try

Author: gatorsmile

python/pyspark/sql/catalog.py

@@ -270,8 +270,8 @@ def registerFunction(self, name, f, returnType=StringType()):
 
         if hasattr(f, 'asNondeterministic'):
             udf = UserDefinedFunction(f.func, returnType=returnType, name=name,
-                                      evalType=PythonEvalType.SQL_BATCHED_UDF)
-            udf = udf if (f._deterministic) else udf.asNondeterministic()
+                                      evalType=PythonEvalType.SQL_BATCHED_UDF,
+                                      deterministic=f.deterministic)
         else:
             udf = UserDefinedFunction(f, returnType=returnType, name=name,
                                       evalType=PythonEvalType.SQL_BATCHED_UDF)

python/pyspark/sql/tests.py

@@ -382,15 +382,18 @@ def test_non_deterministic_udf(self):
         import random
         from pyspark.sql.functions import udf
         random_udf = udf(lambda: random.randint(6, 6), IntegerType()).asNondeterministic()
-        self.assertEqual(random_udf._deterministic, False)
+        self.assertEqual(random_udf.deterministic, False)
         random_udf1 = self.spark.catalog.registerFunction("randInt", random_udf, StringType())
-        self.assertEqual(random_udf1._deterministic, False)
+        self.assertEqual(random_udf1.deterministic, False)
         [row] = self.spark.sql("SELECT randInt()").collect()
         self.assertEqual(row[0], "6")
         [row] = self.spark.range(1).select(random_udf1()).collect()
         self.assertEqual(row[0], "6")
         [row] = self.spark.range(1).select(random_udf()).collect()
         self.assertEqual(row[0], 6)
+        pydoc.render_doc(lambda: random.randint(6, 6), IntegerType())
+        pydoc.render_doc(random_udf)
+        pydoc.render_doc(random_udf1)
 
     def test_chained_udf(self):
         self.spark.catalog.registerFunction("double", lambda x: x + x, IntegerType())