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https://issues.apache.org/jira/browse/SPARK-22239?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16410521#comment-16410521
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Herman van Hovell commented on SPARK-22239:
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Unbouded windows should be relatively straightforward. We buffer all the rows
in a window before we apply the function window functions. We could turn that
buffer into vectors and send it to python. There are two key issues here:
* Very large windows, we shouldn't pass these to python in one go.
* Nice python integration. We might need to create a python window frame
processor.
> User-defined window functions with pandas udf
> ---------------------------------------------
>
> Key: SPARK-22239
> URL: https://issues.apache.org/jira/browse/SPARK-22239
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.2.0
> Environment:
> Reporter: Li Jin
> Priority: Major
>
> Window function is another place we can benefit from vectored udf and add
> another useful function to the pandas_udf suite.
> Example usage (preliminary):
> {code:java}
> w = Window.partitionBy('id').orderBy('time').rangeBetween(-200, 0)
> @pandas_udf(DoubleType())
> def ema(v1):
> return v1.ewm(alpha=0.5).mean().iloc[-1]
> df.withColumn('v1_ema', ema(df.v1).over(window))
> {code}
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