Spark GraphX Aggregation Summation

问题

I'm trying to compute the sum of node values in a spark graphx graph. In short the graph is a tree and the top node (root) should sum all children and their children. My graph is actually a tree that looks like this and the expected summed value should be 1850:

                                     +----+
                     +--------------->    |  VertexID 14
                     |               |    |  Value: 1000
                 +---+--+            +----+
    +------------>      | VertexId 11
    |            |      | Value:     +----+
    |            +------+ Sum of 14 & 24  |  VertexId 24
+---++                +-------------->    |  Value: 550
|    | VertexId 20                   +----+
|    | Value:
+----++Sum of 11 & 911
      |
      |           +-----+
      +----------->     | VertexId 911
                  |     | Value: 300
                  +-----+

The first stab at this looks like this:

val vertices: RDD[(VertexId, Int)] =
      sc.parallelize(Array((20L, 0)
        , (11L, 0)
        , (14L, 1000)
        , (24L, 550)
        , (911L, 300)
      ))

  //note that the last value in the edge is for factor (positive or negative)
    val edges: RDD[Edge[Int]] =
      sc.parallelize(Array(
        Edge(14L, 11L, 1),
        Edge(24L, 11L, 1),
        Edge(11L, 20L, 1),
        Edge(911L, 20L, 1)
      ))

    val dataItemGraph = Graph(vertices, edges)


    val sum: VertexRDD[(Int, BigDecimal, Int)] = dataItemGraph.aggregateMessages[(Int, BigDecimal, Int)](
      sendMsg = { triplet => triplet.sendToDst(1, triplet.srcAttr, 1) },
      mergeMsg = { (a, b) => (a._1, a._2 * a._3 + b._2 * b._3, 1) }
    )

    sum.collect.foreach(println)

This returns the following:

(20,(1,300,1))
(11,(1,1550,1))

It's doing the sum for vertex 11 but it's not rolling up to the root node (vertex 20). What am I missing or is there a better way of doing this? Of course the tree can be of arbitrary size and each vertex can have an arbitrary number of children edges.

回答1:

Given the graph is directed (as in you example it seems to be) it should be possible to write a Pregel program that does what you're asking for:

val result = 
 dataItemGraph.pregel(0, activeDirection = EdgeDirection.Out)(
  (_, vd, msg) => msg + vd, 
  t => Iterator((t.dstId, t.srcAttr)), 
  (x, y) => x + y
 )

 result.vertices.collect().foreach(println)

// Output is:
// (24,550)
// (20,1850)
// (14,1000)
// (11,1550)
// (911,300)

I'm using EdgeDirection.Out so that the messages are being send only from bottom to up (otherwise we would get into an endless loop).

来源：https://stackoverflow.com/questions/41451947/spark-graphx-aggregation-summation

标签

scala

apache-spark

spark-graphx