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

Senior

May 2026

Summary

Anthropic system design round for a software engineer role. The question was a full deep-dive into building a file deduplication tool, which sounds manageable until they keep pushing on edge cases and complexity analysis.

Questions Asked (1)

Design and implement a file deduplication tool that, given a root directory, finds all groups of duplicate files. You need to handle nested directories, symbolic links and cycles, large files efficiently, and explain your data structures, algorithm, complexity, and test cases.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The multi-stage filtering part is what they really wanted to hear.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a multi-stage algorithm: traverse the directory with cycle detection, group files by size, and hash only files with matching sizes to confirm duplicates. Discuss trade-offs between hashing full files vs. partial hashes, and explain how to handle symbolic links and large files efficiently.

Pro tip: Emphasize that you would first group by file size to avoid hashing unique files, and mention using a fast non-cryptographic hash (e.g., xxHash) for initial grouping, then a cryptographic hash (e.g., SHA-256) only for final confirmation to balance speed and collision resistance.

1. Clarify Requirements and Constraints

Ask about expected scale (number of files, directory depth), whether to follow symbolic links, how to handle cycles, and if hard links should be considered duplicates. Confirm if the tool should be recursive and if it needs to handle permission errors.

2. Design the Algorithm

Outline a multi-pass approach: traverse the directory tree while tracking visited inodes to avoid cycles; group files by size; for groups with >1 file, compute a fast hash (e.g., xxHash) of a small chunk or full file; for remaining collisions, compute a cryptographic hash (e.g., SHA-256) to confirm duplicates.

3. Explain Data Structures and Complexity

Describe using a map from size to list of file paths, a set of visited inodes for cycle detection, and a map from hash to list of file paths. Analyze time complexity as O(N) for traversal plus O(M * H) for hashing, where M is number of files with duplicate sizes and H is hashing cost; space complexity O(N).

4. Address Edge Cases and Trade-offs

Discuss handling symbolic links (follow or skip based on requirements), cycles (via visited inode set), large files (stream hashing, partial hashing), permission errors (skip and log), and hard links (treat as duplicates or not). Compare full-file hashing vs. partial hashing vs. byte-by-byte comparison.

5. Propose Test Cases

Suggest test cases: empty directory, single file, no duplicates, multiple duplicates, nested directories, symbolic links (to files and directories), cycles, large files, files with same size but different content, and files with same content but different names.

Key Points to Mention

Cycle detection using visited inodes (e.g., via os.stat().st_ino) to avoid infinite loops with symbolic links.
Two-phase hashing: fast non-cryptographic hash for initial grouping, then cryptographic hash for final confirmation to reduce I/O and CPU.
Grouping by file size first to minimize the number of files that need hashing.
Streaming file reads to handle large files without loading them entirely into memory.
Trade-offs between following symbolic links (risk of cycles, duplicates) vs. skipping them (missing potential duplicates).
Complexity analysis: O(N) traversal, O(M * H) hashing, where M is files with duplicate sizes; space O(N).

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