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From "Hyukjin Kwon (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-25599) Stateful aggregation in PySpark
Date Wed, 21 Nov 2018 10:13:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-25599?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Hyukjin Kwon resolved SPARK-25599.
----------------------------------
    Resolution: Duplicate

> Stateful aggregation in PySpark
> -------------------------------
>
>                 Key: SPARK-25599
>                 URL: https://issues.apache.org/jira/browse/SPARK-25599
>             Project: Spark
>          Issue Type: New Feature
>          Components: PySpark
>    Affects Versions: 2.3.0
>            Reporter: Vincent Grosbois
>            Priority: Minor
>
> Hi!
>  
> From PySpark, I am trying to define a custom aggregator *that is accumulating state*.
Is it possible in Spark 2.3 ?
> AFAIK, it is now possible to define a custom UDAF in PySpark since Spark 2.3 (cf [How
to define and use a User-Defined Aggregate Function in Spark SQL?|https://stackoverflow.com/questions/32100973/how-to-define-and-use-a-user-defined-aggregate-function-in-spark-sql]),
by calling {{pandas_udf}} with the {{PandasUDFType.GROUPED_AGG}} keyword.
> However given that it is just taking a function as a parameter I don't think it is possible
to carry state around during the aggregation with this function.
> From Scala, I see it is possible to have stateful aggregation by either extending {{UserDefinedAggregateFunction}}
or {{org.apache.spark.sql.expressions.Aggregator}} , but is there a similar thing I can do
on python-side only?
> If no, is this planned in a future release?
> thanks!



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