Architect a high-throughput Ride Matching Engine
Last updated: April 12, 2026
Quick Overview
Design a high-throughput ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Shopify
System Design
Software Engineer
Shopify
April 12, 2026Software Engineer
Onsite
System Design
Hard
374
8
4,811 solved
Design a high-throughput ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Shopify 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
Failure handling and fault tolerance
Partitioning and sharding strategies
Caching strategies (local, distributed, CDN)
Requirements gathering and capacity estimation
Database selection and data modeling
Load balancing and horizontal scaling
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?
- What would the deployment pipeline look like for this system?
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Requirements
Functional Requirements
- Real-time Ride Matching: Match riders with drivers in real-time based on proximity and availability.
- User Management: Handle user profiles for both riders and...
Capacity Estimation
Back-of-envelope Calculations
- User Base: Assume a user base of 10 million active users, with 30% (3 million) making requests at peak times.
- Request Rate: If each user makes an averag...
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