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perf(encoding): add benchmark for data chunk encoding (risingwavelabs…
…#9035) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| // Copyright 2023 RisingWave Labs | ||
| // | ||
| // Licensed 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 criterion::{criterion_group, criterion_main, Criterion}; | ||
| use risingwave_common::test_utils::rand_chunk; | ||
| use risingwave_common::types::DataType; | ||
|
|
||
| static SEED: u64 = 998244353u64; | ||
| static CHUNK_SIZES: &[usize] = &[128, 1024]; | ||
| static NULL_RATIOS: &[f64] = &[0.0, 0.01, 0.1]; | ||
|
|
||
| struct DataChunkBenchCase { | ||
| pub name: String, | ||
| pub data_types: Vec<DataType>, | ||
| } | ||
|
|
||
| impl DataChunkBenchCase { | ||
| pub fn new(name: &str, data_types: Vec<DataType>) -> Self { | ||
| Self { | ||
| name: name.to_string(), | ||
| data_types, | ||
| } | ||
| } | ||
| } | ||
|
|
||
| fn bench_data_chunk_encoding(c: &mut Criterion) { | ||
| let test_cases = vec![ | ||
| DataChunkBenchCase::new("Int16", vec![DataType::Int16]), | ||
| DataChunkBenchCase::new("String", vec![DataType::Varchar]), | ||
| DataChunkBenchCase::new("Int16 and String", vec![DataType::Int16, DataType::Varchar]), | ||
| ]; | ||
| for case in test_cases { | ||
| for null_ratio in NULL_RATIOS { | ||
| for chunk_size in CHUNK_SIZES { | ||
| let id = format!( | ||
| "data chunk encoding: {}, {} rows, Pr[null]={}", | ||
| case.name, chunk_size, null_ratio | ||
| ); | ||
| let chunk = rand_chunk::gen_chunk(&case.data_types, *chunk_size, SEED, *null_ratio); | ||
| c.bench_function(&id, |b| b.iter(|| chunk.serialize())); | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
|
||
| criterion_group!(benches, bench_data_chunk_encoding); | ||
| criterion_main!(benches); |
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|
@@ -13,4 +13,5 @@ | |
| // limitations under the License. | ||
|
|
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| pub mod rand_array; | ||
| pub mod rand_chunk; | ||
| pub mod test_stream_chunk; | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,54 @@ | ||
| // Copyright 2023 RisingWave Labs | ||
| // | ||
| // Licensed 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 crate::array::column::Column; | ||
| use crate::array::serial_array::SerialArray; | ||
| use crate::array::{ | ||
| BoolArray, DataChunk, DateArray, DecimalArray, F32Array, F64Array, I16Array, I32Array, | ||
| I64Array, IntervalArray, TimeArray, TimestampArray, Utf8Array, | ||
| }; | ||
| use crate::test_utils::rand_array::seed_rand_array_ref; | ||
| use crate::types::DataType; | ||
| use crate::util::schema_check; | ||
|
|
||
| pub fn gen_chunk(data_types: &[DataType], size: usize, seed: u64, null_ratio: f64) -> DataChunk { | ||
| let mut columns = vec![]; | ||
|
|
||
| for d in data_types { | ||
| columns.push(Column::new(match d { | ||
| DataType::Boolean => seed_rand_array_ref::<BoolArray>(size, seed, null_ratio), | ||
| DataType::Int16 => seed_rand_array_ref::<I16Array>(size, seed, null_ratio), | ||
| DataType::Int32 => seed_rand_array_ref::<I32Array>(size, seed, null_ratio), | ||
| DataType::Int64 => seed_rand_array_ref::<I64Array>(size, seed, null_ratio), | ||
| DataType::Float32 => seed_rand_array_ref::<F32Array>(size, seed, null_ratio), | ||
| DataType::Float64 => seed_rand_array_ref::<F64Array>(size, seed, null_ratio), | ||
| DataType::Decimal => seed_rand_array_ref::<DecimalArray>(size, seed, null_ratio), | ||
| DataType::Date => seed_rand_array_ref::<DateArray>(size, seed, null_ratio), | ||
| DataType::Varchar => seed_rand_array_ref::<Utf8Array>(size, seed, null_ratio), | ||
| DataType::Time => seed_rand_array_ref::<TimeArray>(size, seed, null_ratio), | ||
| DataType::Serial => seed_rand_array_ref::<SerialArray>(size, seed, null_ratio), | ||
| DataType::Timestamp => seed_rand_array_ref::<TimestampArray>(size, seed, null_ratio), | ||
| DataType::Timestamptz => seed_rand_array_ref::<I64Array>(size, seed, null_ratio), | ||
| DataType::Interval => seed_rand_array_ref::<IntervalArray>(size, seed, null_ratio), | ||
| DataType::Struct(_) | DataType::Bytea | DataType::Jsonb => { | ||
| todo!() | ||
| } | ||
| DataType::List { datatype: _ } => { | ||
| todo!() | ||
| } | ||
| })); | ||
| } | ||
| schema_check::schema_check(data_types, &columns).unwrap(); | ||
| DataChunk::new(columns, size) | ||
| } |