Marko Filipovic’s Post

I used to think docker run nginx is just one simple command. But then I asked myself: 👉 What actually happens after I hit Enter? And honestly… that question changed how I understand systems. Because behind that one command — there’s an entire workflow happening in seconds 👇 So I built something to visualize it. 💡 An interactive simulation that shows: → How Docker CLI talks to the daemon → How images are pulled from the registry → How containers are actually created → What really happens inside the Linux kernel And I didn’t stop there — I documented the complete flow step-by-step. 📌 What you’ll understand after this: ✔ Real Docker architecture (not just theory) ✔ Role of containerd & runc ✔ How namespaces & cgroups actually work ✔ What’s really running inside a container 💻 Interactive simulation link 👉 https://lnkd.in/dT72PNgC 📄 Full PDF guide links In Comments👇 This is not just learning Docker. This is understanding how systems actually work. If you're preparing for DevOps or UpSkilling— this perspective makes a huge difference. Would love your feedback 🙌 #Docker #DevOps #CloudComputing #Kubernetes #SoftwareEngineering #LearningInPublic #SystemDesign #LearnwithHarinesh

🐳 Top Docker Commands Every Developer Should Know If you're working with Docker, mastering a few core commands can make your workflow faster, cleaner, and more efficient. Here are some essential Docker commands every developer should know: 🔹 1. Check Docker Version docker --version 🔹 2. Pull an Image from Docker Hub docker pull nginx 🔹 3. List Images docker images 🔹 4. Run a Container docker run -d -p 3000:3000 node-app 🔹 5. List Running Containers docker ps 🔹 6. List All Containers (including stopped) docker ps -a 🔹 7. Stop a Container docker stop <container_id> 🔹 8. Remove a Container docker rm <container_id> 🔹 9. Remove an Image docker rmi <image_id> 🔹 10. View Logs docker logs <container_id> 🔹 11. Execute Command Inside Container docker exec -it <container_id> bash 🔹 12. Build an Image docker build -t my-app . 🔹 13. Docker Compose Up docker-compose up -d 🔹 14. Docker Compose Down docker-compose down 💡 Pro Tip You don’t need to memorize everything — but knowing these commands can cover 80% of real-world Docker use cases. Mastering Docker CLI is a big step toward becoming a DevOps-ready developer 🚀 #Docker #DevOps #Containerization #WebDevelopment #CloudComputing #CICD #SoftwareEngineering #BackendDevelopment #TechSkills #Programming

🐳 Day 66: Docker Command Deep Dive Debugging a messy Docker Compose setup today reminded me why I love this command: docker-compose ps -a Ever been in that situation where you're staring at your screen wondering "what containers did this compose file actually create?" This little gem shows you EVERYTHING - running, stopped, crashed containers - the whole family tree of your compose project. 🎯 Use Cases: Beginner: You ran docker-compose up but some services aren't working. Use this to quickly see which containers failed to start or exited unexpectedly. Pro Level 1: During deployment rollbacks, use this to verify which version of containers are actually running vs what you expected to deploy. Pro Level 2: When inheriting legacy projects, this helps you map the actual container landscape against the docker-compose.yml file to spot any orphaned or missing services. 💡 Pro Tip: Remember "ps = Process Status" and the "-a" means "all" (just like regular docker ps -a). Think of it as your compose project's family photo - everyone's included, even the ones that didn't make it! 📸 The beauty is in the details - you'll see container names, status, ports, and commands all in one clean table. Super handy for those "why isn't this working" moments we all have. What's your go-to debugging command for Docker issues? Drop it in the comments! Tomorrow brings another command worth mastering 🚀 #Docker #DevOps #Containers #DockerCompose #TechTips #Developer My YT channel Link: https://lnkd.in/d99x27ve

