Spark组件之GraphX学习3--Structural Operators：subgraph

更多代码请见：​​https://github.com/xubo245/SparkLearning​​

1解释

子图，过滤

结构化操作有多个

class Graph[VD, ED] {
  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]
  def groupEdges(merge: (ED, ED) => ED): Graph[VD,ED]
}

源码：抽象类，没具体实现

/**
   * Restricts the graph to only the vertices and edges satisfying the predicates. The resulting
   * subgraph satisifies
   *
   * {{{
   * V' = {v : for all v in V where vpred(v)}
   * E' = {(u,v): for all (u,v) in E where epred((u,v)) && vpred(u) && vpred(v)}
   * }}}
   *
   * @param epred the edge predicate, which takes a triplet and
   * evaluates to true if the edge is to remain in the subgraph.  Note
   * that only edges where both vertices satisfy the vertex
   * predicate are considered.
   *
   * @param vpred the vertex predicate, which takes a vertex object and
   * evaluates to true if the vertex is to be included in the subgraph
   *
   * @return the subgraph containing only the vertices and edges that
   * satisfy the predicates
   */
  def subgraph(
      epred: EdgeTriplet[VD, ED] => Boolean = (x => true),
      vpred: (VertexId, VD) => Boolean = ((v, d) => true))
    : Graph[VD, ED]

2.代码：

/**
 * @author xubo
 * ref http://spark.apache.org/docs/1.5.2/graphx-programming-guide.html
 * time 20160503
 */

package org.apache.spark.graphx.learning
import org.apache.spark._
import org.apache.spark.graphx._
// To make some of the examples work we will also need RDD
import org.apache.spark.rdd.RDD

object GraphOperators {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("gettingStart").setMaster("local[4]")
    // Assume the SparkContext has already been constructed
    val sc = new SparkContext(conf)

    // Create an RDD for the vertices
    val users: RDD[(VertexId, (String, String))] =
      sc.parallelize(Array((3L, ("rxin", "student")), (7L, ("jgonzal", "postdoc")),
        (5L, ("franklin", "prof")), (2L, ("istoica", "prof")),
        (4L, ("peter", "student"))))
    // Create an RDD for edges
    val relationships: RDD[Edge[String]] =
      sc.parallelize(Array(Edge(3L, 7L, "collab"), Edge(5L, 3L, "advisor"),
        Edge(2L, 5L, "colleague"), Edge(5L, 7L, "pi"),
        Edge(4L, 0L, "student"), Edge(5L, 0L, "colleague")))
    // Define a default user in case there are relationship with missing user
    val defaultUser = ("John Doe", "Missing")
    // Build the initial Graph
    val graph = Graph(users, relationships, defaultUser)
    // Notice that there is a user 0 (for which we have no information) connected to users
    // 4 (peter) and 5 (franklin).
    println("vertices:");
    graph.subgraph(each=> each.srcId != 100L).vertices.collect.foreach(println)
    println("\ntriplets:");
    graph.triplets.map(
      triplet => triplet.srcAttr._1 + " is the " + triplet.attr + " of " + triplet.dstAttr._1).collect.foreach(println(_))

    // Remove missing vertices as well as the edges to connected to them
    println("\nemove missing vertices as well as the edges to connected to them:");
//    val validGraph = graph.subgraph(epred, vpred)

    val validGraph = graph.subgraph(vpred = (id, attr) => attr._2 != "Missing")
    // The valid subgraph will disconnect users 4 and 5 by removing user 0
    println("new vertices:");
    validGraph.vertices.collect.foreach(println(_))
    
    println("\nnew triplets:");
    validGraph.triplets.map(
      triplet => triplet.srcAttr._1 + " is the " + triplet.attr + " of " + triplet.dstAttr._1).collect.foreach(println(_))
  }

}

3.结果：

过滤掉了顶点第二个属性为Missing的点及其相关边

vertices:
(4,(peter,student))
(0,(John Doe,Missing))
(5,(franklin,prof))
(2,(istoica,prof))
(3,(rxin,student))
(7,(jgonzal,postdoc))

triplets:
rxin is the collab of jgonzal
istoica is the colleague of franklin
franklin is the advisor of rxin
franklin is the pi of jgonzal
peter is the student of John Doe
franklin is the colleague of John Doe

emove missing vertices as well as the edges to connected to them:
new vertices:
(4,(peter,student))
(5,(franklin,prof))
(2,(istoica,prof))
(3,(rxin,student))
(7,(jgonzal,postdoc))

new triplets:
rxin is the collab of jgonzal
istoica is the colleague of franklin
franklin is the advisor of rxin
franklin is the pi of jgonzal

参考

【1】 http://spark.apache.org/docs/1.5.2/graphx-programming-guide.html

【2】​​https://github.com/xubo245/SparkLearning​​