Tag
12 items tagged DevOps.
A troubleshooting sequence (get, describe, logs, events, exec, port-forward) applied to four broken workloads: a crashing container, a missing image, a missing ConfigMap and an empty Service. Real events, real fixes.
What readiness, liveness and startup probes do (with a failing one of each), how requests decide scheduling and limits decide OOMKilled and throttling, what the cgroup counters show, and how termination grace works.
Manual scaling, rolling updates and rollback as an operator sees them, and a HorizontalPodAutoscaler on kind with metrics-server: real utilisation figures, a 2-to-6 scale-up under load, and the scale-down window.
How a request finds a pod: cluster DNS, the Service ClusterIP, the EndpointSlice the selector fills, port versus targetPort, headless Services and Ingress in outline; each inspected on a kind cluster, mistakes included.
Deployments, StatefulSets, DaemonSets, Jobs and CronJobs on a real three-node cluster: what kubectl reports, what happens when you delete a pod of each kind, why a DaemonSet skips the control plane, and which to use.
The operating model behind the CI templates, ApplicationSets, Kyverno guardrails and observability stack on this site: what a team hands over, what the platform returns, where the golden path ends and enforcement begins.
Kyverno as a platform guardrail system: baseline and production policy layers keyed on namespace labels, delivered by Argo CD from one repository, rolled out audit-first, with scoped expiring exceptions.
One platform-owned GitLab CI template instead of a pipeline per repository: spec:inputs with validation, a consumer that includes it by project and tag, extension points, versioning, and what a consumer can override.
Make a pipeline produce the same result every time and fail usefully: pinned images and lockfiles, a cache keyed on the lockfile, artifacts versus caches, needs, retries and timeouts, and build kept apart from deploy.
What a delivery pipeline does between a push and a deployment: stages and jobs, runners, checkout, build, test, artifacts, environments and promotion, with a GitLab CI pipeline you can execute locally and watch fail.
Rolling updates, blue/green switches and canary releases built from plain Deployments and Services on a kind cluster, with the health checks, gates and promotion steps that decide when each one is right.
Make rollback a deploy, not a rebuild: immutable artifacts and image digests, a failed rollout detected with rollout status and conditions, kubectl rollout undo on a real failure, and migrations planned separately.