Hi all!
I'm using Spark 2.0.1 with two workers (one executor each) with 20Gb each.
And run following code:
JavaRDD<MatrixEntry> entries = ...; // filing the dataCoordinateMatrix
cmatrix = new CoordinateMatrix(entries.rdd());BlockMatrix matrix =
cmatrix.toBlockMatrix(100, 1000);BlockMatrix cooc =
matrix.transpose().multiply(matrix);
My matrix is approx 8 000 000 x 3000, but only 10 000 000 cells have
meaningful value. During multiplication I always get:
17/01/24 08:03:10 WARN TaskMemoryManager: leak 1322.6 MB memory from
org.apache.spark.util.collection.ExternalAppendOnlyMap@649e701917/01/24
08:03:10 ERROR Executor: Exception in task 1.0 in stage 57.0 (TID
83664)
java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.mllib.linalg.DenseMatrix$.zeros(Matrices.scala:453)
at
org.apache.spark.mllib.linalg.Matrix$class.multiply(Matrices.scala:101)
at
org.apache.spark.mllib.linalg.SparseMatrix.multiply(Matrices.scala:565)
at
org.apache.spark.mllib.linalg.distributed.BlockMatrix$$anonfun$23$$anonfun$apply$9$$anonfun$apply$11.apply(BlockMatrix.scala:483)
at
org.apache.spark.mllib.linalg.distributed.BlockMatrix$$anonfun$23$$anonfun$apply$9$$anonfun$apply$11.apply(BlockMatrix.scala:480)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at
org.apache.spark.mllib.linalg.distributed.BlockMatrix$$anonfun$23$$anonfun$apply$9.apply(BlockMatrix.scala:480)
at
org.apache.spark.mllib.linalg.distributed.BlockMatrix$$anonfun$23$$anonfun$apply$9.apply(BlockMatrix.scala:479)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at
scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at
org.apache.spark.util.collection.CompactBuffer$$anon$1.foreach(CompactBuffer.scala:115)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at
org.apache.spark.util.collection.CompactBuffer.foreach(CompactBuffer.scala:30)
at
scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at
org.apache.spark.util.collection.CompactBuffer.flatMap(CompactBuffer.scala:30)
at
org.apache.spark.mllib.linalg.distributed.BlockMatrix$$anonfun$23.apply(BlockMatrix.scala:479)
at
org.apache.spark.mllib.linalg.distributed.BlockMatrix$$anonfun$23.apply(BlockMatrix.scala:478)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at
org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:192)
at
org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
Now I'm even trying to use only one core per executor. What can be the
problem? And how can I debug it and find root cause? What could I miss in
spark configuration?
I've already tried increasing spark.default.parallelism and decreasing
blocks size for BlockMatrix.
Thanks.
