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Posted to commits@datafusion.apache.org by gi...@apache.org on 2026/02/22 07:40:25 UTC
(datafusion) branch main updated: bench: Add IN list benchmarks for non-constant list expressions (#20444)
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The following commit(s) were added to refs/heads/main by this push:
new bfc012e638 bench: Add IN list benchmarks for non-constant list expressions (#20444)
bfc012e638 is described below
commit bfc012e63852826f68e970a993c4c25cfcbddf43
Author: Zhang Xiaofeng <zh...@gmail.com>
AuthorDate: Sun Feb 22 15:40:02 2026 +0800
bench: Add IN list benchmarks for non-constant list expressions (#20444)
## Which issue does this PR close?
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- Relates to #20427 .
## Rationale for this change
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Why are you proposing this change? If this is already explained clearly
in the issue then this section is not needed.
Explaining clearly why changes are proposed helps reviewers understand
your changes and offer better suggestions for fixes.
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The existing `in_list` benchmarks only cover the static filter path
(constant literal lists), which uses HashSet lookup. There are no
benchmarks for the dynamic evaluation path, triggered when the IN list
contains non-constant expressions such as column references (e.g., `a IN
(b, c, d)`). Adding these benchmarks establishes a baseline for
measuring the impact upcoming optimizations to the dynamic path. (see
#20428).
## 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 criterion benchmarks for the dynamic IN list evaluation path:
- `bench_dynamic_int32`: Int32 column references, list sizes [3, 8, 28]
× match rates [0%, 50%, 100%] × null rates [0%, 20%]
- `bench_dynamic_utf8`: Utf8 column references, list sizes [3, 8, 28] ×
match rates [0%, 50%, 100%]
## Are these changes tested?
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2. Serve as another way to document the expected behavior of the code
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Yes. The benchmarks compile and run correctly. No implementation code is
changed.
## Are there any user-facing changes?
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---
datafusion/physical-expr/benches/in_list.rs | 166 ++++++++++++++++++++++++++++
1 file changed, 166 insertions(+)
diff --git a/datafusion/physical-expr/benches/in_list.rs b/datafusion/physical-expr/benches/in_list.rs
index 954715d0e5..021d8259cd 100644
--- a/datafusion/physical-expr/benches/in_list.rs
+++ b/datafusion/physical-expr/benches/in_list.rs
@@ -23,6 +23,7 @@ use arrow::datatypes::{Field, Schema};
use arrow::record_batch::RecordBatch;
use criterion::{Criterion, criterion_group, criterion_main};
use datafusion_common::ScalarValue;
+use datafusion_physical_expr::PhysicalExpr;
use datafusion_physical_expr::expressions::{col, in_list, lit};
use rand::distr::Alphanumeric;
use rand::prelude::*;
@@ -50,7 +51,9 @@ fn random_string(rng: &mut StdRng, len: usize) -> String {
}
const IN_LIST_LENGTHS: [usize; 4] = [3, 8, 28, 100];
+const LIST_WITH_COLUMNS_LENGTHS: [usize; 3] = [3, 8, 28];
const NULL_PERCENTS: [f64; 2] = [0., 0.2];
+const MATCH_PERCENTS: [f64; 3] = [0.0, 0.5, 1.0];
const STRING_LENGTHS: [usize; 3] = [3, 12, 100];
const ARRAY_LENGTH: usize = 8192;
@@ -219,6 +222,165 @@ fn bench_realistic_mixed_strings<A>(
}
}
+/// Benchmarks the column-reference evaluation path (no static filter) by including
+/// a column reference in the IN list, which prevents static filter creation.
+///
+/// This simulates SQL like:
+/// ```sql
+/// CREATE TABLE t (a INT, b0 INT, b1 INT, b2 INT);
+/// SELECT * FROM t WHERE a IN (b0, b1, b2);
+/// ```
+///
+/// - `values`: the "needle" column (`a`)
+/// - `list_cols`: the "haystack" columns (`b0`, `b1`, …)
+fn do_bench_with_columns(
+ c: &mut Criterion,
+ name: &str,
+ values: ArrayRef,
+ list_cols: &[ArrayRef],
+) {
+ let mut fields = vec![Field::new("a", values.data_type().clone(), true)];
+ let mut columns: Vec<ArrayRef> = vec![values];
+
+ // Build list expressions: column refs (forces non-constant evaluation path)
+ let schema_fields: Vec<Field> = list_cols
+ .iter()
+ .enumerate()
+ .map(|(i, col_arr)| {
+ let name = format!("b{i}");
+ fields.push(Field::new(&name, col_arr.data_type().clone(), true));
+ columns.push(Arc::clone(col_arr));
+ Field::new(&name, col_arr.data_type().clone(), true)
+ })
+ .collect();
+
+ let schema = Schema::new(fields);
+ let list_exprs: Vec<Arc<dyn PhysicalExpr>> = schema_fields
+ .iter()
+ .map(|f| col(f.name(), &schema).unwrap())
+ .collect();
+
+ let expr = in_list(col("a", &schema).unwrap(), list_exprs, &false, &schema).unwrap();
+ let batch = RecordBatch::try_new(Arc::new(schema), columns).unwrap();
+
+ c.bench_function(name, |b| {
+ b.iter(|| black_box(expr.evaluate(black_box(&batch)).unwrap()))
+ });
+}
+
+/// Benchmarks the IN list path with column references for Int32 arrays.
