nathanb9 commented on code in PR #22322: URL: https://github.com/apache/datafusion/pull/22322#discussion_r3275421349
########## datafusion/core/benches/multi_group_by.rs: ########## @@ -0,0 +1,219 @@ +// 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 multi-column GROUP BY performance. +//! +//! Tests the performance of grouping across different cardinality +//! scenarios and column counts. Uses Parquet files so that column +//! statistics (min/max) are available to the optimizer for heuristic +//! decisions about GroupValues implementation selection. +//! +//! The benchmark pre-plans the query and only measures execution time +//! (excludes planning and I/O setup overhead). + +use arrow::array::{ArrayRef, Int32Array, RecordBatch}; +use arrow::datatypes::{DataType, Field, Schema}; +use criterion::{Criterion, criterion_group, criterion_main}; +use datafusion::prelude::{SessionConfig, SessionContext}; +use parking_lot::Mutex; +use parquet::arrow::ArrowWriter; +use parquet::file::properties::WriterProperties; +use rand::rngs::StdRng; +use rand::{Rng, SeedableRng}; +use std::hint::black_box; +use std::sync::Arc; +use tempfile::NamedTempFile; +use tokio::runtime::Runtime; + +const NUM_ROWS: usize = 1_000_000; +const BATCH_SIZE: usize = 8192; + +fn build_group_by_sql(num_cols: usize) -> String { + let cols: Vec<String> = (0..num_cols).map(|i| format!("col_{i}")).collect(); + let col_list = cols.join(", "); + format!("SELECT {col_list} FROM t GROUP BY {col_list}") +} + +fn generate_parquet_file(num_cols: usize, cardinality: usize) -> NamedTempFile { + let mut rng = StdRng::seed_from_u64(42); + let fields: Vec<Field> = (0..num_cols) + .map(|i| Field::new(format!("col_{i}"), DataType::Int32, false)) + .collect(); + let schema = Arc::new(Schema::new(fields)); + + let mut temp_file = tempfile::Builder::new() + .prefix("multi_group_by") + .suffix(".parquet") + .tempfile() + .unwrap(); + + let props = WriterProperties::builder() + .set_max_row_group_row_count(Some(NUM_ROWS)) + .build(); + + let mut writer = + ArrowWriter::try_new(&mut temp_file, Arc::clone(&schema), Some(props)).unwrap(); + + let num_batches = NUM_ROWS / BATCH_SIZE; + for _ in 0..num_batches { + let columns: Vec<ArrayRef> = (0..num_cols) + .map(|_| { + let values: Vec<i32> = (0..BATCH_SIZE) + .map(|_| rng.random_range(0..cardinality as i32)) + .collect(); + Arc::new(Int32Array::from(values)) as ArrayRef + }) + .collect(); + let batch = RecordBatch::try_new(Arc::clone(&schema), columns).unwrap(); + writer.write(&batch).unwrap(); + } + + writer.close().unwrap(); + temp_file +} + +struct BenchContext { + ctx: Arc<Mutex<SessionContext>>, + _temp_file: NamedTempFile, +} + +#[expect(clippy::needless_pass_by_value)] +fn query(ctx: Arc<Mutex<SessionContext>>, rt: &Runtime, sql: &str) { + let df = rt.block_on(ctx.lock().sql(sql)).unwrap(); + black_box(rt.block_on(df.collect()).unwrap()); +} + +fn prepare_context(rt: &Runtime, num_cols: usize, cardinality: usize) -> BenchContext { + let temp_file = generate_parquet_file(num_cols, cardinality); + let path = temp_file.path().to_str().unwrap().to_string(); + + let config = SessionConfig::new().with_target_partitions(1); + let ctx = SessionContext::new_with_config(config); + rt.block_on(async { + ctx.register_parquet("t", &path, Default::default()) + .await + .unwrap(); + // Warm the OS page cache + let df = ctx.sql(&build_group_by_sql(num_cols)).await.unwrap(); + let _ = df.collect().await.unwrap(); + }); + + BenchContext { + ctx: Arc::new(Mutex::new(ctx)), + _temp_file: temp_file, + } +} + +fn criterion_benchmark(c: &mut Criterion) { + let rt = Runtime::new().unwrap(); + + // === Experiment 1: Fixed ~100-1000 groups, vary column count === + let b_ctx = prepare_context(&rt, 2, 10); // 10^2 = 100 groups + c.bench_function("fixed_groups_cols_2_grp_100", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(2))) + }); + + let b_ctx = prepare_context(&rt, 3, 5); // 5^3 = 125 groups + c.bench_function("fixed_groups_cols_3_grp_125", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(3))) + }); + + let b_ctx = prepare_context(&rt, 4, 3); // 3^4 = 81 groups + c.bench_function("fixed_groups_cols_4_grp_81", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(4))) + }); + + let b_ctx = prepare_context(&rt, 6, 3); // 3^6 = 729 groups + c.bench_function("fixed_groups_cols_6_grp_729", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(6))) + }); + + let b_ctx = prepare_context(&rt, 8, 2); // 2^8 = 256 groups + c.bench_function("fixed_groups_cols_8_grp_256", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(8))) + }); + + let b_ctx = prepare_context(&rt, 10, 2); // 2^10 = 1024 groups + c.bench_function("fixed_groups_cols_10_grp_1024", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(10))) + }); + + // === Experiment 1b: High groups (~1M), vary column count === + let b_ctx = prepare_context(&rt, 2, 1000); // 1000^2 = 1M groups + c.bench_function("high_groups_cols_2_grp_1M", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(2))) + }); + + let b_ctx = prepare_context(&rt, 3, 100); // 100^3 = 1M groups + c.bench_function("high_groups_cols_3_grp_1M", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(3))) + }); + + let b_ctx = prepare_context(&rt, 4, 32); // 32^4 = ~1M groups + c.bench_function("high_groups_cols_4_grp_1M", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(4))) + }); + + let b_ctx = prepare_context(&rt, 6, 10); // 10^6 = 1M groups + c.bench_function("high_groups_cols_6_grp_1M", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(6))) + }); + + let b_ctx = prepare_context(&rt, 8, 6); // 6^8 = ~1.7M groups + c.bench_function("high_groups_cols_8_grp_1M", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(8))) + }); + + let b_ctx = prepare_context(&rt, 10, 4); // 4^10 = ~1M groups + c.bench_function("high_groups_cols_10_grp_1M", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(10))) + }); + + // === Experiment 2: Fixed 4 columns, vary group count === + let b_ctx = prepare_context(&rt, 4, 2); // 2^4 = 16 groups + c.bench_function("fixed_4cols_grp_16", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(4))) + }); + + let b_ctx = prepare_context(&rt, 4, 5); // 5^4 = 625 groups + c.bench_function("fixed_4cols_grp_625", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(4))) + }); + + let b_ctx = prepare_context(&rt, 4, 10); // 10^4 = 10K groups + c.bench_function("fixed_4cols_grp_10000", |b| { + b.iter(|| query(b_ctx.ctx.clone(), &rt, &build_group_by_sql(4))) + }); + + let b_ctx = prepare_context(&rt, 4, 30); // 30^4 = 810K groups Review Comment: Just addresed by using sequential enumeration (`global_row % num_distinct_groups` decomposed per-column) which guarantees the exact number of distinct groups matches the label. 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