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https://issues.apache.org/jira/browse/SPARK-20396?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16111554#comment-16111554
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Hyukjin Kwon commented on SPARK-20396:
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User 'icexelloss' has created a pull request for this issue:
https://github.com/apache/spark/pull/18732
> groupBy().apply() with pandas udf in pyspark
> --------------------------------------------
>
> Key: SPARK-20396
> URL: https://issues.apache.org/jira/browse/SPARK-20396
> Project: Spark
> Issue Type: New Feature
> Components: PySpark, SQL
> Affects Versions: 2.1.0
> Reporter: Li Jin
>
> split-apply-merge is a common pattern when analyzing data. It is implemented
> in many popular data analyzing libraries such as Spark, Pandas, R, and etc.
> Split and merge operations in these libraries are similar to each other,
> mostly implemented by certain grouping operators. For instance, Spark
> DataFrame has groupBy, Pandas DataFrame has groupby. Therefore, for users
> familiar with either Spark DataFrame or pandas DataFrame, it is not difficult
> for them to understand how grouping works in the other library. However,
> apply is more native to different libraries and therefore, quite different
> between libraries. A pandas user knows how to use apply to do curtain
> transformation in pandas might not know how to do the same using pyspark.
> Also, the current implementation of passing data from the java executor to
> python executor is not efficient, there is opportunity to speed it up using
> Apache Arrow. This feature can enable use cases that uses Spark's grouping
> operators such as groupBy, rollUp, cube, window and Pandas's native apply
> operator.
> Related work:
> SPARK-13534
> This enables faster data serialization between Pyspark and Pandas using
> Apache Arrow. Our work will be on top of this and use the same serialization
> for pandas udf.
> SPARK-12919 and SPARK-12922
> These implemented two functions: dapply and gapply in Spark R which
> implements the similar split-apply-merge pattern that we want to implement
> with Pyspark.
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