CI/CD Reliability: Scenario-Based Questions

Automated rollback ensures fast recovery from faulty deployments by reverting to the last known good version. It’s essential in fast-moving environments where uptime and user trust are critical.

🔁 Rollback Triggers

• Health Check Failures: Liveness/readiness probes fail post-deploy.
• Canary Metrics Breach: Elevated 5xx rates, high latency, or dropped sessions.
• Monitoring Alerts: SLOs or custom thresholds breached after release.

🧰 Rollback Mechanisms

• Kubernetes: Use kubectl rollout undo deployment/app-name or helm rollback.
• AWS: CodeDeploy or Lambda with rollback on failed health checks.
• GitOps: ArgoCD or Flux automatically revert if sync deviates from target state.
• Version Control: Tag stable builds and deploy by reverting to previous artifact in pipeline.

⚙️ Pipeline Implementation

• Set up health checks and validation gates as a post-deploy stage.
• Use conditional logic in Jenkins/GitHub Actions/GitLab CI to revert on failure:
• Store deployment metadata (timestamp, artifact hash, commit ID) for traceability and rollback targeting.

✅ Best Practices

• Keep previous versions readily available (e.g., artifacts in S3, Docker registries).
• Make rollback scripts idempotent and safe.
• Alert stakeholders automatically during rollbacks.

🚫 Common Mistakes

• Not validating rollback success — assuming it worked without checks.
• Manual intervention required in “automated” rollback paths.
• Neglecting to roll back DB migrations or data changes.

📌 Real-World Insight

Leading tech teams treat rollback as a first-class feature — just as important as deployments themselves. Automating this ensures higher availability, fewer wake-up calls, and faster incident resolution.