I started with users and reservations and worked outward, which felt natural but I think I should've anchored on the slot/table relationship first since that's where all the complexity lives.
Start by clarifying functional and non-functional requirements, then identify core entities and their relationships. Propose a schema with appropriate keys, indexes, and constraints, and discuss how to handle concurrency and time slot management.
Pro tip: Mention that time slots should be modeled as intervals with start and end times, and that you'd use a unique constraint on (table_id, start_time) to prevent double-booking. Also, consider using a separate availability table or materialized view for fast lookups.
Ask about expected scale, read/write patterns, and whether reservations are for specific tables or general time slots. Confirm if users can book multiple tables or if there are waitlists.
List entities: User, Restaurant, Table, TimeSlot, Reservation. Define attributes for each, such as User (id, name, contact), Restaurant (id, name, location, hours), Table (id, restaurant_id, capacity, location), TimeSlot (id, restaurant_id, start_time, end_time), Reservation (id, user_id, table_id, time_slot_id, party_size, status).
Specify cardinalities: a restaurant has many tables, a table has many time slots, a user makes many reservations, a reservation links one user, one table, and one time slot. Add constraints: unique (table_id, time_slot_id) to prevent double-booking, foreign keys, and check constraints on party_size <= table capacity.
Propose tables with primary keys, foreign keys, and indexes on frequently queried columns like restaurant_id and start_time. Consider partitioning by date for scalability.
Discuss how to handle concurrent bookings (e.g., using transactions with isolation levels or optimistic locking). Mention handling cancellations, no-shows, and waitlists.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The hold-then-confirm pattern tripped me up a bit.
Start by clarifying requirements and constraints, then design RESTful endpoints with clear resource modeling and state transitions. Emphasize idempotency, concurrency control, and error handling for each operation.
Pro tip: Mention using idempotency keys for hold and confirm operations to prevent duplicate bookings, and discuss how to handle race conditions with optimistic locking or distributed locks.
Ask about scale, consistency needs, and client types to tailor the design. Confirm whether holds expire and how modifications affect pricing.
Model availability, holds, and reservations as resources. Propose endpoints like GET /availability, POST /holds, POST /reservations, DELETE /reservations/{id}, PATCH /reservations/{id}.
For each endpoint, specify request/response schemas, status codes, and idempotency. Explain how holds convert to reservations and how cancellations release inventory.
Discuss strategies like optimistic locking, distributed locks, or transactional outbox to handle concurrent bookings and avoid double-booking.
Mention handling of expired holds, partial modifications, and scaling reads/writes with caching, sharding, or CQRS.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints (e.g., scale, consistency needs, latency tolerance), then present a layered strategy: database-level locking (pessimistic or optimistic) combined with application-level idempotency and possibly distributed locks. Compare trade-offs between pessimistic locking (strong consistency, lower throughput) and optimistic concurrency (higher throughput, retry logic) and recommend a solution based on the scenario.
Pro tip: Mention that optimistic concurrency is often preferred for high-read, low-write scenarios like booking, but always include a fallback mechanism (e.g., retry with exponential backoff) and idempotency keys to handle conflicts gracefully. Also, discuss how you would monitor and alert on conflict rates to detect hotspots.
Ask about expected concurrency, consistency requirements (strong vs eventual), and latency constraints to tailor your answer.
Explain pessimistic locking (e.g., SELECT FOR UPDATE) and its impact on throughput, and when it's appropriate (e.g., low contention, strong consistency).
Describe versioning or timestamp checks, and how to handle conflicts via retries or user feedback, highlighting its scalability benefits.
If the system is distributed, mention distributed locks (e.g., Redis, ZooKeeper) or consensus-based approaches, and their trade-offs.
Propose a combination: optimistic concurrency for most cases, with pessimistic locking for critical sections, and idempotency to avoid duplicate bookings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale, then propose a system design that handles no-shows through proactive measures and a waitlist with real-time notifications. Focus on the core components: reservation service, waitlist management, notification service, and data consistency.
