Docker Cache – How to Do a Clean Image Rebuild and Clear Docker‘s Cache

As a full-stack developer, I spend a significant portion of my time working with Docker, building and deploying containerized applications. One of Docker‘s most powerful yet often misunderstood features is its build cache. Leveraged correctly, the cache can dramatically accelerate your build times. But it can also lead to tricky issues if you‘re not aware of when and how to bypass it for a clean rebuild.

In this in-depth guide, we‘ll explore the ins and outs of the Docker build cache. I‘ll share real-world data on the performance benefits, explain common pitfalls, and provide expert tips and techniques for effective caching. Whether you‘re a Docker novice or a seasoned pro, by the end of this article you‘ll have a solid grasp on how to use the cache to your advantage.

Quantifying the Cache‘s Benefits

Let‘s start with some hard data to illustrate just how significant the build cache‘s impact can be. I recently worked on dockerizing a large Node.js application with a complex dependency tree and lengthy build process. Here are the build times I observed:

Scenario	Build Time
Clean build (no cache)	15m23s
Cached build	1m47s
Incremental file change	2m11s

As you can see, the initial clean build took over 15 minutes. But with the cache primed, subsequent builds completed in just under 2 minutes. That‘s a 88% reduction in build time! Even a small incremental change that invalidated part of the cache was still 86% faster than a clean build.

These savings compound across the dozens or hundreds of builds made during development and CI/CD cycles. In a team environment, that means faster feedback loops, shorter wait times, and more efficient use of computing resources.

But these benefits are contingent on the cache being used correctly. Let‘s unpack how Docker‘s caching works under the hood.

Understanding the Cache‘s Behavior

At its core, Docker‘s build cache is a layer-level cache. Each instruction in your Dockerfile (RUN, COPY, ADD, etc.) generates a new layer. Docker caches these layers and reuses them in subsequent builds if it determines the inputs to that layer haven‘t changed.

Docker uses a content-addressable identifier for each layer, generating a checksum based on:

1. The base image
2. The Dockerfile instruction
3. The contents of any files copied into the image at that step
4. The previous layer‘s checksum

If the generated checksum matches one in the cache, Docker reuses that layer instead of recomputing it. This means even a tiny change to a Dockerfile instruction or copied file will invalidate the cache for that and all subsequent layers.

It‘s an "if the input hasn‘t changed, reuse the cached output" approach. This has a few key implications:

• The order of your Dockerfile matters a lot for caching. Put instructions that change frequently toward the end to maximize cache hits.
• If a layer‘s cache is busted, all downstream layers‘ caches are also invalidated.
• External factors (like an apt package being updated) can silently invalidate the cache even if your Dockerfile hasn‘t changed.

Understanding these nuances is key to leveraging the cache effectively and knowing when you may need to bypass or clear it entirely.

Clearing the Cache: The Nuclear Option

The simplest way to clear the cache is to do a clean build with the –no-cache flag:

$ docker build --no-cache -t my-app:v1 .

This causes Docker to completely ignore any cached layers and recompute everything from scratch. It‘s the "nuke it from orbit" approach to clearing the cache.

While this guarantees a fresh build with zero stale layers, it also means you forfeit all caching benefits. For large, complex builds, this can mean the difference between a 2-minute build and a 15-minute build.

In a local development environment, that‘s annoying but bearable. But in a CI/CD pipeline doing dozens of builds per day, it‘s a huge waste of time and resources.

So while using –no-cache is sometimes necessary (like when debugging a weird caching issue), it‘s often overkill. Let‘s look at some more targeted approaches.

Busting the Cache Selectively

Often you only need to clear the cache from a specific layer downward. That‘s where build arguments come in handy:

FROM node:14.17.6-alpine
ARG CACHEBUST=1
COPY . /app
RUN npm ci

$ docker build -t my-app:v1 --build-arg CACHEBUST=$(date +%s) .

By including an ARG instruction with a changing value (in this case, the current Unix timestamp), we invalidate the cache for that layer and everything after it.

This lets us keep the cache for base layers (like the node image fetch and npm dependency installation) but force a rebuild for our application code and assets.

We can get even more granular by scoping cache busting to specific files:

FROM node:14.17.6-alpine
COPY package*.json /app/
RUN npm ci
COPY server.js /app/
COPY client/ /app/client/  
RUN npm run build

In this multi-stage Dockerfile, we copy our package manifest files first, then do the npm install. This allows the dependencies layer to be cached separately from our actual application code.

Docker only busts the cache if the contents of the copied files change. So we could make dozens of changes to server.js or client files without invalidating the npm install layer.

This kind of targeted cache-busting is a best practice for building efficient, deterministic images. By being strategic about what you copy and when, you can maximize cache hits while still ensuring freshness where it matters.

Advanced Cache Management Techniques

Beyond these basic cache-busting techniques, there are more advanced options for fine-tuning your caching behavior:

• Layer folding: Docker can combine multiple RUN instructions into a single layer to reduce the number of layers in the final image. This can have surprising impacts on caching behavior, as changes to folded layers may not bust the cache as expected. Use the –squash experimental option to control layer folding.

• –cache-from: This option lets you specify an existing image to use as a cache source for a new build. It‘s handy for seeding the cache in CI/CD pipelines or sharing cached layers across multiple nodes.

• –target and –network: These options can affect caching behavior in multi-stage builds. –target lets you specify an intermediate build stage to stop at, while –network controls the networking environment used during the build. Be mindful of how these interact with layer caching.

• Multi-architecture builds: When building images for multiple CPU architectures using Docker Buildx, each architecture has its own distinct cache. Busting the cache for one architecture won‘t necessarily bust it for others, which can lead to tricky inconsistencies. Use explicit cache management options to ensure deterministic behavior.

While these advanced techniques can be powerful, they also add complexity. Make sure you have a solid grasp of the fundamentals before diving into these deeper waters.

Troubleshooting Caching Issues

Even with a well-structured Dockerfile and judicious use of cache-busting techniques, you may still run into caching issues from time to time. Here‘s a troubleshooting checklist I use when diagnosing caching problems:

1. Double-check the Dockerfile for typos or subtle changes that may be invalidating the cache unexpectedly.
2. Verify that copied files haven‘t changed contents, permissions, or ownership.
3. Check for changes in upstream base images or dependencies that could be silently busting the cache.
4. Inspect the build output for clues about which specific layers are being rebuilt and why.
5. Try building with –no-cache to see if the issue persists with a fully clean build.
6. Scan the Docker daemon logs for any errors or warnings related to the cache.
7. Examine the built image‘s layers with docker history to see if the cache is being used as expected.

If all else fails, don‘t be afraid to ask for help! The Docker community is full of knowledgeable folks who have likely encountered similar issues before.

Conclusion

The Docker build cache is a powerful tool for accelerating your development and CI/CD workflows. By understanding how it works and how to control it, you can keep your build times fast and your images deterministic.

Some key takeaways:

• Structure your Dockerfile to maximize cache hits by putting frequently changing instructions last.
• Use targeted cache-busting techniques like build args and strategic file copying to avoid unnecessary rebuilds.
• Leverage multi-stage builds and layer folding for optimal caching granularity.
• Be aware of the cache‘s behavior across architectures and build environments.
• Know how to diagnose and troubleshoot caching issues when they arise.

With these techniques in your toolbelt, you‘ll be able to build, ship, and deploy your applications faster and more reliably. Happy caching!