我是Spark和GraphX的新手，并使用其算法进行了一些实验来查找连接的组件。我注意到图表的结构似乎对性能有很大的影响。
能够计算具有数百万个顶点和边的图，但对于某组图，算法没有及时完成，但最终以onLoggingImpression(Ad)失败。
该算法似乎存在包含长路径的图形的问题。例如，对于此图OutOfMemoryError: GC overhead limit exceeded，计算失败。但是，当我添加传递边时，计算立即完成：
{ (i,i+1) | i <- {1..200} }这样的图表也没问题：
{ (i,j) | i <- {1..200}, j <- {i+1,200} }
以下是重现问题的最小示例：
{ (i,1) | i <- {1..200} }
import org.apache.spark._
import org.apache.spark.graphx._
import org.apache.spark.graphx.lib._
import org.apache.spark.storage.StorageLevel
import scala.collection.mutable
object Matching extends Logging {
def main(args: Array[String]): Unit = {
val fname = "input.graph"
val optionsList = args.drop(1).map { arg =>
arg.dropWhile(_ == '-').split('=') match {
case Array(opt, v) => opt -> v
case _ => throw new IllegalArgumentException("Invalid argument: " + arg)
}
}
val options = mutable.Map(optionsList: _*)
val conf = new SparkConf()
GraphXUtils.registerKryoClasses(conf)
val partitionStrategy: Option[PartitionStrategy] = options.remove("partStrategy")
.map(PartitionStrategy.fromString(_))
val edgeStorageLevel = options.remove("edgeStorageLevel")
.map(StorageLevel.fromString(_)).getOrElse(StorageLevel.MEMORY_ONLY)
val vertexStorageLevel = options.remove("vertexStorageLevel")
.map(StorageLevel.fromString(_)).getOrElse(StorageLevel.MEMORY_ONLY)
val sc = new SparkContext(conf.setAppName("ConnectedComponents(" + fname + ")"))
val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
edgeStorageLevel = edgeStorageLevel,
vertexStorageLevel = vertexStorageLevel).cache()
log.info("Loading graph...")
val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
log.info("Loading graph...done")
log.info("Computing connected components...")
val cc = ConnectedComponents.run(graph)
log.info("Computed connected components...done")
sc.stop()
}
}
文件可以看到这个（10个节点，连接它们的9条边）：
input.graph当它失败时，它会挂起1 2
2 3
3 4
4 5
5 6
6 7
7 8
8 9
9 10
。错误消息是：
ConnectedComponents.run(graph)我正在运行本地Spark节点并使用以下选项启动JVM：
Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.util.regex.Pattern.compile(Pattern.java:1054)
at java.lang.String.replace(String.java:2239)
at org.apache.spark.util.Utils$.getFormattedClassName(Utils.scala:1632)
at org.apache.spark.storage.RDDInfo$$anonfun$1.apply(RDDInfo.scala:58)
at org.apache.spark.storage.RDDInfo$$anonfun$1.apply(RDDInfo.scala:58)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.storage.RDDInfo$.fromRdd(RDDInfo.scala:58)
at org.apache.spark.scheduler.StageInfo$$anonfun$1.apply(StageInfo.scala:80)
at org.apache.spark.scheduler.StageInfo$$anonfun$1.apply(StageInfo.scala:80)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.scheduler.StageInfo$.fromStage(StageInfo.scala:80)
at org.apache.spark.scheduler.Stage.<init>(Stage.scala:99)
at org.apache.spark.scheduler.ShuffleMapStage.<init>(ShuffleMapStage.scala:44)
at org.apache.spark.scheduler.DAGScheduler.newShuffleMapStage(DAGScheduler.scala:317)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$newOrUsedShuffleStage(DAGScheduler.scala:352)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$getShuffleMapStage$1.apply(DAGScheduler.scala:286)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$getShuffleMapStage$1.apply(DAGScheduler.scala:285)
at scala.collection.Iterator$class.foreach(Iterator.scala:742)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.mutable.Stack.foreach(Stack.scala:170)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$getShuffleMapStage(DAGScheduler.scala:285)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$visit$1$1.apply(DAGScheduler.scala:389)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$visit$1$1.apply(DAGScheduler.scala:386)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.scheduler.DAGScheduler.visit$1(DAGScheduler.scala:386)
at org.apache.spark.scheduler.DAGScheduler.getParentStages(DAGScheduler.scala:398)
你能帮助我理解为什么它有这个玩具图（201个节点和200个边缘）的问题，但另一方面可以解决一个在80秒内有数百万个边缘的真实图形？ （在这两个示例中，我使用相同的设置和配置。）
更新
也可以在spark-shell中复制：
-Dspark.master=local -Dspark.local.dir=/home/phil/tmp/spark-tmp -Xms8g -Xmx8g
我创建了一个错误报告：SPARK-15042