Spark GraphX图计算简单案例【代码实现，源码分析】

一.简介

　　参考：https://www.cnblogs.com/yszd/p/10186556.html

二.代码实现　　

package big.data.analyse.graphx

import org.apache.log4j.{Level, Logger}
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession

class VertexProperty()
case class UserProperty(val name: String) extends VertexProperty
case class ProductProperty(val name: String, val price: Double) extends VertexProperty

/*class Graph[VD, ED]{
  val vertices: VertexRDD[VD]
  val edges: EdgeRDD[ED]
}*/

/**
  * Created by zhen on 2019/10/4.
  */
object GraphXTest {
  /**
    * 设置日志级别
    */
  Logger.getLogger("org").setLevel(Level.WARN)
  def main(args: Array[String]) {
    val spark = SparkSession.builder().appName("GraphXTest").master("local[2]").getOrCreate()
    val sc = spark.sparkContext
    /**
      * 创建vertices的RDD
      */
    val users : RDD[(VertexId, (String, String))] = sc.parallelize(
      Array((3L, ("Spark", "GraphX")), (7L, ("Hadoop", "Java")),
            (5L, ("HBase", "Mysql")), (2L, ("Hive", "Mysql"))))

    /**
      * 创建edges的RDD
      */
    val relationships: RDD[Edge[String]] = sc.parallelize(
      Array(Edge(3L, 7L, "Fast"), Edge(5L, 3L, "Relation"),
      Edge(2L, 5L, "colleague"), Edge(5L, 7L, "colleague")))

    /**
      * 定义默认用户
      */
    val defualtUser = ("Machical", "Missing")

    /**
      * 构建初始化图
      */
    val graph = Graph(users, relationships, defualtUser)

    /**
      * 使用三元组视图呈现顶点之间关系
      */
    val facts : RDD[String] = graph.triplets.map(triplet =>
      triplet.srcAttr._1 + " is the " + triplet.attr + " with " + triplet.dstAttr._1)
    facts.collect().foreach(println)

    graph.vertices.foreach(println) //顶点
    graph.edges.foreach(println) //边
    graph.ops.degrees.foreach(println) // 各顶点的度
    graph.triplets.foreach(println) // 顶点，边，关系
    println(graph.ops.numEdges) // 边的数量
    println(graph.ops.numVertices) // 顶点的数量
  }
}

三.结果

　　1.三元组视图

　　　　

　　2.顶点

　　　　

　　3.边

　　　　

　　4.各顶点的度

　　　　

　　5.三元组视图

　　　　

　　6.边/顶点数量

　　　　

四.源码分析

class Graph[VD, ED] {
   // Information about the Graph
　　val numEdges: Long
　　val numVertices:Long
　　val inDegrees: VertexRDD[Int]
　　val outDegrees: VertexRDD[Int]
　　val degrees: VertexRDD[Int]
　　
   // Views of the graph as collections
　　val vertices: VertexRDD[VD]
　　val edges: EdgeRDD[ED]
　　val triplets: RDD[EdgeTriplet[VD,ED]]
　
 　//Functions for caching graphs
　　def persist(newLevel1:StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]//默认存储级别为MEMORY_ONLY
　　def cache(): Graph[VD, ED]
　　def unpersistVertices(blocking: Boolean = true): Graph[VD, ED]

　　// Change the partitioning heuristic
　　def partitionBy(partitionStrategy: PartitionStrategy)

　　// Transform vertex and edge attributes
　　def mapVertices[VD2](map: (VertexId, VD) => VD2): Graph[VD2, ED]
　　def mapEdges[ED2](map: Edge[ED] => ED2): Graph[VD, ED2]
　　def mapEdges[ED2](map: (PartitionID, Iterator[Edge[ED]]) => Iterator[ED2]): Graph[VD, ED2]
　　def mapTriplets[ED2](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2]
　　def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2]): Graph[VD, ED2]

　　// Modify the graph structure
　　def reverse: Graph[VD, ED]
　　def subgraph(epred: EdgeTriplet[VD,ED] => Boolean,vpred: (VertexId, VD) => Boolean): Graph[VD, ED]
　　def mask[VD2, ED2](other: Graph[VD2, ED2]): Graph[VD, ED] // 返回当前图和其它图的公共子图
　　def groupEdges(merge: (ED, ED) => ED): Graph[VD,ED]

　　// Join RDDs with the graph　　
　　def joinVertices[U](table: RDD[(VertexId, U)])(mapFunc: (VertexId, VD, U) => VD): Graph[VD, ED]
　　def outerJoinVertices[U, VD2](other: RDD[(VertexId, U)])(mapFunc: (VertexId, VD, Option[U]))
　　
　　// Aggregate information about adjacent triplets
　　def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexId]]
　　def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[Array[(VertexId, VD)]]
　　def aggregateMessages[Msg: ClassTag](sendMsg: EdgeContext[VD, ED, Msg] => Unit, merageMsg: (Msg, Msg) => Msg, tripletFields: TripletFields: TripletFields = TripletFields.All): VertexRDD[A]
　　
　　//Iterative graph-parallel computation
　　def pregel[A](initialMsg: A, maxIterations: Int, activeDirection: EdgeDiection)(vprog: (VertexId, VD, A) => VD, sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)], mergeMsg: (A, A) => A): Graph[VD, ED]
　　
　　// Basic graph algorithms
　　def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double]
　　def connectedComponents(): Graph[VertexId, ED]
　　def triangleCount(): Graph[Int, ED]
　　def stronglyConnectedComponents(numIter: Int): Graph[VertexId, ED]
}