Add a shuffle parameter to coalesce.

This is useful for when you want just 1 output file (part-00000) but
still up the upstream RDD to be computed in parallel.

1 files changed, 5 insertions, 1 deletions

diff --git a/core/src/test/scala/spark/RDDSuite.scala b/core/src/test/scala/spark/RDDSuite.scala
index 9739ba869b..bcbb472f6c 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, PartitionPruningRDD, ShuffledRDD}
 
 class RDDSuite extends FunSuite with LocalSparkContext {
 
@@ -154,6 +154,10 @@ class RDDSuite extends FunSuite with LocalSparkContext {
     assert(coalesced4.collect().toList === (1 to 10).toList)
     assert(coalesced4.glom().collect().map(_.toList).toList ===
       (1 to 10).map(x => List(x)).toList)
+
+    // we can optionally shuffle to keep the upstream parallel
+    val coalesced5 = data.coalesce(1, shuffle = true)
+    assert(coalesced5.dependencies.head.rdd.dependencies.head.rdd.asInstanceOf[ShuffledRDD[_, _]] ne null)
   }
 
   test("zipped RDDs") {