Design a large-scale A/B Testing Platform
Last updated: October 16, 2025
Quick Overview
Design a multi-tenant a/b testing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Amazon
October 16, 202537
12
809 solved
Design a multi-tenant a/b testing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Amazon typically last 45-60 minutes. You are expected to drive the conversation, starting from requirements gathering through to a detailed architecture. The interviewer will evaluate your ability to handle ambiguity and make practical engineering decisions.
What the Interviewer Expects
- Clearly define functional and non-functional requirements
- Propose a reasonable high-level architecture with core components
- Choose appropriate data storage solutions with basic justification
- Discuss basic scaling strategies (horizontal scaling, caching)
- Identify potential bottlenecks and suggest simple solutions
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How do you ensure data consistency across multiple services?
- How would you handle a region-wide outage?
- What monitoring and alerting would you set up on day one?
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Requirements
Functional Requirements
- Experiment Creation: Users can define experiments including variants, metrics, and targeting criteria.
- Traffic Allocation: The system should allocate traffic ...
Capacity Estimation
Assuming Amazon has about 300 million active users and we plan to run experiments on 10% of them:
- User base for experiments: 300 million * 10% = 30 million users.
- Requests per experiment: ...
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