33 posts tagged with "Kubernetes"

You know Kubernetes. You've run minikube start a hundred times, maybe even wrestled with kubeadm on bare metal. But running Kubernetes in production is a different animal — one where the control plane going down at 3 AM means your pager goes off instead of someone else's. Amazon EKS takes that control plane problem off your plate and lets you focus on what actually matters: deploying and scaling your workloads.

Running a single Kubernetes cluster is straightforward until your application needs to survive a regional outage, comply with data sovereignty laws, or serve users on three continents without 200ms latency. At that point, you need multiple clusters — and that changes everything about how you deploy, network, and manage workloads.

Here is a number that should make you uncomfortable: the average Kubernetes cluster runs at 20-35% resource utilization. That means you are paying for three nodes but only using one node's worth of compute. Multiply that across dev, staging, and production clusters, and you are burning thousands of dollars a month on idle capacity. The good news — most of this waste is fixable with the right tools and a few YAML changes.

Your microservices architecture has grown to forty services. You need mutual TLS between all of them, but implementing certificate management in every service is a nightmare. You need traffic splitting for canary deployments, but your Ingress controller only handles north-south traffic. You need to answer "why is service A slow when calling service B?" but your application has no distributed tracing. A service mesh handles all of this at the infrastructure level, without changing a single line of application code.

Deploying a stateless web server to Kubernetes is straightforward — a Deployment, a Service, done. But try deploying a PostgreSQL cluster with streaming replication, automatic failover, point-in-time recovery, and backup schedules using just Deployments and StatefulSets. You end up with a mountain of init scripts, sidecar containers, and CronJobs that break every time you upgrade. Operators solve this by encoding all that operational knowledge into software that runs inside the cluster.

You have a CI pipeline that builds your container image and runs tests. The last step runs kubectl apply -f manifests/ against the production cluster. It works, until someone SSH-es into the server and runs kubectl edit deployment to "hotfix" something. Now your Git repository says one thing and the cluster says another. Nobody knows what is actually running in production. This is the exact problem GitOps solves.