[SYSTEMDS-919] OOM Error in `RDDConverterUtilsExt`

• Type: Bug
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: SystemML 0.11
• Epic Link: MLContext Performance Improvements
• Assignee: Matthias Boehm
• Reporter: Mike Dusenberry
• Created: 14/Sep/16 19:38
• Updated: 21/Sep/16 22:11
• Resolved: 21/Sep/16 22:11

Description

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:

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)