Anthropic·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Apr 2026

Summary

Coding round at Anthropic for a software engineer role. Just the one question but it had some depth to it.

Questions Asked (1)

Design and implement a file deduplication system.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

More interesting than it sounds at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, file sizes, exact vs. near-duplicate, storage constraints) and then propose a two-phase approach: first, use a fast hash (e.g., MD5/SHA-1) to group potential duplicates, then confirm with a cryptographic hash (e.g., SHA-256) or byte-by-byte comparison. Discuss trade-offs between time, space, and accuracy, and outline a scalable architecture using chunking, distributed storage, and indexing.

Pro tip: Emphasize the importance of chunk-level deduplication for large files and explain how content-defined chunking (e.g., Rabin fingerprinting) avoids the boundary-shift problem, showing depth beyond basic hashing.

1. Clarify Requirements and Constraints

Ask about scale (number of files, total size), file types, acceptable false positive/negative rates, latency requirements, and whether near-duplicate detection is needed. This shapes the entire design.

2. Design Core Deduplication Algorithm

Propose a multi-stage approach: quick hash (e.g., MD5) to bucket candidates, then strong hash (e.g., SHA-256) for exact duplicates. For large files, use chunking (fixed or content-defined) and hash each chunk to enable partial deduplication.

3. Architect Scalable Storage and Indexing

Outline a distributed system: use a metadata store (e.g., key-value store) mapping hashes to file locations, and a blob store for unique chunks. Discuss sharding, replication, and consistency trade-offs (e.g., eventual vs. strong).

4. Address Performance and Trade-offs

Discuss time/space trade-offs: hashing cost vs. storage savings, memory vs. disk-based indexes, and parallelization. Mention techniques like bloom filters to reduce disk lookups.

5. Handle Edge Cases and Extensions

Cover handling of hash collisions (verify with byte comparison), small files, empty files, and concurrency. Optionally, discuss near-duplicate detection using locality-sensitive hashing (LSH) or similarity metrics.

Key Points to Mention

Use of cryptographic hashes (SHA-256) for exact duplicate detection and collision resistance.
Content-defined chunking (e.g., Rabin fingerprinting) for efficient large-file deduplication and resilience to insertions/deletions.
Trade-offs between fixed-size and variable-size chunking, and their impact on deduplication ratio and metadata overhead.
Scalability considerations: distributed hash tables, sharding, and replication for handling petabytes of data.
Performance optimizations: bloom filters to avoid unnecessary disk reads, parallel processing, and caching.
Handling hash collisions by verifying with byte-by-byte comparison or using a second hash.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.