Design a large-scale Ride Matching Platform
Last updated: May 17, 2026
Quick Overview
Design a geo-distributed ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Supabase
May 17, 202642
7
916 solved
Design a geo-distributed ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Supabase goes beyond model selection. This Onsite question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
What the Interviewer Expects
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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
- How would you debug a model that works well offline but poorly online?
- What is your model retraining strategy?
- How would you handle a 10x increase in prediction requests?
- How would you ensure fairness and reduce bias in the model?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For large-scale Ride Matching Platform, key functional requirements inclu...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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