diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
index f8d54a8f738ce741693a70001bb385b9b5785389..e86d7ef76779867181ab3885c257864375623919 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
+++ b/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))
diff --git a/graph/src/main/scala/org/apache/spark/graph/Graph.scala b/graph/src/main/scala/org/apache/spark/graph/Graph.scala
index acfdc4378b0a0cd612832e8e3860e53690215827..f5b4c57f72902167d0560ed98a1cafbd72b2a439 100644
--- a/graph/src/main/scala/org/apache/spark/graph/Graph.scala
+++ b/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
@@ -12,21 +12,21 @@ import org.apache.spark.rdd.RDD
* operations return new graphs.
*
* @see GraphOps for additional graph member functions.
- *
+ *
* @note The majority of the graph operations are implemented in
* `GraphOps`. All the convenience operations are defined in the
* `GraphOps` class which may be shared across multiple graph
* implementations.
*
* @tparam VD the vertex attribute type
- * @tparam ED the edge attribute type
+ * @tparam ED the edge attribute type
*/
abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
/**
* Get the vertices and their data.
*
- * @note vertex ids are unique.
+ * @note vertex ids are unique.
* @return An RDD containing the vertices in this graph
*
* @see Vertex for the vertex type.
@@ -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
@@ -100,7 +105,7 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
* @tparam VD2 the new vertex data type
*
* @example We might use this operation to change the vertex values
- * from one type to another to initialize an algorithm.
+ * from one type to another to initialize an algorithm.
* {{{
* val rawGraph: Graph[(), ()] = Graph.textFile("hdfs://file")
* val root = 42
@@ -190,7 +195,7 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
* @return the subgraph containing only the vertices and edges that
* satisfy the predicates.
*/
- def subgraph(epred: EdgeTriplet[VD,ED] => Boolean = (x => true),
+ def subgraph(epred: EdgeTriplet[VD,ED] => Boolean = (x => true),
vpred: (Vid, VD) => Boolean = ((v,d) => true) ): Graph[VD, ED]
@@ -255,12 +260,12 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
* @param reduceFunc the user defined reduce function which should
* be commutative and assosciative and is used to combine the output
* of the map phase.
- *
+ *
* @example We can use this function to compute the inDegree of each
* vertex
* {{{
* val rawGraph: Graph[(),()] = Graph.textFile("twittergraph")
- * val inDeg: RDD[(Vid, Int)] =
+ * val inDeg: RDD[(Vid, Int)] =
* mapReduceTriplets[Int](et => Array((et.dst.id, 1)), _ + _)
* }}}
*
@@ -269,12 +274,12 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
* Graph API in that enables neighborhood level computation. For
* example this function can be used to count neighbors satisfying a
* predicate or implement PageRank.
- *
+ *
*/
def mapReduceTriplets[A: ClassManifest](
mapFunc: EdgeTriplet[VD, ED] => Array[(Vid, A)],
reduceFunc: (A, A) => A)
- : VertexSetRDD[A]
+ : VertexSetRDD[A]
/**
@@ -296,11 +301,11 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
* @example This function is used to update the vertices with new
* values based on external data. For example we could add the out
* degree to each vertex record
- *
+ *
* {{{
* val rawGraph: Graph[(),()] = Graph.textFile("webgraph")
* val outDeg: RDD[(Vid, Int)] = rawGraph.outDegrees()
- * val graph = rawGraph.outerJoinVertices(outDeg) {
+ * val graph = rawGraph.outerJoinVertices(outDeg) {
* (vid, data, optDeg) => optDeg.getOrElse(0)
* }
* }}}
@@ -337,7 +342,7 @@ object Graph {
* (i.e., the undirected degree).
*
* @param rawEdges the RDD containing the set of edges in the graph
- *
+ *
* @return a graph with edge attributes containing the count of
* duplicate edges and vertex attributes containing the total degree
* of each vertex.
