Trie vs Hash Map for Prefix Matching in DSA C++ - 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 the root to nodes form words or prefixes.

beginner

How does a Hash Map handle prefix matching?

A Hash Map stores complete keys and their values. For prefix matching, it requires checking all keys to find those starting with the prefix, which can be slow for many keys.

intermediate

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

Trie allows direct traversal along the prefix characters, quickly reaching all words sharing that prefix without scanning all keys, making prefix queries faster than Hash Map.

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, while Hash Map stores only complete keys and values.

intermediate

In what scenario might a Hash Map be preferred over a Trie for prefix matching?

When the dataset is small or prefix queries are rare, a Hash Map is simpler and uses less memory, so it might be preferred despite slower prefix matching.

Which data structure allows fast prefix matching by traversing characters?

A Trie

B Hash Map

C Array

D Stack

Trie stores characters in nodes allowing direct traversal along prefixes, making prefix matching fast.

What is a main disadvantage of using a Trie?

A Slow prefix search

B Cannot store strings

C No support for prefix queries

D High memory usage

Trie can consume more memory because it stores nodes for each character.

How does a Hash Map find keys with a given prefix?

A Direct traversal of prefix nodes

B Uses a stack to store prefixes

C Iterate all keys and check prefix

D Uses binary search

Hash Map requires checking all keys to find those starting with the prefix.

When is a Hash Map preferred over a Trie for prefix matching?

A Large dataset with many prefix queries

B Small dataset or rare prefix queries

C When memory is unlimited

D When keys are numeric only

Hash Map is simpler and uses less memory, suitable for small datasets or rare prefix queries.

What does each node in a Trie represent?

A A character

B A complete word

C A number

D A hash value

Each node in a Trie represents a single character.

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

Think about how characters are stored and accessed in a Trie.

List advantages and disadvantages of using a Trie versus a Hash Map for prefix matching.

Consider speed and memory trade-offs.