Optimize Docker Builds with Layer Order

Most people rebuild containers. I tried fixing one. Another step in building in public. This week I ran into a Docker issue where a container was in an exited state. Instead of deleting and recreating, I stopped and checked what was actually wrong. Looked into volume mapping and port configs using docker inspect, then simply restarted it with docker start. The service came back and was accessible on port 8085. Biggest lesson: troubleshooting matters more than just knowing how to build. Follow along — more coming next week #backend #docker #devops #buildinpublic

🐳 Learning Docker… I’ve been using Docker for a while, but recently started understanding the basics more clearly. Things like: 1. Images vs Containers 2. Layers and how they affect build time 3. What a registry does 4. Why tags matter Still learning, but it’s starting to make more sense now. I wrote a short blog to capture my understanding: 🔗 https://lnkd.in/gxSG4v_p Would love to hear — what helped you understand Docker better? #Docker #LearningInPublic #DevOps

🚀 Learning Update | Docker & DevOps Fundamentals Here’s what I worked on recently: 🔹 Docker Concepts Studied core Docker concepts including: • Dockerfile • Image layers & caching • Best practices for efficient builds 🔹 Hands-on Implementation Created a multi-stage Dockerfile for a Node.js application to improve build efficiency. 🔹 Optimization Reduced image size using: • .dockerignore • Slim base images • Layer caching techniques ⚡ 🔹 Docker Compose Setup Built a setup with: • Node.js service • PostgreSQL service 🔹 Testing & Configuration • Verified services build, run, and communicate correctly • Configured environment variables, volume mounts, and health checks 🔹 Code Sharing Pushed Dockerfile and docker-compose.yml to GitHub for reference and reuse. Strengthening my DevOps fundamentals step by step. #Docker #DevOps #NodeJS #PostgreSQL #LearningInPublic #GrowthMindset

Most Docker + Kubernetes tutorials stop at Minikube with a hello-world container. That teaches you the commands. It doesn't teach you what actually matters in production. Here's what the hello-world tutorials leave out: - Layer ordering in your Dockerfile determines whether your CI pipeline takes 30 seconds or 8 minutes. Copy package.json before your source code. The dependency install layer is cached as long as dependencies don't change. - Standalone pods don't belong in production. If a pod dies, nothing replaces it. That's what Deployments and ReplicaSets are for. - Liveness and readiness probes are not the same thing. Liveness restarts a broken container. Readiness removes a temporarily overloaded one from the load balancer. Hitting the same endpoint for both is the mistake that turns a traffic spike into a cascading restart loop. - Resource requests and limits are not optional. Without requests, the scheduler can't make informed placement decisions. Without limits, one memory leak can starve every other pod on the node. - maxUnavailable: 0 and maxSurge: 1 in your rolling update strategy is what gives you zero-downtime deployments. The default settings don't guarantee this. We published a full Docker Kubernetes tutorial for 2026, from writing a production-quality Dockerfile with multi-stage builds, to Deployments, Services, Ingress, ConfigMaps, Secrets, and HPA. Real YAML, real commands, with the reasoning behind each decision. Link in comments. #docker #kubernetes #devops #platform #engineering

Ever had this moment where everything is running perfectly… and suddenly your Docker container just stops working? No code changes. No clear error. Just broken. Most of the time, it’s not a big failure—it’s something small hiding in the setup: Missing or incorrect environment variables A dependency not included inside the image A cached Docker layer not updating A version mismatch between services The frustrating part is Docker doesn’t always explain it clearly—it just fails quietly. So how do you actually fix it? You don’t guess—you isolate. Start with logs (docker logs <container>). Then check what’s actually inside the container using docker exec. If things still look off, rebuild without cache (--no-cache). And always verify versions and dependencies in your image. The real trick is simple: don’t look at Docker as “one system”—break it into small parts and test step by step. Once you do that, those “random issues” stop feeling random. #Docker #DevOps #Debugging #SoftwareEngineering #Containers