@@ -368,10 +373,10 @@ object Graph {
rawEdges.map { case (s, t) => Edge(s, t, 1) }
}
// Determine unique vertices
- /** @todo Should this reduceByKey operation be indexed? */
- val vertices: RDD[(Vid, Int)] =
+ /** @todo Should this reduceByKey operation be indexed? */
+ val vertices: RDD[(Vid, Int)] =
edges.flatMap{ case Edge(s, t, cnt) => Array((s, 1), (t, 1)) }.reduceByKey(_ + _)
-
+
// Return graph
GraphImpl(vertices, edges, 0)
}
@@ -392,7 +397,7 @@ object Graph {
*
*/
def apply[VD: ClassManifest, ED: ClassManifest](
- vertices: RDD[(Vid,VD)],
+ vertices: RDD[(Vid,VD)],
edges: RDD[Edge[ED]]): Graph[VD, ED] = {
val defaultAttr: VD = null.asInstanceOf[VD]
Graph(vertices, edges, defaultAttr, (a:VD,b:VD) => a)
@@ -416,7 +421,7 @@ object Graph {
*
*/
def apply[VD: ClassManifest, ED: ClassManifest](
- vertices: RDD[(Vid,VD)],
+ vertices: RDD[(Vid,VD)],
edges: RDD[Edge[ED]],
defaultVertexAttr: VD,
mergeFunc: (VD, VD) => VD): Graph[VD, ED] = {
diff --git a/graph/src/main/scala/org/apache/spark/graph/Pregel.scala b/graph/src/main/scala/org/apache/spark/graph/Pregel.scala
index 501e593e917eae3cf4df6940b138d2ff9a5d2c0b..3b4d3c0df2a51ca178194f4623ff02e4baa960ec 100644
--- a/graph/src/main/scala/org/apache/spark/graph/Pregel.scala
+++ b/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
}
diff --git a/graph/src/main/scala/org/apache/spark/graph/VertexSetRDD.scala b/graph/src/main/scala/org/apache/spark/graph/VertexSetRDD.scala
index 8611d2f0ce1a98da2adcfd08e6c232dd107b5904..e8b8bb32280f394e3e240fe701675529b7876ebc 100644
--- a/graph/src/main/scala/org/apache/spark/graph/VertexSetRDD.scala
+++ b/graph/src/main/scala/org/apache/spark/graph/VertexSetRDD.scala
@@ -28,7 +28,7 @@ import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveKeyOpenHa
* The `VertexSetIndex` maintains the per-partition mapping from
* vertex id to the corresponding location in the per-partition values
* array. This class is meant to be an opaque type.
- *
+ *
*/
class VertexSetIndex(private[spark] val rdd: RDD[VertexIdToIndexMap]) {
/**
@@ -55,7 +55,7 @@ class VertexSetIndex(private[spark] val rdd: RDD[VertexIdToIndexMap]) {
* In addition to providing the basic RDD[(Vid,V)] functionality the
* VertexSetRDD exposes an index member which can be used to "key"
* other VertexSetRDDs
- *
+ *
* @tparam V the vertex attribute associated with each vertex in the
* set.
*
@@ -84,7 +84,7 @@ class VertexSetIndex(private[spark] val rdd: RDD[VertexIdToIndexMap]) {
class VertexSetRDD[@specialized V: ClassManifest](
@transient val index: VertexSetIndex,
@transient val valuesRDD: RDD[ ( Array[V], BitSet) ])
- extends RDD[(Vid, V)](index.rdd.context,
+ extends RDD[(Vid, V)](index.rdd.context,
List(new OneToOneDependency(index.rdd), new OneToOneDependency(valuesRDD)) ) {
@@ -100,32 +100,32 @@ class VertexSetRDD[@specialized V: ClassManifest](
* An internal representation which joins the block indices with the values
* This is used by the compute function to emulate RDD[(Vid, V)]
*/
- protected[spark] val tuples =
+ protected[spark] val tuples =
new ZippedRDD(index.rdd.context, index.rdd, valuesRDD)
/**
- * The partitioner is defined by the index.
+ * The partitioner is defined by the index.
*/
override val partitioner = index.rdd.partitioner
-
+
/**
* The actual partitions are defined by the tuples.
*/
- override def getPartitions: Array[Partition] = tuples.getPartitions
-
+ override def getPartitions: Array[Partition] = tuples.getPartitions
+
/**
- * The preferred locations are computed based on the preferred
- * locations of the tuples.
+ * The preferred locations are computed based on the preferred
+ * locations of the tuples.
*/
- override def getPreferredLocations(s: Partition): Seq[String] =
+ override def getPreferredLocations(s: Partition): Seq[String] =
tuples.getPreferredLocations(s)
/**
- * Caching an VertexSetRDD causes the index and values to be cached separately.
+ * Caching an VertexSetRDD causes the index and values to be cached separately.
*/
override def persist(newLevel: StorageLevel): VertexSetRDD[V] = {
index.persist(newLevel)
@@ -143,7 +143,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
/**
- * Provide the RDD[(K,V)] equivalent output.
