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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 spark.rdd
import spark.{Utils, OneToOneDependency, RDD, SparkContext, Partition, TaskContext}
import java.io.{ObjectOutputStream, IOException}
private[spark] class ZippedPartition[T: ClassManifest, U: ClassManifest](
idx: Int,
@transient rdd1: RDD[T],
@transient rdd2: RDD[U]
) extends Partition {
var partition1 = rdd1.partitions(idx)
var partition2 = rdd2.partitions(idx)
override val index: Int = idx
def partitions = (partition1, partition2)
@throws(classOf[IOException])
private def writeObject(oos: ObjectOutputStream) {
// Update the reference to parent partition at the time of task serialization
partition1 = rdd1.partitions(idx)
partition2 = rdd2.partitions(idx)
oos.defaultWriteObject()
}
}
class ZippedRDD[T: ClassManifest, U: ClassManifest](
sc: SparkContext,
var rdd1: RDD[T],
var rdd2: RDD[U])
extends RDD[(T, U)](sc, List(new OneToOneDependency(rdd1), new OneToOneDependency(rdd2))) {
override def getPartitions: Array[Partition] = {
if (rdd1.partitions.size != rdd2.partitions.size) {
throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions")
}
val array = new Array[Partition](rdd1.partitions.size)
for (i <- 0 until rdd1.partitions.size) {
array(i) = new ZippedPartition(i, rdd1, rdd2)
}
array
}
override def compute(s: Partition, context: TaskContext): Iterator[(T, U)] = {
val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions
rdd1.iterator(partition1, context).zip(rdd2.iterator(partition2, context))
}
override def getPreferredLocations(s: Partition): Seq[String] = {
// Note that as number of slaves in cluster increase, the computed preferredLocations can become small : so we might need
// to look at alternate strategies to alleviate this. (If there are no (or very small number of preferred locations), we
// will end up transferred the blocks to 'any' node in the cluster - paying with n/w and cache cost.
// Maybe pick one or the other ? (so that atleast one block is local ?).
// Choose node which is hosting 'larger' of the blocks ?
// Look at rack locality to ensure chosen host is atleast rack local to both hosting node ?, etc (would be good to defer this if possible)
val (partition1, partition2) = s.asInstanceOf[ZippedPartition[T, U]].partitions
val pref1 = rdd1.preferredLocations(partition1)
val pref2 = rdd2.preferredLocations(partition2)
// exact match - instance local and host local.
val exactMatchLocations = pref1.intersect(pref2)
// remove locations which are already handled via exactMatchLocations, and intersect where both partitions are node local.
val otherNodeLocalPref1 = pref1.filter(loc => ! exactMatchLocations.contains(loc)).map(loc => Utils.parseHostPort(loc)._1)
val otherNodeLocalPref2 = pref2.filter(loc => ! exactMatchLocations.contains(loc)).map(loc => Utils.parseHostPort(loc)._1)
val otherNodeLocalLocations = otherNodeLocalPref1.intersect(otherNodeLocalPref2)
// Can have mix of instance local (hostPort) and node local (host) locations as preference !
exactMatchLocations ++ otherNodeLocalLocations
}
override def clearDependencies() {
super.clearDependencies()
rdd1 = null
rdd2 = null
}
}
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