Prefix Search Using Trie in DSA Typescript - Time & Space Complexity

Time Complexity: Prefix Search Using Trie

O(m)

Understanding Time Complexity

We want to understand how fast we can find words that start with a given prefix using a Trie.

The question is: how does the search time grow as the prefix length or number of words changes?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

class TrieNode {
  children: Map = new Map();
  isWord: boolean = false;
}

function startsWith(root: TrieNode, prefix: string): boolean {
  let node = root;
  for (const char of prefix) {
    if (!node.children.has(char)) return false;
    node = node.children.get(char)!;
  }
  return true;
}
    

This code checks if any word in the Trie starts with the given prefix by following nodes for each prefix character.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through each character of the prefix string.
• How many times: Exactly once per character in the prefix (length = m).

How Execution Grows With Input

Each character in the prefix requires one step to check in the Trie.

Input Size (prefix length m)	Approx. Operations
10	10 steps
100	100 steps
1000	1000 steps

Pattern observation: The time grows linearly with the prefix length, not with the total number of words stored.

Final Time Complexity

Time Complexity: O(m)

This means the search time depends only on how long the prefix is, not on how many words are in the Trie.

Common Mistake

[X] Wrong: "The search time depends on the total number of words stored in the Trie."

[OK] Correct: The search only follows nodes for the prefix characters, so it depends on prefix length, not total words.

Interview Connect

Understanding this helps you explain why Tries are efficient for prefix searches, a common real-world problem like autocomplete.

Self-Check

"What if we changed the prefix search to also return all words starting with the prefix? How would the time complexity change?"

Practice

1. What is the main purpose of using a Trie data structure in prefix search?

easy

A. To efficiently find all words that start with a given prefix

B. To sort words alphabetically

C. To store words in reverse order

D. To count the total number of words

Solution

1. Step 1: Understand Trie usage

   A Trie stores words by their prefixes, allowing quick prefix-based lookups.

2. Step 2: Identify prefix search goal

   Prefix search means finding all words starting with a given prefix efficiently.

3. Final Answer:

   To efficiently find all words that start with a given prefix -> Option A

4. Quick Check:

   Trie = Prefix search [OK]

Hint: Trie is built for prefix-based fast word retrieval [OK]

Common Mistakes:

• Confusing Trie with sorting structure
• Thinking Trie stores words reversed
• Assuming Trie counts total words only

2. Which of the following is the correct TypeScript syntax to define a Trie node with children as a Map and an endOfWord boolean?

easy

A. class TrieNode { children: object; endOfWord: string; constructor() { this.children = {}; this.endOfWord = ''; } }

B. class TrieNode { children: string[]; endOfWord: boolean; constructor() { this.children = []; this.endOfWord = false; } }

C. class TrieNode { children: Map; endOfWord: boolean; constructor() { this.children = new Map(); this.endOfWord = false; } }

D. class TrieNode { children: Map; endOfWord: boolean; constructor() { this.children = new Map(); this.endOfWord = false; } }

Solution

1. Step 1: Check children type

   Children should be a Map with string keys and TrieNode values for prefix characters.

2. Step 2: Check endOfWord type

   endOfWord is a boolean indicating if node ends a word.

3. Final Answer:

   class TrieNode { children: Map; endOfWord: boolean; constructor() { this.children = new Map(); this.endOfWord = false; } } -> Option D

4. Quick Check:

   Map + boolean endOfWord = class TrieNode { children: Map; endOfWord: boolean; constructor() { this.children = new Map(); this.endOfWord = false; } } [OK]

Hint: Use Map for children and boolean for endOfWord [OK]

Common Mistakes:

• Using array instead of Map for children
• Using wrong key type in Map
• Setting endOfWord as string instead of boolean

3. Given the following TypeScript code snippet, what will be the output of console.log(trie.startsWith('app'))?

class TrieNode {
  children = new Map();
  endOfWord = false;
}

class Trie {
  root = new TrieNode();

  insert(word: string) {
    let node = this.root;
    for (const char of word) {
      if (!node.children.has(char)) {
        node.children.set(char, new TrieNode());
      }
      node = node.children.get(char)!;
    }
    node.endOfWord = true;
  }

  startsWith(prefix: string): boolean {
    let node = this.root;
    for (const char of prefix) {
      if (!node.children.has(char)) return false;
      node = node.children.get(char)!;
    }
    return true;
  }
}

const trie = new Trie();
trie.insert('apple');
trie.insert('app');
trie.insert('bat');

medium

A. true

B. false

C. undefined

D. Error at runtime

Solution

1. Step 1: Insert words into Trie

   Words 'apple', 'app', and 'bat' are inserted, so prefix 'app' exists in Trie.

2. Step 2: Check startsWith method

   Method returns true if prefix characters exist in Trie nodes, which they do for 'app'.

3. Final Answer:

   true -> Option A

4. Quick Check:

   Prefix 'app' found = true [OK]

Hint: If prefix inserted, startsWith returns true [OK]

Common Mistakes:

• Confusing startsWith with search for whole word
• Expecting false if prefix is a word itself
• Thinking startsWith returns endOfWord status

4. The following TypeScript code tries to implement a startsWith method for a Trie but always returns false. What is the bug?

startsWith(prefix: string): boolean {
  let node = this.root;
  for (const char of prefix) {
    if (!node.children.has(char)) return false;
    node = node.children.get(char);
  }
  return false;
}

medium

A. It should check endOfWord before returning

B. It should use a for loop with index instead of for-of

C. It should return true after the loop if prefix exists

D. It should initialize node with a new TrieNode

Solution

1. Step 1: Analyze loop behavior

   The loop correctly traverses nodes for each prefix character.

2. Step 2: Check return statement

   Method returns false always after loop, so never returns true even if prefix found.

3. Step 3: Fix return logic

   Return true after loop if all prefix chars found.

4. Final Answer:

   It should return true after the loop if prefix exists -> Option C

5. Quick Check:

   Return true after loop if prefix found [OK]

Hint: Return true after loop if prefix nodes exist [OK]

Common Mistakes:

• Returning false at end regardless
• Checking endOfWord unnecessarily
• Incorrect node initialization

5. You want to extend a Trie to return all words starting with a given prefix. Which approach correctly implements this in TypeScript? A) Traverse to prefix node, then recursively collect all words below. B) Store all words in an array and filter by prefix each time. C) Use a hash map to map prefixes to words directly. D) Insert reversed words and search suffixes instead.

hard

A. Store all words in an array and filter by prefix each time.

B. Traverse to prefix node, then recursively collect all words below.

C. Use a hash map to map prefixes to words directly.

D. Insert reversed words and search suffixes instead.

Solution

1. Step 1: Understand prefix search extension

   To get all words with prefix, first find node matching prefix.

2. Step 2: Collect words recursively

   From prefix node, recursively traverse children to collect all complete words.

3. Step 3: Evaluate other options

   Filtering array or hash map is less efficient; reversed words search suffixes, not prefixes.

4. Final Answer:

   Traverse to prefix node, then recursively collect all words below. -> Option B

5. Quick Check:

   Recursive traversal from prefix node = Traverse to prefix node, then recursively collect all words below. [OK]

Hint: Find prefix node, then DFS to collect words [OK]

Common Mistakes:

• Filtering all words each time (slow)
• Using hash map without prefix traversal
• Confusing prefix with suffix search