I started with the naive approach, just querying all active orders and filtering by location, and the interviewer let me dig myself into that hole for a bit before nudging me toward geospatial indexing.
Start by clarifying requirements and scale, then propose a geospatial data model that maps disaster regions to properties using spatial indexes. Walk through the end-to-end flow: ingest events, query affected entities, and notify via a scalable, idempotent pipeline. Emphasize trade-offs between freshness, accuracy, and cost.
Pro tip: Mention using a geospatial database like PostGIS or a specialized service (e.g., Redis Geo, Elasticsearch) and discuss how to handle boundary cases like overlapping disasters and notification deduplication.
Ask about expected QPS, number of properties, disaster event frequency, and freshness SLA. Define what 'timely' means (e.g., <1 minute) and whether notifications are push or pull.
Model properties with lat/long and disaster regions as polygons. Use a geospatial index (e.g., R-tree, geohash) to enable fast spatial queries. Consider storing disaster events with affected regions and time validity.
On disaster event, query the geospatial index to find properties within affected regions. Join with orders, hosts, and guests. Handle overlapping events and deduplicate notifications.
Design an asynchronous pipeline: event ingestion -> matching -> notification queue -> delivery. Ensure idempotency and retries. Discuss freshness: near-real-time matching, but notifications may be batched.
Handle many concurrent disasters by partitioning geospatial index, using message queues for backpressure, and scaling notification workers horizontally. Consider caching and rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.