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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/09/10 05:14:20 UTC
[jira] [Assigned] (SPARK-17488) TakeAndOrder will OOM when the data
is very large
[ https://issues.apache.org/jira/browse/SPARK-17488?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-17488:
------------------------------------
Assignee: Apache Spark
> TakeAndOrder will OOM when the data is very large
> -------------------------------------------------
>
> Key: SPARK-17488
> URL: https://issues.apache.org/jira/browse/SPARK-17488
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 1.6.2, 2.0.0
> Environment: spark1.6
> Reporter: cen yuhai
> Assignee: Apache Spark
>
> In function Utils.takeOrdered, it will sort all data in memory, when the data is very large, It will OOM.
> {code}
> 16/08/22 16:18:42 ERROR executor.Executor: Exception in task 139.0 in stage 5.0 (TID 6420)
> java.lang.OutOfMemoryError: Java heap space
> at org.spark-project.guava.collect.Ordering.leastOf(Ordering.java:657)
> at org.apache.spark.util.collection.Utils$.takeOrdered(Utils.scala:37)
> at org.apache.spark.sql.execution.TakeOrderedAndProject$$anonfun$9.apply(basicOperators.scala:227)
> at org.apache.spark.sql.execution.TakeOrderedAndProject$$anonfun$9.apply(basicOperators.scala:226)
> 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$20.apply(RDD.scala:710)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:74)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:104)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:247)
> 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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