[jira] [Updated] (SPARK-14363) Executor OOM due to a memory leak in Sorter

2016-04-15 Thread Sean Owen (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-14363?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sean Owen updated SPARK-14363:
--
Assignee: Sital Kedia

> Executor OOM due to a memory leak in Sorter
> ---
>
> Key: SPARK-14363
> URL: https://issues.apache.org/jira/browse/SPARK-14363
> Project: Spark
>  Issue Type: Bug
>  Components: Shuffle
>Affects Versions: 1.6.1
>Reporter: Sital Kedia
>Assignee: Sital Kedia
> Fix For: 1.6.2, 2.0.0
>
>
> While running a Spark job, we see that the job fails because of executor OOM 
> with following stack trace - 
> {code}
> java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
>   at 
> org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
>   at 
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
>   at 
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
>   at 
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
>   at 
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
>   at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
>   at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:89)
>   at org.apache.spark.executor.Executor$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}
> The issue is that there is a memory leak in the Sorter.  When the 
> UnsafeExternalSorter spills the data to disk, it does not free up the 
> underlying pointer array. As a result, we see a lot of executor OOM and also 
> memory under utilization.
> This is a regression partially introduced in PR 
> https://github.com/apache/spark/pull/9241



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[jira] [Updated] (SPARK-14363) Executor OOM due to a memory leak in Sorter

2016-04-10 Thread Sital Kedia (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-14363?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sital Kedia updated SPARK-14363:

Description: 
While running a Spark job, we see that the job fails because of executor OOM 
with following stack trace - 
{code}
java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
at 
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$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}

The issue is that there is a memory leak in the Sorter.  When the 
UnsafeExternalSorter spills the data to disk, it does not free up the 
underlying pointer array. As a result, we see a lot of executor OOM and also 
memory under utilization.

This is a regression partially introduced in PR 
https://github.com/apache/spark/pull/9241

  was:
While running a Spark job, we see that the job fails because of executor OOM 
with following stack trace - 
{code}
java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
at 
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
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.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd

[jira] [Updated] (SPARK-14363) Executor OOM due to a memory leak in Sorter

2016-04-10 Thread Sital Kedia (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-14363?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sital Kedia updated SPARK-14363:

Description: 
While running a Spark job, we see that the job fails because of executor OOM 
with following stack trace - 
{code}
java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
at 
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at 
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$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}

The issue is that there is a memory leak in the Sorter.  When the 
UnsafeExternalSorter spills the data to disk, it does not free up the 
underlying pointer array. As a result, we see a lot of executor OOM and also 
memory under utilization.

  was:
While running a Spark job, we see that the job fails because of executor OOM 
with following stack trace - 
{code}
java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
at 
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
at 
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
at 
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
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.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(MapPa

[jira] [Updated] (SPARK-14363) Executor OOM due to a memory leak in Sorter

2016-04-10 Thread Sital Kedia (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-14363?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Sital Kedia updated SPARK-14363:

Summary: Executor OOM due to a memory leak in Sorter  (was: Executor OOM 
while trying to acquire new page from the memory manager)

> Executor OOM due to a memory leak in Sorter
> ---
>
> Key: SPARK-14363
> URL: https://issues.apache.org/jira/browse/SPARK-14363
> Project: Spark
>  Issue Type: Bug
>  Components: Shuffle
>Affects Versions: 1.6.1
>Reporter: Sital Kedia
>
> While running a Spark job, we see that the job fails because of executor OOM 
> with following stack trace - 
> {code}
> java.lang.OutOfMemoryError: Unable to acquire 76 bytes of memory, got 0
>   at 
> org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
>   at 
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:326)
>   at 
> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertRecord(UnsafeExternalSorter.java:341)
>   at 
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:91)
>   at 
> org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:168)
>   at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
>   at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
>   at 
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:728)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at 
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>   at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
>   at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:89)
>   at org.apache.spark.executor.Executor$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}



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