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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2018/08/20 03:14:00 UTC
[jira] [Resolved] (SPARK-25144) distinct on Dataset leads to
exception due to Managed memory leak detected
[ https://issues.apache.org/jira/browse/SPARK-25144?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-25144.
----------------------------------
Resolution: Cannot Reproduce
{code}
scala> case class Foo(bar: Option[String])
defined class Foo
scala> val ds = List(Foo(Some("bar"))).toDS
ds: org.apache.spark.sql.Dataset[Foo] = [bar: string]
scala> val result = ds.flatMap(_.bar).distinct
result: org.apache.spark.sql.Dataset[String] = [value: string]
scala> result.rdd.isEmpty
res0: Boolean = false
{code}
I tried in the master. i am unable to reproduce this. Resolving this. It would be nicer if we identify and see if we can backport. Please link this to the duplicate if it's identified.
> distinct on Dataset leads to exception due to Managed memory leak detected
> ----------------------------------------------------------------------------
>
> Key: SPARK-25144
> URL: https://issues.apache.org/jira/browse/SPARK-25144
> Project: Spark
> Issue Type: Bug
> Components: Optimizer, Spark Core, SQL
> Affects Versions: 2.3.1
> Environment: spark 2.3.1
> Reporter: Ayoub Benali
> Priority: Major
>
> The following code example:
> {code}
> case class Foo(bar: Option[String])
> val ds = List(Foo(Some("bar"))).toDS
> val result = ds.flatMap(_.bar).distinct
> result.rdd.isEmpty
> {code}
> Produces the following stacktrace
> {code}
> [info] org.apache.spark.SparkException: Job aborted due to stage failure: Task 42 in stage 7.0 failed 1 times, most recent failure: Lost task 42.0 in stage 7.0 (TID 125, localhost, executor driver): org.apache.spark.SparkException: Managed memory leak detected; size = 16777216 bytes, TID = 125
> [info] at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:358)
> [info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> [info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> [info] at java.lang.Thread.run(Thread.java:748)
> [info]
> [info] Driver stacktrace:
> [info] at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602)
> [info] at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590)
> [info] at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589)
> [info] at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> [info] at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> [info] at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589)
> [info] at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
> [info] at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
> [info] at scala.Option.foreach(Option.scala:257)
> [info] at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
> [info] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1823)
> [info] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772)
> [info] at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761)
> [info] at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> [info] at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
> [info] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
> [info] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
> [info] at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
> [info] at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1358)
> [info] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> [info] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> [info] at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
> [info] at org.apache.spark.rdd.RDD.take(RDD.scala:1331)
> [info] at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply$mcZ$sp(RDD.scala:1466)
> [info] at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply(RDD.scala:1466)
> [info] at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply(RDD.scala:1466)
> [info] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> [info] at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> [info] at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
> [info] at org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1465)
> {code}
> The code example doesn't produce any error when `distinct` function is not called.
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