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Posted to commits@datafusion.apache.org by gi...@apache.org on 2026/04/19 14:11:13 UTC
(datafusion) branch gh-readonly-queue/main/pr-21575-7e1a7104b62d568afe3a02cc7aecb96dd265c238 created (now 9c0edccdb1)
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github-merge-queue[bot] pushed a change to branch gh-readonly-queue/main/pr-21575-7e1a7104b62d568afe3a02cc7aecb96dd265c238
in repository https://gitbox.apache.org/repos/asf/datafusion.git
at 9c0edccdb1 chore: add count distinct group benchmarks (#21575)
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new 9c0edccdb1 chore: add count distinct group benchmarks (#21575)
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(datafusion) 01/01: chore: add count distinct group benchmarks (#21575)
Posted by gi...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
github-merge-queue[bot] pushed a commit to branch gh-readonly-queue/main/pr-21575-7e1a7104b62d568afe3a02cc7aecb96dd265c238
in repository https://gitbox.apache.org/repos/asf/datafusion.git
commit 9c0edccdb164843333b7a09dfd8a00d19a6dc868
Author: Bhargava Vadlamani <11...@users.noreply.github.com>
AuthorDate: Sun Apr 19 07:10:51 2026 -0700
chore: add count distinct group benchmarks (#21575)
## Which issue does this PR close?
Add benchmarks for group accumulators to test :
https://github.com/apache/datafusion/pull/21561
The implementation forks out based on `is_groups_accumulator_supported`
function call. Once this is merged , we should be able to evaluate group
accumulators on count distinct expr
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- Closes #.
## Rationale for this change
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---
.../functions-aggregate/benches/count_distinct.rs | 311 ++++++++++++++++++++-
1 file changed, 308 insertions(+), 3 deletions(-)
diff --git a/datafusion/functions-aggregate/benches/count_distinct.rs b/datafusion/functions-aggregate/benches/count_distinct.rs
index e742a3e5c1..4d9e8c5b67 100644
--- a/datafusion/functions-aggregate/benches/count_distinct.rs
+++ b/datafusion/functions-aggregate/benches/count_distinct.rs
@@ -18,12 +18,13 @@
use std::sync::Arc;
use arrow::array::{
- ArrayRef, Int8Array, Int16Array, Int64Array, UInt8Array, UInt16Array,
+ Array, ArrayRef, Int8Array, Int16Array, Int32Array, Int64Array, UInt8Array,
+ UInt16Array, UInt32Array,
};
use arrow::datatypes::{DataType, Field, Schema};
use criterion::{Criterion, criterion_group, criterion_main};
use datafusion_expr::function::AccumulatorArgs;
-use datafusion_expr::{Accumulator, AggregateUDFImpl};
+use datafusion_expr::{Accumulator, AggregateUDFImpl, EmitTo};
use datafusion_functions_aggregate::count::Count;
use datafusion_physical_expr::expressions::col;
use rand::rngs::StdRng;
@@ -87,6 +88,44 @@ fn create_i16_array(n_distinct: usize) -> Int16Array {
.collect()
}
+fn create_u32_array(n_distinct: usize) -> UInt32Array {
+ let mut rng = StdRng::seed_from_u64(42);
+ (0..BATCH_SIZE)
+ .map(|_| Some(rng.random_range(0..n_distinct as u32)))
+ .collect()
+}
+
+fn create_i32_array(n_distinct: usize) -> Int32Array {
+ let mut rng = StdRng::seed_from_u64(42);
+ (0..BATCH_SIZE)
+ .map(|_| Some(rng.random_range(0..n_distinct as i32)))
+ .collect()
+}
+
+fn prepare_args(data_type: DataType) -> (Arc<Schema>, AccumulatorArgs<'static>) {
+ let schema = Arc::new(Schema::new(vec![Field::new("f", data_type, true)]));
+ let schema_leaked: &'static Schema = Box::leak(Box::new((*schema).clone()));
+ let expr = col("f", schema_leaked).unwrap();
+ let expr_leaked: &'static _ = Box::leak(Box::new(expr));
+ let return_field: Arc<Field> = Field::new("f", DataType::Int64, true).into();
+ let return_field_leaked: &'static _ = Box::leak(Box::new(return_field.clone()));
+ let expr_field = expr_leaked.return_field(schema_leaked).unwrap();
+ let expr_field_leaked: &'static _ = Box::leak(Box::new(expr_field));
+
