Spark组件之GraphX学习9--使用pregel函数求单源最短路径

更多代码请见：https://github.com/xubo245/SparkLearning

1解释

使用pregel函数求单源最短路径

GraphX中的单源点最短路径例子，使用的是类Pregel的方式。

核心部分是三个函数：

1.节点处理消息的函数  vprog: (VertexId, VD, A) => VD （节点id，节点属性，消息） => 节点属性

2.节点发送消息的函数 sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId,A)]   (边元组） => Iterator[(目标节点id，消息)]

3.消息合并函数 mergeMsg: (A, A) => A)    (消息，消息) => 消息

具体请参考【3】
主要代码：

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
)

源码：

/**
* Execute a Pregel-like iterative vertex-parallel abstraction. The
* user-defined vertex-program `vprog` is executed in parallel on
* each vertex receiving any inbound messages and computing a new
* value for the vertex. The `sendMsg` function is then invoked on
* all out-edges and is used to compute an optional message to the
* destination vertex. The `mergeMsg` function is a commutative
* associative function used to combine messages destined to the
* same vertex.
*
* On the first iteration all vertices receive the `initialMsg` and
* on subsequent iterations if a vertex does not receive a message
* then the vertex-program is not invoked.
*
* This function iterates until there are no remaining messages, or
* for `maxIterations` iterations.
*
* @tparam A the Pregel message type
*
* @param initialMsg the message each vertex will receive at the on
* the first iteration
*
* @param maxIterations the maximum number of iterations to run for
*
* @param activeDirection the direction of edges incident to a vertex that received a message in
* the previous round on which to run `sendMsg`. For example, if this is `EdgeDirection.Out`, only
* out-edges of vertices that received a message in the previous round will run.
*
* @param vprog the user-defined vertex program which runs on each
* vertex and receives the inbound message and computes a new vertex
* value. On the first iteration the vertex program is invoked on
* all vertices and is passed the default message. On subsequent
* iterations the vertex program is only invoked on those vertices
* that receive messages.
*
* @param sendMsg a user supplied function that is applied to out
* edges of vertices that received messages in the current
* iteration
*
* @param mergeMsg a user supplied function that takes two incoming
* messages of type A and merges them into a single message of type
* A. ''This function must be commutative and associative and
* ideally the size of A should not increase.''
*
* @return the resulting graph at the end of the computation
*
*/
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)
}

object Pregel extends Logging {

/**
* Execute a Pregel-like iterative vertex-parallel abstraction.  The
* user-defined vertex-program `vprog` is executed in parallel on
* each vertex receiving any inbound messages and computing a new
* value for the vertex.  The `sendMsg` function is then invoked on
* all out-edges and is used to compute an optional message to the
* destination vertex. The `mergeMsg` function is a commutative
* associative function used to combine messages destined to the
* same vertex.
*
* On the first iteration all vertices receive the `initialMsg` and
* on subsequent iterations if a vertex does not receive a message
* then the vertex-program is not invoked.
*
* This function iterates until there are no remaining messages, or
* for `maxIterations` iterations.
*
* @tparam VD the vertex data type
* @tparam ED the edge data type
* @tparam A the Pregel message type
*
* @param graph the input graph.
*
* @param initialMsg the message each vertex will receive at the first
* iteration
*
* @param maxIterations the maximum number of iterations to run for
*
* @param activeDirection the direction of edges incident to a vertex that received a message in
* the previous round on which to run `sendMsg`. For example, if this is `EdgeDirection.Out`, only
* out-edges of vertices that received a message in the previous round will run. The default is
* `EdgeDirection.Either`, which will run `sendMsg` on edges where either side received a message
* in the previous round. If this is `EdgeDirection.Both`, `sendMsg` will only run on edges where
* *both* vertices received a message.
*
* @param vprog the user-defined vertex program which runs on each
* vertex and receives the inbound message and computes a new vertex
* value.  On the first iteration the vertex program is invoked on
* all vertices and is passed the default message.  On subsequent
* iterations the vertex program is only invoked on those vertices
* that receive messages.
*
* @param sendMsg a user supplied function that is applied to out
* edges of vertices that received messages in the current
* iteration
*
* @param mergeMsg a user supplied function that takes two incoming
* messages of type A and merges them into a single message of type
* A.  ''This function must be commutative and associative and
* ideally the size of A should not increase.''
*
* @return the resulting graph at the end of the computation
*
*/
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] =
{
var g = graph.mapVertices((vid, vdata) => vprog(vid, vdata, initialMsg)).cache()
// compute the messages
var messages = g.mapReduceTriplets(sendMsg, mergeMsg)
var activeMessages = messages.count()
// Loop
var prevG: Graph[VD, ED] = null
var i = 0
while (activeMessages > 0 && i < maxIterations) {
// Receive the messages and update the vertices.
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.
messages = g.mapReduceTriplets(
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
}

