Deployment Strategies: Scenario-Based Questions

A Canary Deployment is a progressive release strategy where a new version of an application is deployed to a small subset of users before rolling it out to the entire environment. This helps detect issues early with minimal impact.

🎯 Objectives of Canary Deployments

• Reduce risk of deploying unstable code to all users.
• Enable real-time monitoring and rollback if issues are detected.
• Support rapid iteration with safe validation in live environments.

⚙️ Implementation Approaches

• Kubernetes: Use progressive rollout with Deployment strategies or tools like Argo Rollouts or Flagger.
• AWS: Use CodeDeploy with weighted traffic shifting in ALB or Lambda deployments.
• Istio/Service Mesh: Route a small percentage of traffic to canary version via virtual services.
• Load Balancer-Based: Duplicate deployments behind the same load balancer with separate weights.

🧪 Monitoring Canary Releases

• Track key metrics: error rate, latency, CPU/memory usage, user sessions.
• Set up automated analysis: SLO breach detection, comparison dashboards.
• Use alerting for anomaly detection during the canary window.

✅ Safety Measures

• Start with a low traffic weight (e.g., 1%-5%).
• Define clear rollback conditions (e.g., 5xx errors > threshold).
• Automate rollback using deployment tools or CI/CD policies.
• Gate further rollout on health checks and performance metrics.

🚫 Common Mistakes

• Skipping monitoring — silent errors can slip through undetected.
• Sending all traffic to canary pods for internal testing — defeats purpose.
• Relying on manual rollback without automation.

📌 Real-World Insight

Tech companies like Netflix, Google, and Shopify standardize canary deployments as part of their release process. They use dynamic traffic shaping, real-time alerts, and automated rollbacks to catch issues before full rollout.