Merge pull request #60 from amplab/rxin

Looks good to me.

core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala

@@ -157,6 +157,16 @@ class OpenHashSet[@specialized(Long, Int) T: ClassManifest](
   /** Return the value at the specified position. */
   def getValue(pos: Int): T = _data(pos)
 
+  def iterator() = new Iterator[T] {
+    var pos = nextPos(0)
+    override def hasNext: Boolean = pos != INVALID_POS
+    override def next(): T = {
+      val tmp = getValue(pos)
+      pos = nextPos(pos+1)
+      tmp
+    }
+  }
+
   /** Return the value at the specified position. */
   def getValueSafe(pos: Int): T = {
     assert(_bitset.get(pos))

graph/src/main/scala/org/apache/spark/graph/Graph.scala

@@ -1,7 +1,7 @@
 package org.apache.spark.graph
 
 import org.apache.spark.rdd.RDD
-
+import org.apache.spark.storage.StorageLevel
 
 /**
  * The Graph abstractly represents a graph with arbitrary objects
@@ -70,6 +70,11 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
    */
   val triplets: RDD[EdgeTriplet[VD, ED]]
 
+
+
+  def persist(newLevel: StorageLevel): Graph[VD, ED]
+
+
   /**
    * Return a graph that is cached when first created. This is used to
    * pin a graph in memory enabling multiple queries to reuse the same

graph/src/main/scala/org/apache/spark/graph/GraphKryoRegistrator.scala

@@ -2,7 +2,7 @@ package org.apache.spark.graph
 
 import com.esotericsoftware.kryo.Kryo
 
-import org.apache.spark.graph.impl.{EdgePartition, MessageToPartition}
+import org.apache.spark.graph.impl._
 import org.apache.spark.serializer.KryoRegistrator
 import org.apache.spark.util.collection.BitSet
 
@@ -12,6 +12,8 @@ class GraphKryoRegistrator extends KryoRegistrator {
     kryo.register(classOf[Edge[Object]])
     kryo.register(classOf[MutableTuple2[Object, Object]])
     kryo.register(classOf[MessageToPartition[Object]])
+    kryo.register(classOf[VertexBroadcastMsg[Object]])
+    kryo.register(classOf[AggregationMsg[Object]])
     kryo.register(classOf[(Vid, Object)])
     kryo.register(classOf[EdgePartition[Object]])
     kryo.register(classOf[BitSet])

graph/src/main/scala/org/apache/spark/graph/Pregel.scala

@@ -98,14 +98,14 @@ object Pregel {
     : Graph[VD, ED] = {
 
     // Receive the first set of messages
-    var g = graph.mapVertices( (vid, vdata) => vprog(vid, vdata, initialMsg))
+    var g = graph.mapVertices( (vid, vdata) => vprog(vid, vdata, initialMsg)).cache
 
     var i = 0
     while (i < numIter) {
       // compute the messages
       val messages = g.mapReduceTriplets(sendMsg, mergeMsg)
       // receive the messages
-      g = g.joinVertices(messages)(vprog)
+      g = g.joinVertices(messages)(vprog).cache
       // count the iteration
       i += 1
     }

graph/src/main/scala/org/apache/spark/graph/VertexSetRDD.scala

@@ -22,7 +22,8 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.rdd._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveKeyOpenHashMap}
-
+import org.apache.spark.graph.impl.AggregationMsg
+import org.apache.spark.graph.impl.MsgRDDFunctions._
 
 /**
  * The `VertexSetIndex` maintains the per-partition mapping from
@@ -659,6 +660,43 @@ object VertexSetRDD {
     apply(rdd,index, (v:V) => v, reduceFunc, reduceFunc)
 
 
+  def aggregate[V: ClassManifest](
+    rdd: RDD[AggregationMsg[V]], index: VertexSetIndex,
+    reduceFunc: (V, V) => V): VertexSetRDD[V] = {
+
+    val cReduceFunc = index.rdd.context.clean(reduceFunc)
+    assert(rdd.partitioner == index.rdd.partitioner)
+    // Use the index to build the new values table
+    val values: RDD[ (Array[V], BitSet) ] = index.rdd.zipPartitions(rdd)( (indexIter, tblIter) => {
+      // There is only one map
+      val index = indexIter.next()
+      assert(!indexIter.hasNext)
+      val values = new Array[V](index.capacity)
+      val bs = new BitSet(index.capacity)
+      for (msg <- tblIter) {
+        // Get the location of the key in the index
+        val pos = index.getPos(msg.vid)
+        if ((pos & OpenHashSet.NONEXISTENCE_MASK) != 0) {
+          throw new SparkException("Error: Trying to bind an external index " +
+            "to an RDD which contains keys that are not in the index.")
+        } else {
+          // Get the actual index
+          val ind = pos & OpenHashSet.POSITION_MASK
+          // If this value has already been seen then merge
+          if (bs.get(ind)) {
+            values(ind) = cReduceFunc(values(ind), msg.data)
+          } else { // otherwise just store the new value
+            bs.set(ind)
+            values(ind) = msg.data
+          }
+        }
+      }
+      Iterator((values, bs))
+    })
+    new VertexSetRDD(index, values)
+  }
+
+
   /**
    * Construct a vertex set from an RDD using an existing index and a
    * user defined `combiner` to merge duplicate vertices.

graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala

@@ -5,15 +5,15 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
-
 import org.apache.spark.SparkContext._
 import org.apache.spark.HashPartitioner
 import org.apache.spark.util.ClosureCleaner
 
 import org.apache.spark.graph._
 import org.apache.spark.graph.impl.GraphImpl._
-import org.apache.spark.graph.impl.MessageToPartitionRDDFunctions._
+import org.apache.spark.graph.impl.MsgRDDFunctions._
 import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveKeyOpenHashMap}
 
 
@@ -72,8 +72,6 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
 
   def this() = this(null, null, null, null)
 
-
-
   /**
    * (localVidMap: VertexSetRDD[Pid, VertexIdToIndexMap]) is a version of the
    * vertex data after it is replicated. Within each partition, it holds a map
@@ -86,29 +84,28 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
   @transient val vTableReplicatedValues: RDD[(Pid, Array[VD])] =
     createVTableReplicated(vTable, vid2pid, localVidMap)
 
-
   /** Return a RDD of vertices. */
   @transient override val vertices = vTable
 
-
   /** Return a RDD of edges. */
   @transient override val edges: RDD[Edge[ED]] = {
     eTable.mapPartitions( iter => iter.next()._2.iterator , true )
   }
 
-
   /** Return a RDD that brings edges with its source and destination vertices together. */
   @transient override val triplets: RDD[EdgeTriplet[VD, ED]] =
     makeTriplets(localVidMap, vTableReplicatedValues, eTable)
 
-
+  override def persist(newLevel: StorageLevel): Graph[VD, ED] = {
-  override def cache(): Graph[VD, ED] = {
+    eTable.persist(newLevel)
-    eTable.cache()
+    vid2pid.persist(newLevel)
-    vid2pid.cache()
+    vTable.persist(newLevel)
-    vTable.cache()
+    localVidMap.persist(newLevel)
+    // vTableReplicatedValues.persist(newLevel)
     this
   }
 
+  override def cache(): Graph[VD, ED] = persist(StorageLevel.MEMORY_ONLY)
 
   override def statistics: Map[String, Any] = {
     val numVertices = this.numVertices
@@ -125,7 +122,6 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
       "Min Load" -> minLoad, "Max Load" -> maxLoad)
   }
 
-
   /**
    * Display the lineage information for this graph.
    */
@@ -183,14 +179,12 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
     println(visited)
   } // end of print lineage
 
-
   override def reverse: Graph[VD, ED] = {
     val newEtable = eTable.mapPartitions( _.map{ case (pid, epart) => (pid, epart.reverse) },
       preservesPartitioning = true)
     new GraphImpl(vTable, vid2pid, localVidMap, newEtable)
   }
 
-
   override def mapVertices[VD2: ClassManifest](f: (Vid, VD) => VD2): Graph[VD2, ED] = {
     val newVTable = vTable.mapValuesWithKeys((vid, data) => f(vid, data))
     new GraphImpl(newVTable, vid2pid, localVidMap, eTable)
@@ -202,11 +196,9 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
     new GraphImpl(vTable, vid2pid, localVidMap, newETable)
   }
 
-
   override def mapTriplets[ED2: ClassManifest](f: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2] =
     GraphImpl.mapTriplets(this, f)
 
-
   override def subgraph(epred: EdgeTriplet[VD,ED] => Boolean = (x => true),
     vpred: (Vid, VD) => Boolean = ((a,b) => true) ): Graph[VD, ED] = {
 
@@ -246,7 +238,6 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
     new GraphImpl(newVTable, newVid2Pid, localVidMap, newETable)
   }
 
-
   override def groupEdgeTriplets[ED2: ClassManifest](
     f: Iterator[EdgeTriplet[VD,ED]] => ED2 ): Graph[VD,ED2] = {
       val newEdges: RDD[Edge[ED2]] = triplets.mapPartitions { partIter =>
@@ -271,7 +262,6 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
       new GraphImpl(vTable, vid2pid, localVidMap, newETable)
   }
 
-
   override def groupEdges[ED2: ClassManifest](f: Iterator[Edge[ED]] => ED2 ):
     Graph[VD,ED2] = {
 
@@ -289,8 +279,6 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
       new GraphImpl(vTable, vid2pid, localVidMap, newETable)
   }
 
-
-
   //////////////////////////////////////////////////////////////////////////////////////////////////
   // Lower level transformation methods
   //////////////////////////////////////////////////////////////////////////////////////////////////
@@ -301,7 +289,6 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
     : VertexSetRDD[A] =
     GraphImpl.mapReduceTriplets(this, mapFunc, reduceFunc)
 
