datafusion-functions 53.1.0

Function packages for the DataFusion query engine
Documentation
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// 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::datatypes::{DataType, Field, Float32Type, Float64Type};
use arrow::util::bench_util::create_primitive_array;
use criterion::{Criterion, criterion_group, criterion_main};
use datafusion_common::ScalarValue;
use datafusion_common::config::ConfigOptions;
use datafusion_expr::{ColumnarValue, ScalarFunctionArgs};
use datafusion_functions::math::atan2;
use std::hint::black_box;
use std::sync::Arc;

fn criterion_benchmark(c: &mut Criterion) {
    let atan2_fn = atan2();
    let config_options = Arc::new(ConfigOptions::default());

    for size in [1024, 4096, 8192] {
        let y_f32 = Arc::new(create_primitive_array::<Float32Type>(size, 0.2));
        let x_f32 = Arc::new(create_primitive_array::<Float32Type>(size, 0.2));
        let f32_args = vec![ColumnarValue::Array(y_f32), ColumnarValue::Array(x_f32)];
        let f32_arg_fields = f32_args
            .iter()
            .enumerate()
            .map(|(idx, arg)| {
                Field::new(format!("arg_{idx}"), arg.data_type(), true).into()
            })
            .collect::<Vec<_>>();
        let return_field_f32 = Field::new("f", DataType::Float32, true).into();

        c.bench_function(&format!("atan2 f32 array: {size}"), |b| {
            b.iter(|| {
                black_box(
                    atan2_fn
                        .invoke_with_args(ScalarFunctionArgs {
                            args: f32_args.clone(),
                            arg_fields: f32_arg_fields.clone(),
                            number_rows: size,
                            return_field: Arc::clone(&return_field_f32),
                            config_options: Arc::clone(&config_options),
                        })
                        .unwrap(),
                )
            })
        });

        let y_f64 = Arc::new(create_primitive_array::<Float64Type>(size, 0.2));
        let x_f64 = Arc::new(create_primitive_array::<Float64Type>(size, 0.2));
        let f64_args = vec![ColumnarValue::Array(y_f64), ColumnarValue::Array(x_f64)];
        let f64_arg_fields = f64_args
            .iter()
            .enumerate()
            .map(|(idx, arg)| {
                Field::new(format!("arg_{idx}"), arg.data_type(), true).into()
            })
            .collect::<Vec<_>>();
        let return_field_f64 = Field::new("f", DataType::Float64, true).into();

        c.bench_function(&format!("atan2 f64 array: {size}"), |b| {
            b.iter(|| {
                black_box(
                    atan2_fn
                        .invoke_with_args(ScalarFunctionArgs {
                            args: f64_args.clone(),
                            arg_fields: f64_arg_fields.clone(),
                            number_rows: size,
                            return_field: Arc::clone(&return_field_f64),
                            config_options: Arc::clone(&config_options),
                        })
                        .unwrap(),
                )
            })
        });
    }

    let scalar_f32_args = vec![
        ColumnarValue::Scalar(ScalarValue::Float32(Some(1.0))),
        ColumnarValue::Scalar(ScalarValue::Float32(Some(2.0))),
    ];
    let scalar_f32_arg_fields = vec![
        Field::new("a", DataType::Float32, false).into(),
        Field::new("b", DataType::Float32, false).into(),
    ];
    let return_field_f32 = Field::new("f", DataType::Float32, false).into();

    c.bench_function("atan2 f32 scalar", |b| {
        b.iter(|| {
            black_box(
                atan2_fn
                    .invoke_with_args(ScalarFunctionArgs {
                        args: scalar_f32_args.clone(),
                        arg_fields: scalar_f32_arg_fields.clone(),
                        number_rows: 1,
                        return_field: Arc::clone(&return_field_f32),
                        config_options: Arc::clone(&config_options),
                    })
                    .unwrap(),
            )
        })
    });

    let scalar_f64_args = vec![
        ColumnarValue::Scalar(ScalarValue::Float64(Some(1.0))),
        ColumnarValue::Scalar(ScalarValue::Float64(Some(2.0))),
    ];
    let scalar_f64_arg_fields = vec![
        Field::new("a", DataType::Float64, false).into(),
        Field::new("b", DataType::Float64, false).into(),
    ];
    let return_field_f64 = Field::new("f", DataType::Float64, false).into();

    c.bench_function("atan2 f64 scalar", |b| {
        b.iter(|| {
            black_box(
                atan2_fn
                    .invoke_with_args(ScalarFunctionArgs {
                        args: scalar_f64_args.clone(),
                        arg_fields: scalar_f64_arg_fields.clone(),
                        number_rows: 1,
                        return_field: Arc::clone(&return_field_f64),
                        config_options: Arc::clone(&config_options),
                    })
                    .unwrap(),
            )
        })
    });
}

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