diff --git a/datafusion/core/src/dataframe/mod.rs b/datafusion/core/src/dataframe/mod.rs index f5867881da139..67e2a4780d06c 100644 --- a/datafusion/core/src/dataframe/mod.rs +++ b/datafusion/core/src/dataframe/mod.rs @@ -535,9 +535,26 @@ impl DataFrame { group_expr: Vec, aggr_expr: Vec, ) -> Result { + let is_grouping_set = matches!(group_expr.as_slice(), [Expr::GroupingSet(_)]); + let aggr_expr_len = aggr_expr.len(); let plan = LogicalPlanBuilder::from(self.plan) .aggregate(group_expr, aggr_expr)? .build()?; + let plan = if is_grouping_set { + let grouping_id_pos = plan.schema().fields().len() - 1 - aggr_expr_len; + // For grouping sets we do a project to not expose the internal grouping id + let exprs = plan + .schema() + .columns() + .into_iter() + .enumerate() + .filter(|(idx, _)| *idx != grouping_id_pos) + .map(|(_, column)| Expr::Column(column)) + .collect::>(); + LogicalPlanBuilder::from(plan).project(exprs)?.build()? + } else { + plan + }; Ok(DataFrame { session_state: self.session_state, plan, diff --git a/datafusion/core/src/physical_planner.rs b/datafusion/core/src/physical_planner.rs index 78c70606bf688..cf2a157b04b68 100644 --- a/datafusion/core/src/physical_planner.rs +++ b/datafusion/core/src/physical_planner.rs @@ -692,10 +692,6 @@ impl DefaultPhysicalPlanner { physical_input_schema.clone(), )?); - // update group column indices based on partial aggregate plan evaluation - let final_group: Vec> = - initial_aggr.output_group_expr(); - let can_repartition = !groups.is_empty() && session_state.config().target_partitions() > 1 && session_state.config().repartition_aggregations(); @@ -716,13 +712,7 @@ impl DefaultPhysicalPlanner { AggregateMode::Final }; - let final_grouping_set = PhysicalGroupBy::new_single( - final_group - .iter() - .enumerate() - .map(|(i, expr)| (expr.clone(), groups.expr()[i].1.clone())) - .collect(), - ); + let final_grouping_set = initial_aggr.group_expr().as_final(); Arc::new(AggregateExec::try_new( next_partition_mode, @@ -2345,7 +2335,7 @@ mod tests { .expect("hash aggregate"); assert_eq!( "sum(aggregate_test_100.c3)", - final_hash_agg.schema().field(2).name() + final_hash_agg.schema().field(3).name() ); // we need access to the input to the partial aggregate so that other projects can // implement serde diff --git a/datafusion/expr/src/logical_plan/plan.rs b/datafusion/expr/src/logical_plan/plan.rs index 19e73140b75ca..0292274e57ee3 100644 --- a/datafusion/expr/src/logical_plan/plan.rs +++ b/datafusion/expr/src/logical_plan/plan.rs @@ -21,7 +21,7 @@ use std::cmp::Ordering; use std::collections::{HashMap, HashSet}; use std::fmt::{self, Debug, Display, Formatter}; use std::hash::{Hash, Hasher}; -use std::sync::Arc; +use std::sync::{Arc, OnceLock}; use super::dml::CopyTo; use super::DdlStatement; @@ -2965,6 +2965,15 @@ impl Aggregate { .into_iter() .map(|(q, f)| (q, f.as_ref().clone().with_nullable(true).into())) .collect::>(); + qualified_fields.push(( + None, + Field::new( + Self::INTERNAL_GROUPING_ID, + Self::grouping_id_type(qualified_fields.len()), + false, + ) + .into(), + )); } qualified_fields.extend(exprlist_to_fields(aggr_expr.as_slice(), &input)?); @@ -3016,9 +3025,19 @@ impl Aggregate { }) } + fn is_grouping_set(&self) -> bool { + matches!(self.group_expr.as_slice(), [Expr::GroupingSet(_)]) + } + /// Get the output expressions. fn output_expressions(&self) -> Result> { + static INTERNAL_ID_EXPR: OnceLock = OnceLock::new(); let mut exprs = grouping_set_to_exprlist(self.group_expr.as_slice())?; + if self.is_grouping_set() { + exprs.push(INTERNAL_ID_EXPR.get_or_init(|| { + Expr::Column(Column::from_name(Self::INTERNAL_GROUPING_ID)) + })); + } exprs.extend(self.aggr_expr.iter()); debug_assert!