Kubernetes Troubleshooting: Identify the Broken Layer

☸️ Understanding Kubernetes – 5 Core Building Blocks Before diving deep into Kubernetes, it's important to understand its core building blocks. These are the foundation of every Kubernetes cluster. 📦 1. Container A container is the smallest lightweight unit that runs your application. It packages: • Application code (binary) • Dependencies • Runtime environment Containers are managed by container runtimes like containerd. 🛑 Important: Kubernetes does NOT manage containers directly. ✅ It manages Pods, which run containers. 🧩 2. Pod A Pod is the smallest deployable unit in Kubernetes. • Contains one or more containers • Shares the same network and storage • Managed by controllers (like Deployments) to ensure reliability 👉 You never deploy containers directly in Kubernetes — you deploy Pods. 🖥️ 3. Node A Node is a machine (virtual or physical) where Pods run. Each node includes: • Container runtime (e.g., containerd) • Kubelet (agent communicating with control plane) • Kube-proxy (handles networking rules) 👉 Pods run on Nodes, and Nodes are part of a cluster. 🌐 4. Cluster A Kubernetes Cluster is a complete system that consists of: • Control Plane Nodes → manage the cluster • Worker Nodes → run applications All operations like deploying, scaling, and managing apps happen inside the cluster. 🛠️ 5. kubectl kubectl is the command-line tool used to interact with the cluster. With kubectl, you can: • View cluster resources • Deploy applications • Update or delete resources • Debug issues 👉 Think of kubectl as your remote control for Kubernetes 📌 Example Commands kubectl get pods kubectl get all kubectl apply -f app.yaml kubectl describe pod <name> Understanding these fundamentals is the first step toward mastering Kubernetes and building scalable, containerized applications. #Kubernetes #DevOps #Containers #CloudComputing #K8s #LearningInPublic

Kubernetes stops feeling complex the moment you stop looking at it as a collection of commands and start seeing it as a system that continuously reconciles reality with intention. Over the past weeks i’ve been exploring Kubernetes (k8s) from the inside out, not just running workloads but understanding why the platform behaves the way it does. At its core, kubernetes is not about containers. Containers are just the outcome. The real power lies in its architecture: You declare a desired state. The control plane observes. Controllers compare expectation vs reality. The scheduler decides placement. kubelet executes and maintains workloads on nodes. Everything operates as a continuous feedback loop. Working through a single-node cluster using Minikube and Docker made this especially clear. When you create a Deployment you are not starting containers, you are defining intent. Kubernetes then distributes responsibility across its components to make that intent real and keep it real even when failures occur. Some insights that stand out: • Pods are ephemeral execution units, not long-lived servers • Deployments are state definitions, not runtime processes • Services abstract instability, not just networking • Namespaces introduce logical isolation rather than infrastructure separation • Imperative commands help exploration, but declarative configuration defines reliability The shift from imperative to declarative thinking is where Kubernetes truly clicks. Instead of managing systems step-by-step you design outcomes and let the platform enforce consistency. Kubernetes is not just orchestration, it’s automated operational reasoning encoded into software. #Kubernetes #DevOps #Containerization #SoftwareEngineering #InfrastructureAsCode #CloudComputing #Docker #K8s #Automation #OpenSource

🚨 Most Kubernetes deployments fail not because of bad code — but because of the wrong deployment strategy. I've seen teams take down production with a simple update. Not because they didn't test. But because they chose Recreate when they needed Blue-Green. Here's a complete breakdown of all 6 Kubernetes Deployment Strategies — with real YAML, pros/cons, and when to use each 👇 ♻️ Recreate → Kill all pods, redeploy. Simple. But expect downtime. 🔄 Rolling Update → Replace pods gradually. The safe default for most teams. 🔵🟢 Blue-Green → Two environments. Instant traffic flip. Instant rollback. 🐤 Canary → Ship to 5% of users first. Monitor. Then expand. 🧪 A/B Testing → Route specific users to different versions. Data-driven decisions. 👥 Shadow → Mirror real traffic to new version. Zero user impact. Perfect for risky rewrites. ✅ Each strategy includes: → Architecture diagram → Production-ready YAML → When to use it → Rollback commands → Tool recommendations (Argo Rollouts, Istio, Flagger) 📖 Full blog here 👇 🔗 https://lnkd.in/dJYKUJ-C 💬 Which deployment strategy does your team use in production? Drop it in the comments 👇 #Kubernetes #DevOps #CloudNative #K8s #DeploymentStrategies #BlueGreenDeployment #CanaryDeployment #RollingUpdate #SRE #GitOps #ArgoRollouts #Istio #EKS #AKS #CI_CD #ZeroDowntime #PlatformEngineering #Microservices #Docker #TechOps

