# Performance and Optimization

Optimizing performance is essential for maintaining efficient development and deployment workflows. This section covers the key strategies and tools used in the **addons-server** project for performance and optimization.

## Docker Layer Caching

Docker layer caching is a powerful feature that significantly speeds up the build process by reusing unchanged layers. This section explains the benefits and setup for Docker layer caching in the **addons-server** project.

1. **Benefits of Docker Layer Caching**:
   - **Reduced Build Times**: By caching intermediate layers, Docker can reuse these layers in subsequent builds, reducing the overall build time.
   - **Efficient Resource Usage**: Caching helps save bandwidth and computational resources by avoiding redundant downloads and computations.
   - **Consistency**: Ensures that identical builds produce identical layers, promoting consistency across builds.

2. **Setup for Docker Layer Caching**:
   - **Build Stages**: The Dockerfile uses build stages to isolate dependency installation and other tasks. This ensures that stages are only re-executed when necessary.
   - **Cache Mounts**: The project uses `--mount=type=cache` in the Dockerfile to cache directories across builds. This is particularly useful for caching Python and npm dependencies, speeding up future builds.

   Example snippet from the Dockerfile:

   ```Dockerfile
   RUN --mount=type=cache,target=/root/.cache/pip pip install -r requirements/prod.txt
   RUN --mount=type=cache,target=/root/.npm npm install
   ```

   - **BuildKit**: Ensures BuildKit is enabled to take advantage of advanced caching features:

     ```sh
     export DOCKER_BUILDKIT=1
     ```

   - **GitHub Actions Cache**: The custom action (`./.github/actions/cache-deps`) caches the `/data/olympia/deps` folder, leveraging GitHub Actions cache to improve CI run times.

## Performance Testing

Performance testing is crucial for identifying bottlenecks and optimizing application performance. The **addons-server** project includes various strategies for performance testing and optimization.

1. **Running Performance Tests**:
   - The project uses `pytest` along with plugins like `pytest-split` and `pytest-xdist` to run tests in parallel, significantly reducing test times.
   - Performance-specific tests can be run to measure the responsiveness and efficiency of the application.

2. **Optimization Tips**:
   - **Parallel Testing**: Use `pytest-xdist` to run tests in parallel:

     ```sh
     pytest -n auto
     ```

   - **Test Splitting**: Use `pytest-split` to distribute tests evenly across multiple processes.
   - **Code Profiling**: Use profiling tools to identify slow functions and optimize them.
   - **Database Optimization**: Regularly monitor and optimize database queries to ensure efficient data retrieval and storage.

By implementing these performance and optimization strategies, the **addons-server** project ensures efficient and reliable builds and tests, both locally and in CI environments. For more detailed instructions, refer to the project's Dockerfile, Makefile, and GitHub Actions configurations in the repository.