REL-505 Add new ByteswapPartitioner

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

@@ -55,14 +55,23 @@ object Partitioner {
    * We use two method parameters (rdd, others) to enforce callers passing at least 1 RDD.
    */
   def defaultPartitioner(rdd: RDD[_], others: RDD[_]*): Partitioner = {
+    val shortPartitionerNames = Map(
+      "hash" -> "org.apache.spark.HashPartitioner",
+      "byteswap" -> "org.apache.spark.ByteswapPartitioner"
+    )
+    val defaultPartitionerName = rdd.conf.get("spark.default.partitioner", "hash")
+    val className =
+      shortPartitionerNames.getOrElse(defaultPartitionerName.toLowerCase, defaultPartitionerName)
+    val ctor = Class.forName(className, true, Utils.getContextOrSparkClassLoader)
+      .getConstructor(classOf[Int])
     val bySize = (Seq(rdd) ++ others).sortBy(_.partitions.size).reverse
     for (r <- bySize if r.partitioner.isDefined) {
       return r.partitioner.get
     }
     if (rdd.context.conf.contains("spark.default.parallelism")) {
-      new HashPartitioner(rdd.context.defaultParallelism)
+      ctor.newInstance(rdd.context.defaultParallelism: java.lang.Integer).asInstanceOf[Partitioner]
     } else {
-      new HashPartitioner(bySize.head.partitions.size)
+      ctor.newInstance(bySize.head.partitions.size: java.lang.Integer).asInstanceOf[Partitioner]
     }
   }
 }
@@ -93,6 +102,18 @@ class HashPartitioner(partitions: Int) extends Partitioner {
   override def hashCode: Int = numPartitions
 }
 
+/**
+ * A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using
+ * Java's `Object.hashCode`.  In order to spread-out hashCodes that are divisible by
+ * `numPartitions`, `byteswap32` is applied to the hashCodes before modding by `numPartitions`.
+ */
+class ByteswapPartitioner(partitions: Int) extends HashPartitioner(partitions) {
+  override def getPartition(key: Any): Int = key match {
+    case null => 0
+    case _ => Utils.nonNegativeMod(byteswap32(key.hashCode), numPartitions)
+  }
+}
+
 /**
  * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly
  * equal ranges. The ranges are determined by sampling the content of the RDD passed in.

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

@@ -32,6 +32,16 @@ import scala.util.Try
 
 class PipedRDDSuite extends FunSuite with SharedSparkContext {
 
+  override def beforeAll() {
+    System.setProperty("spark.default.partitioner", "hash")
+    super.beforeAll()
+  }
+
+  override def afterAll() {
+    System.clearProperty("spark.default.partitioner")
+    super.afterAll()
+  }
+
   test("basic pipe") {
     if (testCommandAvailable("cat")) {
       val nums = sc.makeRDD(Array(1, 2, 3, 4), 2)

docs/configuration.md

@@ -488,6 +488,17 @@ Apart from these, the following properties are also available, and may be useful
     (<code>groupByKey</code>, <code>reduceByKey</code>, etc) when not set by user.
   </td>
 </tr>
+<tr>
+  <td><code>spark.default.partitioner</code></td>
+  <td>hash</td>
+  <td>
+    Implementation to use for partitioning key-value data pairs. There are two implementations
+    available:<code>hash</code> and <code>byteswap</code>.  Both are based on the <code>hashCode</code> of
+    the keys <code>mod</code> the number of partitions, but the <code>byteswap</code> partitioner also
+    applies <code>byteswap32</code> to the hash codes, which helps guarantee that all partitions are used
+    even when the hash codes are divisible by a factor of the number of partitions.
+  </td>
+</tr>
 <tr>
   <td><code>spark.broadcast.factory</code></td>
   <td>org.apache.spark.broadcast.<br />TorrentBroadcastFactory</td>

mllib/pom.xml

@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.1--csd-1-SNAPSHOT</version>
+    <version>1.1.1-csd-1-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>