Day 102: Building Smart Feature Rollouts with A/B Testing Framework
Module 4: Building a Complete Distributed Log Platform Week 15: Advanced Operational Features
Today's Mission: Smart Feature Deployment
Today we're building an intelligent A/B testing framework that transforms risky feature launches into controlled, data-driven rollouts. Instead of deploying features to all users simultaneously and hoping for the best, you'll create a system that gradually exposes new functionality while measuring impact in real-time.
What We're Building:
Feature flag service with dynamic configuration
User segmentation engine for targeted testing
Traffic splitting mechanisms for controlled exposure
Real-time metrics collection and analysis
Integration with yesterday's blue/green deployment system
The Production Reality
Netflix runs thousands of A/B tests simultaneously across their platform. When they test a new recommendation algorithm, they don't roll it out to 200 million users at once. Instead, they start with 1% of users, measure engagement metrics, and gradually increase exposure based on results. Your framework will implement these same patterns.
Core Concepts: A/B Testing in Distributed Systems
Feature Flag Architecture
Feature flags act as runtime switches that control feature visibility without code deployment. Unlike static configuration, these flags respond to user attributes, geographical location, and business metrics in real-time.
User Segmentation Strategy
Effective A/B testing requires consistent user assignment. Users must see the same feature variant across sessions and devices. This requires deterministic hashing algorithms and persistent user state management.
Experiment Lifecycle Management
Experiments progress through distinct phases: design, activation, data collection, analysis, and decision. Each phase requires specific system capabilities and safety mechanisms.
