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Subject [GitHub] [spark] cloud-fan commented on a change in pull request #26434: [SPARK-29544] [SQL] optimize skewed partition based on data size
Date Wed, 08 Jan 2020 14:37:33 GMT
cloud-fan commented on a change in pull request #26434: [SPARK-29544] [SQL] optimize skewed
partition based on data size
URL: https://github.com/apache/spark/pull/26434#discussion_r364262035
 
 

 ##########
 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedPartitions.scala
 ##########
 @@ -0,0 +1,255 @@
+/*
+ * 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.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{MapOutputStatistics, MapOutputTrackerMaster, SparkEnv}
+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._
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.internal.SQLConf
+
+case class OptimizeSkewedPartitions(conf: SQLConf) extends Rule[SparkPlan] {
+
+  private val supportedJoinTypes =
+    Inner :: Cross :: LeftSemi :: LeftAnti :: LeftOuter :: RightOuter :: Nil
+
+  /**
+   * A partition is considered as a skewed partition if its size is larger than the median
+   * partition size * spark.sql.adaptive.skewedPartitionFactor and also larger than
+   * spark.sql.adaptive.skewedPartitionSizeThreshold.
+   */
+  private def isSkewed(
+      stats: MapOutputStatistics,
+      partitionId: Int,
+      medianSize: Long): Boolean = {
+    val size = stats.bytesByPartitionId(partitionId)
+    size > medianSize * conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_FACTOR)
&&
+      size > conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_SIZE_THRESHOLD)
+  }
+
+  private def medianSize(stats: MapOutputStatistics): Long = {
+    val numPartitions = stats.bytesByPartitionId.length
+    val bytes = stats.bytesByPartitionId.sorted
+    if (bytes(numPartitions / 2) > 0) bytes(numPartitions / 2) else 1
+  }
+
+  /**
+   * Get the map size of the specific reduce shuffle Id.
+   */
+  private def getMapSizesForReduceId(shuffleId: Int, partitionId: Int): Array[Long] = {
+    val mapOutputTracker = SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
+    mapOutputTracker.shuffleStatuses(shuffleId).mapStatuses.map{_.getSizeForBlock(partitionId)}
+  }
+
+  /**
+   * Split the skewed partition based on the map size and the max split number.
+   */
+  private def getMapStartIndices(stage: ShuffleQueryStageExec, partitionId: Int): Array[Int]
= {
+    val shuffleId = stage.shuffle.shuffleDependency.shuffleHandle.shuffleId
+    val mapPartitionSizes = getMapSizesForReduceId(shuffleId, partitionId)
+    val maxSplits = math.min(conf.getConf(
+      SQLConf.ADAPTIVE_EXECUTION_SKEWED_PARTITION_MAX_SPLITS), mapPartitionSizes.length)
+    val avgPartitionSize = mapPartitionSizes.sum / maxSplits
+    val advisoryPartitionSize = math.max(avgPartitionSize,
+      conf.getConf(SQLConf.SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE))
+    val partitionIndices = mapPartitionSizes.indices
+    val partitionStartIndices = ArrayBuffer[Int]()
+    var postMapPartitionSize = mapPartitionSizes(0)
+    partitionStartIndices += 0
+    partitionIndices.drop(1).foreach { nextPartitionIndex =>
+        val nextMapPartitionSize = mapPartitionSizes(nextPartitionIndex)
+        if (postMapPartitionSize + nextMapPartitionSize > advisoryPartitionSize) {
+          partitionStartIndices += nextPartitionIndex
+          postMapPartitionSize = nextMapPartitionSize
+        } else {
+          postMapPartitionSize += nextMapPartitionSize
+        }
+    }
+
+    if (partitionStartIndices.size > maxSplits) {
+      partitionStartIndices.take(maxSplits).toArray
+    } else partitionStartIndices.toArray
+  }
+
+  private def getStatistics(stage: ShuffleQueryStageExec): MapOutputStatistics = {
+    assert(stage.resultOption.isDefined, "ShuffleQueryStageExec should" +
+      " already be ready when executing OptimizeSkewedPartitions rule")
+    stage.resultOption.get.asInstanceOf[MapOutputStatistics]
+  }
+
+  private def supportSplitOnLeftPartition(joinType: JoinType) = {
+    joinType == Inner || joinType == Cross || joinType == LeftSemi ||
+      joinType == LeftAnti || joinType == LeftOuter
+  }
+
+  private def supportSplitOnRightPartition(joinType: JoinType) = {
+    joinType == Inner || joinType == Cross || joinType == RightOuter
+  }
+
+  private def getNumMappers(stage: ShuffleQueryStageExec): Int = {
+    stage.shuffle.shuffleDependency.rdd.partitions.length
+  }
+
+  def handleSkewJoin(plan: SparkPlan): SparkPlan = plan.transformUp {
+    case smj @ SortMergeJoinExec(leftKeys, rightKeys, joinType, condition,
+      SortExec(_, _, left: ShuffleQueryStageExec, _),
+      SortExec(_, _, right: ShuffleQueryStageExec, _))
