aggregateMessages

def aggregateMessages[Msg: ClassTag](
      sendMsg: EdgeContext[VD, ED, Msg] => Unit,
      mergeMsg: (Msg, Msg) => Msg,
      tripletFields: TripletFields = TripletFields.All)
    : VertexRDD[A]注：聚合信息的核心方法是：aggregateMessages，其操作的本质是sendMsg和mergeMsg，

具体而言，是依次在图的每条边(edgeTriplets)上根据sendMsg函数的要求，把该边上A端的节点信息发送给B端，如:把src节点信息发到dst节点信息，或者把dst节点信息发送到src节点上，
然后在B端调用merge函数将可能收到的多个msg合并成一个msg.

tripletFields字段指定要操作哪些字段，如果仅操作部分字段的话，通过此参数进行限定可提高性能。aggregateMessages方法最终返回一个新的顶点集：VertexRDD，这个新的顶点集中每个vertex节点上包含上聚合后的信息。

collectNeighborIds与collectNeighbors函数就是对aggregateMessages的简单封装以实现聚合相邻节点id和相邻节点的功能

vertecs.txt:
(4,4.0)
(0,0.0)
(1,1.0)
(6,6.0)
(3,3.0)
(7,7.0)
(9,9.0)
(8,8.0)
(5,5.0)
(2,2.0)
edges:
Edge(0,2,1)
Edge(0,3,1)
Edge(0,5,1)
Edge(0,5,1)
Edge(0,7,1)
Edge(0,8,1)
Edge(0,8,1)
Edge(0,8,1)
Edge(0,9,1)
Edge(1,0,1)

package graphx

import org.apache.log4j.{Level, Logger}
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx.util.GraphGenerators


object test07 {

  def main(args:Array[String]){


    //屏蔽日志
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    //设置运行环境
    val conf: SparkConf = new SparkConf().setAppName("my graphx").setMaster("local")
    val sc = new SparkContext(conf)


    //TODO 构造graph
    val graph: Graph[Double, Int] = GraphGenerators.logNormalGraph(sc, numVertices = 10).mapVertices( (id, _) => id.toDouble )
    println("vertecs.txt:")
    graph.vertices.collect().foreach(println)
    println("edges:")
    graph.edges.collect().foreach(println)
    println("triplets:")
    graph.triplets.collect().foreach(println)


    // Compute the number of older followers and their total age
    val olderFollowers: VertexRDD[(PartitionID, Double)] = graph.aggregateMessages[(PartitionID, Double)](
      triplet => { // Map Function
        if (triplet.srcAttr > triplet.dstAttr) {
          triplet.sendToDst(1, triplet.srcAttr) // Send message to destination vertex containing counter and age
        }
      },
      // Add counter and age
      (a, b) => (a._1 + b._1, a._2 + b._2) // Reduce Function
    )

    println("olderFollowers")
    olderFollowers.collect().foreach(println)

    val avgAgeOfOlderFollowers: VertexRDD[Double] = olderFollowers.mapValues({
      (id, value) =>
        value match {
          case (count, totalAge) => totalAge / count
        }
    })

    println("avgAgeOfOlderFollowers")
    avgAgeOfOlderFollowers.collect().foreach(println)



    val avgAgeOfOlderFollowers2: VertexRDD[Double] = olderFollowers.mapValues({
      (id,value)=>value._2/value._1
    })


    println("avgAgeOfOlderFollowers2")
    avgAgeOfOlderFollowers2.collect().foreach(println)


    //节点得出度
    graph.aggregateMessages[Int](_.sendToSrc(1), _ + _).foreach(println)



  }

}