Longest Substring Without Repeating Characters

Instead of discarding the whole accumulated substring when a duplicate character arrives, only the prefix up to that duplicate's previous position becomes obsolete. Recording the most recent index seen for every symbol allows the left boundary to hop directly past that index, ensuring the active span between pointers always holds unique items without restarting from scratch.

Full solution

Jump using last-seen index: track each character's most recent position and snap the left boundary past it on a collision, giving a single O(n) pass instead of the sliding-window set's incremental left-side deletions.

def length_of_longest_substring(s: str) -> int:
    last_seen: dict[str, int] = {}
    left = 0
    best = 0
    for right, ch in enumerate(s):
        if ch in last_seen and last_seen[ch] >= left:
            left = last_seen[ch] + 1
        last_seen[ch] = right
        best = max(best, right - left + 1)
    return best

function lengthOfLongestSubstring(s) {
  const lastSeen = new Map();
  let left = 0;
  let best = 0;
  for (let right = 0; right < s.length; right++) {
    const ch = s[right];
    if (lastSeen.has(ch) && lastSeen.get(ch) >= left) {
      left = lastSeen.get(ch) + 1;
    }
    lastSeen.set(ch, right);
    best = Math.max(best, right - left + 1);
  }
  return best;
}

The theory behind it

Strings — the ground this problem stands on. All Strings problems

What Strings is

A string is an ordered necklace of text characters, like letters printed along a ribbon of paper. Each character sits at an exact numeric slot, holding a glyph such as a letter, punctuation mark, or digit. In many programming languages, ribbons cannot be edited after creation, meaning changing a single character requires pressing an entirely new ribbon from scratch.

When to reach for it

Reach for string techniques when inputs consist of words, DNA sequences, serialized data formats, or sentences. Clues include questions testing palindromes, anagram matches, substring patterns, parenthesis balancing, or character frequency counts. Whenever an algorithm asks to transform capitalization, parse structured tokens, or compute edits between two phrases, string representations are the core subject.

How the pattern works

Think of characters as small integer codes ranging across standard character sets. Frequency tables with fixed sizes often replace heavy hash maps when tallying occurrences. For search tasks, maintain rolling state using character indices or sliding borders. When building output text through repeated appends, accumulate pieces inside a mutable list or string builder rather than concatenating strings directly, avoiding quadratic copy overhead.