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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

Scaling Deployments with Horizontal Pod Autoscaler (HPA)Troubleshooting Kubernetes Pod OOMKilled and CrashLoopBackOff ErrorsTroubleshooting ImagePullBackOff and Registry Authentication Issues

3 Subtopics

Interactive guides & progressions

13 Articles

In-depth technical readings

52 Glossary Terms

Platform terminology defined

3 FAQs

Common questions answered

Kubernetes Observability and Scaling

Monitor, troubleshoot, and auto-scale Kubernetes workloads using Prometheus, Grafana, HPA, Cluster Autoscaler, and production debugging techniques.

You cannot fix what you cannot see. Production Kubernetes clusters generate thousands of metrics, logs, and events every second — and without the right observability stack, incidents become guesswork. This pillar covers how engineering teams at Hotstar, Swiggy, and Zerodha monitor, debug, and automatically scale their Kubernetes workloads.

What This Pillar Covers

  • Setting up Prometheus for metrics collection and alert rules
  • Building Grafana dashboards for pod CPU, memory, and request rate visibility
  • Configuring Alertmanager routing to Slack and PagerDuty
  • Horizontal Pod Autoscaler (HPA) for automatic pod scaling based on CPU and custom metrics
  • Cluster Autoscaler for automatic node provisioning on AWS EKS
  • Troubleshooting OOMKilled, CrashLoopBackOff, and ImagePullBackOff errors in production

Who This Is For

Site reliability engineers, DevOps engineers, and platform engineers responsible for maintaining uptime, diagnosing production incidents, and ensuring Kubernetes workloads scale reliably under traffic spikes.

Frequently Asked Questions

What does the Kubernetes Observability and Scaling concept cover?

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.

How does Kubernetes Observability and Scaling relate to the Kubernetes hub?

Kubernetes Observability and Scaling is a core learning conceptual pillar mapped within the Kubernetes engineering hub of the DevOps Network.

Are these DevOps concepts free to learn?

Yes, all lessons, visual roadmaps, and guides on DevOps Network are 100% free with no paywalls or sign-up gates for learning content.