Trie Search Operation in DSA Typescript - Time & Space Complexity

Time Complexity: Trie Search Operation

O(n)

Understanding Time Complexity

We want to understand how long it takes to find a word in a trie.

The question is: how does the search time grow as the word length changes?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

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

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

This code checks if a word exists in a trie by following each character step-by-step.

Identify Repeating Operations

• Primary operation: Looping through each character of the input word.
• How many times: Exactly once per character, so as many times as the word length.

How Execution Grows With Input

Each extra letter means one more step to check in the trie.

Input Size (n)	Approx. Operations
10	10 steps
100	100 steps
1000	1000 steps

Pattern observation: The time grows directly with the word length, one step per character.

Final Time Complexity

Time Complexity: O(n)

This means the search time grows linearly with the length of the word you are looking for.

Common Mistake

[X] Wrong: "Searching a word in a trie depends on the total number of words stored."

[OK] Correct: The search only depends on the length of the word, not how many words are in the trie.

Interview Connect

Knowing this helps you explain why tries are fast for prefix searches and dictionary lookups.

Self-Check

"What if the trie used an array instead of a map for children? How would the time complexity change?"

Practice

1. What does the search operation in a Trie data structure do?

easy

A. Checks if a given word exists in the Trie

B. Deletes a word from the Trie

C. Inserts a new word into the Trie

D. Counts the total words stored in the Trie

Solution

1. Step 1: Understand the purpose of search in Trie

   The search operation is designed to check if a specific word is present in the Trie data structure.

2. Step 2: Differentiate from other operations

   Insertion adds words, deletion removes words, and counting totals words, but search only checks existence.

3. Final Answer:

   Checks if a given word exists in the Trie -> Option A

4. Quick Check:

   Search = Check existence [OK]

Hint: Search checks presence of word, not insertion or deletion [OK]

Common Mistakes:

• Confusing search with insert operation
• Thinking search deletes words
• Assuming search counts words

2. Which of the following is the correct TypeScript method signature for a Trie search function that returns a boolean?

easy

A. search(word: boolean): boolean

B. search(word: number): string

C. search(word: string): void

D. search(word: string): boolean

Solution

1. Step 1: Identify input and output types for search

   The search method takes a word as a string and returns true or false, so input is string and output is boolean.

2. Step 2: Match method signature with correct types

   Only search(word: string): boolean matches the expected input and output types.

3. Final Answer:

   search(word: string): boolean -> Option D

4. Quick Check:

   Input string, output boolean [OK]

Hint: Search takes string, returns true/false [OK]

Common Mistakes:

• Using wrong input type like number or boolean
• Returning void instead of boolean
• Confusing return type with string

3. Given the following TypeScript Trie search code, what will be the output?

const trie = new Trie();
trie.insert('cat');
trie.insert('car');
trie.insert('dog');
console.log(trie.search('car'));
console.log(trie.search('cap'));

medium

A. true\ntrue

B. true\nfalse

C. false\ntrue

D. false\nfalse

Solution

1. Step 1: Check inserted words in Trie

   The words 'cat', 'car', and 'dog' are inserted into the Trie.

2. Step 2: Search for 'car' and 'cap'

   'car' exists in the Trie, so search returns true. 'cap' was not inserted, so search returns false.

3. Final Answer:

   true\nfalse -> Option B

4. Quick Check:

   Inserted words found, missing words not found [OK]

Hint: Inserted words return true, others false [OK]

Common Mistakes:

• Assuming 'cap' is found because it starts like 'car'
• Mixing true/false order in output
• Forgetting to insert words before search

4. Identify the bug in this TypeScript Trie search method:

search(word: string): boolean {
  let node = this.root;
  for (let i = 0; i <= word.length; i++) {
    const char = word[i];
    if (!node.children.has(char)) return false;
    node = node.children.get(char)!;
  }
  return node.isEndOfWord;
}

medium

A. Loop condition should be i < word.length, not <=

B. Missing check for empty string input

C. Should return true before loop ends

D. Incorrect use of non-null assertion operator (!)

Solution

1. Step 1: Analyze loop boundary in search method

   The loop runs from i = 0 to i <= word.length, which means it accesses word[word.length], an undefined index.

2. Step 2: Correct loop condition to avoid out-of-bounds

   The loop should run while i < word.length to access valid characters only.

3. Final Answer:

   Loop condition should be i < word.length, not <= -> Option A

4. Quick Check:

   Loop boundary off by one [OK]

Hint: Loop over characters with i < length, not <= length [OK]

Common Mistakes:

• Using <= causes undefined access
• Ignoring empty string edge case
• Misusing non-null assertion without checks

5. You want to modify a Trie search method to support prefix search (check if any word starts with given prefix). Which change is correct?

hard

A. Return true only if prefix is a complete word (isEndOfWord true)

B. Return false if prefix length is less than 3 characters

C. Return true as soon as all prefix characters are found, without checking isEndOfWord

D. Return true only if prefix matches exactly one word

Solution

1. Step 1: Understand prefix search goal

   Prefix search should confirm if any word starts with the given prefix, not necessarily a complete word.

2. Step 2: Adjust search logic for prefix

   Return true once all prefix characters are found in sequence, without requiring isEndOfWord to be true.

3. Final Answer:

   Return true as soon as all prefix characters are found, without checking isEndOfWord -> Option C

4. Quick Check:

   Prefix found means true, no full word check needed [OK]

Hint: Prefix search stops after matching prefix, ignores full word flag [OK]

Common Mistakes:

• Checking isEndOfWord for prefix search
• Rejecting short prefixes arbitrarily
• Requiring prefix to match exactly one word