[jira] [Assigned] (SPARK-28006) User-defined grouped transform pandas_udf for window operations

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-28006?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-28006:
------------------------------------

    Assignee: Apache Spark

> User-defined grouped transform pandas_udf for window operations
> ---------------------------------------------------------------
>
>                 Key: SPARK-28006
>                 URL: https://issues.apache.org/jira/browse/SPARK-28006
>             Project: Spark
>          Issue Type: New Feature
>          Components: PySpark
>    Affects Versions: 2.4.3
>            Reporter: Li Jin
>            Assignee: Apache Spark
>            Priority: Major
>
> Currently, pandas_udf supports "grouped aggregate" type that can be used with unbounded and unbounded windows. There is another set of use cases that can benefit from a "grouped transform" type pandas_udf.
> Grouped transform is defined as a N -> N mapping over a group. For example, "compute zscore for values in the group using the grouped mean and grouped stdev", or "rank the values in the group".
> Currently, in order to do this, user needs to use "grouped apply", for example:
> {code:java}
> @pandas_udf(schema, GROUPED_MAP)
> def subtract_mean(pdf)
>     v = pdf['v']
>     pdf['v'] = v - v.mean()
>     return pdf
> df.groupby('id').apply(subtract_mean)
> # +---+----+
> # | id|   v|
> # +---+----+
> # |  1|-0.5|
> # |  1| 0.5|
> # |  2|-3.0|
> # |  2|-1.0|
> # |  2| 4.0|
> # +---+----+{code}
> This approach has a few downside:
>  * Specifying the full return schema is complicated for the user although the function only changes one column.
>  * The column name 'v' inside as part of the udf, makes the udf less reusable.
>  * The entire dataframe is serialized to pass to Python although only one column is needed.
> Here we propose a new type of pandas_udf to work with these types of use cases:
> {code:java}
> df = spark.createDataFrame(
>     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
>     ("id", "v"))
> @pandas_udf('double', GROUPED_XFORM)
> def subtract_mean(v):
>     return v - v.mean()
> w = Window.partitionBy('id')
> df = df.withColumn('v', subtract_mean(df['v']).over(w))
> # +---+----+
> # | id|   v|
> # +---+----+
> # |  1|-0.5|
> # |  1| 0.5|
> # |  2|-3.0|
> # |  2|-1.0|
> # |  2| 4.0|
> # +---+----+{code}
> Which addresses the above downsides.
>  * The user only needs to specify the output type of a single column.
>  * The column being zscored is decoupled from the udf implementation
>  * We only need to send one column to Python worker and concat the result with the original dataframe (this is what grouped aggregate is doing already)
>  
>  



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