Storing the Dockerfile in source control helps you track changes to a Docker image.

Outline for the article

• Hook: a quick scene about chasing changes in a Docker image and why it matters.

• Core idea: the Dockerfile stored in source control is the reliable trail of changes.

• Why this approach beats other ideas (container state saves, per-image branches, or registry notes).

• How to put it into action (simple steps you can actually follow).

• Practical tips and light, real-world tangents (CI, tags, multi-stage builds, .dockerignore).

• Common misconceptions and clarifications.

• Closing thought: reproducibility, collaboration, and making life easier for everyone who touches the project.

A clean, clear path to tracking Docker image changes

Let me ask you something. When you build a Docker image, do you want to rely on a guess about what’s inside, or do you want a solid, human-readable record of every change that went into that image? Most teams pick the latter. They treat the Dockerfile as the blueprint—an explicit, versioned recipe that describes base images, packages, environment variables, and the exact commands used to assemble the final product. When you store that Dockerfile in source control, you create a durable history you can inspect, revert, or improve over time. That’s the heartbeat of good software placement: traceability you can trust.

Why the Dockerfile is the real tracker

Here’s the thing: a container snapshot is ephemeral. It’s convenient to think of a saved state, but that state is not a stable record of decisions. If you rely on a saved container or notes in a registry, you risk drift—the situation where the running image diverges from what you intended. The Dockerfile, on the other hand, is a textual blueprint. It captures decisions like which base image to start with, which packages to install, what environment variables to set, and which commands to run to configure the image. When this file lives in version control, every modification gets a timestamp, a author, and a clear commit message. You can review the history, compare changes side by side, and even revert to a previous version with ease.

For teams, this is more than tidy history. It’s accountability with a human face. You can see who added a package, why a specific base image was chosen, or why a certain environment variable was introduced. It’s not about policing; it’s about clarity. And clarity makes collaboration possible, especially when people join a project midstream or when you need to reproduce a build in a different environment.

What to track in practice

• The Dockerfile itself: the go-to source of truth. It lists the base image, the instructions to install dependencies, and the commands that shape the final image.

• Build context and commands that affect the image: operations like apt-get install, apk add, pip install, npm install, and the order in which steps run matter because they determine layers and caching.

• Base image pinning: tagging a base image (for example, python:3.11-slim) helps lock down the starting point and reduces surprise as upstream images change.

• Environment variables and ARGs: keeping these in the Dockerfile makes the image’s behavior explicit.

• Multi-stage builds (when used): these help you separate build-time dependencies from the final image, and documenting that flow is essential.

A practical way to implement this

• Put the Dockerfile in your Git repository, alongside your application code. This alignment makes it obvious which code builds which image.

• Use meaningful commit messages. A simple pattern helps: “(base) pin to python:3.11-slim; (deps) install libxyz; (config) set APP_ENV=production.” The narrative in the history is invaluable when you’re debugging or rolling back.

• Build automatically from the Dockerfile in CI. Configure the pipeline to pull the repo, run docker build, and test the image. If the build passes, you’ve got confidence that the Dockerfile and your code are in harmony.

• Tag and release images with a versioned label, but keep the Dockerfile under version control. The tag is for runtime consumption, the Dockerfile for traceability.

• Keep a changelog or a short note in the repository that explains why significant changes were made. A sentence or two in a CHANGELOG.md can save hours when someone wonders, “Why did we install this particular package now?”

• Use .dockerignore to keep the build context lean. It’s not glamorous, but it speeds things up and reduces the chance of accidentally including sensitive files.

A little imagination helps

You can think of the Dockerfile as a recipe in a shared cookbook. If a cook adds a new spice, they write it down where everyone can see, not just in a whispered chat. If the oven temperature changes, that note sits in the book, easy to locate, easy to compare with yesterday’s version. People can reproduce the dish exactly, even if they weren’t in the kitchen when the change happened. That same clarity translates to software—especially when teams grow, when parts get swapped, or when you’re debugging why a container behaves differently on another machine.

A few practical tips that keep things calm and productive

• Pin versions where it matters. If you rely on external tooling, specify exact versions or sha256 digests for critical steps. This makes builds deterministic and easier to reason about.

• Use multi-stage builds when appropriate. They’re great for keeping final images lean and for documenting the separation between build-time and runtime actions.

• Document the intent in a short README alongside the Dockerfile. A quick paragraph explaining the purpose of the image, its main use cases, and any known caveats helps newcomers.

• Keep base image updates intentional. When a new base image comes out, assess whether you want to bump it in a controlled way rather than letting it drift in silently.

• Review changes with care. A quick pull request review that checks what changed in the Dockerfile (and why) pays dividends later.

Let’s wander a little, then circle back to the point

If you ever work in a team that ships software quickly, you’ve probably seen a familiar pattern: a clever Dockerfile gets created, a dozen tweaks land in a hurry, and suddenly the image seems to work in one environment but not another. What’s missing in that scene is a reliable, human-readable record of those tweaks. That’s where the Dockerfile in source control shines. It’s not about policing; it’s about making the ride smoother for everyone, from the fresh-eyed newcomer to the veteran engineer.

While we’re on the tangent, consider the mindset of shipping software as a chain of small decisions. Each Dockerfile line is a decision point: should we install this library now or later? Do we need this environment variable for production, or is it a holdover from a previous stage? When you track these decisions in version control, you create a map of intent. That map is incredibly helpful when you’re debugging or when you’re explaining a setup to a teammate who didn’t witness the original development arc.

Common misunderstandings (and a gentle correction)

• Misconception: You should save container states to the repository. Correction: container states are ephemeral and can be idiosyncratic to a runtime environment. The Dockerfile is a stable, portable record that describes how to recreate the image from a clean slate.

• Misconception: A separate Git branch for every image version is best. Correction: branches are a powerful tool, but the real strength comes from the Dockerfile staying in sync with code changes and build context. Versioning can be handled with tags and commits in the Dockerfile plus a clear release process.

• Misconception: Registry notes alone can tell the full story. Correction: registries are great for distribution, but they don’t capture the rationale behind changes. The Dockerfile in source control carries the story.

A final thought to keep in mind

The habit of keeping the Dockerfile in source control is more than a best practice; it’s a cultural move. It signals that you value reproducibility, collaboration, and responsibility. It makes it easier to onboard someone new, because they can read the Dockerfile, see exactly what’s installed, and understand how the image is assembled without wading through disjoint notes or stale screenshots. And when you need to roll things back or audit how an image came to be, you’ve got a clear, navigable trail.

In short, treat the Dockerfile as the single source of truth about your image. Put it in your code repository, maintain it with your other project files, and let the history speak for itself. The image may look polished on the surface, but the real story is in the code that built it. And that story is most honest when it lives where every other part of your project lives: in source control, alongside the code, ready for review, reuse, and shared understanding.

If you’re looking for a mindset to carry forward, here it is: clarity first, then speed. A well-maintained Dockerfile in a well-organized repository keeps both. You’ll thank yourself on a coffee-break morning when you can quickly trace a change, rebuild with confidence, and explain the decision with a few clean lines of history. That’s the rhythm of good container development—predictable, collaborative, and undeniably practical.