Optimizing GitLab CI Pipeline Speed with Multi-Stage Docker Builds

How many commits have you made just to test if something works in the real environment? Push. Wait for the pipeline. It fails. Fix a config. Push again. Wait again. This is what happens when local dev looks nothing like production. Every fix is a commit, every commit is a 10-minute wait, and none of it is feature work. So I built a local dev platform where developers build and test on a real Kubernetes cluster that mirrors production. Same Dockerfile, same manifests, same ingress. - tilt up — see changes in 1 second instead of pushing and waiting - make ci-local — local gitlab pipeline run to catch failures before you push - Push once and it works, not 15 "fix CI" commits I wrote up how I built this. https://lnkd.in/dAQejEUU #Kubernetes #PlatformEngineering #DevOps #Tilt #GitLab

Following up on the first part of our CI/CD integration last week, we’ve just published Part 2 of the series, where I dive deeper into how we integrate Docker with Jenkins Pipelines to support scalable and repeatable automation within store infrastructure. In this article, I focus on the practical side of working with containerized workloads and how we’ve connected the Docker Engine API and Docker Hub repositories directly into our Jenkins pipelines to streamline build and test workflows. Some of the key areas covered in this part: 🐳 Leveraging the Docker Engine API from Jenkins Pipelines 🔗 Integrating Docker Hub as a central image repository ⚙️ Automating image build, tagging, and distribution steps 🧪 Using containerized environments to make test execution more consistent The goal remains the same: reduce manual effort, improve reliability, and make our delivery process easier to maintain as it grows. Read Part 2 here: 👉 https://lnkd.in/d9GHdDQD #JYSK #JYSKtech #Jenkins #Docker #DockerHub #CICD #Automation #InfrastructureEngineering #DevOps #TestAutomation

Building efficient Docker images is crucial for faster deployment and reduced storage costs. Docker multi-stage builds are a game-changer in this regard, enabling developers to slash image size by 80% 🚀. With multi-stage builds, you can separate the build process from the runtime environment, allowing for a much smaller final image. The killer feature of this approach is the ability to use a larger base image for compilation, such as `maven:3.6-jdk-8`, and then copy the resulting artifacts to a smaller image, like `openjdk:8-jre-alpine` 📦. However, one real gotcha to watch out for is the potential for slower build times due to the increased complexity of the build process ⏱️. To mitigate this, it's essential to optimize the build process by minimizing the number of stages and using Docker layer caching effectively. Docker multi-stage builds are a must-use tool for any developer or team looking to optimize their containerized applications. Who should use it? Anyone working with Docker, especially those using CI/CD pipelines to automate their build and deployment process 🚀. What's your favorite Docker optimization technique? #Docker #Containerization #MultiStageBuilds #DevOps #CloudComputing #ContainerOptimization

Most CI/CD pipelines fail for the same reason — no clear stages. After 4 years in DevOps, here's the multi-stage GitHub Actions pipeline I recommend to every engineer on my team: ━━━━━━━━━━━━━━━━━━━ Stage 1 → Test Stage 2 → Build & tag Docker image Stage 3 → Deploy to Staging Stage 4 → Deploy to Production (with manual approval) ━━━━━━━━━━━━━━━━━━━ 3 things that make this bulletproof: 1️⃣ Use needs: to chain jobs — if tests fail, nothing else runs 2️⃣ Tag images with github.sha — every build is fully traceable 3️⃣ Use GitHub Environments for prod — enforces human approval before anything goes live You don't need a complex tool to do this. A single YAML file in .github/workflows/ is enough to build a production-grade pipeline. Save this post for when you set yours up. What does your CI/CD stack look like? Drop it in the comments 👇 #DevOps #GitHubActions #CICD #Docker #Kubernetes #CloudNative #DevOpsEngineer #SoftwareEngineering

Your Kubernetes cluster is lying to you. And you won't find out until prod breaks. Here's a problem most platform engineers don't talk about enough: Config drift across environments. Everything looks identical — dev, staging, prod. Same Helm charts. Same GitOps repo. Same manifests. Then prod goes down. And you spend 3 hours figuring out why staging never caught it. Here's what actually happened: Someone patched a ConfigMap directly on the prod cluster with "kubectl edit" during last month's incident. Just a quick fix. "I'll raise a PR later." They didn't. Now prod is running a config that exists nowhere in Git. Your GitOps tool (ArgoCD, Flux — doesn't matter) shows everything as Synced because drift detection only works if the live state diverges from what's currently in Git. But the patch was never in Git to begin with. This is the gap nobody warns you about: - GitOps doesn't protect you from changes that never entered Git - kubectl diff only compares against what's applied, not what should exist - Multi-cluster setups multiply this problem — 5 clusters, 5 different "versions of truth" - The longer it goes undetected, the harder the blast radius when it surfaces The fix isn't just "don't use kubectl edit" — that battle is already lost in most orgs. The real fix is drift detection as a first-class concern: - Enable ArgoCD's self-heal and prune flags so live state is continuously reconciled - Run kubectl diff in your CI pipeline before every deploy, not just locally - Set up audit logging on your clusters — who ran kubectl commands, and when - Tools like Kyverno or Datree can flag live state mismatches proactively - Treat your cluster state like a database — no manual writes, ever The hardest part isn't the tooling. It's the culture shift of making "I'll fix it in Git later" completely unacceptable. Because in a fast-moving team, "later" is when prod burns. Been burned by config drift before? Drop it in the comments. #Kubernetes #DevOps #PlatformEngineering #GitOps #K8s #SRE #CloudNative