+      if supportedJoinTypes.contains(joinType) =>
+      val leftStats = getStatistics(left)
+      val rightStats = getStatistics(right)
+      val numPartitions = leftStats.bytesByPartitionId.length
+
+      val leftMedSize = medianSize(leftStats)
+      val rightMedSize = medianSize(rightStats)
+      val leftSizeInfo = s"median size: $leftMedSize, max size: ${leftStats.bytesByPartitionId.max}"
+      val rightSizeInfo = s"median size: $rightMedSize," +
+        s" max size: ${rightStats.bytesByPartitionId.max}"
+      logDebug(
+        s"""
+          |Try to optimize skewed join.
+          |Left side partition size: $leftSizeInfo
+          |Right side partition size: $rightSizeInfo
+        """.stripMargin)
+
+      val skewedPartitions = mutable.HashSet[Int]()
+      val subJoins = mutable.ArrayBuffer[SparkPlan]()
+      for (partitionId <- 0 until numPartitions) {
+        val isLeftSkew = isSkewed(leftStats, partitionId, leftMedSize)
+        val isRightSkew = isSkewed(rightStats, partitionId, rightMedSize)
+        val leftMapIdStartIndices = if (isLeftSkew && supportSplitOnLeftPartition(joinType))
{
+          getMapStartIndices(left, partitionId)
+        } else {
+          Array(0)
+        }
+        val rightMapIdStartIndices = if (isRightSkew && supportSplitOnRightPartition(joinType))
{
+          getMapStartIndices(right, partitionId)
+        } else {
+          Array(0)
+        }
+
+        if (leftMapIdStartIndices.length > 1 || rightMapIdStartIndices.length > 1)
{
+          skewedPartitions += partitionId
+          for (i <- 0 until leftMapIdStartIndices.length;
+               j <- 0 until rightMapIdStartIndices.length) {
+            val leftEndMapId = if (i == leftMapIdStartIndices.length - 1) {
+              getNumMappers(left)
+            } else {
+              leftMapIdStartIndices(i + 1)
+            }
+            val rightEndMapId = if (j == rightMapIdStartIndices.length - 1) {
+              getNumMappers(right)
+            } else {
+              rightMapIdStartIndices(j + 1)
+            }
+            // TODO: we may can optimize the sort merge join to broad cast join after
+            //       obtaining the raw data size of per partition,
+            val leftSkewedReader = SkewedShufflePartitionReader(
+              left, partitionId, leftMapIdStartIndices(i), leftEndMapId)
+
+            val rightSkewedReader = SkewedShufflePartitionReader(right, partitionId,
+                rightMapIdStartIndices(j), rightEndMapId)
+            subJoins += SortMergeJoinExec(leftKeys, rightKeys, joinType, condition,
+              leftSkewedReader, rightSkewedReader)
+          }
+        }
+      }
+      logDebug(s"number of skewed partitions is ${skewedPartitions.size}")
+      if (skewedPartitions.nonEmpty) {
+        val optimizedSmj = smj.transformDown {
+          case sort @ SortExec(_, _, shuffleStage: ShuffleQueryStageExec, _) =>
+            val shuffleStage = sort.child.asInstanceOf[ShuffleQueryStageExec]
+            val newStage = shuffleStage.copy(
+              excludedPartitions = skewedPartitions.toSet)
+            newStage.resultOption = shuffleStage.resultOption
+            sort.copy(child = newStage)
+        }
+        subJoins += optimizedSmj
+        UnionExec(subJoins)
+      } else {
+        smj
+      }
+  }
+
+  override def apply(plan: SparkPlan): SparkPlan = {
+    if (!conf.getConf(SQLConf.ADAPTIVE_EXECUTION_SKEWED_JOIN_ENABLED)) {
+      return plan
+    }
+
+    def collectShuffleStages(plan: SparkPlan): Seq[ShuffleQueryStageExec] = plan match {
+      case _: LocalShuffleReaderExec => Nil
+      case _: CoalescedShuffleReaderExec => Nil
+      case stage: ShuffleQueryStageExec => Seq(stage)
+      case _ => plan.children.flatMap(collectShuffleStages)
+    }
+
+    val shuffleStages = collectShuffleStages(plan)
+
+    if (shuffleStages.length == 2) {
+      // Currently we only support handling skewed join for 2 table join.
+      handleSkewJoin(plan)
+    } else {
+      plan
+
+    }
+  }
+}
+
+/**
+ * A wrapper of shuffle query stage, which submits one reduce task to read a single
+ * shuffle partition 'partitionIndex' produced by the mappers in range [startMapId, endMapId).
+ * This is used to handle the skewed partitions.
+ *
+ * @param child It's usually `ShuffleQueryStageExec`, but can be the shuffle exchange
+ *              node during canonicalization.
+ * @param partitionIndex The pre shuffle partition index.
+ * @param startMapId The start map id.
+ * @param endMapId The end map id.
+ */
+case class SkewedShufflePartitionReader(
+    child: QueryStageExec,
+    partitionIndex: Int,
+    startMapId: Int,
 
 Review comment:
   nit: mapIndex

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