Start by clarifying requirements and scale, then design a clean API for encounter detection and data storage, and finally discuss scaling strategies like sharding, partitioning, and asynchronous processing. Emphasize trade-offs and how the system evolves as volume grows.
Pro tip: Demonstrate awareness of real-world constraints by discussing idempotency and exactly-once processing, and how you'd handle late-arriving or out-of-order events in a distributed system.
Ask questions to understand expected encounter volume, latency needs, consistency requirements, and what constitutes an 'encounter' (e.g., proximity, duration). This sets the stage for design decisions.
Define endpoints for reporting encounters, querying encounter history, and subscribing to encounter events. Consider REST vs. gRPC, and include idempotency keys for reliable ingestion.
Choose a storage solution (e.g., time-series DB, wide-column store) that handles high write throughput and efficient time-range queries. Model encounters with entity IDs, timestamps, location, and metadata.
Discuss partitioning (e.g., by time or entity), sharding, and using a message queue for asynchronous processing. Address hot partitions and how to scale reads and writes independently.
Summarize trade-offs (e.g., consistency vs. availability, cost vs. performance) and outline how the system can evolve, such as adding caching, tiered storage, or real-time analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.