Design a ticket-purchasing platform for a popular concert with sudden spikes up to millions of requests per second. How would you prevent overselling and ensure fairness? Discuss admission control (rate limiting, token buckets), virtual waiting rooms/queuing, anti-bot measures, idempotent order creation, inventory reservation vs. immediate deduction, and techniques to avoid hot-spot keys. Detail the data model and partitioning, cache usage, consistency strategy (optimistic/pessimistic locking, outbox/inbox, retries), and exactly-once or at-least-once semantics. Provide capacity estimates (QPS, p99 latency), failure handling (timeouts, dead letters), and monitoring/alerting.

This question evaluates skills in scalable distributed system architecture, capacity planning, reliability engineering, and operational observability for high-throughput, low-latency services.

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Design a high-volume ticketing system | TikTok Interview Question

Design a High-Spike Ticket Purchasing Platform

Context

A popular concert sale can trigger sudden spikes of up to millions of requests per second (RPS). You need to design a backend that:

Prevents overselling.
Ensures fairness among users.
Remains resilient and observable under extreme load.

Assume: mobile-heavy traffic, a global audience, and both reserved seating and general admission are possible. Payments are processed through third-party gateways.

Requirements

Admission control at the edge (e.g., rate limiting, token bucket/leaky bucket).
Virtual waiting room and queueing model to smooth spikes and enforce fairness.
Anti-bot defenses.
Idempotent order creation and deduplication.
Inventory management: reservation-with-TTL vs immediate deduction.
Avoiding hot-spot keys under skewed access patterns.
Data model and partitioning strategy.
Caching strategy.
Consistency strategy (optimistic/pessimistic locking, outbox/inbox, retries).
Delivery semantics: exactly-once vs at-least-once.
Capacity estimates (QPS, p99 latency goals).
Failure handling (timeouts, circuit breakers, dead letters).
Monitoring and alerting.

Context

A popular concert sale can trigger sudden spikes of up to millions of requests per second (RPS). You need to design a backend that:

Prevents overselling.

Ensures fairness among users.

Remains resilient and observable under extreme load.

Assume: mobile-heavy traffic, a global audience, and both reserved seating and general admission are possible. Payments are processed through third-party gateways.

Requirements

Admission control at the edge (e.g., rate limiting, token bucket/leaky bucket).

Virtual waiting room and queueing model to smooth spikes and enforce fairness.

Anti-bot defenses.

Idempotent order creation and deduplication.

Inventory management: reservation-with-TTL vs immediate deduction.

Avoiding hot-spot keys under skewed access patterns.

Data model and partitioning strategy.

Caching strategy.

Consistency strategy (optimistic/pessimistic locking, outbox/inbox, retries).

Delivery semantics: exactly-once vs at-least-once.

Capacity estimates (QPS, p99 latency goals).

Failure handling (timeouts, circuit breakers, dead letters).

Monitoring and alerting.

Design a high-volume ticketing system

Quick Overview

Design a High-Spike Ticket Purchasing Platform

Context

Requirements

Solution

Comments (0)

Design a high-volume ticketing system

Quick Overview

Design a High-Spike Ticket Purchasing Platform

Context

Requirements

Solution

Comments (0)