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Posted to issues@spark.apache.org by "Kris Mok (JIRA)" <ji...@apache.org> on 2018/01/05 01:08:00 UTC
[jira] [Updated] (SPARK-22966) Spark SQL should handle Python UDFs
that return a datetime.date or datetime.datetime
[ https://issues.apache.org/jira/browse/SPARK-22966?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Kris Mok updated SPARK-22966:
-----------------------------
Description:
Currently, in Spark SQL, if a Python UDF returns a {{datetime.date}} (which should correspond to a Spark SQL {{date}} type) or {{datetime.datetime}} (which should correspond to a Spark SQL {{timestamp}} type), it gets unpickled into a {{java.util.Calendar}} which Spark SQL doesn't understand internally, and will thus give incorrect results.
e.g.
{code:none}
>>> import datetime
>>> from pyspark.sql import *
>>> py_date = udf(datetime.date)
>>> spark.range(1).select(py_date(lit(2017), lit(10), lit(30)) == lit(datetime.date(2017, 10, 30))).show()
+----------------------------------------+
|(date(2017, 10, 30) = DATE '2017-10-30')|
+----------------------------------------+
| false|
+----------------------------------------+
{code}
(changing the definition of {{py_date}} from {{udf(date)}} to {{udf(date, 'date')}} doesn't work either)
We should correctly handle Python UDFs that return objects of such types.
was:
Currently, in Spark SQL, if a Python UDF returns a {{datetime.date}} (which should correspond to a Spark SQL {{date}} type) or {{datetime.datetime}} (which should correspond to a Spark SQL {{timestamp}} type), it gets unpickled into a {{java.util.Calendar}} which Spark SQL doesn't understand internally, and will thus give incorrect results.
e.g.
{code:python}
>>> import datetime
>>> from pyspark.sql import *
>>> py_date = udf(datetime.date)
>>> spark.range(1).select(py_date(lit(2017), lit(10), lit(30)) == lit(datetime.date(2017, 10, 30))).show()
+----------------------------------------+
|(date(2017, 10, 30) = DATE '2017-10-30')|
+----------------------------------------+
| false|
+----------------------------------------+
{code}
(changing the definition of {{py_date}} from {{udf(date)}} to {{udf(date, 'date')}} doesn't work either)
We should correctly handle Python UDFs that return objects of such types.
> Spark SQL should handle Python UDFs that return a datetime.date or datetime.datetime
> ------------------------------------------------------------------------------------
>
> Key: SPARK-22966
> URL: https://issues.apache.org/jira/browse/SPARK-22966
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.2.0, 2.2.1
> Reporter: Kris Mok
>
> Currently, in Spark SQL, if a Python UDF returns a {{datetime.date}} (which should correspond to a Spark SQL {{date}} type) or {{datetime.datetime}} (which should correspond to a Spark SQL {{timestamp}} type), it gets unpickled into a {{java.util.Calendar}} which Spark SQL doesn't understand internally, and will thus give incorrect results.
> e.g.
> {code:none}
> >>> import datetime
> >>> from pyspark.sql import *
> >>> py_date = udf(datetime.date)
> >>> spark.range(1).select(py_date(lit(2017), lit(10), lit(30)) == lit(datetime.date(2017, 10, 30))).show()
> +----------------------------------------+
> |(date(2017, 10, 30) = DATE '2017-10-30')|
> +----------------------------------------+
> | false|
> +----------------------------------------+
> {code}
> (changing the definition of {{py_date}} from {{udf(date)}} to {{udf(date, 'date')}} doesn't work either)
> We should correctly handle Python UDFs that return objects of such types.
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