+ * Provide the RDD[(K,V)] equivalent output.
*/
override def compute(part: Partition, context: TaskContext): Iterator[(Vid, V)] = {
tuples.compute(part, context).flatMap { case (indexMap, (values, bs) ) =>
@@ -154,19 +154,19 @@ class VertexSetRDD[@specialized V: ClassManifest](
/**
* Restrict the vertex set to the set of vertices satisfying the
- * given predicate.
- *
+ * given predicate.
+ *
* @param pred the user defined predicate
*
* @note The vertex set preserves the original index structure
* which means that the returned RDD can be easily joined with
- * the original vertex-set. Furthermore, the filter only
- * modifies the bitmap index and so no new values are allocated.
+ * the original vertex-set. Furthermore, the filter only
+ * modifies the bitmap index and so no new values are allocated.
*/
override def filter(pred: Tuple2[Vid,V] => Boolean): VertexSetRDD[V] = {
val cleanPred = index.rdd.context.clean(pred)
- val newValues = index.rdd.zipPartitions(valuesRDD){
- (keysIter: Iterator[VertexIdToIndexMap],
+ val newValues = index.rdd.zipPartitions(valuesRDD){
+ (keysIter: Iterator[VertexIdToIndexMap],
valuesIter: Iterator[(Array[V], BitSet)]) =>
val index = keysIter.next()
assert(keysIter.hasNext == false)
@@ -174,7 +174,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
assert(valuesIter.hasNext == false)
// Allocate the array to store the results into
val newBS = new BitSet(index.capacity)
- // Iterate over the active bits in the old bitset and
+ // Iterate over the active bits in the old bitset and
// evaluate the predicate
var ind = bs.nextSetBit(0)
while(ind >= 0) {
@@ -193,7 +193,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
/**
* Pass each vertex attribute through a map function and retain the
* original RDD's partitioning and index.
- *
+ *
* @tparam U the type returned by the map function
*
* @param f the function applied to each value in the RDD
@@ -204,12 +204,12 @@ class VertexSetRDD[@specialized V: ClassManifest](
def mapValues[U: ClassManifest](f: V => U): VertexSetRDD[U] = {
val cleanF = index.rdd.context.clean(f)
val newValuesRDD: RDD[ (Array[U], BitSet) ] =
- valuesRDD.mapPartitions(iter => iter.map{
+ valuesRDD.mapPartitions(iter => iter.map{
case (values, bs: BitSet) =>
val newValues = new Array[U](values.size)
bs.iterator.foreach { ind => newValues(ind) = cleanF(values(ind)) }
(newValues, bs)
- }, preservesPartitioning = true)
+ }, preservesPartitioning = true)
new VertexSetRDD[U](index, newValuesRDD)
} // end of mapValues
@@ -217,7 +217,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
/**
* Pass each vertex attribute along with the vertex id through a map
* function and retain the original RDD's partitioning and index.
- *
+ *
* @tparam U the type returned by the map function
*
* @param f the function applied to each vertex id and vertex
@@ -229,8 +229,8 @@ class VertexSetRDD[@specialized V: ClassManifest](
def mapValuesWithKeys[U: ClassManifest](f: (Vid, V) => U): VertexSetRDD[U] = {
val cleanF = index.rdd.context.clean(f)
val newValues: RDD[ (Array[U], BitSet) ] =
- index.rdd.zipPartitions(valuesRDD){
- (keysIter: Iterator[VertexIdToIndexMap],
+ index.rdd.zipPartitions(valuesRDD){
+ (keysIter: Iterator[VertexIdToIndexMap],
valuesIter: Iterator[(Array[V], BitSet)]) =>
val index = keysIter.next()
assert(keysIter.hasNext == false)
@@ -254,7 +254,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
* vertices that are in both this and the other vertex set.
*
* @tparam W the attribute type of the other VertexSet
- *
+ *
* @param other the other VertexSet with which to join.
* @return a VertexSetRDD containing only the vertices in both this
* and the other VertexSet and with tuple attributes.
@@ -324,7 +324,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
* any vertex in this VertexSet then a `None` attribute is generated
*
* @tparam W the attribute type of the other VertexSet
- *
+ *
* @param other the other VertexSet with which to join.
