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| 1 | +// Copyright 2023 RisingWave Labs |
| 2 | +// |
| 3 | +// Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +// you may not use this file except in compliance with the License. |
| 5 | +// You may obtain a copy of the License at |
| 6 | +// |
| 7 | +// http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +// |
| 9 | +// Unless required by applicable law or agreed to in writing, software |
| 10 | +// distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +// See the License for the specific language governing permissions and |
| 13 | +// limitations under the License. |
| 14 | + |
| 15 | +use criterion::{criterion_group, criterion_main, Criterion}; |
| 16 | +use itertools::Itertools; |
| 17 | +use risingwave_common::array::column::Column; |
| 18 | +use risingwave_common::array::serial_array::SerialArray; |
| 19 | +use risingwave_common::array::{ |
| 20 | + ArrayBuilderImpl, BoolArray, DataChunk, DecimalArray, F32Array, F64Array, I16Array, I32Array, |
| 21 | + I64Array, IntervalArray, NaiveDateArray, NaiveDateTimeArray, NaiveTimeArray, Utf8Array, |
| 22 | +}; |
| 23 | +use risingwave_common::hash::{calc_hash_key_kind, HashKey, HashKeyDispatcher}; |
| 24 | +use risingwave_common::test_utils::rand_array::seed_rand_array_ref; |
| 25 | +use risingwave_common::types::DataType; |
| 26 | + |
| 27 | +static SEED: u64 = 998244353u64; |
| 28 | +static CHUNK_SIZES: &[usize] = &[128, 1024]; |
| 29 | +static NULL_RATIOS: &[f64] = &[0.0, 0.01, 0.1]; |
| 30 | + |
| 31 | +trait Case: Send + 'static { |
| 32 | + fn bench(&self, c: &mut Criterion); |
| 33 | +} |
| 34 | +type BoxedCase = Box<dyn Case>; |
| 35 | + |
| 36 | +struct HashKeyBenchCaseBuilder { |
| 37 | + pub data_types: Vec<DataType>, |
| 38 | + pub describe: String, |
| 39 | +} |
| 40 | +impl HashKeyBenchCaseBuilder { |
| 41 | + pub fn gen_cases(self) -> Vec<BoxedCase> { |
| 42 | + self.dispatch() |
| 43 | + } |
| 44 | +} |
| 45 | +impl HashKeyDispatcher for HashKeyBenchCaseBuilder { |
| 46 | + type Output = Vec<BoxedCase>; |
| 47 | + |
| 48 | + fn dispatch_impl<K: HashKey>(self) -> Self::Output { |
| 49 | + let mut ret: Vec<BoxedCase> = vec![]; |
| 50 | + for null_ratio in NULL_RATIOS { |
| 51 | + for chunk_size in CHUNK_SIZES { |
| 52 | + let id = format!( |
| 53 | + "{}, key type: {:?}, chunk size {}, null ratio {}", |
| 54 | + self.describe, |
| 55 | + calc_hash_key_kind(self.data_types()), |
| 56 | + chunk_size, |
| 57 | + null_ratio |
| 58 | + ); |
| 59 | + let input_chunk = gen_chunk(self.data_types(), *chunk_size, SEED, *null_ratio); |
| 60 | + ret.push(Box::new(HashKeyBenchCase::<K>::new( |
| 61 | + id, |
| 62 | + input_chunk, |
| 63 | + self.data_types.clone(), |
| 64 | + ))); |
| 65 | + } |
| 66 | + } |
| 67 | + ret |
| 68 | + } |
| 69 | + |
| 70 | + fn data_types(&self) -> &[DataType] { |
| 71 | + &self.data_types |
| 72 | + } |
| 73 | +} |
| 74 | + |
| 75 | +struct HashKeyBenchCase<K: HashKey> { |
| 76 | + id: String, |
| 77 | + input_chunk: DataChunk, |
| 78 | + keys: Vec<K>, |
| 79 | + data_types: Vec<DataType>, |
| 80 | + col_idxes: Vec<usize>, |
| 81 | +} |
