[SPARK-11496][GRAPHX] Parallel implementation of personalized pagerank

graphx/pom.xml

@@ -46,6 +46,11 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-mllib-local_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
     <dependency>
       <groupId>org.apache.xbean</groupId>
       <artifactId>xbean-asm5-shaded</artifactId>

graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala

@@ -20,9 +20,10 @@ package org.apache.spark.graphx
 import scala.reflect.ClassTag
 import scala.util.Random
 
-import org.apache.spark.SparkException
 import org.apache.spark.graphx.lib._
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkException
 
 /**
  * Contains additional functionality for [[Graph]]. All operations are expressed in terms of the
@@ -391,6 +392,15 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali
     PageRank.runUntilConvergenceWithOptions(graph, tol, resetProb, Some(src))
   }
 
+  /**
+   * Run parallel personalized PageRank for a given array of source vertices, such
+   * that all random walks are started relative to the source vertices
+   */
+  def staticParallelPersonalizedPageRank(sources: Array[VertexId], numIter: Int,
+    resetProb: Double = 0.15) : Graph[Vector, Double] = {
+    PageRank.runParallelPersonalizedPageRank(graph, numIter, resetProb, sources)
+  }
+
   /**
    * Run Personalized PageRank for a fixed number of iterations with
    * with all iterations originating at the source node

graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala

@@ -19,8 +19,11 @@ package org.apache.spark.graphx.lib
 
 import scala.reflect.ClassTag
 
+import breeze.linalg.{Vector => BV}
+
 import org.apache.spark.graphx._
 import org.apache.spark.internal.Logging
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 
 /**
  * PageRank algorithm implementation. There are two implementations of PageRank implemented.
@@ -162,6 +165,85 @@ object PageRank extends Logging {
     rankGraph
   }
 
+  /**
+   * Run Personalized PageRank for a fixed number of iterations, for a
+   * set of starting nodes in parallel. Returns a graph with vertex attributes
+   * containing the pagerank relative to all starting nodes (as a sparse vector) and
+   * edge attributes the normalized edge weight
+   *
+   * @tparam VD The original vertex attribute (not used)
+   * @tparam ED The original edge attribute (not used)
+   *
+   * @param graph The graph on which to compute personalized pagerank
+   * @param numIter The number of iterations to run
+   * @param resetProb The random reset probability
+   * @param sources The list of sources to compute personalized pagerank from
+   * @return the graph with vertex attributes
+   *         containing the pagerank relative to all starting nodes (as a sparse vector) and
+   *         edge attributes the normalized edge weight
+   */
+  def runParallelPersonalizedPageRank[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED],
+    numIter: Int, resetProb: Double = 0.15,
+    sources: Array[VertexId]): Graph[Vector, Double] = {
+    // TODO if one sources vertex id is outside of the int range
+    // we won't be able to store its activations in a sparse vector
+    val zero = Vectors.sparse(sources.size, List()).asBreeze
+    val sourcesInitMap = sources.zipWithIndex.map { case (vid, i) =>
+      val v = Vectors.sparse(sources.size, Array(i), Array(resetProb)).asBreeze
+      (vid, v)
+    }.toMap
+    val sc = graph.vertices.sparkContext
+    val sourcesInitMapBC = sc.broadcast(sourcesInitMap)
+    // Initialize the PageRank graph with each edge attribute having
+    // weight 1/outDegree and each source vertex with attribute 1.0.
+    var rankGraph = graph
+      // Associate the degree with each vertex
+      .outerJoinVertices(graph.outDegrees) { (vid, vdata, deg) => deg.getOrElse(0) }
+      // Set the weight on the edges based on the degree
+      .mapTriplets(e => 1.0 / e.srcAttr, TripletFields.Src)
+      .mapVertices { (vid, attr) =>
+        if (sourcesInitMapBC.value contains vid) {
+          sourcesInitMapBC.value(vid)
+        } else {
+          zero
+        }
+      }
+
+    var i = 0
+    while (i < numIter) {
+      val prevRankGraph = rankGraph
+      // Propagates the message along outbound edges
+      // and adding start nodes back in with activation resetProb
+      val rankUpdates = rankGraph.aggregateMessages[BV[Double]](
+        ctx => ctx.sendToDst(ctx.srcAttr :* ctx.attr),
+        (a : BV[Double], b : BV[Double]) => a :+ b, TripletFields.Src)
+
+      rankGraph = rankGraph.joinVertices(rankUpdates) {
+        (vid, oldRank, msgSum) =>
+          val popActivations: BV[Double] = msgSum :* (1.0 - resetProb)
+          val resetActivations = if (sourcesInitMapBC.value contains vid) {
+            sourcesInitMapBC.value(vid)
+          } else {
+            zero
+          }
+          popActivations :+ resetActivations
+        }.cache()
+
+      rankGraph.edges.foreachPartition(x => {}) // also materializes rankGraph.vertices
+      prevRankGraph.vertices.unpersist(false)
+      prevRankGraph.edges.unpersist(false)
+
+      logInfo(s"Parallel Personalized PageRank finished iteration $i.")
+
+      i += 1
+    }
+
+    rankGraph
+      .mapVertices { (vid, attr) =>
+        Vectors.fromBreeze(attr)
+      }
+  }
+
   /**
    * Run a dynamic version of PageRank returning a graph with vertex attributes containing the
    * PageRank and edge attributes containing the normalized edge weight.

graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala

@@ -118,11 +118,29 @@ class PageRankSuite extends SparkFunSuite with LocalSparkContext {
       val dynamicRanks = starGraph.personalizedPageRank(0, 0, resetProb).vertices.cache()
       assert(compareRanks(staticRanks2, dynamicRanks) < errorTol)
 
+      val parallelStaticRanks1 = starGraph
+        .staticParallelPersonalizedPageRank(Array(0), 1, resetProb).mapVertices {
+          case (vertexId, vector) => vector(0)
+        }.vertices.cache()
+      assert(compareRanks(staticRanks1, parallelStaticRanks1) < errorTol)
+
+      val parallelStaticRanks2 = starGraph
+        .staticParallelPersonalizedPageRank(Array(0, 1), 2, resetProb).mapVertices {
+          case (vertexId, vector) => vector(0)
+        }.vertices.cache()
+      assert(compareRanks(staticRanks2, parallelStaticRanks2) < errorTol)
+
       // We have one outbound edge from 1 to 0
       val otherStaticRanks2 = starGraph.staticPersonalizedPageRank(1, numIter = 2, resetProb)
         .vertices.cache()
       val otherDynamicRanks = starGraph.personalizedPageRank(1, 0, resetProb).vertices.cache()
+      val otherParallelStaticRanks2 = starGraph
+        .staticParallelPersonalizedPageRank(Array(0, 1), 2, resetProb).mapVertices {
+          case (vertexId, vector) => vector(1)
+        }.vertices.cache()
       assert(compareRanks(otherDynamicRanks, otherStaticRanks2) < errorTol)
+      assert(compareRanks(otherStaticRanks2, otherParallelStaticRanks2) < errorTol)
+      assert(compareRanks(otherDynamicRanks, otherParallelStaticRanks2) < errorTol)
     }
   } // end of test Star PersonalPageRank
 
@@ -177,6 +195,12 @@ class PageRankSuite extends SparkFunSuite with LocalSparkContext {
       val dynamicRanks = chain.personalizedPageRank(4, tol, resetProb).vertices
 
       assert(compareRanks(staticRanks, dynamicRanks) < errorTol)
+
+      val parallelStaticRanks = chain
+        .staticParallelPersonalizedPageRank(Array(4), numIter, resetProb).mapVertices {
+          case (vertexId, vector) => vector(0)
+        }.vertices.cache()
+      assert(compareRanks(staticRanks, parallelStaticRanks) < errorTol)
     }
   }
 }