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Posted to issues@spark.apache.org by "Dongjoon Hyun (Jira)" <ji...@apache.org> on 2020/03/16 22:52:07 UTC
[jira] [Updated] (SPARK-29040) Support pyspark.createDataFrame from
a pyarrow.Table
[ https://issues.apache.org/jira/browse/SPARK-29040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun updated SPARK-29040:
----------------------------------
Affects Version/s: (was: 3.0.0)
3.1.0
> Support pyspark.createDataFrame from a pyarrow.Table
> ----------------------------------------------------
>
> Key: SPARK-29040
> URL: https://issues.apache.org/jira/browse/SPARK-29040
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 3.1.0
> Reporter: Bryan Cutler
> Priority: Major
>
> PySpark {{createDataFrame}} currently supports creating a spark DataFrame from Pandas, using Arrow if enabled. This could be extended to accept a {{pyarrow.Table}} which has the added benefit of being able to efficiently use columns with nested struct types.
> It is possible to convert a pyarrow.Table with nested columns into a pandas.DataFrame, but the data becomes dictionaries, and is not a performant way to parallelize the data.
> Time/Date columns would need to be handled specially, since pyspark currently uses pandas to convert Arrow data of these types to the required Spark internal format.
> This follows from a mailing list discussion at http://apache-spark-user-list.1001560.n3.nabble.com/question-about-pyarrow-Table-to-pyspark-DataFrame-conversion-td36110.html
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