Re: Heap Problem with Checkpoints

[Piotr Nowojski]

Hi,

What kind of messages are those “logs about S3 operations”? Did you try to 
google search them? Maybe it’s a known S3 issue?

Another approach is please use some heap space analyser from which you can 
backtrack classes that are referencing those “memory leaks” and again try to 
google any known memory issues.

It also could just mean, that it’s not a memory leak, but you just need to 
allocate more heap space for your JVM (and memory consumption will stabilise at 
some point).

Piotrek

> On 8 Jun 2018, at 18:32, Fabian Wollert  wrote:
> 
> Hi, in this email thread 
> 
>  here, i tried to set up S3 as a filesystem backend for checkpoints. Now 
> everything is working (Flink V1.5.0), but the JobMaster is accumulating Heap 
> space, with eventually killing itself with HeapSpace OOM after several hours. 
> If I don't enable Checkpointing, then everything is fine. I'm using the Flink 
> S3 Shaded Libs (tried both the Hadoop and the Presto lib, no difference in 
> this regard) from the tutorial. my checkpoint settings are this (job level):
> 
> env.enableCheckpointing(1000);
> env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
> env.getCheckpointConfig().setMinPauseBetweenCheckpoints(5000);
> env.getCheckpointConfig().setCheckpointTimeout(6);
> env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
> 
> Another clue why i suspect the S3 Checkpointing is that the heapspace dump 
> contains a lot of char[] objects with some logs about S3 operations.
> 
> anyone has an idea where to look further on this?
> 
> Cheers
> 
> --
> 
> Fabian Wollert
> Zalando SE
> 
> E-Mail: fabian.woll...@zalando.de
>  
> 
> Tamara-Danz-Straße 1
> 10243 Berlin
> Fax: +49 (0)30 2759 46 93
> E-mail: legalnot...@zalando.co.uk 
> Notifications of major holdings (Sec. 33, 38, 39 WpHG):  +49 (0)30 2000889349
> 
> Management Board:
> Robert Gentz, David Schneider, Rubin Ritter
> 
> Chairman of the Supervisory Board:
> Lothar Lanz
> 
> Person responsible for providing the contents of Zalando SE acc. to Art. 55 
> RStV [Interstate Broadcasting Agreement]: Rubin Ritter
> Registered at the Local Court Charlottenburg Berlin, HRB 158855 B
> VAT registration number: DE 260543043

Re: Heap Problem with Checkpoints

[Piotr Nowojski]

Hi,

Can you search the logs/std err/std output for log entries like:

log.warn("Failed to locally delete blob “ …) ?

I see in the code, that if file deletion fails for whatever the reason, 
TransientBlobCleanupTask can loop indefinitely trying to remove it over and 
over again. That might be ok, however it’s doing it without any back off time 
as fast as possible.

To confirm this, could you take couple of thread dumps and check whether some 
thread is spinning in 
org.apache.flink.runtime.blob.TransientBlobCleanupTask#run ?

If that’s indeed a case, the question would be why file deletion fails?

