I started with the mobile vs in-store split and that turned out to be the right move.
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that unifies mobile app and in-store register flows through a shared backend. Focus on key components like order management, payment processing, and inventory sync, and discuss trade-offs around consistency, latency, and offline support.
Pro tip: Emphasize idempotency and exactly-once payment processing to avoid double charges, and discuss how you'd handle offline scenarios for in-store registers—these are common real-world pitfalls that demonstrate production experience.
Ask questions to scope the system: expected scale (orders per day, peak load), payment methods, offline support, and consistency needs. Distinguish between mobile app (online) and in-store register (possibly offline) flows.
Propose a microservices-based architecture with an API gateway, order service, payment service, inventory service, and notification service. Show how both mobile and in-store clients interact with the same backend via APIs.
Detail the order lifecycle (cart, checkout, payment, fulfillment) and payment integration (e.g., Stripe, idempotency keys, retries). Discuss inventory synchronization and how to handle concurrent orders.
Discuss trade-offs: consistency vs. availability (CAP), latency vs. durability, and offline mode for registers. Cover edge cases like payment failures, network partitions, and refunds.
Explain how to scale horizontally (load balancing, sharding), ensure fault tolerance (circuit breakers, retries), and monitor the system (logging, metrics, alerts).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a line-item plus modifier table approach.
Start by clarifying the requirements and constraints, then propose a flexible schema that separates base products from modifiers, using a many-to-many relationship. Discuss trade-offs between normalization and denormalization, and consider how the model supports queries, pricing, and inventory.
Pro tip: Mention that modifiers often have their own attributes (e.g., price adjustments, default selections) and that you'd model them as first-class entities to avoid hardcoding. Also, consider using a JSON column for modifier selections in the order line for flexibility, but be aware of query performance implications.
Ask about expected query patterns, scale, and whether modifiers affect pricing or inventory. This ensures the model aligns with business needs.
Define entities like Product, ModifierGroup, Modifier, Order, and OrderItem. Recognize that modifiers can be grouped (e.g., size, milk) and may have multiple options.
Model many-to-many relationships: Product to ModifierGroup, ModifierGroup to Modifier, and OrderItem to selected Modifiers. Use junction tables to capture selections and any additional attributes like quantity.
Decide how modifier price adjustments are stored (e.g., on Modifier) and how they affect OrderItem total. Consider inventory impact if modifiers consume stock.
Compare normalized relational design with denormalized JSON storage. Highlight pros and cons regarding flexibility, query complexity, and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business domain and key requirements (e.g., order flow, real-time updates, consistency needs). Then propose a service decomposition based on bounded contexts, explaining how each service owns its data and communicates via events or APIs. Finally, discuss trade-offs like coupling, latency, and operational complexity, and how you'd evolve the architecture over time.
Pro tip: Emphasize that service boundaries should align with business capabilities and team ownership, not just technical layers. Also, mention that you'd start with a modular monolith or a few coarse-grained services and split further only when justified by scaling or team autonomy needs.
Ask questions to understand the business domain, expected scale, consistency requirements, and team structure. Identify core entities and workflows (e.g., order placement, payment processing, kitchen fulfillment).
Map business capabilities to bounded contexts (e.g., Ordering, Payment, Kitchen, Inventory, Loyalty). Ensure each context has a clear responsibility and minimal overlap.
For each service, specify its API/events and the data it owns. Decide on synchronous (REST/gRPC) vs asynchronous (events) communication based on coupling and latency needs.
Analyze trade-offs: consistency vs availability, latency, operational overhead, and team autonomy. Explain how you'd evolve the decomposition (e.g., start with a modular monolith, split when needed).
Recap the proposed decomposition, highlighting how it meets requirements. Invite feedback and discuss potential failure modes or scaling strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: is this about retrying a failed payment request or preventing duplicate charges when a request times out? Then propose an idempotency key mechanism combined with a state machine for payment attempts, and discuss how to handle partial failures (e.g., network timeouts) by querying the payment provider's status before retrying.
Pro tip: Emphasize that idempotency keys must be generated client-side and stored server-side with a unique constraint, and that you should never blindly retry a payment without first checking the transaction status with the provider.
Ask whether the concern is duplicate charges from retries, partial failures (e.g., timeout after provider processed), or both. Identify the payment provider's capabilities (idempotency support, status query API).
Use a client-generated idempotency key (UUID) sent with each payment request. The server stores this key with a unique constraint and returns the same result for repeated requests with the same key.
Track each payment attempt through states: initiated, pending, succeeded, failed, unknown. On failure or timeout, transition to 'unknown' and trigger a reconciliation process to query the provider's status.
Only retry if the payment is in a retryable state (e.g., failed due to network error) and after confirming with the provider that the original attempt did not succeed. Use exponential backoff with jitter.
Implement a background job that periodically reconciles 'unknown' payments by querying the provider. Log all attempts and set up alerts for discrepancies to catch double-charge bugs early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Trickier than it sounds because redeeming points needs to be atomic with the payment.
Start by clarifying requirements and constraints, then propose a high-level architecture that separates loyalty concerns from core checkout, and finally dive into key design decisions like data consistency, failure handling, and trade-offs. Emphasize idempotency, eventual consistency, and user experience during redemption.
Pro tip: Demonstrate awareness of business impact by discussing how to handle partial failures (e.g., points deducted but order fails) and proposing compensating transactions or sagas. Also, mention observability and metrics to monitor loyalty system health.
Ask questions to understand scale, latency requirements, consistency needs, and integration points (e.g., existing loyalty service, checkout flow). Identify if points earning/redemption should be synchronous or asynchronous.
Propose a modular design with a dedicated loyalty service that exposes APIs for earning and redeeming points. Integrate with checkout via orchestration or choreography, ensuring loose coupling.
Detail the sequence for earning points (e.g., after order completion) and redeeming points (e.g., during checkout). Discuss idempotency keys, validation, and how to handle insufficient points.
Explain how to maintain consistency between checkout and loyalty systems using patterns like saga, two-phase commit, or event-driven eventual consistency. Cover rollback and compensation for failures.
Compare synchronous vs asynchronous integration, strong vs eventual consistency, and monolithic vs microservices. Highlight scalability considerations like caching, sharding, and rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what metrics (sales totals, popular items), granularity (per store, per day), and latency (real-time vs batch). Then propose a scalable data pipeline that ingests point-of-sale data from all stores, aggregates it in a data warehouse, and serves it via a reporting API or dashboard, discussing trade-offs between batch and streaming.
Pro tip: Emphasize data modeling and partitioning strategies (e.g., by store and date) to ensure efficient queries and cost-effective storage, and mention how you'd handle late-arriving data and ensure data consistency across stores.
Ask about the scale (number of stores, transactions per day), required freshness (real-time, hourly, daily), and specific metrics (e.g., total sales, top-selling items per store).
Propose how to collect data from each store: batch uploads to cloud storage or streaming events via a message queue like Kafka, ensuring reliability and idempotency.
Select a data warehouse (e.g., BigQuery, Redshift) for aggregated data and a processing engine (e.g., Spark, Flink) for transformations; discuss partitioning and indexing for performance.
Outline how to compute sales totals and popular items: use SQL or batch jobs to group by store, item, and time window; consider incremental updates and handling late data.
Expose results via an API or dashboard, implement caching for frequent queries, and set up monitoring for data quality and pipeline health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.