Progressive Delivery Techniques

Progressive delivery is a set of techniques that release features gradually, enabling testing, monitoring, and safe rollbacks.

Why Progressive Delivery Matters

  • Reduces risk by releasing to a subset of users
  • Validates features in real-world conditions
  • Enables immediate rollback if issues arise
  • Provides data-driven insights for feature adoption

Workflow Example

  1. Deploy feature behind a flag or canary
  2. Monitor metrics and logs for anomalies
  3. Gradually increase traffic or audience
  4. Enable full rollout after validation
  5. Rollback if errors exceed thresholds

Visual Diagram

flowchart TD A[New Feature Deployment] --> B[Canary/Feature Flag] B --> C[Monitor Metrics] C --> D{Stable?} D -->|Yes| E[Increase Traffic Gradually] D -->|No| F[Rollback] E --> G[Full Release]

Sample Code Snippet

def deploy_feature(flag_enabled):
    if flag_enabled:
        print("Feature is live for users.")
    else:
        print("Feature is hidden behind a flag.")
# Example usage
deploy_feature(True)  # Feature is live for users.
deploy_feature(False) # Feature is hidden behind a flag.

Best Practices

  • Use metrics to guide rollout
  • Automate traffic shifting and rollback
  • Limit exposure for risky features
  • Document all rollout steps

Common Pitfalls

  • Rushing full rollout without monitoring
  • Ignoring feedback or anomalies
  • Not automating rollback

Conclusion

Progressive delivery ensures safer, monitored, and data-driven deployments, minimizing risk and improving user experience.