Spark组件之GraphX学习7--随机图生成和reduce最大或最小出度/入度/度

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

1解释

通过自定义函数

reduce最大或最小出度/入度/度

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.learningimport org.apache.spark.SparkConfimport org.apache.spark.SparkContextimport org.apache.spark.graphx.Graphimport org.apache.spark.graphx.VertexIdimport org.apache.spark.graphx.VertexRDDimport org.apache.spark.graphx.util.GraphGeneratorsobject GraphGeneratorsAndMaxMin {  def main(args: Array[String]): Unit = {    val conf = new SparkConf().setAppName("GraphOperatorsStructuralMask").setMaster("local[4]")    // Assume the SparkContext has already been constructed    val sc = new SparkContext(conf)    // Import random graph generation library    // Create a graph with "age" as the vertex property.  Here we use a random graph for simplicity.    val graph: Graph[Double, Int] =      GraphGenerators.logNormalGraph(sc, numVertices = 5).mapVertices((id, _) => id.toDouble)    // Compute the number of older followers and their total age    println("Graph:");    println("sc.defaultParallelism:" + sc.defaultParallelism);    println("vertices:");    graph.vertices.collect.foreach(println(_))    println("edges:");    graph.edges.collect.foreach(println(_))    println("count:" + graph.edges.count);    println("\ndegrees");    graph.degrees.foreach(println)    println("\ninDegrees");    graph.inDegrees.foreach(println)    println("\noutDegrees");    graph.outDegrees.foreach(println)    // Define a reduce operation to compute the highest degree vertex    def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = {      if (a._2 > b._2) a else b    }    // Define a reduce operation to compute the highest degree vertex    def min(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = {      if (a._2 < b._2) a else b    }    // Compute the max degrees    val maxInDegree: (VertexId, Int) = graph.inDegrees.reduce(max)    val maxOutDegree: (VertexId, Int) = graph.outDegrees.reduce(max)    val maxDegrees: (VertexId, Int) = graph.degrees.reduce(max)    println("\nmax:");    println("maxDegree:" + (graph.degrees.reduce(max)));    println("maxInDegree:" + graph.inDegrees.reduce(max));    println("maxoutDegree:" + graph.outDegrees.reduce(max));     println("\nmin:");    println("minDegree:" + (graph.degrees.reduce(min)));    println("minInDegree:" + graph.inDegrees.reduce(min));    println("minoutDegree:" + graph.outDegrees.reduce(min));    println("end");  }}

3.结果：

Graph:sc.defaultParallelism:4vertices:(4,4.0)(0,0.0)(1,1.0)(2,2.0)(3,3.0)edges:Edge(0,2,1)Edge(0,3,1)Edge(0,3,1)Edge(0,3,1)Edge(1,0,1)Edge(1,0,1)Edge(1,2,1)Edge(2,0,1)Edge(2,2,1)Edge(2,4,1)Edge(3,1,1)Edge(3,1,1)Edge(3,2,1)Edge(4,0,1)Edge(4,4,1)count:15degrees(2,7)(1,5)(3,6)(4,4)(0,8)inDegrees(1,2)(4,2)(0,4)(3,3)(2,4)outDegrees(3,3)(1,3)(2,3)(4,2)(0,4)max:maxDegree:(0,8)maxInDegree:(0,4)maxoutDegree:(0,4)min:maxDegree:(4,4)maxInDegree:(1,2)maxoutDegree:(4,2)end

参考

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

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