[jira] [Created] (SPARK-9429) TriangleCount: job aborted due to stage failure

["YangBaoxing (JIRA)" <ji...@apache.org>]

YangBaoxing created SPARK-9429:
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             Summary: 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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