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Single multi groupby v2 #11777
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,43 +1,2 @@ | ||
| SELECT "UserID", "SearchPhrase", COUNT(*) FROM hits GROUP BY "UserID", "SearchPhrase" ORDER BY COUNT(*) DESC LIMIT 10; | ||
| SELECT "UserID", concat("SearchPhrase", repeat('hello', 20)) as s, COUNT(*) FROM hits GROUP BY "UserID", s ORDER BY COUNT(*) DESC LIMIT 10; |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| // 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::{ | ||
| datatypes::{DataType, Field, Schema}, | ||
| record_batch::RecordBatch, | ||
| }; | ||
| use arrow_array::{Int64Array, StringArray}; | ||
| use criterion::{criterion_group, criterion_main, Criterion}; | ||
| use datafusion::prelude::SessionContext; | ||
| use datafusion::{datasource::MemTable, error::Result}; | ||
| use futures::executor::block_on; | ||
| use std::sync::Arc; | ||
| use tokio::runtime::Runtime; | ||
|
|
||
| async fn query(ctx: &mut SessionContext, sql: &str) { | ||
| let rt = Runtime::new().unwrap(); | ||
|
|
||
| // execute the query | ||
| let df = rt.block_on(ctx.sql(sql)).unwrap(); | ||
| criterion::black_box(rt.block_on(df.collect()).unwrap()); | ||
| } | ||
|
|
||
| fn create_context(array_len: usize, batch_size: usize) -> Result<SessionContext> { | ||
| // define a schema. | ||
| let schema = Arc::new(Schema::new(vec![ | ||
| Field::new("a", DataType::Int64, false), | ||
| Field::new("b", DataType::Utf8, false), | ||
| ])); | ||
|
|
||
| // define data. | ||
| let batches = (0..array_len / batch_size) | ||
| .map(|_i| { | ||
| let data1 = (0..batch_size) | ||
| .into_iter() | ||
| .map(|x| x as i64) | ||
| .collect::<Vec<_>>(); | ||
| let data2 = (0..batch_size) | ||
| .into_iter() | ||
| .map(|j| format!("a{j}")) | ||
| .collect::<Vec<_>>(); | ||
|
|
||
| RecordBatch::try_new( | ||
| schema.clone(), | ||
| vec![ | ||
| Arc::new(Int64Array::from(data1)), | ||
| Arc::new(StringArray::from(data2)), | ||
| ], | ||
| ) | ||
| .unwrap() | ||
| }) | ||
| .collect::<Vec<_>>(); | ||
|
|
||
| let ctx = SessionContext::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 = 2000000; // 2M rows | ||
| let batch_size = array_len; | ||
|
|
||
| c.bench_function("benchmark", |b| { | ||
| let mut ctx = create_context(array_len, batch_size).unwrap(); | ||
| b.iter(|| block_on(query(&mut ctx, "select a, b, count(*) from t group by a, b order by count(*) desc limit 10"))) | ||
| }); | ||
| } | ||
|
|
||
| criterion_group! { | ||
| name = benches; | ||
| // This can be any expression that returns a `Criterion` object. | ||
| config = Criterion::default().sample_size(10); | ||
| targets = criterion_benchmark | ||
| } | ||
| criterion_main!(benches); | ||
|
|
||
| // Previous result | ||
| // reuse-hash | ||
| // benchmark time: [2.5999 s 6.3132 s 11.062 s] | ||
| // Found 1 outliers among 10 measurements (10.00%) | ||
|
|
||
| // main | ||
| // benchmark time: [4.1404 s 8.4601 s 13.226 s] | ||
|
|
||
| // Latest reuslt | ||
| // single-multi-groupby | ||
| // Gnuplot not found, using plotters backend | ||
| // benchmark time: [82.970 ms 98.723 ms 111.32 ms] | ||
| // change: [-99.328% -98.932% -97.777%] (p = 0.00 < 0.05) | ||
| // Performance has improved. | ||
| // Found 2 outliers among 10 measurements (20.00%) | ||
| // 2 (20.00%) high mild | ||
|
|
||
| // main (I guess this improves because of the change Check hashes first during probing the aggr hash table #11718, but I didn't verify) | ||
| // Gnuplot not found, using plotters backend | ||
| // Benchmarking benchmark: Warming up for 3.0000 s | ||
