Design low-latency large-scale hotel booking system

You are asked to design the backend for a large-scale hotel booking system that runs behind a very high-traffic consumer app (think a TikTok-like app where a hotel goes viral and suddenly millions of users click into the same property).

Users can:

• Browse hotels for a city and date range.
• View near real-time availability and prices for a specific hotel.
• Place a booking request and receive a confirmation or rejection.

The interviewer gives you the high-level requirements and asks you to reason carefully about business properties first, then derive the architecture and key trade-offs.

Functional requirements

• Search for hotels by city, date range, and basic filters.
• For a given hotel and date range, show up-to-date availability (rooms left) and price.
• Create a booking for a chosen hotel, room type, and date range.
• Guarantee that the same room-night is not double-booked.
• Optionally support cancellation (you can keep this high-level).

Non-functional requirements

• High concurrency:
• Assume up to ~100k read requests/sec (availability checks) and ~10k booking attempts/sec at peak.
• Low latency:
• P99 latency for availability checks and booking confirmation should be < 200 ms end-to-end from the client’s perspective.
• High availability:
• The system must continue working if individual nodes or even a whole zone go down.
• Consistency characteristics:
• Weak/eventual consistency is acceptable for displaying availability counts on the UI. A user may occasionally see an outdated count.
• Strong correctness is required for final booking confirmation (no double-booking).
• Message loss tolerance:
• For streaming / push-style availability updates to clients, it is acceptable if some update messages are lost (they will be refreshed soon anyway).
• It is not acceptable to lose actual booking requests or confirmations.
• Hotspot handling:
• Some hotels may become extremely hot (e.g., after a viral video), causing a huge, skewed load.
• The system should avoid concentrating all traffic for one hot hotel on a single node.
• Latency vs reliability trade-off:
• You should discuss protocol choices (e.g., HTTP vs WebSocket vs UDP or similar) and how they impact latency and reliability.

Specific discussion points the interviewer cares about

1. Latency vs reliability:

• When and why might you choose a low-overhead protocol such as UDP or WebSocket-style persistent connections instead of plain HTTP for certain flows?
• Which parts of the system can tolerate message loss, and which cannot?

1. Preventing double bookings for the same hotel/room:

• Under high concurrency, how do you ensure two users cannot both successfully book the last available room-night?
• Discuss techniques such as rate limiting, request coalescing/merging, throttling, and concurrency control (locks, atomic counters, queues, etc.).

1. Sharding / horizontal scaling for hot hotels:

• How would you horizontally partition the load for a very hot hotel?
• Compare strategies like sharding by room (e.g., room ID or room type) versus bucketizing by user (e.g., hashing on user ID) and discuss pros/cons.

Assume you are free to pick any technologies (e.g., relational vs NoSQL databases, caches like Redis, message queues like Kafka, etc.).

Task: Design the system at a high level:

• Identify the major components/services.
• Propose a data model for hotels, rooms, inventory, and bookings.
• Describe the end-to-end flows for availability lookup and booking confirmation.
• Explain how your design achieves:
• Low latency with acceptable reliability trade-offs.
• No double-booking despite high concurrency.
• Good handling of hot hotels via sharding/partitioning.
• Explicitly call out the key trade-offs and why you made those choices.