-
   override def outerJoinVertices[U: ClassManifest, VD2: ClassManifest]
     (updates: RDD[(Vid, U)])(updateF: (Vid, VD, Option[U]) => VD2)
     : Graph[VD2, ED] = {
@@ -309,15 +296,9 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
     val newVTable = vTable.leftJoin(updates)(updateF)
     new GraphImpl(newVTable, vid2pid, localVidMap, eTable)
   }
-
-
 } // end of class GraphImpl
 
 
-
-
-
-
 object GraphImpl {
 
   def apply[VD: ClassManifest, ED: ClassManifest](
@@ -327,7 +308,6 @@ object GraphImpl {
     apply(vertices, edges, defaultVertexAttr, (a:VD, b:VD) => a)
   }
 
-
   def apply[VD: ClassManifest, ED: ClassManifest](
     vertices: RDD[(Vid, VD)],
     edges: RDD[Edge[ED]],
@@ -353,7 +333,6 @@ object GraphImpl {
     new GraphImpl(vtable, vid2pid, localVidMap, etable)
   }
 
-
   /**
    * Create the edge table RDD, which is much more efficient for Java heap storage than the
    * normal edges data structure (RDD[(Vid, Vid, ED)]).
@@ -375,7 +354,7 @@ object GraphImpl {
       //val part: Pid = canonicalEdgePartitionFunction2D(e.srcId, e.dstId, numPartitions, ceilSqrt)
 
       // Should we be using 3-tuple or an optimized class
-      MessageToPartition(part, (e.srcId, e.dstId, e.attr))
+      new MessageToPartition(part, (e.srcId, e.dstId, e.attr))
     }
     .partitionBy(new HashPartitioner(numPartitions))
     .mapPartitionsWithIndex( (pid, iter) => {
@@ -389,7 +368,6 @@ object GraphImpl {
     }, preservesPartitioning = true).cache()
   }
 
-
   protected def createVid2Pid[ED: ClassManifest](
     eTable: RDD[(Pid, EdgePartition[ED])],
     vTableIndex: VertexSetIndex): VertexSetRDD[Array[Pid]] = {
@@ -398,7 +376,7 @@ object GraphImpl {
       val vSet = new VertexSet
       edgePartition.foreach(e => {vSet.add(e.srcId); vSet.add(e.dstId)})
       vSet.iterator.map { vid => (vid.toLong, pid) }
-    }
+    }.partitionBy(vTableIndex.rdd.partitioner.get)
     VertexSetRDD[Pid, ArrayBuffer[Pid]](preAgg, vTableIndex,
       (p: Pid) => ArrayBuffer(p),
       (ab: ArrayBuffer[Pid], p:Pid) => {ab.append(p); ab},
@@ -406,7 +384,6 @@ object GraphImpl {
       .mapValues(a => a.toArray).cache()
   }
 
-
   protected def createLocalVidMap[ED: ClassManifest](eTable: RDD[(Pid, EdgePartition[ED])]):
     RDD[(Pid, VertexIdToIndexMap)] = {
     eTable.mapPartitions( _.map{ case (pid, epart) =>
@@ -419,7 +396,6 @@ object GraphImpl {
     }, preservesPartitioning = true).cache()
   }
 
-
   protected def createVTableReplicated[VD: ClassManifest](
       vTable: VertexSetRDD[VD],
       vid2pid: VertexSetRDD[Array[Pid]],
@@ -428,7 +404,10 @@ object GraphImpl {
     // Join vid2pid and vTable, generate a shuffle dependency on the joined
     // result, and get the shuffle id so we can use it on the slave.
     val msgsByPartition = vTable.zipJoinFlatMap(vid2pid) { (vid, vdata, pids) =>
-      pids.iterator.map { pid => MessageToPartition(pid, (vid, vdata)) }
+      // TODO(rxin): reuse VertexBroadcastMessage
+      pids.iterator.map { pid =>
+        new VertexBroadcastMsg[VD](pid, vid, vdata)
+      }
     }.partitionBy(replicationMap.partitioner.get).cache()
 
     replicationMap.zipPartitions(msgsByPartition){
@@ -438,8 +417,8 @@ object GraphImpl {
       // Populate the vertex array using the vidToIndex map
       val vertexArray = new Array[VD](vidToIndex.capacity)
       for (msg <- msgsIter) {
-        val ind = vidToIndex.getPos(msg.data._1) & OpenHashSet.POSITION_MASK
+        val ind = vidToIndex.getPos(msg.vid) & OpenHashSet.POSITION_MASK
-        vertexArray(ind) = msg.data._2
+        vertexArray(ind) = msg.data
       }
       Iterator((pid, vertexArray))
     }.cache()
@@ -447,7 +426,6 @@ object GraphImpl {
     // @todo assert edge table has partitioner
   }
 
