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

Senior
Apr 2026

Summary

Airbnb system design round for a software engineer role, focused on designing a booking system. Pretty standard stuff for this company but still a lot to cover in one session.

Questions Asked (1)

Q1

Design a booking system.

System DesignData ModelingTechnical Trade-offs
Author's notes

Classic Airbnb question, probably the most predictable thing they could have asked.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then design a high-level architecture covering core entities like users, listings, and bookings. Dive into data modeling for availability and concurrency, and discuss trade-offs around consistency, scalability, and user experience.

Pro tip: Emphasize how you handle double bookings and race conditions using techniques like optimistic locking or distributed transactions, as this is a critical challenge in booking systems.

1. Clarify Requirements

Ask questions to understand scope: Is this for hotels, flights, or home rentals? What are the key features (search, booking, payment)? What are the scale and consistency requirements?

2. High-Level Design

Sketch the main components: client apps, API gateway, services (user, listing, booking, payment), databases, and caches. Define the core data entities and their relationships.

3. Data Modeling & Availability

Design schemas for listings, availability calendars, and bookings. Discuss how to efficiently query availability and handle updates, considering time zones and date ranges.

4. Concurrency & Consistency

Address race conditions and double bookings. Compare approaches like pessimistic vs. optimistic locking, distributed transactions, and eventual consistency with compensating actions.

5. Scalability & Trade-offs

Discuss scaling reads/writes, caching strategies, sharding, and trade-offs between consistency, availability, and latency. Mention monitoring and failure handling.

Key Points to Mention

  • Handling double bookings and race conditions with locking or transactions
  • Data model for availability (e.g., calendar table, date ranges, or bitmap)
  • Search and filtering scalability (e.g., Elasticsearch, geospatial indexing)
  • Payment integration and idempotency to avoid duplicate charges
  • Caching strategies for frequently accessed data (e.g., Redis)
  • Trade-offs between strong consistency and high availability (CAP theorem)

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