[jira] [Assigned] (SPARK-23011) Prepend missing grouping columns in groupby apply

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

     [ https://issues.apache.org/jira/browse/SPARK-23011?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-23011:
------------------------------------

    Assignee: Apache Spark

> Prepend missing grouping columns in groupby apply
> -------------------------------------------------
>
>                 Key: SPARK-23011
>                 URL: https://issues.apache.org/jira/browse/SPARK-23011
>             Project: Spark
>          Issue Type: Sub-task
>          Components: PySpark
>    Affects Versions: 2.3.0
>            Reporter: Li Jin
>            Assignee: Apache Spark
>
> The current semantics of groupby apply is that the output schema of groupby apply is the same as the output schema of the UDF. Because grouping column is usually useful to users, users often need to output grouping columns in the UDF. To further explain, consider the following example:
> {code:java}
> import statsmodels.api as sm
> # df has four columns: id, y, x1, x2
> group_column = 'id'
> y_column = 'y'
> x_columns = ['x1', 'x2']
> schema = df.select(group_column, *x_columns).schema
> @pandas_udf(schema, PandasUDFType.GROUP_MAP)
> # Input/output are both a pandas.DataFrame
> def ols(pdf):
>     group_key = pdf[group_column].iloc[0]
>     y = pdf[y_column]
>     X = pdf[x_columns]
>       X = sm.add_constant(X)
>     model = sm.OLS(y, X).fit()
>     return pd.DataFrame([[group_key] + [model.params[i] for i in   x_columns]], columns=[group_column] + x_columns)
> beta = df.groupby(group_column).apply(ols)
> {code}
> Although the UDF (linear regression) has nothing to do with the grouping column, the user needs to deal with grouping column in the UDF. In other words, the UDF is tightly coupled with the grouping column.
> Here I propose we prepend grouping columns that are not returned by the UDF to the result of groupby apply. With this change, users can write UDFs that are decoupled from the grouping column:
> {code:java}
> import statsmodels.api as sm
> # df has four columns: id, y, x1, x2
> group_column = 'id'
> y_column = 'y'
> x_columns = ['x1', 'x2']
> schema = df.select(*x_columns).schema
> @pandas_udf(schema, PandasUDFType.GROUP_MAP)
> # Input/output are both a pandas.DataFrame
> def ols(pdf):
>     y = pdf[y_column]
>     X = pdf[x_columns]
>       X = sm.add_constant(X)
>     model = sm.OLS(y, X).fit()
>     return pd.DataFrame([[model.params[i] for i in   x_columns]], columns=x_columns)
> beta = df.groupby(group_column).apply(ols)
> {code}



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