Start by clarifying functional and non-functional requirements, then sketch a high-level architecture with core services (ride matching, location tracking, pricing, etc.). Dive into data modeling and matching algorithms, emphasizing scalability and fault tolerance through partitioning, replication, and eventual consistency.
Pro tip: Proactively discuss trade-offs between consistency and availability (e.g., using eventual consistency for location updates) and how you'd handle edge cases like driver disconnections or surge pricing during high load.
Ask questions to understand scale (e.g., millions of rides per day), latency requirements, and key features (matching, tracking, payments). Define functional and non-functional requirements to guide design.
Outline major components: API gateway, ride matching service, location service, driver/rider services, and data stores. Use a microservices architecture with event-driven communication for scalability.
Design schemas for rides, drivers, riders, and locations. Choose appropriate databases: relational for transactions, NoSQL for high-throughput location data, and geospatial indexes for matching.
Explain the matching algorithm (e.g., using geohashing and nearest-neighbor search) and how to handle real-time location updates via WebSockets or long polling. Discuss consistency models for location data.
Describe partitioning (e.g., by city or geohash), replication, and load balancing. Address failure scenarios: driver disconnection, service outages, and data loss prevention with backups and retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.