-
   def makeTriplets[VD: ClassManifest, ED: ClassManifest](
     localVidMap: RDD[(Pid, VertexIdToIndexMap)],
     vTableReplicatedValues: RDD[(Pid, Array[VD]) ],
@@ -461,7 +439,6 @@ object GraphImpl {
     }
   }
 
-
   def mapTriplets[VD: ClassManifest, ED: ClassManifest, ED2: ClassManifest](
     g: GraphImpl[VD, ED],
     f: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2] = {
@@ -483,7 +460,6 @@ object GraphImpl {
     new GraphImpl(g.vTable, g.vid2pid, g.localVidMap, newETable)
   }
 
-
   def mapReduceTriplets[VD: ClassManifest, ED: ClassManifest, A: ClassManifest](
     g: GraphImpl[VD, ED],
     mapFunc: EdgeTriplet[VD, ED] => Array[(Vid, A)],
@@ -495,33 +471,35 @@ object GraphImpl {
     // Map and preaggregate
     val preAgg = g.eTable.zipPartitions(g.localVidMap, g.vTableReplicatedValues){
       (edgePartitionIter, vidToIndexIter, vertexArrayIter) =>
-      val (pid, edgePartition) = edgePartitionIter.next()
+      val (_, edgePartition) = edgePartitionIter.next()
       val (_, vidToIndex) = vidToIndexIter.next()
       val (_, vertexArray) = vertexArrayIter.next()
       assert(!edgePartitionIter.hasNext)
       assert(!vidToIndexIter.hasNext)
       assert(!vertexArrayIter.hasNext)
       assert(vidToIndex.capacity == vertexArray.size)
+      // Reuse the vidToIndex map to run aggregation.
       val vmap = new PrimitiveKeyOpenHashMap[Vid, VD](vidToIndex, vertexArray)
-      // We can reuse the vidToIndex map for aggregation here as well.
+      // TODO(jegonzal): This doesn't allow users to send messages to arbitrary vertices.
-      /** @todo Since this has the downside of not allowing "messages" to arbitrary
-       * vertices we should consider just using a fresh map.
-       */
       val msgArray = new Array[A](vertexArray.size)
       val msgBS = new BitSet(vertexArray.size)
       // Iterate over the partition
       val et = new EdgeTriplet[VD, ED]
-      edgePartition.foreach{e =>
+
+      edgePartition.foreach { e =>
         et.set(e)
         et.srcAttr = vmap(e.srcId)
         et.dstAttr = vmap(e.dstId)
-        mapFunc(et).foreach{ case (vid, msg) =>
+        // TODO(rxin): rewrite the foreach using a simple while loop to speed things up.
+        // Also given we are only allowing zero, one, or two messages, we can completely unroll
+        // the for loop.
+        mapFunc(et).foreach { case (vid, msg) =>
           // verify that the vid is valid
           assert(vid == et.srcId || vid == et.dstId)
           // Get the index of the key
           val ind = vidToIndex.getPos(vid) & OpenHashSet.POSITION_MASK
           // Populate the aggregator map
-          if(msgBS.get(ind)) {
+          if (msgBS.get(ind)) {
             msgArray(ind) = reduceFunc(msgArray(ind), msg)
           } else {
             msgArray(ind) = msg
@@ -530,20 +508,19 @@ object GraphImpl {
         }
       }
       // construct an iterator of tuples Iterator[(Vid, A)]
-      msgBS.iterator.map( ind => (vidToIndex.getValue(ind), msgArray(ind)) )
+      msgBS.iterator.map { ind =>
+        new AggregationMsg[A](vidToIndex.getValue(ind), msgArray(ind))
+      }
     }.partitionBy(g.vTable.index.rdd.partitioner.get)
     // do the final reduction reusing the index map
-    VertexSetRDD(preAgg, g.vTable.index, reduceFunc)
+    VertexSetRDD.aggregate(preAgg, g.vTable.index, reduceFunc)
   }
 
-
   protected def edgePartitionFunction1D(src: Vid, dst: Vid, numParts: Pid): Pid = {
     val mixingPrime: Vid = 1125899906842597L
     (math.abs(src) * mixingPrime).toInt % numParts
   }
 
-
-
   /**
    * This function implements a classic 2D-Partitioning of a sparse matrix.
    * Suppose we have a graph with 11 vertices that we want to partition
@@ -596,7 +573,6 @@ object GraphImpl {
     (col * ceilSqrtNumParts + row) % numParts
   }
 
-
   /**
    * Assign edges to an aribtrary machine corresponding to a
    * random vertex cut.
@@ -605,7 +581,6 @@ object GraphImpl {
     math.abs((src, dst).hashCode()) % numParts
   }
 
-
   /**
    * @todo This will only partition edges to the upper diagonal
    * of the 2D processor space.
@@ -622,4 +597,3 @@ object GraphImpl {
   }
 
