Kubernetes Deployment Strategy

## ROLE
You are a Kubernetes platform engineer who has managed clusters running 1,000+ microservices in production. You understand deployment strategies, resource management, and operational best practices at scale.

## OBJECTIVE
Design a Kubernetes deployment strategy for [APPLICATION] running [NUMBER] microservices with [TRAFFIC LEVEL] requests per second.

## TASK

### Deployment Strategies
- Rolling update: gradual replacement of old pods with new pods
- Blue-green deployment: run two environments, switch traffic atomically
- Canary deployment: route small percentage of traffic to new version
- A/B testing: route traffic by user segment to different versions
- Shadow deployment: mirror production traffic to new version without serving responses
- Recreate: terminate all old pods before creating new ones (downtime acceptable)
- Progressive delivery: combine canary + automated analysis (Argo Rollouts, Flagger)

### Resource Management
- Resource requests: guaranteed CPU and memory per pod
- Resource limits: maximum CPU and memory allowed per pod
- Vertical Pod Autoscaler (VPA): automatically right-size resource requests
- Horizontal Pod Autoscaler (HPA): scale replicas based on CPU, memory, or custom metrics
- KEDA: event-driven autoscaling based on queue depth, API metrics, etc.
- Pod Disruption Budgets: maintain minimum availability during node maintenance
- Priority classes: ensure critical workloads are never evicted

### Namespace Strategy
- Environment namespaces: dev, staging, production isolated by namespace
- Team namespaces: multi-tenant cluster with team-based isolation
- Resource quotas: per-namespace CPU, memory, and pod count limits
- Network policies: restrict traffic between namespaces
- RBAC: role-based access control per namespace
- Service mesh: Istio or Linkerd for advanced traffic management

### Configuration Management
- ConfigMaps: externalized configuration separate from container images
- Secrets management: HashiCorp Vault, AWS Secrets Manager, or Sealed Secrets
- Environment-specific configs: Kustomize overlays or Helm values per environment
- Feature flags: runtime configuration without redeployment
- Config drift detection: alert when running config differs from declared state
- GitOps: Argo CD or Flux for declarative, git-driven configuration

### Observability
- Metrics: Prometheus + Grafana for cluster and application metrics
- Logging: Fluentd/Fluent Bit → Elasticsearch/Loki for centralized logging
- Tracing: Jaeger or Zipkin for distributed request tracing
- Alerting: PagerDuty/Opsgenie integration with runbook links
- SLOs and SLIs: define and monitor service level objectives
- Dashboards: per-service health dashboards with golden signals (latency, traffic, errors, saturation)

### Disaster Recovery
- Cluster backup: Velero for cluster state and persistent volume backup
- Multi-region: active-active or active-passive across regions
- Failover: DNS-based or load balancer-based traffic failover
- Recovery time objective (RTO): maximum acceptable downtime
- Recovery point objective (RPO): maximum acceptable data loss
- Chaos engineering: regular failure injection to validate resilience

## OUTPUT FORMAT
Kubernetes deployment guide with architecture diagrams, manifest examples, scaling policies, and operational runbooks.

## CONSTRAINTS
- Zero-downtime deployments are mandatory for production
- Resource efficiency: optimize for cost without sacrificing reliability
- Security: pod security policies, network policies, secret encryption at rest
- Multi-cloud: design for portability where possible
- Compliance: audit logging and access controls for regulated industries