I started with the usual stuff, namespace, metadata server, chunked storage, replication.
Start by clarifying requirements and scale, then design a high-level architecture that separates metadata management from data storage. Discuss trade-offs between consistency, availability, and partition tolerance, and dive into key components like chunking, replication, and fault tolerance.
Pro tip: Relate your design to Databricks' ecosystem (e.g., Delta Lake, Spark) and emphasize how your choices support analytics workloads and multi-cloud environments.
Ask about scale (file sizes, number of files, clients), consistency needs, latency, and durability. Determine if the system is for analytics, general-purpose, or specific workloads.
Propose a master-slave or peer-to-peer architecture. Separate metadata (namespace, permissions) from data storage. Consider using a distributed metadata store like ZooKeeper or etcd.
Explain how files are split into chunks/blocks, stored across data nodes, and replicated for fault tolerance. Discuss chunk size trade-offs (e.g., 64MB vs 128MB).
Describe the consistency model (e.g., strong vs eventual). Explain replication strategies, handling node failures, and ensuring data durability (e.g., checksums, re-replication).
Discuss how the system scales horizontally, handles metadata bottlenecks, and optimizes for throughput (e.g., data locality, caching). Mention monitoring and load balancing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.