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

Trie vs Hash Map for Prefix Matching

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

🎯 Goal: You will create a small program that stores contact names and then finds all names starting with a given prefix using two methods: a Trie and a Hash Map. This will help you understand how these data structures work for prefix matching.

📋 What You'll Learn

Create a list of contact names

Create a prefix string to search

Implement prefix matching using a Trie

Implement prefix matching using a Hash Map

Print the matching contacts from both methods

💡 Why This Matters

🌍 Real World

Search features in phone contacts, autocomplete in search engines, and dictionary word lookups use prefix matching to quickly find relevant results.

💼 Career

Understanding Tries and Hash Maps for prefix matching is useful for software engineers working on search engines, text editors, and any application requiring fast lookup of strings.

1

Create the list of contacts

Create a std::vector<std::string> called contacts with these exact names: "Alice", "Bob", "Alfred", "Bobby", and "Alex".

DSA C++

# Create the contacts vector with the given names
# Your code here

Hint

Use std::vector<std::string> and initialize it with the exact names inside curly braces.

2

Create the prefix to search

Create a std::string variable called prefix and set it to "Al".

DSA C++

#include 
#include 
#include 

std::vector contacts = {"Alice", "Bob", "Alfred", "Bobby", "Alex"};

# Create the prefix string
# Your code here

Hint

Use std::string prefix = "Al"; to create the prefix variable.

3

Implement prefix matching using a Trie

Implement a simple Trie class with insert and startsWith methods. Insert all contacts into the Trie. Then create a function getContactsWithPrefixTrie that returns a std::vector<std::string> of all contacts starting with prefix using the Trie.

DSA C++

#include 
#include 
#include 

std::vector contacts = {"Alice", "Bob", "Alfred", "Bobby", "Alex"};

std::string prefix = "Al";

// Implement Trie class and getContactsWithPrefixTrie function
// Your code here

Hint

Build a Trie with insert and startsWithNode methods. Use a depth-first search to collect all words starting with the prefix.

4

Print matching contacts from Trie and Hash Map

Print the contacts starting with prefix found by getContactsWithPrefixTrie. Then create a std::unordered_map<std::string, bool> called contactMap with all contacts as keys. Use a loop to find and print all contacts starting with prefix from contactMap.

DSA C++

#include 
#include 
#include 
#include 

std::vector contacts = {"Alice", "Bob", "Alfred", "Bobby", "Alex"};

std::string prefix = "Al";

// Trie and helper functions already defined above

int main() {
    // Print contacts from Trie
    // Your code here

    // Create contactMap and print contacts starting with prefix
    // Your code here

    return 0;
}

Hint

Use getContactsWithPrefixTrie to get and print contacts from the Trie. Then create a std::unordered_map<std::string, bool> with all contacts and print those starting with prefix by checking if pair.first.find(prefix) == 0.

Practice

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

easy

A. Linked List, because it stores elements in order

B. Hash Map, because it stores keys with constant time lookup

C. Array, because it allows sequential search of prefixes

D. Trie, because it stores prefixes explicitly and allows fast prefix search

Solution

1. Step 1: Understand prefix search requirements

   Prefix search needs to quickly find if any string starts with a given prefix.

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

   Trie stores prefixes explicitly, allowing fast traversal to check prefixes. Hash Map stores full keys, so prefix search requires checking many keys or extra processing.

3. Final Answer:

   Trie, because it stores prefixes explicitly and allows fast prefix search -> Option D

4. Quick Check:

   Prefix search = Trie [OK]

Hint: Prefix search is fastest with Trie due to explicit prefix nodes [OK]

Common Mistakes:

• Thinking hash map can do prefix search in constant time
• Confusing prefix search with exact key search
• Ignoring the need to check multiple keys in hash map

2. Which of the following is the correct way to declare a Trie node in C++ for lowercase English letters?

easy

A. struct TrieNode { TrieNode* children[26]; bool isEnd; };

B. struct TrieNode { map children; bool isEnd; };

C. struct TrieNode { vector<int> children; bool isEnd; };

D. struct TrieNode { unordered_map children; bool isEnd; };

Solution

1. Step 1: Identify correct children type for fixed alphabet

   For lowercase English letters, fixed size array of 26 pointers is efficient.

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

   struct TrieNode { TrieNode* children[26]; bool isEnd; }; uses TrieNode* children[26], which is correct. struct TrieNode { map children; bool isEnd; }; uses map, which is valid but slower. Options A and C use incorrect types.

