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Databricks·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate
Jun 2026

Summary

Databricks SWE interview with a system design question around a book marketplace. The API design piece was more demanding than I expected, they really pushed back on anything that felt like lazy field additions.

Questions Asked (1)

Q1

Design a system for buying and selling books. How would you handle it at scale, and walk through your API design decisions?

System DesignAPI & IntegrationsTechnical Trade-offs
Author's notes

The part that tripped me up was the API design.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then design the core data model and API endpoints for buying and selling books. Discuss how to scale each component (database, caching, search, messaging) and justify trade-offs in API design (e.g., REST vs. GraphQL, pagination, idempotency).

Pro tip: At Databricks, emphasize how you'd leverage a lakehouse architecture for analytics and real-time inventory, and discuss consistency models (e.g., strong vs. eventual) for transactions like purchases.

1. Clarify Requirements

Ask about scale (users, books, transactions), read/write ratio, consistency needs, and features like search, reviews, and payments. Define SLAs for latency and availability.

2. Design Data Model and API

Outline entities (User, Book, Listing, Order, Payment) and relationships. Design RESTful endpoints (e.g., POST /books, GET /books/{id}, POST /orders) with proper HTTP methods, status codes, and versioning.

3. Scale Components

Choose scalable storage (e.g., distributed SQL for transactions, NoSQL for catalog, search engine for queries). Use caching, CDN, and message queues for async processing. Discuss sharding, replication, and load balancing.

4. Address Consistency and Reliability

Explain how to handle transactions (e.g., two-phase commit, saga pattern) and idempotency for payments. Discuss monitoring, rate limiting, and fault tolerance.

5. Justify Trade-offs

Compare API design choices (REST vs. GraphQL, pagination strategies) and scaling approaches (SQL vs. NoSQL, caching). Tie decisions back to requirements and Databricks' tech stack.

Key Points to Mention

  • API design: RESTful endpoints, versioning, pagination, filtering, and idempotency for POST requests.
  • Database choices: ACID-compliant for orders vs. eventually consistent for catalog; use of indexes and read replicas.
  • Caching strategies: Redis for session and hot data, CDN for static assets.
  • Search: Elasticsearch or similar for full-text search and faceted filtering.
  • Scalability: horizontal scaling, sharding, load balancing, and async processing with queues.
  • Consistency: strong consistency for inventory and payments, eventual for reviews and recommendations.

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