Piotrek

> On 18 Jun 2018, at 15:48, Fabian Wollert  wrote:
> 
> Hi Piotrek, thx a lot for your answer and sry for the late response. I was 
> running some more tests, but i still got the same problem. I was analyzing a 
> heap dump already with VisualVM, and thats how i got to the intention that it 
> was some S3 logging, but seems like i was wrong. on the newer tests, the heap 
> dump says the following (this time i used Eclipse MemoryAnalyzer): 
> 
> 
> 
> 
> Are you aware of problems with the BlobServer not cleaning up properly? I 
> tried also using a bigger instance, but this never stabilizes, it just keeps 
> increasing (gave it already 10GB+ Heap) ...
> 
> Cheers
> 
> --
> 
> Fabian Wollert
> Zalando SE
> 
> E-Mail: fabian.woll...@zalando.de 
> 
> 
> 
> Am Mo., 11. Juni 2018 um 10:46 Uhr schrieb Piotr Nowojski 
> mailto:pi...@data-artisans.com>>:
> Hi,
> 
> What kind of messages are those “logs about S3 operations”? Did you try to 
> google search them? Maybe it’s a known S3 issue?
> 
> Another approach is please use some heap space analyser from which you can 
> backtrack classes that are referencing those “memory leaks” and again try to 
> google any known memory issues.
> 
> It also could just mean, that it’s not a memory leak, but you just need to 
> allocate more heap space for your JVM (and memory consumption will stabilise 
> at some point).
> 
> Piotrek
> 
>> On 8 Jun 2018, at 18:32, Fabian Wollert > > wrote:
>> 
>> Hi, in this email thread 
>> 
>>  here, i tried to set up S3 as a filesystem backend for checkpoints. Now 
>> everything is working (Flink V1.5.0), but the JobMaster is accumulating Heap 
>> space, with eventually killing itself with HeapSpace OOM after several 
>> hours. If I don't enable Checkpointing, then everything is fine. I'm using 
>> the Flink S3 Shaded Libs (tried both the Hadoop and the Presto lib, no 
>> difference in this regard) from the tutorial. my checkpoint settings are 
>> this (job level):
>> 
>> env.enableCheckpointing(1000);
>> env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
>> env.getCheckpointConfig().setMinPauseBetweenCheckpoints(5000);
>> env.getCheckpointConfig().setCheckpointTimeout(6);
>> env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
>> 
>> Another clue why i suspect the S3 Checkpointing is that the heapspace dump 
>> contains a lot of char[] objects with some logs about S3 operations.
>> 
>> anyone has an idea where to look further on this?
>> 
>> Cheers
>> 
>> --
>> 
>> Fabian Wollert
>> Zalando SE
>> 
>> E-Mail: fabian.woll...@zalando.de
>>  
>> 
>> Tamara-Danz-Straße 1
>> 10243 Berlin
>> Fax: +49 (0)30 2759 46 93
>> E-mail: legalnot...@zalando.co.uk 
>> Notifications of major holdings (Sec. 33, 38, 39 WpHG):  +49 (0)30 2000889349
>> 
>> Management Board:
>> Robert Gentz, David Schneider, Rubin Ritter
>> 
>> Chairman of the Supervisory Board:
>> Lothar Lanz
>> 
>> Person responsible for providing the contents of Zalando SE acc. to Art. 55 
>> RStV [Interstate Broadcasting Agreement]: Rubin Ritter
>> Registered at the Local Court Charlottenburg Berlin, HRB 158855 B
>> VAT registration number: DE 260543043
> 

Re: Heap Problem with Checkpoints

[Piotr Nowojski]

Hi,

I was looking in this more, and I have couple of suspicions, but it’s still 
hard to tell which is correct. Could you for example place a breakpoint (or add 
a code there to print a stack trace) in
org.apache.log4j.helpers.AppenderAttachableImpl#addAppender
And check who is calling it? Since it seems like this method is responsible for 
the growing number of ConsoleAppenders consumption.

