This question has so many layers it's kind of ridiculous.
Start by clarifying functional and non-functional requirements, then design a data model that captures watch requests with date ranges and listing availability. Propose a scalable architecture for matching cancellations to watches and delivering notifications, and discuss how hosts can query watch counts efficiently.
Pro tip: Emphasize idempotency and deduplication in notifications to avoid spamming users when multiple cancellations occur, and discuss how to handle time zones and date range overlaps correctly.
Ask questions to understand scale, notification channels (email, push, SMS), latency expectations, and whether watches are per listing or per host. Clarify if users can watch multiple date ranges and if hosts need real-time counts.
Define entities: User, Listing, Watch (user_id, listing_id, start_date, end_date, status), and Availability (listing_id, date, status). Consider using a time-series or interval-based storage for availability and watches.
Design a pipeline: when a cancellation occurs, publish an event to a message queue. A matching service consumes events, queries watches that overlap the freed dates, and triggers notifications via a notification service. Ensure scalability with sharding by listing_id.
Provide an API endpoint for hosts to get watch counts per listing, possibly with caching or a materialized view updated asynchronously to avoid impacting write performance.
Address consistency vs. latency, storage costs for watches, and how to handle expired watches. Mention indexing strategies (e.g., composite index on listing_id and date range) and potential use of geospatial or interval trees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.