I went straight to the visual layer and started sketching out components before really thinking about the data flow underneath.
Start by clarifying the product context and user needs, then outline the core components of the price alert system (data model, tracking service, notification service, and UI). Focus on how the UI integrates with PayPal's payment and commerce ecosystem, and discuss trade-offs around real-time updates, scalability, and user experience.
Pro tip: Emphasize idempotency and exactly-once notification delivery to avoid duplicate alerts, and discuss how to handle price fluctuations during checkout to maintain trust.
Ask questions to understand the target users (buyers, merchants), alert types (price drop, back in stock), and scale (number of users, products). Define whether alerts are for PayPal's own products or third-party merchants.
Sketch the main components: a tracking service that monitors prices, a rules engine for alert conditions, a notification service (email, push, SMS), and a UI for managing alerts. Show how they interact with PayPal's payment and identity systems.
Design schemas for alerts, user preferences, and price history. Consider using a time-series database for price data and a relational database for user alerts. Discuss indexing for efficient queries.
Describe the key screens: alert creation (select product, set target price), alert management (list, edit, delete), and notification preferences. Highlight real-time updates and mobile responsiveness.
Discuss trade-offs: polling vs. webhooks for price updates, push vs. pull for notifications, and consistency vs. availability. Address scaling to millions of users with sharding and caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.