[jira] [Resolved] (HIVE-5170) Sorted Bucketed Partitioned Insert hard-codes the reducer count == bucket count

["Gopal V (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HIVE-5170?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Gopal V resolved HIVE-5170.
---------------------------
    Resolution: Done

> Sorted Bucketed Partitioned Insert hard-codes the reducer count == bucket count
> -------------------------------------------------------------------------------
>
>                 Key: HIVE-5170
>                 URL: https://issues.apache.org/jira/browse/HIVE-5170
>             Project: Hive
>          Issue Type: Bug
>          Components: Query Processor
>    Affects Versions: 0.12.0
>         Environment: Ubuntu LXC
>            Reporter: Gopal V
>
> When performing a hive sorted-partitioned insert, the insert optimizer hard-codes the number of output files to the actual bucket count of the table.
> https://github.com/apache/hive/blob/trunk/ql/src/java/org/apache/hadoop/hive/ql/parse/SemanticAnalyzer.java#L4852
> We need at least that many reducers or if limited, switch to multi-spray (as implemented already), but more reducers is wasteful as long as the HiveKey only contains the partition columns.
> At this point, we're limited to reducers = n-bucket still, which is a problem for partitioning requests which need to insert nearly a terabyte of data into a single-digit bucket count and four-digit partition count.
> Since that is routed by the hasCode of the HiveKey, we can ensure that works by modifying the HiveKey to handle n-buckets internally.
> Basically it should only generate hashCode = (sort_cols.hashCode() % n) routing only to n reducers over-all, despite how many we spin up.
> So far so good with the hard-coded reducer count.
> But provided we fix the issues brought up by HIVE-5169, the insert becomes friendlier to a higher reducer count as well.
> At this juncture, we can modify the hashCode to be slightly more interesting.
> hashCode = (part_cols.hashCode()*31 + (sort_cols.hashCode() % n)) 
> This generates somewhere between n to partition_count * n unique hash-codes.
> Since the sort-order & bucketing has to be maintained per-partition dir, distributing this equally across any number of reducers will result in the scale-out of the reducer count.
> This will allow a reducer count that will allow for far faster inserts of ORC data into a partitioned/sorted table.



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