Spark GraphX 聚合操作

package Spark_GraphX

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx._
import org.apache.spark.graphx.util.GraphGenerators

/**
  * 计算每一个用户的追随者数量和追随者的平均年龄
  */
object Graphx_聚合操作 {
  def main(args: Array[String]): Unit = {
      val conf=new SparkConf().setAppName("聚合操作").setMaster("local[2]")
      val sc=new SparkContext(conf)
    //随机生成具有100个顶点的图,图的结构也随机
    val graph:Graph[Double,Int]=GraphGenerators.logNormalGraph(sc,numVertices =100).mapVertices((id,_)=>id.toDouble)
    graph.vertices.foreach(println)
    graph.edges.foreach(println)
    val olderFollowers:VertexRDD[(Int,Double)]=graph.aggregateMessages[(Int,Double)](
      triplet=>{
        if(triplet.srcAttr>triplet.dstAttr){    //如果源顶点的属性大于目的顶点的属性
          triplet.sendToDst(1,triplet.srcAttr)   //源-->目的 发送个数1 和源顶点的属性
        }
      },
    (a,b)=>(a._1+b._1,a._2+b._2)     //累加统计结果
    )
    olderFollowers.values.foreach(println)
    val avgAgeOfOlderFollowers:VertexRDD[Double]=olderFollowers.mapValues((id,value)=>value match {case (count,totalAge)=>totalAge/count})
     println("*********************")
      avgAgeOfOlderFollowers.foreach(println)
  }

}
(34,34.0)
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Edge(50,1,1)
Edge(0,58,1)
Edge(50,25,1)
Edge(1,4,1)
Edge(51,0,1)
Edge(1,5,1)
Edge(51,1,1)
Edge(1,6,1)
Edge(51,2,1)
Edge(1,10,1)
Edge(1,27,1)
Edge(1,27,1)
Edge(1,29,1)
Edge(1,33,1)
Edge(1,34,1)
Edge(1,37,1)
Edge(1,39,1)
Edge(1,42,1)
Edge(51,8,1)
Edge(1,45,1)
Edge(51,9,1)
Edge(1,46,1)
Edge(51,10,1)
Edge(1,47,1)
Edge(51,13,1)
Edge(1,48,1)
Edge(51,13,1)
Edge(1,48,1)
Edge(51,14,1)
Edge(5,5,1)
Edge(5,10,1)
Edge(5,12,1)
Edge(5,13,1)
Edge(5,13,1)
Edge(5,15,1)
Edge(5,17,1)
Edge(5,17,1)
Edge(5,17,1)
Edge(5,17,1)
Edge(5,20,1)
Edge(5,20,1)
Edge(5,22,1)
...........................     省略
...........................     省略
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(59,3208.0)
*********************
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posted @ 2017-11-24 15:45  soyosuyang  阅读(604)  评论(0编辑  收藏  举报