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Improve performance of
find_in_set
function (#14020)
* Improve performance of 'find_in_set' function Signed-off-by: Tai Le Manh <[email protected]> * Remove clippy warnings * Support scalar args for 'find_in_set' function Signed-off-by: Tai Le Manh <[email protected]> --------- Signed-off-by: Tai Le Manh <[email protected]>
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// 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. | ||
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extern crate criterion; | ||
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use arrow::array::{StringArray, StringViewArray}; | ||
use arrow::datatypes::DataType; | ||
use arrow::util::bench_util::{ | ||
create_string_array_with_len, create_string_view_array_with_len, | ||
}; | ||
use criterion::{black_box, criterion_group, criterion_main, Criterion, SamplingMode}; | ||
use datafusion_common::ScalarValue; | ||
use datafusion_expr::{ColumnarValue, ScalarFunctionArgs}; | ||
use rand::distributions::Alphanumeric; | ||
use rand::prelude::StdRng; | ||
use rand::{Rng, SeedableRng}; | ||
use std::sync::Arc; | ||
use std::time::Duration; | ||
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/// gen_arr(4096, 128, 0.1, 0.1, true) will generate a StringViewArray with | ||
/// 4096 rows, each row containing a string with 128 random characters. | ||
/// around 10% of the rows are null, around 10% of the rows are non-ASCII. | ||
fn gen_args_array( | ||
n_rows: usize, | ||
str_len_chars: usize, | ||
null_density: f32, | ||
utf8_density: f32, | ||
is_string_view: bool, // false -> StringArray, true -> StringViewArray | ||
) -> Vec<ColumnarValue> { | ||
let mut rng = StdRng::seed_from_u64(42); | ||
let rng_ref = &mut rng; | ||
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let num_elements = 5; // 5 elements separated by comma | ||
let utf8 = "DataFusionДатаФусион数据融合📊🔥"; // includes utf8 encoding with 1~4 bytes | ||
let corpus_char_count = utf8.chars().count(); | ||
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let mut output_set_vec: Vec<Option<String>> = Vec::with_capacity(n_rows); | ||
let mut output_element_vec: Vec<Option<String>> = Vec::with_capacity(n_rows); | ||
for _ in 0..n_rows { | ||
let rand_num = rng_ref.gen::<f32>(); // [0.0, 1.0) | ||
if rand_num < null_density { | ||
output_element_vec.push(None); | ||
output_set_vec.push(None); | ||
} else if rand_num < null_density + utf8_density { | ||
// Generate random UTF-8 string with comma separators | ||
let mut generated_string = String::with_capacity(str_len_chars); | ||
for i in 0..num_elements { | ||
for _ in 0..str_len_chars { | ||
let idx = rng_ref.gen_range(0..corpus_char_count); | ||
let char = utf8.chars().nth(idx).unwrap(); | ||
generated_string.push(char); | ||
} | ||
if i < num_elements - 1 { | ||
generated_string.push(','); | ||
} | ||
} | ||
output_element_vec.push(Some(random_element_in_set(&generated_string))); | ||
output_set_vec.push(Some(generated_string)); | ||
} else { | ||
// Generate random ASCII-only string with comma separators | ||
let mut generated_string = String::with_capacity(str_len_chars); | ||
for i in 0..num_elements { | ||
for _ in 0..str_len_chars { | ||
let c = rng_ref.sample(Alphanumeric); | ||
generated_string.push(c as char); | ||
} | ||
if i < num_elements - 1 { | ||
generated_string.push(','); | ||
} | ||
} | ||
output_element_vec.push(Some(random_element_in_set(&generated_string))); | ||
output_set_vec.push(Some(generated_string)); | ||
} | ||
} | ||
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if is_string_view { | ||
let set_array: StringViewArray = output_set_vec.into_iter().collect(); | ||
let element_array: StringViewArray = output_element_vec.into_iter().collect(); | ||
vec![ | ||
ColumnarValue::Array(Arc::new(element_array)), | ||
ColumnarValue::Array(Arc::new(set_array)), | ||
] | ||
} else { | ||
let set_array: StringArray = output_set_vec.clone().into_iter().collect(); | ||
let element_array: StringArray = output_element_vec.into_iter().collect(); | ||
