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From "Naden Franciscus (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-10309) Some tasks failed with Unable to acquire memory
Date Sat, 05 Sep 2015 07:37:45 GMT

    [ https://issues.apache.org/jira/browse/SPARK-10309?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14731862#comment-14731862
] 

Naden Franciscus edited comment on SPARK-10309 at 9/5/15 7:37 AM:
------------------------------------------------------------------

Why is this targeted for 1.6 ? We are finding this issue with basic Spark SQL executions in
our applications. Is the expectation that Tungsten sort will be disabled in an upcoming checkin
?

Job aborted due to stage failure: Task 1 in stage 25.0 failed 4 times, most recent failure:
Lost task 1.3 in stage 25.0 (TID 3962, 39.6.64.17): java.io.IOException: Unable to acquire
16777216 bytes of memory
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
	at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
	at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146)
	at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
	at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
	at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50)
	at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83)
	at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82)
	at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
	at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278)
	at scala.collection.immutable.List.foreach(List.scala:318)



was (Author: nadenf):
Why is this targeted for 1.6 ? We are finding this issue with basic Spark SQL executions in
our applications.

Job aborted due to stage failure: Task 1 in stage 25.0 failed 4 times, most recent failure:
Lost task 1.3 in stage 25.0 (TID 3962, 39.6.64.17): java.io.IOException: Unable to acquire
16777216 bytes of memory
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
	at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
	at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
	at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146)
	at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
	at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
	at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.prepare(MapPartitionsWithPreparationRDD.scala:50)
	at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:83)
	at org.apache.spark.rdd.ZippedPartitionsBaseRDD$$anonfun$tryPrepareParents$1.applyOrElse(ZippedPartitionsRDD.scala:82)
	at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
	at scala.collection.TraversableLike$$anonfun$collect$1.apply(TraversableLike.scala:278)
	at scala.collection.immutable.List.foreach(List.scala:318)


> Some tasks failed with Unable to acquire memory
> -----------------------------------------------
>
>                 Key: SPARK-10309
>                 URL: https://issues.apache.org/jira/browse/SPARK-10309
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.5.0
>            Reporter: Davies Liu
>
> While running Q53 of TPCDS (scale = 1500) on 24 nodes cluster (12G memory on executor):
> {code}
> java.io.IOException: Unable to acquire 33554432 bytes of memory
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.<init>(UnsafeExternalSorter.java:138)
>         at org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.create(UnsafeExternalSorter.java:106)
>         at org.apache.spark.sql.execution.UnsafeExternalRowSorter.<init>(UnsafeExternalRowSorter.java:68)
>         at org.apache.spark.sql.execution.TungstenSort.org$apache$spark$sql$execution$TungstenSort$$preparePartition$1(sort.scala:146)
>         at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
>         at org.apache.spark.sql.execution.TungstenSort$$anonfun$doExecute$3.apply(sort.scala:169)
>         at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:45)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>         at org.apache.spark.scheduler.Task.run(Task.scala:88)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>         at java.lang.Thread.run(Thread.java:745)
> {code}
> The task could finished after retry.



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