Docker volume analyzer

Docker Volume Analyzer is a tool designed to simplify the management of Docker volumes. It provides features to:

• Visualize the containers associated with each volume.
• Explore the contents of volumes and delete files if needed.
• Retrieve basic information about volumes, such as size and usage.
• Streamline volume management tasks for improved efficiency.

This project aims to make Docker volume management more intuitive and user-friendly.

Installation

Prerequisites

• Python >=3.13,<4.0.0
• Poetry >=2.1.2 installed globally (installation link)
• Docker installed locally (if you want to analyze volumes)

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1. Clone the project

git clone https://github.com/glefer/docker-volumes-analyzer.git
cd docker-volumes-analyzer

2. Install dependencies

poetry install

3. Run the application

poetry run start

⚠️ The application uses the Docker socket at the standard location: /var/run/docker.sock

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🐳 Usage via Docker

Don't want to install Python? Simply use the Docker image:

docker run --rm -v /var/run/docker.sock:/var/run/docker.sock -ti glefer/docker-volumes-analyzer:latest

Use a specific version

docker run --rm -v /var/run/docker.sock:/var/run/docker.sock -ti glefer/docker-volumes-analyzer:0.1.0

Running the Application in Different Modes

The application supports multiple modes of operation. You can specify the mode using the APP_MODE environment variable. The available modes are:

• CLI mode (start): Runs the application in command-line interface mode.
• Web development mode (web): Starts the application in web development mode.
• Gunicorn mode (gunicorn): Runs the application using Gunicorn as the WSGI server.

Using Docker

You can run the application in different modes by setting the APP_MODE environment variable when running the Docker container.

CLI mode

docker run --rm -e APP_MODE=start -v /var/run/docker.sock:/var/run/docker.sock -ti glefer/docker-volumes-analyzer:latest

Web development mode

docker run -e APP_MODE=web -v /var/run/docker.sock:/var/run/docker.sock glefer/docker-volumes-analyzer:latest

Web production mode (gunicorn)

docker run -e APP_MODE=gunicorn -v /var/run/docker.sock:/var/run/docker.sock glefer/docker-volumes-analyzer:latest

Using python locally

If you prefer to run the application locally without Docker, you can use the entrypoint.sh script directly. Make sure you have all dependencies installed via Poetry.

CLI mode

APP_MODE=start scripts/entrypoint.sh

Web development mode

APP_MODE=web scripts/entrypoint.sh

Web production mode (gunicorn)

APP_MODE=gunicorn scripts/entrypoint.sh

Prometheus

When running the application in web or gunicorn mode, it exposes a Prometheus metrics endpoint at /metrics. This endpoint provides detailed metrics about Docker volumes, such as:

• Total number of Docker volumes.
• Size of individual Docker volumes (in bytes).

For more information about the metrics exposed and how to integrate them with Prometheus, refer to the Prometheus documentation.

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Run tests

poetry run pytest

With coverage:

poetry run pytest --cov=docker_volume_analyzer

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🛠 Development

Start a virtual shell:

poetry shell

Formatting and checks:

poetry run pre-commit run --all-files

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🔧 Project structure

.
├── src/
│   └── docker_volume_analyzer/
│       └── main.py   # Entry point
├── tests/
├── README.md
├── pyproject.toml
└── poetry.lock

Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/my-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/my-feature).
5. Open a pull request.

For major changes, please open an issue first to discuss what you would like to change.

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📝 License

This project is licensed under the MIT license — see the LICENSE file.

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👨‍💻 Author

github.com/glefer