Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/9276#discussion_r43577893
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/ExchangeCoordinator.scala
---
@@ -0,0 +1,251 @@
+/*
+ * 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
+
+import java.util.{Map => JMap, HashMap => JHashMap}
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
+import org.apache.spark.{Logging, SimpleFutureAction, ShuffleDependency,
MapOutputStatistics}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+
+/**
+ * A coordinator used to determines how we shuffle data between stages
generated by Spark SQL.
+ * Right now, the work of this coordinator is to determine the number of
post-shuffle partitions
+ * for a stage that needs to fetch shuffle data from one or multiple
stages.
+ *
+ * A coordinator is constructed with two parameters, `numExchanges` and
+ * `targetPostShuffleInputSize`. `numExchanges` is used to indicated that
how many [[Exchange]]s
+ * that will be registered to this coordinator. So, when we start to do
any actual work, we have
+ * a way to make sure that we have got expected number of [[Exchange]]s.
+ * `targetPostShuffleInputSize` is the targeted size of a post-shuffle
partition's input data size.
+ * With this parameter, we can estimate the number of post-shuffle
partitions. This parameter
+ * is configured through
`spark.sql.adaptive.shuffle.targetPostShuffleInputSize`.
+ *
+ * The workflow of this coordinator is described as follows:
+ * - Before the execution of a [[SparkPlan]], for an [[Exchange]]
operator,
+ * if an [[ExchangeCoordinator]] is assigned to it, it registers itself
to this coordinator.
+ * This happens in the `doPrepare` method.
+ * - Once we start to execute a physical plan, an [[Exchange]] registered
to this coordinator will
+ * call `postShuffleRDD` to get its corresponding post-shuffle [[RDD]].
If this coordinator has
+ * made the decision on how to shuffle data, this [[Exchange]] will
immediately get its
+ * corresponding post-shuffle [[RDD]].
+ * - If this coordinator has not made the decision on how to shuffle
data, it will ask those
+ * registered [[Exchange]]s to submit their pre-shuffle stages. Then,
based on the the size
+ * statistics of pre-shuffle partitions, this coordinator will
determine the number of
+ * post-shuffle partitions and pack multiple pre-shuffle partitions
with continuous indices
+ * to a post-shuffle partition whenever necessary.
+ * - Finally, this coordinator will create post-shuffle [[RDD]]s for all
registered [[Exchange]]s.
+ * So, when an [[Exchange]] calls `postShuffleRDD`, this coordinator
can lookup the
+ * corresponding [[RDD]].
+ *
+ * The strategy used to determine the number of post-shuffle partitions is
described as follows.
+ * To determine the number of post-shuffle partitions, we have a target
input size for a
+ * post-shuffle partition. Once we have size statistics of pre-shuffle
partitions from stages
+ * corresponding to the registered [[Exchange]]s, we will do a pass of
those statistics and
+ * pack pre-shuffle partitions with continuous indices to a single
post-shuffle partition until
+ * the size of a post-shuffle partition is equal or greater than the
target size.
+ * For example, we have two stages with the following pre-shuffle
partition size statistics:
+ * stage 1: [100 MB, 20 MB, 100 MB, 10MB, 30 MB]
+ * stage 2: [10 MB, 10 MB, 70 MB, 5 MB, 5 MB]
+ * assuming the target input size is 128 MB, we will have three
post-shuffle partitions,
+ * which are:
+ * - post-shuffle partition 0: pre-shuffle partition 0 and 1
+ * - post-shuffle partition 1: pre-shuffle partition 2
+ * - post-shuffle partition 2: pre-shuffle partition 3 and 4
+ *
+ * If `minNumPostShufflePartitions` is defined. This ExchangeCoordinator
will try to enforce
+ * the minimal number of post-shuffle partitions to this number.
+ */
+private[sql] class ExchangeCoordinator(
+ numExchanges: Int,
+ advisoryTargetPostShuffleInputSize: Long,
+ minNumPostShufflePartitions: Option[Int] = None)
+ extends Logging {
+
+ // The registered Exchange operators.
+ private[this] val exchanges = ArrayBuffer[Exchange]()
+
+ // This map that is used to lookup the post-shuffle RDD for an Exchange
operator.
+ private[this] val postShuffleRDDs: JMap[Exchange, ShuffledRowRDD] =
+ new JHashMap[Exchange, ShuffledRowRDD](numExchanges)
+
+ // A boolean indicates if this coordinator has made decision on how to
shuffle data.
+ @volatile private[this] var estimated: Boolean = false
+
+ /**
+ * Registers an [[Exchange]] operator to this coordinator. This method
is only allowed to be
+ * called in the `doPrepare` method of an [[Exchange]] operator.
+ */
+ def registerExchange(exchange: Exchange): Unit = synchronized {
+ exchanges += exchange
+ }
+
+ def isEstimated: Boolean = estimated
+
+ /**
+ * Estimates partition start indices for post-shuffle partitions based on
+ * mapOutputStatistics provided by all pre-shuffle stages.
+ */
+ private[sql] def estimatePartitionStartIndices(
+ mapOutputStatistics: Array[MapOutputStatistics]): Array[Int] = {
+ // At here, we have mapOutputStatistics.length <= numExchange, it is
because we do not submit
+ // a stage if the number of partitions of the RDD is 0.
