package main.scalaimport org.apache.spark.graphx.{Edge, Graph, VertexId}import org.apache.spark.rdd.RDDimport org.apache.spark.{SparkConf, SparkContext}object graph_test {    // define hadoop_home directory  System.setProperty("hadoop.home.dir","E:/zhuangji/winutil/")  def main(args:Array[String]):Unit={    val conf=new SparkConf().setMaster("local[2]").setAppName("graph_test")    val sc=new SparkContext(conf)    // VertexRDD & EdgeRDD to build graph    val users:RDD[(VertexId,(String,String))]=      sc.parallelize(Array((3L,("rxin","student")),(7L,("jgonzal","postdoc")),                          (5L,("franklin","prof")),(2L,("istoica","prof"))))    val relationships:RDD[Edge[String]]=      sc.parallelize(Array(Edge(3L,7L,"collab"),Edge(5L,3L,"advisor"),                           Edge(2L,5L,"colleague"),Edge(5L,7L,"pi")))    val defaultUser=("John Doe","Missing")    val graph=Graph(users,relationships,defaultUser)    // graph.vertices & graph.edges to query graph    println(graph.vertices.filter{case (id,(name,pos))=>pos=="prof"}.count)    println(graph.edges.filter{case Edge(s,d,r)=>s<d}.count)  // 两者    println(graph.edges.filter(e=>e.srcId<e.dstId).count)  // 等价        // 三元组视图 graph.triplets could also query a graph    val facts:RDD[String]=      graph.triplets.map(triplet=>         triplet.srcAttr._1 + " is the " + triplet.attr + " of " + triplet.dstAttr._1)    facts.collect.foreach(println(_))  }}