Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/20288#discussion_r161964247
--- Diff: python/pyspark/sql/catalog.py ---
@@ -224,92 +225,18 @@ def dropGlobalTempView(self, viewName):
"""
self._jcatalog.dropGlobalTempView(viewName)
- @ignore_unicode_prefix
- @since(2.0)
def registerFunction(self, name, f, returnType=None):
- """Registers a Python function (including lambda function) or a
:class:`UserDefinedFunction`
- as a UDF. The registered UDF can be used in SQL statements.
-
- :func:`spark.udf.register` is an alias for
:func:`spark.catalog.registerFunction`.
-
- In addition to a name and the function itself, `returnType` can be
optionally specified.
- 1) When f is a Python function, `returnType` defaults to a string.
The produced object must
- match the specified type. 2) When f is a
:class:`UserDefinedFunction`, Spark uses the return
- type of the given UDF as the return type of the registered UDF.
The input parameter
- `returnType` is None by default. If given by users, the value must
be None.
-
- :param name: name of the UDF in SQL statements.
- :param f: a Python function, or a wrapped/native
UserDefinedFunction. The UDF can be either
- row-at-a-time or vectorized.
- :param returnType: the return type of the registered UDF.
- :return: a wrapped/native :class:`UserDefinedFunction`
-
- >>> strlen = spark.catalog.registerFunction("stringLengthString",
len)
- >>> spark.sql("SELECT stringLengthString('test')").collect()
- [Row(stringLengthString(test)=u'4')]
-
- >>> spark.sql("SELECT 'foo' AS
text").select(strlen("text")).collect()
- [Row(stringLengthString(text)=u'3')]
-
- >>> from pyspark.sql.types import IntegerType
- >>> _ = spark.catalog.registerFunction("stringLengthInt", len,
IntegerType())
- >>> spark.sql("SELECT stringLengthInt('test')").collect()
- [Row(stringLengthInt(test)=4)]
-
- >>> from pyspark.sql.types import IntegerType
- >>> _ = spark.udf.register("stringLengthInt", len, IntegerType())
- >>> spark.sql("SELECT stringLengthInt('test')").collect()
- [Row(stringLengthInt(test)=4)]
-
- >>> from pyspark.sql.types import IntegerType
- >>> from pyspark.sql.functions import udf
- >>> slen = udf(lambda s: len(s), IntegerType())
- >>> _ = spark.udf.register("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
- >>> random_udf = udf(lambda: random.randint(0, 100),
IntegerType()).asNondeterministic()
- >>> new_random_udf = spark.catalog.registerFunction("random_udf",
random_udf)
- >>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP
- [Row(random_udf()=82)]
- >>> spark.range(1).select(new_random_udf()).collect() # doctest:
+SKIP
- [Row(<lambda>()=26)]
-
- >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
- >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP
- ... def add_one(x):
- ... return x + 1
- ...
- >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP
- >>> spark.sql("SELECT add_one(id) FROM range(3)").collect() #
doctest: +SKIP
- [Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]
- """
-
- # This is to check whether the input function is a wrapped/native
UserDefinedFunction
- if hasattr(f, 'asNondeterministic'):
- if returnType is not None:
- raise TypeError(
- "Invalid returnType: None is expected when f is a
UserDefinedFunction, "
- "but got %s." % returnType)
- if f.evalType not in [PythonEvalType.SQL_BATCHED_UDF,
- PythonEvalType.SQL_PANDAS_SCALAR_UDF]:
- raise ValueError(
- "Invalid f: f must be either SQL_BATCHED_UDF or
SQL_PANDAS_SCALAR_UDF")
- register_udf = UserDefinedFunction(f.func,
returnType=f.returnType, name=name,
- evalType=f.evalType,
-
deterministic=f.deterministic)
- return_udf = f
- else:
- if returnType is None:
- returnType = StringType()
- register_udf = UserDefinedFunction(f, returnType=returnType,
name=name,
-
evalType=PythonEvalType.SQL_BATCHED_UDF)
- return_udf = register_udf._wrapped()
- self._jsparkSession.udf().registerPython(name, register_udf._judf)
- return return_udf
+ warnings.warn(
+ "Deprecated in 2.3.0. Use spark.udf.register instead.",
+ DeprecationWarning)
+ return self._sparkSession.udf.register(name, f, returnType)
+ # Reuse the docstring from UDFRegistration but with few notes.
+ registerFunction.__doc__ = """%s
+ .. note:: :func:`spark.catalog.registerFunction` is an alias
+ for :func:`spark.udf.register`.
+ .. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register`
instead.
--- End diff --
Do we have any plan (e.g. 3.0.0) to remove this alias?
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