Lowering mapred.job.shuffle.input.buffer.percent would be the option to choose.
Maybe GC wasn't releasing memory fast enough for in memory shuffling. 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 > > >> > > > > > >
