Let's design a hotel booking system. Focus on the core functionalities required for users to search, book, and manage their reservations. Consider the following use cases:

• User searches for available hotels: A user specifies a location, check-in date, check-out date, and number of guests to find available hotels.
• User views hotel details: A user can view detailed information about a hotel, including amenities, room types, and pricing.
• User books a room: A user selects a room type and books it for the specified dates and guests. The system should handle payment processing (assume a simplified payment gateway).
• User manages their bookings: A user can view, modify (e.g., change dates or room type), or cancel their existing bookings. Cancellation policies should be considered.
• Hotel admin manages hotel details and availability: Hotel administrators can add or update hotel information, room types, and availability calendars.

Let's start by discussing the high-level architecture and key components involved in such a system.

Hotel Booking System Design

Let's design a hotel booking system. I've worked on similar distributed systems at Amazon, so I'll leverage that experience here. I'm currently a Principal Engineer based in Seattle, so I'm used to dealing with high-scale, resilient service design.

Requirements

Here's a breakdown of the system's requirements. I'll focus on the core functionality for brevity. Let me know if you'd like to dive deeper into any specific aspect.

• Functional Requirements:
  • Users should be able to search for hotels by location, dates, and number of guests.
  • Users should be able to view hotel details (rooms, amenities, prices).
  • Users should be able to book rooms.
  • Users should be able to cancel bookings.
  • Hotels should be able to manage their room availability and pricing.
  • The system should handle concurrent bookings and prevent overbooking.
• Non-Functional Requirements:
  • Scalability: The system should handle a large number of users and bookings.
  • Availability: The system should be highly available and fault-tolerant.
  • Consistency: The system should ensure data consistency, especially regarding room availability.
  • Performance: The system should provide fast search and booking responses.
  • Security: The system should protect user data and prevent unauthorized access.

High-Level Design

We can break down the system into several key components:

1. User Interface (UI): The front-end for users to interact with the system (web and mobile).
2. API Gateway: Entry point for all requests, handling authentication, authorization, rate limiting, and routing requests to the appropriate services.
3. Search Service: Responsible for searching hotels based on user criteria.
4. Hotel Service: Manages hotel information, room details, and pricing.
5. Booking Service: Handles booking requests, manages booking records, and communicates with other services.
6. Payment Service: Processes payments for bookings. (We'll assume a 3rd party integration here to keep the scope reasonable).
7. Notification Service: Sends email/SMS confirmations and updates to users.
8. Cache: Used to cache frequently accessed data (e.g., hotel details, search results) to improve performance.
9. Database: Stores all persistent data (hotel information, bookings, user data).

Communication: The services will primarily communicate via REST APIs. For asynchronous tasks (e.g., sending notifications), we can use a message queue (e.g., Kafka or RabbitMQ).

[Diagram: A simple block diagram showing these components and their interactions would be helpful here. Unfortunately, I cannot draw diagrams.]

Data Model

Here's a simplified data model, focusing on the core entities:

Table	Columns	Data Type	Description
Hotels	hotelid (PK), name, location, description, amenities	INT, VARCHAR	Stores information about hotels.
Rooms	roomid (PK), hotelid (FK), roomtype, capacity, price	INT, INT, VARCHAR, INT, DECIMAL	Stores information about rooms within a hotel.
Bookings	bookingid (PK), userid (FK), roomid (FK), checkindate, checkoutdate, numguests, totalprice, bookingstatus	INT, INT, INT, DATE, DATE, INT, DECIMAL, ENUM	Stores booking information, including user, room, dates, and status (e.g., confirmed, cancelled).
Users	userid (PK), username, password, email	INT, VARCHAR	Stores user account information.
Availability	roomid (PK, FK), date (PK), availablerooms	INT, DATE, INT	Stores the number of available rooms for a given room type on a specific date. This helps prevent overbooking. A composite primary key is used.

API Design

Here are some key API endpoints:

• Search:
  • GET /hotels?location={location}&checkInDate={checkInDate}&checkOutDate={checkOutDate}&guests={guests}: Searches for hotels based on criteria. Returns a list of hotel IDs and summary info.
• Hotel Details:
  • GET /hotels/{hotelId}: Retrieves detailed information about a specific hotel.
• Room Details:
  • GET /hotels/{hotelId}/rooms/{roomId}: Retrieves details of a specific room within a hotel
• Booking:
  • POST /bookings: Creates a new booking.
    • Request body: {userId, roomId, checkInDate, checkOutDate, numGuests}
    • Response: bookingId
• Cancellation:
  • DELETE /bookings/{bookingId}: Cancels an existing booking.
• **Hotel Management (Authentication Required):
  • POST /hotels: Add a new hotel. (Restricted Access)
  • PUT /hotels/{hotelId}: Update a hotel (Restricted Access)
  • PUT /hotels/{hotelId}/rooms/{roomId}: Update a room (Restricted Access)

Tradeoffs

• Consistency vs. Availability: We can use a distributed database (e.g., Cassandra or DynamoDB) to achieve high availability. However, this may come at the cost of eventual consistency. For critical operations like booking, we need strong consistency. We can achieve this using distributed transactions or optimistic locking with retries.
• Monolith vs. Microservices: I've described a microservices architecture which gives us scalability and independent deployments. A monolithic architecture might be easier to develop initially, but less scalable and maintainable in the long run.

Alternative Approaches

• CQRS (Command Query Responsibility Segregation): For the search service, we can use CQRS. We can have a separate read model optimized for fast searching. The write model updates the read model asynchronously. This would improve search performance.
  • Pros: Improved search performance.
  • Cons: Increased complexity.
• Serverless Architecture: We could implement some of the services (e.g., Notification Service) using serverless functions (e.g., AWS Lambda). This can reduce operational overhead and costs.
  • Pros: Reduced operational overhead, pay-per-use pricing.
  • Cons: Cold starts, vendor lock-in.

Edge Cases

• Overbooking: The availability table is designed to prevent overbooking. When a booking request comes in, we first check the availability table to ensure that there are enough rooms available. If so, we decrement the available_rooms count and create the booking. We use a transaction to ensure atomicity.
• Concurrent Bookings: Multiple users might try to book the same room at the same time. We can use optimistic locking on the availability table to handle this. If the update fails due to a conflict, we retry the booking.
• Payment Failures: If the payment fails, we need to rollback the booking and release the room. We can use a saga pattern to handle this.
• Cancellation Policy: We need to implement a cancellation policy that defines the terms and conditions for cancelling bookings. This could include a cancellation fee or a deadline for cancellations.

Future Considerations

• Recommendation Engine: We could add a recommendation engine to suggest hotels based on user preferences and past bookings.
• Loyalty Program: We could implement a loyalty program to reward frequent users.
• Integration with External Services: We could integrate with other services, such as flight booking services or car rental services, to provide a more complete travel booking experience.
• Real-time Availability Updates: Implement real-time updates for room availability using WebSockets to improve the user experience. This would require updating the caching strategy.