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From "Pi Song (JIRA)" <j...@apache.org>
Subject [jira] Commented: (PIG-171) Top K
Date Thu, 03 Apr 2008 14:02:24 GMT

    [ https://issues.apache.org/jira/browse/PIG-171?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12585114#action_12585114
] 

Pi Song commented on PIG-171:
-----------------------------

Small interesting bit here:-
- For TOPK, you keep K rows in first level aggregation.
- For LIMIT N to M, you keep M rows in first level aggregation.
- If M-N = 1 and M is really big, the amount of data kept for doing LIMIT can be ridiculously
too much.






> Top K
> -----
>
>                 Key: PIG-171
>                 URL: https://issues.apache.org/jira/browse/PIG-171
>             Project: Pig
>          Issue Type: New Feature
>            Reporter: Amir Youssefi
>            Assignee: Amir Youssefi
>
> Frequently, users are interested on Top results (especially Top K rows) . This can be
implemented efficiently in Pig /Map Reduce settings to deliver rapid results and low Network
Bandwidth/Memory usage.
>  
>  Key point is to prune all data on the map side and keep only small set of rows with
Top criteria . We can do it in Algebraic function (combiner) with multiple value output. Only
a small data-set gets out of mapper node.
> The same idea is applicable to solve variants of this problem:
>   - An Algebraic Function for 'Top K Rows'
>   - An Algebraic Function for 'Top K' values ('Top Rank K' and 'Top Dense Rank K')
>   - TOP K ORDER BY.
> Another words implementation is similar to combiners for aggregate functions but instead
of one value we get multiple ones. 
> I will add a sample implementation for Top K Rows and possibly TOP K ORDER BY to clarify
details.

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