GraphX API

图的基本概念和术语这里介绍，这里以示例来做基本演示与理解

外部依赖除了基本的spark依赖之外，还需导入spark-GraphX

• 构建如下关系图，圈内数字分别表示各个点的id，以（name,job）作为各点的属性，各点之间形成关系图，边的权重表示为点与点之间的联系
  

图的构建与图信息的查看

• 创建图操作（以下为操作步骤，代码在main方法中执行）

//创建sparkContext
val conf = new SparkConf().setAppName("ghx").setMaster("local[*]")
val sc = new SparkContext(conf)

//创建所有的点
val vertices= sc.makeRDD(Seq((3L,("zl","stu")),(5L,("zs","prof")),(2L,("ls","prof")),(7L,("ww","pstdoc"))))
val edges = sc.makeRDD(Seq(Edge(2L,5L,"tongshi"),Edge(5L,3L,"jiao"),Edge(5L,7L,"pi"),Edge(3L,7L,"wen")))
//创建图
val graph = Graph(vertices,edges)

• 查看图信息

1. 产看边点数量
   形成的图关系，构建了4个点以及四条有向的边

println(graph.numEdges,graph.numVertices)

//打印结果
（4,4）

2. 查看点属性和边属性

//点属性
graph.vertices.foreach(x=>println(x._1,x._2))
//打印结果
(2,(ls,prof))
(7,(ww,pstdoc))
(5,(zs,prof))
(3,(zl,stu))

//边属性
graph.edges.foreach(x=>println(x.srcId,x.dstId,x.attr))
//打印结果
(5,7,pi)
(2,5,tongshi)
(3,7,wen)
(5,3,jiao)


//通过triplets查看点边的所有值
//srcId(起始点的id) srcAttr(起始点的属性值) dstId(到达点的id) dstAttr(到达点的属性值) attr(权重关系)
graph.triplets.foreach(x=>println(x.srcId,x.srcAttr,x.dstId,x.dstAttr,x.attr))
//打印结果
(5,(zs,prof),3,(zl,stu),jiao)
(2,(ls,prof),5,(zs,prof),tongshi)
(5,(zs,prof),7,(ww,pstdoc),pi)
(3,(zl,stu),7,(ww,pstdoc),wen)

3. 查看出度和入度

• 出度：指从当前顶点指向其他顶点的边的数量
• 入度：其他顶点指向当前顶点的边的数量

//出度
graph.outDegrees.foreach(x=>println(x))
//打印结果
(5,2)
(2,1)
(3,1)

//入度
graph.inDegrees.foreach(x=>println(x))
//打印结果
(3,1)
(5,1)
(7,2)

//出度入度之和
graph.degrees.foreach(x=>println(x))
//打印结果
(2,1)
(5,3)
(3,2)
(7,2)

图的算子

1. 属性算子map

//改变点的值(一边之改变点的属性值)
//如下两种写法相同：只取name的值进行修改
graph.mapVertices{case(vertexId,(name,job))=>(vertexId,name)}
graph.mapVertices{case(vertexId,attr)=>(vertexId,attr._1+"hello")}

//改变边的值    graph.mapEdges(e=>Edge(e.srcId,e.dstId,e.attr+",Hello"))

2. 结构算子

//改变边的方向
graph.reverse

//生成满足顶点与边条件的子图
graph.subgraph(vpred=(id,attr)=>attr._2.contains("pro"))

3. join算子

//join 合并操作 如下创建新的点与原构建图进行join操作
//将相同id的点的属性值拼接
val newPoin = sc.parallelize(Array((3L,"hehe"),(5L,"xixi"),(4L,"cici")))
    graph.joinVertices(newPoin)((id,src,newval)=>(src._1+"@"+newval,src._2)).vertices.foreach(f=>println(f._2))
//打印结果
(ww,pstdoc)
(ls,prof)
(zs@xixi,prof)
(zl@hehe,stu)


//此外还有outerJoinVertices相当于outerjoin
outerJoinVertices

pageRank应用

通过pageRank找出用户社交网络最重要的用户

object myGrp extends App {
  val spark = SparkSession.builder().master("local[1]").appName("users").getOrCreate()

  val tweeters = Array((1L, ("Alice", 28)), (2L, ("Bob", 27)), (3L, ("Charlie", 65)), (4L, ("David", 42)), (5L, ("Ed", 55)), (6L, ("Fran", 50)))
  val vects = spark.sparkContext.parallelize(tweeters)

  val followRelations = Array(Edge[Int](2L, 1L, 7), Edge[Int](2L, 4L, 2), Edge[Int](3L, 2L, 4),  Edge[Int](3L, 6L, 3), Edge[Int](4L, 1L, 1), Edge[Int](5L, 2L, 2), Edge[Int](5L, 3L, 8), Edge[Int](5L, 6L, 3))

  val edges = spark.sparkContext.parallelize(followRelations)

  val grah = Graph(vects,edges)
  val cc = grah.pageRank(0.001)
  cc.vertices.sortBy(_._2,false).foreach(println)
}

pregel应用求最短距离

求顶点5到各个点的最短距离

object mypregel extends App {
  //创建SparkContext
  val conf = new SparkConf().setMaster("local[*]").setAppName("pregel")
  val sc = SparkContext.getOrCreate(conf)

  //创建点
  val vertexs = sc.makeRDD(Seq((1L,"park"),(2L,"company"),(3L,"restaurant"),
    (4L,"hospital"),(5L,"home"),(6L,"school")))

  //创建边
  val edges=sc.makeRDD(Seq(Edge(2L,1L,7),Edge(2L,4L,2),Edge(3L,2L,4),Edge(3L,6L,3),
    Edge(4L,1L,1), Edge(2L,5L,2),Edge(5L,3L,8),Edge(5L,6L,3)))

  //创建图
  val graph = Graph(vertexs,edges)

  //计算顶点5到其他各个点的最短距离
  //起始点id
  val srcVertexId = 5L
  //起始点赋值为0，其余的点全部赋值为无穷大
  val initialGraph = graph.mapVertices{
    case (id,destination) => if(id==srcVertexId) 0.0 else Double.PositiveInfinity
  }
  //调用pregel
  val pregelGraph = initialGraph.pregel(
    Double.PositiveInfinity,
    Int.MaxValue,
    EdgeDirection.Out
  )(
    (id:VertexId,vd:Double,distMsg:Double)=>{
      val minDist = math.min(vd,distMsg)
      println(s"顶点${id},属性${vd},接收到消息${distMsg},合并后的属性${minDist}")
      minDist
    },
    (edgeTriplet:EdgeTriplet[Double,PartitionID])=>{
      if (edgeTriplet.srcAttr + edgeTriplet.attr< edgeTriplet.dstAttr){
        println(s"顶点${edgeTriplet.srcId}给顶点${edgeTriplet.dstId} 发送消息 ${edgeTriplet.srcAttr + edgeTriplet.attr}")
        Iterator[(VertexId, Double)]((edgeTriplet.dstId, edgeTriplet.srcAttr + edgeTriplet.attr))
      }else{
        Iterator.empty
      }
    },
    (msg1:Double,msg2:Double)=> math.min(msg1,msg2)
  )

  //输出结果
  pregelGraph.triplets.collect().foreach(println)
  println(pregelGraph.vertices.collect().mkString(" "))

  sc.stop()
}