← Databricks Interview Insights
This is a massive question and I underestimated how much they'd push on each layer.
Start by clarifying functional and non-functional requirements, then design a scalable data model that supports core entities like books, users, orders, and inventory. Walk through the architecture, focusing on how to handle scale through partitioning, caching, and distributed processing, and discuss trade-offs at each decision point.
Pro tip: Emphasize how Databricks' unified data platform can simplify the architecture by handling both transactional and analytical workloads, and discuss the importance of data consistency and fault tolerance in a distributed system.
Ask questions to understand the scope: expected user base, read/write patterns, latency requirements, and features like search, recommendations, and payments. Define functional and non-functional requirements.
Identify core entities (e.g., Book, User, Order, Inventory, Review) and their relationships. Choose appropriate data stores (e.g., relational for transactions, NoSQL for catalog, search index for queries) and discuss schema design.
Propose a high-level architecture with services for catalog, search, cart, orders, and payments. Discuss scaling strategies: sharding, replication, caching, CDN, and asynchronous processing.
Discuss consistency vs. availability, SQL vs. NoSQL, and batch vs. stream processing. Explain how choices impact scalability, cost, and complexity.
Highlight how Databricks can unify data engineering, analytics, and ML workloads, enabling real-time insights and recommendations while simplifying operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the follow-up that tripped me up the most.
Start by clarifying the system's requirements (e.g., scale, consistency needs, overselling tolerance) and then compare the two reservation strategies. Discuss how each approach handles concurrency, user experience, and system complexity, and propose a hybrid solution if appropriate.
Pro tip: Acknowledge that the 'right' answer depends on business priorities—e.g., preventing overselling vs. maximizing conversion—and show how you'd measure and iterate based on metrics like cart abandonment and inventory accuracy.
Ask about expected traffic, inventory accuracy needs, and business impact of overselling or stockouts. This sets the stage for a tailored answer.
Describe how reserving inventory when items are added to the cart reduces overselling but can lead to inventory being held by inactive users. Mention the need for expiration policies and potential impact on conversion.
Describe how reserving at checkout minimizes held inventory but risks overselling and a poor user experience if items become unavailable. Discuss the need for fast, atomic operations and potential retries.
Contrast the two approaches in terms of consistency, scalability, user experience, and system complexity. Highlight scenarios where each is preferable.
Suggest a hybrid approach, such as soft reservations with TTL on add-to-cart and hard reservations at checkout, or using a distributed lock or optimistic concurrency. Discuss how to handle failures and scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the workload characteristics (read/write ratio, data size, query patterns) and then propose a multi-layer caching strategy (client, CDN, application, database) with appropriate invalidation and consistency mechanisms. Emphasize trade-offs between cache freshness, latency, and cost, and tie your choices to Databricks' scale and reliability requirements.
Pro tip: Mention that cache invalidation is the hardest part and propose a hybrid approach (TTL + event-driven purge) to balance consistency and performance. Also, highlight the importance of monitoring cache hit ratio and using it to drive capacity planning.
Ask about read/write ratio, data size, update frequency, latency SLOs, and consistency requirements to tailor the caching strategy.
Propose caching at CDN edge, application level (e.g., Redis), and database query cache, explaining what data goes where and why.
Describe how to keep caches fresh: TTLs, event-driven invalidation, versioning, and handling stale reads during updates.
Discuss sharding, replication, failover, and how to handle cache stampedes and hot keys under high traffic.
Explain metrics to track (hit ratio, latency, eviction rate) and how to iterate on cache configuration and CDN rules.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then propose a high-level architecture with separate write and read paths. Explain how eventual consistency is handled through asynchronous processing, idempotency, and reconciliation, and discuss trade-offs between consistency and availability.
Pro tip: Emphasize that eventual consistency is acceptable for ratings as long as the system is monotonic and converges; use techniques like versioning and conflict-free replicated data types (CRDTs) to avoid anomalies.
Ask about scale, read/write patterns, consistency needs, and features like aggregation, moderation, and user history. Establish whether strong consistency is required for any part.
Propose a schema for reviews (e.g., review_id, user_id, item_id, rating, text, timestamp) and separate storage for aggregates (e.g., average rating, count). Choose databases (e.g., Cassandra for writes, Redis for caching) based on access patterns.
For writes, use a queue to asynchronously process reviews and update aggregates. For reads, serve from caches or materialized views, ensuring low latency. Discuss how to handle updates and deletes.
Explain mechanisms like idempotent consumers, versioning, and periodic reconciliation to ensure convergence. Discuss how to handle conflicts (e.g., last-write-wins) and provide read-your-writes consistency if needed.
Discuss trade-offs between consistency, latency, and cost. Propose monitoring for lag, error rates, and data drift, and outline a rollback or repair strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.