Spark组件之GraphX学习10--PageRank学习和使用（From examples）

更多代码请见：https://github.com/xubo245/SparkLearning

1解释

原理在参考【3】中讲的很详细，包括MapReduce情况下的。

源码：

  /**
   * Run a dynamic version of PageRank returning a graph with vertex attributes containing the
   * PageRank and edge attributes containing the normalized edge weight.
   *
   * @see [[org.apache.spark.graphx.lib.PageRank$#runUntilConvergence]]
   */
  def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = {
    PageRank.runUntilConvergence(graph, tol, resetProb)
  }

调用了：

/**
   * Run a dynamic version of PageRank returning a graph with vertex attributes containing the
   * PageRank and edge attributes containing 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 PageRank
   * @param tol the tolerance allowed at convergence (smaller => more accurate).
   * @param resetProb the random reset probability (alpha)
   *
   * @return the graph containing with each vertex containing the PageRank and each edge
   *         containing the normalized weight.
   */
  def runUntilConvergence[VD: ClassTag, ED: ClassTag](
    graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15): Graph[Double, Double] =
  {
      runUntilConvergenceWithOptions(graph, tol, resetProb)
  }

  /**
   * Run a dynamic version of PageRank returning a graph with vertex attributes containing the
   * PageRank and edge attributes containing 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 PageRank
   * @param tol the tolerance allowed at convergence (smaller => more accurate).
   * @param resetProb the random reset probability (alpha)
   * @param srcId the source vertex for a Personalized Page Rank (optional)
   *
   * @return the graph containing with each vertex containing the PageRank and each edge
   *         containing the normalized weight.
   */
  def runUntilConvergenceWithOptions[VD: ClassTag, ED: ClassTag](
      graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15,
      srcId: Option[VertexId] = None): Graph[Double, Double] =
  {
    // Initialize the pagerankGraph with each edge attribute
    // having weight 1/outDegree and each vertex with attribute 1.0.
    val pagerankGraph: Graph[(Double, 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 (initalPR, delta = 0)
      .mapVertices( (id, attr) => (0.0, 0.0) )
      .cache()

    val personalized = srcId.isDefined
    val src: VertexId = srcId.getOrElse(-1L)


    // Define the three functions needed to implement PageRank in the GraphX
    // version of Pregel
    def vertexProgram(id: VertexId, attr: (Double, Double), msgSum: Double): (Double, Double) = {
      val (oldPR, lastDelta) = attr
      val newPR = oldPR + (1.0 - resetProb) * msgSum
      (newPR, newPR - oldPR)
    }

    def personalizedVertexProgram(id: VertexId, attr: (Double, Double),
      msgSum: Double): (Double, Double) = {
      val (oldPR, lastDelta) = attr
      var teleport = oldPR
      val delta = if (src==id) 1.0 else 0.0
      teleport = oldPR*delta

      val newPR = teleport + (1.0 - resetProb) * msgSum
      (newPR, newPR - oldPR)
    }

    def sendMessage(edge: EdgeTriplet[(Double, Double), Double]) = {
      if (edge.srcAttr._2 > tol) {
        Iterator((edge.dstId, edge.srcAttr._2 * edge.attr))
      } else {
        Iterator.empty
      }
    }

    def messageCombiner(a: Double, b: Double): Double = a + b

    // The initial message received by all vertices in PageRank
    val initialMessage = resetProb / (1.0 - resetProb)

    // Execute a dynamic version of Pregel.
    val vp = if (personalized) {
      (id: VertexId, attr: (Double, Double), msgSum: Double) =>
        personalizedVertexProgram(id, attr, msgSum)
    } else {
      (id: VertexId, attr: (Double, Double), msgSum: Double) =>
        vertexProgram(id, attr, msgSum)
    }

    Pregel(pagerankGraph, initialMessage, activeDirection = EdgeDirection.Out)(
      vp, sendMessage, messageCombiner)
      .mapVertices((vid, attr) => attr._1)
  } // end of deltaPageRank

2.代码：

/**
 * @author xubo
 * ref http://spark.apache.org/docs/1.5.2/graphx-programming-guide.html
 * time 20160503
 */

package org.apache.spark.graphx.learning

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph
import org.apache.spark.graphx.Graph.graphToGraphOps
import org.apache.spark.graphx.VertexId
import org.apache.spark.graphx.util.GraphGenerators
import org.apache.spark.graphx.GraphLoader

object PageRank {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("PageRank").setMaster("local[4]")
    val sc = new SparkContext(conf)

    // Load the edges as a graph
    val graph = GraphLoader.edgeListFile(sc, "file/data/graphx/input/followers.txt")
    // Run PageRank
    val ranks = graph.pageRank(0.0001).vertices
    // Join the ranks with the usernames
    val users = sc.textFile("file/data/graphx/input/users.txt").map { line =>
      val fields = line.split(",")
      (fields(0).toLong, fields(1))
    }
    val ranksByUsername = users.join(ranks).map {
      case (id, (username, rank)) => (username, rank)
    }
    // Print the result
    println(ranksByUsername.collect().mkString("\n"))
  }
}

直接调用

<pre code_snippet_id="1671863" snippet_file_name="blog_20160504_3_6713227" name="code" class="plain">   val ranks = graph.pageRank(0.0001).vertices

就行了，0.0001为前后两次收敛的误差阈值，小于这个阈值时则结束计算，越小精度越到

数据：

followers.txt：

2 1
4 1
1 2
6 3
7 3
7 6
6 7
3 7

users.txt<span style="font-family: Arial, Helvetica, sans-serif; font-size: 12px; background-color: rgb(255, 255, 255);">:</span>

1,BarackObama,Barack Obama
2,ladygaga,Goddess of Love
3,jeresig,John Resig
4,justinbieber,Justin Bieber
6,matei_zaharia,Matei Zaharia
7,odersky,Martin Odersky
8,anonsys

图：

<img src="http://img.blog.csdn.net/20160504185030083?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/Center" alt="" />

3.结果：

(justinbieber,0.15)
(matei_zaharia,0.7013599933629602)
(ladygaga,1.390049198216498)
(BarackObama,1.4588814096664682)
(jeresig,0.9993442038507723)
(odersky,1.2973176314422592)

第8个顶点由于没有与任何顶点连接，故join时就没了

参考

【1】 http://spark.apache.org/docs/1.5.2/graphx-programming-guide.html

【2】https://github.com/xubo245/SparkLearning

【3】http://blog.jobbole.com/71431/