Github user ueshin commented on a diff in the pull request:
https://github.com/apache/spark/pull/20217#discussion_r160872774
--- Diff: python/pyspark/sql/catalog.py ---
@@ -255,26 +255,67 @@ def registerFunction(self, name, f,
returnType=StringType()):
>>> _ = spark.udf.register("stringLengthInt", len, IntegerType())
>>> spark.sql("SELECT stringLengthInt('test')").collect()
[Row(stringLengthInt(test)=4)]
+ """
+
+ # This is to check whether the input function is a wrapped/native
UserDefinedFunction
+ if hasattr(f, 'asNondeterministic'):
+ raise ValueError("Please use registerUDF for registering UDF.
The expected function of "
+ "registerFunction is a Python function
(including lambda function)")
+ udf = UserDefinedFunction(f, returnType=returnType, name=name,
+ evalType=PythonEvalType.SQL_BATCHED_UDF)
+ self._jsparkSession.udf().registerPython(name, udf._judf)
+ return udf._wrapped()
+
+ @ignore_unicode_prefix
+ @since(2.3)
+ def registerUDF(self, name, f):
+ """Registers a :class:`UserDefinedFunction`. The registered UDF
can be used in SQL
+ statement.
+
+ :param name: name of the UDF
+ :param f: a wrapped/native UserDefinedFunction. The UDF can be
either row-at-a-time or
+ scalar vectorized. Grouped vectorized UDFs are not
supported.
+ :return: a wrapped :class:`UserDefinedFunction`
+
+ >>> from pyspark.sql.types import IntegerType
+ >>> from pyspark.sql.functions import udf
+ >>> slen = udf(lambda s: len(s), IntegerType())
+ >>> _ = spark.udf.registerUDF("slen", slen)
+ >>> spark.sql("SELECT slen('test')").collect()
+ [Row(slen(test)=4)]
>>> import random
>>> from pyspark.sql.functions import udf
- >>> from pyspark.sql.types import IntegerType, StringType
+ >>> from pyspark.sql.types import IntegerType
>>> random_udf = udf(lambda: random.randint(0, 100),
IntegerType()).asNondeterministic()
- >>> newRandom_udf = spark.catalog.registerFunction("random_udf",
random_udf, StringType())
+ >>> newRandom_udf = spark.catalog.registerUDF("random_udf",
random_udf)
>>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP
- [Row(random_udf()=u'82')]
+ [Row(random_udf()=82)]
>>> spark.range(1).select(newRandom_udf()).collect() # doctest:
+SKIP
- [Row(random_udf()=u'62')]
+ [Row(random_udf()=62)]
+
+ >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
+ >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP
+ ... def add_one(x):
+ ... return x + 1
+ ...
+ >>> _ = spark.udf.registerUDF("add_one", add_one) # doctest: +SKIP
+ >>> spark.sql("SELECT add_one(id) FROM range(10)").collect() #
doctest: +SKIP
--- End diff --
Although this will be skipped, we should show the result like
`[Row(add_one(id)=1), Row(add_one(id)=2), ...]`.
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