3. Final Answer:

   struct TrieNode { TrieNode* children[26]; bool isEnd; }; -> Option A

4. Quick Check:

   Fixed alphabet Trie node = array of 26 pointers [OK]

Hint: Use array of 26 pointers for fixed lowercase letters [OK]

Common Mistakes:

• Using vector<int> instead of pointers for children
• Using unordered_map with wrong key type
• Confusing map with array for fixed alphabets

3. What will be the output of the following C++ code snippet?

struct TrieNode {
  TrieNode* children[26] = {nullptr};
  bool isEnd = false;
};

TrieNode* root = new TrieNode();

// Insert "cat"
string word = "cat";
TrieNode* node = root;
for (char c : word) {
  int index = c - 'a';
  if (!node->children[index])
    node->children[index] = new TrieNode();
  node = node->children[index];
}
node->isEnd = true;

// Check prefix "ca"
node = root;
string prefix = "ca";
bool found = true;
for (char c : prefix) {
  int index = c - 'a';
  if (!node->children[index]) {
    found = false;
    break;
  }
  node = node->children[index];
}
cout << (found ? "Prefix exists" : "Prefix not found") << endl;

medium

A. Prefix exists

B. Prefix not found

C. Compilation error

D. Runtime error

Solution

1. Step 1: Insert "cat" into Trie

   Each character 'c', 'a', 't' creates nodes if missing. After insertion, path for "cat" exists.

2. Step 2: Check prefix "ca"

   Traverse nodes for 'c' and 'a'. Both nodes exist, so prefix is found.

3. Final Answer:

   Prefix exists -> Option A

4. Quick Check:

   Inserted "cat" contains prefix "ca" [OK]

Hint: Traverse nodes for prefix; if all exist, prefix exists [OK]

Common Mistakes:

• Assuming prefix must be a full word
• Forgetting to check each character node
• Confusing isEnd with prefix existence

4. Identify the bug in this C++ Trie prefix search code snippet:

bool startsWith(TrieNode* root, const string& prefix) {
  TrieNode* node = root;
  for (char c : prefix) {
    int idx = c - 'a';
    if (node->children[idx] == nullptr)
      return false;
    node = node->children[idx];
  }
  return node->isEnd;
}

medium

A. Index calculation is wrong, should be c - 'A'

B. Should return true after loop, not node->isEnd

C. Missing null check for root pointer

D. Loop should iterate over root->children, not prefix

Solution

1. Step 1: Understand prefix search logic

   Prefix search should return true if all prefix nodes exist, regardless of isEnd.

2. Step 2: Identify incorrect return statement

   Returning node->isEnd checks if prefix is a full word, not just a prefix. It should return true after loop.

3. Final Answer:

   Should return true after loop, not node->isEnd -> Option B

4. Quick Check:

   Prefix search returns true if prefix nodes exist [OK]

Hint: Prefix search returns true if path exists, ignore isEnd [OK]

Common Mistakes:

• Confusing prefix existence with full word existence
• Incorrect index calculation for characters
• Not checking for null root pointer

5. You need to implement a system that supports fast prefix matching for millions of strings with dynamic insertions and lookups. Which approach is best and why? Option A: Use a sorted array of strings and binary search for prefixes. Option B: Use a Hash Map storing all strings and check prefixes by scanning keys. Option C: Use a Trie for prefix matching. Option D: Use a linked list of strings and linear search for prefixes.

hard

A. Sorted array, because binary search is fast for prefix queries

B. Hash Map, because it has constant time lookup for exact keys

C. Trie, because it provides efficient prefix search and dynamic insertions

D. Linked list, because it is simple to implement

Solution

1. Step 1: Analyze requirements for millions of strings with dynamic insertions

   Dynamic insertions require efficient updates; prefix matching needs fast queries.

2. Step 2: Compare data structures for prefix matching and insertions

   Trie supports fast prefix search and efficient insertions. Hash Map is fast for exact keys but slow for prefix. Sorted array requires costly insertions. Linked list is slow for both.

3. Final Answer:

   Trie, because it provides efficient prefix search and dynamic insertions -> Option C

4. Quick Check:

   Dynamic prefix search = Trie [OK]

Hint: For dynamic prefix search, Trie beats hash map and arrays [OK]

Common Mistakes:

• Choosing hash map for prefix search without scanning keys
• Ignoring insertion cost in sorted arrays
• Underestimating linked list search time