LeetCode #1233 — MEDIUM

Remove Sub-Folders from the Filesystem

Move from brute-force thinking to an efficient approach using array strategy.

Solve on LeetCode
The Problem

Problem Statement

Given a list of folders folder, return the folders after removing all sub-folders in those folders. You may return the answer in any order.

If a folder[i] is located within another folder[j], it is called a sub-folder of it. A sub-folder of folder[j] must start with folder[j], followed by a "/". For example, "/a/b" is a sub-folder of "/a", but "/b" is not a sub-folder of "/a/b/c".

The format of a path is one or more concatenated strings of the form: '/' followed by one or more lowercase English letters.

  • For example, "/leetcode" and "/leetcode/problems" are valid paths while an empty string and "/" are not.

Example 1:

Input: folder = ["/a","/a/b","/c/d","/c/d/e","/c/f"]
Output: ["/a","/c/d","/c/f"]
Explanation: Folders "/a/b" is a subfolder of "/a" and "/c/d/e" is inside of folder "/c/d" in our filesystem.

Example 2:

Input: folder = ["/a","/a/b/c","/a/b/d"]
Output: ["/a"]
Explanation: Folders "/a/b/c" and "/a/b/d" will be removed because they are subfolders of "/a".

Example 3:

Input: folder = ["/a/b/c","/a/b/ca","/a/b/d"]
Output: ["/a/b/c","/a/b/ca","/a/b/d"]

Constraints:

  • 1 <= folder.length <= 4 * 104
  • 2 <= folder[i].length <= 100
  • folder[i] contains only lowercase letters and '/'.
  • folder[i] always starts with the character '/'.
  • Each folder name is unique.
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: Given a list of folders folder, return the folders after removing all sub-folders in those folders. You may return the answer in any order. If a folder[i] is located within another folder[j], it is called a sub-folder of it. A sub-folder of folder[j] must start with folder[j], followed by a "/". For example, "/a/b" is a sub-folder of "/a", but "/b" is not a sub-folder of "/a/b/c". The format of a path is one or more concatenated strings of the form: '/' followed by one or more lowercase English letters. For example, "/leetcode" and "/leetcode/problems" are valid paths while an empty string and "/" are not.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Trie

Example 1

["/a","/a/b","/c/d","/c/d/e","/c/f"]

Example 2

["/a","/a/b/c","/a/b/d"]

Example 3

["/a/b/c","/a/b/ca","/a/b/d"]
Step 02

Core Insight

What unlocks the optimal approach

  • Sort the folders lexicographically.
  • Insert the current element in an array and then loop until we get rid of all of their subfolders, repeat this until no element is left.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Upper-end input sizes
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #1233: Remove Sub-Folders from the Filesystem
class Solution {
    public List<String> removeSubfolders(String[] folder) {
        Arrays.sort(folder);
        List<String> ans = new ArrayList<>();
        ans.add(folder[0]);
        for (int i = 1; i < folder.length; ++i) {
            int m = ans.get(ans.size() - 1).length();
            int n = folder[i].length();
            if (m >= n
                || !(ans.get(ans.size() - 1).equals(folder[i].substring(0, m))
                    && folder[i].charAt(m) == '/')) {
                ans.add(folder[i]);
            }
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n × log n × m)
Space
O(m)

Approach Breakdown

HASH SET
O(N × L) time
O(N × L) space

Store all N words in a hash set. Each insert/lookup hashes the entire word of length L, giving O(L) per operation. Prefix queries require checking every stored word against the prefix — O(N × L) per prefix search. Space is O(N × L) for storing all characters.

TRIE
O(L) time
O(N × L) space

Each operation (insert, search, prefix) takes O(L) time where L is the word length — one node visited per character. Total space is bounded by the sum of all stored word lengths. Tries win over hash sets when you need prefix matching: O(L) prefix search vs. checking every stored word.

Shortcut: One node per character → O(L) per operation. Prefix queries are what make tries worthwhile.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.