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

SeniorPrefer not to say
May 2026

Summary

System design round at Meta for a software engineer role, full hour spent on Ticketmaster from scratch. Dense problem with a lot of moving parts and I don't think I covered everything as well as I should have.

Questions Asked (1)

Q1

Design a large-scale event ticketing platform like Ticketmaster. Walk through the data model for events, venues, and seats, how users browse and search, how you handle concurrent seat selection without overselling, the purchase and payment flow, handling bursty traffic during popular on-sales, a waiting room or queue mechanism, fraud and bot mitigation, and consistency guarantees around seat ownership.

System DesignData ModelingTechnical Trade-offs
Author's notes

This is basically a full system design in one prompt and the scope is brutal.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that separates read-heavy browsing from write-heavy purchase flows. Dive into the data model for events, venues, and seats, and explain how to handle concurrency, bursty traffic, and fraud with specific technologies and trade-offs. Emphasize consistency for seat ownership and discuss the waiting room as a load-shedding mechanism.

Pro tip: Proactively discuss trade-offs between strong and eventual consistency, and how you would handle failures (e.g., payment timeouts) to show production maturity. Also, mention that you would use a combination of techniques like optimistic locking and distributed locks, but be specific about where each is appropriate.

1. Clarify Requirements and Scale

Ask about expected traffic (e.g., millions of concurrent users during on-sales), consistency needs, and key features. Define functional and non-functional requirements to scope the design.

2. Design Data Model and Storage

Outline entities: events, venues, seats, users, orders, and payments. Choose appropriate databases (e.g., relational for transactions, NoSQL for catalog) and discuss indexing for search.

3. Architect Browsing and Search

Design read-optimized services using caching, CDNs, and search engines (e.g., Elasticsearch). Explain how to handle high read throughput and keep data fresh.

4. Handle Seat Selection and Purchase Flow

Detail concurrency control (e.g., optimistic locking, distributed locks) to prevent overselling. Describe the purchase flow with payment integration, idempotency, and consistency guarantees.

5. Address Bursty Traffic, Queueing, and Fraud

Implement a waiting room with rate limiting and queueing (e.g., using Redis or Kafka). Discuss bot mitigation via CAPTCHAs, device fingerprinting, and anomaly detection.

Key Points to Mention

  • Data model: events, venues, seats with sections/rows, and relationships; use of relational DB for transactions and NoSQL for catalog.
  • Concurrency control: optimistic locking with versioning, distributed locks (e.g., Redis Redlock), and database transactions to prevent overselling.
  • Caching and CDN for read-heavy browsing; search using Elasticsearch with facets and filters.
  • Waiting room implementation: queue with rate limiting, virtual waiting room, and fair FIFO ordering.
  • Fraud and bot mitigation: CAPTCHA, device fingerprinting, behavioral analysis, and rate limiting per user/IP.
  • Consistency guarantees: strong consistency for seat ownership using ACID transactions; eventual consistency for browsing and analytics.

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