如何为使用官方 docker-compose 运行的 Airflow 设置远程调试？

Question

我试图关注这篇文章： https ://medium.com/@andrewhharmon/apache-airflow-using-pycharm-and-docker-for-remote-debugging-b2d1edf83d9d。

有问题的部分是：

• 创建 Python Interpreter 时应该选择哪个容器？
• 设置调试配置以直接运行 Airflow 的二进制文件是否正确？ 当我使用气流工作者作为解释器并在调试模式下运行时，我得到了

data-pipeline-airflow-worker-1  | /home/airflow/.local/lib/python3.7/site-packages/airflow/configuration.py:360: FutureWarning: The auth_backends setting in [api] has had airflow.api.auth.backend.session added in the running config, which is needed by the UI. Please update your config before Apache Airflow 3.0.
data-pipeline-airflow-worker-1  |   FutureWarning,
data-pipeline-airflow-worker-1  | 
data-pipeline-airflow-worker-1  | airflow command error: the following arguments are required: GROUP_OR_COMMAND, see help above.
data-pipeline-airflow-worker-1  | usage: airflow [-h] GROUP_OR_COMMAND ...
data-pipeline-airflow-worker-1  | 
data-pipeline-airflow-worker-1  | positional arguments:
data-pipeline-airflow-worker-1  |   GROUP_OR_COMMAND
data-pipeline-airflow-worker-1  | 
data-pipeline-airflow-worker-1  |     Groups:
data-pipeline-airflow-worker-1  |       celery         Celery components
data-pipeline-airflow-worker-1  |       config         View configuration
data-pipeline-airflow-worker-1  |       connections    Manage connections
data-pipeline-airflow-worker-1  |       dags           Manage DAGs
data-pipeline-airflow-worker-1  |       db             Database operations
data-pipeline-airflow-worker-1  |       jobs           Manage jobs
data-pipeline-airflow-worker-1  |       kubernetes     Tools to help run the KubernetesExecutor
data-pipeline-airflow-worker-1  |       pools          Manage pools
data-pipeline-airflow-worker-1  |       providers      Display providers
data-pipeline-airflow-worker-1  |       roles          Manage roles
data-pipeline-airflow-worker-1  |       tasks          Manage tasks
data-pipeline-airflow-worker-1  |       users          Manage users
data-pipeline-airflow-worker-1  |       variables      Manage variables

Answer 1

好的，我找到了答案，您需要在气流工作者中设置 Python 解释器。 对于脚本路径，它应该是/home/airflow/.local/bin/airflow并且参数应该是tasks test [dag_id] [task_id] [start_date] 。 我正在使用气流 2.3.2