Kris Mok created SPARK-22966:
--------------------------------
Summary: 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: 2.2.1, 2.2.0
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: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.
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