Build a geo-distributed Ride Matching Pipeline
Last updated: February 20, 2026
Quick Overview
Design a geo-distributed ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Zoom
February 20, 2026289
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2,429 solved
Design a geo-distributed ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Zoom 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 would the deployment pipeline look like for this system?
- How would you optimize costs as the system scales?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
Functional Requirements
- User Registration and Authentication: Users must be able to create accounts and authenticate securely.
- Ride Request Submission: Users can submit ride requests...
Capacity Estimation
Assuming Zoom's ride matching system aims to handle 10 million users:
- Peak Load: Estimate around 1 million concurrent users during peak hours, with each user generating 1 request every 30 second...
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