Ok, but what does it means? I did not change the core files of spark, so is
it a bug there?
PS: on small datasets (<500 Mb) I have no problem.
Am 25.06.2015 18:02 schrieb "Ted Yu" <yuzhih...@gmail.com>:
> The assertion failure from TriangleCount.scala corresponds with the
> following lines:
>
> g.outerJoinVertices(counters) {
> (vid, _, optCounter: Option[Int]) =>
> val dblCount = optCounter.getOrElse(0)
> // double count should be even (divisible by two)
> assert((dblCount & 1) == 0)
>
> Cheers
>
> On Thu, Jun 25, 2015 at 6:20 AM, Roman Sokolov <ole...@gmail.com> wrote:
>
>> Hello!
>> I am trying to compute number of triangles with GraphX. But get memory
>> error or heap size, even though the dataset is very small (1Gb). I run the
>> code in spark-shell, having 16Gb RAM machine (also tried with 2 workers on
>> separate machines 8Gb RAM each). So I have 15x more memory than the dataset
>> size is, but it is not enough. What should I do with terabytes sized
>> datasets? How do people process it? Read a lot of documentation and 2 Spark
>> books, and still have no clue :(
>>
>> Tried to run with the options, no effect:
>> ./bin/spark-shell --executor-memory 6g --driver-memory 9g
>> --total-executor-cores 100
>>
>> The code is simple:
>>
>> val graph = GraphLoader.edgeListFile(sc,
>> "/home/ubuntu/data/soc-LiveJournal1/lj.stdout",
>> edgeStorageLevel = StorageLevel.MEMORY_AND_DISK_SER,
>> vertexStorageLevel =
>> StorageLevel.MEMORY_AND_DISK_SER).partitionBy(PartitionStrategy.RandomVertexCut)
>>
>> println(graph.numEdges)
>> println(graph.numVertices)
>>
>> val triangleNum = graph.triangleCount().vertices.map(x => x._2).reduce(_
>> + _)/3
>>
>> (dataset is from here:
>> http://konect.uni-koblenz.de/downloads/tsv/soc-LiveJournal1.tar.bz2 first
>> two lines contain % characters, so have to be removed).
>>
>>
>> UPD: today tried on 32Gb machine (from spark shell again), now got
>> another error:
>>
>> [Stage 8:> (0 +
>> 4) / 32]15/06/25 13:03:05 WARN ShippableVertexPartitionOps: Joining two
>> VertexPartitions with different indexes is slow.
>> 15/06/25 13:03:05 ERROR Executor: Exception in task 3.0 in stage 8.0 (TID
>> 227)
>> 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.VertexPartitionBaseOps.leftJoin(VertexPartitionBaseOps.scala:133)
>> at
>> org.apache.spark.graphx.impl.VertexRDDImpl$$anonfun$3.apply(VertexRDDImpl.scala:159)
>> at
>> org.apache.spark.graphx.impl.VertexRDDImpl$$anonfun$3.apply(VertexRDDImpl.scala:156)
>> at
>> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
>> at org.apache.spark.graphx.VertexRDD.compute(VertexRDD.scala:71)
>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
>> at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
>> at org.apache.spark.scheduler.Task.run(Task.scala:70)
>> at org.apache.spark.executor.Executor$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)
>>
>>
>>
>>
>>
>>
>> --
>> Best regards, Roman Sokolov
>>
>>
>>
>
