[jira] [Updated] (SPARK-7712) Window Function Improvements

["Herman van Hovell (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7712?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Herman van Hovell updated SPARK-7712:
-------------------------------------
    Summary: Window Function Improvements  (was: Native Spark Window Functions & Performance Improvements )

> Window Function Improvements
> ----------------------------
>
>                 Key: SPARK-7712
>                 URL: https://issues.apache.org/jira/browse/SPARK-7712
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 1.4.0
>            Reporter: Herman van Hovell
>            Priority: Critical
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Hi All,
> After playing with the current spark window implementation, I tried to take this to next level. My main goal is/was to address the following issues: Native Spark SQL & Performance.
> *Native Spark SQL*
> The current implementation uses Hive UDAFs as its aggregation mechanism. We try to address the following issues  by moving to a more 'native' Spark SQL approach:
> - Window functions require Hive. Some people (mostly by accident) use Spark SQL without Hive. Usage of UDAFs is still supported though.
> - Adding your own Aggregates requires you to write them in Hive instead of native Spark SQL.
> - Hive UDAFs are very well written and quite quick, but they are opaque in processing and memory management; this makes them hard to optimize. By using 'Native' Spark SQL constructs we can actually do alot more optimization, for example AggregateEvaluation style Window processing (this would require us to move some of the code out of the AggregateEvaluation class into some Common base class), or Tungten style memory management.
> *Performance*
> - Much better performance (10x) in running cases (e.g. BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) and UNBOUDED FOLLOWING cases. The current implementation in spark uses a sliding window approach in these cases. This means that an aggregate is maintained for every row, so space usage is N (N being the number of rows). This also means that all these aggregates all need to be updated separately, this takes N*(N-1)/2 updates. The running case differs from the Sliding case because we are only adding data to an aggregate function (no reset is required), we only need to maintain one aggregate (like in the UNBOUNDED PRECEDING AND UNBOUNDED case), update the aggregate for each row, and get the aggregate value after each update. This is what the new implementation does. This approach only uses 1 buffer, and only requires N updates; I am currently working on data with window sizes of 500-1000 doing running sums and this saves a lot of time. The CURRENT ROW AND UNBOUNDED FOLLOWING case also uses this approach and the fact that aggregate operations are communitative, there is one twist though it will process the input buffer in reverse.
> - Fewer comparisons in the sliding case. The current implementation determines frame boundaries for every input row. The new implementation makes more use of the fact that the window is sorted, maintains the boundaries, and only moves them when the current row order changes. This is a minor improvement.
> - A single Window node is able to process all types of Frames for the same Partitioning/Ordering. This saves a little time/memory spent buffering and managing partitions.
> - A lot of the staging code is moved from the execution phase to the initialization phase. Minor performance improvement, and improves readability of the execution code.
> The original work including some benchmarking code for the running case can be here: https://github.com/hvanhovell/spark-window
> A PR has been created, this is still work in progress, and can be found here: https://github.com/apache/spark/pull/6278
> Comments, feedback and other discussion is much appreciated.



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