sparkGraphX 基础操作

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转载最新推荐文章于 2024-12-16 00:00:00 发布 · 223 阅读

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原文链接：https://my.oschina.net/puwenchao/blog/1531907

文章标签：

#大数据 #python #开发工具

本文介绍如何使用Apache Spark的GraphX库进行图数据处理与分析。通过具体示例展示了图的基本操作，包括构建图、筛选子图、计算顶点度数等。此外还演示了如何实现三角形计数、PageRank网页排名算法及最短路径算法。

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1. 目的

由数组形成graph并展示基础操作

2. 素材

由Array生成vertexRDD和edgeRDD

3. 代码

/**
  * Created by puwenchao on 2016-07-06.
  */
package test
import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.graphx._
import org.apache.spark.graphx.lib.ShortestPaths
import org.apache.spark.rdd.RDD

object triangle {
  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 = new SparkConf().setAppName("triangle").setMaster("local")
    val sc = new SparkContext(conf)

    //设置顶点和边，注意顶点和边都是用元组定义的Array
    //顶点的数据类型是VD:(String,Int)
    val vertexArray = Array(
      (3L, ("rxin", 28)),
      (7L, ("jgonzal", 27)),
      (5L, ("franklin", 65)),
      (2L, ("istoica", 42))
    )
    //边的数据类型ED:Int
    val edgeArray = Array(
      Edge(3L, 7L, 7),
      Edge(5L, 3L, 2),
      Edge(2L, 5L, 4),
      Edge(5L, 7L, 3)
    )

    //构造vertexRDD和edgeRDD
    val vertexRDD: RDD[(Long, (String, Int))] = sc.parallelize(vertexArray)
    val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)
    //构造图Graph[VD,ED]
    val graph: Graph[(String, Int), Int] = Graph(vertexRDD, edgeRDD)
    //筛选有效子图
    val subgraph= graph.subgraph(vpred = (id,attr) => attr._2 != "Missing")
    println("subgraph:")
    subgraph.vertices.collect().foreach(println(_))


    //三元组
    val e=graph.triplets
    println("\ntriplets:")
    e.take(10).foreach {println}

    //总边数
    val f=graph.numEdges
    println("\nnumEdges:"+f)

    //总顶点数
    val g=graph.numVertices
    println("\nnumVertices:"+g)

    //顶点入度
    val h=graph.inDegrees
    println("\ninDegrees:"+h.collect.mkString(","))

    //顶点出度
    val i=graph.outDegrees
    println("\noutDegrees:"+i.collect.mkString(","))

    //筛选来源点ID>目标点ID
    val j=graph.edges.filter { case Edge(src, dst, prop) => src > dst }.count
    println("\nedge-filter:"+j)

    //三角计数
    val l=graph.triangleCount.vertices
    println("\ntriangleCount:")
    l.foreach(println)

    //网页排序算法
    val m=graph.pageRank(0.1).vertices
    println("\npageRank:")
    m.foreach(println)

    //最短路径
    val landmarks = Seq(7).map(_.toLong)
    val o=ShortestPaths.run(graph,landmarks).vertices
    println("\nShortestPaths:")
    o.foreach(println)

    sc.stop()
  }
}

4. 输出

    subgraph:
    (3,(rxin,28))
    (7,(jgonzal,27))
    (5,(franklin,65))
    (2,(istoica,42))

    triplets:
    ((2,(istoica,42)),(5,(franklin,65)),4)
    ((3,(rxin,28)),(7,(jgonzal,27)),7)
    ((5,(franklin,65)),(3,(rxin,28)),2)
    ((5,(franklin,65)),(7,(jgonzal,27)),3)

numEdges:4

numVertices:4

inDegrees:(3,1),(7,2),(5,1)

outDegrees:(3,1),(5,2),(2,1)

edge-filter:1

    triangleCount:
    (3,1)
    (7,1)
    (5,1)
    (2,0)

    pageRank:
    (3,0.2679375)
    (7,0.39543749999999994)
    (5,0.27749999999999997)
    (2,0.15)

    ShortestPaths:
    (3,Map(7 -> 1))
    (7,Map(7 -> 0))
    (5,Map(7 -> 1))
    (2,Map(7 -> 2))

转载于:https://my.oschina.net/puwenchao/blog/1531907