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.