vec![ | ||
ColumnarValue::Array(Arc::new(element_array)), | ||
ColumnarValue::Array(Arc::new(set_array)), | ||
] | ||
} | ||
} | ||
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fn random_element_in_set(string: &str) -> String { | ||
let elements: Vec<&str> = string.split(',').collect(); | ||
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if elements.is_empty() || (elements.len() == 1 && elements[0].is_empty()) { | ||
return String::new(); | ||
} | ||
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let mut rng = StdRng::seed_from_u64(44); | ||
let random_index = rng.gen_range(0..elements.len()); | ||
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elements[random_index].to_string() | ||
} | ||
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fn gen_args_scalar( | ||
n_rows: usize, | ||
str_len_chars: usize, | ||
null_density: f32, | ||
is_string_view: bool, // false -> StringArray, true -> StringViewArray | ||
) -> Vec<ColumnarValue> { | ||
let str_list = "Apache,DataFusion,SQL,Query,Engine".to_string(); | ||
if is_string_view { | ||
let string = | ||
create_string_view_array_with_len(n_rows, null_density, str_len_chars, false); | ||
vec![ | ||
ColumnarValue::Array(Arc::new(string)), | ||
ColumnarValue::Scalar(ScalarValue::Utf8(Some(str_list))), | ||
] | ||
} else { | ||
let string = | ||
create_string_array_with_len::<i32>(n_rows, null_density, str_len_chars); | ||
vec![ | ||
ColumnarValue::Array(Arc::new(string)), | ||
ColumnarValue::Scalar(ScalarValue::Utf8(Some(str_list))), | ||
] | ||
} | ||
} | ||
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fn criterion_benchmark(c: &mut Criterion) { | ||
// All benches are single batch run with 8192 rows | ||
let find_in_set = datafusion_functions::unicode::find_in_set(); | ||
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let n_rows = 8192; | ||
for str_len in [8, 32, 1024] { | ||
let mut group = c.benchmark_group("find_in_set"); | ||
group.sampling_mode(SamplingMode::Flat); | ||
group.sample_size(50); | ||
group.measurement_time(Duration::from_secs(10)); | ||
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let args = gen_args_array(n_rows, str_len, 0.1, 0.5, false); | ||
group.bench_function(format!("string_len_{}", str_len), |b| { | ||
b.iter(|| { | ||
black_box(find_in_set.invoke_with_args(ScalarFunctionArgs { | ||
args: args.clone(), | ||
number_rows: n_rows, | ||
return_type: &DataType::Int32, | ||
})) | ||
}) | ||
}); | ||
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let args = gen_args_array(n_rows, str_len, 0.1, 0.5, true); | ||
group.bench_function(format!("string_view_len_{}", str_len), |b| { | ||
b.iter(|| { | ||
black_box(find_in_set.invoke_with_args(ScalarFunctionArgs { | ||
args: args.clone(), | ||
number_rows: n_rows, | ||
return_type: &DataType::Int32, | ||
})) | ||
}) | ||
}); | ||
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group.finish(); | ||
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let mut group = c.benchmark_group("find_in_set_scalar"); | ||
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let args = gen_args_scalar(n_rows, str_len, 0.1, false); | ||
group.bench_function(format!("string_len_{}", str_len), |b| { | ||
b.iter(|| { | ||
black_box(find_in_set.invoke_with_args(ScalarFunctionArgs { | ||
args: args.clone(), | ||
number_rows: n_rows, | ||
return_type: &DataType::Int32, | ||
})) | ||
}) | ||
}); | ||
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let args = gen_args_scalar(n_rows, str_len, 0.1, true); | ||
group.bench_function(format!("string_view_len_{}", str_len), |b| { | ||
b.iter(|| { | ||
black_box(find_in_set.invoke_with_args(ScalarFunctionArgs { | ||
args: args.clone(), | ||
number_rows: n_rows, | ||
return_type: &DataType::Int32, | ||
})) | ||
}) | ||
}); | ||
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group.finish(); | ||
} | ||
} | ||
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criterion_group!(benches, criterion_benchmark); | ||
criterion_main!(benches); |
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