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Salesforce·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
Jun 2026

Summary

System design round at Salesforce, just one question the whole time. They wanted Ticketmaster, which sounds like a classic but there's a lot of surface area to cover and I definitely didn't pace myself well.

Questions Asked (1)

Q1

Design a large-scale ticket booking and event management platform similar to Ticketmaster.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with the usual capacity estimates and that ate up more time than I wanted.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., events per day, concurrent users, peak load), then design a high-level architecture focusing on core components like event catalog, seat inventory, booking, and payment. Dive into critical challenges such as concurrency control for seat locking, data consistency, and scalability, discussing trade-offs and technologies (e.g., distributed locking, caching, sharding).

Pro tip: Emphasize the importance of handling peak traffic and preventing double-booking through optimistic locking or distributed locks, and discuss how to ensure high availability and fault tolerance. Show awareness of real-world constraints like payment gateway integration and regulatory compliance.

1. Clarify Requirements and Scale

Ask questions to understand expected traffic, number of events, concurrent users, and peak load scenarios. Define functional and non-functional requirements (e.g., latency, consistency, availability).

2. High-Level Architecture

Outline main components: event service, seat inventory service, booking service, payment service, notification service, and user management. Sketch data flow and interactions.

3. Deep Dive into Critical Components

Focus on seat selection and locking mechanism to prevent double-booking, using techniques like optimistic concurrency control or distributed locks. Discuss data modeling for events, seats, and bookings.

4. Scalability and Performance

Explain how to scale each component: caching (e.g., Redis for seat maps), database sharding (by event or geography), load balancing, and asynchronous processing for notifications.

5. Trade-offs and Reliability

Discuss trade-offs between consistency and availability (CAP theorem), and how to handle failures (e.g., retries, idempotency, circuit breakers). Mention monitoring and alerting.

Key Points to Mention

  • Concurrency control for seat reservation (e.g., optimistic locking, distributed locks with Redis or ZooKeeper)
  • Data modeling: events, venues, seats, bookings, and users; use of relational vs NoSQL databases
  • Caching strategies for seat maps and event details to reduce database load
  • Sharding and partitioning strategies for scalability (e.g., by event ID or geographic region)
  • Asynchronous processing for payment confirmation, email/SMS notifications, and ticket generation
  • Handling peak traffic with auto-scaling, queueing, and rate limiting

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.