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

Common Docker Mistakes I Made (So You Don’t Have To) 🙂 After working with Docker in real projects and VPS deployments, I realized something: Most problems were not advanced issues. They were basic mistakes repeated again and again. Here are some mistakes I made early on: 📌 1. Building images locally every time I used to build images on my laptop and push to Docker Hub. Result: • high CPU usage • slow builds • storage issues Fix: Moved builds to GitHub Actions. 📌 2. Not cleaning Docker regularly I didn’t remove unused images and containers. Result: • VPS storage filled up • server became unstable Fix: Use: docker system prune -a regularly. 📌 3. Ignoring logs When something failed, I guessed instead of checking logs. Result: • wasted time • wrong assumptions Fix: docker logs container_name Logs solve most problems. 📌 4. Hardcoding configs I sometimes put values directly in code. Result: • deployment issues • environment mismatch Fix: Use .env properly. 📌 5. No clear project structure At the beginning, everything was messy on the VPS. Result: • difficult debugging • hard to scale Fix: Use a clean docker-compose based structure. After all these mistakes, one thing became clear: Docker is simple — but discipline is required. If you follow a clean process, Docker becomes extremely powerful. Lesson: You don’t need to be perfect from the start. You just need to learn from your mistakes and improve your system. In the next post, I’ll share my complete VPS deployment architecture — how everything connects in production. #Docker #DevOps #SoftwareEngineering #VPS #BuildInPublic

Kubernetes doesn't care how much you know. It only cares if your YAML matches your reality. I spent 4 hours fighting a cluster today. It started with a simple request: Build a 3-replica Nginx deployment. Expose it. Update the image. The lab manual said it would take 15 minutes. It took me half a day. I broke everything. I typed kubeclt instead of kubectl while the clock was ticking. I tried to mix create and apply commands like they were interchangeable (they aren't). I spent 20 minutes staring at a JSONPath error because I forgot a single dot in {.spec}. My cluster didn't just throw errors; it threw them with attitude. But here is the thing about SRE work: We don't get paid for the days everything works. We get paid for the 4 hours of frustration that leads to that one moment of "Oh, that’s why." The error isn't the enemy. The error is the documentation you haven't read yet. If you’re feeling like an imposter because you can’t get a simple Pod to spin up, you’re not failing. You’re just in the middle of a debugging session that hasn’t paid off yet. I mapped out the mental model that finally saved my cluster today. Save this for when your terminal starts screaming at you. What’s the one error message that still makes your heart rate spike even after years in the industry?

These days almost every developer knows how to use Docker. We know how to write a Dockerfile, use Docker Compose, push/pull images, reduce image size, configure networks, volumes, and run containers. But recently I started thinking about something different. What problems were engineers actually trying to solve when they built Docker? The famous line "It works on my machine, but not in production" perfectly describes the chaos developers faced for years. When you dig deeper, Docker mainly solves four core problems: • Isolation – using Linux namespaces • Resource control – using cgroups • Portability – build once, run anywhere • Storage efficiency – layered images with UnionFS Under the hood, Docker is a beautiful piece of engineering built around components like: • Docker CLI • Docker Daemon • containerd • runc • Docker Registry I wrote a short blog explaining the engineering problems behind Docker and its architecture. Blog Link:- https://lnkd.in/gRG2AvQd Sometimes understanding why a technology exists is more valuable than just knowing how to use it. #docker #devops #kubernetes #containers #softwareengineering

