Autocomplete System with Trie in DSA C# - Build from Scratch

Autocomplete System with Trie

📖 Scenario: Imagine you are building a search box for a website. When users start typing, you want to suggest words that start with what they typed. This helps users find what they want faster.

🎯 Goal: Build a simple autocomplete system using a Trie data structure. You will create the Trie, add words to it, search for words starting with a prefix, and print the suggestions.

📋 What You'll Learn

Create a TrieNode class with a dictionary for children and a boolean to mark end of word

Create a Trie class with methods to insert words and search for words starting with a prefix

Insert a list of given words into the Trie

Search for words starting with a given prefix and collect suggestions

Print the list of suggestions

💡 Why This Matters

🌍 Real World

Autocomplete systems are used in search engines, messaging apps, and code editors to help users type faster and find results quickly.

💼 Career

Understanding Tries and autocomplete logic is useful for software engineers working on search features, text processing, and user experience improvements.

1

Create TrieNode class and Trie class with Insert method

Create a class called TrieNode with a public dictionary called Children of type Dictionary<char, TrieNode> and a public boolean called IsEndOfWord. Then create a class called Trie with a public TrieNode field called Root initialized in the constructor. Add a public method Insert in Trie that takes a string word and inserts it into the Trie by creating nodes for each character and marking the last node's IsEndOfWord as true.

DSA C#

// Your code here to create TrieNode and Trie classes with Insert method

Hint

Think of TrieNode as a box holding letters and a flag if a word ends there. The Trie has a root box. Insert adds letters one by one.

2

Insert given words into the Trie

Create a list of strings called words with these exact values: "apple", "app", "application", "bat", "ball". Then create a Trie object called trie and insert each word from words into trie using the Insert method.

DSA C#

using System;
using System.Collections.Generic;

// Insert the given words into the trie
// Your code here

Hint

Use a list to hold the words. Then loop through the list and insert each word into the trie.

3

Add Search method to find words starting with a prefix

Add a public method Search in the Trie class that takes a string prefix and returns a list of strings. This method should find the node matching the prefix and then collect all words starting from that node by traversing the Trie. Use a helper private method CollectWords that takes a TrieNode, a string prefix, and a list of strings results to add all words starting from that node to results. The Search method returns the list of words starting with the prefix.

DSA C#

using System;
using System.Collections.Generic;

// Add Search and CollectWords methods inside Trie class
// Your code here

Hint

First find the node matching the prefix. Then use recursion to find all words below that node.

4

Search for prefix and print suggestions

In the Main method, call trie.Search with the prefix "app" and store the result in a variable called suggestions. Then print each word in suggestions on its own line.

DSA C#

using System;
using System.Collections.Generic;

// Search for prefix "app" and print suggestions
// Your code here

Hint

Call Search with "app" and print each word in the returned list on its own line.

Practice

1. What is the main advantage of using a Trie over a HashSet for implementing an autocomplete system?

easy

A. Trie uses less memory than HashSet for all cases

B. Trie supports prefix-based search efficiently, while HashSet requires checking all words

C. Trie is faster for exact word lookup than HashSet

D. Trie automatically sorts words alphabetically unlike HashSet

Solution

1. Step 1: Understand autocomplete requirement

   Autocomplete needs to find all words that start with a given prefix quickly.

2. Step 2: Compare Trie vs HashSet

   Trie stores characters level by level, so prefix traversal is O(prefix length). HashSet requires scanning all words to check which ones start with the prefix.

3. Final Answer:

   Trie supports prefix-based search efficiently, while HashSet requires checking all words -> Option B

4. Quick Check:

   Trie = prefix search in O(m), HashSet = O(n*m) for prefix [OK]

Hint: Trie shares common prefixes among words - natural fit for autocomplete [OK]

Common Mistakes:

• Assuming Trie always uses less memory - it can use more for sparse word sets
• Thinking exact lookup is why Trie beats HashSet - HashSet is actually faster for exact lookup
• Assuming Trie sorts automatically - it doesn't sort, traversal order depends on children insertion

2. Which C# code correctly inserts a word into a Trie node structure where each node has a Dictionary<char, TrieNode> Children and a bool IsEndOfWord?

easy

A. node.Children[c] = new TrieNode(); node = node.Children[c];

B. if (!node.Children.ContainsKey(c)) node.Children[c] = new TrieNode(); node = node.Children[c];

C. node.Children.Add(c); node = node.Children[c];

D. node.Children[c] = c; node = node.Children[c];

Solution

1. Step 1: Check if child exists

   Before creating a new node, check if the character key already exists in Children to avoid overwriting existing subtrees.

