Design a large-scale Ride Matching Platform
Last updated: January 31, 2026
Quick Overview
Design a event-driven ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Anthropic
January 31, 202660
5
3,732 solved
Design a event-driven ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Anthropic 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
- What would the deployment pipeline look like for this system?
- How would you optimize costs as the system scales?
- What monitoring and alerting would you set up on day one?
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Requirements
Functional Requirements
- User Registration and Authentication: Users (drivers and riders) must be able to register, log in, and manage their profiles securely.
- Ride Request Submission...
Capacity Estimation
Assuming Anthropic aims to handle 10 million ride requests per day:
- Peak Traffic: If we consider peak hours, let's estimate 25% of requests occur during a 4-hour window (2.5 million requests). -...
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