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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/02/18 03:49:00 UTC
[jira] [Resolved] (SPARK-26887) Create datetime.date directly
instead of creating datetime64[ns] as intermediate data.
[ https://issues.apache.org/jira/browse/SPARK-26887?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-26887.
----------------------------------
Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 23795
[https://github.com/apache/spark/pull/23795]
> Create datetime.date directly instead of creating datetime64[ns] as intermediate data.
> --------------------------------------------------------------------------------------
>
> Key: SPARK-26887
> URL: https://issues.apache.org/jira/browse/SPARK-26887
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.4.0
> Reporter: Takuya Ueshin
> Assignee: Takuya Ueshin
> Priority: Major
> Fix For: 3.0.0
>
>
> Currently {{DataFrame.toPandas()}} with arrow enabled or {{ArrowStreamPandasSerializer}} for pandas UDF with pyarrow<0.12 creates {{datetime64[ns]}} type series as intermediate data and then convert to {{datetime.date}} series, but the intermediate {{datetime64[ns]}} might cause an overflow even if the date is valid.
> {noformat}
> >>> import datetime
> >>>
> >>> t = [datetime.date(2262, 4, 12), datetime.date(2263, 4, 12)]
> >>>
> >>> df = spark.createDataFrame(t, 'date')
> >>> df.show()
> +----------+
> | value|
> +----------+
> |2262-04-12|
> |2263-04-12|
> +----------+
> >>>
> >>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
> >>>
> >>> df.toPandas()
> value
> 0 1677-09-21
> 1 1678-09-21
> {noformat}
> We should avoid creating such intermediate data and create {{datetime.date}} series directly instead.
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