Implementation: Gating, Analytics, and Dual Path Management

Core Concept
Holdout implementation requires feature gating, dual-path code management, and analytics infrastructure to track both experiences over extended periods.
Feature Gating
Every feature check becomes: if (!isHoldout(user) && featureEnabled(feature, user)). Holdout check must come first and short-circuit. This pattern ensures holdout users never see new features regardless of experiment assignment.
Centralize the isHoldout check in a single service. Every feature team imports this check rather than implementing their own. This prevents bugs where one team forgets the holdout check.
Dual Path Management
Old code paths must be maintained for holdout users. When you refactor the recommendation system, holdout users still need the old recommender. This creates maintenance burden: bug fixes in both paths, testing both paths, potential divergence over time.
⚠️ Key Trade-off: Some changes cannot have dual paths (infrastructure migrations, API deprecations). These break holdout validity. Accept that holdout measures application-layer changes only, not infrastructure.
Analytics Infrastructure
Track holdout status on every event. Build dashboards comparing holdout vs production on key long-term metrics. Set up automated alerts when holdout deviates significantly. Report weekly/monthly on cumulative impact.

💡 Key Takeaways

✓Feature check pattern: if (!isHoldout(user) && featureEnabled(feature, user)) - holdout check first

✓Centralize isHoldout check in single service to prevent teams forgetting the check

✓Old code paths must be maintained for holdout users, creating dual maintenance burden

✓Infrastructure changes (migrations, API deprecations) cannot have dual paths, breaking holdout validity

📌 Interview Tips

1When implementing: describe centralized isHoldout service that all feature teams import

2For infrastructure: explain that holdout measures application-layer changes only, not infrastructure

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