Thanks Ted. My understanding is that MAPREDUCE-1182 is included in the
0.20.2 release. We upgraded our cluster to 0.20.2 this weekend and re-ran the
same job scenarios. Running with mapred.reduce.parallel.copies set to 1 and
continue to have the same Java heap space error.
-----Original Message-----
From: Ted Yu [mailto:[email protected]]
Sent: Tuesday, March 09, 2010 12:56 PM
To: [email protected]
Subject: Re: Shuffle In Memory OutOfMemoryError
This issue has been resolved in
http://issues.apache.org/jira/browse/MAPREDUCE-1182
Please apply the patch
M1182-1v20.patch<http://issues.apache.org/jira/secure/attachment/12424116/M1182-1v20.patch>
On Sun, Mar 7, 2010 at 3:57 PM, Andy Sautins <[email protected]>wrote:
>
> Thanks Ted. Very helpful. You are correct that I misunderstood the code
> at ReduceTask.java:1535. I missed the fact that it's in a IOException catch
> block. My mistake. That's what I get for being in a rush.
>
> For what it's worth I did re-run the job with
> mapred.reduce.parallel.copies set with values from 5 all the way down to 1.
> All failed with the same error:
>
> Error: java.lang.OutOfMemoryError: Java heap space
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1508)
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutput(ReduceTask.java:1408)
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(ReduceTask.java:1261)
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceTask.java:1195)
>
>
> So from that it does seem like something else might be going on, yes? I
> need to do some more research.
>
> I appreciate your insights.
>
> Andy
>
> -----Original Message-----
> From: Ted Yu [mailto:[email protected]]
> Sent: Sunday, March 07, 2010 3:38 PM
> To: [email protected]
> Subject: Re: Shuffle In Memory OutOfMemoryError
>
> My observation is based on this call chain:
> MapOutputCopier.run() calling copyOutput() calling getMapOutput() calling
> ramManager.canFitInMemory(decompressedLength)
>
> Basically ramManager.canFitInMemory() makes decision without considering
> the
> number of MapOutputCopiers that are running. Thus 1.25 * 0.7 of total heap
> may be used in shuffling if default parameters were used.
> Of course, you should check the value for mapred.reduce.parallel.copies to
> see if it is 5. If it is 4 or lower, my reasoning wouldn't apply.
>
> About ramManager.unreserve() call, ReduceTask.java from hadoop 0.20.2 only
> has 2731 lines. So I have to guess the location of the code snippet you
> provided.
> I found this around line 1535:
> } catch (IOException ioe) {
> LOG.info("Failed to shuffle from " +
> mapOutputLoc.getTaskAttemptId(),
> ioe);
>
> // Inform the ram-manager
> ramManager.closeInMemoryFile(mapOutputLength);
> ramManager.unreserve(mapOutputLength);
>
> // Discard the map-output
> try {
> mapOutput.discard();
> } catch (IOException ignored) {
> LOG.info("Failed to discard map-output from " +
> mapOutputLoc.getTaskAttemptId(), ignored);
> }
> Please confirm the line number.
>
> If we're looking at the same code, I am afraid I don't see how we can
> improve it. First, I assume IOException shouldn't happen that often.
> Second,
> mapOutput.discard() just sets:
> data = null;
> for in memory case. Even if we call mapOutput.discard() before
> ramManager.unreserve(), we don't know when GC would kick in and make more
> memory available.
> Of course, given the large number of map outputs in your system, it became
> more likely that the root cause from my reasoning made OOME happen sooner.
>
> Thanks
>
> >
> On Sun, Mar 7, 2010 at 1:03 PM, Andy Sautins <[email protected]
> >wrote:
>
> >
> > Ted,
> >
> > I'm trying to follow the logic in your mail and I'm not sure I'm
> > following. If you would mind helping me understand I would appreciate
> it.
> >
> > Looking at the code maxSingleShuffleLimit is only used in determining
> if
> > the copy _can_ fit into memory:
> >
> > boolean canFitInMemory(long requestedSize) {
> > return (requestedSize < Integer.MAX_VALUE &&
> > requestedSize < maxSingleShuffleLimit);
> > }
> >
> > It also looks like the RamManager.reserve should wait until memory is
> > available so it should hit a memory limit for that reason.
> >
> > What does seem a little strange to me is the following (
> ReduceTask.java
> > starting at 2730 ):
> >
> > // Inform the ram-manager
> > ramManager.closeInMemoryFile(mapOutputLength);
> > ramManager.unreserve(mapOutputLength);
> >
> > // Discard the map-output
> > try {
> > mapOutput.discard();
> > } catch (IOException ignored) {
> > LOG.info("Failed to discard map-output from " +
> > mapOutputLoc.getTaskAttemptId(), ignored);
> > }
> > mapOutput = null;
> >
> > So to me that looks like the ramManager unreserves the memory before
> the
> > mapOutput is discarded. Shouldn't the mapOutput be discarded _before_
> the
> > ramManager unreserves the memory? If the memory is unreserved before the
> > actual underlying data references are removed then it seems like another
> > thread can try to allocate memory ( ReduceTask.java:2730 ) before the
> > previous memory is disposed ( mapOutput.discard() ).
