Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/20288#discussion_r162230241 --- Diff: python/pyspark/sql/context.py --- @@ -172,113 +173,29 @@ def range(self, start, end=None, step=1, numPartitions=None): """ return self.sparkSession.range(start, end, step, numPartitions) - @ignore_unicode_prefix @since(1.2) 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:`sqlContext.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 = sqlContext.registerFunction("stringLengthString", lambda x: len(x)) - >>> sqlContext.sql("SELECT stringLengthString('test')").collect() - [Row(stringLengthString(test)=u'4')] - - >>> sqlContext.sql("SELECT 'foo' AS text").select(strlen("text")).collect() - [Row(stringLengthString(text)=u'3')] - - >>> from pyspark.sql.types import IntegerType - >>> _ = sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() - [Row(stringLengthInt(test)=4)] - - >>> from pyspark.sql.types import IntegerType - >>> _ = sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlContext.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()) - >>> _ = sqlContext.udf.register("slen", slen) - >>> sqlContext.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 = sqlContext.registerFunction("random_udf", random_udf) - >>> sqlContext.sql("SELECT random_udf()").collect() # doctest: +SKIP - [Row(random_udf()=82)] - >>> sqlContext.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 - ... - >>> _ = sqlContext.udf.register("add_one", add_one) # doctest: +SKIP - >>> sqlContext.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)] + """An alias for :func:`spark.udf.register`. + See :meth:`pyspark.sql.UDFRegistration.register`. + + .. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register` instead. --- End diff -- It shows the doc as below: ![2018-01-18 10 28 46](https://user-images.githubusercontent.com/6477701/35076515-379756f4-fc3c-11e7-99db-447fb466c626.png) I checked the link, `pyspark.sql.UDFRegistration.register` is correct.
--- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org