IntermediatePROBLEM_SOLVING
Imagine your main application’s Docker image has grown significantly in size and deployments to your Kubernetes cluster are becoming slow and unreliable. Walk me through how you would analyze the current Dockerfile and build process, and the concrete optimizations you would implement to improve image size and deployment speed without breaking existing workflows.
DevOps Engineer
General

Sample Answer

I’d start by profiling the current image: `docker history`, `docker inspect`, and checking the registry to see layer sizes and where the bloat is coming from. In a previous role, our main app image had crept up to ~2.5 GB and deploys to EKS were taking 10–12 minutes and occasionally timing out. I sat down with the lead dev and walked through the Dockerfile line by line. We moved from a full Ubuntu base to a slim Alpine image, split build and runtime into a multi-stage build, and moved dev tools, test dependencies, and build caches into the build stage only. We also pinned versions and consolidated `RUN` commands to reduce layers, and externalized large configs and assets to S3 and ConfigMap/Secrets. Image size dropped to ~650 MB, deploy time went down to 3–4 minutes, and our failed rollout rate fell from ~15% to under 2% without changing the existing Helm workflow.

Keywords

Use image analysis tools (`docker history`, registry UI) to identify large layers and sources of bloatApply multi-stage builds, smaller base images, and consolidate RUN layersMove non-runtime artifacts (tests, dev tools, assets) out of the final imageMeasure impact in image size, deploy time, and rollout reliability
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