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Posted to commits@datafusion.apache.org by gi...@apache.org on 2026/08/31 08:28:22 UTC
(datafusion) branch main updated: bench: add grouped aggregate filter benchmarks (#24748)
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github-merge-queue[bot] pushed a commit to branch main
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The following commit(s) were added to refs/heads/main by this push:
new 180c461a97 bench: add grouped aggregate filter benchmarks (#24748)
180c461a97 is described below
commit 180c461a977dda4d2d15f54a98c88092674367ad
Author: linfeng <33...@users.noreply.github.com>
AuthorDate: Mon Aug 31 08:17:47 2026 +0000
bench: add grouped aggregate filter benchmarks (#24748)
## Which issue does this PR close?
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- Closes #.
## Rationale for this change
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your changes and offer better suggestions for fixes.
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behavior, rather than the implementation.
For example, "The code in `foo.rs` doesn't handle nulls" is a symptom of
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implementation. "COUNT(DISTINCT) returns wrong results when the column
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Add a microbenchmark for measuring grouped aggregate `FILTER`
performance across different filter selectivities and aggregate layouts.
## What changes are included in this PR?
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There is no need to duplicate the description in the issue here, but it
is sometimes worth providing a summary of the individual changes in this
PR.
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- Add an `AggregateExec` microbenchmark for grouped aggregates with
`FILTER`.
- Cover one aggregate, two aggregates sharing a filter, and two
aggregates using distinct filters.
- Cover bare-column and multiplication arguments for the
single-aggregate layout.
- Cover unfiltered, 1%, 10%, 50%, 90%, 99%, and 100% filter
selectivities.
## Are these changes tested?
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1. Prevent the code from being accidentally broken by subsequent changes
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Yes.
## Are there any user-facing changes?
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No.
---
datafusion/physical-plan/Cargo.toml | 5 +
.../physical-plan/benches/aggregate_filter.rs | 286 +++++++++++++++++++++
2 files changed, 291 insertions(+)
diff --git a/datafusion/physical-plan/Cargo.toml b/datafusion/physical-plan/Cargo.toml
index cc30651b44..f8703e04cd 100644
--- a/datafusion/physical-plan/Cargo.toml
+++ b/datafusion/physical-plan/Cargo.toml
@@ -131,6 +131,11 @@ harness = false
name = "aggregate_vectorized"
required-features = ["test_utils"]
+[[bench]]
+harness = false
+name = "aggregate_filter"
+required-features = ["test_utils"]
+
[[bench]]
harness = false
name = "compute_statistics"
diff --git a/datafusion/physical-plan/benches/aggregate_filter.rs b/datafusion/physical-plan/benches/aggregate_filter.rs
new file mode 100644
index 0000000000..ae97cb7388
--- /dev/null
+++ b/datafusion/physical-plan/benches/aggregate_filter.rs
@@ -0,0 +1,286 @@
+// 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.
+
+//! Benchmarks grouped aggregate `FILTER` processing through `AggregateExec`.
+//!
+//! The benchmark includes group-key, filter, and aggregate-argument evaluation,
+//! group interning, and `GroupsAccumulator` updates. It intentionally uses an
+//! in-memory physical plan to exclude SQL planning and file I/O.
