Spark GraphX概念及操作示例

原创

wx59658a994b151 2022-11-02 15:13:01 博主文章分类：spark ©著作权

文章标签 spark 大数据 graphx apache scala 文章分类 运维

©著作权归作者所有：来自51CTO博客作者wx59658a994b151的原创作品，请联系作者获取转载授权，否则将追究法律责任

引言:为什么要学计算

许多大数据以大规模图或网络的形式呈现
许多非图结构的大数据,常会被转换为图模型进行分析
图数据结构很好地表达了数据之间的关联性

一、图的基本概念

图是由顶点集合(vertex)及顶点间的关系集合（边edge）组成的一种网状数据结构
通常为二元组:Graph=(V,E)
可以对事物之间的关系建模
应用场景
在地图应用中寻找最短路径
社交网络关系
网页间超链接关系

图的术语-1

顶点（Vertex）

边（Edge）

Graph=(V,E)

集合V={v1,v2,v3}

集合E={(v1,v2),(v1,v3),(v2,v3)}

图的术语-2

Spark GraphX概念及操作示例_apache

图的术语-3

Spark GraphX概念及操作示例_scala_02

图的术语-4

Spark GraphX概念及操作示例_大数据_03

图的经典表示法

Spark GraphX概念及操作示例_apache_04

二、Spark GraphX简介

GraphX是Spark提供分布式图计算API
GraphX特点
基于内存实现了数据的复用与快速读取
通过弹性分布式属性图（Property Graph）统一了图视图与表视图
与Spark Streaming、Spark SQL和Spark MLlib等无缝衔接

三、 IDEA中应用示例

(1)

package cn.kgc.spark.graphX

import org.apache.spark.SparkContext
import org.apache.spark.graphx.{Edge, Graph}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession

/**
  * Created by wangchunhui on 2021/1/12 11:39
  */
object Demo01_CreateGraph {
  def main(args: Array[String]): Unit = {
    // 创建SparkSession
    val spark: SparkSession = SparkSession.builder()
      .appName(this.getClass.getName)
      .master("local[4]")
      .getOrCreate()
    // 创建SparkContext
    val sc: SparkContext = spark.sparkContext

    // 构建点集合的RDD
    val vertices: RDD[(Long, Int)] = sc.makeRDD(Seq((1L,1),(2L,2),(3L,3)))
    // 构建边集合的RDD
    val edges: RDD[Edge[Int]] = sc.makeRDD(Seq(Edge(1L,2L,1),Edge(2L,3L,2)))
    // 通过点集合和边集合构件图
    val graph: Graph[Int, Int] = Graph(vertices,edges)
    // 打印图的顶点, 顶点id-->顶点值
    graph.vertices.foreach(x=>println(s"${x._1}-->${x._2}"))
    // 打印图的边
    graph.edges.foreach(x=>println(s"src:${x.srcId},dst:${x.dstId},attr:${x.attr}"))
    // triplets带有属性的点和边
    graph.triplets.foreach(x=>println(x.toString()))
  }
}

(2)

package cn.kgc.spark.graphX

import org.apache.spark.SparkContext
import org.apache.spark.graphx.{Edge, Graph}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession

/**
  * Created by wangchunhui on 2021/1/12 11:57
  */
object Demo02_APPlication1 {
  def main(args: Array[String]): Unit = {
    // 创建SparkSession
    val spark: SparkSession = SparkSession.builder()
      .appName(this.getClass.getName)
      .master("local[4]")
      .getOrCreate()
    // 创建SparkContext
    val sc: SparkContext = spark.sparkContext
    // 构建用户的点集合
    val users: RDD[(Long, (String, String))] = sc.parallelize(Array(
      (3L, ("rxin", "student")),
      (7L, ("jgonzal", "postdoc")),
      (5L, ("franklin", "professor")),
      (2L, ("istoica", "professor"))
    ))
    // 构建关系的边集合
    val relationships: RDD[Edge[String]] = sc.parallelize(Array(
      (Edge(3L, 7L, "collaborator")),
      (Edge(5L, 3L, "advisor")),
      (Edge(2L, 5L, "colleague")),
      (Edge(5L, 7L, "PI"))
    ))
    val graph = Graph(users,relationships)
    // 打印图的顶点, 顶点id-->顶点值
    graph.vertices.foreach(x=>println(s"${x._1}-->${x._2}"))
    // 打印图的边
    graph.edges.foreach(x=>println(s"src:${x.srcId},dst:${x.dstId},attr:${x.attr}"))
    // triplets带有属性的点和边
    graph.triplets.foreach(x=>println(x.toString()))
  }
}

