Update an existing Docker image with a new tag or by pulling the latest version.

Updating a Docker image isn’t about magic tricks. It’s about clean versioning, clear steps, and knowing which command actually changes what. If you’re sorting through Docker topics, you’ll hear a few different paths. Let me lay out the landscape and keep it practical.

A quick scenario you might see in learning materials

Suppose someone asks which method updates an existing image. Here are the options in a typical quiz-style setup:

• A) Using docker push with a new tag

• B) By running docker build with a new tag or docker pull

• C) With docker update command

• D) Through docker commit on a running container

The correct answer? B — By running docker build with a new tag or docker pull. Let me explain why, and what each option really does.

First, what updating an image means

Think of an image as a stack of layers. Each layer adds or changes something. When you “update” an image, you’re creating a new image that reflects your changes. The old image isn’t magically changed in place. You end up with a newer version that you can tag distinctly, then use just like the old one, but with improvements, fixes, or new features.

Option A: docker push with a new tag

Pushing is about sharing. If you’ve built a new image locally, you can tag it (say, myapp:1.2) and push it to a registry. But pushing alone doesn’t create that new image—it assumes you already built it. If you haven’t built a new image, pushing won’t update anything on your side. So, this path isn’t the direct way to update an image; it’s how you distribute your updated image once you’ve built it.

Option C: docker update command

This one trips people up because the phrase sounds plausible. In Docker, the update command doesn’t modify an image. It’s used to alter container resource settings (like memory limits or CPU shares) for a running or paused container. It doesn’t rewrite or enhance image layers. So, it won’t help you create a newer image version.

Option D: docker commit on a running container

This can be handy in a pinch. If you’re tweaking a container on the fly and want to capture its current state as a new image, docker commit can do that. But it isn’t the cleanest approach for versioning. The resulting image can be idiosyncratic and hard to reproduce, especially if you’re trying to keep change logs tidy. For robust version control and repeatable builds, most teams lean toward docker build with a Dockerfile (and a new tag) instead.

Why docker build with a new tag or docker pull is the right move

• docker build with a new tag: This is the standard, repeatable path. You modify your Dockerfile or the build context, run docker build, and create a brand-new image with a fresh tag (for example, 1.2 or 2.0). This gives you a clean, documented history of changes, and you can push that new tag to a registry for consistent deployment across environments.

• docker pull: This fetches a newer version from a registry. If someone published a newer image version, pulling can update your local store to that latest version. It’s a way to synchronize with upstream changes, but it doesn’t rewrite an existing local image in place. You typically pull to refresh what you already have, or to bring in a newer version for your use.

A practical path you can actually follow

Here’s a straightforward workflow you can rely on when you need a newer image version.

1. Make your changes

• Update your source files, the Dockerfile, or the build context.

• If you’re aiming for a new release, bump the version tag or add a semantic tag (like v1.2.3 or 1.2).

2. Build the new image with a fresh tag

• Run: docker build -t yourimage:1.2 .

• This creates a new image with the 1.2 tag. You can inspect it with docker images.

3. Test the new image locally

• Run a container from the new image to verify behavior.

• If you use Docker Compose, update the tag in the compose file and bring the stack up again.

4. Tag for registry delivery (optional but common)

• If you’re pushing to a registry, tag it for the repo: docker tag yourimage:1.2 yourrepo/yourimage:1.2

• Then push: docker push yourrepo/yourimage:1.2

5. Or pull a newer image from a registry

• If someone else published the updated image, you can pull it: docker pull yourrepo/yourimage:1.2

• If you’re maintaining a local workflow, you’ll likely re-run containers from the updated tag rather than trying to “patch” a running container.

6. Recreate containers to use the new image

• Don’t run a new container on top of an old one if you want the update. Stop the old container and start a new one based on the updated image.

• If you’re using orchestration (like docker-compose or Kubernetes), redeploy with the new image tag.

A few practical notes that keep things smooth

• Tag with care: Use meaningful, incremental versions and avoid relying on “latest” in production. It can become a brittle habit when you want predictable rollouts.

• Keep the history legible: Maintain a changelog or notes that explain what changed in each image version. That helps everyone understand the move from, say, 1.1 to 1.2.

• Clean up old images: Periodically prune unused images to reclaim disk space. But don’t blow away images your deployments still rely on.

• Use multi-stage builds: If you’re optimizing image size, multi-stage builds can shave off build-time dependencies from the final image. It’s a small win that compounds over time.

• Verify security updates: When you update an image, you might be updating dependencies and base images. Build steps should include scanning for vulnerabilities and updating base layers as needed.

A quick mental model you can carry

Imagine you’re updating a recipe. You don’t replace the box in the pantry. You bake a new batch with a new label, test it, and then decide whether to replace the old box in your routines or keep both versions handy for different meals. Docker works similarly: you create a new image with a new tag, test it, and decide how and when to use it. The idea is clear traceability and repeatability, not a one-off patch.

Common stumbling blocks and how to avoid them

• Mixing container runs with image updates: It’s tempting to think you can fix a problem by tweaking a running container. Translation: that doesn’t improve the image itself. You’ll want to adjust the Dockerfile or build arguments, then create a fresh image and redeploy.

• Skipping tests on new builds: A new tag is just a label unless the image actually works as intended. A quick local test before pushing saves you a lot of back-and-forth.

• Relying on “latest” in production: It sounds convenient, but it’s a trap for unpredictable deployments. Use explicit tags so you can pin exactly what’s running in each environment.

A few lines about automation

If you’re doing this regularly, automation helps a ton. A simple CI workflow can:

• Trigger on code changes to rebuild the image with a new tag.

• Run a basic test suite against the new image.

• Push to the registry if tests pass.

• Update a deployment manifest to point to the new tag and roll out the change.

When you want to rely on the registry instead of rebuilding from scratch, you can still benefit from automation by periodically pulling base images and rebuilding the downstream images with updated layers. It keeps your stack cohesive and less brittle.

Putting it all together

To update an existing Docker image the right way, you’re mostly looking at two reliable avenues: build a new image with a fresh tag or pull a newer image from a registry. The other options—changing container settings with docker update or saving a container’s state with docker commit—have their place, but they aren’t the cleanest paths for long-term image versioning and reproducibility. Pushing a new tag matters for distribution, but it doesn’t substitute for creating that new image in the first place.

So the bottom line is simple: if you want an updated image, build a new one with a new tag, or pull the newer version from a registry, and then recreate your containers to use that updated image. It’s a small workflow, but it pays off in clarity, reliability, and smoother deployments.

If you’re exploring Docker topics and want a steady compass, remember this pattern. It keeps your images tidy, your deployments predictable, and your learning grounded in solid, reproducible steps. And that, more than anything, makes working with containers feel a lot less mysterious and a lot more approachable.