I went straight for a city-to-events dictionary built at init time, which felt right.
Start by clarifying requirements and defining the domain models (Customer, City, Event, Subscription). Then propose an internal data structure that maps cities to events and customers to their city, optimizing for the query 'notify customer of all events in their city'. Finally, analyze the time and space complexity of the notification operation and discuss trade-offs.
Pro tip: Mention that in a real system, you'd likely need to handle scale with sharding by city and use a pub/sub system for notifications, but for this exercise, focus on the core data structure and complexity.
Ask about expected scale, whether events are static or dynamic, and if notifications are real-time or batch. Confirm that the primary operation is retrieving all events for a customer's city.
Identify key entities: Customer (with city), Event (with city, date, etc.), and possibly a Subscription or Notification service. Define relationships between them.
Propose a hash map from city to a list of events (e.g., HashMap<String, List<Event>>). Optionally, maintain a map from customer to city for quick lookup.
For a customer query: O(1) to find city, O(1) to get events list, O(k) to iterate and notify, where k is number of events in that city. Discuss space complexity O(E + C) where E is events and C is customers.
Consider alternatives like indexing by city and date, or using a graph. Mention scalability concerns (e.g., sharding, caching) and how to handle updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.