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

SeniorPrefer not to say

Jun 2026

Summary

Walmart Labs system design round, one big question about building a Ticketmaster-style platform. The scope was wide enough that I kept second-guessing whether to go deeper or broader, and I'm still not sure I struck the right balance.

Questions Asked (1)

Design a ticket purchasing and event discovery system similar to Ticketmaster, covering everything from browsing events to checkout, seat locking, and cancellations.

System DesignTechnical Trade-offsData Modeling

Author's notes

This one sprawled fast.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then design the system in layers: event discovery, seat selection and locking, checkout, and cancellation. Focus on the critical challenge of seat locking and inventory consistency under high concurrency, using distributed locking and transactional guarantees. Conclude by discussing trade-offs, scalability, and how Walmart Labs' scale might influence design choices.

Pro tip: Emphasize idempotency and graceful degradation—e.g., using idempotent APIs for checkout and a queue-based approach for seat locking to handle spikes. This shows you understand real-world reliability concerns beyond just happy-path design.

1. Clarify Requirements and Scope

Ask about expected scale (events, users, concurrent bookings), consistency vs. availability trade-offs, and key features like search, recommendations, payments, and cancellations. Define non-functional requirements such as latency, throughput, and durability.

2. High-Level Architecture and Data Model

Sketch core services: event catalog, search, seat inventory, booking, payment, and notification. Define data entities (Event, Venue, Seat, User, Order, Ticket) and their relationships, choosing appropriate storage (e.g., SQL for transactions, NoSQL for catalog, search index for discovery).

3. Deep Dive: Seat Locking and Checkout

Explain how to handle seat selection and locking: use a distributed lock (e.g., Redis) with TTL, or optimistic concurrency with versioning. Detail the checkout flow: reserve seats, process payment, confirm order, and release locks on failure. Ensure idempotency and handle race conditions.

4. Scalability, Reliability, and Trade-offs

Discuss scaling reads (caching, CDN, read replicas) and writes (sharding, queues). Address failure modes: lock expiration, payment timeouts, and double-booking. Compare consistency models (strong vs. eventual) and their impact on user experience.

5. Wrap-up: Cancellations and Extensions

Cover cancellation flow: release seats, refund payment, update inventory. Mention potential extensions like dynamic pricing, waitlists, or analytics. Summarize key design decisions and how they meet requirements.

Key Points to Mention

Distributed locking mechanisms (e.g., Redis Redlock, ZooKeeper) with TTL to prevent double-booking and handle lock expiration.
Idempotent API design for checkout and cancellation to avoid duplicate charges or seat releases.
Database choice: ACID-compliant SQL for bookings vs. NoSQL for event catalog; use of transactions and isolation levels.
Caching strategies (e.g., Redis, CDN) for event discovery and seat maps to reduce latency and load.
Handling high concurrency: queue-based seat locking, optimistic vs. pessimistic concurrency control, and rate limiting.
Trade-offs between consistency and availability (CAP theorem) and how they affect user experience during peak demand.

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