Canary Deployment
Deployment strategy where a new version is gradually rolled out to a small percentage of traffic before full deployment.
Canary deployments roll out new versions gradually – first 1-5% traffic, then more with stable metrics, immediate rollback on issues.
Explanation
Canary deployments initially route 1-5% of traffic to the new version while monitoring KPIs. With stable metrics, the traffic share is gradually increased. On issues, traffic immediately rolls back to the stable version.
Marketing Relevance
Canary deployments minimize risk during ML model updates and critical system changes.
Common Pitfalls
Too-fast traffic ramping. Not waiting for statistical significance. No automatic rollback triggers.
Origin & History
The name comes from canaries in coal mines. Google and Netflix pioneered canary deployments in the ML context. Argo Rollouts (2019) and Flagger brought Kubernetes-native canary automation.
Comparisons & Differences
Canary Deployment vs. Blue-Green Deployment
Blue-green switches all traffic at once; canary increases the traffic share gradually.
Canary Deployment vs. Shadow Deployment
Shadow deployments mirror traffic without user impact; canary deployments route real user traffic to the new version.