Spark GraphX Pregel API

Spark GraphX Pregel API

图本身是递归数据结构，顶点的属性依赖于它们邻居的属性，这些邻居的属性又依赖于自己邻居的属性。所以许多重要的图算法都是迭代的重新计算每个顶点的属性，直到满足某个确定的条件。一系列的graph-parallel抽象已经被提出来用来表达这些迭代算法。GraphX公开了一个类似Pregel的操作，它是广泛使用的Pregel和GraphLab抽象的一个融合。

在GraphX中，更高级的Pregel操作是一个约束到图拓扑的批量同步（bulk-synchronous）并行消息抽象。Pregel操作者执行一系列的超级步骤（super steps），在这些步骤中，顶点从之前的超级步骤中接收进入(inbound)消息的总和，为顶点属性计算一个新的值，然后在以后的超级步骤中发送消息到邻居顶点。不像Pregel而更像GraphLab，消息作为一个边三元组的函数被并行计算，消息计算既访问了源顶点特征也访问了目的顶点特征。在超级步中，没有收到消息的顶点被跳过。当没有消息遗留时，Pregel操作停止迭代并返回最终的图。

注意，与更标准的Pregel实现不同的是，GraphX中的顶点仅仅能发送信息给邻居顶点，并利用用户自定义的消息函数构造消息。这些限制允许在GraphX进行额外的优化。

一下是 Pregel操作(ClassTag[A]):Graph[VD,ED])的类型签名以及实现草图（注意，访问graph.cache已经被删除）

class GraphOps[VD, ED] {
  def pregel[A]
      (initialMsg: A,
       maxIter: Int = Int.MaxValue,
       activeDir: EdgeDirection = EdgeDirection.Out)
      (vprog: (VertexId, VD, A) => VD,
       sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)],
       mergeMsg: (A, A) => A)
    : Graph[VD, ED] = {
    // Receive the initial message at each vertex
    var g = mapVertices( (vid, vdata) => vprog(vid, vdata, initialMsg) ).cache()
    // compute the messages
    var messages = g.mapReduceTriplets(sendMsg, mergeMsg)
    var activeMessages = messages.count()
    // Loop until no messages remain or maxIterations is achieved
    var i = 0
    while (activeMessages > 0 && i < maxIterations) {
      // Receive the messages: -----------------------------------------------------------------------
      // Run the vertex program on all vertices that receive messages
      val newVerts = g.vertices.innerJoin(messages)(vprog).cache()
      // Merge the new vertex values back into the graph
      g = g.outerJoinVertices(newVerts) { (vid, old, newOpt) => newOpt.getOrElse(old) }.cache()
      // Send Messages: ------------------------------------------------------------------------------
      // Vertices that didn't receive a message above don't appear in newVerts and therefore don't
      // get to send messages.  More precisely the map phase of mapReduceTriplets is only invoked
      // on edges in the activeDir of vertices in newVerts
      messages = g.mapReduceTriplets(sendMsg, mergeMsg, Some((newVerts, activeDir))).cache()
      activeMessages = messages.count()
      i += 1
    }
    g
  }
}

注意，pregel有两个参数列表（graph.pregel(list1)(list2)）。第一个参数列表包含配置参数初始消息、最大迭代数、发送消息的边的方向（默认是沿边方向出）。第二个参数列表包含用户自定义的函数用来接收消息（vprog）、计算消息（sendMsg）、合并消息（mergeMsg）。

我们可以用Pregel操作表达计算单源最短路径( single source shortest path)。

import org.apache.spark.graphx._
// Import random graph generation library
import org.apache.spark.graphx.util.GraphGenerators
// A graph with edge attributes containing distances
val graph: Graph[Int, Double] =
  GraphGenerators.logNormalGraph(sc, numVertices = 100).mapEdges(e => e.attr.toDouble)
val sourceId: VertexId = 42 // The ultimate source
// Initialize the graph such that all vertices except the root have distance infinity.
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
  )
println(sssp.vertices.collect.mkString("n"))