Dockerfile Caching

Dockerfile caching with Alokai Cloud's Docker image registry can help speed up builds. Docker leverages a layered caching mechanism to optimize image builds. Each instruction (like COPY, RUN, etc.) creates a layer, and Docker stores the output of each layer for reuse on Alokai Cloud. This significantly speeds up subsequent builds if the Dockerfile hasn't changed and the base image remains the same.

However, it's crucial to remember that changes to a Dockerfile instruction can invalidate the cache for that layer and all subsequent layers. This is because subsequent layers rely on the outputs of previous ones.

Caching Behavior in GitHub CI

Whenever your GitHub Actions workflow triggers a build, it will attempt to utilize the cached layers stored on Alokai Cloud's Docker image registry. If the cache for a particular layer is unavailable or invalid, that layer and all subsequent layers will be rebuilt.

Smart Caching in the Provided Dockerfile

The default Dockerfile demonstrates several techniques for achieving smart caching:

1. Separating Dependency Installation and Application Code:

• The Dockerfile uses multi-stage builds with three stages: base, builder, and runner.
• The base stage serves as a base for both the builder and runner stages.
• The builder stage focuses on installing dependencies. It copies package.json, yarn.lock, and other configuration files, then installs dependencies using yarn install.
• This approach ensures that changes in application code (./apps/storefront-* directories) won't invalidate the cache for dependency installation (as long as package.json and yarn.lock remain the same).

2. Multi-Stage Build Benefits:

• This strategy keeps the final image (runner) smaller because it doesn't include unnecessary build tools (yarn, npm) that were only needed for the build process.
• It also improves security by separating the build environment from the final runtime environment.

3. Early package.json Copy:

• The Dockerfile strategically copies package.json before any application code. This ensures that the yarn install command only runs when the dependencies themselves (reflected in package.json) change.

4. Caching During Subsequent Builds:

• If package.json and its dependencies haven't changed, Docker will reuse the cached layer from the builder stage, significantly speeding up subsequent builds.

Examples of Cache Invalidation vs. Persistence

Scenario 1: Cache Invalidation

Change in package.json: Modifying dependencies or scripts in package.json necessitates a re-run of the yarn install command to ensure proper dependency resolution. This invalidates the cache for the yarn install layer and all subsequent layers, including those copying the application code and building it.

Scenario 2: Cache Persistence

Change in application code (./apps/storefront-unified-nextjs/ or ./apps/storefront-unified-nuxt/ directory): As long as package.json and its dependencies remain the same, changes within the application code won't invalidate the yarn install cache. Only the RUN yarn turbo run build layer and subsequent copying/building steps will be re-executed.

Additional Considerations

• Base Image Updates: Updating the base image (node:18-alpine in our case) will always invalidate the cache, as the underlying environment changes.
• Context Changes: Any modifications in the context directory (the directory containing your Dockerfile and other build files) can trigger a cache invalidation. By understanding Dockerfile caching and applying these optimization techniques, you can ensure faster and more efficient builds for your storefront application.

Importance of Caution During Modifications

Cache Invalidation:

Any modifications to the Dockerfile or the context directory (containing the Dockerfile and other build files) can invalidate the cache for subsequent layers. This is because subsequent layers depend on the outputs of previous ones.

For example, if you add a new file to the context directory that's not explicitly excluded, it will trigger a full rebuild.

Dependency Management:

Changes to package.json or yarn.lock will invalidate the cache for the yarn install layer and all subsequent layers, even if the application code itself hasn't changed. This ensures that the image uses the correct dependencies.

Understanding Cache Behavior:

It's crucial to understand how changes to your Dockerfile or context directory affect the cache. This knowledge helps you structure your Dockerfile for optimal caching and avoid unnecessary rebuilds.

By following these principles and carefully considering potential cache invalidation scenarios, you can maintain an efficient Docker build process for your storefront application.

Cache Invalidation Rules

For detailed information on cache invalidation rules, refer to the official Docker documentation on Build Cache Invalidation https://docs.docker.com/build/cache/invalidation/.