Github user zero323 commented on a diff in the pull request:
https://github.com/apache/spark/pull/16537#discussion_r95681106
--- Diff: python/pyspark/sql/tests.py ---
@@ -429,6 +429,11 @@ def test_udf_with_input_file_name(self):
row =
self.spark.read.json(filePath).select(sourceFile(input_file_name())).first()
self.assertTrue(row[0].find("people1.json") != -1)
+ def test_udf_should_validate_input_args(self):
+ from pyspark.sql.functions import udf
+
+ self.assertRaises(TypeError, udf(lambda x: x), None)
--- End diff --
It is pretty well covered by existing `udf` tests. The more the merrier but
I am not sure what can be added with duplicating other test cases.
Do you think we should try to some type validation of the number of
arguments?
Pros:
- It is easy to implement with `inspect` or `func.__code__` for plain
Python objects.
- It is nice to fail without starting a complex job.
Cons:
- It most likely won't work well for C extensions and such.
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