(exprs.len() == self.schema.fields().len()); Ok(exprs) @@ -3030,6 +3049,41 @@ impl Aggregate { pub fn group_expr_len(&self) -> Result { grouping_set_expr_count(&self.group_expr) } + + /// Returns the data type of the grouping id. + /// The grouping ID value is a bitmask where each set bit + /// indicates that the corresponding grouping expression is + /// null + pub fn grouping_id_type(group_exprs: usize) -> DataType { + if group_exprs <= 8 { + DataType::UInt8 + } else if group_exprs <= 16 { + DataType::UInt16 + } else if group_exprs <= 32 { + DataType::UInt32 + } else { + DataType::UInt64 + } + } + + /// Internal column used when the aggregation is a grouping set. + /// + /// This column contains a bitmask where each bit represents a grouping + /// expression. The least significant bit corresponds to the rightmost + /// grouping expression. A bit value of 0 indicates that the corresponding + /// column is included in the grouping set, while a value of 1 means it is excluded. + /// + /// For example, for the grouping expressions CUBE(a, b), the grouping ID + /// column will have the following values: + /// 0b00: Both `a` and `b` are included + /// 0b01: `b` is excluded + /// 0b10: `a` is excluded + /// 0b11: Both `a` and `b` are excluded + /// + /// This internal column is necessary because excluded columns are replaced + /// with `NULL` values. To handle these cases correctly, we must distinguish + /// between an actual `NULL` value in a column and a column being excluded from the set. + pub const INTERNAL_GROUPING_ID: &'static str = "__grouping_id"; } // Manual implementation needed because of `schema` field. Comparison excludes this field. diff --git a/datafusion/expr/src/utils.rs b/datafusion/expr/src/utils.rs index fa927595040e5..02b36d0feab94 100644 --- a/datafusion/expr/src/utils.rs +++ b/datafusion/expr/src/utils.rs @@ -61,7 +61,17 @@ pub fn exprlist_to_columns(expr: &[Expr], accum: &mut HashSet) -> Result /// Count the number of distinct exprs in a list of group by expressions. If the /// first element is a `GroupingSet` expression then it must be the only expr. pub fn grouping_set_expr_count(group_expr: &[Expr]) -> Result { - grouping_set_to_exprlist(group_expr).map(|exprs| exprs.len()) + if let Some(Expr::GroupingSet(grouping_set)) = group_expr.first() { + if group_expr.len() > 1 { + return plan_err!( + "Invalid group by expressions, GroupingSet must be the only expression" + ); + } + // Groupings sets have an additional interal column for the grouping id + Ok(grouping_set.distinct_expr().len() + 1) + } else { + grouping_set_to_exprlist(group_expr).map(|exprs| exprs.len()) + } } /// The [power set] (or powerset) of a set S is the set of all subsets of S, \ diff --git a/datafusion/optimizer/src/single_distinct_to_groupby.rs b/datafusion/optimizer/src/single_distinct_to_groupby.rs index 1c22c2a4375ad..74251e5caad2b 100644 --- a/datafusion/optimizer/src/single_distinct_to_groupby.rs +++ b/datafusion/optimizer/src/single_distinct_to_groupby.rs @@ -355,7 +355,7 @@ mod tests { .build()?; // Should not be optimized - let expected = "Aggregate: groupBy=[[GROUPING SETS ((test.a), (test.b))]], aggr=[[count(DISTINCT test.c)]] [a:UInt32;N, b:UInt32;N, count(DISTINCT test.c):Int64]\ + let expected = "Aggregate: groupBy=[[GROUPING SETS ((test.a), (test.b))]], aggr=[[count(DISTINCT test.c)]] [a:UInt32;N, b:UInt32;N, __grouping_id:UInt8, count(DISTINCT test.c):Int64]\ \n TableScan: test [a:UInt32, b:UInt32, c:UInt32]"; assert_optimized_plan_equal(plan, expected) @@ -373,7 +373,7 @@ mod tests { .build()?; // Should not be optimized - let expected = "Aggregate: groupBy=[[CUBE (test.a, test.b)]], aggr=[[count(DISTINCT test.c)]] [a:UInt32;N, b:UInt32;N, count(DISTINCT test.c):Int64]\ + let expected = "Aggregate: groupBy=[[CUBE (test.a, test.b)]], aggr=[[count(DISTINCT test.c)]] [a:UInt32;N, b:UInt32;N, __grouping_id:UInt8, count(DISTINCT test.c):Int64]\ \n TableScan: test [a:UInt32, b:UInt32, c:UInt32]"; assert_optimized_plan_equal(plan, expected) @@ -392,7 +392,7 @@ mod tests { .build()?; // Should not be optimized - let expected = "Aggregate: groupBy=[[ROLLUP (test.a, test.b)]], aggr=[[count(DISTINCT test.c)]] [a:UInt32;N, b:UInt32;N, count(DISTINCT test.c):Int64]\ + let expected = "Aggregate: groupBy=[[ROLLUP (test.a, test.b)]], aggr=[[count(DISTINCT test.c)]] [a:UInt32;N, b:UInt32;N, __grouping_id:UInt8, count(DISTINCT test.c):Int64]\ \n TableScan: test [a:UInt32, b:UInt32, c:UInt32]"; assert_optimized_plan_equal(plan, expected) diff --git a/datafusion/physical-plan/src/aggregates/mod.rs b/datafusion/physical-plan/src/aggregates/mod.rs index 9466ff6dd4591..f9dd973c814e4 100644 --- a/datafusion/physical-plan/src/aggregates/mod.rs +++ b/datafusion/physical-plan/src/aggregates/mod.rs @@ -36,10 +36,11 @@ use crate::{ use arrow::array::ArrayRef; use arrow::datatypes::{Field, Schema, SchemaRef}; use arrow::record_batch::RecordBatch; +use arrow_array::{UInt16Array, UInt32Array, UInt64Array, UInt8Array}; use datafusion_common::stats::Precision; use datafusion_common::{internal_err, not_impl_err, Result}; use datafusion_execution::TaskContext; -use datafusion_expr::Accumulator; +use datafusion_expr::{Accumulator, Aggregate}; use datafusion_physical_expr::{ equivalence::{collapse_lex_req, ProjectionMapping}, expressions::Column, @@ -211,13 +212,99 @@ impl PhysicalGroupBy { .collect() } + /// The number of expressions in the output schema. + fn num_output_exprs(&self) -> usize { + let mut num_exprs = self.expr.len(); + if !self.is_single() { + num_exprs += 1 + } + num_exprs + } + /// Return grouping expressions as they occur in the output schema. pub fn output_exprs(&self) -> Vec> { - self.expr - .iter() - .enumerate() - .map(|(index, (_, name))| Arc::new(Column::new(name, index)) as _) - .collect() + let num_output_exprs = self.num_output_exprs(); + let mut output_exprs = Vec::with_capacity(num_output_exprs); + output_exprs.extend( + self.expr + .iter() + .enumerate() + .take(num_output_exprs) + .map(|(index, (_, name))| Arc::new(Column::new(name, index)) as _), + ); + if !self.is_single() { + output_exprs.push(Arc::new(Column::new( + Aggregate::INTERNAL_GROUPING_ID, + self.expr.len(), + )) as _); + } + output_exprs + } + + /// Returns the number expression as grouping keys. + fn num_group_exprs(&self) -> usize { + if self.is_single() { + self.expr.len() + } else { + self.expr.len() + 1 + } + } + + /// Returns the fields that are used as the grouping keys. + fn group_fields(&self, input_schema: &Schema) -> Result> { + let mut fields = Vec::with_capacity(self.num_group_exprs()); + for ((expr, name), group_expr_nullable) in + self.expr.iter().zip(self.exprs_nullable().into_iter()) + { + fields.push( + Field::new( + name, + expr.data_type(input_schema)?, + group_expr_nullable || expr.nullable(input_schema)?, + ) + .with_metadata( + get_field_metadata(expr, input_schema).unwrap_or_default(), + ), + ); + } + if !self.is_single() { + fields.push(Field::new( + Aggregate::INTERNAL_GROUPING_ID, + Aggregate::grouping_id_type(self.expr.len()), + false, + )); + } + Ok(fields) + } + + /// Returns