Designing a Hotel Management System

Introduction

Booking platforms must prevent double-bookings during high traffic. I designed a Hotel Management System that manages reservations, ensures transactional isolation, and processes payments securely.

Background

If two users book the same room at the same time, database queries run concurrently. A naive database read-then-write check results in both reservations being processed, causing operational issues. Relational databases address this using database locking or transaction isolation levels.

Implementation

We structured our booking queries using PostgreSQL transaction blocks with explicit row locking (FOR UPDATE). This blocks concurrent queries from reading the room's booking state until the active transaction completes.

Challenges

Implementing reservation locks introduced operational challenges:

• Deadlocks: Concurrent database operations locking different rows in conflicting order, causing database timeouts.
• Slow transactions: Keeping database connections locked during slow payment gateway API calls, causing connection pool exhaustion.

Solutions

We solved this by establishing database patterns:

1. Outbox Payment Processing: We created bookings in a pending state, released database locks immediately, and processed payments asynchronously in background tasks.
2. Order-based Queries: Enforcing strict order sorting (like room_id alphabetical order) in all multi-row SQL queries to prevent deadlocks.

Results

Releasing locks before payment processing reduced lock times from 2 seconds to under 5ms, and double-booking errors dropped to zero under high load.

Conclusion

Preventing race conditions requires transactional discipline. By using row locking, optimizing transaction scopes, and processing payments asynchronously, developers build reliable booking systems.