> >
> > Not sure that makes sense. One thing to note is that the particular
> job
> > that is failing does have a good number ( 200k+ ) of map outputs. The
> large
> > number of small map outputs may be why we are triggering a problem.
> >
> > Thanks again for your thoughts.
> >
> > Andy
> >
> >
> > -----Original Message-----
> > From: Jacob R Rideout [mailto:[email protected]]
> > Sent: Sunday, March 07, 2010 1:21 PM
> > To: [email protected]
> > Cc: Andy Sautins; Ted Yu
> > Subject: Re: Shuffle In Memory OutOfMemoryError
> >
> > Ted,
> >
> > Thank you. I filled MAPREDUCE-1571 to cover this issue. I might have
> > some time to write a patch later this week.
> >
> > Jacob Rideout
> >
> > On Sat, Mar 6, 2010 at 11:37 PM, Ted Yu <[email protected]> wrote:
> > > I think there is mismatch (in ReduceTask.java) between:
> > > this.numCopiers = conf.getInt("mapred.reduce.parallel.copies", 5);
> > > and:
> > > maxSingleShuffleLimit = (long)(maxSize *
> > > MAX_SINGLE_SHUFFLE_SEGMENT_FRACTION);
> > > where MAX_SINGLE_SHUFFLE_SEGMENT_FRACTION is 0.25f
> > >
> > > because
> > > copiers = new ArrayList<MapOutputCopier>(numCopiers);
> > > so the total memory allocated for in-mem shuffle is 1.25 * maxSize
> > >
> > > A JIRA should be filed to correlate the constant 5 above and
> > > MAX_SINGLE_SHUFFLE_SEGMENT_FRACTION.
> > >
> > > Cheers
> > >
> > > On Sat, Mar 6, 2010 at 8:31 AM, Jacob R Rideout <
> [email protected]
> > >wrote:
> > >
> > >> Hi all,
> > >>
> > >> We are seeing the following error in our reducers of a particular job:
> > >>
> > >> Error: java.lang.OutOfMemoryError: Java heap space
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1508)
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutput(ReduceTask.java:1408)
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(ReduceTask.java:1261)
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceTask.java:1195)
> > >>
> > >>
> > >> After enough reducers fail the entire job fails. This error occurs
> > >> regardless of whether mapred.compress.map.output is true. We were able
> > >> to avoid the issue by reducing mapred.job.shuffle.input.buffer.percent
> > >> to 20%. Shouldn't the framework via ShuffleRamManager.canFitInMemory
> > >> and.ShuffleRamManager.reserve correctly detect the the memory
> > >> available for allocation? I would think that with poor configuration
> > >> settings (and default settings in particular) the job may not be as
> > >> efficient, but wouldn't die.
> > >>
> > >> Here is some more context in the logs, I have attached the full
> > >> reducer log here: http://gist.github.com/323746
> > >>
> > >>
> > >> 2010-03-06 07:54:49,621 INFO org.apache.hadoop.mapred.ReduceTask:
> > >> Shuffling 4191933 bytes (435311 raw bytes) into RAM from
> > >> attempt_201003060739_0002_m_000061_0
> > >> 2010-03-06 07:54:50,222 INFO org.apache.hadoop.mapred.ReduceTask: Task
> > >> attempt_201003060739_0002_r_000000_0: Failed fetch #1 from
> > >> attempt_201003060739_0002_m_000202_0
> > >> 2010-03-06 07:54:50,223 WARN org.apache.hadoop.mapred.ReduceTask:
> > >> attempt_201003060739_0002_r_000000_0 adding host
> > >> hd37.dfs.returnpath.net to penalty box, next contact in 4 seconds
> > >> 2010-03-06 07:54:50,223 INFO org.apache.hadoop.mapred.ReduceTask:
> > >> attempt_201003060739_0002_r_000000_0: Got 1 map-outputs from previous
> > >> failures
> > >> 2010-03-06 07:54:50,223 FATAL org.apache.hadoop.mapred.TaskRunner:
> > >> attempt_201003060739_0002_r_000000_0 : Map output copy failure :
> > >> java.lang.OutOfMemoryError: Java heap space
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1508)
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutput(ReduceTask.java:1408)
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(ReduceTask.java:1261)
> > >> at
> > >>
> >
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceTask.java:1195)
> > >>
> > >>
> > >> We tried this both in 0.20.1 and 0.20.2. We had hoped MAPREDUCE-1182
> > >> would address the issue in 0.20.2, but it did not. Does anyone have
> > >> any comments or suggestions? Is this a bug I should file a JIRA for?
> > >>
> > >> Jacob Rideout
> > >> Return Path
> > >>
> > >
> >
>
