Feature Flags: Implementation at Scale

This article is part of my series on feature flags. Check out the complete article and series on my blog.

When teams first implement feature flags, they often start with a simple vision: "We'll just add a few toggles to control our deployments!" But as systems scale, these "simple toggles" evolve into complex distributed systems that can either empower or cripple your entire architecture.

The Hidden Complexity

Let's look at how most teams start. The implementation usually looks something like this:

class SimpleFeatureFlag {
  async isEnabled(feature: string): Promise<boolean> {
    const config = await this.store.get(feature)
    return config.enabled // Seems simple enough, right?
  }
}

"It's just a boolean check!" we tell ourselves. But the reality of scaling systems quickly proves otherwise.

What starts as a simple toggle inevitably grows as requirements become clear. You need:

• Caching for performance
• Circuit breakers for reliability
• Access controls for security
• Audit trails for compliance
• Analytics for business insights

The Real Challenges

As systems scale, different features demand different treatment. Consider this evolution in design:

class StrategyBasedConfigStore extends ConfigurationStore {
  async getFeatureConfig(feature: string): Promise<FeatureConfig> {
    const strategy = this.getStrategyForFeature(feature)
    switch (strategy) {
      case 'ALWAYS_FRESH':
        // Critical payment features need real-time updates
        return this.getFreshConfig(feature)
      case 'CACHE_FIRST':
        // UI features can tolerate some staleness for better performance
        return this.getCacheFirstConfig(feature)
      case 'CACHED_WITH_BACKGROUND_REFRESH':
        // Best of both worlds for less critical features
        this.refreshInBackground(feature)
        return this.getCachedConfig(feature)
      default:
        return super.getFeatureConfig(feature)
    }
  }
}

At this point, you're not just managing features – you're building a distributed system that needs to handle:

• Cache invalidation (one of the hard problems in computer science)
• Race conditions in updates
• Network failures and timeouts
• Inconsistent states across services
• The dreaded "thundering herd" problem

The Security Considerations

Remember that A/B test you launched six months ago? The one testing a new rate limiter bypass for premium users? Yeah, it's still running, and now it's accidentally bypassing your new security controls. 🤦‍♂️

This is why proper lifecycle management isn't just a "nice to have" – it's critical infrastructure:

class SecureFeatureManager {
  async updateFeature(
    feature: string,
    update: FeatureUpdate,
    context: SecurityContext
  ): Promise<void> {
    // 🔒 Security first!
    await this.verifyAccess(feature, context);
    if (this.isHighRiskChange(update)) {
      // 🤔 Should this change really go straight to production?
      await this.requestApproval(feature, update);
    }
    await this.store.transaction(async (tx) => {
      // 📝 Always leave a paper trail
      await this.auditLog.record(feature, update, context);
      await this.store.update(feature, update);
    });
  }
}

Want to Learn More?

This post only scratches the surface of building feature flags at scale. In the full article, I dive deeper into:

• Building multi-level caching architectures that actually work
• Implementing circuit breakers and graceful degradation
• Managing feature flag dependencies effectively
• Real-world monitoring and analytics strategies
• Patterns for handling failure modes gracefully

The article is part of a larger series that explores everything from basic concepts to advanced implementation patterns.

Read the full article →