[jira] [Closed] (SPARK-12774) DataFrame.mapPartitions apply function operates on Pandas DataFrame instead of a generator or rows

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

     [ https://issues.apache.org/jira/browse/SPARK-12774?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

holdenk closed SPARK-12774.
---------------------------
    Resolution: Won't Fix

In some ways yes avoiding unecessary iteration can be good, but allowing Spark to spill is also important. That being said - map and map partitions have been temporarily removed from DataFrame while the dataset API is sorted out in Python so I don't think this is likely to get in (although maybe worth being involved in the new dataframe API discussions if you are interested.

> DataFrame.mapPartitions apply function operates on Pandas DataFrame instead of a generator or rows
> --------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-12774
>                 URL: https://issues.apache.org/jira/browse/SPARK-12774
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark
>            Reporter: Josh
>              Labels: dataframe, mapPartitions, pandas
>
> Currently DataFrame.mapPatitions is analogous to DataFrame.rdd.mapPatitions in both Spark and pySpark. The function that is applied to each partition _f_ must operate on a list generator. This is however very inefficient in Python. It would be more logical and efficient if the apply function _f_  operated on Pandas DataFrames instead and also returned a DataFrame. This avoids unnecessary iteration in Python which is slow.
> Currently:
> {code}
> def apply_function(rows):
>     df = pd.DataFrame(list(rows))
>     df = df % 100   # Do something on df
>     return df.values.tolist()
> table = sqlContext.read.parquet("")
> table = table.mapPatitions(apply_function)
> {code}
> New apply function would accept a Pandas DataFrame and return a DataFrame:
> {code}
> def apply_function(df):
>     df = df % 100   # Do something on df
>     return df
> {code}



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