Trie vs Hash Map for Prefix Matching in DSA Typescript - Key Differences

Recall & Review

beginner

What is a Trie data structure?

A Trie is a tree-like data structure that stores strings by sharing common prefixes. Each node represents a character, and paths from root to nodes form words or prefixes.

beginner

How does a Hash Map store data for prefix matching?

A Hash Map stores key-value pairs where keys are usually full strings. For prefix matching, it requires checking all keys or storing prefixes explicitly, which can be inefficient.

intermediate

Why is Trie more efficient than Hash Map for prefix matching?

Trie allows direct traversal along characters of the prefix, making prefix search fast and efficient. Hash Map lacks this structure and may need to check many keys.

intermediate

What is a downside of using Trie compared to Hash Map?

Trie can use more memory because it stores nodes for each character, even if some prefixes are rare. Hash Map can be simpler and use less memory for exact key lookups.

intermediate

In which scenario is Hash Map preferred over Trie for prefix matching?

When the dataset is small or prefix queries are rare, Hash Map is simpler and faster to implement. Trie is better for large datasets with many prefix queries.

Which data structure is designed to efficiently handle prefix searches?

A Hash Map

B Trie

C Stack

D Queue

Trie stores characters in a tree structure allowing fast prefix traversal.

What is a common disadvantage of using Trie compared to Hash Map?

A Cannot store strings

B Slower prefix search

C Uses more memory

D No support for exact match

Trie nodes for each character can consume more memory than Hash Map keys.

How does Hash Map perform prefix matching?

A Checking all keys or storing prefixes explicitly

B Direct traversal by characters

C Using a tree structure

D Using linked lists

Hash Map requires scanning keys or storing prefixes, which is less efficient.

When is Hash Map preferred over Trie for prefix matching?

A Large dataset with many prefix queries

B When keys are numeric

C When memory is unlimited

D Small dataset or rare prefix queries

Hash Map is simpler and faster for small datasets or few prefix queries.

What does each node in a Trie represent?

A A character

B A number

C A full word

D A hash code

Each Trie node represents a single character in the stored strings.

Explain how a Trie supports efficient prefix matching compared to a Hash Map.

Think about how each character leads to the next in a Trie.

Describe scenarios where you would choose a Hash Map over a Trie for prefix matching.

Consider dataset size and frequency of prefix searches.

Practice

1. Which data structure is generally more efficient for prefix matching in a large set of strings?

easy

A. Trie, because it stores prefixes explicitly and allows fast prefix queries

B. Hash Map, because it stores keys with constant time lookup

C. Array, because it stores elements in order

D. Linked List, because it allows sequential access

Solution

1. Step 1: Understand prefix matching requirements

   Prefix matching means finding all strings that start with a given prefix efficiently.

2. Step 2: Compare Trie and Hash Map for prefix queries

   Trie stores prefixes explicitly in its structure, allowing quick traversal to all words sharing a prefix. Hash Map only allows exact key lookups, not prefix searches.

3. Final Answer:

   Trie, because it stores prefixes explicitly and allows fast prefix queries -> Option A

4. Quick Check:

   Prefix matching = Trie [OK]

Hint: Trie stores prefixes; Hash Map does not support prefix search [OK]

Common Mistakes:

• Thinking Hash Map supports prefix search efficiently
• Confusing prefix matching with exact key lookup
• Assuming arrays or linked lists are efficient for prefix queries

2. Which of the following TypeScript code snippets correctly initializes a Trie node for prefix matching?

easy

A. class TrieNode { children: object = {}; isEnd: number = 0; }

B. class TrieNode { children: string[] = []; isEnd: boolean = true; }

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

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

Solution

1. Step 1: Identify correct data types for TrieNode properties

   Children should map characters (string) to TrieNode objects. isEnd should be boolean to mark word end.

2. Step 2: Check each option's correctness

   class TrieNode { children: Map = new Map(); isEnd: boolean = false; } uses Map and boolean isEnd, which is correct. Others use wrong types or incorrect isEnd values.

