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https://issues.apache.org/jira/browse/HIVE-562?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12719842#action_12719842
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Namit Jain commented on HIVE-562:
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It can be handled in a couple of ways -
1. create another level of indirection, and then swap those units
independently. For eg, instead of storing 'N' values with a key 'k', store that
'k' maps to 'k1', 'k2'.....'kn'
and then read all keys when needed.
2. the query is rewritten to a bunch of independent queries - at some layer. So
instead of joining L and S:
a. S is broken into S1..Sn
b. Join L with S1..Sn
c. Merge the above results.
This can be done by the compiler or at the query specification time.
> join does not work well if there is a very large skew in keys
> -------------------------------------------------------------
>
> Key: HIVE-562
> URL: https://issues.apache.org/jira/browse/HIVE-562
> Project: Hadoop Hive
> Issue Type: Improvement
> Components: Query Processor
> Affects Versions: 0.4.0
> Reporter: Namit Jain
>
> Only the last table is streamed in case of regular joins.
> So, for any other table, or for any small table (in case of map-joins), if
> the number of values for a given key are very large, it does not scale.
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