| 82 | + |
| 83 | +impl<K: HashKey> HashKeyBenchCase<K> { |
| 84 | + pub fn new(id: String, input_chunk: DataChunk, data_types: Vec<DataType>) -> Self { |
| 85 | + // please check the `bench_vec_dser` and `bench_deser` method when want to bench not full |
| 86 | + // `col_idxes` |
| 87 | + let col_idxes = (0..input_chunk.columns().len()).collect_vec(); |
| 88 | + let keys = HashKey::build(&col_idxes, &input_chunk).unwrap(); |
| 89 | + Self { |
| 90 | + id, |
| 91 | + input_chunk, |
| 92 | + keys, |
| 93 | + data_types, |
| 94 | + col_idxes, |
| 95 | + } |
| 96 | + } |
| 97 | + |
| 98 | + pub fn bench_vec_ser(&self, c: &mut Criterion) { |
| 99 | + let vectorize_serialize_id = "vec ser ".to_string() + &self.id; |
| 100 | + c.bench_function(&vectorize_serialize_id, |b| { |
| 101 | + b.iter(|| K::build(&self.col_idxes, &self.input_chunk).unwrap()) |
| 102 | + }); |
| 103 | + } |
| 104 | + |
| 105 | + pub fn bench_vec_deser(&self, c: &mut Criterion) { |
| 106 | + let vectorize_deserialize_id = "vec deser ".to_string() + &self.id; |
| 107 | + c.bench_function(&vectorize_deserialize_id, |b| { |
| 108 | + let mut array_builders = self |
| 109 | + .input_chunk |
| 110 | + .columns() |
| 111 | + .iter() |
| 112 | + .map(|c| c.array_ref().create_builder(self.input_chunk.capacity())) |
| 113 | + .collect::<Vec<ArrayBuilderImpl>>(); |
| 114 | + b.iter(|| { |
| 115 | + for key in &self.keys { |
| 116 | + key.deserialize_to_builders(&mut array_builders[..], &self.data_types) |
| 117 | + .unwrap(); |
| 118 | + } |
| 119 | + }) |
| 120 | + }); |
| 121 | + } |
| 122 | + |
| 123 | + pub fn bench_deser(&self, c: &mut Criterion) { |
| 124 | + let vectorize_deserialize_id = "row deser ".to_string() + &self.id; |
| 125 | + c.bench_function(&vectorize_deserialize_id, |b| { |
| 126 | + b.iter(|| { |
| 127 | + for key in &self.keys { |
| 128 | + key.deserialize(&self.data_types).unwrap(); |
| 129 | + } |
| 130 | + }) |
| 131 | + }); |
| 132 | + } |
| 133 | +} |
| 134 | +impl<K: HashKey> Case for HashKeyBenchCase<K> { |
| 135 | + fn bench(&self, c: &mut Criterion) { |
| 136 | + self.bench_vec_ser(c); |
| 137 | + self.bench_vec_deser(c); |
| 138 | + self.bench_deser(c); |
| 139 | + } |
| 140 | +} |
| 141 | + |
| 142 | +fn gen_chunk(data_types: &[DataType], size: usize, seed: u64, null_ratio: f64) -> DataChunk { |
| 143 | + let mut columns = vec![]; |
| 144 | + |
| 145 | + for d in data_types { |
| 146 | + columns.push(Column::new(match d { |
| 147 | + DataType::Boolean => seed_rand_array_ref::<BoolArray>(size, seed, null_ratio), |
| 148 | + DataType::Int16 => seed_rand_array_ref::<I16Array>(size, seed, null_ratio), |
| 149 | + DataType::Int32 => seed_rand_array_ref::<I32Array>(size, seed, null_ratio), |
| 150 | + DataType::Int64 => seed_rand_array_ref::<I64Array>(size, seed, null_ratio), |
| 151 | + DataType::Float32 => seed_rand_array_ref::<F32Array>(size, seed, null_ratio), |
| 152 | + DataType::Float64 => seed_rand_array_ref::<F64Array>(size, seed, null_ratio), |
| 153 | + DataType::Decimal => seed_rand_array_ref::<DecimalArray>(size, seed, null_ratio), |
