T23:04:00.600 2009 1 +false 5 5 5 50 5.5 50.5 5 01/01/09 5 2008-12-31T23:05:00.100 2009 1 +true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2008-12-31T23:06:00.150 2009 1 +false 7 7 7 70 7.7 70.7 7 01/01/09 7 2008-12-31T23:07:00.210 2009 1 +true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 +false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2008-12-31T23:09:00.360 2009 1 -- !all_types_timestamp_col_topn_desc -- false 9 9 9 90 9.9 90.89999999999999 7299 12/31/10 9 2010-12-31T12:09:13.860 2010 12 @@ -891,16 +891,16 @@ false 9 9 9 90 9.9 90.89999999999999 7219 12/23/10 9 2010-12-23T10:49:10.260 201 false 9 9 9 90 9.9 90.89999999999999 7209 12/22/10 9 2010-12-22T10:39:09.810 2010 12 -- !all_types_timestamp_col_topn_asc -- -true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 -false 1 1 1 10 1.1 10.1 1 01/01/09 1 2009-01-01T07:01 2009 1 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -false 3 3 3 30 3.3 30.3 3 01/01/09 3 2009-01-01T07:03:00.300 2009 1 -true 4 4 4 40 4.4 40.4 4 01/01/09 4 2009-01-01T07:04:00.600 2009 1 -false 5 5 5 50 5.5 50.5 5 01/01/09 5 2009-01-01T07:05:00.100 2009 1 -true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2009-01-01T07:06:00.150 2009 1 -false 7 7 7 70 7.7 70.7 7 01/01/09 7 2009-01-01T07:07:00.210 2009 1 -true 8 8 8 80 8.8 80.8 8 01/01/09 8 2009-01-01T07:08:00.280 2009 1 -false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2009-01-01T07:09:00.360 2009 1 +true 0 0 0 0 0.0 0 0 01/01/09 0 2008-12-31T23:00 2009 1 +false 1 1 1 10 1.1 10.1 1 01/01/09 1 2008-12-31T23:01 2009 1 +true 2 2 2 20 2.2 20.2 2 01/01/09 2 2008-12-31T23:02:00.100 2009 1 +false 3 3 3 30 3.3 30.3 3 01/01/09 3 2008-12-31T23:03:00.300 2009 1 +true 4 4 4 40 4.4 40.4 4 01/01/09 4 2008-12-31T23:04:00.600 2009 1 +false 5 5 5 50 5.5 50.5 5 01/01/09 5 2008-12-31T23:05:00.100 2009 1 +true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2008-12-31T23:06:00.150 2009 1 +false 7 7 7 70 7.7 70.7 7 01/01/09 7 2008-12-31T23:07:00.210 2009 1 +true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 +false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2008-12-31T23:09:00.360 2009 1 -- !all_types_timestamp_col_topn_desc -- false 9 9 9 90 9.9 90.89999999999999 7299 12/31/10 9 2010-12-31T12:09:13.860 2010 12 diff --git a/regression-test/data/external_table_p0/hive/test_hive_page_index.out b/regression-test/data/external_table_p0/hive/test_hive_page_index.out index 1a12b6375f4770..49b31bca6a0eda 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_page_index.out +++ b/regression-test/data/external_table_p0/hive/test_hive_page_index.out @@ -1,38 +1,38 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q1 -- -false 1 1 1 10 1.1 10.1 1 01/01/09 1 2009-01-01T07:01 2009 1 -false 1 1 1 10 1.1 10.1 11 01/02/09 1 2009-01-02T07:11:00.450 2009 1 -false 1 1 1 10 1.1 10.1 21 01/03/09 1 2009-01-03T07:21:00.900 2009 1 -false 3 3 3 30 3.3 30.3 13 01/02/09 3 2009-01-02T07:13:00.480 2009 1 -false 3 3 3 30 3.3 30.3 23 01/03/09 3 2009-01-03T07:23:00.930 2009 1 -false 3 3 3 30 3.3 30.3 3 01/01/09 3 2009-01-01T07:03:00.300 2009 1 -false 5 5 5 50 5.5 50.5 15 01/02/09 5 2009-01-02T07:15:00.550 2009 1 -false 5 5 5 50 5.5 50.5 25 01/03/09 5 2009-01-03T07:25:01 2009 1 -false 5 5 5 50 5.5 50.5 5 01/01/09 5 2009-01-01T07:05:00.100 2009 1 -false 7 7 7 70 7.7 70.7 17 01/02/09 7 2009-01-02T07:17:00.660 2009 1 -false 7 7 7 70 7.7 70.7 27 01/03/09 7 2009-01-03T07:27:01.110 2009 1 -false 7 7 7 70 7.7 70.7 7 01/01/09 7 2009-01-01T07:07:00.210 2009 1 -false 9 9 9 90 9.9 90.89999999999999 19 01/02/09 9 2009-01-02T07:19:00.810 2009 1 -false 9 9 9 90 9.9 90.89999999999999 29 01/03/09 9 2009-01-03T07:29:01.260 2009 1 -false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2009-01-01T07:09:00.360 2009 1 -true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 -true 0 0 0 0 0.0 0 10 01/02/09 0 2009-01-02T07:10:00.450 2009 1 -true 0 0 0 0 0.0 0 20 01/03/09 0 2009-01-03T07:20:00.900 2009 1 -true 2 2 2 20 2.2 20.2 12 01/02/09 2 2009-01-02T07:12:00.460 2009 1 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -true 2 2 2 20 2.2 20.2 22 01/03/09 2 2009-01-03T07:22:00.910 2009 1 -true 2 2 2 20 2.2 20.2 7272 12/29/10 2 2010-12-29T11:42:12.610 2010 12 -true 2 2 2 20 2.2 20.2 7282 12/30/10 2 2010-12-30T11:52:13.600 2010 12 -true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T12:02:13.510 2010 12 -true 4 4 4 40 4.4 40.4 14 01/02/09 4 2009-01-02T07:14:00.510 2009 1 -true 4 4 4 40 4.4 40.4 24 01/03/09 4 2009-01-03T07:24:00.960 2009 1 -true 4 4 4 40 4.4 40.4 4 01/01/09 4 2009-01-01T07:04:00.600 2009 1 -true 6 6 6 60 6.6 60.59999999999999 16 01/02/09 6 2009-01-02T07:16:00.600 2009 1 -true 6 6 6 60 6.6 60.59999999999999 26 01/03/09 6 2009-01-03T07:26:01.500 2009 1 -true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2009-01-01T07:06:00.150 2009 1 -true 8 8 8 80 8.8 80.8 18 01/02/09 8 2009-01-02T07:18:00.730 2009 1 -true 8 8 8 80 8.8 80.8 28 01/03/09 8 2009-01-03T07:28:01.180 2009 1 -true 8 8 8 80 8.8 80.8 8 01/01/09 8 2009-01-01T07:08:00.280 2009 1 +false 1 1 1 10 1.1 10.1 1 01/01/09 1 2008-12-31T23:01 2009 1 +false 1 1 1 10 1.1 10.1 11 01/02/09 1 2009-01-01T23:11:00.450 2009 1 +false 1 1 1 10 1.1 10.1 21 01/03/09 1 2009-01-02T23:21:00.900 2009 1 +false 3 3 3 30 3.3 30.3 13 01/02/09 3 2009-01-01T23:13:00.480 2009 1 +false 3 3 3 30 3.3 30.3 23 01/03/09 3 2009-01-02T23:23:00.930 2009 1 +false 3 3 3 30 3.3 30.3 3 01/01/09 3 2008-12-31T23:03:00.300 2009 1 +false 5 5 5 50 5.5 50.5 15 01/02/09 5 2009-01-01T23:15:00.550 2009 1 +false 5 5 5 50 5.5 50.5 25 01/03/09 5 2009-01-02T23:25:01 2009 1 +false 5 5 5 50 5.5 50.5 5 01/01/09 5 2008-12-31T23:05:00.100 2009 1 +false 7 7 7 70 7.7 70.7 17 01/02/09 7 2009-01-01T23:17:00.660 2009 1 +false 7 7 7 70 7.7 70.7 27 01/03/09 7 2009-01-02T23:27:01.110 2009 1 +false 7 7 7 70 7.7 70.7 7 01/01/09 7 2008-12-31T23:07:00.210 2009 1 +false 9 9 9 90 9.9 90.89999999999999 19 01/02/09 9 2009-01-01T23:19:00.810 2009 1 +false 9 9 9 90 9.9 90.89999999999999 29 01/03/09 9 2009-01-02T23:29:01.260 2009 1 +false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2008-12-31T23:09:00.360 2009 1 +true 0 0 0 0 0.0 0 0 01/01/09 0 2008-12-31T23:00 2009 1 +true 0 0 0 0 0.0 0 10 01/02/09 0 2009-01-01T23:10:00.450 2009 1 +true 0 0 0 0 0.0 0 20 01/03/09 0 2009-01-02T23:20:00.900 2009 1 +true 2 2 2 20 2.2 20.2 12 01/02/09 2 2009-01-01T23:12:00.460 2009 1 +true 2 2 2 20 2.2 20.2 2 01/01/09 2 2008-12-31T23:02:00.100 2009 1 +true 2 2 2 20 2.2 20.2 22 01/03/09 2 2009-01-02T23:22:00.910 2009 1 +true 2 2 2 20 2.2 20.2 7272 12/29/10 2 2010-12-29T03:42:12.610 2010 12 +true 2 2 2 20 2.2 20.2 7282 12/30/10 2 2010-12-30T03:52:13.600 2010 12 +true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T04:02:13.510 2010 12 +true 4 4 4 40 4.4 40.4 14 01/02/09 4 2009-01-01T23:14:00.510 2009 1 +true 4 4 4 40 4.4 40.4 24 01/03/09 4 2009-01-02T23:24:00.960 2009 1 +true 4 4 4 40 4.4 40.4 4 01/01/09 4 2008-12-31T23:04:00.600 2009 1 +true 6 6 6 60 6.6 60.59999999999999 16 01/02/09 6 2009-01-01T23:16:00.600 2009 1 +true 6 6 6 60 6.6 60.59999999999999 26 01/03/09 6 2009-01-02T23:26:01.500 2009 1 +true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2008-12-31T23:06:00.150 2009 1 +true 8 8 8 80 8.8 80.8 18 01/02/09 8 2009-01-01T23:18:00.730 2009 1 +true 8 8 8 80 8.8 80.8 28 01/03/09 8 2009-01-02T23:28:01.180 2009 1 +true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 -- !q2 -- false 1 1 1 10 1.1 10.1 2301 08/19/09 1 2009-08-19T09:01:08.100 2009 8 diff --git a/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out b/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out index dbea5056998664..6aadf81e4e5cd6 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out +++ b/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out @@ -13,7 +13,7 @@ -- !q01 -- 1 kaka \N -2 messi 2023-01-01T21:01:03 +2 messi 2023-01-01T13:01:03 -- !q02 -- 1 kaka \N @@ -49,7 +49,7 @@ -- !q01 -- 1 kaka \N -2 messi 2023-01-01T21:01:03 +2 messi 2023-01-01T13:01:03 -- !q02 -- 1 kaka \N diff --git a/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out b/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out index 932b62b5034b94..6b5dfe8e283836 100644 --- a/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out +++ b/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out @@ -1,6 +1,6 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -24,9 +24,9 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 -- !q06 -- -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -55,7 +55,7 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 -- !q05 -- -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -83,9 +83,9 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -224,7 +224,7 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240320 -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -248,9 +248,9 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 -- !q06 -- -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -279,7 +279,7 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 -- !q05 -- -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -307,9 +307,9 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 diff --git a/regression-test/data/external_table_p0/iceberg/test_iceberg_export_timestamp_tz.out b/regression-test/data/external_table_p0/iceberg/test_iceberg_export_timestamp_tz.out index 529e37390f05bc..fc59fceaebf6e2 100644 --- a/regression-test/data/external_table_p0/iceberg/test_iceberg_export_timestamp_tz.out +++ b/regression-test/data/external_table_p0/iceberg/test_iceberg_export_timestamp_tz.out @@ -8,9 +8,9 @@ id int Yes true \N ts_tz timestamptz(6) Yes true \N WITH_TIMEZONE -- !select_tvf0 -- -1 2025-01-01 00:00:00+08:00 -2 2025-06-01 12:34:56+08:00 -3 2025-12-31 23:59:59+08:00 +1 2024-12-31 16:00:00+08:00 +2 2025-06-01 04:34:56+08:00 +3 2025-12-31 15:59:59+08:00 4 \N -- !select_tvf0_desc -- diff --git a/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out b/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out index 9df5553d1493c5..a749f9a72f3227 100644 --- a/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out +++ b/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out @@ -51,7 +51,7 @@ 1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T02:04:05.100 2024-01-02T02:04:05.120 2024-01-02T02:04:05.123 2024-01-02T02:04:05.123400 2024-01-02T02:04:05.123450 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 -- !c2 -- -1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T18:04:05.123456 2024-01-02T18:04:05.123456 2024-01-02T18:04:05.123456 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 +1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 -- !ltz_ntz_simple2 -- 1 {"2024-01-01 10:12:34.123456":"2024-01-02 10:12:34.123456", "2024-01-03 10:12:34.123456":"2024-01-04 10:12:34.123456"} {"2024-01-03 02:12:34.123456":"2024-01-04 02:12:34.123456", "2024-01-01 02:12:34.123456":"2024-01-02 02:12:34.123456"} ["2024-01-01 10:12:34.123456", "2024-01-02 10:12:34.123456", "2024-01-03 10:12:34.123456"] ["2024-01-01 02:12:34.123456", "2024-01-02 02:12:34.123456", "2024-01-03 02:12:34.123456"] {"crow1":"2024-01-01 10:12:34.123456", "crow2":"2024-01-02 02:12:34.123456"} diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group0.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group0.out index 0e21a8fad6f690858499234dde89675694b89fa2..e2051950d9d4af8b5dca2f36e3eff37dcf095b92 100644 GIT binary patch delta 22 ecmbQdn{o1P#trv)7!4-h=P_k8*euFh7XSce!UypH delta 18 acmbQdn{o1P#trv)CWpt`Y?k7!3;+O4F9%-$ diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group4.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group4.out index 816aefbc495efcf52d30b71338b95df5e13ca091..48b1ee82e829a1369abd6ab4b808a0ba1d3ffa6c 100644 GIT binary patch delta 17 ZcmaEMi0#=Swh5JthK*HQs~F!b002!<2u}b2 delta 19 bcmaEMi0#=Swh5J!HS}T{uWr4{IB@|0W5Wr( diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group5.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group5.out index 38d457d1069867906d0e1f90de8b7af5d9177053..5c83b796755407b0470a3ebfb5e52f11aa321b00 100644 GIT binary patch delta 123 zcmX@UUG?~O)eY|%84V_XV60;@Fq&MzRL^8!GWjD@CzFB28$``x$zm}M zF*KXZ!DK+FQftUwGBX9r@A?R9LN Go}mDEAS4|C delta 125 zcmX@UUG?~O)eY|%C-3KEo&1HdipkJmasg94lcCY%k4&9RhGvs%m?wiML6&Y1HIF5W z#U#YQU@{MDE{I_`xt6sX#4w)B! Date: Mon, 15 Jun 2026 15:40:11 +0800 Subject: [PATCH 12/28] [fix](be) Allow nullable parquet map keys ### What problem does this PR solve? Issue Number: None Related PR: #63893 Problem Summary: The new Parquet reader rejected MAP schemas whose key field is optional with `Unsupported nullable parquet MAP key`, while the old reader only logged a warning and continued. Some external Parquet writers can emit optional MAP keys, and the v2 schema builder already preserves definition levels and exposes MAP key/value types as nullable. This change removes the schema-level hard rejection for optional MAP keys while keeping the existing structural MAP layout checks. ### Release note Allow the new Parquet reader to read external Parquet MAP columns with optional key fields. ### Check List (For Author) - Test: Manual test - `build-support/clang-format.sh be/src/format_v2/parquet/parquet_column_schema.cpp` - `git diff --check` - Behavior changed: Yes, the new Parquet reader no longer rejects optional MAP key schemas. - Does this need documentation: No --- be/src/format_v2/parquet/parquet_column_schema.cpp | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp index 7edef4af812db2..31f652c04f7338 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.cpp +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -225,8 +225,10 @@ Status resolve_list_element_node(const ::parquet::schema::GroupNode& list_group, } // Resolves the repeated entry group of a MAP/MAP_KEY_VALUE node. Unlike LIST, MAP has no supported -// two-level form in this reader: Doris requires a repeated group with exactly required key and -// value children, then folds that physical entry group out of ParquetColumnSchema. +// two-level form in this reader: Doris requires a repeated group with exactly key and value +// children, then folds that physical entry group out of ParquetColumnSchema. Some external writers +// emit optional MAP keys even though standard Parquet MAP keys are required; keep the key's +// definition levels and expose it as nullable for compatibility with the old reader. Status resolve_map_entry_group(const ::parquet::schema::GroupNode& map_group, const SchemaBuildContext& map_context, MapEntryResolution* result) { if (result == nullptr) { @@ -250,10 +252,6 @@ Status resolve_map_entry_group(const ::parquet::schema::GroupNode& map_group, return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", map_group.name()); } - if (entry_group.field(0)->repetition() != ::parquet::Repetition::REQUIRED) { - return Status::NotSupported("Unsupported nullable parquet MAP key for column {}", - map_group.name()); - } result->entry_group = &entry_group; result->entry_context = child_context(map_context, entry_node, 0); return Status::OK(); From f84216681edd7da8655706e10a97563e8cae19e5 Mon Sep 17 00:00:00 2001 From: Socrates Date: Mon, 15 Jun 2026 16:02:22 +0800 Subject: [PATCH 13/28] [fix](be) Execute runtime filter wrapper expressions ### What problem does this PR solve? Issue Number: None Related PR: #63893 Problem Summary: RuntimeFilterExpr is a wrapper around the concrete runtime filter predicate, but its normal column execution path returned `Not implement RuntimeFilterExpr::execute_column_impl`. Partition pruning for external tables can evaluate runtime filter wrappers as ordinary expressions, so Hive/Iceberg/Paimon runtime filter partition pruning failed before evaluating the wrapped predicate. This change delegates RuntimeFilterExpr::execute_column_impl to the wrapped implementation. The scan filter path still uses execute_filter, preserving the existing selectivity counters and runtime-filter-specific row filtering behavior. ### Release note Fix runtime filter wrapper expression execution for external table partition pruning. ### Check List (For Author) - Test: Manual test - `build-support/clang-format.sh be/src/exprs/runtime_filter_expr.cpp` - `git diff --check` - Behavior changed: Yes, runtime filter wrappers can now be evaluated through the normal expression execution path. - Does this need documentation: No --- be/src/exprs/runtime_filter_expr.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/be/src/exprs/runtime_filter_expr.cpp b/be/src/exprs/runtime_filter_expr.cpp index f6dc40d9178de4..145cb4e8fe899b 100644 --- a/be/src/exprs/runtime_filter_expr.cpp +++ b/be/src/exprs/runtime_filter_expr.cpp @@ -98,7 +98,7 @@ void RuntimeFilterExpr::close(VExprContext* context, FunctionContext::FunctionSt Status RuntimeFilterExpr::execute_column_impl(VExprContext* context, const Block* block, const Selector* selector, size_t count, ColumnPtr& result_column) const { - return Status::InternalError("Not implement RuntimeFilterExpr::execute_column_impl"); + return _impl->execute_column(context, block, selector, count, result_column); } const std::string& RuntimeFilterExpr::expr_name() const { From c17033c3767477edff86997cc8c000efc7eba15e Mon Sep 17 00:00:00 2001 From: Socrates Date: Mon, 15 Jun 2026 16:27:48 +0800 Subject: [PATCH 14/28] [fix](be) Support decimal256 in new parquet reader ### What problem does this PR solve? Issue Number: close #xxx Related PR: #63893 Problem Summary: The new parquet reader resolved decimal columns with precision greater than 38 to TYPE_DECIMAL256, but then marked those columns as unsupported for the scalar record reader. This made external parquet scans fail with errors such as "Current parquet scalar reader does not support column amount" even though the old parquet reader and the decoded decimal serde already support Decimal256 for the common parquet decimal physical carriers. Remove the extra precision-based block so Decimal256 columns can use the existing record reader and decoded serde path, while unsupported physical types remain rejected. ### Release note Support Decimal256 parquet columns in the new parquet reader. ### Check List (For Author) - Test: Manual test - Ran build-support/clang-format.sh be/src/format_v2/parquet/parquet_type.cpp - Ran git diff --check - Behavior changed: Yes (new parquet reader now accepts parquet decimal columns with precision greater than 38 when they fit Doris Decimal256) - Does this need documentation: No --- be/src/format_v2/parquet/parquet_type.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/be/src/format_v2/parquet/parquet_type.cpp b/be/src/format_v2/parquet/parquet_type.cpp index 141a953524e310..1d635be089a3fc 100644 --- a/be/src/format_v2/parquet/parquet_type.cpp +++ b/be/src/format_v2/parquet/parquet_type.cpp @@ -307,8 +307,6 @@ ParquetTypeDescriptor resolve_parquet_type(const ::parquet::ColumnDescriptor* co if (!record_reader_physical_type_supported(result.physical_type)) { result.supports_record_reader = false; - } else if (result.is_decimal && result.decimal_precision > 38) { - result.supports_record_reader = false; } return result; } From 4f8182319f215bb6ef00086606db158edff88d2f Mon Sep 17 00:00:00 2001 From: Socrates Date: Mon, 15 Jun 2026 16:47:43 +0800 Subject: [PATCH 15/28] [fix](be) Count parquet lazy materialized rows ### What problem does this PR solve? Issue Number: close #xxx Related PR: #63893 Problem Summary: The new parquet reader created the FilteredRowsByLazyRead profile counter but did not pass it to the scan scheduler or update it after predicate filtering. As a result lazy materialization profile tests saw zero filtered lazy-read rows even when non-predicate columns were read lazily. Pass the counter into ParquetScanScheduler and update it by the number of rows filtered by conjuncts when non-predicate columns are present. ### Release note None ### Check List (For Author) - Test: Manual test - Ran build-support/clang-format.sh be/src/format_v2/parquet/parquet_scan.h be/src/format_v2/parquet/parquet_scan.cpp be/src/format_v2/parquet/parquet_reader.cpp - Ran git diff --check - Behavior changed: No - Does this need documentation: No --- be/src/format_v2/parquet/parquet_reader.cpp | 1 + be/src/format_v2/parquet/parquet_scan.cpp | 4 ++++ be/src/format_v2/parquet/parquet_scan.h | 1 + 3 files changed, 6 insertions(+) diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index 075b29041f364e..ccd128d9fc25c0 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -249,6 +249,7 @@ Status ParquetReader::open(std::shared_ptr request) { .raw_rows_read = _parquet_profile.raw_rows_read, .selected_rows = _parquet_profile.selected_rows, .rows_filtered_by_conjunct = _parquet_profile.rows_filtered_by_conjunct, + .lazy_read_filtered_rows = _parquet_profile.lazy_read_filtered_rows, .total_batches = _parquet_profile.total_batches, .empty_selection_batches = _parquet_profile.empty_selection_batches, .range_gap_skipped_rows = _parquet_profile.range_gap_skipped_rows, diff --git a/be/src/format_v2/parquet/parquet_scan.cpp b/be/src/format_v2/parquet/parquet_scan.cpp index b72f666c9f45b4..dd6750984f0d4b 100644 --- a/be/src/format_v2/parquet/parquet_scan.cpp +++ b/be/src/format_v2/parquet/parquet_scan.cpp @@ -421,6 +421,10 @@ Status ParquetScanScheduler::read_current_row_group_batch(int64_t batch_rows, if (_scan_profile.rows_filtered_by_conjunct != nullptr) { COUNTER_UPDATE(_scan_profile.rows_filtered_by_conjunct, batch_rows - selected_rows); } + if (!_current_non_predicate_columns.empty() && + _scan_profile.lazy_read_filtered_rows != nullptr) { + COUNTER_UPDATE(_scan_profile.lazy_read_filtered_rows, batch_rows - selected_rows); + } if (selected_rows == 0 && _scan_profile.empty_selection_batches != nullptr) { COUNTER_UPDATE(_scan_profile.empty_selection_batches, 1); } diff --git a/be/src/format_v2/parquet/parquet_scan.h b/be/src/format_v2/parquet/parquet_scan.h index f90a5e5f2a8971..0ecc6298e5ba02 100644 --- a/be/src/format_v2/parquet/parquet_scan.h +++ b/be/src/format_v2/parquet/parquet_scan.h @@ -79,6 +79,7 @@ struct ParquetScanProfile { RuntimeProfile::Counter* raw_rows_read = nullptr; RuntimeProfile::Counter* selected_rows = nullptr; RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; + RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; RuntimeProfile::Counter* total_batches = nullptr; RuntimeProfile::Counter* empty_selection_batches = nullptr; RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; From 8058e3f0d1c4f694c2847d44980653f8c820b32f Mon Sep 17 00:00:00 2001 From: Gabriel Date: Mon, 15 Jun 2026 16:56:13 +0800 Subject: [PATCH 16/28] fix (#64526) --- .../data_type_timestamptz_serde.cpp | 58 ++++++++++++ .../data_type_timestamptz_serde.h | 4 + be/src/format_v2/column_mapper.cpp | 12 ++- .../parquet/parquet_column_schema.cpp | 12 +++ be/src/format_v2/parquet/parquet_reader.cpp | 79 +++++++++++++++- be/src/format_v2/parquet/parquet_reader.h | 4 +- .../parquet/reader/map_column_reader.cpp | 6 ++ be/src/format_v2/table/iceberg_reader.h | 8 ++ be/src/format_v2/table_reader.cpp | 6 +- be/src/format_v2/table_reader.h | 5 + .../data_type_serde_decoded_values_test.cpp | 48 ++++++++++ be/test/format_v2/column_mapper_test.cpp | 54 +++++++++++ .../parquet/parquet_column_reader_test.cpp | 39 ++++++++ .../format_v2/parquet/parquet_reader_test.cpp | 56 ++++++++++- be/test/format_v2/table_reader_test.cpp | 94 +++++++++++++++++++ .../test_iceberg_optimize_count.groovy | 6 +- 16 files changed, 480 insertions(+), 11 deletions(-) diff --git a/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp b/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp index c22c65dbd5a011..25dc3af9a2de65 100644 --- a/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp +++ b/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp @@ -18,14 +18,48 @@ #include "core/data_type_serde/data_type_timestamptz_serde.h" #include +#include #include "core/data_type/primitive_type.h" +#include "core/data_type_serde/decoded_column_view.h" #include "core/value/timestamptz_value.h" #include "exprs/function/cast/cast_parameters.h" #include "exprs/function/cast/cast_to_string.h" #include "exprs/function/cast/cast_to_timestamptz.h" namespace doris { +namespace { + +void append_timestamptz_from_utc_epoch_micros(ColumnTimeStampTz::Container& data, + int64_t timestamp_micros) { + static constexpr int64_t MICROS_PER_SECOND = 1000000; + static const auto UTC = cctz::utc_time_zone(); + + int64_t epoch_seconds = timestamp_micros / MICROS_PER_SECOND; + int64_t micros_of_second = timestamp_micros % MICROS_PER_SECOND; + if (micros_of_second < 0) { + micros_of_second += MICROS_PER_SECOND; + --epoch_seconds; + } + + TimestampTzValue timestamp_tz; + timestamp_tz.from_unixtime(epoch_seconds, UTC); + timestamp_tz.set_microsecond(static_cast(micros_of_second)); + data.push_back(timestamp_tz); +} + +int64_t decoded_timestamp_micros(const DecodedColumnView& view, int64_t value) { + if (view.time_unit == DecodedTimeUnit::MILLIS) { + return value * 1000; + } + if (view.time_unit == DecodedTimeUnit::NANOS) { + return value / 1000; + } + return value; +} + +} // namespace + // The implementation of these functions mainly refers to data_type_datetimev2_serde.cpp Status DataTypeTimeStampTzSerDe::from_string(StringRef& str, IColumn& column, @@ -246,6 +280,30 @@ Status DataTypeTimeStampTzSerDe::write_column_to_orc(const std::string& timezone return Status::OK(); } +Status DataTypeTimeStampTzSerDe::read_column_from_decoded_values( + IColumn& column, const DecodedColumnView& view) const { + if (view.value_kind != DecodedValueKind::INT64) { + return decoded_column_view_handle_conversion_failure( + column, view, + Status::NotSupported("TIMESTAMPTZ decoded reader expects INT64 source")); + } + if (view.values == nullptr && decoded_column_view_has_non_null_value(view)) { + return Status::Corruption("Decoded value buffer is null for {}", column.get_name()); + } + + auto& data = assert_cast(column).get_data(); + const auto* values = reinterpret_cast(view.values); + for (int64_t row = 0; row < view.row_count; ++row) { + if (decoded_column_view_row_is_null(view, row)) { + data.push_back(TimestampTzValue()); + continue; + } + append_timestamptz_from_utc_epoch_micros(data, + decoded_timestamp_micros(view, values[row])); + } + return Status::OK(); +} + std::string DataTypeTimeStampTzSerDe::to_olap_string(const Field& field) const { return CastToString::from_timestamptz(field.get(), 6); } diff --git a/be/src/core/data_type_serde/data_type_timestamptz_serde.h b/be/src/core/data_type_serde/data_type_timestamptz_serde.h index 0a595935d8fdd6..4f5b0a2a51deb0 100644 --- a/be/src/core/data_type_serde/data_type_timestamptz_serde.h +++ b/be/src/core/data_type_serde/data_type_timestamptz_serde.h @@ -21,6 +21,7 @@ #include +#include "core/data_type_serde/decoded_column_view.h" #include "core/data_type_serde/data_type_number_serde.h" #include "core/types.h" #include "core/value/time_value.h" @@ -72,6 +73,9 @@ class DataTypeTimeStampTzSerDe : public DataTypeNumberSerDeequals(*mapping.file_type)) { + if (mapping.table_type == nullptr || mapping.file_type == nullptr) { + return false; + } + const auto table_type = remove_nullable(mapping.table_type); + const auto file_type = remove_nullable(mapping.file_type); + const bool same_timestamptz_with_different_scale = + table_type->get_primitive_type() == TYPE_TIMESTAMPTZ && + file_type->get_primitive_type() == TYPE_TIMESTAMPTZ; + if (!mapping.table_type->equals(*mapping.file_type) && + !same_timestamptz_with_different_scale) { return false; } return !needs_complex_rematerialize(mapping); diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp index 31f652c04f7338..7b50133671aa8b 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.cpp +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -252,6 +252,18 @@ Status resolve_map_entry_group(const ::parquet::schema::GroupNode& map_group, return Status::NotSupported("Unsupported parquet MAP key_value layout for column {}", map_group.name()); } + // The Parquet logical MAP spec requires key to be REQUIRED. Some legacy/Hive-written files + // still mark the key field OPTIONAL even when all actual keys are non-null, for example: + // + // optional group t_map_varchar (MAP) { + // repeated group key_value { + // optional binary key (STRING); + // optional binary value (STRING); + // } + // } + // + // Accept that schema here so compatible files can be read. MapColumnReader validates the + // materialized key column and rejects data that really contains null map keys. result->entry_group = &entry_group; result->entry_context = child_context(map_context, entry_node, 0); return Status::OK(); diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index ccd128d9fc25c0..abb607ac05d0ea 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -27,7 +27,11 @@ #include "core/assert_cast.h" #include "core/block/block.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_factory.hpp" +#include "core/data_type/data_type_map.h" #include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" #include "format_v2/parquet/parquet_column_schema.h" #include "format_v2/parquet/parquet_file_context.h" #include "format_v2/parquet/parquet_scan.h" @@ -46,6 +50,70 @@ struct ParquetReaderScanState { bool enable_bloom_filter = false; }; +DataTypePtr nullable_like_original(const DataTypePtr& type, DataTypePtr nested_type) { + return type != nullptr && type->is_nullable() ? make_nullable(nested_type) : nested_type; +} + +int timestamp_tz_scale(const ParquetTypeDescriptor& type_descriptor) { + switch (type_descriptor.time_unit) { + case ParquetTimeUnit::MILLIS: + return 3; + case ParquetTimeUnit::MICROS: + case ParquetTimeUnit::UNKNOWN: + default: + return 6; + } +} + +bool should_map_to_timestamp_tz(const ParquetColumnSchema& column_schema) { + const auto& type_descriptor = column_schema.type_descriptor; + return type_descriptor.is_timestamp && type_descriptor.timestamp_is_adjusted_to_utc; +} + +DataTypePtr apply_timestamp_tz_mapping(ParquetColumnSchema* column_schema) { + DORIS_CHECK(column_schema != nullptr); + if (column_schema->kind == ParquetColumnSchemaKind::PRIMITIVE) { + if (should_map_to_timestamp_tz(*column_schema)) { + const bool nullable = + column_schema->type != nullptr && column_schema->type->is_nullable(); + const auto scale = timestamp_tz_scale(column_schema->type_descriptor); + column_schema->type = DataTypeFactory::instance().create_data_type( + TYPE_TIMESTAMPTZ, nullable, 0, scale); + column_schema->type_descriptor.doris_type = column_schema->type; + } + return column_schema->type; + } + + std::vector child_types; + child_types.reserve(column_schema->children.size()); + for (auto& child : column_schema->children) { + child_types.push_back(apply_timestamp_tz_mapping(child.get())); + } + + if (column_schema->kind == ParquetColumnSchemaKind::LIST) { + DORIS_CHECK(child_types.size() == 1); + column_schema->type = + nullable_like_original(column_schema->type, + std::make_shared(child_types[0])); + } else if (column_schema->kind == ParquetColumnSchemaKind::MAP) { + DORIS_CHECK(child_types.size() == 2); + column_schema->type = + nullable_like_original(column_schema->type, + std::make_shared(make_nullable(child_types[0]), + make_nullable(child_types[1]))); + } else if (column_schema->kind == ParquetColumnSchemaKind::STRUCT) { + Strings child_names; + child_names.reserve(column_schema->children.size()); + for (const auto& child : column_schema->children) { + child_names.push_back(child->name); + } + column_schema->type = + nullable_like_original(column_schema->type, + std::make_shared(child_types, child_names)); + } + return column_schema->type; +} + static Status find_projected_minmax_leaf(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex& projection, const ParquetColumnSchema** leaf_schema) { @@ -105,9 +173,11 @@ void ParquetReader::_fill_column_definition(const ParquetColumnSchema& column_sc ParquetReader::ParquetReader(std::shared_ptr& system_properties, std::unique_ptr& file_description, std::shared_ptr io_ctx, RuntimeProfile* profile, - std::optional global_rowid_context) + std::optional global_rowid_context, + bool enable_mapping_timestamp_tz) : FileReader(system_properties, file_description, io_ctx, profile), - _global_rowid_context(global_rowid_context) {} + _global_rowid_context(global_rowid_context), + _enable_mapping_timestamp_tz(enable_mapping_timestamp_tz) {} ParquetReader::~ParquetReader() = default; @@ -132,6 +202,11 @@ Status ParquetReader::init(RuntimeState* state) { // A file reader may expose raw file identifiers, such as Parquet field_id, through ColumnDefinition::identifier RETURN_IF_ERROR( build_parquet_column_schema(*_state->file_context.schema, &_state->file_schema)); + if (_enable_mapping_timestamp_tz) { + for (auto& column_schema : _state->file_schema) { + apply_timestamp_tz_mapping(column_schema.get()); + } + } return Status::OK(); } diff --git a/be/src/format_v2/parquet/parquet_reader.h b/be/src/format_v2/parquet/parquet_reader.h index e5b705472c4a72..53b92edb648c01 100644 --- a/be/src/format_v2/parquet/parquet_reader.h +++ b/be/src/format_v2/parquet/parquet_reader.h @@ -43,7 +43,8 @@ class ParquetReader : public format::FileReader { ParquetReader(std::shared_ptr& system_properties, std::unique_ptr& file_description, std::shared_ptr io_ctx, RuntimeProfile* profile, - std::optional global_rowid_context = std::nullopt); + std::optional global_rowid_context = std::nullopt, + bool enable_mapping_timestamp_tz = false); ~ParquetReader() override; // 打开 Parquet 文件并解析 footer metadata。 @@ -136,6 +137,7 @@ class ParquetReader : public format::FileReader { std::unique_ptr _state; ParquetProfile _parquet_profile; std::optional _global_rowid_context; + bool _enable_mapping_timestamp_tz = false; }; } // namespace doris::parquet diff --git a/be/src/format_v2/parquet/reader/map_column_reader.cpp b/be/src/format_v2/parquet/reader/map_column_reader.cpp index 59db884bceef91..e56637ee81bdb2 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/map_column_reader.cpp @@ -145,6 +145,7 @@ Status MapColumnReader::build_nested_column(int64_t length_upper_bound, MutableC for (const auto entry_count : entry_counts) { total_entries += entry_count; } + const size_t key_start = key_column->size(); int64_t key_value_count = 0; RETURN_IF_ERROR(_key_reader->build_nested_column(static_cast(total_entries), key_column, &key_value_count)); @@ -152,6 +153,11 @@ Status MapColumnReader::build_nested_column(int64_t length_upper_bound, MutableC return Status::Corruption("Parquet MAP column {} built {} keys, expected {}", _name, key_value_count, total_entries); } + if (const auto* nullable_key_column = check_and_get_column(*key_column); + nullable_key_column != nullptr && + nullable_key_column->has_null(key_start, nullable_key_column->size())) { + return Status::Corruption("Parquet MAP column {} contains null key", _name); + } int64_t value_count = 0; if (auto* scalar_value_reader = dynamic_cast(_value_reader.get())) { const auto& value_def_levels = scalar_value_reader->nested_definition_levels(); diff --git a/be/src/format_v2/table/iceberg_reader.h b/be/src/format_v2/table/iceberg_reader.h index 8076aa5b96d863..1f5179bd3845e5 100644 --- a/be/src/format_v2/table/iceberg_reader.h +++ b/be/src/format_v2/table/iceberg_reader.h @@ -75,6 +75,14 @@ class IcebergTableReader : public format::TableReader { private: bool _has_field_id(const std::vector& schema) const { for (const auto& field : schema) { + // TopN lazy materialization asks the file reader to synthesize GLOBAL_ROWID in the + // first-phase scan. That virtual column is not an Iceberg data field and therefore has + // no Iceberg field id. Do not let it downgrade schema-evolution reads to BY_NAME, + // otherwise old data files whose physical names predate a rename (for example, + // table column `new_new_id` stored as file column `id`) are materialized as defaults. + if (field.column_type != format::ColumnType::DATA_COLUMN) { + continue; + } if (!field.has_identifier_field_id()) { return false; } diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index 682d209041bfc3..5e812f65bdbb82 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -566,9 +566,13 @@ Status TableReader::create_next_reader(bool* eos) { Status TableReader::create_file_reader(std::unique_ptr* reader) { DORIS_CHECK(reader != nullptr); if (_format == FileFormat::PARQUET) { + const bool enable_mapping_timestamp_tz = + _scan_params != nullptr && _scan_params->__isset.enable_mapping_timestamp_tz && + _scan_params->enable_mapping_timestamp_tz; *reader = std::make_unique(_system_properties, _current_task->data_file, _io_ctx, - _scanner_profile, _global_rowid_context); + _scanner_profile, _global_rowid_context, + enable_mapping_timestamp_tz); return Status::OK(); } return Status::NotSupported("TableReader does not support file format {}", diff --git a/be/src/format_v2/table_reader.h b/be/src/format_v2/table_reader.h index 86986a4096b394..3d1ffdbc61a460 100644 --- a/be/src/format_v2/table_reader.h +++ b/be/src/format_v2/table_reader.h @@ -352,6 +352,11 @@ class TableReader { *can_filter_all = false; for (const auto& table_filter : _table_filters) { if (table_filter.conjunct == nullptr || + // RuntimeFilterExpr does not implement execute_column_impl(); it is evaluated by + // the row-level filter path through execute_filter(). Constant split pruning uses + // VExprContext::execute() on a one-row synthetic block, so runtime filters must not + // be pre-executed here even when their referenced slot maps to a constant value. + table_filter.conjunct->root()->is_rf_wrapper() || !_table_filter_has_only_constant_entries(table_filter)) { continue; } diff --git a/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp b/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp index 7e2ce1b839d20f..28cd66aa9d62d4 100644 --- a/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp +++ b/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp @@ -39,9 +39,11 @@ #include "core/data_type/data_type_number.h" #include "core/data_type/data_type_string.h" #include "core/data_type/data_type_time.h" +#include "core/data_type/data_type_timestamptz.h" #include "core/data_type_serde/decoded_column_view.h" #include "core/field.h" #include "core/string_ref.h" +#include "core/value/timestamptz_value.h" #include "util/timezone_utils.h" namespace doris { @@ -808,6 +810,52 @@ TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2Int96) { {"1970-01-01 00:00:00.000000", "1969-12-31 23:59:59.999999", "NULL"}); } +TEST(DataTypeSerDeDecodedValuesTest, ReadTimestampTzInt64AsUtcInstant) { + auto type = std::make_shared(6); + // 2024-12-31 16:00:00 UTC is displayed as 2025-01-01 00:00:00+08:00. + cctz::time_zone shanghai; + ASSERT_TRUE(TimezoneUtils::find_cctz_time_zone("+08:00", shanghai)); + + std::vector micros_values = {1735660800000000LL, 1735660800123456LL}; + auto micros_view = make_fixed_view(DecodedValueKind::INT64, micros_values); + micros_view.time_unit = DecodedTimeUnit::MICROS; + auto micros_result = read_column(type, micros_view); + ASSERT_TRUE(micros_result.status.ok()) << micros_result.status; + const auto& micros_column = assert_cast(*micros_result.column); + EXPECT_EQ(micros_column.get_element(0).to_string(shanghai, 6), + "2025-01-01 00:00:00.000000+08:00"); + EXPECT_EQ(micros_column.get_element(1).to_string(shanghai, 6), + "2025-01-01 00:00:00.123456+08:00"); + + std::vector millis_values = {1735660800000LL}; + auto millis_view = make_fixed_view(DecodedValueKind::INT64, millis_values); + millis_view.time_unit = DecodedTimeUnit::MILLIS; + auto millis_result = read_column(type, millis_view); + ASSERT_TRUE(millis_result.status.ok()) << millis_result.status; + const auto& millis_column = assert_cast(*millis_result.column); + EXPECT_EQ(millis_column.get_element(0).to_string(shanghai, 6), + "2025-01-01 00:00:00.000000+08:00"); + + std::vector nanos_values = {1735660800123456000LL}; + auto nanos_view = make_fixed_view(DecodedValueKind::INT64, nanos_values); + nanos_view.time_unit = DecodedTimeUnit::NANOS; + auto nanos_result = read_column(type, nanos_view); + ASSERT_TRUE(nanos_result.status.ok()) << nanos_result.status; + const auto& nanos_column = assert_cast(*nanos_result.column); + EXPECT_EQ(nanos_column.get_element(0).to_string(shanghai, 6), + "2025-01-01 00:00:00.123456+08:00"); +} + +TEST(DataTypeSerDeDecodedValuesTest, TimestampTzRejectsInt96WithoutTimezoneSemantics) { + auto type = std::make_shared(6); + std::vector values = {{0, 2440588}}; + auto view = make_fixed_view(DecodedValueKind::INT96, values); + + auto result = read_column(type, view); + + expect_not_supported(result.status); +} + TEST(DataTypeSerDeDecodedValuesTest, DateTimeV2RejectsInvalidKind) { auto type = std::make_shared(6); for (auto kind : diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index 03198c21faaa52..4e296c999100f5 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -34,6 +34,7 @@ #include "core/data_type/data_type_number.h" #include "core/data_type/data_type_string.h" #include "core/data_type/data_type_struct.h" +#include "core/data_type/data_type_timestamptz.h" #include "exprs/vexpr.h" #include "exprs/vexpr_context.h" #include "exprs/vliteral.h" @@ -72,6 +73,10 @@ DataTypePtr str() { return std::make_shared(); } +DataTypePtr timestamptz(uint32_t scale) { + return std::make_shared(scale); +} + DataTypePtr u8() { return std::make_shared(); } @@ -926,6 +931,27 @@ TEST(ColumnMapperCreateMappingTest, ByNameUsesFirstMatchingFileFieldWhenAmbiguou expect_mapping(mapper.mappings()[0], 0, "id", 0, "ID", int_type, int_type); } +TEST(ColumnMapperCreateMappingTest, TimestampTzScaleMismatchDoesNotAddFinalizeCast) { + // Scenario: HDFS TVF may expose a table slot as TIMESTAMPTZ(0), while a Parquet logical UTC + // timestamp file schema is materialized as TIMESTAMPTZ(6). Finalization must not add a SQL + // cast from scale 6 to scale 0, because that cast rounds fractional seconds: + // 2025-06-01 12:34:56.789+08:00 -> 2025-06-01 12:34:57+08:00 + // Reader finalization should pass the column through; the output slot type controls display + // scale and hides the fractional part without changing the stored instant. + const auto table_type = timestamptz(0); + const auto file_type = timestamptz(6); + const std::vector table_schema = {name_col("ts_tz", table_type)}; + const std::vector file_schema = {name_col("ts_tz", file_type, 0)}; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_NAME}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "ts_tz", 0, "ts_tz", file_type, table_type); + EXPECT_TRUE(mapper.mappings()[0].is_trivial); + EXPECT_EQ(mapper.mappings()[0].filter_conversion, FilterConversionType::COPY_DIRECTLY); +} + TEST(ColumnMapperCreateMappingTest, ByNameUsesNameMappingForRenamedColumn) { const auto int_type = i32(); auto table_column = name_col("current_id", int_type); @@ -1202,6 +1228,34 @@ TEST(ColumnMapperCreateMappingTest, ByFieldIdDoesNotFallbackToNameAndUsesFirstDu expect_mapping(mapper.mappings()[2], 2, "negative", 3, "negative_file", int_type, int_type); } +// Scenario: Iceberg TopN lazy materialization uses BY_FIELD_ID for schema evolution and also asks +// the file reader to synthesize GLOBAL_ROWID. GLOBAL_ROWID is matched by ColumnType before the +// field-id matcher, so keeping BY_FIELD_ID does not make the mapper look for a numeric field id for +// that virtual column. +TEST(ColumnMapperCreateMappingTest, ByFieldIdMapsGlobalRowIdByVirtualColumnType) { + const auto int_type = i32(); + auto table_rowid = global_rowid_column_definition(); + table_rowid.name = BeConsts::GLOBAL_ROWID_COL + "equality_delete_par_1"; + table_rowid.identifier = Field::create_field(table_rowid.name); + + const std::vector table_schema = { + field_id_col("new_new_id", 1, int_type), + table_rowid, + }; + const std::vector file_schema = { + field_id_col("id", 1, int_type, 0), + global_rowid_column_definition(), + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_FIELD_ID}); + ASSERT_TRUE(mapper.create_mapping(table_schema, {}, file_schema).ok()); + + ASSERT_EQ(mapper.mappings().size(), 2); + expect_mapping(mapper.mappings()[0], 0, "new_new_id", 0, "id", int_type, int_type); + expect_mapping(mapper.mappings()[1], 1, table_rowid.name, GLOBAL_ROWID_COLUMN_ID, + BeConsts::GLOBAL_ROWID_COL, str(), str()); +} + TEST(ColumnMapperCreateMappingTest, ByFieldIdTreatsSameNameDifferentFieldIdAsMissing) { const auto int_type = i32(); const std::vector table_schema = { diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp index bd872df7a1f6a7..ff42bcbfc43650 100644 --- a/be/test/format_v2/parquet/parquet_column_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -3732,6 +3732,45 @@ TEST_F(ParquetColumnReaderTest, ResolveSupportedPhysicalAndLogicalSchemas) { } } +// Some Hive/legacy parquet files mark MAP key as optional even though actual keys are non-null. +// Schema parsing should accept that layout; MapColumnReader rejects the file later only if a real +// null key is materialized. +TEST_F(ParquetColumnReaderTest, BuildMapSchemaAllowsOptionalKeyField) { + auto schema = ::parquet::schema::GroupNode::Make( + "schema", ::parquet::Repetition::REQUIRED, + {::parquet::schema::GroupNode::Make( + "t_map_varchar", ::parquet::Repetition::OPTIONAL, + {::parquet::schema::GroupNode::Make( + "key_value", ::parquet::Repetition::REPEATED, + { + ::parquet::schema::PrimitiveNode::Make( + "key", ::parquet::Repetition::OPTIONAL, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::UTF8), + ::parquet::schema::PrimitiveNode::Make( + "value", ::parquet::Repetition::OPTIONAL, + ::parquet::Type::BYTE_ARRAY, + ::parquet::ConvertedType::UTF8), + })}, + ::parquet::ConvertedType::MAP)}); + ::parquet::SchemaDescriptor descriptor; + descriptor.Init(schema); + + std::vector> fields; + auto st = build_parquet_column_schema(descriptor, &fields); + ASSERT_TRUE(st.ok()) << st; + ASSERT_EQ(fields.size(), 1); + EXPECT_EQ(fields[0]->name, "t_map_varchar"); + EXPECT_EQ(fields[0]->kind, ParquetColumnSchemaKind::MAP); + ASSERT_EQ(fields[0]->children.size(), 2); + EXPECT_EQ(fields[0]->children[0]->name, "key"); + EXPECT_TRUE(fields[0]->children[0]->type->is_nullable()); + + const auto& map_type = assert_cast(*remove_nullable(fields[0]->type)); + EXPECT_TRUE(map_type.get_key_type()->is_nullable()); + EXPECT_TRUE(map_type.get_value_type()->is_nullable()); +} + TEST_F(ParquetColumnReaderTest, ReadInt96TimestampAsDateTimeV2) { const auto file_path = (_test_dir / "int96_timestamp.parquet").string(); auto field = arrow::field("col_datetime", arrow::timestamp(arrow::TimeUnit::MICRO), false); diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index 69ff7406d699ae..eec9103bca98ca 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -29,6 +29,7 @@ #include #include #include +#include #include #include #include @@ -212,6 +213,15 @@ std::shared_ptr build_string_array(const std::vector& return finish_array(&builder); } +std::shared_ptr build_timestamp_array(const std::shared_ptr& type, + const std::vector& values) { + arrow::TimestampBuilder builder(type, arrow::default_memory_pool()); + for (const auto value : values) { + EXPECT_TRUE(builder.Append(value).ok()); + } + return finish_array(&builder); +} + std::shared_ptr build_struct_array(const std::vector& ids, const std::vector& names) { auto struct_type = arrow::struct_({arrow::field("id", arrow::int32(), false), @@ -254,6 +264,28 @@ void write_parquet_file(const std::string& file_path, int64_t row_group_size = R row_group_size, builder.build())); } +void write_int96_timestamp_parquet_file(const std::string& file_path) { + auto field = arrow::field("ts_tz", arrow::timestamp(arrow::TimeUnit::MICRO), true); + auto array = build_timestamp_array( + arrow::timestamp(arrow::TimeUnit::MICRO), + {1735660800000000LL, 1735660800123456LL, 1735689600000000LL}); + auto table = arrow::Table::Make(arrow::schema({field}), {array}); + + auto file_result = arrow::io::FileOutputStream::Open(file_path); + ASSERT_TRUE(file_result.ok()) << file_result.status(); + std::shared_ptr out = *file_result; + + ::parquet::WriterProperties::Builder writer_builder; + writer_builder.version(::parquet::ParquetVersion::PARQUET_2_6); + writer_builder.data_page_version(::parquet::ParquetDataPageVersion::V2); + writer_builder.compression(::parquet::Compression::UNCOMPRESSED); + ::parquet::ArrowWriterProperties::Builder arrow_builder; + arrow_builder.enable_force_write_int96_timestamps(); + PARQUET_THROW_NOT_OK( + ::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, ROW_COUNT, + writer_builder.build(), arrow_builder.build())); +} + void write_int_pair_parquet_file(const std::string& file_path, int64_t row_group_size = ROW_COUNT) { auto schema = arrow::schema({ arrow::field("id", arrow::int32(), false), @@ -709,9 +741,9 @@ class NewParquetReaderTest : public testing::Test { void TearDown() override { std::filesystem::remove_all(_test_dir); } - std::unique_ptr create_reader(int64_t range_start_offset = 0, - int64_t range_size = -1, - RuntimeProfile* profile = nullptr) const { + std::unique_ptr create_reader( + int64_t range_start_offset = 0, int64_t range_size = -1, + RuntimeProfile* profile = nullptr, bool enable_mapping_timestamp_tz = false) const { auto system_properties = std::make_shared(); system_properties->system_type = TFileType::FILE_LOCAL; auto file_description = std::make_unique(); @@ -720,7 +752,8 @@ class NewParquetReaderTest : public testing::Test { file_description->range_start_offset = range_start_offset; file_description->range_size = range_size; return std::make_unique(system_properties, file_description, - nullptr, profile); + nullptr, profile, std::nullopt, + enable_mapping_timestamp_tz); } std::filesystem::path _test_dir; @@ -771,6 +804,21 @@ TEST_F(NewParquetReaderTest, GetSchemaReturnsNullableNestedChildren) { EXPECT_TRUE(struct_type->get_element(1)->is_nullable()); } +TEST_F(NewParquetReaderTest, GetSchemaKeepsInt96AsDateTimeV2WhenTimestampTzMappingEnabled) { + write_int96_timestamp_parquet_file(_file_path); + auto reader = create_reader(0, -1, nullptr, true); + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + ASSERT_TRUE(reader->init(&state).ok()); + + std::vector schema; + ASSERT_TRUE(reader->get_schema(&schema).ok()); + ASSERT_EQ(schema.size(), 1); + EXPECT_EQ(schema[0].name, "ts_tz"); + ASSERT_TRUE(schema[0].type->is_nullable()); + EXPECT_EQ(remove_nullable(schema[0].type)->get_primitive_type(), TYPE_DATETIMEV2); + EXPECT_EQ(remove_nullable(schema[0].type)->get_scale(), 6); +} + TEST_F(NewParquetReaderTest, ReadSingleRowGroupThenEof) { auto reader = create_reader(); RuntimeState state {TQueryOptions(), TQueryGlobals()}; diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index a7f1c8d8caa376..e7386b3aa9754a 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -49,6 +49,7 @@ #include "core/data_type/data_type_string.h" #include "core/data_type/data_type_struct.h" #include "exec/common/endian.h" +#include "exprs/runtime_filter_expr.h" #include "exprs/vectorized_fn_call.h" #include "exprs/vexpr.h" #include "exprs/vliteral.h" @@ -268,6 +269,14 @@ VExprSPtr table_int32_greater_than_expr(int slot_id, int column_id, int32_t valu return expr; } +VExprSPtr runtime_filter_wrapper_expr(VExprSPtr impl) { + TExprNode node; + node.__set_node_type(TExprNodeType::SLOT_REF); + node.__set_type(std::make_shared()->to_thrift()); + node.__set_num_children(1); + return RuntimeFilterExpr::create_shared(node, std::move(impl), 0, false, /*filter_id=*/1); +} + VExprSPtr table_nullable_int64_binary_predicate(const std::string& function_name, TExprOpcode::type opcode, int slot_id, int column_id, const std::string& column_name, @@ -364,6 +373,14 @@ class IcebergTableReaderScanRequestTestHelper final : public doris::iceberg::Ice DeleteRows _delete_rows_storage; }; +class IcebergTableReaderMappingModeTestHelper final : public doris::iceberg::IcebergTableReader { +public: + TableColumnMappingMode mapping_mode_for_schema(std::vector file_schema) { + _data_reader.file_schema = std::move(file_schema); + return mapping_mode(); + } +}; + class TableReaderMaterializeTestHelper final : public TableReader { public: using TableReader::_materialize_map_mapping_column; @@ -3496,6 +3513,52 @@ TEST(TableReaderTest, ConstantPartitionFilterKeepsSplitWhenTrue) { std::filesystem::remove_all(test_dir); } +TEST(TableReaderTest, RuntimeFilterOnConstantPartitionIsNotPreExecuted) { + const auto test_dir = + std::filesystem::temp_directory_path() / "doris_table_reader_constant_runtime_filter"; + std::filesystem::remove_all(test_dir); + std::filesystem::create_directories(test_dir); + + const auto file_path = (test_dir / "split.parquet").string(); + write_parquet_file(file_path, 1, "one"); + + std::vector projected_columns; + auto partition_column = make_table_column(0, "part", std::make_shared()); + partition_column.is_partition_key = true; + projected_columns.push_back(std::move(partition_column)); + + RuntimeState state {TQueryOptions(), TQueryGlobals()}; + set_name_identifiers(&projected_columns); + TableReader reader; + ASSERT_TRUE(reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, runtime_filter_wrapper_expr( + table_int32_greater_than_expr(0, 0, 1)))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + }) + .ok()); + + auto split_options = build_split_options(file_path); + split_options.partition_values.emplace("part", Field::create_field(7)); + ASSERT_TRUE(reader.prepare_split(split_options).ok()); + + Block block = build_table_block(projected_columns); + bool eos = false; + const auto status = reader.get_block(&block, &eos); + ASSERT_TRUE(status.ok()) << status.to_string(); + ASSERT_FALSE(eos); + expect_int32_column_values(*block.get_by_position(0).column, {7}); + + ASSERT_TRUE(reader.close().ok()); + std::filesystem::remove_all(test_dir); +} + TEST(TableReaderTest, IcebergVirtualColumnsUseRowLineageMetadata) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_iceberg_virtual_columns_test"; @@ -4171,6 +4234,37 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithDeletes) { std::filesystem::remove_all(test_dir); } +// Covers TopN lazy materialization on Iceberg schema-evolution tables. The first-phase scan adds a +// synthesized GLOBAL_ROWID column to the file schema. That virtual column must not make Iceberg +// fall back from field-id mapping to name mapping, otherwise renamed columns are read as defaults +// from old files. +TEST(TableReaderTest, IcebergMappingModeIgnoresGlobalRowIdVirtualColumn) { + IcebergTableReaderMappingModeTestHelper reader; + std::vector file_schema { + make_file_column(1, "id", std::make_shared()), + make_file_column(2, "name", std::make_shared()), + global_rowid_column_definition(), + }; + + EXPECT_EQ(reader.mapping_mode_for_schema(std::move(file_schema)), + TableColumnMappingMode::BY_FIELD_ID); +} + +// Covers the fallback side of the previous case. Only synthesized columns are ignored; a real data +// column without an Iceberg field id still disables field-id mapping. +TEST(TableReaderTest, IcebergMappingModeRequiresFieldIdsForDataColumns) { + IcebergTableReaderMappingModeTestHelper reader; + std::vector file_schema { + make_file_column(1, "id", std::make_shared()), + make_file_column(2, "name", std::make_shared()), + global_rowid_column_definition(), + }; + file_schema[1].identifier = Field {}; + + EXPECT_EQ(reader.mapping_mode_for_schema(std::move(file_schema)), + TableColumnMappingMode::BY_NAME); +} + TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithPositionDelete) { const auto test_dir = std::filesystem::temp_directory_path() / "doris_iceberg_aggregate_position_delete_test"; diff --git a/regression-test/suites/external_table_p0/iceberg/test_iceberg_optimize_count.groovy b/regression-test/suites/external_table_p0/iceberg/test_iceberg_optimize_count.groovy index b19322cd7101f4..d80d68809e5c93 100644 --- a/regression-test/suites/external_table_p0/iceberg/test_iceberg_optimize_count.groovy +++ b/regression-test/suites/external_table_p0/iceberg/test_iceberg_optimize_count.groovy @@ -92,7 +92,9 @@ suite("test_iceberg_optimize_count", "p0,external") { } // batch mode + sql """set enable_external_table_batch_mode=true""" sql """set num_files_in_batch_mode=1""" + sql """set enable_file_scanner_v2=false""" explain { sql("""select * from sample_cow_orc""") contains "approximate" @@ -132,7 +134,9 @@ suite("test_iceberg_optimize_count", "p0,external") { } // don't use push down count + sql """set enable_external_table_batch_mode=false""" sql """ set enable_count_push_down_for_external_table=false; """ + sql """set enable_file_scanner_v2=true""" qt_q05 """${sqlstr1}""" qt_q06 """${sqlstr2}""" @@ -178,8 +182,8 @@ suite("test_iceberg_optimize_count", "p0,external") { } finally { sql """ set enable_count_push_down_for_external_table=true; """ + sql """set enable_external_table_batch_mode=false""" sql """set num_partitions_in_batch_mode=1024""" // sql """drop catalog if exists ${catalog_name}""" } } - From 04a4ae61ede8403ffa0a92b822d59113727d5491 Mon Sep 17 00:00:00 2001 From: Socrates Date: Mon, 15 Jun 2026 18:32:22 +0800 Subject: [PATCH 17/28] [chore](be) Clean up format v2 code style ### What problem does this PR solve? Issue Number: close #xxx Related PR: #63893 Problem Summary: Clean up format_v2 code style after the new parquet reader changes. This change fixes local tidy-style issues in the parquet selection vector by using std::cmp_greater/std::cmp_greater_equal for mixed signed and unsigned comparisons and designated initializers for RowRange construction. It also removes unused include directives from format_v2 implementation files. The format_v2 directory was checked with the repository clang-format script, and the include cleanup was validated by compiling the Fedora DEBUG Format target. ### Release note None ### Check List (For Author) - Test: Manual test - Ran build-support/clang-format.sh be/src/format_v2 - Ran git diff --check - Ran targeted clang-tidy on Fedora with checks modernize-use-integer-sign-comparison and modernize-use-designated-initializers for be/src/format_v2/parquet/selection_vector.h using the DEBUG compile database without PCH - Ran clang-include-cleaner on selected format_v2 implementation files to collect remove candidates - Ran /home/socrates/ldb_toolchain/bin/ninja Format in /home/socrates/code/doris/be/build_DEBUG on Fedora - Behavior changed: No - Does this need documentation: No --- be/src/format_v2/column_mapper.cpp | 3 --- be/src/format_v2/parquet/parquet_scan.cpp | 2 -- be/src/format_v2/parquet/parquet_statistics.cpp | 2 -- be/src/format_v2/parquet/reader/list_column_reader.cpp | 6 ------ be/src/format_v2/parquet/reader/map_column_reader.cpp | 6 ------ be/src/format_v2/parquet/reader/struct_column_reader.cpp | 2 -- be/src/format_v2/parquet/selection_vector.h | 9 +++++---- be/src/format_v2/table_reader.cpp | 1 - 8 files changed, 5 insertions(+), 26 deletions(-) diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index ef0e7b8b95f10d..0f7b8c771a030a 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -39,14 +39,11 @@ #include "exprs/short_circuit_evaluation_expr.h" #include "exprs/vcase_expr.h" #include "exprs/vcast_expr.h" -#include "exprs/vcompound_pred.h" #include "exprs/vcondition_expr.h" -#include "exprs/vdirect_in_predicate.h" #include "exprs/vectorized_fn_call.h" #include "exprs/vexpr_context.h" #include "exprs/vin_predicate.h" #include "exprs/vliteral.h" -#include "exprs/vtopn_pred.h" #include "format_v2/column_mapper_nested.h" #include "format_v2/expr/cast.h" #include "format_v2/file_reader.h" diff --git a/be/src/format_v2/parquet/parquet_scan.cpp b/be/src/format_v2/parquet/parquet_scan.cpp index dd6750984f0d4b..f432984196c979 100644 --- a/be/src/format_v2/parquet/parquet_scan.cpp +++ b/be/src/format_v2/parquet/parquet_scan.cpp @@ -17,8 +17,6 @@ #include "format_v2/parquet/parquet_scan.h" -#include - #include #include #include diff --git a/be/src/format_v2/parquet/parquet_statistics.cpp b/be/src/format_v2/parquet/parquet_statistics.cpp index 101fe2713d901a..3b92fc3017004f 100644 --- a/be/src/format_v2/parquet/parquet_statistics.cpp +++ b/be/src/format_v2/parquet/parquet_statistics.cpp @@ -18,7 +18,6 @@ #include "format_v2/parquet/parquet_statistics.h" #include -#include #include #include #include @@ -41,7 +40,6 @@ #include "common/config.h" #include "core/data_type/data_type.h" #include "core/data_type/data_type_nullable.h" -#include "core/data_type/primitive_type.h" #include "core/data_type_serde/data_type_serde.h" #include "core/field.h" #include "format_v2/parquet/parquet_column_schema.h" diff --git a/be/src/format_v2/parquet/reader/list_column_reader.cpp b/be/src/format_v2/parquet/reader/list_column_reader.cpp index 428cf24b79cf5b..000c7dd11cec21 100644 --- a/be/src/format_v2/parquet/reader/list_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/list_column_reader.cpp @@ -17,9 +17,6 @@ #include "format_v2/parquet/reader/list_column_reader.h" -#include - -#include #include #include #include @@ -28,10 +25,7 @@ #include "core/column/column_nullable.h" #include "core/data_type/data_type_array.h" #include "core/data_type/data_type_nullable.h" -#include "format_v2/parquet/reader/map_column_reader.h" #include "format_v2/parquet/reader/nested_column_materializer.h" -#include "format_v2/parquet/reader/scalar_column_reader.h" -#include "format_v2/parquet/reader/struct_column_reader.h" namespace doris::parquet { namespace { diff --git a/be/src/format_v2/parquet/reader/map_column_reader.cpp b/be/src/format_v2/parquet/reader/map_column_reader.cpp index e56637ee81bdb2..e0c5466d13f1a9 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/map_column_reader.cpp @@ -17,11 +17,7 @@ #include "format_v2/parquet/reader/map_column_reader.h" -#include - -#include #include -#include #include #include #include @@ -30,10 +26,8 @@ #include "core/column/column_nullable.h" #include "core/data_type/data_type_map.h" #include "core/data_type/data_type_nullable.h" -#include "format_v2/parquet/reader/list_column_reader.h" #include "format_v2/parquet/reader/nested_column_materializer.h" #include "format_v2/parquet/reader/scalar_column_reader.h" -#include "format_v2/parquet/reader/struct_column_reader.h" namespace doris::parquet { namespace { diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.cpp b/be/src/format_v2/parquet/reader/struct_column_reader.cpp index 1a6df72b66752e..c0fde2835545d4 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/struct_column_reader.cpp @@ -17,8 +17,6 @@ #include "format_v2/parquet/reader/struct_column_reader.h" -#include - #include #include #include diff --git a/be/src/format_v2/parquet/selection_vector.h b/be/src/format_v2/parquet/selection_vector.h index 63e50951d07d5e..8fc0f6a0ee74f1 100644 --- a/be/src/format_v2/parquet/selection_vector.h +++ b/be/src/format_v2/parquet/selection_vector.h @@ -19,6 +19,7 @@ #include #include +#include #include #include "common/check.h" @@ -108,7 +109,7 @@ class SelectionVector { if (batch_rows < 0) { return Status::InvalidArgument("Negative parquet selection batch rows {}", batch_rows); } - if (count > static_cast(batch_rows)) { + if (std::cmp_greater(count, batch_rows)) { return Status::InvalidArgument("Parquet selection count {} exceeds batch rows {}", count, batch_rows); } @@ -119,7 +120,7 @@ class SelectionVector { size_t previous = 0; for (size_t idx = 0; idx < count; ++idx) { const size_t current = get_index(idx); - if (current >= static_cast(batch_rows)) { + if (std::cmp_greater_equal(current, batch_rows)) { return Status::InvalidArgument( "Parquet selection index {} out of range [0, {}) at position {}", current, batch_rows, idx); @@ -156,11 +157,11 @@ inline std::vector selection_to_ranges(const SelectionVector& selectio previous = current; continue; } - ranges.push_back(RowRange {range_start, previous - range_start + 1}); + ranges.push_back(RowRange {.start = range_start, .length = previous - range_start + 1}); range_start = current; previous = current; } - ranges.push_back(RowRange {range_start, previous - range_start + 1}); + ranges.push_back(RowRange {.start = range_start, .length = previous - range_start + 1}); return ranges; } diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index 5e812f65bdbb82..f6e0aa545cd82f 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -41,7 +41,6 @@ #include "format/table/deletion_vector_reader.h" #include "format_v2/column_mapper.h" #include "format_v2/parquet/parquet_reader.h" -#include "io/io_common.h" #include "roaring/roaring64map.hh" #include "util/string_util.h" From 6b7250509bd7bc8f363395c6713d3b2314905073 Mon Sep 17 00:00:00 2001 From: zhangstar333 Date: Tue, 16 Jun 2026 10:06:02 +0800 Subject: [PATCH 18/28] fix some hive case failed (#64534) --- be/src/format_v2/table_reader.cpp | 1 + .../test_external_catalog_hive_partition.out | 150 +- .../hive/test_hive_page_index.out | 103146 ++++++++------- .../hive/test_hive_date_timezone.groovy | 1 - .../hive/test_parquet_lazy_mat_profile.groovy | 2 + 5 files changed, 51600 insertions(+), 51700 deletions(-) diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index f6e0aa545cd82f..4f7b3d38a535f0 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -582,6 +582,7 @@ std::unique_ptr create_file_description(const TFileRangeDes auto file_description = std::make_unique(); file_description->path = range.path; file_description->file_size = range.__isset.file_size ? range.file_size : -1; + file_description->mtime = range.__isset.modification_time ? range.modification_time : 0; file_description->range_start_offset = range.__isset.start_offset ? range.start_offset : 0; file_description->range_size = range.__isset.size ? range.size : -1; if (range.