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_r353150281
 
 

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 File path: 
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedPartitions.scala
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 @@ -0,0 +1,281 @@
+/*
+ * 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 scala.concurrent.duration.Duration
+
+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
+import org.apache.spark.util.ThreadUtils
+
+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.adaptiveSkewedFactor &&
+      size > conf.adaptiveSkewedSizeThreshold
+  }
+
+  private def medianSize(stats: MapOutputStatistics): Long = {
+    val bytesLen = stats.bytesByPartitionId.length
+    val bytes = stats.bytesByPartitionId.sorted
+    if (bytes(bytesLen / 2) > 0) bytes(bytesLen / 2) else 1
+  }
+
+  /*
+  * Get all the map data size for specific reduce partitionId.
+  */
+  def getMapSizeForSpecificPartition(partitionId: Int, shuffleId: Int): 
Array[Long] = {
+    val mapOutputTracker = 
SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
+    mapOutputTracker.shuffleStatuses.get(shuffleId).
+      get.mapStatuses.map{_.getSizeForBlock(partitionId)}
+  }
+
+  /*
+  * Split the mappers based on the map size of specific skewed reduce 
partitionId.
+  */
+  def splitMappersBasedDataSize(mapPartitionSize: Array[Long], numMappers: 
Int): Array[Int] = {
+    val advisoryTargetPostShuffleInputSize = conf.targetPostShuffleInputSize
+    val partitionStartIndices = ArrayBuffer[Int]()
+    var i = 0
+    var postMapPartitionSize: Long = mapPartitionSize(i)
+    partitionStartIndices += i
+    while (i < numMappers && i + 1 < numMappers) {
+      val nextIndex = if (i + 1 < numMappers) {
+        i + 1
+      } else numMappers -1
+
+      if (postMapPartitionSize + mapPartitionSize(nextIndex) > 
advisoryTargetPostShuffleInputSize) {
+        postMapPartitionSize = mapPartitionSize(nextIndex)
+        partitionStartIndices += nextIndex
+      } else {
+        postMapPartitionSize += mapPartitionSize(nextIndex)
+      }
+      i += 1
+    }
+    partitionStartIndices.toArray
+  }
+
+  /**
+   * We split the partition into several splits. Each split reads the data 
from several map outputs
+   * ranging from startMapId to endMapId(exclusive). This method calculates 
the split number and
+   * the startMapId for all splits.
+   */
+  private def estimateMapIdStartIndices(
+    stage: QueryStageExec,
+    partitionId: Int,
+    medianSize: Long): Array[Int] = {
+    val dependency = getShuffleStage(stage).plan.shuffleDependency
+    val shuffleId = dependency.shuffleHandle.shuffleId
+    val mapSize = getMapSizeForSpecificPartition(partitionId, shuffleId)
+    val numMappers = dependency.rdd.partitions.length
+    splitMappersBasedDataSize(mapSize, numMappers)
+  }
+
+  private def getShuffleStage(queryStage: QueryStageExec): 
ShuffleQueryStageExec = {
+    queryStage match {
+      case stage: ShuffleQueryStageExec => stage
+      case ReusedQueryStageExec(_, stage: ShuffleQueryStageExec, _) => stage
+    }
+  }
+
+  private def getStatistics(queryStage: QueryStageExec): MapOutputStatistics = 
{
+    val shuffleStage = queryStage match {
+      case stage: ShuffleQueryStageExec => stage
+      case ReusedQueryStageExec(_, stage: ShuffleQueryStageExec, _) => stage
+    }
+    val metrics = shuffleStage.mapOutputStatisticsFuture
+    assert(metrics.isCompleted, "ShuffleQueryStageExec should already be 
ready")
+    ThreadUtils.awaitResult(metrics, Duration.Zero)
+  }
+
+  /**
+   * Base optimization support check: the join type is supported and plan 
statistics is available.
+   * Note that for some join types(like left outer), whether a certain 
partition can be optimized
+   * also depends on the filed isSkewAndSupportsSplit.
+   */
+  private def supportOptimization(
+    joinType: JoinType,
+    leftStage: QueryStageExec,
+    rightStage: QueryStageExec): Boolean = {
+    val joinTypeSupported = supportedJoinTypes.contains(joinType)
+    val shuffleStageCheck = 
ShuffleQueryStageExec.isShuffleQueryStageExec(leftStage) &&
+      ShuffleQueryStageExec.isShuffleQueryStageExec(rightStage)
+    val statisticsReady: Boolean = if (shuffleStageCheck) {
+      getStatistics(leftStage) != null && getStatistics(rightStage) != null
 
 Review comment:
   how can the stats be null?

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