diff options
3 files changed, 152 insertions, 32 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala index 551c339b19..5f35a58364 100644 --- a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala +++ b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala @@ -38,12 +38,13 @@ object SynthBenchmark { * Options: * -app "pagerank" or "cc" for pagerank or connected components. (Default: pagerank) * -niters the number of iterations of pagerank to use (Default: 10) - * -numVertices the number of vertices in the graph (Default: 1000000) + * -nverts the number of vertices in the graph (Default: 1000000) * -numEPart the number of edge partitions in the graph (Default: number of cores) * -partStrategy the graph partitioning strategy to use * -mu the mean parameter for the log-normal graph (Default: 4.0) * -sigma the stdev parameter for the log-normal graph (Default: 1.3) * -degFile the local file to save the degree information (Default: Empty) + * -seed seed to use for RNGs (Default: -1, picks seed randomly) */ def main(args: Array[String]) { val options = args.map { @@ -62,6 +63,7 @@ object SynthBenchmark { var mu: Double = 4.0 var sigma: Double = 1.3 var degFile: String = "" + var seed: Int = -1 options.foreach { case ("app", v) => app = v @@ -72,6 +74,7 @@ object SynthBenchmark { case ("mu", v) => mu = v.toDouble case ("sigma", v) => sigma = v.toDouble case ("degFile", v) => degFile = v + case ("seed", v) => seed = v.toInt case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt) } @@ -85,7 +88,7 @@ object SynthBenchmark { // Create the graph println(s"Creating graph...") val unpartitionedGraph = GraphGenerators.logNormalGraph(sc, numVertices, - numEPart.getOrElse(sc.defaultParallelism), mu, sigma) + numEPart.getOrElse(sc.defaultParallelism), mu, sigma, seed) // Repartition the graph val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_)).cache() @@ -113,7 +116,7 @@ object SynthBenchmark { println(s"Total PageRank = $totalPR") } else if (app == "cc") { println("Running Connected Components") - val numComponents = graph.connectedComponents.vertices.map(_._2).distinct() + val numComponents = graph.connectedComponents.vertices.map(_._2).distinct().count() println(s"Number of components = $numComponents") } val runTime = System.currentTimeMillis() - startTime diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala index 60149548ab..b8309289fe 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala @@ -40,7 +40,7 @@ object GraphGenerators { val RMATd = 0.25 /** - * Generate a graph whose vertex out degree is log normal. + * Generate a graph whose vertex out degree distribution is log normal. * * The default values for mu and sigma are taken from the Pregel paper: * @@ -48,33 +48,36 @@ object GraphGenerators { * Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. * Pregel: a system for large-scale graph processing. SIGMOD '10. * - * @param sc - * @param numVertices - * @param mu - * @param sigma - * @return + * If the seed is -1 (default), a random seed is chosen. Otherwise, use + * the user-specified seed. + * + * @param sc Spark Context + * @param numVertices number of vertices in generated graph + * @param numEParts (optional) number of partitions + * @param mu (optional, default: 4.0) mean of out-degree distribution + * @param sigma (optional, default: 1.3) standard deviation of out-degree distribution + * @param seed (optional, default: -1) seed for RNGs, -1 causes a random seed to be chosen + * @return Graph object */ - def logNormalGraph(sc: SparkContext, numVertices: Int, numEParts: Int, - mu: Double = 4.0, sigma: Double = 1.3): Graph[Long, Int] = { - val vertices = sc.parallelize(0 until numVertices, numEParts).map { src => - // Initialize the random number generator with the source vertex id - val rand = new Random(src) - val degree = math.min(numVertices.toLong, math.exp(rand.nextGaussian() * sigma + mu).toLong) - (src.toLong, degree) + def logNormalGraph( + sc: SparkContext, numVertices: Int, numEParts: Int = 0, mu: Double = 4.0, + sigma: Double = 1.3, seed: Long = -1): Graph[Long, Int] = { + + val evalNumEParts = if (numEParts == 0) sc.defaultParallelism else numEParts + + // Enable deterministic seeding + val seedRand = if (seed == -1) new Random() else new Random(seed) + val seed1 = seedRand.nextInt() + val seed2 = seedRand.nextInt() + + val vertices: RDD[(VertexId, Long)] = sc.parallelize(0 until numVertices, evalNumEParts).map { + src => (src, sampleLogNormal(mu, sigma, numVertices, seed = (seed1 ^ src))) } + val edges = vertices.flatMap { case (src, degree) => - new Iterator[Edge[Int]] { - // Initialize the random number generator