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https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13092522#comment-13092522
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Binglin Chang commented on MAPREDUCE-2841:
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bq. The goal of the last few implementations was a predictable memory
footprint.
Predictable memory foot print is very important, I strongly agree.
Observations of our cluster's memory status shows that most OOM or high memory
consumption
cases are caused by big Key/Value in InputReader, Key/Value writable and merge.
Sort&spill
is much more stable.
I think in many cases predictable memory control is enough, rather than precise
memory control,
since it's impractical. We can use some dynamic memory if it is in a predicable
range,
for example +/-20%, +-30%, etc.
Just an idea, what if memory related configurations can be a random variable,
with mean & variance? Can this leads to better resource utilization? A fixed
memory bound
always means application will request more memory than they really need.
> Task level native optimization
> ------------------------------
>
> Key: MAPREDUCE-2841
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-2841
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Components: task
> Environment: x86-64 Linux
> Reporter: Binglin Chang
> Assignee: Binglin Chang
> Attachments: MAPREDUCE-2841.v1.patch, dualpivot-0.patch,
> dualpivotv20-0.patch
>
>
> I'm recently working on native optimization for MapTask based on JNI.
> The basic idea is that, add a NativeMapOutputCollector to handle k/v pairs
> emitted by mapper, therefore sort, spill, IFile serialization can all be done
> in native code, preliminary test(on Xeon E5410, jdk6u24) showed promising
> results:
> 1. Sort is about 3x-10x as fast as java(only binary string compare is
> supported)
> 2. IFile serialization speed is about 3x of java, about 500MB/s, if hardware
> CRC32C is used, things can get much faster(1G/s).
> 3. Merge code is not completed yet, so the test use enough io.sort.mb to
> prevent mid-spill
> This leads to a total speed up of 2x~3x for the whole MapTask, if
> IdentityMapper(mapper does nothing) is used.
> There are limitations of course, currently only Text and BytesWritable is
> supported, and I have not think through many things right now, such as how to
> support map side combine. I had some discussion with somebody familiar with
> hive, it seems that these limitations won't be much problem for Hive to
> benefit from those optimizations, at least. Advices or discussions about
> improving compatibility are most welcome:)
> Currently NativeMapOutputCollector has a static method called canEnable(),
> which checks if key/value type, comparator type, combiner are all compatible,
> then MapTask can choose to enable NativeMapOutputCollector.
> This is only a preliminary test, more work need to be done. I expect better
> final results, and I believe similar optimization can be adopt to reduce task
> and shuffle too.
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