[jira] [Created] (SPARK-22966) Spark SQL should handle Python UDFs that return a datetime.date or datetime.datetime

["Kris Mok (JIRA)" <ji...@apache.org>]

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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