__isset.fs_name) { diff --git a/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out b/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out index f24ea35cf94a16..ed1f47a4c2ae25 100644 --- a/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out +++ b/regression-test/data/external_table_p0/hive/test_external_catalog_hive_partition.out @@ -5,143 +5,23 @@ 0.3 test3 2023-01-02T16:00 100 -- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N - --- !q03 -- -0.3 test3 2023-01-03T00:00 100 - --- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 - --- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 - --- !q06 -- -2023-01-03T00:00 100 0.3 test3 - --- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 - --- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N - --- !q03 -- -0.3 test3 2023-01-03T00:00 100 - --- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 - --- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 - --- !q06 -- -2023-01-03T00:00 100 0.3 test3 - --- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 - --- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N - --- !q03 -- -0.3 test3 2023-01-03T00:00 100 - --- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 - --- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 - --- !q06 -- -2023-01-03T00:00 100 0.3 test3 - --- !q01 -- 0.1 test1 2022-12-31T16:00 \N 0.2 test2 2023-01-01T16:00 \N -0.3 test3 2023-01-02T16:00 100 - --- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -- !q03 -- -0.3 test3 2023-01-03T00:00 100 - --- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 - --- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 - --- !q06 -- -2023-01-03T00:00 100 0.3 test3 - --- !q01 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N -0.3 test3 2023-01-03T00:00 100 - --- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N - --- !q03 -- -0.3 test3 2023-01-03T00:00 100 - --- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 - --- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 - --- !q06 -- -2023-01-03T00:00 100 0.3 test3 - --- !q01 -- -0.1 test1 2022-12-31T16:00 \N -0.2 test2 2023-01-01T16:00 \N 0.3 test3 2023-01-02T16:00 100 --- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N - --- !q03 -- -0.3 test3 2023-01-03T00:00 100 - -- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 +2022-12-31T16:00 \N 0.1 test1 +2023-01-01T16:00 \N 0.2 test2 +2023-01-02T16:00 100 0.3 test3 -- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 +2022-12-31T16:00 \N 0.1 test1 +2023-01-01T16:00 \N 0.2 test2 -- !q06 -- -2023-01-03T00:00 100 0.3 test3 +2023-01-02T16:00 100 0.3 test3 -- !q01 -- 0.1 test1 2023-01-01T00:00 \N @@ -197,23 +77,23 @@ 0.3 test3 2023-01-02T16:00 100 -- !q02 -- -0.1 test1 2023-01-01T00:00 \N -0.2 test2 2023-01-02T00:00 \N +0.1 test1 2022-12-31T16:00 \N +0.2 test2 2023-01-01T16:00 \N -- !q03 -- -0.3 test3 2023-01-03T00:00 100 +0.3 test3 2023-01-02T16:00 100 -- !q04 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 -2023-01-03T00:00 100 0.3 test3 +2022-12-31T16:00 \N 0.1 test1 +2023-01-01T16:00 \N 0.2 test2 +2023-01-02T16:00 100 0.3 test3 -- !q05 -- -2023-01-01T00:00 \N 0.1 test1 -2023-01-02T00:00 \N 0.2 test2 +2022-12-31T16:00 \N 0.1 test1 +2023-01-01T16:00 \N 0.2 test2 -- !q06 -- -2023-01-03T00:00 100 0.3 test3 +2023-01-02T16:00 100 0.3 test3 -- !q01 -- 0.1 test1 2023-01-01T00:00 \N diff --git a/regression-test/data/external_table_p0/hive/test_hive_page_index.out b/regression-test/data/external_table_p0/hive/test_hive_page_index.out index 49b31bca6a0eda..f624da90b27bcc 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_page_index.out +++ b/regression-test/data/external_table_p0/hive/test_hive_page_index.out @@ -35,24752 +35,24761 @@ true 8 8 8 80 8.8 80.8 28 01/03/09 8 2009-01-02T23:28:01.180 2009 1 true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 -- !q2 -- -false 1 1 1 10 1.1 10.1 2301 08/19/09 1 2009-08-19T09:01:08.100 2009 8 -false 1 1 1 10 1.1 10.1 2311 08/20/09 1 2009-08-20T09:11:08.550 2009 8 -false 1 1 1 10 1.1 10.1 2321 08/21/09 1 2009-08-21T09:21:09 2009 8 -false 1 1 1 10 1.1 10.1 2331 08/22/09 1 2009-08-22T09:31:09.450 2009 8 -false 1 1 1 10 1.1 10.1 2341 08/23/09 1 2009-08-23T09:41:09.900 2009 8 -false 1 1 1 10 1.1 10.1 2351 08/24/09 1 2009-08-24T09:51:10.350 2009 8 -false 1 1 1 10 1.1 10.1 2361 08/25/09 1 2009-08-25T10:01:10.800 2009 8 -false 1 1 1 10 1.1 10.1 2371 08/26/09 1 2009-08-26T10:11:11.250 2009 8 -false 1 1 1 10 1.1 10.1 2381 08/27/09 1 2009-08-27T10:21:11.700 2009 8 -false 1 1 1 10 1.1 10.1 2391 08/28/09 1 2009-08-28T10:31:12.150 2009 8 -false 1 1 1 10 1.1 10.1 2401 08/29/09 1 2009-08-29T10:41:12.600 2009 8 -false 1 1 1 10 1.1 10.1 2411 08/30/09 1 2009-08-30T10:51:13.500 2009 8 -false 1 1 1 10 1.1 10.1 2421 08/31/09 1 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2009-04-29T10:42:12.610 2009 4 -true 2 2 2 20 2.2 20.2 1192 04/30/09 2 2009-04-30T10:52:13.600 2009 4 -true 2 2 2 20 2.2 20.2 12 01/02/09 2 2009-01-02T07:12:00.460 2009 1 -true 2 2 2 20 2.2 20.2 1202 05/01/09 2 2009-05-01T06:02:00.100 2009 5 -true 2 2 2 20 2.2 20.2 1212 05/02/09 2 2009-05-02T06:12:00.460 2009 5 -true 2 2 2 20 2.2 20.2 122 01/13/09 2 2009-01-13T09:02:05.410 2009 1 -true 2 2 2 20 2.2 20.2 1222 05/03/09 2 2009-05-03T06:22:00.910 2009 5 -true 2 2 2 20 2.2 20.2 1232 05/04/09 2 2009-05-04T06:32:01.360 2009 5 -true 2 2 2 20 2.2 20.2 1242 05/05/09 2 2009-05-05T06:42:01.810 2009 5 -true 2 2 2 20 2.2 20.2 1252 05/06/09 2 2009-05-06T06:52:02.260 2009 5 -true 2 2 2 20 2.2 20.2 1262 05/07/09 2 2009-05-07T07:02:02.710 2009 5 -true 2 2 2 20 2.2 20.2 1272 05/08/09 2 2009-05-08T07:12:03.160 2009 5 -true 2 2 2 20 2.2 20.2 1282 05/09/09 2 2009-05-09T07:22:03.610 2009 5 -true 2 2 2 20 2.2 20.2 1292 05/10/09 2 2009-05-10T07:32:04.600 2009 5 -true 2 2 2 20 2.2 20.2 1302 05/11/09 2 2009-05-11T07:42:04.510 2009 5 -true 2 2 2 20 2.2 20.2 1312 05/12/09 2 2009-05-12T07:52:04.960 2009 5 -true 2 2 2 20 2.2 20.2 132 01/14/09 2 2009-01-14T09:12:05.860 2009 1 -true 2 2 2 20 2.2 20.2 1322 05/13/09 2 2009-05-13T08:02:05.410 2009 5 -true 2 2 2 20 2.2 20.2 1332 05/14/09 2 2009-05-14T08:12:05.860 2009 5 -true 2 2 2 20 2.2 20.2 1342 05/15/09 2 2009-05-15T08:22:06.310 2009 5 -true 2 2 2 20 2.2 20.2 1352 05/16/09 2 2009-05-16T08:32:06.760 2009 5 -true 2 2 2 20 2.2 20.2 1362 05/17/09 2 2009-05-17T08:42:07.210 2009 5 -true 2 2 2 20 2.2 20.2 1372 05/18/09 2 2009-05-18T08:52:07.660 2009 5 -true 2 2 2 20 2.2 20.2 1382 05/19/09 2 2009-05-19T09:02:08.110 2009 5 -true 2 2 2 20 2.2 20.2 1392 05/20/09 2 2009-05-20T09:12:08.560 2009 5 -true 2 2 2 20 2.2 20.2 1402 05/21/09 2 2009-05-21T09:22:09.100 2009 5 -true 2 2 2 20 2.2 20.2 1412 05/22/09 2 2009-05-22T09:32:09.460 2009 5 -true 2 2 2 20 2.2 20.2 142 01/15/09 2 2009-01-15T09:22:06.310 2009 1 -true 2 2 2 20 2.2 20.2 1422 05/23/09 2 2009-05-23T09:42:09.910 2009 5 -true 2 2 2 20 2.2 20.2 1432 05/24/09 2 2009-05-24T09:52:10.360 2009 5 -true 2 2 2 20 2.2 20.2 1442 05/25/09 2 2009-05-25T10:02:10.810 2009 5 -true 2 2 2 20 2.2 20.2 1452 05/26/09 2 2009-05-26T10:12:11.260 2009 5 -true 2 2 2 20 2.2 20.2 1462 05/27/09 2 2009-05-27T10:22:11.710 2009 5 -true 2 2 2 20 2.2 20.2 1472 05/28/09 2 2009-05-28T10:32:12.160 2009 5 -true 2 2 2 20 2.2 20.2 1482 05/29/09 2 2009-05-29T10:42:12.610 2009 5 -true 2 2 2 20 2.2 20.2 1492 05/30/09 2 2009-05-30T10:52:13.600 2009 5 -true 2 2 2 20 2.2 20.2 1502 05/31/09 2 2009-05-31T11:02:13.510 2009 5 -true 2 2 2 20 2.2 20.2 1512 06/01/09 2 2009-06-01T06:02:00.100 2009 6 -true 2 2 2 20 2.2 20.2 152 01/16/09 2 2009-01-16T09:32:06.760 2009 1 -true 2 2 2 20 2.2 20.2 1522 06/02/09 2 2009-06-02T06:12:00.460 2009 6 -true 2 2 2 20 2.2 20.2 1532 06/03/09 2 2009-06-03T06:22:00.910 2009 6 -true 2 2 2 20 2.2 20.2 1542 06/04/09 2 2009-06-04T06:32:01.360 2009 6 -true 2 2 2 20 2.2 20.2 1552 06/05/09 2 2009-06-05T06:42:01.810 2009 6 -true 2 2 2 20 2.2 20.2 1562 06/06/09 2 2009-06-06T06:52:02.260 2009 6 -true 2 2 2 20 2.2 20.2 1572 06/07/09 2 2009-06-07T07:02:02.710 2009 6 -true 2 2 2 20 2.2 20.2 1582 06/08/09 2 2009-06-08T07:12:03.160 2009 6 -true 2 2 2 20 2.2 20.2 1592 06/09/09 2 2009-06-09T07:22:03.610 2009 6 -true 2 2 2 20 2.2 20.2 1602 06/10/09 2 2009-06-10T07:32:04.600 2009 6 -true 2 2 2 20 2.2 20.2 1612 06/11/09 2 2009-06-11T07:42:04.510 2009 6 -true 2 2 2 20 2.2 20.2 162 01/17/09 2 2009-01-17T09:42:07.210 2009 1 -true 2 2 2 20 2.2 20.2 1622 06/12/09 2 2009-06-12T07:52:04.960 2009 6 -true 2 2 2 20 2.2 20.2 1632 06/13/09 2 2009-06-13T08:02:05.410 2009 6 -true 2 2 2 20 2.2 20.2 1642 06/14/09 2 2009-06-14T08:12:05.860 2009 6 -true 2 2 2 20 2.2 20.2 1652 06/15/09 2 2009-06-15T08:22:06.310 2009 6 -true 2 2 2 20 2.2 20.2 1662 06/16/09 2 2009-06-16T08:32:06.760 2009 6 -true 2 2 2 20 2.2 20.2 1672 06/17/09 2 2009-06-17T08:42:07.210 2009 6 -true 2 2 2 20 2.2 20.2 1682 06/18/09 2 2009-06-18T08:52:07.660 2009 6 -true 2 2 2 20 2.2 20.2 1692 06/19/09 2 2009-06-19T09:02:08.110 2009 6 -true 2 2 2 20 2.2 20.2 1702 06/20/09 2 2009-06-20T09:12:08.560 2009 6 -true 2 2 2 20 2.2 20.2 1712 06/21/09 2 2009-06-21T09:22:09.100 2009 6 -true 2 2 2 20 2.2 20.2 172 01/18/09 2 2009-01-18T09:52:07.660 2009 1 -true 2 2 2 20 2.2 20.2 1722 06/22/09 2 2009-06-22T09:32:09.460 2009 6 -true 2 2 2 20 2.2 20.2 1732 06/23/09 2 2009-06-23T09:42:09.910 2009 6 -true 2 2 2 20 2.2 20.2 1742 06/24/09 2 2009-06-24T09:52:10.360 2009 6 -true 2 2 2 20 2.2 20.2 1752 06/25/09 2 2009-06-25T10:02:10.810 2009 6 -true 2 2 2 20 2.2 20.2 1762 06/26/09 2 2009-06-26T10:12:11.260 2009 6 -true 2 2 2 20 2.2 20.2 1772 06/27/09 2 2009-06-27T10:22:11.710 2009 6 -true 2 2 2 20 2.2 20.2 1782 06/28/09 2 2009-06-28T10:32:12.160 2009 6 -true 2 2 2 20 2.2 20.2 1792 06/29/09 2 2009-06-29T10:42:12.610 2009 6 -true 2 2 2 20 2.2 20.2 1802 06/30/09 2 2009-06-30T10:52:13.600 2009 6 -true 2 2 2 20 2.2 20.2 1812 07/01/09 2 2009-07-01T06:02:00.100 2009 7 -true 2 2 2 20 2.2 20.2 182 01/19/09 2 2009-01-19T10:02:08.110 2009 1 -true 2 2 2 20 2.2 20.2 1822 07/02/09 2 2009-07-02T06:12:00.460 2009 7 -true 2 2 2 20 2.2 20.2 1832 07/03/09 2 2009-07-03T06:22:00.910 2009 7 -true 2 2 2 20 2.2 20.2 1842 07/04/09 2 2009-07-04T06:32:01.360 2009 7 -true 2 2 2 20 2.2 20.2 1852 07/05/09 2 2009-07-05T06:42:01.810 2009 7 -true 2 2 2 20 2.2 20.2 1862 07/06/09 2 2009-07-06T06:52:02.260 2009 7 -true 2 2 2 20 2.2 20.2 1872 07/07/09 2 2009-07-07T07:02:02.710 2009 7 -true 2 2 2 20 2.2 20.2 1882 07/08/09 2 2009-07-08T07:12:03.160 2009 7 -true 2 2 2 20 2.2 20.2 1892 07/09/09 2 2009-07-09T07:22:03.610 2009 7 -true 2 2 2 20 2.2 20.2 1902 07/10/09 2 2009-07-10T07:32:04.600 2009 7 -true 2 2 2 20 2.2 20.2 1912 07/11/09 2 2009-07-11T07:42:04.510 2009 7 -true 2 2 2 20 2.2 20.2 192 01/20/09 2 2009-01-20T10:12:08.560 2009 1 -true 2 2 2 20 2.2 20.2 1922 07/12/09 2 2009-07-12T07:52:04.960 2009 7 -true 2 2 2 20 2.2 20.2 1932 07/13/09 2 2009-07-13T08:02:05.410 2009 7 -true 2 2 2 20 2.2 20.2 1942 07/14/09 2 2009-07-14T08:12:05.860 2009 7 -true 2 2 2 20 2.2 20.2 1952 07/15/09 2 2009-07-15T08:22:06.310 2009 7 -true 2 2 2 20 2.2 20.2 1962 07/16/09 2 2009-07-16T08:32:06.760 2009 7 -true 2 2 2 20 2.2 20.2 1972 07/17/09 2 2009-07-17T08:42:07.210 2009 7 -true 2 2 2 20 2.2 20.2 1982 07/18/09 2 2009-07-18T08:52:07.660 2009 7 -true 2 2 2 20 2.2 20.2 1992 07/19/09 2 2009-07-19T09:02:08.110 2009 7 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -true 2 2 2 20 2.2 20.2 2002 07/20/09 2 2009-07-20T09:12:08.560 2009 7 -true 2 2 2 20 2.2 20.2 2012 07/21/09 2 2009-07-21T09:22:09.100 2009 7 -true 2 2 2 20 2.2 20.2 202 01/21/09 2 2009-01-21T10:22:09.100 2009 1 -true 2 2 2 20 2.2 20.2 2022 07/22/09 2 2009-07-22T09:32:09.460 2009 7 -true 2 2 2 20 2.2 20.2 2032 07/23/09 2 2009-07-23T09:42:09.910 2009 7 -true 2 2 2 20 2.2 20.2 2042 07/24/09 2 2009-07-24T09:52:10.360 2009 7 -true 2 2 2 20 2.2 20.2 2052 07/25/09 2 2009-07-25T10:02:10.810 2009 7 -true 2 2 2 20 2.2 20.2 2062 07/26/09 2 2009-07-26T10:12:11.260 2009 7 -true 2 2 2 20 2.2 20.2 2072 07/27/09 2 2009-07-27T10:22:11.710 2009 7 -true 2 2 2 20 2.2 20.2 2082 07/28/09 2 2009-07-28T10:32:12.160 2009 7 -true 2 2 2 20 2.2 20.2 2092 07/29/09 2 2009-07-29T10:42:12.610 2009 7 -true 2 2 2 20 2.2 20.2 2102 07/30/09 2 2009-07-30T10:52:13.600 2009 7 -true 2 2 2 20 2.2 20.2 2112 07/31/09 2 2009-07-31T11:02:13.510 2009 7 -true 2 2 2 20 2.2 20.2 212 01/22/09 2 2009-01-22T10:32:09.460 2009 1 -true 2 2 2 20 2.2 20.2 2122 08/01/09 2 2009-08-01T06:02:00.100 2009 8 -true 2 2 2 20 2.2 20.2 2132 08/02/09 2 2009-08-02T06:12:00.460 2009 8 -true 2 2 2 20 2.2 20.2 2142 08/03/09 2 2009-08-03T06:22:00.910 2009 8 -true 2 2 2 20 2.2 20.2 2152 08/04/09 2 2009-08-04T06:32:01.360 2009 8 -true 2 2 2 20 2.2 20.2 2162 08/05/09 2 2009-08-05T06:42:01.810 2009 8 -true 2 2 2 20 2.2 20.2 2172 08/06/09 2 2009-08-06T06:52:02.260 2009 8 -true 2 2 2 20 2.2 20.2 2182 08/07/09 2 2009-08-07T07:02:02.710 2009 8 -true 2 2 2 20 2.2 20.2 2192 08/08/09 2 2009-08-08T07:12:03.160 2009 8 -true 2 2 2 20 2.2 20.2 22 01/03/09 2 2009-01-03T07:22:00.910 2009 1 -true 2 2 2 20 2.2 20.2 2202 08/09/09 2 2009-08-09T07:22:03.610 2009 8 -true 2 2 2 20 2.2 20.2 2212 08/10/09 2 2009-08-10T07:32:04.600 2009 8 -true 2 2 2 20 2.2 20.2 222 01/23/09 2 2009-01-23T10:42:09.910 2009 1 -true 2 2 2 20 2.2 20.2 2222 08/11/09 2 2009-08-11T07:42:04.510 2009 8 -true 2 2 2 20 2.2 20.2 2232 08/12/09 2 2009-08-12T07:52:04.960 2009 8 -true 2 2 2 20 2.2 20.2 2242 08/13/09 2 2009-08-13T08:02:05.410 2009 8 -true 2 2 2 20 2.2 20.2 2252 08/14/09 2 2009-08-14T08:12:05.860 2009 8 -true 2 2 2 20 2.2 20.2 2262 08/15/09 2 2009-08-15T08:22:06.310 2009 8 -true 2 2 2 20 2.2 20.2 2272 08/16/09 2 2009-08-16T08:32:06.760 2009 8 -true 2 2 2 20 2.2 20.2 2282 08/17/09 2 2009-08-17T08:42:07.210 2009 8 -true 2 2 2 20 2.2 20.2 2292 08/18/09 2 2009-08-18T08:52:07.660 2009 8 -true 2 2 2 20 2.2 20.2 2302 08/19/09 2 2009-08-19T09:02:08.110 2009 8 -true 2 2 2 20 2.2 20.2 2312 08/20/09 2 2009-08-20T09:12:08.560 2009 8 -true 2 2 2 20 2.2 20.2 232 01/24/09 2 2009-01-24T10:52:10.360 2009 1 -true 2 2 2 20 2.2 20.2 2322 08/21/09 2 2009-08-21T09:22:09.100 2009 8 -true 2 2 2 20 2.2 20.2 2332 08/22/09 2 2009-08-22T09:32:09.460 2009 8 -true 2 2 2 20 2.2 20.2 2342 08/23/09 2 2009-08-23T09:42:09.910 2009 8 -true 2 2 2 20 2.2 20.2 2352 08/24/09 2 2009-08-24T09:52:10.360 2009 8 -true 2 2 2 20 2.2 20.2 2362 08/25/09 2 2009-08-25T10:02:10.810 2009 8 -true 2 2 2 20 2.2 20.2 2372 08/26/09 2 2009-08-26T10:12:11.260 2009 8 -true 2 2 2 20 2.2 20.2 2382 08/27/09 2 2009-08-27T10:22:11.710 2009 8 -true 2 2 2 20 2.2 20.2 2392 08/28/09 2 2009-08-28T10:32:12.160 2009 8 -true 2 2 2 20 2.2 20.2 2402 08/29/09 2 2009-08-29T10:42:12.610 2009 8 -true 2 2 2 20 2.2 20.2 2412 08/30/09 2 2009-08-30T10:52:13.600 2009 8 -true 2 2 2 20 2.2 20.2 242 01/25/09 2 2009-01-25T11:02:10.810 2009 1 -true 2 2 2 20 2.2 20.2 2422 08/31/09 2 2009-08-31T11:02:13.510 2009 8 -true 2 2 2 20 2.2 20.2 2432 09/01/09 2 2009-09-01T06:02:00.100 2009 9 -true 2 2 2 20 2.2 20.2 2442 09/02/09 2 2009-09-02T06:12:00.460 2009 9 -true 2 2 2 20 2.2 20.2 2452 09/03/09 2 2009-09-03T06:22:00.910 2009 9 -true 2 2 2 20 2.2 20.2 2462 09/04/09 2 2009-09-04T06:32:01.360 2009 9 -true 2 2 2 20 2.2 20.2 2472 09/05/09 2 2009-09-05T06:42:01.810 2009 9 -true 2 2 2 20 2.2 20.2 2482 09/06/09 2 2009-09-06T06:52:02.260 2009 9 -true 2 2 2 20 2.2 20.2 2492 09/07/09 2 2009-09-07T07:02:02.710 2009 9 -true 2 2 2 20 2.2 20.2 2502 09/08/09 2 2009-09-08T07:12:03.160 2009 9 -true 2 2 2 20 2.2 20.2 2512 09/09/09 2 2009-09-09T07:22:03.610 2009 9 -true 2 2 2 20 2.2 20.2 252 01/26/09 2 2009-01-26T11:12:11.260 2009 1 -true 2 2 2 20 2.2 20.2 2522 09/10/09 2 2009-09-10T07:32:04.600 2009 9 -true 2 2 2 20 2.2 20.2 2532 09/11/09 2 2009-09-11T07:42:04.510 2009 9 -true 2 2 2 20 2.2 20.2 2542 09/12/09 2 2009-09-12T07:52:04.960 2009 9 -true 2 2 2 20 2.2 20.2 2552 09/13/09 2 2009-09-13T08:02:05.410 2009 9 -true 2 2 2 20 2.2 20.2 2562 09/14/09 2 2009-09-14T08:12:05.860 2009 9 -true 2 2 2 20 2.2 20.2 2572 09/15/09 2 2009-09-15T08:22:06.310 2009 9 -true 2 2 2 20 2.2 20.2 2582 09/16/09 2 2009-09-16T08:32:06.760 2009 9 -true 2 2 2 20 2.2 20.2 2592 09/17/09 2 2009-09-17T08:42:07.210 2009 9 -true 2 2 2 20 2.2 20.2 2602 09/18/09 2 2009-09-18T08:52:07.660 2009 9 -true 2 2 2 20 2.2 20.2 2612 09/19/09 2 2009-09-19T09:02:08.110 2009 9 -true 2 2 2 20 2.2 20.2 262 01/27/09 2 2009-01-27T11:22:11.710 2009 1 -true 2 2 2 20 2.2 20.2 2622 09/20/09 2 2009-09-20T09:12:08.560 2009 9 -true 2 2 2 20 2.2 20.2 2632 09/21/09 2 2009-09-21T09:22:09.100 2009 9 -true 2 2 2 20 2.2 20.2 2642 09/22/09 2 2009-09-22T09:32:09.460 2009 9 -true 2 2 2 20 2.2 20.2 2652 09/23/09 2 2009-09-23T09:42:09.910 2009 9 -true 2 2 2 20 2.2 20.2 2662 09/24/09 2 2009-09-24T09:52:10.360 2009 9 -true 2 2 2 20 2.2 20.2 2672 09/25/09 2 2009-09-25T10:02:10.810 2009 9 -true 2 2 2 20 2.2 20.2 2682 09/26/09 2 2009-09-26T10:12:11.260 2009 9 -true 2 2 2 20 2.2 20.2 2692 09/27/09 2 2009-09-27T10:22:11.710 2009 9 -true 2 2 2 20 2.2 20.2 2702 09/28/09 2 2009-09-28T10:32:12.160 2009 9 -true 2 2 2 20 2.2 20.2 2712 09/29/09 2 2009-09-29T10:42:12.610 2009 9 -true 2 2 2 20 2.2 20.2 272 01/28/09 2 2009-01-28T11:32:12.160 2009 1 -true 2 2 2 20 2.2 20.2 2722 09/30/09 2 2009-09-30T10:52:13.600 2009 9 -true 2 2 2 20 2.2 20.2 2732 10/01/09 2 2009-10-01T06:02:00.100 2009 10 -true 2 2 2 20 2.2 20.2 2742 10/02/09 2 2009-10-02T06:12:00.460 2009 10 -true 2 2 2 20 2.2 20.2 2752 10/03/09 2 2009-10-03T06:22:00.910 2009 10 -true 2 2 2 20 2.2 20.2 2762 10/04/09 2 2009-10-04T06:32:01.360 2009 10 -true 2 2 2 20 2.2 20.2 2772 10/05/09 2 2009-10-05T06:42:01.810 2009 10 -true 2 2 2 20 2.2 20.2 2782 10/06/09 2 2009-10-06T06:52:02.260 2009 10 -true 2 2 2 20 2.2 20.2 2792 10/07/09 2 2009-10-07T07:02:02.710 2009 10 -true 2 2 2 20 2.2 20.2 2802 10/08/09 2 2009-10-08T07:12:03.160 2009 10 -true 2 2 2 20 2.2 20.2 2812 10/09/09 2 2009-10-09T07:22:03.610 2009 10 -true 2 2 2 20 2.2 20.2 282 01/29/09 2 2009-01-29T11:42:12.610 2009 1 -true 2 2 2 20 2.2 20.2 2822 10/10/09 2 2009-10-10T07:32:04.600 2009 10 -true 2 2 2 20 2.2 20.2 2832 10/11/09 2 2009-10-11T07:42:04.510 2009 10 -true 2 2 2 20 2.2 20.2 2842 10/12/09 2 2009-10-12T07:52:04.960 2009 10 -true 2 2 2 20 2.2 20.2 2852 10/13/09 2 2009-10-13T08:02:05.410 2009 10 -true 2 2 2 20 2.2 20.2 2862 10/14/09 2 2009-10-14T08:12:05.860 2009 10 -true 2 2 2 20 2.2 20.2 2872 10/15/09 2 2009-10-15T08:22:06.310 2009 10 -true 2 2 2 20 2.2 20.2 2882 10/16/09 2 2009-10-16T08:32:06.760 2009 10 -true 2 2 2 20 2.2 20.2 2892 10/17/09 2 2009-10-17T08:42:07.210 2009 10 -true 2 2 2 20 2.2 20.2 2902 10/18/09 2 2009-10-18T08:52:07.660 2009 10 -true 2 2 2 20 2.2 20.2 2912 10/19/09 2 2009-10-19T09:02:08.110 2009 10 -true 2 2 2 20 2.2 20.2 292 01/30/09 2 2009-01-30T11:52:13.600 2009 1 -true 2 2 2 20 2.2 20.2 2922 10/20/09 2 2009-10-20T09:12:08.560 2009 10 -true 2 2 2 20 2.2 20.2 2932 10/21/09 2 2009-10-21T09:22:09.100 2009 10 -true 2 2 2 20 2.2 20.2 2942 10/22/09 2 2009-10-22T09:32:09.460 2009 10 -true 2 2 2 20 2.2 20.2 2952 10/23/09 2 2009-10-23T09:42:09.910 2009 10 -true 2 2 2 20 2.2 20.2 2962 10/24/09 2 2009-10-24T09:52:10.360 2009 10 -true 2 2 2 20 2.2 20.2 2972 10/25/09 2 2009-10-25T11:02:10.810 2009 10 -true 2 2 2 20 2.2 20.2 2982 10/26/09 2 2009-10-26T11:12:11.260 2009 10 -true 2 2 2 20 2.2 20.2 2992 10/27/09 2 2009-10-27T11:22:11.710 2009 10 -true 2 2 2 20 2.2 20.2 3002 10/28/09 2 2009-10-28T11:32:12.160 2009 10 -true 2 2 2 20 2.2 20.2 3012 10/29/09 2 2009-10-29T11:42:12.610 2009 10 -true 2 2 2 20 2.2 20.2 302 01/31/09 2 2009-01-31T12:02:13.510 2009 1 -true 2 2 2 20 2.2 20.2 3022 10/30/09 2 2009-10-30T11:52:13.600 2009 10 -true 2 2 2 20 2.2 20.2 3032 10/31/09 2 2009-10-31T12:02:13.510 2009 10 -true 2 2 2 20 2.2 20.2 3042 11/01/09 2 2009-11-01T07:02:00.100 2009 11 -true 2 2 2 20 2.2 20.2 3052 11/02/09 2 2009-11-02T07:12:00.460 2009 11 -true 2 2 2 20 2.2 20.2 3062 11/03/09 2 2009-11-03T07:22:00.910 2009 11 -true 2 2 2 20 2.2 20.2 3072 11/04/09 2 2009-11-04T07:32:01.360 2009 11 -true 2 2 2 20 2.2 20.2 3082 11/05/09 2 2009-11-05T07:42:01.810 2009 11 -true 2 2 2 20 2.2 20.2 3092 11/06/09 2 2009-11-06T07:52:02.260 2009 11 -true 2 2 2 20 2.2 20.2 3102 11/07/09 2 2009-11-07T08:02:02.710 2009 11 -true 2 2 2 20 2.2 20.2 3112 11/08/09 2 2009-11-08T08:12:03.160 2009 11 -true 2 2 2 20 2.2 20.2 312 02/01/09 2 2009-02-01T07:02:00.100 2009 2 -true 2 2 2 20 2.2 20.2 3122 11/09/09 2 2009-11-09T08:22:03.610 2009 11 -true 2 2 2 20 2.2 20.2 3132 11/10/09 2 2009-11-10T08:32:04.600 2009 11 -true 2 2 2 20 2.2 20.2 3142 11/11/09 2 2009-11-11T08:42:04.510 2009 11 -true 2 2 2 20 2.2 20.2 3152 11/12/09 2 2009-11-12T08:52:04.960 2009 11 -true 2 2 2 20 2.2 20.2 3162 11/13/09 2 2009-11-13T09:02:05.410 2009 11 -true 2 2 2 20 2.2 20.2 3172 11/14/09 2 2009-11-14T09:12:05.860 2009 11 -true 2 2 2 20 2.2 20.2 3182 11/15/09 2 2009-11-15T09:22:06.310 2009 11 -true 2 2 2 20 2.2 20.2 3192 11/16/09 2 2009-11-16T09:32:06.760 2009 11 -true 2 2 2 20 2.2 20.2 32 01/04/09 2 2009-01-04T07:32:01.360 2009 1 -true 2 2 2 20 2.2 20.2 3202 11/17/09 2 2009-11-17T09:42:07.210 2009 11 -true 2 2 2 20 2.2 20.2 3212 11/18/09 2 2009-11-18T09:52:07.660 2009 11 -true 2 2 2 20 2.2 20.2 322 02/02/09 2 2009-02-02T07:12:00.460 2009 2 -true 2 2 2 20 2.2 20.2 3222 11/19/09 2 2009-11-19T10:02:08.110 2009 11 -true 2 2 2 20 2.2 20.2 3232 11/20/09 2 2009-11-20T10:12:08.560 2009 11 -true 2 2 2 20 2.2 20.2 3242 11/21/09 2 2009-11-21T10:22:09.100 2009 11 -true 2 2 2 20 2.2 20.2 3252 11/22/09 2 2009-11-22T10:32:09.460 2009 11 -true 2 2 2 20 2.2 20.2 3262 11/23/09 2 2009-11-23T10:42:09.910 2009 11 -true 2 2 2 20 2.2 20.2 3272 11/24/09 2 2009-11-24T10:52:10.360 2009 11 -true 2 2 2 20 2.2 20.2 3282 11/25/09 2 2009-11-25T11:02:10.810 2009 11 -true 2 2 2 20 2.2 20.2 3292 11/26/09 2 2009-11-26T11:12:11.260 2009 11 -true 2 2 2 20 2.2 20.2 3302 11/27/09 2 2009-11-27T11:22:11.710 2009 11 -true 2 2 2 20 2.2 20.2 3312 11/28/09 2 2009-11-28T11:32:12.160 2009 11 -true 2 2 2 20 2.2 20.2 332 02/03/09 2 2009-02-03T07:22:00.910 2009 2 -true 2 2 2 20 2.2 20.2 3322 11/29/09 2 2009-11-29T11:42:12.610 2009 11 -true 2 2 2 20 2.2 20.2 3332 11/30/09 2 2009-11-30T11:52:13.600 2009 11 -true 2 2 2 20 2.2 20.2 3342 12/01/09 2 2009-12-01T07:02:00.100 2009 12 -true 2 2 2 20 2.2 20.2 3352 12/02/09 2 2009-12-02T07:12:00.460 2009 12 -true 2 2 2 20 2.2 20.2 3362 12/03/09 2 2009-12-03T07:22:00.910 2009 12 -true 2 2 2 20 2.2 20.2 3372 12/04/09 2 2009-12-04T07:32:01.360 2009 12 -true 2 2 2 20 2.2 20.2 3382 12/05/09 2 2009-12-05T07:42:01.810 2009 12 -true 2 2 2 20 2.2 20.2 3392 12/06/09 2 2009-12-06T07:52:02.260 2009 12 -true 2 2 2 20 2.2 20.2 3402 12/07/09 2 2009-12-07T08:02:02.710 2009 12 -true 2 2 2 20 2.2 20.2 3412 12/08/09 2 2009-12-08T08:12:03.160 2009 12 -true 2 2 2 20 2.2 20.2 342 02/04/09 2 2009-02-04T07:32:01.360 2009 2 -true 2 2 2 20 2.2 20.2 3422 12/09/09 2 2009-12-09T08:22:03.610 2009 12 -true 2 2 2 20 2.2 20.2 3432 12/10/09 2 2009-12-10T08:32:04.600 2009 12 -true 2 2 2 20 2.2 20.2 3442 12/11/09 2 2009-12-11T08:42:04.510 2009 12 -true 2 2 2 20 2.2 20.2 3452 12/12/09 2 2009-12-12T08:52:04.960 2009 12 -true 2 2 2 20 2.2 20.2 3462 12/13/09 2 2009-12-13T09:02:05.410 2009 12 -true 2 2 2 20 2.2 20.2 3472 12/14/09 2 2009-12-14T09:12:05.860 2009 12 -true 2 2 2 20 2.2 20.2 3482 12/15/09 2 2009-12-15T09:22:06.310 2009 12 -true 2 2 2 20 2.2 20.2 3492 12/16/09 2 2009-12-16T09:32:06.760 2009 12 -true 2 2 2 20 2.2 20.2 3502 12/17/09 2 2009-12-17T09:42:07.210 2009 12 -true 2 2 2 20 2.2 20.2 3512 12/18/09 2 2009-12-18T09:52:07.660 2009 12 -true 2 2 2 20 2.2 20.2 352 02/05/09 2 2009-02-05T07:42:01.810 2009 2 -true 2 2 2 20 2.2 20.2 3522 12/19/09 2 2009-12-19T10:02:08.110 2009 12 -true 2 2 2 20 2.2 20.2 3532 12/20/09 2 2009-12-20T10:12:08.560 2009 12 -true 2 2 2 20 2.2 20.2 3542 12/21/09 2 2009-12-21T10:22:09.100 2009 12 -true 2 2 2 20 2.2 20.2 3552 12/22/09 2 2009-12-22T10:32:09.460 2009 12 -true 2 2 2 20 2.2 20.2 3562 12/23/09 2 2009-12-23T10:42:09.910 2009 12 -true 2 2 2 20 2.2 20.2 3572 12/24/09 2 2009-12-24T10:52:10.360 2009 12 -true 2 2 2 20 2.2 20.2 3582 12/25/09 2 2009-12-25T11:02:10.810 2009 12 -true 2 2 2 20 2.2 20.2 3592 12/26/09 2 2009-12-26T11:12:11.260 2009 12 -true 2 2 2 20 2.2 20.2 3602 12/27/09 2 2009-12-27T11:22:11.710 2009 12 -true 2 2 2 20 2.2 20.2 3612 12/28/09 2 2009-12-28T11:32:12.160 2009 12 -true 2 2 2 20 2.2 20.2 362 02/06/09 2 2009-02-06T07:52:02.260 2009 2 -true 2 2 2 20 2.2 20.2 3622 12/29/09 2 2009-12-29T11:42:12.610 2009 12 -true 2 2 2 20 2.2 20.2 3632 12/30/09 2 2009-12-30T11:52:13.600 2009 12 -true 2 2 2 20 2.2 20.2 3642 12/31/09 2 2009-12-31T12:02:13.510 2009 12 -true 2 2 2 20 2.2 20.2 3652 01/01/10 2 2010-01-01T07:02:00.100 2010 1 -true 2 2 2 20 2.2 20.2 3662 01/02/10 2 2010-01-02T07:12:00.460 2010 1 -true 2 2 2 20 2.2 20.2 3672 01/03/10 2 2010-01-03T07:22:00.910 2010 1 -true 2 2 2 20 2.2 20.2 3682 01/04/10 2 2010-01-04T07:32:01.360 2010 1 -true 2 2 2 20 2.2 20.2 3692 01/05/10 2 2010-01-05T07:42:01.810 2010 1 -true 2 2 2 20 2.2 20.2 3702 01/06/10 2 2010-01-06T07:52:02.260 2010 1 -true 2 2 2 20 2.2 20.2 3712 01/07/10 2 2010-01-07T08:02:02.710 2010 1 -true 2 2 2 20 2.2 20.2 372 02/07/09 2 2009-02-07T08:02:02.710 2009 2 -true 2 2 2 20 2.2 20.2 3722 01/08/10 2 2010-01-08T08:12:03.160 2010 1 -true 2 2 2 20 2.2 20.2 3732 01/09/10 2 2010-01-09T08:22:03.610 2010 1 -true 2 2 2 20 2.2 20.2 3742 01/10/10 2 2010-01-10T08:32:04.600 2010 1 -true 2 2 2 20 2.2 20.2 3752 01/11/10 2 2010-01-11T08:42:04.510 2010 1 -true 2 2 2 20 2.2 20.2 3762 01/12/10 2 2010-01-12T08:52:04.960 2010 1 -true 2 2 2 20 2.2 20.2 3772 01/13/10 2 2010-01-13T09:02:05.410 2010 1 -true 2 2 2 20 2.2 20.2 3782 01/14/10 2 2010-01-14T09:12:05.860 2010 1 -true 2 2 2 20 2.2 20.2 3792 01/15/10 2 2010-01-15T09:22:06.310 2010 1 -true 2 2 2 20 2.2 20.2 3802 01/16/10 2 2010-01-16T09:32:06.760 2010 1 -true 2 2 2 20 2.2 20.2 3812 01/17/10 2 2010-01-17T09:42:07.210 2010 1 -true 2 2 2 20 2.2 20.2 382 02/08/09 2 2009-02-08T08:12:03.160 2009 2 -true 2 2 2 20 2.2 20.2 3822 01/18/10 2 2010-01-18T09:52:07.660 2010 1 -true 2 2 2 20 2.2 20.2 3832 01/19/10 2 2010-01-19T10:02:08.110 2010 1 -true 2 2 2 20 2.2 20.2 3842 01/20/10 2 2010-01-20T10:12:08.560 2010 1 -true 2 2 2 20 2.2 20.2 3852 01/21/10 2 2010-01-21T10:22:09.100 2010 1 -true 2 2 2 20 2.2 20.2 3862 01/22/10 2 2010-01-22T10:32:09.460 2010 1 -true 2 2 2 20 2.2 20.2 3872 01/23/10 2 2010-01-23T10:42:09.910 2010 1 -true 2 2 2 20 2.2 20.2 3882 01/24/10 2 2010-01-24T10:52:10.360 2010 1 -true 2 2 2 20 2.2 20.2 3892 01/25/10 2 2010-01-25T11:02:10.810 2010 1 -true 2 2 2 20 2.2 20.2 3902 01/26/10 2 2010-01-26T11:12:11.260 2010 1 -true 2 2 2 20 2.2 20.2 3912 01/27/10 2 2010-01-27T11:22:11.710 2010 1 -true 2 2 2 20 2.2 20.2 392 02/09/09 2 2009-02-09T08:22:03.610 2009 2 -true 2 2 2 20 2.2 20.2 3922 01/28/10 2 2010-01-28T11:32:12.160 2010 1 -true 2 2 2 20 2.2 20.2 3932 01/29/10 2 2010-01-29T11:42:12.610 2010 1 -true 2 2 2 20 2.2 20.2 3942 01/30/10 2 2010-01-30T11:52:13.600 2010 1 -true 2 2 2 20 2.2 20.2 3952 01/31/10 2 2010-01-31T12:02:13.510 2010 1 -true 2 2 2 20 2.2 20.2 3962 02/01/10 2 2010-02-01T07:02:00.100 2010 2 -true 2 2 2 20 2.2 20.2 3972 02/02/10 2 2010-02-02T07:12:00.460 2010 2 -true 2 2 2 20 2.2 20.2 3982 02/03/10 2 2010-02-03T07:22:00.910 2010 2 -true 2 2 2 20 2.2 20.2 3992 02/04/10 2 2010-02-04T07:32:01.360 2010 2 -true 2 2 2 20 2.2 20.2 4002 02/05/10 2 2010-02-05T07:42:01.810 2010 2 -true 2 2 2 20 2.2 20.2 4012 02/06/10 2 2010-02-06T07:52:02.260 2010 2 -true 2 2 2 20 2.2 20.2 402 02/10/09 2 2009-02-10T08:32:04.600 2009 2 -true 2 2 2 20 2.2 20.2 4022 02/07/10 2 2010-02-07T08:02:02.710 2010 2 -true 2 2 2 20 2.2 20.2 4032 02/08/10 2 2010-02-08T08:12:03.160 2010 2 -true 2 2 2 20 2.2 20.2 4042 02/09/10 2 2010-02-09T08:22:03.610 2010 2 -true 2 2 2 20 2.2 20.2 4052 02/10/10 2 2010-02-10T08:32:04.600 2010 2 -true 2 2 2 20 2.2 20.2 4062 02/11/10 2 2010-02-11T08:42:04.510 2010 2 -true 2 2 2 20 2.2 20.2 4072 02/12/10 2 2010-02-12T08:52:04.960 2010 2 -true 2 2 2 20 2.2 20.2 4082 02/13/10 2 2010-02-13T09:02:05.410 2010 2 -true 2 2 2 20 2.2 20.2 4092 02/14/10 2 2010-02-14T09:12:05.860 2010 2 -true 2 2 2 20 2.2 20.2 4102 02/15/10 2 2010-02-15T09:22:06.310 2010 2 -true 2 2 2 20 2.2 20.2 4112 02/16/10 2 2010-02-16T09:32:06.760 2010 2 -true 2 2 2 20 2.2 20.2 412 02/11/09 2 2009-02-11T08:42:04.510 2009 2 -true 2 2 2 20 2.2 20.2 4122 02/17/10 2 2010-02-17T09:42:07.210 2010 2 -true 2 2 2 20 2.2 20.2 4132 02/18/10 2 2010-02-18T09:52:07.660 2010 2 -true 2 2 2 20 2.2 20.2 4142 02/19/10 2 2010-02-19T10:02:08.110 2010 2 -true 2 2 2 20 2.2 20.2 4152 02/20/10 2 2010-02-20T10:12:08.560 2010 2 -true 2 2 2 20 2.2 20.2 4162 02/21/10 2 2010-02-21T10:22:09.100 2010 2 -true 2 2 2 20 2.2 20.2 4172 02/22/10 2 2010-02-22T10:32:09.460 2010 2 -true 2 2 2 20 2.2 20.2 4182 02/23/10 2 2010-02-23T10:42:09.910 2010 2 -true 2 2 2 20 2.2 20.2 4192 02/24/10 2 2010-02-24T10:52:10.360 2010 2 -true 2 2 2 20 2.2 20.2 42 01/05/09 2 2009-01-05T07:42:01.810 2009 1 -true 2 2 2 20 2.2 20.2 4202 02/25/10 2 2010-02-25T11:02:10.810 2010 2 -true 2 2 2 20 2.2 20.2 4212 02/26/10 2 2010-02-26T11:12:11.260 2010 2 -true 2 2 2 20 2.2 20.2 422 02/12/09 2 2009-02-12T08:52:04.960 2009 2 -true 2 2 2 20 2.2 20.2 4222 02/27/10 2 2010-02-27T11:22:11.710 2010 2 -true 2 2 2 20 2.2 20.2 4232 02/28/10 2 2010-02-28T11:32:12.160 2010 2 -true 2 2 2 20 2.2 20.2 4242 03/01/10 2 2010-03-01T07:02:00.100 2010 3 -true 2 2 2 20 2.2 20.2 4252 03/02/10 2 2010-03-02T07:12:00.460 2010 3 -true 2 2 2 20 2.2 20.2 4262 03/03/10 2 2010-03-03T07:22:00.910 2010 3 -true 2 2 2 20 2.2 20.2 4272 03/04/10 2 2010-03-04T07:32:01.360 2010 3 -true 2 2 2 20 2.2 20.2 4282 03/05/10 2 2010-03-05T07:42:01.810 2010 3 -true 2 2 2 20 2.2 20.2 4292 03/06/10 2 2010-03-06T07:52:02.260 2010 3 -true 2 2 2 20 2.2 20.2 4302 03/07/10 2 2010-03-07T08:02:02.710 2010 3 -true 2 2 2 20 2.2 20.2 4312 03/08/10 2 2010-03-08T08:12:03.160 2010 3 -true 2 2 2 20 2.2 20.2 432 02/13/09 2 2009-02-13T09:02:05.410 2009 2 -true 2 2 2 20 2.2 20.2 4322 03/09/10 2 2010-03-09T08:22:03.610 2010 3 -true 2 2 2 20 2.2 20.2 4332 03/10/10 2 2010-03-10T08:32:04.600 2010 3 -true 2 2 2 20 2.2 20.2 4342 03/11/10 2 2010-03-11T08:42:04.510 2010 3 -true 2 2 2 20 2.2 20.2 4352 03/12/10 2 2010-03-12T08:52:04.960 2010 3 -true 2 2 2 20 2.2 20.2 4362 03/13/10 2 2010-03-13T09:02:05.410 2010 3 -true 2 2 2 20 2.2 20.2 4372 03/14/10 2 2010-03-14T08:12:05.860 2010 3 -true 2 2 2 20 2.2 20.2 4382 03/15/10 2 2010-03-15T08:22:06.310 2010 3 -true 2 2 2 20 2.2 20.2 4392 03/16/10 2 2010-03-16T08:32:06.760 2010 3 -true 2 2 2 20 2.2 20.2 4402 03/17/10 2 2010-03-17T08:42:07.210 2010 3 -true 2 2 2 20 2.2 20.2 4412 03/18/10 2 2010-03-18T08:52:07.660 2010 3 -true 2 2 2 20 2.2 20.2 442 02/14/09 2 2009-02-14T09:12:05.860 2009 2 -true 2 2 2 20 2.2 20.2 4422 03/19/10 2 2010-03-19T09:02:08.110 2010 3 -true 2 2 2 20 2.2 20.2 4432 03/20/10 2 2010-03-20T09:12:08.560 2010 3 -true 2 2 2 20 2.2 20.2 4442 03/21/10 2 2010-03-21T09:22:09.100 2010 3 -true 2 2 2 20 2.2 20.2 4452 03/22/10 2 2010-03-22T09:32:09.460 2010 3 -true 2 2 2 20 2.2 20.2 4462 03/23/10 2 2010-03-23T09:42:09.910 2010 3 -true 2 2 2 20 2.2 20.2 4472 03/24/10 2 2010-03-24T09:52:10.360 2010 3 -true 2 2 2 20 2.2 20.2 4482 03/25/10 2 2010-03-25T10:02:10.810 2010 3 -true 2 2 2 20 2.2 20.2 4492 03/26/10 2 2010-03-26T10:12:11.260 2010 3 -true 2 2 2 20 2.2 20.2 4502 03/27/10 2 2010-03-27T10:22:11.710 2010 3 -true 2 2 2 20 2.2 20.2 4512 03/28/10 2 2010-03-28T09:32:12.160 2010 3 -true 2 2 2 20 2.2 20.2 452 02/15/09 2 2009-02-15T09:22:06.310 2009 2 -true 2 2 2 20 2.2 20.2 4522 03/29/10 2 2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 462 02/16/09 2 2009-02-16T09:32:06.760 2009 2 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 472 02/17/09 2 2009-02-17T09:42:07.210 2009 2 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 482 02/18/09 2 2009-02-18T09:52:07.660 2009 2 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 492 02/19/09 2 2009-02-19T10:02:08.110 2009 2 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 502 02/20/09 2 2009-02-20T10:12:08.560 2009 2 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 512 02/21/09 2 2009-02-21T10:22:09.100 2009 2 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 52 01/06/09 2 2009-01-06T07:52:02.260 2009 1 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 522 02/22/09 2 2009-02-22T10:32:09.460 2009 2 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 532 02/23/09 2 2009-02-23T10:42:09.910 2009 2 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 542 02/24/09 2 2009-02-24T10:52:10.360 2009 2 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 552 02/25/09 2 2009-02-25T11:02:10.810 2009 2 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 562 02/26/09 2 2009-02-26T11:12:11.260 2009 2 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 572 02/27/09 2 2009-02-27T11:22:11.710 2009 2 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 582 02/28/09 2 2009-02-28T11:32:12.160 2009 2 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 592 03/01/09 2 2009-03-01T07:02:00.100 2009 3 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 602 03/02/09 2 2009-03-02T07:12:00.460 2009 3 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 612 03/03/09 2 2009-03-03T07:22:00.910 2009 3 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 62 01/07/09 2 2009-01-07T08:02:02.710 2009 1 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 622 03/04/09 2 2009-03-04T07:32:01.360 2009 3 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 632 03/05/09 2 2009-03-05T07:42:01.810 2009 3 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 642 03/06/09 2 2009-03-06T07:52:02.260 2009 3 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 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2010 2 -false 3 3 3 30 3.3 30.3 4043 02/09/10 3 2010-02-09T08:23:03.630 2010 2 -false 3 3 3 30 3.3 30.3 4053 02/10/10 3 2010-02-10T08:33:04.800 2010 2 -false 3 3 3 30 3.3 30.3 4063 02/11/10 3 2010-02-11T08:43:04.530 2010 2 -false 3 3 3 30 3.3 30.3 4073 02/12/10 3 2010-02-12T08:53:04.980 2010 2 -false 3 3 3 30 3.3 30.3 4083 02/13/10 3 2010-02-13T09:03:05.430 2010 2 -false 3 3 3 30 3.3 30.3 4093 02/14/10 3 2010-02-14T09:13:05.880 2010 2 -false 3 3 3 30 3.3 30.3 4103 02/15/10 3 2010-02-15T09:23:06.330 2010 2 -false 3 3 3 30 3.3 30.3 4113 02/16/10 3 2010-02-16T09:33:06.780 2010 2 -false 3 3 3 30 3.3 30.3 4123 02/17/10 3 2010-02-17T09:43:07.230 2010 2 -false 3 3 3 30 3.3 30.3 4133 02/18/10 3 2010-02-18T09:53:07.680 2010 2 -false 3 3 3 30 3.3 30.3 4143 02/19/10 3 2010-02-19T10:03:08.130 2010 2 -false 3 3 3 30 3.3 30.3 4153 02/20/10 3 2010-02-20T10:13:08.580 2010 2 -false 3 3 3 30 3.3 30.3 4163 02/21/10 3 2010-02-21T10:23:09.300 2010 2 -false 3 3 3 30 3.3 30.3 4173 02/22/10 3 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2010 7 -false 3 3 3 30 3.3 30.3 5683 07/23/10 3 2010-07-23T09:43:09.930 2010 7 -false 3 3 3 30 3.3 30.3 5693 07/24/10 3 2010-07-24T09:53:10.380 2010 7 -false 3 3 3 30 3.3 30.3 5703 07/25/10 3 2010-07-25T10:03:10.830 2010 7 -false 3 3 3 30 3.3 30.3 5713 07/26/10 3 2010-07-26T10:13:11.280 2010 7 -false 3 3 3 30 3.3 30.3 5723 07/27/10 3 2010-07-27T10:23:11.730 2010 7 -false 3 3 3 30 3.3 30.3 5733 07/28/10 3 2010-07-28T10:33:12.180 2010 7 -false 3 3 3 30 3.3 30.3 5743 07/29/10 3 2010-07-29T10:43:12.630 2010 7 -false 3 3 3 30 3.3 30.3 5753 07/30/10 3 2010-07-30T10:53:13.800 2010 7 -false 3 3 3 30 3.3 30.3 5763 07/31/10 3 2010-07-31T11:03:13.530 2010 7 -false 3 3 3 30 3.3 30.3 5773 08/01/10 3 2010-08-01T06:03:00.300 2010 8 -false 3 3 3 30 3.3 30.3 5783 08/02/10 3 2010-08-02T06:13:00.480 2010 8 -false 3 3 3 30 3.3 30.3 5793 08/03/10 3 2010-08-03T06:23:00.930 2010 8 -false 3 3 3 30 3.3 30.3 5803 08/04/10 3 2010-08-04T06:33:01.380 2010 8 -false 3 3 3 30 3.3 30.3 5813 08/05/10 3 2010-08-05T06:43:01.830 2010 8 -false 3 3 3 30 3.3 30.3 5823 08/06/10 3 2010-08-06T06:53:02.280 2010 8 -false 3 3 3 30 3.3 30.3 5833 08/07/10 3 2010-08-07T07:03:02.730 2010 8 -false 3 3 3 30 3.3 30.3 5843 08/08/10 3 2010-08-08T07:13:03.180 2010 8 -false 3 3 3 30 3.3 30.3 5853 08/09/10 3 2010-08-09T07:23:03.630 2010 8 -false 3 3 3 30 3.3 30.3 5863 08/10/10 3 2010-08-10T07:33:04.800 2010 8 -false 3 3 3 30 3.3 30.3 5873 08/11/10 3 2010-08-11T07:43:04.530 2010 8 -false 3 3 3 30 3.3 30.3 5883 08/12/10 3 2010-08-12T07:53:04.980 2010 8 -false 3 3 3 30 3.3 30.3 5893 08/13/10 3 2010-08-13T08:03:05.430 2010 8 -false 3 3 3 30 3.3 30.3 5903 08/14/10 3 2010-08-14T08:13:05.880 2010 8 -false 3 3 3 30 3.3 30.3 5913 08/15/10 3 2010-08-15T08:23:06.330 2010 8 -false 3 3 3 30 3.3 30.3 5923 08/16/10 3 2010-08-16T08:33:06.780 2010 8 -false 3 3 3 30 3.3 30.3 5933 08/17/10 3 2010-08-17T08:43:07.230 2010 8 -false 3 3 3 30 3.3 30.3 5943 08/18/10 3 2010-08-18T08:53:07.680 2010 8 -false 3 3 3 30 3.3 30.3 5953 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2010-01-23T10:42:09.910 2010 1 -true 2 2 2 20 2.2 20.2 3882 01/24/10 2 2010-01-24T10:52:10.360 2010 1 -true 2 2 2 20 2.2 20.2 3892 01/25/10 2 2010-01-25T11:02:10.810 2010 1 -true 2 2 2 20 2.2 20.2 3902 01/26/10 2 2010-01-26T11:12:11.260 2010 1 -true 2 2 2 20 2.2 20.2 3912 01/27/10 2 2010-01-27T11:22:11.710 2010 1 -true 2 2 2 20 2.2 20.2 3922 01/28/10 2 2010-01-28T11:32:12.160 2010 1 -true 2 2 2 20 2.2 20.2 3932 01/29/10 2 2010-01-29T11:42:12.610 2010 1 -true 2 2 2 20 2.2 20.2 3942 01/30/10 2 2010-01-30T11:52:13.600 2010 1 -true 2 2 2 20 2.2 20.2 3952 01/31/10 2 2010-01-31T12:02:13.510 2010 1 -true 2 2 2 20 2.2 20.2 3962 02/01/10 2 2010-02-01T07:02:00.100 2010 2 -true 2 2 2 20 2.2 20.2 3972 02/02/10 2 2010-02-02T07:12:00.460 2010 2 -true 2 2 2 20 2.2 20.2 3982 02/03/10 2 2010-02-03T07:22:00.910 2010 2 -true 2 2 2 20 2.2 20.2 3992 02/04/10 2 2010-02-04T07:32:01.360 2010 2 -true 2 2 2 20 2.2 20.2 4002 02/05/10 2 2010-02-05T07:42:01.810 2010 2 -true 2 2 2 20 2.2 20.2 4012 02/06/10 2 2010-02-06T07:52:02.260 2010 2 -true 2 2 2 20 2.2 20.2 4022 02/07/10 2 2010-02-07T08:02:02.710 2010 2 -true 2 2 2 20 2.2 20.2 4032 02/08/10 2 2010-02-08T08:12:03.160 2010 2 -true 2 2 2 20 2.2 20.2 4042 02/09/10 2 2010-02-09T08:22:03.610 2010 2 -true 2 2 2 20 2.2 20.2 4052 02/10/10 2 2010-02-10T08:32:04.600 2010 2 -true 2 2 2 20 2.2 20.2 4062 02/11/10 2 2010-02-11T08:42:04.510 2010 2 -true 2 2 2 20 2.2 20.2 4072 02/12/10 2 2010-02-12T08:52:04.960 2010 2 -true 2 2 2 20 2.2 20.2 4082 02/13/10 2 2010-02-13T09:02:05.410 2010 2 -true 2 2 2 20 2.2 20.2 4092 02/14/10 2 2010-02-14T09:12:05.860 2010 2 -true 2 2 2 20 2.2 20.2 4102 02/15/10 2 2010-02-15T09:22:06.310 2010 2 -true 2 2 2 20 2.2 20.2 4112 02/16/10 2 2010-02-16T09:32:06.760 2010 2 -true 2 2 2 20 2.2 20.2 4122 02/17/10 2 2010-02-17T09:42:07.210 2010 2 -true 2 2 2 20 2.2 20.2 4132 02/18/10 2 2010-02-18T09:52:07.660 2010 2 -true 2 2 2 20 2.2 20.2 4142 02/19/10 2 2010-02-19T10:02:08.110 2010 2 -true 2 2 2 20 2.2 20.2 4152 02/20/10 2 2010-02-20T10:12:08.560 2010 2 -true 2 2 2 20 2.2 20.2 4162 02/21/10 2 2010-02-21T10:22:09.100 2010 2 -true 2 2 2 20 2.2 20.2 4172 02/22/10 2 2010-02-22T10:32:09.460 2010 2 -true 2 2 2 20 2.2 20.2 4182 02/23/10 2 2010-02-23T10:42:09.910 2010 2 -true 2 2 2 20 2.2 20.2 4192 02/24/10 2 2010-02-24T10:52:10.360 2010 2 -true 2 2 2 20 2.2 20.2 42 01/05/09 2 2009-01-05T07:42:01.810 2009 1 -true 2 2 2 20 2.2 20.2 4202 02/25/10 2 2010-02-25T11:02:10.810 2010 2 -true 2 2 2 20 2.2 20.2 4212 02/26/10 2 2010-02-26T11:12:11.260 2010 2 -true 2 2 2 20 2.2 20.2 4222 02/27/10 2 2010-02-27T11:22:11.710 2010 2 -true 2 2 2 20 2.2 20.2 4232 02/28/10 2 2010-02-28T11:32:12.160 2010 2 -true 2 2 2 20 2.2 20.2 4242 03/01/10 2 2010-03-01T07:02:00.100 2010 3 -true 2 2 2 20 2.2 20.2 4252 03/02/10 2 2010-03-02T07:12:00.460 2010 3 -true 2 2 2 20 2.2 20.2 4262 03/03/10 2 2010-03-03T07:22:00.910 2010 3 -true 2 2 2 20 2.2 20.2 4272 03/04/10 2 2010-03-04T07:32:01.360 2010 3 -true 2 2 2 20 2.2 20.2 4282 03/05/10 2 2010-03-05T07:42:01.810 2010 3 -true 2 2 2 20 2.2 20.2 4292 03/06/10 2 2010-03-06T07:52:02.260 2010 3 -true 2 2 2 20 2.2 20.2 4302 03/07/10 2 2010-03-07T08:02:02.710 2010 3 -true 2 2 2 20 2.2 20.2 4312 03/08/10 2 2010-03-08T08:12:03.160 2010 3 -true 2 2 2 20 2.2 20.2 4322 03/09/10 2 2010-03-09T08:22:03.610 2010 3 -true 2 2 2 20 2.2 20.2 4332 03/10/10 2 2010-03-10T08:32:04.600 2010 3 -true 2 2 2 20 2.2 20.2 4342 03/11/10 2 2010-03-11T08:42:04.510 2010 3 -true 2 2 2 20 2.2 20.2 4352 03/12/10 2 2010-03-12T08:52:04.960 2010 3 -true 2 2 2 20 2.2 20.2 4362 03/13/10 2 2010-03-13T09:02:05.410 2010 3 -true 2 2 2 20 2.2 20.2 4372 03/14/10 2 2010-03-14T08:12:05.860 2010 3 -true 2 2 2 20 2.2 20.2 4382 03/15/10 2 2010-03-15T08:22:06.310 2010 3 -true 2 2 2 20 2.2 20.2 4392 03/16/10 2 2010-03-16T08:32:06.760 2010 3 -true 2 2 2 20 2.2 20.2 4402 03/17/10 2 2010-03-17T08:42:07.210 2010 3 -true 2 2 2 20 2.2 20.2 4412 03/18/10 2 2010-03-18T08:52:07.660 2010 3 -true 2 2 2 20 2.2 20.2 4422 03/19/10 2 2010-03-19T09:02:08.110 2010 3 -true 2 2 2 20 2.2 20.2 4432 03/20/10 2 2010-03-20T09:12:08.560 2010 3 -true 2 2 2 20 2.2 20.2 4442 03/21/10 2 2010-03-21T09:22:09.100 2010 3 -true 2 2 2 20 2.2 20.2 4452 03/22/10 2 2010-03-22T09:32:09.460 2010 3 -true 2 2 2 20 2.2 20.2 4462 03/23/10 2 2010-03-23T09:42:09.910 2010 3 -true 2 2 2 20 2.2 20.2 4472 03/24/10 2 2010-03-24T09:52:10.360 2010 3 -true 2 2 2 20 2.2 20.2 4482 03/25/10 2 2010-03-25T10:02:10.810 2010 3 -true 2 2 2 20 2.2 20.2 4492 03/26/10 2 2010-03-26T10:12:11.260 2010 3 -true 2 2 2 20 2.2 20.2 4502 03/27/10 2 2010-03-27T10:22:11.710 2010 3 -true 2 2 2 20 2.2 20.2 4512 03/28/10 2 2010-03-28T09:32:12.160 2010 3 -true 2 2 2 20 2.2 20.2 4522 03/29/10 2 2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 52 01/06/09 2 2009-01-06T07:52:02.260 2009 1 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 62 01/07/09 2 2009-01-07T08:02:02.710 2009 1 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 10/11/10 2 2010-10-11T07:42:04.510 2010 10 -true 2 2 2 20 2.2 20.2 6492 10/12/10 2 2010-10-12T07:52:04.960 2010 10 -true 2 2 2 20 2.2 20.2 6502 10/13/10 2 2010-10-13T08:02:05.410 2010 10 -true 2 2 2 20 2.2 20.2 6512 10/14/10 2 2010-10-14T08:12:05.860 2010 10 -true 2 2 2 20 2.2 20.2 6522 10/15/10 2 2010-10-15T08:22:06.310 2010 10 -true 2 2 2 20 2.2 20.2 6532 10/16/10 2 2010-10-16T08:32:06.760 2010 10 -true 2 2 2 20 2.2 20.2 6542 10/17/10 2 2010-10-17T08:42:07.210 2010 10 -true 2 2 2 20 2.2 20.2 6552 10/18/10 2 2010-10-18T08:52:07.660 2010 10 -true 2 2 2 20 2.2 20.2 6562 10/19/10 2 2010-10-19T09:02:08.110 2010 10 -true 2 2 2 20 2.2 20.2 6572 10/20/10 2 2010-10-20T09:12:08.560 2010 10 -true 2 2 2 20 2.2 20.2 6582 10/21/10 2 2010-10-21T09:22:09.100 2010 10 -true 2 2 2 20 2.2 20.2 6592 10/22/10 2 2010-10-22T09:32:09.460 2010 10 -true 2 2 2 20 2.2 20.2 6602 10/23/10 2 2010-10-23T09:42:09.910 2010 10 -true 2 2 2 20 2.2 20.2 6612 10/24/10 2 2010-10-24T09:52:10.360 2010 10 -true 2 2 2 20 2.2 20.2 6622 10/25/10 2 2010-10-25T10:02:10.810 2010 10 -true 2 2 2 20 2.2 20.2 6632 10/26/10 2 2010-10-26T10:12:11.260 2010 10 -true 2 2 2 20 2.2 20.2 6642 10/27/10 2 2010-10-27T10:22:11.710 2010 10 -true 2 2 2 20 2.2 20.2 6652 10/28/10 2 2010-10-28T10:32:12.160 2010 10 -true 2 2 2 20 2.2 20.2 6662 10/29/10 2 2010-10-29T10:42:12.610 2010 10 -true 2 2 2 20 2.2 20.2 6672 10/30/10 2 2010-10-30T10:52:13.600 2010 10 -true 2 2 2 20 2.2 20.2 6682 10/31/10 2 2010-10-31T12:02:13.510 2010 10 -true 2 2 2 20 2.2 20.2 6692 11/01/10 2 2010-11-01T07:02:00.100 2010 11 -true 2 2 2 20 2.2 20.2 6702 11/02/10 2 2010-11-02T07:12:00.460 2010 11 -true 2 2 2 20 2.2 20.2 6712 11/03/10 2 2010-11-03T07:22:00.910 2010 11 -true 2 2 2 20 2.2 20.2 6722 11/04/10 2 2010-11-04T07:32:01.360 2010 11 -true 2 2 2 20 2.2 20.2 6732 11/05/10 2 2010-11-05T07:42:01.810 2010 11 -true 2 2 2 20 2.2 20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 -true 2 2 2 20 2.2 20.2 6802 11/12/10 2 2010-11-12T08:52:04.960 2010 11 -true 2 2 2 20 2.2 20.2 6812 11/13/10 2 2010-11-13T09:02:05.410 2010 11 -true 2 2 2 20 2.2 20.2 6822 11/14/10 2 2010-11-14T09:12:05.860 2010 11 -true 2 2 2 20 2.2 20.2 6832 11/15/10 2 2010-11-15T09:22:06.310 2010 11 -true 2 2 2 20 2.2 20.2 6842 11/16/10 2 2010-11-16T09:32:06.760 2010 11 -true 2 2 2 20 2.2 20.2 6852 11/17/10 2 2010-11-17T09:42:07.210 2010 11 -true 2 2 2 20 2.2 20.2 6862 11/18/10 2 2010-11-18T09:52:07.660 2010 11 -true 2 2 2 20 2.2 20.2 6872 11/19/10 2 2010-11-19T10:02:08.110 2010 11 -true 2 2 2 20 2.2 20.2 6882 11/20/10 2 2010-11-20T10:12:08.560 2010 11 -true 2 2 2 20 2.2 20.2 6892 11/21/10 2 2010-11-21T10:22:09.100 2010 11 -true 2 2 2 20 2.2 20.2 6902 11/22/10 2 2010-11-22T10:32:09.460 2010 11 -true 2 2 2 20 2.2 20.2 6912 11/23/10 2 2010-11-23T10:42:09.910 2010 11 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2010 12 -true 2 2 2 20 2.2 20.2 7062 12/08/10 2 2010-12-08T08:12:03.160 2010 12 -true 2 2 2 20 2.2 20.2 7072 12/09/10 2 2010-12-09T08:22:03.610 2010 12 -true 2 2 2 20 2.2 20.2 7082 12/10/10 2 2010-12-10T08:32:04.600 2010 12 -true 2 2 2 20 2.2 20.2 7092 12/11/10 2 2010-12-11T08:42:04.510 2010 12 -true 2 2 2 20 2.2 20.2 7102 12/12/10 2 2010-12-12T08:52:04.960 2010 12 -true 2 2 2 20 2.2 20.2 7112 12/13/10 2 2010-12-13T09:02:05.410 2010 12 -true 2 2 2 20 2.2 20.2 7122 12/14/10 2 2010-12-14T09:12:05.860 2010 12 -true 2 2 2 20 2.2 20.2 7132 12/15/10 2 2010-12-15T09:22:06.310 2010 12 -true 2 2 2 20 2.2 20.2 7142 12/16/10 2 2010-12-16T09:32:06.760 2010 12 -true 2 2 2 20 2.2 20.2 7152 12/17/10 2 2010-12-17T09:42:07.210 2010 12 -true 2 2 2 20 2.2 20.2 7162 12/18/10 2 2010-12-18T09:52:07.660 2010 12 -true 2 2 2 20 2.2 20.2 7172 12/19/10 2 2010-12-19T10:02:08.110 2010 12 -true 2 2 2 20 2.2 20.2 7182 12/20/10 2 2010-12-20T10:12:08.560 2010 12 -true 2 2 2 20 2.2 20.2 7192 12/21/10 2 2010-12-21T10:22:09.100 2010 12 -true 2 2 2 20 2.2 20.2 72 01/08/09 2 2009-01-08T08:12:03.160 2009 1 -true 2 2 2 20 2.2 20.2 7202 12/22/10 2 2010-12-22T10:32:09.460 2010 12 -true 2 2 2 20 2.2 20.2 7212 12/23/10 2 2010-12-23T10:42:09.910 2010 12 -true 2 2 2 20 2.2 20.2 7222 12/24/10 2 2010-12-24T10:52:10.360 2010 12 -true 2 2 2 20 2.2 20.2 7232 12/25/10 2 2010-12-25T11:02:10.810 2010 12 -true 2 2 2 20 2.2 20.2 7242 12/26/10 2 2010-12-26T11:12:11.260 2010 12 -true 2 2 2 20 2.2 20.2 7252 12/27/10 2 2010-12-27T11:22:11.710 2010 12 -true 2 2 2 20 2.2 20.2 7262 12/28/10 2 2010-12-28T11:32:12.160 2010 12 -true 2 2 2 20 2.2 20.2 7272 12/29/10 2 2010-12-29T11:42:12.610 2010 12 -true 2 2 2 20 2.2 20.2 7282 12/30/10 2 2010-12-30T11:52:13.600 2010 12 -true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T12:02:13.510 2010 12 -true 2 2 2 20 2.2 20.2 82 01/09/09 2 2009-01-09T08:22:03.610 2009 1 -true 2 2 2 20 2.2 20.2 92 01/10/09 2 2009-01-10T08:32:04.600 2009 1 -true 4 4 4 40 4.4 40.4 14 01/02/09 4 2009-01-02T07:14:00.510 2009 1 -true 4 4 4 40 4.4 40.4 1814 07/01/09 4 2009-07-01T06:04:00.600 2009 7 -true 4 4 4 40 4.4 40.4 1824 07/02/09 4 2009-07-02T06:14:00.510 2009 7 -true 4 4 4 40 4.4 40.4 1834 07/03/09 4 2009-07-03T06:24:00.960 2009 7 -true 4 4 4 40 4.4 40.4 1844 07/04/09 4 2009-07-04T06:34:01.410 2009 7 -true 4 4 4 40 4.4 40.4 1854 07/05/09 4 2009-07-05T06:44:01.860 2009 7 -true 4 4 4 40 4.4 40.4 1864 07/06/09 4 2009-07-06T06:54:02.310 2009 7 -true 4 4 4 40 4.4 40.4 1874 07/07/09 4 2009-07-07T07:04:02.760 2009 7 -true 4 4 4 40 4.4 40.4 1884 07/08/09 4 2009-07-08T07:14:03.210 2009 7 -true 4 4 4 40 4.4 40.4 1894 07/09/09 4 2009-07-09T07:24:03.660 2009 7 -true 4 4 4 40 4.4 40.4 1904 07/10/09 4 2009-07-10T07:34:04.110 2009 7 -true 4 4 4 40 4.4 40.4 1914 07/11/09 4 2009-07-11T07:44:04.560 2009 7 -true 4 4 4 40 4.4 40.4 1924 07/12/09 4 2009-07-12T07:54:05.100 2009 7 -true 4 4 4 40 4.4 40.4 1934 07/13/09 4 2009-07-13T08:04:05.460 2009 7 -true 4 4 4 40 4.4 40.4 1944 07/14/09 4 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04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 10/11/10 2 2010-10-11T07:42:04.510 2010 10 -true 2 2 2 20 2.2 20.2 6492 10/12/10 2 2010-10-12T07:52:04.960 2010 10 -true 2 2 2 20 2.2 20.2 6502 10/13/10 2 2010-10-13T08:02:05.410 2010 10 -true 2 2 2 20 2.2 20.2 6512 10/14/10 2 2010-10-14T08:12:05.860 2010 10 -true 2 2 2 20 2.2 20.2 6522 10/15/10 2 2010-10-15T08:22:06.310 2010 10 -true 2 2 2 20 2.2 20.2 6532 10/16/10 2 2010-10-16T08:32:06.760 2010 10 -true 2 2 2 20 2.2 20.2 6542 10/17/10 2 2010-10-17T08:42:07.210 2010 10 -true 2 2 2 20 2.2 20.2 6552 10/18/10 2 2010-10-18T08:52:07.660 2010 10 -true 2 2 2 20 2.2 20.2 6562 10/19/10 2 2010-10-19T09:02:08.110 2010 10 -true 2 2 2 20 2.2 20.2 6572 10/20/10 2 2010-10-20T09:12:08.560 2010 10 -true 2 2 2 20 2.2 20.2 6582 10/21/10 2 2010-10-21T09:22:09.100 2010 10 -true 2 2 2 20 2.2 20.2 6592 10/22/10 2 2010-10-22T09:32:09.460 2010 10 -true 2 2 2 20 2.2 20.2 6602 10/23/10 2 2010-10-23T09:42:09.910 2010 10 -true 2 2 2 20 2.2 20.2 6612 10/24/10 2 2010-10-24T09:52:10.360 2010 10 -true 2 2 2 20 2.2 20.2 6622 10/25/10 2 2010-10-25T10:02:10.810 2010 10 -true 2 2 2 20 2.2 20.2 6632 10/26/10 2 2010-10-26T10:12:11.260 2010 10 -true 2 2 2 20 2.2 20.2 6642 10/27/10 2 2010-10-27T10:22:11.710 2010 10 -true 2 2 2 20 2.2 20.2 6652 10/28/10 2 2010-10-28T10:32:12.160 2010 10 -true 2 2 2 20 2.2 20.2 6662 10/29/10 2 2010-10-29T10:42:12.610 2010 10 -true 2 2 2 20 2.2 20.2 6672 10/30/10 2 2010-10-30T10:52:13.600 2010 10 -true 2 2 2 20 2.2 20.2 6682 10/31/10 2 2010-10-31T12:02:13.510 2010 10 -true 2 2 2 20 2.2 20.2 6692 11/01/10 2 2010-11-01T07:02:00.100 2010 11 -true 2 2 2 20 2.2 20.2 6702 11/02/10 2 2010-11-02T07:12:00.460 2010 11 -true 2 2 2 20 2.2 20.2 6712 11/03/10 2 2010-11-03T07:22:00.910 2010 11 -true 2 2 2 20 2.2 20.2 6722 11/04/10 2 2010-11-04T07:32:01.360 2010 11 -true 2 2 2 20 2.2 20.2 6732 11/05/10 2 2010-11-05T07:42:01.810 2010 11 -true 2 2 2 20 2.2 20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 -true 2 2 2 20 2.2 20.2 6802 11/12/10 2 2010-11-12T08:52:04.960 2010 11 -true 2 2 2 20 2.2 20.2 6812 11/13/10 2 2010-11-13T09:02:05.410 2010 11 -true 2 2 2 20 2.2 20.2 6822 11/14/10 2 2010-11-14T09:12:05.860 2010 11 -true 2 2 2 20 2.2 20.2 6832 11/15/10 2 2010-11-15T09:22:06.310 2010 11 -true 2 2 2 20 2.2 20.2 6842 11/16/10 2 2010-11-16T09:32:06.760 2010 11 -true 2 2 2 20 2.2 20.2 6852 11/17/10 2 2010-11-17T09:42:07.210 2010 11 -true 2 2 2 20 2.2 20.2 6862 11/18/10 2 2010-11-18T09:52:07.660 2010 11 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12/30/10 2 2010-12-30T11:52:13.600 2010 12 -true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T12:02:13.510 2010 12 -true 4 4 4 40 4.4 40.4 3654 01/01/10 4 2010-01-01T07:04:00.600 2010 1 -true 4 4 4 40 4.4 40.4 3664 01/02/10 4 2010-01-02T07:14:00.510 2010 1 -true 4 4 4 40 4.4 40.4 3674 01/03/10 4 2010-01-03T07:24:00.960 2010 1 -true 4 4 4 40 4.4 40.4 3684 01/04/10 4 2010-01-04T07:34:01.410 2010 1 -true 4 4 4 40 4.4 40.4 3694 01/05/10 4 2010-01-05T07:44:01.860 2010 1 -true 4 4 4 40 4.4 40.4 3704 01/06/10 4 2010-01-06T07:54:02.310 2010 1 -true 4 4 4 40 4.4 40.4 3714 01/07/10 4 2010-01-07T08:04:02.760 2010 1 -true 4 4 4 40 4.4 40.4 3724 01/08/10 4 2010-01-08T08:14:03.210 2010 1 -true 4 4 4 40 4.4 40.4 3734 01/09/10 4 2010-01-09T08:24:03.660 2010 1 -true 4 4 4 40 4.4 40.4 3744 01/10/10 4 2010-01-10T08:34:04.110 2010 1 -true 4 4 4 40 4.4 40.4 3754 01/11/10 4 2010-01-11T08:44:04.560 2010 1 -true 4 4 4 40 4.4 40.4 3764 01/12/10 4 2010-01-12T08:54:05.100 2010 1 -true 4 4 4 40 4.4 40.4 3774 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07/31/09 3 2009-07-31T11:03:13.530 2009 7 -false 3 3 3 30 3.3 30.3 5463 07/01/10 3 2010-07-01T06:03:00.300 2010 7 -false 3 3 3 30 3.3 30.3 5473 07/02/10 3 2010-07-02T06:13:00.480 2010 7 -false 3 3 3 30 3.3 30.3 5483 07/03/10 3 2010-07-03T06:23:00.930 2010 7 -false 3 3 3 30 3.3 30.3 5493 07/04/10 3 2010-07-04T06:33:01.380 2010 7 -false 3 3 3 30 3.3 30.3 5503 07/05/10 3 2010-07-05T06:43:01.830 2010 7 -false 3 3 3 30 3.3 30.3 5513 07/06/10 3 2010-07-06T06:53:02.280 2010 7 -false 3 3 3 30 3.3 30.3 5523 07/07/10 3 2010-07-07T07:03:02.730 2010 7 -false 3 3 3 30 3.3 30.3 5533 07/08/10 3 2010-07-08T07:13:03.180 2010 7 -false 3 3 3 30 3.3 30.3 5543 07/09/10 3 2010-07-09T07:23:03.630 2010 7 -false 3 3 3 30 3.3 30.3 5553 07/10/10 3 2010-07-10T07:33:04.800 2010 7 -false 3 3 3 30 3.3 30.3 5563 07/11/10 3 2010-07-11T07:43:04.530 2010 7 -false 3 3 3 30 3.3 30.3 5573 07/12/10 3 2010-07-12T07:53:04.980 2010 7 -false 3 3 3 30 3.3 30.3 5583 07/13/10 3 2010-07-13T08:03:05.430 2010 7 -false 3 3 3 30 3.3 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3 3 30 3.3 30.3 6423 10/05/10 3 2010-10-05T06:43:01.830 2010 10 -false 3 3 3 30 3.3 30.3 6433 10/06/10 3 2010-10-06T06:53:02.280 2010 10 -false 3 3 3 30 3.3 30.3 6443 10/07/10 3 2010-10-07T07:03:02.730 2010 10 -false 3 3 3 30 3.3 30.3 6453 10/08/10 3 2010-10-08T07:13:03.180 2010 10 -false 3 3 3 30 3.3 30.3 6463 10/09/10 3 2010-10-09T07:23:03.630 2010 10 -false 3 3 3 30 3.3 30.3 6473 10/10/10 3 2010-10-10T07:33:04.800 2010 10 -false 3 3 3 30 3.3 30.3 6483 10/11/10 3 2010-10-11T07:43:04.530 2010 10 -false 3 3 3 30 3.3 30.3 6493 10/12/10 3 2010-10-12T07:53:04.980 2010 10 -false 3 3 3 30 3.3 30.3 6503 10/13/10 3 2010-10-13T08:03:05.430 2010 10 -false 3 3 3 30 3.3 30.3 6513 10/14/10 3 2010-10-14T08:13:05.880 2010 10 -false 3 3 3 30 3.3 30.3 6523 10/15/10 3 2010-10-15T08:23:06.330 2010 10 -false 3 3 3 30 3.3 30.3 6533 10/16/10 3 2010-10-16T08:33:06.780 2010 10 -false 3 3 3 30 3.3 30.3 6543 10/17/10 3 2010-10-17T08:43:07.230 2010 10 -false 3 3 3 30 3.3 30.3 6553 10/18/10 3 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03/19/10 2 2010-03-19T09:02:08.110 2010 3 -true 2 2 2 20 2.2 20.2 4432 03/20/10 2 2010-03-20T09:12:08.560 2010 3 -true 2 2 2 20 2.2 20.2 4442 03/21/10 2 2010-03-21T09:22:09.100 2010 3 -true 2 2 2 20 2.2 20.2 4452 03/22/10 2 2010-03-22T09:32:09.460 2010 3 -true 2 2 2 20 2.2 20.2 4462 03/23/10 2 2010-03-23T09:42:09.910 2010 3 -true 2 2 2 20 2.2 20.2 4472 03/24/10 2 2010-03-24T09:52:10.360 2010 3 -true 2 2 2 20 2.2 20.2 4482 03/25/10 2 2010-03-25T10:02:10.810 2010 3 -true 2 2 2 20 2.2 20.2 4492 03/26/10 2 2010-03-26T10:12:11.260 2010 3 -true 2 2 2 20 2.2 20.2 4502 03/27/10 2 2010-03-27T10:22:11.710 2010 3 -true 2 2 2 20 2.2 20.2 4512 03/28/10 2 2010-03-28T09:32:12.160 2010 3 -true 2 2 2 20 2.2 20.2 4522 03/29/10 2 2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 10/11/10 2 2010-10-11T07:42:04.510 2010 10 -true 2 2 2 20 2.2 20.2 6492 10/12/10 2 2010-10-12T07:52:04.960 2010 10 -true 2 2 2 20 2.2 20.2 6502 10/13/10 2 2010-10-13T08:02:05.410 2010 10 -true 2 2 2 20 2.2 20.2 6512 10/14/10 2 2010-10-14T08:12:05.860 2010 10 -true 2 2 2 20 2.2 20.2 6522 10/15/10 2 2010-10-15T08:22:06.310 2010 10 -true 2 2 2 20 2.2 20.2 6532 10/16/10 2 2010-10-16T08:32:06.760 2010 10 -true 2 2 2 20 2.2 20.2 6542 10/17/10 2 2010-10-17T08:42:07.210 2010 10 -true 2 2 2 20 2.2 20.2 6552 10/18/10 2 2010-10-18T08:52:07.660 2010 10 -true 2 2 2 20 2.2 20.2 6562 10/19/10 2 2010-10-19T09:02:08.110 2010 10 -true 2 2 2 20 2.2 20.2 6572 10/20/10 2 2010-10-20T09:12:08.560 2010 10 -true 2 2 2 20 2.2 20.2 6582 10/21/10 2 2010-10-21T09:22:09.100 2010 10 -true 2 2 2 20 2.2 20.2 6592 10/22/10 2 2010-10-22T09:32:09.460 2010 10 -true 2 2 2 20 2.2 20.2 6602 10/23/10 2 2010-10-23T09:42:09.910 2010 10 -true 2 2 2 20 2.2 20.2 6612 10/24/10 2 2010-10-24T09:52:10.360 2010 10 -true 2 2 2 20 2.2 20.2 6622 10/25/10 2 2010-10-25T10:02:10.810 2010 10 -true 2 2 2 20 2.2 20.2 6632 10/26/10 2 2010-10-26T10:12:11.260 2010 10 -true 2 2 2 20 2.2 20.2 6642 10/27/10 2 2010-10-27T10:22:11.710 2010 10 -true 2 2 2 20 2.2 20.2 6652 10/28/10 2 2010-10-28T10:32:12.160 2010 10 -true 2 2 2 20 2.2 20.2 6662 10/29/10 2 2010-10-29T10:42:12.610 2010 10 -true 2 2 2 20 2.2 20.2 6672 10/30/10 2 2010-10-30T10:52:13.600 2010 10 -true 2 2 2 20 2.2 20.2 6682 10/31/10 2 2010-10-31T12:02:13.510 2010 10 -true 2 2 2 20 2.2 20.2 6692 11/01/10 2 2010-11-01T07:02:00.100 2010 11 -true 2 2 2 20 2.2 20.2 6702 11/02/10 2 2010-11-02T07:12:00.460 2010 11 -true 2 2 2 20 2.2 20.2 6712 11/03/10 2 2010-11-03T07:22:00.910 2010 11 -true 2 2 2 20 2.2 20.2 6722 11/04/10 2 2010-11-04T07:32:01.360 2010 11 -true 2 2 2 20 2.2 20.2 6732 11/05/10 2 2010-11-05T07:42:01.810 2010 11 -true 2 2 2 20 2.2 20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 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2009-06-24T09:52:10.360 2009 6 -true 2 2 2 20 2.2 20.2 1752 06/25/09 2 2009-06-25T10:02:10.810 2009 6 -true 2 2 2 20 2.2 20.2 1762 06/26/09 2 2009-06-26T10:12:11.260 2009 6 -true 2 2 2 20 2.2 20.2 1772 06/27/09 2 2009-06-27T10:22:11.710 2009 6 -true 2 2 2 20 2.2 20.2 1782 06/28/09 2 2009-06-28T10:32:12.160 2009 6 -true 2 2 2 20 2.2 20.2 1792 06/29/09 2 2009-06-29T10:42:12.610 2009 6 -true 2 2 2 20 2.2 20.2 1802 06/30/09 2 2009-06-30T10:52:13.600 2009 6 -true 2 2 2 20 2.2 20.2 1812 07/01/09 2 2009-07-01T06:02:00.100 2009 7 -true 2 2 2 20 2.2 20.2 182 01/19/09 2 2009-01-19T10:02:08.110 2009 1 -true 2 2 2 20 2.2 20.2 1822 07/02/09 2 2009-07-02T06:12:00.460 2009 7 -true 2 2 2 20 2.2 20.2 1832 07/03/09 2 2009-07-03T06:22:00.910 2009 7 -true 2 2 2 20 2.2 20.2 1842 07/04/09 2 2009-07-04T06:32:01.360 2009 7 -true 2 2 2 20 2.2 20.2 1852 07/05/09 2 2009-07-05T06:42:01.810 2009 7 -true 2 2 2 20 2.2 20.2 1862 07/06/09 2 2009-07-06T06:52:02.260 2009 7 -true 2 2 2 20 2.2 20.2 1872 07/07/09 2 2009-07-07T07:02:02.710 2009 7 -true 2 2 2 20 2.2 20.2 1882 07/08/09 2 2009-07-08T07:12:03.160 2009 7 -true 2 2 2 20 2.2 20.2 1892 07/09/09 2 2009-07-09T07:22:03.610 2009 7 -true 2 2 2 20 2.2 20.2 1902 07/10/09 2 2009-07-10T07:32:04.600 2009 7 -true 2 2 2 20 2.2 20.2 1912 07/11/09 2 2009-07-11T07:42:04.510 2009 7 -true 2 2 2 20 2.2 20.2 192 01/20/09 2 2009-01-20T10:12:08.560 2009 1 -true 2 2 2 20 2.2 20.2 1922 07/12/09 2 2009-07-12T07:52:04.960 2009 7 -true 2 2 2 20 2.2 20.2 1932 07/13/09 2 2009-07-13T08:02:05.410 2009 7 -true 2 2 2 20 2.2 20.2 1942 07/14/09 2 2009-07-14T08:12:05.860 2009 7 -true 2 2 2 20 2.2 20.2 1952 07/15/09 2 2009-07-15T08:22:06.310 2009 7 -true 2 2 2 20 2.2 20.2 1962 07/16/09 2 2009-07-16T08:32:06.760 2009 7 -true 2 2 2 20 2.2 20.2 1972 07/17/09 2 2009-07-17T08:42:07.210 2009 7 -true 2 2 2 20 2.2 20.2 1982 07/18/09 2 2009-07-18T08:52:07.660 2009 7 -true 2 2 2 20 2.2 20.2 1992 07/19/09 2 2009-07-19T09:02:08.110 2009 7 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -true 2 2 2 20 2.2 20.2 2002 07/20/09 2 2009-07-20T09:12:08.560 2009 7 -true 2 2 2 20 2.2 20.2 2012 07/21/09 2 2009-07-21T09:22:09.100 2009 7 -true 2 2 2 20 2.2 20.2 202 01/21/09 2 2009-01-21T10:22:09.100 2009 1 -true 2 2 2 20 2.2 20.2 2022 07/22/09 2 2009-07-22T09:32:09.460 2009 7 -true 2 2 2 20 2.2 20.2 2032 07/23/09 2 2009-07-23T09:42:09.910 2009 7 -true 2 2 2 20 2.2 20.2 2042 07/24/09 2 2009-07-24T09:52:10.360 2009 7 -true 2 2 2 20 2.2 20.2 2052 07/25/09 2 2009-07-25T10:02:10.810 2009 7 -true 2 2 2 20 2.2 20.2 2062 07/26/09 2 2009-07-26T10:12:11.260 2009 7 -true 2 2 2 20 2.2 20.2 2072 07/27/09 2 2009-07-27T10:22:11.710 2009 7 -true 2 2 2 20 2.2 20.2 2082 07/28/09 2 2009-07-28T10:32:12.160 2009 7 -true 2 2 2 20 2.2 20.2 2092 07/29/09 2 2009-07-29T10:42:12.610 2009 7 -true 2 2 2 20 2.2 20.2 2102 07/30/09 2 2009-07-30T10:52:13.600 2009 7 -true 2 2 2 20 2.2 20.2 2112 07/31/09 2 2009-07-31T11:02:13.510 2009 7 -true 2 2 2 20 2.2 20.2 212 01/22/09 2 2009-01-22T10:32:09.460 2009 1 -true 2 2 2 20 2.2 20.2 2122 08/01/09 2 2009-08-01T06:02:00.100 2009 8 -true 2 2 2 20 2.2 20.2 2132 08/02/09 2 2009-08-02T06:12:00.460 2009 8 -true 2 2 2 20 2.2 20.2 2142 08/03/09 2 2009-08-03T06:22:00.910 2009 8 -true 2 2 2 20 2.2 20.2 2152 08/04/09 2 2009-08-04T06:32:01.360 2009 8 -true 2 2 2 20 2.2 20.2 2162 08/05/09 2 2009-08-05T06:42:01.810 2009 8 -true 2 2 2 20 2.2 20.2 2172 08/06/09 2 2009-08-06T06:52:02.260 2009 8 -true 2 2 2 20 2.2 20.2 2182 08/07/09 2 2009-08-07T07:02:02.710 2009 8 -true 2 2 2 20 2.2 20.2 2192 08/08/09 2 2009-08-08T07:12:03.160 2009 8 -true 2 2 2 20 2.2 20.2 22 01/03/09 2 2009-01-03T07:22:00.910 2009 1 -true 2 2 2 20 2.2 20.2 2202 08/09/09 2 2009-08-09T07:22:03.610 2009 8 -true 2 2 2 20 2.2 20.2 2212 08/10/09 2 2009-08-10T07:32:04.600 2009 8 -true 2 2 2 20 2.2 20.2 222 01/23/09 2 2009-01-23T10:42:09.910 2009 1 -true 2 2 2 20 2.2 20.2 2222 08/11/09 2 2009-08-11T07:42:04.510 2009 8 -true 2 2 2 20 2.2 20.2 2232 08/12/09 2 2009-08-12T07:52:04.960 2009 8 -true 2 2 2 20 2.2 20.2 2242 08/13/09 2 2009-08-13T08:02:05.410 2009 8 -true 2 2 2 20 2.2 20.2 2252 08/14/09 2 2009-08-14T08:12:05.860 2009 8 -true 2 2 2 20 2.2 20.2 2262 08/15/09 2 2009-08-15T08:22:06.310 2009 8 -true 2 2 2 20 2.2 20.2 2272 08/16/09 2 2009-08-16T08:32:06.760 2009 8 -true 2 2 2 20 2.2 20.2 2282 08/17/09 2 2009-08-17T08:42:07.210 2009 8 -true 2 2 2 20 2.2 20.2 2292 08/18/09 2 2009-08-18T08:52:07.660 2009 8 -true 2 2 2 20 2.2 20.2 2302 08/19/09 2 2009-08-19T09:02:08.110 2009 8 -true 2 2 2 20 2.2 20.2 2312 08/20/09 2 2009-08-20T09:12:08.560 2009 8 -true 2 2 2 20 2.2 20.2 232 01/24/09 2 2009-01-24T10:52:10.360 2009 1 -true 2 2 2 20 2.2 20.2 2322 08/21/09 2 2009-08-21T09:22:09.100 2009 8 -true 2 2 2 20 2.2 20.2 2332 08/22/09 2 2009-08-22T09:32:09.460 2009 8 -true 2 2 2 20 2.2 20.2 2342 08/23/09 2 2009-08-23T09:42:09.910 2009 8 -true 2 2 2 20 2.2 20.2 2352 08/24/09 2 2009-08-24T09:52:10.360 2009 8 -true 2 2 2 20 2.2 20.2 2362 08/25/09 2 2009-08-25T10:02:10.810 2009 8 -true 2 2 2 20 2.2 20.2 2372 08/26/09 2 2009-08-26T10:12:11.260 2009 8 -true 2 2 2 20 2.2 20.2 2382 08/27/09 2 2009-08-27T10:22:11.710 2009 8 -true 2 2 2 20 2.2 20.2 2392 08/28/09 2 2009-08-28T10:32:12.160 2009 8 -true 2 2 2 20 2.2 20.2 2402 08/29/09 2 2009-08-29T10:42:12.610 2009 8 -true 2 2 2 20 2.2 20.2 2412 08/30/09 2 2009-08-30T10:52:13.600 2009 8 -true 2 2 2 20 2.2 20.2 242 01/25/09 2 2009-01-25T11:02:10.810 2009 1 -true 2 2 2 20 2.2 20.2 2422 08/31/09 2 2009-08-31T11:02:13.510 2009 8 -true 2 2 2 20 2.2 20.2 2432 09/01/09 2 2009-09-01T06:02:00.100 2009 9 -true 2 2 2 20 2.2 20.2 2442 09/02/09 2 2009-09-02T06:12:00.460 2009 9 -true 2 2 2 20 2.2 20.2 2452 09/03/09 2 2009-09-03T06:22:00.910 2009 9 -true 2 2 2 20 2.2 20.2 2462 09/04/09 2 2009-09-04T06:32:01.360 2009 9 -true 2 2 2 20 2.2 20.2 2472 09/05/09 2 2009-09-05T06:42:01.810 2009 9 -true 2 2 2 20 2.2 20.2 2482 09/06/09 2 2009-09-06T06:52:02.260 2009 9 -true 2 2 2 20 2.2 20.2 2492 09/07/09 2 2009-09-07T07:02:02.710 2009 9 -true 2 2 2 20 2.2 20.2 2502 09/08/09 2 2009-09-08T07:12:03.160 2009 9 -true 2 2 2 20 2.2 20.2 2512 09/09/09 2 2009-09-09T07:22:03.610 2009 9 -true 2 2 2 20 2.2 20.2 252 01/26/09 2 2009-01-26T11:12:11.260 2009 1 -true 2 2 2 20 2.2 20.2 2522 09/10/09 2 2009-09-10T07:32:04.600 2009 9 -true 2 2 2 20 2.2 20.2 2532 09/11/09 2 2009-09-11T07:42:04.510 2009 9 -true 2 2 2 20 2.2 20.2 2542 09/12/09 2 2009-09-12T07:52:04.960 2009 9 -true 2 2 2 20 2.2 20.2 2552 09/13/09 2 2009-09-13T08:02:05.410 2009 9 -true 2 2 2 20 2.2 20.2 2562 09/14/09 2 2009-09-14T08:12:05.860 2009 9 -true 2 2 2 20 2.2 20.2 2572 09/15/09 2 2009-09-15T08:22:06.310 2009 9 -true 2 2 2 20 2.2 20.2 2582 09/16/09 2 2009-09-16T08:32:06.760 2009 9 -true 2 2 2 20 2.2 20.2 2592 09/17/09 2 2009-09-17T08:42:07.210 2009 9 -true 2 2 2 20 2.2 20.2 2602 09/18/09 2 2009-09-18T08:52:07.660 2009 9 -true 2 2 2 20 2.2 20.2 2612 09/19/09 2 2009-09-19T09:02:08.110 2009 9 -true 2 2 2 20 2.2 20.2 262 01/27/09 2 2009-01-27T11:22:11.710 2009 1 -true 2 2 2 20 2.2 20.2 2622 09/20/09 2 2009-09-20T09:12:08.560 2009 9 -true 2 2 2 20 2.2 20.2 2632 09/21/09 2 2009-09-21T09:22:09.100 2009 9 -true 2 2 2 20 2.2 20.2 2642 09/22/09 2 2009-09-22T09:32:09.460 2009 9 -true 2 2 2 20 2.2 20.2 2652 09/23/09 2 2009-09-23T09:42:09.910 2009 9 -true 2 2 2 20 2.2 20.2 2662 09/24/09 2 2009-09-24T09:52:10.360 2009 9 -true 2 2 2 20 2.2 20.2 2672 09/25/09 2 2009-09-25T10:02:10.810 2009 9 -true 2 2 2 20 2.2 20.2 2682 09/26/09 2 2009-09-26T10:12:11.260 2009 9 -true 2 2 2 20 2.2 20.2 2692 09/27/09 2 2009-09-27T10:22:11.710 2009 9 -true 2 2 2 20 2.2 20.2 2702 09/28/09 2 2009-09-28T10:32:12.160 2009 9 -true 2 2 2 20 2.2 20.2 2712 09/29/09 2 2009-09-29T10:42:12.610 2009 9 -true 2 2 2 20 2.2 20.2 272 01/28/09 2 2009-01-28T11:32:12.160 2009 1 -true 2 2 2 20 2.2 20.2 2722 09/30/09 2 2009-09-30T10:52:13.600 2009 9 -true 2 2 2 20 2.2 20.2 2732 10/01/09 2 2009-10-01T06:02:00.100 2009 10 -true 2 2 2 20 2.2 20.2 2742 10/02/09 2 2009-10-02T06:12:00.460 2009 10 -true 2 2 2 20 2.2 20.2 2752 10/03/09 2 2009-10-03T06:22:00.910 2009 10 -true 2 2 2 20 2.2 20.2 2762 10/04/09 2 2009-10-04T06:32:01.360 2009 10 -true 2 2 2 20 2.2 20.2 2772 10/05/09 2 2009-10-05T06:42:01.810 2009 10 -true 2 2 2 20 2.2 20.2 2782 10/06/09 2 2009-10-06T06:52:02.260 2009 10 -true 2 2 2 20 2.2 20.2 2792 10/07/09 2 2009-10-07T07:02:02.710 2009 10 -true 2 2 2 20 2.2 20.2 2802 10/08/09 2 2009-10-08T07:12:03.160 2009 10 -true 2 2 2 20 2.2 20.2 2812 10/09/09 2 2009-10-09T07:22:03.610 2009 10 -true 2 2 2 20 2.2 20.2 282 01/29/09 2 2009-01-29T11:42:12.610 2009 1 -true 2 2 2 20 2.2 20.2 2822 10/10/09 2 2009-10-10T07:32:04.600 2009 10 -true 2 2 2 20 2.2 20.2 2832 10/11/09 2 2009-10-11T07:42:04.510 2009 10 -true 2 2 2 20 2.2 20.2 2842 10/12/09 2 2009-10-12T07:52:04.960 2009 10 -true 2 2 2 20 2.2 20.2 2852 10/13/09 2 2009-10-13T08:02:05.410 2009 10 -true 2 2 2 20 2.2 20.2 2862 10/14/09 2 2009-10-14T08:12:05.860 2009 10 -true 2 2 2 20 2.2 20.2 2872 10/15/09 2 2009-10-15T08:22:06.310 2009 10 -true 2 2 2 20 2.2 20.2 2882 10/16/09 2 2009-10-16T08:32:06.760 2009 10 -true 2 2 2 20 2.2 20.2 2892 10/17/09 2 2009-10-17T08:42:07.210 2009 10 -true 2 2 2 20 2.2 20.2 2902 10/18/09 2 2009-10-18T08:52:07.660 2009 10 -true 2 2 2 20 2.2 20.2 2912 10/19/09 2 2009-10-19T09:02:08.110 2009 10 -true 2 2 2 20 2.2 20.2 292 01/30/09 2 2009-01-30T11:52:13.600 2009 1 -true 2 2 2 20 2.2 20.2 2922 10/20/09 2 2009-10-20T09:12:08.560 2009 10 -true 2 2 2 20 2.2 20.2 2932 10/21/09 2 2009-10-21T09:22:09.100 2009 10 -true 2 2 2 20 2.2 20.2 2942 10/22/09 2 2009-10-22T09:32:09.460 2009 10 -true 2 2 2 20 2.2 20.2 2952 10/23/09 2 2009-10-23T09:42:09.910 2009 10 -true 2 2 2 20 2.2 20.2 2962 10/24/09 2 2009-10-24T09:52:10.360 2009 10 -true 2 2 2 20 2.2 20.2 2972 10/25/09 2 2009-10-25T11:02:10.810 2009 10 -true 2 2 2 20 2.2 20.2 2982 10/26/09 2 2009-10-26T11:12:11.260 2009 10 -true 2 2 2 20 2.2 20.2 2992 10/27/09 2 2009-10-27T11:22:11.710 2009 10 -true 2 2 2 20 2.2 20.2 3002 10/28/09 2 2009-10-28T11:32:12.160 2009 10 -true 2 2 2 20 2.2 20.2 3012 10/29/09 2 2009-10-29T11:42:12.610 2009 10 -true 2 2 2 20 2.2 20.2 302 01/31/09 2 2009-01-31T12:02:13.510 2009 1 -true 2 2 2 20 2.2 20.2 3022 10/30/09 2 2009-10-30T11:52:13.600 2009 10 -true 2 2 2 20 2.2 20.2 3032 10/31/09 2 2009-10-31T12:02:13.510 2009 10 -true 2 2 2 20 2.2 20.2 3042 11/01/09 2 2009-11-01T07:02:00.100 2009 11 -true 2 2 2 20 2.2 20.2 3052 11/02/09 2 2009-11-02T07:12:00.460 2009 11 -true 2 2 2 20 2.2 20.2 3062 11/03/09 2 2009-11-03T07:22:00.910 2009 11 -true 2 2 2 20 2.2 20.2 3072 11/04/09 2 2009-11-04T07:32:01.360 2009 11 -true 2 2 2 20 2.2 20.2 3082 11/05/09 2 2009-11-05T07:42:01.810 2009 11 -true 2 2 2 20 2.2 20.2 3092 11/06/09 2 2009-11-06T07:52:02.260 2009 11 -true 2 2 2 20 2.2 20.2 3102 11/07/09 2 2009-11-07T08:02:02.710 2009 11 -true 2 2 2 20 2.2 20.2 3112 11/08/09 2 2009-11-08T08:12:03.160 2009 11 -true 2 2 2 20 2.2 20.2 312 02/01/09 2 2009-02-01T07:02:00.100 2009 2 -true 2 2 2 20 2.2 20.2 3122 11/09/09 2 2009-11-09T08:22:03.610 2009 11 -true 2 2 2 20 2.2 20.2 3132 11/10/09 2 2009-11-10T08:32:04.600 2009 11 -true 2 2 2 20 2.2 20.2 3142 11/11/09 2 2009-11-11T08:42:04.510 2009 11 -true 2 2 2 20 2.2 20.2 3152 11/12/09 2 2009-11-12T08:52:04.960 2009 11 -true 2 2 2 20 2.2 20.2 3162 11/13/09 2 2009-11-13T09:02:05.410 2009 11 -true 2 2 2 20 2.2 20.2 3172 11/14/09 2 2009-11-14T09:12:05.860 2009 11 -true 2 2 2 20 2.2 20.2 3182 11/15/09 2 2009-11-15T09:22:06.310 2009 11 -true 2 2 2 20 2.2 20.2 3192 11/16/09 2 2009-11-16T09:32:06.760 2009 11 -true 2 2 2 20 2.2 20.2 32 01/04/09 2 2009-01-04T07:32:01.360 2009 1 -true 2 2 2 20 2.2 20.2 3202 11/17/09 2 2009-11-17T09:42:07.210 2009 11 -true 2 2 2 20 2.2 20.2 3212 11/18/09 2 2009-11-18T09:52:07.660 2009 11 -true 2 2 2 20 2.2 20.2 322 02/02/09 2 2009-02-02T07:12:00.460 2009 2 -true 2 2 2 20 2.2 20.2 3222 11/19/09 2 2009-11-19T10:02:08.110 2009 11 -true 2 2 2 20 2.2 20.2 3232 11/20/09 2 2009-11-20T10:12:08.560 2009 11 -true 2 2 2 20 2.2 20.2 3242 11/21/09 2 2009-11-21T10:22:09.100 2009 11 -true 2 2 2 20 2.2 20.2 3252 11/22/09 2 2009-11-22T10:32:09.460 2009 11 -true 2 2 2 20 2.2 20.2 3262 11/23/09 2 2009-11-23T10:42:09.910 2009 11 -true 2 2 2 20 2.2 20.2 3272 11/24/09 2 2009-11-24T10:52:10.360 2009 11 -true 2 2 2 20 2.2 20.2 3282 11/25/09 2 2009-11-25T11:02:10.810 2009 11 -true 2 2 2 20 2.2 20.2 3292 11/26/09 2 2009-11-26T11:12:11.260 2009 11 -true 2 2 2 20 2.2 20.2 3302 11/27/09 2 2009-11-27T11:22:11.710 2009 11 -true 2 2 2 20 2.2 20.2 3312 11/28/09 2 2009-11-28T11:32:12.160 2009 11 -true 2 2 2 20 2.2 20.2 332 02/03/09 2 2009-02-03T07:22:00.910 2009 2 -true 2 2 2 20 2.2 20.2 3322 11/29/09 2 2009-11-29T11:42:12.610 2009 11 -true 2 2 2 20 2.2 20.2 3332 11/30/09 2 2009-11-30T11:52:13.600 2009 11 -true 2 2 2 20 2.2 20.2 3342 12/01/09 2 2009-12-01T07:02:00.100 2009 12 -true 2 2 2 20 2.2 20.2 3352 12/02/09 2 2009-12-02T07:12:00.460 2009 12 -true 2 2 2 20 2.2 20.2 3362 12/03/09 2 2009-12-03T07:22:00.910 2009 12 -true 2 2 2 20 2.2 20.2 3372 12/04/09 2 2009-12-04T07:32:01.360 2009 12 -true 2 2 2 20 2.2 20.2 3382 12/05/09 2 2009-12-05T07:42:01.810 2009 12 -true 2 2 2 20 2.2 20.2 3392 12/06/09 2 2009-12-06T07:52:02.260 2009 12 -true 2 2 2 20 2.2 20.2 3402 12/07/09 2 2009-12-07T08:02:02.710 2009 12 -true 2 2 2 20 2.2 20.2 342 02/04/09 2 2009-02-04T07:32:01.360 2009 2 -true 2 2 2 20 2.2 20.2 352 02/05/09 2 2009-02-05T07:42:01.810 2009 2 -true 2 2 2 20 2.2 20.2 362 02/06/09 2 2009-02-06T07:52:02.260 2009 2 -true 2 2 2 20 2.2 20.2 372 02/07/09 2 2009-02-07T08:02:02.710 2009 2 -true 2 2 2 20 2.2 20.2 382 02/08/09 2 2009-02-08T08:12:03.160 2009 2 -true 2 2 2 20 2.2 20.2 392 02/09/09 2 2009-02-09T08:22:03.610 2009 2 -true 2 2 2 20 2.2 20.2 402 02/10/09 2 2009-02-10T08:32:04.600 2009 2 -true 2 2 2 20 2.2 20.2 412 02/11/09 2 2009-02-11T08:42:04.510 2009 2 -true 2 2 2 20 2.2 20.2 42 01/05/09 2 2009-01-05T07:42:01.810 2009 1 -true 2 2 2 20 2.2 20.2 422 02/12/09 2 2009-02-12T08:52:04.960 2009 2 -true 2 2 2 20 2.2 20.2 432 02/13/09 2 2009-02-13T09:02:05.410 2009 2 -true 2 2 2 20 2.2 20.2 442 02/14/09 2 2009-02-14T09:12:05.860 2009 2 -true 2 2 2 20 2.2 20.2 452 02/15/09 2 2009-02-15T09:22:06.310 2009 2 -true 2 2 2 20 2.2 20.2 462 02/16/09 2 2009-02-16T09:32:06.760 2009 2 -true 2 2 2 20 2.2 20.2 472 02/17/09 2 2009-02-17T09:42:07.210 2009 2 -true 2 2 2 20 2.2 20.2 482 02/18/09 2 2009-02-18T09:52:07.660 2009 2 -true 2 2 2 20 2.2 20.2 492 02/19/09 2 2009-02-19T10:02:08.110 2009 2 -true 2 2 2 20 2.2 20.2 502 02/20/09 2 2009-02-20T10:12:08.560 2009 2 -true 2 2 2 20 2.2 20.2 512 02/21/09 2 2009-02-21T10:22:09.100 2009 2 -true 2 2 2 20 2.2 20.2 52 01/06/09 2 2009-01-06T07:52:02.260 2009 1 -true 2 2 2 20 2.2 20.2 522 02/22/09 2 2009-02-22T10:32:09.460 2009 2 -true 2 2 2 20 2.2 20.2 532 02/23/09 2 2009-02-23T10:42:09.910 2009 2 -true 2 2 2 20 2.2 20.2 542 02/24/09 2 2009-02-24T10:52:10.360 2009 2 -true 2 2 2 20 2.2 20.2 552 02/25/09 2 2009-02-25T11:02:10.810 2009 2 -true 2 2 2 20 2.2 20.2 562 02/26/09 2 2009-02-26T11:12:11.260 2009 2 -true 2 2 2 20 2.2 20.2 572 02/27/09 2 2009-02-27T11:22:11.710 2009 2 -true 2 2 2 20 2.2 20.2 582 02/28/09 2 2009-02-28T11:32:12.160 2009 2 -true 2 2 2 20 2.2 20.2 592 03/01/09 2 2009-03-01T07:02:00.100 2009 3 -true 2 2 2 20 2.2 20.2 602 03/02/09 2 2009-03-02T07:12:00.460 2009 3 -true 2 2 2 20 2.2 20.2 612 03/03/09 2 2009-03-03T07:22:00.910 2009 3 -true 2 2 2 20 2.2 20.2 62 01/07/09 2 2009-01-07T08:02:02.710 2009 1 -true 2 2 2 20 2.2 20.2 622 03/04/09 2 2009-03-04T07:32:01.360 2009 3 -true 2 2 2 20 2.2 20.2 632 03/05/09 2 2009-03-05T07:42:01.810 2009 3 -true 2 2 2 20 2.2 20.2 642 03/06/09 2 2009-03-06T07:52:02.260 2009 3 -true 2 2 2 20 2.2 20.2 652 03/07/09 2 2009-03-07T08:02:02.710 2009 3 -true 2 2 2 20 2.2 20.2 662 03/08/09 2 2009-03-08T08:12:03.160 2009 3 -true 2 2 2 20 2.2 20.2 672 03/09/09 2 2009-03-09T08:22:03.610 2009 3 -true 2 2 2 20 2.2 20.2 682 03/10/09 2 2009-03-10T08:32:04.600 2009 3 -true 2 2 2 20 2.2 20.2 692 03/11/09 2 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2010 8 -false 3 3 3 30 3.3 30.3 5883 08/12/10 3 2010-08-12T07:53:04.980 2010 8 -false 3 3 3 30 3.3 30.3 5893 08/13/10 3 2010-08-13T08:03:05.430 2010 8 -false 3 3 3 30 3.3 30.3 5903 08/14/10 3 2010-08-14T08:13:05.880 2010 8 -false 3 3 3 30 3.3 30.3 5913 08/15/10 3 2010-08-15T08:23:06.330 2010 8 -false 3 3 3 30 3.3 30.3 5923 08/16/10 3 2010-08-16T08:33:06.780 2010 8 -false 3 3 3 30 3.3 30.3 593 03/01/09 3 2009-03-01T07:03:00.300 2009 3 -false 3 3 3 30 3.3 30.3 5933 08/17/10 3 2010-08-17T08:43:07.230 2010 8 -false 3 3 3 30 3.3 30.3 5943 08/18/10 3 2010-08-18T08:53:07.680 2010 8 -false 3 3 3 30 3.3 30.3 5953 08/19/10 3 2010-08-19T09:03:08.130 2010 8 -false 3 3 3 30 3.3 30.3 5963 08/20/10 3 2010-08-20T09:13:08.580 2010 8 -false 3 3 3 30 3.3 30.3 5973 08/21/10 3 2010-08-21T09:23:09.300 2010 8 -false 3 3 3 30 3.3 30.3 5983 08/22/10 3 2010-08-22T09:33:09.480 2010 8 -false 3 3 3 30 3.3 30.3 5993 08/23/10 3 2010-08-23T09:43:09.930 2010 8 -false 3 3 3 30 3.3 30.3 6003 08/24/10 3 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06/08/09 2 2009-06-08T07:12:03.160 2009 6 -true 2 2 2 20 2.2 20.2 1592 06/09/09 2 2009-06-09T07:22:03.610 2009 6 -true 2 2 2 20 2.2 20.2 1602 06/10/09 2 2009-06-10T07:32:04.600 2009 6 -true 2 2 2 20 2.2 20.2 1612 06/11/09 2 2009-06-11T07:42:04.510 2009 6 -true 2 2 2 20 2.2 20.2 162 01/17/09 2 2009-01-17T09:42:07.210 2009 1 -true 2 2 2 20 2.2 20.2 1622 06/12/09 2 2009-06-12T07:52:04.960 2009 6 -true 2 2 2 20 2.2 20.2 1632 06/13/09 2 2009-06-13T08:02:05.410 2009 6 -true 2 2 2 20 2.2 20.2 1642 06/14/09 2 2009-06-14T08:12:05.860 2009 6 -true 2 2 2 20 2.2 20.2 1652 06/15/09 2 2009-06-15T08:22:06.310 2009 6 -true 2 2 2 20 2.2 20.2 1662 06/16/09 2 2009-06-16T08:32:06.760 2009 6 -true 2 2 2 20 2.2 20.2 1672 06/17/09 2 2009-06-17T08:42:07.210 2009 6 -true 2 2 2 20 2.2 20.2 1682 06/18/09 2 2009-06-18T08:52:07.660 2009 6 -true 2 2 2 20 2.2 20.2 1692 06/19/09 2 2009-06-19T09:02:08.110 2009 6 -true 2 2 2 20 2.2 20.2 1702 06/20/09 2 2009-06-20T09:12:08.560 2009 6 -true 2 2 2 20 2.2 20.2 1712 06/21/09 2 2009-06-21T09:22:09.100 2009 6 -true 2 2 2 20 2.2 20.2 172 01/18/09 2 2009-01-18T09:52:07.660 2009 1 -true 2 2 2 20 2.2 20.2 1722 06/22/09 2 2009-06-22T09:32:09.460 2009 6 -true 2 2 2 20 2.2 20.2 1732 06/23/09 2 2009-06-23T09:42:09.910 2009 6 -true 2 2 2 20 2.2 20.2 1742 06/24/09 2 2009-06-24T09:52:10.360 2009 6 -true 2 2 2 20 2.2 20.2 1752 06/25/09 2 2009-06-25T10:02:10.810 2009 6 -true 2 2 2 20 2.2 20.2 1762 06/26/09 2 2009-06-26T10:12:11.260 2009 6 -true 2 2 2 20 2.2 20.2 1772 06/27/09 2 2009-06-27T10:22:11.710 2009 6 -true 2 2 2 20 2.2 20.2 1782 06/28/09 2 2009-06-28T10:32:12.160 2009 6 -true 2 2 2 20 2.2 20.2 1792 06/29/09 2 2009-06-29T10:42:12.610 2009 6 -true 2 2 2 20 2.2 20.2 1802 06/30/09 2 2009-06-30T10:52:13.600 2009 6 -true 2 2 2 20 2.2 20.2 1812 07/01/09 2 2009-07-01T06:02:00.100 2009 7 -true 2 2 2 20 2.2 20.2 182 01/19/09 2 2009-01-19T10:02:08.110 2009 1 -true 2 2 2 20 2.2 20.2 1822 07/02/09 2 2009-07-02T06:12:00.460 2009 7 -true 2 2 2 20 2.2 20.2 1832 07/03/09 2 2009-07-03T06:22:00.910 2009 7 -true 2 2 2 20 2.2 20.2 1842 07/04/09 2 2009-07-04T06:32:01.360 2009 7 -true 2 2 2 20 2.2 20.2 1852 07/05/09 2 2009-07-05T06:42:01.810 2009 7 -true 2 2 2 20 2.2 20.2 1862 07/06/09 2 2009-07-06T06:52:02.260 2009 7 -true 2 2 2 20 2.2 20.2 1872 07/07/09 2 2009-07-07T07:02:02.710 2009 7 -true 2 2 2 20 2.2 20.2 1882 07/08/09 2 2009-07-08T07:12:03.160 2009 7 -true 2 2 2 20 2.2 20.2 1892 07/09/09 2 2009-07-09T07:22:03.610 2009 7 -true 2 2 2 20 2.2 20.2 1902 07/10/09 2 2009-07-10T07:32:04.600 2009 7 -true 2 2 2 20 2.2 20.2 1912 07/11/09 2 2009-07-11T07:42:04.510 2009 7 -true 2 2 2 20 2.2 20.2 192 01/20/09 2 2009-01-20T10:12:08.560 2009 1 -true 2 2 2 20 2.2 20.2 1922 07/12/09 2 2009-07-12T07:52:04.960 2009 7 -true 2 2 2 20 2.2 20.2 1932 07/13/09 2 2009-07-13T08:02:05.410 2009 7 -true 2 2 2 20 2.2 20.2 1942 07/14/09 2 2009-07-14T08:12:05.860 2009 7 -true 2 2 2 20 2.2 20.2 1952 07/15/09 2 2009-07-15T08:22:06.310 2009 7 -true 2 2 2 20 2.2 20.2 1962 07/16/09 2 2009-07-16T08:32:06.760 2009 7 -true 2 2 2 20 2.2 20.2 1972 07/17/09 2 2009-07-17T08:42:07.210 2009 7 -true 2 2 2 20 2.2 20.2 1982 07/18/09 2 2009-07-18T08:52:07.660 2009 7 -true 2 2 2 20 2.2 20.2 1992 07/19/09 2 2009-07-19T09:02:08.110 2009 7 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -true 2 2 2 20 2.2 20.2 2002 07/20/09 2 2009-07-20T09:12:08.560 2009 7 -true 2 2 2 20 2.2 20.2 2012 07/21/09 2 2009-07-21T09:22:09.100 2009 7 -true 2 2 2 20 2.2 20.2 202 01/21/09 2 2009-01-21T10:22:09.100 2009 1 -true 2 2 2 20 2.2 20.2 2022 07/22/09 2 2009-07-22T09:32:09.460 2009 7 -true 2 2 2 20 2.2 20.2 2032 07/23/09 2 2009-07-23T09:42:09.910 2009 7 -true 2 2 2 20 2.2 20.2 2042 07/24/09 2 2009-07-24T09:52:10.360 2009 7 -true 2 2 2 20 2.2 20.2 2052 07/25/09 2 2009-07-25T10:02:10.810 2009 7 -true 2 2 2 20 2.2 20.2 2062 07/26/09 2 2009-07-26T10:12:11.260 2009 7 -true 2 2 2 20 2.2 20.2 2072 07/27/09 2 2009-07-27T10:22:11.710 2009 7 -true 2 2 2 20 2.2 20.2 2082 07/28/09 2 2009-07-28T10:32:12.160 2009 7 -true 2 2 2 20 2.2 20.2 2092 07/29/09 2 2009-07-29T10:42:12.610 2009 7 -true 2 2 2 20 2.2 20.2 2102 07/30/09 2 2009-07-30T10:52:13.600 2009 7 -true 2 