[jira] [Updated] (PIG-4601) Implement Merge CoGroup for Spark engine

["liyunzhang_intel (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/PIG-4601?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

liyunzhang_intel updated PIG-4601:
----------------------------------
    Status: Patch Available  (was: Open)

> 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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