Create deployment to host trinity-cache in Kubernetes

Create a production-grade Kubernetes deployment for Trinity Cache that enables scalable, secure, and observable operation across environments (dev → staging → production).

This issue proposes designing and implementing Kubernetes manifests / Helm chart (or equivalent) covering deployment, configuration, networking, persistence, autoscaling, and operational concerns.

🎯 Goals

• Provide a fully production-ready Kubernetes setup
• Support horizontal scalability and high availability
• Ensure secure configuration management
• Enable observability and operational readiness
• Allow environment-specific configuration
• Offer simple install + upgrade workflow

❌ Non-goals

• Kubernetes operator (initially)
• Multi-cluster federation
• Deep cloud-provider-specific infrastructure automation

🧱 Proposed Architecture
Workload

• Deployment or StatefulSet (depending on persistence requirements)
• Rolling updates with zero downtime
• Resource requests/limits tuned for cache workload

Networking

• ClusterIP service
• Optional ingress support
• TLS termination strategy (ingress or sidecar)

Configuration

• ConfigMaps for non-sensitive config
• Secrets for credentials
• Environment-based overrides
• Persistence (if required)
• PVC support with configurable storage class
• Backup strategy defined

Scaling

• Horizontal Pod Autoscaler (CPU/memory or custom metrics)
• Pod disruption budget

Security

• Non-root container
• Pod security context
• Network policies
• Secret management patterns

Observability

• Liveness & readiness probes
• Metrics endpoint integration
• Prometheus annotations
• Structured logging
• Tracing hooks (optional)

Implementation Options (to discuss)

• Helm chart (recommended)
• Kustomize overlays
• Raw manifests
• Hybrid Helm + Kustomize

Acceptance Criteria

• Helm chart or equivalent deployment artifacts
• Example values for dev/staging/prod
• Horizontal scaling verified
• Rolling upgrade tested
• Resource limits documented
• Security best practices applied
• Metrics and probes implemented
• CI workflow validating manifests
• Documentation added to README

Testing Strategy

• Kind or k3d integration test
• Upgrade test (N → N+1)
• Failure injection (pod kill / node drain)
• Load testing with realistic cache traffic

Documentation Requirements

• Quick start install guide
• Production tuning guide
• Scaling recommendations
• Observability setup
• Backup/restore (if persistence exists)
• Troubleshooting section

Future Enhancements

• Kubernetes operator
• Service mesh integration
• Multi-region active-active
• Advanced autoscaling (KEDA)
• Chaos testing automation