Spark GraphX 属性图操作

package Spark_GraphX

import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object 属性图 {
  def main(args: Array[String]): Unit = {
    val conf=new SparkConf().setAppName("SimpleGraphX").setMaster("local[4]")
    val sc=new SparkContext(conf)
   //定义顶点
    val users:RDD[(VertexId,(String,String))]=sc.parallelize(Array((3L,("soyo","student")),(7L,("soyo2","postdoc")),(5L,("xiaozhou","professor")),(2L,("xiaocui","professor"))))
    //定义边
    val relationships:RDD[Edge[String]]=sc.parallelize(Array(Edge(3L,7L,"collab"),Edge(5L,3L,"advisor"),Edge(2L,5L,"colleague"),Edge(5L,7L,"parent")))
   //定义默认的作者,以防与不存在的作者有边
    val defaultUser=("Jone","Dance")
    val graph=Graph(users,relationships,defaultUser)
    println("*****************")
    println("找到图中属性是student的点")
    graph.vertices.filter{case (id,(name,occupation))=>occupation=="student"}.collect.foreach{case(id,(name,occupation))=>println(s"$name is $occupation")}
    println("--------------------------")
    println("找到途中边的属性是advisor的边")
    graph.edges.filter(x=>x.attr=="advisor").collect().foreach(x=>println(s"${x.srcId} to ${x.dstId} 属性为 ${x.attr}"))
    println("--------------------------")
    println("找到图中的最大出度,入度,度数")
    println("最大的出度:"+graph.outDegrees.reduce(max))
    println("最大的入度:"+graph.inDegrees.reduce(max))
    println("最大的度数:"+graph.degrees.reduce(max))
    //Scala 可直接调用Java程序
    // System.out.print("hello word")
    //属性操作
    println("------------------------")
    println("给图中每个顶点的职业属性上加上“spark字符串")
    graph.mapVertices{case (id,(name,occupation))=>(id,(name,occupation+"Spark"))}.vertices.collect.foreach(x=>println(s"${x._2._1} is ${x._2._2} : ${x._1} : ${x._2}"))
    println("------------------------")
    println("给途中每个元组的Edge的属性值设置为源顶点属性值+边的属性值+目标定点属性值:")
   graph.mapTriplets(x=>x.srcAttr._2+"+"+x.attr+"+"+x.dstAttr._2).edges.collect().foreach(println)
    //可以证明：属性操作下,图的结构都不受影响.
    graph.mapTriplets(x=>x.srcId+x.dstId).edges.collect().foreach(println)
   //结构操作 :triplets(表示边)
    /*
     reverse操作返回一个所有边方向取反的新图.该反转操作并没有修改图中顶点,边的属性,更没有增加边的数量.
     subgraph操作主要利用顶点和边进行判断,返回的新图中包含满足判断要求的顶点,边.该操作常用于一些情景,比如：限制感兴趣的图顶点和边,删除损坏连接.
     */
    println("------结构操作---------")
    graph.triplets.map(x=>x.srcAttr._1+" is the "+x.attr+" of "+x.dstAttr._1).foreach(println)
    println("-------删除职业是postdoc的节点,构建子图----------")
    val validGraph=graph.subgraph(vpred=(id,attr)=>attr._2!="postdoc")
    validGraph.vertices.foreach(println)
    validGraph.triplets.map(x=>x.srcAttr._1+" is the "+x.attr+" of "+x.dstAttr._1).foreach(println)
    println("----------构建职业是professor的子图,并打印子图的顶点--------")
    val subGraph=graph.subgraph(vpred = (id,attr)=>attr._2=="professor")
    subGraph.vertices.collect().foreach(x=>println(s"${x._2._1} is ${x._2._2}"))

  }
  //VertexId：顶点,Int：度数
  def max(a:(VertexId,Int),b:(VertexId,Int)):(VertexId,Int)={
    if(a._2>b._2)a else b
  }

}

结果：

*****************
找到图中属性是student的点
soyo is student
--------------------------
找到途中边的属性是advisor的边
 to  属性为 advisor
--------------------------
找到图中的最大出度,入度,度数
最大的出度:(,)
最大的入度:(,)
最大的度数:(,)
------------------------
给图中每个顶点的职业属性上加上“spark字符串
 is (xiaozhou,professorSpark) :  : (,(xiaozhou,professorSpark))
 is (xiaocui,professorSpark) :  : (,(xiaocui,professorSpark))
 is (soyo,studentSpark) :  : (,(soyo,studentSpark))
 is (soyo2,postdocSpark) :  : (,(soyo2,postdocSpark))
------------------------
给途中每个元组的Edge的属性值设置为源顶点属性值+边的属性值+目标定点属性值:
Edge(,,student+collab+postdoc)
Edge(,,professor+advisor+student)
Edge(,,professor+colleague+professor)
Edge(,,professor+parent+postdoc)
Edge(,,)
Edge(,,)
Edge(,,)
Edge(,,)
------结构操作---------
xiaozhou is the parent of soyo2
soyo is the collab of soyo2
xiaozhou is the advisor of soyo
xiaocui is the colleague of xiaozhou
-------删除职业是postdoc的节点,构建子图----------
(,(xiaozhou,professor))
(,(xiaocui,professor))
(,(soyo,student))
xiaozhou is the advisor of soyo
xiaocui is the colleague of xiaozhou
----------构建职业是professor的子图,并打印子图的顶点--------
xiaozhou is professor
xiaocui is professor