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https://issues.apache.org/jira/browse/PIG-1846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12992119#comment-12992119
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Thejas M Nair commented on PIG-1846:
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One way to mitigate the problem of skew in above above example query is to add
another group-by statement which uses both gender and user as group-by key, and
does a partial aggregation. It will introduce and additional MR job. The 2nd MR
job will be effectively using only 2 reducers, but the work that needs to be
done in the reduce of the 2nd MR job will be very little.
{code}
USER_DATA = load 'file' as (USER, GENDER, AGE);
USER_GROUP_GENDER_PART = group USER_DATA by (GENDER, USER) parallel 100;
-- there is only one distinct user per row since the USER column is one of
group-by colums, so just project 1 as count
DIST_USER_PER_GENDER_PART = foreach USER_GROUP_GENDER_PART generate
group.GENDER as GENDER, 1 as USER_COUNT;
USER_GROUP_GENDER = group DIST_USER_PER_GENDER_PART by GENDER;
-- map-side combiner will do most of the work in parallel, reduce will need to
process few small records
DIST_USER_PER_GENDER = foreach USER_GROUP_GENDER generate GENDER,
SUM(USER_GROUP_GENDER.USER_COUNT);
{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
>
> 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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