* @return a VertexSetRDD containing all the vertices in this
* VertexSet with `None` attributes used for Vertices missing in the
@@ -365,7 +365,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
* VertexSet then a `None` attribute is generated
*
* @tparam W the attribute type of the other VertexSet
- *
+ *
* @param other the other VertexSet with which to join.
* @param merge the function used combine duplicate vertex
* attributes
@@ -398,28 +398,28 @@ class VertexSetRDD[@specialized V: ClassManifest](
/**
- * For each key k in `this` or `other`, return a resulting RDD that contains a
+ * For each key k in `this` or `other`, return a resulting RDD that contains a
* tuple with the list of values for that key in `this` as well as `other`.
*/
/*
- def cogroup[W: ClassManifest](other: RDD[(Vid, W)], partitioner: Partitioner):
+ def cogroup[W: ClassManifest](other: RDD[(Vid, W)], partitioner: Partitioner):
VertexSetRDD[(Seq[V], Seq[W])] = {
//RDD[(K, (Seq[V], Seq[W]))] = {
other match {
case other: VertexSetRDD[_] if index == other.index => {
- // if both RDDs share exactly the same index and therefore the same
- // super set of keys then we simply merge the value RDDs.
- // However it is possible that both RDDs are missing a value for a given key in
+ // if both RDDs share exactly the same index and therefore the same
+ // super set of keys then we simply merge the value RDDs.
+ // However it is possible that both RDDs are missing a value for a given key in
// which case the returned RDD should have a null value
- val newValues: RDD[(IndexedSeq[(Seq[V], Seq[W])], BitSet)] =
+ val newValues: RDD[(IndexedSeq[(Seq[V], Seq[W])], BitSet)] =
valuesRDD.zipPartitions(other.valuesRDD){
- (thisIter, otherIter) =>
+ (thisIter, otherIter) =>
val (thisValues, thisBS) = thisIter.next()
assert(!thisIter.hasNext)
val (otherValues, otherBS) = otherIter.next()
assert(!otherIter.hasNext)
- /**
- * @todo consider implementing this with a view as in leftJoin to
+ /**
+ * @todo consider implementing this with a view as in leftJoin to
* reduce array allocations
*/
val newValues = new Array[(Seq[V], Seq[W])](thisValues.size)
@@ -428,20 +428,20 @@ class VertexSetRDD[@specialized V: ClassManifest](
var ind = newBS.nextSetBit(0)
while(ind >= 0) {
val a = if (thisBS.get(ind)) Seq(thisValues(ind)) else Seq.empty[V]
- val b = if (otherBS.get(ind)) Seq(otherValues(ind)) else Seq.empty[W]
+ val b = if (otherBS.get(ind)) Seq(otherValues(ind)) else Seq.empty[W]
newValues(ind) = (a, b)
ind = newBS.nextSetBit(ind+1)
}
Iterator((newValues.toIndexedSeq, newBS))
}
- new VertexSetRDD(index, newValues)
+ new VertexSetRDD(index, newValues)
}
- case other: VertexSetRDD[_]
+ case other: VertexSetRDD[_]
if index.rdd.partitioner == other.index.rdd.partitioner => {
// If both RDDs are indexed using different indices but with the same partitioners
// then we we need to first merge the indicies and then use the merged index to
// merge the values.
- val newIndex =
+ val newIndex =
index.rdd.zipPartitions(other.index.rdd)(
(thisIter, otherIter) => {
val thisIndex = thisIter.next()
@@ -463,7 +463,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
List(newIndex).iterator
}).cache()
// Use the new index along with the this and the other indices to merge the values
- val newValues: RDD[(IndexedSeq[(Seq[V], Seq[W])], BitSet)] =
+ val newValues: RDD[(IndexedSeq[(Seq[V], Seq[W])], BitSet)] =
newIndex.zipPartitions(tuples, other.tuples)(
(newIndexIter, thisTuplesIter, otherTuplesIter) => {
// Get the new index for this partition
@@ -507,7 +507,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
case None => throw new SparkException("An index must have a partitioner.")