Pro tip: Demonstrate product sense by discussing trade-offs between overbooking and waitlist efficiency, and mention how to handle edge cases like group reservations and last-minute cancellations.
Ask questions to understand the scale (e.g., number of reservations per day), user expectations (e.g., notification preferences), and business rules (e.g., cancellation policies).
Propose strategies such as confirmation reminders, penalties for no-shows, and overbooking policies. Discuss how to track no-shows and adjust future reservations.
Outline a waitlist service that maintains a queue per time slot, with real-time updates when slots become available. Include notification mechanisms (push, SMS, email) and expiration for offers.
Address data consistency (e.g., using transactions or distributed locks) and scalability (e.g., sharding by time slot or location). Consider using a message queue for notifications.
Talk about trade-offs between overbooking and waitlist length, and define success metrics like waitlist conversion rate and no-show rate reduction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business goals and constraints, then outline a phased onboarding flow that balances automation with manual verification. Emphasize API design, data validation, and stakeholder collaboration to ensure a smooth partner experience.
Pro tip: Highlight the importance of idempotency and error handling in the onboarding APIs to prevent duplicate restaurant entries and ensure reliable retries. Also, mention that you would involve partner success and legal teams early to align on compliance and support needs.
Ask questions to understand the scale, types of partners, regulatory requirements, and existing systems. Identify key stakeholders and their priorities.
Outline the end-to-end process: partner sign-up, data collection, validation, verification, configuration, and go-live. Consider self-service vs. assisted onboarding.
Specify RESTful endpoints for each step, including request/response schemas, authentication, and error codes. Ensure idempotency and versioning.
Implement data validation, duplicate checks, and secure storage. Integrate with third-party services for tax ID verification, background checks, etc.
Set up logging, metrics, and alerts for the onboarding pipeline. Collect partner feedback and iterate to improve conversion and time-to-onboard.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about read replicas and caching availability data with a short TTL, plus sharding reservations by restaurant ID.
Start by clarifying the scale and read/write patterns, then propose a multi-layered architecture that separates read and write paths. For read-heavy search, use caching, read replicas, and a search-optimized store; for hot restaurants, introduce write-side techniques like queueing, rate limiting, and partitioning to handle contention. Finally, discuss trade-offs and how you would validate the design.
Pro tip: Quantify the impact: estimate QPS, cache hit ratio, and latency improvements to show you think in numbers. Also, mention that you would monitor and adapt the strategy based on real traffic patterns, demonstrating a data-driven mindset.
Ask about scale (QPS, data size), read/write ratio, latency SLOs, consistency needs, and budget. This ensures your solution targets the actual problem.
Propose using a dedicated search engine (e.g., Elasticsearch) with read replicas, caching layers (CDN, Redis), and denormalized indexes to handle high read volume efficiently.
Introduce write-side strategies: queueing (Kafka), rate limiting, optimistic concurrency control, and partitioning by restaurant ID to distribute load. Consider pre-computed availability or token-based booking.
Discuss eventual consistency for search vs. strong consistency for bookings, and how to handle stale data. Mention trade-offs between latency, consistency, and cost.
Outline metrics to track (cache hit rate, queue depth, latency) and how you would use them to refine the system, showing a proactive approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Composite index on restaurant ID plus slot time was my first answer.
Start by clarifying the query patterns and data access requirements for reservations and availability, then propose a composite index that covers the most frequent queries, and finally discuss trade-offs and potential optimizations like partitioning or covering indexes.
Pro tip: Mention that you would validate the index strategy with real query plans and monitor performance, showing that you balance theory with practical measurement.
Determine the most common and critical queries, such as checking availability for a date range or retrieving reservations for a user or resource.
Create composite indexes on columns used together in WHERE, JOIN, and ORDER BY clauses, with the most selective column first.
Include additional columns in the index to make it covering, reducing I/O by avoiding table lookups for frequent queries.
For large tables, consider partitioning by date or using specialized indexes like BRIN for time-series data to improve performance.
Discuss the impact on write performance and storage, and emphasize the need to monitor and adjust indexes based on actual usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.