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Posted to issues@spark.apache.org by "Ayoub Benali (JIRA)" <ji...@apache.org> on 2018/08/17 12:48:00 UTC
[jira] [Created] (SPARK-25144) distinct on Dataset leads to
exception due to Managed memory leak detected
Ayoub Benali created SPARK-25144:
------------------------------------
Summary: 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
The following code example:
{code:scala}
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:bash}
[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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