 } // end of object GraphImpl
-

graph/src/main/scala/org/apache/spark/graph/impl/MessageToPartition.scala

@@ -1,10 +1,35 @@
 package org.apache.spark.graph.impl
 
 import org.apache.spark.Partitioner
-import org.apache.spark.graph.Pid
+import org.apache.spark.graph.{Pid, Vid}
 import org.apache.spark.rdd.{ShuffledRDD, RDD}
 
 
+class VertexBroadcastMsg[@specialized(Int, Long, Double, Boolean) T](
+    @transient var partition: Pid,
+    var vid: Vid,
+    var data: T)
+  extends Product2[Pid, (Vid, T)] {
+
+  override def _1 = partition
+
+  override def _2 = (vid, data)
+
+  override def canEqual(that: Any): Boolean = that.isInstanceOf[VertexBroadcastMsg[_]]
+}
+
+
+class AggregationMsg[@specialized(Int, Long, Double, Boolean) T](var vid: Vid, var data: T)
+  extends Product2[Vid, T] {
+
+  override def _1 = vid
+
+  override def _2 = data
+
+  override def canEqual(that: Any): Boolean = that.isInstanceOf[AggregationMsg[_]]
+}
+
+
 /**
  * A message used to send a specific value to a partition.
  * @param partition index of the target partition.
@@ -22,15 +47,42 @@ class MessageToPartition[@specialized(Int, Long, Double, Char, Boolean/*, AnyRef
   override def canEqual(that: Any): Boolean = that.isInstanceOf[MessageToPartition[_]]
 }
 
-/**
+
- * Companion object for MessageToPartition.
+class VertexBroadcastMsgRDDFunctions[T: ClassManifest](self: RDD[VertexBroadcastMsg[T]]) {
- */
+  def partitionBy(partitioner: Partitioner): RDD[VertexBroadcastMsg[T]] = {
-object MessageToPartition {
+    val rdd = new ShuffledRDD[Pid, (Vid, T), VertexBroadcastMsg[T]](self, partitioner)
-  def apply[T](partition: Pid, value: T) = new MessageToPartition(partition, value)
+
+    // Set a custom serializer if the data is of int or double type.
+    if (classManifest[T] == ClassManifest.Int) {
+      rdd.setSerializer(classOf[IntVertexBroadcastMsgSerializer].getName)
+    } else if (classManifest[T] == ClassManifest.Long) {
+      rdd.setSerializer(classOf[LongVertexBroadcastMsgSerializer].getName)
+    } else if (classManifest[T] == ClassManifest.Double) {
+      rdd.setSerializer(classOf[DoubleVertexBroadcastMsgSerializer].getName)
+    }
+    rdd
+  }
 }
 
 
-class MessageToPartitionRDDFunctions[T: ClassManifest](self: RDD[MessageToPartition[T]]) {
+class AggregationMessageRDDFunctions[T: ClassManifest](self: RDD[AggregationMsg[T]]) {
+  def partitionBy(partitioner: Partitioner): RDD[AggregationMsg[T]] = {
+    val rdd = new ShuffledRDD[Vid, T, AggregationMsg[T]](self, partitioner)
+
+    // Set a custom serializer if the data is of int or double type.
+    if (classManifest[T] == ClassManifest.Int) {
+      rdd.setSerializer(classOf[IntAggMsgSerializer].getName)
+    } else if (classManifest[T] == ClassManifest.Long) {
+      rdd.setSerializer(classOf[LongAggMsgSerializer].getName)
+    } else if (classManifest[T] == ClassManifest.Double) {
+      rdd.setSerializer(classOf[DoubleAggMsgSerializer].getName)
+    }
+    rdd
+  }
+}
+
+
+class MsgRDDFunctions[T: ClassManifest](self: RDD[MessageToPartition[T]]) {
 
   /**
    * Return a copy of the RDD partitioned using the specified partitioner.
@@ -42,8 +94,16 @@ class MessageToPartitionRDDFunctions[T: ClassManifest](self: RDD[MessageToPartit
 }
 
 
-object MessageToPartitionRDDFunctions {
+object MsgRDDFunctions {
   implicit def rdd2PartitionRDDFunctions[T: ClassManifest](rdd: RDD[MessageToPartition[T]]) = {
-    new MessageToPartitionRDDFunctions(rdd)
+    new MsgRDDFunctions(rdd)
+  }
+
+  implicit def rdd2vertexMessageRDDFunctions[T: ClassManifest](rdd: RDD[VertexBroadcastMsg[T]]) = {
+    new VertexBroadcastMsgRDDFunctions(rdd)
+  }
+
+  implicit def rdd2aggMessageRDDFunctions[T: ClassManifest](rdd: RDD[AggregationMsg[T]]) = {
+    new AggregationMessageRDDFunctions(rdd)
   }
 }

