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https://issues.apache.org/jira/browse/MAPREDUCE-7180?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16784444#comment-16784444
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David Mollitor commented on MAPREDUCE-7180:
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Thanks [~wilfreds].
I have opened [YARN-9347]. I think a V1 of this feature can build on
[YARN-9347]. If the growth-factor is 0, the entire application exits
immediately, if the growth factor is defined, the target container is retried
with a larger container (N times).
I'm not sure how I can put further emphasis on this idea of _let the end user
and or admin sort it out_. That should be an absolute *last* resort. Software
should automate things and make our lives easier. No end user or admin wants
to be called into work in the middle of night to resolve an issue with a failed
workflow. This change would at least give a chance for users to sort it out
when they get into the office the next day, on their schedule. And, speaking
from experience, the the first attempt at a corrective action a user/admin
implements is to simply increase the memory, by some arbitrary amount, and
retry the application again. The software should just do this automatically.
Thanks.
> Relaunching Failed Containers
> -----------------------------
>
> Key: MAPREDUCE-7180
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-7180
> Project: Hadoop Map/Reduce
> Issue Type: New Feature
> Components: mrv1, mrv2
> Reporter: David Mollitor
> Priority: Major
>
> In my experience, it is very common that a MR job completely fails because a
> single Mapper/Reducer container is using more memory than has been reserved
> in YARN. The following message is logging the the MapReduce
> ApplicationMaster:
> {code}
> Container [pid=46028,containerID=container_e54_1435155934213_16721_01_003666]
> is running beyond physical memory limits.
> Current usage: 1.0 GB of 1 GB physical memory used; 2.7 GB of 2.1 GB virtual
> memory used. Killing container.
> {code}
> In this case, the container is re-launched on another node, and of course, it
> is killed again for the same reason. This process happens three (maybe
> four?) times before the entire MapReduce job fails. It's often said that the
> definition of insanity is doing the same thing over and over and expecting
> different results.
> For all intents and purposes, the amount of resources requested by Mappers
> and Reducers is a fixed amount; based on the default configuration values.
> Users can set the memory on a per-job basis, but it's a pain, not exact, and
> requires intimate knowledge of the MapReduce framework and its memory usage
> patterns.
> I propose that if the MR ApplicationMaster detects that a container is killed
> because of this specific memory resource constraint, that it requests a
> larger container for the subsequent task attempt.
> For example, increase the requested memory size by 50% each time the
> container fails and the task is retried. This will prevent many Job failures
> and allow for additional memory tuning, per-Job, after the fact, to get
> better performance (v.s. fail/succeed).
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