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Kubernetes Essentials for Developers

Understand Kubernetes from a developer's perspective. Learn deployments, services, and debugging without becoming a cluster admin.

B
Bootspring Team
Engineering
August 5, 2025
5 min read

Kubernetes (K8s) orchestrates containers at scale. While platform teams manage clusters, developers need to understand K8s concepts to deploy and debug applications effectively. This guide covers what developers actually need to know.

Core Concepts

Pods

The smallest deployable unit. Usually one container, sometimes sidecars:

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Deployments

Manage pod replicas and updates:

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Services

Expose pods to network traffic:

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Configuration Management

ConfigMaps

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Using in deployments:

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Secrets

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Using secrets:

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Health Checks

Liveness and Readiness Probes

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Liveness: Is the container alive? Failure triggers restart. Readiness: Can it receive traffic? Failure removes from service. Startup: For slow-starting apps, delays other probes.

Debugging Applications

Common Commands

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Debugging Checklist

Pod not starting: 1. kubectl describe pod <name> - Check events 2. kubectl logs <name> - Check application logs 3. Verify image exists and is pullable 4. Check resource limits (OOMKilled?) Pod running but not accessible: 1. kubectl get endpoints <service> - Any endpoints? 2. Verify selector labels match 3. Check service port configuration 4. Test with port-forward Application errors: 1. kubectl logs -f <name> - Live logs 2. kubectl exec -it <name> -- sh - Interactive debug 3. Check ConfigMaps and Secrets mounted correctly 4. Verify environment variables

Deployment Strategies

Rolling Update (Default)

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Blue-Green with Services

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Resource Management

Requests and Limits

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Horizontal Pod Autoscaler

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Ingress

Expose to Internet

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Jobs and CronJobs

One-Time Jobs

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Scheduled Jobs

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Local Development

Minikube

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Skaffold

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Conclusion

Kubernetes knowledge empowers developers to deploy confidently and debug effectively. Focus on deployments, services, config management, and debugging—leave cluster administration to platform teams.

AI helps generate manifests, troubleshoot issues, and understand complex configurations. Start with simple deployments, add health checks, then expand to autoscaling and advanced patterns as needed.

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