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https://issues.apache.org/jira/browse/HIVE-7989?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ankit Kamboj reopened HIVE-7989:
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> Optimize Windowing function performance for row frames
> ------------------------------------------------------
>
> Key: HIVE-7989
> URL: https://issues.apache.org/jira/browse/HIVE-7989
> Project: Hive
> Issue Type: Improvement
> Components: PTF-Windowing
> Affects Versions: 0.13.0
> Reporter: Ankit Kamboj
>
> To find aggregate value for each row, current windowing function
> implementation creates a new aggregation buffer for each row, iterates over
> all the rows in respective window frame, puts them in buffer and then finds
> the aggregated value. This causes bottleneck for partitions with huge number
> of rows because this process runs in n-square complexity (n being rows in a
> partition) for each partition. So, if there are multiple partitions in a
> dataset, each with millions of rows, aggregation for all rows will take days
> to finish.
> There is scope of optimization for row frames, for following cases:
> a) For UNBOUNDED PRECEDING start and bounded end: Instead of iterating on
> window frame again for each row, we can slide the end one row at a time and
> aggregate, since we know the start is fixed for each row. This will have
> running time linear to the size of partition (O(n)).
> b) For bounded start and UNBOUNDED FOLLOWING end: Instead of iterating on
> window frame again for each row, we can slide the start one row at a time and
> aggregate in reverse, since we know the end is fixed for each row. This will
> have running time linear to the size of partition (O(n)).
> Also, In general for both row and value frames, we don't need to iterate over
> the range and re-create aggregation buffer if the start as well as end remain
> same. Instead, can re-use the previously created aggregation buffer.
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