pyflink.table.udf.udf#

udf(f: Optional[Union[Callable, pyflink.table.udf.ScalarFunction, Type]] = None, input_types: Optional[Union[List[pyflink.table.types.DataType], pyflink.table.types.DataType, str, List[str]]] = None, result_type: Optional[Union[pyflink.table.types.DataType, str]] = None, deterministic: Optional[bool] = None, name: Optional[str] = None, func_type: str = 'general') → Union[pyflink.table.udf.UserDefinedScalarFunctionWrapper, Callable][source]#

Helper method for creating a user-defined function.

Example:

>>> add_one = udf(lambda i: i + 1, DataTypes.BIGINT(), DataTypes.BIGINT())

>>> # The input_types is optional.
>>> @udf(result_type=DataTypes.BIGINT())
... def add(i, j):
...     return i + j

>>> # Specify result_type via string.
>>> @udf(result_type='BIGINT')
... def add(i, j):
...     return i + j

>>> class SubtractOne(ScalarFunction):
...     def eval(self, i):
...         return i - 1
>>> subtract_one = udf(SubtractOne(), DataTypes.BIGINT(), DataTypes.BIGINT())

Parameters

f – lambda function or user-defined function.
input_types – optional, the input data types.
result_type – the result data type.
deterministic – the determinism of the function’s results. True if and only if a call to this function is guaranteed to always return the same result given the same parameters. (default True)
name – the function name.
func_type – the type of the python function, available value: general, pandas, (default: general)
udf_type – the type of the python function, available value: general, pandas, (default: general)

Returns

UserDefinedScalarFunctionWrapper or function.

New in version 1.10.0.