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Design a ticketing purchase system

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of large-scale system design and distributed systems concepts, including concurrency control, consistency models, real-time fanout, payment idempotency, and failure handling for high-concurrency ticketing with interactive seat maps.

  • hard
  • Meta
  • System Design
  • Software Engineer

Design a ticketing purchase system

Company: Meta

Role: Software Engineer

Category: System Design

Difficulty: hard

Interview Round: Onsite

Design a high-concurrency ticket purchasing system for events with seat maps. Requirements: seat selection, seat holds with TTL, preventing oversells under spikes, wait queues, payments with idempotency and retries, refunds/chargebacks, anti-bot measures and rate limiting, audit logs, and real-time availability updates. Discuss data model, APIs, locking strategy (pessimistic vs. optimistic), consistency model, scaling (sharding, caching), and failure handling.

Quick Answer: This question evaluates understanding of large-scale system design and distributed systems concepts, including concurrency control, consistency models, real-time fanout, payment idempotency, and failure handling for high-concurrency ticketing with interactive seat maps.

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Meta logo
Meta
Aug 14, 2025, 12:00 AM
Software Engineer
Onsite
System Design
3
0

System Design: High-Concurrency Ticket Purchasing With Seat Maps

You are asked to design an end-to-end ticketing system for large on-sale events (e.g., concerts, sports) with interactive seat maps and extreme traffic spikes. The system must handle concurrent seat selection, payment, and real-time updates while preventing oversells.

Functional Requirements

  • Interactive seat selection on a seat map (single and multi-seat selection, contiguous seats).
  • Seat holds with TTL (e.g., 2–5 minutes) and renewal, visible across devices.
  • Purchasing flow with payment authorization/capture, idempotency, and retries.
  • Refunds and chargebacks handling with appropriate state transitions.
  • Wait queues during spikes; fair admission and anti-bot protections.
  • Real-time availability updates to clients.
  • Audit logging for compliance and investigations.

Non-Functional Requirements

  • Prevent oversells under massive concurrent demand.
  • High write throughput and burst tolerance (tens of thousands of holds/s).
  • Low-latency seat availability reads (<150 ms p95) and updates.
  • Horizontally scalable (sharding, caching) and resilient to failures.
  • Clear consistency model and locking approach.

Deliverables

Discuss and justify:

  1. Data model and key constraints.
  2. Public APIs and core flows (hold, purchase, refund, real-time updates).
  3. Concurrency control and locking (pessimistic vs. optimistic).
  4. Consistency model and how you prevent oversells.
  5. Scaling strategy (sharding, caching, queueing, real-time fanout).
  6. Failure handling, idempotency, retries, and auditability.

Solution

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