When it comes to building and scaling feature flag systems, there are several key principles that can help ensure success. In this article, we'll explore 11 essential principles for developing a reliable, easy-to-maintain, and scalable feature flag system that's capable of handling heavy loads.

Enable Runtime Control

A scalable feature management system evaluates flags at runtime. This means that flags should be dynamic, not static. If you need to restart your application to turn on a flag, that's configuration, not a feature flag. A large-scale feature flag system that enables runtime control should have, at minimum, the following components: a service to manage feature flags, a database or data store, an API layer, a feature flag SDK, and a continuous update mechanism.

Feature Flag System Components

  • Feature Flag Control Service: A service that acts as the control plane for your feature flags, managing all flag configurations. The scope of this service should reflect the boundaries of your organization.
  • Database or data store: A robust, scalable, and highly available database or data store that stores feature flag configurations reliably. Common options include SQL databases, NoSQL databases, or key-value stores.
  • API layer: An API layer that exposes endpoints for your application to interact with the Feature Flag Control Service. This API should allow your application to request feature flag configurations.
  • Feature flag SDK: An easy-to-use interface for fetching flag configurations and evaluating feature flags at runtime. When considering feature flags in your application, the call to the SDK should query the local cache, and the SDK should ask the central service for updates in the background.
  • Continuous update mechanism: An update mechanism that enables dynamic updates to feature flag configurations without requiring application restarts or redeployments. The SDK should handle subscriptions or polling to the Feature Flag Control Service for updates.

Make Flags Short-Lived

The most common use case for feature flags is to manage the rollout of new functionality. Once a rollout is complete, you should remove the feature flag from your code and archive it. Remove any old code paths that the new functionality replaces. Avoid using feature flags for static application configuration. Application configuration should be consistent, long-lived, and loaded during application startup.

Strategies for Large Organizations Implementing Feature Flags

To succeed with feature flags in a large organization, follow these strategies:

  • Set flag expiration dates: Assign expiration dates to feature flags to track which flags are no longer needed. A good feature flag management tool will alert you to expired flags, making it easier to maintain your codebase.
  • Treat feature flags like technical debt: Incorporate tasks to remove outdated feature flags into your sprint or project planning, just as you would with technical debt. Feature flags add complexity to your code by introducing multiple code paths that need context and maintenance.
  • Archive old flags: When feature flags are no longer in use, archive them after removing them from the codebase. This archive serves as an important audit log of feature flags and allows you to revive flags if you need to restore an older version of your application.

Prioritize Availability Over Consistency

Your application shouldn’t have any dependency on the availability of your feature flag system. Robust feature flag systems avoid relying on real-time flag evaluations because the unavailability of the feature flag system will cause application downtime, outages, degraded performance, or even a complete failure of your application.

Conclusion

In this article, we've explored 11 essential principles for building and scaling feature flag systems that are capable of handling heavy loads. By following these principles, you can create a reliable, easy-to-maintain, and scalable feature flag system that enables runtime control, makes flags short-lived, prioritizes availability over consistency, and more.