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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-6120) DecisionTree.save uses too much Java heap space for default spark shell settings
Date Tue, 03 Mar 2015 02:10:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6120?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14344323#comment-14344323
] 

Apache Spark commented on SPARK-6120:
-------------------------------------

User 'jkbradley' has created a pull request for this issue:
https://github.com/apache/spark/pull/4864

> DecisionTree.save uses too much Java heap space for default spark shell settings
> --------------------------------------------------------------------------------
>
>                 Key: SPARK-6120
>                 URL: https://issues.apache.org/jira/browse/SPARK-6120
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.3.0
>            Reporter: Joseph K. Bradley
>            Assignee: Joseph K. Bradley
>
> When the Python DecisionTree example in the programming guide is run, it runs out of
Java Heap Space:
> {code}
> scala> model.save(sc, "myModelPath")
> [Stage 12:>                                                                      
                                                                 (0 + 8) / 8]15/03/02 14:19:16
ERROR Executor: Exception in task 1.0 in stage 12.0 (TID 22)
> java.lang.OutOfMemoryError: Java heap space
> 	at parquet.bytes.CapacityByteArrayOutputStream.initSlabs(CapacityByteArrayOutputStream.java:65)
> 	at parquet.bytes.CapacityByteArrayOutputStream.<init>(CapacityByteArrayOutputStream.java:57)
> 	at parquet.column.values.plain.PlainValuesWriter.<init>(PlainValuesWriter.java:45)
> 	at parquet.column.values.dictionary.DictionaryValuesWriter.<init>(DictionaryValuesWriter.java:102)
> 	at parquet.column.values.dictionary.DictionaryValuesWriter$PlainDoubleDictionaryValuesWriter.<init>(DictionaryValuesWriter.java:471)
> 	at parquet.column.ParquetProperties.getValuesWriter(ParquetProperties.java:111)
> 	at parquet.column.impl.ColumnWriterImpl.<init>(ColumnWriterImpl.java:74)
> 	at parquet.column.impl.ColumnWriteStoreImpl.newMemColumn(ColumnWriteStoreImpl.java:68)
> 	at parquet.column.impl.ColumnWriteStoreImpl.getColumnWriter(ColumnWriteStoreImpl.java:56)
> 	at parquet.io.MessageColumnIO$MessageColumnIORecordConsumer.<init>(MessageColumnIO.java:178)
> 	at parquet.io.MessageColumnIO.getRecordWriter(MessageColumnIO.java:369)
> 	at parquet.hadoop.InternalParquetRecordWriter.initStore(InternalParquetRecordWriter.java:108)
> 	at parquet.hadoop.InternalParquetRecordWriter.<init>(InternalParquetRecordWriter.java:94)
> 	at parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:64)
> 	at parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:282)
> 	at parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:252)
> 	at org.apache.spark.sql.parquet.ParquetRelation2.org$apache$spark$sql$parquet$ParquetRelation2$$writeShard$1(newParquet.scala:620)
> 	at org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:641)
> 	at org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:641)
> 	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
> 	at org.apache.spark.scheduler.Task.run(Task.scala:64)
> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:197)
> 	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)
> {code}
> When saving using JSON format instead of Parquet, this works.  It seems to be caused
by Parquet requiring a lot of metadata to describe the schema.
> I'm labeling this a bug since it should succeed with the default spark-shell settings.
 Potential fixes are:
> * increasing spark-shell default heap space settings (This is probably too hard to agree
on currently.)
> * not using Parquet for storage (This would be good for small examples but probably worse
for large models, where Parquet would be more efficient than other formats.)
> * compressing the schema (The various values in the DecisionTree model could be flattened
into a single Seq of Double.  This may be the best option for now.)
> Notes:
> * This happens in both pyspark and Scala shells.
> * Increasing driver memory to 1g (from the default of 512m) makes this succeed.
> * Running other examples such as NaiveBayes with the default of 512m works.
> * This is a bit strange in that the actual size of the saved model on disk is small (86K
on disk for me).



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