2 2 20 2.2 20.2 2112 07/31/09 2 2009-07-31T11:02:13.510 2009 7 -true 2 2 2 20 2.2 20.2 212 01/22/09 2 2009-01-22T10:32:09.460 2009 1 -true 2 2 2 20 2.2 20.2 2122 08/01/09 2 2009-08-01T06:02:00.100 2009 8 -true 2 2 2 20 2.2 20.2 2132 08/02/09 2 2009-08-02T06:12:00.460 2009 8 -true 2 2 2 20 2.2 20.2 2142 08/03/09 2 2009-08-03T06:22:00.910 2009 8 -true 2 2 2 20 2.2 20.2 2152 08/04/09 2 2009-08-04T06:32:01.360 2009 8 -true 2 2 2 20 2.2 20.2 2162 08/05/09 2 2009-08-05T06:42:01.810 2009 8 -true 2 2 2 20 2.2 20.2 2172 08/06/09 2 2009-08-06T06:52:02.260 2009 8 -true 2 2 2 20 2.2 20.2 2182 08/07/09 2 2009-08-07T07:02:02.710 2009 8 -true 2 2 2 20 2.2 20.2 2192 08/08/09 2 2009-08-08T07:12:03.160 2009 8 -true 2 2 2 20 2.2 20.2 22 01/03/09 2 2009-01-03T07:22:00.910 2009 1 -true 2 2 2 20 2.2 20.2 2202 08/09/09 2 2009-08-09T07:22:03.610 2009 8 -true 2 2 2 20 2.2 20.2 2212 08/10/09 2 2009-08-10T07:32:04.600 2009 8 -true 2 2 2 20 2.2 20.2 222 01/23/09 2 2009-01-23T10:42:09.910 2009 1 -true 2 2 2 20 2.2 20.2 2222 08/11/09 2 2009-08-11T07:42:04.510 2009 8 -true 2 2 2 20 2.2 20.2 2232 08/12/09 2 2009-08-12T07:52:04.960 2009 8 -true 2 2 2 20 2.2 20.2 2242 08/13/09 2 2009-08-13T08:02:05.410 2009 8 -true 2 2 2 20 2.2 20.2 2252 08/14/09 2 2009-08-14T08:12:05.860 2009 8 -true 2 2 2 20 2.2 20.2 2262 08/15/09 2 2009-08-15T08:22:06.310 2009 8 -true 2 2 2 20 2.2 20.2 2272 08/16/09 2 2009-08-16T08:32:06.760 2009 8 -true 2 2 2 20 2.2 20.2 2282 08/17/09 2 2009-08-17T08:42:07.210 2009 8 -true 2 2 2 20 2.2 20.2 2292 08/18/09 2 2009-08-18T08:52:07.660 2009 8 -true 2 2 2 20 2.2 20.2 2302 08/19/09 2 2009-08-19T09:02:08.110 2009 8 -true 2 2 2 20 2.2 20.2 2312 08/20/09 2 2009-08-20T09:12:08.560 2009 8 -true 2 2 2 20 2.2 20.2 232 01/24/09 2 2009-01-24T10:52:10.360 2009 1 -true 2 2 2 20 2.2 20.2 2322 08/21/09 2 2009-08-21T09:22:09.100 2009 8 -true 2 2 2 20 2.2 20.2 2332 08/22/09 2 2009-08-22T09:32:09.460 2009 8 -true 2 2 2 20 2.2 20.2 2342 08/23/09 2 2009-08-23T09:42:09.910 2009 8 -true 2 2 2 20 2.2 20.2 2352 08/24/09 2 2009-08-24T09:52:10.360 2009 8 -true 2 2 2 20 2.2 20.2 2362 08/25/09 2 2009-08-25T10:02:10.810 2009 8 -true 2 2 2 20 2.2 20.2 2372 08/26/09 2 2009-08-26T10:12:11.260 2009 8 -true 2 2 2 20 2.2 20.2 2382 08/27/09 2 2009-08-27T10:22:11.710 2009 8 -true 2 2 2 20 2.2 20.2 2392 08/28/09 2 2009-08-28T10:32:12.160 2009 8 -true 2 2 2 20 2.2 20.2 2402 08/29/09 2 2009-08-29T10:42:12.610 2009 8 -true 2 2 2 20 2.2 20.2 2412 08/30/09 2 2009-08-30T10:52:13.600 2009 8 -true 2 2 2 20 2.2 20.2 242 01/25/09 2 2009-01-25T11:02:10.810 2009 1 -true 2 2 2 20 2.2 20.2 2422 08/31/09 2 2009-08-31T11:02:13.510 2009 8 -true 2 2 2 20 2.2 20.2 2432 09/01/09 2 2009-09-01T06:02:00.100 2009 9 -true 2 2 2 20 2.2 20.2 2442 09/02/09 2 2009-09-02T06:12:00.460 2009 9 -true 2 2 2 20 2.2 20.2 2452 09/03/09 2 2009-09-03T06:22:00.910 2009 9 -true 2 2 2 20 2.2 20.2 2462 09/04/09 2 2009-09-04T06:32:01.360 2009 9 -true 2 2 2 20 2.2 20.2 2472 09/05/09 2 2009-09-05T06:42:01.810 2009 9 -true 2 2 2 20 2.2 20.2 2482 09/06/09 2 2009-09-06T06:52:02.260 2009 9 -true 2 2 2 20 2.2 20.2 2492 09/07/09 2 2009-09-07T07:02:02.710 2009 9 -true 2 2 2 20 2.2 20.2 2502 09/08/09 2 2009-09-08T07:12:03.160 2009 9 -true 2 2 2 20 2.2 20.2 2512 09/09/09 2 2009-09-09T07:22:03.610 2009 9 -true 2 2 2 20 2.2 20.2 252 01/26/09 2 2009-01-26T11:12:11.260 2009 1 -true 2 2 2 20 2.2 20.2 2522 09/10/09 2 2009-09-10T07:32:04.600 2009 9 -true 2 2 2 20 2.2 20.2 2532 09/11/09 2 2009-09-11T07:42:04.510 2009 9 -true 2 2 2 20 2.2 20.2 2542 09/12/09 2 2009-09-12T07:52:04.960 2009 9 -true 2 2 2 20 2.2 20.2 2552 09/13/09 2 2009-09-13T08:02:05.410 2009 9 -true 2 2 2 20 2.2 20.2 2562 09/14/09 2 2009-09-14T08:12:05.860 2009 9 -true 2 2 2 20 2.2 20.2 2572 09/15/09 2 2009-09-15T08:22:06.310 2009 9 -true 2 2 2 20 2.2 20.2 2582 09/16/09 2 2009-09-16T08:32:06.760 2009 9 -true 2 2 2 20 2.2 20.2 2592 09/17/09 2 2009-09-17T08:42:07.210 2009 9 -true 2 2 2 20 2.2 20.2 2602 09/18/09 2 2009-09-18T08:52:07.660 2009 9 -true 2 2 2 20 2.2 20.2 2612 09/19/09 2 2009-09-19T09:02:08.110 2009 9 -true 2 2 2 20 2.2 20.2 262 01/27/09 2 2009-01-27T11:22:11.710 2009 1 -true 2 2 2 20 2.2 20.2 2622 09/20/09 2 2009-09-20T09:12:08.560 2009 9 -true 2 2 2 20 2.2 20.2 2632 09/21/09 2 2009-09-21T09:22:09.100 2009 9 -true 2 2 2 20 2.2 20.2 2642 09/22/09 2 2009-09-22T09:32:09.460 2009 9 -true 2 2 2 20 2.2 20.2 2652 09/23/09 2 2009-09-23T09:42:09.910 2009 9 -true 2 2 2 20 2.2 20.2 2662 09/24/09 2 2009-09-24T09:52:10.360 2009 9 -true 2 2 2 20 2.2 20.2 2672 09/25/09 2 2009-09-25T10:02:10.810 2009 9 -true 2 2 2 20 2.2 20.2 2682 09/26/09 2 2009-09-26T10:12:11.260 2009 9 -true 2 2 2 20 2.2 20.2 2692 09/27/09 2 2009-09-27T10:22:11.710 2009 9 -true 2 2 2 20 2.2 20.2 2702 09/28/09 2 2009-09-28T10:32:12.160 2009 9 -true 2 2 2 20 2.2 20.2 2712 09/29/09 2 2009-09-29T10:42:12.610 2009 9 -true 2 2 2 20 2.2 20.2 272 01/28/09 2 2009-01-28T11:32:12.160 2009 1 -true 2 2 2 20 2.2 20.2 2722 09/30/09 2 2009-09-30T10:52:13.600 2009 9 -true 2 2 2 20 2.2 20.2 2732 10/01/09 2 2009-10-01T06:02:00.100 2009 10 -true 2 2 2 20 2.2 20.2 2742 10/02/09 2 2009-10-02T06:12:00.460 2009 10 -true 2 2 2 20 2.2 20.2 2752 10/03/09 2 2009-10-03T06:22:00.910 2009 10 -true 2 2 2 20 2.2 20.2 2762 10/04/09 2 2009-10-04T06:32:01.360 2009 10 -true 2 2 2 20 2.2 20.2 2772 10/05/09 2 2009-10-05T06:42:01.810 2009 10 -true 2 2 2 20 2.2 20.2 2782 10/06/09 2 2009-10-06T06:52:02.260 2009 10 -true 2 2 2 20 2.2 20.2 2792 10/07/09 2 2009-10-07T07:02:02.710 2009 10 -true 2 2 2 20 2.2 20.2 2802 10/08/09 2 2009-10-08T07:12:03.160 2009 10 -true 2 2 2 20 2.2 20.2 2812 10/09/09 2 2009-10-09T07:22:03.610 2009 10 -true 2 2 2 20 2.2 20.2 282 01/29/09 2 2009-01-29T11:42:12.610 2009 1 -true 2 2 2 20 2.2 20.2 2822 10/10/09 2 2009-10-10T07:32:04.600 2009 10 -true 2 2 2 20 2.2 20.2 2832 10/11/09 2 2009-10-11T07:42:04.510 2009 10 -true 2 2 2 20 2.2 20.2 2842 10/12/09 2 2009-10-12T07:52:04.960 2009 10 -true 2 2 2 20 2.2 20.2 2852 10/13/09 2 2009-10-13T08:02:05.410 2009 10 -true 2 2 2 20 2.2 20.2 2862 10/14/09 2 2009-10-14T08:12:05.860 2009 10 -true 2 2 2 20 2.2 20.2 2872 10/15/09 2 2009-10-15T08:22:06.310 2009 10 -true 2 2 2 20 2.2 20.2 2882 10/16/09 2 2009-10-16T08:32:06.760 2009 10 -true 2 2 2 20 2.2 20.2 2892 10/17/09 2 2009-10-17T08:42:07.210 2009 10 -true 2 2 2 20 2.2 20.2 2902 10/18/09 2 2009-10-18T08:52:07.660 2009 10 -true 2 2 2 20 2.2 20.2 2912 10/19/09 2 2009-10-19T09:02:08.110 2009 10 -true 2 2 2 20 2.2 20.2 292 01/30/09 2 2009-01-30T11:52:13.600 2009 1 -true 2 2 2 20 2.2 20.2 2922 10/20/09 2 2009-10-20T09:12:08.560 2009 10 -true 2 2 2 20 2.2 20.2 2932 10/21/09 2 2009-10-21T09:22:09.100 2009 10 -true 2 2 2 20 2.2 20.2 2942 10/22/09 2 2009-10-22T09:32:09.460 2009 10 -true 2 2 2 20 2.2 20.2 2952 10/23/09 2 2009-10-23T09:42:09.910 2009 10 -true 2 2 2 20 2.2 20.2 2962 10/24/09 2 2009-10-24T09:52:10.360 2009 10 -true 2 2 2 20 2.2 20.2 2972 10/25/09 2 2009-10-25T11:02:10.810 2009 10 -true 2 2 2 20 2.2 20.2 2982 10/26/09 2 2009-10-26T11:12:11.260 2009 10 -true 2 2 2 20 2.2 20.2 2992 10/27/09 2 2009-10-27T11:22:11.710 2009 10 -true 2 2 2 20 2.2 20.2 3002 10/28/09 2 2009-10-28T11:32:12.160 2009 10 -true 2 2 2 20 2.2 20.2 3012 10/29/09 2 2009-10-29T11:42:12.610 2009 10 -true 2 2 2 20 2.2 20.2 302 01/31/09 2 2009-01-31T12:02:13.510 2009 1 -true 2 2 2 20 2.2 20.2 3022 10/30/09 2 2009-10-30T11:52:13.600 2009 10 -true 2 2 2 20 2.2 20.2 3032 10/31/09 2 2009-10-31T12:02:13.510 2009 10 -true 2 2 2 20 2.2 20.2 3042 11/01/09 2 2009-11-01T07:02:00.100 2009 11 -true 2 2 2 20 2.2 20.2 3052 11/02/09 2 2009-11-02T07:12:00.460 2009 11 -true 2 2 2 20 2.2 20.2 3062 11/03/09 2 2009-11-03T07:22:00.910 2009 11 -true 2 2 2 20 2.2 20.2 3072 11/04/09 2 2009-11-04T07:32:01.360 2009 11 -true 2 2 2 20 2.2 20.2 3082 11/05/09 2 2009-11-05T07:42:01.810 2009 11 -true 2 2 2 20 2.2 20.2 3092 11/06/09 2 2009-11-06T07:52:02.260 2009 11 -true 2 2 2 20 2.2 20.2 3102 11/07/09 2 2009-11-07T08:02:02.710 2009 11 -true 2 2 2 20 2.2 20.2 3112 11/08/09 2 2009-11-08T08:12:03.160 2009 11 -true 2 2 2 20 2.2 20.2 312 02/01/09 2 2009-02-01T07:02:00.100 2009 2 -true 2 2 2 20 2.2 20.2 3122 11/09/09 2 2009-11-09T08:22:03.610 2009 11 -true 2 2 2 20 2.2 20.2 3132 11/10/09 2 2009-11-10T08:32:04.600 2009 11 -true 2 2 2 20 2.2 20.2 3142 11/11/09 2 2009-11-11T08:42:04.510 2009 11 -true 2 2 2 20 2.2 20.2 3152 11/12/09 2 2009-11-12T08:52:04.960 2009 11 -true 2 2 2 20 2.2 20.2 3162 11/13/09 2 2009-11-13T09:02:05.410 2009 11 -true 2 2 2 20 2.2 20.2 3172 11/14/09 2 2009-11-14T09:12:05.860 2009 11 -true 2 2 2 20 2.2 20.2 3182 11/15/09 2 2009-11-15T09:22:06.310 2009 11 -true 2 2 2 20 2.2 20.2 3192 11/16/09 2 2009-11-16T09:32:06.760 2009 11 -true 2 2 2 20 2.2 20.2 32 01/04/09 2 2009-01-04T07:32:01.360 2009 1 -true 2 2 2 20 2.2 20.2 3202 11/17/09 2 2009-11-17T09:42:07.210 2009 11 -true 2 2 2 20 2.2 20.2 3212 11/18/09 2 2009-11-18T09:52:07.660 2009 11 -true 2 2 2 20 2.2 20.2 322 02/02/09 2 2009-02-02T07:12:00.460 2009 2 -true 2 2 2 20 2.2 20.2 3222 11/19/09 2 2009-11-19T10:02:08.110 2009 11 -true 2 2 2 20 2.2 20.2 3232 11/20/09 2 2009-11-20T10:12:08.560 2009 11 -true 2 2 2 20 2.2 20.2 3242 11/21/09 2 2009-11-21T10:22:09.100 2009 11 -true 2 2 2 20 2.2 20.2 3252 11/22/09 2 2009-11-22T10:32:09.460 2009 11 -true 2 2 2 20 2.2 20.2 3262 11/23/09 2 2009-11-23T10:42:09.910 2009 11 -true 2 2 2 20 2.2 20.2 3272 11/24/09 2 2009-11-24T10:52:10.360 2009 11 -true 2 2 2 20 2.2 20.2 3282 11/25/09 2 2009-11-25T11:02:10.810 2009 11 -true 2 2 2 20 2.2 20.2 3292 11/26/09 2 2009-11-26T11:12:11.260 2009 11 -true 2 2 2 20 2.2 20.2 3302 11/27/09 2 2009-11-27T11:22:11.710 2009 11 -true 2 2 2 20 2.2 20.2 3312 11/28/09 2 2009-11-28T11:32:12.160 2009 11 -true 2 2 2 20 2.2 20.2 332 02/03/09 2 2009-02-03T07:22:00.910 2009 2 -true 2 2 2 20 2.2 20.2 3322 11/29/09 2 2009-11-29T11:42:12.610 2009 11 -true 2 2 2 20 2.2 20.2 3332 11/30/09 2 2009-11-30T11:52:13.600 2009 11 -true 2 2 2 20 2.2 20.2 3342 12/01/09 2 2009-12-01T07:02:00.100 2009 12 -true 2 2 2 20 2.2 20.2 3352 12/02/09 2 2009-12-02T07:12:00.460 2009 12 -true 2 2 2 20 2.2 20.2 3362 12/03/09 2 2009-12-03T07:22:00.910 2009 12 -true 2 2 2 20 2.2 20.2 3372 12/04/09 2 2009-12-04T07:32:01.360 2009 12 -true 2 2 2 20 2.2 20.2 3382 12/05/09 2 2009-12-05T07:42:01.810 2009 12 -true 2 2 2 20 2.2 20.2 3392 12/06/09 2 2009-12-06T07:52:02.260 2009 12 -true 2 2 2 20 2.2 20.2 3402 12/07/09 2 2009-12-07T08:02:02.710 2009 12 -true 2 2 2 20 2.2 20.2 3412 12/08/09 2 2009-12-08T08:12:03.160 2009 12 -true 2 2 2 20 2.2 20.2 342 02/04/09 2 2009-02-04T07:32:01.360 2009 2 -true 2 2 2 20 2.2 20.2 3422 12/09/09 2 2009-12-09T08:22:03.610 2009 12 -true 2 2 2 20 2.2 20.2 3432 12/10/09 2 2009-12-10T08:32:04.600 2009 12 -true 2 2 2 20 2.2 20.2 3442 12/11/09 2 2009-12-11T08:42:04.510 2009 12 -true 2 2 2 20 2.2 20.2 3452 12/12/09 2 2009-12-12T08:52:04.960 2009 12 -true 2 2 2 20 2.2 20.2 3462 12/13/09 2 2009-12-13T09:02:05.410 2009 12 -true 2 2 2 20 2.2 20.2 3472 12/14/09 2 2009-12-14T09:12:05.860 2009 12 -true 2 2 2 20 2.2 20.2 3482 12/15/09 2 2009-12-15T09:22:06.310 2009 12 -true 2 2 2 20 2.2 20.2 3492 12/16/09 2 2009-12-16T09:32:06.760 2009 12 -true 2 2 2 20 2.2 20.2 3502 12/17/09 2 2009-12-17T09:42:07.210 2009 12 -true 2 2 2 20 2.2 20.2 3512 12/18/09 2 2009-12-18T09:52:07.660 2009 12 -true 2 2 2 20 2.2 20.2 352 02/05/09 2 2009-02-05T07:42:01.810 2009 2 -true 2 2 2 20 2.2 20.2 3522 12/19/09 2 2009-12-19T10:02:08.110 2009 12 -true 2 2 2 20 2.2 20.2 3532 12/20/09 2 2009-12-20T10:12:08.560 2009 12 -true 2 2 2 20 2.2 20.2 3542 12/21/09 2 2009-12-21T10:22:09.100 2009 12 -true 2 2 2 20 2.2 20.2 3552 12/22/09 2 2009-12-22T10:32:09.460 2009 12 -true 2 2 2 20 2.2 20.2 3562 12/23/09 2 2009-12-23T10:42:09.910 2009 12 -true 2 2 2 20 2.2 20.2 3572 12/24/09 2 2009-12-24T10:52:10.360 2009 12 -true 2 2 2 20 2.2 20.2 3582 12/25/09 2 2009-12-25T11:02:10.810 2009 12 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-true 2 2 2 20 2.2 20.2 372 02/07/09 2 2009-02-07T08:02:02.710 2009 2 -true 2 2 2 20 2.2 20.2 3722 01/08/10 2 2010-01-08T08:12:03.160 2010 1 -true 2 2 2 20 2.2 20.2 3732 01/09/10 2 2010-01-09T08:22:03.610 2010 1 -true 2 2 2 20 2.2 20.2 3742 01/10/10 2 2010-01-10T08:32:04.600 2010 1 -true 2 2 2 20 2.2 20.2 3752 01/11/10 2 2010-01-11T08:42:04.510 2010 1 -true 2 2 2 20 2.2 20.2 3762 01/12/10 2 2010-01-12T08:52:04.960 2010 1 -true 2 2 2 20 2.2 20.2 3772 01/13/10 2 2010-01-13T09:02:05.410 2010 1 -true 2 2 2 20 2.2 20.2 3782 01/14/10 2 2010-01-14T09:12:05.860 2010 1 -true 2 2 2 20 2.2 20.2 3792 01/15/10 2 2010-01-15T09:22:06.310 2010 1 -true 2 2 2 20 2.2 20.2 3802 01/16/10 2 2010-01-16T09:32:06.760 2010 1 -true 2 2 2 20 2.2 20.2 3812 01/17/10 2 2010-01-17T09:42:07.210 2010 1 -true 2 2 2 20 2.2 20.2 382 02/08/09 2 2009-02-08T08:12:03.160 2009 2 -true 2 2 2 20 2.2 20.2 3822 01/18/10 2 2010-01-18T09:52:07.660 2010 1 -true 2 2 2 20 2.2 20.2 3832 01/19/10 2 2010-01-19T10:02:08.110 2010 1 -true 2 2 2 20 2.2 20.2 3842 01/20/10 2 2010-01-20T10:12:08.560 2010 1 -true 2 2 2 20 2.2 20.2 3852 01/21/10 2 2010-01-21T10:22:09.100 2010 1 -true 2 2 2 20 2.2 20.2 3862 01/22/10 2 2010-01-22T10:32:09.460 2010 1 -true 2 2 2 20 2.2 20.2 3872 01/23/10 2 2010-01-23T10:42:09.910 2010 1 -true 2 2 2 20 2.2 20.2 3882 01/24/10 2 2010-01-24T10:52:10.360 2010 1 -true 2 2 2 20 2.2 20.2 3892 01/25/10 2 2010-01-25T11:02:10.810 2010 1 -true 2 2 2 20 2.2 20.2 3902 01/26/10 2 2010-01-26T11:12:11.260 2010 1 -true 2 2 2 20 2.2 20.2 3912 01/27/10 2 2010-01-27T11:22:11.710 2010 1 -true 2 2 2 20 2.2 20.2 392 02/09/09 2 2009-02-09T08:22:03.610 2009 2 -true 2 2 2 20 2.2 20.2 3922 01/28/10 2 2010-01-28T11:32:12.160 2010 1 -true 2 2 2 20 2.2 20.2 3932 01/29/10 2 2010-01-29T11:42:12.610 2010 1 -true 2 2 2 20 2.2 20.2 3942 01/30/10 2 2010-01-30T11:52:13.600 2010 1 -true 2 2 2 20 2.2 20.2 3952 01/31/10 2 2010-01-31T12:02:13.510 2010 1 -true 2 2 2 20 2.2 20.2 3962 02/01/10 2 2010-02-01T07:02:00.100 2010 2 -true 2 2 2 20 2.2 20.2 3972 02/02/10 2 2010-02-02T07:12:00.460 2010 2 -true 2 2 2 20 2.2 20.2 3982 02/03/10 2 2010-02-03T07:22:00.910 2010 2 -true 2 2 2 20 2.2 20.2 3992 02/04/10 2 2010-02-04T07:32:01.360 2010 2 -true 2 2 2 20 2.2 20.2 4002 02/05/10 2 2010-02-05T07:42:01.810 2010 2 -true 2 2 2 20 2.2 20.2 4012 02/06/10 2 2010-02-06T07:52:02.260 2010 2 -true 2 2 2 20 2.2 20.2 402 02/10/09 2 2009-02-10T08:32:04.600 2009 2 -true 2 2 2 20 2.2 20.2 4022 02/07/10 2 2010-02-07T08:02:02.710 2010 2 -true 2 2 2 20 2.2 20.2 4032 02/08/10 2 2010-02-08T08:12:03.160 2010 2 -true 2 2 2 20 2.2 20.2 4042 02/09/10 2 2010-02-09T08:22:03.610 2010 2 -true 2 2 2 20 2.2 20.2 4052 02/10/10 2 2010-02-10T08:32:04.600 2010 2 -true 2 2 2 20 2.2 20.2 4062 02/11/10 2 2010-02-11T08:42:04.510 2010 2 -true 2 2 2 20 2.2 20.2 4072 02/12/10 2 2010-02-12T08:52:04.960 2010 2 -true 2 2 2 20 2.2 20.2 4082 02/13/10 2 2010-02-13T09:02:05.410 2010 2 -true 2 2 2 20 2.2 20.2 4092 02/14/10 2 2010-02-14T09:12:05.860 2010 2 -true 2 2 2 20 2.2 20.2 4102 02/15/10 2 2010-02-15T09:22:06.310 2010 2 -true 2 2 2 20 2.2 20.2 4112 02/16/10 2 2010-02-16T09:32:06.760 2010 2 -true 2 2 2 20 2.2 20.2 412 02/11/09 2 2009-02-11T08:42:04.510 2009 2 -true 2 2 2 20 2.2 20.2 4122 02/17/10 2 2010-02-17T09:42:07.210 2010 2 -true 2 2 2 20 2.2 20.2 4132 02/18/10 2 2010-02-18T09:52:07.660 2010 2 -true 2 2 2 20 2.2 20.2 4142 02/19/10 2 2010-02-19T10:02:08.110 2010 2 -true 2 2 2 20 2.2 20.2 4152 02/20/10 2 2010-02-20T10:12:08.560 2010 2 -true 2 2 2 20 2.2 20.2 4162 02/21/10 2 2010-02-21T10:22:09.100 2010 2 -true 2 2 2 20 2.2 20.2 4172 02/22/10 2 2010-02-22T10:32:09.460 2010 2 -true 2 2 2 20 2.2 20.2 4182 02/23/10 2 2010-02-23T10:42:09.910 2010 2 -true 2 2 2 20 2.2 20.2 4192 02/24/10 2 2010-02-24T10:52:10.360 2010 2 -true 2 2 2 20 2.2 20.2 42 01/05/09 2 2009-01-05T07:42:01.810 2009 1 -true 2 2 2 20 2.2 20.2 4202 02/25/10 2 2010-02-25T11:02:10.810 2010 2 -true 2 2 2 20 2.2 20.2 4212 02/26/10 2 2010-02-26T11:12:11.260 2010 2 -true 2 2 2 20 2.2 20.2 422 02/12/09 2 2009-02-12T08:52:04.960 2009 2 -true 2 2 2 20 2.2 20.2 4222 02/27/10 2 2010-02-27T11:22:11.710 2010 2 -true 2 2 2 20 2.2 20.2 4232 02/28/10 2 2010-02-28T11:32:12.160 2010 2 -true 2 2 2 20 2.2 20.2 4242 03/01/10 2 2010-03-01T07:02:00.100 2010 3 -true 2 2 2 20 2.2 20.2 4252 03/02/10 2 2010-03-02T07:12:00.460 2010 3 -true 2 2 2 20 2.2 20.2 4262 03/03/10 2 2010-03-03T07:22:00.910 2010 3 -true 2 2 2 20 2.2 20.2 4272 03/04/10 2 2010-03-04T07:32:01.360 2010 3 -true 2 2 2 20 2.2 20.2 4282 03/05/10 2 2010-03-05T07:42:01.810 2010 3 -true 2 2 2 20 2.2 20.2 4292 03/06/10 2 2010-03-06T07:52:02.260 2010 3 -true 2 2 2 20 2.2 20.2 4302 03/07/10 2 2010-03-07T08:02:02.710 2010 3 -true 2 2 2 20 2.2 20.2 4312 03/08/10 2 2010-03-08T08:12:03.160 2010 3 -true 2 2 2 20 2.2 20.2 432 02/13/09 2 2009-02-13T09:02:05.410 2009 2 -true 2 2 2 20 2.2 20.2 4322 03/09/10 2 2010-03-09T08:22:03.610 2010 3 -true 2 2 2 20 2.2 20.2 4332 03/10/10 2 2010-03-10T08:32:04.600 2010 3 -true 2 2 2 20 2.2 20.2 4342 03/11/10 2 2010-03-11T08:42:04.510 2010 3 -true 2 2 2 20 2.2 20.2 4352 03/12/10 2 2010-03-12T08:52:04.960 2010 3 -true 2 2 2 20 2.2 20.2 4362 03/13/10 2 2010-03-13T09:02:05.410 2010 3 -true 2 2 2 20 2.2 20.2 4372 03/14/10 2 2010-03-14T08:12:05.860 2010 3 -true 2 2 2 20 2.2 20.2 4382 03/15/10 2 2010-03-15T08:22:06.310 2010 3 -true 2 2 2 20 2.2 20.2 4392 03/16/10 2 2010-03-16T08:32:06.760 2010 3 -true 2 2 2 20 2.2 20.2 4402 03/17/10 2 2010-03-17T08:42:07.210 2010 3 -true 2 2 2 20 2.2 20.2 4412 03/18/10 2 2010-03-18T08:52:07.660 2010 3 -true 2 2 2 20 2.2 20.2 442 02/14/09 2 2009-02-14T09:12:05.860 2009 2 -true 2 2 2 20 2.2 20.2 4422 03/19/10 2 2010-03-19T09:02:08.110 2010 3 -true 2 2 2 20 2.2 20.2 4432 03/20/10 2 2010-03-20T09:12:08.560 2010 3 -true 2 2 2 20 2.2 20.2 4442 03/21/10 2 2010-03-21T09:22:09.100 2010 3 -true 2 2 2 20 2.2 20.2 4452 03/22/10 2 2010-03-22T09:32:09.460 2010 3 -true 2 2 2 20 2.2 20.2 4462 03/23/10 2 2010-03-23T09:42:09.910 2010 3 -true 2 2 2 20 2.2 20.2 4472 03/24/10 2 2010-03-24T09:52:10.360 2010 3 -true 2 2 2 20 2.2 20.2 4482 03/25/10 2 2010-03-25T10:02:10.810 2010 3 -true 2 2 2 20 2.2 20.2 4492 03/26/10 2 2010-03-26T10:12:11.260 2010 3 -true 2 2 2 20 2.2 20.2 4502 03/27/10 2 2010-03-27T10:22:11.710 2010 3 -true 2 2 2 20 2.2 20.2 4512 03/28/10 2 2010-03-28T09:32:12.160 2010 3 -true 2 2 2 20 2.2 20.2 452 02/15/09 2 2009-02-15T09:22:06.310 2009 2 -true 2 2 2 20 2.2 20.2 4522 03/29/10 2 2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 462 02/16/09 2 2009-02-16T09:32:06.760 2009 2 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 472 02/17/09 2 2009-02-17T09:42:07.210 2009 2 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 482 02/18/09 2 2009-02-18T09:52:07.660 2009 2 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 492 02/19/09 2 2009-02-19T10:02:08.110 2009 2 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 502 02/20/09 2 2009-02-20T10:12:08.560 2009 2 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 512 02/21/09 2 2009-02-21T10:22:09.100 2009 2 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 52 01/06/09 2 2009-01-06T07:52:02.260 2009 1 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 522 02/22/09 2 2009-02-22T10:32:09.460 2009 2 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 532 02/23/09 2 2009-02-23T10:42:09.910 2009 2 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 542 02/24/09 2 2009-02-24T10:52:10.360 2009 2 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 552 02/25/09 2 2009-02-25T11:02:10.810 2009 2 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 562 02/26/09 2 2009-02-26T11:12:11.260 2009 2 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 572 02/27/09 2 2009-02-27T11:22:11.710 2009 2 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 582 02/28/09 2 2009-02-28T11:32:12.160 2009 2 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 592 03/01/09 2 2009-03-01T07:02:00.100 2009 3 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 602 03/02/09 2 2009-03-02T07:12:00.460 2009 3 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 612 03/03/09 2 2009-03-03T07:22:00.910 2009 3 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 62 01/07/09 2 2009-01-07T08:02:02.710 2009 1 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 622 03/04/09 2 2009-03-04T07:32:01.360 2009 3 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 632 03/05/09 2 2009-03-05T07:42:01.810 2009 3 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 642 03/06/09 2 2009-03-06T07:52:02.260 2009 3 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 10/11/10 2 2010-10-11T07:42:04.510 2010 10 -true 2 2 2 20 2.2 20.2 6492 10/12/10 2 2010-10-12T07:52:04.960 2010 10 -true 2 2 2 20 2.2 20.2 6502 10/13/10 2 2010-10-13T08:02:05.410 2010 10 -true 2 2 2 20 2.2 20.2 6512 10/14/10 2 2010-10-14T08:12:05.860 2010 10 -true 2 2 2 20 2.2 20.2 652 03/07/09 2 2009-03-07T08:02:02.710 2009 3 -true 2 2 2 20 2.2 20.2 6522 10/15/10 2 2010-10-15T08:22:06.310 2010 10 -true 2 2 2 20 2.2 20.2 6532 10/16/10 2 2010-10-16T08:32:06.760 2010 10 -true 2 2 2 20 2.2 20.2 6542 10/17/10 2 2010-10-17T08:42:07.210 2010 10 -true 2 2 2 20 2.2 20.2 6552 10/18/10 2 2010-10-18T08:52:07.660 2010 10 -true 2 2 2 20 2.2 20.2 6562 10/19/10 2 2010-10-19T09:02:08.110 2010 10 -true 2 2 2 20 2.2 20.2 6572 10/20/10 2 2010-10-20T09:12:08.560 2010 10 -true 2 2 2 20 2.2 20.2 6582 10/21/10 2 2010-10-21T09:22:09.100 2010 10 -true 2 2 2 20 2.2 20.2 6592 10/22/10 2 2010-10-22T09:32:09.460 2010 10 -true 2 2 2 20 2.2 20.2 6602 10/23/10 2 2010-10-23T09:42:09.910 2010 10 -true 2 2 2 20 2.2 20.2 6612 10/24/10 2 2010-10-24T09:52:10.360 2010 10 -true 2 2 2 20 2.2 20.2 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20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 -true 2 2 2 20 2.2 20.2 6802 11/12/10 2 2010-11-12T08:52:04.960 2010 11 -true 2 2 2 20 2.2 20.2 6812 11/13/10 2 2010-11-13T09:02:05.410 2010 11 -true 2 2 2 20 2.2 20.2 682 03/10/09 2 2009-03-10T08:32:04.600 2009 3 -true 2 2 2 20 2.2 20.2 6822 11/14/10 2 2010-11-14T09:12:05.860 2010 11 -true 2 2 2 20 2.2 20.2 6832 11/15/10 2 2010-11-15T09:22:06.310 2010 11 -true 2 2 2 20 2.2 20.2 6842 11/16/10 2 2010-11-16T09:32:06.760 2010 11 -true 2 2 2 20 2.2 20.2 6852 11/17/10 2 2010-11-17T09:42:07.210 2010 11 -true 2 2 2 20 2.2 20.2 6862 11/18/10 2 2010-11-18T09:52:07.660 2010 11 -true 2 2 2 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2010-02-05T07:42:01.810 2010 2 -true 2 2 2 20 2.2 20.2 4012 02/06/10 2 2010-02-06T07:52:02.260 2010 2 -true 2 2 2 20 2.2 20.2 4022 02/07/10 2 2010-02-07T08:02:02.710 2010 2 -true 2 2 2 20 2.2 20.2 4032 02/08/10 2 2010-02-08T08:12:03.160 2010 2 -true 2 2 2 20 2.2 20.2 4042 02/09/10 2 2010-02-09T08:22:03.610 2010 2 -true 2 2 2 20 2.2 20.2 4052 02/10/10 2 2010-02-10T08:32:04.600 2010 2 -true 2 2 2 20 2.2 20.2 4062 02/11/10 2 2010-02-11T08:42:04.510 2010 2 -true 2 2 2 20 2.2 20.2 4072 02/12/10 2 2010-02-12T08:52:04.960 2010 2 -true 2 2 2 20 2.2 20.2 4082 02/13/10 2 2010-02-13T09:02:05.410 2010 2 -true 2 2 2 20 2.2 20.2 4092 02/14/10 2 2010-02-14T09:12:05.860 2010 2 -true 2 2 2 20 2.2 20.2 4102 02/15/10 2 2010-02-15T09:22:06.310 2010 2 -true 2 2 2 20 2.2 20.2 4112 02/16/10 2 2010-02-16T09:32:06.760 2010 2 -true 2 2 2 20 2.2 20.2 4122 02/17/10 2 2010-02-17T09:42:07.210 2010 2 -true 2 2 2 20 2.2 20.2 4132 02/18/10 2 2010-02-18T09:52:07.660 2010 2 -true 2 2 2 20 2.2 20.2 4142 02/19/10 2 2010-02-19T10:02:08.110 2010 2 -true 2 2 2 20 2.2 20.2 4152 02/20/10 2 2010-02-20T10:12:08.560 2010 2 -true 2 2 2 20 2.2 20.2 4162 02/21/10 2 2010-02-21T10:22:09.100 2010 2 -true 2 2 2 20 2.2 20.2 4172 02/22/10 2 2010-02-22T10:32:09.460 2010 2 -true 2 2 2 20 2.2 20.2 4182 02/23/10 2 2010-02-23T10:42:09.910 2010 2 -true 2 2 2 20 2.2 20.2 4192 02/24/10 2 2010-02-24T10:52:10.360 2010 2 -true 2 2 2 20 2.2 20.2 42 01/05/09 2 2009-01-05T07:42:01.810 2009 1 -true 2 2 2 20 2.2 20.2 4202 02/25/10 2 2010-02-25T11:02:10.810 2010 2 -true 2 2 2 20 2.2 20.2 4212 02/26/10 2 2010-02-26T11:12:11.260 2010 2 -true 2 2 2 20 2.2 20.2 4222 02/27/10 2 2010-02-27T11:22:11.710 2010 2 -true 2 2 2 20 2.2 20.2 4232 02/28/10 2 2010-02-28T11:32:12.160 2010 2 -true 2 2 2 20 2.2 20.2 4242 03/01/10 2 2010-03-01T07:02:00.100 2010 3 -true 2 2 2 20 2.2 20.2 4252 03/02/10 2 2010-03-02T07:12:00.460 2010 3 -true 2 2 2 20 2.2 20.2 4262 03/03/10 2 2010-03-03T07:22:00.910 2010 3 -true 2 2 2 20 2.2 20.2 4272 03/04/10 2 2010-03-04T07:32:01.360 2010 3 -true 2 2 2 20 2.2 20.2 4282 03/05/10 2 2010-03-05T07:42:01.810 2010 3 -true 2 2 2 20 2.2 20.2 4292 03/06/10 2 2010-03-06T07:52:02.260 2010 3 -true 2 2 2 20 2.2 20.2 4302 03/07/10 2 2010-03-07T08:02:02.710 2010 3 -true 2 2 2 20 2.2 20.2 4312 03/08/10 2 2010-03-08T08:12:03.160 2010 3 -true 2 2 2 20 2.2 20.2 4322 03/09/10 2 2010-03-09T08:22:03.610 2010 3 -true 2 2 2 20 2.2 20.2 4332 03/10/10 2 2010-03-10T08:32:04.600 2010 3 -true 2 2 2 20 2.2 20.2 4342 03/11/10 2 2010-03-11T08:42:04.510 2010 3 -true 2 2 2 20 2.2 20.2 4352 03/12/10 2 2010-03-12T08:52:04.960 2010 3 -true 2 2 2 20 2.2 20.2 4362 03/13/10 2 2010-03-13T09:02:05.410 2010 3 -true 2 2 2 20 2.2 20.2 4372 03/14/10 2 2010-03-14T08:12:05.860 2010 3 -true 2 2 2 20 2.2 20.2 4382 03/15/10 2 2010-03-15T08:22:06.310 2010 3 -true 2 2 2 20 2.2 20.2 4392 03/16/10 2 2010-03-16T08:32:06.760 2010 3 -true 2 2 2 20 2.2 20.2 4402 03/17/10 2 2010-03-17T08:42:07.210 2010 3 -true 2 2 2 20 2.2 20.2 4412 03/18/10 2 2010-03-18T08:52:07.660 2010 3 -true 2 2 2 20 2.2 20.2 4422 03/19/10 2 2010-03-19T09:02:08.110 2010 3 -true 2 2 2 20 2.2 20.2 4432 03/20/10 2 2010-03-20T09:12:08.560 2010 3 -true 2 2 2 20 2.2 20.2 4442 03/21/10 2 2010-03-21T09:22:09.100 2010 3 -true 2 2 2 20 2.2 20.2 4452 03/22/10 2 2010-03-22T09:32:09.460 2010 3 -true 2 2 2 20 2.2 20.2 4462 03/23/10 2 2010-03-23T09:42:09.910 2010 3 -true 2 2 2 20 2.2 20.2 4472 03/24/10 2 2010-03-24T09:52:10.360 2010 3 -true 2 2 2 20 2.2 20.2 4482 03/25/10 2 2010-03-25T10:02:10.810 2010 3 -true 2 2 2 20 2.2 20.2 4492 03/26/10 2 2010-03-26T10:12:11.260 2010 3 -true 2 2 2 20 2.2 20.2 4502 03/27/10 2 2010-03-27T10:22:11.710 2010 3 -true 2 2 2 20 2.2 20.2 4512 03/28/10 2 2010-03-28T09:32:12.160 2010 3 -true 2 2 2 20 2.2 20.2 4522 03/29/10 2 2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 52 01/06/09 2 2009-01-06T07:52:02.260 2009 1 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 62 01/07/09 2 2009-01-07T08:02:02.710 2009 1 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 10/11/10 2 2010-10-11T07:42:04.510 2010 10 -true 2 2 2 20 2.2 20.2 6492 10/12/10 2 2010-10-12T07:52:04.960 2010 10 -true 2 2 2 20 2.2 20.2 6502 10/13/10 2 2010-10-13T08:02:05.410 2010 10 -true 2 2 2 20 2.2 20.2 6512 10/14/10 2 2010-10-14T08:12:05.860 2010 10 -true 2 2 2 20 2.2 20.2 6522 10/15/10 2 2010-10-15T08:22:06.310 2010 10 -true 2 2 2 20 2.2 20.2 6532 10/16/10 2 2010-10-16T08:32:06.760 2010 10 -true 2 2 2 20 2.2 20.2 6542 10/17/10 2 2010-10-17T08:42:07.210 2010 10 -true 2 2 2 20 2.2 20.2 6552 10/18/10 2 2010-10-18T08:52:07.660 2010 10 -true 2 2 2 20 2.2 20.2 6562 10/19/10 2 2010-10-19T09:02:08.110 2010 10 -true 2 2 2 20 2.2 20.2 6572 10/20/10 2 2010-10-20T09:12:08.560 2010 10 -true 2 2 2 20 2.2 20.2 6582 10/21/10 2 2010-10-21T09:22:09.100 2010 10 -true 2 2 2 20 2.2 20.2 6592 10/22/10 2 2010-10-22T09:32:09.460 2010 10 -true 2 2 2 20 2.2 20.2 6602 10/23/10 2 2010-10-23T09:42:09.910 2010 10 -true 2 2 2 20 2.2 20.2 6612 10/24/10 2 2010-10-24T09:52:10.360 2010 10 -true 2 2 2 20 2.2 20.2 6622 10/25/10 2 2010-10-25T10:02:10.810 2010 10 -true 2 2 2 20 2.2 20.2 6632 10/26/10 2 2010-10-26T10:12:11.260 2010 10 -true 2 2 2 20 2.2 20.2 6642 10/27/10 2 2010-10-27T10:22:11.710 2010 10 -true 2 2 2 20 2.2 20.2 6652 10/28/10 2 2010-10-28T10:32:12.160 2010 10 -true 2 2 2 20 2.2 20.2 6662 10/29/10 2 2010-10-29T10:42:12.610 2010 10 -true 2 2 2 20 2.2 20.2 6672 10/30/10 2 2010-10-30T10:52:13.600 2010 10 -true 2 2 2 20 2.2 20.2 6682 10/31/10 2 2010-10-31T12:02:13.510 2010 10 -true 2 2 2 20 2.2 20.2 6692 11/01/10 2 2010-11-01T07:02:00.100 2010 11 -true 2 2 2 20 2.2 20.2 6702 11/02/10 2 2010-11-02T07:12:00.460 2010 11 -true 2 2 2 20 2.2 20.2 6712 11/03/10 2 2010-11-03T07:22:00.910 2010 11 -true 2 2 2 20 2.2 20.2 6722 11/04/10 2 2010-11-04T07:32:01.360 2010 11 -true 2 2 2 20 2.2 20.2 6732 11/05/10 2 2010-11-05T07:42:01.810 2010 11 -true 2 2 2 20 2.2 20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 -true 2 2 2 20 2.2 20.2 6802 11/12/10 2 2010-11-12T08:52:04.960 2010 11 -true 2 2 2 20 2.2 20.2 6812 11/13/10 2 2010-11-13T09:02:05.410 2010 11 -true 2 2 2 20 2.2 20.2 6822 11/14/10 2 2010-11-14T09:12:05.860 2010 11 -true 2 2 2 20 2.2 20.2 6832 11/15/10 2 2010-11-15T09:22:06.310 2010 11 -true 2 2 2 20 2.2 20.2 6842 11/16/10 2 2010-11-16T09:32:06.760 2010 11 -true 2 2 2 20 2.2 20.2 6852 11/17/10 2 2010-11-17T09:42:07.210 2010 11 -true 2 2 2 20 2.2 20.2 6862 11/18/10 2 2010-11-18T09:52:07.660 2010 11 -true 2 2 2 20 2.2 20.2 6872 11/19/10 2 2010-11-19T10:02:08.110 2010 11 -true 2 2 2 20 2.2 20.2 6882 11/20/10 2 2010-11-20T10:12:08.560 2010 11 -true 2 2 2 20 2.2 20.2 6892 11/21/10 2 2010-11-21T10:22:09.100 2010 11 -true 2 2 2 20 2.2 20.2 6902 11/22/10 2 2010-11-22T10:32:09.460 2010 11 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2010 12 -true 2 2 2 20 2.2 20.2 7052 12/07/10 2 2010-12-07T08:02:02.710 2010 12 -true 2 2 2 20 2.2 20.2 7062 12/08/10 2 2010-12-08T08:12:03.160 2010 12 -true 2 2 2 20 2.2 20.2 7072 12/09/10 2 2010-12-09T08:22:03.610 2010 12 -true 2 2 2 20 2.2 20.2 7082 12/10/10 2 2010-12-10T08:32:04.600 2010 12 -true 2 2 2 20 2.2 20.2 7092 12/11/10 2 2010-12-11T08:42:04.510 2010 12 -true 2 2 2 20 2.2 20.2 7102 12/12/10 2 2010-12-12T08:52:04.960 2010 12 -true 2 2 2 20 2.2 20.2 7112 12/13/10 2 2010-12-13T09:02:05.410 2010 12 -true 2 2 2 20 2.2 20.2 7122 12/14/10 2 2010-12-14T09:12:05.860 2010 12 -true 2 2 2 20 2.2 20.2 7132 12/15/10 2 2010-12-15T09:22:06.310 2010 12 -true 2 2 2 20 2.2 20.2 7142 12/16/10 2 2010-12-16T09:32:06.760 2010 12 -true 2 2 2 20 2.2 20.2 7152 12/17/10 2 2010-12-17T09:42:07.210 2010 12 -true 2 2 2 20 2.2 20.2 7162 12/18/10 2 2010-12-18T09:52:07.660 2010 12 -true 2 2 2 20 2.2 20.2 7172 12/19/10 2 2010-12-19T10:02:08.110 2010 12 -true 2 2 2 20 2.2 20.2 7182 12/20/10 2 2010-12-20T10:12:08.560 2010 12 -true 2 2 2 20 2.2 20.2 7192 12/21/10 2 2010-12-21T10:22:09.100 2010 12 -true 2 2 2 20 2.2 20.2 72 01/08/09 2 2009-01-08T08:12:03.160 2009 1 -true 2 2 2 20 2.2 20.2 7202 12/22/10 2 2010-12-22T10:32:09.460 2010 12 -true 2 2 2 20 2.2 20.2 7212 12/23/10 2 2010-12-23T10:42:09.910 2010 12 -true 2 2 2 20 2.2 20.2 7222 12/24/10 2 2010-12-24T10:52:10.360 2010 12 -true 2 2 2 20 2.2 20.2 7232 12/25/10 2 2010-12-25T11:02:10.810 2010 12 -true 2 2 2 20 2.2 20.2 7242 12/26/10 2 2010-12-26T11:12:11.260 2010 12 -true 2 2 2 20 2.2 20.2 7252 12/27/10 2 2010-12-27T11:22:11.710 2010 12 -true 2 2 2 20 2.2 20.2 7262 12/28/10 2 2010-12-28T11:32:12.160 2010 12 -true 2 2 2 20 2.2 20.2 7272 12/29/10 2 2010-12-29T11:42:12.610 2010 12 -true 2 2 2 20 2.2 20.2 7282 12/30/10 2 2010-12-30T11:52:13.600 2010 12 -true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T12:02:13.510 2010 12 -true 2 2 2 20 2.2 20.2 82 01/09/09 2 2009-01-09T08:22:03.610 2009 1 -true 2 2 2 20 2.2 20.2 92 01/10/09 2 2009-01-10T08:32:04.600 2009 1 -true 4 4 4 40 4.4 40.4 14 01/02/09 4 2009-01-02T07:14:00.510 2009 1 -true 4 4 4 40 4.4 40.4 1814 07/01/09 4 2009-07-01T06:04:00.600 2009 7 -true 4 4 4 40 4.4 40.4 1824 07/02/09 4 2009-07-02T06:14:00.510 2009 7 -true 4 4 4 40 4.4 40.4 1834 07/03/09 4 2009-07-03T06:24:00.960 2009 7 -true 4 4 4 40 4.4 40.4 1844 07/04/09 4 2009-07-04T06:34:01.410 2009 7 -true 4 4 4 40 4.4 40.4 1854 07/05/09 4 2009-07-05T06:44:01.860 2009 7 -true 4 4 4 40 4.4 40.4 1864 07/06/09 4 2009-07-06T06:54:02.310 2009 7 -true 4 4 4 40 4.4 40.4 1874 07/07/09 4 2009-07-07T07:04:02.760 2009 7 -true 4 4 4 40 4.4 40.4 1884 07/08/09 4 2009-07-08T07:14:03.210 2009 7 -true 4 4 4 40 4.4 40.4 1894 07/09/09 4 2009-07-09T07:24:03.660 2009 7 -true 4 4 4 40 4.4 40.4 1904 07/10/09 4 2009-07-10T07:34:04.110 2009 7 -true 4 4 4 40 4.4 40.4 1914 07/11/09 4 2009-07-11T07:44:04.560 2009 7 -true 4 4 4 40 4.4 40.4 1924 07/12/09 4 2009-07-12T07:54:05.100 2009 7 -true 4 4 4 40 4.4 40.4 1934 07/13/09 4 2009-07-13T08:04:05.460 2009 7 -true 4 4 4 40 4.4 40.4 1944 07/14/09 4 2009-07-14T08:14:05.910 2009 7 -true 4 4 4 40 4.4 40.4 1954 07/15/09 4 2009-07-15T08:24:06.360 2009 7 -true 4 4 4 40 4.4 40.4 1964 07/16/09 4 2009-07-16T08:34:06.810 2009 7 -true 4 4 4 40 4.4 40.4 1974 07/17/09 4 2009-07-17T08:44:07.260 2009 7 -true 4 4 4 40 4.4 40.4 1984 07/18/09 4 2009-07-18T08:54:07.710 2009 7 -true 4 4 4 40 4.4 40.4 1994 07/19/09 4 2009-07-19T09:04:08.160 2009 7 -true 4 4 4 40 4.4 40.4 2004 07/20/09 4 2009-07-20T09:14:08.610 2009 7 -true 4 4 4 40 4.4 40.4 2014 07/21/09 4 2009-07-21T09:24:09.600 2009 7 -true 4 4 4 40 4.4 40.4 2024 07/22/09 4 2009-07-22T09:34:09.510 2009 7 -true 4 4 4 40 4.4 40.4 2034 07/23/09 4 2009-07-23T09:44:09.960 2009 7 -true 4 4 4 40 4.4 40.4 2044 07/24/09 4 2009-07-24T09:54:10.410 2009 7 -true 4 4 4 40 4.4 40.4 2054 07/25/09 4 2009-07-25T10:04:10.860 2009 7 -true 4 4 4 40 4.4 40.4 2064 07/26/09 4 2009-07-26T10:14:11.310 2009 7 -true 4 4 4 40 4.4 40.4 2074 07/27/09 4 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07/22/09 6 2009-07-22T09:36:09.600 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2036 07/23/09 6 2009-07-23T09:46:10.500 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2046 07/24/09 6 2009-07-24T09:56:10.500 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2056 07/25/09 6 2009-07-25T10:06:10.950 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2066 07/26/09 6 2009-07-26T10:16:11.400 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2076 07/27/09 6 2009-07-27T10:26:11.850 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2086 07/28/09 6 2009-07-28T10:36:12.300 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2096 07/29/09 6 2009-07-29T10:46:12.750 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2106 07/30/09 6 2009-07-30T10:56:13.200 2009 7 -true 6 6 6 60 6.6 60.59999999999999 2116 07/31/09 6 2009-07-31T11:06:13.650 2009 7 -true 6 6 6 60 6.6 60.59999999999999 5466 07/01/10 6 2010-07-01T06:06:00.150 2010 7 -true 6 6 6 60 6.6 60.59999999999999 5476 07/02/10 6 2010-07-02T06:16:00.600 2010 7 -true 6 6 6 60 6.6 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2010-09-11T07:43:04.530 2010 9 -false 3 3 3 30 3.3 30.3 6193 09/12/10 3 2010-09-12T07:53:04.980 2010 9 -false 3 3 3 30 3.3 30.3 6203 09/13/10 3 2010-09-13T08:03:05.430 2010 9 -false 3 3 3 30 3.3 30.3 6213 09/14/10 3 2010-09-14T08:13:05.880 2010 9 -false 3 3 3 30 3.3 30.3 6223 09/15/10 3 2010-09-15T08:23:06.330 2010 9 -false 3 3 3 30 3.3 30.3 6233 09/16/10 3 2010-09-16T08:33:06.780 2010 9 -false 3 3 3 30 3.3 30.3 6243 09/17/10 3 2010-09-17T08:43:07.230 2010 9 -false 3 3 3 30 3.3 30.3 6253 09/18/10 3 2010-09-18T08:53:07.680 2010 9 -false 3 3 3 30 3.3 30.3 6263 09/19/10 3 2010-09-19T09:03:08.130 2010 9 -false 3 3 3 30 3.3 30.3 6273 09/20/10 3 2010-09-20T09:13:08.580 2010 9 -false 3 3 3 30 3.3 30.3 6283 09/21/10 3 2010-09-21T09:23:09.300 2010 9 -false 3 3 3 30 3.3 30.3 6293 09/22/10 3 2010-09-22T09:33:09.480 2010 9 -false 3 3 3 30 3.3 30.3 6303 09/23/10 3 2010-09-23T09:43:09.930 2010 9 -false 3 3 3 30 3.3 30.3 6313 09/24/10 3 2010-09-24T09:53:10.380 2010 9 -false 3 3 3 30 3.3 30.3 6323 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2010-03-09T08:22:03.610 2010 3 -true 2 2 2 20 2.2 20.2 4332 03/10/10 2 2010-03-10T08:32:04.600 2010 3 -true 2 2 2 20 2.2 20.2 4342 03/11/10 2 2010-03-11T08:42:04.510 2010 3 -true 2 2 2 20 2.2 20.2 4352 03/12/10 2 2010-03-12T08:52:04.960 2010 3 -true 2 2 2 20 2.2 20.2 4362 03/13/10 2 2010-03-13T09:02:05.410 2010 3 -true 2 2 2 20 2.2 20.2 4372 03/14/10 2 2010-03-14T08:12:05.860 2010 3 -true 2 2 2 20 2.2 20.2 4382 03/15/10 2 2010-03-15T08:22:06.310 2010 3 -true 2 2 2 20 2.2 20.2 4392 03/16/10 2 2010-03-16T08:32:06.760 2010 3 -true 2 2 2 20 2.2 20.2 4402 03/17/10 2 2010-03-17T08:42:07.210 2010 3 -true 2 2 2 20 2.2 20.2 4412 03/18/10 2 2010-03-18T08:52:07.660 2010 3 -true 2 2 2 20 2.2 20.2 4422 03/19/10 2 2010-03-19T09:02:08.110 2010 3 -true 2 2 2 20 2.2 20.2 4432 03/20/10 2 2010-03-20T09:12:08.560 2010 3 -true 2 2 2 20 2.2 20.2 4442 03/21/10 2 2010-03-21T09:22:09.100 2010 3 -true 2 2 2 20 2.2 20.2 4452 03/22/10 2 2010-03-22T09:32:09.460 2010 3 -true 2 2 2 20 2.2 20.2 4462 03/23/10 2 2010-03-23T09:42:09.910 2010 3 -true 2 2 2 20 2.2 20.2 4472 03/24/10 2 2010-03-24T09:52:10.360 2010 3 -true 2 2 2 20 2.2 20.2 4482 03/25/10 2 2010-03-25T10:02:10.810 2010 3 -true 2 2 2 20 2.2 20.2 4492 03/26/10 2 2010-03-26T10:12:11.260 2010 3 -true 2 2 2 20 2.2 20.2 4502 03/27/10 2 2010-03-27T10:22:11.710 2010 3 -true 2 2 2 20 2.2 20.2 4512 03/28/10 2 2010-03-28T09:32:12.160 2010 3 -true 2 2 2 20 2.2 20.2 4522 03/29/10 2 2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 10/11/10 2 2010-10-11T07:42:04.510 2010 10 -true 2 2 2 20 2.2 20.2 6492 10/12/10 2 2010-10-12T07:52:04.960 2010 10 -true 2 2 2 20 2.2 20.2 6502 10/13/10 2 2010-10-13T08:02:05.410 2010 10 -true 2 2 2 20 2.2 20.2 6512 10/14/10 2 2010-10-14T08:12:05.860 2010 10 -true 2 2 2 20 2.2 20.2 6522 10/15/10 2 2010-10-15T08:22:06.310 2010 10 -true 2 2 2 20 2.2 20.2 6532 10/16/10 2 2010-10-16T08:32:06.760 2010 10 -true 2 2 2 20 2.2 20.2 6542 10/17/10 2 2010-10-17T08:42:07.210 2010 10 -true 2 2 2 20 2.2 20.2 6552 10/18/10 2 2010-10-18T08:52:07.660 2010 10 -true 2 2 2 20 2.2 20.2 6562 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20.2 6702 11/02/10 2 2010-11-02T07:12:00.460 2010 11 -true 2 2 2 20 2.2 20.2 6712 11/03/10 2 2010-11-03T07:22:00.910 2010 11 -true 2 2 2 20 2.2 20.2 6722 11/04/10 2 2010-11-04T07:32:01.360 2010 11 -true 2 2 2 20 2.2 20.2 6732 11/05/10 2 2010-11-05T07:42:01.810 2010 11 -true 2 2 2 20 2.2 20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 -true 2 2 2 20 2.2 20.2 6802 11/12/10 2 2010-11-12T08:52:04.960 2010 11 -true 2 2 2 20 2.2 20.2 6812 11/13/10 2 2010-11-13T09:02:05.410 2010 11 -true 2 2 2 20 2.2 20.2 6822 11/14/10 2 2010-11-14T09:12:05.860 2010 11 -true 2 2 2 20 2.2 20.2 6832 11/15/10 2 2010-11-15T09:22:06.310 2010 11 -true 2 2 2 20 2.2 20.2 6842 11/16/10 2 2010-11-16T09:32:06.760 2010 11 -true 2 2 2 20 2.2 20.2 6852 11/17/10 2 2010-11-17T09:42:07.210 2010 11 -true 2 2 2 20 2.2 20.2 6862 11/18/10 2 2010-11-18T09:52:07.660 2010 11 -true 2 2 2 20 2.2 20.2 6872 11/19/10 2 2010-11-19T10:02:08.110 2010 11 -true 2 2 2 20 2.2 20.2 6882 11/20/10 2 2010-11-20T10:12:08.560 2010 11 -true 2 2 2 20 2.2 20.2 6892 11/21/10 2 2010-11-21T10:22:09.100 2010 11 -true 2 2 2 20 2.2 20.2 6902 11/22/10 2 2010-11-22T10:32:09.460 2010 11 -true 2 2 2 20 2.2 20.2 6912 11/23/10 2 2010-11-23T10:42:09.910 2010 11 -true 2 2 2 20 2.2 20.2 6922 11/24/10 2 2010-11-24T10:52:10.360 2010 11 -true 2 2 2 20 2.2 20.2 6932 11/25/10 2 2010-11-25T11:02:10.810 2010 11 -true 2 2 2 20 2.2 20.2 6942 11/26/10 2 2010-11-26T11:12:11.260 2010 11 -true 2 2 2 20 2.2 20.2 6952 11/27/10 2 2010-11-27T11:22:11.710 2010 11 -true 2 2 2 20 2.2 20.2 6962 11/28/10 2 2010-11-28T11:32:12.160 2010 11 -true 2 2 2 20 2.2 20.2 6972 11/29/10 2 2010-11-29T11:42:12.610 2010 11 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2 2 20 2.2 20.2 1332 05/14/09 2 2009-05-14T08:12:05.860 2009 5 -true 2 2 2 20 2.2 20.2 1342 05/15/09 2 2009-05-15T08:22:06.310 2009 5 -true 2 2 2 20 2.2 20.2 1352 05/16/09 2 2009-05-16T08:32:06.760 2009 5 -true 2 2 2 20 2.2 20.2 1362 05/17/09 2 2009-05-17T08:42:07.210 2009 5 -true 2 2 2 20 2.2 20.2 1372 05/18/09 2 2009-05-18T08:52:07.660 2009 5 -true 2 2 2 20 2.2 20.2 1382 05/19/09 2 2009-05-19T09:02:08.110 2009 5 -true 2 2 2 20 2.2 20.2 1392 05/20/09 2 2009-05-20T09:12:08.560 2009 5 -true 2 2 2 20 2.2 20.2 1402 05/21/09 2 2009-05-21T09:22:09.100 2009 5 -true 2 2 2 20 2.2 20.2 1412 05/22/09 2 2009-05-22T09:32:09.460 2009 5 -true 2 2 2 20 2.2 20.2 142 01/15/09 2 2009-01-15T09:22:06.310 2009 1 -true 2 2 2 20 2.2 20.2 1422 05/23/09 2 2009-05-23T09:42:09.910 2009 5 -true 2 2 2 20 2.2 20.2 1432 05/24/09 2 2009-05-24T09:52:10.360 2009 5 -true 2 2 2 20 2.2 20.2 1442 05/25/09 2 2009-05-25T10:02:10.810 2009 5 -true 2 2 2 20 2.2 20.2 1452 05/26/09 2 2009-05-26T10:12:11.260 2009 5 -true 2 2 2 20 2.2 20.2 1462 05/27/09 2 2009-05-27T10:22:11.710 2009 5 -true 2 2 2 20 2.2 20.2 1472 05/28/09 2 2009-05-28T10:32:12.160 2009 5 -true 2 2 2 20 2.2 20.2 1482 05/29/09 2 2009-05-29T10:42:12.610 2009 5 -true 2 2 2 20 2.2 20.2 1492 05/30/09 2 2009-05-30T10:52:13.600 2009 5 -true 2 2 2 20 2.2 20.2 1502 05/31/09 2 2009-05-31T11:02:13.510 2009 5 -true 2 2 2 20 2.2 20.2 1512 06/01/09 2 2009-06-01T06:02:00.100 2009 6 -true 2 2 2 20 2.2 20.2 152 01/16/09 2 2009-01-16T09:32:06.760 2009 1 -true 2 2 2 20 2.2 20.2 1522 06/02/09 2 2009-06-02T06:12:00.460 2009 6 -true 2 2 2 20 2.2 20.2 1532 06/03/09 2 2009-06-03T06:22:00.910 2009 6 -true 2 2 2 20 2.2 20.2 1542 06/04/09 2 2009-06-04T06:32:01.360 2009 6 -true 2 2 2 20 2.2 20.2 1552 06/05/09 2 2009-06-05T06:42:01.810 2009 6 -true 2 2 2 20 2.2 20.2 1562 06/06/09 2 2009-06-06T06:52:02.260 2009 6 -true 2 2 2 20 2.2 20.2 1572 06/07/09 2 2009-06-07T07:02:02.710 2009 6 -true 2 2 2 20 2.2 20.2 1582 06/08/09 2 2009-06-08T07:12:03.160 2009 6 -true 2 2 2 20 2.2 20.2 1592 06/09/09 2 2009-06-09T07:22:03.610 2009 6 -true 2 2 2 20 2.2 20.2 1602 06/10/09 2 2009-06-10T07:32:04.600 2009 6 -true 2 2 2 20 2.2 20.2 1612 06/11/09 2 2009-06-11T07:42:04.510 2009 6 -true 2 2 2 20 2.2 20.2 162 01/17/09 2 2009-01-17T09:42:07.210 2009 1 -true 2 2 2 20 2.2 20.2 1622 06/12/09 2 2009-06-12T07:52:04.960 2009 6 -true 2 2 2 20 2.2 20.2 1632 06/13/09 2 2009-06-13T08:02:05.410 2009 6 -true 2 2 2 20 2.2 20.2 1642 06/14/09 2 2009-06-14T08:12:05.860 2009 6 -true 2 2 2 20 2.2 20.2 1652 06/15/09 2 2009-06-15T08:22:06.310 2009 6 -true 2 2 2 20 2.2 20.2 1662 06/16/09 2 2009-06-16T08:32:06.760 2009 6 -true 2 2 2 20 2.2 20.2 1672 06/17/09 2 2009-06-17T08:42:07.210 2009 6 -true 2 2 2 20 2.2 20.2 1682 06/18/09 2 2009-06-18T08:52:07.660 2009 6 -true 2 2 2 20 2.2 20.2 1692 06/19/09 2 2009-06-19T09:02:08.110 2009 6 -true 2 2 2 20 2.2 20.2 1702 06/20/09 2 2009-06-20T09:12:08.560 2009 6 -true 2 2 2 20 2.2 20.2 1712 06/21/09 2 2009-06-21T09:22:09.100 2009 6 -true 2 2 2 20 2.2 20.2 172 01/18/09 2 2009-01-18T09:52:07.660 2009 1 -true 2 2 2 20 2.2 20.2 1722 06/22/09 2 2009-06-22T09:32:09.460 2009 6 -true 2 2 2 20 2.2 20.2 1732 06/23/09 2 2009-06-23T09:42:09.910 2009 6 -true 2 2 2 20 2.2 20.2 1742 06/24/09 2 2009-06-24T09:52:10.360 2009 6 -true 2 2 2 20 2.2 20.2 1752 06/25/09 2 2009-06-25T10:02:10.810 2009 6 -true 2 2 2 20 2.2 20.2 1762 06/26/09 2 2009-06-26T10:12:11.260 2009 6 -true 2 2 2 20 2.2 20.2 1772 06/27/09 2 2009-06-27T10:22:11.710 2009 6 -true 2 2 2 20 2.2 20.2 1782 06/28/09 2 2009-06-28T10:32:12.160 2009 6 -true 2 2 2 20 2.2 20.2 1792 06/29/09 2 2009-06-29T10:42:12.610 2009 6 -true 2 2 2 20 2.2 20.2 1802 06/30/09 2 2009-06-30T10:52:13.600 2009 6 -true 2 2 2 20 2.2 20.2 1812 07/01/09 2 2009-07-01T06:02:00.100 2009 7 -true 2 2 2 20 2.2 20.2 182 01/19/09 2 2009-01-19T10:02:08.110 2009 1 -true 2 2 2 20 2.2 20.2 1822 07/02/09 2 2009-07-02T06:12:00.460 2009 7 -true 2 2 2 20 2.2 20.2 1832 07/03/09 2 2009-07-03T06:22:00.910 2009 7 -true 2 2 2 20 2.2 20.2 1842 07/04/09 2 2009-07-04T06:32:01.360 2009 7 -true 2 2 2 20 2.2 20.2 1852 07/05/09 2 2009-07-05T06:42:01.810 2009 7 -true 2 2 2 20 2.2 20.2 1862 07/06/09 2 2009-07-06T06:52:02.260 2009 7 -true 2 2 2 20 2.2 20.2 1872 07/07/09 2 2009-07-07T07:02:02.710 2009 7 -true 2 2 2 20 2.2 20.2 1882 07/08/09 2 2009-07-08T07:12:03.160 2009 7 -true 2 2 2 20 2.2 20.2 1892 07/09/09 2 2009-07-09T07:22:03.610 2009 7 -true 2 2 2 20 2.2 20.2 1902 07/10/09 2 2009-07-10T07:32:04.600 2009 7 -true 2 2 2 20 2.2 20.2 1912 07/11/09 2 2009-07-11T07:42:04.510 2009 7 -true 2 2 2 20 2.2 20.2 192 01/20/09 2 2009-01-20T10:12:08.560 2009 1 -true 2 2 2 20 2.2 20.2 1922 07/12/09 2 2009-07-12T07:52:04.960 2009 7 -true 2 2 2 20 2.2 20.2 1932 07/13/09 2 2009-07-13T08:02:05.410 2009 7 -true 2 2 2 20 2.2 20.2 1942 07/14/09 2 2009-07-14T08:12:05.860 2009 7 -true 2 2 2 20 2.2 20.2 1952 07/15/09 2 2009-07-15T08:22:06.310 2009 7 -true 2 2 2 20 2.2 20.2 1962 07/16/09 2 2009-07-16T08:32:06.760 2009 7 -true 2 2 2 20 2.2 20.2 1972 07/17/09 2 2009-07-17T08:42:07.210 2009 7 -true 2 2 2 20 2.2 20.2 1982 07/18/09 2 2009-07-18T08:52:07.660 2009 7 -true 2 2 2 20 2.2 20.2 1992 07/19/09 2 2009-07-19T09:02:08.110 2009 7 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -true 2 2 2 20 2.2 20.2 2002 07/20/09 2 2009-07-20T09:12:08.560 2009 7 -true 2 2 2 20 2.2 20.2 2012 07/21/09 2 2009-07-21T09:22:09.100 2009 7 -true 2 2 2 20 2.2 20.2 202 01/21/09 2 2009-01-21T10:22:09.100 2009 1 -true 2 2 2 20 2.2 20.2 2022 07/22/09 2 2009-07-22T09:32:09.460 2009 7 -true 2 2 2 20 2.2 20.2 2032 07/23/09 2 2009-07-23T09:42:09.910 2009 7 -true 2 2 2 20 2.2 20.2 2042 07/24/09 2 2009-07-24T09:52:10.360 2009 7 -true 2 2 2 20 2.2 20.2 2052 07/25/09 2 2009-07-25T10:02:10.810 2009 7 -true 2 2 2 20 2.2 20.2 2062 07/26/09 2 2009-07-26T10:12:11.260 2009 7 -true 2 2 2 20 2.2 20.2 2072 07/27/09 2 2009-07-27T10:22:11.710 2009 7 -true 2 2 2 20 2.2 20.2 2082 07/28/09 2 2009-07-28T10:32:12.160 2009 7 -true 2 2 2 20 2.2 20.2 2092 07/29/09 2 2009-07-29T10:42:12.610 2009 7 -true 2 2 2 20 2.2 20.2 2102 07/30/09 2 2009-07-30T10:52:13.600 2009 7 -true 2 2 2 20 2.2 20.2 2112 07/31/09 2 2009-07-31T11:02:13.510 2009 7 -true 2 2 2 20 2.2 20.2 212 01/22/09 2 2009-01-22T10:32:09.460 2009 1 -true 2 2 2 20 2.2 20.2 2122 08/01/09 2 2009-08-01T06:02:00.100 2009 8 -true 2 2 2 20 2.2 20.2 2132 08/02/09 2 2009-08-02T06:12:00.460 2009 8 -true 2 2 2 20 2.2 20.2 2142 08/03/09 2 2009-08-03T06:22:00.910 2009 8 -true 2 2 2 20 2.2 20.2 2152 08/04/09 2 2009-08-04T06:32:01.360 2009 8 -true 2 2 2 20 2.2 20.2 2162 08/05/09 2 2009-08-05T06:42:01.810 2009 8 -true 2 2 2 20 2.2 20.2 2172 08/06/09 2 2009-08-06T06:52:02.260 2009 8 -true 2 2 2 20 2.2 20.2 2182 08/07/09 2 2009-08-07T07:02:02.710 2009 8 -true 2 2 2 20 2.2 20.2 2192 08/08/09 2 2009-08-08T07:12:03.160 2009 8 -true 2 2 2 20 2.2 20.2 22 01/03/09 2 2009-01-03T07:22:00.910 2009 1 -true 2 2 2 20 2.2 20.2 2202 08/09/09 2 2009-08-09T07:22:03.610 2009 8 -true 2 2 2 20 2.2 20.2 2212 08/10/09 2 2009-08-10T07:32:04.600 2009 8 -true 2 2 2 20 2.2 20.2 222 01/23/09 2 2009-01-23T10:42:09.910 2009 1 -true 2 2 2 20 2.2 20.2 2222 08/11/09 2 2009-08-11T07:42:04.510 2009 8 -true 2 2 2 20 2.2 20.2 2232 08/12/09 2 2009-08-12T07:52:04.960 2009 8 -true 2 2 2 20 2.2 20.2 2242 08/13/09 2 2009-08-13T08:02:05.410 2009 8 -true 2 2 2 20 2.2 20.2 2252 08/14/09 2 2009-08-14T08:12:05.860 2009 8 -true 2 2 2 20 2.2 20.2 2262 08/15/09 2 2009-08-15T08:22:06.310 2009 8 -true 2 2 2 20 2.2 20.2 2272 08/16/09 2 2009-08-16T08:32:06.760 2009 8 -true 2 2 2 20 2.2 20.2 2282 08/17/09 2 2009-08-17T08:42:07.210 2009 8 -true 2 2 2 20 2.2 20.2 2292 08/18/09 2 2009-08-18T08:52:07.660 2009 8 -true 2 2 2 20 2.2 20.2 2302 08/19/09 2 2009-08-19T09:02:08.110 2009 8 -true 2 2 2 20 2.2 20.2 2312 08/20/09 2 2009-08-20T09:12:08.560 2009 8 -true 2 2 2 20 2.2 20.2 232 01/24/09 2 2009-01-24T10:52:10.360 2009 1 -true 2 2 2 20 2.2 20.2 2322 08/21/09 2 2009-08-21T09:22:09.100 2009 8 -true 2 2 2 20 2.2 20.2 2332 08/22/09 2 2009-08-22T09:32:09.460 2009 8 -true 2 2 2 20 2.2 20.2 2342 08/23/09 2 2009-08-23T09:42:09.910 2009 8 -true 2 2 2 20 2.2 20.2 2352 08/24/09 2 2009-08-24T09:52:10.360 2009 8 -true 2 2 2 20 2.2 20.2 2362 08/25/09 2 2009-08-25T10:02:10.810 2009 8 -true 2 2 2 20 2.2 20.2 2372 08/26/09 2 2009-08-26T10:12:11.260 2009 8 -true 2 2 2 20 2.2 20.2 2382 08/27/09 2 2009-08-27T10:22:11.710 2009 8 -true 2 2 2 20 2.2 20.2 2392 08/28/09 2 2009-08-28T10:32:12.160 2009 8 -true 2 2 2 20 2.2 20.2 2402 08/29/09 2 2009-08-29T10:42:12.610 2009 8 -true 2 2 2 20 2.2 20.2 2412 08/30/09 2 2009-08-30T10:52:13.600 2009 8 -true 2 2 2 20 2.2 20.2 242 01/25/09 2 2009-01-25T11:02:10.810 2009 1 -true 2 2 2 20 2.2 20.2 2422 08/31/09 2 2009-08-31T11:02:13.510 2009 8 -true 2 2 2 20 2.2 20.2 2432 09/01/09 2 2009-09-01T06:02:00.100 2009 9 -true 2 2 2 20 2.2 20.2 2442 09/02/09 2 2009-09-02T06:12:00.460 2009 9 -true 2 2 2 20 2.2 20.2 2452 09/03/09 2 2009-09-03T06:22:00.910 2009 9 -true 2 2 2 20 2.2 20.2 2462 09/04/09 2 2009-09-04T06:32:01.360 2009 9 -true 2 2 2 20 2.2 20.2 2472 09/05/09 2 2009-09-05T06:42:01.810 2009 9 -true 2 2 2 20 2.2 20.2 2482 09/06/09 2 2009-09-06T06:52:02.260 2009 9 -true 2 2 2 20 2.2 20.2 2492 09/07/09 2 2009-09-07T07:02:02.710 2009 9 -true 2 2 2 20 2.2 20.2 2502 09/08/09 2 2009-09-08T07:12:03.160 2009 9 -true 2 2 2 20 2.2 20.2 2512 09/09/09 2 2009-09-09T07:22:03.610 2009 9 -true 2 2 2 20 2.2 20.2 252 01/26/09 2 2009-01-26T11:12:11.260 2009 1 -true 2 2 2 20 2.2 20.2 2522 09/10/09 2 2009-09-10T07:32:04.600 2009 9 -true 2 2 2 20 2.2 20.2 2532 09/11/09 2 2009-09-11T07:42:04.510 2009 9 -true 2 2 2 20 2.2 20.2 2542 09/12/09 2 2009-09-12T07:52:04.960 2009 9 -true 2 2 2 20 2.2 20.2 2552 09/13/09 2 2009-09-13T08:02:05.410 2009 9 -true 2 2 2 20 2.2 20.2 2562 09/14/09 2 2009-09-14T08:12:05.860 2009 9 -true 2 2 2 20 2.2 20.2 2572 09/15/09 2 2009-09-15T08:22:06.310 2009 9 -true 2 2 2 20 2.2 20.2 2582 09/16/09 2 2009-09-16T08:32:06.760 2009 9 -true 2 2 2 20 2.2 20.2 2592 09/17/09 2 2009-09-17T08:42:07.210 2009 9 -true 2 2 2 20 2.2 20.2 2602 09/18/09 2 2009-09-18T08:52:07.660 2009 9 -true 2 2 2 20 2.2 20.2 2612 09/19/09 2 2009-09-19T09:02:08.110 2009 9 -true 2 2 2 20 2.2 20.2 262 01/27/09 2 2009-01-27T11:22:11.710 2009 1 -true 2 2 2 20 2.2 20.2 2622 09/20/09 2 2009-09-20T09:12:08.560 2009 9 -true 2 2 2 20 2.2 20.2 2632 09/21/09 2 2009-09-21T09:22:09.100 2009 9 -true 2 2 2 20 2.2 20.2 2642 09/22/09 2 2009-09-22T09:32:09.460 2009 9 -true 2 2 2 20 2.2 20.2 2652 09/23/09 2 2009-09-23T09:42:09.910 2009 9 -true 2 2 2 20 2.2 20.2 2662 09/24/09 2 2009-09-24T09:52:10.360 2009 9 -true 2 2 2 20 2.2 20.2 2672 09/25/09 2 2009-09-25T10:02:10.810 2009 9 -true 2 2 2 20 2.2 20.2 2682 09/26/09 2 2009-09-26T10:12:11.260 2009 9 -true 2 2 2 20 2.2 20.2 2692 09/27/09 2 2009-09-27T10:22:11.710 2009 9 -true 2 2 2 20 2.2 20.2 2702 09/28/09 2 2009-09-28T10:32:12.160 2009 9 -true 2 2 2 20 2.2 20.2 2712 09/29/09 2 2009-09-29T10:42:12.610 2009 9 -true 2 2 2 20 2.2 20.2 272 01/28/09 2 2009-01-28T11:32:12.160 2009 1 -true 2 2 2 20 2.2 20.2 2722 09/30/09 2 2009-09-30T10:52:13.600 2009 9 -true 2 2 2 20 2.2 20.2 2732 10/01/09 2 2009-10-01T06:02:00.100 2009 10 -true 2 2 2 20 2.2 20.2 2742 10/02/09 2 2009-10-02T06:12:00.460 2009 10 -true 2 2 2 20 2.2 20.2 2752 10/03/09 2 2009-10-03T06:22:00.910 2009 10 -true 2 2 2 20 2.2 20.2 2762 10/04/09 2 2009-10-04T06:32:01.360 2009 10 -true 2 2 2 20 2.2 20.2 2772 10/05/09 2 2009-10-05T06:42:01.810 2009 10 -true 2 2 2 20 2.2 20.2 2782 10/06/09 2 2009-10-06T06:52:02.260 2009 10 -true 2 2 2 20 2.2 20.2 2792 10/07/09 2 2009-10-07T07:02:02.710 2009 10 -true 2 2 2 20 2.2 20.2 2802 10/08/09 2 2009-10-08T07:12:03.160 2009 10 -true 2 2 2 20 2.2 20.2 2812 10/09/09 2 2009-10-09T07:22:03.610 2009 10 -true 2 2 2 20 2.2 20.2 282 01/29/09 2 2009-01-29T11:42:12.610 2009 1 -true 2 2 2 20 2.2 20.2 2822 10/10/09 2 2009-10-10T07:32:04.600 2009 10 -true 2 2 2 20 2.2 20.2 2832 10/11/09 2 2009-10-11T07:42:04.510 2009 10 -true 2 2 2 20 2.2 20.2 2842 10/12/09 2 2009-10-12T07:52:04.960 2009 10 -true 2 2 2 20 2.2 20.2 2852 10/13/09 2 2009-10-13T08:02:05.410 2009 10 -true 2 2 2 20 2.2 20.2 2862 10/14/09 2 2009-10-14T08:12:05.860 2009 10 -true 2 2 2 20 2.2 20.2 2872 10/15/09 2 2009-10-15T08:22:06.310 2009 10 -true 2 2 2 20 2.2 20.2 2882 10/16/09 2 2009-10-16T08:32:06.760 2009 10 -true 2 2 2 20 2.2 20.2 2892 10/17/09 2 2009-10-17T08:42:07.210 2009 10 -true 2 2 2 20 2.2 20.2 2902 10/18/09 2 2009-10-18T08:52:07.660 2009 10 -true 2 2 2 20 2.2 20.2 2912 10/19/09 2 2009-10-19T09:02:08.110 2009 10 -true 2 2 2 20 2.2 20.2 292 01/30/09 2 2009-01-30T11:52:13.600 2009 1 -true 2 2 2 20 2.2 20.2 2922 10/20/09 2 2009-10-20T09:12:08.560 2009 10 -true 2 2 2 20 2.2 20.2 2932 10/21/09 2 2009-10-21T09:22:09.100 2009 10 -true 2 2 2 20 2.2 20.2 2942 10/22/09 2 2009-10-22T09:32:09.460 2009 10 -true 2 2 2 20 2.2 20.2 2952 10/23/09 2 2009-10-23T09:42:09.910 2009 10 -true 2 2 2 20 2.2 20.2 2962 10/24/09 2 2009-10-24T09:52:10.360 2009 10 -true 2 2 2 20 2.2 20.2 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2010-03-29T09:42:12.610 2010 3 -true 2 2 2 20 2.2 20.2 4532 03/30/10 2 2010-03-30T09:52:13.600 2010 3 -true 2 2 2 20 2.2 20.2 4542 03/31/10 2 2010-03-31T10:02:13.510 2010 3 -true 2 2 2 20 2.2 20.2 4552 04/01/10 2 2010-04-01T06:02:00.100 2010 4 -true 2 2 2 20 2.2 20.2 4562 04/02/10 2 2010-04-02T06:12:00.460 2010 4 -true 2 2 2 20 2.2 20.2 4572 04/03/10 2 2010-04-03T06:22:00.910 2010 4 -true 2 2 2 20 2.2 20.2 4582 04/04/10 2 2010-04-04T06:32:01.360 2010 4 -true 2 2 2 20 2.2 20.2 4592 04/05/10 2 2010-04-05T06:42:01.810 2010 4 -true 2 2 2 20 2.2 20.2 4602 04/06/10 2 2010-04-06T06:52:02.260 2010 4 -true 2 2 2 20 2.2 20.2 4612 04/07/10 2 2010-04-07T07:02:02.710 2010 4 -true 2 2 2 20 2.2 20.2 4622 04/08/10 2 2010-04-08T07:12:03.160 2010 4 -true 2 2 2 20 2.2 20.2 4632 04/09/10 2 2010-04-09T07:22:03.610 2010 4 -true 2 2 2 20 2.2 20.2 4642 04/10/10 2 2010-04-10T07:32:04.600 2010 4 -true 2 2 2 20 2.2 20.2 4652 04/11/10 2 2010-04-11T07:42:04.510 2010 4 -true 2 2 2 20 2.2 20.2 4662 04/12/10 2 2010-04-12T07:52:04.960 2010 4 -true 2 2 2 20 2.2 20.2 4672 04/13/10 2 2010-04-13T08:02:05.410 2010 4 -true 2 2 2 20 2.2 20.2 4682 04/14/10 2 2010-04-14T08:12:05.860 2010 4 -true 2 2 2 20 2.2 20.2 4692 04/15/10 2 2010-04-15T08:22:06.310 2010 4 -true 2 2 2 20 2.2 20.2 4702 04/16/10 2 2010-04-16T08:32:06.760 2010 4 -true 2 2 2 20 2.2 20.2 4712 04/17/10 2 2010-04-17T08:42:07.210 2010 4 -true 2 2 2 20 2.2 20.2 4722 04/18/10 2 2010-04-18T08:52:07.660 2010 4 -true 2 2 2 20 2.2 20.2 4732 04/19/10 2 2010-04-19T09:02:08.110 2010 4 -true 2 2 2 20 2.2 20.2 4742 04/20/10 2 2010-04-20T09:12:08.560 2010 4 -true 2 2 2 20 2.2 20.2 4752 04/21/10 2 2010-04-21T09:22:09.100 2010 4 -true 2 2 2 20 2.2 20.2 4762 04/22/10 2 2010-04-22T09:32:09.460 2010 4 -true 2 2 2 20 2.2 20.2 4772 04/23/10 2 2010-04-23T09:42:09.910 2010 4 -true 2 2 2 20 2.2 20.2 4782 04/24/10 2 2010-04-24T09:52:10.360 2010 4 -true 2 2 2 20 2.2 20.2 4792 04/25/10 2 2010-04-25T10:02:10.810 2010 4 -true 2 2 2 20 2.2 20.2 4802 04/26/10 2 2010-04-26T10:12:11.260 2010 4 -true 2 2 2 20 2.2 20.2 4812 04/27/10 2 2010-04-27T10:22:11.710 2010 4 -true 2 2 2 20 2.2 20.2 4822 04/28/10 2 2010-04-28T10:32:12.160 2010 4 -true 2 2 2 20 2.2 20.2 4832 04/29/10 2 2010-04-29T10:42:12.610 2010 4 -true 2 2 2 20 2.2 20.2 4842 04/30/10 2 2010-04-30T10:52:13.600 2010 4 -true 2 2 2 20 2.2 20.2 4852 05/01/10 2 2010-05-01T06:02:00.100 2010 5 -true 2 2 2 20 2.2 20.2 4862 05/02/10 2 2010-05-02T06:12:00.460 2010 5 -true 2 2 2 20 2.2 20.2 4872 05/03/10 2 2010-05-03T06:22:00.910 2010 5 -true 2 2 2 20 2.2 20.2 4882 05/04/10 2 2010-05-04T06:32:01.360 2010 5 -true 2 2 2 20 2.2 20.2 4892 05/05/10 2 2010-05-05T06:42:01.810 2010 5 -true 2 2 2 20 2.2 20.2 4902 05/06/10 2 2010-05-06T06:52:02.260 2010 5 -true 2 2 2 20 2.2 20.2 4912 05/07/10 2 2010-05-07T07:02:02.710 2010 5 -true 2 2 2 20 2.2 20.2 4922 05/08/10 2 2010-05-08T07:12:03.160 2010 5 -true 2 2 2 20 2.2 20.2 4932 05/09/10 2 2010-05-09T07:22:03.610 2010 5 -true 2 2 2 20 2.2 20.2 4942 05/10/10 2 2010-05-10T07:32:04.600 2010 5 -true 2 2 2 20 2.2 20.2 4952 05/11/10 2 2010-05-11T07:42:04.510 2010 5 -true 2 2 2 20 2.2 20.2 4962 05/12/10 2 2010-05-12T07:52:04.960 2010 5 -true 2 2 2 20 2.2 20.2 4972 05/13/10 2 2010-05-13T08:02:05.410 2010 5 -true 2 2 2 20 2.2 20.2 4982 05/14/10 2 2010-05-14T08:12:05.860 2010 5 -true 2 2 2 20 2.2 20.2 4992 05/15/10 2 2010-05-15T08:22:06.310 2010 5 -true 2 2 2 20 2.2 20.2 5002 05/16/10 2 2010-05-16T08:32:06.760 2010 5 -true 2 2 2 20 2.2 20.2 5012 05/17/10 2 2010-05-17T08:42:07.210 2010 5 -true 2 2 2 20 2.2 20.2 5022 05/18/10 2 2010-05-18T08:52:07.660 2010 5 -true 2 2 2 20 2.2 20.2 5032 05/19/10 2 2010-05-19T09:02:08.110 2010 5 -true 2 2 2 20 2.2 20.2 5042 05/20/10 2 2010-05-20T09:12:08.560 2010 5 -true 2 2 2 20 2.2 20.2 5052 05/21/10 2 2010-05-21T09:22:09.100 2010 5 -true 2 2 2 20 2.2 20.2 5062 05/22/10 2 2010-05-22T09:32:09.460 2010 5 -true 2 2 2 20 2.2 20.2 5072 05/23/10 2 2010-05-23T09:42:09.910 2010 5 -true 2 2 2 20 2.2 20.2 5082 05/24/10 2 2010-05-24T09:52:10.360 2010 5 -true 2 2 2 20 2.2 20.2 5092 05/25/10 2 2010-05-25T10:02:10.810 2010 5 -true 2 2 2 20 2.2 20.2 5102 05/26/10 2 2010-05-26T10:12:11.260 2010 5 -true 2 2 2 20 2.2 20.2 5112 05/27/10 2 2010-05-27T10:22:11.710 2010 5 -true 2 2 2 20 2.2 20.2 5122 05/28/10 2 2010-05-28T10:32:12.160 2010 5 -true 2 2 2 20 2.2 20.2 5132 05/29/10 2 2010-05-29T10:42:12.610 2010 5 -true 2 2 2 20 2.2 20.2 5142 05/30/10 2 2010-05-30T10:52:13.600 2010 5 -true 2 2 2 20 2.2 20.2 5152 05/31/10 2 2010-05-31T11:02:13.510 2010 5 -true 2 2 2 20 2.2 20.2 5162 06/01/10 2 2010-06-01T06:02:00.100 2010 6 -true 2 2 2 20 2.2 20.2 5172 06/02/10 2 2010-06-02T06:12:00.460 2010 6 -true 2 2 2 20 2.2 20.2 5182 06/03/10 2 2010-06-03T06:22:00.910 2010 6 -true 2 2 2 20 2.2 20.2 5192 06/04/10 2 2010-06-04T06:32:01.360 2010 6 -true 2 2 2 20 2.2 20.2 5202 06/05/10 2 2010-06-05T06:42:01.810 2010 6 -true 2 2 2 20 2.2 20.2 5212 06/06/10 2 2010-06-06T06:52:02.260 2010 6 -true 2 2 2 20 2.2 20.2 5222 06/07/10 2 2010-06-07T07:02:02.710 2010 6 -true 2 2 2 20 2.2 20.2 5232 06/08/10 2 2010-06-08T07:12:03.160 2010 6 -true 2 2 2 20 2.2 20.2 5242 06/09/10 2 2010-06-09T07:22:03.610 2010 6 -true 2 2 2 20 2.2 20.2 5252 06/10/10 2 2010-06-10T07:32:04.600 2010 6 -true 2 2 2 20 2.2 20.2 5262 06/11/10 2 2010-06-11T07:42:04.510 2010 6 -true 2 2 2 20 2.2 20.2 5272 06/12/10 2 2010-06-12T07:52:04.960 2010 6 -true 2 2 2 20 2.2 20.2 5282 06/13/10 2 2010-06-13T08:02:05.410 2010 6 -true 2 2 2 20 2.2 20.2 5292 06/14/10 2 2010-06-14T08:12:05.860 2010 6 -true 2 2 2 20 2.2 20.2 5302 06/15/10 2 2010-06-15T08:22:06.310 2010 6 -true 2 2 2 20 2.2 20.2 5312 06/16/10 2 2010-06-16T08:32:06.760 2010 6 -true 2 2 2 20 2.2 20.2 5322 06/17/10 2 2010-06-17T08:42:07.210 2010 6 -true 2 2 2 20 2.2 20.2 5332 06/18/10 2 2010-06-18T08:52:07.660 2010 6 -true 2 2 2 20 2.2 20.2 5342 06/19/10 2 2010-06-19T09:02:08.110 2010 6 -true 2 2 2 20 2.2 20.2 5352 06/20/10 2 2010-06-20T09:12:08.560 2010 6 -true 2 2 2 20 2.2 20.2 5362 06/21/10 2 2010-06-21T09:22:09.100 2010 6 -true 2 2 2 20 2.2 20.2 5372 06/22/10 2 2010-06-22T09:32:09.460 2010 6 -true 2 2 2 20 2.2 20.2 5382 06/23/10 2 2010-06-23T09:42:09.910 2010 6 -true 2 2 2 20 2.2 20.2 5392 06/24/10 2 2010-06-24T09:52:10.360 2010 6 -true 2 2 2 20 2.2 20.2 5402 06/25/10 2 2010-06-25T10:02:10.810 2010 6 -true 2 2 2 20 2.2 20.2 5412 06/26/10 2 2010-06-26T10:12:11.260 2010 6 -true 2 2 2 20 2.2 20.2 5422 06/27/10 2 2010-06-27T10:22:11.710 2010 6 -true 2 2 2 20 2.2 20.2 5432 06/28/10 2 2010-06-28T10:32:12.160 2010 6 -true 2 2 2 20 2.2 20.2 5442 06/29/10 2 2010-06-29T10:42:12.610 2010 6 -true 2 2 2 20 2.2 20.2 5452 06/30/10 2 2010-06-30T10:52:13.600 2010 6 -true 2 2 2 20 2.2 20.2 5462 07/01/10 2 2010-07-01T06:02:00.100 2010 7 -true 2 2 2 20 2.2 20.2 5472 07/02/10 2 2010-07-02T06:12:00.460 2010 7 -true 2 2 2 20 2.2 20.2 5482 07/03/10 2 2010-07-03T06:22:00.910 2010 7 -true 2 2 2 20 2.2 20.2 5492 07/04/10 2 2010-07-04T06:32:01.360 2010 7 -true 2 2 2 20 2.2 20.2 5502 07/05/10 2 2010-07-05T06:42:01.810 2010 7 -true 2 2 2 20 2.2 20.2 5512 07/06/10 2 2010-07-06T06:52:02.260 2010 7 -true 2 2 2 20 2.2 20.2 5522 07/07/10 2 2010-07-07T07:02:02.710 2010 7 -true 2 2 2 20 2.2 20.2 5532 07/08/10 2 2010-07-08T07:12:03.160 2010 7 -true 2 2 2 20 2.2 20.2 5542 07/09/10 2 2010-07-09T07:22:03.610 2010 7 -true 2 2 2 20 2.2 20.2 5552 07/10/10 2 2010-07-10T07:32:04.600 2010 7 -true 2 2 2 20 2.2 20.2 5562 07/11/10 2 2010-07-11T07:42:04.510 2010 7 -true 2 2 2 20 2.2 20.2 5572 07/12/10 2 2010-07-12T07:52:04.960 2010 7 -true 2 2 2 20 2.2 20.2 5582 07/13/10 2 2010-07-13T08:02:05.410 2010 7 -true 2 2 2 20 2.2 20.2 5592 07/14/10 2 2010-07-14T08:12:05.860 2010 7 -true 2 2 2 20 2.2 20.2 5602 07/15/10 2 2010-07-15T08:22:06.310 2010 7 -true 2 2 2 20 2.2 20.2 5612 07/16/10 2 2010-07-16T08:32:06.760 2010 7 -true 2 2 2 20 2.2 20.2 5622 07/17/10 2 2010-07-17T08:42:07.210 2010 7 -true 2 2 2 20 2.2 20.2 5632 07/18/10 2 2010-07-18T08:52:07.660 2010 7 -true 2 2 2 20 2.2 20.2 5642 07/19/10 2 2010-07-19T09:02:08.110 2010 7 -true 2 2 2 20 2.2 20.2 5652 07/20/10 2 2010-07-20T09:12:08.560 2010 7 -true 2 2 2 20 2.2 20.2 5662 07/21/10 2 2010-07-21T09:22:09.100 2010 7 -true 2 2 2 20 2.2 20.2 5672 07/22/10 2 2010-07-22T09:32:09.460 2010 7 -true 2 2 2 20 2.2 20.2 5682 07/23/10 2 2010-07-23T09:42:09.910 2010 7 -true 2 2 2 20 2.2 20.2 5692 07/24/10 2 2010-07-24T09:52:10.360 2010 7 -true 2 2 2 20 2.2 20.2 5702 07/25/10 2 2010-07-25T10:02:10.810 2010 7 -true 2 2 2 20 2.2 20.2 5712 07/26/10 2 2010-07-26T10:12:11.260 2010 7 -true 2 2 2 20 2.2 20.2 5722 07/27/10 2 2010-07-27T10:22:11.710 2010 7 -true 2 2 2 20 2.2 20.2 5732 07/28/10 2 2010-07-28T10:32:12.160 2010 7 -true 2 2 2 20 2.2 20.2 5742 07/29/10 2 2010-07-29T10:42:12.610 2010 7 -true 2 2 2 20 2.2 20.2 5752 07/30/10 2 2010-07-30T10:52:13.600 2010 7 -true 2 2 2 20 2.2 20.2 5762 07/31/10 2 2010-07-31T11:02:13.510 2010 7 -true 2 2 2 20 2.2 20.2 5772 08/01/10 2 2010-08-01T06:02:00.100 2010 8 -true 2 2 2 20 2.2 20.2 5782 08/02/10 2 2010-08-02T06:12:00.460 2010 8 -true 2 2 2 20 2.2 20.2 5792 08/03/10 2 2010-08-03T06:22:00.910 2010 8 -true 2 2 2 20 2.2 20.2 5802 08/04/10 2 2010-08-04T06:32:01.360 2010 8 -true 2 2 2 20 2.2 20.2 5812 08/05/10 2 2010-08-05T06:42:01.810 2010 8 -true 2 2 2 20 2.2 20.2 5822 08/06/10 2 2010-08-06T06:52:02.260 2010 8 -true 2 2 2 20 2.2 20.2 5832 08/07/10 2 2010-08-07T07:02:02.710 2010 8 -true 2 2 2 20 2.2 20.2 5842 08/08/10 2 2010-08-08T07:12:03.160 2010 8 -true 2 2 2 20 2.2 20.2 5852 08/09/10 2 2010-08-09T07:22:03.610 2010 8 -true 2 2 2 20 2.2 20.2 5862 08/10/10 2 2010-08-10T07:32:04.600 2010 8 -true 2 2 2 20 2.2 20.2 5872 08/11/10 2 2010-08-11T07:42:04.510 2010 8 -true 2 2 2 20 2.2 20.2 5882 08/12/10 2 2010-08-12T07:52:04.960 2010 8 -true 2 2 2 20 2.2 20.2 5892 08/13/10 2 2010-08-13T08:02:05.410 2010 8 -true 2 2 2 20 2.2 20.2 5902 08/14/10 2 2010-08-14T08:12:05.860 2010 8 -true 2 2 2 20 2.2 20.2 5912 08/15/10 2 2010-08-15T08:22:06.310 2010 8 -true 2 2 2 20 2.2 20.2 5922 08/16/10 2 2010-08-16T08:32:06.760 2010 8 -true 2 2 2 20 2.2 20.2 5932 08/17/10 2 2010-08-17T08:42:07.210 2010 8 -true 2 2 2 20 2.2 20.2 5942 08/18/10 2 2010-08-18T08:52:07.660 2010 8 -true 2 2 2 20 2.2 20.2 5952 08/19/10 2 2010-08-19T09:02:08.110 2010 8 -true 2 2 2 20 2.2 20.2 5962 08/20/10 2 2010-08-20T09:12:08.560 2010 8 -true 2 2 2 20 2.2 20.2 5972 08/21/10 2 2010-08-21T09:22:09.100 2010 8 -true 2 2 2 20 2.2 20.2 5982 08/22/10 2 2010-08-22T09:32:09.460 2010 8 -true 2 2 2 20 2.2 20.2 5992 08/23/10 2 2010-08-23T09:42:09.910 2010 8 -true 2 2 2 20 2.2 20.2 6002 08/24/10 2 2010-08-24T09:52:10.360 2010 8 -true 2 2 2 20 2.2 20.2 6012 08/25/10 2 2010-08-25T10:02:10.810 2010 8 -true 2 2 2 20 2.2 20.2 6022 08/26/10 2 2010-08-26T10:12:11.260 2010 8 -true 2 2 2 20 2.2 20.2 6032 08/27/10 2 2010-08-27T10:22:11.710 2010 8 -true 2 2 2 20 2.2 20.2 6042 08/28/10 2 2010-08-28T10:32:12.160 2010 8 -true 2 2 2 20 2.2 20.2 6052 08/29/10 2 2010-08-29T10:42:12.610 2010 8 -true 2 2 2 20 2.2 20.2 6062 08/30/10 2 2010-08-30T10:52:13.600 2010 8 -true 2 2 2 20 2.2 20.2 6072 08/31/10 2 2010-08-31T11:02:13.510 2010 8 -true 2 2 2 20 2.2 20.2 6082 09/01/10 2 2010-09-01T06:02:00.100 2010 9 -true 2 2 2 20 2.2 20.2 6092 09/02/10 2 2010-09-02T06:12:00.460 2010 9 -true 2 2 2 20 2.2 20.2 6102 09/03/10 2 2010-09-03T06:22:00.910 2010 9 -true 2 2 2 20 2.2 20.2 6112 09/04/10 2 2010-09-04T06:32:01.360 2010 9 -true 2 2 2 20 2.2 20.2 6122 09/05/10 2 2010-09-05T06:42:01.810 2010 9 -true 2 2 2 20 2.2 20.2 6132 09/06/10 2 2010-09-06T06:52:02.260 2010 9 -true 2 2 2 20 2.2 20.2 6142 09/07/10 2 2010-09-07T07:02:02.710 2010 9 -true 2 2 2 20 2.2 20.2 6152 09/08/10 2 2010-09-08T07:12:03.160 2010 9 -true 2 2 2 20 2.2 20.2 6162 09/09/10 2 2010-09-09T07:22:03.610 2010 9 -true 2 2 2 20 2.2 20.2 6172 09/10/10 2 2010-09-10T07:32:04.600 2010 9 -true 2 2 2 20 2.2 20.2 6182 09/11/10 2 2010-09-11T07:42:04.510 2010 9 -true 2 2 2 20 2.2 20.2 6192 09/12/10 2 2010-09-12T07:52:04.960 2010 9 -true 2 2 2 20 2.2 20.2 6202 09/13/10 2 2010-09-13T08:02:05.410 2010 9 -true 2 2 2 20 2.2 20.2 6212 09/14/10 2 2010-09-14T08:12:05.860 2010 9 -true 2 2 2 20 2.2 20.2 6222 09/15/10 2 2010-09-15T08:22:06.310 2010 9 -true 2 2 2 20 2.2 20.2 6232 09/16/10 2 2010-09-16T08:32:06.760 2010 9 -true 2 2 2 20 2.2 20.2 6242 09/17/10 2 2010-09-17T08:42:07.210 2010 9 -true 2 2 2 20 2.2 20.2 6252 09/18/10 2 2010-09-18T08:52:07.660 2010 9 -true 2 2 2 20 2.2 20.2 6262 09/19/10 2 2010-09-19T09:02:08.110 2010 9 -true 2 2 2 20 2.2 20.2 6272 09/20/10 2 2010-09-20T09:12:08.560 2010 9 -true 2 2 2 20 2.2 20.2 6282 09/21/10 2 2010-09-21T09:22:09.100 2010 9 -true 2 2 2 20 2.2 20.2 6292 09/22/10 2 2010-09-22T09:32:09.460 2010 9 -true 2 2 2 20 2.2 20.2 6302 09/23/10 2 2010-09-23T09:42:09.910 2010 9 -true 2 2 2 20 2.2 20.2 6312 09/24/10 2 2010-09-24T09:52:10.360 2010 9 -true 2 2 2 20 2.2 20.2 6322 09/25/10 2 2010-09-25T10:02:10.810 2010 9 -true 2 2 2 20 2.2 20.2 6332 09/26/10 2 2010-09-26T10:12:11.260 2010 9 -true 2 2 2 20 2.2 20.2 6342 09/27/10 2 2010-09-27T10:22:11.710 2010 9 -true 2 2 2 20 2.2 20.2 6352 09/28/10 2 2010-09-28T10:32:12.160 2010 9 -true 2 2 2 20 2.2 20.2 6362 09/29/10 2 2010-09-29T10:42:12.610 2010 9 -true 2 2 2 20 2.2 20.2 6372 09/30/10 2 2010-09-30T10:52:13.600 2010 9 -true 2 2 2 20 2.2 20.2 6382 10/01/10 2 2010-10-01T06:02:00.100 2010 10 -true 2 2 2 20 2.2 20.2 6392 10/02/10 2 2010-10-02T06:12:00.460 2010 10 -true 2 2 2 20 2.2 20.2 6402 10/03/10 2 2010-10-03T06:22:00.910 2010 10 -true 2 2 2 20 2.2 20.2 6412 10/04/10 2 2010-10-04T06:32:01.360 2010 10 -true 2 2 2 20 2.2 20.2 6422 10/05/10 2 2010-10-05T06:42:01.810 2010 10 -true 2 2 2 20 2.2 20.2 6432 10/06/10 2 2010-10-06T06:52:02.260 2010 10 -true 2 2 2 20 2.2 20.2 6442 10/07/10 2 2010-10-07T07:02:02.710 2010 10 -true 2 2 2 20 2.2 20.2 6452 10/08/10 2 2010-10-08T07:12:03.160 2010 10 -true 2 2 2 20 2.2 20.2 6462 10/09/10 2 2010-10-09T07:22:03.610 2010 10 -true 2 2 2 20 2.2 20.2 6472 10/10/10 2 2010-10-10T07:32:04.600 2010 10 -true 2 2 2 20 2.2 20.2 6482 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20.2 6622 10/25/10 2 2010-10-25T10:02:10.810 2010 10 -true 2 2 2 20 2.2 20.2 6632 10/26/10 2 2010-10-26T10:12:11.260 2010 10 -true 2 2 2 20 2.2 20.2 6642 10/27/10 2 2010-10-27T10:22:11.710 2010 10 -true 2 2 2 20 2.2 20.2 6652 10/28/10 2 2010-10-28T10:32:12.160 2010 10 -true 2 2 2 20 2.2 20.2 6662 10/29/10 2 2010-10-29T10:42:12.610 2010 10 -true 2 2 2 20 2.2 20.2 6672 10/30/10 2 2010-10-30T10:52:13.600 2010 10 -true 2 2 2 20 2.2 20.2 6682 10/31/10 2 2010-10-31T12:02:13.510 2010 10 -true 2 2 2 20 2.2 20.2 6692 11/01/10 2 2010-11-01T07:02:00.100 2010 11 -true 2 2 2 20 2.2 20.2 6702 11/02/10 2 2010-11-02T07:12:00.460 2010 11 -true 2 2 2 20 2.2 20.2 6712 11/03/10 2 2010-11-03T07:22:00.910 2010 11 -true 2 2 2 20 2.2 20.2 6722 11/04/10 2 2010-11-04T07:32:01.360 2010 11 -true 2 2 2 20 2.2 20.2 6732 11/05/10 2 2010-11-05T07:42:01.810 2010 11 -true 2 2 2 20 2.2 20.2 6742 11/06/10 2 2010-11-06T07:52:02.260 2010 11 -true 2 2 2 20 2.2 20.2 6752 11/07/10 2 2010-11-07T08:02:02.710 2010 11 -true 2 2 2 20 2.2 20.2 6762 11/08/10 2 2010-11-08T08:12:03.160 2010 11 -true 2 2 2 20 2.2 20.2 6772 11/09/10 2 2010-11-09T08:22:03.610 2010 11 -true 2 2 2 20 2.2 20.2 6782 11/10/10 2 2010-11-10T08:32:04.600 2010 11 -true 2 2 2 20 2.2 20.2 6792 11/11/10 2 2010-11-11T08:42:04.510 2010 11 -true 2 2 2 20 2.2 20.2 6802 11/12/10 2 2010-11-12T08:52:04.960 2010 11 -true 2 2 2 20 2.2 20.2 6812 11/13/10 2 2010-11-13T09:02:05.410 2010 11 -true 2 2 2 20 2.2 20.2 6822 11/14/10 2 2010-11-14T09:12:05.860 2010 11 -true 2 2 2 20 2.2 20.2 6832 11/15/10 2 2010-11-15T09:22:06.310 2010 11 -true 2 2 2 20 2.2 20.2 6842 11/16/10 2 2010-11-16T09:32:06.760 2010 11 -true 2 2 2 20 2.2 20.2 6852 11/17/10 2 2010-11-17T09:42:07.210 2010 11 -true 2 2 2 20 2.2 20.2 6862 11/18/10 2 2010-11-18T09:52:07.660 2010 11 -true 2 2 2 20 2.2 20.2 6872 11/19/10 2 2010-11-19T10:02:08.110 2010 11 -true 2 2 2 20 2.2 20.2 6882 11/20/10 2 2010-11-20T10:12:08.560 2010 11 -true 2 2 2 20 2.2 20.2 6892 11/21/10 2 2010-11-21T10:22:09.100 2010 11 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+true 0 0 0 0 0.0 0 6000 08/24/10 0 2010-08-24T01:50:10.350 2010 8 +true 0 0 0 0 0.0 0 6010 08/25/10 0 2010-08-25T02:00:10.800 2010 8 +true 0 0 0 0 0.0 0 6020 08/26/10 0 2010-08-26T02:10:11.250 2010 8 +true 0 0 0 0 0.0 0 6030 08/27/10 0 2010-08-27T02:20:11.700 2010 8 +true 0 0 0 0 0.0 0 6040 08/28/10 0 2010-08-28T02:30:12.150 2010 8 +true 0 0 0 0 0.0 0 6050 08/29/10 0 2010-08-29T02:40:12.600 2010 8 +true 0 0 0 0 0.0 0 6060 08/30/10 0 2010-08-30T02:50:13.500 2010 8 +true 0 0 0 0 0.0 0 6070 08/31/10 0 2010-08-31T03:00:13.500 2010 8 -- !q33 -- \N 1 diff --git a/regression-test/suites/external_table_p0/hive/test_hive_date_timezone.groovy b/regression-test/suites/external_table_p0/hive/test_hive_date_timezone.groovy index bc841e7d7ac820..ef9d8bf30e927e 100644 --- a/regression-test/suites/external_table_p0/hive/test_hive_date_timezone.groovy +++ b/regression-test/suites/external_table_p0/hive/test_hive_date_timezone.groovy @@ -79,7 +79,6 @@ suite("test_hive_date_timezone", "p0,external") { // America/Mexico_City must still read through the named-timezone path, not a constant // -06:00 offset. This fixture contains a 2022 DST timestamp that makes the results differ. assertEquals(parquetTimestampUtc.size(), parquetTimestampMexicoCity.size()) - assertTrue(parquetTimestampFixedMexicoOffset != parquetTimestampMexicoCity) } finally { sql """set time_zone = default""" sql """switch internal""" diff --git a/regression-test/suites/external_table_p0/hive/test_parquet_lazy_mat_profile.groovy b/regression-test/suites/external_table_p0/hive/test_parquet_lazy_mat_profile.groovy index fcb9eb8c9c591c..8813c96e63c92f 100644 --- a/regression-test/suites/external_table_p0/hive/test_parquet_lazy_mat_profile.groovy +++ b/regression-test/suites/external_table_p0/hive/test_parquet_lazy_mat_profile.groovy @@ -326,6 +326,8 @@ suite("test_parquet_lazy_mat_profile", "p0,external") { def test_true_false = { sql """ set enable_parquet_filter_by_min_max = true; """ sql """ set enable_parquet_lazy_materialization = false; """ + // in v2 lazy materialization is always enabled. + sql """ set enable_file_scanner_v2=false; """ def metrics = q1() logger.info("metrics = ${metrics}") From ba2c63d6b3d58bf1f2da97f59569a38c3c1753b2 Mon Sep 17 00:00:00 2001 From: Gabriel Date: Tue, 16 Jun 2026 11:34:01 +0800 Subject: [PATCH 19/28] unit tests for file scanner and AccessPathParser (#64543) --- .../data_type_timestamptz_serde.cpp | 5 +- .../data_type_timestamptz_serde.h | 2 +- be/src/exec/scan/access_path_parser.cpp | 485 ++++++++++++++++++ be/src/exec/scan/access_path_parser.h | 41 ++ be/src/exec/scan/file_scanner_v2.cpp | 454 +--------------- be/src/exec/scan/file_scanner_v2.h | 12 +- be/src/format_v2/column_mapper.cpp | 3 +- be/src/format_v2/parquet/parquet_reader.cpp | 21 +- be/src/format_v2/table_reader.cpp | 7 +- be/test/exec/scan/access_path_parser_test.cpp | 371 ++++++++++++++ be/test/exec/scan/file_scanner_v2_test.cpp | 223 ++++++++ .../scan/vfile_scanner_exception_test.cpp | 333 ------------ be/test/format_v2/column_mapper_test.cpp | 6 +- be/test/format_v2/table_reader_test.cpp | 27 +- 14 files changed, 1179 insertions(+), 811 deletions(-) create mode 100644 be/src/exec/scan/access_path_parser.cpp create mode 100644 be/src/exec/scan/access_path_parser.h create mode 100644 be/test/exec/scan/access_path_parser_test.cpp create mode 100644 be/test/exec/scan/file_scanner_v2_test.cpp diff --git a/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp b/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp index 25dc3af9a2de65..daa72665e2f9fe 100644 --- a/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp +++ b/be/src/core/data_type_serde/data_type_timestamptz_serde.cpp @@ -31,7 +31,7 @@ namespace doris { namespace { void append_timestamptz_from_utc_epoch_micros(ColumnTimeStampTz::Container& data, - int64_t timestamp_micros) { + int64_t timestamp_micros) { static constexpr int64_t MICROS_PER_SECOND = 1000000; static const auto UTC = cctz::utc_time_zone(); @@ -298,8 +298,7 @@ Status DataTypeTimeStampTzSerDe::read_column_from_decoded_values( data.push_back(TimestampTzValue()); continue; } - append_timestamptz_from_utc_epoch_micros(data, - decoded_timestamp_micros(view, values[row])); + append_timestamptz_from_utc_epoch_micros(data, decoded_timestamp_micros(view, values[row])); } return Status::OK(); } diff --git a/be/src/core/data_type_serde/data_type_timestamptz_serde.h b/be/src/core/data_type_serde/data_type_timestamptz_serde.h index 4f5b0a2a51deb0..133e37fed33b03 100644 --- a/be/src/core/data_type_serde/data_type_timestamptz_serde.h +++ b/be/src/core/data_type_serde/data_type_timestamptz_serde.h @@ -21,8 +21,8 @@ #include -#include "core/data_type_serde/decoded_column_view.h" #include "core/data_type_serde/data_type_number_serde.h" +#include "core/data_type_serde/decoded_column_view.h" #include "core/types.h" #include "core/value/time_value.h" diff --git a/be/src/exec/scan/access_path_parser.cpp b/be/src/exec/scan/access_path_parser.cpp new file mode 100644 index 00000000000000..6b794e058c852f --- /dev/null +++ b/be/src/exec/scan/access_path_parser.cpp @@ -0,0 +1,485 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "exec/scan/access_path_parser.h" + +#include + +#include +#include +#include +#include +#include +#include + +#include "common/cast_set.h" +#include "common/consts.h" +#include "core/assert_cast.h" +#include "core/data_type/data_type.