spark MLlib 学习

spark GraphX-用户关系

import org.apache.spark.graphx.{VertexRDD, VertexId, Graph, Edge}import org.apache.spark.rdd.RDDimport org.apache.spark.{SparkConf, SparkContext}object UserRelationship {  def main(args: Array[String]) {    val conf = new SparkConf()      .setMaster("local[1]")      .setAppName(" user relationship ")    val sc = new SparkContext(conf)    // id , name , age    val vertexArray = Array(      (1L, ("Alice", 28)),      (2L, ("Bob", 27)),      (3L, ("Charlie", 65)),      (4L, ("David", 42)),      (5L, ("Ed", 55)),      (6L, ("Fran", 50)))    val edgeArray = Array(      Edge(2L, 1L, 7),      Edge(2L, 4L, 2),      Edge(3L, 2L, 4),      Edge(3L, 6L, 3),      Edge(4L, 1L, 1),      Edge(5L, 2L, 2),      Edge(5L, 3L, 8),      Edge(5L, 6L, 3))    /*    Edge have a scrId and a dstId corresponding to the source and destination    vertexArray and edgeArray variables     */    val vertexRDD: RDD[(Long, (String, Int))] = sc.parallelize(vertexArray)    val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)    val graph: Graph[(String, Int), Int] = Graph(vertexRDD, edgeRDD)    println("find whose age > 30")    // find whose age > 30    graph.vertices.filter {      case (id, (name, age)) => age > 30    }.collect.foreach {      case (id, (name, age)) => println(s"$name is $age")    }    println(" find the attribute > 5 ")    //find the attribute > 5    graph.edges.filter(e => e.attr > 5)      .collect.foreach(e => println(s"{$e.srcId} to s{$e.destId} att ${e.attr}"))    println("edge attr >5")    // edge attr >5    for (triple <- graph.triplets.filter(t => t.attr > 5).collect) {      println(s"${triple.srcAttr._1} likes ${triple.dstAttr._1}")    }    //Degrees操作 找出图中最大的出度 入度 度数    def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = {      if (a._2 > b._2) a else b    }    println("the max outDegrees and inDegress Degress is :")    println("max of outDegrees:" + graph.outDegrees.reduce(max) +      "max of inDegrees:" + graph.inDegrees.reduce(max) +      "max of Degrees:" + graph.degrees.reduce(max))    //顶点的转换操作，顶点age + 10    graph.mapVertices {      case (id, (name, age)) => (id, (name, age + 10))    }.vertices.collect.foreach(v => println(s"${v._2._1} is ${v._2._2}"))    //    graph.mapEdges(e => e.attr * 2).edges.collect      .foreach(e => println(s"${e.srcId} to ${e.dstId} attr ${e.attr}"))    println("顶点年纪>30的子图：")    val subGraph = graph.subgraph(vpred = (id, vd) => vd._2 >= 30)    println("子图所有顶点：")    subGraph.vertices.collect.foreach(v => println(s"${v._2._1} is ${v._2._2}"))    println("subGraph edges:")    subGraph.edges.collect.foreach(e => println(s"${e.srcId} to ${e.dstId} att $e.attr"))    println("collection:")    val inDegrees: VertexRDD[Int] = graph.inDegrees    case class User(name: String, age: Int, inDeg: Int, outDeg: Int)    //创建一个新图，顶点VD的数据类型为User，并从graph做类型转换    val initialUserGraph: Graph[User, Int] = graph.mapVertices {      case (id, (name, age)) => User(name, age, 0, 0)    }    // fill in the degree information    val userGraph = initialUserGraph.outerJoinVertices(initialUserGraph.inDegrees) {      case (id, u, inDegOpt) => User(u.name, u.age, inDegOpt.getOrElse(0), u.outDeg)    }.outerJoinVertices(initialUserGraph.outDegrees) {      case (id, u, outDegOpt) => User(u.name, u.age, u.inDeg, outDegOpt.getOrElse(0))    }  }}