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

Senior
Apr 2026

Summary

Amazon solutions architect interview, system design focus. The main question was a big distributed systems problem and it went about as well as you'd expect when you're winging the capacity math on the spot.

Questions Asked (1)

Q1

How would you design a large-scale ticketing system capable of handling millions of concurrent users during peak demand events like major sales or high-traffic launches?

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

Jumped straight to the queue layer because that felt safe, but I underestimated how much they wanted to hear about the seat reservation problem specifically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., millions of concurrent users, peak events) and then propose a high-level architecture that separates concerns: read-heavy browsing vs. write-heavy purchasing. Focus on scalability, availability, and consistency trade-offs, and dive into critical components like inventory management, queueing, and database sharding.

Pro tip: Emphasize idempotency and exactly-once processing for payment and ticket allocation to prevent double-booking and duplicate charges, which are common pitfalls in high-concurrency systems.

1. Clarify Requirements and Scale

Ask questions to understand expected read/write ratios, peak QPS, consistency needs, and budget constraints. Define functional and non-functional requirements.

2. High-Level Architecture

Sketch a layered architecture with CDN, load balancers, stateless services, caching, and a message queue for asynchronous processing. Separate read and write paths.

3. Deep Dive into Critical Components

Detail inventory management (e.g., using distributed locks or optimistic concurrency), database sharding, and caching strategies. Discuss how to handle hot keys and contention.

4. Address Scalability and Reliability

Explain auto-scaling, multi-region deployment, failover, and monitoring. Discuss trade-offs between consistency and availability (CAP theorem).

5. Summarize and Discuss Trade-offs

Recap the design, highlighting key decisions and their trade-offs. Mention potential bottlenecks and how to mitigate them.

Key Points to Mention

  • Use of a virtual waiting room or queue to smooth traffic spikes and prevent overload.
  • Database sharding and partitioning strategies for tickets and users.
  • Caching layers (e.g., Redis) for frequently accessed data like event details and seat maps.
  • Idempotent APIs and distributed transactions to ensure exactly-once ticket allocation.
  • Asynchronous processing with message queues (e.g., SQS, Kafka) for order fulfillment and notifications.
  • Monitoring, alerting, and chaos engineering to ensure system resilience during peak events.

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