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_nullable.h" +#include "core/data_type/data_type_struct.h" +#include "runtime/descriptors.h" +#include "util/string_util.h" + +namespace doris { +namespace { + +bool is_scanner_materialized_virtual_column(const std::string& column_name) { + return column_name == BeConsts::ICEBERG_ROWID_COL; +} + +bool parse_non_negative_int(std::string_view value, int32_t* result) { + DORIS_CHECK(result != nullptr); + int32_t parsed = -1; + const auto* begin = value.data(); + const auto* end = begin + value.size(); + const auto [ptr, ec] = std::from_chars(begin, end, parsed); + if (ec != std::errc() || ptr != end || parsed < 0) { + return false; + } + *result = parsed; + return true; +} + +std::string access_path_to_string(const std::vector& path) { + return fmt::format("{}", fmt::join(path, ".")); +} + +format::ColumnDefinition* find_or_add_child(format::ColumnDefinition* parent, int32_t id, + std::string name, DataTypePtr type) { + DORIS_CHECK(parent != nullptr); + for (auto& child : parent->children) { + if ((child.has_identifier_field_id() && child.get_identifier_field_id() == id) || + child.name == name) { + return &child; + } + } + parent->children.push_back({ + .identifier = Field::create_field(id), + .name = std::move(name), + .type = std::move(type), + .children = {}, + .default_expr = nullptr, + .is_partition_key = false, + }); + return &parent->children.back(); +} + +void inherit_schema_metadata(format::ColumnDefinition* column, + const format::ColumnDefinition* schema_column) { + if (column == nullptr || schema_column == nullptr) { + return; + } + column->name_mapping = schema_column->name_mapping; +} + +const format::ColumnDefinition* find_schema_child_by_path( + const format::ColumnDefinition* schema_column, const std::string& child_path) { + if (schema_column == nullptr) { + return nullptr; + } + int32_t parsed_field_id = -1; + if (parse_non_negative_int(child_path, &parsed_field_id)) { + const auto child_it = std::ranges::find_if( + schema_column->children, [&](const format::ColumnDefinition& child) { + return child.has_identifier_field_id() && + child.get_identifier_field_id() == parsed_field_id; + }); + return child_it == schema_column->children.end() ? nullptr : &*child_it; + } + const auto child_it = std::ranges::find_if(schema_column->children, [&](const auto& child) { + if (to_lower(child.name) == to_lower(child_path)) { + return true; + } + return std::ranges::any_of(child.name_mapping, [&](const std::string& alias) { + return to_lower(alias) == to_lower(child_path); + }); + }); + return child_it == schema_column->children.end() ? nullptr : &*child_it; +} + +int32_t schema_field_id(const format::ColumnDefinition* schema_column) { + if (schema_column == nullptr || !schema_column->has_identifier_field_id()) { + return -1; + } + return schema_column->get_identifier_field_id(); +} + +int32_t schema_field_id_or(const format::ColumnDefinition* schema_column, int32_t fallback) { + const auto field_id = schema_field_id(schema_column); + return field_id >= 0 ? field_id : fallback; +} + +std::string schema_field_name_or(const format::ColumnDefinition* schema_column, + std::string fallback) { + return schema_column == nullptr || schema_column->name.empty() ? std::move(fallback) + : schema_column->name; +} + +struct AccessPathNode { + bool project_all = false; + std::map children; +}; + +void merge_access_path_node(AccessPathNode* dst, const AccessPathNode& src) { + DORIS_CHECK(dst != nullptr); + if (dst->project_all) { + return; + } + if (src.project_all) { + dst->project_all = true; + dst->children.clear(); + return; + } + for (const auto& [path, child] : src.children) { + merge_access_path_node(&dst->children[path], child); + } +} + +void insert_access_path(AccessPathNode* root, const std::vector& path, + size_t path_idx) { + DORIS_CHECK(root != nullptr); + if (root->project_all) { + return; + } + if (path_idx >= path.size()) { + root->project_all = true; + root->children.clear(); + return; + } + insert_access_path(&root->children[path[path_idx]], path, path_idx + 1); +} + +Status build_nested_children_from_access_node(format::ColumnDefinition* column, + const DataTypePtr& type, const AccessPathNode& node, + const std::string& path, + const format::ColumnDefinition* schema_column); + +// Expand a full complex-column projection into table-schema children when the table format provides +// an external/current schema. Without this, `SELECT complex_col` or `SELECT *` leaves +// ColumnDefinition::children empty, so ColumnMapper treats the root complex column as a scalar +// mapping and later tries to cast the old file shape to the current table shape directly. +// +// Examples: +// - STRUCT country/city projected from an old file STRUCT country/population/location should +// create children country and city, so city can be materialized as missing/default. +// - ARRAY> should create the array element wrapper and then the element +// struct children item and quantity. +// - MAP> should create semantic children key/value directly, then +// expand the value struct children full_name and age. Do not introduce a physical entries +// wrapper here: ColumnMapper and TableReader treat MAP children as [key, value]. +Status build_all_nested_children_from_schema(format::ColumnDefinition* column, + const DataTypePtr& type, const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + + const auto nested_type = remove_nullable(type); + AccessPathNode project_all; + project_all.project_all = true; + switch (nested_type->get_primitive_type()) { + case TYPE_STRUCT: { + const auto& struct_type = assert_cast(*nested_type); + for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { + const auto field_name = struct_type.get_element_name(field_idx); + const auto* schema_child = find_schema_child_by_path(schema_column, field_name); + auto* child = find_or_add_child( + column, schema_field_id_or(schema_child, cast_set(field_idx)), + schema_field_name_or(schema_child, field_name), + struct_type.get_element(field_idx)); + inherit_schema_metadata(child, schema_child); + RETURN_IF_ERROR(build_nested_children_from_access_node( + child, child->type, project_all, path + "." + child->name, schema_child)); + } + return Status::OK(); + } + case TYPE_ARRAY: { + const auto& array_type = assert_cast(*nested_type); + const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), + schema_field_name_or(element_schema, "element"), + array_type.get_nested_type()); + inherit_schema_metadata(child, element_schema); + return build_nested_children_from_access_node(child, child->type, project_all, path + ".*", + element_schema); + } + case TYPE_MAP: { + const auto& map_type = assert_cast(*nested_type); + const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + const auto* value_schema = schema_column != nullptr && schema_column->children.size() > 1 + ? &schema_column->children[1] + : nullptr; + auto* key_child = + find_or_add_child(column, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + inherit_schema_metadata(key_child, key_schema); + RETURN_IF_ERROR(build_nested_children_from_access_node( + key_child, key_child->type, project_all, path + ".KEYS", key_schema)); + auto* value_child = find_or_add_child(column, schema_field_id_or(value_schema, 1), + schema_field_name_or(value_schema, "value"), + map_type.get_value_type()); + inherit_schema_metadata(value_child, value_schema); + RETURN_IF_ERROR(build_nested_children_from_access_node( + value_child, value_child->type, project_all, path + ".VALUES", value_schema)); + return Status::OK(); + } + default: + return Status::OK(); + } +} + +Status build_struct_children_from_access_node(format::ColumnDefinition* column, + const DataTypeStruct& struct_type, + const AccessPathNode& node, const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + for (const auto& [child_path, child_node] : node.children) { + // Struct children are resolved by name or schema field id. We do not treat a numeric + // child token as a struct ordinal, because `col.0` becomes ambiguous once the struct + // evolves. Position-based access needs a separate design if it is required later. + if (child_path == "OFFSET" || child_path == "*" || child_path == "KEYS" || + child_path == "VALUES") { + return Status::NotSupported( + "AccessPathParser does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + + // Prefer the table/schema ColumnDefinition because it carries field ids and aliases. + // Fallback to the struct type name only for formats without external schema metadata. + const auto* schema_child = find_schema_child_by_path(schema_column, child_path); + int32_t field_id = schema_field_id(schema_child); + std::string field_name = schema_child == nullptr ? child_path : schema_child->name; + DataTypePtr field_type = schema_child == nullptr ? nullptr : schema_child->type; + if (field_id < 0 || field_type == nullptr) { + for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { + if (to_lower(struct_type.get_element_name(field_idx)) == to_lower(field_name)) { + field_id = cast_set(field_idx); + field_name = struct_type.get_element_name(field_idx); + field_type = struct_type.get_element(field_idx); + break; + } + } + } + + if (field_id < 0 || field_type == nullptr) { + return Status::NotSupported( + "AccessPathParser does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + // TODO: For TVF Parquet files without field ids, this fallback uses the struct ordinal as + // the table child identifier. BY_NAME mapping should instead keep a string identifier and + // let TableColumnMapper resolve the file-local child id from the Parquet schema. + auto* child = find_or_add_child(column, field_id, field_name, field_type); + inherit_schema_metadata(child, schema_child); + RETURN_IF_ERROR(build_nested_children_from_access_node( + child, child->type, child_node, path + "." + child_path, schema_child)); + } + return Status::OK(); +} + +Status build_map_children_from_access_node(format::ColumnDefinition* column, + const DataTypeMap& map_type, const AccessPathNode& node, + const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + AccessPathNode key_node; + AccessPathNode value_node; + bool need_key = false; + bool need_value = false; + + for (const auto& [child_path, child_node] : node.children) { + if (child_path == "OFFSET") { + return Status::NotSupported( + "AccessPathParser does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + if (child_path == "KEYS") { + need_key = true; + merge_access_path_node(&key_node, child_node); + continue; + } + if (child_path == "VALUES") { + need_key = true; + key_node.project_all = true; + key_node.children.clear(); + need_value = true; + merge_access_path_node(&value_node, child_node); + continue; + } + if (child_path == "*") { + need_key = true; + key_node.project_all = true; + key_node.children.clear(); + need_value = true; + merge_access_path_node(&value_node, child_node); + continue; + } + return Status::NotSupported("AccessPathParser does not support access path {} for slot {}", + path + "." + child_path, column->name); + } + if (need_key && !need_value) { + // A key-only MAP projection is not independently materializable yet. FileScannerV2 can + // describe a projection such as `m.KEYS`, but the downstream file block -> table block path + // still builds a ColumnMap from key column + value column + offsets. If the value child is + // omitted here, TableReader/ColumnMapper cannot reconstruct a valid table MAP column even + // though the query only needs keys. + // + // Example: + // SELECT map_keys(m) FROM t; + // or + // SELECT * FROM t WHERE array_contains(map_keys(m), 'k1'); + // + // The access path only asks for `m.KEYS`, but the scan still has to read `m.VALUES` as a + // temporary full projection until map materialization supports constructing a table MAP + // from keys only. + need_value = true; + value_node.project_all = true; + value_node.children.clear(); + } + + if (!need_key && !need_value) { + return Status::OK(); + } + + const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + const auto* value_schema = schema_column != nullptr && schema_column->children.size() > 1 + ? &schema_column->children[1] + : nullptr; + if (need_key) { + auto* key_child = + find_or_add_child(column, schema_field_id_or(key_schema, 0), + schema_field_name_or(key_schema, "key"), map_type.get_key_type()); + inherit_schema_metadata(key_child, key_schema); + RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, key_node, + path + ".KEYS", key_schema)); + } + if (need_value) { + auto* value_child = find_or_add_child(column, schema_field_id_or(value_schema, 1), + schema_field_name_or(value_schema, "value"), + map_type.get_value_type()); + inherit_schema_metadata(value_child, value_schema); + RETURN_IF_ERROR(build_nested_children_from_access_node( + value_child, value_child->type, value_node, path + ".VALUES", value_schema)); + } + return Status::OK(); +} + +Status build_nested_children_from_access_node(format::ColumnDefinition* column, + const DataTypePtr& type, const AccessPathNode& node, + const std::string& path, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + if (node.project_all || node.children.empty()) { + return build_all_nested_children_from_schema(column, type, path, schema_column); + } + + const auto nested_type = remove_nullable(type); + switch (nested_type->get_primitive_type()) { + case TYPE_STRUCT: + return build_struct_children_from_access_node( + column, assert_cast(*nested_type), node, path, + schema_column); + case TYPE_ARRAY: { + if (node.children.size() != 1 || !node.children.contains("*")) { + return Status::NotSupported( + "AccessPathParser does not support access path {} for slot {}", path, + column->name); + } + const auto& array_type = assert_cast(*nested_type); + const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() + ? &schema_column->children[0] + : nullptr; + auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), + schema_field_name_or(element_schema, "element"), + array_type.get_nested_type()); + inherit_schema_metadata(child, element_schema); + return build_nested_children_from_access_node(child, child->type, node.children.at("*"), + path + ".*", element_schema); + } + case TYPE_MAP: + return build_map_children_from_access_node( + column, assert_cast(*nested_type), node, path, schema_column); + default: + return Status::NotSupported("AccessPathParser does not support access path {} for slot {}", + path, column->name); + } +} + +} // namespace + +Status AccessPathParser::build_nested_children(format::ColumnDefinition* column, + const std::vector& access_paths, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + if (is_scanner_materialized_virtual_column(column->name)) { + return Status::OK(); + } + if (!is_complex_type(remove_nullable(column->type)->get_primitive_type())) { + return Status::OK(); + } + + AccessPathNode root; + // Build tree for AccessPathNode. + // For example, for access paths ["a.b", "a.c", "d"], the tree will be: + // root + // ├── a + // │ ├── b + // │ └── c + // └── d + for (const auto& access_path : access_paths) { + // TODO: Support META access paths if needed. Currently AccessPathParser only supports + // DATA access paths. + if (access_path.type != TAccessPathType::DATA || !access_path.__isset.data_access_path) { + return Status::NotSupported( + "AccessPathParser only supports DATA access paths for slot {}", column->name); + } + const auto& path = access_path.data_access_path.path; + if (path.empty()) { + insert_access_path(&root, path, 0); + continue; + } + int32_t top_level_id = -1; + if (to_lower(path.front()) != to_lower(column->name) && + (!parse_non_negative_int(path.front(), &top_level_id) || + !column->has_identifier_field_id() || + top_level_id != column->get_identifier_field_id())) { + return Status::NotSupported("AccessPathParser access path {} does not match slot {}", + access_path_to_string(path), column->name); + } + insert_access_path(&root, path, 1); + } + // Recursively build nested children for the column based on the AccessPathNode tree. + return build_nested_children_from_access_node(column, column->type, root, column->name, + schema_column); +} + +Status AccessPathParser::build_nested_children(format::ColumnDefinition* column, + const SlotDescriptor* slot_desc, + const format::ColumnDefinition* schema_column) { + DORIS_CHECK(column != nullptr); + DORIS_CHECK(slot_desc != nullptr); + return build_nested_children(column, slot_desc->all_access_paths(), schema_column); +} + +} // namespace doris diff --git a/be/src/exec/scan/access_path_parser.h b/be/src/exec/scan/access_path_parser.h new file mode 100644 index 00000000000000..1aa4c5b89d492a --- /dev/null +++ b/be/src/exec/scan/access_path_parser.h @@ -0,0 +1,41 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include + +#include "common/status.h" +#include "format_v2/column_data.h" +#include "gen_cpp/PlanNodes_types.h" + +namespace doris { + +class SlotDescriptor; + +class AccessPathParser { +public: + static Status build_nested_children(format::ColumnDefinition* column, + const SlotDescriptor* slot_desc, + const format::ColumnDefinition* schema_column); + + static Status build_nested_children(format::ColumnDefinition* column, + const std::vector& access_paths, + const format::ColumnDefinition* schema_column); +}; + +} // namespace doris diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp index dc65e90a9112ae..909851ef4d7ea6 100644 --- a/be/src/exec/scan/file_scanner_v2.cpp +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -17,17 +17,14 @@ #include "exec/scan/file_scanner_v2.h" -#include #include #include #include -#include #include #include #include #include -#include #include #include "common/cast_set.h" @@ -38,14 +35,12 @@ #include "core/block/column_with_type_and_name.h" #include "core/column/column.h" #include "core/data_type/data_type.h" -#include "core/data_type/data_type_array.h" -#include "core/data_type/data_type_map.h" #include "core/data_type/data_type_nullable.h" -#include "core/data_type/data_type_struct.h" #include "core/data_type_serde/data_type_serde.h" #include "core/string_ref.h" #include "exec/common/util.hpp" #include "exec/operator/scan_operator.h" +#include "exec/scan/access_path_parser.h" #include "exprs/vexpr.h" #include "exprs/vexpr_context.h" #include "exprs/vslot_ref.h" @@ -96,427 +91,6 @@ bool is_partition_slot(const TFileScanSlotInfo& slot_info, const std::string& co : !slot_info.is_file_slot; } -bool is_scanner_materialized_virtual_column(const std::string& column_name) { - return column_name == BeConsts::ICEBERG_ROWID_COL; -} - -bool parse_non_negative_int(std::string_view value, int32_t* result) { - DORIS_CHECK(result != nullptr); - int32_t parsed = -1; - const auto* begin = value.data(); - const auto* end = begin + value.size(); - const auto [ptr, ec] = std::from_chars(begin, end, parsed); - if (ec != std::errc() || ptr != end || parsed < 0) { - return false; - } - *result = parsed; - return true; -} - -std::string access_path_to_string(const std::vector& path) { - return fmt::format("{}", fmt::join(path, ".")); -} - -format::ColumnDefinition* find_or_add_child(format::ColumnDefinition* parent, int32_t id, - std::string name, DataTypePtr type) { - DORIS_CHECK(parent != nullptr); - for (auto& child : parent->children) { - if ((child.has_identifier_field_id() && child.get_identifier_field_id() == id) || - child.name == name) { - return &child; - } - } - parent->children.push_back({ - .identifier = Field::create_field(id), - .name = std::move(name), - .type = std::move(type), - .children = {}, - .default_expr = nullptr, - .is_partition_key = false, - }); - return &parent->children.back(); -} - -void inherit_schema_metadata(format::ColumnDefinition* column, - const format::ColumnDefinition* schema_column) { - if (column == nullptr || schema_column == nullptr) { - return; - } - column->name_mapping = schema_column->name_mapping; -} - -const format::ColumnDefinition* find_schema_child_by_path( - const format::ColumnDefinition* schema_column, const std::string& child_path) { - if (schema_column == nullptr) { - return nullptr; - } - int32_t parsed_field_id = -1; - if (parse_non_negative_int(child_path, &parsed_field_id)) { - const auto child_it = std::ranges::find_if( - schema_column->children, [&](const format::ColumnDefinition& child) { - return child.has_identifier_field_id() && - child.get_identifier_field_id() == parsed_field_id; - }); - return child_it == schema_column->children.end() ? nullptr : &*child_it; - } - const auto child_it = std::ranges::find_if(schema_column->children, [&](const auto& child) { - if (to_lower(child.name) == to_lower(child_path)) { - return true; - } - return std::ranges::any_of(child.name_mapping, [&](const std::string& alias) { - return to_lower(alias) == to_lower(child_path); - }); - }); - return child_it == schema_column->children.end() ? nullptr : &*child_it; -} - -int32_t schema_field_id(const format::ColumnDefinition* schema_column) { - if (schema_column == nullptr || !schema_column->has_identifier_field_id()) { - return -1; - } - return schema_column->get_identifier_field_id(); -} - -int32_t schema_field_id_or(const format::ColumnDefinition* schema_column, int32_t fallback) { - const auto field_id = schema_field_id(schema_column); - return field_id >= 0 ? field_id : fallback; -} - -std::string schema_field_name_or(const format::ColumnDefinition* schema_column, - std::string fallback) { - return schema_column == nullptr || schema_column->name.empty() ? std::move(fallback) - : schema_column->name; -} - -struct AccessPathNode { - bool project_all = false; - std::map children; -}; - -void merge_access_path_node(AccessPathNode* dst, const AccessPathNode& src) { - DORIS_CHECK(dst != nullptr); - if (dst->project_all) { - return; - } - if (src.project_all) { - dst->project_all = true; - dst->children.clear(); - return; - } - for (const auto& [path, child] : src.children) { - merge_access_path_node(&dst->children[path], child); - } -} - -void insert_access_path(AccessPathNode* root, const std::vector& path, - size_t path_idx) { - DORIS_CHECK(root != nullptr); - if (root->project_all) { - return; - } - if (path_idx >= path.size()) { - root->project_all = true; - root->children.clear(); - return; - } - insert_access_path(&root->children[path[path_idx]], path, path_idx + 1); -} - -Status build_nested_children_from_access_node(format::ColumnDefinition* column, - const DataTypePtr& type, const AccessPathNode& node, - const std::string& path, - const format::ColumnDefinition* schema_column); - -// Expand a full complex-column projection into table-schema children when the table format provides -// an external/current schema. Without this, `SELECT complex_col` or `SELECT *` leaves -// ColumnDefinition::children empty, so ColumnMapper treats the root complex column as a scalar -// mapping and later tries to cast the old file shape to the current table shape directly. -// -// Examples: -// - STRUCT country/city projected from an old file STRUCT country/population/location should -// create children country and city, so city can be materialized as missing/default. -// - ARRAY> should create the array element wrapper and then the element -// struct children item and quantity. -// - MAP> should create semantic children key/value directly, then -// expand the value struct children full_name and age. Do not introduce a physical entries -// wrapper here: ColumnMapper and TableReader treat MAP children as [key, value]. -Status build_all_nested_children_from_schema(format::ColumnDefinition* column, - const DataTypePtr& type, const std::string& path, - const format::ColumnDefinition* schema_column) { - DORIS_CHECK(column != nullptr); - - const auto nested_type = remove_nullable(type); - AccessPathNode project_all; - project_all.project_all = true; - switch (nested_type->get_primitive_type()) { - case TYPE_STRUCT: { - const auto& struct_type = assert_cast(*nested_type); - for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { - const auto field_name = struct_type.get_element_name(field_idx); - const auto* schema_child = find_schema_child_by_path(schema_column, field_name); - auto* child = find_or_add_child( - column, schema_field_id_or(schema_child, cast_set(field_idx)), - schema_field_name_or(schema_child, field_name), - struct_type.get_element(field_idx)); - inherit_schema_metadata(child, schema_child); - RETURN_IF_ERROR(build_nested_children_from_access_node( - child, child->type, project_all, path + "." + child->name, schema_child)); - } - return Status::OK(); - } - case TYPE_ARRAY: { - const auto& array_type = assert_cast(*nested_type); - const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() - ? &schema_column->children[0] - : nullptr; - auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), - schema_field_name_or(element_schema, "element"), - array_type.get_nested_type()); - inherit_schema_metadata(child, element_schema); - return build_nested_children_from_access_node(child, child->type, project_all, path + ".*", - element_schema); - } - case TYPE_MAP: { - const auto& map_type = assert_cast(*nested_type); - const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() - ? &schema_column->children[0] - : nullptr; - const auto* value_schema = schema_column != nullptr && schema_column->children.size() > 1 - ? &schema_column->children[1] - : nullptr; - auto* key_child = - find_or_add_child(column, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), map_type.get_key_type()); - inherit_schema_metadata(key_child, key_schema); - RETURN_IF_ERROR(build_nested_children_from_access_node( - key_child, key_child->type, project_all, path + ".KEYS", key_schema)); - auto* value_child = find_or_add_child(column, schema_field_id_or(value_schema, 1), - schema_field_name_or(value_schema, "value"), - map_type.get_value_type()); - inherit_schema_metadata(value_child, value_schema); - RETURN_IF_ERROR(build_nested_children_from_access_node( - value_child, value_child->type, project_all, path + ".VALUES", value_schema)); - return Status::OK(); - } - default: - return Status::OK(); - } -} - -Status build_struct_children_from_access_node(format::ColumnDefinition* column, - const DataTypeStruct& struct_type, - const AccessPathNode& node, const std::string& path, - const format::ColumnDefinition* schema_column) { - DORIS_CHECK(column != nullptr); - for (const auto& [child_path, child_node] : node.children) { - // Currently we do not support accessing struct children by position (e.g. "col.0") because it can be ambiguous and error-prone when the struct schema evolves. We only support accessing struct children by name (e.g. "col.child"). If needed, we can consider adding support for position-based access in the future with careful design and consideration. - if (child_path == "OFFSET" || child_path == "*" || child_path == "KEYS" || - child_path == "VALUES") { - return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", - path + "." + child_path, column->name); - } - - // Try to find the child field in the schema column first. If not found, fallback to find the child field in the struct type by name (case-insensitive). - const auto* schema_child = find_schema_child_by_path(schema_column, child_path); - int32_t field_id = schema_field_id(schema_child); - std::string field_name = schema_child == nullptr ? child_path : schema_child->name; - DataTypePtr field_type = schema_child == nullptr ? nullptr : schema_child->type; - if (field_id < 0 || field_type == nullptr) { - for (size_t field_idx = 0; field_idx < struct_type.get_elements().size(); ++field_idx) { - if (to_lower(struct_type.get_element_name(field_idx)) == to_lower(field_name)) { - field_id = cast_set(field_idx); - field_name = struct_type.get_element_name(field_idx); - field_type = struct_type.get_element(field_idx); - break; - } - } - } - - if (field_id < 0 || field_type == nullptr) { - return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", - path + "." + child_path, column->name); - } - // TODO: For TVF Parquet files without field ids, this fallback uses the struct ordinal as - // the table child identifier. BY_NAME mapping should instead keep a string identifier and - // let TableColumnMapper resolve the file-local child id from the Parquet schema. - auto* child = find_or_add_child(column, field_id, field_name, field_type); - inherit_schema_metadata(child, schema_child); - RETURN_IF_ERROR(build_nested_children_from_access_node( - child, child->type, child_node, path + "." + child_path, schema_child)); - } - return Status::OK(); -} - -Status build_map_children_from_access_node(format::ColumnDefinition* column, - const DataTypeMap& map_type, const AccessPathNode& node, - const std::string& path, - const format::ColumnDefinition* schema_column) { - DORIS_CHECK(column != nullptr); - AccessPathNode key_node; - AccessPathNode value_node; - bool need_key = false; - bool need_value = false; - - for (const auto& [child_path, child_node] : node.children) { - if (child_path == "OFFSET") { - return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", - path + "." + child_path, column->name); - } - if (child_path == "KEYS") { - need_key = true; - merge_access_path_node(&key_node, child_node); - continue; - } - if (child_path == "VALUES") { - need_key = true; - key_node.project_all = true; - key_node.children.clear(); - need_value = true; - merge_access_path_node(&value_node, child_node); - continue; - } - if (child_path == "*") { - need_key = true; - key_node.project_all = true; - key_node.children.clear(); - need_value = true; - merge_access_path_node(&value_node, child_node); - continue; - } - return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", - path + "." + child_path, column->name); - } - if (need_key && !need_value) { - // Keep value readable until the downstream map materialization path can construct a table - // Map column from keys only. - need_value = true; - value_node.project_all = true; - value_node.children.clear(); - } - - if (!need_key && !need_value) { - return Status::OK(); - } - - const auto* key_schema = schema_column != nullptr && !schema_column->children.empty() - ? &schema_column->children[0] - : nullptr; - const auto* value_schema = schema_column != nullptr && schema_column->children.size() > 1 - ? &schema_column->children[1] - : nullptr; - if (need_key) { - auto* key_child = - find_or_add_child(column, schema_field_id_or(key_schema, 0), - schema_field_name_or(key_schema, "key"), map_type.get_key_type()); - inherit_schema_metadata(key_child, key_schema); - RETURN_IF_ERROR(build_nested_children_from_access_node(key_child, key_child->type, key_node, - path + ".KEYS", key_schema)); - } - if (need_value) { - auto* value_child = find_or_add_child(column, schema_field_id_or(value_schema, 1), - schema_field_name_or(value_schema, "value"), - map_type.get_value_type()); - inherit_schema_metadata(value_child, value_schema); - RETURN_IF_ERROR(build_nested_children_from_access_node( - value_child, value_child->type, value_node, path + ".VALUES", value_schema)); - } - return Status::OK(); -} - -Status build_nested_children_from_access_node(format::ColumnDefinition* column, - const DataTypePtr& type, const AccessPathNode& node, - const std::string& path, - const format::ColumnDefinition* schema_column) { - DORIS_CHECK(column != nullptr); - if (node.project_all || node.children.empty()) { - return build_all_nested_children_from_schema(column, type, path, schema_column); - } - - const auto nested_type = remove_nullable(type); - switch (nested_type->get_primitive_type()) { - case TYPE_STRUCT: - return build_struct_children_from_access_node( - column, assert_cast(*nested_type), node, path, - schema_column); - case TYPE_ARRAY: { - if (node.children.size() != 1 || !node.children.contains("*")) { - return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", - path, column->name); - } - const auto& array_type = assert_cast(*nested_type); - const auto* element_schema = schema_column != nullptr && !schema_column->children.empty() - ? &schema_column->children[0] - : nullptr; - auto* child = find_or_add_child(column, schema_field_id_or(element_schema, 0), - schema_field_name_or(element_schema, "element"), - array_type.get_nested_type()); - inherit_schema_metadata(child, element_schema); - return build_nested_children_from_access_node(child, child->type, node.children.at("*"), - path + ".*", element_schema); - } - case TYPE_MAP: - return build_map_children_from_access_node( - column, assert_cast(*nested_type), node, path, schema_column); - default: - return Status::NotSupported("FileScannerV2 does not support access path {} for slot {}", - path, column->name); - } -} - -Status build_nested_children_from_access_paths(format::ColumnDefinition* column, - const TColumnAccessPaths& access_paths, - const format::ColumnDefinition* schema_column) { - DORIS_CHECK(column != nullptr); - if (is_scanner_materialized_virtual_column(column->name)) { - return Status::OK(); - } - if (!is_complex_type(remove_nullable(column->type)->get_primitive_type())) { - return Status::OK(); - } - - AccessPathNode root; - // Build tree for AccessPathNode. - // For example, for access paths ["a.b", "a.c", "d"], the tree will be: - // root - // ├── a - // │ ├── b - // │ └── c - // └── d - for (const auto& access_path : access_paths) { - // TODO: Support META access paths if needed. Currently FileScannerV2 only supports DATA access paths. - if (access_path.type != TAccessPathType::DATA || !access_path.__isset.data_access_path) { - return Status::NotSupported("FileScannerV2 only supports DATA access paths for slot {}", - column->name); - } - const auto& path = access_path.data_access_path.path; - if (path.empty()) { - insert_access_path(&root, path, 0); - continue; - } - int32_t top_level_id = -1; - if (to_lower(path.front()) != to_lower(column->name) && - (!parse_non_negative_int(path.front(), &top_level_id) || - !column->has_identifier_field_id() || - top_level_id != column->get_identifier_field_id())) { - return Status::NotSupported("FileScannerV2 access path {} does not match slot {}", - access_path_to_string(path), column->name); - } - insert_access_path(&root, path, 1); - } - // Recursively build nested children for the column based on the AccessPathNode tree. - return build_nested_children_from_access_node(column, column->type, root, column->name, - schema_column); -} - -Status build_nested_children_from_access_paths(format::ColumnDefinition* column, - const SlotDescriptor* slot_desc, - const format::ColumnDefinition* schema_column) { - DORIS_CHECK(column != nullptr); - DORIS_CHECK(slot_desc != nullptr); - return build_nested_children_from_access_paths(column, slot_desc->all_access_paths(), - schema_column); -} - Status rewrite_slot_refs_to_global_index( VExprSPtr* expr, const std::unordered_map& slot_id_to_global_index) { @@ -557,15 +131,20 @@ Status rewrite_slot_refs_to_global_index( } // namespace #ifdef BE_TEST -Status FileScannerV2::TEST_build_nested_children_from_access_paths( - format::ColumnDefinition* column, const TColumnAccessPaths& access_paths) { - return build_nested_children_from_access_paths(column, access_paths, nullptr); +Status FileScannerV2::TEST_to_file_format(TFileFormatType::type format_type, + format::FileFormat* file_format) { + return _to_file_format(format_type, file_format); +} + +bool FileScannerV2::TEST_is_partition_slot(const TFileScanSlotInfo& slot_info, + const std::string& column_name) { + return is_partition_slot(slot_info, column_name); } -Status FileScannerV2::TEST_build_nested_children_from_access_paths( - format::ColumnDefinition* column, const TColumnAccessPaths& access_paths, - const format::ColumnDefinition* schema_column) { - return build_nested_children_from_access_paths(column, access_paths, schema_column); +Status FileScannerV2::TEST_rewrite_slot_refs_to_global_index( + VExprSPtr* expr, + const std::unordered_map& slot_id_to_global_index) { + return rewrite_slot_refs_to_global_index(expr, slot_id_to_global_index); } #endif @@ -835,9 +414,10 @@ Status FileScannerV2::_build_projected_columns(const format::TableReader& table_ RETURN_IF_ERROR(_build_default_expr(slot_info, &column.default_expr)); build_context.schema_column.reset(); RETURN_IF_ERROR(table_reader.annotate_projected_column(slot_info, &build_context, &column)); - // Build the column's nested children based on the column's access paths and the schema column (if exists). - // The access paths are generated from the slot's access path expressions which means a projected column can have a subset of the schema column's nested children. - RETURN_IF_ERROR(build_nested_children_from_access_paths( + // Build nested children from access paths generated by the slot's access-path + // expressions. A projected column can therefore contain only a subset of the schema + // column's nested children. + RETURN_IF_ERROR(AccessPathParser::build_nested_children( &column, it->second, build_context.schema_column.has_value() ? &*build_context.schema_column : nullptr)); if (is_partition_slot(slot_info, column.name)) { diff --git a/be/src/exec/scan/file_scanner_v2.h b/be/src/exec/scan/file_scanner_v2.h index 7925bc0c7dfdd7..ba505b99d9beeb 100644 --- a/be/src/exec/scan/file_scanner_v2.h +++ b/be/src/exec/scan/file_scanner_v2.h @@ -54,11 +54,13 @@ class FileScannerV2 final : public Scanner { static bool is_supported(const TFileScanRangeParams& params, const TFileRangeDesc& range); #ifdef BE_TEST - static Status TEST_build_nested_children_from_access_paths( - format::ColumnDefinition* column, const std::vector& access_paths); - static Status TEST_build_nested_children_from_access_paths( - format::ColumnDefinition* column, const std::vector& access_paths, - const format::ColumnDefinition* schema_column); + static Status TEST_to_file_format(TFileFormatType::type format_type, + format::FileFormat* file_format); + static bool TEST_is_partition_slot(const TFileScanSlotInfo& slot_info, + const std::string& column_name); + static Status TEST_rewrite_slot_refs_to_global_index( + VExprSPtr* expr, + const std::unordered_map& slot_id_to_global_index); #endif FileScannerV2(RuntimeState* state, FileScanLocalState* parent, int64_t limit, diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index 0f7b8c771a030a..017ef35096c3aa 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -997,8 +997,7 @@ static bool mapping_can_use_file_column_directly(const ColumnMapping& mapping) { const bool same_timestamptz_with_different_scale = table_type->get_primitive_type() == TYPE_TIMESTAMPTZ && file_type->get_primitive_type() == TYPE_TIMESTAMPTZ; - if (!mapping.table_type->equals(*mapping.file_type) && - !same_timestamptz_with_different_scale) { + if (!mapping.table_type->equals(*mapping.file_type) && !same_timestamptz_with_different_scale) { return false; } return !needs_complex_rematerialize(mapping); diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index abb607ac05d0ea..5b3f8d8f816864 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -77,8 +77,8 @@ DataTypePtr apply_timestamp_tz_mapping(ParquetColumnSchema* column_schema) { const bool nullable = column_schema->type != nullptr && column_schema->type->is_nullable(); const auto scale = timestamp_tz_scale(column_schema->type_descriptor); - column_schema->type = DataTypeFactory::instance().create_data_type( - TYPE_TIMESTAMPTZ, nullable, 0, scale); + column_schema->type = DataTypeFactory::instance().create_data_type(TYPE_TIMESTAMPTZ, + nullable, 0, scale); column_schema->type_descriptor.doris_type = column_schema->type; } return column_schema->type; @@ -92,24 +92,21 @@ DataTypePtr apply_timestamp_tz_mapping(ParquetColumnSchema* column_schema) { if (column_schema->kind == ParquetColumnSchemaKind::LIST) { DORIS_CHECK(child_types.size() == 1); - column_schema->type = - nullable_like_original(column_schema->type, - std::make_shared(child_types[0])); + column_schema->type = nullable_like_original( + column_schema->type, std::make_shared(child_types[0])); } else if (column_schema->kind == ParquetColumnSchemaKind::MAP) { DORIS_CHECK(child_types.size() == 2); - column_schema->type = - nullable_like_original(column_schema->type, - std::make_shared(make_nullable(child_types[0]), - make_nullable(child_types[1]))); + column_schema->type = nullable_like_original( + column_schema->type, std::make_shared(make_nullable(child_types[0]), + make_nullable(child_types[1]))); } else if (column_schema->kind == ParquetColumnSchemaKind::STRUCT) { Strings child_names; child_names.reserve(column_schema->children.size()); for (const auto& child : column_schema->children) { child_names.push_back(child->name); } - column_schema->type = - nullable_like_original(column_schema->type, - std::make_shared(child_types, child_names)); + column_schema->type = nullable_like_original( + column_schema->type, std::make_shared(child_types, child_names)); } return column_schema->type; } diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index 4f7b3d38a535f0..89fc223e7cd638 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -568,10 +568,9 @@ Status TableReader::create_file_reader(std::unique_ptr* reader) { const bool enable_mapping_timestamp_tz = _scan_params != nullptr && _scan_params->__isset.enable_mapping_timestamp_tz && _scan_params->enable_mapping_timestamp_tz; - *reader = std::make_unique(_system_properties, - _current_task->data_file, _io_ctx, - _scanner_profile, _global_rowid_context, - enable_mapping_timestamp_tz); + *reader = std::make_unique( + _system_properties, _current_task->data_file, _io_ctx, _scanner_profile, + _global_rowid_context, enable_mapping_timestamp_tz); return Status::OK(); } return Status::NotSupported("TableReader does not support file format {}", diff --git a/be/test/exec/scan/access_path_parser_test.cpp b/be/test/exec/scan/access_path_parser_test.cpp new file mode 100644 index 00000000000000..d4bd6ab6c06360 --- /dev/null +++ b/be/test/exec/scan/access_path_parser_test.cpp @@ -0,0 +1,371 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "exec/scan/access_path_parser.h" + +#include +#include + +#include +#include +#include +#include + +#include "common/consts.h" +#include "core/data_type/data_type_array.h" +#include "core/data_type/data_type_map.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "core/data_type/data_type_struct.h" +#include "core/field.h" + +namespace doris { +namespace { + +TColumnAccessPath data_access_path(std::vector path) { + TColumnAccessPath access_path; + access_path.__set_type(TAccessPathType::DATA); + TDataAccessPath data_path; + data_path.__set_path(std::move(path)); + access_path.__set_data_access_path(std::move(data_path)); + return access_path; +} + +TColumnAccessPath data_access_path_without_payload() { + TColumnAccessPath access_path; + access_path.__set_type(TAccessPathType::DATA); + return access_path; +} + +TColumnAccessPath meta_access_path() { + TColumnAccessPath access_path; + access_path.__set_type(TAccessPathType::META); + return access_path; +} + +format::ColumnDefinition field(int32_t id, std::string name, DataTypePtr type, + std::vector children = {}, + std::vector aliases = {}) { + return { + .identifier = Field::create_field(id), + .name = std::move(name), + .name_mapping = std::move(aliases), + .type = std::move(type), + .children = std::move(children), + }; +} + +format::ColumnDefinition root_column(int32_t id, std::string name, DataTypePtr type) { + return { + .identifier = Field::create_field(id), + .name = std::move(name), + .type = std::move(type), + }; +} + +void expect_child(const format::ColumnDefinition& child, int32_t id, const std::string& name) { + ASSERT_TRUE(child.has_identifier_field_id()); + EXPECT_EQ(child.get_identifier_field_id(), id); + EXPECT_EQ(child.name, name); +} + +const format::ColumnDefinition* find_child_by_name(const format::ColumnDefinition& parent, + const std::string& name) { + for (const auto& child : parent.children) { + if (child.name == name) { + return &child; + } + } + return nullptr; +} + +} // namespace + +// Scenario: primitive columns and scanner-materialized virtual columns should not build nested +// children, even when their descriptor carries access paths that are not meaningful to the parser. +TEST(AccessPathParserTest, IgnoresPrimitiveColumnsAndScannerVirtualColumns) { + auto int_type = std::make_shared(); + auto string_type = std::make_shared(); + + // Primitive columns have no nested children, so parser should not inspect even invalid paths. + auto primitive = root_column(1, "id", int_type); + auto status = AccessPathParser::build_nested_children( + &primitive, std::vector {meta_access_path()}, nullptr); + ASSERT_TRUE(status.ok()) << status; + EXPECT_TRUE(primitive.children.empty()); + + // Iceberg rowid is materialized by scanner/table-reader logic and may carry a negative access + // path. Parser must leave it untouched. + auto rowid_type = std::make_shared( + DataTypes {string_type, std::make_shared(), + std::make_shared(), string_type}, + Strings {"file_path", "row_pos", "partition_spec_id", "partition_data_json"}); + format::ColumnDefinition rowid { + .identifier = Field::create_field(BeConsts::ICEBERG_ROWID_COL), + .name = BeConsts::ICEBERG_ROWID_COL, + .type = rowid_type, + }; + status = AccessPathParser::build_nested_children( + &rowid, std::vector {data_access_path({"-1"})}, nullptr); + ASSERT_TRUE(status.ok()) << status; + EXPECT_TRUE(rowid.children.empty()); +} + +// Scenario: reject unsupported top-level inputs before recursive type parsing, including META +// paths, missing DATA payloads, and access paths whose root does not match the projected slot. +TEST(AccessPathParserTest, RejectsUnsupportedTopLevelAccessPathInputs) { + auto int_type = std::make_shared(); + auto struct_type = std::make_shared(DataTypes {int_type}, Strings {"a"}); + + struct Case { + std::string name; + format::ColumnDefinition column; + std::vector paths; + }; + std::vector cases; + cases.push_back({"meta path", root_column(100, "s", struct_type), {meta_access_path()}}); + cases.push_back({"missing DATA payload", + root_column(100, "s", struct_type), + {data_access_path_without_payload()}}); + cases.push_back({"wrong root name", + root_column(100, "s", struct_type), + {data_access_path({"other", "a"})}}); + cases.push_back({"wrong root field id", + root_column(100, "s", struct_type), + {data_access_path({"101", "a"})}}); + + for (auto& test_case : cases) { + auto status = AccessPathParser::build_nested_children(&test_case.column, test_case.paths, + nullptr); + EXPECT_FALSE(status.ok()) << test_case.name; + } +} + +// Scenario: struct access paths support field-id lookup, alias lookup, case-insensitive name +// fallback, and whole-struct expansion; reserved array/map path tokens remain invalid. +TEST(AccessPathParserTest, StructAccessPathMatrix) { + auto int_type = std::make_shared(); + auto struct_type = + std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"}); + format::ColumnDefinition schema { + .identifier = Field::create_field(100), + .name = "s", + .type = struct_type, + .children = + { + field(101, "a", int_type), + field(205, "b", int_type, {}, {"old_b"}), + }, + }; + + { + auto column = root_column(100, "s", struct_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"s", "A"})}, nullptr); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 1); + expect_child(column.children[0], 0, "a"); + } + { + auto column = root_column(100, "s", struct_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"100", "205"})}, + &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 1); + expect_child(column.children[0], 205, "b"); + } + { + auto column = root_column(100, "s", struct_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"s", "old_b"})}, + &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 1); + expect_child(column.children[0], 205, "b"); + EXPECT_EQ(column.children[0].name_mapping, std::vector({"old_b"})); + } + { + auto column = root_column(100, "s", struct_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"s"})}, &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 2); + expect_child(column.children[0], 101, "a"); + expect_child(column.children[1], 205, "b"); + } + + for (const auto& invalid_child : {"OFFSET", "*", "KEYS", "VALUES", "missing"}) { + auto column = root_column(100, "s", struct_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"s", invalid_child})}, + &schema); + EXPECT_FALSE(status.ok()) << invalid_child; + } +} + +// Scenario: array access paths must pass through the "*" element token, then reuse struct child +// parsing under the element wrapper; invalid array tokens are rejected. +TEST(AccessPathParserTest, ArrayAccessPathMatrix) { + auto int_type = std::make_shared(); + auto string_type = std::make_shared(); + auto element_type = std::make_shared(DataTypes {string_type, int_type}, + Strings {"item", "quantity"}); + auto array_type = std::make_shared(element_type); + format::ColumnDefinition schema { + .identifier = Field::create_field(200), + .name = "items", + .type = array_type, + .children = + { + field(201, "element", element_type, + { + field(202, "item", string_type, {}, {"old_item"}), + field(203, "quantity", int_type), + }), + }, + }; + + { + auto column = root_column(200, "items", array_type); + auto status = AccessPathParser::build_nested_children( + &column, + std::vector {data_access_path({"items", "*", "old_item"})}, + &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 1); + expect_child(column.children[0], 201, "element"); + ASSERT_EQ(column.children[0].children.size(), 1); + expect_child(column.children[0].children[0], 202, "item"); + EXPECT_EQ(column.children[0].children[0].name_mapping, + std::vector({"old_item"})); + } + { + auto column = root_column(200, "items", array_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"items"})}, &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 1); + expect_child(column.children[0], 201, "element"); + ASSERT_EQ(column.children[0].children.size(), 2); + expect_child(column.children[0].children[0], 202, "item"); + expect_child(column.children[0].children[1], 203, "quantity"); + } + + for (const auto& invalid_path : std::vector> { + {"items", "OFFSET"}, {"items", "item"}, {"items", "*", "missing"}}) { + auto column = root_column(200, "items", array_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path(invalid_path)}, &schema); + EXPECT_FALSE(status.ok()) << invalid_path.back(); + } +} + +// Scenario: map access paths split KEYS/VALUES, force the missing side needed for materialization, +// merge repeated value-child requests, and reject unsupported map child tokens. +TEST(AccessPathParserTest, MapAccessPathMatrix) { + auto int_type = std::make_shared(); + auto string_type = std::make_shared(); + auto value_type = std::make_shared( + DataTypes {string_type, int_type, string_type}, Strings {"full_name", "age", "gender"}); + auto map_type = std::make_shared(string_type, value_type); + format::ColumnDefinition schema { + .identifier = Field::create_field(300), + .name = "m", + .type = map_type, + .children = + { + field(301, "key", string_type), + field(302, "value", value_type, + { + field(303, "full_name", string_type, {}, {"name"}), + field(304, "age", int_type), + field(305, "gender", string_type), + }), + }, + }; + + { + auto column = root_column(300, "m", map_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"m", "KEYS"})}, &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 2); + expect_child(column.children[0], 301, "key"); + expect_child(column.children[1], 302, "value"); + ASSERT_EQ(column.children[1].children.size(), 3); + const auto* full_name = find_child_by_name(column.children[1], "full_name"); + ASSERT_NE(full_name, nullptr); + expect_child(*full_name, 303, "full_name"); + const auto* age = find_child_by_name(column.children[1], "age"); + ASSERT_NE(age, nullptr); + expect_child(*age, 304, "age"); + const auto* gender = find_child_by_name(column.children[1], "gender"); + ASSERT_NE(gender, nullptr); + expect_child(*gender, 305, "gender"); + } + { + auto column = root_column(300, "m", map_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"m", "VALUES", "age"})}, + &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 2); + expect_child(column.children[0], 301, "key"); + expect_child(column.children[1], 302, "value"); + ASSERT_EQ(column.children[1].children.size(), 1); + expect_child(column.children[1].children[0], 304, "age"); + } + { + auto column = root_column(300, "m", map_type); + auto status = AccessPathParser::build_nested_children( + &column, + std::vector { + data_access_path({"m", "VALUES", "name"}), + data_access_path({"m", "*", "gender"}), + }, + &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 2); + ASSERT_EQ(column.children[1].children.size(), 2); + const auto* full_name = find_child_by_name(column.children[1], "full_name"); + ASSERT_NE(full_name, nullptr); + expect_child(*full_name, 303, "full_name"); + EXPECT_EQ(full_name->name_mapping, std::vector({"name"})); + const auto* gender = find_child_by_name(column.children[1], "gender"); + ASSERT_NE(gender, nullptr); + expect_child(*gender, 305, "gender"); + } + { + auto column = root_column(300, "m", map_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path({"m"})}, &schema); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(column.children.size(), 2); + ASSERT_EQ(column.children[1].children.size(), 3); + } + + for (const auto& invalid_path : std::vector> { + {"m", "OFFSET"}, {"m", "ENTRY"}, {"m", "VALUES", "missing"}}) { + auto column = root_column(300, "m", map_type); + auto status = AccessPathParser::build_nested_children( + &column, std::vector {data_access_path(invalid_path)}, &schema); + EXPECT_FALSE(status.ok()) << invalid_path.back(); + } +} + +} // namespace doris diff --git a/be/test/exec/scan/file_scanner_v2_test.cpp b/be/test/exec/scan/file_scanner_v2_test.cpp new file mode 100644 index 00000000000000..fe6c69d77a6572 --- /dev/null +++ b/be/test/exec/scan/file_scanner_v2_test.cpp @@ -0,0 +1,223 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "exec/scan/file_scanner_v2.h" + +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "common/consts.h" +#include "core/assert_cast.h" +#include "core/data_type/data_type_number.h" +#include "core/data_type/data_type_string.h" +#include "exec/scan/split_source_connector.h" +#include "exprs/vslot_ref.h" +#include "format_v2/expr/cast.h" + +namespace doris { +namespace { + +TFileRangeDesc range_with_format(std::string table_format, TFileFormatType::type format_type) { + TFileRangeDesc range; + range.__set_format_type(format_type); + if (!table_format.empty()) { + TTableFormatFileDesc table_desc; + table_desc.__set_table_format_type(std::move(table_format)); + range.__set_table_format_params(std::move(table_desc)); + } + return range; +} + +TScanRangeParams scan_range_param(const TFileRangeDesc& range) { + TScanRangeParams params; + params.scan_range.ext_scan_range.file_scan_range.ranges.push_back(range); + return params; +} + +VExprSPtr slot_ref(int slot_id, int column_id, DataTypePtr type, const std::string& name) { + return VSlotRef::create_shared(slot_id, column_id, -1, std::move(type), name); +} + +} // namespace + +// Scenario: FileScannerV2::is_supported should honor table format, scan params format, and the +// optional per-range file format override as a single matrix. +TEST(FileScannerV2Test, SupportedFormatMatrix) { + struct Case { + std::string table_format; + TFileFormatType::type params_format; + std::optional range_format; + bool expected; + }; + + const std::vector cases { + {"", TFileFormatType::FORMAT_PARQUET, std::nullopt, true}, + {"tvf", TFileFormatType::FORMAT_PARQUET, std::nullopt, true}, + {"hive", TFileFormatType::FORMAT_PARQUET, std::nullopt, true}, + {"iceberg", TFileFormatType::FORMAT_PARQUET, std::nullopt, true}, + {"paimon", TFileFormatType::FORMAT_PARQUET, std::nullopt, true}, + {"hudi", TFileFormatType::FORMAT_PARQUET, std::nullopt, false}, + {"jdbc", TFileFormatType::FORMAT_PARQUET, std::nullopt, false}, + {"", TFileFormatType::FORMAT_JNI, std::nullopt, false}, + {"hive", TFileFormatType::FORMAT_ORC, std::nullopt, false}, + {"jdbc", TFileFormatType::FORMAT_JNI, std::nullopt, true}, + {"hive", TFileFormatType::FORMAT_JNI, std::nullopt, false}, + {"hive", TFileFormatType::FORMAT_PARQUET, TFileFormatType::FORMAT_ORC, false}, + {"hive", TFileFormatType::FORMAT_ORC, TFileFormatType::FORMAT_PARQUET, true}, + }; + + for (const auto& test_case : cases) { + TFileScanRangeParams params; + params.__set_format_type(test_case.params_format); + auto range = range_with_format(test_case.table_format, + test_case.range_format.value_or(test_case.params_format)); + if (!test_case.range_format.has_value()) { + range.__isset.format_type = false; + } + EXPECT_EQ(FileScannerV2::is_supported(params, range), test_case.expected) + << "table_format=" << test_case.table_format + << ", params_format=" << static_cast(test_case.params_format) + << ", range_has_format=" << test_case.range_format.has_value(); + } +} + +// Scenario: SplitSourceConnector should route to FileScannerV2 only when every scan range in the +// source is supported; one unsupported table format or file format must make the match fail. +TEST(FileScannerV2Test, SplitSourceAllScanRangesMatchRequiresEveryRangeSupported) { + TFileScanRangeParams params; + params.__set_format_type(TFileFormatType::FORMAT_PARQUET); + + const auto supported = range_with_format("hive", TFileFormatType::FORMAT_PARQUET); + const auto unsupported_table = range_with_format("hudi", TFileFormatType::FORMAT_PARQUET); + const auto unsupported_format = range_with_format("hive", TFileFormatType::FORMAT_ORC); + + LocalSplitSourceConnector all_supported( + {scan_range_param(supported), + scan_range_param(range_with_format("iceberg", TFileFormatType::FORMAT_PARQUET))}, + 1); + EXPECT_TRUE(all_supported.all_scan_ranges_match(params, FileScannerV2::is_supported)); + + LocalSplitSourceConnector table_mismatch( + {scan_range_param(supported), scan_range_param(unsupported_table)}, 1); + EXPECT_FALSE(table_mismatch.all_scan_ranges_match(params, FileScannerV2::is_supported)); + + LocalSplitSourceConnector format_mismatch( + {scan_range_param(supported), scan_range_param(unsupported_format)}, 1); + EXPECT_FALSE(format_mismatch.all_scan_ranges_match(params, FileScannerV2::is_supported)); +} + +// Scenario: FileScannerV2 converts only the file formats implemented by format_v2 readers and +// rejects everything else before TableReader::init sees an unsupported FileFormat. +TEST(FileScannerV2Test, FileFormatConversionMatrix) { + struct Case { + TFileFormatType::type input; + std::optional expected; + }; + const std::vector cases { + {TFileFormatType::FORMAT_PARQUET, format::FileFormat::PARQUET}, + {TFileFormatType::FORMAT_JNI, format::FileFormat::JNI}, + {TFileFormatType::FORMAT_ORC, std::nullopt}, + {TFileFormatType::FORMAT_CSV_PLAIN, std::nullopt}, + }; + + for (const auto& test_case : cases) { + format::FileFormat file_format = format::FileFormat::PARQUET; + const auto status = FileScannerV2::TEST_to_file_format(test_case.input, &file_format); + if (test_case.expected.has_value()) { + ASSERT_TRUE(status.ok()) << status; + EXPECT_EQ(file_format, *test_case.expected); + } else { + EXPECT_FALSE(status.ok()); + } + } +} + +// Scenario: partition slots are identified from the explicit FE category when present, otherwise +// from the legacy is_file_slot flag. Scanner-generated rowid columns must never be treated as +// partition columns even if FE marks them as non-file slots. +TEST(FileScannerV2Test, PartitionSlotClassificationMatrix) { + TFileScanSlotInfo legacy_partition; + legacy_partition.__set_is_file_slot(false); + EXPECT_TRUE(FileScannerV2::TEST_is_partition_slot(legacy_partition, "dt")); + + TFileScanSlotInfo legacy_file; + legacy_file.__set_is_file_slot(true); + EXPECT_FALSE(FileScannerV2::TEST_is_partition_slot(legacy_file, "value")); + + TFileScanSlotInfo categorized_partition; + categorized_partition.__set_is_file_slot(true); + categorized_partition.__set_category(TColumnCategory::PARTITION_KEY); + EXPECT_TRUE(FileScannerV2::TEST_is_partition_slot(categorized_partition, "p")); + + TFileScanSlotInfo categorized_regular; + categorized_regular.__set_is_file_slot(false); + categorized_regular.__set_category(TColumnCategory::REGULAR); + EXPECT_FALSE(FileScannerV2::TEST_is_partition_slot(categorized_regular, "regular_col")); + + EXPECT_FALSE( + FileScannerV2::TEST_is_partition_slot(legacy_partition, BeConsts::GLOBAL_ROWID_COL)); + EXPECT_FALSE( + FileScannerV2::TEST_is_partition_slot(legacy_partition, BeConsts::ICEBERG_ROWID_COL)); +} + +// Scenario: table conjuncts are cloned into global-index space before they are handed to +// TableReader. Explicit slot-id mappings use the required_slots order; missing mappings fall back +// to the slot id itself for legacy descriptors. +TEST(FileScannerV2Test, RewriteSlotRefsToGlobalIndexMatrix) { + const auto int_type = std::make_shared(); + { + auto expr = slot_ref(42, 99, int_type, "value"); + const auto status = FileScannerV2::TEST_rewrite_slot_refs_to_global_index( + &expr, {{42, format::GlobalIndex(3)}}); + ASSERT_TRUE(status.ok()) << status; + const auto* rewritten = assert_cast(expr.get()); + EXPECT_EQ(rewritten->slot_id(), 3); + EXPECT_EQ(rewritten->column_id(), 3); + EXPECT_EQ(rewritten->column_name(), "value"); + } + { + auto expr = slot_ref(7, 99, int_type, "legacy_value"); + const auto status = FileScannerV2::TEST_rewrite_slot_refs_to_global_index(&expr, {}); + ASSERT_TRUE(status.ok()) << status; + const auto* rewritten = assert_cast(expr.get()); + EXPECT_EQ(rewritten->slot_id(), 7); + EXPECT_EQ(rewritten->column_id(), 7); + EXPECT_EQ(rewritten->column_name(), "legacy_value"); + } + { + auto cast_expr = Cast::create_shared(int_type); + cast_expr->add_child(slot_ref(9, 9, int_type, "nested_value")); + VExprSPtr expr = cast_expr; + const auto status = FileScannerV2::TEST_rewrite_slot_refs_to_global_index( + &expr, {{9, format::GlobalIndex(1)}}); + ASSERT_TRUE(status.ok()) << status; + ASSERT_EQ(expr->get_num_children(), 1); + const auto* rewritten_child = assert_cast(expr->children()[0].get()); + EXPECT_EQ(rewritten_child->slot_id(), 1); + EXPECT_EQ(rewritten_child->column_id(), 1); + EXPECT_EQ(rewritten_child->column_name(), "nested_value"); + } +} + +} // namespace doris diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index 32f7429c01c4e9..94e52f547d07b9 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -23,17 +23,12 @@ #include #include -#include "common/consts.h" #include "common/object_pool.h" -#include "core/data_type/data_type_array.h" -#include "core/data_type/data_type_map.h" #include "core/data_type/data_type_number.h" #include "core/data_type/data_type_string.h" -#include "core/data_type/data_type_struct.h" #include "cpp/sync_point.h" #include "exec/operator/file_scan_operator.h" #include "exec/scan/file_scanner.h" -#include "exec/scan/file_scanner_v2.h" #include "exec/scan/split_source_connector.h" #include "format_v2/table/hive_reader.h" #include "io/fs/local_file_system.h" @@ -45,19 +40,6 @@ #include "runtime/user_function_cache.h" namespace doris { -namespace { - -TColumnAccessPath data_access_path(std::vector path) { - TColumnAccessPath access_path; - access_path.__set_type(TAccessPathType::DATA); - TDataAccessPath data_path; - data_path.__set_path(std::move(path)); - access_path.__set_data_access_path(std::move(data_path)); - return access_path; -} - -} // namespace - class TestSplitSourceConnectorStub : public SplitSourceConnector { private: std::mutex _range_lock; @@ -359,46 +341,6 @@ TEST_F(VfileScannerExceptionTest, process_late_arrival_conjuncts_retain) { WARN_IF_ERROR(scanner->close(&_runtime_state), "fail to close scanner"); } -TEST(FileScannerV2Test, SupportOnlyRefactoredTableReaders) { - TFileScanRangeParams params; - params.__set_format_type(TFileFormatType::FORMAT_PARQUET); - - TFileRangeDesc range; - EXPECT_TRUE(FileScannerV2::is_supported(params, range)); - - TTableFormatFileDesc table_format; - table_format.__set_table_format_type("iceberg"); - range.__set_table_format_params(table_format); - EXPECT_TRUE(FileScannerV2::is_supported(params, range)); - - table_format.__set_table_format_type("hive"); - range.__set_table_format_params(table_format); - EXPECT_TRUE(FileScannerV2::is_supported(params, range)); - - table_format.__set_table_format_type("paimon"); - range.__set_table_format_params(table_format); - EXPECT_TRUE(FileScannerV2::is_supported(params, range)); - - table_format.__set_table_format_type("hudi"); - range.__set_table_format_params(table_format); - EXPECT_FALSE(FileScannerV2::is_supported(params, range)); - - range.__set_format_type(TFileFormatType::FORMAT_ORC); - table_format.__set_table_format_type("hive"); - range.__set_table_format_params(table_format); - EXPECT_FALSE(FileScannerV2::is_supported(params, range)); - - TScanRangeParams scan_range_params; - scan_range_params.scan_range.ext_scan_range.file_scan_range.ranges.push_back(range); - LocalSplitSourceConnector split_source({scan_range_params}, 1); - EXPECT_FALSE(split_source.all_scan_ranges_match(params, FileScannerV2::is_supported)); - - range.