SubhamSinghal commented on code in PR #24160: URL: https://github.com/apache/datafusion/pull/24160#discussion_r3737295969
########## datafusion/physical-plan/benches/piecewise_merge_join_semi_anti.rs: ########## @@ -0,0 +1,228 @@ +// 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. + +//! Criterion benchmark comparing existence (LeftSemi / LeftAnti) joins over a single +//! range predicate (`left.key < right.key`) evaluated two ways: +//! +//! - `PiecewiseMergeJoinExec` (with the required `SortExec` on the buffered/left side, +//! as the physical planner would insert), and +//! - `NestedLoopJoinExec`, which is the fallback used when +//! `enable_piecewise_merge_join` is off. +//! +//! Both plans compute the same result, so this measures the win from routing an +//! inequality-correlated `EXISTS` / `NOT EXISTS` to PWMJ instead of the O(n*m) +//! nested-loop join. The `SortExec` is included on the PWMJ side because it is a real +//! cost of that plan. +//! +//! ## Axes +//! - **join type**: LeftSemi (`EXISTS`) and LeftAnti (`NOT EXISTS`). +//! - **selectivity**: the fraction of left rows that have at least one matching right +//! row, controlled by shifting the right-side key range. Semi output size grows with +//! selectivity; Anti output size shrinks. + +use std::sync::Arc; + +use arrow::array::{Int32Array, RecordBatch}; +use arrow::compute::SortOptions; +use arrow::datatypes::{DataType, Field, Schema, SchemaRef}; +use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main}; +use datafusion_common::JoinSide; +use datafusion_common::JoinType; +use datafusion_execution::TaskContext; +use datafusion_expr::Operator; +use datafusion_physical_expr::expressions::{BinaryExpr, Column}; +use datafusion_physical_expr::{LexOrdering, PhysicalSortExpr}; +use datafusion_physical_plan::joins::utils::{ColumnIndex, JoinFilter}; +use datafusion_physical_plan::joins::{NestedLoopJoinExec, PiecewiseMergeJoinExec}; +use datafusion_physical_plan::sorts::sort::SortExec; +use datafusion_physical_plan::test::TestMemoryExec; +use datafusion_physical_plan::{ExecutionPlan, PhysicalExpr, collect}; +use tokio::runtime::Runtime; + +/// Two-column schema: (`key`, `payload`). +fn schema() -> SchemaRef { + Arc::new(Schema::new(vec![ + Field::new("key", DataType::Int32, false), + Field::new("payload", DataType::Int32, false), + ])) +} + +/// Build a single-partition input of `num_rows` rows. Keys are drawn from +/// `[key_offset, key_offset + key_span)` in a fixed, reproducible pattern (no RNG so +/// the benchmark is deterministic). +fn build_exec( + num_rows: usize, + key_offset: i32, + key_span: i32, + schema: &SchemaRef, +) -> Arc<dyn ExecutionPlan> { + let keys: Vec<i32> = (0..num_rows) + .map(|i| key_offset + (i as i32 * 2_654_435_761u32 as i32).rem_euclid(key_span)) + .collect(); + let payload: Vec<i32> = (0..num_rows as i32).collect(); + let batch = RecordBatch::try_new( + Arc::clone(schema), + vec![ + Arc::new(Int32Array::from(keys)), + Arc::new(Int32Array::from(payload)), + ], + ) + .unwrap(); + + // Slice into 8192-row batches to mirror a realistic streamed input. + let batch_size = 8192; + let mut batches = Vec::new(); + let mut offset = 0; + while offset < batch.num_rows() { + let len = (batch.num_rows() - offset).min(batch_size); + batches.push(batch.slice(offset, len)); + offset += len; + } + TestMemoryExec::try_new_exec(&[batches], Arc::clone(schema), None).unwrap() +} + +/// `PiecewiseMergeJoinExec` over `left.key < right.key`, with the required `SortExec` +/// on the buffered (left) side. `<` requires the buffered side sorted descending. +fn pwmj_plan( + left: Arc<dyn ExecutionPlan>, + right: Arc<dyn ExecutionPlan>, + join_type: JoinType, +) -> Arc<dyn ExecutionPlan> { + let sort = LexOrdering::new(vec![PhysicalSortExpr::new( + Arc::new(Column::new("key", 0)), + SortOptions::new(true, true), + )]) + .unwrap(); + let sorted_left = Arc::new(SortExec::new(sort, left)); + + let on: (Arc<dyn PhysicalExpr>, Arc<dyn PhysicalExpr>) = ( + Arc::new(Column::new("key", 0)), + Arc::new(Column::new("key", 0)), + ); + Arc::new( + PiecewiseMergeJoinExec::try_new( + sorted_left, + right, + on, + Operator::Lt, + join_type, + 1, + ) + .unwrap(), + ) +} + +/// `NestedLoopJoinExec` over the same `left.key < right.key` predicate. +fn nlj_plan( + left: Arc<dyn ExecutionPlan>, + right: Arc<dyn ExecutionPlan>, + join_type: JoinType, +) -> Arc<dyn ExecutionPlan> { + let intermediate_schema = Schema::new(vec![ + Field::new("key", DataType::Int32, false), + Field::new("key", DataType::Int32, false), + ]); + let expr = Arc::new(BinaryExpr::new( + Arc::new(Column::new("key", 0)), + Operator::Lt, + Arc::new(Column::new("key", 1)), + )) as Arc<dyn PhysicalExpr>; + let column_indices = vec![ + ColumnIndex { + index: 0, + side: JoinSide::Left, + }, + ColumnIndex { + index: 0, + side: JoinSide::Right, + }, + ]; + let filter = JoinFilter::new(expr, column_indices, Arc::new(intermediate_schema)); + Arc::new( + NestedLoopJoinExec::try_new(left, right, Some(filter), &join_type, None).unwrap(), + ) +} + +fn run(plan: Arc<dyn ExecutionPlan>, rt: &Runtime) -> usize { + let task_ctx = Arc::new(TaskContext::default()); + rt.block_on(async { + let batches = collect(plan, task_ctx).await.unwrap(); + batches.iter().map(|b| b.num_rows()).sum() + }) +} + +fn bench_pwmj_semi_anti(c: &mut Criterion) { + let rt = Runtime::new().unwrap(); + let s = schema(); + + // Left (buffered) is deliberately smaller than right (streamed); the streamed side + // drives the loop in both operators. + let left_rows = 20_000; + let right_rows = 20_000; + let key_span = 10_000; + + // Selectivity is set by how far the right key range sits above the left range. + // - "high": right keys mostly above left keys -> most left rows match (Semi large) + // - "low": right keys mostly below left keys -> few left rows match (Anti large) + let regimes: [(&str, i32); 2] = [("sel_high", key_span), ("sel_low", -key_span)]; Review Comment: resolved in d52523e2778758326e74bb14733eca25ff28eb71 ########## datafusion/physical-plan/benches/piecewise_merge_join_semi_anti.rs: ########## @@ -0,0 +1,228 @@ +// 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. + +//! Criterion benchmark comparing existence (LeftSemi / LeftAnti) joins over a single +//! range predicate (`left.key < right.key`) evaluated two ways: +//! +//! - `PiecewiseMergeJoinExec` (with the required `SortExec` on the buffered/left side, +//! as the physical planner would insert), and +//! - `NestedLoopJoinExec`, which is the fallback used when +//! `enable_piecewise_merge_join` is off. +//! +//! Both plans compute the same result, so this measures the win from routing an +//! inequality-correlated `EXISTS` / `NOT EXISTS` to PWMJ instead of the O(n*m) +//! nested-loop join. The `SortExec` is