← Databricks Interview Insights
I started with functional requirements which felt right but I spent too long on the basics like open/read/write and didn't get to the interesting stuff fast enough.
Start by clarifying requirements (file sizes, consistency, durability, access patterns) and then propose a high-level architecture with a metadata service and data storage layer. Explain how each CRUD operation works, how horizontal scaling is achieved via sharding and replication, and discuss trade-offs like consistency vs. availability and metadata bottlenecks.
Pro tip: Emphasize the separation of metadata and data, and discuss how to handle large files with chunking and erasure coding for durability. Also, mention real-world systems like HDFS, GFS, or S3 to ground your design.
Ask about expected file sizes, read/write ratios, consistency requirements, durability, and scale (number of files, total storage). State your assumptions clearly.
Propose a distributed architecture with separate metadata and data layers. Metadata service manages file hierarchy, permissions, and chunk locations; data nodes store file chunks.
Define APIs for Create (upload), Read (download), Update (append/overwrite), and Delete. Explain how each operation interacts with metadata and data nodes, including chunking and replication.
Describe horizontal scaling via sharding metadata (e.g., by namespace) and adding data nodes. Discuss replication, erasure coding, and consistency models (e.g., eventual vs. strong).
Discuss trade-offs: consistency vs. availability, metadata bottleneck, small files problem. Mention optimizations like caching, batching, and garbage collection for deletes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.