I pressed send key a bit early.
I will have to dig a bit deeper.
Hopefully someone can find reader.close() call after which I will look for
another possible root cause :-)
On Wed, Mar 10, 2010 at 7:48 PM, Ted Yu <yuzhih...@gmail.com> wrote:
> Thanks to Andy for the log he provided.
>
> You can see from the log below that size increased steadily from 341535057
> to 408181692, approaching maxSize. Then OOME:
>
>
> 2010-03-10 18:38:32,936 INFO org.apache.hadoop.mapred.ReduceTask: reserve:
> pos=start requestedSize=3893000 size=341535057 numPendingRequests=0
> maxSize=417601952
> 2010-03-10 18:38:32,936 INFO org.apache.hadoop.mapred.ReduceTask: reserve:
> pos=end requestedSize=3893000 size=345428057 numPendingRequests=0
> maxSize=417601952
> ...
> 2010-03-10 18:38:35,950 INFO org.apache.hadoop.mapred.ReduceTask: reserve:
> pos=end requestedSize=635753 size=408181692 numPendingRequests=0
> maxSize=417601952
> 2010-03-10 18:38:36,603 INFO org.apache.hadoop.mapred.ReduceTask: Task
> attempt_201003101826_0001_r_000004_0: Failed fetch #1 from
> attempt_201003101826_0001_m_000875_0
>
> 2010-03-10 18:38:36,603 WARN org.apache.hadoop.mapred.ReduceTask:
> attempt_201003101826_0001_r_000004_0 adding host hd17.dfs.returnpath.netto
> penalty box, next contact in 4 seconds
> 2010-03-10 18:38:36,604 INFO org.apache.hadoop.mapred.ReduceTask:
> attempt_201003101826_0001_r_000004_0: Got 1 map-outputs from previous
> failures
> 2010-03-10 18:38:36,605 FATAL org.apache.hadoop.mapred.TaskRunner:
> attempt_201003101826_0001_r_000004_0 : Map output copy failure :
> java.lang.OutOfMemoryError: Java heap space
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1513)
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutput(ReduceTask.java:1413)
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(ReduceTask.java:1266)
> at
> org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceTask.java:1200)
>
> Looking at the call to unreserve() in ReduceTask, two were for IOException
> and the other was for Sanity check (line 1557). Meaning they wouldn't be
> called in normal execution path.
>
> I see one call in IFile.InMemoryReader close() method:
> // Inform the RamManager
> ramManager.unreserve(bufferSize);
>
> And InMemoryReader is used in createInMemorySegments():
> Reader<K, V> reader =
> new InMemoryReader<K, V>(ramManager, mo.mapAttemptId,
> mo.data, 0, mo.data.length);
>
> But I don't see reader.close() in ReduceTask file.
>
> On Wed, Mar 10, 2010 at 3:34 PM, Chris Douglas <chri...@yahoo-inc.com>wrote:
>
>> I don't think this OOM is a framework bug per se, and given the
>> rewrite/refactoring of the shuffle in MAPREDUCE-318 (in 0.21), tuning the
>> 0.20 shuffle semantics is likely not worthwhile (though data informing
>> improvements to trunk would be excellent). Most likely (and tautologically),
>> ReduceTask simply requires more memory than is available and the job failure
>> can be avoided by either 0) increasing the heap size or 1) lowering
>> mapred.shuffle.input.buffer.percent. Most of the tasks we run have a heap of
>> 1GB. For a reduce fetching >200k map outputs, that's a reasonable, even
>> stingy amount of space. -C
>>
>>
>> On Mar 10, 2010, at 5:26 AM, Ted Yu wrote:
>>
>> I verified that size and maxSize are long. This means MR-1182 didn't
>>> resolve
>>> Andy's issue.
>>>
>>> According to Andy:
>>> At the beginning of the job there are 209,754 pending map tasks and 32
>>> pending reduce tasks
>>>
>>> My guess is that GC wasn't reclaiming memory fast enough, leading to OOME
>>> because of large number of in-memory shuffle candidates.
