Kubernetes at Scale

After managing production Kubernetes clusters at Google serving millions of requests daily, I've learned that theory and practice often diverge. Here's what actually matters in production.

The Reality of "It Works on My Machine"

Local Kubernetes (minikube, kind) is great for development, but production is a different beast. Here's what changes:

Resource Constraints

• Local: Unlimited resources (relatively)
• Production: Every CPU cycle and MB of RAM costs money
• Lesson: Set realistic resource limits and requests from day one

Network Complexity

• Local: Simple networking, everything works
• Production: Network policies, service meshes, ingress controllers
• Lesson: Test network policies early and often

State Management

• Local: StatefulSets "just work"
• Production: Persistent volumes, backup strategies, disaster recovery
• Lesson: Treat state as a first-class concern

The Three Pillars of Production Kubernetes

1. Observability

You can't fix what you can't see. Our stack:

• Metrics: Prometheus + Grafana
• Logs: Centralized logging with structured logs
• Traces: Distributed tracing for request flows
• Alerts: Smart alerting (not alert fatigue)

2. Reliability

99.9% uptime isn't luck, it's engineering:

• Pod Disruption Budgets (PDBs)
• Horizontal Pod Autoscaling (HPA)
• Cluster autoscaling
• Multi-zone deployments
• Regular chaos engineering

3. Security

Security isn't optional:

• Network policies by default
• Pod Security Standards
• RBAC with least privilege
• Regular security scans
• Secrets management (never in Git!)

Common Pitfalls and Solutions

Pitfall 1: Over-engineering

Problem: Using every Kubernetes feature because it exists Solution: Start simple, add complexity only when needed

Pitfall 2: Ignoring Resource Limits

Problem: Pods without resource limits causing cluster instability Solution: Always set requests and limits, use LimitRanges

Pitfall 3: Stateful Applications

Problem: Treating stateful apps like stateless ones Solution: Use StatefulSets, understand storage classes, plan for backups

Pitfall 4: Configuration Management

Problem: ConfigMaps and Secrets sprawl Solution: Use tools like Kustomize or Helm, version everything

Performance Optimization

Real-world optimizations that made a difference:

1. Node Affinity: Place pods strategically
2. Pod Anti-Affinity: Spread replicas across nodes
3. Resource Quotas: Prevent resource hogging
4. Vertical Pod Autoscaling: Right-size your pods
5. Cluster Autoscaling: Scale nodes based on demand

The Human Factor

Technology is only half the battle:

• Documentation: Write runbooks for common issues
• Training: Ensure team understands Kubernetes concepts
• Incident Response: Practice incident handling
• Blameless Postmortems: Learn from failures

Conclusion

Kubernetes is powerful but complex. Success comes from:

• Understanding fundamentals deeply
• Starting simple and iterating
• Prioritizing observability and reliability
• Learning from production incidents
• Sharing knowledge with your team

Remember: Kubernetes is a tool, not a goal. Use it to solve real problems, not because it's trendy.

🐋 Like orcas hunting in pods, successful Kubernetes deployments require coordination, communication, and continuous learning.