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7 changes: 1 addition & 6 deletions datafusion/functions/Cargo.toml
Original file line number Diff line number Diff line change
Expand Up @@ -308,12 +308,7 @@ required-features = ["string_expressions"]

[[bench]]
harness = false
name = "left"
required-features = ["unicode_expressions"]

[[bench]]
harness = false
name = "right"
name = "left_right"
required-features = ["unicode_expressions"]

[[bench]]
Expand Down
140 changes: 0 additions & 140 deletions datafusion/functions/benches/left.rs

This file was deleted.

166 changes: 166 additions & 0 deletions datafusion/functions/benches/left_right.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,166 @@
// 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 std::hint::black_box;
use std::sync::Arc;

use arrow::array::{ArrayRef, Int64Array};
use arrow::datatypes::{DataType, Field};
use arrow::util::bench_util::{
create_string_array_with_len, create_string_view_array_with_len,
};
use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main};
use datafusion_common::config::ConfigOptions;
use datafusion_expr::{ColumnarValue, ScalarFunctionArgs};
use datafusion_functions::unicode::{left, right};

fn create_args(
size: usize,
str_len: usize,
use_negative: bool,
is_string_view: bool,
) -> Vec<ColumnarValue> {
let string_arg = if is_string_view {
ColumnarValue::Array(Arc::new(create_string_view_array_with_len(
size, 0.1, str_len, true,
)))
} else {
ColumnarValue::Array(Arc::new(create_string_array_with_len::<i32>(
size, 0.1, str_len,
)))
};

// For negative n, we want to trigger the double-iteration code path
let n_values: Vec<i64> = if use_negative {
(0..size).map(|i| -((i % 10 + 1) as i64)).collect()
} else {
(0..size).map(|i| (i % 10 + 1) as i64).collect()
};
let n_array = Arc::new(Int64Array::from(n_values));

vec![
string_arg,
ColumnarValue::Array(Arc::clone(&n_array) as ArrayRef),
]
}

fn criterion_benchmark(c: &mut Criterion) {
let left_function = left();
let right_function = right();

for function in [left_function, right_function] {
for is_string_view in [false, true] {
for size in [1024, 4096] {
let function_name = function.name();
let mut group = c.benchmark_group(format!("{function_name} size={size}"));

// Benchmark with positive n (no optimization needed)
let mut bench_name = if is_string_view {
"string_view_array positive n"
} else {
"string_array positive n"
};
let return_type = if is_string_view {
DataType::Utf8View
} else {
DataType::Utf8
};

let args = create_args(size, 32, false, is_string_view);
group.bench_function(BenchmarkId::new(bench_name, size), |b| {
let arg_fields = args
.iter()
.enumerate()
.map(|(idx, arg)| {
Field::new(format!("arg_{idx}"), arg.data_type(), true).into()
})
.collect::<Vec<_>>();
let config_options = Arc::new(ConfigOptions::default());

b.iter(|| {
black_box(
function
.invoke_with_args(ScalarFunctionArgs {
args: args.clone(),
arg_fields: arg_fields.clone(),
number_rows: size,
return_field: Field::new(
"f",
return_type.clone(),
true,
)
.into(),
config_options: Arc::clone(&config_options),
})
.expect("should work"),
)
})
});

// Benchmark with negative n (triggers optimization)
bench_name = if is_string_view {
"string_view_array negative n"
} else {
"string_array negative n"
};
let return_type = if is_string_view {
DataType::Utf8View
} else {
DataType::Utf8
};

let args = create_args(size, 32, true, is_string_view);
group.bench_function(BenchmarkId::new(bench_name, size), |b| {
let arg_fields = args
.iter()
.enumerate()
.map(|(idx, arg)| {
Field::new(format!("arg_{idx}"), arg.data_type(), true).into()
})
.collect::<Vec<_>>();
let config_options = Arc::new(ConfigOptions::default());

b.iter(|| {
black_box(
function
.invoke_with_args(ScalarFunctionArgs {
args: args.clone(),
arg_fields: arg_fields.clone(),
number_rows: size,
return_field: Field::new(
"f",
return_type.clone(),
true,
)
.into(),
config_options: Arc::clone(&config_options),
})
.expect("should work"),
)
})
});

Comment on lines +73 to +158

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medium

The code for benchmarking with positive and negative n is very similar and can be refactored to reduce duplication. You can introduce a loop over [false, true] for use_negative and move the common benchmarking logic inside.

                let return_type = if is_string_view {
                    DataType::Utf8View
                } else {
                    DataType::Utf8
                };

                for use_negative in [false, true] {
                    let bench_name = format!(
                        "{} {}",
                        if is_string_view {
                            "string_view_array"
                        } else {
                            "string_array"
                        },
                        if use_negative { "negative n" } else { "positive n" }
                    );

                    let args = create_args(size, 32, use_negative, is_string_view);
                    group.bench_function(BenchmarkId::new(bench_name, size), |b| {
                        let arg_fields = args
                            .iter()
                            .enumerate()
                            .map(|(idx, arg)| {
                                Field::new(format!("arg_{idx}"), arg.data_type(), true).into()
                            })
                            .collect::<Vec<_>>();
                        let config_options = Arc::new(ConfigOptions::default());

                        b.iter(|| {
                            black_box(
                                function
                                    .invoke_with_args(ScalarFunctionArgs {
                                        args: args.clone(),
                                        arg_fields: arg_fields.clone(),
                                        number_rows: size,
                                        return_field: Field::new(
                                            "f",
                                            return_type.clone(),
                                            true,
                                        )
                                        .into(),
                                        config_options: Arc::clone(&config_options),
                                    })
                                    .expect("should work"),
                            )
                        })
                    });
                }

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Owner Author

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value:good-to-have; category:bug; feedback: The Gemini AI reviewer is correct! There is a code duplication in the benchmark test that could be avoided by adding a new for loop for the negative/positive flag.Prevent maintaining the same code twice.

group.finish();
}
}
}
}

criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);
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