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https://issues.apache.org/jira/browse/SPARK-5480?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14324349#comment-14324349
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Stephane Maarek commented on SPARK-5480:
----------------------------------------
Hi Sean,
We have included the following code before and after the graph gets created:
println(s"Vertices count ${vertices.count}")
println(s"Edges count ${edges.count}")
val defaultArticle = ("Missing", None, List.empty, None)
// create the graph, making sure we default to a defaultArticle when we
have a missing relation (prevents nulls)
val graph = Graph(vertices, edges, defaultArticle).cache
println(s"After graph: Vertices count ${graph.vertices.count}")
println(s"After graph: Edges count ${graph.edges.count}")
What we see on multiple runs with exact same configuration is that the count of
edges and nodes before the graph is created is always the same.
The constant:
Vertices count: 192190
Edges count: 4582582
After graph:
(trial one - generated the error)
After graph: Vertices count: 2450854
After graph: Edges count: 4188635
(trial two - terminated correctly)
After graph: Vertices count: 2450854
After graph: Edges count: 4582582
(trial three - generated the error)
After graph: Vertices count: 2450854
After graph: Edges count: 4000218
As we can replicate the issue, please let us know if we should add any code to
help you debug.
Our code is deterministic, so before creating the graph we always see the same
output.
What's odd is that after creating the graph, the vertices count is constant,
but the edges count varies....
> GraphX pageRank: java.lang.ArrayIndexOutOfBoundsException:
> -----------------------------------------------------------
>
> Key: SPARK-5480
> URL: https://issues.apache.org/jira/browse/SPARK-5480
> Project: Spark
> Issue Type: Bug
> Components: GraphX
> Affects Versions: 1.2.0
> Environment: Yarn client
> Reporter: Stephane Maarek
>
> Running the following code:
> val subgraph = graph.subgraph (
> vpred = (id,article) => //working predicate)
> ).cache()
> println( s"Subgraph contains ${subgraph.vertices.count} nodes and
> ${subgraph.edges.count} edges")
> val prGraph = subgraph.staticPageRank(5).cache
> val titleAndPrGraph = subgraph.outerJoinVertices(prGraph.vertices) {
> (v, title, rank) => (rank.getOrElse(0.0), title)
> }
> titleAndPrGraph.vertices.top(13) {
> Ordering.by((entry: (VertexId, (Double, _))) => entry._2._1)
> }.foreach(t => println(t._2._2._1 + ": " + t._2._1 + ", id:" + t._1))
> Returns a graph with 5000 nodes and 4000 edges.
> Then it crashes during the PageRank with the following:
> 15/01/29 05:51:07 INFO scheduler.TaskSetManager: Starting task 125.0 in stage
> 39.0 (TID 1808, *HIDDEN, PROCESS_LOCAL, 2059 bytes)
> 15/01/29 05:51:07 WARN scheduler.TaskSetManager: Lost task 107.0 in stage
> 39.0 (TID 1794, *HIDDEN): java.lang.ArrayIndexOutOfBoundsException: -1
> at
> org.apache.spark.graphx.util.collection.GraphXPrimitiveKeyOpenHashMap$mcJI$sp.apply$mcJI$sp(GraphXPrimitiveKeyOpenHashMap.scala:64)
> at
> org.apache.spark.graphx.impl.EdgePartition.updateVertices(EdgePartition.scala:91)
> at
> org.apache.spark.graphx.impl.ReplicatedVertexView$$anonfun$2$$anonfun$apply$1.apply(ReplicatedVertexView.scala:75)
> at
> org.apache.spark.graphx.impl.ReplicatedVertexView$$anonfun$2$$anonfun$apply$1.apply(ReplicatedVertexView.scala:73)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at
> org.apache.spark.graphx.impl.EdgeRDDImpl$$anonfun$mapEdgePartitions$1.apply(EdgeRDDImpl.scala:110)
> at
> org.apache.spark.graphx.impl.EdgeRDDImpl$$anonfun$mapEdgePartitions$1.apply(EdgeRDDImpl.scala:108)
> at org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:601)
> at org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:601)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:61)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:228)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:61)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:228)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:56)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
> 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)
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