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
