Josh created SPARK-12774:
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Summary: 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
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:python}
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:python}
def apply_function(df):
df = df % 100 # Do something on df
return df
{code}
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