Design ticketing and coding practice platforms

You are asked two separate system design questions.

1. Design an online event ticketing platform (like Ticketmaster)

Design a large-scale web service where users can:

• Browse events (concerts, sports, theater) by city/date/venue.
• View seating charts and real-time seat availability.
• Select specific seats and purchase tickets.
• Receive a digital ticket/QR code.

Assume:

• Millions of users, with traffic spikes when popular events go on sale.
• Each event has a finite set of seats, and overselling must not happen.
• There may be waitlists or temporary seat holds while a user is checking out.

Describe a high-level design that covers:

• Main functional requirements and non-functional goals (availability, consistency, latency).
• Core APIs (e.g., search events, hold seats, purchase tickets).
• Data model for events, venues, seats, reservations, and orders.
• How to prevent double-booking the same seat under high concurrency.
• Overall architecture: services, databases, caches, message queues.
• Strategies for scaling, handling flash-sale spikes, and ensuring reliability.

2. Design an online coding practice & interview-prep platform

Design a web platform where users can:

• Browse and search a catalog of coding problems by tags/difficulty.
• View problem statements and constraints.
• Write code in the browser in multiple languages.
• Run code against sample tests and hidden test cases.
• Submit solutions and see whether they passed, with basic statistics.

Assume:

• Millions of registered users, globally distributed.
• Spiky traffic (e.g., evenings and weekends are busier).
• Code must run in a secure, resource-isolated environment.

Describe a high-level design that covers:

• Key functional requirements and non-functional goals.
• Core APIs (e.g., list problems, get problem details, submit code, get results).
• Data model for users, problems, test cases, submissions, and results.
• Architecture for the code-execution service (sandboxing, job queue, worker nodes).
• Storage choices for code, problem metadata, and execution logs.
• Strategies for scaling, ensuring fairness in resource usage, and system reliability.

For both systems, state reasonable assumptions and provide a clear, component-level architecture with major trade-offs explained.