Github user vlad17 commented on a diff in the pull request:
https://github.com/apache/spark/pull/13778#discussion_r67894162
--- Diff: python/pyspark/sql/tests.py ---
@@ -558,6 +558,18 @@ def check_datatype(datatype):
_verify_type(PythonOnlyPoint(1.0, 2.0), PythonOnlyUDT())
self.assertRaises(ValueError, lambda: _verify_type([1.0, 2.0],
PythonOnlyUDT()))
+ schema = StructType().add("key", LongType()).add("val",
PythonOnlyUDT())
+ df = self.spark.createDataFrame(
+ [(i % 3, PythonOnlyPoint(float(i), float(i))) for i in
range(10)],
+ schema=schema)
+ df.show()
--- End diff --
`DataFrame.show()` gives unnecessary stringification, so this test ends up
testing unnecessary stuff (in fact it would fail if the UDT didn't have
`__str__`. I would use `collect()` to force materialization instead.
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