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

Senior
May 2026

Summary

System design round at Databricks for a software engineering role. The problem was a price-watching service for books, which sounds manageable until you start thinking through the scale and timing constraints.

Questions Asked (1)

Q1

Design a book price-watching system where users set a target price and a time window (like 24 hours), the system polls multiple bookstores for price updates, and automatically purchases the book if the price drops to or below the target.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

I started with the obvious stuff: a job scheduler polling each bookstore's API on some interval, a database of user requests with expiry timestamps, and a comparison step that triggers a purchase.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then sketch a high-level architecture with core components like user service, price poller, and purchase executor. Dive into data models, API design, and trade-offs around polling frequency, scalability, and reliability, emphasizing idempotency and fault tolerance.

Pro tip: Highlight the importance of idempotent purchase operations and exactly-once semantics to avoid duplicate orders, and discuss how to handle partial failures in a distributed system.

1. Clarify Requirements

Ask questions to understand scale, latency, consistency needs, and constraints (e.g., number of users, books, bookstores, purchase guarantees).

2. High-Level Design

Outline main components: user service, watchlist manager, price poller, price comparator, purchase executor, and notification service. Describe data flow.

3. Deep Dive into Key Components

Detail the price polling mechanism (scheduling, frequency, backoff), data storage (user watches, price history), and purchase execution (idempotency, retries).

4. Address Scalability and Reliability

Discuss partitioning, load balancing, fault tolerance, and monitoring. Explain how to handle bookstore API failures and rate limits.

5. Discuss Trade-offs and Alternatives

Compare polling vs. webhooks, synchronous vs. asynchronous processing, and consistency models. Justify design choices.

Key Points to Mention

  • Idempotency and exactly-once semantics for purchase execution to prevent duplicate orders.
  • Efficient polling strategies: adaptive polling intervals, backoff, and batching to reduce load on bookstore APIs.
  • Data model design: storing user watches, price history, and purchase attempts with appropriate indexes.
  • Handling bookstore API rate limits and failures with retries, circuit breakers, and fallbacks.
  • Scalability considerations: sharding by user or book, using message queues for asynchronous processing.
  • Consistency and correctness: ensuring price comparisons are accurate and purchases only occur when conditions are met.

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