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The following commit(s) were added to refs/heads/main by this push:
new 1d2b389599 perf: Optimize `contains` for scalar search arg (#19529)
1d2b389599 is described below
commit 1d2b3895996a5371ff750c1cc76b7fd715920414
Author: Andy Grove <[email protected]>
AuthorDate: Mon Dec 29 20:41:05 2025 -0700
perf: Optimize `contains` for scalar search arg (#19529)
## Which issue does this PR close?
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enhancements and this helps us generate change logs for our releases.
You can link an issue to this PR using the GitHub syntax. For example
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- Closes #.
## 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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This PR is an alternative to
https://github.com/apache/datafusion/pull/19514 that replaces the use of
`make_scalar_function` with a new `make_scalar_function_columnar` that
avoids expanding scalar values to arrays for each batch.
| Benchmark | Old Code | New Code | Improvement |
|--------------------------------------------|----------|----------|-------------|
| contains_StringViewArray_scalar_strlen_8 | ~97 µs | ~34 µs | 2.8x
faster |
| contains_StringViewArray_scalar_strlen_32 | ~175 µs | ~37 µs | 4.7x
faster |
| contains_StringViewArray_scalar_strlen_128 | ~332 µs | ~42 µs | 7.9x
faster |
| contains_StringViewArray_scalar_strlen_512 | ~371 µs | ~88 µs | 4.2x
faster |
## What changes are included in this PR?
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is sometimes worth providing a summary of the individual changes in this
PR.
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## Are these changes tested?
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Existing tests
## Are there any user-facing changes?
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---
datafusion/functions/Cargo.toml | 5 +
datafusion/functions/benches/contains.rs | 185 ++++++++++++++++++++++++++++
datafusion/functions/src/string/contains.rs | 89 ++++++++-----
3 files changed, 248 insertions(+), 31 deletions(-)
diff --git a/datafusion/functions/Cargo.toml b/datafusion/functions/Cargo.toml
index d85a269c7f..9555e828ca 100644
--- a/datafusion/functions/Cargo.toml
+++ b/datafusion/functions/Cargo.toml
@@ -260,6 +260,11 @@ harness = false
name = "find_in_set"
required-features = ["unicode_expressions"]
+[[bench]]
+harness = false
+name = "contains"
+required-features = ["string_expressions"]
+
[[bench]]
harness = false
name = "starts_with"
diff --git a/datafusion/functions/benches/contains.rs
b/datafusion/functions/benches/contains.rs
new file mode 100644
index 0000000000..052eff3886
--- /dev/null
+++ b/datafusion/functions/benches/contains.rs
@@ -0,0 +1,185 @@
+// 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.
+
+extern crate criterion;
+
+use arrow::array::{StringArray, StringViewArray};
+use arrow::datatypes::{DataType, Field};
+use criterion::{Criterion, criterion_group, criterion_main};
+use datafusion_common::ScalarValue;
+use datafusion_common::config::ConfigOptions;
+use datafusion_expr::{ColumnarValue, ScalarFunctionArgs};
+use rand::distr::Alphanumeric;
+use rand::prelude::StdRng;
+use rand::{Rng, SeedableRng};
+use std::hint::black_box;
+use std::sync::Arc;
+
+/// Generate a StringArray/StringViewArray with random ASCII strings
+fn gen_string_array(
+ n_rows: usize,
+ str_len: usize,
+ is_string_view: bool,
+) -> ColumnarValue {
+ let mut rng = StdRng::seed_from_u64(42);
+ let strings: Vec<Option<String>> = (0..n_rows)
+ .map(|_| {
+ let s: String = (&mut rng)
+ .sample_iter(&Alphanumeric)
+ .take(str_len)
+ .map(char::from)
+ .collect();
+ Some(s)
+ })
+ .collect();
+
+ if is_string_view {
+ ColumnarValue::Array(Arc::new(StringViewArray::from(strings)))
+ } else {
+ ColumnarValue::Array(Arc::new(StringArray::from(strings)))
+ }
+}
+
+/// Generate a scalar search string
+fn gen_scalar_search(search_str: &str, is_string_view: bool) -> ColumnarValue {
+ if is_string_view {
+
ColumnarValue::Scalar(ScalarValue::Utf8View(Some(search_str.to_string())))
+ } else {
+ ColumnarValue::Scalar(ScalarValue::Utf8(Some(search_str.to_string())))
+ }
+}
+
+/// Generate an array of search strings (same string repeated)
