airflow/CONTRIBUTORS_QUICK_START_PYCHARM.rst at main · eth-easl/airflow

Setup your project

Open your IDE or source code editor and select the option to clone the repository
Paste the repository link in the URL field and submit.

It requires "airflow-env" virtual environment configured locally.

Airflow is by default configured to use SQLite database. Configuration can be seen on local machine ~/airflow/airflow.cfg under sql_alchemy_conn.
Installing required dependency for MySQL connection in airflow-env on local machine.
```
$ pyenv activate airflow-env
$ pip install PyMySQL
```
Now set sql_alchemy_conn = mysql+pymysql://root:@127.0.0.1:23306/airflow?charset=utf8mb4 in file ~/airflow/airflow.cfg on local machine.

Add Interpreter to PyCharm pointing interpreter path to ~/.pyenv/versions/airflow-env/bin/python, which is virtual environment airflow-env created with pyenv earlier. For adding an Interpreter go to File -> Setting -> Project: airflow -> Python Interpreter.
In PyCharm IDE open airflow project, directory /files/dags of local machine is by default mounted to docker machine when breeze airflow is started. So any DAG file present in this directory will be picked automatically by scheduler running in docker machine and same can be seen on http://127.0.0.1:28080.
Copy any example DAG present in the /airflow/example_dags directory to /files/dags/.
Add a __main__ block at the end of your DAG file to make it runnable. It will run a back_fill job:
```
if __name__ == "__main__":
    dag.clear()
    dag.run()
```
Add AIRFLOW__CORE__EXECUTOR=DebugExecutor to Environment variable of Run Configuration.
- Click on Add configuration
- Add Script Path and Environment Variable to new Python configuration
Now Debug an example dag and view the entries in tables such as dag_run, xcom etc in MySQL Workbench.