with the source vertex id - val rand = new Random(src) - var i = 0 - override def hasNext(): Boolean = { i < degree } - override def next(): Edge[Int] = { - val nextEdge = Edge[Int](src, rand.nextInt(numVertices), i) - i += 1 - nextEdge - } - } + generateRandomEdges(src.toInt, degree.toInt, numVertices, seed = (seed2 ^ src)) } + Graph(vertices, edges, 0) } @@ -82,9 +85,10 @@ object GraphGenerators { // the edge data is the weight (default 1) val RMATc = 0.15 - def generateRandomEdges(src: Int, numEdges: Int, maxVertexId: Int): Array[Edge[Int]] = { - val rand = new Random() - Array.fill(maxVertexId) { Edge[Int](src, rand.nextInt(maxVertexId), 1) } + def generateRandomEdges( + src: Int, numEdges: Int, maxVertexId: Int, seed: Long = -1): Array[Edge[Int]] = { + val rand = if (seed == -1) new Random() else new Random(seed) + Array.fill(numEdges) { Edge[Int](src, rand.nextInt(maxVertexId), 1) } } /** @@ -97,9 +101,12 @@ object GraphGenerators { * @param mu the mean of the normal distribution * @param sigma the standard deviation of the normal distribution * @param maxVal exclusive upper bound on the value of the sample + * @param seed optional seed */ - private def sampleLogNormal(mu: Double, sigma: Double, maxVal: Int): Int = { - val rand = new Random() + private[spark] def sampleLogNormal( + mu: Double, sigma: Double, maxVal: Int, seed: Long = -1): Int = { + val rand = if (seed == -1) new Random() else new Random(seed) + val sigmaSq = sigma * sigma val m = math.exp(mu + sigmaSq / 2.0) // expm1 is exp(m)-1 with better accuracy for tiny m diff --git a/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala new file mode 100644 index 0000000000..b346d4db2e --- /dev/null +++ b/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.graphx.util + +import org.scalatest.FunSuite + +import org.apache.spark.graphx.LocalSparkContext + +class GraphGeneratorsSuite extends FunSuite with LocalSparkContext { + + test("GraphGenerators.generateRandomEdges") { + val src = 5 + val numEdges10 = 10 + val numEdges20 = 20 + val maxVertexId = 100 + + val edges10 = GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId) + assert(edges10.length == numEdges10) + + val correctSrc = edges10.forall(e => e.srcId == src) + assert(correctSrc) + + val correctWeight = edges10.forall(e => e.attr == 1) + assert(correctWeight) + + val correctRange = edges10.forall(e => e.dstId >= 0 && e.dstId <= maxVertexId) + assert(correctRange) + + val edges20 = GraphGenerators.generateRandomEdges(src, numEdges20, maxVertexId) + assert(edges20.length == numEdges20) + + val edges10_round1 = + GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId, seed = 12345) + val edges10_round2 = + GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId, seed = 12345) + assert(edges10_round1.zip(edges10_round2).forall { case (e1, e2) => + e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr + }) + + val edges10_round3 = + GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId, seed = 3467) + assert(!edges10_round1.zip(edges10_round3).forall { case (e1, e2) => + e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr + }) + } + + test("GraphGenerators.sampleLogNormal") { + val mu = 4.0 + val sigma = 1.3 + val maxVal = 100 + + val dstId = GraphGenerators.sampleLogNormal(mu, sigma, maxVal) + assert(dstId < maxVal) + + val dstId_round1 = GraphGenerators.sampleLogNormal(mu, sigma, maxVal, 12345) + val dstId_round2 = GraphGenerators.sampleLogNormal(mu, sigma, maxVal, 12345) + assert(dstId_round1 == dstId_round2) + + val dstId_round3 = GraphGenerators.sampleLogNormal(mu, sigma, maxVal, 789) + assert(dstId_round1 != dstId_round3) + } + + test("GraphGenerators.logNormalGraph") { + withSpark { sc => + val mu = 4.0 + val sigma = 1.3 + val numVertices100 = 100 + + val graph = GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma) + assert(graph.vertices.count() == numVertices100) + + val graph_round1 = + GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma, seed = 12345) + val graph_round2 = + GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma, seed = 12345) + + val graph_round1_edges = graph_round1.edges.collect() + val graph_round2_edges = graph_round2.edges.collect() + + assert(graph_round1_edges.zip(graph_round2_edges).forall { case (e1, e2) => + e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr + }) + + val graph_round3 = + GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma, seed = 567) + + val graph_round3_edges = graph_round3.edges.collect() + + assert(!graph_round1_edges.zip(graph_round3_edges).forall { case (e1, e2) => + e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr + }) + } + } + +} |