2. Step 2: Create only if missing

   Using ContainsKey check ensures we reuse existing nodes and only create new ones when the character path doesn't exist yet.

3. Final Answer:

   if (!node.Children.ContainsKey(c)) node.Children[c] = new TrieNode(); node = node.Children[c]; -> Option B

4. Quick Check:

   Always check ContainsKey before adding to avoid overwriting existing paths [OK]

Hint: Always guard with ContainsKey before inserting a new Trie node [OK]

Common Mistakes:

• Option A overwrites existing children, destroying previously inserted words
• Option C uses Add which throws if key already exists
• Option D stores char as value instead of TrieNode

3. What will the following C# Trie prefix search return when searching for prefix "ca" in a Trie containing ["cat", "car", "card", "dog"]?

List<string> results = new List<string>();
void DFS(TrieNode node, string current) {
    if (node.IsEndOfWord) results.Add(current);
    foreach (var kvp in node.Children)
        DFS(kvp.Value, current + kvp.Key);
}
TrieNode prefixNode = GetPrefixNode("ca");
if (prefixNode != null) DFS(prefixNode, "ca");

medium

A. ["cat", "car", "card", "dog"]

B. ["ca"]

C. ["cat", "car", "card"]

D. []

Solution

1. Step 1: Find prefix node

   GetPrefixNode("ca") traverses c→a and returns the node at 'a'. "dog" is unreachable from this node.

2. Step 2: DFS from prefix node

   DFS explores all children from 'a': t (cat), r (car), r→d (card). All three have IsEndOfWord=true.

3. Final Answer:

   ["cat", "car", "card"] -> Option C

4. Quick Check:

   Only words under prefix "ca" subtree are returned, "dog" excluded [OK]

Hint: DFS from prefix node returns only words in that subtree - "dog" is in a different branch [OK]

Common Mistakes:

• Including 'dog' - it's in a completely separate Trie branch
• Returning only 'ca' - 'ca' itself is not marked IsEndOfWord
• Returning empty - prefix 'ca' exists in the Trie so DFS finds words

4. Identify the bug in this C# autocomplete prefix search:

bool StartsWith(string prefix) {
    TrieNode node = root;
    foreach (char c in prefix) {
        if (!node.Children.ContainsKey(c))
            return true;
        node = node.Children[c];
    }
    return false;
}

medium

A. Should return false when character not found, and true at the end

B. Should use a List instead of Dictionary for Children

C. Should check IsEndOfWord instead of ContainsKey

D. Should start from the last character of the prefix

Solution

1. Step 1: Trace the logic

   When a character is not found in Children, it means the prefix doesn't exist - should return false, not true. And after traversing all prefix characters successfully, the prefix exists - should return true.

2. Step 2: Identify the swap

   The return values are swapped: 'return true' and 'return false' are in each other's place.

3. Final Answer:

   Should return false when character not found, and true at the end -> Option A

4. Quick Check:

   Missing prefix char = false, full prefix traversed = true [OK]

Hint: If you can't follow the path, prefix is absent = false; if path complete = true [OK]

Common Mistakes:

• Thinking Dictionary is wrong - Dictionary is correct for Children
• Checking IsEndOfWord - StartsWith only checks if prefix path exists, not word end
• Starting from last character - prefix traversal always goes left to right

5. In a Trie-based autocomplete system with 100,000 words averaging 8 characters each, what is the time complexity to retrieve all words matching a prefix of length p, where k is the number of matching words and m is the average word length?

hard

A. O(n) where n is total number of words

B. O(p + k * m) - traverse prefix then DFS over matching words

C. O(p * n) - check prefix against each word

D. O(1) - Trie provides constant time autocomplete

Solution

1. Step 1: Cost of reaching prefix node

   Traversing p characters to reach the prefix node costs O(p).

2. Step 2: Cost of DFS to collect results

   DFS visits all nodes under the prefix subtree. With k matching words of average length m, this costs O(k * m) in the worst case.

3. Final Answer:

   O(p + k * m) -> Option B

4. Quick Check:

   Prefix traversal O(p) + result collection O(k*m) = O(p + k*m) [OK]

Hint: Trie autocomplete = prefix path O(p) + collect results O(k*m), independent of total word count n [OK]

Common Mistakes:

• O(n) - Trie doesn't scan all words, only the prefix subtree
• O(1) - result collection depends on number and length of matching words
• O(p*n) - that's the HashSet approach, not Trie