Docker Caching

Understanding how our Dockerfile structure is designed to save you valuable development time

How Docker build caching works

Docker builds your application in layers, with each instruction in a Dockerfile creating a new layer. When you rebuild your image, Docker can reuse unchanged layers from its cache rather than recreating them. This is one of Docker’s most powerful features for development efficiency.

Think of Docker’s caching mechanism like building a layered cake. If the bottom layers haven’t changed, Docker won’t bake them again—it simply reuses those existing layers and only bakes the changed ones on top.

Our dockerfile design philosophy

Our Dockerfile structure is deliberately organized to maximize cache efficiency. You might notice that we handle dependencies before copying your application code:

# System dependencies first
COPY .dockerignore setup.sh* ./
RUN if [ -f ./setup.sh ]; then ... fi

# Then Python dependencies
COPY .dockerignore requirements.txt* ./
RUN if [ -f ./requirements.txt ]; then ... fi

# Finally, application code
COPY . .

This isn’t arbitrary—it follows a “least likely to change” to “most likely to change” progression. System dependencies rarely change, Python package requirements change occasionally, and your application code changes frequently during development.

Why this matters for development speed

When you’re iterating on your application, you typically make frequent changes to your code while your dependencies remain stable. Because we’ve structured the Dockerfile to install dependencies first, Docker can reuse those cached layers and only rebuild the layers containing your updated code.

Without this approach, even a small code change would trigger a complete reinstallation of all dependencies—turning a 5-second rebuild into a 5-minute one.

The real-world impact

Consider a typical development cycle: you change a few lines of code, rebuild, test, and repeat. With our caching strategy, each rebuild might take seconds instead of minutes. Over the course of a day, this could save hours of waiting time.

The most significant performance gain comes from keeping your requirements.txt and setup.sh files stable. When these files remain unchanged, Docker can reuse the expensive dependency installation layers, dramatically reducing build times.

Trade-offs and considerations

While our approach optimizes for development speed, it does mean that if you do need to update dependencies, you’ll invalidate several cache layers. This is an intentional trade-off—we’ve prioritized the more common workflow (code changes) over the less frequent one (dependency updates).

Understanding this caching behavior helps you develop more efficiently and explains why we’ve structured our Docker environment the way we have.