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Posted to issues@spark.apache.org by "Wenchen Fan (Jira)" <ji...@apache.org> on 2020/01/12 07:40:00 UTC
[jira] [Resolved] (SPARK-27296) Efficient User Defined Aggregators
[ https://issues.apache.org/jira/browse/SPARK-27296?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan resolved SPARK-27296.
---------------------------------
Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 25024
[https://github.com/apache/spark/pull/25024]
> Efficient User Defined Aggregators
> -----------------------------------
>
> Key: SPARK-27296
> URL: https://issues.apache.org/jira/browse/SPARK-27296
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL, Structured Streaming
> Affects Versions: 2.3.3, 2.4.0, 3.0.0
> Reporter: Erik Erlandson
> Assignee: Erik Erlandson
> Priority: Major
> Labels: performance, usability
> Fix For: 3.0.0
>
>
> Spark's UDAFs appear to be serializing and de-serializing to/from the MutableAggregationBuffer for each row. This gist shows a small reproducing UDAF and a spark shell session:
> [https://gist.github.com/erikerlandson/3c4d8c6345d1521d89e0d894a423046f]
> The UDAF and its compantion UDT are designed to count the number of times that ser/de is invoked for the aggregator. The spark shell session demonstrates that it is executing ser/de on every row of the data frame.
> Note, Spark's pre-defined aggregators do not have this problem, as they are based on an internal aggregating trait that does the correct thing and only calls ser/de at points such as partition boundaries, presenting final results, etc.
> This is a major problem for UDAFs, as it means that every UDAF is doing a massive amount of unnecessary work per row, including but not limited to Row object allocations. For a more realistic UDAF having its own non trivial internal structure it is obviously that much worse.
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