Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Anthropic software engineer interview that was basically a filesystem problem dressed up as a systems question. More depth required than I expected, especially around the I/O optimization side of things.

Questions Asked (1)

Given access to a directory tree, write code to find all groups of files that have identical byte content. Walk through your approach to minimizing disk I/O, handling large files, permission errors, and symbolic links.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with hashing right away and the interviewer immediately asked why I wasn't grouping by size first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a multi-pass approach that uses file size as a cheap filter, partial hashes (e.g., first/last few KB) to reduce I/O, and full hashes only when necessary. Discuss how to handle edge cases like permission errors, symbolic links, and large files, and emphasize minimizing disk I/O through caching and incremental hashing.

Pro tip: Mention that you would use a streaming hash (e.g., SHA-256) and read files in chunks to avoid loading entire large files into memory, and that you would cache file metadata (size, mtime) to skip re-hashing unchanged files in subsequent runs.

1. Clarify requirements and constraints

Ask about the expected scale (number of files, total size), whether the directory tree is static or changing, and if there are any specific performance or memory constraints. Confirm that 'identical byte content' means exact match, not similarity.

2. Design a multi-pass algorithm

First, traverse the directory tree and group files by size (cheap metadata). For groups with more than one file, compute a quick partial hash (e.g., first and last 4KB) to further narrow candidates. Finally, compute full hashes only for files that still collide, minimizing disk I/O.

3. Address edge cases and errors

Handle permission errors by logging and skipping inaccessible files. For symbolic links, decide whether to follow them (with cycle detection) or treat them as distinct entries; typically, avoid following to prevent loops and duplicate content. For large files, use streaming reads with a fixed buffer size.

4. Optimize I/O and memory

Use memory-mapped files or buffered streams to read efficiently. Cache hashes and metadata to avoid re-computation. Consider parallelizing hashing with a thread pool, but be mindful of disk contention.

5. Discuss trade-offs and alternatives

Compare hashing algorithms (MD5 vs SHA-256) for speed vs collision resistance. Mention that for very large datasets, a distributed approach or external sorting might be needed. Also, discuss whether to store results in memory or on disk.

Key Points to Mention

Use file size as a first-pass filter to avoid unnecessary hashing.
Compute partial hashes (e.g., first/last few KB) to reduce I/O before full hashing.
Stream files in chunks to handle large files without high memory usage.
Handle permission errors gracefully by skipping and logging.
Decide on symbolic link policy: follow with cycle detection or skip.
Cache hashes and metadata to optimize repeated runs.

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