The scope was bigger than I initially mapped out.
Start by clarifying functional and non-functional requirements, then design the core data model and APIs for availability search, table management, and reservation lifecycle. Focus on concurrency control for double-booking prevention and scalability for high-traffic periods, while addressing the needs of both customers and restaurant operators.
Pro tip: Proactively discuss how to handle race conditions during peak booking times using techniques like optimistic locking or distributed locks, and mention how to design for idempotency to avoid duplicate reservations from retries.
Ask questions to understand expected scale, user roles (customers, restaurant operators), key features (search, booking, cancellation, waitlist), and non-functional needs like availability, consistency, and latency.
Define entities such as Restaurant, Table, Reservation, User, and TimeSlot, and choose appropriate databases (e.g., relational for transactions, NoSQL for scalability) with indexing for efficient availability queries.
Outline RESTful or GraphQL APIs for searching availability, creating/canceling reservations, and managing tables. Describe the reservation lifecycle states (e.g., held, confirmed, canceled, completed) and transitions.
Explain how to prevent double-booking using transactions, optimistic locking, or distributed locks. Discuss idempotency keys for reservation creation and handling of race conditions during high demand.
Discuss partitioning, caching, read replicas, and asynchronous processing for notifications. Consider search optimization (e.g., geospatial indexes) and how to handle peak loads with rate limiting and queueing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent most of my energy and honestly where it got interesting.
Start by clarifying requirements such as scale, consistency needs, and latency constraints. Then discuss concurrency control mechanisms like optimistic and pessimistic locking, and how to apply them at different layers (database, application, cache). Finally, address trade-offs and edge cases like distributed transactions and failure recovery.
Pro tip: Mention that the best solution depends on the specific use case—sometimes a simple database unique constraint is enough, while other times you need a distributed lock. Also, highlight the importance of idempotency and handling race conditions in a distributed environment.
Ask about expected traffic, consistency requirements (strong vs eventual), and whether the system is distributed. This shapes the appropriate solution.
Discuss optimistic vs pessimistic locking, and select based on contention levels. For high contention, pessimistic locking may be better; for low contention, optimistic locking can improve throughput.
Decide where to enforce locking: database (unique constraints, SELECT FOR UPDATE), application (in-memory locks), or distributed (Redis, ZooKeeper). Consider using a combination for robustness.
If distributed, discuss distributed locks, consensus algorithms (e.g., Raft), and handling network partitions. Mention idempotency to avoid duplicate bookings on retries.
Compare performance, scalability, and complexity. Discuss failure scenarios (e.g., lock expiration, deadlocks) and mitigation strategies like timeouts and retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a slots-based approach rather than pure interval math, which they seemed fine with.
Start by clarifying the functional requirements (e.g., single vs. multi-location, time zone handling, booking rules) and non-functional requirements (scale, consistency, latency). Then propose a normalized schema with core entities (Table, Reservation, AvailabilitySlot) and discuss how to efficiently query and update availability, including indexing and concurrency control.
Pro tip: Mention that availability slots can be precomputed and stored as materialized views or separate tables to avoid expensive real-time joins, and highlight the trade-off between storage cost and query performance.
Ask about scale, booking rules (e.g., duration, buffer times), time zones, and whether tables can be combined. This ensures the design meets actual needs.
Define entities like Table, Reservation, and AvailabilitySlot, and their relationships (e.g., a table has many slots, a reservation references a slot). Consider using a separate table for time slots to simplify queries.
Propose tables with primary/foreign keys, and indexes on frequently queried columns (e.g., date, table_id). Discuss normalization vs. denormalization for performance.
Explain how to handle concurrent bookings (e.g., using transactions, optimistic locking, or unique constraints) to prevent double-booking.
Discuss how to efficiently retrieve availability (e.g., precomputed slots, caching) and handle time zone conversions. Mention potential partitioning or sharding for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about caching availability windows at a coarse granularity and invalidating on any booking or cancellation.
Start by clarifying the requirements: read-heavy workload, low-latency lookups, and tolerance for stale data. Then propose a multi-layer caching strategy (client, CDN, application, distributed cache) with appropriate eviction policies and cache invalidation mechanisms. Finally, discuss trade-offs like consistency vs. availability and how to handle cache misses or failures gracefully.
Pro tip: Emphasize the importance of monitoring cache hit ratio and latency, and be ready to discuss how you'd handle cache stampede or thundering herd problems with techniques like request coalescing or probabilistic early expiration.
Ask about read/write ratio, data size, acceptable staleness, and latency SLAs to tailor the caching strategy.
Suggest caching at multiple levels: client-side, CDN, application-level (in-memory), and distributed cache (e.g., Redis) to reduce load on the primary datastore.
Discuss eviction policies (LRU, LFU) and invalidation strategies (TTL, write-through, write-behind, event-driven) based on consistency needs.
Explain how to handle cache misses, failures, and high concurrency (e.g., circuit breakers, request coalescing, replication).
Acknowledge trade-offs (consistency vs. latency, cost) and mention key metrics (hit ratio, latency, eviction rate) to monitor.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I ran through cancellations and no-shows fine.
Start by clarifying the system context and requirements, then systematically address each edge case by describing how you would model state transitions, handle concurrency, and ensure data consistency. Emphasize a robust design that uses idempotent operations, event-driven updates, and appropriate data structures to manage cancellations, no-shows, walk-ins, and waitlists.
Pro tip: Demonstrate foresight by discussing how you would monitor and log these edge cases to detect patterns (e.g., frequent no-shows) and iteratively improve the system, showing you think beyond just handling them.
Ask questions to understand the domain: Is this for appointments, reservations, or resource scheduling? What are the business rules for cancellations, no-shows, walk-ins, and waitlists? What are the consistency and latency requirements?
Define entities like Appointment, Customer, Resource, and WaitlistEntry. Outline state transitions (e.g., Booked -> Cancelled, Booked -> NoShow, Waitlisted -> Booked) and how they interact.
For cancellations: handle refunds, notifications, and slot release. For no-shows: define detection (time-based), penalties, and slot reallocation. For walk-ins: manage real-time availability and queueing. For waitlists: implement priority, notification, and expiration.
Discuss techniques like optimistic locking, transactions, or event sourcing to prevent double-booking and ensure waitlist promotions are atomic. Consider idempotency for operations like cancellation.
Explain how the design scales (e.g., sharding by resource, caching availability) and how you would monitor edge cases (metrics, logs) to improve the system. Mention trade-offs between consistency and availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: scale, latency, reliability, and user preferences. Then propose a high-level architecture with decoupled services, message queues, and channel-specific adapters, and dive into key components like scheduling, retries, and idempotency.
Pro tip: Emphasize idempotency and exactly-once delivery semantics, as duplicate notifications can erode user trust. Also, mention the importance of respecting user preferences and quiet hours to avoid spamming.
Ask about expected volume, latency requirements, delivery guarantees, and user preference management. Understand the types of notifications (confirmation vs. reminder) and their timing.
Propose a decoupled system with an API gateway, notification service, message queue (e.g., Kafka), and channel-specific workers. Use a database to store notification templates and user preferences.
Design a scheduler that triggers notifications based on events (e.g., reservation created) and time-based reminders. Use a distributed cron or delay queue for reminders.
Implement retries with exponential backoff, dead-letter queues, and idempotency keys to handle failures. Ensure exactly-once delivery where possible, or at-least-once with deduplication.
Discuss metrics (success rate, latency), logging, and alerting. Scale horizontally by adding workers and partitioning the queue. Consider rate limiting and third-party service limits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.