← Anthropic Interview Insights
My first instinct was to just hash everything and compare, which is fine but I jumped to it too fast without talking through the tradeoffs.
Start by clarifying requirements (e.g., file size, scope, performance constraints) and then propose a multi-stage approach: first group files by size, then by a quick hash of the first few bytes, and finally by a full cryptographic hash to confirm duplicates. Discuss trade-offs between accuracy, speed, and memory usage, and consider edge cases like empty files and symbolic links.
Pro tip: Mention that you can optimize by only hashing files with matching sizes and using a fast non-cryptographic hash (like xxHash) for initial filtering, then a cryptographic hash (like SHA-256) for final verification to avoid collisions. Also, note that you should handle large files by streaming rather than loading entirely into memory.
Ask about the file system scale (number of files, total size), performance requirements (time vs. space), and whether the solution should be exact or approximate. Also clarify if symbolic links, empty files, or special files should be considered.
Traverse the file system and group files by their size. Files with unique sizes cannot be duplicates, so this reduces the candidate set significantly.
For each group of files with the same size, compute a hash. To optimize, first hash only the first few KB (or a sample) to quickly eliminate non-duplicates, then compute a full hash (e.g., SHA-256) for remaining candidates.
Group files by their full hash. Files sharing the same hash are duplicates (assuming a strong hash). Report groups of duplicate files, possibly with their paths and sizes.
Talk about parallelizing the traversal and hashing, using a database or in-memory map for large scale, and the trade-offs between hash collision risk and speed. Mention incremental updates if the file system changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.