The core of it was separating metadata from actual file content.
Start by clarifying requirements (file sizes, access patterns, consistency needs) and then present a high-level architecture covering storage, metadata, and chunking. Focus on how chunking enables parallel uploads/downloads, deduplication, and fault tolerance, and discuss trade-offs like chunk size and metadata overhead.
Pro tip: Emphasize that chunk size is a critical trade-off: smaller chunks improve parallelism and deduplication but increase metadata and request overhead; larger chunks reduce overhead but limit parallelism. Mention that real systems like Dropbox use 4MB chunks, and consider content-defined chunking for deduplication.
Ask about expected file sizes, upload/download patterns, consistency requirements, and budget. This scopes the design and shows you avoid over-engineering.
Outline components: client, API gateway, metadata service, chunk storage (e.g., object store like S3), and a database for metadata. Explain how they interact.
Describe how files are split into chunks (fixed-size vs. content-defined), chunk size selection, and how chunks are named and stored. Discuss deduplication and integrity checks (hashing).
Detail the process: client requests upload, receives chunk URLs, uploads chunks in parallel, and finalizes with metadata commit. For download, client fetches metadata and chunks in parallel.
Discuss trade-offs (chunk size, consistency vs. availability, cost) and how the system scales (sharding metadata, CDN for downloads, garbage collection for orphaned chunks).
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