the output fields of the group by. + /// + /// This might be different from the `group_fields` that might contain internal expressions that + /// should not be part of the output schema. + fn output_fields(&self, input_schema: &Schema) -> Result> { + let mut fields = self.group_fields(input_schema)?; + fields.truncate(self.num_output_exprs()); + Ok(fields) + } + + /// Returns the `PhysicalGroupBy` for a final aggregation if `self` is used for a partial + /// aggregation. + pub fn as_final(&self) -> PhysicalGroupBy { + let expr: Vec<_> = + self.output_exprs() + .into_iter() + .zip( + self.expr.iter().map(|t| t.1.clone()).chain(std::iter::once( + Aggregate::INTERNAL_GROUPING_ID.to_owned(), + )), + ) + .collect(); + let num_exprs = expr.len(); + Self { + expr, + null_expr: vec![], + groups: vec![vec![false; num_exprs]], + } } } @@ -321,13 +408,7 @@ impl AggregateExec { input: Arc, input_schema: SchemaRef, ) -> Result { - let schema = create_schema( - &input.schema(), - &group_by.expr, - &aggr_expr, - group_by.exprs_nullable(), - mode, - )?; + let schema = create_schema(&input.schema(), &group_by, &aggr_expr, mode)?; let schema = Arc::new(schema); AggregateExec::try_new_with_schema( @@ -789,25 +870,12 @@ impl ExecutionPlan for AggregateExec { fn create_schema( input_schema: &Schema, - group_expr: &[(Arc, String)], + group_by: &PhysicalGroupBy, aggr_expr: &[AggregateFunctionExpr], - group_expr_nullable: Vec, mode: AggregateMode, ) -> Result { - let mut fields = Vec::with_capacity(group_expr.len() + aggr_expr.len()); - for (index, (expr, name)) in group_expr.iter().enumerate() { - fields.push( - Field::new( - name, - expr.data_type(input_schema)?, - // In cases where we have multiple grouping sets, we will use NULL expressions in - // order to align the grouping sets. So the field must be nullable even if the underlying - // schema field is not. - group_expr_nullable[index] || expr.nullable(input_schema)?, - ) - .with_metadata(get_field_metadata(expr, input_schema).unwrap_or_default()), - ) - } + let mut fields = Vec::with_capacity(group_by.num_output_exprs() + aggr_expr.len()); + fields.extend(group_by.output_fields(input_schema)?); match mode { AggregateMode::Partial => { @@ -833,9 +901,8 @@ fn create_schema( )) } -fn group_schema(schema: &Schema, group_count: usize) -> SchemaRef { - let group_fields = schema.fields()[0..group_count].to_vec(); - Arc::new(Schema::new(group_fields)) +fn group_schema(input_schema: &Schema, group_by: &PhysicalGroupBy) -> Result { + Ok(Arc::new(Schema::new(group_by.group_fields(input_schema)?))) } /// Determines the lexical ordering requirement for an aggregate expression. @@ -1142,6 +1209,27 @@ fn evaluate_optional( .collect() } +fn group_id_array(group: &[bool], batch: &RecordBatch) -> Result { + if group.len() > 64 { + return not_impl_err!( + "Grouping sets with more than 64 columns are not supported" + ); + } + let group_id = group.iter().fold(0u64, |acc, &is_null| { + (acc << 1) | if is_null { 1 } else { 0 } + }); + let num_rows = batch.num_rows(); + if group.len() <= 8 { + Ok(Arc::new(UInt8Array::from(vec![group_id as u8; num_rows]))) + } else if group.len() <= 16 { + Ok(Arc::new(UInt16Array::from(vec![group_id as u16; num_rows]))) + } else if group.len() <= 32 { + Ok(Arc::new(UInt32Array::from(vec![group_id as u32; num_rows]))) + } else { + Ok(Arc::new(UInt64Array::from(vec![group_id; num_rows]))) + } +} + /// Evaluate a group by expression against a `RecordBatch` /// /// Arguments: @@ -1174,23 +1262,24 @@ pub(crate) fn evaluate_group_by( }) .collect::>>()?; - Ok(group_by + group_by .groups .iter() .map(|group| { - group - .iter() - .enumerate() - .map(|(idx, is_null)| { - if *is_null { - Arc::clone(&null_exprs[idx]) - } else { - Arc::clone(&exprs[idx]) - } - }) - .collect() + let mut group_values = Vec::with_capacity(group_by.num_group_exprs()); + group_values.extend(group.iter().enumerate().map(|(idx, is_null)| { + if *is_null { + Arc::clone(&null_exprs[idx]) + } else { + Arc::clone(&exprs[idx]) + } + })); + if !group_by.is_single() { + group_values.push(group_id_array(group, batch)?); + } + Ok(group_values) }) - .collect()) + .collect() } #[cfg(test)] @@ -1348,21 +1437,21 @@ mod tests { ) -> Result<()> { let input_schema = input.schema(); - let grouping_set = PhysicalGroupBy { - expr: vec![ + let grouping_set = PhysicalGroupBy::new( + vec![ (col("a", &input_schema)?, "a".to_string()), (col("b", &input_schema)?, "b".to_string()), ], - null_expr: vec![ + vec![ (lit(ScalarValue::UInt32(None)), "a".to_string()), (lit(ScalarValue::Float64(None)), "b".to_string()), ], - groups: vec![ + vec![ vec![false, true], // (a, NULL) vec![true, false], // (NULL, b) vec![false, false], // (a,b) ], - }; + ); let aggregates = vec![AggregateExprBuilder::new(count_udaf(), vec![lit(1i8)]) .schema(Arc::clone(&input_schema)) @@ -1392,63 +1481,56 @@ mod tests { // In spill mode, we test with the limited memory, if the mem usage exceeds, // we trigger the early emit rule, which turns out the partial aggregate result. vec![ - "+---+-----+-----------------+", - "| a | b | COUNT(1)[count] |", - "+---+-----+-----------------+", - "| | 1.0 | 1 |", - "| | 1.0 | 1 |", - "| | 2.0 | 1 |", - "| | 2.0 | 1 |", - "| | 3.0 | 1 |", - "| | 3.0 | 1 |", - "| | 4.0 | 1 |", - "| | 4.0 | 1 |", - "| 2 | | 1 |", - "| 2 | | 1 |", - "| 2 | 1.0 | 1 |", - "| 2 | 1.0 | 1 |", - "| 3 | | 1 |", - "| 3 | | 2 |", - "| 3 | 2.0 | 2 |", - "| 3 | 3.0 | 1 |", - "| 4 | | 1 |", - "| 4 | | 2 |", - "| 4 | 3.0 | 1 |", - "| 4 | 4.0 | 2 |", - "+---+-----+-----------------+", + "+---+-----+---------------+-----------------+", + "| a | b | __grouping_id | COUNT(1)[count] |", + "+---+-----+---------------+-----------------+", + "| | 1.0 | 2 | 1 |", + "| | 1.0 | 2 | 1 |", + "| | 2.0 | 2 | 1 |", + "| | 2.0 | 2 | 1 |", + "| | 3.0 | 2 | 1 |", + "| | 3.0 | 2 | 1 |", + "| | 4.0 | 2 | 1 |", + "| | 4.0 | 2 | 1 |", + "| 2 | | 1 | 1 |", + "| 2 | | 1 | 1 |", + "| 2 | 1.0 | 0 | 1 |", + "| 2 | 1.0 | 0 | 1 |", + "| 3 | | 1 | 1 |", + "| 3 | | 1 | 2 |", + "| 3 | 2.0 | 0 | 2 |", + "| 3 | 3.0 | 0 | 1 |", + "| 4 | | 1 | 1 |", + "| 4 | | 1 | 2 |", + "| 4 | 3.0 | 0 | 1 |", + "| 4 | 4.0 | 0 | 2 |", + "+---+-----+---------------+-----------------+", ] } else { vec![ - "+---+-----+-----------------+", - "| a | b | COUNT(1)[count] |", - "+---+-----+-----------------+", - "| | 1.0 | 2 |", - "| | 2.0 | 2 |", - "| | 3.0 | 2 |", - "| | 4.0 | 2 |", - "| 2 | | 2 |", - "| 2 | 1.0 | 2 |", - "| 3 | | 3 |", - "| 3 | 2.0 | 2 |", - "| 3 | 3.0 | 1 |", - "| 4 | | 3 |", - "| 4 | 3.0 | 1 |", - "| 4 | 4.0 | 2 |", - "+---+-----+-----------------+", + "+---+-----+---------------+-----------------+", + "| a | b | __grouping_id | COUNT(1)[count] |", + "+---+-----+---------------+-----------------+", + "| | 1.0 | 2 | 2 |", + "| | 2.0 | 2 | 2 |", + "| | 3.0 | 2 | 2 |", + "| | 4.0 | 2 | 2 |", + "| 2 | | 1 | 2 |", + "| 2 | 1.0 | 0 | 2 |", + "| 3 | | 1 | 3 |", + "| 3 | 2.0 | 0 | 2 |", + "| 3 | 3.0 | 0 | 1 |", + "| 4 | | 1 | 3 |", + "| 4 | 3.0 | 0 | 1 |", + "| 4 | 4.0 | 0 | 2 |", + "+---+-----+---------------+-----------------+", ] }; assert_batches_sorted_eq!