Spark学习笔记--Graphx

浅谈Graphx: http://blog.csdn.net/shangwen_/article/details/38645601

Pregel: http://blog.csdn.net/shangwen_/article/details/38479835

Bagel: http://ju.outofmemory.cn/entry/712

Graphx的主要接口：

基本信息接口(numEdges , num Vertices , degrees(in/out) )
聚合操作 (mapVertices , mapEdges , mapTriplets)
转换接口 (mapReduceTriplets , collectNeighbors)
结构操作 (reverse , subgraph , mask , groupEdges)
缓存操作 (cache , unpersistVertices)

要点：

每个图由3个RDD组成

名称	对应RDD	包含的属性
Vertices	VertexRDD	ID、点属性
Edges	EdgeRDD	源顶点的ID，目标顶点的ID，边属性
Triplets		源顶点ID，源顶点属性，边属性，目标顶点ID，目标顶点属性

Triplets其实是对Vertices和Edges做了join操作
点分割、边分割

应用：

基于最大连通图的社区发现
基于三角形计数的关系衡量
基于随机游走的用户属性传播

注意：

GraphX通过引入*Resilient Distributed Property Graph*（一种点和边都带属性的有向多图）扩展了Spark RDD这种抽象数据结构，这种Property Graph拥有两种Table和Graph两种视图（及视图对应的一套API），而只有一份物理存储。
Table视图将视图看成Vertex Property Table和Edge Property Table等的组合，这些组合继承了Spark RDD的API(filter,map等)。
Graph视图上包括reverse/subgraph/mapV(E)/joinV(E)/mrTriplets等操作。

Graph上的函数：（官网）

/** Summary of the functionality in the property graph */
class Graph[VD, ED] {
  // Information about the Graph ===================================================================
  val numEdges: Long
  val numVertices: Long
  val inDegrees: VertexRDD[Int]
  val outDegrees: VertexRDD[Int]
  val degrees: VertexRDD[Int]
  // Views of the graph as collections =============================================================
  val vertices: VertexRDD[VD]
  val edges: EdgeRDD[ED]
  val triplets: RDD[EdgeTriplet[VD, ED]]
  // Functions for caching graphs ==================================================================
  def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]
  def cache(): Graph[VD, ED]
  def unpersistVertices(blocking: Boolean = true): Graph[VD, ED]
  // Change the partitioning heuristic  ============================================================
  def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]
  // Transform vertex and edge attributes ==========================================================
  def mapVertices[VD2](map: (VertexID, VD) => VD2): Graph[VD2, ED]
  def mapEdges[ED2](map: Edge[ED] => ED2): Graph[VD, ED2]
  def mapEdges[ED2](map: (PartitionID, Iterator[Edge[ED]]) => Iterator[ED2]): Graph[VD, ED2]
  def mapTriplets[ED2](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2]
  def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2])
    : Graph[VD, ED2]
  // Modify the graph structure ====================================================================
  def reverse: Graph[VD, ED]
  def subgraph(
      epred: EdgeTriplet[VD,ED] => Boolean = (x => true),
      vpred: (VertexID, VD) => Boolean = ((v, d) => true))
    : Graph[VD, ED]
  def mask[VD2, ED2](other: Graph[VD2, ED2]): Graph[VD, ED]
  def groupEdges(merge: (ED, ED) => ED): Graph[VD, ED]
  // Join RDDs with the graph ======================================================================
  def joinVertices[U](table: RDD[(VertexID, U)])(mapFunc: (VertexID, VD, U) => VD): Graph[VD, ED]
  def outerJoinVertices[U, VD2](other: RDD[(VertexID, U)])
      (mapFunc: (VertexID, VD, Option[U]) => VD2)
    : Graph[VD2, ED]
  // Aggregate information about adjacent triplets =================================================
  def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexID]]
  def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[Array[(VertexID, VD)]]
  def aggregateMessages[Msg: ClassTag](
      sendMsg: EdgeContext[VD, ED, Msg] => Unit,
      mergeMsg: (Msg, Msg) => Msg,
      tripletFields: TripletFields = TripletFields.All)
    : VertexRDD[A]
  // Iterative graph-parallel computation ==========================================================
  def pregel[A](initialMsg: A, maxIterations: Int, activeDirection: EdgeDirection)(
      vprog: (VertexID, VD, A) => VD,
      sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexID,A)],
      mergeMsg: (A, A) => A)
    : Graph[VD, ED]
  // Basic graph algorithms ========================================================================
  def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double]
  def connectedComponents(): Graph[VertexID, ED]
  def triangleCount(): Graph[Int, ED]
  def stronglyConnectedComponents(numIter: Int): Graph[VertexID, ED]
}

pregel函数参数解释：

VD:顶点的数据类型。
ED:边的数据类型
A：Pregel message的类型。

graph：输入的图
initialMsg:在第一次迭代的时候顶点收到的消息。
maxIterations：迭代的次数
vprog：用户定义的顶点程序运行在每一个顶点中，负责接收进来的信息，和计算新的顶点值。在第一次迭代的时候，所有的顶点程序将会被默认的defaultMessage调用，在次轮迭代中，顶点程序只有接收到message才会被调用。
sendMsg：用户提供的函数，应用于边缘顶点在当前迭代中接收message
mergeMsg：用户提供定义的函数，将两个类型为A的message合并为一个类型为A的message。（thisfunction must be commutative and associative and ideally the size of A shouldnot increase）

示例：

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[Long, 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"))