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Design a Ride-sharing Service (like Uber/Lyft)

Build a scalable ride-sharing platform with real-time matching, geospatial indexing, GPS tracking, dynamic pricing, and global multi-city support.

Interview Context

In a real interview, start by understanding the scope and scale. Don't assume - ask specific questions about user base, geographic coverage, and success metrics. This conversation shapes your entire architecture.

Q: What's the expected scale of operations?
A: We need to support 50M daily active users (15M drivers, 35M riders) across 100+ cities globally with 10M+ daily rides.
Your Analysis:
At this scale, we need to handle 3.75M location updates/second and 2K ride requests/second during peak hours. This drives our architecture toward distributed systems with geographic sharding and real-time processing capabilities.
Q: What are the core functional requirements?
A: Real-time driver-rider matching, GPS tracking with 4-second update intervals, dynamic pricing, trip management, payment processing, and multi-stop routing for pooled rides.
Your Analysis:
The real-time matching requirement means we need sub-5 second matching times with 99.9% availability. GPS tracking at this frequency requires efficient geospatial indexing and streaming data architecture.
Q: What are the latency and availability requirements?
A: Driver-rider matching must complete within 5 seconds P95, location updates within 1-2 seconds, 99.9% uptime SLA, and support for 1M concurrent users.
Your Analysis:
These SLAs require multi-region deployment, sophisticated caching strategies, and graceful degradation patterns. We'll need circuit breakers and fallback mechanisms for critical paths.
Q: Are there specific geographic or regulatory constraints?
A: Global deployment with city-specific regulations, different payment methods per region, varying driver licensing requirements, and GDPR/data privacy compliance.
Your Analysis:
This requires a flexible configuration system per city, localized payment gateways, and region-specific data residency policies. Each city may have different surge pricing caps and driver qualification rules.
Q: What level of ride personalization and optimization is needed?
A: Support for different vehicle types (economy, premium, shared), driver ratings > 4.0, accessibility options, and ride pooling with optimized routing.
Your Analysis:
Vehicle type matching adds complexity to our geospatial indexing - we need separate indices per vehicle category. Ride pooling requires sophisticated routing algorithms that can handle multi-stop optimization in real-time.

Key Requirements Summary

Functional Requirements
  • • Real-time driver-rider matching (<5 sec)
  • • GPS location tracking (4-sec intervals)
  • • Dynamic pricing and surge algorithms
  • • Multi-stop ride pooling optimization
  • • Payment processing and split billing
  • • Driver and rider rating systems
Non-Functional Requirements
  • • 50M DAU (15M drivers, 35M riders)
  • • 10M+ daily rides globally
  • • 99.9% availability SLA
  • • Support for 100+ cities
  • • 1M concurrent active users
  • • Multi-region, GDPR compliant
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Interview Practice Questions

Practice these open-ended questions to prepare for system design interviews. Think through each scenario and discuss trade-offs.

1

Global Ride Sharing Platform: Design a Uber-scale ride sharing system supporting 100M+ users across 100+ cities globally. Address multi-region deployment, local regulations, payment methods, cultural preferences, and disaster recovery across continents.

2

Autonomous Vehicle Integration: Extend the ride sharing platform to support autonomous vehicles alongside human drivers. Address vehicle routing, remote monitoring, safety protocols, liability handling, and mixed fleet management.

3

Multi-Modal Transportation Hub: Build a comprehensive transportation platform integrating ride sharing, public transit, bike sharing, scooters, and walking directions. Address unified routing, cross-modal transfers, integrated payments, and real-time scheduling.

4

Safety & Trust Platform: Design comprehensive safety features including driver verification, real-time trip monitoring, emergency response, user reporting, predictive risk assessment, and integration with local emergency services.

5

Enterprise & B2B Solutions: Build enterprise ride sharing solutions for corporate accounts, healthcare transportation, goods delivery, and logistics. Address billing integration, reporting, compliance, priority booking, and fleet management.

6

Machine Learning Optimization: Integrate ML for demand prediction, dynamic pricing, route optimization, fraud detection, driver quality scoring, and personalized user experience. Address real-time inference, model updates, and A/B testing at scale.