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feat: partition_statistics() for HashJoinExec #20711

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024f732
feat: `partition_statistics()` for HashJoinExec
jonathanc-n Mar 4, 2026
d7aafa6
Merge branch 'main' into hj-partition-stats
jonathanc-n Mar 4, 2026
327551e
Merge branch 'main' into hj-partition-stats
jonathanc-n Mar 5, 2026
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253 changes: 251 additions & 2 deletions datafusion/core/tests/physical_optimizer/partition_statistics.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,9 @@ mod test {
use datafusion_catalog::TableProvider;
use datafusion_common::Result;
use datafusion_common::stats::Precision;
use datafusion_common::{ColumnStatistics, ScalarValue, Statistics};
use datafusion_common::{
ColumnStatistics, JoinType, NullEquality, ScalarValue, Statistics,
};
use datafusion_execution::TaskContext;
use datafusion_execution::config::SessionConfig;
use datafusion_expr::{WindowFrame, WindowFunctionDefinition};
Expand All @@ -45,7 +47,9 @@ mod test {
use datafusion_physical_plan::common::compute_record_batch_statistics;
use datafusion_physical_plan::empty::EmptyExec;
use datafusion_physical_plan::filter::FilterExec;
use datafusion_physical_plan::joins::{CrossJoinExec, NestedLoopJoinExec};
use datafusion_physical_plan::joins::{
CrossJoinExec, HashJoinExec, NestedLoopJoinExec, PartitionMode,
};
use datafusion_physical_plan::limit::{GlobalLimitExec, LocalLimitExec};
use datafusion_physical_plan::placeholder_row::PlaceholderRowExec;
use datafusion_physical_plan::projection::{ProjectionExec, ProjectionExpr};
Expand Down Expand Up @@ -1354,4 +1358,249 @@ mod test {

Ok(())
}

#[tokio::test]
async fn test_hash_join_partition_statistics() -> Result<()> {
// Create left table scan and coalesce to 1 partition for CollectLeft mode
let left_scan = create_scan_exec_with_statistics(None, Some(2)).await;
let left_scan_coalesced = Arc::new(CoalescePartitionsExec::new(left_scan.clone()))
as Arc<dyn ExecutionPlan>;

// Create right table scan with different table name
let right_create_table_sql = "CREATE EXTERNAL TABLE t2 (id INT NOT NULL, date DATE) \
STORED AS PARQUET LOCATION './tests/data/test_statistics_per_partition'\
PARTITIONED BY (date) \
WITH ORDER (id ASC);";
let right_scan =
create_scan_exec_with_statistics(Some(right_create_table_sql), Some(2)).await;

// Create join condition: t1.id = t2.id
let on = vec![(
Arc::new(Column::new("id", 0)) as Arc<dyn PhysicalExpr>,
Arc::new(Column::new("id", 0)) as Arc<dyn PhysicalExpr>,
)];

// Test CollectLeft mode - left child must have 1 partition
let collect_left_join = Arc::new(HashJoinExec::try_new(
left_scan_coalesced,
Arc::clone(&right_scan),
on.clone(),
None,
&JoinType::Inner,
None,
PartitionMode::CollectLeft,
NullEquality::NullEqualsNothing,
false,
)?) as Arc<dyn ExecutionPlan>;

// Test partition statistics for CollectLeft mode
let statistics = (0..collect_left_join.output_partitioning().partition_count())
.map(|idx| collect_left_join.partition_statistics(Some(idx)))
.collect::<Result<Vec<_>>>()?;

// Check that we have the expected number of partitions
assert_eq!(statistics.len(), 2);

// For collect left mode, the min/max values are from the entire left table and the specific partition of the right table.
let expected_p0_statistics = Statistics {
num_rows: Precision::Inexact(2),
total_byte_size: Precision::Absent,
column_statistics: vec![
// Left id column: all partitions (id 1..4)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Int32(Some(4))),
min_value: Precision::Exact(ScalarValue::Int32(Some(1))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(16),
},
// Left date column: all partitions (2025-03-01..2025-03-04)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_04,
))),
min_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_01,
))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(16),
},
// Right id column: partition 0 only (id 3..4)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Int32(Some(4))),
min_value: Precision::Exact(ScalarValue::Int32(Some(3))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(8),
},
// Right date column: partition 0 only (2025-03-01..2025-03-02)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_02,
))),
min_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_01,
))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(8),
},
],
};
assert_eq!(statistics[0], expected_p0_statistics);

// Test Partitioned mode
let partitioned_join = Arc::new(HashJoinExec::try_new(
Arc::clone(&left_scan),
Arc::clone(&right_scan),
on.clone(),
None,
&JoinType::Inner,
None,
PartitionMode::Partitioned,
NullEquality::NullEqualsNothing,
false,
)?) as Arc<dyn ExecutionPlan>;

// Test partition statistics for Partitioned mode
let statistics = (0..partitioned_join.output_partitioning().partition_count())
.map(|idx| partitioned_join.partition_statistics(Some(idx)))
.collect::<Result<Vec<_>>>()?;

// Check that we have the expected number of partitions
assert_eq!(statistics.len(), 2);

