Design an Online Ticket Booking System: LLD Guide

I remember the first time I tried to book tickets for a popular movie. The website crashed, and I missed out! That’s when I started wondering about the magic behind these systems. How do they handle so much traffic and keep everything running smoothly?

Let's break down the low-level design (LLD) of an online ticket booking system, like BookMyShow.

Why This Matters?

Understanding the LLD of a system like this is crucial for any software engineer. It touches upon key concepts like:

• Scalability
• Concurrency
• Database design
• Design patterns

It’s not just about booking tickets; it’s about building robust and scalable systems.

Key Components

Let's identify the core components needed for our ticket booking system:

1. User Interface (UI): Allows users to browse movies, events, and venues.
2. Catalog Service: Manages information about movies, events, and venues.
3. Booking Service: Handles the booking process, including seat selection and payment.
4. Payment Gateway: Integrates with payment providers to process transactions.
5. Notification Service: Sends booking confirmations and updates to users.
6. Database: Stores information about users, events, bookings, and payments.

Database Schema

A well-designed database schema is essential for efficient data management. Here's a simplified schema:

• Users Table: user_id, name, email, password, etc.
• Events Table: event_id, title, description, venue_id, start_time, end_time, etc.
• Venues Table: venue_id, name, address, capacity, etc.
• Seats Table: seat_id, venue_id, seat_number, seat_type, etc.
• Bookings Table: booking_id, user_id, event_id, seat_id, booking_time, payment_id, etc.
• Payments Table: payment_id, booking_id, amount, payment_time, payment_method, etc.

Design Patterns

Several design patterns can be applied to build a scalable and maintainable system:

• Singleton Pattern: Used for managing unique resources like configuration settings or database connections. Read more about Singleton Pattern here.
• Factory Pattern: Used for creating different types of notifications (e.g., email, SMS) based on user preferences. Read more about Factory Pattern here.
• Observer Pattern: Used for notifying users about booking confirmations or updates. Read more about Observer Pattern here.
• Strategy Pattern: Used for implementing different payment methods (e.g., credit card, UPI). Read more about Strategy Pattern here.

Sequence Diagram

Let's illustrate the booking flow with a sequence diagram:

1. User selects an event and seats.
2. The UI sends a booking request to the Booking Service.
3. The Booking Service checks seat availability.
4. If seats are available, the Booking Service reserves the seats.
5. The Booking Service redirects the user to the Payment Gateway.
6. The Payment Gateway processes the payment.
7. Upon successful payment, the Payment Gateway notifies the Booking Service.
8. The Booking Service confirms the booking and generates a booking confirmation.
9. The Notification Service sends a booking confirmation to the user.

Concurrency Handling

Concurrency is a critical aspect of ticket booking systems. Multiple users might try to book the same seat simultaneously. Here’s how we can handle it:

• Optimistic Locking: Check if the seat is still available before confirming the booking. If not, ask the user to select another seat.
• Pessimistic Locking: Lock the seat when a user selects it, preventing other users from booking it until the booking is confirmed or canceled.

Scalability Considerations

To handle a large number of users and events, consider the following scalability strategies:

• Load Balancing: Distribute traffic across multiple servers to prevent overload.
• Caching: Cache frequently accessed data (e.g., event details, venue information) to reduce database load.
• Database Sharding: Split the database into multiple shards to distribute the load.
• Microservices: Decompose the system into smaller, independent services that can be scaled independently.

Code Example (Java)

Here’s a simplified example of how you might implement the Booking Service in Java:

java
public class BookingService {
    private SeatRepository seatRepository;
    private PaymentGateway paymentGateway;
    private NotificationService notificationService;

    public BookingConfirmation bookSeats(User user, Event event, List<Seat> seats) {
        // Check seat availability
        if (!seatRepository.areSeatsAvailable(event, seats)) {
            throw new SeatsNotAvailableException("Seats are not available");
        }

        // Reserve seats
        seatRepository.reserveSeats(event, seats);

        // Process payment
        PaymentConfirmation paymentConfirmation = paymentGateway.processPayment(user, event, seats);

        // Confirm booking
        BookingConfirmation bookingConfirmation = new BookingConfirmation(user, event, seats, paymentConfirmation);

        // Send notification
        notificationService.sendBookingConfirmation(user, bookingConfirmation);

        return bookingConfirmation;
    }
}

FAQs

1. How do you handle seat selection and prevent double booking?

• Use optimistic or pessimistic locking to manage concurrency during seat selection.

2. What database is best for a ticket booking system?

• Relational databases like MySQL or PostgreSQL are commonly used for their ACID properties. NoSQL databases like Cassandra can be used for scalability.

3. How do you integrate with payment gateways?

• Use APIs provided by payment gateways like Stripe or PayPal to process transactions securely.

4. How do you handle booking cancellations and refunds?

• Implement a cancellation policy and process refunds through the payment gateway.

5. How do you ensure the system is highly available?

• Use load balancing, replication, and failover mechanisms to ensure high availability.

Wrapping Up

Designing an online ticket booking system involves several key considerations, including database design, concurrency handling, and scalability. By applying appropriate design patterns and architectural principles, you can build a robust and scalable system that meets the needs of millions of users.

If you're eager to put these concepts into practice, check out Coudo AI's movie ticket API problem. It’s a hands-on way to solidify your understanding and tackle real-world challenges. Keep building, keep learning, and always aim for that smooth booking experience!