Removed the mapSideCombine option in CoGroupedRDD.

1 files changed, 5 insertions, 33 deletions

diff --git a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
index c540cd36eb..019b12d2d5 100644
--- a/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/spark/rdd/CoGroupedRDD.scala
@@ -23,7 +23,7 @@ import java.util.{HashMap => JHashMap}
 import scala.collection.JavaConversions
 import scala.collection.mutable.ArrayBuffer
 
-import spark.{Aggregator, Partition, Partitioner, RDD, SparkEnv, TaskContext}
+import spark.{Partition, Partitioner, RDD, SparkEnv, TaskContext}
 import spark.{Dependency, OneToOneDependency, ShuffleDependency}
 
 
@@ -52,13 +52,6 @@ class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep])
   override def hashCode(): Int = idx
 }
 
-private[spark] class CoGroupAggregator
-  extends Aggregator[Any, Any, ArrayBuffer[Any]](
-    { x => ArrayBuffer(x) },
-    { (b, x) => b += x },
-    { (b1, b2) => b1 ++ b2 })
-  with Serializable
-
 
 /**
  * A RDD that cogroups its parents. For each key k in parent RDDs, the resulting RDD contains a
@@ -66,34 +59,21 @@ private[spark] class CoGroupAggregator
  *
  * @param rdds parent RDDs.
  * @param part partitioner used to partition the shuffle output.
- * @param mapSideCombine flag indicating whether to merge values before shuffle step. If the flag
- *                       is on, Spark does an extra pass over the data on the map side to merge
- *                       all values belonging to the same key together. This can reduce the amount
- *                       of data shuffled if and only if the number of distinct keys is very small,
- *                       and the ratio of key size to value size is also very small.
  */
 class CoGroupedRDD[K](
   @transient var rdds: Seq[RDD[(K, _)]],
   part: Partitioner,
-  val mapSideCombine: Boolean = false,
   val serializerClass: String = null)
   extends RDD[(K, Seq[Seq[_]])](rdds.head.context, Nil) {
 
-  private val aggr = new CoGroupAggregator
-
   override def getDependencies: Seq[Dependency[_]] = {
-    rdds.map { rdd =>
+    rdds.map { rdd: RDD[(K, _)] =>
       if (rdd.partitioner == Some(part)) {
         logInfo("Adding one-to-one dependency with " + rdd)
         new OneToOneDependency(rdd)
       } else {
         logInfo("Adding shuffle dependency with " + rdd)
-        if (mapSideCombine) {
-          val mapSideCombinedRDD = rdd.mapPartitions(aggr.combineValuesByKey(_), true)
-          new ShuffleDependency[Any, ArrayBuffer[Any]](mapSideCombinedRDD, part, serializerClass)
-        } else {
-          new ShuffleDependency[Any, Any](rdd.asInstanceOf[RDD[(Any, Any)]], part, serializerClass)
-        }
+        new ShuffleDependency[Any, Any](rdd.asInstanceOf[RDD[(Any, Any)]], part, serializerClass)
       }
     }
   }
@@ -145,16 +125,8 @@ class CoGroupedRDD[K](
       case ShuffleCoGroupSplitDep(shuffleId) => {
         // Read map outputs of shuffle
         val fetcher = SparkEnv.get.shuffleFetcher
-        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, ser).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, ser).foreach {
-            case (key, value) => getSeq(key)(depNum) += value
-          }
+        fetcher.fetch[K, Any](shuffleId, split.index, context.taskMetrics, ser).foreach {
+          case (key, value) => getSeq(key)(depNum) += value
         }
       }
     }