}
// Shuffle the other RDD using the partitioner for this index
- val otherShuffled =
+ val otherShuffled =
if (other.partitioner == Some(partitioner)) {
other
} else {
@@ -527,7 +527,7 @@ class VertexSetRDD[@specialized V: ClassManifest](
// populate the newValues with the values in this VertexSetRDD
for ((k,i) <- thisIndex) {
if (thisBS.get(i)) {
- newValues(i) = (Seq(thisValues(i)), ArrayBuffer.empty[W])
+ newValues(i) = (Seq(thisValues(i)), ArrayBuffer.empty[W])
newBS.set(i)
}
}
@@ -538,28 +538,28 @@ class VertexSetRDD[@specialized V: ClassManifest](
if(newBS.get(ind)) {
newValues(ind)._2.asInstanceOf[ArrayBuffer[W]].append(w)
} else {
- // If the other key was in the index but not in the values
- // of this indexed RDD then create a new values entry for it
+ // If the other key was in the index but not in the values
+ // of this indexed RDD then create a new values entry for it
newBS.set(ind)
newValues(ind) = (Seq.empty[V], ArrayBuffer(w))
- }
+ }
} else {
// update the index
val ind = newIndex.size
newIndex.put(k, ind)
newBS.set(ind)
// Update the values
- newValues.append( (Seq.empty[V], ArrayBuffer(w) ) )
+ newValues.append( (Seq.empty[V], ArrayBuffer(w) ) )
}
}
Iterator( (newIndex, (newValues.toIndexedSeq, newBS)) )
}).cache()
- // Extract the index and values from the above RDD
+ // Extract the index and values from the above RDD
val newIndex = groups.mapPartitions(_.map{ case (kMap,vAr) => kMap }, true)
- val newValues: RDD[(IndexedSeq[(Seq[V], Seq[W])], BitSet)] =
+ val newValues: RDD[(IndexedSeq[(Seq[V], Seq[W])], BitSet)] =
groups.mapPartitions(_.map{ case (kMap,vAr) => vAr }, true)
-
+
new VertexSetRDD[(Seq[V], Seq[W])](new VertexSetIndex(newIndex), newValues)
}
}
@@ -583,7 +583,7 @@ object VertexSetRDD {
*
* @param rdd the collection of vertex-attribute pairs
*/
- def apply[V: ClassManifest](rdd: RDD[(Vid,V)]): VertexSetRDD[V] =
+ def apply[V: ClassManifest](rdd: RDD[(Vid,V)]): VertexSetRDD[V] =
apply(rdd, (a:V, b:V) => a )
/**
@@ -591,7 +591,7 @@ object VertexSetRDD {
* where duplicate entries are merged using the reduceFunc
*
* @tparam V the vertex attribute type
- *
+ *
* @param rdd the collection of vertex-attribute pairs
* @param reduceFunc the function used to merge attributes of
* duplicate vertices.
@@ -602,12 +602,12 @@ object VertexSetRDD {
// Preaggregate and shuffle if necessary
val preAgg = rdd.partitioner match {
case Some(p) => rdd
- case None =>
+ case None =>
val partitioner = new HashPartitioner(rdd.partitions.size)
// Preaggregation.
val aggregator = new Aggregator[Vid, V, V](v => v, cReduceFunc, cReduceFunc)
rdd.mapPartitions(aggregator.combineValuesByKey, true).partitionBy(partitioner)
- }
+ }
val groups = preAgg.mapPartitions( iter => {
val hashMap = new PrimitiveKeyOpenHashMap[Vid, V]
@@ -629,8 +629,8 @@ object VertexSetRDD {
/**
* Construct a vertex set from an RDD using an existing index.
- *
- * @note duplicate vertices are discarded arbitrarily
+ *
+ * @note duplicate vertices are discarded arbitrarily
*
* @tparam V the vertex attribute type
* @param rdd the rdd containing vertices
@@ -638,13 +638,13 @@ object VertexSetRDD {
* in RDD
*/
def apply[V: ClassManifest](
- rdd: RDD[(Vid,V)], index: VertexSetIndex): VertexSetRDD[V] =
+ rdd: RDD[(Vid,V)], index: VertexSetIndex): VertexSetRDD[V] =
apply(rdd, index, (a:V,b:V) => a)
/**
* Construct a vertex set from an RDD using an existing index and a
- * user defined `combiner` to merge duplicate vertices.
+ * user defined `combiner` to merge duplicate vertices.
*
* @tparam V the vertex attribute type
* @param rdd the rdd containing vertices
@@ -655,13 +655,13 @@ object VertexSetRDD {
*/
def apply[V: ClassManifest](
rdd: RDD[(Vid,V)], index: VertexSetIndex,
- reduceFunc: (V, V) => V): VertexSetRDD[V] =
+ reduceFunc: (V, V) => V): VertexSetRDD[V] =
apply(rdd,index, (v:V) => v, reduceFunc, reduceFunc)
-
+
/**
* Construct a vertex set from an RDD using an existing index and a
- * user defined `combiner` to merge duplicate vertices.