| // Warning: Unable to complete 10 samples in 5.0s. You may wish to increase target time to 23.3s. | ||
| // benchmark time: [660.82 ms 1.3354 s 2.1247 s] | ||
| // change: [+675.04% +1377.8% +2344.1%] (p = 0.00 < 0.05) | ||
| // Performance has regressed. | ||
| // Found 1 outliers among 10 measurements (10.00%) | ||
| // 1 (10.00%) high mild | ||
|
|
||
|
|
||
| // Gnuplot not found, using plotters backend | ||
| // Benchmarking benchmark: Warming up for 3.0000 s | ||
| // Warning: Unable to complete 10 samples in 5.0s. You may wish to increase target time to 8.2s or enable flat sampling. | ||
| // benchmark time: [150.22 ms 154.31 ms 158.06 ms] | ||
| // change: [+254.76% +261.92% +270.28%] (p = 0.00 < 0.05) | ||
| // Performance has regressed. | ||
| // Found 1 outliers among 10 measurements (10.00%) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,95 @@ | ||
| // 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::{ | ||
| datatypes::{DataType, Field, Schema}, | ||
| record_batch::RecordBatch, | ||
| }; | ||
| use arrow_array::{Int64Array, StringArray}; | ||
| use criterion::{criterion_group, criterion_main, Criterion}; | ||
| use datafusion::prelude::SessionContext; | ||
| use datafusion::{datasource::MemTable, error::Result}; | ||
| use futures::executor::block_on; | ||
| use std::sync::Arc; | ||
| use tokio::runtime::Runtime; | ||
|
|
||
| async fn query(ctx: &mut SessionContext, sql: &str) { | ||
| let rt = Runtime::new().unwrap(); | ||
|
|
||
| // execute the query | ||
| let df = rt.block_on(ctx.sql(sql)).unwrap(); | ||
| criterion::black_box(rt.block_on(df.collect()).unwrap()); | ||
| } | ||
|
|
||
| fn create_context(array_len: usize, batch_size: usize) -> Result<SessionContext> { | ||
| // define a schema. | ||
| let schema = Arc::new(Schema::new(vec![ | ||
| Field::new("a", DataType::Int64, false), | ||
| Field::new("b", DataType::Utf8, false), | ||
| ])); | ||
|
|
||
| // define data. | ||
| let batches = (0..array_len / batch_size) | ||
| .map(|_i| { | ||
| let data1 = (0..batch_size).into_iter().map(|x| (x % 4 > 1) as i64).collect::<Vec<_>>(); | ||
| let data2 = (0..batch_size).into_iter().map(|j| format!("a{}", (j % 2))).collect::<Vec<_>>(); | ||
|
|
||
| RecordBatch::try_new( | ||
| schema.clone(), | ||
| vec![ | ||
| Arc::new(Int64Array::from(data1)), | ||
| Arc::new(StringArray::from(data2)), | ||
| ], | ||
| ) | ||
| .unwrap() | ||
| }) | ||
| .collect::<Vec<_>>(); | ||
|
|
||
| let ctx = SessionContext::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 = 2000000; // 2M rows | ||
| let batch_size = array_len; | ||
|
|
||
| c.bench_function("benchmark", |b| { | ||
| let mut ctx = create_context(array_len, batch_size).unwrap(); | ||
| b.iter(|| block_on(query(&mut ctx, "select a, b, count(*) from t group by a, b order by count(*) desc limit 10"))) | ||
| }); | ||
| } | ||
|
|
||
| criterion_group! { | ||
| name = benches; | ||
| // This can be any expression that returns a `Criterion` object. | ||
| config = Criterion::default().sample_size(10); | ||
| targets = criterion_benchmark | ||
| } | ||
| criterion_main!(benches); | ||
|
|
||
| // single-multi-groupby-v2 | ||
| // Gnuplot not found, using plotters backend | ||
| // benchmark time: [41.512 ms 41.931 ms 42.318 ms] | ||
| // change: [-6.5657% -5.6393% -4.6452%] (p = 0.00 < 0.05) | ||
| // Performance has improved. | ||
| // Found 1 outliers among 10 measurements (10.00%) | ||
| // 1 (10.00%) high mild | ||
|
Contributor
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There was a problem hiding this comment. single-multi-groupby-v2 main |
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I got 2.1296 for main