Walmart Labs·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

System design round at Walmart Labs focused entirely on building a Ticketmaster-style platform. One long question, lots of ground to cover, and the seat-locking piece under contention is where things got interesting.

Questions Asked (1)

Design a large-scale ticketing platform (like Ticketmaster) that supports event browsing, seat selection, ticket purchasing, and refunds without overselling seats.

System DesignTechnical Trade-offsData Modeling

Author's notes

This one is deceptively wide.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design the core data model and APIs for browsing, seat selection, purchasing, and refunds. Focus on preventing overselling through concurrency control and transactional integrity, and discuss trade-offs between consistency, availability, and performance.

Pro tip: Emphasize that overselling is prevented by combining seat locking with a two-phase commit or saga pattern, and that refunds must be idempotent to avoid double refunds. Also, consider using a queue for high-demand events to serialize purchases.

1. Clarify Requirements and Scale

Ask about expected traffic, number of events, concurrent users, and consistency requirements. Define functional and non-functional requirements.

2. High-Level Architecture

Outline components: API gateway, event service, seat inventory service, order service, payment service, and notification service. Discuss data stores (SQL vs NoSQL) and caching.

3. Data Model and Seat Management

Design schema for events, venues, seats, and orders. Explain how to represent seat availability and handle seat selection with temporary locks.

4. Concurrency and Overselling Prevention

Detail mechanisms like optimistic/pessimistic locking, distributed locks, or queue-based serialization to ensure atomic seat booking. Discuss trade-offs.

5. Purchase and Refund Flows

Describe the purchase flow with payment integration and the refund flow with idempotency and consistency. Address failure scenarios and compensation.

Key Points to Mention

Use of distributed transactions or sagas to maintain consistency across services.
Seat locking with TTL to prevent double booking during selection.
Idempotent refund processing to handle retries and avoid double refunds.
Caching strategies for event browsing to handle read-heavy traffic.
Database sharding or partitioning by event to scale writes.
Queue-based approach for high-demand events to serialize purchases and prevent overselling.

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