(expected, &result); - let groups = partial_aggregate.group_expr().expr().to_vec(); - let merge = Arc::new(CoalescePartitionsExec::new(partial_aggregate)); - let final_group: Vec<(Arc, String)> = groups - .iter() - .map(|(_expr, name)| Ok((col(name, &input_schema)?, name.clone()))) - .collect::>()?; - - let final_grouping_set = PhysicalGroupBy::new_single(final_group); + let final_grouping_set = grouping_set.as_final(); let task_ctx = if spill { new_spill_ctx(4, 3160) @@ -1468,26 +1550,26 @@ mod tests { let result = common::collect(merged_aggregate.execute(0, Arc::clone(&task_ctx))?).await?; let batch = concat_batches(&result[0].schema(), &result)?; - assert_eq!(batch.num_columns(), 3); + assert_eq!(batch.num_columns(), 4); assert_eq!(batch.num_rows(), 12); let expected = vec![ - "+---+-----+----------+", - "| a | b | COUNT(1) |", - "+---+-----+----------+", - "| | 1.0 | 2 |", - "| | 2.0 | 2 |", - "| | 3.0 | 2 |", - "| | 4.0 | 2 |", - "| 2 | | 2 |", - "| 2 | 1.0 | 2 |", - "| 3 | | 3 |", - "| 3 | 2.0 | 2 |", - "| 3 | 3.0 | 1 |", - "| 4 | | 3 |", - "| 4 | 3.0 | 1 |", - "| 4 | 4.0 | 2 |", - "+---+-----+----------+", + "+---+-----+---------------+----------+", + "| a | b | __grouping_id | COUNT(1) |", + "+---+-----+---------------+----------+", + "| | 1.0 | 2 | 2 |", + "| | 2.0 | 2 | 2 |", + "| | 3.0 | 2 | 2 |", + "| | 4.0 | 2 | 2 |", + "| 2 | | 1 | 2 |", + "| 2 | 1.0 | 0 | 2 |", + "| 3 | | 1 | 3 |", + "| 3 | 2.0 | 0 | 2 |", + "| 3 | 3.0 | 0 | 1 |", + "| 4 | | 1 | 3 |", + "| 4 | 3.0 | 0 | 1 |", + "| 4 | 4.0 | 0 | 2 |", + "+---+-----+---------------+----------+", ]; assert_batches_sorted_eq!(&expected, &result); @@ -1503,11 +1585,11 @@ mod tests { async fn check_aggregates(input: Arc, spill: bool) -> Result<()> { let input_schema = input.schema(); - let grouping_set = PhysicalGroupBy { - expr: vec![(col("a", &input_schema)?, "a".to_string())], - null_expr: vec![], - groups: vec![vec![false]], - }; + let grouping_set = PhysicalGroupBy::new( + vec![(col("a", &input_schema)?, "a".to_string())], + vec![], + vec![vec![false]], + ); let aggregates: Vec = vec![ @@ -1563,13 +1645,7 @@ mod tests { let merge = Arc::new(CoalescePartitionsExec::new(partial_aggregate)); - let final_group: Vec<(Arc, String)> = grouping_set - .expr - .iter() - .map(|(_expr, name)| Ok((col(name, &input_schema)?, name.clone()))) - .collect::>()?; - - let final_grouping_set = PhysicalGroupBy::new_single(final_group); + let final_grouping_set = grouping_set.as_final(); let merged_aggregate = Arc::new(AggregateExec::try_new( AggregateMode::Final, @@ -1825,11 +1901,11 @@ mod tests { let task_ctx = Arc::new(task_ctx); let groups_none = PhysicalGroupBy::default(); - let groups_some = PhysicalGroupBy { - expr: vec![(col("a", &input_schema)?, "a".to_string())], - null_expr: vec![], - groups: vec![vec![false]], - }; + let groups_some = PhysicalGroupBy::new( + vec![(col("a", &input_schema)?, "a".to_string())], + vec![], + vec![vec![false]], + ); // something that allocates within the aggregator let aggregates_v0: Vec = @@ -2306,7 +2382,7 @@ mod tests { )?); let aggregate_exec = Arc::new(AggregateExec::try_new( - AggregateMode::Partial, + AggregateMode::Single, groups, aggregates.clone(), vec![None], @@ -2318,13 +2394,13 @@ mod tests { collect(aggregate_exec.execute(0, Arc::new(TaskContext::default()))?).await?; let expected = [ - "+-----+-----+-------+----------+", - "| a | b | const | 1[count] |", - "+-----+-----+-------+----------+", - "| | 0.0 | | 32768 |", - "| 0.0 | | | 32768 |", - "| | | 1 | 32768 |", - "+-----+-----+-------+----------+", + "+-----+-----+-------+---------------+-------+", + "| a | b | const | __grouping_id | 1 |", + "+-----+-----+-------+---------------+-------+", + "| | | 1 | 6 | 32768 |", + "| | 0.0 | | 5 | 32768 |", + "| 0.0 | | | 3 | 32768 |", + "+-----+-----+-------+---------------+-------+", ]; assert_batches_sorted_eq!