I started with the session lifecycle because that felt like the spine of the whole thing: open register, scan items, apply discounts, collect payment, close.
Start by clarifying functional and non-functional requirements, then design a modular system with clear separation of concerns (e.g., product catalog, pricing engine, payment processing, receipt generation, session management). Walk through the core checkout flow, then discuss data models and trade-offs, and finally explain how to extend the design for loyalty, returns, and split payments using extensible patterns like strategy or plugin architectures.
Pro tip: Emphasize idempotency and consistency in payment and receipt generation to handle retries and failures gracefully, and mention that you'd use feature flags or modular services to roll out extensions without disrupting existing operations.
Ask about expected scale (transactions per second, number of registers), consistency needs (e.g., inventory accuracy), and integration points (payment gateways, loyalty systems). Define core use cases and constraints.
Outline modules: Product Catalog (with SKU lookup), Pricing Engine (handles discounts, taxes), Payment Processor (integrates with external gateways), Receipt Generator, and Register Session Manager. Define key entities like Product, Price, Transaction, Receipt, and Session.
Describe the sequence: scan item → lookup product → calculate price → add to cart → apply payment → generate receipt → close session. Highlight error handling, concurrency (e.g., multiple registers), and idempotency for payment and receipt.
Compare monolithic vs. microservices, SQL vs. NoSQL for product catalog, synchronous vs. asynchronous payment processing, and consistency models. Justify choices based on requirements.
Explain how to add loyalty programs (e.g., strategy pattern for discount rules), returns (reverse transaction flow with refund processing), and split payments (payment aggregation with multiple payment methods). Use interfaces and modular design to minimize impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.