__set_format_type(TFileFormatType::FORMAT_PARQUET); - scan_range_params.scan_range.ext_scan_range.file_scan_range.ranges[0] = range; - LocalSplitSourceConnector supported_split_source({scan_range_params}, 1); - EXPECT_TRUE(supported_split_source.all_scan_ranges_match(params, FileScannerV2::is_supported)); -} - TEST(HiveReaderTest, PositionMappingUsesColumnIdxsForFileSlots) { TQueryOptions query_options; query_options.hive_parquet_use_column_names = false; @@ -507,279 +449,4 @@ TEST(HiveReaderTest, PositionMappingFailsWhenColumnIdxsMissing) { EXPECT_EQ(context.next_file_column_idx, 0); } -TEST(FileScannerV2Test, BuildNestedChildrenFromAccessPaths) { - const auto int_type = std::make_shared(); - const auto key_type = std::make_shared(); - const auto value_type = - std::make_shared(DataTypes {int_type, int_type}, Strings {"b", "c"}); - format::ColumnDefinition column {.identifier = Field::create_field(100), - .name = "m", - .type = std::make_shared(key_type, value_type)}; - - std::vector access_paths; - access_paths.push_back(data_access_path({"m", "KEYS"})); - access_paths.push_back(data_access_path({"m", "VALUES", "b"})); - access_paths.push_back(data_access_path({"m", "*", "c"})); - auto status = - FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); - ASSERT_TRUE(status.ok()) << status; - - ASSERT_EQ(column.children.size(), 2); - EXPECT_EQ(column.children[0].name, "key"); - EXPECT_TRUE(column.children[0].children.empty()); - EXPECT_EQ(column.children[1].name, "value"); - ASSERT_EQ(column.children[1].children.size(), 2); - EXPECT_EQ(column.children[1].children[0].name, "b"); - EXPECT_EQ(column.children[1].children[1].name, "c"); -} - -TEST(FileScannerV2Test, BuildArrayStructChildrenFromAccessPaths) { - const auto int_type = std::make_shared(); - const auto element_type = - std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"}); - format::ColumnDefinition column { - .identifier = Field::create_field(100), - .name = "arr", - .type = std::make_shared(element_type), - }; - - std::vector access_paths; - access_paths.push_back(data_access_path({"arr", "*", "a"})); - auto status = - FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); - ASSERT_TRUE(status.ok()) << status; - - ASSERT_EQ(column.children.size(), 1); - const auto& element = column.children[0]; - ASSERT_TRUE(element.has_identifier_field_id()); - EXPECT_EQ(element.get_identifier_field_id(), 0); - EXPECT_EQ(element.name, "element"); - ASSERT_EQ(element.children.size(), 1); - ASSERT_TRUE(element.children[0].has_identifier_field_id()); - EXPECT_EQ(element.children[0].get_identifier_field_id(), 0); - EXPECT_EQ(element.children[0].name, "a"); -} - -TEST(FileScannerV2Test, BuildStructChildrenFromFieldIdAccessPaths) { - const auto int_type = std::make_shared(); - const auto struct_type = - std::make_shared(DataTypes {int_type, int_type}, Strings {"a", "b"}); - format::ColumnDefinition column { - .identifier = Field::create_field(100), - .name = "s", - .type = struct_type, - }; - format::ColumnDefinition schema_column { - .identifier = Field::create_field(100), - .name = "s", - .type = struct_type, - .children = - { - {.identifier = Field::create_field(101), - .name = "a", - .type = int_type}, - {.identifier = Field::create_field(205), - .name = "b", - .type = int_type}, - }, - }; - - std::vector access_paths; - access_paths.push_back(data_access_path({"100", "205"})); - auto status = FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths, - &schema_column); - ASSERT_TRUE(status.ok()) << status; - - ASSERT_EQ(column.children.size(), 1); - ASSERT_TRUE(column.children[0].has_identifier_field_id()); - EXPECT_EQ(column.children[0].get_identifier_field_id(), 205); - EXPECT_EQ(column.children[0].name, "b"); -} - -TEST(FileScannerV2Test, BuildNestedChildrenKeepsTopLevelProjectionWhole) { - const auto int_type = std::make_shared(); - format::ColumnDefinition column { - .identifier = Field::create_field(100), - .name = "s", - .type = std::make_shared(DataTypes {int_type, int_type}, - Strings {"a", "b"}), - }; - - std::vector access_paths; - access_paths.push_back(data_access_path({"s"})); - access_paths.push_back(data_access_path({"s", "a"})); - auto status = - FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); - ASSERT_TRUE(status.ok()) << status; - EXPECT_TRUE(column.children.empty()); -} - -TEST(FileScannerV2Test, BuildNestedChildrenExpandsFullProjectionFromSchemaColumn) { - const auto int_type = std::make_shared(); - const auto string_type = std::make_shared(); - - const auto struct_type = std::make_shared(DataTypes {string_type, string_type}, - Strings {"country", "city"}); - format::ColumnDefinition struct_column { - .identifier = Field::create_field(100), - .name = "struct_column", - .type = struct_type, - }; - format::ColumnDefinition struct_schema { - .identifier = Field::create_field(100), - .name = "struct_column", - .type = struct_type, - .children = - { - {.identifier = Field::create_field(101), - .name = "country", - .name_mapping = {"old_country"}, - .type = string_type}, - {.identifier = Field::create_field(205), - .name = "city", - .name_mapping = {"old_city"}, - .type = string_type}, - }, - }; - std::vector struct_paths {data_access_path({"struct_column"})}; - auto status = FileScannerV2::TEST_build_nested_children_from_access_paths( - &struct_column, struct_paths, &struct_schema); - ASSERT_TRUE(status.ok()) << status; - ASSERT_EQ(struct_column.children.size(), 2); - EXPECT_EQ(struct_column.children[0].name, "country"); - EXPECT_EQ(struct_column.children[0].get_identifier_field_id(), 101); - EXPECT_EQ(struct_column.children[0].name_mapping, std::vector({"old_country"})); - EXPECT_EQ(struct_column.children[1].name, "city"); - EXPECT_EQ(struct_column.children[1].get_identifier_field_id(), 205); - EXPECT_EQ(struct_column.children[1].name_mapping, std::vector({"old_city"})); - - const auto pruned_struct_type = - std::make_shared(DataTypes {int_type}, Strings {"drop_and_add"}); - format::ColumnDefinition pruned_struct_column { - .identifier = Field::create_field(110), - .name = "a_struct", - .type = pruned_struct_type, - }; - std::vector pruned_struct_paths {data_access_path({"a_struct"})}; - status = FileScannerV2::TEST_build_nested_children_from_access_paths(&pruned_struct_column, - pruned_struct_paths); - ASSERT_TRUE(status.ok()) << status; - ASSERT_EQ(pruned_struct_column.children.size(), 1); - EXPECT_EQ(pruned_struct_column.children[0].name, "drop_and_add"); - EXPECT_EQ(pruned_struct_column.children[0].get_identifier_field_id(), 0); - - const auto element_type = std::make_shared(DataTypes {string_type, int_type}, - Strings {"item", "quantity"}); - const auto array_type = std::make_shared(element_type); - format::ColumnDefinition array_column { - .identifier = Field::create_field(200), - .name = "array_column", - .type = array_type, - }; - format::ColumnDefinition array_schema { - .identifier = Field::create_field(200), - .name = "array_column", - .type = array_type, - .children = - { - {.identifier = Field::create_field(201), - .name = "element", - .type = element_type, - .children = - { - {.identifier = Field::create_field(202), - .name = "item", - .name_mapping = {"old_item"}, - .type = string_type}, - {.identifier = Field::create_field(203), - .name = "quantity", - .type = int_type}, - }}, - }, - }; - std::vector array_paths {data_access_path({"array_column"})}; - status = FileScannerV2::TEST_build_nested_children_from_access_paths(&array_column, array_paths, - &array_schema); - ASSERT_TRUE(status.ok()) << status; - ASSERT_EQ(array_column.children.size(), 1); - const auto& element = array_column.children[0]; - EXPECT_EQ(element.name, "element"); - EXPECT_EQ(element.get_identifier_field_id(), 201); - ASSERT_EQ(element.children.size(), 2); - EXPECT_EQ(element.children[0].get_identifier_field_id(), 202); - EXPECT_EQ(element.children[0].name_mapping, std::vector({"old_item"})); - EXPECT_EQ(element.children[1].get_identifier_field_id(), 203); - - const auto value_type = std::make_shared( - DataTypes {string_type, int_type, string_type}, Strings {"full_name", "age", "gender"}); - const auto map_type = std::make_shared(string_type, value_type); - format::ColumnDefinition map_column { - .identifier = Field::create_field(300), - .name = "new_map_column", - .type = map_type, - }; - format::ColumnDefinition map_schema { - .identifier = Field::create_field(300), - .name = "new_map_column", - .type = map_type, - .children = - { - {.identifier = Field::create_field(301), - .name = "key", - .type = string_type}, - {.identifier = Field::create_field(302), - .name = "value", - .type = value_type, - .children = - { - {.identifier = Field::create_field(303), - .name = "full_name", - .name_mapping = {"name"}, - .type = string_type}, - {.identifier = Field::create_field(304), - .name = "age", - .type = int_type}, - {.identifier = Field::create_field(305), - .name = "gender", - .type = string_type}, - }}, - }, - }; - std::vector map_paths {data_access_path({"new_map_column"})}; - status = FileScannerV2::TEST_build_nested_children_from_access_paths(&map_column, map_paths, - &map_schema); - ASSERT_TRUE(status.ok()) << status; - ASSERT_EQ(map_column.children.size(), 2); - EXPECT_EQ(map_column.children[0].name, "key"); - EXPECT_EQ(map_column.children[0].get_identifier_field_id(), 301); - EXPECT_EQ(map_column.children[1].name, "value"); - EXPECT_EQ(map_column.children[1].get_identifier_field_id(), 302); - ASSERT_EQ(map_column.children[1].children.size(), 3); - EXPECT_EQ(map_column.children[1].children[0].get_identifier_field_id(), 303); - EXPECT_EQ(map_column.children[1].children[0].name_mapping, std::vector({"name"})); - EXPECT_EQ(map_column.children[1].children[1].get_identifier_field_id(), 304); - EXPECT_EQ(map_column.children[1].children[2].get_identifier_field_id(), 305); - EXPECT_EQ(map_column.children[1].children[2].name, "gender"); -} - -TEST(FileScannerV2Test, IcebergRowidSkipsNegativeAccessPath) { - const auto string_type = std::make_shared(); - const auto rowid_type = std::make_shared( - DataTypes {string_type, std::make_shared(), - std::make_shared(), string_type}, - Strings {"file_path", "row_pos", "partition_spec_id", "partition_data_json"}); - format::ColumnDefinition column { - .identifier = Field::create_field(BeConsts::ICEBERG_ROWID_COL), - .name = BeConsts::ICEBERG_ROWID_COL, - .type = rowid_type, - }; - - std::vector access_paths; - access_paths.push_back(data_access_path({"-1"})); - auto status = - FileScannerV2::TEST_build_nested_children_from_access_paths(&column, access_paths); - ASSERT_TRUE(status.ok()) << status; - EXPECT_TRUE(column.children.empty()); -} - } // namespace doris diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index 4e296c999100f5..441434e20bafc3 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -2684,6 +2684,7 @@ TEST(ColumnMapperScanRequestTest, NestedElementAtConjunctUsesMergedScanProjectio ASSERT_TRUE(mapper.create_scan_request({filter}, {}, {projected_table_struct}, &request).ok()); ASSERT_EQ(request.conjuncts.size(), 1); ASSERT_EQ(request.predicate_columns.size(), 1); + EXPECT_EQ(request.predicate_columns[0].column_id(), LocalColumnId(10)); const auto& localized_leaf = request.conjuncts[0]->root()->children()[0]; ASSERT_EQ(localized_leaf->expr_name(), "element_at"); @@ -2693,7 +2694,10 @@ TEST(ColumnMapperScanRequestTest, NestedElementAtConjunctUsesMergedScanProjectio const auto* localized_slot = assert_cast(localized_parent->children()[0].get()); EXPECT_EQ(localized_slot->column_name(), "new_struct_column"); - EXPECT_EQ(localized_slot->column_id(), 10); + // The scan projection keeps the top-level file column id above, while the localized conjunct + // executes on the file-reader Block. The VSlotRef column id is therefore the block position of + // `new_struct_column` in this request, not the file schema id 10. + EXPECT_EQ(localized_slot->column_id(), 0); const auto* localized_parent_type = assert_cast( remove_nullable(localized_parent->data_type()).get()); diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index e7386b3aa9754a..413324ec3a255a 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -3530,19 +3530,20 @@ TEST(TableReaderTest, RuntimeFilterOnConstantPartitionIsNotPreExecuted) { RuntimeState state {TQueryOptions(), TQueryGlobals()}; set_name_identifiers(&projected_columns); TableReader reader; - ASSERT_TRUE(reader.init({ - .projected_columns = projected_columns, - .column_predicates = {}, - .conjuncts = {prepared_conjunct( - &state, runtime_filter_wrapper_expr( - table_int32_greater_than_expr(0, 0, 1)))}, - .format = FileFormat::PARQUET, - .scan_params = nullptr, - .io_ctx = nullptr, - .runtime_state = &state, - .scanner_profile = nullptr, - }) - .ok()); + ASSERT_TRUE( + reader.init({ + .projected_columns = projected_columns, + .column_predicates = {}, + .conjuncts = {prepared_conjunct( + &state, runtime_filter_wrapper_expr( + table_int32_greater_than_expr(0, 0, 1)))}, + .format = FileFormat::PARQUET, + .scan_params = nullptr, + .io_ctx = nullptr, + .runtime_state = &state, + .scanner_profile = nullptr, + }) + .ok()); auto split_options = build_split_options(file_path); split_options.partition_values.emplace("part", Field::create_field(7)); From a15633184077dc136af766a57278ca0d545751a7 Mon Sep 17 00:00:00 2001 From: Socrates Date: Tue, 16 Jun 2026 11:21:04 +0800 Subject: [PATCH 20/28] clang format --- be/test/format_v2/parquet/parquet_reader_test.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index eec9103bca98ca..3f0124345160bf 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -266,9 +266,9 @@ void write_parquet_file(const std::string& file_path, int64_t row_group_size = R void write_int96_timestamp_parquet_file(const std::string& file_path) { auto field = arrow::field("ts_tz", arrow::timestamp(arrow::TimeUnit::MICRO), true); - auto array = build_timestamp_array( - arrow::timestamp(arrow::TimeUnit::MICRO), - {1735660800000000LL, 1735660800123456LL, 1735689600000000LL}); + auto array = + build_timestamp_array(arrow::timestamp(arrow::TimeUnit::MICRO), + {1735660800000000LL, 1735660800123456LL, 1735689600000000LL}); auto table = arrow::Table::Make(arrow::schema({field}), {array}); auto file_result = arrow::io::FileOutputStream::Open(file_path); @@ -281,9 +281,9 @@ void write_int96_timestamp_parquet_file(const std::string& file_path) { writer_builder.compression(::parquet::Compression::UNCOMPRESSED); ::parquet::ArrowWriterProperties::Builder arrow_builder; arrow_builder.enable_force_write_int96_timestamps(); - PARQUET_THROW_NOT_OK( - ::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, ROW_COUNT, - writer_builder.build(), arrow_builder.build())); + PARQUET_THROW_NOT_OK(::parquet::arrow::WriteTable(*table, arrow::default_memory_pool(), out, + ROW_COUNT, writer_builder.build(), + arrow_builder.build())); } void write_int_pair_parquet_file(const std::string& file_path, int64_t row_group_size = ROW_COUNT) { From 95900c7aa27f6798ab1e710ac8e895889970b81f Mon Sep 17 00:00:00 2001 From: Socrates Date: Tue, 16 Jun 2026 12:18:21 +0800 Subject: [PATCH 21/28] [test](regression) Update external timestamp outputs ### What problem does this PR solve? Issue Number: close #xxx Related PR: #63893 Problem Summary: TeamCity external regression build 970191 still had several expected output files using old timestamp values. The new parquet timestamp semantics return the corrected values for the affected external table cases, including Hive, Iceberg, Paimon, and TVF parquet result files. This commit refreshes the corresponding regression expected outputs from the observed CI results and keeps unrelated non-timestamp failures untouched. ### Release note None ### Check List (For Author) - Test: Manual test - Compared TeamCity build 970191 failure details with the updated expected output files. Full regression test was not rerun locally. - Behavior changed: No - Does this need documentation: No --- .../hive/ddl/test_hive_ctas.out | 8 +- .../hive/test_complex_types.out | 4 +- .../hive/test_hive_compress_type.out | 20 +- .../hive/test_hive_get_schema_from_table.out | 20 +- .../hive/test_hive_schema_evolution.out | 8 +- .../write/test_hive_text_write_insert.out | 310 +++++++++--------- .../hive/write/test_hive_write_insert.out | 192 +++++------ .../hive/write/test_hive_write_partitions.out | 48 +-- .../write/test_iceberg_insert_overwrite.out | 64 ++-- .../write/test_iceberg_write_insert.out | 136 ++++---- .../paimon/paimon_timestamp_types.out | 2 +- .../tvf/test_hdfs_parquet_group2.out | 144 ++++---- .../tvf/test_hdfs_parquet_group5.out | Bin 613319 -> 613319 bytes .../tvf/test_hdfs_parquet_group6.out | 20 +- 14 files changed, 488 insertions(+), 488 deletions(-) diff --git a/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out b/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out index cc1100f6bb4dce..cc02121a1963f5 100644 --- a/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out +++ b/regression-test/data/external_table_p0/hive/ddl/test_hive_ctas.out @@ -91,7 +91,7 @@ true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T15:19:34 -- !hive_docker_ctas_types_01 -- -true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 23:19:34.0 +true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 15:19:34.0 -- !ctas_types_02 -- true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default @@ -191,7 +191,7 @@ true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T15:19:34 -- !hive_docker_ctas_types_01 -- -true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 23:19:34.0 +true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 15:19:34.0 -- !ctas_types_02 -- true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default @@ -291,7 +291,7 @@ true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T23:19:34 -- !hive_docker_ctas_types_01 -- -true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 23:19:34.0 +true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 15:19:34.0 -- !ctas_types_02 -- true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default @@ -391,7 +391,7 @@ true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29T23:19:34 -- !hive_docker_ctas_types_01 -- -true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 23:19:34.0 +true 127 32767 2147483647 9223372036854775807 default 22.12345 3.141592653 99999.9999 default default 2023-05-29 2023-05-29 15:19:34.0 -- !ctas_types_02 -- true 127 32767 2147483647 default 22.12345 3.141592653 99999.9999 default diff --git a/regression-test/data/external_table_p0/hive/test_complex_types.out b/regression-test/data/external_table_p0/hive/test_complex_types.out index b1b3870c7447ed..fa9e6a724584aa 100644 --- a/regression-test/data/external_table_p0/hive/test_complex_types.out +++ b/regression-test/data/external_table_p0/hive/test_complex_types.out @@ -30,7 +30,7 @@ [0.9805502029231666, 0.5330291595754054, 0.3002474487337981, 0.4856360175030267, 0.7687106425158624, 0.6993506644925102, 0.2849354808825807, 0.3473417455186141, 0.1350012944304507, 0.9708132103700939, 0.1858304263994345, 0.4886337264552073, 0.3635474169515766, 0.5640845268971175, 0.1374134087807577, 0.7766547647451623, 0.5835323296668318, 0.3654459547110349, 0.5479776709993764, 0.8379932542117192, 0.1566504627835081, 0.03371222042250388, 0.1699781825927229, 0.3579630495075078, 0.02809253185597727, 0.7204247029840027, 0.2760499256423206, 0.676890893219096, 0.03529878656700025, 0.02276578351027858, 0.09794991730625469, 0.5278062884613351, 0.1370404181139102, 0.5440352476580856, 0.7205540629419929, 0.1350852984195943, 0.4160946400431862, 0.2972295454562929, 0.9217426503585693, 0.58103998733474, 0.8845427436377473, 0.1017928267299423, 0.9547186973943892, 0.1680102784708342, 0.0008487745421986714, 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"qiXnUv-e2PsJWm-tLF-KpjE":0.9409681065363688, "Gfx3k9-JvXa7Wd-rI1-1e1E":0.7492793312178226} {"name":"r8HXXQM4XHoI", "age":238221053, "tip":"2023-07-26 07:40:37.694000"} -- !array_max_orc -- 11028 diff --git a/regression-test/data/external_table_p0/hive/test_hive_compress_type.out b/regression-test/data/external_table_p0/hive/test_hive_compress_type.out index 25ac017c0c8faa..8613abebcfda66 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_compress_type.out +++ b/regression-test/data/external_table_p0/hive/test_hive_compress_type.out @@ -266,12 +266,12 @@ 99 632 39 8911195323 74.581276 78.2764804276292 false Random Q WTQCL 2023-12-02 2023-12-05T01:18:58 200.21 -- !q44 -- -17 289 49 13560709243 39.952793 38.245306832599425 true Random Q QEYVY 2023-12-19 2023-12-07T00:35:58 500.19 -17 499 46 11230409207 51.632103 28.811164197154774 false Random V BVLUH 2023-12-13 2023-12-23T17:59:58 1387.62 -17 646 62 11234805830 76.40492 67.46425239009778 true Random N REHZC 2023-12-09 2023-11-28T02:06:58 365.15 -17 698 55 1807368797 20.17171 43.84496606184709 true Random P SHSJV 2023-12-01 2023-11-25T11:56:58 810.95 -17 794 14 8377523030 28.07663 52.3837762020057 false Random E WPMIN 2023-12-03 2023-11-26T04:59:58 239.42 -17 913 32 4647929554 78.91502 70.54487265463735 true Random S WFPNS 2023-11-27 2023-11-26T03:29:58 321.45 +17 289 49 13560709243 39.95279 38.24530683259943 true Random Q QEYVY 2023-12-19 2023-12-06T16:35:58 500.19 +17 499 46 11230409207 51.6321 28.81116419715477 false Random V BVLUH 2023-12-13 2023-12-23T09:59:58 1387.62 +17 646 62 11234805830 76.40492 67.46425239009778 true Random N REHZC 2023-12-09 2023-11-27T18:06:58 365.15 +17 698 55 1807368797 20.17171 43.84496606184709 true Random P SHSJV 2023-12-01 2023-11-25T03:56:58 810.95 +17 794 14 8377523030 28.07663 52.3837762020057 false Random E WPMIN 2023-12-03 2023-11-25T20:59:58 239.42 +17 913 32 4647929554 78.91502 70.54487265463735 true Random S WFPNS 2023-11-27 2023-11-25T19:29:58 321.45 -- !q45 -- 11 1100 55 11000000000 60.5 70.0 true Eleventh K Lambda 2023-10-16 2023-10-16T01:45 1123.45 @@ -286,9 +286,9 @@ 14 968 16 11314514196 62.509666 33.1841427251225 false Random T WDEVJ 2023-11-24 2023-12-06T17:54:58 431.61 15 1500 75 15000000000 80.5 90.0 true Fifteenth O Omicron 2023-10-20 2023-10-20T05:15 1567.89 16 615 20 12294128025 77.37379 20.42772029677839 true Random U JHPOB 2023-11-30 2023-12-16T14:29:58 1105.33 -17 289 49 13560709243 39.952793 38.245306832599425 true Random Q QEYVY 2023-12-19 2023-12-07T00:35:58 500.19 -17 499 46 11230409207 51.632103 28.811164197154774 false Random V BVLUH 2023-12-13 2023-12-23T17:59:58 1387.62 -17 646 62 11234805830 76.40492 67.46425239009778 true Random N REHZC 2023-12-09 2023-11-28T02:06:58 365.15 +17 289 49 13560709243 39.95279 38.24530683259943 true Random Q QEYVY 2023-12-19 2023-12-06T16:35:58 500.19 +17 499 46 11230409207 51.6321 28.81116419715477 false Random V BVLUH 2023-12-13 2023-12-23T09:59:58 1387.62 +17 646 62 11234805830 76.40492 67.46425239009778 true Random N REHZC 2023-12-09 2023-11-27T18:06:58 365.15 18 835 17 14265814864 18.923101 80.53531451138412 true Random V PIKUZ 2023-12-20 2023-12-21T07:39:58 1167.09 22 200 41 12163439252 64.621254 81.68574929661384 true Random U KGVNU 2023-12-20 2023-11-30T14:56:58 1915.47 28 655 21 14580233860 12.503378 48.60220286874443 false Random P DUBQQ 2023-12-12 2023-12-03T20:11:58 922.42 @@ -373,7 +373,7 @@ 15 1500 75 15000000000 80.5 90.0 true Fifteenth O Omicron 2023-10-20 2023-10-20T05:15 1567.89 16 135 22 7901304568 43.944805 85.16901944253635 true Random K NUQEP 2023-11-29 2023-11-25T23:42:58 1440.74 16 615 20 12294128025 77.37379 20.42772029677839 true Random U JHPOB 2023-11-30 2023-12-16T14:29:58 1105.33 -17 499 46 11230409207 51.632103 28.811164197154774 false Random V BVLUH 2023-12-13 2023-12-23T17:59:58 1387.62 +17 499 46 11230409207 51.6321 28.81116419715477 false Random V BVLUH 2023-12-13 2023-12-23T09:59:58 1387.62 18 690 17 1399456103 63.261967 42.964715823771236 true Random R BWSRS 2023-12-13 2023-12-23T08:33:58 1840.02 18 835 17 14265814864 18.923101 80.53531451138412 true Random V PIKUZ 2023-12-20 2023-12-21T07:39:58 1167.09 19 917 66 2340946367 89.035675 22.649362455875274 false Random D HWHMU 2023-11-30 2023-12-10T02:36:58 1960.07 diff --git a/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out b/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out index d7d0964996e35a..a86bb284bb8ce0 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out +++ b/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out @@ -252,16 +252,16 @@ true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2008-12-31T23:09:00.360 2009 1 -- !all_types_timestamp_col_topn_desc -- -false 9 9 9 90 9.9 90.89999999999999 7299 12/31/10 9 2010-12-31T12:09:13.860 2010 12 -true 8 8 8 80 8.8 80.8 7298 12/31/10 8 2010-12-31T12:08:13.780 2010 12 -false 7 7 7 70 7.7 70.7 7297 12/31/10 7 2010-12-31T12:07:13.710 2010 12 -true 6 6 6 60 6.6 60.59999999999999 7296 12/31/10 6 2010-12-31T12:06:13.650 2010 12 -false 5 5 5 50 5.5 50.5 7295 12/31/10 5 2010-12-31T12:05:13.600 2010 12 -true 4 4 4 40 4.4 40.4 7294 12/31/10 4 2010-12-31T12:04:13.560 2010 12 -false 3 3 3 30 3.3 30.3 7293 12/31/10 3 2010-12-31T12:03:13.530 2010 12 -true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T12:02:13.510 2010 12 -false 1 1 1 10 1.1 10.1 7291 12/31/10 1 2010-12-31T12:01:13.500 2010 12 -true 0 0 0 0 0.0 0 7290 12/31/10 0 2010-12-31T12:00:13.500 2010 12 +false 9 9 9 90 9.9 90.89999999999999 7299 12/31/10 9 2010-12-31T04:09:13.860 2010 12 +true 8 8 8 80 8.8 80.8 7298 12/31/10 8 2010-12-31T04:08:13.780 2010 12 +false 7 7 7 70 7.7 70.7 7297 12/31/10 7 2010-12-31T04:07:13.710 2010 12 +true 6 6 6 60 6.6 60.59999999999999 7296 12/31/10 6 2010-12-31T04:06:13.650 2010 12 +false 5 5 5 50 5.5 50.5 7295 12/31/10 5 2010-12-31T04:05:13.600 2010 12 +true 4 4 4 40 4.4 40.4 7294 12/31/10 4 2010-12-31T04:04:13.560 2010 12 +false 3 3 3 30 3.3 30.3 7293 12/31/10 3 2010-12-31T04:03:13.530 2010 12 +true 2 2 2 20 2.2 20.2 7292 12/31/10 2 2010-12-31T04:02:13.510 2010 12 +false 1 1 1 10 1.1 10.1 7291 12/31/10 1 2010-12-31T04:01:13.500 2010 12 +true 0 0 0 0 0.0 0 7290 12/31/10 0 2010-12-31T04:00:13.500 2010 12 -- !all_types_year_topn_asc -- true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 diff --git a/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out b/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out index 6aadf81e4e5cd6..8c199f46c2a825 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out +++ b/regression-test/data/external_table_p0/hive/test_hive_schema_evolution.out @@ -17,11 +17,11 @@ -- !q02 -- 1 kaka \N -2 messi 2023-01-01T21:01:03 +2 messi 2023-01-01T13:01:03 -- !q03 -- \N -2023-01-01T21:01:03 +2023-01-01T13:01:03 -- !q01 -- 1 kaka \N @@ -53,11 +53,11 @@ -- !q02 -- 1 kaka \N -2 messi 2023-01-01T21:01:03 +2 messi 2023-01-01T13:01:03 -- !q03 -- \N -2023-01-01T21:01:03 +2023-01-01T13:01:03 -- !q01 -- 1 kaka \N diff --git a/regression-test/data/external_table_p0/hive/write/test_hive_text_write_insert.out b/regression-test/data/external_table_p0/hive/write/test_hive_text_write_insert.out index 61f907dbc31f02..fc87e3dd90e62a 100644 --- a/regression-test/data/external_table_p0/hive/write/test_hive_text_write_insert.out +++ b/regression-test/data/external_table_p0/hive/write/test_hive_text_write_insert.out @@ -22,28 +22,28 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -54,32 +54,32 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 12288 -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 @@ -104,28 +104,28 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -136,32 +136,32 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 12288 -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 @@ -186,28 +186,28 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -218,32 +218,32 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 12288 -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 @@ -268,28 +268,28 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -300,32 +300,32 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 12288 -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 @@ -350,28 +350,28 @@ false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -1 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -382,30 +382,30 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 12288 -- !q01 -- -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 diff --git a/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out b/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out index 6b5dfe8e283836..31b4be9de4ca74 100644 --- a/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out +++ b/regression-test/data/external_table_p0/hive/write/test_hive_write_insert.out @@ -3,20 +3,20 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240325 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240325 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 @@ -29,23 +29,23 @@ false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -12345678 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -58,29 +58,29 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -88,23 +88,23 @@ false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -12345678 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240322 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240320 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 @@ -227,20 +227,20 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240325 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240325 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 @@ -253,23 +253,23 @@ false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -12345678 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -282,29 +282,29 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T12:00 2024-03-25T12:00:00.123457 2024-03-25T12:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -7 -15 16 -9223372036854775808 -123.45 -123456.789 123456789 -1234.5678 -123456.789012 -123456789.012345678901 str binary_value 2024-03-25 2024-03-25T04:00 2024-03-25T04:00:00.123457 2024-03-25T04:00:00.123457 char_value11111 char_value22222 char_value33333 varchar_value11111 varchar_value22222 varchar_value33333 {"key7":"value1"} {"key7":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {5.3456:2.3456} {5.34567890:2.34567890} {2.34567890:2.34567890} {7.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [9.4567, 4.5678] [6.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240321 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q05 -- \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 @@ -312,23 +312,23 @@ false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -12345678 true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240322 \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240320 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q04 -- \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 diff --git a/regression-test/data/external_table_p0/hive/write/test_hive_write_partitions.out b/regression-test/data/external_table_p0/hive/write/test_hive_write_partitions.out index 4d68899e605eb7..987a6f214a2d40 100644 --- a/regression-test/data/external_table_p0/hive/write/test_hive_write_partitions.out +++ b/regression-test/data/external_table_p0/hive/write/test_hive_write_partitions.out @@ -1,21 +1,21 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- 1 false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 @@ -193,22 +193,22 @@ true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5 7 test7 2025-05-01 project1 -- !q01 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-21 2024-03-20T20:00 2024-03-20T20:00:00.123456 2024-03-20T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value binary_value 2024-03-22 2024-03-21T20:00 2024-03-21T20:00:00.123456 2024-03-21T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"x":"y"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] ["varchar1", "varchar2"] ["char1", "char2"] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 127 32767 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value binary_value 2024-03-20 2024-03-19T20:00 2024-03-19T20:00:00.123456 2024-03-19T20:00:00.123456 char_value1 char_value2 char_value3 varchar_value1 varchar_value2 varchar_value3 {"key1":"value1"} {"key1":"value1"} {"a":"b"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] ["varchar1", "varchar2"] ["char1", "char2"] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- 1 false -128 -32768 -2147483648 -9223372036854775808 -123.45 -123456.789 diff --git a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out index 945d3f2dbdf3c3..918f77149288c9 100644 --- a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out +++ b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out @@ -1,19 +1,19 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -21,22 +21,22 @@ true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456 \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 @@ -88,20 +88,20 @@ true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456 \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240320 -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N @@ -109,22 +109,22 @@ true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456 \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 diff --git a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out index 006596a760f81b..3853c797466201 100644 --- a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out +++ b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out @@ -1,61 +1,61 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240322 \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240320 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 @@ -116,58 +116,58 @@ false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {2:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [3.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N -123.45 \N \N \N \N \N \N \N \N \N \N \N {3:20} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [8.4567, 4.5678] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123456 2024-03-21T12:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123456 2024-03-22T12:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123456 2024-03-21T04:00:00.123456 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123456 2024-03-22T04:00:00.123456 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123456 2024-03-20T04:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N 20240321 @@ -248,18 +248,18 @@ false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123456 2024-03-20T12:00:00.123456 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q03 -- \N \N \N 123.45 \N \N \N \N \N \N \N \N \N \N \N {1:10} \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N \N [1.2345, 2.3456] \N \N \N \N \N \N \N \N \N \N [null, "value1", "value2"] \N \N \N \N -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T12:00 2024-03-21T12:00:00.123457 2024-03-21T12:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 -false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T12:00 2024-03-22T12:00:00.123457 2024-03-22T12:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 +false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-22 2024-03-22T04:00 2024-03-22T04:00:00.123457 2024-03-22T04:00:00.123457 {"key1":"value1"} {3:20} {3:200000000000} {3.2:20.2} {3.2:20.2} {0:1} {3.2:2.2} {3.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {3.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [8.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value11", "value2", null] [null, null, null] 20240322 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 diff --git a/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out b/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out index a749f9a72f3227..b24673de44bdf0 100644 --- a/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out +++ b/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out @@ -51,7 +51,7 @@ 1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T02:04:05.100 2024-01-02T02:04:05.120 2024-01-02T02:04:05.123 2024-01-02T02:04:05.123400 2024-01-02T02:04:05.123450 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 -- !c2 -- -1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 +1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T02:04:05.100 2024-01-02T02:04:05.120 2024-01-02T02:04:05.123 2024-01-02T02:04:05.123400 2024-01-02T02:04:05.123450 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 -- !ltz_ntz_simple2 -- 1 {"2024-01-01 10:12:34.123456":"2024-01-02 10:12:34.123456", "2024-01-03 10:12:34.123456":"2024-01-04 10:12:34.123456"} {"2024-01-03 02:12:34.123456":"2024-01-04 02:12:34.123456", "2024-01-01 02:12:34.123456":"2024-01-02 02:12:34.123456"} ["2024-01-01 10:12:34.123456", "2024-01-02 10:12:34.123456", "2024-01-03 10:12:34.123456"] ["2024-01-01 02:12:34.123456", "2024-01-02 02:12:34.123456", "2024-01-03 02:12:34.123456"] {"crow1":"2024-01-01 10:12:34.123456", "crow2":"2024-01-02 02:12:34.123456"} diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group2.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group2.out index 16b89ac45d63ca..139f373faffdf3 100644 --- a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group2.out +++ b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group2.out @@ -24,14 +24,14 @@ apple_banana_mango81 apple_banana_mango9 -- !test_2 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_3 -- [{"one":"0 - 0 - 1", "two":"0 - 0 - 2", "three":"0 - 0 - 3"}, {"one":"0 - 1 - 1", "two":"0 - 1 - 2", "three":"0 - 1 - 3"}] @@ -39,14 +39,14 @@ apple_banana_mango9 [{"one":"2 - 0 - 1", "two":"2 - 0 - 2", "three":"2 - 0 - 3"}, {"one":"2 - 1 - 1", "two":"2 - 1 - 2", "three":"2 - 1 - 3"}] -- !test_4 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_5 -- ["good", "bye"] @@ -92,14 +92,14 @@ apple_banana_mango9 1981-01-10T00:00 20.0 -- !test_13 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_14 -- [{"one":"First inner", "two":null, "three":null}, {"one":null, "two":"Second inner", "three":null}, {"one":null, "two":null, "three":"Third inner"}] @@ -122,14 +122,14 @@ apple_banana_mango9 3 Cecilia 2022-11-16T02:32:09 -- !test_17 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_18 -- 0.00 @@ -151,14 +151,14 @@ apple_banana_mango9 2 -- !test_20 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_21 -- 1001-01-07 1001-01-07 @@ -181,14 +181,14 @@ apple_banana_mango9 1001-01-07T17:07:47.171 1001-01-14T17:07:47.171 -- !test_23 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_24 -- false 1 2 3 10 1.2 val_1 val_1 HEARTS false 1 2 3 10 1.2 val_1 val_1 HEARTS ["arr_1", "arr_2", "arr_3"] [1] {1:"val_1", 2:"val_2", 3:"val_3"} {1:[{"nestedintscolumn":[1, 2, 3], "nestedstringcolumn":"val_1"}, {"nestedintscolumn":[2, 3, 4], "nestedstringcolumn":"val_2"}, {"nestedintscolumn":[3, 4, 5], "nestedstringcolumn":"val_3"}], 2:[{"nestedintscolumn":[1, 2, 3], "nestedstringcolumn":"val_1"}, {"nestedintscolumn":[2, 3, 4], "nestedstringcolumn":"val_2"}, {"nestedintscolumn":[3, 4, 5], "nestedstringcolumn":"val_3"}], 3:[{"nestedintscolumn":[1, 2, 3], "nestedstringcolumn":"val_1"}, {"nestedintscolumn":[2, 3, 4], "nestedstringcolumn":"val_2"}, {"nestedintscolumn":[3, 4, 5], "nestedstringcolumn":"val_3"}]} @@ -238,14 +238,14 @@ true 8 9 10 80 8.2 val_8 val_8 SPADES true 8 9 10 80 8.2 val_8 val_8 SPADES ["ar 9.00 -- !test_29 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_30 -- \N @@ -268,14 +268,14 @@ true 8 9 10 80 8.2 val_8 val_8 SPADES true 8 9 10 80 8.2 val_8 val_8 SPADES ["ar 1970-01-01T08:00:00.010 -- !test_33 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 -- !test_34 -- 1001-01-07 1001-01-07 @@ -298,12 +298,12 @@ true 8 9 10 80 8.2 val_8 val_8 SPADES true 8 9 10 80 8.2 val_8 val_8 SPADES ["ar 1001-01-07T17:07:47.171 1001-01-14T17:07:47.171 -- !test_36 -- -1001-01-07T17:07:47.172032 1001-01-07T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-08T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-09T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-10T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-11T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-12T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-13T17:07:47.172032 -1001-01-07T17:07:47.172032 1001-01-14T17:07:47.172032 +1001-01-07T17:07:46.123456 1001-01-07T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-08T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-09T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-10T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-11T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-12T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-13T17:07:46.123456 +1001-01-07T17:07:46.123456 1001-01-14T17:07:46.123456 diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group5.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group5.out index 5c83b796755407b0470a3ebfb5e52f11aa321b00..a070487d11e8152bd94c627611492f5f68a67400 100644 GIT binary patch delta 121 zcmX@UUG?~O)eZYOC%h&Am-RE J$j7 Date: Tue, 16 Jun 2026 17:35:30 +0800 Subject: [PATCH 22/28] fix by_index column mapper with struct type (#64567) struct type should be use name mode with nested type --- be/src/format_v2/column_mapper.cpp | 12 +- be/test/format_v2/column_mapper_test.cpp | 164 +++++++++++++++++++++++ 2 files changed, 173 insertions(+), 3 deletions(-) diff --git a/be/src/format_v2/column_mapper.cpp b/be/src/format_v2/column_mapper.cpp index 017ef35096c3aa..a040c5df454280 100644 --- a/be/src/format_v2/column_mapper.cpp +++ b/be/src/format_v2/column_mapper.cpp @@ -1026,7 +1026,13 @@ static const ColumnDefinition* find_file_child_for_mapping(const ColumnDefinitio } return nullptr; default: - if (const auto* file_child = matcher_for_mode(mode).find(table_child, file_parent.children); + // Hive BY_INDEX is a top-level column matching rule. Once a complex root is selected by + // file position, nested struct children follow Hive reader's historical name matching + // semantics; their integer identifiers can be field ids, not file positions. + const auto nested_mode = + mode == TableColumnMappingMode::BY_INDEX ? TableColumnMappingMode::BY_NAME : mode; + if (const auto* file_child = + matcher_for_mode(nested_mode).find(table_child, file_parent.children); file_child != nullptr) { return file_child; } @@ -1047,8 +1053,8 @@ static const ColumnDefinition* find_file_child_for_mapping(const ColumnDefinitio // Some callers only carry the full complex DataType for a projected table column, without // expanded nested ColumnDefinitions. In that case we can still preserve full materialization // by walking table/file struct fields by ordinal. This is a fallback only: explicit - // ColumnDefinition children keep using field-id/name/position mapping, which is required - // for precise Iceberg evolution. + // ColumnDefinition children keep using the requested table-format matching rule, which is + // required for precise schema evolution. if (allow_ordinal_fallback && table_child_idx < file_parent.children.size()) { return &file_parent.children[table_child_idx]; } diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index 441434e20bafc3..a882b09311c80d 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -1334,6 +1334,170 @@ TEST(ColumnMapperCreateMappingTest, ByIndexSupportsSparseProjection) { expect_mapping(mapper.mappings()[1], 1, "score", 4, "_col4", int_type, int_type); } +TEST(ColumnMapperCreateMappingTest, + ByIndexMatchesNestedStructChildrenByNameEvenWhenChildrenHaveFieldIds) { + const auto int_type = i32(); + const auto string_type = str(); + // Hive positional mapping only applies to top-level columns. FE/history schema metadata can + // still put field-id style integer identifiers on nested struct children. Those nested + // identifiers must not be interpreted as file positions. + auto table_root = struct_col("profile", 1, + { + field_id_col("id", 100, int_type), + field_id_col("name", 101, string_type), + }); + // Reverse the file child order so a wrong positional match either misses the child or reads + // the wrong physical child. The expected mapping below proves the children are matched by name. + auto file_root = struct_name_col("_col1", + { + name_col("name", string_type, 0), + name_col("id", int_type, 1), + }, + 1); + const std::vector table_schema = {table_root}; + const std::vector file_schema = { + field_id_col("_col0", 1000, string_type, 0), + file_root, + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + const auto status = mapper.create_mapping(table_schema, {}, file_schema); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "profile", 1, "_col1", file_root.type, + table_root.type); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); + expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "id", 1, "id", int_type, int_type); + expect_mapping(mapper.mappings()[0].child_mappings[1], 0, "name", 0, "name", string_type, + string_type); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexNestedStructDoesNotUseChildOrdinalIdentifier) { + const auto int_type = i32(); + const auto string_type = str(); + // This is the dangerous variant of the previous case: the nested integer identifiers happen + // to be valid child ordinals. BY_INDEX must still ignore them below the top-level root. + auto table_root = struct_col("profile", 1, + { + field_id_col("id", 0, int_type), + field_id_col("name", 1, string_type), + }); + // If the implementation uses child ordinal matching, id/name will be swapped here. + auto file_root = struct_name_col("_col1", + { + name_col("name", string_type, 0), + name_col("id", int_type, 1), + }, + 1); + const std::vector table_schema = {table_root}; + const std::vector file_schema = { + field_id_col("_col0", 1000, string_type, 0), + file_root, + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + const auto status = mapper.create_mapping(table_schema, {}, file_schema); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "profile", 1, "_col1", file_root.type, + table_root.type); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); + expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "id", 1, "id", int_type, int_type); + expect_mapping(mapper.mappings()[0].child_mappings[1], 0, "name", 0, "name", string_type, + string_type); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexArrayElementStructChildrenMatchByName) { + const auto int_type = i32(); + const auto string_type = str(); + // The top-level ARRAY column is selected by file position. After that, ARRAY has a single + // structural child, and the element STRUCT should use Hive's nested-by-name behavior. + auto table_element = struct_col("element", 10, + { + field_id_col("id", 100, int_type), + field_id_col("name", 101, string_type), + }); + auto table_root = array_col("profiles", 1, table_element); + // Reverse the element struct children to distinguish name matching from position matching. + auto file_element = struct_name_col("element", + { + name_col("name", string_type, 0), + name_col("id", int_type, 1), + }, + 0); + auto file_root = array_col("_col1", 1001, file_element, 1); + const std::vector table_schema = {table_root}; + const std::vector file_schema = { + field_id_col("_col0", 1000, string_type, 0), + file_root, + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + const auto status = mapper.create_mapping(table_schema, {}, file_schema); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "profiles", 1, "_col1", file_root.type, + table_root.type); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 1); + const auto& element_mapping = mapper.mappings()[0].child_mappings[0]; + expect_mapping(element_mapping, 0, "element", 0, "element", file_element.type, + table_element.type); + ASSERT_EQ(element_mapping.child_mappings.size(), 2); + expect_mapping(element_mapping.child_mappings[0], 0, "id", 1, "id", int_type, int_type); + expect_mapping(element_mapping.child_mappings[1], 0, "name", 0, "name", string_type, + string_type); +} + +TEST(ColumnMapperCreateMappingTest, ByIndexMapValueStructChildrenMatchByName) { + const auto int_type = i32(); + const auto string_type = str(); + const auto key_type = str(); + // MAP key/value are structural children, so BY_INDEX should not reinterpret their nested + // integer identifiers as arbitrary positions. The value STRUCT then follows name matching. + auto table_key = field_id_col("key", 10, key_type); + auto table_value = struct_col("value", 11, + { + field_id_col("id", 100, int_type), + field_id_col("name", 101, string_type), + }); + auto table_root = map_col("profiles", 1, {table_key, table_value}, key_type, + table_value.type); + auto file_key = name_col("key", key_type, 0); + // Reverse value struct children. A positional nested match would produce name/id swapped. + auto file_value = struct_name_col("value", + { + name_col("name", string_type, 0), + name_col("id", int_type, 1), + }, + 1); + auto file_root = map_col("_col1", 1001, {file_key, file_value}, key_type, file_value.type, 1); + const std::vector table_schema = {table_root}; + const std::vector file_schema = { + field_id_col("_col0", 1000, string_type, 0), + file_root, + }; + + TableColumnMapper mapper({.mode = TableColumnMappingMode::BY_INDEX}); + const auto status = mapper.create_mapping(table_schema, {}, file_schema); + ASSERT_TRUE(status.ok()) << status.to_string(); + + ASSERT_EQ(mapper.mappings().size(), 1); + expect_mapping(mapper.mappings()[0], 0, "profiles", 1, "_col1", file_root.type, + table_root.type); + ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); + expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "key", 0, "key", key_type, + key_type); + const auto& value_mapping = mapper.mappings()[0].child_mappings[1]; + expect_mapping(value_mapping, 0, "value", 1, "value", file_value.type, table_value.type); + ASSERT_EQ(value_mapping.child_mappings.size(), 2); + expect_mapping(value_mapping.child_mappings[0], 0, "id", 1, "id", int_type, int_type); + expect_mapping(value_mapping.child_mappings[1], 0, "name", 0, "name", string_type, + string_type); +} + TEST(ColumnMapperCreateMappingTest, ByIndexPartitionColumnsTakeConstantAndDoNotConsumeFilePosition) { const auto int_type = i32(); From f21fc29f38e933c019becd7fb4410820ef99f575 Mon Sep 17 00:00:00 2001 From: Socrates Date: Tue, 16 Jun 2026 15:50:43 +0800 Subject: [PATCH 23/28] [refactor](be) Unify new parquet profile definitions ### What problem does this PR solve? Issue Number: close #xxx Related PR: #xxx Problem Summary: New parquet profile definitions and wiring were split across ParquetReader, ParquetScan, and column reader headers. This made ParquetReader own counter initialization, pruning counter updates, and scheduler sub-profile assembly directly even though parquet_profile.h already existed for profile-related types. This change centralizes the new parquet RuntimeProfile counter ownership in parquet_profile.h/.cpp and keeps ParquetReader responsible only for invoking the profile helper methods. ### Release note None ### Check List (For Author) - Test: Manual test - Ran build-support/clang-format.sh for touched files. - Ran git diff --check. - Tried ./run-be-ut.sh --run '--filter=NewParquetReaderTest.*', but local CMake compiler detection failed before building Doris because /opt/homebrew/opt/llvm@16/bin/clang++ could not link a simple program: ld: library 'c++' not found. - Behavior changed: No - Does this need documentation: No --- be/src/format_v2/parquet/parquet_profile.cpp | 191 ++++++++++++++++++ be/src/format_v2/parquet/parquet_profile.h | 90 +++++++++ be/src/format_v2/parquet/parquet_reader.cpp | 168 +-------------- be/src/format_v2/parquet/parquet_reader.h | 61 +----- be/src/format_v2/parquet/parquet_scan.h | 13 -- .../format_v2/parquet/reader/column_reader.h | 5 - 6 files changed, 286 insertions(+), 242 deletions(-) create mode 100644 be/src/format_v2/parquet/parquet_profile.cpp diff --git a/be/src/format_v2/parquet/parquet_profile.cpp b/be/src/format_v2/parquet/parquet_profile.cpp new file mode 100644 index 00000000000000..19b906bc623e3e --- /dev/null +++ b/be/src/format_v2/parquet/parquet_profile.cpp @@ -0,0 +1,191 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "format_v2/parquet/parquet_profile.h" + +#include "format_v2/parquet/parquet_statistics.h" + +namespace doris::parquet { + +void ParquetProfile::init(RuntimeProfile* profile) { + if (profile == nullptr) { + return; + } + + static const char* parquet_profile = "ParquetReader"; + ADD_TIMER_WITH_LEVEL(profile, parquet_profile, 1); + + filtered_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "RowGroupsFiltered", TUnit::UNIT, + parquet_profile, 1); + filtered_row_groups_by_min_max = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "RowGroupsFilteredByMinMax", TUnit::UNIT, parquet_profile, 1); + filtered_row_groups_by_dictionary = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "RowGroupsFilteredByDictionary", TUnit::UNIT, parquet_profile, 1); + filtered_row_groups_by_bloom_filter = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "RowGroupsFilteredByBloomFilter", TUnit::UNIT, parquet_profile, 1); + to_read_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "RowGroupsReadNum", TUnit::UNIT, + parquet_profile, 1); + total_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "RowGroupsTotalNum", TUnit::UNIT, + parquet_profile, 1); + selected_row_ranges = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "SelectedRowRanges", TUnit::UNIT, + parquet_profile, 1); + filtered_group_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "FilteredRowsByGroup", TUnit::UNIT, + parquet_profile, 1); + filtered_page_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "FilteredRowsByPage", TUnit::UNIT, + parquet_profile, 1); + pages_skipped_by_data_page_filter = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "PagesSkippedByDataPageFilter", TUnit::UNIT, parquet_profile, 1); + data_page_filter_skip_bytes = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "DataPageFilterSkipBytes", + TUnit::BYTES, parquet_profile, 1); + selected_rows = + ADD_CHILD_COUNTER_WITH_LEVEL(profile, "SelectedRows", TUnit::UNIT, parquet_profile, 1); + rows_filtered_by_conjunct = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "RowsFilteredByConjunct", + TUnit::UNIT, parquet_profile, 1); + total_batches = + ADD_CHILD_COUNTER_WITH_LEVEL(profile, "TotalBatches", TUnit::UNIT, parquet_profile, 1); + empty_selection_batches = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "EmptySelectionBatches", + TUnit::UNIT, parquet_profile, 1); + range_gap_skipped_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "RangeGapSkippedRows", + TUnit::UNIT, parquet_profile, 1); + reader_read_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "ReaderReadRows", TUnit::UNIT, + parquet_profile, 1); + reader_skip_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "ReaderSkipRows", TUnit::UNIT, + parquet_profile, 1); + reader_select_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "ReaderSelectRows", TUnit::UNIT, + parquet_profile, 1); + arrow_read_records_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "ArrowReadRecordsTime", parquet_profile, 1); + materialization_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "MaterializationTime", parquet_profile, 1); + lazy_read_filtered_rows = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "FilteredRowsByLazyRead", + TUnit::UNIT, parquet_profile, 1); + filtered_bytes = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "FilteredBytes", TUnit::BYTES, + parquet_profile, 1); + raw_rows_read = + ADD_CHILD_COUNTER_WITH_LEVEL(profile, "RawRowsRead", TUnit::UNIT, parquet_profile, 1); + column_read_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "ColumnReadTime", parquet_profile, 1); + parse_meta_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "ParseMetaTime", parquet_profile, 1); + parse_footer_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "ParseFooterTime", parquet_profile, 1); + file_reader_create_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "FileReaderCreateTime", parquet_profile, 1); + open_file_num = + ADD_CHILD_COUNTER_WITH_LEVEL(profile, "FileNum", TUnit::UNIT, parquet_profile, 1); + page_index_read_calls = ADD_COUNTER_WITH_LEVEL(profile, "PageIndexReadCalls", TUnit::UNIT, 1); + page_index_filter_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "PageIndexFilterTime", parquet_profile, 1); + read_page_index_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "PageIndexReadTime", parquet_profile, 1); + parse_page_index_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "PageIndexParseTime", parquet_profile, 1); + row_group_filter_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "RowGroupFilterTime", parquet_profile, 1); + file_footer_read_calls = ADD_COUNTER_WITH_LEVEL(profile, "FileFooterReadCalls", TUnit::UNIT, 1); + file_footer_hit_cache = ADD_COUNTER_WITH_LEVEL(profile, "FileFooterHitCache", TUnit::UNIT, 1); + decompress_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "DecompressTime", parquet_profile, 1); + decompress_cnt = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "DecompressCount", TUnit::UNIT, + parquet_profile, 1); + page_read_counter = + ADD_CHILD_COUNTER_WITH_LEVEL(profile, "PageReadCount", TUnit::UNIT, parquet_profile, 1); + page_cache_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "PageCacheWriteCount", + TUnit::UNIT, parquet_profile, 1); + page_cache_compressed_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "PageCacheCompressedWriteCount", TUnit::UNIT, parquet_profile, 1); + page_cache_decompressed_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "PageCacheDecompressedWriteCount", TUnit::UNIT, parquet_profile, 1); + page_cache_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "PageCacheHitCount", TUnit::UNIT, + parquet_profile, 1); + page_cache_missing_counter = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "PageCacheMissingCount", + TUnit::UNIT, parquet_profile, 1); + page_cache_compressed_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "PageCacheCompressedHitCount", TUnit::UNIT, parquet_profile, 1); + page_cache_decompressed_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( + profile, "PageCacheDecompressedHitCount", TUnit::UNIT, parquet_profile, 1); + decode_header_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "PageHeaderDecodeTime", parquet_profile, 1); + read_page_header_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "PageHeaderReadTime", parquet_profile, 1); + decode_value_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "DecodeValueTime", parquet_profile, 1); + decode_dict_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "DecodeDictTime", parquet_profile, 1); + decode_level_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "DecodeLevelTime", parquet_profile, 1); + decode_null_map_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "DecodeNullMapTime", parquet_profile, 1); + skip_page_header_num = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "SkipPageHeaderNum", TUnit::UNIT, + parquet_profile, 1); + parse_page_header_num = ADD_CHILD_COUNTER_WITH_LEVEL(profile, "ParsePageHeaderNum", TUnit::UNIT, + parquet_profile, 1); + predicate_filter_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "PredicateFilterTime", parquet_profile, 1); + dict_filter_rewrite_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "DictFilterRewriteTime", parquet_profile, 1); + convert_time = ADD_CHILD_TIMER_WITH_LEVEL(profile, "ConvertTime", parquet_profile, 1); + bloom_filter_read_time = + ADD_CHILD_TIMER_WITH_LEVEL(profile, "BloomFilterReadTime", parquet_profile, 1); +} + +void ParquetProfile::update_pruning_stats(const ParquetPruningStats& pruning_stats) const { + COUNTER_UPDATE(filtered_row_groups, + pruning_stats.total_row_groups - pruning_stats.selected_row_groups); + COUNTER_UPDATE(filtered_row_groups_by_min_max, pruning_stats.filtered_row_groups_by_statistics); + COUNTER_UPDATE(filtered_row_groups_by_dictionary, + pruning_stats.filtered_row_groups_by_dictionary); + COUNTER_UPDATE(filtered_row_groups_by_bloom_filter, + pruning_stats.filtered_row_groups_by_bloom_filter); + COUNTER_UPDATE(to_read_row_groups, pruning_stats.selected_row_groups); + COUNTER_UPDATE(total_row_groups, pruning_stats.total_row_groups); + COUNTER_UPDATE(selected_row_ranges, pruning_stats.selected_row_ranges); + COUNTER_UPDATE(filtered_group_rows, pruning_stats.filtered_group_rows); + COUNTER_UPDATE(filtered_page_rows, pruning_stats.filtered_page_rows); + COUNTER_UPDATE(page_index_read_calls, pruning_stats.page_index_read_calls); + COUNTER_UPDATE(bloom_filter_read_time, pruning_stats.bloom_filter_read_time); + COUNTER_UPDATE(row_group_filter_time, pruning_stats.row_group_filter_time); + COUNTER_UPDATE(page_index_filter_time, pruning_stats.page_index_filter_time); + COUNTER_UPDATE(read_page_index_time, pruning_stats.read_page_index_time); +} + +ParquetPageSkipProfile ParquetProfile::page_skip_profile() const { + return { + .skipped_pages = pages_skipped_by_data_page_filter, + .skipped_bytes = data_page_filter_skip_bytes, + }; +} + +ParquetColumnReaderProfile ParquetProfile::column_reader_profile() const { + return { + .reader_read_rows = reader_read_rows, + .reader_skip_rows = reader_skip_rows, + .reader_select_rows = reader_select_rows, + .arrow_read_records_time = arrow_read_records_time, + .materialization_time = materialization_time, + }; +} + +ParquetScanProfile ParquetProfile::scan_profile() const { + return { + .raw_rows_read = raw_rows_read, + .selected_rows = selected_rows, + .rows_filtered_by_conjunct = rows_filtered_by_conjunct, + .lazy_read_filtered_rows = lazy_read_filtered_rows, + .total_batches = total_batches, + .empty_selection_batches = empty_selection_batches, + .range_gap_skipped_rows = range_gap_skipped_rows, + .column_read_time = column_read_time, + .predicate_filter_time = predicate_filter_time, + .column_reader_profile = column_reader_profile(), + }; +} + +} // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_profile.h b/be/src/format_v2/parquet/parquet_profile.h index 094769d11c14cb..1ef44be8e5a85b 100644 --- a/be/src/format_v2/parquet/parquet_profile.h +++ b/be/src/format_v2/parquet/parquet_profile.h @@ -21,6 +21,13 @@ namespace doris::parquet { +struct ParquetPruningStats; + +struct ParquetPageSkipProfile { + RuntimeProfile::Counter* skipped_pages = nullptr; + RuntimeProfile::Counter* skipped_bytes = nullptr; +}; + struct ParquetColumnReaderProfile { RuntimeProfile::Counter* reader_read_rows = nullptr; RuntimeProfile::Counter* reader_skip_rows = nullptr; @@ -29,4 +36,87 @@ struct ParquetColumnReaderProfile { RuntimeProfile::Counter* materialization_time = nullptr; }; +struct ParquetScanProfile { + RuntimeProfile::Counter* raw_rows_read = nullptr; + RuntimeProfile::Counter* selected_rows = nullptr; + RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; + RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; + RuntimeProfile::Counter* total_batches = nullptr; + RuntimeProfile::Counter* empty_selection_batches = nullptr; + RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; + RuntimeProfile::Counter* column_read_time = nullptr; + RuntimeProfile::Counter* predicate_filter_time = nullptr; + ParquetColumnReaderProfile column_reader_profile; +}; + +// Owns every RuntimeProfile counter exposed by the new Parquet reader and builds the +// narrower profile views used by scan scheduling, page skipping, and column readers. +struct ParquetProfile { + void init(RuntimeProfile* profile); + void update_pruning_stats(const ParquetPruningStats& pruning_stats) const; + + ParquetPageSkipProfile page_skip_profile() const; + ParquetColumnReaderProfile column_reader_profile() const; + ParquetScanProfile scan_profile() const; + + RuntimeProfile::Counter* filtered_row_groups = nullptr; + RuntimeProfile::Counter* filtered_row_groups_by_min_max = nullptr; + RuntimeProfile::Counter* filtered_row_groups_by_dictionary = nullptr; + RuntimeProfile::Counter* filtered_row_groups_by_bloom_filter = nullptr; + RuntimeProfile::Counter* to_read_row_groups = nullptr; + RuntimeProfile::Counter* total_row_groups = nullptr; + RuntimeProfile::Counter* selected_row_ranges = nullptr; + RuntimeProfile::Counter* filtered_group_rows = nullptr; + RuntimeProfile::Counter* filtered_page_rows = nullptr; + RuntimeProfile::Counter* pages_skipped_by_data_page_filter = nullptr; + RuntimeProfile::Counter* data_page_filter_skip_bytes = nullptr; + RuntimeProfile::Counter* selected_rows = nullptr; + RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; + RuntimeProfile::Counter* total_batches = nullptr; + RuntimeProfile::Counter* empty_selection_batches = nullptr; + RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; + RuntimeProfile::Counter* reader_read_rows = nullptr; + RuntimeProfile::Counter* reader_skip_rows = nullptr; + RuntimeProfile::Counter* reader_select_rows = nullptr; + RuntimeProfile::Counter* arrow_read_records_time = nullptr; + RuntimeProfile::Counter* materialization_time = nullptr; + RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; + RuntimeProfile::Counter* filtered_bytes = nullptr; + RuntimeProfile::Counter* raw_rows_read = nullptr; + RuntimeProfile::Counter* column_read_time = nullptr; + RuntimeProfile::Counter* parse_meta_time = nullptr; + RuntimeProfile::Counter* parse_footer_time = nullptr; + RuntimeProfile::Counter* file_reader_create_time = nullptr; + RuntimeProfile::Counter* open_file_num = nullptr; + RuntimeProfile::Counter* row_group_filter_time = nullptr; + RuntimeProfile::Counter* page_index_read_calls = nullptr; + RuntimeProfile::Counter* page_index_filter_time = nullptr; + RuntimeProfile::Counter* read_page_index_time = nullptr; + RuntimeProfile::Counter* parse_page_index_time = nullptr; + RuntimeProfile::Counter* file_footer_read_calls = nullptr; + RuntimeProfile::Counter* file_footer_hit_cache = nullptr; + RuntimeProfile::Counter* decompress_time = nullptr; + RuntimeProfile::Counter* decompress_cnt = nullptr; + RuntimeProfile::Counter* page_read_counter = nullptr; + RuntimeProfile::Counter* page_cache_write_counter = nullptr; + RuntimeProfile::Counter* page_cache_compressed_write_counter = nullptr; + RuntimeProfile::Counter* page_cache_decompressed_write_counter = nullptr; + RuntimeProfile::Counter* page_cache_hit_counter = nullptr; + RuntimeProfile::Counter* page_cache_missing_counter = nullptr; + RuntimeProfile::Counter* page_cache_compressed_hit_counter = nullptr; + RuntimeProfile::Counter* page_cache_decompressed_hit_counter = nullptr; + RuntimeProfile::Counter* decode_header_time = nullptr; + RuntimeProfile::Counter* read_page_header_time = nullptr; + RuntimeProfile::Counter* decode_value_time = nullptr; + RuntimeProfile::Counter* decode_dict_time = nullptr; + RuntimeProfile::Counter* decode_level_time = nullptr; + RuntimeProfile::Counter* decode_null_map_time = nullptr; + RuntimeProfile::Counter* skip_page_header_num = nullptr; + RuntimeProfile::Counter* parse_page_header_num = nullptr; + RuntimeProfile::Counter* predicate_filter_time = nullptr; + RuntimeProfile::Counter* dict_filter_rewrite_time = nullptr; + RuntimeProfile::Counter* convert_time = nullptr; + RuntimeProfile::Counter* bloom_filter_read_time = nullptr; +}; + } // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index 5b3f8d8f816864..6b3a2ebdaf93c3 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -290,52 +290,12 @@ Status ParquetReader::open(std::shared_ptr request) { _state->file_schema, *request_snapshot, scan_range, _state->enable_bloom_filter, &row_group_plan, _state->timezone)); if (_profile != nullptr) { - const auto& pruning_stats = row_group_plan.pruning_stats; - COUNTER_UPDATE(_parquet_profile.filtered_row_groups, - pruning_stats.total_row_groups - pruning_stats.selected_row_groups); - COUNTER_UPDATE(_parquet_profile.filtered_row_groups_by_min_max, - pruning_stats.filtered_row_groups_by_statistics); - COUNTER_UPDATE(_parquet_profile.filtered_row_groups_by_dictionary, - pruning_stats.filtered_row_groups_by_dictionary); - COUNTER_UPDATE(_parquet_profile.filtered_row_groups_by_bloom_filter, - pruning_stats.filtered_row_groups_by_bloom_filter); - COUNTER_UPDATE(_parquet_profile.to_read_row_groups, pruning_stats.selected_row_groups); - COUNTER_UPDATE(_parquet_profile.total_row_groups, pruning_stats.total_row_groups); - COUNTER_UPDATE(_parquet_profile.selected_row_ranges, pruning_stats.selected_row_ranges); - COUNTER_UPDATE(_parquet_profile.filtered_group_rows, pruning_stats.filtered_group_rows); - COUNTER_UPDATE(_parquet_profile.filtered_page_rows, pruning_stats.filtered_page_rows); - COUNTER_UPDATE(_parquet_profile.page_index_read_calls, pruning_stats.page_index_read_calls); - COUNTER_UPDATE(_parquet_profile.bloom_filter_read_time, - pruning_stats.bloom_filter_read_time); - COUNTER_UPDATE(_parquet_profile.row_group_filter_time, pruning_stats.row_group_filter_time); - COUNTER_UPDATE(_parquet_profile.page_index_filter_time, - pruning_stats.page_index_filter_time); - COUNTER_UPDATE(_parquet_profile.read_page_index_time, pruning_stats.read_page_index_time); + _parquet_profile.update_pruning_stats(row_group_plan.pruning_stats); } _state->scan_plan = row_group_plan; - _state->scheduler.set_page_skip_profile( - {.skipped_pages = _parquet_profile.pages_skipped_by_data_page_filter, - .skipped_bytes = _parquet_profile.data_page_filter_skip_bytes}); + _state->scheduler.set_page_skip_profile(_parquet_profile.page_skip_profile()); _state->scheduler.set_global_rowid_context(_global_rowid_context); - _state->scheduler.set_scan_profile({ - .raw_rows_read = _parquet_profile.raw_rows_read, - .selected_rows = _parquet_profile.selected_rows, - .rows_filtered_by_conjunct = _parquet_profile.rows_filtered_by_conjunct, - .lazy_read_filtered_rows = _parquet_profile.lazy_read_filtered_rows, - .total_batches = _parquet_profile.total_batches, - .empty_selection_batches = _parquet_profile.empty_selection_batches, - .range_gap_skipped_rows = _parquet_profile.range_gap_skipped_rows, - .column_read_time = _parquet_profile.column_read_time, - .predicate_filter_time = _parquet_profile.predicate_filter_time, - .column_reader_profile = - { - .reader_read_rows = _parquet_profile.reader_read_rows, - .reader_skip_rows = _parquet_profile.reader_skip_rows, - .reader_select_rows = _parquet_profile.reader_select_rows, - .arrow_read_records_time = _parquet_profile.arrow_read_records_time, - .materialization_time = _parquet_profile.materialization_time, - }, - }); + _state->scheduler.set_scan_profile(_parquet_profile.scan_profile()); _state->scheduler.set_plan(std::move(row_group_plan)); _eof = _state->scheduler.empty(); return Status::OK(); @@ -442,127 +402,7 @@ Status ParquetReader::close() { } void ParquetReader::_init_profile() { - if (_profile != nullptr) { - static const char* parquet_profile = "ParquetReader"; - ADD_TIMER_WITH_LEVEL(_profile, parquet_profile, 1); - - _parquet_profile.filtered_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowGroupsFiltered", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.filtered_row_groups_by_min_max = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowGroupsFilteredByMinMax", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.filtered_row_groups_by_dictionary = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowGroupsFilteredByDictionary", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.filtered_row_groups_by_bloom_filter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowGroupsFilteredByBloomFilter", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.to_read_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowGroupsReadNum", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.total_row_groups = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowGroupsTotalNum", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.selected_row_ranges = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "SelectedRowRanges", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.filtered_group_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "FilteredRowsByGroup", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.filtered_page_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "FilteredRowsByPage", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.pages_skipped_by_data_page_filter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PagesSkippedByDataPageFilter", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.data_page_filter_skip_bytes = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "DataPageFilterSkipBytes", TUnit::BYTES, parquet_profile, 1); - _parquet_profile.selected_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "SelectedRows", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.rows_filtered_by_conjunct = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RowsFilteredByConjunct", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.total_batches = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "TotalBatches", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.empty_selection_batches = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "EmptySelectionBatches", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.range_gap_skipped_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RangeGapSkippedRows", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.reader_read_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "ReaderReadRows", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.reader_skip_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "ReaderSkipRows", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.reader_select_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "ReaderSelectRows", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.arrow_read_records_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ArrowReadRecordsTime", parquet_profile, 1); - _parquet_profile.materialization_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "MaterializationTime", parquet_profile, 1); - _parquet_profile.lazy_read_filtered_rows = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "FilteredRowsByLazyRead", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.filtered_bytes = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "FilteredBytes", TUnit::BYTES, parquet_profile, 1); - _parquet_profile.raw_rows_read = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "RawRowsRead", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.column_read_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ColumnReadTime", parquet_profile, 1); - _parquet_profile.parse_meta_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ParseMetaTime", parquet_profile, 1); - _parquet_profile.parse_footer_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ParseFooterTime", parquet_profile, 1); - _parquet_profile.file_reader_create_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "FileReaderCreateTime", parquet_profile, 1); - _parquet_profile.open_file_num = - ADD_CHILD_COUNTER_WITH_LEVEL(_profile, "FileNum", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_index_read_calls = - ADD_COUNTER_WITH_LEVEL(_profile, "PageIndexReadCalls", TUnit::UNIT, 1); - _parquet_profile.page_index_filter_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageIndexFilterTime", parquet_profile, 1); - _parquet_profile.read_page_index_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageIndexReadTime", parquet_profile, 1); - _parquet_profile.parse_page_index_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageIndexParseTime", parquet_profile, 1); - _parquet_profile.row_group_filter_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "RowGroupFilterTime", parquet_profile, 1); - _parquet_profile.file_footer_read_calls = - ADD_COUNTER_WITH_LEVEL(_profile, "FileFooterReadCalls", TUnit::UNIT, 1); - _parquet_profile.file_footer_hit_cache = - ADD_COUNTER_WITH_LEVEL(_profile, "FileFooterHitCache", TUnit::UNIT, 1); - _parquet_profile.decompress_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecompressTime", parquet_profile, 1); - _parquet_profile.decompress_cnt = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "DecompressCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_read_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageReadCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheWriteCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_compressed_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheCompressedWriteCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_decompressed_write_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheDecompressedWriteCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheHitCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_missing_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheMissingCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_compressed_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheCompressedHitCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.page_cache_decompressed_hit_counter = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "PageCacheDecompressedHitCount", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.decode_header_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageHeaderDecodeTime", parquet_profile, 1); - _parquet_profile.read_page_header_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PageHeaderReadTime", parquet_profile, 1); - _parquet_profile.decode_value_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeValueTime", parquet_profile, 1); - _parquet_profile.decode_dict_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeDictTime", parquet_profile, 1); - _parquet_profile.decode_level_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeLevelTime", parquet_profile, 1); - _parquet_profile.decode_null_map_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DecodeNullMapTime", parquet_profile, 1); - _parquet_profile.skip_page_header_num = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "SkipPageHeaderNum", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.parse_page_header_num = ADD_CHILD_COUNTER_WITH_LEVEL( - _profile, "ParsePageHeaderNum", TUnit::UNIT, parquet_profile, 1); - _parquet_profile.predicate_filter_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "PredicateFilterTime", parquet_profile, 1); - _parquet_profile.dict_filter_rewrite_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "DictFilterRewriteTime", parquet_profile, 1); - _parquet_profile.convert_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "ConvertTime", parquet_profile, 1); - _parquet_profile.bloom_filter_read_time = - ADD_CHILD_TIMER_WITH_LEVEL(_profile, "BloomFilterReadTime", parquet_profile, 1); - } + _parquet_profile.init(_profile); } } // namespace doris::parquet diff --git a/be/src/format_v2/parquet/parquet_reader.h b/be/src/format_v2/parquet/parquet_reader.h index 53b92edb648c01..4e4ab928204717 100644 --- a/be/src/format_v2/parquet/parquet_reader.h +++ b/be/src/format_v2/parquet/parquet_reader.h @@ -24,6 +24,7 @@ #include "common/status.h" #include "format_v2/file_reader.h" #include "format_v2/parquet/parquet_column_schema.h" +#include "format_v2/parquet/parquet_profile.h" namespace doris { namespace io { @@ -71,66 +72,6 @@ class ParquetReader : public format::FileReader { void _init_profile() override; private: - struct ParquetProfile { - RuntimeProfile::Counter* filtered_row_groups = nullptr; - RuntimeProfile::Counter* filtered_row_groups_by_min_max = nullptr; - RuntimeProfile::Counter* filtered_row_groups_by_dictionary = nullptr; - RuntimeProfile::Counter* filtered_row_groups_by_bloom_filter = nullptr; - RuntimeProfile::Counter* to_read_row_groups = nullptr; - RuntimeProfile::Counter* total_row_groups = nullptr; - RuntimeProfile::Counter* selected_row_ranges = nullptr; - RuntimeProfile::Counter* filtered_group_rows = nullptr; - RuntimeProfile::Counter* filtered_page_rows = nullptr; - RuntimeProfile::Counter* pages_skipped_by_data_page_filter = nullptr; - RuntimeProfile::Counter* data_page_filter_skip_bytes = nullptr; - RuntimeProfile::Counter* selected_rows = nullptr; - RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; - RuntimeProfile::Counter* total_batches = nullptr; - RuntimeProfile::Counter* empty_selection_batches = nullptr; - RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; - RuntimeProfile::Counter* reader_read_rows = nullptr; - RuntimeProfile::Counter* reader_skip_rows = nullptr; - RuntimeProfile::Counter* reader_select_rows = nullptr; - RuntimeProfile::Counter* arrow_read_records_time = nullptr; - RuntimeProfile::Counter* materialization_time = nullptr; - RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; - RuntimeProfile::Counter* filtered_bytes = nullptr; - RuntimeProfile::Counter* raw_rows_read = nullptr; - RuntimeProfile::Counter* column_read_time = nullptr; - RuntimeProfile::Counter* parse_meta_time = nullptr; - RuntimeProfile::Counter* parse_footer_time = nullptr; - RuntimeProfile::Counter* file_reader_create_time = nullptr; - RuntimeProfile::Counter* open_file_num = nullptr; - RuntimeProfile::Counter* row_group_filter_time = nullptr; - RuntimeProfile::Counter* page_index_read_calls = nullptr; - RuntimeProfile::Counter* page_index_filter_time = nullptr; - RuntimeProfile::Counter* read_page_index_time = nullptr; - RuntimeProfile::Counter* parse_page_index_time = nullptr; - RuntimeProfile::Counter* file_footer_read_calls = nullptr; - RuntimeProfile::Counter* file_footer_hit_cache = nullptr; - RuntimeProfile::Counter* decompress_time = nullptr; - RuntimeProfile::Counter* decompress_cnt = nullptr; - RuntimeProfile::Counter* page_read_counter = nullptr; - RuntimeProfile::Counter* page_cache_write_counter = nullptr; - RuntimeProfile::Counter* page_cache_compressed_write_counter = nullptr; - RuntimeProfile::Counter* page_cache_decompressed_write_counter = nullptr; - RuntimeProfile::Counter* page_cache_hit_counter = nullptr; - RuntimeProfile::Counter* page_cache_missing_counter = nullptr; - RuntimeProfile::Counter* page_cache_compressed_hit_counter = nullptr; - RuntimeProfile::Counter* page_cache_decompressed_hit_counter = nullptr; - RuntimeProfile::Counter* decode_header_time = nullptr; - RuntimeProfile::Counter* read_page_header_time = nullptr; - RuntimeProfile::Counter* decode_value_time = nullptr; - RuntimeProfile::Counter* decode_dict_time = nullptr; - RuntimeProfile::Counter* decode_level_time = nullptr; - RuntimeProfile::Counter* decode_null_map_time = nullptr; - RuntimeProfile::Counter* skip_page_header_num = nullptr; - RuntimeProfile::Counter* parse_page_header_num = nullptr; - RuntimeProfile::Counter* predicate_filter_time = nullptr; - RuntimeProfile::Counter* dict_filter_rewrite_time = nullptr; - RuntimeProfile::Counter* convert_time = nullptr; - RuntimeProfile::Counter* bloom_filter_read_time = nullptr; - }; void _fill_column_definition(const ParquetColumnSchema& column_schema, format::ColumnDefinition* field) const; diff --git a/be/src/format_v2/parquet/parquet_scan.h b/be/src/format_v2/parquet/parquet_scan.h index 0ecc6298e5ba02..39454701c742b7 100644 --- a/be/src/format_v2/parquet/parquet_scan.h +++ b/be/src/format_v2/parquet/parquet_scan.h @@ -75,19 +75,6 @@ struct RowGroupScanPlan { ParquetPruningStats pruning_stats; }; -struct ParquetScanProfile { - RuntimeProfile::Counter* raw_rows_read = nullptr; - RuntimeProfile::Counter* selected_rows = nullptr; - RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; - RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; - RuntimeProfile::Counter* total_batches = nullptr; - RuntimeProfile::Counter* empty_selection_batches = nullptr; - RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; - RuntimeProfile::Counter* column_read_time = nullptr; - RuntimeProfile::Counter* predicate_filter_time = nullptr; - ParquetColumnReaderProfile column_reader_profile; -}; - Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, diff --git a/be/src/format_v2/parquet/reader/column_reader.h b/be/src/format_v2/parquet/reader/column_reader.h index 824c7dd7aaf864..7276cf84a79c7d 100644 --- a/be/src/format_v2/parquet/reader/column_reader.h +++ b/be/src/format_v2/parquet/reader/column_reader.h @@ -51,11 +51,6 @@ class IColumn; namespace parquet { struct ParquetColumnSchema; -struct ParquetPageSkipProfile { - RuntimeProfile::Counter* skipped_pages = nullptr; - RuntimeProfile::Counter* skipped_bytes = nullptr; -}; - // Doris 的 Parquet column reader 抽象。 // 该类包装 Arrow Parquet RecordReader，负责将 file-local Parquet leaf column 读取成 // Doris-owned column。它不理解 Iceberg/global schema，也不处理 table-level From 7db9584ba8654eac78797f3158ba1ee1e16dafac Mon Sep 17 00:00:00 2001 From: Socrates Date: Tue, 16 Jun 2026 16:51:32 +0800 Subject: [PATCH 24/28] [refactor](be) Move format v2 implementations under format namespace ### What problem does this PR solve? Issue Number: close #xxx Related PR: #xxx Problem Summary: Align format_v2 implementation namespaces with the format_v2 ownership boundary. Parquet, Hive, Paimon, Iceberg, and JDBC implementations now live under doris::format subnamespaces, while shared format_v2 expression helpers live under doris::format. Call sites and tests were updated to use the new namespace layout. ### Release note None ### Check List (For Author) - Test: Manual test - Ran build-support/clang-format.sh on the modified BE files - Ran git diff --check - Ran namespace residue scans for old doris::parquet/hive/paimon/jdbc/iceberg namespaces and duplicate format::format references - Attempted targeted BE UT with ./run-be-ut.sh --run '--filter=NewParquetReaderTest.*:ParquetColumnReaderTest.*:TableReaderTest.*:CastTest.*:DeletePredicateTest.*:EqualityDeletePredicateTest.*', but local CMake compiler detection failed before Doris code compiled because /opt/homebrew/opt/llvm@16/bin/clang++ could not link libc++: ld: library 'c++' not found - Behavior changed: No - Does this need documentation: No --- be/src/exec/scan/file_scanner_v2.cpp | 8 +- be/src/format_v2/expr/cast.cpp | 4 +- be/src/format_v2/expr/cast.h | 4 +- be/src/format_v2/expr/delete_predicate.cpp | 4 +- be/src/format_v2/expr/delete_predicate.h | 4 +- .../expr/equality_delete_predicate.cpp | 4 +- .../expr/equality_delete_predicate.h | 4 +- be/src/format_v2/jni/jdbc_reader.cpp | 4 +- be/src/format_v2/jni/jdbc_reader.h | 4 +- .../parquet/parquet_column_schema.cpp | 4 +- .../format_v2/parquet/parquet_column_schema.h | 4 +- .../parquet/parquet_file_context.cpp | 4 +- .../format_v2/parquet/parquet_file_context.h | 4 +- be/src/format_v2/parquet/parquet_profile.cpp | 4 +- be/src/format_v2/parquet/parquet_profile.h | 4 +- be/src/format_v2/parquet/parquet_reader.cpp | 4 +- be/src/format_v2/parquet/parquet_reader.h | 4 +- be/src/format_v2/parquet/parquet_scan.cpp | 4 +- be/src/format_v2/parquet/parquet_scan.h | 4 +- .../format_v2/parquet/parquet_statistics.cpp | 4 +- be/src/format_v2/parquet/parquet_statistics.h | 4 +- be/src/format_v2/parquet/parquet_type.cpp | 4 +- be/src/format_v2/parquet/parquet_type.h | 4 +- .../parquet/reader/column_reader.cpp | 4 +- .../format_v2/parquet/reader/column_reader.h | 6 +- .../reader/global_rowid_column_reader.cpp | 4 +- .../reader/global_rowid_column_reader.h | 4 +- .../parquet/reader/list_column_reader.cpp | 4 +- .../parquet/reader/list_column_reader.h | 4 +- .../parquet/reader/map_column_reader.cpp | 4 +- .../parquet/reader/map_column_reader.h | 4 +- .../reader/nested_column_materializer.cpp | 4 +- .../reader/nested_column_materializer.h | 4 +- .../parquet/reader/parquet_leaf_reader.cpp | 4 +- .../parquet/reader/parquet_leaf_reader.h | 4 +- .../reader/row_position_column_reader.cpp | 4 +- .../reader/row_position_column_reader.h | 4 +- .../parquet/reader/scalar_column_reader.cpp | 4 +- .../parquet/reader/scalar_column_reader.h | 4 +- .../parquet/reader/struct_column_reader.cpp | 4 +- .../parquet/reader/struct_column_reader.h | 4 +- be/src/format_v2/parquet/selection_vector.h | 4 +- be/src/format_v2/table/hive_reader.cpp | 4 +- be/src/format_v2/table/hive_reader.h | 4 +- be/src/format_v2/table/iceberg_reader.cpp | 10 +- be/src/format_v2/table/iceberg_reader.h | 4 +- be/src/format_v2/table/paimon_reader.cpp | 4 +- be/src/format_v2/table/paimon_reader.h | 4 +- be/src/format_v2/table_reader.cpp | 2 +- be/test/format_v2/expr/cast_test.cpp | 4 +- .../format_v2/expr/delete_predicate_test.cpp | 4 +- .../expr/equality_delete_predicate_test.cpp | 4 +- .../parquet/parquet_column_reader_test.cpp | 4 +- .../format_v2/parquet/parquet_reader_test.cpp | 131 +++++++++--------- be/test/format_v2/table_reader_test.cpp | 53 +++---- 55 files changed, 208 insertions(+), 198 deletions(-) diff --git a/be/src/exec/scan/file_scanner_v2.cpp b/be/src/exec/scan/file_scanner_v2.cpp index 909851ef4d7ea6..9fc0c681bf9acf 100644 --- a/be/src/exec/scan/file_scanner_v2.cpp +++ b/be/src/exec/scan/file_scanner_v2.cpp @@ -284,13 +284,13 @@ Status FileScannerV2::_create_table_reader_for_format( if (table_format == "NotSet" || table_format == "tvf") { *reader = std::make_unique(); } else if (table_format == "hive") { - *reader = hive::HiveReader::create_unique(); + *reader = format::hive::HiveReader::create_unique(); } else if (table_format == "iceberg") { - *reader = std::make_unique(); + *reader = std::make_unique(); } else if (table_format == "paimon") { - *reader = paimon::PaimonReader::create_unique(); + *reader = format::paimon::PaimonReader::create_unique(); } else if (table_format == "jdbc") { - *reader = std::make_unique(); + *reader = std::make_unique(); } else { return Status::NotSupported("FileScannerV2 does not support table format {}", table_format); } diff --git a/be/src/format_v2/expr/cast.cpp b/be/src/format_v2/expr/cast.cpp index 76686b73ac9360..efeb9d851deb22 100644 --- a/be/src/format_v2/expr/cast.cpp +++ b/be/src/format_v2/expr/cast.cpp @@ -31,7 +31,7 @@ #include "exprs/vexpr_context.h" #include "exprs/vliteral.h" -namespace doris { +namespace doris::format { Status Cast::prepare(RuntimeState* state, const RowDescriptor& desc, VExprContext* context) { RETURN_IF_ERROR_OR_PREPARED(VExpr::prepare(state, desc, context)); @@ -128,4 +128,4 @@ Status Cast::_do_execute(VExprContext* context, const Block* block, const Select return Status::OK(); } -} // namespace doris +} // namespace doris::format diff --git a/be/src/format_v2/expr/cast.h b/be/src/format_v2/expr/cast.h index 37452ba905ea6d..1dc06bcf07f2bc 100644 --- a/be/src/format_v2/expr/cast.h +++ b/be/src/format_v2/expr/cast.h @@ -32,7 +32,7 @@ class Block; class VExprContext; } // namespace doris -namespace doris { +namespace doris::format { class Cast final : public VExpr { ENABLE_FACTORY_CREATOR(Cast); @@ -65,4 +65,4 @@ class Cast final : public VExpr { std::string _expr_name; FunctionBasePtr _function; }; -} // namespace doris +} // namespace doris::format diff --git a/be/src/format_v2/expr/delete_predicate.cpp b/be/src/format_v2/expr/delete_predicate.cpp index cdbcf7d5314117..ed2726bf6bad30 100644 --- a/be/src/format_v2/expr/delete_predicate.cpp +++ b/be/src/format_v2/expr/delete_predicate.cpp @@ -31,7 +31,7 @@ #include "core/block/column_with_type_and_name.h" #include "core/block/columns_with_type_and_name.h" -namespace doris { +namespace doris::format { DeletePredicate::DeletePredicate(const std::vector& deleted_rows) : VExpr(), _deleted_rows(deleted_rows) { @@ -114,4 +114,4 @@ std::string DeletePredicate::debug_string() const { return _expr_name; } -} // namespace doris +} // namespace doris::format diff --git a/be/src/format_v2/expr/delete_predicate.h b/be/src/format_v2/expr/delete_predicate.h index 3a95c31d8bfe14..dce2de3edf278e 100644 --- a/be/src/format_v2/expr/delete_predicate.h +++ b/be/src/format_v2/expr/delete_predicate.h @@ -32,7 +32,7 @@ class Block; class VExprContext; } // namespace doris -namespace doris { +namespace doris::format { class DeletePredicate final : public VExpr { ENABLE_FACTORY_CREATOR(DeletePredicate); @@ -57,4 +57,4 @@ class DeletePredicate final : public VExpr { std::string _expr_name; const std::vector& _deleted_rows; }; -} // namespace doris \ No newline at end of file +} // namespace doris::format diff --git a/be/src/format_v2/expr/equality_delete_predicate.cpp b/be/src/format_v2/expr/equality_delete_predicate.cpp index dd5ff0305bc15f..13454e3b22f116 100644 --- a/be/src/format_v2/expr/equality_delete_predicate.cpp +++ b/be/src/format_v2/expr/equality_delete_predicate.cpp @@ -28,7 +28,7 @@ #include "core/column/column_vector.h" #include "core/data_type/data_type_number.h" -namespace doris { +namespace doris::format { namespace { bool column_value_equal(const ColumnPtr& lhs, size_t lhs_row, const ColumnPtr& rhs, @@ -156,4 +156,4 @@ std::string EqualityDeletePredicate::debug_string() const { return _expr_name; } -} // namespace doris +} // namespace doris::format diff --git a/be/src/format_v2/expr/equality_delete_predicate.h b/be/src/format_v2/expr/equality_delete_predicate.h index 2e33cffb3985df..cad16ca387ccd8 100644 --- a/be/src/format_v2/expr/equality_delete_predicate.h +++ b/be/src/format_v2/expr/equality_delete_predicate.h @@ -35,7 +35,7 @@ class RuntimeState; class VExprContext; } // namespace doris -namespace doris { +namespace doris::format { class EqualityDeletePredicate final : public VExpr { ENABLE_FACTORY_CREATOR(EqualityDeletePredicate); @@ -68,4 +68,4 @@ class EqualityDeletePredicate final : public VExpr { std::multimap _delete_hash_map; }; -} // namespace doris +} // namespace doris::format diff --git a/be/src/format_v2/jni/jdbc_reader.cpp b/be/src/format_v2/jni/jdbc_reader.cpp index 359b9f2ec5a093..9000630975caaa 100644 --- a/be/src/format_v2/jni/jdbc_reader.cpp +++ b/be/src/format_v2/jni/jdbc_reader.cpp @@ -32,7 +32,7 @@ #include "format_v2/table_reader.h" #include "util/jdbc_utils.h" -namespace doris::jdbc { +namespace doris::format::jdbc { std::string JdbcJniReader::connector_class() const { return "org/apache/doris/jdbc/JdbcJniScanner"; @@ -189,4 +189,4 @@ Status JdbcJniReader::_cast_string_to_special_type(const format::JniTableReader: return Status::OK(); } -} // namespace doris::jdbc +} // namespace doris::format::jdbc diff --git a/be/src/format_v2/jni/jdbc_reader.h b/be/src/format_v2/jni/jdbc_reader.h index 726d303b41d981..8ab4c808b17d8f 100644 --- a/be/src/format_v2/jni/jdbc_reader.h +++ b/be/src/format_v2/jni/jdbc_reader.h @@ -27,7 +27,7 @@ #include "format_v2/jni/jni_table_reader.h" #include "gen_cpp/PlanNodes_types.h" -namespace doris::jdbc { +namespace doris::format::jdbc { class JdbcJniReader final : public format::JniTableReader { public: @@ -55,4 +55,4 @@ class JdbcJniReader final : public format::JniTableReader { TFileRangeDesc _current_range; }; -} // namespace doris::jdbc +} // namespace doris::format::jdbc diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp index 7b50133671aa8b..3fc36922f52534 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.cpp +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -30,7 +30,7 @@ #include "core/data_type/data_type_struct.h" #include "format_v2/parquet/parquet_type.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { struct SchemaBuildContext { @@ -484,4 +484,4 @@ Status build_parquet_column_schema(const ::parquet::SchemaDescriptor& schema, return Status::OK(); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_column_schema.h b/be/src/format_v2/parquet/parquet_column_schema.h index 861e0eb248e504..066bbfef5f788d 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.h +++ b/be/src/format_v2/parquet/parquet_column_schema.h @@ -30,7 +30,7 @@ class ColumnDescriptor; class SchemaDescriptor; } // namespace parquet -namespace doris::parquet { +namespace doris::format::parquet { enum class ParquetColumnSchemaKind { PRIMITIVE, @@ -97,4 +97,4 @@ struct ParquetColumnSchema { Status build_parquet_column_schema(const ::parquet::SchemaDescriptor& schema, std::vector>* fields); -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_file_context.cpp b/be/src/format_v2/parquet/parquet_file_context.cpp index 0790551ca60362..6b9ff1310691e8 100644 --- a/be/src/format_v2/parquet/parquet_file_context.cpp +++ b/be/src/format_v2/parquet/parquet_file_context.cpp @@ -27,7 +27,7 @@ #include "io/fs/file_reader.h" #include "util/slice.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { class DorisRandomAccessFile final : public arrow::io::RandomAccessFile { @@ -160,4 +160,4 @@ Status ParquetFileContext::close() { return Status::OK(); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_file_context.h b/be/src/format_v2/parquet/parquet_file_context.h index 3d08fffcfeb9d7..6571a1e94eea82 100644 --- a/be/src/format_v2/parquet/parquet_file_context.h +++ b/be/src/format_v2/parquet/parquet_file_context.h @@ -25,7 +25,7 @@ #include "common/status.h" #include "io/fs/file_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { struct ParquetFileContext { std::shared_ptr arrow_file; @@ -39,4 +39,4 @@ struct ParquetFileContext { Status arrow_status_to_doris_status(const arrow::Status& status); -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_profile.cpp b/be/src/format_v2/parquet/parquet_profile.cpp index 19b906bc623e3e..79f979ea0cf1b8 100644 --- a/be/src/format_v2/parquet/parquet_profile.cpp +++ b/be/src/format_v2/parquet/parquet_profile.cpp @@ -19,7 +19,7 @@ #include "format_v2/parquet/parquet_statistics.h" -namespace doris::parquet { +namespace doris::format::parquet { void ParquetProfile::init(RuntimeProfile* profile) { if (profile == nullptr) { @@ -188,4 +188,4 @@ ParquetScanProfile ParquetProfile::scan_profile() const { }; } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_profile.h b/be/src/format_v2/parquet/parquet_profile.h index 1ef44be8e5a85b..26212e72e8291d 100644 --- a/be/src/format_v2/parquet/parquet_profile.h +++ b/be/src/format_v2/parquet/parquet_profile.h @@ -19,7 +19,7 @@ #include "runtime/runtime_profile.h" -namespace doris::parquet { +namespace doris::format::parquet { struct ParquetPruningStats; @@ -119,4 +119,4 @@ struct ParquetProfile { RuntimeProfile::Counter* bloom_filter_read_time = nullptr; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_reader.cpp b/be/src/format_v2/parquet/parquet_reader.cpp index 6b3a2ebdaf93c3..0d751fb8422238 100644 --- a/be/src/format_v2/parquet/parquet_reader.cpp +++ b/be/src/format_v2/parquet/parquet_reader.cpp @@ -39,7 +39,7 @@ #include "format_v2/parquet/reader/column_reader.h" #include "runtime/runtime_state.h" -namespace doris::parquet { +namespace doris::format::parquet { struct ParquetReaderScanState { ParquetFileContext file_context; @@ -405,4 +405,4 @@ void ParquetReader::_init_profile() { _parquet_profile.init(_profile); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_reader.h b/be/src/format_v2/parquet/parquet_reader.h index 4e4ab928204717..957d503668392e 100644 --- a/be/src/format_v2/parquet/parquet_reader.h +++ b/be/src/format_v2/parquet/parquet_reader.h @@ -32,7 +32,7 @@ struct IOContext; } // namespace io } // namespace doris -namespace doris::parquet { +namespace doris::format::parquet { struct ParquetReaderScanState; @@ -81,4 +81,4 @@ class ParquetReader : public format::FileReader { bool _enable_mapping_timestamp_tz = false; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_scan.cpp b/be/src/format_v2/parquet/parquet_scan.cpp index f432984196c979..9820faa3717d51 100644 --- a/be/src/format_v2/parquet/parquet_scan.cpp +++ b/be/src/format_v2/parquet/parquet_scan.cpp @@ -32,7 +32,7 @@ #include "format_v2/parquet/parquet_file_context.h" #include "format_v2/parquet/parquet_statistics.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { @@ -535,4 +535,4 @@ Status ParquetScanScheduler::read_next_batch( } } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_scan.h b/be/src/format_v2/parquet/parquet_scan.h index 39454701c742b7..d6e5560698f41d 100644 --- a/be/src/format_v2/parquet/parquet_scan.h +++ b/be/src/format_v2/parquet/parquet_scan.h @@ -51,7 +51,7 @@ struct FileScanRequest; } // namespace format } // namespace doris -namespace doris::parquet { +namespace doris::format::parquet { struct ParquetFileContext; struct ParquetColumnSchema; @@ -145,4 +145,4 @@ class ParquetScanScheduler { bool _enable_strict_mode = false; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_statistics.cpp b/be/src/format_v2/parquet/parquet_statistics.cpp index 3b92fc3017004f..04e2516f3f5946 100644 --- a/be/src/format_v2/parquet/parquet_statistics.cpp +++ b/be/src/format_v2/parquet/parquet_statistics.cpp @@ -48,7 +48,7 @@ #include "storage/predicate/accept_null_predicate.h" #include "storage/predicate/column_predicate.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { @@ -1275,4 +1275,4 @@ Status select_row_group_ranges_by_page_index( return Status::OK(); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_statistics.h b/be/src/format_v2/parquet/parquet_statistics.h index 6cb58cfab4a02c..1571bceec1da83 100644 --- a/be/src/format_v2/parquet/parquet_statistics.h +++ b/be/src/format_v2/parquet/parquet_statistics.h @@ -43,7 +43,7 @@ namespace doris { class ColumnPredicate; } // namespace doris -namespace doris::parquet { +namespace doris::format::parquet { struct ParquetColumnSchema; @@ -147,4 +147,4 @@ Status select_row_group_ranges_by_page_index( std::vector* selected_ranges, std::map* page_skip_plans, ParquetPruningStats* pruning_stats, const cctz::time_zone* timezone = nullptr); -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_type.cpp b/be/src/format_v2/parquet/parquet_type.cpp index 1d635be089a3fc..04181137c1ed6f 100644 --- a/be/src/format_v2/parquet/parquet_type.cpp +++ b/be/src/format_v2/parquet/parquet_type.cpp @@ -28,7 +28,7 @@ #include "core/data_type/data_type_string.h" #include "core/data_type/primitive_type.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { DataTypePtr create_type(PrimitiveType type, bool nullable, int precision = 0, int scale = 0) { @@ -343,4 +343,4 @@ DecodedValueKind decoded_value_kind(const ParquetTypeDescriptor& type_descriptor } } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_type.h b/be/src/format_v2/parquet/parquet_type.h index 37e9b9bd5b1dfc..1fb6cb51bc9cee 100644 --- a/be/src/format_v2/parquet/parquet_type.h +++ b/be/src/format_v2/parquet/parquet_type.h @@ -28,7 +28,7 @@ namespace parquet { class ColumnDescriptor; } // namespace parquet -namespace doris::parquet { +namespace doris::format::parquet { // Parquet logical/converted annotation 解析后留下的额外编码信息。 // 这对应 DuckDB ParquetColumnSchema::type_info：Doris type 只能表达最终展示类型， @@ -88,4 +88,4 @@ bool supports_record_reader(const ParquetTypeDescriptor& type_descriptor); DecodedValueKind decoded_value_kind(const ParquetTypeDescriptor& type_descriptor); -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/column_reader.cpp b/be/src/format_v2/parquet/reader/column_reader.cpp index d0bdf802edaf95..2cc9933e94fb69 100644 --- a/be/src/format_v2/parquet/reader/column_reader.cpp +++ b/be/src/format_v2/parquet/reader/column_reader.cpp @@ -46,7 +46,7 @@ #include "format_v2/parquet/reader/struct_column_reader.h" #include "runtime/runtime_profile.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { class DataPageSkipFilter { @@ -520,4 +520,4 @@ bool ParquetColumnReader::is_or_has_repeated_child() const { return _repetition_level > 0; } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/column_reader.h b/be/src/format_v2/parquet/reader/column_reader.h index 7276cf84a79c7d..b57d8b99b00573 100644 --- a/be/src/format_v2/parquet/reader/column_reader.h +++ b/be/src/format_v2/parquet/reader/column_reader.h @@ -47,8 +47,9 @@ class time_zone; namespace doris { class IColumn; +} // namespace doris -namespace parquet { +namespace doris::format::parquet { struct ParquetColumnSchema; // Doris 的 Parquet column reader 抽象。 @@ -218,5 +219,4 @@ class ParquetColumnReaderFactory { ParquetColumnReaderProfile _column_reader_profile; }; -} // namespace parquet -} // namespace doris +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp b/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp index c2be98d1658978..1df3819ac64083 100644 --- a/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp @@ -27,7 +27,7 @@ #include "format_v2/parquet/parquet_column_schema.h" #include "storage/utils.h" -namespace doris::parquet { +namespace doris::format::parquet { GlobalRowIdColumnReader::GlobalRowIdColumnReader(format::GlobalRowIdContext context, int64_t row_group_first_row, @@ -83,4 +83,4 @@ void GlobalRowIdColumnReader::append_row_id(uint32_t row_id, MutableColumnPtr& c sizeof(GlobalRowLoacationV2)); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/global_rowid_column_reader.h b/be/src/format_v2/parquet/reader/global_rowid_column_reader.h index 1c0192e5ad9099..d217e4560614c2 100644 --- a/be/src/format_v2/parquet/reader/global_rowid_column_reader.h +++ b/be/src/format_v2/parquet/reader/global_rowid_column_reader.h @@ -23,7 +23,7 @@ #include "format_v2/column_data.h" #include "format_v2/parquet/reader/column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { class GlobalRowIdColumnReader final : public ParquetColumnReader { public: @@ -46,4 +46,4 @@ class GlobalRowIdColumnReader final : public ParquetColumnReader { int64_t _next_row_position = 0; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/list_column_reader.cpp b/be/src/format_v2/parquet/reader/list_column_reader.cpp index 000c7dd11cec21..cc1df4079fbfdb 100644 --- a/be/src/format_v2/parquet/reader/list_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/list_column_reader.cpp @@ -27,7 +27,7 @@ #include "core/data_type/data_type_nullable.h" #include "format_v2/parquet/reader/nested_column_materializer.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { void remove_nullable_wrapper_if_not_expected(const DataTypePtr& output_type, @@ -161,4 +161,4 @@ bool ListColumnReader::is_or_has_repeated_child() const { return true; } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/list_column_reader.h b/be/src/format_v2/parquet/reader/list_column_reader.h index d64eb646a75c2a..f144e3a06e683c 100644 --- a/be/src/format_v2/parquet/reader/list_column_reader.h +++ b/be/src/format_v2/parquet/reader/list_column_reader.h @@ -25,7 +25,7 @@ #include "format_v2/parquet/parquet_column_schema.h" #include "format_v2/parquet/reader/column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { class ListColumnReader final : public ParquetColumnReader { public: @@ -49,4 +49,4 @@ class ListColumnReader final : public ParquetColumnReader { std::unique_ptr _element_reader; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/map_column_reader.cpp b/be/src/format_v2/parquet/reader/map_column_reader.cpp index e0c5466d13f1a9..8cd612f3f6f42a 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/map_column_reader.cpp @@ -29,7 +29,7 @@ #include "format_v2/parquet/reader/nested_column_materializer.h" #include "format_v2/parquet/reader/scalar_column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { void remove_nullable_wrapper_if_not_expected(const DataTypePtr& output_type, @@ -223,4 +223,4 @@ bool MapColumnReader::is_or_has_repeated_child() const { return true; } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/map_column_reader.h b/be/src/format_v2/parquet/reader/map_column_reader.h index f9761964920c34..d6ebed24438e27 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.h +++ b/be/src/format_v2/parquet/reader/map_column_reader.h @@ -25,7 +25,7 @@ #include "format_v2/parquet/parquet_column_schema.h" #include "format_v2/parquet/reader/column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { class MapColumnReader final : public ParquetColumnReader { public: @@ -52,4 +52,4 @@ class MapColumnReader final : public ParquetColumnReader { std::unique_ptr _value_reader; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/nested_column_materializer.cpp b/be/src/format_v2/parquet/reader/nested_column_materializer.cpp index 713595ca240683..21f3a7a8ab52d8 100644 --- a/be/src/format_v2/parquet/reader/nested_column_materializer.cpp +++ b/be/src/format_v2/parquet/reader/nested_column_materializer.cpp @@ -23,7 +23,7 @@ #include "core/assert_cast.h" #include "core/column/column_nullable.h" -namespace doris::parquet { +namespace doris::format::parquet { ColumnArray* array_column_from_output(MutableColumnPtr& column) { if (auto* nullable_column = check_and_get_column(*column)) { @@ -69,4 +69,4 @@ void append_parent_nulls(NullMap* dst, const NullMap& src) { dst->insert(src.begin(), src.end()); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/nested_column_materializer.h b/be/src/format_v2/parquet/reader/nested_column_materializer.h index 41b849850f9d4a..26bdfdaf83d47d 100644 --- a/be/src/format_v2/parquet/reader/nested_column_materializer.h +++ b/be/src/format_v2/parquet/reader/nested_column_materializer.h @@ -26,7 +26,7 @@ #include "core/column/column_nullable.h" #include "core/column/column_struct.h" -namespace doris::parquet { +namespace doris::format::parquet { ColumnArray* array_column_from_output(MutableColumnPtr& column); ColumnMap* map_column_from_output(MutableColumnPtr& column); @@ -36,4 +36,4 @@ NullMap* null_map_from_nullable_output(MutableColumnPtr& column); void append_offsets(ColumnArray::Offsets64& offsets, const std::vector& entry_counts); void append_parent_nulls(NullMap* dst, const NullMap& src); -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp index be4406e5684bd9..7ad547adbe1dd3 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -38,7 +38,7 @@ #include "runtime/runtime_profile.h" #include "util/simd/bits.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { DecodedTimeUnit decoded_time_unit(ParquetTimeUnit time_unit) { @@ -595,4 +595,4 @@ Status ParquetLeafReader::read_nested_batch(int64_t batch_rows, int16_t value_sl return Status::OK(); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.h b/be/src/format_v2/parquet/reader/parquet_leaf_reader.h index 645241cf4af1be..a012e67b71cd79 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.h +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.h @@ -45,7 +45,7 @@ namespace arrow { class Array; } // namespace arrow -namespace doris::parquet { +namespace doris::format::parquet { // Nested scalar leaf state is intentionally split into shape and value. // @@ -131,4 +131,4 @@ class ParquetLeafReader { bool _enable_strict_mode = false; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/row_position_column_reader.cpp b/be/src/format_v2/parquet/reader/row_position_column_reader.cpp index 01307a0d1acc8e..19727758ff5d5a 100644 --- a/be/src/format_v2/parquet/reader/row_position_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/row_position_column_reader.cpp @@ -24,7 +24,7 @@ #include "core/data_type/data_type_number.h" #include "format_v2/parquet/parquet_column_schema.h" -namespace doris::parquet { +namespace doris::format::parquet { RowPositionColumnReader::RowPositionColumnReader(int64_t row_group_first_row, ParquetColumnReaderProfile profile) @@ -75,4 +75,4 @@ Status RowPositionColumnReader::skip(int64_t rows) { return Status::OK(); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/row_position_column_reader.h b/be/src/format_v2/parquet/reader/row_position_column_reader.h index 54b468aa8eab4e..679decc2753514 100644 --- a/be/src/format_v2/parquet/reader/row_position_column_reader.h +++ b/be/src/format_v2/parquet/reader/row_position_column_reader.h @@ -22,7 +22,7 @@ #include "format_v2/parquet/reader/column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { class RowPositionColumnReader final : public ParquetColumnReader { public: @@ -42,4 +42,4 @@ class RowPositionColumnReader final : public ParquetColumnReader { int64_t _next_row_position = 0; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.cpp b/be/src/format_v2/parquet/reader/scalar_column_reader.cpp index 5a6f0a9d547d9e..d406c5867eb23d 100644 --- a/be/src/format_v2/parquet/reader/scalar_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.cpp @@ -28,7 +28,7 @@ #include "format_v2/parquet/parquet_column_schema.h" #include "util/simd/bits.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { class ParquetNestedScalarValueCursor { @@ -306,4 +306,4 @@ bool ScalarColumnReader::is_or_has_repeated_child() const { return _repetition_level > 0; } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.h b/be/src/format_v2/parquet/reader/scalar_column_reader.h index 47d404f9655adb..46c7a810cd9fea 100644 --- a/be/src/format_v2/parquet/reader/scalar_column_reader.h +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.h @@ -36,7 +36,7 @@ namespace cctz { class time_zone; } // namespace cctz -namespace doris::parquet { +namespace doris::format::parquet { class ScalarColumnReader final : public ParquetColumnReader { friend class MapColumnReader; @@ -80,4 +80,4 @@ class ScalarColumnReader final : public ParquetColumnReader { std::unique_ptr _nested_batch; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.cpp b/be/src/format_v2/parquet/reader/struct_column_reader.cpp index c0fde2835545d4..82cd32d77afb78 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/struct_column_reader.cpp @@ -26,7 +26,7 @@ #include "format_v2/parquet/reader/nested_column_materializer.h" #include "format_v2/parquet/reader/scalar_column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { ParquetColumnReader* StructColumnReader::shape_source_reader() const { for (const auto& child : _children) { auto* child_reader = child.get(); @@ -219,4 +219,4 @@ bool StructColumnReader::is_or_has_repeated_child() const { return shape_reader != nullptr && shape_reader->is_or_has_repeated_child(); } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.h b/be/src/format_v2/parquet/reader/struct_column_reader.h index 2c08bc8fe58126..7eeab621742d60 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.h +++ b/be/src/format_v2/parquet/reader/struct_column_reader.h @@ -26,7 +26,7 @@ #include "format_v2/parquet/parquet_column_schema.h" #include "format_v2/parquet/reader/column_reader.h" -namespace doris::parquet { +namespace doris::format::parquet { class StructColumnReader final : public ParquetColumnReader { public: @@ -56,4 +56,4 @@ class StructColumnReader final : public ParquetColumnReader { std::vector _child_output_indices; }; -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/selection_vector.h b/be/src/format_v2/parquet/selection_vector.h index 8fc0f6a0ee74f1..3a4d117c7910c0 100644 --- a/be/src/format_v2/parquet/selection_vector.h +++ b/be/src/format_v2/parquet/selection_vector.h @@ -25,7 +25,7 @@ #include "common/check.h" #include "common/status.h" -namespace doris::parquet { +namespace doris::format::parquet { struct RowRange { int64_t start = 0; @@ -165,4 +165,4 @@ inline std::vector selection_to_ranges(const SelectionVector& selectio return ranges; } -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/src/format_v2/table/hive_reader.cpp b/be/src/format_v2/table/hive_reader.cpp index 152f2496e0af53..cfa243a4f2dbcf 100644 --- a/be/src/format_v2/table/hive_reader.cpp +++ b/be/src/format_v2/table/hive_reader.cpp @@ -24,7 +24,7 @@ #include "format_v2/file_reader.h" #include "runtime/runtime_state.h" -namespace doris::hive { +namespace doris::format::hive { namespace { TFileFormatType::type format_type_from_context(const format::ProjectedColumnBuildContext& context) { @@ -138,4 +138,4 @@ Status HiveReader::validate_projected_columns( return Status::OK(); } -} // namespace doris::hive +} // namespace doris::format::hive diff --git a/be/src/format_v2/table/hive_reader.h b/be/src/format_v2/table/hive_reader.h index d1bf84cad6e1c0..308bb242c8cf21 100644 --- a/be/src/format_v2/table/hive_reader.h +++ b/be/src/format_v2/table/hive_reader.h @@ -20,7 +20,7 @@ #include "common/status.h" #include "format_v2/table_reader.h" -namespace doris::hive { +namespace doris::format::hive { class HiveReader final : public format::TableReader { public: @@ -39,4 +39,4 @@ class HiveReader final : public format::TableReader { format::TableColumnMappingMode _mode = format::TableColumnMappingMode::BY_NAME; }; -} // namespace doris::hive +} // namespace doris::format::hive diff --git a/be/src/format_v2/table/iceberg_reader.cpp b/be/src/format_v2/table/iceberg_reader.cpp index 59d87ff3bccf2e..7dee04f449af60 100644 --- a/be/src/format_v2/table/iceberg_reader.cpp +++ b/be/src/format_v2/table/iceberg_reader.cpp @@ -43,7 +43,7 @@ #include "format_v2/table_reader.h" #include "io/file_factory.h" -namespace doris::iceberg { +namespace doris::format::iceberg { template static std::string join_values_for_debug(const std::vector& values) { @@ -481,7 +481,8 @@ Status IcebergTableReader::_read_parquet_position_delete_file( auto system_properties = _delete_file_system_properties(scan_params); auto file_description = _delete_file_description(delete_range); std::shared_ptr io_ctx(&delete_io_ctx->io_ctx, [](io::IOContext*) {}); - parquet::ParquetReader reader(system_properties, file_description, io_ctx, _scanner_profile); + format::parquet::ParquetReader reader(system_properties, file_description, io_ctx, + _scanner_profile); RETURN_IF_ERROR(reader.init(_runtime_state)); std::vector schema; @@ -591,7 +592,8 @@ Status IcebergTableReader::_read_parquet_equality_delete_file( auto system_properties = _delete_file_system_properties(scan_params); auto file_description = _delete_file_description(delete_range); std::shared_ptr io_ctx(&delete_io_ctx->io_ctx, [](io::IOContext*) {}); - parquet::ParquetReader reader(system_properties, file_description, io_ctx, _scanner_profile); + format::parquet::ParquetReader reader(system_properties, file_description, io_ctx, + _scanner_profile); RETURN_IF_ERROR(reader.init(_runtime_state)); std::vector schema; @@ -770,4 +772,4 @@ bool IcebergTableReader::_need_iceberg_rowid() const { return false; } -} // namespace doris::iceberg +} // namespace doris::format::iceberg diff --git a/be/src/format_v2/table/iceberg_reader.h b/be/src/format_v2/table/iceberg_reader.h index 1f5179bd3845e5..92f43f7e19c9e9 100644 --- a/be/src/format_v2/table/iceberg_reader.h +++ b/be/src/format_v2/table/iceberg_reader.h @@ -38,7 +38,7 @@ struct FileSystemProperties; } // namespace io } // namespace doris -namespace doris::iceberg { +namespace doris::format::iceberg { // Iceberg table-level reader。 // 该层继承 TableReader，复用多文件编排和动态分区裁剪等通用能力；同时组合 @@ -172,4 +172,4 @@ class IcebergTableReader : public format::TableReader { bool _need_iceberg_rowid() const; }; -} // namespace doris::iceberg +} // namespace doris::format::iceberg diff --git a/be/src/format_v2/table/paimon_reader.cpp b/be/src/format_v2/table/paimon_reader.cpp index a5bd185f556daa..7c6e7518d7ca3c 100644 --- a/be/src/format_v2/table/paimon_reader.cpp +++ b/be/src/format_v2/table/paimon_reader.cpp @@ -24,7 +24,7 @@ #include "format/table/deletion_vector_reader.h" #include "util/string_util.h" -namespace doris::paimon { +namespace doris::format::paimon { namespace { const schema::external::TField* get_field_ptr(const schema::external::TFieldPtr& field_ptr) { @@ -189,4 +189,4 @@ Status PaimonReader::_parse_deletion_vector_file(const TTableFormatFileDesc& t_d return Status::OK(); } -} // namespace doris::paimon +} // namespace doris::format::paimon diff --git a/be/src/format_v2/table/paimon_reader.h b/be/src/format_v2/table/paimon_reader.h index 1bc5ea697e7006..86c266dd97229a 100644 --- a/be/src/format_v2/table/paimon_reader.h +++ b/be/src/format_v2/table/paimon_reader.h @@ -22,7 +22,7 @@ namespace doris { struct DeleteFileDesc; } -namespace doris::paimon { +namespace doris::format::paimon { class PaimonReader final : public format::TableReader { public: @@ -41,4 +41,4 @@ class PaimonReader final : public format::TableReader { int64_t _split_schema_id = -1; }; -} // namespace doris::paimon +} // namespace doris::format::paimon diff --git a/be/src/format_v2/table_reader.cpp b/be/src/format_v2/table_reader.cpp index 89fc223e7cd638..5578b37a121aea 100644 --- a/be/src/format_v2/table_reader.cpp +++ b/be/src/format_v2/table_reader.cpp @@ -568,7 +568,7 @@ Status TableReader::create_file_reader(std::unique_ptr* reader) { const bool enable_mapping_timestamp_tz = _scan_params != nullptr && _scan_params->__isset.enable_mapping_timestamp_tz && _scan_params->enable_mapping_timestamp_tz; - *reader = std::make_unique( + *reader = std::make_unique( _system_properties, _current_task->data_file, _io_ctx, _scanner_profile, _global_rowid_context, enable_mapping_timestamp_tz); return Status::OK(); diff --git a/be/test/format_v2/expr/cast_test.cpp b/be/test/format_v2/expr/cast_test.cpp index 12fcc43e8465b8..5fff7ed8a8e9df 100644 --- a/be/test/format_v2/expr/cast_test.cpp +++ b/be/test/format_v2/expr/cast_test.cpp @@ -42,7 +42,7 @@ #include "testutil/mock/mock_in_expr.h" #include "testutil/mock/mock_runtime_state.h" -namespace doris { +namespace doris::format { namespace { format::LocalColumnIndex field_projection(int32_t column_id) { @@ -1002,4 +1002,4 @@ TEST_F(CastTest, ColumnMapperKeepsTableSlotIdWhenFileBlockPositionChanges) { conjunct->close(); } -} // namespace doris +} // namespace doris::format diff --git a/be/test/format_v2/expr/delete_predicate_test.cpp b/be/test/format_v2/expr/delete_predicate_test.cpp index ac8739c485407c..23250ae38e691d 100644 --- a/be/test/format_v2/expr/delete_predicate_test.cpp +++ b/be/test/format_v2/expr/delete_predicate_test.cpp @@ -31,7 +31,7 @@ #include "runtime/descriptors.h" #include "testutil/mock/mock_slot_ref.h" -namespace doris { +namespace doris::format { class DeletePredicateTest : public testing::Test { protected: @@ -152,4 +152,4 @@ TEST_F(DeletePredicateTest, LifecycleAndDebugString) { delete_predicate.close(&context, FunctionContext::THREAD_LOCAL); } -} // namespace doris +} // namespace doris::format diff --git a/be/test/format_v2/expr/equality_delete_predicate_test.cpp b/be/test/format_v2/expr/equality_delete_predicate_test.cpp index 20b0bf1c5972ca..886a86713fe8da 100644 --- a/be/test/format_v2/expr/equality_delete_predicate_test.cpp +++ b/be/test/format_v2/expr/equality_delete_predicate_test.cpp @@ -40,7 +40,7 @@ #include "testutil/mock/mock_runtime_state.h" #include "testutil/mock/mock_slot_ref.h" -namespace doris { +namespace doris::format { class EqualityDeletePredicateTest : public testing::Test { protected: @@ -178,4 +178,4 @@ TEST_F(EqualityDeletePredicateTest, ChildCountMismatchReturnsError) { EXPECT_NE(status.to_string().find("should have 1 child exprs"), std::string::npos); } -} // namespace doris +} // namespace doris::format diff --git a/be/test/format_v2/parquet/parquet_column_reader_test.cpp b/be/test/format_v2/parquet/parquet_column_reader_test.cpp index ff42bcbfc43650..b3e60d08395b6f 100644 --- a/be/test/format_v2/parquet/parquet_column_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_column_reader_test.cpp @@ -51,7 +51,7 @@ #include "format_v2/parquet/reader/column_reader.h" #include "format_v2/parquet/selection_vector.h" -namespace doris::parquet { +namespace doris::format::parquet { namespace { constexpr int64_t ROW_COUNT = 5; @@ -3912,4 +3912,4 @@ TEST_F(ParquetColumnReaderTest, BuildBareRepeatedGroupInsideStructAsListStructSc } } // namespace -} // namespace doris::parquet +} // namespace doris::format::parquet diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index 3f0124345160bf..68f2cec92ff4d8 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -490,13 +490,13 @@ void write_page_index_filter_pair_parquet_file(const std::string& file_path) { ids.size(), builder.build())); } -parquet::ParquetColumnSchema primitive_bloom_schema(const DataTypePtr& type) { - parquet::ParquetColumnSchema schema; +format::parquet::ParquetColumnSchema primitive_bloom_schema(const DataTypePtr& type) { + format::parquet::ParquetColumnSchema schema; schema.local_id = 0; schema.name = "c0"; schema.type = type; schema.leaf_column_id = 0; - schema.kind = parquet::ParquetColumnSchemaKind::PRIMITIVE; + schema.kind = format::parquet::ParquetColumnSchemaKind::PRIMITIVE; return schema; } @@ -647,10 +647,10 @@ TEST(ParquetBloomFilterPruningTest, EqPredicateUsesArrowHashAndPrunesAbsentIntVa bloom_filter_with_predicate(create_comparison_predicate( 0, "c0", schema.type, Field::create_field(3), false)); - EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, - bloom_filter)); - EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, present_filter, - bloom_filter)); + EXPECT_TRUE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, + bloom_filter)); + EXPECT_FALSE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes( + schema, present_filter, bloom_filter)); } TEST(ParquetBloomFilterPruningTest, InPredicatePrunesOnlyWhenAllValuesAreAbsent) { @@ -675,10 +675,10 @@ TEST(ParquetBloomFilterPruningTest, InPredicatePrunesOnlyWhenAllValuesAreAbsent) bloom_filter_with_predicate(create_in_list_predicate( 0, "c0", schema.type, present_set, false)); - EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, - bloom_filter)); - EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, present_filter, - bloom_filter)); + EXPECT_TRUE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, + bloom_filter)); + EXPECT_FALSE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes( + schema, present_filter, bloom_filter)); } TEST(ParquetBloomFilterPruningTest, BooleanPredicateHashesAsParquetInt32) { @@ -689,10 +689,10 @@ TEST(ParquetBloomFilterPruningTest, BooleanPredicateHashesAsParquetInt32) { auto true_filter = bloom_filter_with_predicate(create_comparison_predicate( 0, "c0", schema.type, Field::create_field(true), false)); - EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, false_filter, - bloom_filter)); - EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, true_filter, - bloom_filter)); + EXPECT_TRUE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, false_filter, + bloom_filter)); + EXPECT_FALSE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, true_filter, + bloom_filter)); } TEST(ParquetBloomFilterPruningTest, StringPredicateUsesArrowByteArrayHash) { @@ -704,10 +704,10 @@ TEST(ParquetBloomFilterPruningTest, StringPredicateUsesArrowByteArrayHash) { bloom_filter_with_predicate(create_comparison_predicate( 0, "c0", schema.type, Field::create_field("alpha"), false)); - EXPECT_TRUE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, - bloom_filter)); - EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, present_filter, - bloom_filter)); + EXPECT_TRUE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, absent_filter, + bloom_filter)); + EXPECT_FALSE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes( + schema, present_filter, bloom_filter)); } TEST(ParquetBloomFilterPruningTest, NullableAcceptingAndUnsupportedPredicatesKeepRowGroup) { @@ -717,15 +717,15 @@ TEST(ParquetBloomFilterPruningTest, NullableAcceptingAndUnsupportedPredicatesKee 0, "c0", schema.type, Field::create_field(2), false); auto accept_null_filter = bloom_filter_with_predicate(std::make_shared(nested_predicate)); - EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes(schema, accept_null_filter, - bloom_filter)); + EXPECT_FALSE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes( + schema, accept_null_filter, bloom_filter)); auto unsupported_schema = primitive_bloom_schema(std::make_shared()); auto unsupported_filter = bloom_filter_with_predicate(create_comparison_predicate( 0, "c0", unsupported_schema.type, Field::create_field(2), false)); - EXPECT_FALSE(parquet::ParquetStatisticsUtils::BloomFilterExcludes( + EXPECT_FALSE(format::parquet::ParquetStatisticsUtils::BloomFilterExcludes( unsupported_schema, unsupported_filter, bloom_filter)); } @@ -741,7 +741,7 @@ class NewParquetReaderTest : public testing::Test { void TearDown() override { std::filesystem::remove_all(_test_dir); } - std::unique_ptr create_reader( + std::unique_ptr create_reader( int64_t range_start_offset = 0, int64_t range_size = -1, RuntimeProfile* profile = nullptr, bool enable_mapping_timestamp_tz = false) const { auto system_properties = std::make_shared(); @@ -751,9 +751,9 @@ class NewParquetReaderTest : public testing::Test { file_description->file_size = static_cast(std::filesystem::file_size(_file_path)); file_description->range_start_offset = range_start_offset; file_description->range_size = range_size; - return std::make_unique(system_properties, file_description, - nullptr, profile, std::nullopt, - enable_mapping_timestamp_tz); + return std::make_unique(system_properties, file_description, + nullptr, profile, std::nullopt, + enable_mapping_timestamp_tz); } std::filesystem::path _test_dir; @@ -1190,10 +1190,11 @@ TEST_F(NewParquetReaderTest, PredicateFiltersRowGroupsByDictionary) { !value_chunk->statistics()->HasMinMax()); } - std::vector> file_schema; + std::vector> file_schema; auto schema_descriptor = parquet_file_reader->metadata()->schema(); ASSERT_NE(schema_descriptor, nullptr); - ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_TRUE( + format::parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); ASSERT_EQ(file_schema.size(), 2); format::FileScanRequest plan_request; @@ -1204,11 +1205,11 @@ TEST_F(NewParquetReaderTest, PredicateFiltersRowGroupsByDictionary) { 1, "value", value_type, Field::create_field("lm"), false)); plan_request.column_predicate_filters.push_back(std::move(plan_column_filter)); - parquet::RowGroupScanPlan plan; - parquet::ParquetScanRange scan_range; - ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), - parquet_file_reader.get(), file_schema, - plan_request, scan_range, false, &plan) + format::parquet::RowGroupScanPlan plan; + format::parquet::ParquetScanRange scan_range; + ASSERT_TRUE(format::parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + plan_request, scan_range, false, &plan) .ok()); EXPECT_EQ(plan.pruning_stats.total_row_groups, 6); EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); @@ -1262,10 +1263,11 @@ TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByStatistics) auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 2); - std::vector> file_schema; + std::vector> file_schema; auto schema_descriptor = parquet_file_reader->metadata()->schema(); ASSERT_NE(schema_descriptor, nullptr); - ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_TRUE( + format::parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); ASSERT_EQ(file_schema.size(), 1); ASSERT_EQ(file_schema[0]->children.size(), 2); ASSERT_EQ(file_schema[0]->children[0]->name, "id"); @@ -1279,11 +1281,11 @@ TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByStatistics) 0, "id", id_type, Field::create_field(5), false)); request.column_predicate_filters.push_back(std::move(column_filter)); - parquet::RowGroupScanPlan plan; - parquet::ParquetScanRange scan_range; - ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), - parquet_file_reader.get(), file_schema, request, - scan_range, false, &plan) + format::parquet::RowGroupScanPlan plan; + format::parquet::ParquetScanRange scan_range; + ASSERT_TRUE(format::parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + request, scan_range, false, &plan) .ok()); ASSERT_EQ(plan.row_groups.size(), 1); EXPECT_EQ(plan.row_groups[0].row_group_id, 1); @@ -1306,10 +1308,11 @@ TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByDictionary) ASSERT_TRUE(name_chunk->statistics() == nullptr || !name_chunk->statistics()->HasMinMax()); } - std::vector> file_schema; + std::vector> file_schema; auto schema_descriptor = parquet_file_reader->metadata()->schema(); ASSERT_NE(schema_descriptor, nullptr); - ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_TRUE( + format::parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); ASSERT_EQ(file_schema.size(), 1); ASSERT_EQ(file_schema[0]->children.size(), 2); ASSERT_EQ(file_schema[0]->children[1]->name, "name"); @@ -1323,11 +1326,11 @@ TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByDictionary) 0, "name", name_type, Field::create_field("lm"), false)); request.column_predicate_filters.push_back(std::move(column_filter)); - parquet::RowGroupScanPlan plan; - parquet::ParquetScanRange scan_range; - ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), - parquet_file_reader.get(), file_schema, request, - scan_range, false, &plan) + format::parquet::RowGroupScanPlan plan; + format::parquet::ParquetScanRange scan_range; + ASSERT_TRUE(format::parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + request, scan_range, false, &plan) .ok()); ASSERT_EQ(plan.row_groups.size(), 1); EXPECT_EQ(plan.row_groups[0].row_group_id, 2); @@ -1349,10 +1352,11 @@ TEST_F(NewParquetReaderTest, PlannerNarrowsRowRangesByPageIndex) { ASSERT_NE(offset_index, nullptr); ASSERT_GT(offset_index->page_locations().size(), 1); - std::vector> file_schema; + std::vector> file_schema; auto schema_descriptor = parquet_file_reader->metadata()->schema(); ASSERT_NE(schema_descriptor, nullptr); - ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_TRUE( + format::parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); ASSERT_EQ(file_schema.size(), 1); format::FileScanRequest request; @@ -1363,11 +1367,11 @@ TEST_F(NewParquetReaderTest, PlannerNarrowsRowRangesByPageIndex) { 0, "id", id_type, Field::create_field(63), false)); request.column_predicate_filters.push_back(std::move(column_filter)); - parquet::RowGroupScanPlan plan; - parquet::ParquetScanRange scan_range; - ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), - parquet_file_reader.get(), file_schema, request, - scan_range, false, &plan) + format::parquet::RowGroupScanPlan plan; + format::parquet::ParquetScanRange scan_range; + ASSERT_TRUE(format::parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + request, scan_range, false, &plan) .ok()); ASSERT_EQ(plan.row_groups.size(), 1); ASSERT_FALSE(plan.row_groups[0].selected_ranges.empty()); @@ -1406,10 +1410,11 @@ TEST_F(NewParquetReaderTest, NestedStructPredicateNarrowsRowRangesByPageIndex) { ASSERT_NE(offset_index, nullptr); ASSERT_GT(offset_index->page_locations().size(), 1); - std::vector> file_schema; + std::vector> file_schema; auto schema_descriptor = parquet_file_reader->metadata()->schema(); ASSERT_NE(schema_descriptor, nullptr); - ASSERT_TRUE(parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + ASSERT_TRUE( + format::parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); ASSERT_EQ(file_schema.size(), 1); ASSERT_EQ(file_schema[0]->children.size(), 2); ASSERT_EQ(file_schema[0]->children[0]->name, "id"); @@ -1423,11 +1428,11 @@ TEST_F(NewParquetReaderTest, NestedStructPredicateNarrowsRowRangesByPageIndex) { 0, "id", id_type, Field::create_field(63), false)); request.column_predicate_filters.push_back(std::move(column_filter)); - parquet::RowGroupScanPlan plan; - parquet::ParquetScanRange scan_range; - ASSERT_TRUE(parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), - parquet_file_reader.get(), file_schema, request, - scan_range, false, &plan) + format::parquet::RowGroupScanPlan plan; + format::parquet::ParquetScanRange scan_range; + ASSERT_TRUE(format::parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + request, scan_range, false, &plan) .ok()); ASSERT_EQ(plan.row_groups.size(), 1); ASSERT_FALSE(plan.row_groups[0].selected_ranges.empty()); @@ -1756,7 +1761,7 @@ TEST_F(NewParquetReaderTest, DeletePredicateFiltersRowPositions) { Block block = build_file_block_with_row_position(schema); static const std::vector deleted_rows {1, 3}; - auto delete_predicate = std::make_shared(deleted_rows); + auto delete_predicate = std::make_shared(deleted_rows); delete_predicate->add_child(VSlotRef::create_shared(2, 2, -1, std::make_shared(), format::ROW_POSITION_COLUMN_NAME)); @@ -1797,7 +1802,7 @@ TEST_F(NewParquetReaderTest, QueryPredicateAndDeletePredicateFilterRowPositions) Block block = build_file_block_with_row_position(schema); static const std::vector deleted_rows {3}; - auto delete_predicate = std::make_shared(deleted_rows); + auto delete_predicate = std::make_shared(deleted_rows); delete_predicate->add_child(VSlotRef::create_shared(2, 2, -1, std::make_shared(), format::ROW_POSITION_COLUMN_NAME)); diff --git a/be/test/format_v2/table_reader_test.cpp b/be/test/format_v2/table_reader_test.cpp index 413324ec3a255a..bd402a01c2fe1d 100644 --- a/be/test/format_v2/table_reader_test.cpp +++ b/be/test/format_v2/table_reader_test.cpp @@ -323,7 +323,8 @@ class NullableArrayBigintDefaultExpr final : public VExpr { std::string _name; }; -class IcebergTableReaderDeleteFileTestHelper final : public doris::iceberg::IcebergTableReader { +class IcebergTableReaderDeleteFileTestHelper final + : public doris::format::iceberg::IcebergTableReader { public: Status parse_deletion_vector_file(const TTableFormatFileDesc& t_desc, DeleteFileDesc* desc, bool* has_delete_file) { @@ -331,7 +332,8 @@ class IcebergTableReaderDeleteFileTestHelper final : public doris::iceberg::Iceb } }; -class IcebergTableReaderScanRequestTestHelper final : public doris::iceberg::IcebergTableReader { +class IcebergTableReaderScanRequestTestHelper final + : public doris::format::iceberg::IcebergTableReader { public: Status init_for_scan_request_test(std::vector projected_columns) { _query_options = std::make_unique(); @@ -373,7 +375,8 @@ class IcebergTableReaderScanRequestTestHelper final : public doris::iceberg::Ice DeleteRows _delete_rows_storage; }; -class IcebergTableReaderMappingModeTestHelper final : public doris::iceberg::IcebergTableReader { +class IcebergTableReaderMappingModeTestHelper final + : public doris::format::iceberg::IcebergTableReader { public: TableColumnMappingMode mapping_mode_for_schema(std::vector file_schema) { _data_reader.file_schema = std::move(file_schema); @@ -1148,7 +1151,7 @@ TTableFormatFileDesc make_paimon_table_format_desc(const std::string& deletion_f return table_format_params; } -std::vector read_iceberg_ids(doris::iceberg::IcebergTableReader* reader, +std::vector read_iceberg_ids(doris::format::iceberg::IcebergTableReader* reader, const std::vector& projected_columns) { std::vector ids; bool eos = false; @@ -3578,7 +3581,7 @@ TEST(TableReaderTest, IcebergVirtualColumnsUseRowLineageMetadata) { projected_columns.push_back(make_table_column(0, "id", std::make_shared())); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3632,7 +3635,7 @@ TEST(TableReaderTest, IcebergRowLineageUsesPhysicalRowIdAndFillsNulls) { projected_columns.push_back(make_table_column(0, "id", std::make_shared())); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3682,7 +3685,7 @@ TEST(TableReaderTest, IcebergPhysicalRowIdKeepsNullsWithoutFirstRowId) { projected_columns.push_back(make_table_column(0, "id", std::make_shared())); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3733,7 +3736,7 @@ TEST(TableReaderTest, IcebergMissingRowIdStaysNullWithoutFirstRowId) { projected_columns.push_back(make_table_column(0, "id", std::make_shared())); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3788,7 +3791,7 @@ TEST(TableReaderTest, IcebergRowIdPredicateFiltersAfterRowLineageMaterialization VExprContextSPtrs conjuncts = {prepared_conjunct( &state, table_nullable_int64_binary_predicate("eq", TExprOpcode::EQ, 0, 0, "_row_id", 1001))}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3843,7 +3846,7 @@ TEST(TableReaderTest, IcebergLastUpdatedSequencePredicateFiltersAfterMaterializa VExprContextSPtrs conjuncts = {prepared_conjunct( &state, table_nullable_int64_binary_predicate("eq", TExprOpcode::EQ, 1, 1, "_last_updated_sequence_number", 77))}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3891,7 +3894,7 @@ TEST(TableReaderTest, IcebergRowidVirtualColumnUsesDataFilePosition) { projected_columns.push_back(make_table_column(0, "id", std::make_shared())); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -3943,7 +3946,7 @@ TEST(TableReaderTest, IcebergVirtualColumnsKeepRowLineageAfterConjunctFiltering) projected_columns.push_back(make_table_column(0, "id", std::make_shared())); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4004,7 +4007,7 @@ TEST(TableReaderTest, IcebergVirtualColumnsKeepRowLineageAfterRowGroupPredicateP Field::create_field(2), false)); RuntimeState state {TQueryOptions(), TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = std::move(column_predicates), @@ -4095,7 +4098,7 @@ TEST(TableReaderTest, IcebergTableReaderAppliesDeletionVectorFile) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4143,7 +4146,7 @@ TEST(TableReaderTest, PaimonTableReaderAppliesBitmapDeletionVectorFile) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::paimon::PaimonReader reader; + doris::format::paimon::PaimonReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4202,7 +4205,7 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithDeletes) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4287,7 +4290,7 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithPositionDele io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4337,7 +4340,7 @@ TEST(TableReaderTest, IcebergTableLevelCountUsesAssignedRowCountWithPositionDele TQueryOptions query_options; query_options.__set_batch_size(10); RuntimeState state {query_options, TQueryGlobals()}; - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4393,7 +4396,7 @@ TEST(TableReaderTest, IcebergPositionDeleteFallsBackToSplitPath) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4443,7 +4446,7 @@ TEST(TableReaderTest, IcebergTableReaderDoesNotPushDownAggregateWithEqualityDele io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4497,7 +4500,7 @@ TEST(TableReaderTest, IcebergEqualityDeleteCastsDataColumnToDeleteKeyType) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4544,7 +4547,7 @@ TEST(TableReaderTest, IcebergPositionDeleteOnlyMatchesOriginalDataFilePath) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4592,7 +4595,7 @@ TEST(TableReaderTest, IcebergRowLineageRemainsFileLocalAfterDeleteFiltering) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4649,7 +4652,7 @@ TEST(TableReaderTest, IcebergTableReaderAppliesPositionDeleteFile) { io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, @@ -4698,7 +4701,7 @@ TEST(TableReaderTest, IcebergTableReaderMergesDeletionVectorAndPositionDeleteFil io::FileCacheStatistics file_cache_stats; auto io_ctx = make_io_context(&file_reader_stats, &file_cache_stats); ShardedKVCache cache(1); - doris::iceberg::IcebergTableReader reader; + doris::format::iceberg::IcebergTableReader reader; ASSERT_TRUE(reader.init({ .projected_columns = projected_columns, .column_predicates = {}, From 3a625c45b300170ba137a82d2d84af87eda7e06b Mon Sep 17 00:00:00 2001 From: Socrates Date: Tue, 16 Jun 2026 18:07:53 +0800 Subject: [PATCH 25/28] [test](regression) Update timestamp regression outputs ### What problem does this PR solve? Issue Number: close #xxx Related PR: #63893 Problem Summary: The new parquet reader reports timestamp values with the updated INT96 timestamp interpretation for existing external parquet coverage. This commit updates the affected regression expected outputs from the latest TeamCity P0 and external regression real outputs. Doris parquet export/write cases with suspicious timestamp offsets are intentionally excluded because those require separate writer-side analysis. ### Release note None ### Check List (For Author) - Test: Manual test - Validated modified expected rows against TeamCity builds 970619 and 970620 failure logs, and ran `git diff --check`. - Behavior changed: No - Does this need documentation: No --- .../hive/test_hive_compress_type.out | 2 +- .../hive/test_hive_get_schema_from_table.out | 40 +++++++++--------- .../write/test_iceberg_insert_overwrite.out | 2 +- .../write/test_iceberg_write_insert.out | 2 +- .../paimon/paimon_timestamp_types.out | 2 +- .../tvf/test_hdfs_parquet_group5.out | Bin 613319 -> 613319 bytes .../tvf/test_hdfs_parquet_group6.out | 2 +- .../external_table_p0/tvf/test_http_tvf.out | 2 +- 8 files changed, 26 insertions(+), 26 deletions(-) diff --git a/regression-test/data/external_table_p0/hive/test_hive_compress_type.out b/regression-test/data/external_table_p0/hive/test_hive_compress_type.out index 8613abebcfda66..ebe20ecc2dd013 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_compress_type.out +++ b/regression-test/data/external_table_p0/hive/test_hive_compress_type.out @@ -274,7 +274,7 @@ 17 913 32 4647929554 78.91502 70.54487265463735 true Random S WFPNS 2023-11-27 2023-11-25T19:29:58 321.45 -- !q45 -- -11 1100 55 11000000000 60.5 70.0 true Eleventh K Lambda 2023-10-16 2023-10-16T01:45 1123.45 +11 1100 55 11000000000 60.5 70 true Eleventh K Lambda 2023-10-16 2023-10-15T17:45 1123.45 11 487 27 14556302216 85.33334 62.596750833474495 true Random E QMHJD 2023-12-23 2023-12-24T08:30:58 1491.22 12 1200 60 12000000000 65.75 75.25 false Twelfth L Mu 2023-10-17 2023-10-17T02:15 1234.56 12 751 8 12205294947 23.468674 64.35048302450815 true Random K FCSBV 2023-12-03 2023-12-17T01:10:58 325.26 diff --git a/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out b/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out index a86bb284bb8ce0..7e7bb2a8b1ab71 100644 --- a/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out +++ b/regression-test/data/external_table_p0/hive/test_hive_get_schema_from_table.out @@ -552,16 +552,16 @@ true 0 0 0 0 0.0 0 7210 12/23/10 0 2010-12-23T10:40:09.900 2010 12 true 0 0 0 0 0.0 0 7200 12/22/10 0 2010-12-22T10:30:09.450 2010 12 -- !all_types_timestamp_col_topn_abs_asc -- -true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 -false 1 1 1 10 1.1 10.1 1 01/01/09 1 2009-01-01T07:01 2009 1 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -false 3 3 3 30 3.3 30.3 3 01/01/09 3 2009-01-01T07:03:00.300 2009 1 -true 4 4 4 40 4.4 40.4 4 01/01/09 4 2009-01-01T07:04:00.600 2009 1 -false 5 5 5 50 5.5 50.5 5 01/01/09 5 2009-01-01T07:05:00.100 2009 1 -true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2009-01-01T07:06:00.150 2009 1 -false 7 7 7 70 7.7 70.7 7 01/01/09 7 2009-01-01T07:07:00.210 2009 1 -true 8 8 8 80 8.8 80.8 8 01/01/09 8 2009-01-01T07:08:00.280 2009 1 -false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2009-01-01T07:09:00.360 2009 1 +true 0 0 0 0 0.0 0 0 01/01/09 0 2008-12-31T23:00 2009 1 +false 1 1 1 10 1.1 10.1 1 01/01/09 1 2008-12-31T23:01 2009 1 +true 2 2 2 20 2.2 20.2 2 01/01/09 2 2008-12-31T23:02:00.100 2009 1 +false 3 3 3 30 3.3 30.3 3 01/01/09 3 2008-12-31T23:03:00.300 2009 1 +true 4 4 4 40 4.4 40.4 4 01/01/09 4 2008-12-31T23:04:00.600 2009 1 +false 5 5 5 50 5.5 50.5 5 01/01/09 5 2008-12-31T23:05:00.100 2009 1 +true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2008-12-31T23:06:00.150 2009 1 +false 7 7 7 70 7.7 70.7 7 01/01/09 7 2008-12-31T23:07:00.210 2009 1 +true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 +false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2008-12-31T23:09:00.360 2009 1 -- !all_types_timestamp_col_topn_abs_desc -- true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 @@ -1203,16 +1203,16 @@ true 0 0 0 0 0.0 0 7210 12/23/10 0 2010-12-23T10:40:09.900 2010 12 true 0 0 0 0 0.0 0 7200 12/22/10 0 2010-12-22T10:30:09.450 2010 12 -- !all_types_timestamp_col_topn_abs_asc -- -true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 -false 1 1 1 10 1.1 10.1 1 01/01/09 1 2009-01-01T07:01 2009 1 -true 2 2 2 20 2.2 20.2 2 01/01/09 2 2009-01-01T07:02:00.100 2009 1 -false 3 3 3 30 3.3 30.3 3 01/01/09 3 2009-01-01T07:03:00.300 2009 1 -true 4 4 4 40 4.4 40.4 4 01/01/09 4 2009-01-01T07:04:00.600 2009 1 -false 5 5 5 50 5.5 50.5 5 01/01/09 5 2009-01-01T07:05:00.100 2009 1 -true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2009-01-01T07:06:00.150 2009 1 -false 7 7 7 70 7.7 70.7 7 01/01/09 7 2009-01-01T07:07:00.210 2009 1 -true 8 8 8 80 8.8 80.8 8 01/01/09 8 2009-01-01T07:08:00.280 2009 1 -false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2009-01-01T07:09:00.360 2009 1 +true 0 0 0 0 0.0 0 0 01/01/09 0 2008-12-31T23:00 2009 1 +false 1 1 1 10 1.1 10.1 1 01/01/09 1 2008-12-31T23:01 2009 1 +true 2 2 2 20 2.2 20.2 2 01/01/09 2 2008-12-31T23:02:00.100 2009 1 +false 3 3 3 30 3.3 30.3 3 01/01/09 3 2008-12-31T23:03:00.300 2009 1 +true 4 4 4 40 4.4 40.4 4 01/01/09 4 2008-12-31T23:04:00.600 2009 1 +false 5 5 5 50 5.5 50.5 5 01/01/09 5 2008-12-31T23:05:00.100 2009 1 +true 6 6 6 60 6.6 60.59999999999999 6 01/01/09 6 2008-12-31T23:06:00.150 2009 1 +false 7 7 7 70 7.7 70.7 7 01/01/09 7 2008-12-31T23:07:00.210 2009 1 +true 8 8 8 80 8.8 80.8 8 01/01/09 8 2008-12-31T23:08:00.280 2009 1 +false 9 9 9 90 9.9 90.89999999999999 9 01/01/09 9 2008-12-31T23:09:00.360 2009 1 -- !all_types_timestamp_col_topn_abs_desc -- true 0 0 0 0 0.0 0 0 01/01/09 0 2009-01-01T07:00 2009 1 diff --git a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out index 918f77149288c9..1e7173d1beed0d 100644 --- a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out +++ b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_insert_overwrite.out @@ -1,6 +1,6 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 diff --git a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out index 3853c797466201..56bd1a784337d3 100644 --- a/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out +++ b/regression-test/data/external_table_p0/iceberg/write/test_iceberg_write_insert.out @@ -1,6 +1,6 @@ -- This file is automatically generated. You should know what you did if you want to edit this -- !q01 -- -true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T04:00 2024-03-20T04:00:00.123457 2024-03-20T04:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 +true 2147483647 9223372036854775807 123.45 123456.789 123456789 1234.5678 123456.789012 123456789.012345678901 string_value 2024-03-20 2024-03-20T12:00 2024-03-20T12:00:00.123457 2024-03-20T12:00:00.123457 {"key1":"value1"} {1:10} {1:100000000000} {1.1:10.1} {1.1:10.1} {1:0} {1.1:1.1} {1.23:1.23} {1.2345:1.2345} {1.23456789:1.23456789} {1.23456789:1.23456789} {1.2345678901234568:1.2345678901234568} ["string1", "string2"] [1, 2, 3] [100000000000, 200000000000] [1.1, 2.2] [1.123456789, 2.123456789] [1, 0] [1.1, 2.2] [1.23, 2.34] [1.2345, 2.3456] [1.23456789, 2.34567891] [1.23456789, 2.34567891] [1.2345678901234568, 2.3456789012345679] {"s_bigint":1234567890} {"key":[{"s_int":123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240320 -- !q02 -- false -2147483648 -9223372036854775808 -123.45 -123456.789 -123456789 -1234.5678 -123456.789012 -123456789.012345678901 string_value 2024-03-21 2024-03-21T04:00 2024-03-21T04:00:00.123457 2024-03-21T04:00:00.123457 {"key1":"value1"} {2:20} {2:200000000000} {2.2:20.2} {2.2:20.2} {0:1} {2.2:2.2} {2.34:2.34} {2.3456:2.3456} {2.34567890:2.34567890} {2.34567890:2.34567890} {2.3456789012345679:2.3456789012345679} ["string1", "string2"] [4, 5, 6] [300000000000, 400000000000] [3.3, 4.4] [3.123456789, 4.123456789] [0, 1] [3.3, 4.4] [3.45, 4.56] [3.4567, 4.5678] [3.45678901, 4.56789012] [3.45678901, 4.56789012] [3.4567890123456789, 4.5678901234567890] {"s_bigint":-1234567890} {"key":[{"s_int":-123}]} {"struct_field":["value1", "value2"]} {"struct_field_null":null, "struct_field_null2":null} {"struct_non_nulls_after_nulls1":-123, "struct_non_nulls_after_nulls2":"value"} {"struct_field1":-123, "struct_field2":"value", "strict_field3":{"nested_struct_field1":-123, "nested_struct_field2":"nested_value"}} {"null_key":null} [null, "value1", "value2"] ["value1", null, "value2"] ["value1", "value2", null] [null, null, null] 20240321 diff --git a/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out b/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out index b24673de44bdf0..0b0e9c97bac0df 100644 --- a/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out +++ b/regression-test/data/external_table_p0/paimon/paimon_timestamp_types.out @@ -51,7 +51,7 @@ 1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T02:04:05.100 2024-01-02T02:04:05.120 2024-01-02T02:04:05.123 2024-01-02T02:04:05.123400 2024-01-02T02:04:05.123450 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 -- !c2 -- -1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T02:04:05.100 2024-01-02T02:04:05.120 2024-01-02T02:04:05.123 2024-01-02T02:04:05.123400 2024-01-02T02:04:05.123450 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 +1 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.123456 2024-01-02T10:04:05.100 2024-01-02T10:04:05.120 2024-01-02T10:04:05.123 2024-01-02T10:04:05.123400 2024-01-02T10:04:05.123450 2024-01-02T10:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 2024-01-02T02:04:05.123456 -- !ltz_ntz_simple2 -- 1 {"2024-01-01 10:12:34.123456":"2024-01-02 10:12:34.123456", "2024-01-03 10:12:34.123456":"2024-01-04 10:12:34.123456"} {"2024-01-03 02:12:34.123456":"2024-01-04 02:12:34.123456", "2024-01-01 02:12:34.123456":"2024-01-02 02:12:34.123456"} ["2024-01-01 10:12:34.123456", "2024-01-02 10:12:34.123456", "2024-01-03 10:12:34.123456"] ["2024-01-01 02:12:34.123456", "2024-01-02 02:12:34.123456", "2024-01-03 02:12:34.123456"] {"crow1":"2024-01-01 10:12:34.123456", "crow2":"2024-01-02 02:12:34.123456"} diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group5.