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)
  }

}

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Edge(5,17,1)
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...........................     省略
...........................     省略
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*********************
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