Rabin Karp String Matching in DSA C++ - Build from Scratch

Rabin Karp String Matching

📖 Scenario: Imagine you are building a simple search tool that finds a small word inside a big text. This is like looking for a friend's name in a long list of names quickly.

🎯 Goal: You will build a program using the Rabin Karp algorithm to find if a small pattern exists inside a larger text. You will use hashing to speed up the search.

📋 What You'll Learn

Create variables for the text and pattern strings

Set up variables for hashing like base and modulus

Implement the Rabin Karp algorithm to find the pattern in the text

Print the starting index of the pattern if found, or -1 if not found

💡 Why This Matters

🌍 Real World

Searching for a word or phrase inside large documents or databases quickly.

💼 Career

Useful for software developers working on text editors, search engines, or data processing tools.

1

Create the text and pattern strings

Create a string variable called text with the value "abracadabra" and a string variable called pattern with the value "cada".

DSA C++

// Your code here

Hint

Use string type and assign the exact values given.

2

Set up hashing variables

Add an int variable called base and set it to 256. Add an int variable called modulus and set it to 101.

DSA C++

string text = "abracadabra";
string pattern = "cada";
// Your code here

Hint

Use int type for both variables and assign the exact numbers.

3

Implement Rabin Karp algorithm to find the pattern

Write a function called rabinKarpSearch that takes text, pattern, base, and modulus as parameters and returns the starting index of the first occurrence of pattern in text. Use rolling hash technique inside the function.

DSA C++

string text = "abracadabra";
string pattern = "cada";
int base = 256;
int modulus = 101;

// Your code here

Hint

Use rolling hash to compare pattern hash with text hash and check characters if hashes match.

4

Print the result of the search

Call the function rabinKarpSearch with text, pattern, base, and modulus and print the returned index.

DSA C++

string text = "abracadabra";
string pattern = "cada";
int base = 256;
int modulus = 101;

int rabinKarpSearch(const string& text, const string& pattern, int base, int modulus) {
    int n = text.size();
    int m = pattern.size();
    if (m > n) return -1;

    int patternHash = 0;
    int textHash = 0;
    int h = 1;

    for (int i = 0; i < m - 1; i++) {
        h = (h * base) % modulus;
    }

    for (int i = 0; i < m; i++) {
        patternHash = (base * patternHash + pattern[i]) % modulus;
        textHash = (base * textHash + text[i]) % modulus;
    }

    for (int i = 0; i <= n - m; i++) {
        if (patternHash == textHash) {
            int j = 0;
            for (; j < m; j++) {
                if (text[i + j] != pattern[j])
                    break;
            }
            if (j == m) return i;
        }
        if (i < n - m) {
            textHash = (base * (textHash - text[i] * h) + text[i + m]) % modulus;
            if (textHash < 0) textHash += modulus;
        }
    }
    return -1;
}

// Your code here

Hint

Call the function and print the result using cout.

Practice

1. What is the main idea behind the Rabin Karp String Matching algorithm?

easy

A. Compare each character one by one without hashing

B. Use hashing to find pattern matches efficiently

C. Sort the text and pattern before matching

D. Use recursion to find all pattern occurrences

Solution

1. Step 1: Understand Rabin Karp's approach

   The algorithm uses a hash function to convert the pattern and text substrings into numbers.

2. Step 2: Use hashing for efficient matching

   By comparing hash values instead of characters, it quickly finds potential matches and then verifies them.

3. Final Answer:

   Use hashing to find pattern matches efficiently -> Option B

4. Quick Check:

   Hashing = Efficient matching [OK]

Hint: Remember: Rabin Karp uses hashing for fast pattern search [OK]

Common Mistakes:

• Thinking it compares characters directly always
• Assuming it sorts strings before matching
• Confusing it with recursive search methods

2. Which of the following is the correct way to update the rolling hash in Rabin Karp when moving from substring s[i..i+m-1] to s[i+1..i+m]?

easy

A. Add leftmost char's hash, multiply by base, subtract new char's hash

B. Multiply entire hash by base and add new char's hash only

C. Remove leftmost char's hash, multiply by base, add new char's hash

D. Recalculate hash from scratch for new substring

Solution

1. Step 1: Understand rolling hash update

   Remove the leftmost character's contribution, multiply remaining hash by base, then add new character's value.

2. Step 2: Apply formula for next substring

   This keeps hash calculation efficient without recomputing from scratch.

3. Final Answer:

   Remove leftmost char's hash, multiply by base, add new char's hash -> Option C

4. Quick Check:

   Rolling hash update = Remove + multiply + add [OK]

Hint: Rolling hash = remove old char + shift + add new char [OK]

Common Mistakes:

• Recalculating hash fully each time
• Adding instead of removing old char's hash
• Multiplying after adding new char instead of before

3. What will be the output of the following C++ code snippet using Rabin Karp to find pattern "abc" in text "ababc"?

std::string text = "ababc";
std::string pattern = "abc";
// Assume correct Rabin Karp implementation
std::vector<int> matches = rabinKarpSearch(text, pattern);
for (int pos : matches) std::cout << pos << " ";

medium

A. 1

B. 0 2

C. No output

D. 2

Solution

1. Step 1: Identify pattern occurrences in text

   Pattern "abc" appears starting at index 2 in "ababc" (0-based indexing).

2. Step 2: Confirm no other matches

   Substring at index 0 is "aba", not "abc"; index 1 is "bab", not "abc".

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Pattern found at index 2 [OK]

Hint: Check substring positions carefully for exact matches [OK]

Common Mistakes:

• Assuming match at index 0 incorrectly
• Confusing overlapping substrings
• Using 1-based indexing instead of 0-based

4. Identify the bug in this Rabin Karp rolling hash update snippet:

hash = (hash - text[i] * pow(base, m-1)) * base + text[i+m];

What is the likely problem?

medium

A. Missing modulo operation causing overflow

B. Wrong power used in subtraction term

C. Adding instead of subtracting old char's hash

D. Incorrect base multiplier order

Solution

1. Step 1: Check arithmetic operations in rolling hash

   The formula subtracts old char's hash, multiplies by base, adds new char's value.

2. Step 2: Identify missing modulo usage

   Without modulo, hash values can overflow integer limits causing wrong results.

3. Final Answer:

   Missing modulo operation causing overflow -> Option A

4. Quick Check:

   Modulo needed to prevent overflow [OK]

Hint: Always apply modulo after each hash update [OK]

Common Mistakes:

• Ignoring modulo leading to wrong hash values
• Using wrong power exponent
• Mixing addition and subtraction signs

5. Given a large text and multiple patterns, how can Rabin Karp be adapted to efficiently find all pattern occurrences?

hard

A. Use a combined hash set of all patterns and check text substrings against it

B. Run Rabin Karp separately for each pattern on the entire text

C. Sort all patterns and use binary search on text substrings

D. Use recursion to match patterns one by one

Solution

1. Step 1: Understand multi-pattern matching challenge

   Running Rabin Karp separately for each pattern is inefficient for many patterns.

2. Step 2: Use combined hashing approach

   Hash all patterns and store in a set. Then slide over text, hashing substrings and checking set membership.

3. Step 3: Efficiently find all matches

   This reduces repeated work and speeds up multi-pattern search.

4. Final Answer:

   Use a combined hash set of all patterns and check text substrings against it -> Option A

5. Quick Check:

   Combined hashing = efficient multi-pattern search [OK]

Hint: Hash all patterns once, then check text substrings [OK]

Common Mistakes:

• Running separate searches for each pattern
• Sorting patterns instead of hashing
• Using recursion unnecessarily