Additional considerations for Ticketmaster’s design#
We can still improve on this design in some ways:
Time-out mechanism#
An active reservation system holds seats for a set period to prevent double booking. Implementing a time-out ensures that reserved seats are released if payment isn’t completed within the time frame.
For example, Ticketmaster might hold a reserved seat for 10 minutes. If the user doesn’t pay within this period, the reservation is automatically canceled, and the seat becomes available for others to book. This is often implemented using background tasks or scheduled jobs that periodically check for expired reservations.
Data structures#
Track seat reservations using hash maps or in-memory data stores, enabling fast lookups and releasing expired holds.
Hash maps or in-memory data stores like Redis are excellent choices. For instance, each seat can be represented as a key in the hash map, with its value indicating the reservation status (e.g., “available,” “reserved,” “booked”). This allows the system to quickly check a seat’s availability and update its status when a reservation is made, confirmed, or canceled.
Handling wait-lists#
Popular events often sell out, leaving many users disappointed. A wait-list service provides a fair way to manage this demand. Implement a priority queue to manage the wait-list and notify users if seats become available.
A priority queue is a suitable data structure to manage the wait-list efficiently. This ensures that users who join the wait-list earlier get priority when seats become available. For example, if someone cancels their booking, the system can automatically offer the seat to the first person on the wait-list.
Message queue integration #
Use a message queue (e.g., RabbitMQ) to handle wait-list notifications and seat availability updates.
A message queue can handle wait-list notifications and seat availability updates in a ticketing system. When seats become available, the system can send messages to the queue. Consumers (i.e., wait-listed users) can receive these messages and attempt to book the seats. This decouples the different components of the system, improving scalability and reliability.
This kind of discussion is important at the end of your System Design interview when you are evaluating your design.
Challenges in Ticketmaster’s design#
Ticketmaster is a unique System Design problem due to the challenges it presents. It is quite different from the usual System Design problems like designing social media apps (Instagram, Twitter/X), streaming services (YouTube, Netflix), Ride-hailing apps (Uber, Lyft), etc., because it relies on high concurrency to be considered functional.
Concurrency handling#
Handling concurrent requests for the same seats can lead to conflicts and potential overselling.
Solutions#
Optimistic locking: This method uses versioning to detect conflicts. A version field is attached to each seat record. When a user initiates a booking, the system records the initial version. Upon booking confirmation, the system checks if the version remains unchanged. If modified, the transaction is rolled back, indicating another user has acquired the seat.
Pessimistic locking: This approach employs exclusive locks on seat records. When a user initiates a booking, the system acquires a lock on the corresponding seat record. Other transactions attempting to access the same record are blocked or queued until the lock is released. This guarantees exclusive access but can increase latency, particularly under high contention.
Distributed locking: A centralized lock manager is necessary for distributed systems. Tools such as Redis or Zookeeper can fulfill this role. When a user attempts to book a seat, the system acquires a distributed lock associated with that seat. This lock is managed across all servers, preventing concurrent access from any user regardless of their connection point.