How to Leverage Multi-Stage Builds in Python

Leveraging multi-stage builds in Python for better application performance

Docker has revolutionized the way we create and deploy applications. One key feature that makes Docker images so much faster and more impactful is the multi-stage build. Let's take a look at multi-stage builds in Python application and how you can drastically reduce your Docker image size.

Docker's multi-stage builds feature lets you create multiple build phases within the same Docker file. Each phase can be used to compile code, install dependencies, or build artifacts. The final image only contains the artifacts from the last stage, effectively discarding any unnecessary files from intermediate build stages and resulting in smaller, more efficient images.

 Multi-stage builds in the Python application include the steps, directory structure, and simple flask app, multi-stage docker file; in the docker file's first stage, the user can use the official Python image to install the application's dependencies from requirements.txt.

Runtime is the second phase; a thin, lightweight version of the Python image is used to copy only application code and build dependencies directly from the build phase. This means that build dependencies are not included in the final image.

During a multi-stage build in Python, only the end-user's runtime artifacts are included. This dramatically reduces the image size compared to a traditional Docker build, which provides for intermediate build files. Eliminating build-time dependencies reduces the attack surface and vulnerability of an end-user image. Cleaner builds in multi-stage builds ensure that runtime and build-time dependencies are separated, simplifying image maintenance and debugging.

Reducing Image Size

To compare the sizes of the images constructed using a multi-stage approach and a traditional approach. Before the multi-stage build, a single-stage Docker build, without optimization, can have an image size of 300MB. After the multi-stage build, the image size can be reduced to 50–70MB. This is a significant decrease in image size, which leads to faster deployments and better resource utilization.

Multi-stage builds in Python can help reduce Docker image sizes and improve security and maintainability by separating build time from runtime dependencies. Docker is constantly improving and adding new features. MSTs are just one example of how MSTs can enhance your development and deployment processes. Integrating best practices like MSTs into your Docker workflow will help you deliver faster, safer, and more efficient applications.