Experimentation Platform Architecture
Understanding the anatomy of large-scale online experimentation platforms
Sources: This page is based on research papers "The Anatomy of a Large-Scale Online Experimentation Platform" by Gupta et al. and "Extensible Experimentation Platform: Effective AB Test Analysis at Scale" by Bajpai et al., which describe the architecture of large-scale experimentation platforms. The concepts have been generalized to apply to experimentation platforms broadly.
Overview of Experimentation Platforms
Large-scale experimentation platforms enable organizations to run thousands of concurrent A/B tests across their products and services. These platforms consist of several interconnected components that work together to manage the entire experimentation lifecycle.
Modern experimentation platforms handle everything from experiment design and user assignment to data collection, analysis, and decision-making. They are designed to be scalable, reliable, and provide trustworthy results that can guide product development decisions.
Core Components of an Experimentation Platform
The fundamental building blocks that make up a large-scale experimentation system
Experiment Management
Systems for creating, configuring, and managing experiments, including traffic allocation, targeting rules, and experiment lifecycle.
Assignment Service
Infrastructure for assigning users to experiment variants in a consistent, scalable manner with minimal latency impact.
Data Collection
Systems for logging experiment exposures and outcome metrics, ensuring data quality and completeness.
Analysis System
Computation engines for processing experiment data, applying statistical methods, and generating results.
Experimentation Portal
User interfaces for creating experiments, viewing results, and making data-driven decisions.
Platform Services
Supporting infrastructure including authentication, authorization, monitoring, and alerting systems.
Assignment Service Architecture
How users are assigned to experiment variants at scale with minimal latency
The assignment service is a critical component that determines which users see which variants of an experiment. It must operate with extremely low latency (typically milliseconds) and high reliability, as it's often in the critical path of user experiences.
Key Requirements:
- • Consistent assignment (users see the same variant across sessions)
- • Low latency (typically <10ms)
- • High availability (99.99%+ uptime)
- • Support for complex targeting rules
- • Ability to handle traffic allocation changes
Assignment Service Implementation Approaches
| Approach | Description | Advantages | Challenges |
|---|---|---|---|
| Client-side Assignment | Assignment logic runs in client SDKs, with configuration downloaded from servers |
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| Server-side Assignment | Dedicated assignment services handle all experiment allocation decisions |
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| Hybrid Approach | Combination of client and server-side assignment with caching strategies |
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Scaling Challenges and Solutions
How large-scale experimentation platforms address the challenges of scale
Large-scale experimentation platforms face significant challenges as they grow to support thousands of concurrent experiments across billions of users. These challenges require specialized architectural approaches and solutions.
Assignment Service Scaling
- Challenge: Handling billions of assignment decisions daily with sub-10ms latency
- Solutions:
- Distributed caching architectures
- Local evaluation with config synchronization
- Hierarchical assignment services
- Edge computing for regional assignment decisions
Data Pipeline Scaling
- Challenge: Processing petabytes of experimentation data with reasonable latency
- Solutions:
- Distributed processing frameworks (Spark, Flink)
- Data partitioning strategies
- Incremental processing pipelines
- Tiered storage architectures
Analysis System Scaling
- Challenge: Computing results for thousands of metrics across thousands of experiments
- Solutions:
- Parallel computation frameworks
- Pre-aggregation of common metrics
- Materialized views and caching
- Specialized statistical computation engines
Portal Scaling
- Challenge: Supporting thousands of users and experiments with responsive interfaces
- Solutions:
- Microservice architectures
- Client-side rendering with efficient APIs
- Progressive loading of data
- Specialized query optimization for UI patterns