Implementing Docker Image Optimization to Reduce Build Times and Registry Storage Usage
Introduction/Issue:
As the number of Dockerized services in our system grew, we noticed that the Docker images were becoming increasingly large, causing longer build times and consuming significant registry storage. This impacted on our CI/CD pipelines, leading to slower deployments and higher storage costs in our Docker registry.
Why we need to do it/Cause of the issue:
The root causes of the bloated images included:
Unnecessary Dependencies: Some images were built with unnecessary libraries and tools that were not required for production.
Lack of Multi-Stage Builds: The Dockerfiles did not use multi-stage builds, leading to larger images with redundant files.Caching Inefficiencies: Improper layer ordering in the Dockerfiles caused frequent invalidation of cached layers, increasing build times.
Unused Base Images: Old and unused base images were not being cleaned up, taking up unnecessary registry space.
How do we solve it:
To address these issues, we implemented the following optimizations:
Switching to Minimal Base Images:
We replaced heavy base images like ubuntu:latest with lightweight alternatives such as alpine wherever possible:
FROM alpine:latest
RUN apk add –no-cache python3
This reduced the image sizes significantly, sometimes by more than 50%.
Using Multi-Stage Builds:
We updated our Dockerfiles to use multi-stage builds to separate build dependencies from runtime requirements. For example:
Stage 1: Build
FROM golang:1.19 AS builder
WORKDIR /app
COPY . .
RUN go build -o my-app
Stage 2: Runtime
FROM alpine:latest
WORKDIR /app
COPY –from=builder /app/my-app .
CMD [“./my-app”]
This approach reduced image sizes by keeping only the essential files in the final image.
Automating Unused Image Cleanup:
We implemented a regular cleanup process to remove old and unused images from the Docker registry using a script:
docker image prune -a –filter “until=24h”
Conclusion: By adopting lightweight base images, multi-stage builds and automated image cleanup, we reduced Docker image sizes by an average of 40%, decreased build times by 25%, and cut storage usage in our registry by 30%. These optimizations not only improved pipeline efficiency but also reduced operational costs, enabling faster and more efficient deployments.