🚨 Kubernetes Pods Not Working? Follow This Troubleshooting Flow! While working with Kubernetes, one of the most common challenges developers face is debugging why a Pod, Service, or Ingress is not working correctly. While working on my CampusX project, I had the opportunity to explore Kubernetes and understand how deployments actually work in real-world environment I recently came across this excellent Kubernetes Troubleshooting Flowchart from Learnk8s, which provides a systematic way to debug deployment issues. Instead of randomly trying commands, this guide helps you diagnose problems step-by-step. 🔍 Key troubleshooting steps covered in the flowchart: ✅ Check Pod status kubectl get pods Identify Pending, CrashLoopBackOff, or ImagePullBackOff issues ✅ Inspect logs and events kubectl logs <pod-name> kubectl describe pod <pod-name> ✅ Verify container image & registry Check image name and tag Confirm private registry access ✅ Check Services and Endpoints kubectl describe service <service-name> Verify selectors match pod labels ✅ Debug networking kubectl port-forward to test connectivity ✅ Verify Ingress configuration Check backend services Validate ingress rules 💡 Takeaway: Most Kubernetes issues can be solved faster when you follow a structured debugging approach instead of trial-and-error. This flowchart is a great cheat sheet for DevOps engineers, cloud engineers, and platform engineers working with Kubernetes clusters. 📌 Saving this for future debugging sessions! #Kubernetes #DevOps #CloudComputing #Containers #K8s #PlatformEngineering #LearnK8s #Troubleshooting

🚨 Most Kubernetes deployments fail not because of bad code — but because of the wrong deployment strategy. I've seen teams take down production with a simple update. Not because they didn't test. But because they chose Recreate when they needed Blue-Green. Here's a complete breakdown of all 6 Kubernetes Deployment Strategies — with real YAML, pros/cons, and when to use each 👇 ♻️ Recreate → Kill all pods, redeploy. Simple. But expect downtime. 🔄 Rolling Update → Replace pods gradually. The safe default for most teams. 🔵🟢 Blue-Green → Two environments. Instant traffic flip. Instant rollback. 🐤 Canary → Ship to 5% of users first. Monitor. Then expand. 🧪 A/B Testing → Route specific users to different versions. Data-driven decisions. 👥 Shadow → Mirror real traffic to new version. Zero user impact. Perfect for risky rewrites. ✅ Each strategy includes: → Architecture diagram → Production-ready YAML → When to use it → Rollback commands → Tool recommendations (Argo Rollouts, Istio, Flagger) 📖 Full blog here 👇 🔗 https://lnkd.in/dYrszykr 💬 Which deployment strategy does your team use in production? Drop it in the comments 👇 #Kubernetes #DevOps #CloudNative #K8s #DeploymentStrategies #BlueGreenDeployment #CanaryDeployment #RollingUpdate #SRE #GitOps #ArgoRollouts #Istio #EKS #AKS #CI_CD #ZeroDowntime #PlatformEngineering #Microservices #Docker #TechOps

Kubernetes upgrades are not cluster housekeeping anymore. They are platform engineering work. And One thing many teams still underestimate is this: The version bump is rarely the hard part. The real work sits in everything around it: ingress, storage drivers, autoscaling, observability agents, policy layers, GitOps controllers, Helm charts, and the workload assumptions application teams have been carrying for months. That is exactly why upgrades reveal platform maturity faster than almost anything else. Take a practical example. If you are on Kubernetes 1.32 and want to get to 1.34, you do not just “upgrade the cluster.” In a kubeadm-managed setup, you move from 1.32 to 1.33 first, and then from 1.33 to 1.34. Somewhere in that path, you are not only validating the control plane. You are checking whether your add-ons, manifests, controllers, and operational habits still hold. And this is where the platform lens matters. In 1.33, direct use of the Endpoints API was officially deprecated in favor of EndpointSlices. On paper, that can look like a small note in release documentation. In reality, it can surface old scripts, controllers, internal tooling, and troubleshooting practices that teams forgot they were still depending on. That is why mature teams do not approach upgrades as maintenance windows alone. They approach them as a coordinated platform change: i. compatibility mapping, ii. staging validation, iii. workload disruption planning, iv. rollback design, v. and clear ownership between platform and application teams. A strong platform is not one that avoids change. It is one that can absorb change without turning every upgrade into organizational stress. Kubernetes maturity is not measured by how quickly a cluster was provisioned. It is measured by how confidently the platform can evolve when production is already depending on it. #Kubernetes #PlatformEngineering #CloudEngineering #DevOps #EKS #GKE #PlatformTools #Containerisation #K8s #DOKS #PatchManagement #Versioning #PlatformUpgrades