g
} // end of apply

2.代码：

/**
* @author xubo
* ref http://spark.apache.org/docs/1.5.2/graphx-programming-guide.html * time 20160503
*/

package org.apache.spark.graphx.learning

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph
import org.apache.spark.graphx.Graph.graphToGraphOps
import org.apache.spark.graphx.VertexId
import org.apache.spark.graphx.util.GraphGenerators

object Pregeloperator {

def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("CollectingNeighbors").setMaster("local[4]")
// Assume the SparkContext has already been constructed
val sc = new SparkContext(conf)
// A graph with edge attributes containing distances
val graph: Graph[Long, Double] =
GraphGenerators.logNormalGraph(sc, numVertices = 5).mapEdges(e => e.attr.toDouble)
val sourceId: VertexId = 2 // The ultimate source
// Initialize the graph such that all vertices except the root have distance infinity.

println("graph:");
println("vertices:");
graph.vertices.collect.foreach(println)
println("edges:");
graph.edges.collect.foreach(println)
println();

val initialGraph = graph.mapVertices((id, _) => if (id == sourceId) 0.0 else Double.PositiveInfinity)
println("initialGraph:");
println("vertices:");
initialGraph.vertices.collect.foreach(println)
println("edges:");
initialGraph.edges.collect.foreach(println)
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();
println("sssp:");
println("vertices:");
println(sssp.vertices.collect.mkString("\n"))
println("edges:");
sssp.edges.collect.foreach(println)
}
}

3.结果：

graph:
vertices:
(4,3)
(0,3)
(1,2)
(2,3)
(3,4)
edges:
Edge(0,0,1.0)
Edge(0,0,1.0)
Edge(0,4,1.0)
Edge(1,1,1.0)
Edge(1,3,1.0)
Edge(2,1,1.0)
Edge(2,1,1.0)
Edge(2,1,1.0)
Edge(3,1,1.0)
Edge(3,2,1.0)
Edge(3,2,1.0)
Edge(3,4,1.0)
Edge(4,0,1.0)
Edge(4,2,1.0)
Edge(4,4,1.0)

initialGraph:
vertices:
(4,Infinity)
(0,Infinity)
(1,Infinity)
(2,0.0)
(3,Infinity)
edges:
Edge(0,0,1.0)
Edge(0,0,1.0)
Edge(0,4,1.0)
Edge(1,1,1.0)
Edge(1,3,1.0)
Edge(2,1,1.0)
Edge(2,1,1.0)
Edge(2,1,1.0)
Edge(3,1,1.0)
Edge(3,2,1.0)
Edge(3,2,1.0)
Edge(3,4,1.0)
Edge(4,0,1.0)
Edge(4,2,1.0)
Edge(4,4,1.0)
2016-05-04 14:43:01 WARN  BlockManager:71 - Block rdd_23_1 already exists on this machine; not re-adding it

sssp:
vertices:
(4,3.0)
(0,4.0)
(1,1.0)
(2,0.0)
(3,2.0)
edges:
Edge(0,0,1.0)
Edge(0,0,1.0)
Edge(0,4,1.0)
Edge(1,1,1.0)
Edge(1,3,1.0)
Edge(2,1,1.0)
Edge(2,1,1.0)
Edge(2,1,1.0)
Edge(3,1,1.0)
Edge(3,2,1.0)
Edge(3,2,1.0)
Edge(3,4,1.0)
Edge(4,0,1.0)
Edge(4,2,1.0)
Edge(4,4,1.0)

分析：

由上诉结果画图可得：



黑色部分为初始化图Graph的点和边，initGraph会将除了第二个节点外的所有节点的值初始化为无穷大，自己设为0，然后从0开始pregel处理。红色部分为实际求单源最短路径可能的路线，所以节点2到节点1为1，到3为2，到4为3，到0为4

参考

【1】 http://spark.apache.org/docs/1.5.2/graphx-programming-guide.html

【2】https://github.com/xubo245/SparkLearning

【3】http://blog.csdn.net/li385805776/article/details/20487219