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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