Github user wangyum commented on a diff in the pull request:
https://github.com/apache/spark/pull/20303#discussion_r164070918
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/QueryStage.scala
---
@@ -0,0 +1,222 @@
+/*
+ * 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 scala.concurrent.{ExecutionContext, Future}
+import scala.concurrent.duration.Duration
+
+import org.apache.spark.MapOutputStatistics
+import org.apache.spark.broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.exchange._
+import
org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate
+import org.apache.spark.util.ThreadUtils
+
+/**
+ * In adaptive execution mode, an execution plan is divided into multiple
QueryStages. Each
+ * QueryStage is a sub-tree that runs in a single stage.
+ */
+abstract class QueryStage extends UnaryExecNode {
+
+ var child: SparkPlan
+
+ // Ignore this wrapper for canonicalizing.
+ override def doCanonicalize(): SparkPlan = child.canonicalized
+
+ override def output: Seq[Attribute] = child.output
+
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+ /**
+ * Execute childStages and wait until all stages are completed. Use a
thread pool to avoid
+ * blocking on one child stage.
+ */
+ def executeChildStages(): Unit = {
+ // Handle broadcast stages
+ val broadcastQueryStages: Seq[BroadcastQueryStage] = child.collect {
+ case bqs: BroadcastQueryStageInput => bqs.childStage
+ }
+ val broadcastFutures = broadcastQueryStages.map { queryStage =>
+ Future { queryStage.prepareBroadcast() }(QueryStage.executionContext)
+ }
+
+ // Submit shuffle stages
+ val executionId =
sqlContext.sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+ val shuffleQueryStages: Seq[ShuffleQueryStage] = child.collect {
+ case sqs: ShuffleQueryStageInput => sqs.childStage
+ }
+ val shuffleStageFutures = shuffleQueryStages.map { queryStage =>
+ Future {
+ SQLExecution.withExecutionId(sqlContext.sparkContext, executionId)
{
+ queryStage.execute()
+ }
+ }(QueryStage.executionContext)
+ }
+
+ ThreadUtils.awaitResult(
+ Future.sequence(broadcastFutures)(implicitly,
QueryStage.executionContext), Duration.Inf)
+ ThreadUtils.awaitResult(
+ Future.sequence(shuffleStageFutures)(implicitly,
QueryStage.executionContext), Duration.Inf)
+ }
+
+ /**
+ * Before executing the plan in this query stage, we execute all child
stages, optimize the plan
+ * in this stage and determine the reducer number based on the child
stages' statistics. Finally
+ * we do a codegen for this query stage and update the UI with the new
plan.
+ */
+ def prepareExecuteStage(): Unit = {
+ // 1. Execute childStages
+ executeChildStages()
+ // It is possible to optimize this stage's plan here based on the
child stages' statistics.
+
+ // 2. Determine reducer number
+ val queryStageInputs: Seq[ShuffleQueryStageInput] = child.collect {
+ case input: ShuffleQueryStageInput => input
+ }
+ val childMapOutputStatistics =
queryStageInputs.map(_.childStage.mapOutputStatistics)
+ .filter(_ != null).toArray
+ if (childMapOutputStatistics.length > 0) {
+ val exchangeCoordinator = new ExchangeCoordinator(
+ conf.targetPostShuffleInputSize,
+ conf.minNumPostShufflePartitions)
+
+ val partitionStartIndices =
+
exchangeCoordinator.estimatePartitionStartIndices(childMapOutputStatistics)
+ child = child.transform {
+ case ShuffleQueryStageInput(childStage, output, _) =>
+ ShuffleQueryStageInput(childStage, output,
Some(partitionStartIndices))
+ }
+ }
+
+ // 3. Codegen and update the UI
+ child = CollapseCodegenStages(sqlContext.conf).apply(child)
--- End diff --
Change this line to:
```scala
child = child match {
case s: WholeStageCodegenExec => s
case other => CollapseCodegenStages(sqlContext.conf).apply(other)
}
```
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