cloud-fan commented on code in PR #57742: URL: https://github.com/apache/spark/pull/57742#discussion_r3713157690
########## sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/AdaptivePartialAggregationBenchmark.scala: ########## @@ -0,0 +1,137 @@ +/* + * 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. + */ + +package org.apache.spark.sql.execution.benchmark + +import org.apache.spark.benchmark.Benchmark +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.internal.SQLConf + +/** + * Benchmark comparing runtime adaptive partial aggregation (see + * [[SQLConf.ADAPTIVE_PARTIAL_AGGREGATION_ENABLED]]) against the static pre-shuffle partial + * aggregation. When the partial aggregation is not reducing rows, the operator streams the + * remaining rows through as single-row partial buffers instead of maintaining (and possibly + * spilling) a large aggregation map. + * + * Each scenario runs the query across the full matrix of whole-stage codegen on/off and the + * feature disabled (`adaptive = F`, the pre-change baseline) vs enabled (`adaptive = T`), over a + * {high, low}-cardinality x {no-spill, on-spill} grid: + * - high-cardinality, no spill: the no-spill tier bypasses, which should win. + * - low-cardinality, no spill: nothing bypasses, which must not regress. + * - high-cardinality, forced regular-map spill: the on-spill tier bypasses instead of spilling, + * which should win. + * - low-cardinality, forced regular-map spill: the ratio is too low for the on-spill tier to + * bypass, so both runs spill identically (no regression). + * + * To run this benchmark: + * {{{ + * 1. build/sbt "sql/Test/runMain + * org.apache.spark.sql.execution.benchmark.AdaptivePartialAggregationBenchmark" + * 2. generate result: SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/Test/runMain + * org.apache.spark.sql.execution.benchmark.AdaptivePartialAggregationBenchmark" + * Results will be written to "benchmarks/AdaptivePartialAggregationBenchmark-results.txt". + * }}} + */ +object AdaptivePartialAggregationBenchmark extends SqlBasedBenchmark { + + override def runBenchmarkSuite(mainArgs: Array[String]): Unit = { + // The upstream `CombineAdjacentAggregation` and `ReplaceHashWithSortAgg` rules would collapse + // or convert these single-partition hash aggregates, so both are disabled to keep the + // Partial+Final `HashAggregateExec` structure the adaptive feature governs. + val fixedPlanConfs = Seq( + SQLConf.COMBINE_ADJACENT_AGGREGATION_ENABLED.key -> "false", + SQLConf.REPLACE_HASH_WITH_SORT_AGG_ENABLED.key -> "false") + + // Adds the (whole-stage codegen, adaptive switch) matrix for `query`. `extraConf` is applied + // to all four cases so the only differences are the two axes. + def addCodegenAdaptiveCases( + benchmark: Benchmark, + query: () => DataFrame, + extraConf: Seq[(String, String)] = Nil): Unit = { + for { + wholeStage <- Seq(true, false) + adaptive <- Seq(false, true) + } { + val adaptiveLabel = if (adaptive) "T" else "F" + val label = s"codegen = $wholeStage, adaptive = $adaptiveLabel" + benchmark.addCase(label) { _ => + withSQLConf( + (Seq( + SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> wholeStage.toString, + SQLConf.ADAPTIVE_PARTIAL_AGGREGATION_ENABLED.key -> adaptive.toString) ++ + fixedPlanConfs ++ extraConf): _*) { + query().noop() + } + } + } + } + + // Fully distinct keys make partial aggregation useless, so the no-spill (Tier 1) sampling tier + // bypasses: the feature should be faster than the baseline that maintains a map entry per row. + runBenchmark("high-cardinality input, no-spill pass-through (Tier 1)") { + val N = 8L << 20 + val benchmark = new Benchmark("adaptive partial agg, high card, no spill", N, + output = output) + addCodegenAdaptiveCases(benchmark, () => distinctKeyedDf(N)) + benchmark.run() + } + + // 1000 distinct keys over a large input: partial aggregation reduces a lot, the no-spill tier + // never fires, and the two runs must match (no regression). + runBenchmark("low-cardinality input, no-spill pass-through (Tier 1)") { + val N = 16L << 20 + val benchmark = new Benchmark("adaptive partial agg, low card, no spill", N, + output = output) + addCodegenAdaptiveCases(benchmark, () => + spark.range(N).selectExpr("id % 1000 as k", "id as v").groupBy("k").agg("v" -> "sum")) + benchmark.run() + } + + // Force the regular map to spill quickly and disable the no-spill tier (huge sample). With + // fully distinct keys the reduction ratio is 1.0, so at the spill boundary the on-spill + // (Tier 2) tier bypasses instead of spilling; the baseline spills repeatedly and falls back Review Comment: Confirmed, thanks. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