included on the PWMJ side because it is a real +//! cost of that plan. +//! +//! ## Axes +//! - **join type**: LeftSemi (`EXISTS`) and LeftAnti (`NOT EXISTS`). +//! - **selectivity**: the fraction of left rows that have at least one matching right +//! row, controlled by shifting the right-side key range. Semi output size grows with +//! selectivity; Anti output size shrinks. + +use std::sync::Arc; + +use arrow::array::{Int32Array, RecordBatch}; +use arrow::compute::SortOptions; +use arrow::datatypes::{DataType, Field, Schema, SchemaRef}; +use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main}; +use datafusion_common::JoinSide; +use datafusion_common::JoinType; +use datafusion_execution::TaskContext; +use datafusion_expr::Operator; +use datafusion_physical_expr::expressions::{BinaryExpr, Column}; +use datafusion_physical_expr::{LexOrdering, PhysicalSortExpr}; +use datafusion_physical_plan::joins::utils::{ColumnIndex, JoinFilter}; +use datafusion_physical_plan::joins::{NestedLoopJoinExec, PiecewiseMergeJoinExec}; +use datafusion_physical_plan::sorts::sort::SortExec; +use datafusion_physical_plan::test::TestMemoryExec; +use datafusion_physical_plan::{ExecutionPlan, PhysicalExpr, collect}; +use tokio::runtime::Runtime; + +/// Two-column schema: (`key`, `payload`). +fn schema() -> SchemaRef { + Arc::new(Schema::new(vec![ + Field::new("key", DataType::Int32, false), + Field::new("payload", DataType::Int32, false), + ])) +} + +/// Build a single-partition input of `num_rows` rows. Keys are drawn from +/// `[key_offset, key_offset + key_span)` in a fixed, reproducible pattern (no RNG so +/// the benchmark is deterministic). +fn build_exec( + num_rows: usize, + key_offset: i32, + key_span: i32, + schema: &SchemaRef, +) -> Arc<dyn ExecutionPlan> { + let keys: Vec<i32> = (0..num_rows) + .map(|i| key_offset + (i as i32 * 2_654_435_761u32 as i32).rem_euclid(key_span)) + .collect(); + let payload: Vec<i32> = (0..num_rows as i32).collect(); + let batch = RecordBatch::try_new( + Arc::clone(schema), + vec![ + Arc::new(Int32Array::from(keys)), + Arc::new(Int32Array::from(payload)), + ], + ) + .unwrap(); + + // Slice into 8192-row batches to mirror a realistic streamed input. + let batch_size = 8192; + let mut batches = Vec::new(); + let mut offset = 0; + while offset < batch.num_rows() { + let len = (batch.num_rows() - offset).min(batch_size); + batches.push(batch.slice(offset, len)); + offset += len; + } + TestMemoryExec::try_new_exec(&[batches], Arc::clone(schema), None).unwrap() +} + +/// `PiecewiseMergeJoinExec` over `left.key < right.key`, with the required `SortExec` +/// on the buffered (left) side. `<` requires the buffered side sorted descending. +fn pwmj_plan( + left: Arc<dyn ExecutionPlan>, + right: Arc<dyn ExecutionPlan>, + join_type: JoinType, +) -> Arc<dyn ExecutionPlan> { + let sort = LexOrdering::new(vec![PhysicalSortExpr::new( + Arc::new(Column::new("key", 0)), + SortOptions::new(true, true), + )]) + .unwrap(); + let sorted_left = Arc::new(SortExec::new(sort, left)); + + let on: (Arc<dyn PhysicalExpr>, Arc<dyn PhysicalExpr>) = ( + Arc::new(Column::new("key", 0)), + Arc::new(Column::new("key", 0)), + ); + Arc::new( + PiecewiseMergeJoinExec::try_new( Review Comment: addressed in d52523e2778758326e74bb14733eca25ff28eb71 -- This is an automated message from the Apache Git Service. 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