Piotrek

> On 20 Jun 2018, at 09:20, Fabian Wollert  wrote:
> 
> Hi Piotr, thx for the hints. I checked the logs of this stack where the 
> previous Heap Dump was from, there are no error messages from the BlobServer, 
> it seems to work properly. 
> 
> But I found another issue in my setup, I had the logging not set up properly, 
> so everything was logging in the default console appender. I changed this now 
> to:
> 
> log4j.rootLogger=INFO, FILE
> log4j.logger.akka=INFO, FILE
> log4j.logger.org.apache.kafka=INFO, FILE
> log4j.logger.org.apache.hadoop=INFO, FILE
> log4j.logger.org.apache.zookeeper=INFO, FILE
> 
> # Log all info in the given file
> log4j.appender.FILE=org.apache.log4j.RollingFileAppender
> log4j.appender.FILE.File=/opt/flink/log/flink.log
> log4j.appender.FILE.MaxFileSize=100MB
> log4j.appender.FILE.MaxBackupIndex=2
> log4j.appender.FILE.layout=org.apache.log4j.PatternLayout
> log4j.appender.FILE.layout.ConversionPattern=%d{-MM-dd HH:mm:ss,SSS} %-5p 
> %c:%L - %m%n
> 
> # suppress the irrelevant (wrong) warnings from the netty channel handler
> log4j.logger.org.jboss.netty.channel.DefaultChannelPipeline=ERROR, FILE
> 
> though I have this setup now, I still see memory increases, but this time it 
> seems again like my first suspicion is valid:
> 
> 
> 
> 
> 
> 
> What I'm here mostly wondering now: Why is still a ConsoleAppender used 
> although I defined RollingFileAppender?
> 
> Sry for the back and forth between different parts of the code. But your help 
> is highly appreciated!
> 
> Cheers
> 
> --
> 
> Fabian Wollert
> Zalando SE
> 
> E-Mail: fabian.woll...@zalando.de
>  
> 
> Am Di., 19. Juni 2018 um 11:55 Uhr schrieb Piotr Nowojski 
> mailto:pi...@data-artisans.com>>:
> Hi,
> 
> Can you search the logs/std err/std output for log entries like:
> 
> log.warn("Failed to locally delete blob “ …) ?
> 
> I see in the code, that if file deletion fails for whatever the reason, 
> TransientBlobCleanupTask can loop indefinitely trying to remove it over and 
> over again. That might be ok, however it’s doing it without any back off time 
> as fast as possible.
> 
> To confirm this, could you take couple of thread dumps and check whether some 
> thread is spinning in 
> org.apache.flink.runtime.blob.TransientBlobCleanupTask#run ?
> 
> If that’s indeed a case, the question would be why file deletion fails?
> 
> Piotrek
> 
>> On 18 Jun 2018, at 15:48, Fabian Wollert > > wrote:
>> 
>> Hi Piotrek, thx a lot for your answer and sry for the late response. I was 
>> running some more tests, but i still got the same problem. I was analyzing a 
>> heap dump already with VisualVM, and thats how i got to the intention that 
>> it was some S3 logging, but seems like i was wrong. on the newer tests, the 
>> heap dump says the following (this time i used Eclipse MemoryAnalyzer): 
>> 
>> 
>> 
>> 
>> Are you aware of problems with the BlobServer not cleaning up properly? I 
>> tried also using a bigger instance, but this never stabilizes, it just keeps 
>> increasing (gave it already 10GB+ Heap) ...
>> 
>> Cheers
>> 
>> --
>> 
>> Fabian Wollert
>> Zalando SE
>> 
>> E-Mail: fabian.woll...@zalando.de 
>> 
>> 
>> 
>> Am Mo., 11. Juni 2018 um 10:46 Uhr schrieb Piotr Nowojski 
>> mailto:pi...@data-artisans.com>>:
>> Hi,
>> 
>> What kind of messages are those “logs about S3 operations”? Did you try to 
>> google search them? Maybe it’s a known S3 issue?
>> 
>> Another approach is please use some heap space analyser from which you can 
>> backtrack classes that are referencing those “memory leaks” and again try to 
>> google any known memory issues.
>> 
>> It also could just mean, that it’s not a memory leak, but you just need to 
>> allocate more heap space for your JVM (and memory consumption will stabilise 
>> at some point).
>> 
>> Piotrek
>> 
>>> On 8 Jun 2018, at 18:32, Fabian Wollert >> > wrote:
>>> 
>>> Hi, in this email thread 
>>> 
>>>  here, i tried to set up S3 as a filesystem backend for checkpoints. Now 
>>> everything is working (Flink V1.5.0), but the JobMaster is accumulating 
>>> Heap space, with eventually killing itself with HeapSpace OOM after several 
>>> hours. If I don't enable Checkpointing, then everything is fine. I'm using 
>>> the Flink S3 Shaded Libs (tried both the Hadoop and the Presto lib, no 
>>> difference in this reg

Re: Heap Problem with Checkpoints

[Piotr Nowojski]

Btw, side questions. Could it be, that you are accessing two different Hadoop 
file systems (two different schemas) or even the same one from two different 
users (encoded in the file system URI) within the same Flink JobMaster?

If so, the answer might be this possible resource leak in Flink:
https://issues.apache.org/jira/browse/FLINK-9626 


