datafusion-functions-nested 48.0.1

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// to you under the Apache License, Version 2.0 (the
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// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
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// software distributed under the License is distributed on an
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// specific language governing permissions and limitations
// under the License.

extern crate criterion;

use arrow::array::{Int32Array, ListArray, StringArray};
use arrow::buffer::{OffsetBuffer, ScalarBuffer};
use arrow::datatypes::{DataType, Field};
use criterion::{black_box, criterion_group, criterion_main, Criterion};
use rand::prelude::ThreadRng;
use rand::Rng;
use std::collections::HashSet;
use std::sync::Arc;

use datafusion_common::ScalarValue;
use datafusion_expr::planner::ExprPlanner;
use datafusion_expr::{ColumnarValue, Expr, ScalarFunctionArgs};
use datafusion_functions_nested::map::map_udf;
use datafusion_functions_nested::planner::NestedFunctionPlanner;

fn keys(rng: &mut ThreadRng) -> Vec<String> {
    let mut keys = HashSet::with_capacity(1000);

    while keys.len() < 1000 {
        keys.insert(rng.random_range(0..10000).to_string());
    }

    keys.into_iter().collect()
}

fn values(rng: &mut ThreadRng) -> Vec<i32> {
    let mut values = HashSet::with_capacity(1000);

    while values.len() < 1000 {
        values.insert(rng.random_range(0..10000));
    }
    values.into_iter().collect()
}

fn criterion_benchmark(c: &mut Criterion) {
    c.bench_function("make_map_1000", |b| {
        let mut rng = rand::rng();
        let keys = keys(&mut rng);
        let values = values(&mut rng);
        let mut buffer = Vec::new();
        for i in 0..1000 {
            buffer.push(Expr::Literal(
                ScalarValue::Utf8(Some(keys[i].clone())),
                None,
            ));
            buffer.push(Expr::Literal(ScalarValue::Int32(Some(values[i])), None));
        }

        let planner = NestedFunctionPlanner {};

        b.iter(|| {
            black_box(
                planner
                    .plan_make_map(buffer.clone())
                    .expect("map should work on valid values"),
            );
        });
    });

    c.bench_function("map_1000", |b| {
        let mut rng = rand::rng();
        let field = Arc::new(Field::new_list_field(DataType::Utf8, true));
        let offsets = OffsetBuffer::new(ScalarBuffer::from(vec![0, 1000]));
        let key_list = ListArray::new(
            field,
            offsets,
            Arc::new(StringArray::from(keys(&mut rng))),
            None,
        );
        let field = Arc::new(Field::new_list_field(DataType::Int32, true));
        let offsets = OffsetBuffer::new(ScalarBuffer::from(vec![0, 1000]));
        let value_list = ListArray::new(
            field,
            offsets,
            Arc::new(Int32Array::from(values(&mut rng))),
            None,
        );
        let keys = ColumnarValue::Scalar(ScalarValue::List(Arc::new(key_list)));
        let values = ColumnarValue::Scalar(ScalarValue::List(Arc::new(value_list)));

        let return_type = map_udf()
            .return_type(&[DataType::Utf8, DataType::Int32])
            .expect("should get return type");
        let arg_fields = vec![
            Field::new("a", keys.data_type(), true).into(),
            Field::new("a", values.data_type(), true).into(),
        ];
        let return_field = Field::new("f", return_type, true).into();

        b.iter(|| {
            black_box(
                map_udf()
                    .invoke_with_args(ScalarFunctionArgs {
                        args: vec![keys.clone(), values.clone()],
                        arg_fields: arg_fields.clone(),
                        number_rows: 1,
                        return_field: Arc::clone(&return_field),
                    })
                    .expect("map should work on valid values"),
            );
        });
    });
}

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