feat(parquet): support pushing bitmaps down to page-level filtering (except for nested columns)

[zhf999]

Purpose

This PR follows PR#371.

• Support pushing selection bitmaps down to Parquet page-level filtering so only matching pages are read when both predicate and bitmap pruning are available.
• Refactor target row-group filtering metadata into a dedicated TargetRowGroup type to carry row-group index, partial-match status, row ranges, and read-range exclusion flags consistently across planning and reading phases.

Changes

• src/paimon/format/parquet/target_row_group.h

  • Introduced TargetRowGroup class and TargetRowGroups alias.
  • Replaced ad-hoc struct usage from row_ranges.h.

• src/paimon/format/parquet/parquet_file_batch_reader.h

  • Updated row-group filtering APIs to operate on TargetRowGroups instead of raw std::vector<int32_t>.
  • Added FindColumnWithOffsetIndex and FilterPagesByBitmap.

• src/paimon/format/parquet/parquet_file_batch_reader.cpp

  • In SetReadSchema, switched row-group candidate list to TargetRowGroups and applied bitmap filter before page-index filtering.
  • Added bitmap-driven row-group/page pruning:
    • For row groups with offset index metadata, it computes per-page row ranges and marks those RGs as partially matched.
    • If any nested columns are included in read schema, bitmap filtering fallbacks to row-group-level.
  • Enhanced FilterRowGroupsByPageIndex to intersect page ranges when RGs are already partially matched by bitmap.

Tests

• Added bitmap + page-level integration tests:
  • BitmapAllPagesSomeRowGroups
  • BitmapPartialPagesSingleRowGroup
  • BitmapAllAndPartialPagesMixed
  • BitmapAllPagesWithPredicate
  • BitmapPartialPagesWithPredicate
  • BitmapMixedWithPredicate
  • NestedMapBitmapFallback
  • NestedListBitmapFallback
  • NestedStructBitmapFallback
• Coverage includes bitmap-only pruning, bitmap+predicate pruning, and mixed partial/full page hit cases.

Datasets

Multiple performance testing were conducted on 2 dataset.

Dataset	File size	num_rows	num_columns	num_rowgroups	page length (in rows)
Dataset#1	6.7GB	2M	1,176	39	1024
Dataset#2	3GB	11M	4	23	1024

Test Methodology

To validate bitmap behavior, we organize tests into six groups based on bitmap distribution patterns. Each group contains five distinct datasets, and the final result reported is the average of these five runs.

Test Cases

1. Matching records are contiguous and only one row is queried.
2. Matching records are contiguous and 100 rows are queried.
3. Matching records are contiguous and 10,000 rows are queried.
4. Matching records are scattered in 5 segments, each segment containing 1 row.
5. Matching records are scattered in 5 segments, each segment containing 100 rows.
6. Matching records are scattered in 100 segments, each segment containing 1 row.

Test Results

On Dataset#1

Branch	Case 1	Case 2	Case3	Case 4	Case 5	Case 6
Main	2.50s	2.45s	3.08s	11.46s	11.34s	103.63s
This PR	0.35s	0.34s	0.96s	1.94s	1.88s	18.80s

On Dataset#2

Branch	Case 1	Case 2	Case3	Case 4	Case 5	Case 6
Main	1.28s	1.28s	1.28s	3.12s	3.07s	6.47s
This PR	10ms	10ms	42ms	35ms	40ms	0.37s

API and Format

• No public API changes.
• No storage format/protocol changes.

Documentation

• No user-facing docs required for this change.

Generative AI tooling

• gpt-5.3-codex

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