/* * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements.  See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License.  You may obtain a copy of the License at * *    http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */package org.apache.spark.graphximport scala.reflect.ClassTagimport org.apache.spark.Logging/** * Implements a Pregel-like bulk-synchronous message-passing API. * * Unlike the original Pregel API, the GraphX Pregel API factors the sendMessage computation over * edges, enables the message sending computation to read both vertex attributes, and constrains * messages to the graph structure.  These changes allow for substantially more efficient * distributed execution while also exposing greater flexibility for graph-based computation. * * @example We can use the Pregel abstraction to implement PageRank: * {{{ * val pagerankGraph: Graph[Double, Double] = 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) *   // Set the vertex attributes to the initial pagerank values *   .mapVertices((id, attr) => 1.0) * * def vertexProgram(id: VertexId, attr: Double, msgSum: Double): Double = *   resetProb + (1.0 - resetProb) * msgSum * def sendMessage(id: VertexId, edge: EdgeTriplet[Double, Double]): Iterator[(VertexId, Double)] = *   Iterator((edge.dstId, edge.srcAttr * edge.attr)) * def messageCombiner(a: Double, b: Double): Double = a + b * val initialMessage = 0.0 * // Execute Pregel for a fixed number of iterations. * Pregel(pagerankGraph, initialMessage, numIter)( *   vertexProgram, sendMessage, messageCombiner) * }}} * */object Pregel extends Logging {  /**   * Execute a Pregel-like iterative vertex-parallel abstraction.  The   * user-defined vertex-program `vprog` is executed in parallel on   * each vertex receiving any inbound messages and computing a new   * value for the vertex.  The `sendMsg` function is then invoked on   * all out-edges and is used to compute an optional message to the   * destination vertex. The `mergeMsg` function is a commutative   * associative function used to combine messages destined to the   * same vertex.   *   * On the first iteration all vertices receive the `initialMsg` and   * on subsequent iterations if a vertex does not receive a message   * then the vertex-program is not invoked.   *   * This function iterates until there are no remaining messages, or   * for `maxIterations` iterations.   *   * @tparam VD the vertex data type   * @tparam ED the edge data type   * @tparam A the Pregel message type   *   * @param graph the input graph.   *   * @param initialMsg the message each vertex will receive at the on   * the first iteration   *   * @param maxIterations the maximum number of iterations to run for   *   * @param activeDirection the direction of edges incident to a vertex that received a message in   * the previous round on which to run `sendMsg`. For example, if this is `EdgeDirection.Out`, only   * out-edges of vertices that received a message in the previous round will run. The default is   * `EdgeDirection.Either`, which will run `sendMsg` on edges where either side received a message   * in the previous round. If this is `EdgeDirection.Both`, `sendMsg` will only run on edges where   * *both* vertices received a message.   *   * @param vprog the user-defined vertex program which runs on each   * vertex and receives the inbound message and computes a new vertex   * value.  On the first iteration the vertex program is invoked on   * all vertices and is passed the default message.  On subsequent   * iterations the vertex program is only invoked on those vertices   * that receive messages.   *   * @param sendMsg a user supplied function that is applied to out   * edges of vertices that received messages in the current   * iteration   *   * @param mergeMsg a user supplied function that takes two incoming   * messages of type A and merges them into a single message of type   * A.  ‘‘This function must be commutative and associative and   * ideally the size of A should not increase.‘‘   *   * @return the resulting graph at the end of the computation   *   */  def apply[VD: ClassTag, ED: ClassTag, A: ClassTag]     (graph: Graph[VD, ED],      initialMsg: A,      maxIterations: Int = Int.MaxValue,      activeDirection: EdgeDirection = EdgeDirection.Either)     (vprog: (VertexId, VD, A) => VD,      sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)],      mergeMsg: (A, A) => A)    : Graph[VD, ED] =  {    var g = graph.mapVertices((vid, vdata) => vprog(vid, vdata, initialMsg)).cache()    // compute the messages    var messages = g.mapReduceTriplets(sendMsg, mergeMsg)    var activeMessages = messages.count()    // Loop    var prevG: Graph[VD, ED] = null    var i = 0    while (activeMessages > 0 && i < maxIterations) {      // Receive the messages. Vertices that didn‘t get any messages do not appear in newVerts.      val newVerts = g.vertices.innerJoin(messages)(vprog).cache()      // Update the graph with the new vertices.      prevG = g      g = g.outerJoinVertices(newVerts) { (vid, old, newOpt) => newOpt.getOrElse(old) }      g.cache()      val oldMessages = messages      // Send new messages. Vertices that didn‘t get any messages don‘t appear in newVerts, so don‘t      // get to send messages. We must cache messages so it can be materialized on the next line,      // allowing us to uncache the previous iteration.      messages = g.mapReduceTriplets(sendMsg, mergeMsg, Some((newVerts, activeDirection))).cache()      // The call to count() materializes `messages`, `newVerts`, and the vertices of `g`. This      // hides oldMessages (depended on by newVerts), newVerts (depended on by messages), and the      // vertices of prevG (depended on by newVerts, oldMessages, and the vertices of g).      activeMessages = messages.count()      logInfo("Pregel finished iteration " + i)      // Unpersist the RDDs hidden by newly-materialized RDDs      oldMessages.unpersist(blocking=false)      newVerts.unpersist(blocking=false)      prevG.unpersistVertices(blocking=false)      prevG.edges.unpersist(blocking=false)      // count the iteration      i += 1    }    g  } // end of apply} // end of class Pregel