maryannxue commented on a change in pull request #25295: [SPARK-28560][SQL] Optimize shuffle reader to local shuffle reader when smj converted to bhj in adaptive execution URL: https://github.com/apache/spark/pull/25295#discussion_r338722992
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeLocalShuffleReader.scala ########## @@ -0,0 +1,132 @@ +/* + * 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.adaptive + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.Attribute +import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.execution.{LeafExecNode, SparkPlan} +import org.apache.spark.sql.execution.exchange.{EnsureRequirements, ShuffleExchangeExec} +import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, BuildLeft, BuildRight} +import org.apache.spark.sql.internal.SQLConf + +case class OptimizeLocalShuffleReader(conf: SQLConf) extends Rule[SparkPlan] { + + def canUseLocalShuffleReaderLeft(join: BroadcastHashJoinExec): Boolean = { + join.buildSide == BuildRight && ShuffleQueryStageExec.isShuffleQueryStageExec(join.left) + } + + def canUseLocalShuffleReaderRight(join: BroadcastHashJoinExec): Boolean = { + join.buildSide == BuildLeft && ShuffleQueryStageExec.isShuffleQueryStageExec(join.right) + } + + override def apply(plan: SparkPlan): SparkPlan = { + if (!conf.getConf(SQLConf.OPTIMIZE_LOCAL_SHUFFLE_READER_ENABLED)) { + return plan + } + + val optimizedPlan = plan.transformDown { + case join: BroadcastHashJoinExec if canUseLocalShuffleReaderRight(join) => + val localReader = LocalShuffleReaderExec(join.right.asInstanceOf[QueryStageExec]) + join.copy(right = localReader) + case join: BroadcastHashJoinExec if canUseLocalShuffleReaderLeft(join) => + val localReader = LocalShuffleReaderExec(join.left.asInstanceOf[QueryStageExec]) + join.copy(left = localReader) + } + + def numExchanges(plan: SparkPlan): Int = { + plan.collect { + case e: ShuffleExchangeExec => e + }.length + } + + val numExchangeBefore = numExchanges(EnsureRequirements(conf).apply(plan)) + val numExchangeAfter = numExchanges(EnsureRequirements(conf).apply(optimizedPlan)) + + if (numExchangeAfter > numExchangeBefore) { + logWarning("OptimizeLocalShuffleReader rule is not applied due" + + " to additional shuffles will be introduced.") + plan + } else { + optimizedPlan + } + } +} + +case class LocalShuffleReaderExec(child: QueryStageExec) extends LeafExecNode { Review comment: Any reason to make `LocalShuffleReaderExec` a LeafNode? There's a potential issue here: we make it a leaf node yet did not visit this node in `createQueryStages`. So a stage can be "not complete yet" but considered complete and thus trigger the creation of parent stages. This might be the root cause of the flaky tests. ``` case q: QueryStageExec => CreateStageResult(newPlan = q, allChildStagesMaterialized = q.resultOption.isDefined, newStages = Seq.empty) case _ => if (plan.children.isEmpty) { CreateStageResult(newPlan = plan, allChildStagesMaterialized = true, newStages = Seq.empty) } else { val results = plan.children.map(createQueryStages) CreateStageResult( newPlan = plan.withNewChildren(results.map(_.newPlan)), allChildStagesMaterialized = results.forall(_.allChildStagesMaterialized), newStages = results.flatMap(_.newStages)) } ``` ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
