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Anthropic·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

System design round at Anthropic for a software engineer role, focused entirely on a file deduplication system. The depth they expected was pretty intense, covering everything from low-level I/O edge cases to concurrency strategy.

Questions Asked (1)

Q1

Design a file deduplication program and walk through the real-world problems it would face in production. Cover things like permission errors, files changing mid-scan, path limits, symlink loops, memory/disk pressure, hash collisions, partial vs full hashing, concurrency, incremental rescans, and safe deletion strategies.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This was basically one giant question with a dozen sub-questions folded inside it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then outline a high-level design with a two-phase approach: scanning and hashing. Systematically walk through each production challenge, explaining trade-offs and mitigation strategies, and conclude with a discussion on safety and incremental updates.

Pro tip: Emphasize that deduplication is not just about finding duplicates but also about safely deleting them; always include a dry-run mode and logging to prevent data loss. Also, mention that using a database to track file metadata and hashes enables efficient incremental rescans.

1. Clarify Requirements and Scope

Ask questions to understand the expected scale, file types, performance requirements, and whether deletion is in scope. This ensures the design meets the actual needs.

2. High-Level Design

Propose a two-phase approach: first, traverse the file system to collect file metadata (size, path, etc.); second, group files by size and compute hashes to identify duplicates. Mention using a database for persistence.

3. Address Production Challenges

Systematically discuss each challenge: permission errors (handle gracefully, log and skip), files changing mid-scan (use file metadata like mtime and size to detect changes, or re-hash), path limits (use long path prefixes on Windows, handle ENAMETOOLONG), symlink loops (track visited inodes or use realpath), memory/disk pressure (stream hashing, use external sort, limit concurrency), hash collisions (use strong hashes like SHA-256, or verify with byte-by-byte comparison), partial vs full hashing (use partial hash for quick filtering, then full hash for confirmation), concurrency (parallelize scanning and hashing with thread pools, but be mindful of I/O bottlenecks), incremental rescans (store hashes and metadata in a database, only rescan changed files), safe deletion (dry-run, move to trash, or hardlink to a quarantine area before deletion).

4. Discuss Trade-offs and Optimizations

Explain the trade-offs between different approaches, such as using partial hashing to reduce I/O vs. full hashing for accuracy, or the overhead of maintaining a database vs. rescanning everything. Mention potential optimizations like using a Bloom filter for quick duplicate detection.

5. Summarize and Conclude

Recap the key points, emphasizing safety and reliability. Suggest a phased implementation: start with a simple version, then add features like incremental scanning and safe deletion.

Key Points to Mention

  • Permission errors: handle gracefully, log and continue, possibly run with elevated privileges if appropriate.
  • Files changing mid-scan: use file metadata (size, mtime) to detect changes; if changed, re-hash or skip.
  • Symlink loops: track visited inodes or use realpath to avoid infinite loops.
  • Memory/disk pressure: stream hashing, use external sorting, limit concurrency, and consider using a database for metadata.
  • Hash collisions: use cryptographic hashes (e.g., SHA-256) and optionally verify with byte-by-byte comparison.
  • Safe deletion: implement dry-run mode, move to trash, or quarantine before deletion; log all actions.

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