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

The Kubernetes mistake that wastes 30 minutes every day: Using a single kubeconfig file for multiple clusters. 😅 Here's what happens: → You run a command → Wrong cluster responds → You panic and double-check everything → Repeat 10 times a day The fix is simple: Separate config files (config.dev, config.uat, config.prod) Set KUBECONFIG to auto-merge them Use kubectl config get-contexts to verify ✅ Switch contexts explicitly: kubectl config use-context admin-dev Bonus: Install k9s for visual cluster management 🎯 Now I know exactly which cluster I'm working with. Time saved per week: ~2.5 hours ⏰ Accidental production changes: 0 🛡️ What's your approach to managing multiple clusters? Would love to hear what's working for you. 💬 #Kubernetes #DevOps #BestPractices

Day 4 learning Kubernetes. Yesterday I learned how Kubernetes decides WHERE pods run. Today I learned how to control WHAT pods are allowed to consume — and how some pods are special enough to bypass the usual rules. Here's what clicked today. 1. Resource Requests & Limits By default, a pod can consume as much CPU and memory as it wants. That's dangerous in a shared cluster. So, you set two things: Requests = the minimum guaranteed resources for the pod to start Limits = the hard ceiling it can never cross If a pod crosses its memory limit — Kubernetes kills it. Immediately. If it crosses CPU — it gets throttled, not killed. One unconfigured pod can starve everything else on the node. This is why limits matter. 2. DaemonSets Some workloads need to run on every single node in your cluster. Log shippers. Monitoring agents. Security scanners. You don't say "give me 3 replicas." You just define the DaemonSet. Kubernetes handles the rest — one pod per node, automatically. A new node joins the cluster? Pod appears. Node is removed? Pod is gone. No manual intervention. Ever. 3. Static Pods This one genuinely surprised me. Most pods go through the API server to get scheduled. Static Pods skip all of that. You place a YAML file in a specific directory on the node. The kubelet picks it up and runs it — no API server, no scheduler involved. Here's the wild part: this is exactly how Kubernetes runs its own control plane components like the API server, etcd, and the scheduler themselves. The cluster bootstraps itself using Static Pods. Mind = blown. Kubernetes gives you layers of control — from how much a pod consumes, to ensuring a pod runs everywhere, to running pods that exist outside the normal system entirely. #kubernetes #cloudnative #CNCF #DevOps

🚀 Kubernetes : Why "Deployment" is the secret sauce of High Availability! 🚀 If you think managing containers is just about running a few Pods, think again. Today my Kubernetes journey was all about the power of Deployments, DaemonSets and StatefulSets. Here is the breakdown of how Kubernetes keeps applications 99.99% available: 🏗️ The Big Three of Pod Management Deployments: The gold standard for stateless apps. They manage replicas, handle gradual updates and ensure your system stays stable even during traffic spikes. DaemonSets: Perfect for background tasks like monitoring (e.g: Prometheus node exporters). A DaemonSet ensures that every single worker node in your cluster runs exactly one instance of a Pod. StatefulSets: The go-to for databases. These are essential when Pods need to maintain a stable identity, hostname and persistent storage. 🛠️ Hands-on Highlights Self-Healing & Scalability: Using ReplicaSets, Kubernetes continuously tracks the desired state. If a Pod fails, it’s reborn. Need to scale? A simple change in the deployment spec handles it all—no manual intervention needed. Rolling Updates: I experimented with strategy: RollingUpdate and minReadySeconds: 10. This allows for smooth transitions where old pods are terminated only after new ones are ready, ensuring zero downtime. Exposing the App: Used ku expose to create NodePort services, allowing external access to my game and database pods via specific ports (like 32001 and 30426). 💻 Quick Command Cheat Sheet ku create deployment testpod1 --image ... --replicas 6 --dry-run -o yaml (Generate manifest) ku apply -f deploy.yml (Deploy the manifest) ku get svc -o wide (Check service ports and external access) Kubernetes isn't just about running code, it's about building a system that heals itself, scales itself and updates itself. Thank you Saikiran Pinapathruni for guidance #Kubernetes #DevOps #CloudComputing #Containerization #K8s