+ require(mapOutputStatistics.length <= numExchanges)
+
+ // Make sure we do get the same number of pre-shuffle partitions for
those stages.
+ val distinctNumPreShufflePartitions =
+ mapOutputStatistics.map(stats =>
stats.bytesByPartitionId.length).distinct
+ require(
+ distinctNumPreShufflePartitions.length == 1,
+ "There should be only one distinct value of the number pre-shuffle
partitions " +
+ "among registered Exchange operator.")
+
+ val numPreShufflePartitions = distinctNumPreShufflePartitions.head
+ val partitionStartIndices = ArrayBuffer[Int]()
+ var postShuffleInputSize = 0L
+
+ // If minNumPostShufflePartitions is defined, it is possible that we
need to use a
+ // value less than advisoryTargetPostShuffleInputSize as the target
input size of
+ // a post shuffle task.
+ val targetPostShuffleInputSize = minNumPostShufflePartitions match {
+ case Some(numPartitions) =>
+ val totalPostShuffleInputSize =
mapOutputStatistics.map(_.bytesByPartitionId.sum).sum
+ // The max at here is to make sure that when we have an empty
table, we
+ // only have a single post-shuffle partition.
+ val maxPostShuffleInputSize =
+ math.max(math.ceil(totalPostShuffleInputSize /
numPartitions.toDouble).toLong, 16)
+ math.min(maxPostShuffleInputSize,
advisoryTargetPostShuffleInputSize)
+
+ case None => advisoryTargetPostShuffleInputSize
+ }
+
+ logInfo(
+ s"advisoryTargetPostShuffleInputSize:
$advisoryTargetPostShuffleInputSize, " +
+ s"targetPostShuffleInputSize $targetPostShuffleInputSize.")
+
+ // The first element of partitionStartIndices is always 0.
+ partitionStartIndices += 0
+
+ var i = 0
+ while (i < numPreShufflePartitions) {
+ // We calculate the total size of ith pre-shuffle partitions from
all pre-shuffle stages.
+ // Then, we add the total size to postShuffleInputSize.
+ var j = 0
+ while (j < mapOutputStatistics.length) {
+ postShuffleInputSize +=
mapOutputStatistics(j).bytesByPartitionId(i)
+ j += 1
+ }
+
+ // If the current postShuffleInputSize is equal or greater than the
+ // targetPostShuffleInputSize, We need to add a new element in
partitionStartIndices.
+ if (postShuffleInputSize >= targetPostShuffleInputSize) {
+ if (i < numPreShufflePartitions - 1) {
+ // Next start index.
+ partitionStartIndices += i + 1
+ } else {
+ // This is the last element. So, we do not need to append the
next start index to
+ // partitionStartIndices.
+ }
+ // reset postShuffleInputSize.
+ postShuffleInputSize = 0L
+ }
+
+ i += 1
+ }
+
+ partitionStartIndices.toArray
+ }
+
+ private def doEstimationIfNecessary(): Unit = {
+ if (!estimated) {
+ // Make sure we have the expected number of registered Exchange
operators.
+ require(exchanges.length == numExchanges)
+
+ val newPostShuffleRDDs = new JHashMap[Exchange,
ShuffledRowRDD](numExchanges)
+
+ // Submit all map stages
+ val shuffleDependencies = ArrayBuffer[ShuffleDependency[Int,
InternalRow, InternalRow]]()
+ val submittedStageFutures =
ArrayBuffer[SimpleFutureAction[MapOutputStatistics]]()
+ var i = 0
+ while (i < numExchanges) {
+ val exchange = exchanges(i)
+ val shuffleDependency = exchange.prepareShuffleDependency()
+ shuffleDependencies += shuffleDependency
+ if (shuffleDependency.rdd.partitions.length != 0) {
+ // submitMapStage does not accept RDD with 0 partition.
+ // So, we will not submit this dependency.
+ submittedStageFutures +=
+
exchange.sqlContext.sparkContext.submitMapStage(shuffleDependency)
+ }
+ i += 1
+ }
+
+ // Wait for the finishes of those submitted map stages.
+ val mapOutputStatistics = new
Array[MapOutputStatistics](submittedStageFutures.length)
+ i = 0
+ while (i < submittedStageFutures.length) {
+ // This call is a blocking call. If the stage has not finished, we
will wait at here.
+ mapOutputStatistics(i) = submittedStageFutures(i).get()
+ i += 1
+ }
+
+ // Now, we estimate partitionStartIndices.
partitionStartIndices.length will be the
+ // number of post-shuffle partitions.
+ val partitionStartIndices =
+ if (mapOutputStatistics.length == 0) {
+ None
+ } else {
+ Some(estimatePartitionStartIndices(mapOutputStatistics))
+ }
+
+ i = 0
+ while (i < numExchanges) {
+ val exchange = exchanges(i)
+ val rdd =
+ exchange.preparePostShuffleRDD(shuffleDependencies(i),
partitionStartIndices)
+ newPostShuffleRDDs.put(exchange, rdd)
+
+ i += 1
+ }
+
+ // Finally, we acquire a lock and set newPostShuffleRDDs and
estimated.
+ this.synchronized {
--- End diff --
After second thought, I think it makes sense to just use the synchronized
to guard the entire method. The code will be easier to understand.
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