Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/20217#discussion_r161126125
--- Diff: python/pyspark/sql/context.py ---
@@ -203,18 +203,48 @@ def registerFunction(self, name, f,
returnType=StringType()):
>>> _ = sqlContext.udf.register("stringLengthInt", lambda x:
len(x), IntegerType())
>>> sqlContext.sql("SELECT stringLengthInt('test')").collect()
[Row(stringLengthInt(test)=4)]
+ """
+ return self.sparkSession.catalog.registerFunction(name, f,
returnType)
+
+ @ignore_unicode_prefix
+ @since(2.3)
+ def registerUDF(self, name, f):
+ """Registers a :class:`UserDefinedFunction`. The registered UDF
can be used in SQL
+ statements.
+
+ :param name: name of the UDF in SQL statements
+ :param f: a wrapped/native UserDefinedFunction. The UDF can be
either row-at-a-time or
+ scalar vectorized. For example, the object returned by
udf or pandas_udf.
+ Grouped vectorized UDFs are not supported.
+ :return: a wrapped :class:`UserDefinedFunction`
--- End diff --
nit: I think this can be `a wrapped/native UserDefinedFunction.`.
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