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https://issues.apache.org/jira/browse/PIG-4601?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15149022#comment-15149022
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Mohit Sabharwal commented on PIG-4601:
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+1 (non-binding), sorry about the delay reviewing the updated patch.
> Implement Merge CoGroup for Spark engine
> ----------------------------------------
>
> Key: PIG-4601
> URL: https://issues.apache.org/jira/browse/PIG-4601
> Project: Pig
> Issue Type: Sub-task
> Components: spark
> Affects Versions: spark-branch
> Reporter: Mohit Sabharwal
> Assignee: liyunzhang_intel
> Fix For: spark-branch
>
> Attachments: PIG-4601_1.patch, PIG-4601_2.patch
>
>
> When doing a cogroup operation, we need do a map-reduce. The target of merge
> cogroup is implementing cogroup only by a single stage(map). But we need to
> guarantee the input data are sorted.
> There is performance improvement for cases when A(big dataset) merge cogroup
> B( small dataset) because we first generate an index file of A then loading A
> according to the index file and B into memory to do cogroup. The performance
> improves because there is no cost of reduce period comparing cogroup.
> How to use
> {code}
> C = cogroup A by c1, B by c1 using 'merge';
> {code}
> Here A and B is sorted.
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