[jira] [Commented] (PIG-1846) optimize queries like - count distinct users for each gender

["Thejas M Nair (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/PIG-1846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13046964#comment-13046964 ] 

Thejas M Nair commented on PIG-1846:
------------------------------------

For the general case, where there is skew on the group-by keys, or the cardinality of the group-by keys  is very low compared to desired parallelism. The usual way of processing it -
{code}
gby = GROUP in BY (c1, c2) PARALLEL 100;
res = FOREACH gby GENERATE group.c1, group.c2, FUNC(distinct in.c3);
{code}

can be converted to - 
{code}
dist_f = FOREACH in GENERATE c1, c2, c3;
dist = DISTINCT dist_f PARALLEL 100;
dist_grp = GROUP dist by c1, c2;
res = FOREACH dist generate c1, c2, FUNC(c3); -- no distinct on c3 required here 
{code}


> optimize queries like - count distinct users for each gender
> ------------------------------------------------------------
>
>                 Key: PIG-1846
>                 URL: https://issues.apache.org/jira/browse/PIG-1846
>             Project: Pig
>          Issue Type: Improvement
>    Affects Versions: 0.9.0
>            Reporter: Thejas M Nair
>             Fix For: 0.10
>
>
> The pig group operation does not usually have to deal with skew on the group-by keys if the foreach statement that works on the results of group has only algebraic functions on the bags. But for some queries like the following, skew can be a problem -
> {code}
> user_data = load 'file' as (user, gender, age);
> user_group_gender = group user_data by gender parallel 100;
> dist_users_per_gender = foreach user_group_gender 
>                         { 
>                              dist_user = distinct user_data.user; 
>                              generate group as gender, COUNT(dist_user) as user_count;
>                         }
> {code}
> Since there are only 2 distinct values of the group-by key, only 2 reducers will actually get used in current implementation. ie, you can't get better performance by adding more reducers.
> Similar problem is there when the data is skewed on the group key. With current implementation, another problem is that pig and MR has to deal with records with extremely large bags that have the large number of distinct user names, which results in high memory utilization and having to spill the bags to disk.
> The query plan should be modified to handle the skew in such cases and make use of more reducers.

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