canary

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