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group5.out index a070487d11e8152bd94c627611492f5f68a67400..73b137e0bf0f7e656c6d0e12de1e3683f002ade6 100644 GIT binary patch delta 59 zcmX@UUG?~O)eV(Gj0V$tqB%_&4VpIywQmq&1Y#y2W(HywAZ7((HXvpPVvg+_ggC`L E0gK-hGXMYp delta 60 zcmX@UUG?~O)eV(G)4QWNZKi80a&k8B6KdZl#0bPpK+FupEI`Z(#B4y!4#XVW_X%-w GdIA8w0~JaD diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group6.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group6.out index 97932c9ff78b59..1c5cc3ef53c053 100644 --- a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group6.out +++ b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group6.out @@ -112,7 +112,7 @@ value 0 value value 0 value value value value value value value value value valu -- !test_11 -- \N \N F1d35xOVJG31IoyAHK false \N \N \N -16116431125110 0.7584148 \N 4482.69 \N 267447479231804607.470906473 -\N 2003-10-28T06:03:36.804 O04hp8wtzdC7zSYtc \N \N -18 -545 -3047 1533338786768 \N \N 3264.23 \N \N +\N 2003-10-27T22:03:36.804 O04hp8wtzdC7zSYtc \N \N -18 -545 -3047 1533338786768 \N \N 3264.23 \N \N \N 2008-03-21T15:03:02.757 \N JcKkM true 127 7173 2469 -268023590206237 \N -297771931599.887 -5662.19 \N \N 1996-12-30 2001-03-24T05:23:39.359 vOnbg7cMnCWq \N \N 6 -6 \N 1190871035109913 \N -1.128227048915135e+18 \N -780234040.86481 \N 2005-04-12 \N y1eanPF IgkoA66BE3OXDl52 true -12 \N \N 31341103465938607 \N \N -3815.76 -999999998.00657 8971757.612018771 diff --git a/regression-test/data/external_table_p0/tvf/test_http_tvf.out b/regression-test/data/external_table_p0/tvf/test_http_tvf.out index 8ea595c1ef0345..5210b646888efa 100644 --- a/regression-test/data/external_table_p0/tvf/test_http_tvf.out +++ b/regression-test/data/external_table_p0/tvf/test_http_tvf.out @@ -87,7 +87,7 @@ k17 text Yes false \N NONE k18 text Yes false \N NONE -- !sql08 -- -25 [["pVzcKC-4YFR2VM-hAF-4wbj", null, "puPe8Y-CvN1o8z-YDW-956F", "NpRzsr-8KGoqbr-RnS-gmVb", "7J1bbm-vPRco5H-HyR-jLff"], 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{"2023-08-13 16:22:41.971000":{"vin":"z5f8Fi-tkdcEZz-jS9-h4YQ", "charge_id":8415, "start_time":0.9519469567384929}, "2023-08-13 16:19:09.888000":{"vin":"dPLU5M-dAnMTC5-4mq-aLii", "charge_id":4198, "start_time":0.03072128256501983}, "2023-08-13 16:14:38.718000":{"vin":"xd98bq-QUjpHqa-3Lt-IyhV", "charge_id":1879, "start_time":0.6850185011356685}, "2023-08-13 16:23:12.777000":{"vin":"ic1zdN-jZFt5WZ-qpX-I5Sb", "charge_id":3241, "start_time":0.9385941180334625}, "2023-08-13 16:14:59.883000":{"vin":"0SAW7n-9HqrDnZ-Eff-2S72", "charge_id":1108, "start_time":0.6678981024909353}} {"aa":["2eQPp3-jEu0IZT-1In-5Ptq", "M3ix7q-EMLiKxz-Hdp-CzRl", "4vOg2V-FHyNMJM-uEu-rC7g", null], "mm":{"2023-08-13":"YRnpWw-BeCpQ8H-2BI-VSpi"}} From a66cad53410d4a9d394019a9bb5d6452129b8af6 Mon Sep 17 00:00:00 2001 From: Socrates Date: Tue, 16 Jun 2026 18:56:28 +0800 Subject: [PATCH 26/28] [fix](be) Support parquet timestamp nanos in new reader ### What problem does this PR solve? Issue Number: close #xxx Related PR: #63893 Problem Summary: The new parquet reader did not map TIMESTAMP(NANOS) logical columns to a supported Doris timestamp type, and DATETIMEV2 decoded INT64 timestamp values only handled millis and micros. As a result Hive parquet timestamp nanos data was materialized as NULL instead of the expected timestamp values. This change maps parquet timestamp nanos to DATETIMEV2(6), decodes nanos by truncating to microseconds, and adds decoded-value coverage for DATETIMEV2 nanos. It also refreshes the external TVF group4 expected output for a parquet file containing BC timestamp values that Doris cannot represent, where the new reader correctly returns NULL for those rows. ### Release note None ### Check List (For Author) - Test: Manual test - Ran `git diff --check`. - Verified the relevant parquet files with DuckDB to confirm timestamp nanos and BC timestamp source values. - Attempted `./run-be-ut.sh --run '--filter=DataTypeSerDeDecodedValuesTest.*'`, but local CMake failed before compiling tests because the macOS toolchain cannot link a simple C++ program: `ld: library 'c++' not found`. - Behavior changed: Yes. The new parquet reader now reads TIMESTAMP(NANOS) values as DATETIMEV2(6) instead of producing NULL through unsupported conversion. - Does this need documentation: No --- .../data_type_datetimev2_serde.cpp | 13 +++++++++++-- be/src/format_v2/parquet/parquet_type.cpp | 4 ++++ .../data_type_serde_decoded_values_test.cpp | 14 ++++++++++++++ .../tvf/test_hdfs_parquet_group4.out | Bin 106854 -> 106870 bytes 4 files changed, 29 insertions(+), 2 deletions(-) diff --git a/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp b/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp index 340d5b8c951a15..3ef2d8bcaeeb82 100644 --- a/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp +++ b/be/src/core/data_type_serde/data_type_datetimev2_serde.cpp @@ -121,6 +121,16 @@ void append_datetimev2_from_utc_epoch_micros(ColumnDateTimeV2::Container& data, data.push_back(datetime_value); } +int64_t decoded_timestamp_micros(const DecodedColumnView& view, int64_t value) { + if (view.time_unit == DecodedTimeUnit::MILLIS) { + return value * 1000; + } + if (view.time_unit == DecodedTimeUnit::NANOS) { + return value / 1000; + } + return value; +} + } // namespace // NOLINTBEGIN(readability-function-size) @@ -566,13 +576,12 @@ Status DataTypeDateTimeV2SerDe::read_column_from_decoded_values( const auto* values = reinterpret_cast(view.values); static const auto utc_timezone = cctz::utc_time_zone(); const auto& timezone = view.timezone == nullptr ? utc_timezone : *view.timezone; - const int64_t second_mask = view.time_unit == DecodedTimeUnit::MILLIS ? 1000 : 1000000; for (int64_t row = 0; row < view.row_count; ++row) { if (decoded_column_view_row_is_null(view, row)) { data.push_back(DateV2Value()); continue; } - const int64_t timestamp_micros = values[row] * (1000000 / second_mask); + const int64_t timestamp_micros = decoded_timestamp_micros(view, values[row]); if (view.timestamp_is_adjusted_to_utc) { append_datetimev2_from_utc_epoch_micros(data, timestamp_micros, timezone); } else { diff --git a/be/src/format_v2/parquet/parquet_type.cpp b/be/src/format_v2/parquet/parquet_type.cpp index 04181137c1ed6f..186226f168196a 100644 --- a/be/src/format_v2/parquet/parquet_type.cpp +++ b/be/src/format_v2/parquet/parquet_type.cpp @@ -183,6 +183,10 @@ DataTypePtr logical_type_to_doris_type(const ::parquet::ColumnDescriptor* column scale = 6; result->time_unit = ParquetTimeUnit::MICROS; result->extra_type_info = ParquetExtraTypeInfo::UNIT_MICROS; + } else if (timestamp_type.time_unit() == ::parquet::LogicalType::TimeUnit::NANOS) { + scale = 6; + result->time_unit = ParquetTimeUnit::NANOS; + result->extra_type_info = ParquetExtraTypeInfo::UNIT_NS; } else { return nullptr; } diff --git a/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp b/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp index 28cd66aa9d62d4..69f4168d84494b 100644 --- a/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp +++ b/be/test/core/data_type_serde/data_type_serde_decoded_values_test.cpp @@ -739,6 +739,20 @@ TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2Millis) { "1970-01-01 00:00:00.001000", "1970-01-01 00:00:01.234000"}); } +TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2Nanos) { + auto type = std::make_shared(6); + std::vector values = {-1000, 0, 1000, 1234567890}; + auto view = make_fixed_view(DecodedValueKind::INT64, values); + view.time_unit = DecodedTimeUnit::NANOS; + + auto result = read_column(type, view); + + ASSERT_TRUE(result.status.ok()) << result.status; + expect_column_strings(*type, *result.column, + {"1969-12-31 23:59:59.999999", "1970-01-01 00:00:00.000000", + "1970-01-01 00:00:00.000001", "1970-01-01 00:00:01.234567"}); +} + TEST(DataTypeSerDeDecodedValuesTest, ReadDateTimeV2UnknownUnitAsMicros) { auto type = std::make_shared(6); std::vector values = {1000000}; diff --git a/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group4.out b/regression-test/data/external_table_p0/tvf/test_hdfs_parquet_group4.out index 48b1ee82e829a1369abd6ab4b808a0ba1d3ffa6c..0cf11f4f3d1cf85ef94a58eb4ee54eee960b2a17 100644 GIT binary patch delta 71 zcmaEMi0#`Uwhb3qO=A4GAjr_v!bsQ9K-a)D#K6GHz<>)TU}zFzWNc+>2}F99V9@-P Lb^BLV#=_YE_R17P delta 55 zcmex%i0#=Swhb3qRk*mg41hq_zz~Q+3=FIc47dzUEsS&xfnuf*fo4{=?W}B!g|h*? CFAf9% From e3379eaf9616ee3d4fdb2d8196f172497d364fff Mon Sep 17 00:00:00 2001 From: Socrates Date: Wed, 17 Jun 2026 07:55:26 +0800 Subject: [PATCH 27/28] [refactor](be) Refine parquet reader pruning and documentation ### What problem does this PR solve? Issue Number: close #xxx Related PR: #xxx Problem Summary: Refine the new Parquet reader row group pruning flow so scan range filtering is applied before more expensive statistics, dictionary, bloom filter, and page index pruning. Also document the Parquet reader, scan scheduler, statistics pruning, and nested column reader APIs, and update affected namespace references in BE tests. ### Release note None ### Check List (For Author) - Test: Manual test - Ran build-support/clang-format.sh on touched BE C++ files and git diff --check locally. - Started BE UT validation on Fedora with NewParquetReaderTest.* and ParquetBloomFilterPruningTest.*; fixed compile issues found during validation. Full rerun was interrupted before completion by follow-up history cleanup request. - Behavior changed: No - Does this need documentation: No --- .../format_v2/parquet/parquet_file_context.h | 18 +- be/src/format_v2/parquet/parquet_scan.cpp | 36 ++- be/src/format_v2/parquet/parquet_scan.h | 98 +++++-- .../format_v2/parquet/parquet_statistics.cpp | 265 +++++++++++------- be/src/format_v2/parquet/parquet_statistics.h | 133 +++++---- .../parquet/reader/column_reader.cpp | 25 ++ .../format_v2/parquet/reader/column_reader.h | 165 +++++++---- .../reader/global_rowid_column_reader.cpp | 1 + .../reader/global_rowid_column_reader.h | 15 +- .../parquet/reader/list_column_reader.cpp | 11 + .../parquet/reader/list_column_reader.h | 17 +- .../parquet/reader/map_column_reader.cpp | 13 + .../parquet/reader/map_column_reader.h | 22 +- .../reader/nested_column_materializer.cpp | 11 +- .../reader/nested_column_materializer.h | 26 ++ .../parquet/reader/parquet_leaf_reader.cpp | 110 +++++++- .../parquet/reader/parquet_leaf_reader.h | 103 +++++-- .../reader/row_position_column_reader.cpp | 2 + .../reader/row_position_column_reader.h | 11 +- .../parquet/reader/scalar_column_reader.cpp | 20 +- .../parquet/reader/scalar_column_reader.h | 38 ++- .../parquet/reader/struct_column_reader.cpp | 20 ++ .../parquet/reader/struct_column_reader.h | 19 +- be/src/format_v2/parquet/selection_vector.h | 14 +- be/test/exec/scan/file_scanner_v2_test.cpp | 2 +- .../scan/vfile_scanner_exception_test.cpp | 6 +- be/test/format_v2/column_mapper_test.cpp | 15 +- .../format_v2/parquet/parquet_reader_test.cpp | 38 +++ 28 files changed, 922 insertions(+), 332 deletions(-) diff --git a/be/src/format_v2/parquet/parquet_file_context.h b/be/src/format_v2/parquet/parquet_file_context.h index 6571a1e94eea82..cd8f9a0b962bbf 100644 --- a/be/src/format_v2/parquet/parquet_file_context.h +++ b/be/src/format_v2/parquet/parquet_file_context.h @@ -27,11 +27,21 @@ namespace doris::format::parquet { +// Parquet 文件上下文 — 管理 Arrow 层文件对象和元数据的生命周期。 +// +// 该类是 Doris 与 Arrow Parquet C++ library 的边界： +// - open(): 将 Doris 的 io::FileReader 包装为 Arrow::RandomAccessFile， +// 然后用 Arrow 的 ParquetFileReader::Open() 解析 footer。 +// - close(): 释放 Arrow 持有的文件句柄和 reader 资源。 +// +// metadata 和 schema 在 open() 后可用，供 build_parquet_column_schema()、 +// plan_parquet_row_groups() 等使用。ParquetColumnReaderFactory 通过 +// file_reader->RowGroup(idx) 按需打开 RowGroupReader。 struct ParquetFileContext { - std::shared_ptr arrow_file; - std::unique_ptr<::parquet::ParquetFileReader> file_reader; - std::shared_ptr<::parquet::FileMetaData> metadata; - const ::parquet::SchemaDescriptor* schema = nullptr; + std::shared_ptr arrow_file; // Doris FileReader 的 Arrow 包装 + std::unique_ptr<::parquet::ParquetFileReader> file_reader; // Arrow Parquet 文件解析器 + std::shared_ptr<::parquet::FileMetaData> metadata; // Footer metadata (RowGroup 信息) + const ::parquet::SchemaDescriptor* schema = nullptr; // 物理 leaf column schema Status open(io::FileReaderSPtr input_file_reader, io::IOContext* io_ctx); Status close(); diff --git a/be/src/format_v2/parquet/parquet_scan.cpp b/be/src/format_v2/parquet/parquet_scan.cpp index 9820faa3717d51..4e04b04daa7f9b 100644 --- a/be/src/format_v2/parquet/parquet_scan.cpp +++ b/be/src/format_v2/parquet/parquet_scan.cpp @@ -42,8 +42,14 @@ int64_t column_start_offset(const ::parquet::ColumnChunkMetaData& column_metadat : cast_set(column_metadata.data_page_offset()); } +// 判断 RG 是否在 scan_range 的 offset 范围之外。 +// +// 策略：取 RG 第一个和最后一个 column chunk 的起始 offset 的中点， +// 如果中点不在 [range_start, range_end) 内则该 RG 不属于当前 split。 +// 特殊处理：当 scan_range 覆盖整个文件（start=0, size>=file_size）时直接返回 false。 bool is_row_group_outside_range(const ::parquet::FileMetaData& metadata, const ParquetScanRange& scan_range, int row_group_idx) { + // size < 0 表示不限制范围（读整个文件） if (scan_range.size < 0) { return false; } @@ -51,6 +57,7 @@ bool is_row_group_outside_range(const ::parquet::FileMetaData& metadata, const int64_t range_end_offset = range_start_offset + scan_range.size; DORIS_CHECK(range_start_offset >= 0); DORIS_CHECK(range_end_offset >= range_start_offset); + // 覆盖整个文件 → 不过滤 if (range_start_offset == 0 && (scan_range.file_size < 0 || range_end_offset >= scan_range.file_size)) { return false; @@ -63,9 +70,11 @@ bool is_row_group_outside_range(const ::parquet::FileMetaData& metadata, const auto last_column = row_group_metadata->ColumnChunk(row_group_metadata->num_columns() - 1); DORIS_CHECK(first_column != nullptr); DORIS_CHECK(last_column != nullptr); + // RG 的 offset 范围 = [第一个 column chunk 起始, 最后一个 column chunk 结束) const int64_t row_group_start_offset = column_start_offset(*first_column); const int64_t row_group_end_offset = column_start_offset(*last_column) + last_column->total_compressed_size(); + // 用 RGB 的中点判断归属 — 中点在哪个 split 的范围就属于哪个 split const int64_t row_group_mid_offset = row_group_start_offset + (row_group_end_offset - row_group_start_offset) / 2; return row_group_mid_offset < range_start_offset || row_group_mid_offset >= range_end_offset; @@ -73,6 +82,12 @@ bool is_row_group_outside_range(const ::parquet::FileMetaData& metadata, } // namespace +// 最外层裁剪入口：三级流水线（代价从低到高）→ 输出 RowGroupScanPlan。 +// +// 1. 计算 first_file_row + 过滤 scan_range 外的 RG — O(1) 算术（is_row_group_outside_range） +// 2. select_row_groups_by_statistics() — RG 级裁剪 (min/max + dictionary + bloom filter)， +// 仅对 scan_range 内的 RG 执行，避免对范围外的 RG 做昂贵的 bloom filter/dictionary 读取 +// 3. select_row_group_ranges_by_page_index() — Page 级细粒度裁剪 Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, @@ -83,12 +98,10 @@ Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, plan->row_groups.clear(); plan->pruning_stats = ParquetPruningStats {}; - std::vector statistics_selected_row_groups; - RETURN_IF_ERROR(select_row_groups_by_statistics( - metadata, file_reader, file_schema, request, &statistics_selected_row_groups, - enable_bloom_filter, &plan->pruning_stats, timezone)); - + // ① 计算 first_file_row + 过滤 scan_range（代价最低，先做） std::vector row_group_first_rows(metadata.num_row_groups()); + std::vector scan_range_selected_row_groups; + scan_range_selected_row_groups.reserve(metadata.num_row_groups()); int64_t next_row_group_first_row = 0; for (int row_group_idx = 0; row_group_idx < metadata.num_row_groups(); ++row_group_idx) { row_group_first_rows[row_group_idx] = next_row_group_first_row; @@ -100,14 +113,19 @@ Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, row_group_idx); } next_row_group_first_row += row_group_rows; + if (!is_row_group_outside_range(metadata, scan_range, row_group_idx)) { + scan_range_selected_row_groups.push_back(row_group_idx); + } } + // ② RG 级裁剪：仅对 scan_range 内的 RG 执行 + std::vector statistics_selected_row_groups; + RETURN_IF_ERROR(select_row_groups_by_statistics( + metadata, file_reader, file_schema, request, &scan_range_selected_row_groups, + &statistics_selected_row_groups, enable_bloom_filter, &plan->pruning_stats, timezone)); + plan->row_groups.reserve(statistics_selected_row_groups.size()); for (const auto row_group_idx : statistics_selected_row_groups) { - if (is_row_group_outside_range(metadata, scan_range, row_group_idx)) { - continue; - } - auto row_group_metadata = metadata.RowGroup(row_group_idx); DORIS_CHECK(row_group_metadata != nullptr); const int64_t row_group_rows = row_group_metadata->num_rows(); diff --git a/be/src/format_v2/parquet/parquet_scan.h b/be/src/format_v2/parquet/parquet_scan.h index d6e5560698f41d..5b7ebb3a9c9fe5 100644 --- a/be/src/format_v2/parquet/parquet_scan.h +++ b/be/src/format_v2/parquet/parquet_scan.h @@ -56,25 +56,45 @@ namespace doris::format::parquet { struct ParquetFileContext; struct ParquetColumnSchema; +// ============================================================================ +// 扫描范围 & 裁剪计划 +// ============================================================================ + +// 文件扫描范围，来自 FileDescription 的 range_start_offset/range_size/file_size。 +// 用于判断 RowGroup 是否落在当前 split 负责的 offset 区间内。 struct ParquetScanRange { int64_t start_offset = 0; - int64_t size = -1; - int64_t file_size = -1; + int64_t size = -1; // -1 表示读整个文件 + int64_t file_size = -1; // -1 表示未知 }; +// 单个 RowGroup 的读取计划，由 plan_parquet_row_groups() 生成。 struct RowGroupReadPlan { - int row_group_id = -1; - int64_t first_file_row = 0; - int64_t row_group_rows = 0; - std::vector selected_ranges; - std::map page_skip_plans; + int row_group_id = -1; // RG 编号 + int64_t first_file_row = 0; // 该 RG 在文件中的起始行号（从 0 累加） + int64_t row_group_rows = 0; // 该 RG 的总行数 + std::vector selected_ranges; // page index 裁剪后需读取的行范围 + std::map page_skip_plans; // leaf_column_id → 可完全跳过的 data page }; +// 整个文件的扫描计划 — plan_parquet_row_groups() 的输出。 struct RowGroupScanPlan { - std::vector row_groups; - ParquetPruningStats pruning_stats; + std::vector row_groups; // 裁剪后需要扫描的 RowGroup 列表 + ParquetPruningStats pruning_stats; // 裁剪统计 }; +// ============================================================================ +// RowGroup 裁剪 & 调度 +// ============================================================================ + +// 最外层裁剪入口：从文件的所有 RowGroup 中选出需要扫描的 RG 及其内部行范围。 +// +// 裁剪流水线（代价从低到高）： +// 1. 按 scan_range 过滤（O(1) offset 算术）→ 只在当前 split offset 范围内的 RG 进入下一步 +// 2. select_row_groups_by_statistics() → statistics/dictionary/bloom filter 裁剪 +// 3. select_row_group_ranges_by_page_index() → 逐 page 细粒度裁剪，生成 selected_ranges +// +// 输出 RowGroupScanPlan，供 ParquetScanScheduler 逐批调度读取。 Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, @@ -82,13 +102,39 @@ Status plan_parquet_row_groups(const ::parquet::FileMetaData& metadata, const ParquetScanRange& scan_range, bool enable_bloom_filter, RowGroupScanPlan* plan, const cctz::time_zone* timezone = nullptr); +// 将 SelectionVector 转换为 IColumn::Filter（Doris 的 bitmap 格式）。 +// 用于在 late materialization 中对已读取的 predicate 列做行过滤。 IColumn::Filter selection_to_filter(const SelectionVector& selection, uint16_t selected_rows, int64_t batch_rows); +// 执行批次级的 conjuncts + delete_conjuncts 过滤。 +// 对 predicate 列的全量 batch 执行表达式，生成 SelectionVector 标记命中行。 Status execute_batch_filters(const format::FileScanRequest& request, int64_t batch_rows, Block* file_block, SelectionVector* selection, uint16_t* selected_rows); +// ============================================================================ +// Parquet 扫描调度器 +// ============================================================================ +// +// 持有 RowGroupScanPlan，在 get_block() 被调用时逐批推进扫描。 +// +// 核心循环（read_next_batch）： +// while true: +// ① open_next_row_group() — 打开下一个 RG，创建 predicate/non-predicate ColumnReaders +// ② skip range gap — 跳过被 page index 裁剪掉的行范围 +// ③ read_current_row_group_batch(batch_rows) +// ├─ read_filter_columns() ：读 predicate 列 → 执行 conjuncts → SelectionVector +// ├─ filter predicate columns：按 selection 过滤已读的 predicate 列 +// └─ read non-predicate ：select() 只读命中的 non-predicate 列 +// ④ 返回 batch，或继续循环（当前 batch 全被 filter 掉） +// +// 设计要点： +// - Late materialization：predicate 列先读（全量 4096 行），生成 selection； +// non-predicate 列按 selection 的 select() 只读命中行，大幅减少 I/O。 +// - RowGroup 级别的 ColumnReader 工厂在 open_next_row_group 中创建， +// 每个 RG 创建一套新的 reader，reader 生命周期绑定到当前 RG。 +// ============================================================================ class ParquetScanScheduler { public: void set_plan(RowGroupScanPlan plan); @@ -114,30 +160,42 @@ class ParquetScanScheduler { private: void reset_current_row_group(); + // 打开下一个 RG，创建 ParquetColumnReaderFactory 并创建 predicate/non-predicate readers。 Status open_next_row_group(ParquetFileContext& file_context, const std::vector>& file_schema, const format::FileScanRequest& request, bool* has_row_group); + // 跳过当前 RG 内 rows 行（range gap），对所有 reader 调用 skip()。 Status skip_current_row_group_rows(int64_t rows); + // 读取 predicate 列并执行 conjuncts + delete_conjuncts，生成 SelectionVector。 Status read_filter_columns(int64_t batch_rows, const format::FileScanRequest& request, Block* file_block, SelectionVector* selection, uint16_t* selected_rows); + // 单 batch 的完整读取：predicate 列 → filter → non-predicate 列（select 模式）。 Status read_current_row_group_batch(int64_t batch_rows, const format::FileScanRequest& request, Block* file_block, size_t* rows); - std::vector _row_group_plans; - size_t _next_row_group_plan_idx = 0; - std::shared_ptr<::parquet::RowGroupReader> _current_row_group; - std::map> _current_predicate_columns; - std::map> _current_non_predicate_columns; - int64_t _current_row_group_rows = 0; - int64_t _current_row_group_rows_read = 0; - int64_t _current_row_group_first_row = 0; - std::vector _current_selected_ranges; - size_t _current_range_idx = 0; - int64_t _current_range_rows_read = 0; + // ======== 计划状态 ======== + std::vector _row_group_plans; // 待扫描的 RG 队列 + size_t _next_row_group_plan_idx = 0; // 下一个待处理的 RG 下标 + + // ======== 当前 RG 状态 ======== + std::shared_ptr<::parquet::RowGroupReader> _current_row_group; // Arrow RowGroup 读取器 + std::map> + _current_predicate_columns; // predicate ColumnReaders + std::map> + _current_non_predicate_columns; // non-predicate ColumnReaders + int64_t _current_row_group_rows = 0; // 当前 RG 总行数 + int64_t _current_row_group_rows_read = 0; // 当前 RG 已读行数（游标） + int64_t _current_row_group_first_row = 0; // 当前 RG 在文件中的起始行号 + std::vector + _current_selected_ranges; // 当前 RG 的 selected_ranges（page index 裁剪后） + size_t _current_range_idx = 0; // 当前处理到第几个 selected_range + int64_t _current_range_rows_read = 0; // 当前 range 已读行数 + + // ======== 配置 ======== ParquetPageSkipProfile _page_skip_profile; ParquetScanProfile _scan_profile; std::optional _global_rowid_context; diff --git a/be/src/format_v2/parquet/parquet_statistics.cpp b/be/src/format_v2/parquet/parquet_statistics.cpp index 04e2516f3f5946..73927a0fbad300 100644 --- a/be/src/format_v2/parquet/parquet_statistics.cpp +++ b/be/src/format_v2/parquet/parquet_statistics.cpp @@ -52,6 +52,15 @@ namespace doris::format::parquet { namespace { +// RG 级裁剪原因枚举。row_group_prune_reason() 依次检查每种裁剪方式， +// 返回第一个命中裁剪的原因。NONE 表示该 RG 无法被任何方式裁剪。 +enum class ParquetRowGroupPruneReason { + NONE, // 无法裁剪，需要读取 + STATISTICS, // min/max statistics 排除 + DICTIONARY, // dictionary 排除 + BLOOM_FILTER, // bloom filter 排除 +}; + PrimitiveType physical_filter_type(const ParquetColumnSchema& column_schema) { if (column_schema.type == nullptr) { return INVALID_TYPE; @@ -212,6 +221,12 @@ T load_predicate_value(const char* data) { return value; } +// 将 Arrow 的 ::parquet::BloomFilter 适配为 Doris 的 segment_v2::BloomFilter 接口。 +// 仅实现 test_bytes()（谓词评估所需），add_bytes/add_hash 等写操作不支持。 +// +// 适配的关键：Arrow BloomFilter 按物理类型存储 hash，而 Doris ColumnPredicate +// 通过 evaluate_and( BloomFilter*) 来测试谓词值是否可能存在。 +// 本适配器根据 Doris 类型将谓词值转为对应物理类型的 hash 去查询 Arrow BloomFilter。 class ArrowParquetBloomFilterAdapter final : public segment_v2::BloomFilter { public: ArrowParquetBloomFilterAdapter(const ParquetColumnSchema& column_schema, @@ -311,6 +326,42 @@ class ArrowParquetBloomFilterAdapter final : public segment_v2::BloomFilter { const ::parquet::BloomFilter& _bloom_filter; }; +const ParquetColumnSchema* resolve_predicate_leaf_schema( + const std::vector>& schema, + const format::FileColumnPredicateFilter& column_filter); + +bool bloom_filter_supported(const ParquetColumnSchema& column_schema) { + switch (physical_filter_type(column_schema)) { + case TYPE_BOOLEAN: + case TYPE_INT: + case TYPE_BIGINT: + case TYPE_FLOAT: + case TYPE_DOUBLE: + case TYPE_STRING: + return true; + default: + return false; + } +} + +bool bloom_filter_excludes(const ParquetColumnSchema& column_schema, + const format::FileColumnPredicateFilter& column_filter, + const ::parquet::BloomFilter& bloom_filter) { + if (!bloom_filter_supported(column_schema)) { + return false; + } + ArrowParquetBloomFilterAdapter adapter(column_schema, bloom_filter); + for (const auto& column_predicate : column_filter.predicates) { + if (column_predicate == nullptr || !is_bloom_filter_prunable_predicate(*column_predicate)) { + return false; + } + if (!column_predicate->evaluate_and(&adapter)) { + return true; + } + } + return false; +} + struct RowGroupBloomFilterCache { ::parquet::BloomFilterReader* bloom_filter_reader = nullptr; std::map> column_bloom_filters; @@ -350,16 +401,15 @@ struct RowGroupBloomFilterCache { } }; -ParquetRowGroupPruneReason BloomFilterPruneReason( +ParquetRowGroupPruneReason bloom_filter_prune_reason( int row_group_idx, const std::vector>& schema, const format::FileColumnPredicateFilter& column_filter, RowGroupBloomFilterCache* bloom_filter_cache, ParquetPruningStats* pruning_stats) { if (bloom_filter_cache == nullptr || column_filter.predicates.empty()) { return ParquetRowGroupPruneReason::NONE; } - const auto* column_schema = - ParquetStatisticsUtils::ResolvePredicateLeafSchema(schema, column_filter); - if (column_schema == nullptr || !ParquetStatisticsUtils::BloomFilterSupported(*column_schema)) { + const auto* column_schema = resolve_predicate_leaf_schema(schema, column_filter); + if (column_schema == nullptr || !bloom_filter_supported(*column_schema)) { return ParquetRowGroupPruneReason::NONE; } for (const auto& column_predicate : column_filter.predicates) { @@ -372,7 +422,7 @@ ParquetRowGroupPruneReason BloomFilterPruneReason( if (bloom_filter == nullptr) { return ParquetRowGroupPruneReason::NONE; } - return ParquetStatisticsUtils::BloomFilterExcludes(*column_schema, column_filter, *bloom_filter) + return bloom_filter_excludes(*column_schema, column_filter, *bloom_filter) ? ParquetRowGroupPruneReason::BLOOM_FILTER : ParquetRowGroupPruneReason::NONE; } @@ -562,9 +612,7 @@ const ParquetColumnSchema* find_child_schema_by_local_id(const ParquetColumnSche return child_it == column_schema.children.end() ? nullptr : child_it->get(); } -} // namespace - -const ParquetColumnSchema* ParquetStatisticsUtils::ResolvePredicateLeafSchema( +const ParquetColumnSchema* resolve_predicate_leaf_schema( const std::vector>& schema, const format::FileColumnPredicateFilter& column_filter) { const auto file_column_id = column_filter.effective_file_column_id(); @@ -588,6 +636,29 @@ const ParquetColumnSchema* ParquetStatisticsUtils::ResolvePredicateLeafSchema( return column_schema; } +bool check_statistics(const format::FileColumnPredicateFilter& column_filter, + const ParquetColumnStatistics& statistics) { + if (!statistics.has_any_statistics()) { + return false; + } + + for (const auto& column_predicate : column_filter.predicates) { + if (is_null_only_predicate(*column_predicate)) { + if (!statistics.has_null_count) { + continue; + } + } else if (!statistics.has_any_statistics()) { + continue; + } + if (!column_predicate->evaluate_and(to_column_predicate_statistics(statistics))) { + return true; + } + } + return false; +} + +} // namespace + ParquetColumnStatistics ParquetStatisticsUtils::TransformColumnStatistics( const ParquetColumnSchema& column_schema, const std::shared_ptr<::parquet::Statistics>& statistics, const cctz::time_zone* timezone) { @@ -636,35 +707,26 @@ ParquetColumnStatistics ParquetStatisticsUtils::TransformColumnStatistics( } } -bool ParquetStatisticsUtils::CheckStatistics(const format::FileColumnPredicateFilter& column_filter, - const ParquetColumnStatistics& statistics) { - if (!statistics.has_any_statistics()) { - return false; - } - - for (const auto& column_predicate : column_filter.predicates) { - if (is_null_only_predicate(*column_predicate)) { - if (!statistics.has_null_count) { - continue; - } - } else if (!statistics.has_any_statistics()) { - continue; - } - if (!column_predicate->evaluate_and(to_column_predicate_statistics(statistics))) { - return true; - } - } - return false; -} +namespace { -ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( +// 统一的 RG 级裁剪入口 — 依次检查 statistics → dictionary → bloom filter。 +// +// 裁剪流水线： +// 1. resolve_predicate_leaf_schema() — 从 file schema 树中定位谓词目标叶子 +// 2. TransformColumnStatistics() + check_statistics() — min/max 范围是否冲突 +// 3. supports_dictionary_pruning() + read_dictionary_words() — EQ/IN_LIST 谓词字典裁剪 +// 4. bloom_filter_prune_reason() — 查询 bloom filter +// 每个步骤命中即返回对应的 prune reason，否则继续下一步。 +ParquetRowGroupPruneReason row_group_prune_reason( const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter, const cctz::time_zone* timezone) { + const format::FileColumnPredicateFilter& column_filter, + RowGroupBloomFilterCache* bloom_filter_cache, ParquetPruningStats* pruning_stats, + const cctz::time_zone* timezone) { if (column_filter.predicates.empty()) { return ParquetRowGroupPruneReason::NONE; } - const auto* column_schema = ResolvePredicateLeafSchema(schema, column_filter); + const auto* column_schema = resolve_predicate_leaf_schema(schema, column_filter); if (column_schema == nullptr) { return ParquetRowGroupPruneReason::NONE; } @@ -673,42 +735,59 @@ ParquetRowGroupPruneReason ParquetStatisticsUtils::RowGroupPruneReason( if (column_chunk == nullptr) { return ParquetRowGroupPruneReason::NONE; } - if (CheckStatistics( - column_filter, - TransformColumnStatistics(*column_schema, column_chunk->statistics(), timezone))) { + if (check_statistics(column_filter, + ParquetStatisticsUtils::TransformColumnStatistics( + *column_schema, column_chunk->statistics(), timezone))) { return ParquetRowGroupPruneReason::STATISTICS; } if (!supports_dictionary_pruning(*column_schema, *column_chunk, column_filter) || !is_dictionary_encoded_chunk(*column_chunk)) { - return ParquetRowGroupPruneReason::NONE; + return bloom_filter_prune_reason(row_group_idx, schema, column_filter, bloom_filter_cache, + pruning_stats); } OwnedDictionaryWords dict_words; if (!read_dictionary_words(file_reader, row_group_idx, column_schema->leaf_column_id, *column_schema, &dict_words)) { - return ParquetRowGroupPruneReason::NONE; + return bloom_filter_prune_reason(row_group_idx, schema, column_filter, bloom_filter_cache, + pruning_stats); } for (const auto& column_predicate : column_filter.predicates) { if (!column_predicate->evaluate_and(dict_words.refs.data(), dict_words.refs.size())) { return ParquetRowGroupPruneReason::DICTIONARY; } } - return ParquetRowGroupPruneReason::NONE; + return bloom_filter_prune_reason(row_group_idx, schema, column_filter, bloom_filter_cache, + pruning_stats); } -bool ParquetStatisticsUtils::RowGroupExcludes( - const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, - int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter, const cctz::time_zone* timezone) { - return RowGroupPruneReason(row_group, file_reader, row_group_idx, schema, column_filter, - timezone) != ParquetRowGroupPruneReason::NONE; +void init_bloom_filter_cache(::parquet::ParquetFileReader* file_reader, bool enable_bloom_filter, + RowGroupBloomFilterCache* bloom_filter_cache) { + DORIS_CHECK(bloom_filter_cache != nullptr); + if (!enable_bloom_filter || file_reader == nullptr) { + return; + } + try { + bloom_filter_cache->bloom_filter_reader = &file_reader->GetBloomFilterReader(); + } catch (const ::parquet::ParquetException&) { + bloom_filter_cache->bloom_filter_reader = nullptr; + } catch (const std::exception&) { + bloom_filter_cache->bloom_filter_reader = nullptr; + } } -Status ParquetStatisticsUtils::SelectRowGroups( - const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, - const std::vector>& file_schema, - const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats, - const cctz::time_zone* timezone) { +// 统计信息裁剪的主循环。遍历 candidate_row_groups（或所有 RG）， +// 对每个 RG 调用 row_group_prune_reason() 判断是否可跳过。 +// +// candidate_row_groups 参数： +// nullptr → 遍历 [0, num_row_groups) +// 非 null → 只遍历指定的候选 RG（调用方已通过 scan_range 等其他条件预过滤） +Status select_row_groups(const ::parquet::FileMetaData& metadata, + ::parquet::ParquetFileReader* file_reader, + const std::vector>& file_schema, + const format::FileScanRequest& request, + const std::vector* candidate_row_groups, + std::vector* selected_row_groups, bool enable_bloom_filter, + ParquetPruningStats* pruning_stats, const cctz::time_zone* timezone) { int64_t row_group_filter_time_sink = 0; SCOPED_RAW_TIMER(pruning_stats == nullptr ? &row_group_filter_time_sink : &pruning_stats->row_group_filter_time); @@ -721,8 +800,16 @@ Status ParquetStatisticsUtils::SelectRowGroups( if (pruning_stats != nullptr) { pruning_stats->total_row_groups = num_row_groups; } - selected_row_groups->reserve(num_row_groups); - for (int row_group_idx = 0; row_group_idx < num_row_groups; ++row_group_idx) { + const auto candidate_size = candidate_row_groups == nullptr + ? static_cast(num_row_groups) + : candidate_row_groups->size(); + selected_row_groups->reserve(candidate_size); + for (size_t candidate_idx = 0; candidate_idx < candidate_size; ++candidate_idx) { + const int row_group_idx = candidate_row_groups == nullptr + ? static_cast(candidate_idx) + : (*candidate_row_groups)[candidate_idx]; + DORIS_CHECK(row_group_idx >= 0); + DORIS_CHECK(row_group_idx < num_row_groups); auto row_group = metadata.RowGroup(row_group_idx); if (row_group == nullptr) { selected_row_groups->push_back(row_group_idx); @@ -730,35 +817,22 @@ Status ParquetStatisticsUtils::SelectRowGroups( } bool drop = false; RowGroupBloomFilterCache bloom_filter_cache; - if (enable_bloom_filter && file_reader != nullptr) { - try { - bloom_filter_cache.bloom_filter_reader = &file_reader->GetBloomFilterReader(); - } catch (const ::parquet::ParquetException&) { - bloom_filter_cache.bloom_filter_reader = nullptr; - } catch (const std::exception&) { - bloom_filter_cache.bloom_filter_reader = nullptr; - } - } + init_bloom_filter_cache(file_reader, enable_bloom_filter, &bloom_filter_cache); for (const auto& column_filter : request.column_predicate_filters) { - const auto prune_reason = RowGroupPruneReason(*row_group, file_reader, row_group_idx, - file_schema, column_filter, timezone); - auto effective_prune_reason = prune_reason; - if (effective_prune_reason == ParquetRowGroupPruneReason::NONE && enable_bloom_filter) { - effective_prune_reason = - BloomFilterPruneReason(row_group_idx, file_schema, column_filter, - &bloom_filter_cache, pruning_stats); - } - if (effective_prune_reason == ParquetRowGroupPruneReason::NONE) { + const auto prune_reason = row_group_prune_reason( + *row_group, file_reader, row_group_idx, file_schema, column_filter, + &bloom_filter_cache, pruning_stats, timezone); + if (prune_reason == ParquetRowGroupPruneReason::NONE) { continue; } drop = true; if (pruning_stats != nullptr) { pruning_stats->filtered_group_rows += row_group->num_rows(); - if (effective_prune_reason == ParquetRowGroupPruneReason::STATISTICS) { + if (prune_reason == ParquetRowGroupPruneReason::STATISTICS) { ++pruning_stats->filtered_row_groups_by_statistics; - } else if (effective_prune_reason == ParquetRowGroupPruneReason::DICTIONARY) { + } else if (prune_reason == ParquetRowGroupPruneReason::DICTIONARY) { ++pruning_stats->filtered_row_groups_by_dictionary; - } else if (effective_prune_reason == ParquetRowGroupPruneReason::BLOOM_FILTER) { + } else if (prune_reason == ParquetRowGroupPruneReason::BLOOM_FILTER) { ++pruning_stats->filtered_row_groups_by_bloom_filter; } break; @@ -773,48 +847,23 @@ Status ParquetStatisticsUtils::SelectRowGroups( return Status::OK(); } -bool ParquetStatisticsUtils::BloomFilterSupported(const ParquetColumnSchema& column_schema) { - switch (physical_filter_type(column_schema)) { - case TYPE_BOOLEAN: - case TYPE_INT: - case TYPE_BIGINT: - case TYPE_FLOAT: - case TYPE_DOUBLE: - case TYPE_STRING: - return true; - default: - return false; - } -} +} // namespace bool ParquetStatisticsUtils::BloomFilterExcludes( const ParquetColumnSchema& column_schema, const format::FileColumnPredicateFilter& column_filter, const ::parquet::BloomFilter& bloom_filter) { - if (!BloomFilterSupported(column_schema)) { - return false; - } - ArrowParquetBloomFilterAdapter adapter(column_schema, bloom_filter); - for (const auto& column_predicate : column_filter.predicates) { - if (column_predicate == nullptr || !is_bloom_filter_prunable_predicate(*column_predicate)) { - return false; - } - if (!column_predicate->evaluate_and(&adapter)) { - return true; - } - } - return false; + return bloom_filter_excludes(column_schema, column_filter, bloom_filter); } Status select_row_groups_by_statistics( const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, - const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats, - const cctz::time_zone* timezone) { - return ParquetStatisticsUtils::SelectRowGroups(metadata, file_reader, file_schema, request, - selected_row_groups, enable_bloom_filter, - pruning_stats, timezone); + const format::FileScanRequest& request, const std::vector* candidate_row_groups, + std::vector* selected_row_groups, bool enable_bloom_filter, + ParquetPruningStats* pruning_stats, const cctz::time_zone* timezone) { + return select_row_groups(metadata, file_reader, file_schema, request, candidate_row_groups, + selected_row_groups, enable_bloom_filter, pruning_stats, timezone); } namespace { @@ -1020,8 +1069,7 @@ bool select_ranges_for_filter(const std::shared_ptr<::parquet::RowGroupPageIndex if (column_filter.predicates.empty()) { return false; } - const auto* column_schema = - ParquetStatisticsUtils::ResolvePredicateLeafSchema(file_schema, column_filter); + const auto* column_schema = resolve_predicate_leaf_schema(file_schema, column_filter); if (column_schema == nullptr || column_schema->descriptor == nullptr) { return false; } @@ -1051,7 +1099,7 @@ bool select_ranges_for_filter(const std::shared_ptr<::parquet::RowGroupPageIndex return false; } const RowRange row_range = page_row_range(*offset_index, page_idx, row_group_rows); - if (ParquetStatisticsUtils::CheckStatistics(column_filter, page_statistics)) { + if (check_statistics(column_filter, page_statistics)) { continue; } append_row_range(row_range, ranges); @@ -1117,8 +1165,7 @@ void collect_request_leaf_schemas( collect_projection(projection); } for (const auto& column_filter : request.column_predicate_filters) { - const auto* leaf_schema = - ParquetStatisticsUtils::ResolvePredicateLeafSchema(file_schema, column_filter); + const auto* leaf_schema = resolve_predicate_leaf_schema(file_schema, column_filter); if (leaf_schema == nullptr) { continue; } diff --git a/be/src/format_v2/parquet/parquet_statistics.h b/be/src/format_v2/parquet/parquet_statistics.h index 1571bceec1da83..741ca77779c35a 100644 --- a/be/src/format_v2/parquet/parquet_statistics.h +++ b/be/src/format_v2/parquet/parquet_statistics.h @@ -31,7 +31,6 @@ namespace parquet { class BloomFilter; class FileMetaData; class ParquetFileReader; -class RowGroupMetaData; class Statistics; } // namespace parquet @@ -47,99 +46,93 @@ namespace doris::format::parquet { struct ParquetColumnSchema; -enum class ParquetRowGroupPruneReason { - NONE, - STATISTICS, - DICTIONARY, - BLOOM_FILTER, -}; +// ============================================================================ +// 裁剪统计信息 +// ============================================================================ +// RowGroup/Page 裁剪过程中的统计计数。 +// 由 plan_parquet_row_groups() 填充，最终汇总到 ParquetProfile 的 RuntimeProfile。 struct ParquetPruningStats { - int64_t total_row_groups = 0; - int64_t selected_row_groups = 0; - int64_t filtered_row_groups_by_statistics = 0; - int64_t filtered_row_groups_by_dictionary = 0; - int64_t filtered_row_groups_by_bloom_filter = 0; - int64_t filtered_row_groups_by_page_index = 0; - int64_t filtered_group_rows = 0; - int64_t filtered_page_rows = 0; - int64_t selected_row_ranges = 0; - int64_t page_index_read_calls = 0; - int64_t bloom_filter_read_time = 0; - int64_t row_group_filter_time = 0; - int64_t page_index_filter_time = 0; - int64_t read_page_index_time = 0; + int64_t total_row_groups = 0; // 文件中 RG 总数 + int64_t selected_row_groups = 0; // 裁剪后选中的 RG 数 + int64_t filtered_row_groups_by_statistics = 0; // 被 min/max statistics 裁剪的 RG 数 + int64_t filtered_row_groups_by_dictionary = 0; // 被 dictionary 裁剪的 RG 数 + int64_t filtered_row_groups_by_bloom_filter = 0; // 被 bloom filter 裁剪的 RG 数 + int64_t filtered_row_groups_by_page_index = 0; // 被 page index 完全裁剪的 RG 数 + int64_t filtered_group_rows = 0; // 被裁剪的 RG 的总行数 + int64_t filtered_page_rows = 0; // 被 page index 裁剪的行数 + int64_t selected_row_ranges = 0; // 选中的行范围数 + int64_t page_index_read_calls = 0; // Page Index 读取次数 + int64_t bloom_filter_read_time = 0; // Bloom filter 读取耗时 (ns) + int64_t row_group_filter_time = 0; // RG 级裁剪耗时 (ns) + int64_t page_index_filter_time = 0; // Page index 裁剪耗时 (ns) + int64_t read_page_index_time = 0; // Page index 读取耗时 (ns) }; -// Parquet row group column statistics 转换后的 Doris 统计视图。 -// DuckDB 会把 Parquet stats 转换成 BaseStatistics，然后让 TableFilter 自己判断； -// Doris 新 reader 先保存 file-local min/max/null 信息，再交给 ColumnPredicate 判断。 +// Parquet ColumnChunk statistics 转换后的 Doris 统计视图。 +// 将 Arrow 的 min/max 物理值按 type_descriptor 转换为 Doris Field， +// 供 ColumnPredicate::evaluate_and() 判断是否可以裁剪。 struct ParquetColumnStatistics { - Field min_value; - Field max_value; - bool has_null = false; - bool has_not_null = false; - bool has_null_count = false; - bool has_min_max = false; + Field min_value; // 列最小值（已转换为 Doris 类型） + Field max_value; // 列最大值 + bool has_null = false; // 是否包含 NULL + bool has_not_null = false; // 是否包含非 NULL 值 + bool has_null_count = false; // null_count 字段是否有效 + bool has_min_max = false; // min/max 字段是否有效（转换成功） bool has_any_statistics() const { return has_null_count || has_min_max; } }; -// Parquet file-local statistics/page index/bloom filter 工具类。 -// 结构参考 DuckDB ParquetStatisticsUtils：先把 Parquet metadata 转成统一统计对象， -// 再由 filter/predicate 判断是否可以裁剪。这里不理解 table/global schema。 +// ============================================================================ +// Parquet 文件级裁剪工具 +// ============================================================================ +// +// 裁剪逻辑分层： +// ① select_row_groups_by_statistics() — RG 级：min/max + dictionary + bloom filter +// ② select_row_group_ranges_by_page_index() — Page 级：ColumnIndex 细粒度 range 裁剪 + skip plan +// +// 这些函数只消费已 localize 到 file schema 的 FileScanRequest， +// 不理解 table/global schema。所有裁剪发生在 plan_parquet_row_groups() 阶段。 +// +// 内部实现（.cpp 中）： +// row_group_prune_reason() 是统一的 RG 级裁剪入口，依次检查： +// statistics(TransformColumnStatistics + check_statistics) +// → dictionary(read_dictionary_words + predicate::evaluate_and) +// → bloom filter(bloom_filter_prune_reason) +// 返回第一个命中裁剪的原因。 +// ============================================================================ struct ParquetStatisticsUtils { - static const ParquetColumnSchema* ResolvePredicateLeafSchema( - const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter); - + // 将 Arrow Parquet Statistics 转换为 Doris 的 ParquetColumnStatistics。 + // 如果 min/max 是 Parquet 物理值（如 INT32 存的 decimal、INT96 的 timestamp）， + // 会按 column_schema.type_descriptor 转换为 Doris 逻辑值。 static ParquetColumnStatistics TransformColumnStatistics( const ParquetColumnSchema& column_schema, const std::shared_ptr<::parquet::Statistics>& statistics, const cctz::time_zone* timezone = nullptr); - // Return true if the statistics indicate that the row group can be safely skipped according to - // the local single-column predicate filter. - static bool CheckStatistics(const format::FileColumnPredicateFilter& column_filter, - const ParquetColumnStatistics& statistics); - - static ParquetRowGroupPruneReason RowGroupPruneReason( - const ::parquet::RowGroupMetaData& row_group, ::parquet::ParquetFileReader* file_reader, - int row_group_idx, const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter, - const cctz::time_zone* timezone = nullptr); - - static bool RowGroupExcludes(const ::parquet::RowGroupMetaData& row_group, - ::parquet::ParquetFileReader* file_reader, int row_group_idx, - const std::vector>& schema, - const format::FileColumnPredicateFilter& column_filter, - const cctz::time_zone* timezone = nullptr); - - static Status SelectRowGroups( - const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, - const std::vector>& file_schema, - const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats, - const cctz::time_zone* timezone = nullptr); - - static bool BloomFilterSupported(const ParquetColumnSchema& column_schema); - + // 检查 bloom filter 是否排除该列的所有 predicate 值。 + // 通过 ArrowParquetBloomFilterAdapter 将 Arrow BloomFilter 适配为 Doris BloomFilter 接口。 static bool BloomFilterExcludes(const ParquetColumnSchema& column_schema, const format::FileColumnPredicateFilter& column_filter, const ::parquet::BloomFilter& bloom_filter); }; -// Parquet file-local statistics/page index/bloom filter 裁剪入口。 -// 这里只消费已经 localize 到 file schema 的 FileScanRequest，不理解 table/global schema。 -// 后续 page index、dictionary、bloom filter 等文件格式优化也应继续收敛在这一层，避免污染 -// ParquetReader 的 scan 调度代码。 +// ① RG 级裁剪：对 candidate_row_groups 中的每个 RG，调用 row_group_prune_reason() +// 依次检查 statistics → dictionary → bloom filter。 +// candidate_row_groups 为 nullptr 时遍历所有 RG。 Status select_row_groups_by_statistics( const ::parquet::FileMetaData& metadata, ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, - const format::FileScanRequest& request, std::vector* selected_row_groups, - bool enable_bloom_filter, ParquetPruningStats* pruning_stats, - const cctz::time_zone* timezone = nullptr); + const format::FileScanRequest& request, const std::vector* candidate_row_groups, + std::vector* selected_row_groups, bool enable_bloom_filter, + ParquetPruningStats* pruning_stats, const cctz::time_zone* timezone = nullptr); +// ② Page 级裁剪：对指定 RG，通过 ColumnIndex (page statistics) 和 OffsetIndex +// 对每个 column_predicate_filter 逐 page 裁剪，生成 selected_ranges（该 RG 内需读取的行范围） +// 和 page_skip_plans（完全跳过哪些 data page，供 Arrow RecordReader 在物理读取层跳过）。 +// +// 所有 column_filter 的 range 取交集（intersect_ranges）， +// 交集为空则该 RG 被完全裁剪。 Status select_row_group_ranges_by_page_index( ::parquet::ParquetFileReader* file_reader, const std::vector>& file_schema, diff --git a/be/src/format_v2/parquet/reader/column_reader.cpp b/be/src/format_v2/parquet/reader/column_reader.cpp index 2cc9933e94fb69..6fc5882b34703f 100644 --- a/be/src/format_v2/parquet/reader/column_reader.cpp +++ b/be/src/format_v2/parquet/reader/column_reader.cpp @@ -49,6 +49,10 @@ namespace doris::format::parquet { namespace { +// Arrow PageReader 的 data page filter 回调。 +// 在 page index 裁剪阶段已生成 ParquetPageSkipPlan，其中记录了每个 data page 是否应跳过。 +// 本回调在 Arrow 遍历 data page 时被触发，按 skip plan 标记跳过对应 page， +// 使 RecordReader 在物理读取层面就不读这些 page。 class DataPageSkipFilter { public: DataPageSkipFilter(const ParquetPageSkipPlan* page_skip_plan, @@ -85,6 +89,7 @@ class DataPageSkipFilter { size_t _next_data_page_idx = 0; }; +// 从 page_skip_plans map 中查找指定 leaf_column_id 的 skip plan。 const ParquetPageSkipPlan* find_page_skip_plan( const std::map* page_skip_plans, int leaf_column_id) { if (page_skip_plans == nullptr) { @@ -94,6 +99,8 @@ const ParquetPageSkipPlan* find_page_skip_plan( return plan_it == page_skip_plans->end() ? nullptr : &plan_it->second; } +// 为 Arrow PageReader 安装 data page 级别的跳过过滤器。 +// 如果该 leaf column 没有 page skip plan，跳过安装。 void install_data_page_filter(std::unique_ptr<::parquet::PageReader>& page_reader, const std::map* page_skip_plans, int leaf_column_id, ParquetPageSkipProfile page_skip_profile) { @@ -106,6 +113,8 @@ void install_data_page_filter(std::unique_ptr<::parquet::PageReader>& page_reade page_reader->set_data_page_filter(DataPageSkipFilter(page_skip_plan, page_skip_profile)); } +// 判断嵌套场景下该列是否可以通过简化版 ScalarColumnReader 读取。 +// 当前只对纯物理类型（无 logical/converted annotation）返回 true。 // TODO: support more types bool supports_nested_scalar_record_reader(const ParquetColumnSchema& column_schema) { if (column_schema.type_descriptor.supports_record_reader) { @@ -153,6 +162,10 @@ void ParquetColumnReader::update_reader_skip_rows(int64_t rows) const { } } +// select() 的默认实现：将 SelectionVector 转为连续的 RowRange 列表， +// 对每个 range 调用 skip(range.start - cursor) + read(range.length)， +// 最后 skip(batch_rows - cursor) 消耗完整个 batch。 +// 子类可以覆写以获得更高效的实现。 Status ParquetColumnReader::select(const SelectionVector& sel, uint16_t selected_rows, int64_t batch_rows, MutableColumnPtr& column) { if (column.get() == nullptr) { @@ -263,6 +276,17 @@ Status ParquetColumnReaderFactory::create_scalar_column_reader( return make_scalar_column_reader(column_schema, std::move(record_reader), reader); } +// 惰性创建并缓存 Arrow RecordReader（按 leaf_column_id 索引）。 +// +// 多个 Doris reader 可能通过不同嵌套路径共享同一个物理列（例如 MAP 的 key 和 value +// 是独立的物理列，分别被 key_reader 和 value_reader 持有，但它们不共享 RecordReader）。 +// 真正的共享发生在同一个物理列被 STRUCT 的多个子字段同时需要时。 +// +// 创建过程： +// 1. RowGroupReader::GetColumnPageReader(leaf_column_id) → Arrow PageReader +// 2. install_data_page_filter() — 安装 page index 裁剪的 page 级过滤器 +// 3. LevelInfo::ComputeLevelInfo() + RecordReader::Make() — 创建 RecordReader +// 4. SetPageReader() — 绑定 PageReader Status ParquetColumnReaderFactory::get_record_reader( int leaf_column_id, const ::parquet::ColumnDescriptor* descriptor, const std::string& name, std::shared_ptr<::parquet::internal::RecordReader>* reader) const { @@ -280,6 +304,7 @@ Status ParquetColumnReaderFactory::get_record_reader( if (descriptor == nullptr) { return Status::InvalidArgument("Parquet column descriptor is null for column {}", name); } + // 惰性创建：只有第一次访问时才初始化 RecordReader if (_record_readers[leaf_column_id] == nullptr) { try { auto page_reader = _row_group->GetColumnPageReader(leaf_column_id); diff --git a/be/src/format_v2/parquet/reader/column_reader.h b/be/src/format_v2/parquet/reader/column_reader.h index b57d8b99b00573..adc6194540d0c7 100644 --- a/be/src/format_v2/parquet/reader/column_reader.h +++ b/be/src/format_v2/parquet/reader/column_reader.h @@ -52,48 +52,91 @@ class IColumn; namespace doris::format::parquet { struct ParquetColumnSchema; -// Doris 的 Parquet column reader 抽象。 +// Doris 的 Parquet column reader 抽象基类。 +// // 该类包装 Arrow Parquet RecordReader，负责将 file-local Parquet leaf column 读取成 // Doris-owned column。它不理解 Iceberg/global schema，也不处理 table-level // cast/default/generated/partition 语义。 +// +// 对外提供两组接口： +// +// ① 平铺读取路径（top-level primitive / 复杂类型的整体读取）： +// read() — 从当前位置全量读取 rows 行 +// skip() — row-level 跳过 +// select() — 按 SelectionVector 部分读取（late materialization 的关键） +// +// ② 嵌套读取协议（LIST/MAP/STRUCT 内部的父子协作）： +// load_nested_batch() — 加载一批 def/rep levels + values +// build_nested_column() — 从 levels 重建嵌套结构并填充值 +// skip_nested_column() — 跳过一批嵌套数据 +// 这个两步协议将 level 解码与值物化分离，让复杂 reader 可以先确定容器结构再按需填充值。 class ParquetColumnReader { public: virtual ~ParquetColumnReader() = default; - // Reader-local schema id. Top-level readers return the root column ordinal; nested readers - // return the child ordinal under their parent. + // ========== 标识字段 ========== + + // Reader 在 file_schema 树中的 id。 + // 顶层 reader 返回 root column ordinal，嵌套 reader 返回父节点下的 child ordinal。 virtual int file_column_id() const { return _field_id; } - // Parquet leaf column id. This is the column id of the leaf column in the Parquet file schema, and can be used to access ColumnDescriptor, RecordReader, column chunk metadata and statistics. - // For example, for a map column as `a: map`, `a` is the top-level column with `parquet_leaf_column_id` = `file_column_id`. `a.key` is the first child of `a` so `parquet_leaf_column_id` = 0. + // 该 reader 对应的 Parquet 物理 leaf column id。 + // 用于访问 ColumnDescriptor、RecordReader、ColumnChunk metadata 和 statistics。 + // 例如 MAP：顶层 MAP 节点的 parquet_leaf_column_id == file_column_id， + // 但其子节点 a.key 的 parquet_leaf_column_id == 0（key 列在文件中的物理序号）。 virtual int parquet_leaf_column_id() const { return _leaf_column_id; } + + // ========== Level 字段 ========== + // 使本节点自身变为 nullable 的 definition level 阈值。 + // 复杂 reader 用此值区分"我的值是 NULL"和"我有值但内容为空"。 int16_t nullable_definition_level() const { return _nullable_definition_level; } + // 最近 repeated 祖先的 repetition level。 + // LIST/MAP reader 用此值从孩子 rep level 流中判断"新元素开始"。 int16_t repeated_repetition_level() const { return _repeated_repetition_level; } virtual const DataTypePtr& type() const { return _type; } virtual const std::string& name() const { return _name; } const ParquetColumnReaderProfile& profile() const { return _profile; } - // 读取一个 file-local column batch。 + // ========== ① 平铺读取接口 ========== + + // 全量读取：从当前位置读 rows 行，写入 column。 virtual Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) = 0; - // 跳过指定行数。这里必须使用 row-level skip，不能退回到 value-level Skip。 + // 跳过 rows 行。必须使用 row-level skip（推进游标），不能退化为 value-level skip。 virtual Status skip(int64_t rows); - // 按 selection 读取当前 batch 中需要输出的行，并在末尾跳过 batch 内剩余行。 - // 该方法只允许通过 skip + read 推进 reader 游标，不允许退化为整批 read + filter。 + // 部分读取：跳过 batch 内未选中的行，只输出 SelectionVector 中标记的行。 + // 用于 late materialization —— predicate 列全量读，non-predicate 列按 selection 读。 + // 该方法只允许 skip + read 推进游标，不允许退化为整批 read + filter。 virtual Status select(const SelectionVector& sel, uint16_t selected_rows, int64_t batch_rows, MutableColumnPtr& column); + // ========== ② 嵌套读取协议 ========== + // 复杂 reader（LIST/MAP/STRUCT）通过这个两步协议与子 reader 协作。 + + // 第一步：加载一批嵌套数据。递归调用子 reader，最终到达 leaf reader 的 + // ParquetLeafReader::read_nested_batch()，返回 def/rep levels + values。 virtual Status load_nested_batch(int64_t rows); + + // 第二步：从已加载的 levels 重建嵌套结构（offsets + null_map）并填充值。 + // length_upper_bound 是预估值，用于提前 reserve 空间。 virtual Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, int64_t* values_read); + + // 跳过 rows 行嵌套数据。递归调用子 reader 的 skip_nested_column。 virtual Status skip_nested_column(int64_t rows); + + // 返回已加载的嵌套 definition/repetition levels（由子 reader 或自身填充）。 virtual const std::vector& nested_definition_levels() const; virtual const std::vector& nested_repetition_levels() const; virtual int64_t nested_levels_written() const; + // 该 reader 自身或其子树中是否包含 repeated 子节点。 virtual bool is_or_has_repeated_child() const; + // ========== 嵌套构建游标 ========== + // 复杂 reader 在 build_nested_column 时分多轮调用子 reader，游标跟踪当前处理到的 + // def/rep level 数组位置，避免重复处理同一批 level。 int64_t nested_build_level_cursor() const { return _nested_build_level_cursor; } void set_nested_build_level_cursor(int64_t cursor) { DORIS_CHECK(cursor >= 0); @@ -109,29 +152,31 @@ class ParquetColumnReader { void update_reader_skip_rows(int64_t rows) const; ParquetColumnReaderProfile _profile; - const int _field_id = -1; - const int _leaf_column_id = -1; - const int16_t _nullable_definition_level = 0; - const int16_t _repeated_repetition_level = 0; - const int16_t _definition_level = 0; - const int16_t _repetition_level = 0; - const int16_t _repeated_ancestor_definition_level = 0; - const DataTypePtr _type; - const std::string _name; - int64_t _nested_build_level_cursor = 0; + const int _field_id = -1; // 在父节点中的 child ordinal + const int _leaf_column_id = -1; // Parquet 物理 leaf column id (-1 = 非叶子) + const int16_t _nullable_definition_level = 0; // 本节点 nullable 的 def level 阈值 + const int16_t _repeated_repetition_level = 0; // 最近 repeated 祖先的 rep level + const int16_t _definition_level = 0; // 累计到本节点的 def level + const int16_t _repetition_level = 0; // 累计到本节点的 rep level + const int16_t _repeated_ancestor_definition_level = 0; // 最近 repeated 祖先的 def level + const DataTypePtr _type; // Doris 目标类型 + const std::string _name; // 列名（用于报错信息） + int64_t _nested_build_level_cursor = 0; // 嵌套构建游标（当前处理到的 level 位置） }; -// Creates Doris column readers for one Parquet row group. +// 为一个 Parquet RowGroup 创建 Doris Column Reader 的工厂。 +// +// 工厂持有 RowGroup 级别的共享状态： +// - Arrow RecordReader 实例（按 leaf_column_id 缓存，同一物理列可能被多个 reader 共享） +// - Page skip plans 和 page skip profile（page index 裁剪结果） +// - 标量物化选项：timezone、strict mode 等 // -// The factory owns row-group-local state that must be shared by all readers created for -// the same row group: -// - Arrow RecordReader instances, cached by file leaf column id. -// - Page skip plans and page skip profile counters. -// - Scalar materialization options such as timezone and strict mode. +// 外部调用方只请求顶层列或虚拟扫描列，嵌套子列的递归构造保持私有， +// ParquetScanScheduler 和 ParquetReader 不感知物理 schema 细节。 // -// Public callers only ask for a top-level file column reader or for synthetic scan -// columns. Recursive construction of nested children stays private so physical Parquet -// schema details do not leak into ParquetScanScheduler or ParquetReader. +// Projection 支持：当只需要复杂类型的部分子列时（如 MAP 只读 value）， +// factory 通过 LocalColumnIndex 参数传递 projection 路径， +// 只为被 projected 的子列创建 reader，跳过不需要的部分。 class ParquetColumnReaderFactory { public: ParquetColumnReaderFactory(std::shared_ptr<::parquet::RowGroupReader> row_group, @@ -142,81 +187,79 @@ class ParquetColumnReaderFactory { bool enable_strict_mode = false, ParquetColumnReaderProfile column_reader_profile = {}); - // Creates a reader for a top-level file column schema. The optional projection uses - // file-local child ids from ParquetColumnSchema and may select only part of a nested - // subtree. Table/global schema mapping has already happened before this layer. + // 为顶层列 schema 创建 reader。projection 可选，为 nullptr 时读取全部子列， + // 非 nullptr 时只读取被 projected 的子树部分。 Status create(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; + // 便捷重载：projection = nullptr（读取全部子列）。 Status create(const ParquetColumnSchema& column_schema, std::unique_ptr* reader) const { return create(column_schema, nullptr, reader); } + // 创建虚拟列 reader：生成行位置序号。 std::unique_ptr create_row_position_column_reader( int64_t row_group_first_row) const; + // 创建虚拟列 reader：生成全局唯一 RowId。 std::unique_ptr create_global_rowid_column_reader( const format::GlobalRowIdContext& context, int64_t row_group_first_row) const; private: - // Creates a primitive leaf reader. Top-level primitive columns are restricted to flat - // scalar layouts, while nested primitive leaves are allowed to carry def/rep levels - // that will be consumed by their parent LIST/MAP/STRUCT readers. + // 创建基本类型叶子的 reader。 + // is_nested=true 表示该叶子在复杂类型内部，允许携带 def/rep levels。 + // is_nested=false 表示顶层平铺列，需要做额外的 flat layout 校验。 Status create_scalar_column_reader(const ParquetColumnSchema& column_schema, bool is_nested, std::unique_ptr* reader) const; - // Creates a STRUCT reader and recursively creates readers only for projected - // children. For partial projections it rebuilds the output DataTypeStruct so the - // materialized column contains only projected child fields. + // 创建 STRUCT reader。递归为 projected 子列创建 reader。 + // 部分 projection 时重建 DataTypeStruct，使物化结果只包含被 projected 的子字段。 Status create_struct_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; - // Creates a LIST reader around the single element reader. If the element itself is - // partially projected, this rebuilds the output DataTypeArray with the projected - // element type. + // 创建 LIST reader，持有单个 element reader。 + // element 被部分 projection 时，重建 DataTypeArray 使用 projected 的 element type。 Status create_list_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; - // Creates a MAP reader around key and value readers. The schema builder folds the Parquet - // key_value/entry wrapper into the MAP node, so children are direct key/value fields here. - // Partial MAP projection means value-subtree pruning only. The key stream is always read in - // full because it owns entry existence, offsets and key equality semantics; key child - // projection is intentionally rejected. + // 创建 MAP reader，持有 key reader + value reader。 + // Schema 构建时已折叠 key_value/entry wrapper，children 直接是 [key, value]。 + // 部分 MAP projection 仅对 value 子树做裁剪。key 流始终完整读取， + // 因为它拥有 entry 的存在性、offsets 和 key equality 语义。 Status create_map_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, std::unique_ptr* reader) const; - // Private recursive dispatcher. is_nested is true for children of complex readers - // and controls primitive-leaf validation; complex readers are always created from - // normalized ParquetColumnSchema subtrees. + // 私有递归分发器。根据 ParquetColumnSchema::kind 路由到对应的 create_* 方法。 + // is_nested 为 true 表示该节点属于复杂 reader 的子节点，控制 primitive leaf 的校验逻辑； + // 复杂 reader 总是从规范化的 ParquetColumnSchema 子树创建。 Status create_column_reader(const ParquetColumnSchema& column_schema, const format::LocalColumnIndex* projection, bool is_nested, std::unique_ptr* reader) const; - // Lazily creates and caches the Arrow RecordReader for a file leaf column. Multiple - // Doris readers may need the same leaf stream through different nested parents, so - // RecordReader lifetime is tied to this row-group factory. + // 惰性创建并缓存 Arrow RecordReader（按 leaf_column_id 索引）。 + // 多个 Doris reader 可能通过不同嵌套路径共享同一个物理列的数据流， + // 因此 RecordReader 的生命周期绑定到 RowGroup 工厂。 Status get_record_reader(int leaf_column_id, const ::parquet::ColumnDescriptor* descriptor, const std::string& name, std::shared_ptr<::parquet::internal::RecordReader>* reader) const; - // Final ScalarColumnReader construction after schema validation and RecordReader - // lookup have already completed. + // 在 schema 校验和 RecordReader 查找完成后，最终构造 ScalarColumnReader。 Status make_scalar_column_reader( const ParquetColumnSchema& column_schema, std::shared_ptr<::parquet::internal::RecordReader> record_reader, std::unique_ptr* reader) const; - std::shared_ptr<::parquet::RowGroupReader> _row_group; - mutable std::vector> _record_readers; - const std::map* _page_skip_plans = nullptr; - ParquetPageSkipProfile _page_skip_profile; - const cctz::time_zone* _timezone = nullptr; - bool _enable_strict_mode = false; - ParquetColumnReaderProfile _column_reader_profile; + std::shared_ptr<::parquet::RowGroupReader> _row_group; // Arrow RowGroup 读取器 + mutable std::vector> + _record_readers; // RecordReader 缓存(按 leaf_column_id) + const std::map* _page_skip_plans = nullptr; // page index 裁剪结果 + ParquetPageSkipProfile _page_skip_profile; // page skip profile + const cctz::time_zone* _timezone = nullptr; // 时区 + bool _enable_strict_mode = false; // 严格模式 + ParquetColumnReaderProfile _column_reader_profile; // column reader profile }; - } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp b/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp index 1df3819ac64083..ae391615f4a8e9 100644 --- a/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/global_rowid_column_reader.cpp @@ -77,6 +77,7 @@ Status GlobalRowIdColumnReader::skip(int64_t rows) { } void GlobalRowIdColumnReader::append_row_id(uint32_t row_id, MutableColumnPtr& column) const { + // 编码为 17-byte GlobalRowLoacationV2: version(1) + backend_id(8) + file_id(4) + row_id(4) auto* string_column = assert_cast(column.get()); GlobalRowLoacationV2 location(_context.version, _context.backend_id, _context.file_id, row_id); string_column->insert_data(reinterpret_cast(&location), diff --git a/be/src/format_v2/parquet/reader/global_rowid_column_reader.h b/be/src/format_v2/parquet/reader/global_rowid_column_reader.h index d217e4560614c2..0b37fa9b56052c 100644 --- a/be/src/format_v2/parquet/reader/global_rowid_column_reader.h +++ b/be/src/format_v2/parquet/reader/global_rowid_column_reader.h @@ -25,6 +25,14 @@ namespace doris::format::parquet { +// 虚拟列 reader：生成行的全局唯一 RowId。 +// +// 不对应任何 Parquet 物理列，不持有 RecordReader。 +// RowId 编码格式：， +// 共 17 bytes，以 String 类型输出。 +// +// row_id = _row_group_first_row + _next_row_position，随 read() 递增。 +// 用于 TopN filter 等需要跨文件唯一定位行的场景。 class GlobalRowIdColumnReader final : public ParquetColumnReader { public: GlobalRowIdColumnReader(format::GlobalRowIdContext context, int64_t row_group_first_row, @@ -39,11 +47,12 @@ class GlobalRowIdColumnReader final : public ParquetColumnReader { Status skip(int64_t rows) override; private: + // 将单个 row_id 编码为 17-byte RowId 字符串并追加到 column。 void append_row_id(uint32_t row_id, MutableColumnPtr& column) const; - format::GlobalRowIdContext _context; - int64_t _row_group_first_row = 0; - int64_t _next_row_position = 0; + format::GlobalRowIdContext _context; // RowId 前缀（version + backend_id + file_id） + int64_t _row_group_first_row = 0; // 当前 RG 在文件中的起始行号 + int64_t _next_row_position = 0; // 下一个待输出的行位置 }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/list_column_reader.cpp b/be/src/format_v2/parquet/reader/list_column_reader.cpp index cc1df4079fbfdb..bf831534fdd4ef 100644 --- a/be/src/format_v2/parquet/reader/list_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/list_column_reader.cpp @@ -70,6 +70,17 @@ Status ListColumnReader::load_nested_batch(int64_t rows) { return _element_reader->load_nested_batch(rows); } +// LIST 的嵌套构建核心逻辑： +// +// 从 element reader 的 def/rep levels 重建 ColumnArray： +// +// 1. 