+
+use std::hint::black_box;
+use std::sync::Arc;
+
+use arrow::array::{BooleanArray, Float64Array, UInt32Array};
+use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
+use arrow::record_batch::RecordBatch;
+use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main};
+use datafusion_execution::TaskContext;
+use datafusion_expr::Operator;
+use datafusion_functions_aggregate::sum::sum_udaf;
+use datafusion_physical_expr::PhysicalExpr;
+use datafusion_physical_expr::aggregate::{AggregateExprBuilder, AggregateFunctionExpr};
+use datafusion_physical_expr::expressions::{BinaryExpr, col};
+use datafusion_physical_plan::aggregates::{
+ AggregateExec, AggregateMode, PhysicalGroupBy,
+};
+use datafusion_physical_plan::test::TestMemoryExec;
+use datafusion_physical_plan::{ExecutionPlan, collect};
+use rand::SeedableRng;
+use rand::rngs::StdRng;
+use rand::seq::SliceRandom;
+use tokio::runtime::Runtime;
+
+const NUM_ROWS: usize = 65_536;
+const BATCH_SIZE: usize = 8_192;
+const GROUP_COUNTS: &[usize] = &[16, 1_024, NUM_ROWS];
+const FILTER_CASES: &[Option<usize>] = &[
+ None,
+ Some(1),
+ Some(10),
+ Some(50),
+ Some(90),
+ Some(99),
+ Some(100),
+];
+
+#[derive(Clone, Copy)]
+enum ArgumentKind {
+ Column,
+ Multiply,
+}
+
+impl ArgumentKind {
+ fn name(self) -> &'static str {
+ match self {
+ Self::Column => "column",
+ Self::Multiply => "multiply",
+ }
+ }
+}
+
+#[derive(Clone, Copy)]
+enum AggregateLayout {
+ OneAggregate,
+ TwoAggregatesSharedFilter,
+ TwoAggregatesDistinctFilters,
+}
+
+impl AggregateLayout {
+ fn name(self) -> &'static str {
+ match self {
+ Self::OneAggregate => "one_aggregate",
+ Self::TwoAggregatesSharedFilter => "two_aggregates_shared_filter",
+ Self::TwoAggregatesDistinctFilters => "two_aggregates_distinct_filters",
+ }
+ }
+
+ fn aggregate_count(self) -> usize {
+ match self {
+ Self::OneAggregate => 1,
+ Self::TwoAggregatesSharedFilter | Self::TwoAggregatesDistinctFilters => 2,
+ }
+ }
+}
+
+fn schema() -> SchemaRef {
+ Arc::new(Schema::new(vec![
+ Field::new("group_key", DataType::UInt32, false),
+ Field::new("value", DataType::Float64, false),
+ Field::new("include_a", DataType::Boolean, false),
+ Field::new("include_b", DataType::Boolean, false),
+ ]))
+}
+
+fn make_filter_mask(filter_percent: usize, seed: u64) -> Vec<bool> {
+ let selected_rows = NUM_ROWS * filter_percent / 100;
+ let mut mask = vec![false; NUM_ROWS];
+ mask[..selected_rows].fill(true);
+ mask.shuffle(&mut StdRng::seed_from_u64(seed));
+ mask
+}
+
+fn make_batches(filter_percent: Option<usize>, num_groups: usize) -> Vec<RecordBatch> {
+ let filter_percent = filter_percent.unwrap_or(100);
+ // Select different rows while keeping both filters' distributions comparable.
+ let include_a = make_filter_mask(filter_percent, 42);
+ let include_b = make_filter_mask(filter_percent, 43);
+ (0..NUM_ROWS)
+ .step_by(BATCH_SIZE)
+ .map(|start| {
+ let end = start + BATCH_SIZE;
+ let group_key = UInt32Array::from_iter_values(
+ (start..end).map(|row| (row % num_groups) as u32),
+ );
+ let value = Float64Array::from_iter_values(
+ (start..end).map(|row| (row % 1_000) as f64),
+ );
+ let include_a = BooleanArray::from(include_a[start..end].to_vec());
+ let include_b = BooleanArray::from(include_b[start..end].to_vec());
+
+ RecordBatch::try_new(
+ schema(),
+ vec![
+ Arc::new(group_key),
+ Arc::new(value),
+ Arc::new(include_a),
