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 <[email protected]> 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
>
>
>
