Abheek Ranjan Das’ Post

🚨 Pods crashing with OOM… but no actual load? Here’s what we learned. We recently hit a strange issue: Pods were throwing OOM errors out of nowhere. ➡️ No traffic spike ➡️ No deployment ➡️ No visible bottleneck Like most teams, our first reaction was: “Let’s increase the memory.” We did… and nothing changed. Costs went up, OOM stayed. So we stopped scaling and started investigating the application instead. And that’s where we found it: A single application environment variable was influencing memory behaviour in an unexpected way. We updated it — and the issue vanished. No extra resources needed. 💡 Key takeaway: Not every problem is a Kubernetes or DevOps issue. Sometimes the real fix is hidden inside the application itself. Always check application behavior before throwing more memory at the pod. #DevOps #Kubernetes #SRE #CloudEngineering 

A small Kubernetes mistake that could’ve cost $2 million…” Two teams. One cluster. And one overlooked detail that nearly brought everything down. Team A deployed the new payments service. Team B rolled out notifications. Within hours — pods started vanishing, configs overwrote each other, and traffic went haywire. The engineers jumped in, debugging nonstop. Pipelines paused. Releases delayed. Customers waiting. It wasn’t just time slipping away — it was money. When your team of 15 engineers spends two full days firefighting instead of shipping features… you’re easily looking at $2 million in lost productivity and opportunity over the quarter. After hours of chaos, the fix turned out painfully simple: Everything was deployed in the default namespace. One cluster. No isolation. Identical resource names. Kubernetes wasn’t wrong — it was just following orders. We created proper namespaces — one for each team — and instantly: ✅ Deployments stabilized ✅ Logs cleaned up ✅ No more collisions ✅ Teams back to delivering value Sometimes the biggest cost in DevOps isn’t your infrastructure bill — it’s the time your engineers spend fixing what structure could’ve prevented. And in our case, one word saved us a small fortune: Namespaces. It is always good to follow best practices. 💬 How do you isolate your workloads — by environment, by team, or by project? #Kubernetes #DevOps #CloudNative #SRE #Engineering #FinOps #Productivity #K8s #RemoteDevOps #OpentoRemote

💡 Hot take: Most of our “modern infrastructure” isn’t modern — it’s just complicated cosplay. We’re drowning in YAML files, container registries, and CI/CD pipelines that take longer than the actual sprint — all because “that’s what industry best practice looks like.” Reality check: Half the time, nobody even remembers what problem those tools were supposed to solve. We call it DevOps maturity. But let’s be honest — it’s often just a shiny Rube Goldberg machine doing the job a bash script could’ve handled. Your uptime isn’t bad because you lack Kubernetes. It’s bad because you’re worshipping complexity instead of understanding context. So yeah — maybe the team deploying via FTP isn’t “behind.” Maybe they’re just not addicted to pain disguised as progress. #DevOps #SoftwareEngineering #TechSatire #EngineeringCulture #KeepItSimple #DeveloperHumor

🔧 Ready to break things on purpose—for a good reason? Dive into the world of chaos engineering and discover how controlled failure can actually strengthen your systems. This blog post breaks down the benefits of introducing chaos into your DevOps strategy—from uncovering hidden vulnerabilities to building rock-solid resilience. If you’re all about proactive problem-solving and building better infrastructure, this one’s for you. #ChaosEngineering #DevOps #ResilienceTesting #RheinwerkComputingBlog 📖 Read the blog and start embracing the chaos: https://hubs.la/Q03R8RjZ0

💥 DevOps Nightmare Series #3: Building Self Healing Infrastructure 🤖 Observability helped us see everything clearly across systems. But what if systems could go one step further and fix themselves before anyone wakes up at 3 AM? 😴 As the company scaled from 10 to 60+ microservices the need for self-healing infrastructure became clear systems that could react recover and restore automatically. 🔧 1️⃣ Automate the Obvious The first step was automating repetitive incidents like pod crashes, disk pressure, and CPU spikes. Using Kubernetes health probes, HPA and PodDisruptionBudgets, the platform could handle restarts and scaling without human intervention. 👉 "If you can detect it, you can automate it." ⚙️ 2️⃣ Build Intelligent Auto Remediation Connecting Prometheus alerts with Opsgenie enabled smarter responses. When something breaks the system doesn't wait it rolls back the change, restarts the pod, or shifts traffic to a healthy node in seconds. 🚀 ☁️ 3️⃣ Make Infrastructure Declarative Everything was defined through Terraform, Helm and GitOps (ArgoCD). When configs drift GitOps detects it and automatically syncs the cluster back to the last known good state. 🔄 No manual patching. No surprises. Just consistency on autopilot. 🧠 4️⃣ Run Disaster Recovery Drills Simulated failovers, DB outages and config rollbacks helped validate that the system could recover itself under real pressure. No scripts, no manual restarts. 💪 🚀 5️⃣ The Results ✅ 3AM alerts dropped by 80%+ ✅ MTTR reduced from hours to minutes ✅ Systems recover automatically before users even notice The dream isn't "zero incidents." It's zero manual recovery. 💬 What's one thing your team has automated recently that made life easier? Always curious to hear what others are doing to sleep better at night. 😄 Stay tuned for the next post which covers designing reliability pipelines that continuously test and improve system stability. 🔥 #DevOps #SRE #CloudEngineering #DubaiTech #GCCTech #KSA #Automation #SelfHealing #Kubernetes #ArgoCD #Terraform

