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https://issues.apache.org/jira/browse/SPARK-9429?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-9429.
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Resolution: Not A Problem
> TriangleCount: job aborted due to stage failure
> -----------------------------------------------
>
> Key: SPARK-9429
> URL: https://issues.apache.org/jira/browse/SPARK-9429
> Project: Spark
> Issue Type: Bug
> Components: GraphX
> Reporter: YangBaoxing
>
> Hi, all !
> When I run the TriangleCount algorithm on my own data, an exception like "Job
> aborted to stage failure: Task 0 in stage 4.0 failed 1 times, most recent
> failure: Lost task 0.0 in stage 4.0 (TID 8, localhost):
> java.lang.AssertionError: assertion failed" occurred. Then I checked the
> source code and found that the problem is in line "assert((dblCount & 1) ==
> 0)". And I also found that it run successfully on Array(0L -> 1L, 1L -> 2L,
> 2L -> 0L) and Array(0L -> 1L, 1L -> 2L, 2L -> 0L, 0L -> 2L, 2L -> 1L, 1L ->
> 0L) while failed on Array(0L -> 1L, 1L -> 2L, 2L -> 0L, 2L -> 1L). It seems
> to be more suitable for all unidirectional or bidirectional graph. Is
> TriangleCount suitable for incomplete bidirectional graph? The complete
> exception as follows:
> Job aborted due to stage failure: Task 0 in stage 4.0 failed 1 times, most
> recent failure: Lost task 0.0 in stage 4.0 (TID 8, localhost):
> java.lang.AssertionError: assertion failed
> at scala.Predef$.assert(Predef.scala:165)
> at
> org.apache.spark.graphx.lib.TriangleCount$$anonfun$7.apply(TriangleCount.scala:90)
> at
> org.apache.spark.graphx.lib.TriangleCount$$anonfun$7.apply(TriangleCount.scala:87)
> at
> org.apache.spark.graphx.impl.VertexPartitionBaseOps.leftJoin(VertexPartitionBaseOps.scala:140)
> at
> org.apache.spark.graphx.impl.VertexRDDImpl$$anonfun$3.apply(VertexRDDImpl.scala:159)
> at
> org.apache.spark.graphx.impl.VertexRDDImpl$$anonfun$3.apply(VertexRDDImpl.scala:156)
> at
> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.graphx.VertexRDD.compute(VertexRDD.scala:71)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
> at org.apache.spark.scheduler.Task.run(Task.scala:70)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
> 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:745)
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