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https://issues.apache.org/jira/browse/MAPREDUCE-6712?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15326214#comment-15326214
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He Tianyi commented on MAPREDUCE-6712:
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Hi, [~templedf]. Thanks for these ideas.
I think the null key solution meets the need. And this is particularly possible
in a customized environment, since application framework can be specialized to
do this (whatever language supported internally), but may be not worth it for a
general platform.
Moving to pyspark is certainly a better solution for these advantages that a
higher level abstraction and Spark computing model brings. However, there is
still a IPC overhead unless we move to a jvm-based language either. Any
suggestions?
> Support grouping values for reducer on java-side
> ------------------------------------------------
>
> Key: MAPREDUCE-6712
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-6712
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Components: contrib/streaming
> Reporter: He Tianyi
> Priority: Minor
>
> In hadoop streaming, with TextInputWriter, reducer program will receive each
> line representing a (k, v) tuple from {{stdin}}, in which values with
> identical key is not grouped.
> This brings some inefficiency, especially for runtimes based on interpreter
> (e.g. cpython), coming from:
> A. user program has to compare key with previous one (but on java side,
> records already come to reducer in groups),
> B. user program has to perform {{read}}, then {{find}} or {{split}} on each
> record. even if there are multiple values with identical key,
> C. if length of key is large, apparently this introduces inefficiency for
> caching,
> Suppose we need another InputWriter. But this is not enough, since the
> interface of {{InputWriter}} defined {{writeKey}} and {{writeValue}}, not
> {{writeValues}}. Though we can compare key in custom InputWriter and group
> them, but this is also inefficient. Some other changes are also needed.
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