canary-releases

What I do

• Design and implement canary release strategies
• Gradually shift traffic from stable to new versions
• Implement automated traffic routing based on metrics
• Create canary analysis and evaluation frameworks
• Build rollback triggers based on error rates and latency
• Monitor and analyze canary performance metrics

When to use me

• When you want to reduce risk of new deployments
• When you need to validate changes with real production traffic
• When deploying to large user bases where failures are costly
• When you want data-driven deployment decisions
• When implementing progressive delivery practices
• When working with microservices architectures

Key Concepts

Traffic Splitting

# Kubernetes Istio virtual service for canary routing
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: myapp
spec:
  hosts:
    - myapp
  http:
    - route:
        - destination:
            host: myapp
            subset: stable
          weight: 90
        - destination:
            host: myapp
            subset: canary
          weight: 10

Metrics-Based Routing

// Prometheus alert for canary analysis
- alert: CanaryHighErrorRate
  expr: |
    rate(http_requests_total{service="canary"}[5m]) 
    / rate(http_requests_total{service="stable"}[5m]) > 0.1
  for: 5m
  labels:
    severity: critical
  annotations:
    summary: "Canary error rate exceeds 10% of stable"
    description: "Automated rollback recommended"

Phased Rollout

1. Initial: 1-5% traffic to canary
2. Validation: Monitor error rates and latency
3. Expansion: Increase to 25%, 50%, 75%
4. Promotion: Full 100% traffic
5. Cleanup: Remove old version

Key Metrics to Monitor

• Error Rate: Compare canary vs. stable
• Latency: P50, P95, P99 response times
• Success Rate: Request completion percentage
• Resource Usage: CPU, memory, network
• Business Metrics: Conversion rates, session duration
• Custom Metrics: Domain-specific indicators

Rollback Triggers

• Error rate exceeds 2x stable environment
• P99 latency exceeds threshold (e.g., 500ms)
• CPU or memory usage exceeds 80%
• Custom business metric degradation
• Manual override triggered