| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| from typing import Callable, Optional, TypeVar |
| |
| from airflow.decorators.base import DecoratedOperator, task_decorator_factory |
| from airflow.operators.python import PythonOperator |
| |
| |
| class _PythonDecoratedOperator(DecoratedOperator, PythonOperator): |
| """ |
| Wraps a Python callable and captures args/kwargs when called for execution. |
| |
| :param python_callable: A reference to an object that is callable |
| :type python_callable: python callable |
| :param op_kwargs: a dictionary of keyword arguments that will get unpacked |
| in your function (templated) |
| :type op_kwargs: dict |
| :param op_args: a list of positional arguments that will get unpacked when |
| calling your callable (templated) |
| :type op_args: list |
| :param multiple_outputs: if set, function return value will be |
| unrolled to multiple XCom values. Dict will unroll to xcom values with keys as keys. |
| Defaults to False. |
| :type multiple_outputs: bool |
| """ |
| |
| template_fields = ('op_args', 'op_kwargs') |
| template_fields_renderers = {"op_args": "py", "op_kwargs": "py"} |
| |
| # since we won't mutate the arguments, we should just do the shallow copy |
| # there are some cases we can't deepcopy the objects (e.g protobuf). |
| shallow_copy_attrs = ('python_callable',) |
| |
| def __init__( |
| self, |
| **kwargs, |
| ) -> None: |
| kwargs_to_upstream = { |
| "python_callable": kwargs["python_callable"], |
| "op_args": kwargs["op_args"], |
| "op_kwargs": kwargs["op_kwargs"], |
| } |
| |
| super().__init__(kwargs_to_upstream=kwargs_to_upstream, **kwargs) |
| |
| |
| T = TypeVar("T", bound=Callable) |
| |
| |
| def python_task( |
| python_callable: Optional[Callable] = None, multiple_outputs: Optional[bool] = None, **kwargs |
| ): |
| """ |
| Python operator decorator. Wraps a function into an Airflow operator. |
| Accepts kwargs for operator kwarg. Can be reused in a single DAG. |
| |
| :param python_callable: Function to decorate |
| :type python_callable: Optional[Callable] |
| :param multiple_outputs: if set, function return value will be |
| unrolled to multiple XCom values. List/Tuples will unroll to xcom values |
| with index as key. Dict will unroll to xcom values with keys as XCom keys. |
| Defaults to False. |
| :type multiple_outputs: bool |
| """ |
| return task_decorator_factory( |
| python_callable=python_callable, |
| multiple_outputs=multiple_outputs, |
| decorated_operator_class=_PythonDecoratedOperator, |
| **kwargs, |
| ) |