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From "Binglin Chang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAPREDUCE-2872) Optimize TaskTracker memory usage
Date Tue, 30 Aug 2011 08:46:38 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-2872?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13093558#comment-13093558
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Binglin Chang commented on MAPREDUCE-2872:
------------------------------------------

bq. what I sense probably a more pressing issue for you is the memory usage of the child tasks.
Yes, statistics of memory consumption for a whole cluster shows that large proportion of memory
is used by map/reduce tasks, especially reduce tasks. 
We don't use memory-monitoring feature cause our cluster don't have this feature... very old
verison:( 
And I notice that many memory related bugs has already been fixed in newer versions. Kill
tasks is a solution, but we also can do some optimizations. Most high memory child task is
caused by big key/values, especially for streaming/pipes.
As MRv1 will stay for a long time in our company internally, we made these changes:
1. Add cache for taskTrackerHttp for TaskCompletionEvents
2. Add cache in TaskID to JobID reference, 
   So average memory of TaskCompletionEvent object drop from 296bytes to some tens of bytes.
3. Use 12byte to record a IndexRecord rather than 24byte(long[3]) 
4. Try to replace Jetty with Netty, using code from 0.23
5. Resource isolaton using cgroup.

As these changes are not good coding practice or require OS support, and TT will not exist
in MRv2, I think I should close this issue.


> Optimize TaskTracker memory usage
> ---------------------------------
>
>                 Key: MAPREDUCE-2872
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-2872
>             Project: Hadoop Map/Reduce
>          Issue Type: Improvement
>          Components: tasktracker
>    Affects Versions: 0.20.203.0
>            Reporter: Binglin Chang
>            Assignee: Binglin Chang
>              Labels: memory, optimization
>
> We observe high memory usage of framework level components on slave node, mainly TaskTracker
& Child, especially for large clusters. To be clear at first, large jobs with 10000-100000
map and >10000 reduce tasks are very common in our offline cluster, and will very likely
continue to grow. This is reasonable because the number of map & reduce slots are in the
same range, and it's impractical for users to reduce their job's task number without execution
time penalty. 
> High memory consumption will:
> * Limit the memory used by up level application; 
> * Reduce page cache space, which plays a  important role in spill, merge, shuffle and
even HDFS performance; 
> * Increase the probability of slave node OOM, which may affect storage layer(HDFS) too.

> A stable TT with predictable memory behavior is desired, this also applies to Child JVM.
> This issue focuses on TaskTracker memory optimization, on our cluster, TaskTracker use
600M+ memory & 300%+(3core+) CPU at peak, and 300M+ memory & much less CPU in average,
so we need to set -Xmx to 1000M for TT to prevent OOM, then the TT memory is in 200M-1200M
range, and 800M in average. 
> Here are some ideas:  
> Jetty http connection use a lot memory when these are many requests in queue, we need
to limit the length of the queue, combine multiple requests into one request, or use netty
just like MR2
> TaskCompletionEvents use a lot memory too if a job have large number of map task, this
won't be a problem in MR2, but can be optimized, A typical TaskCompletionEvent object use
296 bytes memory, a job with 100000 map will use about 30M memory, problem will appear if
there are some big RunningJob in a TaskTracker. There are more memory efficient implementations
for TaskCompletionEvent.
> IndexCache: memory of indexcache varies directly as reduce number, on large cluster 10MB
of indexcache is not enough, 
> we set it to 100MB, again use primitive long[] instead of IndexRecord[] can save 50%
of memory.
> Although some of the above won't be a problem in MR-v2, since MR-v1 is still widely used,
I think optimizations are needed.

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