SimpleGraphX PageRank shell

package week7

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD

object SimpleGraphX {
def main(args: Array[String]) {
//屏蔽日志
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

//设置运行环境
val conf = new SparkConf().setAppName("SimpleGraphX").setMaster("local")
val sc = new SparkContext(conf)

//设置顶点和边，注意顶点和边都是用元组定义的Array
//顶点的数据类型是VD:(String,Int)
val vertexArray = Array(
(1L, ("Alice", 28)),
(2L, ("Bob", 27)),
(3L, ("Charlie", 65)),
(4L, ("David", 42)),
(5L, ("Ed", 55)),
(6L, ("Fran", 50))
)
//边的数据类型ED:Int
val edgeArray = Array(
Edge(2L, 1L, 7),
Edge(2L, 4L, 2),
Edge(3L, 2L, 4),
Edge(3L, 6L, 3),
Edge(4L, 1L, 1),
Edge(5L, 2L, 2),
Edge(5L, 3L, 8),
Edge(5L, 6L, 3)
)

//构造vertexRDD和edgeRDD
val vertexRDD: RDD[(Long, (String, Int))] = sc.parallelize(vertexArray)
val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)

//构造图Graph[VD,ED]
val graph: Graph[(String, Int), Int] = Graph(vertexRDD, edgeRDD)

//***************************************************************************************************
//*******************************          图的属性          *****************************************
//***************************************************************************************************
println("**********************************************************")
println("属性演示")
println("**********************************************************")
//方法一
println("找出图中年龄大于30的顶点方法一：")
graph.vertices.filter { case (id, (name, age)) => age > 30}.collect.foreach {
case (id, (name, age)) => println(s"$name is $age")
}
//方法二
println("找出图中年龄大于30的顶点方法二：")
graph.vertices.filter(v => v._2._2 > 30).collect.foreach(v => println(s"${v._2._1} is ${v._2._2}"))
println

//边操作：找出图中属性大于5的边
println("找出图中属性大于5的边：")
graph.edges.filter(e => e.attr > 5).collect.foreach(e => println(s"${e.srcId} to ${e.dstId} att ${e.attr}"))
println

//triplets操作，((srcId, srcAttr), (dstId, dstAttr), attr)
println("列出所有的tripltes：")
for (triplet <- graph.triplets.collect) {
println(s"${triplet.srcAttr._1} likes ${triplet.dstAttr._1}")
}
println

println("列出边属性>5的tripltes：")
for (triplet <- graph.triplets.filter(t => t.attr > 5).collect) {
println(s"${triplet.srcAttr._1} likes ${triplet.dstAttr._1}")
}
println

//Degrees操作
println("找出图中最大的出度、入度、度数：")
def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = {
if (a._2 > b._2) a else b
}
println("max of outDegrees:" + graph.outDegrees.reduce(max) + " max of inDegrees:" + graph.inDegrees.reduce(max) + " max of Degrees:" + graph.degrees.reduce(max))
println

//***************************************************************************************************
//*******************************          转换操作          *****************************************
//***************************************************************************************************
println("**********************************************************")
println("转换操作")
println("**********************************************************")
println("顶点的转换操作，顶点age + 10：")
graph.mapVertices{ case (id, (name, age)) => (id, (name, age+10))}.vertices.collect.foreach(v => println(s"${v._2._1} is ${v._2._2}"))
println
println("边的转换操作，边的属性*2：")
graph.mapEdges(e=>e.attr*2).edges.collect.foreach(e => println(s"${e.srcId} to ${e.dstId} att ${e.attr}"))
println

//***************************************************************************************************
//*******************************          结构操作          *****************************************
//***************************************************************************************************
println("**********************************************************")
println("结构操作")
println("**********************************************************")
println("顶点年纪>30的子图：")
val subGraph = graph.subgraph(vpred = (id, vd) => vd._2 >= 30)
println("子图所有顶点：")
subGraph.vertices.collect.foreach(v => println(s"${v._2._1} is ${v._2._2}"))
println
println("子图所有边：")
subGraph.edges.collect.foreach(e => println(s"${e.srcId} to ${e.dstId} att ${e.attr}"))
println

//***************************************************************************************************
//*******************************          连接操作          *****************************************
//***************************************************************************************************
println("**********************************************************")
println("连接操作")
println("**********************************************************")
val inDegrees: VertexRDD[Int] = graph.inDegrees
case class User(name: String, age: Int, inDeg: Int, outDeg: Int)

//创建一个新图，顶点VD的数据类型为User，并从graph做类型转换
val initialUserGraph: Graph[User, Int] = graph.mapVertices { case (id, (name, age)) => User(name, age, 0, 0)}

//initialUserGraph与inDegrees、outDegrees（RDD）进行连接，并修改initialUserGraph中inDeg值、outDeg值
val userGraph = initialUserGraph.outerJoinVertices(initialUserGraph.inDegrees) {
case (id, u, inDegOpt) => User(u.name, u.age, inDegOpt.getOrElse(0), u.outDeg)
}.outerJoinVertices(initialUserGraph.outDegrees) {
case (id, u, outDegOpt) => User(u.name, u.age, u.inDeg, outDegOpt.getOrElse(0))
}

println("连接图的属性：")
userGraph.vertices.collect.foreach(v => println(s"${v._2.name} inDeg: ${v._2.inDeg}  outDeg: ${v._2.outDeg}"))
println

println("出度和入读相同的人员：")
userGraph.vertices.filter {
case (id, u) => u.inDeg == u.outDeg
}.collect.foreach {
case (id, property) => println(property.name)
}
println

