[SPARK-22465][Core][WIP] Add a safety-check to RDD defaultPartitioner

 that ignores existing Partitioners, if they are more than a single order of magnitude smaller than the max number of upstream partitions

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

@@ -21,6 +21,7 @@ import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
+import scala.math.log10
 import scala.reflect.ClassTag
 import scala.util.hashing.byteswap32
 
@@ -42,7 +43,9 @@ object Partitioner {
   /**
    * 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.
+   * If any of the RDDs already has a partitioner, and the number of partitions of the
+   * partitioner is either greater than or is less than and within a single order of
+   * magnitude of the max number of upstream partitions, choose that one.
    *
    * 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
@@ -57,7 +60,7 @@ object Partitioner {
   def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner = {
     val rdds = (Seq(rdd) ++ others)
     val hasPartitioner = rdds.filter(_.partitioner.exists(_.numPartitions > 0))
-    if (hasPartitioner.nonEmpty) {
+    if (hasPartitioner.nonEmpty && isEligiblePartitioner(hasPartitioner.maxBy(_.partitions.length), rdds)) {
       hasPartitioner.maxBy(_.partitions.length).partitioner.get
     } else {
       if (rdd.context.conf.contains("spark.default.parallelism")) {
@@ -67,6 +70,16 @@ object Partitioner {
       }
     }
   }
+
+  /**
+   * Returns true if the number of partitions of the RDD is either greater than or is
+   * less than and within a single order of magnitude of the max number of upstream partitions;
+   * otherwise, returns false
+   */
+  private def isEligiblePartitioner(hasMaxPartitioner: RDD[_], rdds: Seq[RDD[_]]): Boolean = {
+    val maxPartitions = rdds.map(_.partitions.length).max
+    log10(maxPartitions).floor - log10(hasMaxPartitioner.getNumPartitions).floor < 1
+  }
 }
 
 /**

core/src/test/scala/org/apache/spark/PartitioningSuite.scala

@@ -259,6 +259,27 @@ class PartitioningSuite extends SparkFunSuite with SharedSparkContext with Priva
     val partitioner = new RangePartitioner(22, rdd)
     assert(partitioner.numPartitions === 3)
   }
+
+  test("defaultPartitioner") {
+    val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 150)
+    val rdd2 = sc
+      .parallelize(Array((1, 2), (2, 3), (2, 4), (3, 4)))
+      .partitionBy(new HashPartitioner(10))
+    val rdd3 = sc
+      .parallelize(Array((1, 6), (7, 8), (3, 10), (5, 12), (13, 14)))
+      .partitionBy(new HashPartitioner(100))
+
+    val partitioner1 = Partitioner.defaultPartitioner(rdd1, rdd2)
+    val partitioner2 = Partitioner.defaultPartitioner(rdd2, rdd3)
+    val partitioner3 = Partitioner.defaultPartitioner(rdd3, rdd1)
+    val partitioner4 = Partitioner.defaultPartitioner(rdd1, rdd2, rdd3)
+
+    assert(partitioner1.numPartitions == rdd1.getNumPartitions)
+    assert(partitioner2.numPartitions == rdd3.getNumPartitions)
+    assert(partitioner3.numPartitions == rdd3.getNumPartitions)
+    assert(partitioner4.numPartitions == rdd3.getNumPartitions)
+
+  }
 }
 
 

core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala

@@ -310,6 +310,28 @@ class PairRDDFunctionsSuite extends SparkFunSuite with SharedSparkContext {
     assert(joined.size > 0)
   }
 
+  // See SPARK-22465
+  test("cogroup between multiple RDD " +
+    "with an order of magnitude difference in number of partitions") {
+    val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 1000)
+    val rdd2 = sc
+      .parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
+      .partitionBy(new HashPartitioner(10))
+    val joined = rdd1.cogroup(rdd2)
+    assert(joined.getNumPartitions == rdd1.getNumPartitions)
+  }
+
+  // See SPARK-22465
+  test("cogroup between multiple RDD" +
+    " with number of partitions similar in order of magnitude") {
+    val rdd1 = sc.parallelize((1 to 1000).map(x => (x, x)), 20)
+    val rdd2 = sc
+      .parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
+      .partitionBy(new HashPartitioner(10))
+    val joined = rdd1.cogroup(rdd2)
+    assert(joined.getNumPartitions == rdd2.getNumPartitions)
+  }
+
   test("rightOuterJoin") {
     val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
     val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))