← Databricks Interview Insights
The scope was massive and I think I spent too long on the catalog and search parts, then had to rush through payments and order management at the end.
Start by clarifying requirements and scale, then sketch a high-level architecture with core services (catalog, search, cart, orders, payments, inventory, reviews, recommendations). Dive into 2-3 critical components like search and inventory, discussing trade-offs and data consistency. Wrap up by addressing scalability, reliability, and monitoring.
Pro tip: At Databricks, emphasize how you'd leverage data and analytics to drive recommendations and business insights, and discuss trade-offs between consistency and availability for inventory and payments.
Ask about expected traffic, data volume, read/write patterns, and consistency needs. Define functional and non-functional requirements.
Outline major services (catalog, search, cart, orders, payments, inventory, reviews, recommendations) and their interactions. Choose appropriate data stores (e.g., SQL for orders, NoSQL for catalog, search engine for search).
Pick 2-3 areas like search (indexing, ranking), inventory (consistency, locking), and payments (idempotency, third-party integration). Discuss design choices and trade-offs.
Explain how to scale each component (caching, sharding, replication), handle failures (retries, circuit breakers), and ensure data consistency (transactions, eventual consistency).
Recap the design, highlight key trade-offs (e.g., consistency vs. availability, latency vs. cost), and suggest monitoring and analytics for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by categorizing the data based on its consistency needs: inventory and orders require strong consistency to prevent overselling and ensure accurate order processing, while reviews and recommendations can tolerate eventual consistency for higher availability and scalability. Then, propose appropriate data stores and techniques for each category, such as ACID transactions for orders and eventual consistency with caching or batch processing for reviews.
Pro tip: Acknowledge that consistency is a spectrum and that the choice involves trade-offs between correctness, latency, and availability; show you understand the business impact of each choice, like how eventual consistency in reviews might lead to stale recommendations but is acceptable, whereas in orders it could cause financial loss.
Classify inventory/orders as transactional, requiring strong consistency (ACID), and reviews/recommendations as analytical/eventual, where slight staleness is acceptable.
For inventory/orders, suggest relational databases or NewSQL (e.g., Spanner, CockroachDB) with strong consistency; for reviews/recommendations, suggest NoSQL, search indexes, or data lakes with eventual consistency.
For strong consistency, use transactions, locking, or consensus protocols; for eventual consistency, use asynchronous replication, caching, and conflict resolution (e.g., last-write-wins).
Discuss how strong consistency may reduce availability and increase latency, while eventual consistency may lead to stale reads; propose mitigations like idempotency, retries, and compensating transactions.
Explain how eventual consistency allows horizontal scaling and low-latency reads for reviews/recommendations, while strong consistency may require partitioning or sharding with careful coordination.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business requirements and scale, then present a normalized relational model for the core entities (catalog, inventory, order) with key attributes and relationships. Discuss trade-offs for performance and scalability, and mention how you would evolve the model for high-volume, distributed environments like Databricks.
Pro tip: Emphasize data integrity and consistency across domains, and proactively discuss how you would handle schema evolution and partitioning for large-scale analytics—this shows you think beyond basic CRUD and understand Databricks' lakehouse paradigm.
Ask about expected scale, read/write patterns, consistency needs, and whether the system is for transactional or analytical workloads. This ensures your model aligns with business goals.
Identify main entities: Product, Category, InventoryItem, Warehouse, Order, OrderItem, Customer. Describe primary keys, foreign keys, and cardinality (e.g., one-to-many, many-to-many).
For each entity, list essential fields (e.g., Product: product_id, name, description, category_id; Inventory: inventory_id, product_id, warehouse_id, quantity, last_updated). Mention data types and constraints.
Discuss indexing, partitioning (e.g., by date or region), and denormalization for read-heavy analytics. Mention how Databricks Delta Lake supports ACID transactions and time travel.
Explain how to manage schema changes (e.g., adding new product attributes) and trade-offs between normalization (integrity) and denormalization (performance). Mention slowly changing dimensions for catalog changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about an inverted index, async sync from the catalog service, and read replicas.
Start by clarifying requirements: scale (number of books, queries per second), latency target, and search features (full-text, filters, facets). Then propose a distributed search architecture using an inverted index, sharding, replication, and caching, while discussing trade-offs between consistency, latency, and cost.
Pro tip: Emphasize the importance of measuring and optimizing the tail latency (p99) rather than just average latency, as it often dominates user experience at scale. Also, mention the need for a feedback loop to continuously monitor and tune the index based on query patterns.
Ask about scale (data size, QPS), latency SLA, search features (full-text, filters, sorting), and consistency needs. This ensures the design meets actual needs.
Propose a distributed search system with components: indexer, query service, storage, and cache. Use an inverted index for efficient text search.
Shard the index by document ID or key to distribute load. Replicate shards for fault tolerance and to scale read throughput.
Implement caching (query results, filters), use SSD storage, optimize index compression, and consider precomputation for common queries.
Discuss trade-offs: consistency vs. latency, cost vs. performance, and how to scale horizontally. Mention monitoring and iterative improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CDN for static assets, application-level cache for product pages, and cache-aside for catalog reads.
Start by clarifying the workload characteristics (read/write ratio, data size, consistency needs) and then propose a multi-layer caching strategy (client, CDN, application, distributed cache, database) with appropriate eviction policies. Emphasize trade-offs between consistency, latency, and cost, and explain how you would handle cache invalidation and stampedes during peak traffic.
Pro tip: At Databricks, where data volumes are massive and queries are complex, mention that you would consider caching at multiple levels (e.g., query results, file metadata, and columnar data blocks) and use adaptive techniques like request coalescing and probabilistic early expiration to prevent thundering herds.
Ask about read/write ratio, data size, latency SLA, consistency requirements, and peak traffic patterns to tailor the caching strategy.
Propose a hierarchy: client-side, CDN, application-level (in-memory), distributed cache (Redis/Memcached), and database query cache, explaining what each layer optimizes.
Discuss eviction policies (LRU, LFU, TTL) and invalidation strategies (write-through, write-behind, TTL-based) based on consistency needs.
Address cache stampede, thundering herd, and hot keys using techniques like request coalescing, jittered TTLs, and circuit breakers.
Explain how you would monitor cache hit ratio, latency, and eviction rates, and adjust the strategy based on metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.