Trie vs Hash Map for Prefix Matching in DSA Rust - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a word into the Trie node's children.

DSA Rust

node.children.insert([1], TrieNode::new());

Drag options to blanks, or click blank then click option'

A'c'

B"c"

Cc

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using double quotes instead of single quotes for char keys.

Trying to insert the whole node instead of a character key.

We insert a character key into the children HashMap. It must be a char literal like 'c'.

2fill in blank

medium

Complete the code to check if the Trie node has a child for the given character.

DSA Rust

if node.children.contains_key(&[1]) {

Drag options to blanks, or click blank then click option'

Ach

B'ch'

C"ch"

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around the variable name, making it a string literal.

Passing the node instead of the character.

The variable ch is a char, so we check contains_key with &ch.

3fill in blank

hard

Fix the error in this code to get a mutable reference to the child node for character ch.

DSA Rust

let child = node.children.get_mut(&[1]).unwrap();

Drag options to blanks, or click blank then click option'

A"ch"

Bch

C'ch'

D&ch

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string literal instead of a char.

Passing a reference when & is already in code.

We must pass &ch where ch is a char variable. So &ch is correct, but since code already has &, just ch is needed.

4fill in blank

hard

Fill both blanks to complete the prefix search in the Trie.

DSA Rust

for ch in prefix.chars() {
    if !node.children.contains_key(&[1]) {
        return false;
    }
    node = node.children.get_mut(&[2]).unwrap();
}
return true;

Drag options to blanks, or click blank then click option'

Ach

B"ch"

C'c'

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using string or char literals instead of the variable.

Using node instead of the character.

We use the variable ch for both contains_key and get_mut calls as the character key.

5fill in blank

hard

Fill all three blanks to create a HashMap comprehension that maps prefixes to their counts.

DSA Rust

let prefix_counts: HashMap<String, usize> = words.iter()
    .flat_map(|word| (1..=word.len()).map(|[1]| word[0..[2]].to_string()))
    .fold(HashMap::new(), |mut acc, prefix| {
        *acc.entry(prefix).or_insert([3]) += 1;
        acc
    });

Drag options to blanks, or click blank then click option'

Ai

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 as initial count which causes double counting on first occurrence.

Using different variable names inconsistently.

We use i as the variable for the range and slice end index, and initialize counts at 0 before incrementing with += 1.

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