LeetCode #395 — MEDIUM

Longest Substring with At Least K Repeating Characters

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

Solve on LeetCode
The Problem

Problem Statement

Given a string s and an integer k, return the length of the longest substring of s such that the frequency of each character in this substring is greater than or equal to k.

if no such substring exists, return 0.

Example 1:

Input: s = "aaabb", k = 3
Output: 3
Explanation: The longest substring is "aaa", as 'a' is repeated 3 times.

Example 2:

Input: s = "ababbc", k = 2
Output: 5
Explanation: The longest substring is "ababb", as 'a' is repeated 2 times and 'b' is repeated 3 times.

Constraints:

  • 1 <= s.length <= 104
  • s consists of only lowercase English letters.
  • 1 <= k <= 105
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 string s and an integer k, return the length of the longest substring of s such that the frequency of each character in this substring is greater than or equal to k. if no such substring exists, return 0.

Baseline thinking

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

Pattern signal: Hash Map · Sliding Window

Example 1

"aaabb"
3

Example 2

"ababbc"
2

Related Problems

  • Longest Subsequence Repeated k Times (longest-subsequence-repeated-k-times)
  • Number of Equal Count Substrings (number-of-equal-count-substrings)
  • Optimal Partition of String (optimal-partition-of-string)
  • Length of Longest Subarray With at Most K Frequency (length-of-longest-subarray-with-at-most-k-frequency)
  • Find Longest Special Substring That Occurs Thrice II (find-longest-special-substring-that-occurs-thrice-ii)
Step 02

Core Insight

What unlocks the optimal approach

  • No official hints in dataset. Start from constraints and look for a monotonic or reusable state.
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 #395: Longest Substring with At Least K Repeating Characters
class Solution {
    private String s;
    private int k;

    public int longestSubstring(String s, int k) {
        this.s = s;
        this.k = k;
        return dfs(0, s.length() - 1);
    }

    private int dfs(int l, int r) {
        int[] cnt = new int[26];
        for (int i = l; i <= r; ++i) {
            ++cnt[s.charAt(i) - 'a'];
        }
        char split = 0;
        for (int i = 0; i < 26; ++i) {
            if (cnt[i] > 0 && cnt[i] < k) {
                split = (char) (i + 'a');
                break;
            }
        }
        if (split == 0) {
            return r - l + 1;
        }
        int i = l;
        int ans = 0;
        while (i <= r) {
            while (i <= r && s.charAt(i) == split) {
                ++i;
            }
            if (i > r) {
                break;
            }
            int j = i;
            while (j <= r && s.charAt(j) != split) {
                ++j;
            }
            int t = dfs(i, j - 1);
            ans = Math.max(ans, t);
            i = j;
        }
        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)
Space
O(k)

Approach Breakdown

BRUTE FORCE
O(n × k) time
O(1) space

For each starting index, scan the next k elements to compute the window aggregate. There are n−k+1 starting positions, each requiring O(k) work, giving O(n × k) total. No extra space since we recompute from scratch each time.

SLIDING WINDOW
O(n) time
O(k) space

The window expands and contracts as we scan left to right. Each element enters the window at most once and leaves at most once, giving 2n total operations = O(n). Space depends on what we track inside the window (a hash map of at most k distinct elements, or O(1) for a fixed-size window).

Shortcut: Each element enters and exits the window once → O(n) amortized, regardless of window size.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.

Shrinking the window only once

Wrong move: Using `if` instead of `while` leaves the window invalid for multiple iterations.

Usually fails on: Over-limit windows stay invalid and produce wrong lengths/counts.

Fix: Shrink in a `while` loop until the invariant is valid again.