+///
+/// Equivalent SQL:
+/// ```sql
+/// CREATE TABLE t (a INT, b0 INT, b1 INT, ...);
+/// SELECT * FROM t WHERE a IN (b0, b1, ...);
+/// ```
+fn bench_with_columns_int32(c: &mut Criterion) {
+ let mut rng = StdRng::seed_from_u64(42);
+
+ for list_size in LIST_WITH_COLUMNS_LENGTHS {
+ for match_percent in MATCH_PERCENTS {
+ for null_percent in NULL_PERCENTS {
+ // Generate the "needle" column
+ let values: Int32Array = (0..ARRAY_LENGTH)
+ .map(|_| {
+ rng.random_bool(1.0 - null_percent)
+ .then(|| rng.random_range(0..1000))
+ })
+ .collect();
+
+ // Generate list columns with controlled match rate
+ let list_cols: Vec<ArrayRef> = (0..list_size)
+ .map(|_| {
+ let col: Int32Array = (0..ARRAY_LENGTH)
+ .map(|row| {
+ if rng.random_bool(1.0 - null_percent) {
+ if rng.random_bool(match_percent) {
+ // Copy from values to create a match
+ if values.is_null(row) {
+ Some(rng.random_range(0..1000))
+ } else {
+ Some(values.value(row))
+ }
+ } else {
+ // Random value (unlikely to match)
+ Some(rng.random_range(1000..2000))
+ }
+ } else {
+ None
+ }
+ })
+ .collect();
+ Arc::new(col) as ArrayRef
+ })
+ .collect();
+
+ do_bench_with_columns(
+ c,
+ &format!(
+ "in_list_cols/Int32/list={}/match={}%/nulls={}%",
+ list_size,
+ (match_percent * 100.0) as u32,
+ (null_percent * 100.0) as u32
+ ),
+ Arc::new(values),
+ &list_cols,
+ );
+ }
+ }
+ }
+}
+
+/// Benchmarks the IN list path with column references for Utf8 arrays.
+///
+/// Equivalent SQL:
+/// ```sql
+/// CREATE TABLE t (a VARCHAR, b0 VARCHAR, b1 VARCHAR, ...);
+/// SELECT * FROM t WHERE a IN (b0, b1, ...);
+/// ```
+fn bench_with_columns_utf8(c: &mut Criterion) {
+ let mut rng = StdRng::seed_from_u64(99);
+
+ for list_size in LIST_WITH_COLUMNS_LENGTHS {
+ for match_percent in MATCH_PERCENTS {
+ // Generate the "needle" column
+ let value_strings: Vec<Option<String>> = (0..ARRAY_LENGTH)
+ .map(|_| rng.random_bool(0.8).then(|| random_string(&mut rng, 12)))
+ .collect();
+ let values: StringArray =
+ value_strings.iter().map(|s| s.as_deref()).collect();
+
+ // Generate list columns with controlled match rate
+ let list_cols: Vec<ArrayRef> = (0..list_size)
+ .map(|_| {
+ let col: StringArray = (0..ARRAY_LENGTH)
+ .map(|row| {
+ if rng.random_bool(match_percent) {
+ // Copy from values to create a match
+ value_strings[row].as_deref()
+ } else {
+ Some("no_match_value_xyz")
+ }
+ })
+ .collect();
+ Arc::new(col) as ArrayRef
+ })
+ .collect();
+
+ do_bench_with_columns(
+ c,
+ &format!(
+ "in_list_cols/Utf8/list={}/match={}%",
+ list_size,
+ (match_percent * 100.0) as u32,
+ ),
+ Arc::new(values),
+ &list_cols,
+ );
+ }
+ }
+}
+
/// Entry point: registers in_list benchmarks for string and numeric array types.
fn criterion_benchmark(c: &mut Criterion) {
let mut rng = StdRng::seed_from_u64(120320);
@@ -266,6 +428,10 @@ fn criterion_benchmark(c: &mut Criterion) {
|rng| rng.random(),
|v| ScalarValue::TimestampNanosecond(Some(v), None),
);
+
+ // Column-reference path benchmarks (non-constant list expressions)
+ bench_with_columns_int32(c);
+ bench_with_columns_utf8(c);
}
criterion_group! {
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