This question is basically a whole system design course crammed into 45 minutes.
Start by clarifying requirements and scale (e.g., number of drivers, orders, update frequency, latency targets), then propose a high-level architecture covering data sources, ingestion, storage, and serving layers. Dive into trade-offs for consistency vs availability, out-of-order handling, and scaling, and finish with fault tolerance and latency optimizations.
Pro tip: Explicitly tie design choices to business impact—e.g., how eventual consistency for driver locations still meets user expectations while enabling horizontal scale—and mention monitoring/alerting for data staleness to show operational maturity.
Ask about expected number of drivers, orders, update frequency, latency SLAs, and consistency needs to scope the design.
Identify sources (driver apps, order service, etc.) and propose a scalable ingestion pipeline (e.g., Kafka) with deduplication and out-of-order handling.
Select appropriate stores for real-time (e.g., Redis, in-memory) and historical/aggregate data (e.g., Cassandra, time-series DB), and design APIs for dashboard queries.
Discuss trade-offs (e.g., eventual consistency for locations, strong for orders), partitioning, replication, and auto-scaling strategies.
Propose caching, CDN, edge processing, and redundancy (multi-AZ, failover) to meet latency and reliability goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.