kubernetes Concepts
Step-by-step explanations, real-world issues, and simplified understanding. Master every angle — from foundational concepts to real-world troubleshooting.
What we cover
Interactive guides & progressions
In-depth technical readings
Monitor, troubleshoot, and auto-scale Kubernetes workloads using Prometheus, Grafana, HPA, Cluster Autoscaler, and production debugging techniques.
Platform terminology defined
Written guides that show you how to apply this concept in real projects.
Progressive delivery lets you ship to 5% of users first and roll back in 30 seconds if something breaks — here is how to implement canary deployments with Argo Rollouts and Flagger on Kubernetes.
Kubernetes clusters routinely waste 40-70% of provisioned resources. Here is the complete playbook for cutting cloud spend without touching your SLOs.
ArgoCD and FluxCD are the two dominant GitOps engines for Kubernetes — this breakdown tells you exactly which one to pick and why.
Key words used in this concept — explained simply, no jargon.
A Kubernetes mechanism for partitioning cluster resources into isolated virtual segments. Namespaces allow multiple teams, projects, or environments to share the same physical cluster while maintaining logical separation of workloads, access controls, and resource quotas. They do not provide network isolation by themselves — that requires Network Policies.
An API object that manages external HTTP and HTTPS access to services inside a cluster. Ingress sits in front of multiple services and acts as a smart router — directing traffic based on hostnames, URL paths, or headers without exposing each service directly to the internet.
A Kubernetes pod status indicating the container repeatedly crashes immediately after starting and the kubelet applies exponential backoff delays before each restart attempt, beginning at 10 seconds and capping at 5 minutes. It signals that something inside the container is fundamentally broken - not a transient network issue.
The primary node agent in Kubernetes that runs on every worker node, responsible for ensuring containers described in PodSpecs are running, healthy, and reporting status back to the control plane.
Kubernetes Observability and Scaling covers a variety of key topic guides, including: Scaling Deployments with Horizontal Pod Autoscaler (HPA), Troubleshooting Kubernetes Pod OOMKilled and CrashLoopBackOff Errors, Troubleshooting ImagePullBackOff and Registry Authentication Issues. Monitor, troubleshoot, and auto-scale Kubernetes workloads using Prometheus, Grafana, HPA, Cluster Autoscaler, and production debugging techniques.
Kubernetes Observability and Scaling is a core learning conceptual pillar mapped within the kubernetes engineering hub of the DevOps Network.
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