You updated your code… now are you manually building, testing, and deploying every time? 😬 CI/CD automates your entire pipeline: 𝗖𝗜 (𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻) → Build + test your code automatically 𝗖𝗗 (𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁) → Deploy to Kubernetes without manual steps 👉 Tools like GitHub Actions or Jenkins handle this flow Typical flow: Code push → Build Docker image → Push to registry → Deploy to Kubernetes Result: faster releases, fewer human errors Think of it like a factory assembly line 🏭 Raw material (code) enters Machines (CI/CD) build & test Finished product is automatically shipped (deployed) Would you trust manual deployments in production… or rely on an automated pipeline every time? 🤔 #Kubernetes #Docker #DevOps #CloudComputing #Containers #Microservices #LearningInPublic #TechLearning #SoftwareEngineering #CloudNative

A lot of CI/CD pipelines still push deployments to Kubernetes. But GitOps flips the model. Instead of your pipeline talking to the cluster, the cluster pulls from Git and keeps itself in sync. That’s exactly what this FluxCD workflow shows 👇 🔹 You push code → CI builds → image goes to registry 🔹 Flux detects changes (Git + registry) 🔹 It updates manifests and reconciles the cluster automatically No direct access from CI to production. No kubectl in pipelines. Why this matters: • Git becomes the single source of truth • Every deployment is auditable and reversible • Drift is automatically corrected • Your cluster is always aligned with what’s declared This “pull-based” model is what makes GitOps powerful and honestly, safer by design. If your pipeline is still doing direct deploys to the cluster, this is a pattern worth rethinking. #GitOps #FluxCD #Kubernetes #DevOps #PlatformEngineering

GitOps changed how I think about deployments. Here's the mental model: Before GitOps: ❌ SSH into server → pull code → restart service → pray ❌ Jenkins pipeline pushes directly to cluster ❌ "Who deployed what?" — nobody knows After GitOps: ✅ Git is the single source of truth ✅ ArgoCD watches the repo and syncs automatically ✅ Every deployment is a Git commit — auditable, reversible ✅ Multi-cluster? Just point ArgoCD at different directories Key decisions I made: 1. Mono-repo for manifests (simpler than multi-repo for our scale) 2. ArgoCD for app deployments, FluxCD for infra components 3. Automated image tag updates via CI → Git commit → ArgoCD sync If you're starting with GitOps, start with ArgoCD + a single cluster. Don't over-engineer day one. Save this for later ♻️ #GitOps #ArgoCD #FluxCD #Kubernetes #DevOps #EKS #Kubernetes #AWS #CICD #PlatformEngineering #GitOps #Terraform #ArgoCD #CloudEngineering #SRE #DevSecOps #BackstageIO #InfrastructureAsCode #GitHub #Docker #DevOpsCommunity #TechCareers #LearningInPublic #BuildInPublic

Every change in your Kubernetes cluster should be auditable, reversible, and self-healing. By default. GitOps makes this possible. With GitOps, you turn your Git repository into the single source of truth for your Kubernetes clusters. In this article, we compare the CNCF Graduated heavyweights for Kubernetes GitOps: ArgoCD and Flux. You'll understand: - Why GitOps matters - How you can install and use ArgoCD and Flux - How GitOps can be improved by using Lens Kubernetes IDE https://lnkd.in/dVBTjWyg #GitOps #Kubernetes #ArgoCD #FluxCD #DevOps #CloudNative #CNCF #K8s #Lens

Deploying backend on Kubernetes and debugging real-world issues — Day 34 Deployed FastAPI backend using Kubernetes (Deployment + Service) as part of my DevOps platform. Faced real production-like challenges: - Health check failures due to DB dependency - Kubernetes using cached Docker images despite rebuilds Resolved by: - Redesigning /health endpoint to remove external dependencies - Forcing image updates using imagePullPolicy: Always - Debugging using kubectl logs and rollout restart Key learning: Health checks must be lightweight and independent. Image versioning and pull behavior are critical in Kubernetes. This step marks my transition from containerization to orchestration. Next focus: Deploying full system (frontend + backend) on Kubernetes Project: https://lnkd.in/gj4YWDis #DevOps #Kubernetes #Docker #CloudEngineering #Microservices