Design a large-scale A/B Testing Platform
Last updated: November 29, 2025
Quick Overview
Design a geo-distributed a/b testing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
DoorDash
November 29, 20256
9
2,486 solved
Design a geo-distributed a/b testing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at DoorDash 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
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 schema migrations with zero downtime?
- How would you optimize costs as the system scales?
- How would you handle a 10x increase in traffic overnight?
- How do you ensure data consistency across multiple services?
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Requirements
Functional Requirements:
- User Segmentation: Ability to define user segments for targeted A/B tests based on geographic location, device type, and user behavior.
- Experiment Creation: ...
Capacity Estimation
To estimate capacity, let's assume:
- Users: 30 million monthly active users.
- A/B Tests: Average of 100 concurrent A/B tests running at any time.
- Requests per Test: Each test generates...