Piotrek

> On 20 Jun 2018, at 13:50, Piotr Nowojski  wrote:
> 
> Hi,
> 
> I was looking in this more, and I have couple of suspicions, but it’s still 
> hard to tell which is correct. Could you for example place a breakpoint (or 
> add a code there to print a stack trace) in
> org.apache.log4j.helpers.AppenderAttachableImpl#addAppender
> And check who is calling it? Since it seems like this method is responsible 
> for the growing number of ConsoleAppenders consumption.
> 
> Piotrek
> 
>> On 20 Jun 2018, at 09:20, Fabian Wollert > > wrote:
>> 
>> Hi Piotr, thx for the hints. I checked the logs of this stack where the 
>> previous Heap Dump was from, there are no error messages from the 
>> BlobServer, it seems to work properly. 
>> 
>> But I found another issue in my setup, I had the logging not set up 
>> properly, so everything was logging in the default console appender. I 
>> changed this now to:
>> 
>> log4j.rootLogger=INFO, FILE
>> log4j.logger.akka=INFO, FILE
>> log4j.logger.org.apache.kafka=INFO, FILE
>> log4j.logger.org.apache.hadoop=INFO, FILE
>> log4j.logger.org.apache.zookeeper=INFO, FILE
>> 
>> # Log all info in the given file
>> log4j.appender.FILE=org.apache.log4j.RollingFileAppender
>> log4j.appender.FILE.File=/opt/flink/log/flink.log
>> log4j.appender.FILE.MaxFileSize=100MB
>> log4j.appender.FILE.MaxBackupIndex=2
>> log4j.appender.FILE.layout=org.apache.log4j.PatternLayout
>> log4j.appender.FILE.layout.ConversionPattern=%d{-MM-dd HH:mm:ss,SSS} 
>> %-5p %c:%L - %m%n
>> 
>> # suppress the irrelevant (wrong) warnings from the netty channel handler
>> log4j.logger.org.jboss.netty.channel.DefaultChannelPipeline=ERROR, FILE
>> 
>> though I have this setup now, I still see memory increases, but this time it 
>> seems again like my first suspicion is valid:
>> 
>> 
>> 
>> 
>> 
>> 
>> What I'm here mostly wondering now: Why is still a ConsoleAppender used 
>> although I defined RollingFileAppender?
>> 
>> Sry for the back and forth between different parts of the code. But your 
>> help is highly appreciated!
>> 
>> Cheers
>> 
>> --
>> 
>> Fabian Wollert
>> Zalando SE
>> 
>> E-Mail: fabian.woll...@zalando.de
>>  
>> 
>> Am Di., 19. Juni 2018 um 11:55 Uhr schrieb Piotr Nowojski 
>> mailto:pi...@data-artisans.com>>:
>> Hi,
>> 
>> Can you search the logs/std err/std output for log entries like:
>> 
>> log.warn("Failed to locally delete blob “ …) ?
>> 
>> I see in the code, that if file deletion fails for whatever the reason, 
>> TransientBlobCleanupTask can loop indefinitely trying to remove it over and 
>> over again. That might be ok, however it’s doing it without any back off 
>> time as fast as possible.
>> 
>> To confirm this, could you take couple of thread dumps and check whether 
>> some thread is spinning in 
>> org.apache.flink.runtime.blob.TransientBlobCleanupTask#run ?
>> 
>> If that’s indeed a case, the question would be why file deletion fails?
>> 
>> Piotrek
>> 
>>> On 18 Jun 2018, at 15:48, Fabian Wollert >> > wrote:
>>> 
>>> Hi Piotrek, thx a lot for your answer and sry for the late response. I was 
>>> running some more tests, but i still got the same problem. I was analyzing 
>>> a heap dump already with VisualVM, and thats how i got to the intention 
>>> that it was some S3 logging, but seems like i was wrong. on the newer 
>>> tests, the heap dump says the following (this time i used Eclipse 
>>> MemoryAnalyzer): 
>>> 
>>> 
>>> 
>>> 
>>> Are you aware of problems with the BlobServer not cleaning up properly? I 
>>> tried also using a bigger instance, but this never stabilizes, it just 
>>> keeps increasing (gave it already 10GB+ Heap) ...
>>> 
>>> Cheers
>>> 
>>> --
>>> 
>>> Fabian Wollert
>>> Zalando SE
>>> 
>>> E-Mail: fabian.woll...@zalando.de 
>>> 
>>> 
>>> 
>>> Am Mo., 11. Juni 2018 um 10:46 Uhr schrieb Piotr Nowojski 
>>> mailto:pi...@data-artisans.com>>:
>>> Hi,
>>> 
>>> What kind of messages are those “logs about S3 operations”? Did you try to 
>>> google search them? Maybe it’s a known S3 issue?
>>> 
>>> Another approach is please use some heap space analyser from which you can 
>>> backtrack classes that are referencing those “memory leaks” and again try 
>>> to google any known memory issues.
>>> 
>>> It also could just mean, that it’s not a memory leak, but you just need to 
>>> allocate more heap space for your JVM (and memory consumption will 
>>> stabilise at some point).
>>> 
>>> Piotrek
>>> 
 On 8 Jun 2018, at 18:32, Fabian Wollert >>> 

Re: Heap Problem with Checkpoints

[Fabian Wollert]

to that last one: i'm accessing S3 from one EC2 instance which has a IAM
Role attached ...

I'll get back to you when i have those stacktraces printed ... will have to
build the project and package the custom version first, might take some
time, and also some vacation is up next ...