graph/src/main/scala/org/apache/spark/graph/impl/Serializers.scala

@@ -0,0 +1,224 @@
+package org.apache.spark.graph.impl
+
+import java.io.{EOFException, InputStream, OutputStream}
+import java.nio.ByteBuffer
+
+import org.apache.spark.serializer._
+
+
+/** A special shuffle serializer for VertexBroadcastMessage[Int]. */
+class IntVertexBroadcastMsgSerializer extends Serializer {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
+      def writeObject[T](t: T) = {
+        val msg = t.asInstanceOf[VertexBroadcastMsg[Int]]
+        writeLong(msg.vid)
+        writeInt(msg.data)
+        this
+      }
+    }
+
+    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
+      override def readObject[T](): T = {
+        new VertexBroadcastMsg[Int](0, readLong(), readInt()).asInstanceOf[T]
+      }
+    }
+  }
+}
+
+/** A special shuffle serializer for VertexBroadcastMessage[Long]. */
+class LongVertexBroadcastMsgSerializer extends Serializer {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
+      def writeObject[T](t: T) = {
+        val msg = t.asInstanceOf[VertexBroadcastMsg[Long]]
+        writeLong(msg.vid)
+        writeLong(msg.data)
+        this
+      }
+    }
+
+    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
+      override def readObject[T](): T = {
+        val a = readLong()
+        val b = readLong()
+        new VertexBroadcastMsg[Long](0, a, b).asInstanceOf[T]
+      }
+    }
+  }
+}
+
+/** A special shuffle serializer for VertexBroadcastMessage[Double]. */
+class DoubleVertexBroadcastMsgSerializer extends Serializer {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
+      def writeObject[T](t: T) = {
+        val msg = t.asInstanceOf[VertexBroadcastMsg[Double]]
+        writeLong(msg.vid)
+        writeDouble(msg.data)
+        this
+      }
+    }
+
+    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
+      def readObject[T](): T = {
+        val a = readLong()
+        val b = readDouble()
+        new VertexBroadcastMsg[Double](0, a, b).asInstanceOf[T]
+      }
+    }
+  }
+}
+
+
+/** A special shuffle serializer for AggregationMessage[Int]. */
+class IntAggMsgSerializer extends Serializer {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
+      def writeObject[T](t: T) = {
+        val msg = t.asInstanceOf[AggregationMsg[Int]]
+        writeLong(msg.vid)
+        writeInt(msg.data)
+        this
+      }
+    }
+
+    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
+      override def readObject[T](): T = {
+        val a = readLong()
+        val b = readInt()
+        new AggregationMsg[Int](a, b).asInstanceOf[T]
+      }
+    }
+  }
+}
+
+/** A special shuffle serializer for AggregationMessage[Long]. */
+class LongAggMsgSerializer extends Serializer {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
+      def writeObject[T](t: T) = {
+        val msg = t.asInstanceOf[AggregationMsg[Long]]
+        writeLong(msg.vid)
+        writeLong(msg.data)
+        this
+      }
+    }
+
+    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
+      override def readObject[T](): T = {
+        val a = readLong()
+        val b = readLong()
+        new AggregationMsg[Long](a, b).asInstanceOf[T]
+      }
+    }
+  }
+}
+
+
+/** A special shuffle serializer for AggregationMessage[Double]. */
+class DoubleAggMsgSerializer extends Serializer {
+  override def newInstance(): SerializerInstance = new ShuffleSerializerInstance {
+
+    override def serializeStream(s: OutputStream) = new ShuffleSerializationStream(s) {
+      def writeObject[T](t: T) = {
+        val msg = t.asInstanceOf[AggregationMsg[Double]]
+        writeLong(msg.vid)
+        writeDouble(msg.data)
+        this
+      }
+    }
+
+    override def deserializeStream(s: InputStream) = new ShuffleDeserializationStream(s) {
+      def readObject[T](): T = {
+        val a = readLong()
+        val b = readDouble()
+        new AggregationMsg[Double](a, b).asInstanceOf[T]
+      }
+    }
+  }
+}
+
+////////////////////////////////////////////////////////////////////////////////
+// Helper classes to shorten the implementation of those special serializers.
+////////////////////////////////////////////////////////////////////////////////
+
+sealed abstract class ShuffleSerializationStream(s: OutputStream) extends SerializationStream {
+  // The implementation should override this one.
+  def writeObject[T](t: T): SerializationStream
+
+  def writeInt(v: Int) {
+    s.write(v >> 24)
+    s.write(v >> 16)
+    s.write(v >> 8)
+    s.write(v)
+  }
+
+  def writeLong(v: Long) {
+    s.write((v >>> 56).toInt)
+    s.write((v >>> 48).toInt)
+    s.write((v >>> 40).toInt)
+    s.write((v >>> 32).toInt)
+    s.write((v >>> 24).toInt)
+    s.write((v >>> 16).toInt)
+    s.write((v >>> 8).toInt)
+    s.write(v.toInt)
+  }
+
+  def writeDouble(v: Double) {
+    writeLong(java.lang.Double.doubleToLongBits(v))
+  }
+
+  override def flush(): Unit = s.flush()
+
+  override def close(): Unit = s.close()
+}
+
+
+sealed abstract class ShuffleDeserializationStream(s: InputStream) extends DeserializationStream {
+  // The implementation should override this one.
+  def readObject[T](): T
+
+  def readInt(): Int = {
+    val first = s.read()
+    if (first < 0) throw new EOFException
+    (first & 0xFF) << 24 | (s.read() & 0xFF) << 16 | (s.read() & 0xFF) << 8 | (s.read() & 0xFF)
+  }
+
+  def readLong(): Long = {
+    val first = s.read()
+    if (first < 0) throw new EOFException()
+    (first.toLong << 56) |
+      (s.read() & 0xFF).toLong << 48 |
+      (s.read() & 0xFF).toLong << 40 |
+      (s.read() & 0xFF).toLong << 32 |
+      (s.read() & 0xFF).toLong << 24 |
+      (s.read() & 0xFF) << 16 |
+      (s.read() & 0xFF) << 8 |
+      (s.read() & 0xFF)
+  }
+
+  def readDouble(): Double = java.lang.Double.longBitsToDouble(readLong())
+
+  override def close(): Unit = s.close()
+}
+
+
+sealed trait ShuffleSerializerInstance extends SerializerInstance {
+
+  override def serialize[T](t: T): ByteBuffer = throw new UnsupportedOperationException
+
+  override def deserialize[T](bytes: ByteBuffer): T = throw new UnsupportedOperationException
+
+  override def deserialize[T](bytes: ByteBuffer, loader: ClassLoader): T =
+    throw new UnsupportedOperationException
+
+  // The implementation should override the following two.
+  override def serializeStream(s: OutputStream): SerializationStream
+  override def deserializeStream(s: InputStream): DeserializationStream
+}