// For partitioned mode, the min/max values are from the specific partition for each side.
let expected_p0_statistics = Statistics {
num_rows: Precision::Inexact(2),
total_byte_size: Precision::Absent,
column_statistics: vec![
// Left id column: partition 0 only (id 3..4)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Int32(Some(4))),
min_value: Precision::Exact(ScalarValue::Int32(Some(3))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(8),
},
// Left date column: partition 0 only (2025-03-01..2025-03-02)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_02,
))),
min_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_01,
))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(8),
},
// Right id column: partition 0 only (id 3..4)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Int32(Some(4))),
min_value: Precision::Exact(ScalarValue::Int32(Some(3))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(8),
},
// Right date column: partition 0 only (2025-03-01..2025-03-02)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_02,
))),
min_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_01,
))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(8),
},
],
};
assert_eq!(statistics[0], expected_p0_statistics);

// Test Auto mode - should fall back to getting all partition statistics
let auto_join = Arc::new(HashJoinExec::try_new(
Arc::clone(&left_scan),
Arc::clone(&right_scan),
on,
None,
&JoinType::Inner,
None,
PartitionMode::Auto,
NullEquality::NullEqualsNothing,
false,
)?) as Arc<dyn ExecutionPlan>;

// Test partition statistics for Auto mode
let statistics = (0..auto_join.output_partitioning().partition_count())
.map(|idx| auto_join.partition_statistics(Some(idx)))
.collect::<Result<Vec<_>>>()?;

// Check that we have the expected number of partitions
assert_eq!(statistics.len(), 2);

// For auto mode, the min/max values are from the entire left and right tables.
let expected_p0_statistics = Statistics {
num_rows: Precision::Inexact(4),
total_byte_size: Precision::Absent,
column_statistics: vec![
// Left id column: all partitions (id 1..4)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Int32(Some(4))),
min_value: Precision::Exact(ScalarValue::Int32(Some(1))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(16),
},
// Left date column: all partitions (2025-03-01..2025-03-04)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_04,
))),
min_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_01,
))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(16),
},
// Right id column: all partitions (id 1..4)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Int32(Some(4))),
min_value: Precision::Exact(ScalarValue::Int32(Some(1))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(16),
},
// Right date column: all partitions (2025-03-01..2025-03-04)
ColumnStatistics {
null_count: Precision::Exact(0),
max_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_04,
))),
min_value: Precision::Exact(ScalarValue::Date32(Some(
DATE_2025_03_01,
))),
sum_value: Precision::Absent,
distinct_count: Precision::Absent,
byte_size: Precision::Exact(16),
},
],
};
assert_eq!(statistics[0], expected_p0_statistics);
Ok(())
}
}
60 changes: 47 additions & 13 deletions datafusion/physical-plan/src/joins/hash_join/exec.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -1442,19 +1442,53 @@ impl ExecutionPlan for HashJoinExec {
}

fn partition_statistics(&self, partition: Option<usize>) -> Result<Statistics> {
if partition.is_some() {
return Ok(Statistics::new_unknown(&self.schema()));
}
// TODO stats: it is not possible in general to know the output size of joins
// There are some special cases though, for example:
// - `A LEFT JOIN B ON A.col=B.col` with `COUNT_DISTINCT(B.col)=COUNT(B.col)`
let stats = estimate_join_statistics(
self.left.partition_statistics(None)?,
self.right.partition_statistics(None)?,
&self.on,
&self.join_type,
&self.join_schema,
)?;
let stats = match (partition, self.mode) {
// For CollectLeft mode, the left side is collected into a single partition,
// so all left partitions are available to each output partition.
// For the right side, we need the specific partition statistics.
(Some(partition), PartitionMode::CollectLeft) => {
let left_stats = self.left.partition_statistics(None)?;
let right_stats = self.right.partition_statistics(Some(partition))?;

estimate_join_statistics(
left_stats,
right_stats,
&self.on,
&self.join_type,
&self.join_schema,
)?
}

// For Partitioned mode, both sides are partitioned, so each output partition
// only has access to the corresponding partition from both sides.
(Some(partition), PartitionMode::Partitioned) => {
let left_stats = self.left.partition_statistics(Some(partition))?;
let right_stats = self.right.partition_statistics(Some(partition))?;

estimate_join_statistics(
left_stats,
right_stats,
&self.on,
&self.join_type,
&self.join_schema,
)?
}

// For Auto mode or when no specific partition is requested, fall back to
// the current behavior of getting all partition statistics.
(None, _) | (Some(_), PartitionMode::Auto) => {
// TODO stats: it is not possible in general to know the output size of joins
// There are some special cases though, for example:
// - `A LEFT JOIN B ON A.col=B.col` with `COUNT_DISTINCT(B.col)=COUNT(B.col)`
estimate_join_statistics(
self.left.partition_statistics(None)?,
self.right.partition_statistics(None)?,
&self.on,
&self.join_type,
&self.join_schema,
)?
}
};
// Project statistics if there is a projection
let stats = stats.project(self.projection.as_ref());
// Apply fetch limit to statistics
Expand Down