NeuralBlitz 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
- Initial: 1-5% traffic to canary
- Validation: Monitor error rates and latency
- Expansion: Increase to 25%, 50%, 75%
- Promotion: Full 100% traffic
- 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