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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.
use arrow::{
array::{Float32Array, Float64Array},
datatypes::{DataType, Field, Schema},
record_batch::RecordBatch,
};
use criterion::{criterion_group, criterion_main, Criterion};
use datafusion::prelude::ExecutionContext;
use datafusion::{datasource::MemTable, error::Result};
use futures::executor::block_on;
use std::sync::Arc;
async fn query(ctx: &mut ExecutionContext, sql: &str) {
// execute the query
let df = ctx.sql(&sql).unwrap();
let results = df.collect().await.unwrap();
// display the relation
for _batch in results {
// println!("num_rows: {}", _batch.num_rows());
}
}
fn create_context(array_len: usize, batch_size: usize) -> Result<ExecutionContext> {
// define a schema.
let schema = Arc::new(Schema::new(vec![
Field::new("f32", DataType::Float32, false),
Field::new("f64", DataType::Float64, false),
]));
// define data.
let batches = (0..array_len / batch_size)
.map(|i| {
RecordBatch::try_new(
schema.clone(),
vec![
Arc::new(Float32Array::from(vec![i as f32; batch_size])),
Arc::new(Float64Array::from(vec![i as f64; batch_size])),
],
)
.unwrap()
})
.collect::<Vec<_>>();
let mut ctx = ExecutionContext::new();
// declare a table in memory. In spark API, this corresponds to createDataFrame(...).
let provider = MemTable::try_new(schema, vec![batches])?;
ctx.register_table("t", Arc::new(provider));
Ok(ctx)
}
fn criterion_benchmark(c: &mut Criterion) {
let array_len = 524_288; // 2^19
let batch_size = 4096; // 2^12
c.bench_function("filter_array", |b| {
let mut ctx = create_context(array_len, batch_size).unwrap();
b.iter(|| block_on(query(&mut ctx, "select f32, f64 from t where f32 >= f64")))
});
c.bench_function("filter_scalar", |b| {
let mut ctx = create_context(array_len, batch_size).unwrap();
b.iter(|| {
block_on(query(
&mut ctx,
"select f32, f64 from t where f32 >= 250 and f64 > 250",
))
})
});
}
criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);