The Kubernetes Iceberg Nobody Talks About Most people think Kubernetes is just kubectl run nginx and Deployments. They’re wrong. ❄️ Kubernetes has layers — and most teams never go past the surface. 🌤️ Above the water: Pods, Deployments, ReplicaSets, ConfigMaps, Services Easy to learn. Easy to demo. Easy to believe you understand Kubernetes. 🌊 Below the water: StatefulSets, DaemonSets, NetworkPolicy, PodSecurityPolicy, GitOps, Cluster Autoscaler This is where real reliability, security, and scale are built. This is where teams either level up — or break production at 3 AM. 🌑 Deep water: Admission Controllers, Mutating Webhooks, Operators, CRDs, Service Mesh, Node Hardening This is where Kubernetes stops being just a container platform and becomes infrastructure engineering. Here’s the truth: Kubernetes isn’t hard. Partial Kubernetes is hard. The more you understand below the surface,the more control you gain above it. #DevOps #Kubernetes #CloudNative #SRE #PlatformEngineering #Containers #GitOps #Helm #Infra #Ops

🚀 Master Docker Like a Pro! Your ultimate guide to Docker, DevOps, Containerization, and Application Security is here! Whether you're just starting out or scaling production systems, this comprehensive documentation delivers practical, real-world insights to elevate your engineering game. 🔍 What’s Inside? 🧱 Containers & Docker Engine 🛠️ Advanced Dockerfile Techniques & Image Optimization 🌐 Networking & Storage for Production 🔐 Secure Deployments with Docker Compose & Private Registry 📊 Monitoring & Logging with Prometheus, Grafana & ELK Stack 🛡️ Docker Security Principles, Best Practices & Performance Tuning 💡 Designed for beginners and seasoned engineers, this guide walks you through step-by-step use cases, from fundamentals to advanced strategies. 📘 Learn. Build. Secure. Scale. #Docker #DevOps #Containers #Security #DevSecOps #CloudEngineering #OpenSource #TechLearning

💻 DevOps Life in a Nutshell: “It works on my machine.” ✅ “Let’s containerize it.” 🐳 “Why is it failing in production?” 😅 “Wait... who changed the YAML again?” 🤦♂️ “Let’s add more logs!” 🧠 “Now the logs are too big.” 💥 DevOps isn’t just a workflow — it’s a daily rollercoaster of automation, caffeine, and chaos 🚀☕ To all my fellow DevOps folks out there — may your pipelines stay green and your servers never go down on Friday evenings 🙏😆 #DevOps #EngineeringHumor #CloudLife #CI/CD #TechHumor #Automation

Observability isn’t about hoarding logs—it’s about actually understanding what your system is doing. If you’re still calling it “observability” but can’t explain a single incident, that’s just expensive logging. So, be honest—how observable are your systems really? #Observability #DevOps #SystemDesign

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗱𝗿𝗮𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝘀𝗽𝗲𝗲𝗱 𝘂𝗽 𝘆𝗼𝘂𝗿 𝘁𝗶𝗺𝗲-𝘁𝗼-𝗺𝗮𝗿𝗸𝗲𝘁? That’s the promise of 𝗚𝗶𝘁𝗢𝗽𝘀 — define your infrastructure and applications in 𝗚𝗶𝘁, your single source of truth, and let the cluster continuously 𝗿𝗲𝗰𝗼𝗻𝗰𝗶𝗹𝗲 to maintain the desired state with 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 and 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗶𝗹𝗶𝘁𝘆. 𝗚𝗶𝘁𝗢𝗽𝘀 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 A study of 660 professionals found that 93% of organizations have already 𝗮𝗱𝗼𝗽𝘁𝗲𝗱 𝗚𝗶𝘁𝗢𝗽𝘀 or use it actively, and 68% plan to expand its use (DevOps .com). Why? Because GitOps brings 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 to deployments, reduces 𝗵𝘂𝗺𝗮𝗻 𝗲𝗿𝗿𝗼𝗿, and bridges development and operations around a 𝘀𝗶𝗻𝗴𝗹𝗲 𝘁𝗿𝘂𝘁𝗵 — enabling 𝗳𝗮𝘀𝘁𝗲𝗿, more 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 delivery. 𝗙𝗿𝗼𝗺 𝗧𝗵𝗲𝗼𝗿𝘆 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗚𝗶𝘁𝗢𝗽𝘀 𝗙𝗹𝘂𝘅𝗖𝗗 brings 𝗚𝗶𝘁𝗢𝗽𝘀 to life by automating the flow between code and cluster state — detecting every change, applying it 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆, and keeping your environment always in 𝘀𝘆𝗻𝗰. 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗮𝗱𝗼𝗽𝘁 𝗚𝗶𝘁𝗢𝗽𝘀? Read the full hands-on guide on Medium: https://lnkd.in/e86xQ68E #GitOps #FluxCD #Kubernetes #DevOps #CloudEngineering