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From "Zheng Shao (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HIVE-503) improvement on distinct: distinguish distinct aggregate function from distinct
Date Fri, 22 May 2009 06:08:45 GMT

    [ https://issues.apache.org/jira/browse/HIVE-503?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12711961#action_12711961
] 

Zheng Shao commented on HIVE-503:
---------------------------------

Can you explain how to do it in one mapreduce job simultaneously?

Also, what does "distinct 1.OK and 2.Failed" mean? I don't think 2 will fail.

> improvement on distinct: distinguish distinct aggregate function from distinct
> ------------------------------------------------------------------------------
>
>                 Key: HIVE-503
>                 URL: https://issues.apache.org/jira/browse/HIVE-503
>             Project: Hadoop Hive
>          Issue Type: Improvement
>            Reporter: Min Zhou
>
> h4.distinct
> # OK
> {code:sql}
> select 
>    col
> from 
>   tbl
> {code}
> # FAILED
> {code:sql}
> select 
>    col1,
>    col2
> from 
>   tbl
> {code}
> h4.distinct aggregate function
> # OK
> {code:sql}
> select 
>    count(distinct col % 10)
> from 
>   tbl
> {code}
> # OK
> {code:sql}
> select 
>    count(distinct col1% 10)
>    count(distinct col1% 9)
> from 
>   tbl
> {code}
> # OK
> {code:sql}
> select 
>    count(distinct col1 % 10)
>    count(distinct col2 % 9)
> from 
>   tbl
> {code}
> # OK
> {code:sql}
> select 
>   sum(distinct col1 % 10),
>   count(distinct col2 % 9)
> from 
>   tbl
> {code}
> # OK
> {code:sql}
> select 
>   max(distinct substr(col1, 1, 10)),
>   count(distinct col2 % 9)
> from 
>   tbl
> {code}
> The keyword "distinct" ofen produce more than one results, so it's impossible removing
two different columns' duplicates in only one mapreduce job, so it failed.
> But the term "distinct aggregate function" with a form like aggregate_function(distinct
....),  is in connection with the term "all aggregate function",  it essentially is an aggregate
function. Only one result each aggregate function will produce,  it's very possible one mapreduce
job could deal with two or more different aggregate expression simultaneously.

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