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Posted to issues@spark.apache.org by "Davies Liu (JIRA)" <ji...@apache.org> on 2016/01/16 00:07:39 UTC
[jira] [Commented] (SPARK-10538)
java.lang.NegativeArraySizeException during join
[ https://issues.apache.org/jira/browse/SPARK-10538?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15102653#comment-15102653 ]
Davies Liu commented on SPARK-10538:
------------------------------------
@mayxine The problem you posted is not related to this JIRA, it could be that rdd1.partitions.length * rdd2.partitions.length is overflow, if the number of partitions of two RDD are too large.
> java.lang.NegativeArraySizeException during join
> ------------------------------------------------
>
> Key: SPARK-10538
> URL: https://issues.apache.org/jira/browse/SPARK-10538
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Maciej Bryński
> Assignee: Davies Liu
> Attachments: java.lang.NegativeArraySizeException.png, screenshot-1.png
>
>
> Hi,
> I've got a problem during joining tables in PySpark. (in my example 20 of them)
> I can observe that during calculation of first partition (on one of consecutive joins) there is a big shuffle read size (294.7 MB / 146 records) vs on others partitions (approx. 272.5 KB / 113 record)
> I can also observe that just before the crash python process going up to few gb of RAM.
> After some time there is an exception:
> {code}
> java.lang.NegativeArraySizeException
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
> at org.apache.spark.sql.execution.TungstenProject$$anonfun$3$$anonfun$apply$3.apply(basicOperators.scala:90)
> at org.apache.spark.sql.execution.TungstenProject$$anonfun$3$$anonfun$apply$3.apply(basicOperators.scala:88)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.insertAll(BypassMergeSortShuffleWriter.java:119)
> at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:73)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:88)
> at org.apache.spark.executor.Executor$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}
> I'm running this on 2 nodes cluster (12 cores, 64 GB RAM)
> Config:
> {code}
> spark.driver.memory 10g
> spark.executor.extraJavaOptions -XX:-UseGCOverheadLimit -XX:+UseParallelGC -Dfile.encoding=UTF8
> spark.executor.memory 60g
> spark.storage.memoryFraction 0.05
> spark.shuffle.memoryFraction 0.75
> spark.driver.maxResultSize 10g
> spark.cores.max 24
> spark.kryoserializer.buffer.max 1g
> spark.default.parallelism 200
> {code}
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