I started with the obvious stuff: skip files that differ in size before doing any hashing, then bucket by hash (SHA-256 is what I went with, MD5 felt like I was inviting a lecture).
Start by clarifying requirements (file sizes, number of files, distributed constraints) and then present a two-phase algorithm: first group files by size, then hash contents to identify identical files. For scaling, discuss partitioning, parallel processing, and handling large-scale data with MapReduce or similar frameworks.
Pro tip: Emphasize that hashing is probabilistic and collisions are possible; mention using cryptographic hashes like SHA-256 and optionally verifying with byte-by-byte comparison for critical applications. Also, highlight the importance of considering file metadata (e.g., permissions, timestamps) when defining 'identical'.
Ask about the scale (number of files, total size), definition of 'identical' (content only or metadata too), and whether the solution must be distributed. This shows you think before coding.
Propose grouping files by size first (since identical files must have same size), then compute a strong hash (e.g., SHA-256) for each file, and group by hash. Optionally verify with byte comparison to handle collisions.
Discuss time and space complexity: O(N) file reads, O(N) memory for hashes. Mention trade-offs between hash strength, collision probability, and performance.
Describe partitioning files across machines (e.g., by file path or size), computing hashes in parallel, then shuffling hashes to group identical ones. Use MapReduce: map each file to (hash, path), reduce by hash to collect paths.
Discuss handling stragglers, fault tolerance, data skew (e.g., many small files), and optimizing network transfer (e.g., only send hashes, not file contents).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.