org.apache.spark.ShuffleDependency Scala Examples

The following examples show how to use org.apache.spark.ShuffleDependency. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
Example 1
Source File: SubtractedRDD.scala    From drizzle-spark   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.rdd

import java.util.{HashMap => JHashMap}

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

import org.apache.spark.Dependency
import org.apache.spark.OneToOneDependency
import org.apache.spark.Partition
import org.apache.spark.Partitioner
import org.apache.spark.ShuffleDependency
import org.apache.spark.SparkEnv
import org.apache.spark.TaskContext


private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
    @transient var rdd1: RDD[_ <: Product2[K, V]],
    @transient var rdd2: RDD[_ <: Product2[K, W]],
    part: Partitioner)
  extends RDD[(K, V)](rdd1.context, Nil) {


  override def getDependencies: Seq[Dependency[_]] = {
    def rddDependency[T1: ClassTag, T2: ClassTag](rdd: RDD[_ <: Product2[T1, T2]])
      : Dependency[_] = {
      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[T1, T2, Any](rdd, part)
      }
    }
    Seq(rddDependency[K, V](rdd1), rddDependency[K, W](rdd2))
  }

  override def getPartitions: Array[Partition] = {
    val array = new Array[Partition](part.numPartitions)
    for (i <- 0 until array.length) {
      // Each CoGroupPartition will depend on rdd1 and rdd2
      array(i) = new CoGroupPartition(i, Seq(rdd1, rdd2).zipWithIndex.map { case (rdd, j) =>
        dependencies(j) match {
          case s: ShuffleDependency[_, _, _] =>
            None
          case _ =>
            Some(new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i)))
        }
      }.toArray)
    }
    array
  }

  override val partitioner = Some(part)

  override def compute(p: Partition, context: TaskContext): Iterator[(K, V)] = {
    val partition = p.asInstanceOf[CoGroupPartition]
    val map = new JHashMap[K, ArrayBuffer[V]]
    def getSeq(k: K): ArrayBuffer[V] = {
      val seq = map.get(k)
      if (seq != null) {
        seq
      } else {
        val seq = new ArrayBuffer[V]()
        map.put(k, seq)
        seq
      }
    }
    def integrate(depNum: Int, op: Product2[K, V] => Unit): Unit = {
      dependencies(depNum) match {
        case oneToOneDependency: OneToOneDependency[_] =>
          val dependencyPartition = partition.narrowDeps(depNum).get.split
          oneToOneDependency.rdd.iterator(dependencyPartition, context)
            .asInstanceOf[Iterator[Product2[K, V]]].foreach(op)

        case shuffleDependency: ShuffleDependency[_, _, _] =>
          val iter = SparkEnv.get.shuffleManager
            .getReader(
              shuffleDependency.shuffleHandle, partition.index, partition.index + 1, context)
            .read()
          iter.foreach(op)
      }
    }

    // the first dep is rdd1; add all values to the map
    integrate(0, t => getSeq(t._1) += t._2)
    // the second dep is rdd2; remove all of its keys
    integrate(1, t => map.remove(t._1))
    map.asScala.iterator.map(t => t._2.iterator.map((t._1, _))).flatten
  }

  override def clearDependencies() {
    super.clearDependencies()
    rdd1 = null
    rdd2 = null
  }

}
Example 2
Source File: SubtractedRDD.scala    From SparkCore   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.rdd

import java.util.{HashMap => JHashMap}

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

import org.apache.spark.Dependency
import org.apache.spark.OneToOneDependency
import org.apache.spark.Partition
import org.apache.spark.Partitioner
import org.apache.spark.ShuffleDependency
import org.apache.spark.SparkEnv
import org.apache.spark.TaskContext
import org.apache.spark.serializer.Serializer


  def setSerializer(serializer: Serializer): SubtractedRDD[K, V, W] = {
    this.serializer = Option(serializer)
    this
  }

  override def getDependencies: Seq[Dependency[_]] = {
    Seq(rdd1, rdd2).map { 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(rdd, part, serializer)
      }
    }
  }

  override def getPartitions: Array[Partition] = {
    val array = new Array[Partition](part.numPartitions)
    for (i <- 0 until array.size) {
      // Each CoGroupPartition will depend on rdd1 and rdd2
      array(i) = new CoGroupPartition(i, Seq(rdd1, rdd2).zipWithIndex.map { case (rdd, j) =>
        dependencies(j) match {
          case s: ShuffleDependency[_, _, _] =>
            new ShuffleCoGroupSplitDep(s.shuffleHandle)
          case _ =>
            new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))
        }
      }.toArray)
    }
    array
  }

  override val partitioner = Some(part)

  override def compute(p: Partition, context: TaskContext): Iterator[(K, V)] = {
    val partition = p.asInstanceOf[CoGroupPartition]
    val map = new JHashMap[K, ArrayBuffer[V]]
    def getSeq(k: K): ArrayBuffer[V] = {
      val seq = map.get(k)
      if (seq != null) {
        seq
      } else {
        val seq = new ArrayBuffer[V]()
        map.put(k, seq)
        seq
      }
    }
    def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit) = dep match {
      case NarrowCoGroupSplitDep(rdd, _, itsSplit) =>
        rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, V]]].foreach(op)

      case ShuffleCoGroupSplitDep(handle) =>
        val iter = SparkEnv.get.shuffleManager
          .getReader(handle, partition.index, partition.index + 1, context)
          .read()
        iter.foreach(op)
    }
    // the first dep is rdd1; add all values to the map
    integrate(partition.deps(0), t => getSeq(t._1) += t._2)
    // the second dep is rdd2; remove all of its keys
    integrate(partition.deps(1), t => map.remove(t._1))
    map.iterator.map { t =>  t._2.iterator.map { (t._1, _) } }.flatten
  }

  override def clearDependencies() {
    super.clearDependencies()
    rdd1 = null
    rdd2 = null
  }

}
Example 3
Source File: ShuffleMapStage.scala    From iolap   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.scheduler

import org.apache.spark.ShuffleDependency
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.BlockManagerId
import org.apache.spark.util.CallSite


  def removeOutputsOnExecutor(execId: String): Unit = {
    var becameUnavailable = false
    for (partition <- 0 until numPartitions) {
      val prevList = outputLocs(partition)
      val newList = prevList.filterNot(_.location.executorId == execId)
      outputLocs(partition) = newList
      if (prevList != Nil && newList == Nil) {
        becameUnavailable = true
        numAvailableOutputs -= 1
      }
    }
    if (becameUnavailable) {
      logInfo("%s is now unavailable on executor %s (%d/%d, %s)".format(
        this, execId, numAvailableOutputs, numPartitions, isAvailable))
    }
  }
}
Example 4
Source File: ShuffleMapStage.scala    From spark1.52   with Apache License 2.0 5 votes vote down vote up 
package org.apache.spark.scheduler

import org.apache.spark.ShuffleDependency
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.BlockManagerId
import org.apache.spark.util.CallSite


  def removeOutputsOnExecutor(execId: String): Unit = {
    var becameUnavailable = false
    for (partition <- 0 until numPartitions) {
      val prevList = outputLocs(partition)
      val newList = prevList.filterNot(_.location.executorId == execId)
      outputLocs(partition) = newList
      //Nil是一个空的List,::向队列的头部追加数据,创造新的列表
      if (prevList != Nil && newList == Nil) {
        becameUnavailable = true
        numAvailableOutputs -= 1
      }
    }
    if (becameUnavailable) {
      logInfo("%s is now unavailable on executor %s (%d/%d, %s)".format(
        this, execId, numAvailableOutputs, numPartitions, isAvailable))
    }
  }
}