airflow python_virtualenv 源码

airflow python_virtualenv 代码

文件路径：/airflow/decorators/python_virtualenv.py

# 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 __future__ import annotations

import inspect
from textwrap import dedent
from typing import Callable, Sequence

from airflow.decorators.base import DecoratedOperator, TaskDecorator, task_decorator_factory
from airflow.operators.python import PythonVirtualenvOperator
from airflow.utils.decorators import remove_task_decorator


class _PythonVirtualenvDecoratedOperator(DecoratedOperator, PythonVirtualenvOperator):
    """
    Wraps a Python callable and captures args/kwargs when called for execution.

    :param python_callable: A reference to an object that is callable
    :param op_kwargs: a dictionary of keyword arguments that will get unpacked
        in your function (templated)
    :param op_args: a list of positional arguments that will get unpacked when
        calling your callable (templated)
    :param multiple_outputs: If set to True, the decorated function's return value will be unrolled to
        multiple XCom values. Dict will unroll to XCom values with its keys as XCom keys. Defaults to False.
    """

    template_fields: Sequence[str] = ('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: Sequence[str] = ('python_callable',)

    custom_operator_name: str = '@task.virtualenv'

    def __init__(self, *, python_callable, op_args, op_kwargs, **kwargs) -> None:
        kwargs_to_upstream = {
            "python_callable": python_callable,
            "op_args": op_args,
            "op_kwargs": op_kwargs,
        }
        super().__init__(
            kwargs_to_upstream=kwargs_to_upstream,
            python_callable=python_callable,
            op_args=op_args,
            op_kwargs=op_kwargs,
            **kwargs,
        )

    def get_python_source(self):
        raw_source = inspect.getsource(self.python_callable)
        res = dedent(raw_source)
        res = remove_task_decorator(res, "@task.virtualenv")
        return res


def virtualenv_task(
    python_callable: Callable | None = None,
    multiple_outputs: bool | None = None,
    **kwargs,
) -> TaskDecorator:
    """Wraps a callable into an Airflow operator to run via a Python virtual environment.

    Accepts kwargs for operator kwarg. Can be reused in a single DAG.

    This function is only used only used during type checking or auto-completion.

    :meta private:

    :param python_callable: Function to decorate
    :param multiple_outputs: If set to True, the decorated function's return value will be unrolled to
        multiple XCom values. Dict will unroll to XCom values with its keys as XCom keys.
        Defaults to False.
    """
    return task_decorator_factory(
        python_callable=python_callable,
        multiple_outputs=multiple_outputs,
        decorated_operator_class=_PythonVirtualenvDecoratedOperator,
        **kwargs,
    )

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