spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Josh (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-12774) DataFrame.mapPartitions apply function operates on Pandas DataFrame instead of a generator or rows
Date Tue, 12 Jan 2016 10:12:39 GMT
Josh created SPARK-12774:
----------------------------

             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}



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message