//***************************************************************************************************
//*******************************          聚合操作          *****************************************
//***************************************************************************************************
println("**********************************************************")
println("聚合操作")
println("**********************************************************")
println("找出年纪最大的追求者：")
val oldestFollower: VertexRDD[(String, Int)] = userGraph.mapReduceTriplets[(String, Int)](
// 将源顶点的属性发送给目标顶点，map过程
edge => Iterator((edge.dstId, (edge.srcAttr.name, edge.srcAttr.age))),
// 得到最大追求者，reduce过程
(a, b) => if (a._2 > b._2) a else b
)

userGraph.vertices.leftJoin(oldestFollower) { (id, user, optOldestFollower) =>
optOldestFollower match {
case None => s"${user.name} does not have any followers."
case Some((name, age)) => s"${name} is the oldest follower of ${user.name}."
}
}.collect.foreach { case (id, str) => println(str)}
println

//找出追求者的平均年纪
println("找出追求者的平均年纪：")
val averageAge: VertexRDD[Double] = userGraph.mapReduceTriplets[(Int, Double)](
// 将源顶点的属性 (1, Age)发送给目标顶点，map过程
edge => Iterator((edge.dstId, (1, edge.srcAttr.age.toDouble))),
// 得到追求着的数量和总年龄
(a, b) => ((a._1 + b._1), (a._2 + b._2))
).mapValues((id, p) => p._2 / p._1)

userGraph.vertices.leftJoin(averageAge) { (id, user, optAverageAge) =>
optAverageAge match {
case None => s"${user.name} does not have any followers."
case Some(avgAge) => s"The average age of ${user.name}\'s followers is $avgAge."
}
}.collect.foreach { case (id, str) => println(str)}
println

//***************************************************************************************************
//*******************************          实用操作          *****************************************
//***************************************************************************************************
println("**********************************************************")
println("聚合操作")
println("**********************************************************")
println("找出5到各顶点的最短：")
val sourceId: VertexId = 5L // 定义源点
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),
triplet => {  // 计算权重
if (triplet.srcAttr + triplet.attr < triplet.dstAttr) {
Iterator((triplet.dstId, triplet.srcAttr + triplet.attr))
} else {
Iterator.empty
}
},
(a,b) => math.min(a,b) // 最短距离
)
println(sssp.vertices.collect.mkString("\n"))

sc.stop()
}
}

package week7

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD

object PageRank {
def main(args: Array[String]) {
//屏蔽日志
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

//设置运行环境
val conf = new SparkConf().setAppName("PageRank").setMaster("local")
val sc = new SparkContext(conf)

//读入数据文件
val articles: RDD[String] = sc.textFile("/home/mmicky/IdeaProjects/data/graphx/graphx-wiki-vertices.txt")
val links: RDD[String] = sc.textFile("/home/mmicky/IdeaProjects/data/graphx/graphx-wiki-edges.txt")

//装载顶点和边
val vertices = articles.map { line =>
val fields = line.split('\t')
(fields(0).toLong, fields(1))
}

val edges = links.map { line =>
val fields = line.split('\t')
Edge(fields(0).toLong, fields(1).toLong, 0)
}

//cache操作
//val graph = Graph(vertices, edges, "").persist(StorageLevel.MEMORY_ONLY_SER)
val graph = Graph(vertices, edges, "").persist()
//graph.unpersistVertices(false)

//测试
println("**********************************************************")
println("获取5个triplet信息")
println("**********************************************************")
graph.triplets.take(5).foreach(println(_))

//pageRank算法里面的时候使用了cache()，故前面persist的时候只能使用MEMORY_ONLY
println("**********************************************************")
println("PageRank计算，获取最有价值的数据")
println("**********************************************************")
val prGraph = graph.pageRank(0.001).cache()

val titleAndPrGraph = graph.outerJoinVertices(prGraph.vertices) {
(v, title, rank) => (rank.getOrElse(0.0), title)
}

titleAndPrGraph.vertices.top(10) {
Ordering.by((entry: (VertexId, (Double, String))) => entry._2._1)
}.foreach(t => println(t._2._2 + ": " + t._2._1))

sc.stop()
}
}

优化
vi spark-defaults.conf
spark.serializer        org.apache.spark.serializer.KryoSerializer
spark.kryo.registrator  org.apache.spark.graphx.GraphKryoRegistrator

import org.apache.spark.graphx._

import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel

//读入数据文件
val articles: RDD[String] = sc.textFile("file:///app/hadoop/data/graphx-wiki-vertices.txt")

val links: RDD[String] = sc.textFile("file:///app/hadoop/data/graphx-wiki-edges.txt")

//装载顶点和边
val vertices = articles.map { line =>

val fields = line.split('\t')

(fields(0).toLong, fields(1))

}

val edges = links.map { line =>

val fields = line.split('\t')

Edge(fields(0).toLong, fields(1).toLong, 0)

}

//cache
//val graph = Graph(vertices, edges, "").persist(StorageLevel.MEMORY_ONLY_SER)
val graph = Graph(vertices, edges, "").persist()
graph.vertices.count
graph.triplets.count

//graph.unpersistVertices(false)

//测试
graph.triplets.take(5).foreach(println(_))

//pageRank算法里面的时候使用了cache()，故前面persist的时候只能使用MEMORY_ONLY
val prGraph = graph.pageRank(0.001).cache()

val titleAndPrGraph = graph.outerJoinVertices(prGraph.vertices) {

(v, title, rank) => (rank.getOrElse(0.0), title)

}

titleAndPrGraph.vertices.top(10) {

Ordering.by((entry: (VertexId, (Double, String))) => entry._2._1)

}.foreach(t => println(t._2._2 + ": " + t._2._1))