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This example shows how to set up PyCharm for local development with Airflow and the Astro CLI. Setting up a local development environment allows you to iterate more quickly when developing DAGs by taking advantage of IDE features.

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Before you start

Before trying this example, make sure you have:

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Configure the Python interpreter

To develop Airflow DAGs in PyCharm, you need to configure at least one Python interpreter. In this example you configure an interpreter using Docker, which allows you to write Python code and interact with running DAGs from within PyCharm.
  1. In your PyCharm preferences go to Build, Execution, Deployment >> Docker and specify how to connect to the Docker daemon. For more, see Connect to Docker.
  2. Go to Project: <your-project-name> >> Python Interpreter. Click the Add Interpreter button on the right, and then click On Docker:
    Interpreter settings
  3. In the window that appears, select Pull or use existing for the Image option. In the Image tag field, select the Docker image that your Airflow environment is running, then click Next.
    Image settings
  4. Wait for PyCharm to finish pulling the Docker image and then click Next.
    Image settings
  5. Ensure the System Interpreter is set to Python 3. By default this path will point to the location of Python 3 on the machine and shouldn’t need to be changed. Then, click Create.
    Image settings
  6. On the original Python Interpreter setup screen, ensure the Python interpreter is set to the docker image and click Apply, followed by OK.
    Image settings

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Write Airflow code with PyCharm

After you configure PyCharm to use the correct interpreter for Airflow, you get multiple advantages like code autocompletion, identifying deprecated or unused imports, and error syntax highlighting. PyCharm will show you when there are deprecated imports in your project:
Deprecated Imports
It can also alert you when an import is unused:
Unused Imports
Like with other Python projects, PyCharm will highlight syntax errors in your Airflow code:
Syntax Highlighting
Lastly, here is an example of PyCharm autocompleting code and showing built-in definitions:
Code Autocompletion

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Manage Airflow Docker containers with PyCharm

With PyCharm configured to use the Python interpreter from Docker, you can connect to your Docker containers directly from PyCharm using the Services pane. If you don’t see this pane, try pressing cmd+8.
Services
From the Services pane, start Docker by clicking the green play button. You’ll see the same containers appear as when you run docker ps after starting your Astro project locally:
Containers
View logs for the containers by clicking on /scheduler, /triggerer, /webserver, or /airflow-dev_2cd823-postgres. Note that these strings might differ based on where the parent directory for your project is located:
Logs
Run Airflow CLI commands by right clicking on /scheduler and selecting Create Terminal to bash into the container:
CLI

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See also