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