Spark GraphX是一个分布式的图处理框架。社交网络中，用户与用户之间会存在错综复杂的联系，如微信、QQ、微博的用户之间的好友、关注等关系，构成了一张巨大的图，单机无法处理，只能使用分布式图处理框架处理，Spark GraphX就是一种分布式图处理框架。
1. POM文件
在项目的pom文件中加上Spark GraphX的包：
<dependency><groupId>org.apache.spark</groupId><artifactId>spark-graphx_2.10</artifactId><version>1.6.0</version></dependency>
2. 设置运行环境
// 设置运行环境val conf = new SparkConf().setAppName("Simple GraphX").setMaster("spark://master:7077").setJars(Seq("E:\\Intellij\\Projects\\SimpleGraphX\\SimpleGraphX.jar"))val sc = new SparkContext(conf)
3. 图的构造
图是由若干顶点和边构成的，Spark GraphX里面的图也是一样的，所以在初始图之前，先要定义若干的顶点和边：
// 顶点val vertexArray = Array((1L,("Alice", 38)),(2L,("Henry", 27)),(3L,("Charlie", 55)),(4L,("Peter", 32)),(5L,("Mike", 35)),(6L,("Kate", 23)))// 边val edgeArray = Array(Edge(2L, 1L, 5),Edge(2L, 4L, 2),Edge(3L, 2L, 7),Edge(3L, 6L, 3),Edge(4L, 1L, 1),Edge(5L, 2L, 3),Edge(5L, 3L, 8),Edge(5L, 6L, 8))
然后再利用点和边生成各自的RDD：
//构造vertexRDD和edgeRDDval vertexRDD:RDD[(Long,(String,Int))] = sc.parallelize(vertexArray)val edgeRDD:RDD[Edge[Int]] = sc.parallelize(edgeArray)
最后利用两个RDD生成图：
// 构造图val graph:Graph[(String,Int),Int] = Graph(vertexRDD, edgeRDD)
4. 图的属性操作
Spark GraphX图的属性包括：
(1) Graph.vertices：图中的所有顶点；
(2) Graph.edges：图中所有的边；
(3) Graph.triplets：由三部分组成，源顶点，目的顶点，以及两个顶点之间的边；
(4) Graph.degrees：图中所有顶点的度；
(5) Graph.inDegrees：图中所有顶点的入度；
(6) Graph.outDegrees：图中所有顶点的出度；
对这些属性的操作，直接上代码：
//图的属性操作println("*************************************************************")println("属性演示")println("*************************************************************")// 方法一println("找出图中年龄大于20的顶点方法之一:")graph.vertices.filter{case(id,(name,age)) => age>20}.collect.foreach {case(id,(name,age)) => println(s"$name is $age")}// 方法二println("找出图中年龄大于20的顶点方法之二:")graph.vertices.filter(v => v._2._2>20).collect.foreach {v => println(s"${v._2._1} is ${v._2._2}")}// 边的操作println("找出图中属性大于3的边:")graph.edges.filter(e => e.attr>3).collect.foreach(e => println(s"${e.srcId} to ${e.dstId} att ${e.attr}"))println// Triplet操作println("列出所有的Triples:")for(triplet <- graph.triplets.collect){println(s"${triplet.srcAttr._1} likes ${triplet.dstAttr._1}")}printlnprintln("列出边属性>3的Triples:")for(triplet <- graph.triplets.filter(t => t.attr > 3).collect){println(s"${triplet.srcAttr._1} likes ${triplet.dstAttr._1}")}println// Degree操作println("找出图中最大的出度,入度,度数:")def max(a:(VertexId,Int), b:(VertexId,Int)):(VertexId,Int) = {if (a._2>b._2) a else b}println("Max of OutDegrees:" + graph.outDegrees.reduce(max))println("Max of InDegrees:" + graph.inDegrees.reduce(max))println("Max of Degrees:" + graph.degrees.reduce(max))println
运行结果：
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/05/22 20:45:35 INFO Slf4jLogger: Slf4jLogger started
17/05/22 20:45:35 INFO Remoting: Starting remoting
17/05/22 20:45:35 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.1.101:53375]
*************************************************************
属性演示
*************************************************************
找出图中年龄大于20的顶点方法之一:
Peter is 32
Alice is 38
Charlie is 55
Mike is 35
找出图中年龄大于20的顶点方法之二:
Peter is 32
Alice is 38
Charlie is 55
Mike is 35
找出图中属性大于3的边:
3 to 2 att 7
5 to 3 att 8
5 to 6 att 8列出所有的Triples:
Henry likes Alice
Henry likes Peter
Charlie likes Henry
Charlie likes Kate
Peter likes Alice
Mike likes Henry
Mike likes Charlie
Mike likes Kate列出边属性>3的Triples:
Charlie likes Henry
Mike likes Charlie
Mike likes Kate找出图中最大的出度,入度,度数:
Max of OutDegrees:(5,3)
Max of InDegrees:(1,2)
Max of Degrees:(2,4)