[jira] [Closed] (SYSTEMML-919) OOM Error in `RDDConverterUtilsExt`

["Mike Dusenberry (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SYSTEMML-919?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mike Dusenberry closed SYSTEMML-919.
------------------------------------

> OOM Error in `RDDConverterUtilsExt`
> -----------------------------------
>
>                 Key: SYSTEMML-919
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-919
>             Project: SystemML
>          Issue Type: Bug
>            Reporter: Mike Dusenberry
>            Assignee: Matthias Boehm
>             Fix For: SystemML 0.11
>
>
> While running a custom algorithm on ~7TB data on Spark, I ran into the following OOM error.  I am using the new MLContext and passing in Spark DataFrames with no provided dimensions.  It's a difficult setup to reproduce, but we should look into this to see if there are improvements that could avoid OOM situations.
> Stack trace:
> {code}
> java.lang.OutOfMemoryError: Java heap space
> 	at java.util.Arrays.copyOf(Arrays.java:3181)
> 	at java.util.ArrayList.grow(ArrayList.java:261)
> 	at java.util.ArrayList.ensureExplicitCapacity(ArrayList.java:235)
> 	at java.util.ArrayList.ensureCapacityInternal(ArrayList.java:227)
> 	at java.util.ArrayList.add(ArrayList.java:458)
> 	at org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt$RowToBinaryBlockFunctionHelper.flushBlocksToList(RDDConverterUtilsExt.java:926)
> 	at org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt$RowToBinaryBlockFunctionHelper.convertToBinaryBlock(RDDConverterUtilsExt.java:862)
> 	at org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt$DataFrameToBinaryBlockFunction.call(RDDConverterUtilsExt.java:587)
> 	at org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt$DataFrameToBinaryBlockFunction.call(RDDConverterUtilsExt.java:574)
> 	at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:192)
> 	at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:192)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
> 	at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
> 	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> 	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> 	at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> 	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> 	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:89)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> 	at java.lang.Thread.run(Thread.java:745)
> {code}



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