## 𝐃𝐨𝐜𝐤𝐞𝐫 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬 ## * docker 𝐩𝐬 -> It shows the running containers. * docker 𝐩𝐬 -a ->This command shows all the containers which are triggered to run the container. * docker 𝐢𝐦𝐚𝐠𝐞𝐬 -> This command shows all the available images in executing instance. * docker 𝐩𝐮𝐥𝐥 (image_name) -> This command pulls image from docker hub. * docker 𝐜𝐫𝐞𝐚𝐭𝐞 (image_name) -> This command create image. * docker 𝐫𝐮𝐧 (image_name) -> This command checks for image in local, if not present it will try to pull from dockerhub, if it is present, it will pull and start the container.  * docker 𝐫𝐦 (container_id) -> This command removes container. * docker 𝐫𝐦𝐢 (image_name) -> This command removes image. * docker 𝐫𝐮𝐧 -𝐝 (image_name) -> This command runs image in background mode. * docker 𝐫𝐮𝐧 -𝐝 -𝐩 (host_port) _ (container_port) -> This command maps host port to container port and start the container. * docker 𝐞𝐱𝐞𝐜 -𝐢𝐭 (container_id) 𝐛𝐚𝐬𝐡 -> This command will open bash terminal for the container, so that we can check whatever required is present in the container.  * docker 𝐢𝐧𝐬𝐩𝐞𝐜𝐭 (𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫_𝐢𝐝) -> This gives details of the container. * docker 𝐬𝐭𝐚𝐭𝐬 -> This command gives stats of the docker resources. * docker 𝐛𝐮𝐢𝐥𝐝 -𝐭 (image_name) -> This command builds the image. * docker 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 ls -> This command lists the available networks. * docker 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 created (network_name) -> This command will create network. * docker 𝐜𝐨𝐦𝐩𝐨𝐬𝐞 -d up -> This command will create all the docker container in compose file. * docker 𝐜𝐨𝐦𝐩𝐨𝐬𝐞 -d down -> This command will destroy all the running docker containers in the compose file. * docker 𝐜𝐨𝐦𝐩𝐨𝐬𝐞 build (component_name) -> This command will build specific component present in compose file. * docker 𝐯𝐨𝐥𝐮𝐦𝐞𝐬 created (volume_name) ->This command will create volume. * docker 𝐯𝐨𝐥𝐮𝐦𝐞𝐬 ls -> This command lists all available volumes. * docker 𝐯𝐨𝐮𝐥𝐦𝐞𝐬 inspect (volume_name) -> This command gets details for particular volume. * docker 𝐥𝐨𝐠𝐬 <container_id> -> This command gives logs for the given container. JoinDevOps #AWSDevOps #Docker #Kubernetes

One thing I have learned working with Dockerfiles: Simple is better. It is tempting to keep adding layers, commands, and complexity. More instructions. More dependencies. More custom scripts. But over time, that makes debugging harder. A clean Dockerfile: · Is easier to understand · Easier to maintain · And easier to troubleshoot Now, before I write one, I ask myself one question: "What is the simplest way to get this application running reliably?" That mindset has saved me a lot of time. No more overcomplicating things that should be straightforward. How do you approach writing Docker files? #Docker #Containers #DevOps #CloudComputing #TheEmpatheticEngineer

🚀 Day 6/30 – Docker Commands (Hands-on) Day 6 of my 30 Days of DevOps Challenge 💻 🔍 What I learned: Today I explored some basic Docker commands that are essential for working with containers. 🔑 Common Docker commands: • docker build – Build an image from a Dockerfile • docker images – List all images • docker run – Run a container • docker ps – List running containers • docker stop – Stop a container • docker rm – Remove a container 💡 Key takeaway: Understanding Docker commands is the first step toward real-world container management. 📌 Hands-on practice makes concepts much clearer than just theory! #DevOps #Day6 #Docker #Containers #LearningJourney #HandsOn

🗓️ Day 28/100 — 100 Days of AWS & DevOps Challenge Today's task: a developer has in-progress work on a feature branch but one specific commit is ready and needs to go to master right now, without dragging the rest of the unfinished work along. This is exactly what git cherry-pick is for. # Find the commit hash on the feature branch $ git log feature --oneline # abc5678 Update info.txt ← this one # Switch to master and cherry-pick it $ git checkout master $ git cherry-pick abc5678 # Push $ git push origin master One commit. Surgically applied. Feature branch untouched. 1. Why not just merge the feature branch? - The feature branch has in-progress commits code that isn't tested, isn't ready, and would break things on master. git merge feature brings ALL of it over. Cherry-pick takes only what's ready. 2. When this pattern matters in production: - A critical bug fix lands on a development branch. You can't merge the whole branch, there are half-finished features alongside the fix. You cherry-pick the fix onto master and onto any active release branches. This is how security patches get backported across multiple versions in open source projects. Same concept, same tool. The command to find a commit by message when you don't have the hash handy: $ git log --all --oneline --grep="Update info.txt" Saves time when the branch has many commits and you're looking for one specific one. Full breakdown on GitHub 👇 https://lnkd.in/gVHV9qPc #DevOps #Git #VersionControl #CherryPick #GitOps #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #CICD #Hotfix

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