GraphX中Pregel单源点最短路径

    package myclass.GraphX
    
    import org.apache.spark.graphx._
    import org.apache.spark.SparkContext
    
    // Import random graph generation library
    
    import org.apache.spark.graphx.util.GraphGenerators
    
    /** * Created by jack on 3/4/14.
     */
    object Pregel {
    	def main(args: Array[String]) {
    		val sc = new SparkContext("local", "pregel test", System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass))
    		// A graph with edge attributes containing distances
    		//初始化一个随机图，节点的度符合对数正态分布,边属性初始化为1
    		val graph: Graph[Int, Double] =
    			GraphGenerators.logNormalGraph(sc, numVertices = 10).mapEdges(e => e.attr.toDouble)
    graph.edges.foreach(println)
    		val sourceId: VertexId = 4 // The ultimate source
    
    		// Initialize the graph such that all vertices except the root have distance infinity.
    		//初始化各节点到原点的距离
    		val initialGraph = graph.mapVertices((id, _) => if (id == sourceId) 0.0 else Double.PositiveInfinity)
    
    		val sssp = initialGraph.pregel(Double.PositiveInfinity)(
    			// Vertex Program，节点处理消息的函数，dist为原节点属性（Double），newDist为消息类型（Double）
    			(id, dist, newDist) => math.min(dist, newDist),
    
    			// Send Message，发送消息函数，返回结果为（目标节点id，消息（即最短距离））
    			triplet => {
    				if (triplet.srcAttr + triplet.attr < triplet.dstAttr) {
    					Iterator((triplet.dstId, triplet.srcAttr + triplet.attr))
    				} else {
    					Iterator.empty
    				}
    			},
    			//Merge Message，对消息进行合并的操作，类似于Hadoop中的combiner
    			(a, b) => math.min(a, b)
    		)
    
    		println(sssp.vertices.collect.mkString("\n"))
    	}
    }