LeetCode #424 — MEDIUM

Longest Repeating Character Replacement

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

The Problem

Problem Statement

You are given a string s and an integer k. You can choose any character of the string and change it to any other uppercase English character. You can perform this operation at most k times.

Return the length of the longest substring containing the same letter you can get after performing the above operations.

Example 1:

Input: s = "ABAB", k = 2
Output: 4
Explanation: Replace the two 'A's with two 'B's or vice versa.

Example 2:

Input: s = "AABABBA", k = 1
Output: 4
Explanation: Replace the one 'A' in the middle with 'B' and form "AABBBBA".
The substring "BBBB" has the longest repeating letters, which is 4.
There may exists other ways to achieve this answer too.

Constraints:

1 <= s.length <= 10⁵
s consists of only uppercase English letters.
0 <= k <= s.length

Step 01

Brute Force Baseline

Problem summary: You are given a string s and an integer k. You can choose any character of the string and change it to any other uppercase English character. You can perform this operation at most k times. Return the length of the longest substring containing the same letter you can get after performing the above operations.

Baseline thinking

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

Pattern signal: Hash Map · Sliding Window

Example 1

"ABAB"
2

Example 2

"AABABBA"
1

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

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
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 #424: Longest Repeating Character Replacement
class Solution {
    public int characterReplacement(String s, int k) {
        int[] cnt = new int[26];
        int l = 0, mx = 0;
        int n = s.length();
        for (int r = 0; r < n; ++r) {
            mx = Math.max(mx, ++cnt[s.charAt(r) - 'A']);
            if (r - l + 1 - mx > k) {
                --cnt[s.charAt(l++) - 'A'];
            }
        }
        return n - l;
    }
}

// Accepted solution for LeetCode #424: Longest Repeating Character Replacement
func characterReplacement(s string, k int) int {
	cnt := [26]int{}
	l, mx := 0, 0
	for r, c := range s {
		cnt[c-'A']++
		mx = max(mx, cnt[c-'A'])
		if r-l+1-mx > k {
			cnt[s[l]-'A']--
			l++
		}
	}
	return len(s) - l
}

# Accepted solution for LeetCode #424: Longest Repeating Character Replacement
class Solution:
    def characterReplacement(self, s: str, k: int) -> int:
        cnt = Counter()
        l = mx = 0
        for r, c in enumerate(s):
            cnt[c] += 1
            mx = max(mx, cnt[c])
            if r - l + 1 - mx > k:
                cnt[s[l]] -= 1
                l += 1
        return len(s) - l

// Accepted solution for LeetCode #424: Longest Repeating Character Replacement
use std::collections::HashMap;

impl Solution {
    pub fn character_replacement(s: String, k: i32) -> i32 {
        let s: Vec<char> = s.chars().collect();
        let (mut res, mut l, mut maxf) = (0, 0, 0);
        let mut count: HashMap<char, u64> = HashMap::new();

        for r in 0..s.len() {
            *count.entry(s[r]).or_default() += 1;
            maxf = maxf.max(*count.get(&s[r]).unwrap());

            while (r - l + 1) - maxf as usize > k as usize {
                *count.get_mut(&s[l]).unwrap() -= 1;
                l += 1;
            }

            res = res.max(r - l + 1);
        }

        res as i32
    }
}

// Accepted solution for LeetCode #424: Longest Repeating Character Replacement
function characterReplacement(s: string, k: number): number {
    const idx = (c: string) => c.charCodeAt(0) - 65;
    const cnt: number[] = Array(26).fill(0);
    const n = s.length;
    let [l, mx] = [0, 0];
    for (let r = 0; r < n; ++r) {
        mx = Math.max(mx, ++cnt[idx(s[r])]);
        if (r - l + 1 - mx > k) {
            --cnt[idx(s[l++])];
        }
    }
    return n - l;
}

Step 06

Interactive Study Demo

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

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(|\Sigma|)

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.