Trie vs Hash Map for Prefix Matching in DSA Rust - Build Both Approaches

Trie vs Hash Map for Prefix Matching

📖 Scenario: Imagine you are building a simple search feature for a phone contacts app. You want to find all contacts whose names start with a certain prefix quickly.

🎯 Goal: Build two ways to find contacts by prefix: one using a HashMap and one using a Trie. Compare how to store and search prefixes.

📋 What You'll Learn

Create a HashMap with contact names as keys and phone numbers as values

Create a Vec of contact names for building a Trie

Implement a simple Trie structure with insert and prefix search methods

Write code to find all contacts starting with a given prefix using both HashMap and Trie

Print the matching contacts for the prefix search

💡 Why This Matters

🌍 Real World

Phone contacts apps, autocomplete search bars, and dictionary word lookups use prefix matching to quickly find relevant entries.

💼 Career

Understanding Tries and HashMaps for prefix matching is useful for software engineers working on search engines, text editors, and mobile apps.

1

Create a HashMap of contacts

Create a HashMap called contacts_map with these exact entries: "Alice" -> "123", "Bob" -> "456", "Charlie" -> "789", "David" -> "101", "Daniel" -> "102".

DSA Rust

# Your code here

Hint

Use HashMap::new() and insert method to add contacts.

2

Create a Vec of contact names for the Trie

Create a Vec called contacts_list containing these exact strings: "Alice", "Bob", "Charlie", "David", "Daniel".

DSA Rust

use std::collections::HashMap;

fn main() {
    let mut contacts_map = HashMap::new();
    contacts_map.insert("Alice", "123");
    contacts_map.insert("Bob", "456");
    contacts_map.insert("Charlie", "789");
    contacts_map.insert("David", "101");
    contacts_map.insert("Daniel", "102");

    // Create contacts_list Vec here
    # Your code here
}

Hint

Use vec![...] to create the list of contact names.

3

Implement Trie and insert contacts

Define a TrieNode struct with a children field as a HashMap<char, TrieNode> and a is_end boolean. Implement insert method for TrieNode to add a word. Then create a TrieNode called root and insert all names from contacts_list into it.

DSA Rust

use std::collections::HashMap;

fn main() {
    let mut contacts_map = HashMap::new();
    contacts_map.insert("Alice", "123");
    contacts_map.insert("Bob", "456");
    contacts_map.insert("Charlie", "789");
    contacts_map.insert("David", "101");
    contacts_map.insert("Daniel", "102");

    let contacts_list = vec!["Alice", "Bob", "Charlie", "David", "Daniel"];

    // Define TrieNode struct and insert method
    // Create root TrieNode
    // Insert each name from contacts_list into root
    # Your code here
}

Hint

Use a recursive or iterative approach to insert each character into the Trie.

4

Search contacts by prefix using HashMap and Trie

Write code to find all contacts starting with the prefix "Da" using contacts_map by checking keys that start with "Da". Then write a method starts_with for TrieNode that returns all words starting with a prefix. Use it to find contacts starting with "Da" from the Trie. Finally, print both results as JSON arrays.

DSA Rust

use std::collections::HashMap;

struct TrieNode {
    children: HashMap,
    is_end: bool,
}

impl TrieNode {
    fn new() -> Self {
        TrieNode {
            children: HashMap::new(),
            is_end: false,
        }
    }

    fn insert(&mut self, word: &str) {
        let mut node = self;
        for ch in word.chars() {
            node = node.children.entry(ch).or_insert(TrieNode::new());
        }
        node.is_end = true;
    }

    // Implement starts_with method to collect words with prefix
    # Your code here
}

fn main() {
    let mut contacts_map = HashMap::new();
    contacts_map.insert("Alice", "123");
    contacts_map.insert("Bob", "456");
    contacts_map.insert("Charlie", "789");
    contacts_map.insert("David", "101");
    contacts_map.insert("Daniel", "102");

    let contacts_list = vec!["Alice", "Bob", "Charlie", "David", "Daniel"];

    let mut root = TrieNode::new();
    for name in &contacts_list {
        root.insert(name);
    }

    // Find and print contacts starting with "Da" using contacts_map and Trie
    # Your code here
}

Hint

Use starts_with on strings for HashMap keys. For Trie, traverse nodes for prefix then collect words recursively.

Practice

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

easy

A. Linked List, because it allows easy insertion and deletion

B. Hash Map, because it provides constant time lookup for any key

C. Array, because it stores elements in contiguous memory

D. Trie, because it stores common prefixes once and allows fast prefix queries

Solution

1. Step 1: Understand prefix matching requirements

   Prefix matching needs to find all strings starting with a given prefix efficiently.

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

   Trie stores common prefixes once and allows traversal by characters, making prefix queries fast. Hash Map is good for exact key lookup but not for prefix searches.