graph/src/main/scala/org/apache/spark/graph/package.scala

@@ -8,8 +8,7 @@ package object graph {
   type Vid = Long
   type Pid = Int
 
-  type VertexHashMap[T] = it.unimi.dsi.fastutil.longs.Long2ObjectOpenHashMap[T]
+  type VertexSet = OpenHashSet[Vid]
-  type VertexSet = it.unimi.dsi.fastutil.longs.LongOpenHashSet
   type VertexArrayList = it.unimi.dsi.fastutil.longs.LongArrayList
 
   //  type VertexIdToIndexMap = it.unimi.dsi.fastutil.longs.Long2IntOpenHashMap

graph/src/test/scala/org/apache/spark/graph/SerializerSuite.scala

@@ -0,0 +1,160 @@
+package org.apache.spark.graph
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.SparkContext
+import org.apache.spark.graph.LocalSparkContext._
+import java.io.{EOFException, ByteArrayInputStream, ByteArrayOutputStream}
+import org.apache.spark.graph.impl._
+import org.apache.spark.graph.impl.MsgRDDFunctions._
+import org.apache.spark._
+
+
+class SerializerSuite extends FunSuite with LocalSparkContext {
+
+  System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+  System.setProperty("spark.kryo.registrator", "org.apache.spark.graph.GraphKryoRegistrator")
+
+  test("TestVertexBroadcastMessageInt") {
+    val outMsg = new VertexBroadcastMsg[Int](3,4,5)
+    val bout = new ByteArrayOutputStream
+    val outStrm = new IntVertexBroadcastMsgSerializer().newInstance().serializeStream(bout)
+    outStrm.writeObject(outMsg)
+    outStrm.writeObject(outMsg)
+    bout.flush
+    val bin = new ByteArrayInputStream(bout.toByteArray)
+    val inStrm = new IntVertexBroadcastMsgSerializer().newInstance().deserializeStream(bin)
+    val inMsg1: VertexBroadcastMsg[Int] = inStrm.readObject()
+    val inMsg2: VertexBroadcastMsg[Int] = inStrm.readObject()
+    assert(outMsg.vid === inMsg1.vid)
+    assert(outMsg.vid === inMsg2.vid)
+    assert(outMsg.data === inMsg1.data)
+    assert(outMsg.data === inMsg2.data)
+
+    intercept[EOFException] {
+      inStrm.readObject()
+    }
+  }
+
+  test("TestVertexBroadcastMessageLong") {
+    val outMsg = new VertexBroadcastMsg[Long](3,4,5)
+    val bout = new ByteArrayOutputStream
+    val outStrm = new LongVertexBroadcastMsgSerializer().newInstance().serializeStream(bout)
+    outStrm.writeObject(outMsg)
+    outStrm.writeObject(outMsg)
+    bout.flush
+    val bin = new ByteArrayInputStream(bout.toByteArray)
+    val inStrm = new LongVertexBroadcastMsgSerializer().newInstance().deserializeStream(bin)
+    val inMsg1: VertexBroadcastMsg[Long] = inStrm.readObject()
+    val inMsg2: VertexBroadcastMsg[Long] = inStrm.readObject()
+    assert(outMsg.vid === inMsg1.vid)
+    assert(outMsg.vid === inMsg2.vid)
+    assert(outMsg.data === inMsg1.data)
+    assert(outMsg.data === inMsg2.data)
+
+    intercept[EOFException] {
+      inStrm.readObject()
+    }
+  }
+
+  test("TestVertexBroadcastMessageDouble") {
+    val outMsg = new VertexBroadcastMsg[Double](3,4,5.0)
+    val bout = new ByteArrayOutputStream
+    val outStrm = new DoubleVertexBroadcastMsgSerializer().newInstance().serializeStream(bout)
+    outStrm.writeObject(outMsg)
+    outStrm.writeObject(outMsg)
+    bout.flush
+    val bin = new ByteArrayInputStream(bout.toByteArray)
+    val inStrm = new DoubleVertexBroadcastMsgSerializer().newInstance().deserializeStream(bin)
