Start by clarifying requirements and defining the core abstractions: a Campaign interface with a method to select customers and generate notifications, and a MarketingEngine that manages a list of campaigns. Then, walk through the data structures and algorithms for each campaign type, emphasizing extensibility via the Strategy pattern. Finally, discuss trade-offs and potential optimizations.
Pro tip: Emphasize that the engine should be open for extension but closed for modification (Open/Closed Principle). Mention that campaigns can be added dynamically, and consider how to handle dependencies like event data and customer locations.
Ask questions to understand constraints: expected scale, real-time vs batch, data sources, and notification channels. Confirm that new campaigns should be pluggable without changing the engine.
Introduce a Campaign interface with methods like selectCustomers and generateNotifications. The MarketingEngine holds a collection of campaigns and delegates execution to them.
For each campaign type, outline the algorithm and data structures: e.g., for same-city events, filter events by city; for closest to next birthday, compute days until next birthday and find nearest event; for 5 closest events, use geospatial indexing (e.g., k-d tree or geohash) to find nearest neighbors.
Explain how new campaigns can be added by implementing the Campaign interface and registering with the engine. Discuss potential performance bottlenecks and how to scale (e.g., caching, precomputation, distributed processing).
Compare approaches for geospatial search (e.g., k-d tree vs geohash), handling ties, missing data, and time zones. Mention testing strategies and how to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a follow-up but it was clearly planned.
Start by clarifying the requirements and constraints of the matching system, then systematically address each edge case (ties, fewer than 5 matches, and message formatting) with concrete strategies. Emphasize trade-offs between simplicity, correctness, and user experience, and propose a design that is scalable and maintainable.
Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle ties deterministically (e.g., using a secondary sort key) and how to avoid overwhelming users with too many notifications. Mention that you would validate the solution with edge-case tests and consider monitoring for anomalies.
Ask questions to understand the system's purpose, expected scale, and what 'matching events' means. Determine if ties need a deterministic resolution and if notifications are user-facing or internal.
Propose a deterministic tie-breaking rule, such as sorting by a secondary attribute (e.g., timestamp, ID) or using a stable sort. Discuss whether ties should be considered equal or if a priority order is needed.
Decide whether to return fewer results, pad with defaults, or trigger a fallback mechanism. Consider user experience: is it better to show fewer results or to relax criteria to get more?
Define a clear, concise message template that includes essential information (e.g., event name, match count). Ensure messages are actionable and avoid spamming users with multiple notifications for the same event.
Evaluate the impact of each decision on performance, complexity, and user satisfaction. Consider how the solution scales with increasing data and whether asynchronous processing is needed for notifications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.