How Docker Works Ever wondered what actually happens when you run a Docker command? Here’s a step-by-step breakdown of how Docker actually works under the hood. 1️⃣ Docker build → Docker reads your Dockerfile line by line. It uses your current folder as the build context. 2️⃣ Each line in the Dockerfile creates a new image layer. These are stored as compressed files inside Docker’s storage. 3️⃣ Docker uses a union filesystem (like OverlayFS) to stack all those layers into a single container filesystem. 4️⃣ Docker run → takes the image, adds a writable layer on top, and that becomes your running container. 5️⃣ A container isn’t a VM — it’s just a process running on your system, isolated from others using Linux features. 6️⃣ Isolation happens with namespaces (PID, network, mounts) + cgroups (controls CPU, memory, I/O). 7️⃣ Docker gives the container a virtual ethernet interface (by default linked to the docker0 bridge). 8️⃣ Port mapping (-p) → Docker sets up iptables rules to forward traffic from your host to the container. 9️⃣ The Docker daemon (dockerd) runs in the background. It handles builds, containers, images, volumes, and networks. 🔟 The Docker CLI talks to the daemon using a REST API (via Unix socket or TCP). 1️⃣1️⃣ Volumes live outside the container layer (in /var/lib/docker/volumes). They survive container restarts. 1️⃣2️⃣ Any change inside a container is temporary. Delete the container and the changes are gone (unless saved to an image or volume). 1️⃣3️⃣ Docker uses content-based hashes for layers — making them reusable, cacheable, and shareable. 1️⃣4️⃣ When you push an image, Docker only uploads the missing layers. Faster, lighter pushes. 1️⃣5️⃣ Bottom line → Docker looks simple on the outside, but under the hood it’s an elegant system of layers, isolation, and APIs that make modern DevOps possible.  What was the most useful concept you learned while working with Docker? #Docker #DevOps #Containers #CloudComputing #Kubernetes

𝗜 𝗿𝗲𝗱𝘂𝗰𝗲𝗱 𝗮 𝗗𝗼𝗰𝗸𝗲𝗿 𝗶𝗺𝗮𝗴𝗲 𝗳𝗿𝗼𝗺 𝟭.𝟱 𝗚𝗕 ➡ 𝟱𝟬 𝗠𝗕 (𝟵𝟱.𝟮% 𝘀𝗺𝗮𝗹𝗹𝗲𝗿). 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄: Bloated images slow down deployments, eat storage, and create security risks. Keeping containers lean is one of the most practical skills in DevOps. 𝟳 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗜 𝗳𝗼𝗹𝗹𝗼𝘄: 1. 𝗨𝘀𝗲 𝘀𝗺𝗮𝗹𝗹 𝗯𝗮𝘀𝗲 𝗶𝗺𝗮𝗴𝗲𝘀 — Alpine or slim variants instead of full OS images. Immediately cuts hundreds of MBs. 2. 𝗠𝘂𝗹𝘁𝗶-𝘀𝘁𝗮𝗴𝗲 𝗯𝘂𝗶𝗹𝗱𝘀 — build in one stage, copy only the final artifact. Dev tools never make it into production. 3. 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 𝗼𝗻𝗹𝘆 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 — every extra package adds size and attack surface. Be strict in production. 4. 𝗖𝗹𝗲𝗮𝗻 𝗰𝗮𝗰𝗵𝗲 𝗮𝗳𝘁𝗲𝗿 𝗶𝗻𝘀𝘁𝗮𝗹𝗹𝘀 — remove cache in the same RUN command so the layer stays lean. 5. 𝗥𝗲𝗱𝘂𝗰𝗲 𝗗𝗼𝗰𝗸𝗲𝗿 𝗹𝗮𝘆𝗲𝗿𝘀 — chain commands with && so each step doesn't create a new layer. 6. 𝗨𝘀𝗲 .𝗱𝗼𝗰𝗸𝗲𝗿𝗶𝗴𝗻𝗼𝗿𝗲 — keeps node_modules, .git, logs, and local configs out of your image context. 7. 𝗗𝗼𝗻'𝘁 𝗿𝘂𝗻 𝗮𝘀 𝗿𝗼𝗼𝘁 — create a dedicated user. Minimal privileges = better security posture. These are not advanced tricks — they're fundamentals. But most beginners skip them. I'm actively applying these while building real 𝗗𝗼𝗰𝗸𝗲𝗿 𝗮𝗻𝗱 𝗗𝗲𝘃𝗢𝗽𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀. Every image I ship, I ask: is this as lean as it can be? Which of these do you already use? 𝗗𝗿𝗼𝗽 𝗶𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 👇 #Docker #DevOps #Linux #Containers #CloudEngineering #AWS #DevOpsJourney #90daysofdevops #Docker