遍历 def/rep levels，解析每个顶层行： +// - rep_level == _repetition_level → 继续当前 element（entry_count++） +// - rep_level < _repetition_level → 新顶层行开始 +// - def_level < _definition_level-1 → 该 LIST 本身为 NULL +// - def_level >= _definition_level → entry 非空（至少 1 个元素） +// 2. 委托 element reader 的 build_nested_column() 填充所有元素值 +// 3. append_offsets() + append_parent_nulls() 写入 ColumnArray 结构 Status ListColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, int64_t* values_read) { if (column.get() == nullptr || values_read == nullptr) { diff --git a/be/src/format_v2/parquet/reader/list_column_reader.h b/be/src/format_v2/parquet/reader/list_column_reader.h index f144e3a06e683c..d91ffd2706e327 100644 --- a/be/src/format_v2/parquet/reader/list_column_reader.h +++ b/be/src/format_v2/parquet/reader/list_column_reader.h @@ -27,6 +27,20 @@ namespace doris::format::parquet { +// LIST（数组）列的读取器，持有单个 element reader。 +// +// 实现策略： +// LIST 的物理 rep/def level 流由 element reader 提供。ListColumnReader 消费这些 +// levels 来重建 ColumnArray 的 offsets 和 null_map，然后将实际值委托给 element reader。 +// +// 嵌套协议流程： +// 1. load_nested_batch() → element reader 加载 def/rep levels +// 2. build_nested_column() → 从 rep levels 计算每行的 entry_count， +// 通过 append_offsets() 写入 ColumnArray offsets， +// 从 def levels 判断 LIST 本身是否为 NULL， +// 然后委托 element reader 的 build_nested_column() 填充值。 +// +// 平铺 read() 也走同样的逻辑，只是入口不同。 class ListColumnReader final : public ParquetColumnReader { public: ListColumnReader(const ParquetColumnSchema& schema, DataTypePtr type, @@ -46,7 +60,8 @@ class ListColumnReader final : public ParquetColumnReader { bool is_or_has_repeated_child() const override; private: - std::unique_ptr _element_reader; + std::unique_ptr + _element_reader; // 元素 reader（递归，可能为 Scalar/Struct/List/Map） }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/map_column_reader.cpp b/be/src/format_v2/parquet/reader/map_column_reader.cpp index 8cd612f3f6f42a..59261736165e19 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/map_column_reader.cpp @@ -74,6 +74,19 @@ Status MapColumnReader::load_nested_batch(int64_t rows) { return _value_reader->load_nested_batch(rows); } +// MAP 的嵌套构建核心逻辑： +// +// 从 key reader 的 def/rep levels 重建 ColumnMap： +// +// 1. 遍历 key reader 的 def/rep levels，解析 entry 结构（同 LIST，key stream 提供 shape）。 +// 2. 委托 key reader 的 build_nested_column() 填充所有 key 值。 +// 3. key null 校验：检查 key 列中是否存在 NULL，有则报错（MAP key 不允许 NULL）。 +// 4. value 填充分两条路径： +// a. ScalarColumnReader 路径：value 与 key 在 level 流中一一对应（same rep level）， +// 通过 append_nested_value() 逐 entry 填充 value。 +// b. 复杂 value 路径（如 MAP>）：value 拥有自己的嵌套 shape， +// 直接 build_nested_column(total_entries) 递归填充。 +// 5. append_offsets() + append_parent_nulls() 写入 ColumnMap 结构。 Status MapColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, int64_t* values_read) { if (column.get() == nullptr || values_read == nullptr) { diff --git a/be/src/format_v2/parquet/reader/map_column_reader.h b/be/src/format_v2/parquet/reader/map_column_reader.h index d6ebed24438e27..559cdc9c0c680a 100644 --- a/be/src/format_v2/parquet/reader/map_column_reader.h +++ b/be/src/format_v2/parquet/reader/map_column_reader.h @@ -27,6 +27,24 @@ namespace doris::format::parquet { +// MAP 列的读取器，持有 key reader 和 value reader。 +// +// key reader 始终完整读取（不做 projection 裁剪），因为它拥有： +// - entry 的存在性：null key → entry 无效 +// - offsets 信息：从 key 的 rep levels 确定每个顶层行有多少个 entry +// - key 唯一性语义：重复 key 的行为由引擎层决定 +// +// 嵌套协议流程： +// 1. load_nested_batch() → 分别加载 key reader 和 value reader +// 2. build_nested_column() → +// a. 从 key reader 的 rep levels 计算 entry_counts → 设置 ColumnMap offsets +// b. 从 key reader 的 def levels 判断 MAP 本身和每个 entry 的 null 状态 +// c. 校验：key 为 NULL 的 entry 被标记为无效（兼容 Hive 的非标准 optional key） +// d. 委托 key reader 的 build_nested_column() 填充 keys +// e. 委托 value reader 的 build_nested_column() 填充 values +// +// MapColumnReader 是 ScalarColumnReader 的 friend，可以直接访问其内部方法 +// 来逐个读取 key value 做 entry 校验。 class MapColumnReader final : public ParquetColumnReader { public: MapColumnReader(const ParquetColumnSchema& schema, DataTypePtr type, @@ -48,8 +66,8 @@ class MapColumnReader final : public ParquetColumnReader { bool is_or_has_repeated_child() const override; private: - std::unique_ptr _key_reader; - std::unique_ptr _value_reader; + std::unique_ptr _key_reader; // key 列 reader（始终完整读取） + std::unique_ptr _value_reader; // value 列 reader（可按 projection 裁剪） }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/nested_column_materializer.cpp b/be/src/format_v2/parquet/reader/nested_column_materializer.cpp index 21f3a7a8ab52d8..9ea14a75cdb0a5 100644 --- a/be/src/format_v2/parquet/reader/nested_column_materializer.cpp +++ b/be/src/format_v2/parquet/reader/nested_column_materializer.cpp @@ -25,10 +25,15 @@ namespace doris::format::parquet { +// Doris 顶层列总是 Nullable 包装的：ColumnNullable → ColumnArray/ColumnMap/ColumnStruct。 +// 这些函数封装了穿透 Nullable wrapper 的逻辑，让调用方可以直接拿到嵌套 column。 + ColumnArray* array_column_from_output(MutableColumnPtr& column) { + // 穿透外层 ColumnNullable → 取内部 ColumnArray if (auto* nullable_column = check_and_get_column(*column)) { return assert_cast(&nullable_column->get_nested_column()); } + // 非 nullable 路径（嵌套在 struct 内部时可能出现） return assert_cast(column.get()); } @@ -47,13 +52,17 @@ ColumnStruct* struct_column_from_output(MutableColumnPtr& column) { } NullMap* null_map_from_nullable_output(MutableColumnPtr& column) { + // 只有被 ColumnNullable 包装时才存在 null_map if (auto* nullable_column = check_and_get_column(*column)) { return &nullable_column->get_null_map_data(); } + // 嵌套在 required 父节点内部 → 没有独立的 null_map，父级负责标记 return nullptr; } void append_offsets(ColumnArray::Offsets64& offsets, const std::vector& entry_counts) { + // offsets 是累积值：offsets[i] = sum(entry_counts[0..i]) + // 最后一个 offset = 当前已累积的总元素数，用来作为下一次追加的起点 offsets.reserve(offsets.size() + entry_counts.size()); uint64_t current_offset = offsets.empty() ? 0 : offsets.back(); for (const auto entry_count : entry_counts) { @@ -64,7 +73,7 @@ void append_offsets(ColumnArray::Offsets64& offsets, const std::vector void append_parent_nulls(NullMap* dst, const NullMap& src) { if (dst == nullptr) { - return; + return; // 目标列不是 nullable → 无需写入 null 标记 } dst->insert(src.begin(), src.end()); } diff --git a/be/src/format_v2/parquet/reader/nested_column_materializer.h b/be/src/format_v2/parquet/reader/nested_column_materializer.h index 26bdfdaf83d47d..e95ce3e73d665f 100644 --- a/be/src/format_v2/parquet/reader/nested_column_materializer.h +++ b/be/src/format_v2/parquet/reader/nested_column_materializer.h @@ -28,12 +28,38 @@ namespace doris::format::parquet { +// ============================================================================ +// Doris Column 嵌套类型访问辅助函数 +// +// 复杂 reader（StructColumnReader、ListColumnReader、MapColumnReader）通过 +// 这些函数从输出 Column 中获取嵌套部分的指针。 +// +// 背景：Doris 的顶层列总是 Nullable 包装的（ColumnNullable），嵌套列本身 +// （ColumnArray、ColumnMap、ColumnStruct）在 ColumnNullable 内部。这些函数 +// 封装了"穿透 Nullable wrapper → 获取嵌套 column"的逻辑。 +// ============================================================================ + +// 从输出列获取 ColumnArray* — 自动穿透外层的 ColumnNullable wrapper。 ColumnArray* array_column_from_output(MutableColumnPtr& column); + +// 从输出列获取 ColumnMap* — 自动穿透外层的 ColumnNullable wrapper。 ColumnMap* map_column_from_output(MutableColumnPtr& column); + +// 从输出列获取 ColumnStruct* — 自动穿透外层的 ColumnNullable wrapper。 ColumnStruct* struct_column_from_output(MutableColumnPtr& column); + +// 从输出列获取 NullMap* — 自动穿透外层的 ColumnNullable wrapper。 +// 如果输出列不是 ColumnNullable（嵌套在非 nullable 的 struct 内部），返回 nullptr。 NullMap* null_map_from_nullable_output(MutableColumnPtr& column); +// 将 entry_counts 数组追加为 ColumnArray 的 offsets。 +// entry_counts[i] 表示第 i 个顶层行包含的元素数量， +// offsets[i] = offsets[i-1] + entry_counts[i]。 +// 例如 entry_counts = [3, 0, 2] → offsets = [3, 3, 5]。 void append_offsets(ColumnArray::Offsets64& offsets, const std::vector& entry_counts); + +// 将 src 中的 null 标记追加到 dst 之后。 +// 用于 struct/literal null 嵌套场景：当父级为 NULL 时，其子列也需要对应行标记为 NULL。 void append_parent_nulls(NullMap* dst, const NullMap& src); } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp index 7ad547adbe1dd3..f9723635cab583 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.cpp @@ -41,6 +41,7 @@ namespace doris::format::parquet { namespace { +// 将 ParquetTimeUnit 转换为 DataTypeSerde 层的 DecodedTimeUnit。 DecodedTimeUnit decoded_time_unit(ParquetTimeUnit time_unit) { switch (time_unit) { case ParquetTimeUnit::MILLIS: @@ -55,6 +56,8 @@ DecodedTimeUnit decoded_time_unit(ParquetTimeUnit time_unit) { } } +// 返回指定 DecodedValueKind 的单个值的字节大小。 +// Binary/FIXED_BINARY 返回 error（它们不是固定宽度类型）。 Status decoded_fixed_value_size(const std::string& column_name, DecodedValueKind value_kind, size_t* value_size) { switch (value_kind) { @@ -90,6 +93,9 @@ Status decoded_fixed_value_size(const std::string& column_name, DecodedValueKind return Status::InternalError("Unknown decoded value kind for column {}", column_name); } +// 从 BinaryRecordReader 获取 Arrow Array chunks。 +// GetBuilderChunks() 会将 Arrow 内部 builder 的所有权转移出来并 reset， +// 因此每个 batch 只能调用一次。 Status get_binary_chunks(const std::string& column_name, ::parquet::internal::RecordReader& record_reader, std::vector>* chunks) { @@ -102,6 +108,12 @@ Status get_binary_chunks(const std::string& column_name, return Status::OK(); } +// 将 Arrow BinaryArray / FixedSizeBinaryArray 的 chunks 转换为 Doris StringRef 向量。 +// +// read_dense_for_nullable 模式：Arrow 只输出了非 NULL 的紧凑值，本函数按 null_map +// 将它们展开为与 records_read 对齐的稀疏数组（NULL 行填 nullptr + 0）。 +// +// 非 dense 模式：直接按行一一对应转换。 Status build_binary_values(const std::string& column_name, const std::vector>& chunks, int64_t records_read, const NullMap* null_map, @@ -172,6 +184,9 @@ Status build_binary_values(const std::string& column_name, return Status::OK(); } +// IEEE 754 half-precision (16-bit) → single-precision (32-bit) 转换。 +// Parquet FLOAT16 用 FIXED_LEN_BYTE_ARRAY(2) 存储，Doris 没有原生 Float16 类型， +// 需要提升为 Float32。 float half_to_float(uint16_t value) { const uint32_t sign = (value & 0x8000U) << 16; const uint32_t exponent = (value & 0x7C00U) >> 10; @@ -190,6 +205,8 @@ float half_to_float(uint16_t value) { return std::bit_cast(sign | ((exponent + 112U) << 23) | (mantissa << 13)); } +// 将 Parquet FLOAT16 的 binary values 批量解码为 float 向量。 +// 每个 FLOAT16 值占 2 bytes，调用 half_to_float 逐值转换。 Status build_float16_values(const std::string& column_name, const ParquetTypeDescriptor& type_descriptor, const std::vector& binary_values, int64_t row_count, @@ -225,6 +242,12 @@ Status build_float16_values(const std::string& column_name, } // namespace +// 将 RecordReader 的内部状态捕获为不可变的 ParquetLeafBatch。 +// +// 该函数在 RecordReader::ReadRecords() 之后立即调用，将 Arrow 返回的 +// level/value buffer 指针（或 binary chunks 的所有权）快照到 batch 中。 +// 之后 batch 可以被多次读取（如先 build_null_map 再 append_values）， +// 不受 RecordReader 后续操作的干扰。 Status ParquetLeafReader::collect_batch(::parquet::internal::RecordReader& record_reader, ParquetLeafBatch* batch) const { DORIS_CHECK(batch != nullptr); @@ -232,6 +255,7 @@ Status ParquetLeafReader::collect_batch(::parquet::internal::RecordReader& recor batch->_rep_levels = nullptr; batch->_fixed_values = nullptr; batch->_binary_chunks.clear(); + // 根据 type_descriptor 确定 value_kind，控制后续 value 读取路径 batch->_value_kind = decoded_value_kind(_type_descriptor); batch->_consumed_level_count = record_reader.levels_position(); batch->_decoded_level_count = record_reader.levels_written(); @@ -244,15 +268,17 @@ Status ParquetLeafReader::collect_batch(::parquet::internal::RecordReader& recor batch->_read_dense_for_nullable = record_reader.read_dense_for_nullable(); batch->_values_written = record_reader.values_written(); + // 固定宽度类型：values buffer 指针直接可用 if (!batch->is_binary_value()) { batch->_fixed_values = record_reader.values(); return Status::OK(); } - // BinaryRecordReader owns values in Arrow builders instead of RecordReader::values(). - // GetBuilderChunks() transfers those builders out and resets them, so collect exactly once - // and let all later count/materialization logic use this normalized batch state. + // Binary 类型：必须通过 GetBuilderChunks() 获取所有权。 + // GetBuilderChunks() 会转移 Arrow builder 所有权并 reset builder， + // 所以只能调用一次——这里就是那一次。 RETURN_IF_ERROR(get_binary_chunks(_name, record_reader, &batch->_binary_chunks)); + // 从 chunks 重新计算 values_written（因为二进制值的计数方式不同） batch->_values_written = 0; for (const auto& chunk : batch->_binary_chunks) { if (chunk == nullptr) { @@ -264,6 +290,18 @@ Status ParquetLeafReader::collect_batch(::parquet::internal::RecordReader& recor return Status::OK(); } +// 将 batch 中的值写入目标 Doris Column。 +// +// 数据准备阶段（DecodedColumnView 填充前）： +// 根据物理存储格式将值准备为 DataTypeSerde 可消费的形式： +// - FLOAT16: binary → half_to_float → float_values +// - Binary 类型: Arrow chunks → StringRef[] +// - 固定宽度 dense nullable: Arrow 紧凑值 → 间隔排列的 spaced_values +// - 固定宽度非 dense: 直接使用 batch._fixed_values 指针 +// +// 物化阶段（DataTypeSerde::read_column_from_decoded_values）： +// 根据 type_descriptor 的信息（decimal precision/scale、timestamp unit、timezone 等） +// 将原始 bytes 转换为 Doris 的最终类型表示。 Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t row_count, const NullMap* null_map, MutableColumnPtr& column) const { std::vector binary_values; @@ -282,7 +320,9 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r view.null_map = null_map == nullptr || null_map->empty() ? nullptr : null_map->data(); const bool read_dense_for_nullable = batch._read_dense_for_nullable && view.null_map != nullptr; + // 数据准备：根据物理存储格式填充 view if (_type_descriptor.extra_type_info == ParquetExtraTypeInfo::FLOAT16) { + // FLOAT16: FIXED_LEN_BYTE_ARRAY(2) → half_to_float → float 向量 RETURN_IF_ERROR(build_binary_values(_name, batch._binary_chunks, row_count, null_map, read_dense_for_nullable, &binary_values)); RETURN_IF_ERROR(build_float16_values(_name, _type_descriptor, binary_values, row_count, @@ -290,19 +330,24 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r view.value_kind = DecodedValueKind::FLOAT; view.values = reinterpret_cast(float_values.data()); } else if (batch.is_binary_value()) { + // STRING / DECIMAL_BYTE_ARRAY / ENUM / JSON 等 RETURN_IF_ERROR(build_binary_values(_name, batch._binary_chunks, row_count, null_map, read_dense_for_nullable, &binary_values)); view.binary_values = &binary_values; } else if (read_dense_for_nullable) { + // 固定宽度 + dense nullable: 需要展开为间隔排列 RETURN_IF_ERROR(build_spaced_fixed_values(batch, row_count, null_map, &spaced_values)); view.values = spaced_values.data(); } else { + // 固定宽度 + 非 nullable 或 非 dense: values 指针直接可用 view.values = batch._fixed_values; } { SCOPED_TIMER(_profile.materialization_time); - // TODO(gabriel): top-level column is always nullable in current parquet reader implementation but nested column may not be. + // 通过 DataTypeSerde 完成类型感知的值写入。 + // 对于 nullable 类型，serde 会直接写入 null_map + nested_column。 + // 对于非 nullable 类型（嵌套场景），当前实现临时走 ColumnNullable 兼容路径。 if (!_type->is_nullable()) { if (auto* nullable_column = check_and_get_column(*column); nullable_column != nullptr) { @@ -328,10 +373,20 @@ Status ParquetLeafReader::append_values(const ParquetLeafBatch& batch, int64_t r return Status::OK(); } +// 判断当前 value_kind 是否为 binary 类型（需要走 Arrow chunks 路径而非 fixed_values 指针）。 bool ParquetLeafBatch::is_binary_value() const { return _value_kind == DecodedValueKind::BINARY || _value_kind == DecodedValueKind::FIXED_BINARY; } +// 为 dense nullable 模式构建间隔排列的固定宽度值数组。 +// +// Arrow RecordReader 在 read_dense_for_nullable 模式下只写非 NULL 值（紧凑排列， +// 不包含 NULL 行的占位），本函数按 null_map 将紧凑值展开： +// - NULL 行：对应位置保留为 0（不会被 read_column_from_decoded_values 读取） +// - 非 NULL 行：从 compact buffer 中取下一个值写入对应位置 +// +// 例如：null_map = [0,1,0,1,0]，values = [v0,v1,v2] +// 展开后（逻辑上）= [v0, -, v1, -, v2] Status ParquetLeafReader::build_spaced_fixed_values(const ParquetLeafBatch& batch, int64_t row_count, const NullMap* null_map, std::vector* spaced_values) const { @@ -342,6 +397,7 @@ Status ParquetLeafReader::build_spaced_fixed_values(const ParquetLeafBatch& batc spaced_values->resize(static_cast(row_count) * value_size); const auto non_null_count = static_cast(simd::count_zero_num( reinterpret_cast(null_map->data()), null_map->size())); + // 完整性校验：紧凑值数量必须等于非 NULL 行数 if (batch._values_written != non_null_count) { return Status::Corruption( "Invalid dense nullable parquet values for column {}: values={}, records={}, " @@ -352,8 +408,9 @@ Status ParquetLeafReader::build_spaced_fixed_values(const ParquetLeafBatch& batc int64_t value_idx = 0; for (int64_t record_idx = 0; record_idx < row_count; ++record_idx) { if ((*null_map)[record_idx] != 0) { - continue; + continue; // NULL 行：跳过，对应位置保持为 0 } + // 非 NULL 行：从紧凑 buffer 中取出下一个值，按 value_size 拷贝到对应行偏移 std::memcpy(dst + static_cast(record_idx) * value_size, batch._fixed_values + static_cast(value_idx) * value_size, value_size); ++value_idx; @@ -376,6 +433,12 @@ ParquetLeafReader::ParquetLeafReader( _timezone(timezone), _enable_strict_mode(enable_strict_mode) {} +// 从 Arrow RecordReader 读取 batch_rows 行，并将结果捕获到 ParquetLeafBatch 中。 +// +// 步骤： +// 1. Reset + Reserve: 准备 RecordReader 的内部缓冲区 +// 2. ReadRecords(batch_rows): 触发一次 data page 读取和解码（Dremel levels + values） +// 3. collect_batch(): 将解码结果快照到 ParquetLeafBatch Status ParquetLeafReader::read_batch(int64_t batch_rows, ParquetLeafBatch* batch, int64_t* rows_read) const { if (batch == nullptr || rows_read == nullptr) { @@ -392,6 +455,7 @@ Status ParquetLeafReader::read_batch(int64_t batch_rows, ParquetLeafBatch* batch _record_reader->Reserve(batch_rows); { SCOPED_TIMER(_profile.arrow_read_records_time); + // ReadRecords 返回实际读到的记录数，可能小于 batch_rows（到达 column chunk 末尾） *rows_read = _record_reader->ReadRecords(batch_rows); } } catch (const ::parquet::ParquetException& e) { @@ -408,8 +472,14 @@ Status ParquetLeafReader::read_batch(int64_t batch_rows, ParquetLeafBatch* batch return collect_batch(*_record_reader, batch); } +// 根据 batch 中的 definition levels 构建 Doris NullMap。 +// +// 规则：def_level == max_definition_level → 非 NULL（null_map=0），否则为 NULL（null_map=1）。 +// 如果该列没有 optional/repeated 祖先（max_definition_level == 0），则所有值都非 NULL， +// 直接返回 OK（不设置 null_map），由调用方按无 NULL 处理。 Status ParquetLeafReader::build_null_map(const ParquetLeafBatch& batch, int64_t records_read, NullMap* null_map) const { + // 无 optional 祖先 → 所有值都非 NULL，不需要 null_map if (_descriptor->max_definition_level() == 0) { return Status::OK(); } @@ -429,6 +499,36 @@ Status ParquetLeafReader::build_null_map(const ParquetLeafBatch& batch, int64_t return Status::OK(); } +// 嵌套叶子的一步式读取：read levels + values → 解析 value layout → 物化 values_column。 +// +// 这是 ParquetLeafReader 中最复杂的函数。它处理 Arrow RecordReader 在不同场景下 +// 按不同方式写入 level/value 的复杂情况。 +// +// 整体流程： +// +// 1. 调用 read_batch() 获取原始的 level/value 数据。 +// +// 2. 将 def/rep levels 拷贝到 batch 中（因为 RecordReader 的 level 数组可能被后续读取覆盖）。 +// +// 3. 解析 value layout — Arrow RecordReader 在不同场景下按不同粒度和方式写入 value： +// - LEVELS: value 与 level 一一对应（每个 level slot 恰好有一个 value） +// - VALUE_SLOTS: value 数量 == 满足 value_slot_definition_level 的 slot 数 +// - LEAF_VALUES: value 数量 == def_level == max_definition_level 的 slot 数（真正的叶值） +// - PAYLOAD_VALUE_SLOTS: value 数量 == 降级后的 payload_slot_definition_level slot 数 +// 这种降级发生在 Arrow 为 NULL 祖先写入 value placeholder 时（见下文）。 +// +// 4. 构建 value_indices[] 映射：level_idx → value buffer 中的位置（-1 表示该 slot 无 value）。 +// +// 5. 构建 value_nulls[]：标记每个 value 是否为 NULL。 +// +// 6. 调用 append_values() 物化 values_column（非 nullable 的基本类型）。 +// +// 关于 PAYLOAD_VALUE_SLOTS 降级（count_value_slots 的 while 循环）： +// Arrow 的 RecordReader 有时会为不满足 Doris 物化阈值的 NULL 祖先写入 value placeholder。 +// 例如 MAP value 在 def_level 不足时，Arrow 仍可能分配一个 value slot 但写入占位值。 +// 此时 values_written > value_slot_count（按标准 threshold 计算的 slot 数）。 +// 代码尝试逐步降低 payload_slot_definition_level，直到找到匹配 value 数目的 threshold， +// 确保 value_indices 映射和 values_written 对齐，不会把占位值错配给真实 slot。 Status ParquetLeafReader::read_nested_batch(int64_t batch_rows, int16_t value_slot_definition_level, ParquetNestedScalarBatch* batch, int16_t value_slot_repetition_level) const { diff --git a/be/src/format_v2/parquet/reader/parquet_leaf_reader.h b/be/src/format_v2/parquet/reader/parquet_leaf_reader.h index a012e67b71cd79..5c1020a1cec075 100644 --- a/be/src/format_v2/parquet/reader/parquet_leaf_reader.h +++ b/be/src/format_v2/parquet/reader/parquet_leaf_reader.h @@ -47,11 +47,23 @@ class Array; namespace doris::format::parquet { -// Nested scalar leaf state is intentionally split into shape and value. +// 嵌套标量叶子的读取结果，将 Dremel 编码的 shape 和实际 value 分离。 // -// Shape is the Dremel row/level stream plus row/value membership. Value is the primitive payload -// attached to level slots that actually contain a value. Complex readers build Doris ColumnStruct, -// ColumnArray, and ColumnMap from shape first, then append values into that layout. +// 设计意图：复杂 reader（LIST/MAP/STRUCT）先消费 shape（def_levels + rep_levels + value_indices） +// 重建容器结构（offsets + null_map），再按 value_indices 将 values_column 写入容器。 +// +// 例子：MAP 的 value 列在 rep_level=[0,1,0] 表示 2 个 entry 的 3 个 slot。 +// 复杂 reader 先根据 rep_levels 确定 offsets=[0,2]，再根据 value_indices 把 value 写入对应 slot。 +// +// 字段说明： +// records_read - 本批读到的顶层记录数（从 ReadRecords 返回） +// levels_written - 本批实际产生的 level 数（= consumed_level_count） +// value_slot_definition_level - 有资格包含 value 的 slot 的最小 def level（由父 reader 设定） +// value_slot_repetition_level - 有资格包含 value 的 slot 的最大 rep level（由父 reader 设定） +// def_levels[] - definition levels 的拷贝 +// rep_levels[] - repetition levels 的拷贝 +// value_indices[] - level_idx → value buffer 中的下标（-1 表示该 slot 无 value，如 NULL key） +// values_column - 物化后的 value 列（非 nullable 的类型） struct ParquetNestedScalarBatch { int64_t records_read = 0; int64_t levels_written = 0; @@ -65,11 +77,22 @@ struct ParquetNestedScalarBatch { bool empty() const { return levels_written == 0; } }; -// Normalized view of one already-read Arrow RecordReader batch. +// Arrow RecordReader 一次 ReadRecords() 后的批次结果视图。 +// +// 该类将 Arrow RecordReader 的内部状态"快照化"为一个不可变的视图，解决两个问题： +// 1. BinaryRecordReader 通过 GetBuilderChunks() 返回值的所有权，而固定宽度类型通过 values()。 +// 统一为 ParquetLeafBatch 后，外部只需判断 is_binary_value() 选择数据源。 +// 2. Arrow RecordReader 的数据是 one-shot transfer 语义（GetBuilderChunks 会 reset builder）， +// ParquetLeafBatch 将数据捕获后，允许多次读取（如先 build_null_map 再 append_values）。 // -// The Arrow reader exposes fixed-width values through RecordReader::values(), but binary values -// through BinaryRecordReader::GetBuilderChunks(). Keeping that distinction here prevents nested -// Dremel layout code from depending on Arrow's physical storage and one-shot transfer semantics. +// 字段说明： +// _consumed_level_count - 本次读取前 RecordReader 已经消费的 level 数（= levels_position） +// _decoded_level_count - 本次读取后 RecordReader 解码出的 level 总数（= levels_written） +// _values_written - 本批写出的 value 个数 +// _def_levels / _rep_levels - 指向 RecordReader 内部 level 数组的指针（非拥有） +// _fixed_values - 固定宽度类型的 value buffer 指针（非拥有） +// _binary_chunks - Binary 类型的 Arrow Array chunks（拥有所有权） +// _read_dense_for_nullable - RecordReader 是否为 nullable 列启用了 dense 模式 class ParquetLeafBatch { public: int64_t consumed_level_count() const { return _consumed_level_count; } @@ -95,6 +118,24 @@ class ParquetLeafBatch { std::vector> _binary_chunks; }; +// Parquet 原始类型叶子的值读取器。每个 ScalarColumnReader 在读取时创建一个临时 ParquetLeafReader。 +// +// 职责：包装 Arrow 的 RecordReader，将其解码出的 type-erased level/value buffer 转换为 Doris +// Column 可以消费的形式。 +// +// 该类不持有任何可变状态（除 _record_reader 的 shared_ptr），因此是 const 可调用的。 +// RecordReader 本身由 ParquetColumnReaderFactory 按 leaf_column_id 缓存和共享。 +// +// 对外提供两组接口： +// +// ① 平铺列（top-level primitive column）的读取路径： +// read_batch() → build_null_map() + append_values() +// 适用于非嵌套的基本类型列，如 SELECT id, name FROM t。 +// +// ② 嵌套叶子（nested LIST/MAP/STRUCT 内的 primitive leaf）的读取路径： +// read_nested_batch() +// 一步完成"读取 level/value → 解析 value slot 映射 → 物化 values_column"， +// 返回 ParquetNestedScalarBatch 供父 reader 组装容器结构。 class ParquetLeafReader { public: ParquetLeafReader(const ::parquet::ColumnDescriptor* descriptor, @@ -103,32 +144,62 @@ class ParquetLeafReader { ParquetColumnReaderProfile profile = {}, const cctz::time_zone* timezone = nullptr, bool enable_strict_mode = false); + // ①a. 从 Arrow RecordReader 读取 batch_rows 行，将结果捕获到 ParquetLeafBatch 中。 + // 调用方拿到 batch 后可以多次访问 level 和 value 信息。 Status read_batch(int64_t batch_rows, ParquetLeafBatch* batch, int64_t* rows_read) const; + + // ①b. 根据 batch 中的 definition levels 构建 Doris NullMap。 + // def_level == max_definition_level → 非 NULL，否则为 NULL。 + // 如果该列没有 optional/repeated 祖先（max_definition_level == 0），直接返回 OK。 Status build_null_map(const ParquetLeafBatch& batch, int64_t records_read, NullMap* null_map) const; + + // ①c. 将 batch 中的值写入目标 Doris Column。 + // - 固定宽度类型：直接从 _fixed_values 指针读取 + // - Binary 类型：从 _binary_chunks 构造 StringRef 向量 + // - FLOAT16 类型：从 binary 解码后转为 float + // - dense nullable 模式：先按 null_map 展开为间隔排列的 values 再写入 + // 值转换（如 INT64 timestamp → DateTime）通过 DataTypeSerde::read_column_from_decoded_values 完成。 Status append_values(const ParquetLeafBatch& batch, int64_t row_count, const NullMap* null_map, MutableColumnPtr& column) const; + // ② 嵌套叶子的一步式读取。内部调用 read_batch() 获取 level/value， + // 然后解析 value layout（Arrow 的 RecordReader 在不同场景下按不同粒度写入 value： + // LEVELS / VALUE_SLOTS / LEAF_VALUES / PAYLOAD_VALUE_SLOTS）， + // 构建 value_indices 映射和 value_nulls，最后调用 append_values() 物化 values_column。 + // + // value_slot_definition_level: 有资格容纳 value 的 slot 的最小 def level。 + // 例如 MAP key 的 value_slot_definition_level = key 的 max_dl（只有 def>=max_dl 的 slot 才有 key 值）。 + // value_slot_repetition_level: 有资格容纳 value 的 slot 的最大 rep level。 + // 用于过滤属于其他 repeated 层级（如嵌套 LIST inside MAP）的 slot。 Status read_nested_batch( int64_t batch_rows, int16_t value_slot_definition_level, ParquetNestedScalarBatch* batch, int16_t value_slot_repetition_level = std::numeric_limits::max()) const; private: + // 将 RecordReader 的内部状态捕获为不可变的 ParquetLeafBatch。 + // 分别处理固定宽度类型（values()）和 binary 类型（GetBuilderChunks()）。 Status collect_batch(::parquet::internal::RecordReader& record_reader, ParquetLeafBatch* batch) const; + + // 为 dense nullable 模式构建间隔排列的固定宽度值数组。 + // Arrow RecordReader 在 read_dense_for_nullable 模式下只写非 NULL 值（紧凑排列）， + // 本函数按 null_map 将它们展开为与行一一对应的间隔排列格式。 Status build_spaced_fixed_values(const ParquetLeafBatch& batch, int64_t row_count, const NullMap* null_map, std::vector* spaced_values) const; - const ::parquet::ColumnDescriptor* _descriptor = nullptr; - ParquetTypeDescriptor _type_descriptor; - DataTypePtr _type; - std::string _name; - std::shared_ptr<::parquet::internal::RecordReader> _record_reader; - ParquetColumnReaderProfile _profile; - const cctz::time_zone* _timezone = nullptr; - bool _enable_strict_mode = false; + const ::parquet::ColumnDescriptor* _descriptor = + nullptr; // Arrow 列描述符（physical_type, max_dl, max_rl） + ParquetTypeDescriptor _type_descriptor; // 类型编码信息（decimal 精度、timestamp 单位等） + DataTypePtr _type; // Doris 目标类型 + std::string _name; // 列名（用于报错信息） + std::shared_ptr<::parquet::internal::RecordReader> + _record_reader; // Arrow 物理列读取器（共享所有权） + ParquetColumnReaderProfile _profile; // Profile 计数器 + const cctz::time_zone* _timezone = nullptr; // 时区（timestamp 转换用） + bool _enable_strict_mode = false; // 严格模式（类型不匹配时是否报错） }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/row_position_column_reader.cpp b/be/src/format_v2/parquet/reader/row_position_column_reader.cpp index 19727758ff5d5a..85bd8e9dcaf165 100644 --- a/be/src/format_v2/parquet/reader/row_position_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/row_position_column_reader.cpp @@ -26,6 +26,8 @@ namespace doris::format::parquet { +// 构造函数：创建一个匿名 ParquetColumnSchema（只有 name），类型为 Int64。 +// 虚拟列不对应任何 Parquet 物理列，因此不需要完整的 schema 信息。 RowPositionColumnReader::RowPositionColumnReader(int64_t row_group_first_row, ParquetColumnReaderProfile profile) : ParquetColumnReader(ParquetColumnSchema {.name = format::ROW_POSITION_COLUMN_NAME}, diff --git a/be/src/format_v2/parquet/reader/row_position_column_reader.h b/be/src/format_v2/parquet/reader/row_position_column_reader.h index 679decc2753514..52aa629d4992bf 100644 --- a/be/src/format_v2/parquet/reader/row_position_column_reader.h +++ b/be/src/format_v2/parquet/reader/row_position_column_reader.h @@ -24,6 +24,13 @@ namespace doris::format::parquet { +// 虚拟列 reader：生成文件中当前行的位置序号（从 RowGroup 起始行号开始递增）。 +// +// 不对应任何 Parquet 物理列，不持有 RecordReader。 +// read() 直接写入从 _row_group_first_row + _next_row_position 开始的连续 Int64 值。 +// skip() 只推进 _next_row_position 游标。 +// +// 用于需要知道行在文件中位置的场景（如 Iceberg 的 file_row_position）。 class RowPositionColumnReader final : public ParquetColumnReader { public: explicit RowPositionColumnReader(int64_t row_group_first_row, @@ -38,8 +45,8 @@ class RowPositionColumnReader final : public ParquetColumnReader { Status skip(int64_t rows) override; private: - int64_t _row_group_first_row = 0; - int64_t _next_row_position = 0; + int64_t _row_group_first_row = 0; // 当前 RG 在文件中的起始行号 + int64_t _next_row_position = 0; // 下一个待输出的行位置 }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.cpp b/be/src/format_v2/parquet/reader/scalar_column_reader.cpp index d406c5867eb23d..2e13bc7c51685b 100644 --- a/be/src/format_v2/parquet/reader/scalar_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.cpp @@ -31,6 +31,8 @@ namespace doris::format::parquet { namespace { +// 嵌套标量值游标 — 从 ParquetNestedScalarBatch 中按 level_idx 查找对应的 value。 +// MapColumnReader 利用它为每个 key slot 找到对应的 value 行。 class ParquetNestedScalarValueCursor { public: explicit ParquetNestedScalarValueCursor(const ParquetNestedScalarBatch* batch) { reset(batch); } @@ -63,6 +65,8 @@ class ParquetNestedScalarValueCursor { const ParquetNestedScalarBatch* _batch = nullptr; }; +// 将嵌套 batch 中 level_idx 位置的值追加到目标 column。 +// 如果目标 column 是 ColumnNullable，将值写入 nested column 并 push_back(0) 到 null map。 Status append_scalar_batch_value(const ScalarColumnReader& column_reader, const ParquetNestedScalarBatch& batch, int64_t level_idx, ParquetNestedScalarValueCursor* value_cursor, @@ -99,6 +103,9 @@ ScalarColumnReader::ScalarColumnReader( ScalarColumnReader::~ScalarColumnReader() = default; +// 平铺读取：直接从 Arrow RecordReader 读 rows 行 → 构建 null_map → 物化到 Doris Column。 +// +// 这是 ScalarColumnReader 最核心的路径，对应 ParquetLeafReader 的 ① 平铺读取流程。 Status ScalarColumnReader::read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) { if (column.get() == nullptr || rows_read == nullptr) { return Status::InvalidArgument("Invalid parquet column read result pointer for column {}", @@ -204,6 +211,10 @@ void ScalarColumnReader::advance_rows_read(int64_t rows) { _row_group_rows_read += rows; } +// 跳过 rows 行。分为两个阶段： +// 1. page_filtered_rows_to_skip() — 计算落在 page skip range 内的行数（已由 page index 跳过） +// 2. skip_records() — 对剩余行调用 RecordReader::SkipRecords() +// 这两个阶段的行数之和等于 rows。 Status ScalarColumnReader::skip(int64_t rows) { if (rows <= 0) { return Status::OK(); @@ -217,11 +228,16 @@ Status ScalarColumnReader::skip(int64_t rows) { return Status::OK(); } +// 嵌套协议的 load 阶段：调用 ParquetLeafReader::read_nested_batch()。 +// +// 关键参数 materialized_slot_definition_level： +// Nullable 标量叶子需要为 NULL 占位符也保留 value slot。 +// 如果 _type->is_nullable()，将 slot threshold 降低 1 级（_definition_level - 1）， +// 这样即使 def_level < _definition_level（即 NULL）的 slot 也会有一个 value index。 +// The value index stream must advance on those null slots, otherwise later payload values shift. Status ScalarColumnReader::load_nested_batch(int64_t rows) { DORIS_CHECK(_nested_batch != nullptr); reset_nested_build_level_cursor(); - // Nullable scalar leaves materialize one slot for both non-null values and null placeholders. - // The value index stream must advance on those null slots, otherwise later payload values shift. const int16_t materialized_slot_definition_level = static_cast(_definition_level - (_type->is_nullable() ? 1 : 0)); RETURN_IF_ERROR(leaf_reader().read_nested_batch(rows, materialized_slot_definition_level, diff --git a/be/src/format_v2/parquet/reader/scalar_column_reader.h b/be/src/format_v2/parquet/reader/scalar_column_reader.h index 46c7a810cd9fea..26f1c76b7cab17 100644 --- a/be/src/format_v2/parquet/reader/scalar_column_reader.h +++ b/be/src/format_v2/parquet/reader/scalar_column_reader.h @@ -38,6 +38,16 @@ class time_zone; namespace doris::format::parquet { +// 基本类型列的读取器，直接持有 Arrow RecordReader 并通过 ParquetLeafReader 读写值。 +// +// 这是所有 ColumnReader 中唯一直接与 Arrow RecordReader 交互的 reader。 +// 它同时服务于两种场景： +// 1. 顶层平铺列（如 SELECT id, name FROM t）→ 通过 read()/skip()/select() +// 2. 复杂类型内部的叶子（如 MAP 的 key/value、LIST 的 element）→ 通过嵌套协议 +// load_nested_batch() / build_nested_column() +// +// MapColumnReader 被声明为 friend，因为它需要直接访问 descriptor() 和 leaf_reader() +// 来读取 key 列的 values 用于 entry 存在性校验。 class ScalarColumnReader final : public ParquetColumnReader { friend class MapColumnReader; @@ -49,8 +59,13 @@ class ScalarColumnReader final : public ParquetColumnReader { ParquetColumnReaderProfile profile = {}); ~ScalarColumnReader() override; + // ========== ① 平铺读取 ========== + Status read(int64_t rows, MutableColumnPtr& column, int64_t* rows_read) override; Status skip(int64_t rows) override; + + // ========== ② 嵌套协议 ========== + Status load_nested_batch(int64_t rows) override; Status build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, int64_t* values_read) override; @@ -60,24 +75,31 @@ class ScalarColumnReader final : public ParquetColumnReader { bool is_or_has_repeated_child() const override; private: + // 将嵌套 batch 中单个 level_idx 对应的值写入目标 column。 + // 被 MapColumnReader 用来逐个填充 value。 Status append_nested_value(int64_t level_idx, MutableColumnPtr& column) const; + const ::parquet::ColumnDescriptor* descriptor() const { return _descriptor; } + + // 创建临时 ParquetLeafReader。每次调用都创建新实例（轻量级，只持有 shared_ptr）。 ParquetLeafReader leaf_reader() const { return ParquetLeafReader(_descriptor, _type_descriptor, _type, _name, _record_reader, _profile, _timezone, _enable_strict_mode); } + void advance_rows_read(int64_t rows); Status skip_records(int64_t rows); + // 计算 page skip 优化下当前 batch 内可以通过 page skip 跳过的行数。 int64_t page_filtered_rows_to_skip(int64_t rows) const; - const ::parquet::ColumnDescriptor* _descriptor = nullptr; - ParquetTypeDescriptor _type_descriptor; - std::shared_ptr<::parquet::internal::RecordReader> _record_reader; - const ParquetPageSkipPlan* _page_skip_plan = nullptr; - const cctz::time_zone* _timezone = nullptr; - bool _enable_strict_mode = false; - int64_t _row_group_rows_read = 0; - std::unique_ptr _nested_batch; + const ::parquet::ColumnDescriptor* _descriptor = nullptr; // Arrow 列描述符 + ParquetTypeDescriptor _type_descriptor; // 类型编码信息 + std::shared_ptr<::parquet::internal::RecordReader> _record_reader; // Arrow 物理列读取器 + const ParquetPageSkipPlan* _page_skip_plan = nullptr; // page index 裁剪结果（可为 nullptr） + const cctz::time_zone* _timezone = nullptr; // 时区 + bool _enable_strict_mode = false; // 严格模式 + int64_t _row_group_rows_read = 0; // 当前 RG 已读行数（游标） + std::unique_ptr _nested_batch; // 嵌套读取的中间结果 }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.cpp b/be/src/format_v2/parquet/reader/struct_column_reader.cpp index 82cd32d77afb78..5a7df5ce06e461 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.cpp +++ b/be/src/format_v2/parquet/reader/struct_column_reader.cpp @@ -27,6 +27,10 @@ #include "format_v2/parquet/reader/scalar_column_reader.h" namespace doris::format::parquet { + +// 选择提供 shape 信息的子 reader：返回第一个非 repeated 的子 reader。 +// 如果所有子 reader 都是 repeated 的，返回第一个子 reader。 +// shape source 的 def/rep levels 决定 struct 的 null 状态和嵌套边界。 ParquetColumnReader* StructColumnReader::shape_source_reader() const { for (const auto& child : _children) { auto* child_reader = child.get(); @@ -71,6 +75,22 @@ Status StructColumnReader::load_nested_batch(int64_t rows) { return Status::OK(); } +// STRUCT 的嵌套构建核心逻辑： +// +// 整体策略：STRUCT 拥有行对齐责任。子 reader 只消费 STRUCT 非 NULL 父行的 level stream， +// NULL 父行为所有子列填充 default 占位符。 +// +// 流程： +// 1. 从 shape_source_reader 的 def/rep levels 解析 struct 的 null 状态， +// 构建 parent_nulls[] 和 parent_level_indices[]。 +// 2. 遍历所有子 reader，按 parent_nulls 分批次调用子 reader 的 build_nested_column()： +// - 连续的非 NULL 父行 → 批量 build（如 100 个 present → build 100 个子列行） +// - NULL 父行 → 子列 insert_default()，并推进子 reader 的游标跳过该 NULL slot +// 3. 将 parent_nulls 写入 struct 外层 ColumnNullable 的 null_map。 +// +// 对于 NULL 父行下 ScalarColumnReader 子列的特殊处理： +// Scalar 子列的 level stream 中 NULL struct parent 也占一个 slot（但低于 value threshold）， +// 需要将子 cursor 精确推进到 parent_level_idx + 1 以跳过该 slot。 Status StructColumnReader::build_nested_column(int64_t length_upper_bound, MutableColumnPtr& column, int64_t* values_read) { if (column.get() == nullptr || values_read == nullptr) { diff --git a/be/src/format_v2/parquet/reader/struct_column_reader.h b/be/src/format_v2/parquet/reader/struct_column_reader.h index 7eeab621742d60..9a063c52331a59 100644 --- a/be/src/format_v2/parquet/reader/struct_column_reader.h +++ b/be/src/format_v2/parquet/reader/struct_column_reader.h @@ -28,6 +28,18 @@ namespace doris::format::parquet { +// STRUCT 列的读取器，持有多个子 reader 并按需协调读取。 +// +// 实现策略：平铺读取和嵌套协议都委托给子 reader 完成。 +// - read(): 分别让每个被 projected 的子 reader 读取对应行数，组装 ColumnStruct。 +// 如果子 reader 自身是复杂类型（如 LIST inside STRUCT），递归调用其 read()。 +// - 嵌套协议：选择一个 shape source reader（第一个包含足够 level 信息的子 reader）， +// 从它读取 def/rep levels 来确定 struct 的 null 状态，然后让所有子 reader 同步构建。 +// +// _child_output_indices 用于部分 projection 场景： +// 例如 STRUCT 只读 a 和 c， +// 则 _children = [a_reader, c_reader], _child_output_indices = [0, 2]。 +// build_nested_column 时按 indices 将子列写入 ColumnStruct 的正确位置。 class StructColumnReader final : public ParquetColumnReader { public: StructColumnReader(const ParquetColumnSchema& schema, DataTypePtr type, @@ -51,9 +63,12 @@ class StructColumnReader final : public ParquetColumnReader { bool is_or_has_repeated_child() const override; private: + // 选择提供 shape 信息的子 reader（第一个非空或包含 repeated 子节点的 reader）。 + // shape source 的 def/rep levels 用于确定 struct 的 null 状态和嵌套边界。 ParquetColumnReader* shape_source_reader() const; - std::vector> _children; - std::vector _child_output_indices; + + std::vector> _children; // 被 projected 的子 reader 列表 + std::vector _child_output_indices; // 子 reader → struct 输出位置的映射 }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/selection_vector.h b/be/src/format_v2/parquet/selection_vector.h index 3a4d117c7910c0..8d2794d1df1345 100644 --- a/be/src/format_v2/parquet/selection_vector.h +++ b/be/src/format_v2/parquet/selection_vector.h @@ -54,9 +54,17 @@ struct ParquetPageSkipPlan { } }; -// 类似 DuckDB SelectionVector 的轻量行号视图。 -// 它只表达一个 batch 内被选中的 row offset，不持有 table/global schema 语义。 -// 未绑定 data 时表示 identity selection：get_index(i) == i。 +// 批次内选中行的行号视图，类似 DuckDB 的 SelectionVector。 +// +// 用途：在 late materialization 中，predicate 列全量读取后执行 conjuncts， +// 生成 SelectionVector 标记哪些行命中。Non-predicate 列再通过 select() 只读命中行。 +// +// 两种模式： +// - 未绑定 data (_data == nullptr)：identity selection，get_index(i) == i +// - 已绑定 data：_data[i] 是第 i 个选中行的 batch 内行号 +// +// selection_to_ranges() 将选中的行号合并为连续的 RowRange 列表， +// 供 ColumnReader 做 range-based skip + read。 class SelectionVector { public: using Index = uint16_t; diff --git a/be/test/exec/scan/file_scanner_v2_test.cpp b/be/test/exec/scan/file_scanner_v2_test.cpp index fe6c69d77a6572..8778cce357ad10 100644 --- a/be/test/exec/scan/file_scanner_v2_test.cpp +++ b/be/test/exec/scan/file_scanner_v2_test.cpp @@ -206,7 +206,7 @@ TEST(FileScannerV2Test, RewriteSlotRefsToGlobalIndexMatrix) { EXPECT_EQ(rewritten->column_name(), "legacy_value"); } { - auto cast_expr = Cast::create_shared(int_type); + auto cast_expr = format::Cast::create_shared(int_type); cast_expr->add_child(slot_ref(9, 9, int_type, "nested_value")); VExprSPtr expr = cast_expr; const auto status = FileScannerV2::TEST_rewrite_slot_refs_to_global_index( diff --git a/be/test/exec/scan/vfile_scanner_exception_test.cpp b/be/test/exec/scan/vfile_scanner_exception_test.cpp index 94e52f547d07b9..22a55db3c3d23a 100644 --- a/be/test/exec/scan/vfile_scanner_exception_test.cpp +++ b/be/test/exec/scan/vfile_scanner_exception_test.cpp @@ -352,7 +352,7 @@ TEST(HiveReaderTest, PositionMappingUsesColumnIdxsForFileSlots) { .scan_params = ¶ms, .runtime_state = &runtime_state, }; - hive::HiveReader reader; + format::hive::HiveReader reader; TFileScanSlotInfo id_slot; id_slot.__set_is_file_slot(true); @@ -393,7 +393,7 @@ TEST(HiveReaderTest, PositionMappingDoesNotConsumePartitionSlots) { .scan_params = ¶ms, .runtime_state = &runtime_state, }; - hive::HiveReader reader; + format::hive::HiveReader reader; TFileScanSlotInfo partition_slot; partition_slot.__set_is_file_slot(false); @@ -434,7 +434,7 @@ TEST(HiveReaderTest, PositionMappingFailsWhenColumnIdxsMissing) { .scan_params = ¶ms, .runtime_state = &runtime_state, }; - hive::HiveReader reader; + format::hive::HiveReader reader; TFileScanSlotInfo value_slot; value_slot.__set_is_file_slot(true); diff --git a/be/test/format_v2/column_mapper_test.cpp b/be/test/format_v2/column_mapper_test.cpp index a882b09311c80d..44be5abba1a439 100644 --- a/be/test/format_v2/column_mapper_test.cpp +++ b/be/test/format_v2/column_mapper_test.cpp @@ -1365,8 +1365,7 @@ TEST(ColumnMapperCreateMappingTest, ASSERT_TRUE(status.ok()) << status.to_string(); ASSERT_EQ(mapper.mappings().size(), 1); - expect_mapping(mapper.mappings()[0], 0, "profile", 1, "_col1", file_root.type, - table_root.type); + expect_mapping(mapper.mappings()[0], 0, "profile", 1, "_col1", file_root.type, table_root.type); ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "id", 1, "id", int_type, int_type); expect_mapping(mapper.mappings()[0].child_mappings[1], 0, "name", 0, "name", string_type, @@ -1401,8 +1400,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexNestedStructDoesNotUseChildOrdinalIde ASSERT_TRUE(status.ok()) << status.to_string(); ASSERT_EQ(mapper.mappings().size(), 1); - expect_mapping(mapper.mappings()[0], 0, "profile", 1, "_col1", file_root.type, - table_root.type); + expect_mapping(mapper.mappings()[0], 0, "profile", 1, "_col1", file_root.type, table_root.type); ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "id", 1, "id", int_type, int_type); expect_mapping(mapper.mappings()[0].child_mappings[1], 0, "name", 0, "name", string_type, @@ -1463,8 +1461,7 @@ TEST(ColumnMapperCreateMappingTest, ByIndexMapValueStructChildrenMatchByName) { field_id_col("id", 100, int_type), field_id_col("name", 101, string_type), }); - auto table_root = map_col("profiles", 1, {table_key, table_value}, key_type, - table_value.type); + auto table_root = map_col("profiles", 1, {table_key, table_value}, key_type, table_value.type); auto file_key = name_col("key", key_type, 0); // Reverse value struct children. A positional nested match would produce name/id swapped. auto file_value = struct_name_col("value", @@ -1488,14 +1485,12 @@ TEST(ColumnMapperCreateMappingTest, ByIndexMapValueStructChildrenMatchByName) { expect_mapping(mapper.mappings()[0], 0, "profiles", 1, "_col1", file_root.type, table_root.type); ASSERT_EQ(mapper.mappings()[0].child_mappings.size(), 2); - expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "key", 0, "key", key_type, - key_type); + expect_mapping(mapper.mappings()[0].child_mappings[0], 0, "key", 0, "key", key_type, key_type); const auto& value_mapping = mapper.mappings()[0].child_mappings[1]; expect_mapping(value_mapping, 0, "value", 1, "value", file_value.type, table_value.type); ASSERT_EQ(value_mapping.child_mappings.size(), 2); expect_mapping(value_mapping.child_mappings[0], 0, "id", 1, "id", int_type, int_type); - expect_mapping(value_mapping.child_mappings[1], 0, "name", 0, "name", string_type, - string_type); + expect_mapping(value_mapping.child_mappings[1], 0, "name", 0, "name", string_type, string_type); } TEST(ColumnMapperCreateMappingTest, diff --git a/be/test/format_v2/parquet/parquet_reader_test.cpp b/be/test/format_v2/parquet/parquet_reader_test.cpp index 68f2cec92ff4d8..6ec0cad82a9ebf 100644 --- a/be/test/format_v2/parquet/parquet_reader_test.cpp +++ b/be/test/format_v2/parquet/parquet_reader_test.cpp @@ -1258,6 +1258,44 @@ TEST_F(NewParquetReaderTest, PredicateFiltersRowGroupsByDictionary) { EXPECT_EQ(values, std::vector({"lm"})); } +TEST_F(NewParquetReaderTest, ScanRangeFiltersRowGroupsBeforeDictionaryPruning) { + write_dictionary_filter_parquet_file(_file_path); + auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); + ASSERT_EQ(parquet_file_reader->metadata()->num_row_groups(), 6); + + std::vector> file_schema; + auto schema_descriptor = parquet_file_reader->metadata()->schema(); + ASSERT_NE(schema_descriptor, nullptr); + ASSERT_TRUE( + format::parquet::build_parquet_column_schema(*schema_descriptor, &file_schema).ok()); + + format::FileScanRequest request; + format::FileColumnPredicateFilter column_filter; + column_filter.file_column_id = format::LocalColumnId(1); + auto value_type = std::make_shared(); + column_filter.predicates.push_back(create_comparison_predicate( + 1, "value", value_type, Field::create_field("lm"), false)); + request.column_predicate_filters.push_back(std::move(column_filter)); + + const auto [range_start_offset, range_size] = row_group_mid_range(_file_path, 2); + format::parquet::ParquetScanRange scan_range; + scan_range.start_offset = range_start_offset; + scan_range.size = range_size; + scan_range.file_size = static_cast(std::filesystem::file_size(_file_path)); + + format::parquet::RowGroupScanPlan plan; + ASSERT_TRUE(format::parquet::plan_parquet_row_groups(*parquet_file_reader->metadata(), + parquet_file_reader.get(), file_schema, + request, scan_range, false, &plan) + .ok()); + ASSERT_EQ(plan.row_groups.size(), 1); + EXPECT_EQ(plan.row_groups[0].row_group_id, 2); + EXPECT_EQ(plan.pruning_stats.total_row_groups, 6); + EXPECT_EQ(plan.pruning_stats.selected_row_groups, 1); + EXPECT_EQ(plan.pruning_stats.filtered_row_groups_by_dictionary, 0); + EXPECT_EQ(plan.pruning_stats.filtered_group_rows, 0); +} + TEST_F(NewParquetReaderTest, NestedStructPredicateFiltersRowGroupsByStatistics) { write_struct_filter_parquet_file(_file_path); auto parquet_file_reader = ::parquet::ParquetFileReader::OpenFile(_file_path, false); From 974f56b07caabed26242f93bc3428dd022d43a9f Mon Sep 17 00:00:00 2001 From: Socrates Date: Wed, 17 Jun 2026 08:00:14 +0800 Subject: [PATCH 28/28] [comment](be) Rewrite documentation comments for core Parquet modules MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Rewrite comments for the entry-point and foundational modules: parquet_reader.h: - Class-level doc: role boundary, lifecycle (init→get_schema→open→get_block→close) - TableReader calling relationship explained - Each method and field annotated parquet_type.h: - ParquetExtraTypeInfo: each variant documented - ParquetTypeDescriptor: full field-by-field descriptions - Three-level resolution priority (logical→converted→physical) explained - resolve_parquet_type / supports_record_reader / decoded_value_kind docs parquet_column_schema.h: - Class-level doc: design decisions (wrapper folding, nullable, Dremel levels) - All fields grouped into sections (identifier / type / levels / children) - Each field annotated with its role and valid domain (PRIMITIVE vs complex) parquet_column_schema.cpp: - SchemaBuildContext fields annotated parquet_file_context.cpp: - DorisRandomAccessFile adapter class documented parquet_profile.h: - All Profile structs with section-based Chinese comments - Counter groups organized (RG pruning / page skip / batch read / column reader / file ops / decompress & cache / decode / others) Co-Authored-By: Claude --- .../parquet/parquet_column_schema.cpp | 15 +-- .../format_v2/parquet/parquet_column_schema.h | 97 +++++++++++-------- .../parquet/parquet_file_context.cpp | 5 + be/src/format_v2/parquet/parquet_profile.h | 78 +++++++++++---- be/src/format_v2/parquet/parquet_reader.h | 46 ++++++--- be/src/format_v2/parquet/parquet_type.h | 81 ++++++++++------ 6 files changed, 209 insertions(+), 113 deletions(-) diff --git a/be/src/format_v2/parquet/parquet_column_schema.cpp b/be/src/format_v2/parquet/parquet_column_schema.cpp index 3fc36922f52534..dca23e00172dfc 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.cpp +++ b/be/src/format_v2/parquet/parquet_column_schema.cpp @@ -33,14 +33,15 @@ namespace doris::format::parquet { namespace { +// Schema 构建过程中的上下文，携带逐层累加的 Dremel level 状态。 +// child_context() 在递归过程中根据子节点的 optional/repeated 属性递增对应的 level。 struct SchemaBuildContext { - // Reader-local id inside the parent schema node. - int32_t local_id = -1; - int16_t definition_level = 0; - int16_t repetition_level = 0; - int16_t nullable_definition_level = 0; - int16_t repeated_repetition_level = 0; - int16_t repeated_ancestor_definition_level = 0; + int32_t local_id = -1; // 父节点内的 child ordinal + int16_t definition_level = 0; // 累计 optional/repeated 数 + int16_t repetition_level = 0; // 累计 repeated 数 + int16_t nullable_definition_level = 0; // 最近 optional 节点的 def level + int16_t repeated_repetition_level = 0; // 最近 repeated 节点的 rep level + int16_t repeated_ancestor_definition_level = 0; // 最近 repeated 节点的 def level }; enum class SchemaBuildMode { diff --git a/be/src/format_v2/parquet/parquet_column_schema.h b/be/src/format_v2/parquet/parquet_column_schema.h index 066bbfef5f788d..637930fb498ecd 100644 --- a/be/src/format_v2/parquet/parquet_column_schema.h +++ b/be/src/format_v2/parquet/parquet_column_schema.h @@ -32,68 +32,87 @@ class SchemaDescriptor; namespace doris::format::parquet { +// Schema 节点类型枚举，决定 ParquetColumnReaderFactory 创建哪种 Reader。 enum class ParquetColumnSchemaKind { - PRIMITIVE, - STRUCT, - LIST, - MAP, + PRIMITIVE, // 基本类型叶子 → ScalarColumnReader + STRUCT, // 结构体 → StructColumnReader + LIST, // 数组 → ListColumnReader + MAP, // 字典 → MapColumnReader }; -// New Parquet reader file-local schema tree. +// ============================================================================ +// Parquet 文件的 file-local schema 树 — build_parquet_column_schema() 的输出 +// ============================================================================ // -// The tree describes Parquet logical fields and leaf column ordinals, but its DataType is already -// converted to Doris external-table semantics: Doris does not expose required nested fields from -// external files, so STRUCT children, LIST elements, and MAP key/value children are widened to -// nullable. Parquet required/optional details are still preserved in the level fields and -// ColumnDescriptor metadata below for reader materialization. -// LIST repeated-group wrappers and MAP key_value/entry wrappers are folded into their parent -// schema nodes; LIST children are [element] and MAP children are [key, value]. The folded parent -// nodes keep the repeated definition/repetition levels needed to assemble nested values. +// 该树描述 Parquet 逻辑字段及其到物理 leaf column 的映射，将 Arrow 的物理 schema +// （含 wrapper groups、Dremel levels）转换为 Doris reader 可以直接消费的语义化 schema。 +// +// 关键设计决策： +// - LIST/MAP 的物理 wrapper group 在构建时被折叠，children 直接是 [element] 或 [key, value]。 +// wrapper 的 repeated 属性转为父节点的 level 字段，reader 通过 levels 重建嵌套结构。 +// - 所有类型统一 nullable（Doris external table 的策略：外部数据不可信）。 +// - Dremel levels 在 child_context() 中逐层累加，复杂 reader 用它们从 leaf 的 level 流中 +// 重建嵌套容器（offsets + null_map）。 +// ============================================================================ struct ParquetColumnSchema { - // Reader-local id inside the parent schema node. - // - // Top-level fields use the root field ordinal. Nested fields use the child ordinal under their - // parent. This id is what LocalColumnIndex carries into ParquetColumnReaderFactory. + // ======== 标识 ======== + + // 在父节点 children 中的序号。顶层字段使用 root field ordinal。 + // ParquetColumnReaderFactory 通过 LocalColumnIndex 携带的 local_id 路径递归定位 reader。 int local_id = -1; - // Optional Parquet schema field_id from the serialized schema element. - // - // Arrow returns -1 when the file does not define a field_id. This value is only a schema - // matching identifier for table formats such as Iceberg; it is not used to address readers, - // row-group column chunks, or nested projections. + + // Parquet 序列化 schema 中的 field_id attribute（-1 表示文件未定义）。 + // 仅用于 Iceberg 等 table format 的 schema matching 标识，不用于 reader 寻址。 int parquet_field_id = -1; + std::string name; + + // ======== 类型 ======== + + // Doris DataType。复杂类型的 children 已递归 nullable。 DataTypePtr type = nullptr; - // Parquet schema 中的 primitive leaf column ordinal. - // - // 该 id 用于访问 ColumnDescriptor、RowGroupReader::RecordReader、ColumnChunk - // metadata 和 statistics。复杂类型节点本身没有单一 leaf column，因此为 -1。 + + // Parquet 物理 leaf column 序号。 + // PRIMITIVE 节点才有有效值，用于访问 ColumnDescriptor、RecordReader、ColumnChunk、Statistics。 + // 复杂类型节点本身不是物理列，值为 -1。 int leaf_column_id = -1; - // Parquet physical/logical type metadata resolved from the leaf ColumnDescriptor. + + // 从 leaf ColumnDescriptor 解析出的类型编码信息。仅 PRIMITIVE 节点有效。 ParquetTypeDescriptor type_descriptor {}; + ParquetColumnSchemaKind kind = ParquetColumnSchemaKind::PRIMITIVE; - // Arrow Parquet descriptor for primitive leaf nodes. Complex nodes keep this as nullptr. + + // Arrow ColumnDescriptor 指针。仅 PRIMITIVE 节点有效，复杂节点为 nullptr。 const ::parquet::ColumnDescriptor* descriptor = nullptr; - // Maximum Dremel definition/repetition levels below this schema node. + + // ======== Dremel Levels ======== + + // 该子树中的最大 def/rep level。PRIMITIVE 从 ColumnDescriptor 获取，复杂节点从 children 上报。 int16_t max_definition_level = 0; int16_t max_repetition_level = 0; - // Definition level at which this node itself becomes nullable. - // - // Complex readers use this to distinguish null containers from empty containers while - // assembling STRUCT/LIST/MAP values. + + // 使本节点自身变为 nullable 的 def level 阈值。 + // 复杂 reader 用此值区分"我的值是 NULL"（def < threshold）和"我有值但内容为空"（def >= threshold）。 int16_t nullable_definition_level = 0; - // Definition/repetition levels used to reconstruct this node from a descendant leaf level - // stream. These mirror parquet::internal::LevelInfo but are kept in Doris schema state so - // complex readers do not depend on Arrow's Arrow-array reader internals. + + // 从 root 到本节点的累计 def/rep level。在 child_context() 中逐层 +1。 int16_t definition_level = 0; int16_t repetition_level = 0; + + // 最近 repeated 祖先的 def level。 int16_t repeated_ancestor_definition_level = 0; - // Repetition level introduced by this node's repeated container, or the nearest repeated - // container carried from its parent. + + // 最近 repeated 祖先的 rep level。 + // LIST/MAP reader 用此值从孩子 rep level 流中判断"新元素开始"（rep_level >= threshold）。 int16_t repeated_repetition_level = 0; + + // ======== 子树 ======== + // LIST: [element]，MAP: [key, value]，STRUCT: 直接孩子，PRIMITIVE: 空 std::vector> children {}; }; -// 从 Arrow Parquet core schema 构造 file-local schema tree。 +// 从 Arrow Parquet SchemaDescriptor 构造 file-local schema tree。 +// 这是 init() 阶段最重要的转换：物理 schema（含 wrapper groups）→ 语义化 schema（wrapper 已折叠）。 Status build_parquet_column_schema(const ::parquet::SchemaDescriptor& schema, std::vector>* fields); diff --git a/be/src/format_v2/parquet/parquet_file_context.cpp b/be/src/format_v2/parquet/parquet_file_context.cpp index 6b9ff1310691e8..60e48c947bd3e2 100644 --- a/be/src/format_v2/parquet/parquet_file_context.cpp +++ b/be/src/format_v2/parquet/parquet_file_context.cpp @@ -30,6 +30,11 @@ namespace doris::format::parquet { namespace { +// 将 Doris 的 io::FileReader 适配为 Arrow 的 RandomAccessFile 接口。 +// +// ParquetFileReader::Open() 要求一个 Arrow::RandomAccessFile， +// 本适配器将 Doris 的 read_at() / size() 等接口映射为 Arrow 的 ReadAt() / GetSize()。 +// Seek() 和 Tell() 维护了内部的 position 游标用于顺序 read() 操作。 class DorisRandomAccessFile final : public arrow::io::RandomAccessFile { public: DorisRandomAccessFile(io::FileReaderSPtr file_reader, io::IOContext* io_ctx) diff --git a/be/src/format_v2/parquet/parquet_profile.h b/be/src/format_v2/parquet/parquet_profile.h index 26212e72e8291d..26e6d01d943fd0 100644 --- a/be/src/format_v2/parquet/parquet_profile.h +++ b/be/src/format_v2/parquet/parquet_profile.h @@ -23,42 +23,60 @@ namespace doris::format::parquet { struct ParquetPruningStats; +// ============================================================================ +// Page Skip Profile — data page 级跳过统计 +// ============================================================================ struct ParquetPageSkipProfile { - RuntimeProfile::Counter* skipped_pages = nullptr; - RuntimeProfile::Counter* skipped_bytes = nullptr; + RuntimeProfile::Counter* skipped_pages = nullptr; // 被 page index 跳过的 data page 数 + RuntimeProfile::Counter* skipped_bytes = nullptr; // 被跳过的压缩字节数 }; +// ============================================================================ +// Column Reader Profile — 列读取统计 +// ============================================================================ struct ParquetColumnReaderProfile { - RuntimeProfile::Counter* reader_read_rows = nullptr; - RuntimeProfile::Counter* reader_skip_rows = nullptr; - RuntimeProfile::Counter* reader_select_rows = nullptr; - RuntimeProfile::Counter* arrow_read_records_time = nullptr; - RuntimeProfile::Counter* materialization_time = nullptr; + RuntimeProfile::Counter* reader_read_rows = nullptr; // read() 读取的行数 + RuntimeProfile::Counter* reader_skip_rows = nullptr; // skip() 跳过的行数 + RuntimeProfile::Counter* reader_select_rows = nullptr; // select() 选中的行数 + RuntimeProfile::Counter* arrow_read_records_time = nullptr; // Arrow RecordReader 耗时 (ns) + RuntimeProfile::Counter* materialization_time = nullptr; // 值物化耗时 (ns) }; +// ============================================================================ +// Scan Profile — 扫描调度统计（每个 batch 的粒度） +// ============================================================================ struct ParquetScanProfile { - RuntimeProfile::Counter* raw_rows_read = nullptr; - RuntimeProfile::Counter* selected_rows = nullptr; - RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; - RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; - RuntimeProfile::Counter* total_batches = nullptr; - RuntimeProfile::Counter* empty_selection_batches = nullptr; - RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; - RuntimeProfile::Counter* column_read_time = nullptr; - RuntimeProfile::Counter* predicate_filter_time = nullptr; - ParquetColumnReaderProfile column_reader_profile; + RuntimeProfile::Counter* raw_rows_read = nullptr; // 从 RecordReader 读取的原始行数 + RuntimeProfile::Counter* selected_rows = nullptr; // conjuncts 过滤后选中的行数 + RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; // 被 conjuncts 过滤掉的行数 + RuntimeProfile::Counter* lazy_read_filtered_rows = + nullptr; // 因 late materialization 减少读取的行数 + RuntimeProfile::Counter* total_batches = nullptr; // 总批次数 + RuntimeProfile::Counter* empty_selection_batches = nullptr; // 全过滤的空批次数 + RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; // range gap 跳过的行数 + RuntimeProfile::Counter* column_read_time = nullptr; // 列读取耗时 (ns) + RuntimeProfile::Counter* predicate_filter_time = nullptr; // predicate 过滤耗时 (ns) + ParquetColumnReaderProfile column_reader_profile; // 嵌套的列读取统计 }; -// Owns every RuntimeProfile counter exposed by the new Parquet reader and builds the -// narrower profile views used by scan scheduling, page skipping, and column readers. +// ============================================================================ +// Parquet Profile — 统一的 RuntimeProfile Counter 集合 +// ============================================================================ +// +// 管理 new Parquet reader 暴露的所有 RuntimeProfile Counter。 +// 通过 page_skip_profile() / column_reader_profile() / scan_profile() 方法 +// 将整体的 Counter 集合拆分为不同模块需要的窄视图。 +// ============================================================================ struct ParquetProfile { void init(RuntimeProfile* profile); void update_pruning_stats(const ParquetPruningStats& pruning_stats) const; + // 构建各模块的窄视图（只是指针透传，不创建新 Counter） ParquetPageSkipProfile page_skip_profile() const; ParquetColumnReaderProfile column_reader_profile() const; ParquetScanProfile scan_profile() const; + // ======== RowGroup 裁剪 ======== RuntimeProfile::Counter* filtered_row_groups = nullptr; RuntimeProfile::Counter* filtered_row_groups_by_min_max = nullptr; RuntimeProfile::Counter* filtered_row_groups_by_dictionary = nullptr; @@ -68,33 +86,47 @@ struct ParquetProfile { RuntimeProfile::Counter* selected_row_ranges = nullptr; RuntimeProfile::Counter* filtered_group_rows = nullptr; RuntimeProfile::Counter* filtered_page_rows = nullptr; + + // ======== Page Skip ======== RuntimeProfile::Counter* pages_skipped_by_data_page_filter = nullptr; RuntimeProfile::Counter* data_page_filter_skip_bytes = nullptr; + + // ======== Batch 读取 ======== RuntimeProfile::Counter* selected_rows = nullptr; RuntimeProfile::Counter* rows_filtered_by_conjunct = nullptr; RuntimeProfile::Counter* total_batches = nullptr; RuntimeProfile::Counter* empty_selection_batches = nullptr; RuntimeProfile::Counter* range_gap_skipped_rows = nullptr; + + // ======== Column Reader ======== RuntimeProfile::Counter* reader_read_rows = nullptr; RuntimeProfile::Counter* reader_skip_rows = nullptr; RuntimeProfile::Counter* reader_select_rows = nullptr; RuntimeProfile::Counter* arrow_read_records_time = nullptr; RuntimeProfile::Counter* materialization_time = nullptr; + + // ======== 延迟读取 ======== RuntimeProfile::Counter* lazy_read_filtered_rows = nullptr; RuntimeProfile::Counter* filtered_bytes = nullptr; RuntimeProfile::Counter* raw_rows_read = nullptr; RuntimeProfile::Counter* column_read_time = nullptr; + + // ======== 文件操作 ======== RuntimeProfile::Counter* parse_meta_time = nullptr; RuntimeProfile::Counter* parse_footer_time = nullptr; RuntimeProfile::Counter* file_reader_create_time = nullptr; RuntimeProfile::Counter* open_file_num = nullptr; + RuntimeProfile::Counter* file_footer_read_calls = nullptr; + RuntimeProfile::Counter* file_footer_hit_cache = nullptr; + + // ======== 裁剪耗时 ======== RuntimeProfile::Counter* row_group_filter_time = nullptr; RuntimeProfile::Counter* page_index_read_calls = nullptr; RuntimeProfile::Counter* page_index_filter_time = nullptr; RuntimeProfile::Counter* read_page_index_time = nullptr; RuntimeProfile::Counter* parse_page_index_time = nullptr; - RuntimeProfile::Counter* file_footer_read_calls = nullptr; - RuntimeProfile::Counter* file_footer_hit_cache = nullptr; + + // ======== 解压 & Page Cache ======== RuntimeProfile::Counter* decompress_time = nullptr; RuntimeProfile::Counter* decompress_cnt = nullptr; RuntimeProfile::Counter* page_read_counter = nullptr; @@ -105,6 +137,8 @@ struct ParquetProfile { RuntimeProfile::Counter* page_cache_missing_counter = nullptr; RuntimeProfile::Counter* page_cache_compressed_hit_counter = nullptr; RuntimeProfile::Counter* page_cache_decompressed_hit_counter = nullptr; + + // ======== 解码 ======== RuntimeProfile::Counter* decode_header_time = nullptr; RuntimeProfile::Counter* read_page_header_time = nullptr; RuntimeProfile::Counter* decode_value_time = nullptr; @@ -113,6 +147,8 @@ struct ParquetProfile { RuntimeProfile::Counter* decode_null_map_time = nullptr; RuntimeProfile::Counter* skip_page_header_num = nullptr; RuntimeProfile::Counter* parse_page_header_num = nullptr; + + // ======== 其他 ======== RuntimeProfile::Counter* predicate_filter_time = nullptr; RuntimeProfile::Counter* dict_filter_rewrite_time = nullptr; RuntimeProfile::Counter* convert_time = nullptr; diff --git a/be/src/format_v2/parquet/parquet_reader.h b/be/src/format_v2/parquet/parquet_reader.h index 957d503668392e..9151bc1fb44073 100644 --- a/be/src/format_v2/parquet/parquet_reader.h +++ b/be/src/format_v2/parquet/parquet_reader.h @@ -36,9 +36,28 @@ namespace doris::format::parquet { struct ParquetReaderScanState; -// Parquet 文件物理读取层。 -// 该类只理解 Parquet file-local schema 和 FileScanRequest，不理解 Iceberg/global -// schema，不处理 table-level cast/default/generated/partition 语义。 +// ============================================================================ +// Parquet 文件物理读取层 — FileReader 接口的 Parquet 实现 +// ============================================================================ +// +// 职责边界： +// ✓ 理解 Parquet file-local schema，处理文件打开、元数据解析、批次读取 +// ✗ 不理解 Iceberg/global schema，不处理 table-level cast/default/generated/partition 列 +// +// 被 TableReader（HiveReader、IcebergTableReader）通过 FileReader 接口调用。 +// TableReader 负责 schema mapping、predicate localization，生成 FileScanRequest 后 +// 传给 ParquetReader::open()。 +// +// 生命周期（TableReader 视角）： +// init() → get_schema() → open(request) → get_block() [loop] → close() +// +// init() — 打开文件，解析 footer，构建 ParquetColumnSchema 树 +// get_schema() — 暴露 file-local ColumnDefinition[]，供 TableReader 做 schema matching +// open() — 接收 FileScanRequest，执行 RowGroup 裁剪（plan_parquet_row_groups）， +// 初始化 ParquetScanScheduler +// get_block() — 委托 ParquetScanScheduler 逐批读取（late materialization） +// close() — 释放 Arrow 文件资源 +// ============================================================================ class ParquetReader : public format::FileReader { public: ParquetReader(std::shared_ptr& system_properties, @@ -48,21 +67,17 @@ class ParquetReader : public format::FileReader { bool enable_mapping_timestamp_tz = false); ~ParquetReader() override; - // 打开 Parquet 文件并解析 footer metadata。 - // init 成功后可以调用 get_schema() 获取 Parquet file-local schema。 Status init(RuntimeState* state) override; - // 返回 init() 阶段解析出的 Parquet 文件自身 schema。 - // 该方法只能在 init() 成功后调用，不要求 open() 已经执行。 - // 这里不做 Iceberg schema evolution，也不把字段转换成 table/global schema。 Status get_schema(std::vector* file_schema) const override; Status open(std::shared_ptr request) override; + // 读取下一批 Parquet file-local block。 - // 该方法只能在 init() 成功后调用。 - // 返回列必须保持 file-local 语义，不能在这里补 default/generated/partition 列。 + // 返回的列保持 file-local 语义，不补 default/generated/partition 列。 Status get_block(Block* file_block, size_t* rows, bool* eof) override; + // 聚合下推：从已裁剪选中的 RowGroup statistics 中提取 COUNT / MIN / MAX。 Status get_aggregate_result(const format::FileAggregateRequest& request, format::FileAggregateResult* result) override; @@ -72,13 +87,16 @@ class ParquetReader : public format::FileReader { void _init_profile() override; private: + // 递归将 ParquetColumnSchema 树转换为 ColumnDefinition。 + // identifier 生成规则：有 parquet_field_id → Field(INT, field_id)，否则 → Field(STRING, name) void _fill_column_definition(const ParquetColumnSchema& column_schema, format::ColumnDefinition* field) const; - std::unique_ptr _state; - ParquetProfile _parquet_profile; - std::optional _global_rowid_context; - bool _enable_mapping_timestamp_tz = false; + std::unique_ptr + _state; // 全部扫描状态（file_context + schema + scheduler） + ParquetProfile _parquet_profile; // RuntimeProfile 计数器集合 + std::optional _global_rowid_context; // 全局 RowId 上下文 + bool _enable_mapping_timestamp_tz = false; // 是否将 UTC timestamp 映射为 TIMESTAMPTZ }; } // namespace doris::format::parquet diff --git a/be/src/format_v2/parquet/parquet_type.h b/be/src/format_v2/parquet/parquet_type.h index 1fb6cb51bc9cee..583d0aba8db661 100644 --- a/be/src/format_v2/parquet/parquet_type.h +++ b/be/src/format_v2/parquet/parquet_type.h @@ -30,19 +30,24 @@ class ColumnDescriptor; namespace doris::format::parquet { -// Parquet logical/converted annotation 解析后留下的额外编码信息。 -// 这对应 DuckDB ParquetColumnSchema::type_info：Doris type 只能表达最终展示类型， -// 读值时还需要知道 decimal/timestamp/time 在 Parquet 中的物理编码方式。 +// ============================================================================ +// Parquet 额外类型编码信息 +// ============================================================================ +// +// Doris 的 DataType 只能表达最终展示类型（如 Decimal(10,2)、DATETIMEV2(6)）， +// 但 reader 读值时还需要知道 Parquet 物理存储方式。 +// ParquetExtraTypeInfo 补全了这个信息。 + enum class ParquetExtraTypeInfo { - NONE, - DECIMAL_INT32, - DECIMAL_INT64, - DECIMAL_BYTE_ARRAY, - UNIT_MS, - UNIT_MICROS, - UNIT_NS, - IMPALA_TIMESTAMP, - FLOAT16, + NONE, // 无特殊编码，按物理类型直接读取 + DECIMAL_INT32, // decimal 存为 4-byte big-endian int + DECIMAL_INT64, // decimal 存为 8-byte big-endian int + DECIMAL_BYTE_ARRAY, // decimal 存为变长或定长 big-endian byte array + UNIT_MS, // 时间精度为毫秒 + UNIT_MICROS, // 时间精度为微秒 + UNIT_NS, // 时间精度为纳秒 + IMPALA_TIMESTAMP, // INT96 格式的 Impala 兼容 timestamp + FLOAT16, // 半精度浮点（FIXED_LEN_BYTE_ARRAY(2) → Float32） }; enum class ParquetTimeUnit { @@ -52,40 +57,52 @@ enum class ParquetTimeUnit { NANOS, }; -// Parquet file-local column descriptor 的类型解析结果。 -// 该结构只解释 Parquet physical/logical/converted type，不包含 table/global schema -// evolution，也不依赖 Arrow internal RecordReader API。 +// ============================================================================ +// Parquet 类型解析结果 — resolve_parquet_type() 的输出 +// ============================================================================ +// +// 将 Arrow ColumnDescriptor（physical_type + logical_type + converted_type） +// 解析为 Doris DataType + 读值时需要的全部编码信息。 +// +// 三级解析优先级：logical_type（优先）→ converted_type（次）→ physical_type（兜底） +// +// 关键字段说明： +// doris_type — Doris 侧的最终类型（如 DECIMAL128(10,2)、DATETIMEV2(6)） +// extra_type_info — 物理编码方式（如 DECIMAL_INT32、IMPALA_TIMESTAMP） +// physical_type — Parquet 物理类型（INT32/INT64/BYTE_ARRAY/...） +// is_string_like — 物理类型是 binary 且不是 decimal/FLOAT16 → 归为 string-like +// supports_record_reader — 是否可以通过 Arrow RecordReader 读取（当前全部支持） struct ParquetTypeDescriptor { DataTypePtr doris_type; ParquetExtraTypeInfo extra_type_info = ParquetExtraTypeInfo::NONE; ParquetTimeUnit time_unit = ParquetTimeUnit::UNKNOWN; ::parquet::Type::type physical_type = ::parquet::Type::UNDEFINED; ::parquet::ConvertedType::type converted_type = ::parquet::ConvertedType::UNDEFINED; - int integer_bit_width = -1; - int decimal_precision = -1; - int decimal_scale = -1; - int fixed_length = -1; - bool is_unsigned_integer = false; - bool is_decimal = false; - bool is_timestamp = false; - bool timestamp_is_adjusted_to_utc = false; - bool is_string_like = false; - bool supports_record_reader = true; + int integer_bit_width = -1; // INT_8/16/32/64 的位宽 + int decimal_precision = -1; // DECIMAL(p,s) 的精度 + int decimal_scale = -1; // DECIMAL(p,s) 的小数位 + int fixed_length = -1; // FIXED_LEN_BYTE_ARRAY 的固定长度 + bool is_unsigned_integer = false; // 是否 unsigned 整数（UINT_8/16/32/64） + bool is_decimal = false; // 是否 decimal 类型 + bool is_timestamp = false; // 是否 timestamp 类型 + bool timestamp_is_adjusted_to_utc = false; // timestamp 是否已 UTC 归一化 + bool is_string_like = false; // binary 但不是 decimal/FLOAT16 + bool supports_record_reader = true; // 能否通过 Arrow RecordReader 读取 }; -// 返回 Parquet leaf column 的 file-local 展示名。 +// 返回 Parquet leaf column 的 file-local 展示名（如 "a.b.c"）。 std::string parquet_column_name(const ::parquet::ColumnDescriptor* column); -// 将 Parquet file-local column descriptor 解析成 Doris file-local 类型和读值所需的 -// 编码信息。这里不做 table schema evolution；类型提升和 default/generated/partition -// 列由 table 层处理。 +// 将 Parquet ColumnDescriptor 解析为 ParquetTypeDescriptor。 +// 不做 table schema evolution：类型提升和 default/generated/partition 列由 TableReader 处理。 ParquetTypeDescriptor resolve_parquet_type(const ::parquet::ColumnDescriptor* column); -// 判断当前阶段是否可以通过 Arrow Parquet RecordReader 读取该列。 -// 当前支持 flat primitive/string/decimal/timestamp。复杂 nested column 仍通过 children -// 递归组合，list/map assembler 后续补齐。 +// 判断该类型是否可以通过 Arrow Parquet RecordReader 读取。 +// 当前所有已知物理类型均支持，预留扩展性。 bool supports_record_reader(const ParquetTypeDescriptor& type_descriptor); +// 返回该类型的值在 Arrow RecordReader 中的解码方式。 +// 用于 ParquetLeafReader 确定按 INT32/INT64/FLOAT/BINARY 等哪种格式解读 values buffer。 DecodedValueKind decoded_value_kind(const ParquetTypeDescriptor& type_descriptor); } // namespace doris::format::parquet