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73. Implement Trie (Prefix Tree)

mediumAsked at Reddit

Implement a trie supporting insert, search, and startsWith. Reddit uses this to test prefix-tree design — the same data structure they use in their autocomplete service for subreddit and username completion.

By Alex Chen, Founder, InterviewChamp.AI · Last verified

Source citations

Public interview reports confirming this problem appears in Reddit loops.

  • Glassdoor (2026-Q1)— Reddit search-team on-site canonical.
  • Blind (2025-12)— Reported on Reddit search and autocomplete rounds.

Problem

A trie or prefix tree is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. Implement the Trie class: Trie(), insert(word), search(word), startsWith(prefix).

Constraints

  • 1 <= word.length, prefix.length <= 2000
  • word and prefix consist only of lowercase English letters.
  • At most 3 * 10^4 calls in total will be made to insert, search, and startsWith.

Examples

Example 1

Input
["Trie","insert","search","search","startsWith","insert","search"] [[],["apple"],["apple"],["app"],["app"],["app"],["app"]]
Output
[null,null,true,false,true,null,true]

Approaches

1. Hash set of words

Store all words in a Set. search trivial; startsWith scans all words.

Time
search O(1), startsWith O(n*L)
Space
O(n*L)
// Anti-pattern: startsWith is linear in word count.

Tradeoff: Fails for many prefix queries.

2. Trie tree (optimal)

Each node has 26 children + an isEnd flag. Walk char-by-char.

Time
O(L) per op
Space
O(N*L)
class Trie {
  constructor() {
    this.root = {};
  }
  insert(word) {
    let node = this.root;
    for (const c of word) {
      if (!node[c]) node[c] = {};
      node = node[c];
    }
    node.isEnd = true;
  }
  search(word) {
    const node = this._find(word);
    return !!node && !!node.isEnd;
  }
  startsWith(prefix) {
    return !!this._find(prefix);
  }
  _find(s) {
    let node = this.root;
    for (const c of s) {
      if (!node[c]) return null;
      node = node[c];
    }
    return node;
  }
}

Tradeoff: O(L) per op. Memory scales with total characters in dictionary.

Reddit-specific tips

Reddit interviewers want the standard children-map node. Bonus signal: discuss memory optimization (use an array of 26 vs. an object) and connect to their autocomplete service.

Common mistakes

  • Marking every node along the path as isEnd (only the terminal one).
  • Conflating search and startsWith (search requires isEnd).
  • Allocating a 26-slot array per node when the alphabet is small (memory hog).

Follow-up questions

An interviewer at Reddit may pivot to one of these next:

  • Design Add and Search Words (LC 211) — with wildcards.
  • Word Search II (LC 212) — Trie + DFS.
  • Longest word in dictionary (LC 720).

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Output

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FAQ

Object vs. fixed array for children?

Object is more memory-efficient for sparse alphabets; fixed-26 array is faster for dense use.

How would delete work?

Walk to the terminal, unset isEnd. If no children, also remove from parent (recursively up).

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