Applied intuition·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Interviewed for a software engineering role at Applied Intuition and got a file system problem that looked straightforward until the follow-ups started stacking up.

Questions Asked (1)

Given a file system, find all groups of duplicate files based on their content.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the general shape of the answer: group by file size first, then hash the contents to confirm duplicates.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and constraints, then propose a multi-stage approach: first group files by size, then compute hashes for files of the same size, and finally compare byte-by-byte to confirm duplicates. Discuss trade-offs between hashing and direct comparison, and consider scalability for large file systems.

Pro tip: Mention that you can use a two-level hashing strategy: a fast hash (e.g., MD5) to quickly eliminate non-duplicates, and a cryptographic hash (e.g., SHA-256) only for files that match on the fast hash, to reduce collision risk. This shows awareness of performance and correctness trade-offs.

1. Clarify Requirements

Ask about file system size, file types, whether symbolic links should be followed, and if the solution should be in-memory or distributed. Clarify if the goal is to find all duplicates or just report them.

2. Design Algorithm

Outline a multi-pass algorithm: traverse the file system, group files by size, then for each size group compute hashes, and finally confirm duplicates with byte-by-byte comparison. Explain why size grouping reduces unnecessary hashing.

3. Analyze Trade-offs

Discuss trade-offs between using hashing (fast but collision risk) and direct comparison (accurate but slow). Consider memory usage, time complexity, and scalability. Mention that hashing can be parallelized.

4. Handle Edge Cases

Address edge cases: empty files, files with same content but different metadata, symbolic links, and permission issues. Explain how to handle them (e.g., skip symlinks or treat them as duplicates if content matches).

5. Optimize and Scale

Propose optimizations: use a distributed file system or MapReduce for very large datasets, cache hashes, and use a database to store file metadata. Discuss incremental updates for dynamic file systems.

Key Points to Mention

Time and space complexity of the algorithm (e.g., O(n) traversal, O(n) space for hashes).
Choice of hash function: MD5 vs SHA-1 vs SHA-256, and collision probability.
Use of file size as a first-level filter to reduce the number of files to hash.
Byte-by-byte comparison as a final verification step to avoid false positives from hash collisions.
Handling of symbolic links and hard links to avoid infinite loops or double-counting.
Scalability considerations: parallel processing, distributed computing, and external sorting for large datasets.

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