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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 ZippedPartitionsPartition(
idx: Int,
@transient rdds: Seq[RDD[_]])
extends Partition {
override val index: Int = idx
var partitionValues = rdds.map(rdd => rdd.partitions(idx))
def partitions = partitionValues
@throws(classOf[IOException])
private def writeObject(oos: ObjectOutputStream) {
// Update the reference to parent split at the time of task serialization
partitionValues = rdds.map(rdd => rdd.partitions(idx))
oos.defaultWriteObject()
}
}
abstract class ZippedPartitionsBaseRDD[V: ClassManifest](
sc: SparkContext,
var rdds: Seq[RDD[_]])
extends RDD[V](sc, rdds.map(x => new OneToOneDependency(x))) {
override def getPartitions: Array[Partition] = {
val sizes = rdds.map(x => x.partitions.size)
if (!sizes.forall(x => x == sizes(0))) {
throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions")
}
val array = new Array[Partition](sizes(0))
for (i <- 0 until sizes(0)) {
array(i) = new ZippedPartitionsPartition(i, rdds)
}
array
}
override def getPreferredLocations(s: Partition): Seq[String] = {
// Note that as number of rdd's increase and/or number of slaves in cluster increase, the computed preferredLocations below
// become diminishingly 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 a node which figures max amount of time ?
// Choose node which is hosting 'larger' of some subset of 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 splits = s.asInstanceOf[ZippedPartitionsPartition].partitions
val rddSplitZip = rdds.zip(splits)
// exact match.
val exactMatchPreferredLocations = rddSplitZip.map(x => x._1.preferredLocations(x._2))
val exactMatchLocations = exactMatchPreferredLocations.reduce((x, y) => x.intersect(y))
// Remove exact match and then do host local match.
val exactMatchHosts = exactMatchLocations.map(Utils.parseHostPort(_)._1)
val matchPreferredHosts = exactMatchPreferredLocations.map(locs => locs.map(Utils.parseHostPort(_)._1))
.reduce((x, y) => x.intersect(y))
val otherNodeLocalLocations = matchPreferredHosts.filter { s => !exactMatchHosts.contains(s) }
otherNodeLocalLocations ++ exactMatchLocations
}
override def clearDependencies() {
super.clearDependencies()
rdds = null
}
}
class ZippedPartitionsRDD2[A: ClassManifest, B: ClassManifest, V: ClassManifest](
sc: SparkContext,
f: (Iterator[A], Iterator[B]) => Iterator[V],
var rdd1: RDD[A],
var rdd2: RDD[B])
extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2)) {
override def compute(s: Partition, context: TaskContext): Iterator[V] = {
val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
f(rdd1.iterator(partitions(0), context), rdd2.iterator(partitions(1), context))
}
override def clearDependencies() {
super.clearDependencies()
rdd1 = null
rdd2 = null
}
}
class ZippedPartitionsRDD3
[A: ClassManifest, B: ClassManifest, C: ClassManifest, V: ClassManifest](
sc: SparkContext,
f: (Iterator[A], Iterator[B], Iterator[C]) => Iterator[V],
var rdd1: RDD[A],
var rdd2: RDD[B],
var rdd3: RDD[C])
extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3)) {
override def compute(s: Partition, context: TaskContext): Iterator[V] = {
val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
f(rdd1.iterator(partitions(0), context),
rdd2.iterator(partitions(1), context),
rdd3.iterator(partitions(2), context))
}
override def clearDependencies() {
super.clearDependencies()
rdd1 = null
rdd2 = null
rdd3 = null
}
}
class ZippedPartitionsRDD4
[A: ClassManifest, B: ClassManifest, C: ClassManifest, D:ClassManifest, V: ClassManifest](
sc: SparkContext,
f: (Iterator[A], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V],
var rdd1: RDD[A],
var rdd2: RDD[B],
var rdd3: RDD[C],
var rdd4: RDD[D])
extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3, rdd4)) {
override def compute(s: Partition, context: TaskContext): Iterator[V] = {
val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
f(rdd1.iterator(partitions(0), context),
rdd2.iterator(partitions(1), context),
rdd3.iterator(partitions(2), context),
rdd4.iterator(partitions(3), context))
}
override def clearDependencies() {
super.clearDependencies()
rdd1 = null
rdd2 = null
rdd3 = null
rdd4 = null
}
}
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