jecsand838 commented on code in PR #8165: URL: https://github.com/apache/arrow-rs/pull/8165#discussion_r2292163077
########## arrow-avro/benches/avro_writer.rs: ########## @@ -0,0 +1,240 @@ +// 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. + +//! Benchmarks for `arrow‑avro` **Decoder** +//! + +extern crate arrow_avro; +extern crate criterion; +extern crate once_cell; + +use arrow_array::{ + types::{Int32Type, Int64Type, TimestampMicrosecondType}, + ArrayRef, BinaryArray, BooleanArray, Float32Array, Float64Array, PrimitiveArray, RecordBatch, +}; +use arrow_avro::writer::AvroWriter; +use arrow_schema::{DataType, Field, Schema, TimeUnit}; +use criterion::{criterion_group, criterion_main, BatchSize, BenchmarkId, Criterion, Throughput}; +use once_cell::sync::Lazy; +use std::io::Cursor; +use std::sync::Arc; +use std::time::Duration; +use tempfile::tempfile; + +const SIZES: [usize; 3] = [100, 10_000, 1_000_000]; + +fn make_bool_array(n: usize) -> BooleanArray { + BooleanArray::from_iter((0..n).map(|i| Some(i % 2 == 0))) +} + +fn make_i32_array(n: usize) -> PrimitiveArray<Int32Type> { + PrimitiveArray::<Int32Type>::from_iter_values((0..n).map(|i| i as i32)) +} + +fn make_i64_array(n: usize) -> PrimitiveArray<Int64Type> { + PrimitiveArray::<Int64Type>::from_iter_values((0..n).map(|i| i as i64)) +} + +fn make_f32_array(n: usize) -> Float32Array { + Float32Array::from_iter_values((0..n).map(|i| i as f32 + 0.5678)) +} + +fn make_f64_array(n: usize) -> Float64Array { + Float64Array::from_iter_values((0..n).map(|i| i as f64 + 0.1234)) +} + +fn make_binary_array(n: usize) -> BinaryArray { + let payloads: Vec<Vec<u8>> = (0..n).map(|i| vec![(i & 0xFF) as u8; 16]).collect(); + let views: Vec<&[u8]> = payloads.iter().map(|v| v.as_slice()).collect(); + BinaryArray::from_vec(views) +} + +fn make_ts_micros_array(n: usize) -> PrimitiveArray<TimestampMicrosecondType> { + let base: i64 = 1_600_000_000_000_000; + PrimitiveArray::<TimestampMicrosecondType>::from_iter_values((0..n).map(|i| base + i as i64)) +} + +fn schema_single(name: &str, dt: DataType) -> Arc<Schema> { + Arc::new(Schema::new(vec![Field::new(name, dt, false)])) +} + +fn schema_mixed() -> Arc<Schema> { + Arc::new(Schema::new(vec![ + Field::new("f1", DataType::Int32, false), + Field::new("f2", DataType::Int64, false), + Field::new("f3", DataType::Binary, false), + Field::new("f4", DataType::Float64, false), + ])) +} + +static BOOLEAN_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Boolean); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_bool_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static INT32_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Int32); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_i32_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static INT64_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Int64); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_i64_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static FLOAT32_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Float32); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_f32_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static FLOAT64_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Float64); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_f64_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static BINARY_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Binary); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_binary_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static TIMESTAMP_US_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_single("field1", DataType::Timestamp(TimeUnit::Microsecond, None)); + SIZES + .iter() + .map(|&n| { + let col: ArrayRef = Arc::new(make_ts_micros_array(n)); + RecordBatch::try_new(schema.clone(), vec![col]).unwrap() + }) + .collect() +}); + +static MIXED_DATA: Lazy<Vec<RecordBatch>> = Lazy::new(|| { + let schema = schema_mixed(); + SIZES + .iter() + .map(|&n| { + let f1: ArrayRef = Arc::new(make_i32_array(n)); + let f2: ArrayRef = Arc::new(make_i64_array(n)); + let f3: ArrayRef = Arc::new(make_binary_array(n)); + let f4: ArrayRef = Arc::new(make_f64_array(n)); + RecordBatch::try_new(schema.clone(), vec![f1, f2, f3, f4]).unwrap() + }) + .collect() +}); + +fn ocf_size_for_batch(batch: &RecordBatch) -> usize { + let schema_owned: Schema = (*batch.schema()).clone(); + let cursor = Cursor::new(Vec::<u8>::with_capacity(1024)); + let mut writer = AvroWriter::new(cursor, schema_owned).expect("create writer"); + writer.write(batch).expect("write batch"); + writer.finish().expect("finish writer"); + let inner = writer.into_inner(); + inner.into_inner().len() +} + +fn bench_writer_scenario(c: &mut Criterion, name: &str, data_sets: &[RecordBatch]) { + let mut group = c.benchmark_group(name); + let schema_owned: Schema = (*data_sets[0].schema()).clone(); + for (idx, &rows) in SIZES.iter().enumerate() { + let batch = &data_sets[idx]; + let bytes = ocf_size_for_batch(batch); + group.throughput(Throughput::Bytes(bytes as u64)); + match rows { + 10_000 => { Review Comment: That's a good call out. I'll push those changes up tonight as well. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