(expected, &output); @@ -2638,30 +2714,30 @@ mod tests { .build()?, ]; - let grouping_set = PhysicalGroupBy { - expr: vec![ + let grouping_set = PhysicalGroupBy::new( + vec![ (col("a", &input_schema)?, "a".to_string()), (col("b", &input_schema)?, "b".to_string()), ], - null_expr: vec![ + vec![ (lit(ScalarValue::Float32(None)), "a".to_string()), (lit(ScalarValue::Float32(None)), "b".to_string()), ], - groups: vec![ + vec![ vec![false, true], // (a, NULL) vec![false, false], // (a,b) ], - }; + ); let aggr_schema = create_schema( &input_schema, - &grouping_set.expr, + &grouping_set, &aggr_expr, - grouping_set.exprs_nullable(), AggregateMode::Final, )?; let expected_schema = Schema::new(vec![ Field::new("a", DataType::Float32, false), Field::new("b", DataType::Float32, true), + Field::new("__grouping_id", DataType::UInt8, false), Field::new("COUNT(a)", DataType::Int64, false), ]); assert_eq!(aggr_schema, expected_schema); diff --git a/datafusion/physical-plan/src/aggregates/row_hash.rs b/datafusion/physical-plan/src/aggregates/row_hash.rs index 9e4968f1123e7..5121e6cc3b354 100644 --- a/datafusion/physical-plan/src/aggregates/row_hash.rs +++ b/datafusion/physical-plan/src/aggregates/row_hash.rs @@ -449,13 +449,13 @@ impl GroupedHashAggregateStream { let aggregate_arguments = aggregates::aggregate_expressions( &agg.aggr_expr, &agg.mode, - agg_group_by.expr.len(), + agg_group_by.num_group_exprs(), )?; // arguments for aggregating spilled data is the same as the one for final aggregation let merging_aggregate_arguments = aggregates::aggregate_expressions( &agg.aggr_expr, &AggregateMode::Final, - agg_group_by.expr.len(), + agg_group_by.num_group_exprs(), )?; let filter_expressions = match agg.mode { @@ -473,7 +473,7 @@ impl GroupedHashAggregateStream { .map(create_group_accumulator) .collect::>()?; - let group_schema = group_schema(&agg_schema, agg_group_by.expr.len()); + let group_schema = group_schema(&agg.input().schema(), &agg_group_by)?; let spill_expr = group_schema .fields .into_iter() diff --git a/datafusion/sql/src/unparser/utils.rs b/datafusion/sql/src/unparser/utils.rs index 8b2530a7499b9..e05df8ba77fc0 100644 --- a/datafusion/sql/src/unparser/utils.rs +++ b/datafusion/sql/src/unparser/utils.rs @@ -15,6 +15,8 @@ // specific language governing permissions and limitations // under the License. +use std::cmp::Ordering; + use datafusion_common::{ internal_err, tree_node::{Transformed, TreeNode}, @@ -169,10 +171,17 @@ fn find_agg_expr<'a>(agg: &'a Aggregate, column: &Column) -> Result Ok(grouping_expr.into_iter().nth(index)), + Ordering::Equal => { + internal_err!( + "Tried to unproject column refereing to internal grouping id" + ) + } + Ordering::Greater => { + Ok(agg.aggr_expr.get(index - grouping_expr.len() - 1)) + } + } } else { Ok(agg.group_expr.iter().chain(agg.aggr_expr.iter()).nth(index)) } diff --git a/datafusion/sqllogictest/test_files/aggregate.slt b/datafusion/sqllogictest/test_files/aggregate.slt index a78ade81eeba5..250fa85cddef1 100644 --- a/datafusion/sqllogictest/test_files/aggregate.slt +++ b/datafusion/sqllogictest/test_files/aggregate.slt @@ -3520,6 +3520,18 @@ SELECT MIN(value), MAX(value) FROM integers_with_nulls ---- 1 5 +# grouping_sets with null values +query II rowsort +SELECT value, min(value) FROM integers_with_nulls GROUP BY CUBE(value) +---- +1 1 +3 3 +4 4 +5 5 +NULL 1 +NULL NULL + + statement ok DROP TABLE integers_with_nulls; @@ -4879,16 +4891,18 @@ query TT EXPLAIN SELECT c2, c3 FROM aggregate_test_100 group by rollup(c2, c3) limit 3; ---- logical_plan -01)Limit: skip=0, fetch=3 -02)--Aggregate: groupBy=[[ROLLUP (aggregate_test_100.c2, aggregate_test_100.c3)]], aggr=[[]] -03)----TableScan: aggregate_test_100 projection=[c2, c3] +01)Projection: aggregate_test_100.c2, aggregate_test_100.c3 +02)--Limit: skip=0, fetch=3 +03)----Aggregate: groupBy=[[ROLLUP (aggregate_test_100.c2, aggregate_test_100.c3)]], aggr=[[]] +04)------TableScan: aggregate_test_100 projection=[c2, c3] physical_plan -01)GlobalLimitExec: skip=0, fetch=3 -02)--AggregateExec: mode=Final, gby=[c2@0 as c2, c3@1 as c3], aggr=[], lim=[3] -03)----CoalescePartitionsExec -04)------AggregateExec: mode=Partial, gby=[(NULL as c2, NULL as c3), (c2@0 as c2, NULL as c3), (c2@0 as c2, c3@1 as c3)], aggr=[] -05)--------RepartitionExec: partitioning=RoundRobinBatch(4), input_partitions=1 -06)----------CsvExec: file_groups={1 group: [[WORKSPACE_ROOT/testing/data/csv/aggregate_test_100.csv]]}, projection=[c2, c3], has_header=true +01)ProjectionExec: expr=[c2@0 as c2, c3@1 as c3] +02)--GlobalLimitExec: skip=0, fetch=3 +03)----AggregateExec: mode=Final, gby=[c2@0 as c2, c3@1 as c3, __grouping_id@2 as __grouping_id], aggr=[], lim=[3] +04)------CoalescePartitionsExec +05)--------AggregateExec: mode=Partial, gby=[(NULL as c2, NULL as c3), (c2@0 as c2, NULL as c3), (c2@0 as c2, c3@1 as c3)], aggr=[] +06)----------RepartitionExec: partitioning=RoundRobinBatch(4), input_partitions=1 +07)------------CsvExec: file_groups={1 group: [[WORKSPACE_ROOT/testing/data/csv/aggregate_test_100.csv]]}, projection=[c2, c3], has_header=true query II SELECT c2, c3 FROM aggregate_test_100 group by rollup(c2, c3) limit 3; diff --git a/datafusion/sqllogictest/test_files/group_by.slt b/datafusion/sqllogictest/test_files/group_by.slt index f561fa9e9ac8d..a80a0891e9770 100644 --- a/datafusion/sqllogictest/test_files/group_by.slt +++ b/datafusion/sqllogictest/test_files/group_by.slt @@ -5152,8 +5152,6 @@ drop table test_case_expr statement ok drop table t; -# TODO: Current grouping set result is not align with Postgres and DuckDB, we might want to change the result -# See https://github.com/apache/datafusion/issues/12570 # test multi group by for binary type with nulls statement ok create table t(a int, b bytea) as values (1, 0xa), (1, 0xa), (2, null), (null, 0xb), (null, 0xb); @@ -5162,11 +5160,14 @@ query I?I select a, b, count(*) from t group by grouping sets ((a, b), (a), (b)); ---- 1 0a 2 -2 NULL 2 -NULL 0b 4 +2 NULL 1 +NULL 0b 2 1 NULL 2 -NULL NULL 3 +2 NULL 1 +NULL NULL 2 NULL 0a 2 +NULL NULL 1 +NULL 0b 2 statement ok drop table t; diff --git a/datafusion/substrait/tests/cases/roundtrip_logical_plan.rs b/datafusion/substrait/tests/cases/roundtrip_logical_plan.rs index 3b7d0fd296105..ce6d1825cd25c 100644 --- a/datafusion/substrait/tests/cases/roundtrip_logical_plan.rs +++ b/datafusion/substrait/tests/cases/roundtrip_logical_plan.rs @@ -294,8 +294,9 @@ async fn aggregate_grouping_sets() -> Result<()> { async fn aggregate_grouping_rollup() -> Result<()> { assert_expected_plan( "SELECT a, c, e, avg(b) FROM data GROUP BY ROLLUP (a, c, e)", - "Aggregate: groupBy=[[GROUPING SETS ((data.a, data.c, data.e), (data.a, data.c), (data.a), ())]], aggr=[[avg(data.b)]]\ - \n TableScan: data projection=[a, b, c, e]", + "Projection: data.a, data.c, data.e, avg(data.b)\ + \n Aggregate: groupBy=[[GROUPING SETS ((data.a, data.c, data.e), (data.a, data.c), (data.a), ())]], aggr=[[avg(data.b)]]\ + \n TableScan: data projection=[a, b, c, e]", true ).await }