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https://issues.apache.org/jira/browse/HIVE-223?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12671052#action_12671052
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Joydeep Sen Sarma commented on HIVE-223:
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the parameters make a lot of sense.
for #1 and #2:
> group by/sort by grouping + distinct key
assuming this means - group by grouping, sort by grouping + distinct.
#3 - this seems like the 'low cardinality, high skew case'. i think high skew
is not an issue in case of low cardinality. if we are dealing with a small
number of groups in the first place - map side aggregates should reduce the
data so much that skews wouldn't matter (ie. fall back to #1)
> when using map-side aggregates - perform single map-reduce group-by
> -------------------------------------------------------------------
>
> Key: HIVE-223
> URL: https://issues.apache.org/jira/browse/HIVE-223
> Project: Hadoop Hive
> Issue Type: Improvement
> Components: Query Processor
> Reporter: Joydeep Sen Sarma
> Assignee: Namit Jain
>
> today even when we do map side aggregates - we do multiple map-reduce jobs.
> however - the reason for doing multiple map-reduce group-bys (for single
> group-bys) was the fear of skews. When we are doing map side aggregates -
> skews should not exist for the most part. There can be two reason for skews:
> - large number of entries for a single grouping set - map side aggregates
> should take care of this
> - badness in hash function that sends too much stuff to one reducer - we
> should be able to take care of this by having good hash functions (and prime
> number reducer counts)
> So i think we should be able to do a single stage map-reduce when doing
> map-side aggregates.
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