+ Arc::new(include_b),
+ ],
+ )
+ .unwrap()
+ })
+ .collect()
+}
+
+fn aggregate_expr(
+ schema: &SchemaRef,
+ aggregate_index: usize,
+ argument_kind: ArgumentKind,
+) -> Arc<AggregateFunctionExpr> {
+ let value = col("value", schema).unwrap();
+ let argument: Arc<dyn PhysicalExpr> = match argument_kind {
+ ArgumentKind::Column => value,
+ ArgumentKind::Multiply => Arc::new(BinaryExpr::new(
+ Arc::clone(&value),
+ Operator::Multiply,
+ value,
+ )),
+ };
+
+ Arc::new(
+ AggregateExprBuilder::new(sum_udaf(), vec![argument])
+ .schema(Arc::clone(schema))
+ .alias(format!("sum_{}_{aggregate_index}", argument_kind.name()))
+ .build()
+ .unwrap(),
+ )
+}
+
+fn make_plan(
+ layout: AggregateLayout,
+ argument_kind: ArgumentKind,
+ filter_percent: Option<usize>,
+ num_groups: usize,
+) -> Arc<dyn ExecutionPlan> {
+ let schema = schema();
+ let batches = make_batches(filter_percent, num_groups);
+ let input =
+ TestMemoryExec::try_new_exec(&[batches], Arc::clone(&schema), None).unwrap();
+ let group_by = PhysicalGroupBy::new_single(vec![(
+ col("group_key", &schema).unwrap(),
+ "group_key".to_string(),
+ )]);
+ let aggregates = (0..layout.aggregate_count())
+ .map(|index| aggregate_expr(&schema, index, argument_kind))
+ .collect::<Vec<_>>();
+
+ let filters = match filter_percent {
+ None => vec![None; layout.aggregate_count()],
+ Some(_) => {
+ let include_a = col("include_a", &schema).unwrap();
+ match layout {
+ AggregateLayout::OneAggregate => vec![Some(include_a)],
+ AggregateLayout::TwoAggregatesSharedFilter => {
+ vec![Some(Arc::clone(&include_a)), Some(include_a)]
+ }
+ AggregateLayout::TwoAggregatesDistinctFilters => {
+ vec![Some(include_a), Some(col("include_b", &schema).unwrap())]
+ }
+ }
+ }
+ };
+
+ Arc::new(
+ AggregateExec::try_new(
+ AggregateMode::Single,
+ group_by,
+ aggregates,
+ filters,
+ input,
+ schema,
+ )
+ .unwrap(),
+ )
+}
+
+fn benchmark_case(
+ c: &mut Criterion,
+ runtime: &Runtime,
+ layout: AggregateLayout,
+ argument_kinds: &[ArgumentKind],
+) {
+ for &num_groups in GROUP_COUNTS {
+ let mut group = c.benchmark_group(format!(
+ "grouped_aggregate_filter/{}/{}_groups",
+ layout.name(),
+ num_groups
+ ));
+
+ for &argument_kind in argument_kinds {
+ for &filter_percent in FILTER_CASES {
+ let case_name = match filter_percent {
+ None => "unfiltered".to_string(),
+ Some(percent) => format!("{percent}_percent"),
+ };
+ let plan = make_plan(layout, argument_kind, filter_percent, num_groups);
+ let task_ctx = Arc::new(TaskContext::default());
+
+ group.bench_function(
+ BenchmarkId::new(argument_kind.name(), case_name),
+ |b| {
+ b.iter(|| {
+ let output = runtime
+ .block_on(collect(
+ Arc::clone(&plan),
+ Arc::clone(&task_ctx),
+ ))
+ .unwrap();
+ black_box(output);
+ });
+ },
+ );
+ }
+ }
+
+ group.finish();
+ }
+}
+
+fn aggregate_filter_benchmark(c: &mut Criterion) {
+ let runtime = Runtime::new().unwrap();
+
+ benchmark_case(
+ c,
+ &runtime,
+ AggregateLayout::OneAggregate,
+ &[ArgumentKind::Column, ArgumentKind::Multiply],
+ );
+ benchmark_case(
+ c,
+ &runtime,
+ AggregateLayout::TwoAggregatesSharedFilter,
+ &[ArgumentKind::Multiply],
+ );
+ benchmark_case(
+ c,
+ &runtime,
+ AggregateLayout::TwoAggregatesDistinctFilters,
+ &[ArgumentKind::Multiply],
+ );
+}
+
+criterion_group!(benches, aggregate_filter_benchmark);
+criterion_main!(benches);
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