I went straight to the geofencing logic and spent probably too long on the proximity detection piece before anyone asked about it.
Start by clarifying requirements and scale, then design a high-level architecture that separates location ingestion, geofence evaluation, and check-out processing. Focus on trade-offs between accuracy, latency, and cost, and discuss how to handle edge cases like GPS drift and offline devices.
Pro tip: Emphasize that automatic check-out is a state transition that should be idempotent and auditable, and propose a grace period or hysteresis to avoid false positives from GPS jitter.
Ask about scale (users, check-ins per second), accuracy needs, latency tolerance, and whether check-out must be immediate or can be delayed. Also clarify what defines a 'geographic boundary' (polygon, radius) and how it's configured.
Propose a pipeline: location updates from clients -> ingestion service (e.g., Kafka) -> geofence evaluation service -> check-out service. Use a database to store active check-ins and geofences.
Discuss algorithms for point-in-polygon or distance-to-center checks, and how to efficiently evaluate many geofences per user (e.g., spatial indexing like geohash or R-tree). Consider using a streaming processor for real-time evaluation.
Design the check-out trigger: when a user is outside all geofences for a sustained period (e.g., 2 minutes) to avoid GPS jitter. Ensure idempotency and handle offline scenarios by queuing events.
Discuss partitioning by user ID, using a distributed cache for active check-ins, and trade-offs between push (client sends updates) vs pull (server polls). Address consistency vs availability and cost implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.