+    val inMsg1: VertexBroadcastMsg[Double] = inStrm.readObject()
+    val inMsg2: VertexBroadcastMsg[Double] = inStrm.readObject()
+    assert(outMsg.vid === inMsg1.vid)
+    assert(outMsg.vid === inMsg2.vid)
+    assert(outMsg.data === inMsg1.data)
+    assert(outMsg.data === inMsg2.data)
+
+    intercept[EOFException] {
+      inStrm.readObject()
+    }
+  }
+
+  test("TestAggregationMessageInt") {
+    val outMsg = new AggregationMsg[Int](4,5)
+    val bout = new ByteArrayOutputStream
+    val outStrm = new IntAggMsgSerializer().newInstance().serializeStream(bout)
+    outStrm.writeObject(outMsg)
+    outStrm.writeObject(outMsg)
+    bout.flush
+    val bin = new ByteArrayInputStream(bout.toByteArray)
+    val inStrm = new IntAggMsgSerializer().newInstance().deserializeStream(bin)
+    val inMsg1: AggregationMsg[Int] = inStrm.readObject()
+    val inMsg2: AggregationMsg[Int] = inStrm.readObject()
+    assert(outMsg.vid === inMsg1.vid)
+    assert(outMsg.vid === inMsg2.vid)
+    assert(outMsg.data === inMsg1.data)
+    assert(outMsg.data === inMsg2.data)
+
+    intercept[EOFException] {
+      inStrm.readObject()
+    }
+  }
+
+  test("TestAggregationMessageLong") {
+    val outMsg = new AggregationMsg[Long](4,5)
+    val bout = new ByteArrayOutputStream
+    val outStrm = new LongAggMsgSerializer().newInstance().serializeStream(bout)
+    outStrm.writeObject(outMsg)
+    outStrm.writeObject(outMsg)
+    bout.flush
+    val bin = new ByteArrayInputStream(bout.toByteArray)
+    val inStrm = new LongAggMsgSerializer().newInstance().deserializeStream(bin)
+    val inMsg1: AggregationMsg[Long] = inStrm.readObject()
+    val inMsg2: AggregationMsg[Long] = inStrm.readObject()
+    assert(outMsg.vid === inMsg1.vid)
+    assert(outMsg.vid === inMsg2.vid)
+    assert(outMsg.data === inMsg1.data)
+    assert(outMsg.data === inMsg2.data)
+
+    intercept[EOFException] {
+      inStrm.readObject()
+    }
+  }
+
+  test("TestAggregationMessageDouble") {
+    val outMsg = new AggregationMsg[Double](4,5.0)
+    val bout = new ByteArrayOutputStream
+    val outStrm = new DoubleAggMsgSerializer().newInstance().serializeStream(bout)
+    outStrm.writeObject(outMsg)
+    outStrm.writeObject(outMsg)
+    bout.flush
+    val bin = new ByteArrayInputStream(bout.toByteArray)
+    val inStrm = new DoubleAggMsgSerializer().newInstance().deserializeStream(bin)
+    val inMsg1: AggregationMsg[Double] = inStrm.readObject()
+    val inMsg2: AggregationMsg[Double] = inStrm.readObject()
+    assert(outMsg.vid === inMsg1.vid)
+    assert(outMsg.vid === inMsg2.vid)
+    assert(outMsg.data === inMsg1.data)
+    assert(outMsg.data === inMsg2.data)
+
+    intercept[EOFException] {
+      inStrm.readObject()
+    }
+  }
+
+  test("TestShuffleVertexBroadcastMsg") {
+    withSpark(new SparkContext("local[2]", "test")) { sc =>
+      val bmsgs = sc.parallelize(0 until 100, 10).map { pid =>
+        new VertexBroadcastMsg[Int](pid, pid, pid)
+      }
+      bmsgs.partitionBy(new HashPartitioner(3)).collect()
+    }
+  }
+
+  test("TestShuffleAggregationMsg") {
+    withSpark(new SparkContext("local[2]", "test")) { sc =>
+      val bmsgs = sc.parallelize(0 until 100, 10).map(pid => new AggregationMsg[Int](pid, pid))
+      bmsgs.partitionBy(new HashPartitioner(3)).collect()
+    }
+  }
+
+}