>>>
>>> My suggestion for Andy would be to:
>>> 1. add -*verbose*:*gc as JVM parameter
>>> 2. modify reserve() slightly to calculate the maximum outstanding
>>> numPendingRequests and print the maximum.
>>>
>>> Based on the output from above two items, we can discuss solution.
>>> My intuition is to place upperbound on numPendingRequests beyond which
>>> canFitInMemory() returns false.
>>> *
>>> My two cents.
>>>
>>> On Tue, Mar 9, 2010 at 11:51 PM, Christopher Douglas
>>> <chri...@yahoo-inc.com>wrote:
>>>
>>> That section of code is unmodified in MR-1182. See the patches/svn log.
>>>> -C
>>>>
>>>> Sent from my iPhone
>>>>
>>>>
>>>> On Mar 9, 2010, at 7:44 PM, "Ted Yu" <yuzhih...@gmail.com> wrote:
>>>>
>>>> I just downloaded hadoop-0.20.2 tar ball from cloudera mirror.
>>>>
>>>>> This is what I see in ReduceTask (line 999):
>>>>> public synchronized boolean reserve(int requestedSize, InputStream
>>>>> in)
>>>>>
>>>>> throws InterruptedException {
>>>>> // Wait till the request can be fulfilled...
>>>>> while ((size + requestedSize) > maxSize) {
>>>>>
>>>>> I don't see the fix from MR-1182.
>>>>>
>>>>> That's why I suggested to Andy that he manually apply MR-1182.
>>>>>
>>>>> Cheers
>>>>>
>>>>> On Tue, Mar 9, 2010 at 5:01 PM, Andy Sautins <
>>>>> andy.saut...@returnpath.net
>>>>>
>>>>>> wrote:
>>>>>>
>>>>>
>>>>>
>>>>> Thanks Christopher.
>>>>>>
>>>>>> The heap size for reduce tasks is configured to be 640M (
>>>>>> mapred.child.java.opts set to -Xmx640m ).
>>>>>>
>>>>>> Andy
>>>>>>
>>>>>> -----Original Message-----
>>>>>> From: Christopher Douglas [mailto:chri...@yahoo-inc.com]
>>>>>> Sent: Tuesday, March 09, 2010 5:19 PM
>>>>>> To: common-user@hadoop.apache.org
>>>>>> Subject: Re: Shuffle In Memory OutOfMemoryError
>>>>>>
>>>>>> No, MR-1182 is included in 0.20.2
>>>>>>
>>>>>> What heap size have you set for your reduce tasks? -C
>>>>>>
>>>>>> Sent from my iPhone
>>>>>>
>>>>>> On Mar 9, 2010, at 2:34 PM, "Ted Yu" <yuzhih...@gmail.com> wrote:
>>>>>>
>>>>>> Andy:
>>>>>>
>>>>>>> You need to manually apply the patch.
>>>>>>>
>>>>>>> Cheers
>>>>>>>
>>>>>>> On Tue, Mar 9, 2010 at 2:23 PM, Andy Sautins <
>>>>>>>
>>>>>>> andy.saut...@returnpath.net
>>>>>>
>>>>>> wrote:
>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>> 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:yuzhih...@gmail.com]
>>>>>>>> Sent: Tuesday, March 09, 2010 12:56 PM
>>>>>>>> To: common-user@hadoop.apache.org
>>>>>>>> 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 <
>>>>>>>>
>>>>>>>> andy.saut...@returnpath.net
>>>>>>>
>>>>>>
>>>>>> 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:yuzhih...@gmail.com]
>>>>>>>>> Sent: Sunday, March 07, 2010 3:38 PM
>>>>>>>>> To: common-user@hadoop.apache.org
>>>>>>>>> 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 <
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> andy.saut...@returnpath.net
>>>>>>>>
>>>>>>>> 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:apa...@jacobrideout.net]
>>>>>>>>>> Sent: Sunday, March 07, 2010 1:21 PM
>>>>>>>>>> To: common-user@hadoop.apache.org
>>>>>>>>>> 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 <yuzhih...@gmail.com>
>>>>>>>>>> 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 <
>>>>>>>>>>>
>>>>>>>>>>> apa...@jacobrideout.net
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> 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
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>
>>
>
