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From "Pi Song (JIRA)" <j...@apache.org>
Subject [jira] Commented: (PIG-171) Top K
Date Wed, 28 May 2008 15:59:45 GMT

    [ https://issues.apache.org/jira/browse/PIG-171?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12600517#action_12600517
] 

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

So, by implementation we always calculate n-tiles before doing real sort, right? If say we
are interested in Top 1000 out of 10000 inputs, that means we can discard all the records
below 90th percentile in the map phase.

However, in order to do this, we will need a deterministic way to calculate n-tiles (The current
way is apparently probabilistic).

> 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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