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perf: Optimize contains for scalar search arg #19514

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5 changes: 5 additions & 0 deletions datafusion/functions/Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -254,3 +254,8 @@ required-features = ["unicode_expressions"]
harness = false
name = "find_in_set"
required-features = ["unicode_expressions"]

[[bench]]
harness = false
name = "contains"
required-features = ["string_expressions"]
185 changes: 185 additions & 0 deletions datafusion/functions/benches/contains.rs
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@@ -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);
119 changes: 69 additions & 50 deletions datafusion/functions/src/string/contains.rs
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Expand Up @@ -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::{ArrayRef, Scalar};
use arrow::compute::contains as arrow_contains;
use arrow::datatypes::DataType;
use arrow::datatypes::DataType::{Boolean, LargeUtf8, Utf8, Utf8View};
use arrow::datatypes::DataType::Boolean;
use datafusion_common::types::logical_string;
use datafusion_common::{DataFusionError, Result, exec_err};
use datafusion_common::{Result, ScalarValue, exec_err};
use datafusion_expr::binary::{binary_to_string_coercion, string_coercion};
use datafusion_expr::{
Coercion, ColumnarValue, Documentation, ScalarFunctionArgs, ScalarUDFImpl, Signature,
Expand Down Expand Up @@ -89,61 +88,81 @@ impl ScalarUDFImpl for ContainsFunc {
}

fn invoke_with_args(&self, args: ScalarFunctionArgs) -> Result<ColumnarValue> {
make_scalar_function(contains, vec![])(&args.args)
}

fn documentation(&self) -> Option<&Documentation> {
self.doc()
}
}
let [str_arg, search_arg] = args.args.as_slice() else {
return exec_err!(
"contains was called with {} arguments, expected 2",
args.args.len()
);
};

/// use `arrow::compute::contains` to do the calculation for contains
fn contains(args: &[ArrayRef]) -> Result<ArrayRef, DataFusionError> {
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())
})
{
let arg0 = if args[0].data_type() == &coercion_data_type {
Arc::clone(&args[0])
} else {
arrow::compute::kernels::cast::cast(&args[0], &coercion_data_type)?
// Determine the common type for coercion
let coercion_type = string_coercion(
&str_arg.data_type(),
&search_arg.data_type(),
)
.or_else(|| {
binary_to_string_coercion(&str_arg.data_type(), &search_arg.data_type())
});

let Some(coercion_type) = coercion_type else {
return exec_err!(
"Unsupported data types {:?}, {:?} for function `contains`.",
str_arg.data_type(),
search_arg.data_type()
);
};
let arg1 = if args[1].data_type() == &coercion_data_type {
Arc::clone(&args[1])
} else {
arrow::compute::kernels::cast::cast(&args[1], &coercion_data_type)?

// Helper to cast an array if needed
let maybe_cast = |arr: &ArrayRef, target: &DataType| -> Result<ArrayRef> {
if arr.data_type() == target {
Ok(Arc::clone(arr))
} else {
Ok(arrow::compute::kernels::cast::cast(arr, target)?)
}
};

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)
match (str_arg, search_arg) {
// Both scalars - just compute directly
(ColumnarValue::Scalar(str_scalar), ColumnarValue::Scalar(search_scalar)) => {
let str_arr = str_scalar.to_array_of_size(1)?;
let search_arr = search_scalar.to_array_of_size(1)?;
let str_arr = maybe_cast(&str_arr, &coercion_type)?;
let search_arr = maybe_cast(&search_arr, &coercion_type)?;
let result = arrow_contains(&str_arr, &search_arr)?;
Ok(ColumnarValue::Scalar(ScalarValue::try_from_array(
&result, 0,
)?))
}
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)
// String is array, search is scalar - use Scalar wrapper for optimization
(ColumnarValue::Array(str_arr), ColumnarValue::Scalar(search_scalar)) => {
let str_arr = maybe_cast(str_arr, &coercion_type)?;
let search_arr = search_scalar.to_array_of_size(1)?;
let search_arr = maybe_cast(&search_arr, &coercion_type)?;
let search_scalar = Scalar::new(search_arr);
let result = arrow_contains(&str_arr, &search_scalar)?;
Ok(ColumnarValue::Array(Arc::new(result)))
}
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)
// String is scalar, search is array - use Scalar wrapper for string
(ColumnarValue::Scalar(str_scalar), ColumnarValue::Array(search_arr)) => {
let str_arr = str_scalar.to_array_of_size(1)?;
let str_arr = maybe_cast(&str_arr, &coercion_type)?;
let str_scalar = Scalar::new(str_arr);
let search_arr = maybe_cast(search_arr, &coercion_type)?;
let result = arrow_contains(&str_scalar, &search_arr)?;
Ok(ColumnarValue::Array(Arc::new(result)))
}
other => {
exec_err!("Unsupported data type {other:?} for function `contains`.")
// Both arrays - pass directly
(ColumnarValue::Array(str_arr), ColumnarValue::Array(search_arr)) => {
let str_arr = maybe_cast(str_arr, &coercion_type)?;
let search_arr = maybe_cast(search_arr, &coercion_type)?;
let result = arrow_contains(&str_arr, &search_arr)?;
Ok(ColumnarValue::Array(Arc::new(result)))
}
}
} else {
exec_err!(
"Unsupported data type {}, {:?} for function `contains`.",
args[0].data_type(),
args[1].data_type()
)
}

fn documentation(&self) -> Option<&Documentation> {
self.doc()
}
}

Expand Down