3. Final Answer:

   Trie, because it stores common prefixes once and allows fast prefix queries -> Option D

4. Quick Check:

   Prefix matching = Trie [OK]

Hint: Prefix queries need shared paths, so Trie is best [OK]

Common Mistakes:

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

2. Which Rust code snippet correctly declares a HashMap to store strings as keys and integers as values?

easy

A. let map = HashMap::new();

B. let map: HashMap = HashMap::new();

C. let map: std::collections::HashMap = std::collections::HashMap::new();

D. let map = HashMap();

Solution

1. Step 1: Check correct HashMap declaration syntax in Rust

   Rust requires specifying types inside angle brackets and calling HashMap::new() without parameters.

2. Step 2: Validate each option

   let map: std::collections::HashMap = std::collections::HashMap::new(); correctly declares a HashMap with String keys and i32 values using full path and new().

3. Final Answer:

   let map: std::collections::HashMap = std::collections::HashMap::new(); -> Option C

4. Quick Check:

   Correct Rust HashMap syntax = let map: std::collections::HashMap = std::collections::HashMap::new(); [OK]

Hint: Use HashMap::new() with type annotations for keys and values [OK]

Common Mistakes:

• Using parentheses instead of ::new()
• Swapping key and value types
• Omitting type annotations when needed

3. Given the following Rust code using a Trie for prefix matching, what will be the output?

struct TrieNode {
    children: std::collections::HashMap,
    is_end: bool,
}

impl TrieNode {
    fn new() -> Self {
        TrieNode { children: std::collections::HashMap::new(), is_end: false }
    }
}

fn main() {
    let mut root = TrieNode::new();
    // Insert "cat"
    let mut node = &mut root;
    for c in "cat".chars() {
        node = node.children.entry(c).or_insert(TrieNode::new());
    }
    node.is_end = true;

    // Check prefix "ca"
    let mut node = &root;
    let mut found = true;
    for c in "ca".chars() {
        if let Some(next) = node.children.get(&c) {
            node = next;
        } else {
            found = false;
            break;
        }
    }
    println!("{}", found);
}

medium

A. false

B. Compilation error

C. true

D. Runtime panic

Solution

1. Step 1: Analyze TrieNode struct definition

   TrieNode { children: HashMap, ... } creates a recursive type without indirection (e.g., Box<TrieNode>). Rust cannot compute the size of TrieNode, resulting in a compilation error.

2. Step 2: Confirm error prevents execution

   Code fails to compile, so no output or runtime behavior occurs.

3. Final Answer:

   Compilation error -> Option B

4. Quick Check:

   Recursive TrieNode without Box = compile error [OK]

Hint: Rust recursive structs require Box or Rc for children [OK]

Common Mistakes:

• Assuming prefix must be a complete word
• Confusing is_end with prefix existence
• Ignoring Rust recursive type size requirements

4. The following Rust code attempts to use a HashMap for prefix matching but has a bug. What is the bug?

use std::collections::HashMap;

fn main() {
    let mut map = HashMap::new();
    map.insert("apple", 1);
    map.insert("app", 2);

    let prefix = "ap";
    let mut found = false;
    for key in map.keys() {
        if key.starts_with(prefix) {
            found = true;
            break;
        }
    }
    println!("{}", found);
}

medium

A. Inefficient prefix search due to linear scan over keys

B. Using &str keys without lifetime causes compilation error

C. HashMap keys are unordered, so starts_with check is invalid

D. No bug; code works correctly for prefix matching

Solution

1. Step 1: Analyze prefix matching approach

   The code scans all keys linearly and checks if they start with the prefix, which works but is inefficient for large data.

2. Step 2: Identify the bug type

   There is no syntax or runtime error, but the approach is inefficient compared to Trie which supports fast prefix queries.

3. Final Answer:

   Inefficient prefix search due to linear scan over keys -> Option A

4. Quick Check:

   Linear scan for prefix = inefficient [OK]

Hint: HashMap needs full key; prefix search requires scanning all keys [OK]

Common Mistakes:

• Thinking code has syntax or runtime errors
• Assuming HashMap keys are ordered
• Ignoring performance implications

5. You need to implement a system in Rust that supports fast insertion and prefix search for millions of words. Which approach is best and why?

// Option A: Use a Trie with HashMap children
// Option B: Use a HashMap storing all words as keys
// Option C: Use a sorted Vec and binary search for prefix
// Option D: Use a LinkedList of words and scan linearly

hard

A. Trie, because it shares prefixes and allows O(k) prefix search where k is prefix length

B. HashMap, because it provides O(1) exact lookup but slow prefix search

C. Sorted Vec, because binary search helps but prefix search needs extra logic

D. LinkedList, because insertion is fast but prefix search is very slow

Solution

1. Step 1: Evaluate insertion and prefix search efficiency

   Trie allows insertion and prefix search in O(k) time, where k is prefix length, by sharing common prefixes.

2. Step 2: Compare other data structures

   HashMap is fast for exact keys but slow for prefix search. Sorted Vec requires complex prefix range search. LinkedList is slow for prefix search.

3. Final Answer:

   Trie, because it shares prefixes and allows O(k) prefix search where k is prefix length -> Option A

4. Quick Check:

   Fast prefix search = Trie [OK]

Hint: Prefix search speed depends on prefix length, Trie is best [OK]

Common Mistakes:

• Choosing HashMap for prefix search
• Ignoring complexity of prefix range search in sorted Vec
• Underestimating LinkedList search cost