3. Final Answer:

   class TrieNode { children: Map = new Map(); isEnd: boolean = false; } -> Option C

4. Quick Check:

   TrieNode children = Map [OK]

Hint: Trie children map chars to nodes; isEnd is boolean [OK]

Common Mistakes:

• Using array instead of Map for children
• Setting isEnd as number instead of boolean
• Mapping children with wrong key or value types

3. What will be the output of the following TypeScript code snippet?

const map = new Map();
map.set('app', true);
map.set('apple', true);
console.log(map.has('app'));
console.log(map.has('appl'));

medium

A. false\ntrue

B. true\ntrue

C. false\nfalse

D. true\nfalse

Solution

1. Step 1: Understand Map key lookups

   Map.has(key) returns true only if exact key exists. 'app' and 'apple' are keys, 'appl' is not.

2. Step 2: Evaluate console.log outputs

   map.has('app') is true; map.has('appl') is false because 'appl' is not a key.

3. Final Answer:

   true\nfalse -> Option D

4. Quick Check:

   Exact keys only: 'app' true, 'appl' false [OK]

Hint: Map.has checks exact keys, not prefixes [OK]

Common Mistakes:

• Assuming map.has supports prefix matching
• Confusing keys 'appl' and 'apple'
• Expecting true for partial keys

4. Consider this buggy TypeScript Trie insert method:

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

What is the main issue causing a runtime error?

medium

A. node.children.get(char) can return undefined, causing assignment to node to fail

B. The loop variable char is not declared properly

C. The isEnd property is set before the loop

D. The root node is not initialized

Solution

1. Step 1: Analyze node.children.get(char) usage

   Even after checking node.children.has(char), node.children.get(char) can theoretically return undefined if the Map is modified concurrently or due to incorrect initialization.

2. Step 2: Identify runtime error cause

   Assigning undefined to node causes subsequent property access to fail, leading to runtime error.

3. Final Answer:

   node.children.get(char) can return undefined, causing assignment to node to fail -> Option A

4. Quick Check:

   Ensure node.children.get(char) is not undefined before assignment [OK]

Hint: Check that node.children.get(char) returns a valid TrieNode before assignment [OK]

Common Mistakes:

• Thinking Map.get() returns undefined despite prior has() check
• Overlooking need for root initialization in constructor
• Assuming object-style children access instead of Map methods

5. You want to implement a prefix search feature that returns all words starting with a given prefix from a large dictionary. Which approach is best and why?

// Approach 1: Store all words in a Hash Map and check each key for prefix match
// Approach 2: Build a Trie from all words and traverse to prefix node to collect matches

hard

A. Approach 1 is better because Hash Map lookups are O(1) and prefix checks are fast

B. Approach 2 is better because Trie traversal to prefix node is efficient and collects matches quickly

C. Both approaches are equally efficient for prefix search

D. Neither approach works well; use a sorted array with binary search instead

Solution

1. Step 1: Evaluate Approach 1 (Hash Map with prefix checks)

   Hash Map allows O(1) exact lookups but requires checking every key for prefix, which is O(n) and inefficient for large data.

2. Step 2: Evaluate Approach 2 (Trie traversal)

   Trie stores prefixes explicitly; traversing to prefix node is O(k) where k is prefix length, then collecting matches is efficient.

3. Step 3: Compare efficiency and scalability

   Trie approach scales better for prefix search due to structured prefix storage and avoids scanning all keys.

4. Final Answer:

   Approach 2 is better because Trie traversal to prefix node is efficient and collects matches quickly -> Option B

5. Quick Check:

   Prefix search = Trie traversal [OK]

Hint: Trie stores prefixes; Hash Map needs full scan for prefix [OK]

Common Mistakes:

• Assuming Hash Map prefix checks are fast
• Ignoring Trie traversal efficiency
• Thinking both approaches perform the same