L2 · Working engineerAdvanced L2~6 min · 4 tests#89

Find Duplicate Files by Content Hash

Group files that share a content hash to find duplicates in O(n) with a dict of lists. Covers hashing large files in chunks with hashlib. Tested live.

The problem

hashes maps a file name to its content hash. Return a list of groups of files that share a hash — only groups with two or more files.

The order of groups and of files inside a group does not matter.

Examples

Example 1

Input

duplicate_files({'file1.txt': 'abc123', 'file2.txt': 'xyz789', 'file3.txt': 'abc123', 'file4.txt': 'pqr456'})

Expected output

[['file1.txt', 'file3.txt']]

+ 3 hidden tests on Submit.

Edge cases to ask about

No duplicates
Several groups
Empty input

Hints

0/3

How an interviewer scores this

0/9

1. Understand the problemRestate it, ask about edge cases and input size.
2. Write a brute-force solutionCorrect first, clever later.
3. Get the correct outputautoAll visible examples pass.
4. Analyse time complexityautoCommit to a Big-O in the Complexity Lab.
5. Analyse space complexityautoExtra memory, not the input.
6. Identify the bottleneckautoMeasure your code — where does it bend?
7. OptimiseUsually: a set or dict instead of a scan.
8. Explain why it worksSay it out loud to Solvi.
9. Test the edge casesautoSubmit passes every hidden test.

Python 3.13 · duplicate_files

def duplicate_files(hashes):
    """Groups of file names sharing a hash (size >= 2)."""
    # Write your solution here
    pass

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Complexity Lab

What does this cost as n grows?

Interviewers score the analysis as much as the code. Commit to an answer first — then check it, and measure your code against the optimal one at growing input sizes.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

Measure it

Runs the function on inputs of size 250 up to 16,000 and records the time and peak memory. Slow solutions stop early — a short curve is itself the answer.

From brute force to optimal

The progression an interviewer wants to hear, one step at a time.

Approach	Time	Space	Idea
Compare every pair of files	O(n²)	O(1)
bestGroup by hash	O(n)	O(n)	In real life: group by size first, hash only same-size files, in chunks.

Walkthrough of the optimal approach (try it yourself first)

Invert the mapping into hash → [files], then keep groups with more than one file.

The real-world follow-up is computing the hashes: group by file size first (different sizes cannot be duplicates), then hash only the candidates with hashlib.sha256, reading in fixed-size chunks so a 4 GB file does not have to fit in memory.

Complexity: O(n) time, O(n) space. One pass to group, one over the groups.

Reveal the reference solution

def duplicate_files(hashes):
    by_hash = {}
    for name, digest in hashes.items():
        by_hash.setdefault(digest, []).append(name)
    return [names for names in by_hash.values() if len(names) > 1]

Follow-ups interviewers ask

Write the hashing step for a directory tree.
Why group by size before hashing?

Frequently asked interview questions

Core interview concepts, complexities, and follow-ups scored by hiring teams.

What is the time complexity of Find Duplicate Files by Content Hash in Python?

The optimal solution runs in O(n) time and O(n) auxiliary space. One pass to group, one over the groups.

What is the brute-force approach, and how do you optimise it?

Compare every pair of files: O(n²) time, O(1) space. Group by hash: O(n) time, O(n) space. In real life: group by size first, hash only same-size files, in chunks.

What follow-up questions do interviewers ask about Find Duplicate Files by Content Hash?

Write the hashing step for a directory tree. Why group by size before hashing?