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https://issues.apache.org/jira/browse/SPARK-1329?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Reynold Xin resolved SPARK-1329.
--------------------------------
Resolution: Fixed
Fix Version/s: 1.0.0
Assignee: Ankur Dave
> ArrayIndexOutOfBoundsException if graphx.Graph has more edge partitions than
> node partitions
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-1329
> URL: https://issues.apache.org/jira/browse/SPARK-1329
> Project: Spark
> Issue Type: Bug
> Components: GraphX
> Affects Versions: 0.9.0
> Reporter: Daniel Darabos
> Assignee: Ankur Dave
> Fix For: 1.0.0
>
>
> To reproduce, let's look at a graph with two nodes in one partition, and two
> edges between them split across two partitions:
> scala> val vs = sc.makeRDD(Seq(1L->null, 2L->null), 1)
> scala> val es = sc.makeRDD(Seq(graphx.Edge(1, 2, null), graphx.Edge(2, 1,
> null)), 2)
> scala> val g = graphx.Graph(vs, es)
> Everything seems fine, until GraphX needs to join the two RDDs:
> scala> g.triplets.collect
> [...]
> java.lang.ArrayIndexOutOfBoundsException: 1
> at
> org.apache.spark.graphx.impl.RoutingTable$$anonfun$2$$anonfun$apply$3.apply(RoutingTable.scala:76)
> at
> org.apache.spark.graphx.impl.RoutingTable$$anonfun$2$$anonfun$apply$3.apply(RoutingTable.scala:75)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> org.apache.spark.graphx.impl.RoutingTable$$anonfun$2.apply(RoutingTable.scala:75)
> at
> org.apache.spark.graphx.impl.RoutingTable$$anonfun$2.apply(RoutingTable.scala:73)
> at org.apache.spark.rdd.RDD$$anonfun$1.apply(RDD.scala:450)
> at org.apache.spark.rdd.RDD$$anonfun$1.apply(RDD.scala:450)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:34)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:71)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:85)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102)
> at org.apache.spark.scheduler.Task.run(Task.scala:53)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:744)
> The bug is fairly obvious in RoutingTable.createPid2Vid() -- it creates an
> array of length vertices.partitions.size, and then looks up partition IDs
> from the edges.partitionsRDD in it.
> A graph usually has more edges than nodes. So it is natural to have more edge
> partitions than node partitions.
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