+ let accumulator_args = AccumulatorArgs {
+ return_field: return_field_leaked.clone(),
+ schema: schema_leaked,
+ expr_fields: std::slice::from_ref(expr_field_leaked),
+ ignore_nulls: false,
+ order_bys: &[],
+ is_reversed: false,
+ name: "count(distinct f)",
+ is_distinct: true,
+ exprs: std::slice::from_ref(expr_leaked),
+ };
+ (schema, accumulator_args)
+}
+
fn count_distinct_benchmark(c: &mut Criterion) {
for pct in [80, 99] {
let n_distinct = BATCH_SIZE * pct / 100;
@@ -148,7 +187,273 @@ fn count_distinct_benchmark(c: &mut Criterion) {
.unwrap()
})
});
+
+ // 32-bit integer types
+ for pct in [80, 99] {
+ let n_distinct = BATCH_SIZE * pct / 100;
+
+ // UInt32
+ let values = Arc::new(create_u32_array(n_distinct)) as ArrayRef;
+ c.bench_function(&format!("count_distinct u32 {pct}% distinct"), |b| {
+ b.iter(|| {
+ let mut accumulator = prepare_accumulator(DataType::UInt32);
+ accumulator
+ .update_batch(std::slice::from_ref(&values))
+ .unwrap()
+ })
+ });
+
+ // Int32
+ let values = Arc::new(create_i32_array(n_distinct)) as ArrayRef;
+ c.bench_function(&format!("count_distinct i32 {pct}% distinct"), |b| {
+ b.iter(|| {
+ let mut accumulator = prepare_accumulator(DataType::Int32);
+ accumulator
+ .update_batch(std::slice::from_ref(&values))
+ .unwrap()
+ })
+ });
+ }
+}
+
+/// Create group indices with uniform distribution
+fn create_uniform_groups(num_groups: usize) -> Vec<usize> {
+ let mut rng = StdRng::seed_from_u64(42);
+ (0..BATCH_SIZE)
+ .map(|_| rng.random_range(0..num_groups))
+ .collect()
+}
+
+/// Create group indices with skewed distribution (80% in 20% of groups)
+fn create_skewed_groups(num_groups: usize) -> Vec<usize> {
+ let mut rng = StdRng::seed_from_u64(42);
+ let hot_groups = (num_groups / 5).max(1);
+ (0..BATCH_SIZE)
+ .map(|_| {
+ if rng.random_range(0..100) < 80 {
+ rng.random_range(0..hot_groups)
+ } else {
+ rng.random_range(0..num_groups)
+ }
+ })
+ .collect()
+}
+
+fn count_distinct_groups_benchmark(c: &mut Criterion) {
+ let count_fn = Count::new();
+
+ let group_counts = [100, 1000, 10000];
+ let cardinalities = [("low", 20), ("mid", 80), ("high", 99)];
+ let distributions = ["uniform", "skewed"];
+
+ // i64 benchmarks
+ for num_groups in group_counts {
+ for (card_name, distinct_pct) in cardinalities {
+ for dist in distributions {
+ let name = format!("i64_g{num_groups}_{card_name}_{dist}");
+ let n_distinct = BATCH_SIZE * distinct_pct / 100;
+ let values = Arc::new(create_i64_array(n_distinct)) as ArrayRef;
+ let group_indices = if dist == "uniform" {
+ create_uniform_groups(num_groups)
+ } else {
+ create_skewed_groups(num_groups)
+ };
+
+ let (_schema, args) = prepare_args(DataType::Int64);
+
+ if count_fn.groups_accumulator_supported(args.clone()) {
+ c.bench_function(&format!("count_distinct_groups {name}"), |b| {
+ b.iter(|| {
+ let mut acc =
+ count_fn.create_groups_accumulator(args.clone()).unwrap();
+ acc.update_batch(
+ std::slice::from_ref(&values),
+ &group_indices,
+ None,
+ num_groups,
+ )
+ .unwrap();
+ acc.evaluate(EmitTo::All).unwrap()
+ })
+ });
+ } else {
+ let arr = values.as_any().downcast_ref::<Int64Array>().unwrap();
+ let mut group_rows: Vec<Vec<i64>> = vec![Vec::new(); num_groups];
+ for (idx, &group_idx) in group_indices.iter().enumerate() {
+ if arr.is_valid(idx) {
+ group_rows[group_idx].push(arr.value(idx));
+ }
+ }
+ let group_arrays: Vec<ArrayRef> = group_rows
+ .iter()
+ .map(|rows| Arc::new(Int64Array::from(rows.clone())) as ArrayRef)
+ .collect();
+
+ c.bench_function(&format!("count_distinct_groups {name}"), |b| {
+ b.iter(|| {
+ let mut accumulators: Vec<_> = (0..num_groups)
+ .map(|_| prepare_accumulator(DataType::Int64))
+ .collect();
+
+ for (group_idx, batch) in group_arrays.iter().enumerate() {
+ if !batch.is_empty() {
+ accumulators[group_idx]
+ .update_batch(std::slice::from_ref(batch))
+ .unwrap();
+ }
+ }
+
+ let _results: Vec<_> = accumulators
+ .iter_mut()
+ .map(|acc| acc.evaluate().unwrap())