+ * user defined `combiner` to merge duplicate vertices.
*
* @tparam V the vertex attribute type
* @param rdd the rdd containing vertices
@@ -675,7 +675,7 @@ object VertexSetRDD {
*
*/
def apply[V: ClassManifest, C: ClassManifest](
- rdd: RDD[(Vid,V)],
+ rdd: RDD[(Vid,V)],
index: VertexSetIndex,
createCombiner: V => C,
mergeValue: (C, V) => C,
@@ -689,7 +689,7 @@ object VertexSetRDD {
case None => throw new SparkException("An index must have a partitioner.")
}
// Preaggregate and shuffle if necessary
- val partitioned =
+ val partitioned =
if (rdd.partitioner != Some(partitioner)) {
// Preaggregation.
val aggregator = new Aggregator[Vid, V, C](cCreateCombiner, cMergeValue,
@@ -732,23 +732,23 @@ object VertexSetRDD {
/**
* Construct an index of the unique vertices. The resulting index
- * can be used to build VertexSets over subsets of the vertices in
+ * can be used to build VertexSets over subsets of the vertices in
* the input.
*/
- def makeIndex(keys: RDD[Vid],
+ def makeIndex(keys: RDD[Vid],
partitioner: Option[Partitioner] = None): VertexSetIndex = {
// @todo: I don't need the boolean its only there to be the second type since I want to shuffle a single RDD
- // Ugly hack :-(. In order to partition the keys they must have values.
+ // Ugly hack :-(. In order to partition the keys they must have values.
val tbl = keys.mapPartitions(_.map(k => (k, false)), true)
// Shuffle the table (if necessary)
val shuffledTbl = partitioner match {
case None => {
if (tbl.partitioner.isEmpty) {
- // @todo: I don't need the boolean its only there to be the second type of the shuffle.
+ // @todo: I don't need the boolean its only there to be the second type of the shuffle.
new ShuffledRDD[Vid, Boolean, (Vid, Boolean)](tbl, Partitioner.defaultPartitioner(tbl))
} else { tbl }
}
- case Some(partitioner) =>
+ case Some(partitioner) =>
tbl.partitionBy(partitioner)
}
diff --git a/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala b/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
index b178e3bb42bb69c4e5a147b9fce5826148217b77..9ce06eb9e8b2cb481857f53a838bbd6eb9ddaada 100644
--- a/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
+++ b/graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
@@ -14,6 +14,7 @@ import org.apache.spark.graph._
import org.apache.spark.graph.impl.GraphImpl._
import org.apache.spark.graph.impl.MessageToPartitionRDDFunctions._
import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
import org.apache.spark.util.collection.{BitSet, OpenHashSet, PrimitiveKeyOpenHashMap}
@@ -102,13 +103,16 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
makeTriplets(localVidMap, vTableReplicatedValues, eTable)
- override def cache(): Graph[VD, ED] = {
- eTable.cache()
- vid2pid.cache()
- vTable.cache()
+ override def persist(newLevel: StorageLevel): Graph[VD, ED] = {
+ eTable.persist(newLevel)
+ vid2pid.persist(newLevel)
+ vTable.persist(newLevel)
+ 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
@@ -398,7 +402,7 @@ 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},
diff --git a/graph/src/main/scala/org/apache/spark/graph/package.scala b/graph/src/main/scala/org/apache/spark/graph/package.scala
index ee28d1429e017c8d9a5a1847665322dca9cfc01c..7b53e9cce82a3a7eb32ff7daa82015965d0e1cd2 100644
--- a/graph/src/main/scala/org/apache/spark/graph/package.scala
+++ b/graph/src/main/scala/org/apache/spark/graph/package.scala
@@ -8,10 +8,9 @@ package object graph {
type Vid = Long
type Pid = Int
- type VertexHashMap[T] = it.unimi.dsi.fastutil.longs.Long2ObjectOpenHashMap[T]
- type VertexSet = it.unimi.dsi.fastutil.longs.LongOpenHashSet
+ type VertexSet = OpenHashSet[Vid]
type VertexArrayList = it.unimi.dsi.fastutil.longs.LongArrayList
-
+
// type VertexIdToIndexMap = it.unimi.dsi.fastutil.longs.Long2IntOpenHashMap
type VertexIdToIndexMap = OpenHashSet[Vid]