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_r332530816
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeLocalShuffleReader.scala ########## @@ -0,0 +1,106 @@ +/* + * 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.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 == BuildLeft && ShuffleQueryStageExec.isShuffleQueryStageExec(join.right) + } + + def canUseLocalShuffleReaderRight(join: BroadcastHashJoinExec): Boolean = { + join.buildSide == BuildRight && ShuffleQueryStageExec.isShuffleQueryStageExec(join.left) + } + + override def apply(plan: SparkPlan): SparkPlan = { + if (!conf.optimizedLocalShuffleReaderEnabled) { + return plan + } + + plan.transformDown{ + case join: BroadcastHashJoinExec if canUseLocalShuffleReaderLeft(join) => + val localReader = LocalShuffleReaderExec(join.right.asInstanceOf[QueryStageExec]) + join.copy(right = localReader) + case join: BroadcastHashJoinExec if canUseLocalShuffleReaderRight(join) => + val localReader = LocalShuffleReaderExec(join.left.asInstanceOf[QueryStageExec]) + join.copy(left = localReader) + } + } Review comment: > I think each rule should try its best to make sure itself is always positive. +1. This is a general assumption in the current Spark optimization framework. So-called cost-based rules check cost independently within themselves. That said, an alternative would be we introduce an idea of "Batch" in AQE optimizations and we choose to adopt or revert the effect of each Batch based on the cost check. But in this case, we simply need to make sure the exchange number is not increased, while the cost model could evolve to be more complicated than what we need here, so let's stick to checking the exchange number in the rule itself. ---------------------------------------------------------------- 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]