| 154 | + DataType::Date => seed_rand_array_ref::<NaiveDateArray>(size, seed, null_ratio), |
| 155 | + DataType::Varchar => seed_rand_array_ref::<Utf8Array>(size, seed, null_ratio), |
| 156 | + DataType::Time => seed_rand_array_ref::<NaiveTimeArray>(size, seed, null_ratio), |
| 157 | + DataType::Serial => seed_rand_array_ref::<SerialArray>(size, seed, null_ratio), |
| 158 | + DataType::Timestamp => { |
| 159 | + seed_rand_array_ref::<NaiveDateTimeArray>(size, seed, null_ratio) |
| 160 | + } |
| 161 | + DataType::Timestamptz => seed_rand_array_ref::<I64Array>(size, seed, null_ratio), |
| 162 | + DataType::Interval => seed_rand_array_ref::<IntervalArray>(size, seed, null_ratio), |
| 163 | + DataType::Struct(_) | DataType::Bytea | DataType::Jsonb => { |
| 164 | + todo!() |
| 165 | + } |
| 166 | + DataType::List { datatype: _ } => { |
| 167 | + todo!() |
| 168 | + } |
| 169 | + })); |
| 170 | + } |
| 171 | + risingwave_common::util::schema_check::schema_check(data_types, &columns).unwrap(); |
| 172 | + DataChunk::new(columns, size) |
| 173 | +} |
| 174 | + |
| 175 | +fn case_builders() -> Vec<HashKeyBenchCaseBuilder> { |
| 176 | + vec![ |
| 177 | + HashKeyBenchCaseBuilder { |
| 178 | + data_types: vec![DataType::Serial], |
| 179 | + describe: "single Serial".to_string(), |
| 180 | + }, |
| 181 | + HashKeyBenchCaseBuilder { |
| 182 | + data_types: vec![DataType::Int32], |
| 183 | + describe: "single int32".to_string(), |
| 184 | + }, |
| 185 | + HashKeyBenchCaseBuilder { |
| 186 | + data_types: vec![DataType::Int64], |
| 187 | + describe: "single int64".to_string(), |
| 188 | + }, |
| 189 | + HashKeyBenchCaseBuilder { |
| 190 | + data_types: vec![DataType::Varchar], |
| 191 | + describe: "single varchar".to_string(), |
| 192 | + }, |
| 193 | + HashKeyBenchCaseBuilder { |
| 194 | + data_types: vec![DataType::Int32, DataType::Int32, DataType::Int32], |
| 195 | + describe: "composite fixed size".to_string(), |
| 196 | + }, |
| 197 | + HashKeyBenchCaseBuilder { |
| 198 | + data_types: vec![DataType::Int32, DataType::Int64, DataType::Int32], |
| 199 | + describe: "composite fixed size2".to_string(), |
| 200 | + }, |
| 201 | + HashKeyBenchCaseBuilder { |
| 202 | + data_types: vec![DataType::Int32, DataType::Varchar], |
| 203 | + describe: "composite fixed and not fixed size".to_string(), |
| 204 | + }, |
| 205 | + HashKeyBenchCaseBuilder { |
| 206 | + data_types: vec![DataType::Int64, DataType::Varchar], |
| 207 | + describe: "composite fixed and not fixed size".to_string(), |
| 208 | + }, |
| 209 | + ] |
| 210 | +} |
| 211 | + |
| 212 | +fn bench_hash_key_encoding(c: &mut Criterion) { |
| 213 | + for case_builder in case_builders() { |
| 214 | + let cases = case_builder.gen_cases(); |
| 215 | + for case in cases { |
| 216 | + case.bench(c); |
| 217 | + } |
| 218 | + } |
| 219 | +} |
| 220 | + |
| 221 | +// `cargo bench -- "vec ser[\s\S]*KeySerialized[\s\S]*null ratio 0$"` bench all the |
| 222 | +// `KeySerialized` hash key vectorized serialize cases with data's null ratio is 0,001 |
| 223 | +criterion_group!(benches, bench_hash_key_encoding); |
| 224 | +criterion_main!(benches); |
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