Use default parallelism if its set.

core/src/main/scala/spark/Partitioner.scala

@@ -10,12 +10,21 @@ abstract class Partitioner extends Serializable {
 }
 
 object Partitioner {
+
+  private val useDefaultParallelism = System.getProperty("spark.default.parallelism") != null
+
   /**
-   * Choose a partitioner to use for a cogroup-like operation between a number of RDDs. If any of
-   * the RDDs already has a partitioner, choose that one, otherwise use a default HashPartitioner.
+   * Choose a partitioner to use for a cogroup-like operation between a number of RDDs.
+   *
+   * If any of the RDDs already has a partitioner, choose that one.
    *
-   * The number of partitions will be the same as the number of partitions in the largest upstream
-   * RDD, as this should be least likely to cause out-of-memory errors.
+   * Otherwise, we use a default HashPartitioner. For the number of partitions, if
+   * spark.default.parallelism is set, then we'll use the value from SparkContext
+   * defaultParallelism, otherwise we'll use the max number of upstream partitions.
+   *
+   * Unless spark.default.parallelism is set, He number of partitions will be the
+   * same as the number of partitions in the largest upstream RDD, as this should
+   * be least likely to cause out-of-memory errors.
    *
    * We use two method parameters (rdd, others) to enforce callers passing at least 1 RDD.
    */
@@ -24,7 +33,11 @@ object Partitioner {
     for (r <- bySize if r.partitioner != None) {
       return r.partitioner.get
     }
-    return new HashPartitioner(bySize.head.partitions.size)
+    if (useDefaultParallelism) {
+      return new HashPartitioner(rdd.context.defaultParallelism)
+    } else {
+      return new HashPartitioner(bySize.head.partitions.size)
+    }
   }
 }
 

core/src/test/scala/spark/ShuffleSuite.scala

@@ -235,7 +235,7 @@ class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
     assert(rdd.values.collect().toList === List("a", "b"))
   }
 
-  test("default partitioner uses split size") {
+  test("default partitioner uses partition size") {
     sc = new SparkContext("local", "test")
     // specify 2000 partitions
     val a = sc.makeRDD(Array(1, 2, 3, 4), 2000)