Build a scalable A/B Testing Pipeline
Last updated: November 23, 2025
Quick Overview
Design a scalable a/b testing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Supabase
November 23, 202563
13
2,784 solved
Design a scalable a/b testing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Supabase asks this during the System Design Round to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
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 would you handle a 10x increase in traffic overnight?
- How would you handle schema migrations with zero downtime?
- How would you migrate from a monolithic to a microservices architecture?
- What happens if one of your database nodes goes down?
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Requirements
Functional Requirements
- User Segmentation: The ability to segment users based on various criteria (e.g., demographics, behavior).
- Experiment Creation: Users can create, update, and d...
Capacity Estimation
Assuming Supabase handles 10 million active users and each user is part of an average of 5 A/B tests, we can estimate the following:
- Total A/B Tests: 10 million users * 5 tests = 50 million A/B...
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