(3)

package cn.kgc.spark.graphX

import org.apache.spark.SparkContext
import org.apache.spark.graphx.{Edge, Graph}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession

/**
  * Created by wangchunhui on 2021/1/12 12:09
  */
object Demo03_Application2 {
  def main(args: Array[String]): Unit = {
    // 创建SparkSession
    val spark: SparkSession = SparkSession.builder()
      .appName(this.getClass.getName)
      .master("local[4]")
      .getOrCreate()
    // 创建SparkContext
    val sc: SparkContext = spark.sparkContext
    // 构建用户的点集合
    val users: RDD[(Long, (String, Int))] = sc.parallelize(Array(
      (1L, ("Alice", 28)),
      (2L, ("Bob", 27)),
      (3L, ("Charlie", 65)),
      (4L, ("David", 42)),
      (5L, ("Ed", 55)),
      (6L, ("Fran", 50))
    ))
    // 构建用户的边集合
    val cntCall: RDD[Edge[Int]] = sc.parallelize(Array(
      Edge(2L, 1L, 7),
      Edge(2L, 4L, 2),
      Edge(3L, 2L, 4),
      Edge(3L, 6L, 3),
      Edge(4L, 1L, 1),
      Edge(5L, 2L, 2),
      Edge(5L, 3L, 8),
      Edge(5L, 6L, 3)
    ))

    val graph = Graph(users, cntCall)

    // 找出大于30岁的用户
    println("大于30岁的用户为: ")
    graph.vertices.filter { case (id, (name, age)) => age > 30 }.foreach(x=>println(x.toString()))
    //graph.vertices.filter(_._2._2>30)

    // 假设打call超过5次，表示真爱。请找出他（她）们
    println("打call超过5次的为:")
    graph.triplets.filter(_.attr>5).foreach(x=>println(s"${x.toString()}"))

  }
}

四、在虚拟机Spark-shell中运行

scala> import org.apache.spark.SparkContext
import org.apache.spark.SparkContext

scala> import org.apache.spark.graphx.{Edge, Graph}
import org.apache.spark.graphx.{Edge, Graph}

scala> import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.RDD

scala> import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.SparkSession

scala> val users: RDD[(Long, (String, Int))] = sc.parallelize(Array(
     |       (1L, ("Alice", 28)),
     |       (2L, ("Bob", 27)),
     |       (3L, ("Charlie", 65)),
     |       (4L, ("David", 42)),
     |       (5L, ("Ed", 55)),
     |       (6L, ("Fran", 50))
     |     ))
users: org.apache.spark.rdd.RDD[(Long, (String, Int))] = ParallelCollectionRDD[0] at parallelize at <console>:28

scala> val cntCall: RDD[Edge[Int]] = sc.parallelize(Array(
     |       Edge(2L, 1L, 7),
     |       Edge(2L, 4L, 2),
     |       Edge(3L, 2L, 4),
     |       Edge(3L, 6L, 3),
     |       Edge(4L, 1L, 1),
     |       Edge(5L, 2L, 2),
     |       Edge(5L, 3L, 8),
     |       Edge(5L, 6L, 3)
     |     ))
cntCall: org.apache.spark.rdd.RDD[org.apache.spark.graphx.Edge[Int]] = ParallelCollectionRDD[1] at parallelize at <console>:28

scala> val graph = Graph(users, cntCall)
graph: org.apache.spark.graphx.Graph[(String, Int),Int] = org.apache.spark.graphx.impl.GraphImpl@10fef3d1

scala> graph.vertices.filter { case (id, (name, age)) => age > 30 }.foreach(x=>println(x.toString()))
[Stage 0:>                                                          (0 + 1) / 1[Stage 0:>                  (0 + 1) / 1][Stage 1:>                  (0 + 0) / 1](4,(David,42))
(6,(Fran,50))
(3,(Charlie,65))
(5,(Ed,55))
                                                                                
scala> graph.triplets.filter(_.attr>5).foreach(x=>println(s"${x.toString()}"))
((2,(Bob,27)),(1,(Alice,28)),7)
((5,(Ed,55)),(3,(Charlie,65)),8)

scala>