Start by clarifying functional and non-functional requirements, then sketch a high-level architecture with separate services for location ingestion, matching, trip management, and ETA computation. Dive into data models and trade-offs for each component, emphasizing scalability, fault tolerance, and handling of peak load and regional outages.
Pro tip: Proactively discuss how you would handle driver location updates at scale—e.g., using a geo-indexed in-memory store like Redis with geohashing—and how you'd degrade gracefully during regional outages by rerouting to nearby regions and queuing requests.
Ask questions to understand expected scale (e.g., millions of drivers/riders), latency requirements, consistency needs, and specific features like ETA accuracy and outage handling. Define the core entities: riders, drivers, trips, and locations.
Outline major components: a location service for driver updates, a matching service for pairing riders with drivers, a trip service for state management, and an ETA service. Describe how they interact via APIs and message queues.
Choose appropriate data stores: e.g., in-memory geo-index (Redis) for real-time driver locations, a relational or NoSQL database for trip state, and a time-series DB for location history. Discuss indexing strategies for nearby queries.
Explain how to partition data (e.g., by region), use consistent hashing, and auto-scale services. Discuss caching, rate limiting, and asynchronous processing to handle spikes in trip requests and location updates.
Describe strategies for regional outages: multi-region deployment, failover to healthy regions, and graceful degradation (e.g., queue requests, fallback to approximate matching). Ensure trip state is replicated and recoverable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.