diff --git a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
index 65b4621b87ed0370a82e8a73769a7b3e28ef33c2..9213513e80914fd7ad67df953e80f04ddc1f07da 100644
--- a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
@@ -2,10 +2,11 @@ package spark.rdd
import java.io.{ObjectOutputStream, IOException}
import java.util.{HashMap => JHashMap}
+
import scala.collection.JavaConversions
import scala.collection.mutable.ArrayBuffer
-import spark.{Aggregator, Logging, Partitioner, RDD, SparkEnv, Partition, TaskContext}
+import spark.{Aggregator, Logging, Partition, Partitioner, RDD, SparkEnv, TaskContext}
import spark.{Dependency, OneToOneDependency, ShuffleDependency}
@@ -28,7 +29,8 @@ private[spark] case class NarrowCoGroupSplitDep(
private[spark] case class ShuffleCoGroupSplitDep(shuffleId: Int) extends CoGroupSplitDep
private[spark]
-class CoGroupPartition(idx: Int, val deps: Seq[CoGroupSplitDep]) extends Partition with Serializable {
+class CoGroupPartition(idx: Int, val deps: Seq[CoGroupSplitDep])
+ extends Partition with Serializable {
override val index: Int = idx
override def hashCode(): Int = idx
}
@@ -40,7 +42,19 @@ private[spark] class CoGroupAggregator
{ (b1, b2) => b1 ++ b2 })
with Serializable
-class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
+
+/**
+ * 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.
+ * @param mapSideCombine flag indicating whether to merge values before shuffle step.
+ */
+class CoGroupedRDD[K](
+ @transient var rdds: Seq[RDD[(K, _)]],
+ part: Partitioner,
+ val mapSideCombine: Boolean = true)
extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) {
private val aggr = new CoGroupAggregator
@@ -52,8 +66,12 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
new OneToOneDependency(rdd)
} else {
logInfo("Adding shuffle dependency with " + rdd)
- val mapSideCombinedRDD = rdd.mapPartitions(aggr.combineValuesByKey(_), true)
- new ShuffleDependency[Any, ArrayBuffer[Any]](mapSideCombinedRDD, part)
+ if (mapSideCombine) {
+ val mapSideCombinedRDD = rdd.mapPartitions(aggr.combineValuesByKey(_), true)
+ new ShuffleDependency[Any, ArrayBuffer[Any]](mapSideCombinedRDD, part)
+ } else {
+ new ShuffleDependency[Any, Any](rdd.asInstanceOf[RDD[(Any, Any)]], part)
+ }
}
}
}
@@ -82,6 +100,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
val numRdds = split.deps.size
// e.g. for `(k, a) cogroup (k, b)`, K -> Seq(ArrayBuffer as, ArrayBuffer bs)
val map = new JHashMap[K, Seq[ArrayBuffer[Any]]]
+
def getSeq(k: K): Seq[ArrayBuffer[Any]] = {
val seq = map.get(k)
if (seq != null) {
@@ -92,6 +111,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
seq
}
}
+
for ((dep, depNum) <- split.deps.zipWithIndex) dep match {
case NarrowCoGroupSplitDep(rdd, _, itsSplit) => {
// Read them from the parent
@@ -102,9 +122,16 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[(K, _)]], part: Partitioner)
case ShuffleCoGroupSplitDep(shuffleId) => {
// Read map outputs of shuffle
val fetcher = SparkEnv.get.shuffleFetcher
- val fetchItr = fetcher.fetch[K, Seq[Any]](shuffleId, split.index, context.taskMetrics)
- for ((k, vs) <- fetchItr) {
- getSeq(k)(depNum) ++= vs
+ if (mapSideCombine) {
+ // With map side combine on, for each key, the shuffle fetcher returns a list of values.
+ fetcher.fetch[K, Seq[Any]](shuffleId, split.index, context.taskMetrics).foreach {
+ case (key, values) => getSeq(key)(depNum) ++= values
+ }
+ } else {
+ // With map side combine off, for each key the shuffle fetcher returns a single value.
+ fetcher.fetch[K, Any](shuffleId, split.index, context.taskMetrics).foreach {
+ case (key, value) => getSeq(key)(depNum) += value
+ }
}
}
}
diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala
index 9739ba869b3182d0fbdc17ae3e9aaa63d859882c..0ff4a4799892960c86a03af927c5fb0af1be3d37 100644
--- a/core/src/test/scala/spark/RDDSuite.scala
+++ b/core/src/test/scala/spark/RDDSuite.scala
@@ -3,7 +3,7 @@ package spark
import scala.collection.mutable.HashMap
import org.scalatest.FunSuite
import spark.SparkContext._
-import spark.rdd.{CoalescedRDD, PartitionPruningRDD}
+import spark.rdd.{CoalescedRDD, CoGroupedRDD, PartitionPruningRDD}
class RDDSuite extends FunSuite with LocalSparkContext {
@@ -123,6 +123,36 @@ class RDDSuite extends FunSuite with LocalSparkContext {
assert(rdd.collect().toList === List(1, 2, 3, 4))
}
+ test("cogrouped RDDs") {
+ sc = new SparkContext("local", "test")
+ val rdd1 = sc.makeRDD(Array((1, "one"), (1, "another one"), (2, "two"), (3, "three")), 2)
+ val rdd2 = sc.makeRDD(Array((1, "one1"), (1, "another one1"), (2, "two1")), 2)
+
+ // Use cogroup function
+ val cogrouped = rdd1.cogroup(rdd2).collectAsMap()
+ assert(cogrouped(1) === (Seq("one", "another one"), Seq("one1", "another one1")))
+ assert(cogrouped(2) === (Seq("two"), Seq("two1")))
+ assert(cogrouped(3) === (Seq("three"), Seq()))
+
+ // Construct CoGroupedRDD directly, with map side combine enabled
+ val cogrouped1 = new CoGroupedRDD[Int](
+ Seq(rdd1.asInstanceOf[RDD[(Int, Any)]], rdd2.asInstanceOf[RDD[(Int, Any)]]),
+ new HashPartitioner(3),
+ true).collectAsMap()
+ assert(cogrouped1(1).toSeq === Seq(Seq("one", "another one"), Seq("one1", "another one1")))
+ assert(cogrouped1(2).toSeq === Seq(Seq("two"), Seq("two1")))
+ assert(cogrouped1(3).toSeq === Seq(Seq("three"), Seq()))
+
+ // Construct CoGroupedRDD directly, with map side combine disabled
+ val cogrouped2 = new CoGroupedRDD[Int](
+ Seq(rdd1.asInstanceOf[RDD[(Int, Any)]], rdd2.asInstanceOf[RDD[(Int, Any)]]),
+ new HashPartitioner(3),
+ false).collectAsMap()
+ assert(cogrouped2(1).toSeq === Seq(Seq("one", "another one"), Seq("one1", "another one1")))
+ assert(cogrouped2(2).toSeq === Seq(Seq("two"), Seq("two1")))
+ assert(cogrouped2(3).toSeq === Seq(Seq("three"), Seq()))
+ }
+
test("coalesced RDDs") {
sc = new SparkContext("local", "test")
val data = sc.parallelize(1 to 10, 10)