I started with the entities which felt right, Room, Customer, Booking, and a service layer.
Start by clarifying requirements (e.g., single hotel vs. chain, room types, booking rules) and then define the core entities (Hotel, Room, Booking, Guest) with their relationships. Focus on the API design for checking availability and booking, ensuring thread safety and efficient date-range queries.
Pro tip: Mention that availability checks and bookings must be atomic to prevent double-booking, and propose using a database transaction or optimistic locking. Also, discuss how to handle date ranges efficiently with interval trees or database indexes.
Ask questions to understand scope: single hotel or chain? What room types? Are there seasonal rates? What are the booking constraints (min/max stay, cancellation)? This ensures the design meets actual needs.
Identify main classes: Hotel, Room, RoomType, Booking, Guest, and possibly Inventory. Define their attributes and relationships, focusing on how bookings link to rooms and date ranges.
Specify methods: checkAvailability(hotelId, roomType, startDate, endDate) and bookRoom(guestId, roomId, startDate, endDate). Include parameters, return types, and error handling.
Explain how to prevent double-booking using transactions, locks, or optimistic concurrency. Discuss isolation levels and potential race conditions.
Discuss indexing strategies (e.g., composite index on roomId and date range) and data structures (interval trees) to speed up availability queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The half-open interval thing tripped me up for a second.
Clarify the exact overlap condition: two ranges overlap if one starts before the other ends, using strict inequalities to allow check-out and check-in on the same day. Then present the condition as a simple boolean expression and discuss edge cases like invalid ranges and inclusive/exclusive boundaries.
Pro tip: Mention that treating check-out as an exclusive boundary (e.g., using half-open intervals [check-in, check-out)) is a common industry convention that naturally avoids same-day conflicts and simplifies the overlap logic.
Confirm that check-out and check-in on the same date should not conflict, and define whether dates are inclusive or exclusive. Establish that each range has a valid start and end with start < end.
State that two ranges A and B overlap if A.start < B.end AND B.start < A.end. Explain that using strict inequalities ensures that touching endpoints (same-day check-out/check-in) are not considered overlapping.
Provide concrete examples: (1) A: Jan 1–5, B: Jan 5–10 → no overlap; (2) A: Jan 1–5, B: Jan 4–6 → overlap. Show how the condition evaluates for each.
Address invalid ranges (start >= end), time zones, and whether dates are stored as date objects or strings. Mention that using half-open intervals [start, end) simplifies reasoning.
Conclude with the boolean expression and note how this logic scales to checking multiple bookings (e.g., iterating or using interval trees) and its importance in booking systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (e.g., scale, consistency needs, latency tolerance) and then propose a layered solution: use a database transaction with row-level locking or optimistic concurrency control to ensure atomicity, and consider distributed locking or a reservation system for high-scale scenarios. Discuss trade-offs between consistency, availability, and performance, and mention how to handle failures and retries.
Pro tip: Emphasize idempotency and graceful degradation: even with locking, network retries can cause duplicate requests, so design APIs to be idempotent and consider a two-phase approach (hold then confirm) to improve user experience while maintaining correctness.
Ask about scale (requests per second), consistency requirements (strong vs eventual), and latency expectations to tailor the solution.
Select an appropriate mechanism: database transactions with row-level locking (e.g., SELECT FOR UPDATE), optimistic concurrency with version numbers, or distributed locks (e.g., Redis, ZooKeeper) for multi-service architectures.
Outline the steps: check availability, acquire lock, verify availability again, create booking, release lock. Ensure atomicity and handle failures with retries and timeouts.
Discuss handling of concurrent requests, network partitions, lock expiration, and idempotency to prevent duplicate bookings from retries.
Compare approaches in terms of consistency, availability, performance, and complexity, and justify your choice based on the clarified requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short discussion, more of a follow-up than a full question.
Start by clarifying the booking model and the exact invariant (e.g., no overlapping time ranges for the same resource). Then present a layered enforcement strategy: database constraints as the last line of defense, with application-level checks and proper transaction isolation to handle concurrency. Discuss trade-offs between different database-level techniques (exclusion constraints, unique indexes, triggers) and how they affect performance and portability.
Pro tip: Mention that while application-level checks are necessary for user experience, the database must be the ultimate enforcer to prevent race conditions; use a concrete example like PostgreSQL's exclusion constraint with a GiST index to show depth.
