Architect a fault-tolerant Ride Matching Engine
Last updated: April 25, 2026
Quick Overview
Design a fault-tolerant ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Square/Block
April 25, 20269
11
605 solved
Design a fault-tolerant ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Square/Block 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
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
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 happens if one of your database nodes goes down?
- How would you migrate from a monolithic to a microservices architecture?
- How would you handle a region-wide outage?
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Requirements
Functional Requirements
- Real-time Ride Matching: The system should match drivers and riders in real-time based on location, destination, and estimated time of arrival.
- User Profiles:...
Capacity Estimation
To estimate capacity, we consider:
- Peak Load: Assume peak hours have double the average traffic. Thus, 230 requests per second.
- Daily Request Volume: 10 million rides/day translates to abo...
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