Cheers


--


*Fabian WollertZalando SE*

E-Mail: fabian.woll...@zalando.de
Phone: +49 152 03479412
Location: ZMAP 



Am Mi., 20. Juni 2018 um 14:14 Uhr schrieb Piotr Nowojski <
pi...@data-artisans.com>:

> Btw, side questions. Could it be, that you are accessing two different
> Hadoop file systems (two different schemas) or even the same one from two
> different users (encoded in the file system URI) within the same Flink
> JobMaster?
>
> If so, the answer might be this possible resource leak in Flink:
> https://issues.apache.org/jira/browse/FLINK-9626
>
> Piotrek
>
> On 20 Jun 2018, at 13:50, Piotr Nowojski  wrote:
>
> Hi,
>
> I was looking in this more, and I have couple of suspicions, but it’s
> still hard to tell which is correct. Could you for example place a
> breakpoint (or add a code there to print a stack trace) in
> org.apache.log4j.helpers.AppenderAttachableImpl#addAppender
> And check who is calling it? Since it seems like this method is
> responsible for the growing number of ConsoleAppenders consumption.
>
> Piotrek
>
> On 20 Jun 2018, at 09:20, Fabian Wollert  wrote:
>
> Hi Piotr, thx for the hints. I checked the logs of this stack where the
> previous Heap Dump was from, there are no error messages from the
> BlobServer, it seems to work properly.
>
> But I found another issue in my setup, I had the logging not set up
> properly, so everything was logging in the default console appender. I
> changed this now to:
>
> log4j.rootLogger=INFO, FILE
> log4j.logger.akka=INFO, FILE
> log4j.logger.org.apache.kafka=INFO, FILE
> log4j.logger.org.apache.hadoop=INFO, FILE
> log4j.logger.org.apache.zookeeper=INFO, FILE
>
> # Log all info in the given file
> log4j.appender.FILE=org.apache.log4j.RollingFileAppender
> log4j.appender.FILE.File=/opt/flink/log/flink.log
> log4j.appender.FILE.MaxFileSize=100MB
> log4j.appender.FILE.MaxBackupIndex=2
> log4j.appender.FILE.layout=org.apache.log4j.PatternLayout
> log4j.appender.FILE.layout.ConversionPattern=%d{-MM-dd HH:mm:ss,SSS}
> %-5p %c:%L - %m%n
>
> # suppress the irrelevant (wrong) warnings from the netty channel handler
> log4j.logger.org.jboss.netty.channel.DefaultChannelPipeline=ERROR, FILE
>
> though I have this setup now, I still see memory increases, but this time
> it seems again like my first suspicion is valid:
>
> 
>
>
> 
>
> What I'm here mostly wondering now: Why is still a ConsoleAppender used
> although I defined RollingFileAppender?
>
> Sry for the back and forth between different parts of the code. But your
> help is highly appreciated!
>
> Cheers
>
> --
>
>
> *Fabian WollertZalando SE*
>
> E-Mail: fabian.woll...@zalando.de
>
>
> Am Di., 19. Juni 2018 um 11:55 Uhr schrieb Piotr Nowojski <
> pi...@data-artisans.com>:
>
>> Hi,
>>
>> Can you search the logs/std err/std output for log entries like:
>>
>> log.warn("Failed to locally delete blob “ …) ?
>>
>> I see in the code, that if file deletion fails for whatever the reason,
>> TransientBlobCleanupTask can loop indefinitely trying to remove it over and
>> over again. That might be ok, however it’s doing it without any back off
>> time as fast as possible.
>>
>> To confirm this, could you take couple of thread dumps and check whether
>> some thread is spinning
>> in org.apache.flink.runtime.blob.TransientBlobCleanupTask#run ?
>>
>> If that’s indeed a case, the question would be why file deletion fails?
>>
>> Piotrek
>>
>> On 18 Jun 2018, at 15:48, Fabian Wollert  wrote:
>>
>> Hi Piotrek, thx a lot for your answer and sry for the late response. I
>> was running some more tests, but i still got the same problem. I was
>> analyzing a heap dump already with VisualVM, and thats how i got to the
>> intention that it was some S3 logging, but seems like i was wrong. on the
>> newer tests, the heap dump says the following (this time i used Eclipse
>> MemoryAnalyzer):
>>
>> 
>> 
>> 
>> Are you aware of problems with the BlobServer not cleaning up properly? I
>> tried also using a bigger instance, but this never stabilizes, it just
>> keeps increasing (gave it already 10GB+ Heap) ...
>>
>> Cheers
>>
>> --
>>
>>
>> *Fabian WollertZalando SE*
>>
>> E-Mail: fabian.woll...@zalando.de
>>
>>
>>
>> Am Mo., 11. Juni 2018 um 10:46 Uhr schrieb Piotr Nowojski <
>> pi...@data-artisans.com>:
>>
>>> Hi,
>>>
>>> What kind of messages are those “logs about S3 operations”? Did you try
>>> to google search them? Maybe it’s a known S3 issue?
>>>
>>> Another approach is please use some heap space analyser from which you
>>> can backtrack classes that are referencing those “memory leaks” and again
>>> try to google any known memory issues.
>>>
>>> It also could just mean, that it’s not a m

