[GitHub] spark pull request #20303: [SPARK-23128][SQL] A new approach to do adaptive ...
Github user yucai commented on a diff in the pull request:
https://github.com/apache/spark/pull/20303#discussion_r164122162
--- 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 partitionStartIndic
[GitHub] spark pull request #20303: [SPARK-23128][SQL] A new approach to do adaptive ...
Github user gczsjdy commented on a diff in the pull request:
https://github.com/apache/spark/pull/20303#discussion_r164089610
--- 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 partitionStartInd
[GitHub] spark pull request #20303: [SPARK-23128][SQL] A new approach to do adaptive ...
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 partitionStartInd
[GitHub] spark pull request #20303: [SPARK-23128][SQL] A new approach to do adaptive ...
GitHub user carsonwang opened a pull request: https://github.com/apache/spark/pull/20303 [SPARK-23128][SQL] A new approach to do adaptive execution in Spark SQL ## What changes were proposed in this pull request? This is the co-work with @yucai , @gczsjdy , @chenghao-intel , @xuanyuanking We'd like to introduce a new approach to do adaptive execution in Spark SQL. The idea is described at https://docs.google.com/document/d/1mpVjvQZRAkD-Ggy6-hcjXtBPiQoVbZGe3dLnAKgtJ4k/edit?usp=sharing ## How was this patch tested? Updated ExchangeCoordinatorSuite. We also tested this with all queries in TPC-DS. You can merge this pull request into a Git repository by running: $ git pull https://github.com/carsonwang/spark AE_1 Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/20303.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #20303 commit 7e9e57859ae35f70b57ece2fd907464392d88c0f Author: Carson Wang Date: 2017-11-22T08:49:01Z Introduce QueryStage for adapative execution commit 2aa185ddec4616b60e7596153c3dd62188e4d145 Author: Carson Wang Date: 2017-12-12T02:19:53Z Add QueryPlan for ExecutedCommandExec commit 7c3d182f6b6c21f437fbef1ea5ebb2843934dbc6 Author: Carson Wang Date: 2017-12-13T05:31:52Z Add config spark.sql.adaptive.maxNumPostShufflePartitions commit e9b3075c7799b8de6038cc6492cd9e54c12f108e Author: Carson Wang Date: 2017-12-13T07:40:08Z update commetns commit 9666c5fa5b42ebabc4e2b099a1f545945e959e6f Author: Carson Wang Date: 2017-12-18T08:38:32Z Fix style commit 9b29a3c5eb20b76c1ef7f897c07ccdd3a98a27b4 Author: Carson Wang Date: 2017-12-26T02:22:45Z fix bug commit e0b98fbed96c1c07ebf2a1a6846576f705cf2c24 Author: Carson Wang Date: 2018-01-17T05:59:51Z update doc and style --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
