name: canary
description: Canary deployment patterns
license: MIT
compatibility: opencode
metadata:
audience: devops-engineer, platform-engineer
category: devops
What I do
- Implement gradual traffic shifting strategies
- Create automated canary deployments with metric analysis
- Design rollback automation based on health checks
- Build observability dashboards for canary analysis
- Integrate canary releases with CI/CD pipelines
- Configure intelligent traffic routing based on user segments
When to use me
- When introducing new features to production
- When validating infrastructure changes
- When testing performance under real load
- When deploying to multiple regions progressively
- When working with A/B testing alongside deployments
- When reducing blast radius of potential issues
Key Concepts
Flagger Canary CRD
apiVersion: flagger.app/v1alpha3
kind: Canary
metadata:
name: myapp
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp
service:
port: 80
canaryAnalysis:
interval: 1m
threshold: 10
maxWeight: 50
stepWeight: 10
metrics:
- name: request-success-rate
threshold: 99
interval: 1m
- name: request-duration
threshold: 500
interval: 1m
Deployment Strategy
- Start with small percentage (1-5%)
- Increase gradually if metrics stay healthy
- Stop and rollback if errors spike
- Analyze at each stage before proceeding
- Full rollout typically takes 30-60 minutes
Analysis Criteria
- Error rate within acceptable threshold
- Response time not degraded
- No memory leaks or resource exhaustion
- Business metrics not negatively impacted
- No adverse effects on dependent services
By