[
https://issues.apache.org/jira/browse/MAPREDUCE-2872?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13093558#comment-13093558
]
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.
--
This message is automatically generated by JIRA.
For more information on JIRA, see: http://www.atlassian.com/software/jira
