spark CoGroupedRDD 源码

  • 2022-10-20
  • 浏览 (362)

spark CoGroupedRDD 代码

文件路径:/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala

/*
 * 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.rdd

import java.io.{IOException, ObjectOutputStream}

import scala.collection.mutable.ArrayBuffer
import scala.reflect.ClassTag

import org.apache.spark._
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.serializer.Serializer
import org.apache.spark.util.Utils
import org.apache.spark.util.collection.{CompactBuffer, ExternalAppendOnlyMap}

/**
 * The references to rdd and splitIndex are transient because redundant information is stored
 * in the CoGroupedRDD object.  Because CoGroupedRDD is serialized separately from
 * CoGroupPartition, if rdd and splitIndex aren't transient, they'll be included twice in the
 * task closure.
 */
private[spark] case class NarrowCoGroupSplitDep(
    @transient rdd: RDD[_],
    @transient splitIndex: Int,
    var split: Partition
  ) extends Serializable {

  @throws(classOf[IOException])
  private def writeObject(oos: ObjectOutputStream): Unit = Utils.tryOrIOException {
    // Update the reference to parent split at the time of task serialization
    split = rdd.partitions(splitIndex)
    oos.defaultWriteObject()
  }
}

/**
 * Stores information about the narrow dependencies used by a CoGroupedRdd.
 *
 * @param narrowDeps maps to the dependencies variable in the parent RDD: for each one to one
 *                   dependency in dependencies, narrowDeps has a NarrowCoGroupSplitDep (describing
 *                   the partition for that dependency) at the corresponding index. The size of
 *                   narrowDeps should always be equal to the number of parents.
 */
private[spark] class CoGroupPartition(
    override val index: Int, val narrowDeps: Array[Option[NarrowCoGroupSplitDep]])
  extends Partition with Serializable {
  override def hashCode(): Int = index
  override def equals(other: Any): Boolean = super.equals(other)
}

/**
 * :: DeveloperApi ::
 * An RDD that cogroups its parents. For each key k in parent RDDs, the resulting RDD contains a
 * tuple with the list of values for that key.
 *
 * @param rdds parent RDDs.
 * @param part partitioner used to partition the shuffle output
 *
 * @note This is an internal API. We recommend users use RDD.cogroup(...) instead of
 * instantiating this directly.
 */
@DeveloperApi
class CoGroupedRDD[K: ClassTag](
    @transient var rdds: Seq[RDD[_ <: Product2[K, _]]],
    part: Partitioner)
  extends RDD[(K, Array[Iterable[_]])](rdds.head.context, Nil) {

  // For example, `(k, a) cogroup (k, b)` produces k -> Array(ArrayBuffer as, ArrayBuffer bs).
  // Each ArrayBuffer is represented as a CoGroup, and the resulting Array as a CoGroupCombiner.
  // CoGroupValue is the intermediate state of each value before being merged in compute.
  private type CoGroup = CompactBuffer[Any]
  private type CoGroupValue = (Any, Int)  // Int is dependency number
  private type CoGroupCombiner = Array[CoGroup]

  private var serializer: Serializer = SparkEnv.get.serializer

  /** Set a serializer for this RDD's shuffle, or null to use the default (spark.serializer) */
  def setSerializer(serializer: Serializer): CoGroupedRDD[K] = {
    this.serializer = serializer
    this
  }

  override def getDependencies: Seq[Dependency[_]] = {
    rdds.map { rdd: RDD[_] =>
      if (rdd.partitioner == Some(part)) {
        logDebug("Adding one-to-one dependency with " + rdd)
        new OneToOneDependency(rdd)
      } else {
        logDebug("Adding shuffle dependency with " + rdd)
        new ShuffleDependency[K, Any, CoGroupCombiner](
          rdd.asInstanceOf[RDD[_ <: Product2[K, _]]], part, serializer)
      }
    }
  }

  override def getPartitions: Array[Partition] = {
    val array = new Array[Partition](part.numPartitions)
    for (i <- array.indices) {
      // Each CoGroupPartition will have a dependency per contributing RDD
      array(i) = new CoGroupPartition(i, rdds.zipWithIndex.map { case (rdd, j) =>
        // Assume each RDD contributed a single dependency, and get it
        dependencies(j) match {
          case s: ShuffleDependency[_, _, _] =>
            None
          case _ =>
            Some(new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i)))
        }
      }.toArray)
    }
    array
  }

  override val partitioner: Some[Partitioner] = Some(part)

  override def compute(s: Partition, context: TaskContext): Iterator[(K, Array[Iterable[_]])] = {
    val split = s.asInstanceOf[CoGroupPartition]
    val numRdds = dependencies.length

    // A list of (rdd iterator, dependency number) pairs
    val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)]
    for ((dep, depNum) <- dependencies.zipWithIndex) dep match {
      case oneToOneDependency: OneToOneDependency[Product2[K, Any]] @unchecked =>
        val dependencyPartition = split.narrowDeps(depNum).get.split
        // Read them from the parent
        val it = oneToOneDependency.rdd.iterator(dependencyPartition, context)
        rddIterators += ((it, depNum))

      case shuffleDependency: ShuffleDependency[_, _, _] =>
        // Read map outputs of shuffle
        val metrics = context.taskMetrics().createTempShuffleReadMetrics()
        val it = SparkEnv.get.shuffleManager
          .getReader(
            shuffleDependency.shuffleHandle, split.index, split.index + 1, context, metrics)
          .read()
        rddIterators += ((it, depNum))
    }

    val map = createExternalMap(numRdds)
    for ((it, depNum) <- rddIterators) {
      map.insertAll(it.map(pair => (pair._1, new CoGroupValue(pair._2, depNum))))
    }
    context.taskMetrics().incMemoryBytesSpilled(map.memoryBytesSpilled)
    context.taskMetrics().incDiskBytesSpilled(map.diskBytesSpilled)
    context.taskMetrics().incPeakExecutionMemory(map.peakMemoryUsedBytes)
    new InterruptibleIterator(context,
      map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
  }

  private def createExternalMap(numRdds: Int)
    : ExternalAppendOnlyMap[K, CoGroupValue, CoGroupCombiner] = {

    val createCombiner: (CoGroupValue => CoGroupCombiner) = value => {
      val newCombiner = Array.fill(numRdds)(new CoGroup)
      newCombiner(value._2) += value._1
      newCombiner
    }
    val mergeValue: (CoGroupCombiner, CoGroupValue) => CoGroupCombiner =
      (combiner, value) => {
      combiner(value._2) += value._1
      combiner
    }
    val mergeCombiners: (CoGroupCombiner, CoGroupCombiner) => CoGroupCombiner =
      (combiner1, combiner2) => {
        var depNum = 0
        while (depNum < numRdds) {
          combiner1(depNum) ++= combiner2(depNum)
          depNum += 1
        }
        combiner1
      }
    new ExternalAppendOnlyMap[K, CoGroupValue, CoGroupCombiner](
      createCombiner, mergeValue, mergeCombiners)
  }

  override def clearDependencies(): Unit = {
    super.clearDependencies()
    rdds = null
  }
}

相关信息

spark 源码目录

相关文章

spark AsyncRDDActions 源码

spark BinaryFileRDD 源码

spark BlockRDD 源码

spark CartesianRDD 源码

spark CheckpointRDD 源码

spark CoalescedRDD 源码

spark DoubleRDDFunctions 源码

spark EmptyRDD 源码

spark HadoopRDD 源码

spark InputFileBlockHolder 源码

0  赞