Re: Heap Problem with Checkpoints

[Ayush Verma]

Hello Piotr, I work with Fabian and have been investigating the memory leak
associated with issues mentioned in this thread. I took a heap dump of our
master node and noticed that there was >1gb (and growing) worth of entries
in the set, /files/, in class *java.io.DeleteOnExitHook*.
Almost all the strings in this set look like,
/tmp/hadoop-root/s3a/output-*.tmp.

This means that the checkpointing code, which uploads the data to s3,
maintains it in a temporary local file, which is supposed to be deleted on
exit of the JVM. In our case, the checkpointing is quite heavy and because
we have a long running flink cluster, it causes this /set/ to grow
unbounded, eventually cause an OOM. Please see these images:

 

 

The culprit seems to be *org.apache.hadoop.fs.s3a.S3AOutputStream*, which
in-turn, calls
*org.apache.hadoop.fs.s3a.S3AFileSystem.createTmpFileForWrite()*. If we
follow the method call chain from there, we end up at
*org.apache.hadoop.fs.LocalDirAllocator.createTmpFileForWrite()*, where we
can see the temp file being created and the method deleteOnExit() being
called.

Maybe instead of relying on *deleteOnExit()* we can keep track of these tmp
files, and as soon as they are no longer required, delete them ourself.



--
Sent from: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/

Re: Heap Problem with Checkpoints

[Piotr Nowojski]

Hi,

Thanks for getting back with more information.

Apparently this is a known bug of JDK since 2003 and is still not resolved:
https://bugs.java.com/view_bug.do?bug_id=4872014 

https://bugs.java.com/view_bug.do?bug_id=6664633 


Code that is using this `deleteOnExit` is not part of a Flink, but an external 
library that we are using (hadoop-aws:2.8.x), so we can not fix it for them and 
this bug should be reported/forwarded to them (I have already done just that 
). More interesting 
S3AOutputStream is already manually deleting those files when they are not 
needed in `org.apache.hadoop.fs.s3a.S3AOutputStream#close`’s finally block:

} finally {
  if (!backupFile.delete()) {
LOG.warn("Could not delete temporary s3a file: {}", backupFile);
  }
  super.close();
}

But this doesn’t remove the entry from DeleteOnExitHook. 

From what I see in the code, flink-s3-fs-presto filesystem implantation that we 
provide doesn’t use deleteOnExit, so if you can switch to this filesystem it 
would solve the problem for you.

Piotrek

> On 9 Aug 2018, at 12:09, Ayush Verma  wrote:
> 
> Hello Piotr, I work with Fabian and have been investigating the memory leak
> associated with issues mentioned in this thread. I took a heap dump of our
> master node and noticed that there was >1gb (and growing) worth of entries
> in the set, /files/, in class *java.io.DeleteOnExitHook*.
> Almost all the strings in this set look like,
> /tmp/hadoop-root/s3a/output-*.tmp.
> 
> This means that the checkpointing code, which uploads the data to s3,
> maintains it in a temporary local file, which is supposed to be deleted on
> exit of the JVM. In our case, the checkpointing is quite heavy and because
> we have a long running flink cluster, it causes this /set/ to grow
> unbounded, eventually cause an OOM. Please see these images:
> 
>  
> 
>  
> 
> The culprit seems to be *org.apache.hadoop.fs.s3a.S3AOutputStream*, which
> in-turn, calls
> *org.apache.hadoop.fs.s3a.S3AFileSystem.createTmpFileForWrite()*. If we
> follow the method call chain from there, we end up at
> *org.apache.hadoop.fs.LocalDirAllocator.createTmpFileForWrite()*, where we
> can see the temp file being created and the method deleteOnExit() being
> called.
> 
> Maybe instead of relying on *deleteOnExit()* we can keep track of these tmp
> files, and as soon as they are no longer required, delete them ourself.
> 
> 
> 
> --
> Sent from: 
> http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/