DevOps

Docker Layer Caching in CI Pipelines Cut Build Times by 70 %

A practical guide to using docker buildx- multi-stage builds- and remote cache backends for lightning-fast container builds

Docker Layer Caching in CI Pipelines Cut Build Times by 70 %
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You push a one-line code change, and your CI/CD pipeline kicks off. You grab a coffee, come back, and it’s still running, stuck on the docker build step. Slow container builds are a silent productivity killer, delaying feedback, slowing down deployments, and frustrating developers. In an ephemeral CI environment where every job starts with a clean slate, Docker often has to rebuild your entire application image from scratch, every single time.

But what if you could make your CI pipeline remember the work it did before? By implementing a smart Docker layer caching strategy, you can slash your container build times, often by 70% or more. This isn’t about compromising on build quality; it’s about working smarter with the tools you already have, primarily Docker’s modern builder engine, BuildKit. This guide will show you how to effectively use layer caching to accelerate your CI pipeline.

Understanding Docker’s Layered Filesystem: The Foundation of Caching

To understand caching, you must first understand how a Docker image is constructed. A Docker image isn’t a single, monolithic file; it’s a collection of read-only layers stacked on top of each other. Each instruction in your Dockerfile (like FROM, RUN, COPY, or ADD) creates a new layer that contains the changes from the previous one.

When you run a build on your local machine, Docker uses this layered structure for caching. Before executing an instruction, Docker checks if a layer with the exact same instruction and parent layer already exists in its local cache. If it does, Docker reuses that layer instead of rebuilding it. This process continues until it finds an instruction that has changed, which causes a layer invalidation. From that point forward, all subsequent layers must be rebuilt.

The problem in a CI/CD environment is that the runner executing your job is typically ephemeral. It’s a fresh virtual machine or container that doesn’t have the local cache from your previous builds. This is why you need a strategy to share the cache between different CI runs.

Enter BuildKit and docker buildx: Modernizing the Build Process

BuildKit is Docker’s next-generation build engine, offering significant improvements over the legacy builder. It’s the default builder in modern Docker versions and is the key to advanced CI docker cache strategies. BuildKit introduces several powerful features:

  • Parallel Build Processing: It can build independent stages of a multi-stage build in parallel.
  • Improved Caching: It offers more sophisticated and granular caching mechanisms.
  • Pluggable Cache Exporters: This is the game-changer for CI. BuildKit can export the build cache to external locations, like a container registry or a CI-native cache.

The docker buildx command is the user-friendly CLI that exposes these powerful BuildKit features, allowing you to create new builder instances and control advanced options like cache exports.

Key Docker Caching Strategies for CI/CD

Optimizing your Docker build performance in a CI pipeline involves two main efforts: structuring your Dockerfile correctly and telling BuildKit where to store and retrieve the cache.

1. Structure Your Dockerfile for Optimal Caching

The order of instructions in your Dockerfile is the single most important factor for effective caching. Remember that once a layer is invalidated, all subsequent layers are rebuilt. Therefore, you should order your instructions from least frequently changing to most frequently changing.

A well-structured Dockerfile for a Python or Node.js application often follows this pattern:

  1. Base Image: This rarely changes.
  2. Working Directory and Environment Variables: These are usually static.
  3. Copy and Install Dependencies: This is a critical step. Copy only the package manifest files (requirements.txt, package.json, pom.xml, etc.) first, and then run the installation command. This layer only changes when your dependencies do, not every time you change your application code.
  4. Copy Application Source Code: This is the most frequently changing part. By placing it last, you ensure that code changes only invalidate this final layer, allowing Docker to reuse all the preceding layers, including the time-consuming dependency installation step.

Additionally, always use multi-stage builds. A multi-stage build allows you to use one image with all the build-time dependencies (like compilers and SDKs) to compile your application, and then copy only the resulting artifacts into a slim, clean final image. This not only dramatically reduces your final docker image size but also improves caching by isolating build-stage dependencies.

2. Choose Your Remote Cache Backend

With docker buildx, you can use flags to manage your cache, telling BuildKit where to pull a pre-existing cache from and where to push the newly built cache layers. You can specify different types of cache backends depending on your CI platform and infrastructure.

Inline Cache

The cache metadata is embedded directly within the layers of the image you build.

  • Pros: The simplest method, requires no extra configuration.
  • Cons: It can bloat your final image with metadata. More importantly, it only caches the final stage of a multi-stage build, making it inefficient. It’s generally not recommended for CI/CD.

Registry Cache

This is a highly effective and popular strategy. The build cache is pushed as a separate manifest object to a container registry alongside your image.

  • Pros: Shareable across any runner or developer with access to the registry. Caches all stages of a multi-stage build.
  • Cons: Requires credentials to your container registry. Can lead to a lot of untagged cache images that may need periodic pruning using your registry’s lifecycle policies.

GitHub Actions Cache

If you use GitHub Actions, this is often the best choice. It uses the native GitHub Actions caching service to store the cache layers.

  • Pros: Deeply integrated into the platform, fast, and requires no external registry credentials.
  • Cons: Specific to GitHub Actions. The cache has an eviction policy (typically 7 days) and size limits.

Practical Example: Docker Layer Caching in GitHub Actions

A typical GitHub Actions workflow that uses the GHA cache backend can dramatically accelerate builds. The conceptual steps are:

  1. Checkout Code: The first step is always to get your source code into the runner environment.
  2. Set up Buildx: Enable the modern BuildKit engine, which is necessary for advanced caching features.
  3. Log in to Registry: If you intend to push the final image to a container registry like Docker Hub, you’ll need a step to authenticate.
  4. Build with Cache Flags: The core of the strategy is the build step. Here, you configure the build command to use the GitHub Actions cache. You’d specify that the build should attempt to pull from the GHA cache and, upon a successful build, push the new layers back to the cache. For maximum effectiveness, you should configure it to cache all layers from every stage of a multi-stage build.

On the first run, the build will be slow as no cache exists. But on subsequent runs, BuildKit will download the cache from the GHA service and skip any unchanged layers, leading to a dramatic speed-up.

By structuring your Dockerfile thoughtfully and leveraging a remote cache backend, you can transform your CI pipeline from a slow-moving bottleneck into a source of rapid feedback. This CI pipeline optimisation is not a one-time trick but a fundamental best practice for any team serious about DevOps and containerization.

Now that your CI pipeline is lightning-fast, ensure your deployed containers are performing as expected. Netdata provides per-second, real-time insights into your running containers, helping you connect deployment changes to performance impacts instantly.