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

Senior

Apr 2026

Summary

Coding round at Anthropic for a SWE role. One problem, real file system access, and you could look up syntax which was a nice change from the usual "pretend you're in a vacuum" setup.

Questions Asked (1)

Write a program that scans a file system and identifies duplicate files, handling real files, extensions, and actual file reading.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The live file system access was something I didn't expect.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., file size, performance, memory constraints) and then outline a multi-stage algorithm: traverse the file system, group files by size, then by a quick hash (e.g., first few KB), and finally by full content hash to confirm duplicates. Emphasize trade-offs between accuracy, speed, and memory usage, and discuss how to handle edge cases like symlinks and permissions.

Pro tip: Mention that you would use a cryptographic hash (like SHA-256) for final comparison to avoid collisions, but use a faster non-cryptographic hash (like xxHash) for the initial grouping to optimize performance. Also, highlight the importance of handling file system traversal efficiently with iterative approaches to avoid stack overflow on deep directories.

1. Clarify Requirements and Constraints

Ask about the expected file system size, performance requirements, memory limits, and whether symbolic links should be followed. This ensures the solution aligns with the interviewer's expectations.

2. Design the Multi-Stage Algorithm

Outline a three-stage approach: first, traverse the file system and group files by size; second, for files of the same size, compute a quick hash of the first few KB to further group; third, compute a full content hash (e.g., SHA-256) to confirm duplicates.

3. Implement Efficient File Traversal and Hashing

Use an iterative traversal (e.g., os.walk or a stack) to avoid recursion limits. For hashing, read files in chunks to handle large files without loading them entirely into memory.

4. Handle Edge Cases and Optimizations

Address edge cases: empty files, files with same content but different names, symlinks, permission errors, and very large files. Discuss optimizations like parallel processing or using a database for very large file systems.

5. Analyze Trade-offs and Complexity

Discuss time and space complexity: O(n) traversal, O(n) space for storing file metadata, and the trade-off between using a quick hash vs. full hash. Mention that the approach minimizes full file reads by filtering with size and quick hash first.

Key Points to Mention

File system traversal techniques (e.g., os.walk, iterative DFS) and handling of symlinks and permissions.
Hashing strategies: using a fast hash for initial grouping and a cryptographic hash for final confirmation to balance speed and accuracy.
Memory management: reading files in chunks and storing only necessary metadata (e.g., file paths and hashes) to avoid high memory usage.
Parallelization: using multi-threading or multi-processing to speed up hashing, especially for large file systems.
Trade-offs between accuracy and performance: e.g., using file size and partial hash reduces the number of full file reads.
Edge cases: empty files, duplicate files with different names, files that change during scanning, and handling of very large files.

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