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Mike Dusenberry commented on SYSTEMML-919: ------------------------------------------ [~mboehm7] Awesome! Fixing/improving this and the recent related JIRAs will be extremely useful for the project I'm working on [~2 weeks till the deadline]. > OOM Error in `RDDConverterUtilsExt` > ----------------------------------- > > Key: SYSTEMML-919 > URL: https://issues.apache.org/jira/browse/SYSTEMML-919 > Project: SystemML > Issue Type: Bug > Reporter: Mike Dusenberry > > 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} -- This message was sent by Atlassian JIRA (v6.3.4#6332)