[jira] Commented: (HIVE-223) when using map-side aggregates - perform single map-reduce group-by

["Joydeep Sen Sarma (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HIVE-223?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12678073#action_12678073 ] 

Joydeep Sen Sarma commented on HIVE-223:
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+      // In case merge is called without an iterate, the 
+      if (pos == -1) {
+        mSum += Double.parseDouble(o);
+        mCount ++;
+      } 

assuming this happens when iterate is called with null - is it correct to include the null entry in the count?

I did some search on web (for ex: http://www.databasejournal.com/features/mssql/article.php/3399931/Working-With-Columns-That-Contain-Null-Values.htm) - it suggests that AVG definition skips nulls ..

anyway - this is inconsistent with the way the iterate is coded (the iterate doesn't bump count for nulls)

> 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
>         Attachments: 223.2.txt, 223.3.txt, 223.patch1.txt, patch.txt
>
>
> 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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