在pregel中顶点有两种状态:活跃状态(active)和不活跃状态(halt)。如果某一个顶点接收到了消息并且需要执行计算那么它就会将自己设置为活跃状态。如果没有接收到消息或者接收到消息,但是发现自己不需要进行计算,那么就会将自己设置为不活跃状态。这种机制的描述如下图:
superstep 0:首先所有节点设置为活跃,并且沿正向边向相邻节点发送自身的属性值。
Superstep 1:所有节点接收到信息,节点1和节点4发现自己接受到的值比自己的大,所以更新自己的节点(这个过程可以看做是计算),并保持活跃。节点2和3没有接收到比自己大的值,所以不计算、不更新。活跃节点继续向相邻节点发送当前自己的属性值。
Superstep 2:节点3接受信息并计算,其它节点没接收到信息或者接收到但是不计算,所以接下来只有节点3活跃并发送消息。
Superstep 3:节点2和4接受到消息但是不计算所以不活跃,所有节点均不活跃,所以计算结束。
在pregel计算框架中有两个核心的函数:sendmessage函数和F(Vertex)节点计算函数。
Import org.apache.spark._ Import org.apache.spark.graphx._ Import org.apache.spark.rdd.RDD
val graph = GraphLoader.edgeListFile(sc,"/Spark/web-Google.txt")
val sourceId: VertexId = 0
然后对图进行初始化:
val initialGraph = graph.mapVertices((id, _) => if (id == sourceId) 0.0 else Double.PositiveInfinity)
val sssp = initialGraph.pregel(Double.PositiveInfinity)( (id, dist, newDist) => math.min(dist, newDist), // Vertex Program triplet => { // Send Message if (triplet.srcAttr + triplet.attr < triplet.dstAttr) { Iterator((triplet.dstId, triplet.srcAttr + triplet.attr)) } else { Iterator.empty } }, (a,b) => math.min(a,b) // Merge Message )
我们可以看到0点到354796的最短路径为11,到291526不可达。
def pregel[A: ClassTag]( initialMsg: A, maxIterations: Int = Int.MaxValue, activeDirection: EdgeDirection = EdgeDirection.Either)( vprog: (VertexId, VD, A) => VD, sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)], mergeMsg: (A, A) => A) : Graph[VD, ED] = { Pregel(graph, initialMsg, maxIterations, activeDirection)(vprog, sendMsg, mergeMsg) }
def apply[VD: ClassTag, ED: ClassTag, A: ClassTag] (graph: Graph[VD, ED], initialMsg: A, maxIterations: Int = Int.MaxValue, activeDirection: EdgeDirection = EdgeDirection.Either) (vprog: (VertexId, VD, A) => VD, sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)], mergeMsg: (A, A) => A) : Graph[VD, ED] = { //要求最大迭代数大于0,不然报错。 require(maxIterations > 0, s"Maximum number of iterations must be greater than 0," + s" but got ${maxIterations}") //第一次迭代,对每个节点用vprog函数计算。 var g = graph.mapVertices((vid, vdata) => vprog(vid, vdata, initialMsg)).cache() // 根据发送、聚合信息的函数计算下次迭代用的信息。 var messages = GraphXUtils.mapReduceTriplets(g, sendMsg, mergeMsg) //数一下还有多少节点活跃 var activeMessages = messages.count() // 下面进入循环迭代 var prevG: Graph[VD, ED] = null var i = 0 while (activeMessages > 0 && i < maxIterations) { // 接受消息并更新节点信息 prevG = g g = g.joinVertices(messages)(vprog).cache() val oldMessages = messages // Send new messages, skipping edges where neither side received a message. We must cache // messages so it can be materialized on the next line, allowing us to uncache the previous /*iteration这里用mapReduceTriplets实现消息的发送和聚合。mapReduceTriplets的*参数中有一个map方法和一个reduce方法,这里的*sendMsg就是map方法,*mergeMsg就是reduce方法 */ messages = GraphXUtils.mapReduceTriplets( g, sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache() // The call to count() materializes `messages` and the vertices of `g`. This hides oldMessages // (depended on by the vertices of g) and the vertices of prevG (depended on by oldMessages // and the vertices of g). activeMessages = messages.count() logInfo("Pregel finished iteration " + i) // Unpersist the RDDs hidden by newly-materialized RDDs oldMessages.unpersist(blocking = false) prevG.unpersistVertices(blocking = false) prevG.edges.unpersist(blocking = false) // count the iteration i += 1 } messages.unpersist(blocking = false) g } // end of apply