Clearly state what 'no-overlap' means: for a given resource (e.g., room, employee), no two bookings can have overlapping time intervals. Specify whether it's inclusive/exclusive of endpoints and if it applies per resource or globally.
Evaluate options: exclusion constraints (PostgreSQL), unique indexes on computed columns (e.g., resource_id + time slot), or triggers. Discuss pros/cons: exclusion constraints are declarative and efficient but not portable; triggers are flexible but can be error-prone and slower.
Explain how the chosen mechanism works under concurrent inserts. For exclusion constraints, the database automatically handles locking; for triggers, you may need SELECT ... FOR UPDATE or SERIALIZABLE isolation to avoid race conditions.
Describe how the application interacts with the database constraint: catch constraint violations and translate them into user-friendly errors. Optionally, perform a pre-check for better UX, but never rely solely on it.
Compare performance impact (index maintenance, trigger overhead), portability across databases, and complexity. Mention alternatives like optimistic locking with versioning or application-level distributed locks, and why they might be insufficient alone.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't get deep into this one, we were running short on time.
Start by clarifying the current booking system's architecture and locking strategy, then propose extensions for cancellation and modification with a focus on concurrency control. Discuss trade-offs between pessimistic and optimistic locking, and how to handle edge cases like partial modifications and cancellation during concurrent access.
Pro tip: Emphasize idempotency and versioning to handle retries and concurrent modifications gracefully, and mention how Apple's scale might require distributed locking or consensus mechanisms like Raft or Paxos.
Ask about the current system's design, expected load, consistency requirements, and whether bookings can be modified partially or fully. Confirm if cancellation is immediate or follows a policy.
Introduce a booking version or status field to track changes, and consider an audit log for modifications. For cancellation, add a status flag and possibly a cancellation reason.
Define new endpoints for cancel and modify operations, ensuring they validate permissions and business rules (e.g., no modification after check-in). Use idempotency keys to handle retries.
For modifications, use optimistic locking with version checks to detect conflicts, or pessimistic locking for high-contention scenarios. For cancellation, ensure atomic status updates to avoid double-booking.
Discuss handling concurrent modify/cancel requests, deadlocks, and distributed locking if the system is sharded. Consider timeouts and retries with exponential backoff.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Reframe the problem from booking a specific room to booking a room type by introducing an abstraction layer that decouples the booking request from physical room IDs. Then describe how to model availability, allocation, and concurrency to support this generalization.
Pro tip: Emphasize that this change shifts the system from a simple CRUD operation to a resource allocation problem, and highlight the need for atomicity and idempotency to handle concurrent bookings gracefully.
Recognize that the core change is moving from a room ID to a room type as the booking unit, which requires separating inventory (rooms) from the booking request.
Design a data model that tracks room types, their inventory, and availability over time, and define an allocation strategy (e.g., first-available, best-fit) to assign a physical room upon booking.
Ensure that multiple concurrent booking requests for the same room type do not oversell by using transactions, locks, or optimistic concurrency control.
Outline the steps: user selects room type and dates, system checks availability, reserves a room atomically, and returns a confirmation with the assigned room ID.
Address scenarios like cancellations, overbooking policies, and how the design scales with increasing inventory and request volume.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then propose a distributed architecture that partitions data by hotel or region to enable horizontal scaling. Emphasize strong consistency mechanisms like distributed transactions or consensus protocols to guarantee per-room booking correctness, and discuss trade-offs between consistency, availability, and latency.
Pro tip: Demonstrate awareness of Apple's emphasis on user experience by highlighting how you would minimize booking conflicts and provide clear feedback to users, even under high load. Also, mention monitoring and alerting for booking anomalies to ensure reliability.
Ask questions to understand expected traffic, number of hotels/rooms, booking patterns, and consistency requirements. Define what 'correctness' means (e.g., no double-booking, accurate availability).
Propose sharding by hotel ID or geographic region to distribute load and allow independent scaling. Discuss how to handle hot partitions and rebalancing.
Use distributed transactions, two-phase commit, or consensus protocols (e.g., Raft, Paxos) to atomically update room availability and bookings. Consider optimistic concurrency control with versioning.
Discuss CAP theorem implications, latency vs. consistency, and how to handle network partitions, node failures, and retries without compromising correctness.
Outline auto-scaling, caching strategies (e.g., read replicas for availability queries), and monitoring/alerting for booking anomalies and system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.