Premature Optimization in Software Development

🔥 Covered All Important Docker commands 👇 ✅ Installation & Version Commands ✅ Image Management ✅ Running Containers (docker run) ✅ Container Management ✅ Logs & Monitoring ✅ Exec & Shell Access ✅ Volumes ✅ Docker Compose ✅ System Cleanup ✅ Full docker run Example ✅ When to Use What #Docker #DevOps #Backend #FullStack #Programming #Tech #Developers

🚀 Learning Kubernetes Through Real Production Challenges Today I focused on understanding real-world issues that happen in Kubernetes clusters instead of just theory. And honestly, this is where the real learning starts. Here are two important production challenges I learned: ⚠️ 1. Resource Sharing Problem When multiple applications run on the same cluster, one heavy pod can consume excessive CPU/Memory and affect others. 💡 Solution: Define proper requests & limits Use ResourceQuota and LimitRange Ensure fair resource distribution across workloads 💥 2. OOMKilled (Out Of Memory Killed) Issue A very common production issue where Kubernetes kills a pod because it exceeds its memory limit. 💡 Solution: Increase memory limits based on real usage Tune JVM heap size for Java applications Use monitoring tools to detect memory spikes Identify and fix memory leaks 🔍 Key takeaway: Kubernetes is not just about deploying pods — it’s about managing resources efficiently and keeping systems stable under real traffic conditions. I’m currently focusing on building strong DevOps fundamentals by understanding real production scenarios instead of only tutorials. #Kubernetes #DevOps #CloudComputing #Docker #SRE #LearningInPublic #Java #SpringBoot generate image as per this linkedin post info

Kubernetes made simple (finally understood it this way) ☸️ When I started learning Kubernetes, everything felt confusing — pods, nodes, clusters, YAML... too many terms. But then I looked at it as a lifecycle, and everything started making sense. Here’s the easiest way to understand it: 🔹 Step 1: Write Code You build your application (Java, Python, Node.js, etc.) 🔹 Step 2: Build Image Package your app into a Docker image 🔹 Step 3: Push Image Store it in a container registry (Docker Hub / ECR) 🔹 Step 4: Deploy to Kubernetes Use YAML or Helm to deploy your app 🔹 Step 5: Run Application Kubernetes creates Pods and runs your app 🔹 Step 6: Scale & Heal Auto scaling + self-healing (if a pod fails, it restarts automatically) 🔹 Step 7: Update Rolling updates with zero downtime 🔹 Step 8: Monitor Track performance using Prometheus & Grafana 💡 The real power of Kubernetes: ✅ No manual deployments ✅ Automatic scaling ✅ Self-healing systems ✅ High availability In simple terms, Kubernetes is like a smart manager that runs your application without you constantly watching it. Once you see it as a flow instead of complex concepts, everything becomes easier. #kubernetes #aws #k8s #devops

🚀 Day 40 : Debugging, Docker & Microservices Learnings Today was one of those deep technical days where everything that can break, does break — and you learn the most. I worked on a MERN-based microservices monorepo setup, dealing with services like API Gateway, Auth, User, Order, and Notification services, all containerized using Docker. 🧩 Key challenges I faced: Turborepo infinite loop issue due to missing workspace configuration Docker containers not reflecting code changes in real-time TypeScript + Express setup errors in service communication Debugging multi-service orchestration issues using Docker Compose 🔧 What I learned: Proper workspace configuration (like pnpm-workspace.yaml) is critical in monorepos Docker caching can be tricky — sometimes rebuilds don’t reflect changes unless handled correctly Microservices debugging is less about single-service logic and more about system-level thinking Small misconfigurations can cascade into large system failures Every bug today added a new layer of understanding about distributed system design, containerization, and monorepo architecture. #SoftwareDevelopment #FullStackDevelopment #MERNStack #Microservices #Docker #DevOps #BackendDevelopment #SystemDesign #NodeJS #TypeScript #CloudComputing #Programming #WebDevelopment #TechLearning #Debugging #Monorepo #Turborepo

Kubernetes co-founder Brendan Burns: AI-generated code will become as invisible as assembly Burns argues that as testing frameworks mature, developers will stop reviewing most code and programming languages will evolve to match. #devops #operations

Real talk: You keep getting “it works on my machine” issues. Different setups, different results. Fix: - Use Docker or a shared environment - Match versions (Node, DB, etc.) - Document setup clearly Same environment = fewer surprises. #Programming #DevOps #WebDev #DevTips

Production deploy or egg hunt? 🐰🍫 If you have to choose… better go with infrastructure that won’t surprise you. Run your application with stability on LetsCloud 🚀 #devlife #programming #cloudcomputing #devops #backend #letscloud

🚀 Docker Cheat Sheet for Developers Tired of “it works on my machine” problems? 🤯 Docker makes your apps run consistently everywhere. Here’s a quick visual guide covering: 🐳 Images 📦 Containers ⚙️ Docker Compose All essential commands in one place — like a Linux-style cheat sheet for daily use. If you're building modern apps, Docker isn’t optional anymore — it’s a must-have skill. #Docker #DevOps #WebDevelopment #MERN #Backend #Programming #SoftwareEngineering

Built a full CI/CD pipeline this week, and it is already production level work. Every time I run git push, here is what happens automatically: GitHub Actions spins up a fresh Ubuntu machine. Installs Python. Runs pytest on my code. If tests pass, it builds a Docker image, tags it with the commit SHA, and pushes it to Docker Hub. If tests fail, the Docker job never runs. The whole thing takes under a minute. Zero manual steps. Zero clicking. Just push and watch. Stack I used on this project: Python app deployed on EC2 Amazon Linux GitHub Actions workflow with two sequential jobs pytest for automated testing Docker image pushed to Docker Hub GitHub Secrets so no credentials ever touch the code The workflow file is 30 lines of YAML. What it actually does behind the scenes took me a while to fully appreciate. Genuinely grateful to Cloudlem (Pvt) Ltd. for the way this programme is structured. We are not doing tutorials. We are building things that real engineering teams ship to production. The difference shows. Week 4 loading. #DevOps #GitHubActions #CICD #Docker #CloudLem

Built a microservice in Go this week. Vehicle Service API — following Extreme Programming principles: → TDD first — 10 tests written before implementation → Clean layered architecture — handler, service, repository → Concurrent-safe with sync.RWMutex → Multi-stage Docker build → GitHub Actions CI/CD — green on first push New language. Same discipline. https://lnkd.in/gN46dbjS #Go #Golang #TDD #CleanCode #Docker #GitHubActions #BackendDevelopment #OpenToWork #Portugal