Trie vs Hash Map for Prefix Matching in DSA C++ - Compare & Choose

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Prefix Search Using Trie

What is the output of the following C++ code that inserts words into a Trie and checks if a prefix exists?

DSA C++

#include <iostream>
#include <unordered_map>
using namespace std;

struct TrieNode {
    unordered_map<char, TrieNode*> children;
    bool isEnd = false;
};

class Trie {
public:
    TrieNode* root;
    Trie() { root = new TrieNode(); }

    void insert(const string& word) {
        TrieNode* node = root;
        for (char c : word) {
            if (!node->children.count(c))
                node->children[c] = new TrieNode();
            node = node->children[c];
        }
        node->isEnd = true;
    }

    bool startsWith(const string& prefix) {
        TrieNode* node = root;
        for (char c : prefix) {
            if (!node->children.count(c))
                return false;
            node = node->children[c];
        }
        return true;
    }
};

int main() {
    Trie trie;
    trie.insert("apple");
    trie.insert("app");
    cout << (trie.startsWith("app") ? "Yes" : "No") << endl;
    cout << (trie.startsWith("apl") ? "Yes" : "No") << endl;
    return 0;
}

A

Yes
Yes

B

No
Yes

C

Yes
No

D

No
No

💡 Hint

Think about whether the prefix exists in the inserted words.

The prefix "app" exists because both "apple" and "app" start with it, so output is "Yes". The prefix "apl" does not exist in any inserted word, so output is "No".

🧠 Conceptual

intermediate

Why Use Trie Over Hash Map for Prefix Matching?

Which of the following is the main advantage of using a Trie over a Hash Map for prefix matching?

A Hash Map uses less memory than Trie for prefix matching.

B Trie allows efficient prefix search without storing all prefixes explicitly.

C Trie requires less code to implement than Hash Map.

D Hash Map can store prefixes faster than Trie.

💡 Hint

Think about how prefix search works in both data structures.

Trie stores characters in a tree structure allowing prefix search by traversing nodes, without storing all prefixes explicitly. Hash Map requires storing all prefixes as keys, which is less efficient.

❓ Predict Output

advanced

Output of Prefix Count Using Hash Map

What is the output of this C++ code that counts how many words start with a prefix using a Hash Map?

DSA C++

#include <iostream>
#include <unordered_map>
#include <vector>
using namespace std;

int main() {
    vector<string> words = {"cat", "car", "cart", "dog"};
    unordered_map<string, int> prefixCount;

    for (const string& word : words) {
        for (int i = 1; i <= word.size(); ++i) {
            prefixCount[word.substr(0, i)]++;
        }
    }

    cout << prefixCount["car"] << endl;
    cout << prefixCount["ca"] << endl;
    cout << prefixCount["do"] << endl;
    cout << prefixCount["c"] << endl;
    return 0;
}

A

1
2
1
2

B

2
2
1
2

C

3
3
1
3

D

2
3
1
3

💡 Hint

Count how many words start with each prefix.

"car" is prefix of "car" and "cart" → 2
"ca" is prefix of "cat", "car", "cart" → 3
"do" is prefix of "dog" → 1
"c" is prefix of "cat", "car", "cart" → 3

🔧 Debug

advanced

Identify the Error in Trie Prefix Search

What error will this C++ code produce when checking prefix existence in a Trie?

DSA C++

#include <iostream>
#include <unordered_map>
using namespace std;

struct TrieNode {
    unordered_map<char, TrieNode*> children;
    bool isEnd = false;
};

class Trie {
public:
    TrieNode* root;
    Trie() { root = new TrieNode(); }

    void insert(const string& word) {
        TrieNode* node = root;
        for (char c : word) {
            if (!node->children[c])
                node->children[c] = new TrieNode();
            node = node->children[c];
        }
        node->isEnd = true;
    }

    bool startsWith(const string& prefix) {
        TrieNode* node = root;
        for (char c : prefix) {
            if (node->children.count(c) == 0)
                return false;
            node = node->children[c];
        }
        return true;
    }
};

int main() {
    Trie trie;
    trie.insert("hello");
    cout << (trie.startsWith("hel") ? "Yes" : "No") << endl;
    return 0;
}

A Runtime error due to accessing map with operator[] on missing key

B No error, outputs Yes

C Always returns false for any prefix

D Compilation error due to missing semicolon

💡 Hint

Check how operator[] behaves on unordered_map when key is missing.

Using operator[] on unordered_map with a missing key inserts a new entry with default value (nullptr here), so the check 'node->children[c] == nullptr' is true only after insertion, but operator[] itself modifies the map unexpectedly, which can cause logic errors or unexpected behavior.

🧠 Conceptual

expert

Memory Efficiency Comparison Between Trie and Hash Map for Prefix Matching

Which statement best describes memory usage when storing a large dictionary for prefix matching using Trie vs Hash Map?

A Trie uses less memory because it shares common prefixes, while Hash Map stores all prefixes separately.

B Hash Map uses less memory because it stores only full words as keys, not prefixes.

C Trie and Hash Map use about the same memory for prefix matching.

D Hash Map uses less memory because it compresses prefixes automatically.

💡 Hint

Think about how prefixes are stored and shared in each data structure.

Trie stores prefixes in shared nodes, so common prefixes are stored once, saving memory. Hash Map stores each prefix as a separate key, leading to higher memory usage.

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