+fn gen_array_search(
+ search_str: &str,
+ n_rows: usize,
+ is_string_view: bool,
+) -> ColumnarValue {
+ let strings: Vec<Option<String>> =
+ (0..n_rows).map(|_| Some(search_str.to_string())).collect();
+
+ if is_string_view {
+ ColumnarValue::Array(Arc::new(StringViewArray::from(strings)))
+ } else {
+ ColumnarValue::Array(Arc::new(StringArray::from(strings)))
+ }
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+ let contains = datafusion_functions::string::contains();
+ let n_rows = 8192;
+ let str_len = 128;
+ let search_str = "xyz"; // A pattern that likely won't be found
+
+ // Benchmark: StringArray with scalar search (the optimized path)
+ let str_array = gen_string_array(n_rows, str_len, false);
+ let scalar_search = gen_scalar_search(search_str, false);
+ let arg_fields = vec![
+ Field::new("a", DataType::Utf8, true).into(),
+ Field::new("b", DataType::Utf8, true).into(),
+ ];
+ let return_field = Field::new("f", DataType::Boolean, true).into();
+ let config_options = Arc::new(ConfigOptions::default());
+
+ c.bench_function("contains_StringArray_scalar_search", |b| {
+ b.iter(|| {
+ black_box(contains.invoke_with_args(ScalarFunctionArgs {
+ args: vec![str_array.clone(), scalar_search.clone()],
+ arg_fields: arg_fields.clone(),
+ number_rows: n_rows,
+ return_field: Arc::clone(&return_field),
+ config_options: Arc::clone(&config_options),
+ }))
+ })
+ });
+
+ // Benchmark: StringArray with array search (for comparison)
+ let array_search = gen_array_search(search_str, n_rows, false);
+ c.bench_function("contains_StringArray_array_search", |b| {
+ b.iter(|| {
+ black_box(contains.invoke_with_args(ScalarFunctionArgs {
+ args: vec![str_array.clone(), array_search.clone()],
+ arg_fields: arg_fields.clone(),
+ number_rows: n_rows,
+ return_field: Arc::clone(&return_field),
+ config_options: Arc::clone(&config_options),
+ }))
+ })
+ });
+
+ // Benchmark: StringViewArray with scalar search (the optimized path)
+ let str_view_array = gen_string_array(n_rows, str_len, true);
+ let scalar_search_view = gen_scalar_search(search_str, true);
+ let arg_fields_view = vec![
+ Field::new("a", DataType::Utf8View, true).into(),
+ Field::new("b", DataType::Utf8View, true).into(),
+ ];
+
+ c.bench_function("contains_StringViewArray_scalar_search", |b| {
+ b.iter(|| {
+ black_box(contains.invoke_with_args(ScalarFunctionArgs {
+ args: vec![str_view_array.clone(), scalar_search_view.clone()],
+ arg_fields: arg_fields_view.clone(),
+ number_rows: n_rows,
+ return_field: Arc::clone(&return_field),
+ config_options: Arc::clone(&config_options),
+ }))
+ })
+ });
+
+ // Benchmark: StringViewArray with array search (for comparison)
+ let array_search_view = gen_array_search(search_str, n_rows, true);
+ c.bench_function("contains_StringViewArray_array_search", |b| {
+ b.iter(|| {
+ black_box(contains.invoke_with_args(ScalarFunctionArgs {
+ args: vec![str_view_array.clone(), array_search_view.clone()],
+ arg_fields: arg_fields_view.clone(),
+ number_rows: n_rows,
+ return_field: Arc::clone(&return_field),
+ config_options: Arc::clone(&config_options),
+ }))
+ })
+ });
+
+ // Benchmark different string lengths with scalar search
+ for str_len in [8, 32, 128, 512] {
+ let str_array = gen_string_array(n_rows, str_len, true);
+ let scalar_search = gen_scalar_search(search_str, true);
+ let arg_fields = vec![
+ Field::new("a", DataType::Utf8View, true).into(),
+ Field::new("b", DataType::Utf8View, true).into(),
+ ];
+
+ c.bench_function(
+ &format!("contains_StringViewArray_scalar_strlen_{str_len}"),
+ |b| {
+ b.iter(|| {
+ black_box(contains.invoke_with_args(ScalarFunctionArgs {
+ args: vec![str_array.clone(), scalar_search.clone()],
+ arg_fields: arg_fields.clone(),
+ number_rows: n_rows,
+ return_field: Arc::clone(&return_field),
+ config_options: Arc::clone(&config_options),
+ }))
+ })
+ },
+ );
+ }
+}
+
+criterion_group!(benches, criterion_benchmark);
+criterion_main!(benches);
diff --git a/datafusion/functions/src/string/contains.rs
b/datafusion/functions/src/string/contains.rs
index b85e0ed796..b7ec95be44 100644
--- a/datafusion/functions/src/string/contains.rs
+++ b/datafusion/functions/src/string/contains.rs
@@ -15,13 +15,12 @@
// specific language governing permissions and limitations
// under the License.
