Optimising Docker Images: Layers, Multi-Stage Builds & Slim Base Images

Every second your CI pipeline spends pushing a bloated Docker image to a registry is a second your deployment is blocked. At scale — dozens of services, hundreds of deploys per day — a 1 GB image versus a 100 MB image isn't a minor aesthetic preference, it's the difference between a 30-second deploy and a 5-minute one. It compounds across your entire fleet, inflates egress costs on AWS/GCP/Azure, and widens your attack surface because every unused package is a potential CVE waiting to be exploited.

The root cause is almost always the same: Dockerfiles written like shell scripts — one giant RUN block, a fat base image chosen for convenience, build tools left behind after compilation, secrets accidentally baked into layers. Docker's union filesystem means every layer is permanent history; you can't 'delete' a file from a previous layer by removing it in a later one — the bytes are still there, just hidden.

By the end of this article you'll be able to diagnose a bloated image using real tooling, rewrite Dockerfiles using multi-stage builds and layer-cache discipline, choose the right minimal base image for your workload, and avoid the production gotchas that catch even experienced engineers off guard. We'll go deep on the internals — because understanding why Docker layers work the way they do is what separates a developer who memorises tricks from one who can solve novel problems.

What is Optimising Docker Images?

Optimising Docker Images is a core concept in DevOps. Rather than starting with a dry definition, let's see it in action and understand why it exists.

// TheCodeForge — Optimising Docker Images example
// Always use meaningful names, not x or n
public class ForgeExample {
    public static void main(String[] args) {
        String topic = "Optimising Docker Images";
        System.out.println("Learning: " + topic + " 🔥");
    }
}

Frequently Asked Questions