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https://issues.apache.org/jira/browse/HADOOP-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12578622#action_12578622
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eric baldeschwieler commented on HADOOP-2429:
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owen, runping, I disagree completely with the assumptions from which you reach
your conclusions.
I think you are not looking at the total system costs. This design ends up
spewing java types everywhere. The fact of the matter is our average user is
not using java to code. Even in Java we end up doing gymnastics to support
standard types and it is making multi-language interoperability difficult and
non-standard. Types end up polluting all of our containers too. This makes
them non-interoperable.
And after paying all this cost, we still end up compromising the integrity of
the type based design, because one can not efficiently merge/sort if one must
instantiate objects to do it, so we require the writing of byte based
comparators. Yuck!
I'm interested in suggestions for a compromise design that raises other
languages to first order citizens in Hadoop and keeps us from having to do
gymnastics to use even native java types.
> The lowest level map-reduce APIs should be byte oriented
> --------------------------------------------------------
>
> Key: HADOOP-2429
> URL: https://issues.apache.org/jira/browse/HADOOP-2429
> Project: Hadoop Core
> Issue Type: Improvement
> Components: mapred
> Reporter: eric baldeschwieler
>
> As discussed here:
> https://issues.apache.org/jira/browse/HADOOP-1986#action_12551237
> The templates, serializers and other complexities that allow map-reduce to
> use arbitrary types complicate the design and lead to lots of object creates
> and other overhead that a byte oriented design would not suffer. I believe
> the lowest level implementation of hadoop map-reduce should have byte string
> oriented APIs (for keys and values). This API would be more performant,
> simpler and more easily cross language.
> The existing API could be maintained as a thin layer on top of the leaner API.
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