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/*
* 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.{ObjectOutputStream, IOException}
import scala.collection.mutable.ArrayBuffer
import org.apache.spark.{InterruptibleIterator, Partition, Partitioner, SparkEnv, TaskContext}
import org.apache.spark.{Dependency, OneToOneDependency, ShuffleDependency}
import org.apache.spark.util.AppendOnlyMap
private[spark] sealed trait CoGroupSplitDep extends Serializable
private[spark] case class NarrowCoGroupSplitDep(
rdd: RDD[_],
splitIndex: Int,
var split: Partition
) extends CoGroupSplitDep {
@throws(classOf[IOException])
private def writeObject(oos: ObjectOutputStream) {
// Update the reference to parent split at the time of task serialization
split = rdd.partitions(splitIndex)
oos.defaultWriteObject()
}
}
private[spark] case class ShuffleCoGroupSplitDep(shuffleId: Int) extends CoGroupSplitDep
private[spark]
class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep])
extends Partition with Serializable {
override val index: Int = idx
override def hashCode(): Int = idx
}
/**
* A 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.
*/
class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: Partitioner)
extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) {
private var serializerClass: String = null
def setSerializer(cls: String): CoGroupedRDD[K] = {
serializerClass = cls
this
}
override def getDependencies: Seq[Dependency[_]] = {
rdds.map { rdd: RDD[_ <: Product2[K, _]] =>
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[Any, Any](rdd, part, serializerClass)
}
}
}
override def getPartitions: Array[Partition] = {
val array = new Array[Partition](part.numPartitions)
for (i <- 0 until array.size) {
// 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[_, _] =>
new ShuffleCoGroupSplitDep(s.shuffleId)
case _ =>
new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))
}
}.toArray)
}
array
}
override val partitioner = Some(part)
override def compute(s: Partition, context: TaskContext): Iterator[(K, Seq[Seq[_]])] = {
val split = s.asInstanceOf[CoGroupPartition]
val numRdds = split.deps.size
// e.g. for `(k, a) cogroup (k, b)`, K -> Seq(ArrayBuffer as, ArrayBuffer bs)
val map = new AppendOnlyMap[K, Seq[ArrayBuffer[Any]]]
val update: (Boolean, Seq[ArrayBuffer[Any]]) => Seq[ArrayBuffer[Any]] = (hadVal, oldVal) => {
if (hadVal) oldVal else Array.fill(numRdds)(new ArrayBuffer[Any])
}
val getSeq = (k: K) => {
map.changeValue(k, update)
}
val ser = SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf)
for ((dep, depNum) <- split.deps.zipWithIndex) dep match {
case NarrowCoGroupSplitDep(rdd, _, itsSplit) => {
// Read them from the parent
rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, Any]]].foreach { kv =>
getSeq(kv._1)(depNum) += kv._2
}
}
case ShuffleCoGroupSplitDep(shuffleId) => {
// Read map outputs of shuffle
val fetcher = SparkEnv.get.shuffleFetcher
fetcher.fetch[Product2[K, Any]](shuffleId, split.index, context, ser).foreach {
kv => getSeq(kv._1)(depNum) += kv._2
}
}
}
new InterruptibleIterator(context, map.iterator)
}
override def clearDependencies() {
super.clearDependencies()
rdds = null
}
}
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