-use crate::utils::make_scalar_function;
-use arrow::array::{Array, ArrayRef, AsArray};
+use arrow::array::{Array, ArrayRef, Scalar};
use arrow::compute::contains as arrow_contains;
use arrow::datatypes::DataType;
use arrow::datatypes::DataType::{Boolean, LargeUtf8, Utf8, Utf8View};
use datafusion_common::types::logical_string;
-use datafusion_common::{DataFusionError, Result, exec_err};
+use datafusion_common::{Result, exec_err};
use datafusion_expr::binary::{binary_to_string_coercion, string_coercion};
use datafusion_expr::{
Coercion, ColumnarValue, Documentation, ScalarFunctionArgs, ScalarUDFImpl,
Signature,
@@ -89,7 +88,7 @@ impl ScalarUDFImpl for ContainsFunc {
}
fn invoke_with_args(&self, args: ScalarFunctionArgs) ->
Result<ColumnarValue> {
- make_scalar_function(contains, vec![])(&args.args)
+ contains(args.args.as_slice())
}
fn documentation(&self) -> Option<&Documentation> {
@@ -97,43 +96,71 @@ impl ScalarUDFImpl for ContainsFunc {
}
}
+fn to_array(value: &ColumnarValue) -> Result<(ArrayRef, bool)> {
+ match value {
+ ColumnarValue::Array(array) => Ok((Arc::clone(array), false)),
+ ColumnarValue::Scalar(scalar) => Ok((scalar.to_array()?, true)),
+ }
+}
+
+/// Helper to call arrow_contains with proper Datum handling.
+/// When an argument is marked as scalar, we wrap it in `Scalar` to tell
arrow's
+/// kernel to use the optimized single-value code path instead of iterating.
+fn call_arrow_contains(
+ haystack: &ArrayRef,
+ haystack_is_scalar: bool,
+ needle: &ArrayRef,
+ needle_is_scalar: bool,
+) -> Result<ColumnarValue> {
+ // Arrow's Datum trait is implemented for ArrayRef, Arc<dyn Array>, and
Scalar<T>
+ // We pass ArrayRef directly when not scalar, or wrap in Scalar when it is
+ let result = match (haystack_is_scalar, needle_is_scalar) {
+ (false, false) => arrow_contains(haystack, needle)?,
+ (false, true) => arrow_contains(haystack,
&Scalar::new(Arc::clone(needle)))?,
+ (true, false) => arrow_contains(&Scalar::new(Arc::clone(haystack)),
needle)?,
+ (true, true) => arrow_contains(
+ &Scalar::new(Arc::clone(haystack)),
+ &Scalar::new(Arc::clone(needle)),
+ )?,
+ };
+
+ // If both inputs were scalar, return a scalar result
+ if haystack_is_scalar && needle_is_scalar {
+ let scalar = datafusion_common::ScalarValue::try_from_array(&result,
0)?;
+ Ok(ColumnarValue::Scalar(scalar))
+ } else {
+ Ok(ColumnarValue::Array(Arc::new(result)))
+ }
+}
+
/// use `arrow::compute::contains` to do the calculation for contains
-fn contains(args: &[ArrayRef]) -> Result<ArrayRef, DataFusionError> {
+fn contains(args: &[ColumnarValue]) -> Result<ColumnarValue> {
+ let (haystack, haystack_is_scalar) = to_array(&args[0])?;
+ let (needle, needle_is_scalar) = to_array(&args[1])?;
+
if let Some(coercion_data_type) =
- string_coercion(args[0].data_type(), args[1].data_type()).or_else(|| {
- binary_to_string_coercion(args[0].data_type(), args[1].data_type())
+ string_coercion(haystack.data_type(), needle.data_type()).or_else(|| {
+ binary_to_string_coercion(haystack.data_type(), needle.data_type())
})
{
- let arg0 = if args[0].data_type() == &coercion_data_type {
- Arc::clone(&args[0])
+ let haystack = if haystack.data_type() == &coercion_data_type {
+ haystack
} else {
- arrow::compute::kernels::cast::cast(&args[0], &coercion_data_type)?
+ arrow::compute::kernels::cast::cast(&haystack,
&coercion_data_type)?
};
- let arg1 = if args[1].data_type() == &coercion_data_type {
- Arc::clone(&args[1])
+ let needle = if needle.data_type() == &coercion_data_type {
+ needle
} else {
- arrow::compute::kernels::cast::cast(&args[1], &coercion_data_type)?
+ arrow::compute::kernels::cast::cast(&needle, &coercion_data_type)?
};
match coercion_data_type {
- Utf8View => {
- let mod_str = arg0.as_string_view();
- let match_str = arg1.as_string_view();
- let res = arrow_contains(mod_str, match_str)?;
- Ok(Arc::new(res) as ArrayRef)
- }
- Utf8 => {
- let mod_str = arg0.as_string::<i32>();
- let match_str = arg1.as_string::<i32>();
- let res = arrow_contains(mod_str, match_str)?;
- Ok(Arc::new(res) as ArrayRef)
- }
- LargeUtf8 => {
- let mod_str = arg0.as_string::<i64>();
- let match_str = arg1.as_string::<i64>();
- let res = arrow_contains(mod_str, match_str)?;
- Ok(Arc::new(res) as ArrayRef)
- }
+ Utf8View | Utf8 | LargeUtf8 => call_arrow_contains(
+ &haystack,
+ haystack_is_scalar,
+ &needle,
+ needle_is_scalar,
+ ),
other => {
exec_err!("Unsupported data type {other:?} for function
`contains`.")
}
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