+ .collect();
+ })
+ });
+ }
+ }
+ }
+ }
+
+ // i32 benchmarks
+ for num_groups in group_counts {
+ for (card_name, distinct_pct) in cardinalities {
+ for dist in distributions {
+ let name = format!("i32_g{num_groups}_{card_name}_{dist}");
+ let n_distinct = BATCH_SIZE * distinct_pct / 100;
+ let values = Arc::new(create_i32_array(n_distinct)) as ArrayRef;
+ let group_indices = if dist == "uniform" {
+ create_uniform_groups(num_groups)
+ } else {
+ create_skewed_groups(num_groups)
+ };
+
+ let (_schema, args) = prepare_args(DataType::Int32);
+
+ if count_fn.groups_accumulator_supported(args.clone()) {
+ c.bench_function(&format!("count_distinct_groups {name}"), |b| {
+ b.iter(|| {
+ let mut acc =
+ count_fn.create_groups_accumulator(args.clone()).unwrap();
+ acc.update_batch(
+ std::slice::from_ref(&values),
+ &group_indices,
+ None,
+ num_groups,
+ )
+ .unwrap();
+ acc.evaluate(EmitTo::All).unwrap()
+ })
+ });
+ } else {
+ let arr = values.as_any().downcast_ref::<Int32Array>().unwrap();
+ let mut group_rows: Vec<Vec<i32>> = vec![Vec::new(); num_groups];
+ for (idx, &group_idx) in group_indices.iter().enumerate() {
+ if arr.is_valid(idx) {
+ group_rows[group_idx].push(arr.value(idx));
+ }
+ }
+ let group_arrays: Vec<ArrayRef> = group_rows
+ .iter()
+ .map(|rows| Arc::new(Int32Array::from(rows.clone())) as ArrayRef)
+ .collect();
+
+ c.bench_function(&format!("count_distinct_groups {name}"), |b| {
+ b.iter(|| {
+ let mut accumulators: Vec<_> = (0..num_groups)
+ .map(|_| prepare_accumulator(DataType::Int32))
+ .collect();
+
+ for (group_idx, batch) in group_arrays.iter().enumerate() {
+ if !batch.is_empty() {
+ accumulators[group_idx]
+ .update_batch(std::slice::from_ref(batch))
+ .unwrap();
+ }
+ }
+
+ let _results: Vec<_> = accumulators
+ .iter_mut()
+ .map(|acc| acc.evaluate().unwrap())
+ .collect();
+ })
+ });
+ }
+ }
+ }
+ }
+
+ // u32 benchmarks
+ for num_groups in group_counts {
+ for (card_name, distinct_pct) in cardinalities {
+ for dist in distributions {
+ let name = format!("u32_g{num_groups}_{card_name}_{dist}");
+ let n_distinct = BATCH_SIZE * distinct_pct / 100;
+ let values = Arc::new(create_u32_array(n_distinct)) as ArrayRef;
+ let group_indices = if dist == "uniform" {
+ create_uniform_groups(num_groups)
+ } else {
+ create_skewed_groups(num_groups)
+ };
+
+ let (_schema, args) = prepare_args(DataType::UInt32);
+
+ if count_fn.groups_accumulator_supported(args.clone()) {
+ c.bench_function(&format!("count_distinct_groups {name}"), |b| {
+ b.iter(|| {
+ let mut acc =
+ count_fn.create_groups_accumulator(args.clone()).unwrap();
+ acc.update_batch(
+ std::slice::from_ref(&values),
+ &group_indices,
+ None,
+ num_groups,
+ )
+ .unwrap();
+ acc.evaluate(EmitTo::All).unwrap()
+ })
+ });
+ } else {
+ let arr = values.as_any().downcast_ref::<UInt32Array>().unwrap();
+ let mut group_rows: Vec<Vec<u32>> = vec![Vec::new(); num_groups];
+ for (idx, &group_idx) in group_indices.iter().enumerate() {
+ if arr.is_valid(idx) {
+ group_rows[group_idx].push(arr.value(idx));
+ }
+ }
+ let group_arrays: Vec<ArrayRef> = group_rows
+ .iter()
+ .map(|rows| Arc::new(UInt32Array::from(rows.clone())) as ArrayRef)
+ .collect();
+
+ c.bench_function(&format!("count_distinct_groups {name}"), |b| {
+ b.iter(|| {
+ let mut accumulators: Vec<_> = (0..num_groups)
+ .map(|_| prepare_accumulator(DataType::UInt32))
+ .collect();
+
+ for (group_idx, batch) in group_arrays.iter().enumerate() {
+ if !batch.is_empty() {
+ accumulators[group_idx]
+ .update_batch(std::slice::from_ref(batch))
+ .unwrap();
+ }
+ }
+
+ let _results: Vec<_> = accumulators
+ .iter_mut()
+ .map(|acc| acc.evaluate().unwrap())
+ .collect();
+ })
+ });
+ }
+ }
+ }
+ }
}
-criterion_group!(benches, count_distinct_benchmark);
+criterion_group!(
+ benches,
+ count_distinct_benchmark,
+ count_distinct_groups_benchmark
+);
criterion_main!(benches);
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