- Team Context: C# development team focused on features
- Challenge: No deployment plan for production
- Constraints: Must use in-house Kubernetes platform
- Scale: Millions of active players per day
- Goal: Team must independently manage application post-launch
graph TD
A[Discovery & Assessment] --> B[Infrastructure as Code & CI/CD]
B --> C[Performance & Observability]
C --> D[Knowledge Transfer & Handover]
style A fill:#e6f7ff,stroke:#1890ff,stroke-width:2px
style B fill:#f6ffed,stroke:#52c41a,stroke-width:2px
style C fill:#fff7e6,stroke:#fa8c16,stroke-width:2px
style D fill:#f9f0ff,stroke:#722ed1,stroke-width:2px
- Discovery & Assessment
- Infrastructure as Code & CI/CD
- Performance & Observability
- Knowledge Transfer & Handover (Ongoing)
Key Activities:
- Understand internal Kubernetes and cloud platform**
- Application architecture review sessions
- Identify stateful vs. stateless components
- Map external dependencies and data flows
- Document configuration requirements
- Establish initial performance baselines
Deliverables:
- Architecture diagram for Kubernetes deployment(ADR)
- Infrastructure requirements document
Containerization Strategy:
- Multi-stage Docker builds optimized for C#/.NET
- Environment-specific configuration management
- Secrets handling approach
Kubernetes Resources:
# Key resources we'll implement:
- Deployments/StatefulSets
- Services (ClusterIP, LoadBalancer)
- Ingress configurations
- ConfigMaps & Secrets
- HorizontalPodAutoscalers
- ServiceMonitor
- Logging configuration
Infrastructure provisioning and lifecycle:
- Terraform("Infrastructure as Code") implementation
- Cluster provisioning
- Dependent services provisioning
- Associated lifecycle automation
CI Pipeline:
flowchart LR
commit[Code Commit] --> build[Build & Test]
build --> scan[Security Scan]
scan --> package[Package Container]
classDef blue fill:#e6f7ff,stroke:#1890ff,stroke-width:2px
classDef green fill:#f6ffed,stroke:#52c41a,stroke-width:2px
classDef orange fill:#fff7e6,stroke:#fa8c16,stroke-width:2px
classDef purple fill:#f9f0ff,stroke:#722ed1,stroke-width:2px
class commit,build,scan blue
class package green
CD Pipeline:
flowchart LR
tag[commit version tag] --> build
build --> package[Container]
package --> devdeploy[Deploy to Dev]
devdeploy --> integration[Integration Tests]
integration --> stagingdeploy[Deploy to Staging]
stagingdeploy --> load[Load Tests]
load --> proddeploy[Deploy to Production]
classDef blue fill:#e6f7ff,stroke:#1890ff,stroke-width:2px
classDef green fill:#f6ffed,stroke:#52c41a,stroke-width:2px
classDef orange fill:#fff7e6,stroke:#fa8c16,stroke-width:2px
classDef purple fill:#f9f0ff,stroke:#722ed1,stroke-width:2px
class tag,build,scan blue
class package,devdeploy green
class integration,stagingdeploy orange
class load,proddeploy purple
Performance Testing Strategy:
- Implement load testing with realistic player scenarios
- Identify bottlenecks through distributed tracing
- Optimize resource utilization
- Test autoscaling policies
- Introduce metrics and SLI
- Establish SLO and buy in SLA - Part 1(Engineers/SRE)
- Deploy per environment infra. service - sandbox env for Grafana, Loki
Observability Stack:
- Application metrics with Prometheus
- Centralized logging with ELK/Loki
- Distributed tracing with Jaeger/OpenTelemetry
- Custom dashboards for game-specific metrics
graph TD
A[Player Request] --> B{Gateway / Ingress}
B --> C[Game Service Pod 1]
B --> D[Game Service Pod 2]
B --> E[Game Service Pod n...]
C --> F[(Primary Database)]
D --> F
E --> F
C --> G[(Redis Cache)]
D --> G
E --> G
F --> H[Database Replica 1]
F --> I[Database Replica 2]
subgraph "Autoscaling"
J[HPA Controller] -. monitors .-> C
J -. monitors .-> D
J -. monitors .-> E
J -. scales .-> K[New Pod Creation]
end
classDef gateway fill:#e6f7ff,stroke:#1890ff,stroke-width:2px
classDef service fill:#f6ffed,stroke:#52c41a,stroke-width:2px
classDef data fill:#fff7e6,stroke:#fa8c16,stroke-width:2px
classDef scaling fill:#f9f0ff,stroke:#722ed1,stroke-width:2px
class A,B gateway
class C,D,E service
class F,G,H,I data
class J,K scaling
Team Enablement:
- Hands-on workshops vs. theoretical training
- "Infrastructure as Code" paired programming sessions
- On-call shadowing program
- War Games - Chaos testing and Disaster recovery drills
- Establish SLO and buy in SLA - Part 2(PM)
- Review application and infra., backup policies in terms of systems to backup and retention policies
SRE:
- Runbooks for common scenarios
- Troubleshooting guides
- Architecture decision records(ADR)
- Post-mortem templates
Stateful Data Handling:
- Session management strategy
- Database scaling approach
- Cache implementation for performance
Security Considerations:
- Network policies for pod communication
- Secret rotation strategy
- Image vulnerability scanning
- RBAC implementation
Scaling Strategy:
- Horizontal pod autoscaling configuration
- Node scaling policies
- Database connection pooling
- Queue-based workload distribution
Overcoming Resistance:
- Start with pain points (current deployment frustrations)
- Demonstrate velocity improvements with automation
- Create early wins through simplified local development
- Implement "DevOps Days" for hands-on practice
Measuring Success:
- Deployment frequency
- Change lead time
- MTTR (Mean Time To Recovery)
- Business metrics
Transition to Self-Sufficiency:
- Weekly office hours
- Incident response training
- Regular architecture reviews
- Continuous improvement cycles
Long-term DevOps Advocacy:
- Monthly capability assessments
- Technical debt reviews
- New tooling evaluations
- Cross-team knowledge sharing - share learnings both side
Thank you for your attention!