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https://issues.apache.org/jira/browse/HIVE-5170?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13774655#comment-13774655
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Gopal V commented on HIVE-5170:
-------------------------------
Tried to do this, unfortunately the FileSinkOperator uses the task-id as the
bucket filename.
So if you have 12 reducers, the last reducer will automatically write it to
00011_0.
This makes it slightly more complex to fix this without writing a new
SortedFileSinkOperator.
> 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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