Design a large-scale Ride Matching Platform
Last updated: April 8, 2026
Quick Overview
Design a event-driven ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Expedia
System Design
Software Engineer
Expedia
April 8, 2026Software Engineer
Onsite
System Design
Medium
31
6
2,628 solved
Design a event-driven ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Expedia asks this during the Onsite 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
- 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
Failure handling and fault tolerance
Message queues and async processing
Consistency models and replication
Database selection and data modeling
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?
- What would the deployment pipeline look like for this system?
- How would you handle a region-wide outage?
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Requirements
Functional Requirements
- Ride Matching: Match riders with available drivers based on location and time.
- Event Notifications: Send real-time notifications to riders and drivers for rid...
Capacity Estimation
Back-of-Envelope Calculations
- QPS (Queries Per Second): Assuming 5 million requests per day, we have:
- 5,000,000 requests / 86,400 seconds = ~58 QPS
- Storage Needs: If we assume a...
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