← Anthropic Interview Insights
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Start by clarifying requirements (scale, file types, exact vs. near-duplicate, storage constraints) and then present a layered design: content-defined chunking with strong hashing for exact dedup, plus optional similarity hashing for near-duplicates. Discuss trade-offs between chunk size, hash collision risk, and metadata storage, and outline a scalable architecture with a metadata index and object store.
Pro tip: Emphasize the importance of measuring deduplication ratio and system overhead in production, and mention that you would start with a simple exact-match solution before adding complexity like similarity detection.
Ask about scale (number of files, total size), file types, whether near-duplicates matter, latency/throughput needs, and storage budget. This ensures the design targets the right problem.
Decide between whole-file hashing (simple, fast) and chunk-level deduplication (better ratio, more complex). For chunking, consider fixed-size vs. content-defined chunking (e.g., Rabin fingerprinting) to handle insertions/deletions.
Outline a metadata store (e.g., key-value store) mapping chunk hashes to storage locations, and an object store for unique chunks. Discuss indexing for fast lookups and garbage collection for orphaned chunks.
Explain how to distribute the system (e.g., sharding by hash), ensure fault tolerance (replication), and handle hash collisions (e.g., byte-wise comparison on collision).
Compare approaches: whole-file vs. chunk-level, fixed vs. variable chunking, and exact vs. near-duplicate detection. Mention performance metrics like dedup ratio, throughput, and storage overhead.
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