Autocomplete System with Trie in DSA Rust - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new Trie node with an empty children map.

DSA Rust

struct TrieNode {
    children: std::collections::HashMap<char, TrieNode>,
    is_end_of_word: bool,
}

impl TrieNode {
    fn new() -> Self {
        TrieNode {
            children: [1],
            is_end_of_word: false,
        }
    }
}

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

AHashSet::new()

BVec::new()

CHashMap::new()

DString::new()

Attempts:

3 left

💡 Hint

Common Mistakes

Using Vec or HashSet instead of HashMap for children.

We use HashMap::new() to create an empty map for children nodes.

2fill in blank

medium

Complete the code to insert a character into the Trie children if it does not exist.

DSA Rust

impl TrieNode {
    fn insert_char(&mut self, ch: char) {
        self.children.entry(ch).or_insert([1]);
    }
}

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

ATrieNode::new()

BNone

CVec::new()

Dtrue

Attempts:

3 left

💡 Hint

Common Mistakes

Inserting None or a boolean instead of a TrieNode.

We insert a new TrieNode for the character if it is not already present.

3fill in blank

hard

Fix the error in the search function to correctly check if a character exists in children.

DSA Rust

impl TrieNode {
    fn search_char(&self, ch: char) -> bool {
        self.children.[1](&ch)
    }
}

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

Aget

Bcontains_key

Cinsert

Dremove

Attempts:

3 left

💡 Hint

Common Mistakes

Using get which returns an Option, or insert/remove which modify the map.

contains_key checks if the map has the given key.

4fill in blank

hard

Fill both blanks to insert a word into the Trie and mark the end of the word.

DSA Rust

impl TrieNode {
    fn insert_word(&mut self, word: &str) {
        let mut node = self;
        for ch in word.chars() {
            node = node.children.entry(ch).or_insert([1]);
        }
        node.[2] = true;
    }
}

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

ATrieNode::new()

Bfalse

Cis_end_of_word

Dtrue

Attempts:

3 left

💡 Hint

Common Mistakes

Setting the flag to false or forgetting to mark the end.

Insert new TrieNodes for characters and mark the last node's is_end_of_word as true.

5fill in blank

hard

Fill all three blanks to collect all words with a given prefix using recursion.

DSA Rust

impl TrieNode {
    fn collect_words(&self, prefix: String, results: &mut Vec<String>) {
        if self.[1] {
            results.push(prefix.clone());
        }
        for (ch, node) in &self.[2] {
            let mut new_prefix = prefix.clone();
            new_prefix.push([3]);
            node.collect_words(new_prefix, results);
        }
    }
}

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

Ais_end_of_word

Bchildren

C*ch

Dch

Attempts:

3 left

💡 Hint

Common Mistakes

Dereferencing ch unnecessarily or using wrong field names.

Check is_end_of_word, iterate over children, and append the character ch to prefix.

Practice

1. What is the main purpose of using a Trie in an autocomplete system?

easy

A. To sort words alphabetically

B. To count the total number of words

C. To efficiently store and search prefixes of words

D. To reverse the input strings

Solution

1. Step 1: Understand the role of Trie in prefix search

   A Trie is a tree-like data structure designed to store strings by their prefixes, allowing quick prefix-based searches.

2. Step 2: Connect Trie usage to autocomplete

   Autocomplete requires finding all words starting with a given prefix efficiently, which a Trie supports well.

3. Final Answer:

   To efficiently store and search prefixes of words -> Option C

4. Quick Check:

   Trie = prefix search [OK]

Hint: Trie stores prefixes for fast autocomplete search [OK]

Common Mistakes:

• Confusing Trie with sorting data structure
• Thinking Trie counts words only
• Assuming Trie reverses strings

2. Which of the following is the correct Rust syntax to define a Trie node struct with children as a HashMap and an is_end boolean?

easy

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

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

C. struct TrieNode { children: Vec<char>, is_end: bool }

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

Solution

1. Step 1: Understand Rust ownership and recursive types

   Rust requires recursive types like TrieNode to use pointers such as Box to avoid infinite size errors.

2. Step 2: Check each option's children type

   struct TrieNode { children: HashMap, is_end: bool } uses HashMap, which is correct for recursive Trie nodes. struct TrieNode { children: HashMap, is_end: bool } lacks Box, causing compile error. struct TrieNode { children: Vec<char>, is_end: bool } uses Vec<char>, which is incorrect for children mapping. struct TrieNode { children: HashMap, is_end: bool } uses HashMap, which is incorrect because keys should be single chars.

3. Final Answer:

   struct TrieNode { children: HashMap, is_end: bool } -> Option A

4. Quick Check:

   Recursive struct needs Box pointer [OK]

Hint: Use Box for recursive struct fields in Rust [OK]

Common Mistakes:

• Omitting Box causes compile errors
• Using Vec instead of HashMap for children
• Using String keys instead of char

3. Given the following Rust code snippet for inserting words into a Trie, what will be the output of searching for prefix "ca"?

let mut trie = Trie::new();
trie.insert("cat");
trie.insert("car");
trie.insert("dog");
let results = trie.search_prefix("ca");
println!("{:?}", results);

medium

A. ["dog"]

B. ["cat", "car"]

C. ["cat"]

D. []

Solution

1. Step 1: Insert words into Trie

   The words "cat", "car", and "dog" are inserted. "cat" and "car" share prefix "ca".

2. Step 2: Search for prefix "ca"

   Searching prefix "ca" returns all words starting with "ca", which are "cat" and "car".

3. Final Answer:

   ["cat", "car"] -> Option B

4. Quick Check:

   Prefix "ca" matches "cat", "car" [OK]

Hint: Search prefix returns all words starting with it [OK]

Common Mistakes:

• Returning only one matching word
• Returning words without the prefix
• Returning empty list incorrectly

4. Identify the bug in this Rust Trie insertion method snippet:

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

medium

A. word.chars() should be collected into a vector first

B. Use of entry() is incorrect for insertion

C. is_end should be set before the loop

D. node should be mutable reference (&mut) to modify children

Solution

1. Step 1: Analyze mutability of node reference

   node is assigned as immutable reference &self.root, but children need to be modified, so node must be mutable reference &mut.

2. Step 2: Understand why mutability is needed for entry()

   entry() modifies the HashMap, so node must be mutable to call entry() and insert new nodes.

3. Final Answer:

   node should be mutable reference (&mut) to modify children -> Option D

4. Quick Check:

   Mutable reference needed for insertion [OK]

Hint: Use &mut to modify Trie nodes during insertion [OK]

Common Mistakes:

• Using immutable reference for mutable operations
• Setting is_end too early
• Unnecessary collection of chars

5. You want to extend a Trie-based autocomplete system to return suggestions sorted by frequency of word usage. Which data structure modification is best to support this efficiently?

hard

A. Store a frequency count at each end node and use a max-heap during search

B. Store words in a vector and sort on every search

C. Use a linked list to store words in insertion order

D. Store frequency counts but ignore them during search

Solution

1. Step 1: Understand frequency storage in Trie nodes

   Storing frequency counts at end nodes tracks how often each word is used.

2. Step 2: Use max-heap to retrieve top frequent words efficiently

   During prefix search, a max-heap can quickly select highest frequency words without sorting all results.

3. Final Answer:

   Store a frequency count at each end node and use a max-heap during search -> Option A

4. Quick Check:

   Frequency + max-heap = efficient top suggestions [OK]

Hint: Use frequency counts + max-heap for fast top suggestions [OK]

Common Mistakes:

• Sorting all words on every search (slow)
• Ignoring frequency data
• Using linked list which is inefficient for sorting