Rabin Karp String Matching in DSA Java - Time & Space Complexity

Time Complexity: Rabin Karp String Matching

O(n + m)

Understanding Time Complexity

We want to understand how the Rabin Karp algorithm's running time changes as the text and pattern sizes grow.

Specifically, how many steps does it take to find a pattern in a text?

Scenario Under Consideration

Analyze the time complexity of the following Rabin Karp code snippet.

public boolean rabinKarp(String text, String pattern) {
    int n = text.length();
    int m = pattern.length();
    int patternHash = hash(pattern);
    int textHash = hash(text.substring(0, m));
    for (int i = 0; i <= n - m; i++) {
        if (patternHash == textHash && text.substring(i, i + m).equals(pattern)) {
            return true;
        }
        if (i < n - m) {
            textHash = rollHash(textHash, text.charAt(i), text.charAt(i + m));
        }
    }
    return false;
}
    

This code slides a window over the text, compares hash values, and checks for matches.

Identify Repeating Operations

Look for loops and repeated checks.

• Primary operation: Loop sliding over the text from start to end minus pattern length.
• How many times: Approximately n - m + 1 times, where n is text length and m is pattern length.

How Execution Grows With Input

As the text gets longer, the number of windows to check grows roughly linearly.

Input Size (n)	Approx. Operations
10	About 10 checks
100	About 100 checks
1000	About 1000 checks

Pattern length m is usually smaller, so the main growth depends on text length n.

Final Time Complexity

Time Complexity: O(n + m) on average

This means the algorithm checks the text in a way that grows mostly in a straight line with the text and pattern sizes.

Common Mistake

[X] Wrong: "Rabin Karp always runs in O(n) time no matter what."

[OK] Correct: In the worst case, many hash collisions cause repeated substring checks, making it slower, closer to O(n*m).

Interview Connect

Understanding Rabin Karp's time complexity helps you explain how hashing speeds up searching and when it might slow down.

Self-Check

"What if we used a different hash function that never caused collisions? How would the time complexity change?"

Practice

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

easy

A. It sorts the text and pattern before matching

B. It compares each character one by one without hashing

C. It uses hashing to find pattern matches efficiently

D. It uses recursion to find the pattern

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.

3. Final Answer:

   It uses hashing to find pattern matches efficiently -> Option C

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 without hashing
• Confusing it with brute force matching
• Assuming it sorts strings before matching

2. Which of the following is the correct way to calculate the rolling hash in Rabin Karp for the next substring?

easy

A. new_hash = (old_hash - old_char * high_base) * base + new_char

B. new_hash = old_hash + old_char + new_char

C. new_hash = old_hash * old_char - new_char

D. new_hash = old_hash / base + new_char * high_base

Solution

1. Step 1: Recall rolling hash formula

   Rolling hash removes the old char's contribution and adds the new char's value.

2. Step 2: Apply formula correctly

   It subtracts old_char * high_base, multiplies by base, then adds new_char.

3. Final Answer:

   new_hash = (old_hash - old_char * high_base) * base + new_char -> Option A

4. Quick Check:

   Rolling hash formula = new_hash = (old_hash - old_char * high_base) * base + new_char [OK]

Hint: Subtract old char, multiply, then add new char for rolling hash [OK]

Common Mistakes:

• Adding old_char instead of subtracting
• Dividing instead of multiplying by base
• Ignoring the high_base multiplier

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

String text = "ababc";
String pattern = "abc";
List<Integer> result = rabinKarpSearch(text, pattern);
System.out.println(result);

Assume rabinKarpSearch returns a list of starting indices where pattern matches.

medium

A. []

B. [0, 2]

C. [1]

D. [2]

Solution

1. Step 1: Identify pattern occurrences in text

   Text: "ababc"; pattern: "abc"; check substrings of length 3: "aba"(0), "bab"(1), "abc"(2).

2. Step 2: Find matching indices

   Only substring starting at index 2 matches "abc".

3. Final Answer:

   [2] -> Option D

4. Quick Check:

   Pattern found at index 2 only [OK]

Hint: Check substrings of pattern length for exact matches [OK]

Common Mistakes:

• Including partial matches like "aba" or "bab"
• Assuming multiple matches without checking substring
• Confusing indices starting at 1 instead of 0

4. Consider this buggy Java snippet for Rabin Karp pattern search:

int base = 256;
int prime = 101;
int highBase = (int) Math.pow(base, pattern.length());
// ... rest of code
// rolling hash update:
currentHash = (currentHash - text.charAt(i) * highBase) * base + text.charAt(i + pattern.length());
currentHash %= prime;

What is the main bug here?

medium

A. highBase should be base^(pattern.length() - 1), not base^pattern.length()

B. Modulo operation should be before subtraction

C. Rolling hash should add old char, not subtract

D. Base should be 128, not 256

Solution

1. Step 1: Check highBase calculation

   High base is used to remove the leftmost character's contribution, so it should be base^(pattern length - 1).

2. Step 2: Identify incorrect power usage

   Using base^(pattern length) removes too much, causing wrong hash updates.

3. Final Answer:

   highBase should be base^(pattern.length() - 1), not base^pattern.length() -> Option A

4. Quick Check:

   Correct highBase power fixes rolling hash [OK]

Hint: Use base^(pattern length - 1) for highBase in rolling hash [OK]

Common Mistakes:

• Using base^pattern.length() instead of base^(pattern.length()-1)
• Ignoring modulo after subtraction
• Confusing addition and subtraction in rolling hash

5. You want to find all occurrences of pattern "aaa" in text "aaaaaa" using Rabin Karp. How many matches will the algorithm find and why?

hard

A. 2, because it skips overlapping matches

B. 4, because overlapping matches are counted

C. 3, because it counts only non-overlapping matches

D. 1, because it stops after first match

Solution

1. Step 1: Identify all substrings of length 3

   Text "aaaaaa" has substrings: "aaa"(0), "aaa"(1), "aaa"(2), "aaa"(3).

2. Step 2: Count overlapping matches

   All these substrings match pattern "aaa", so 4 matches found including overlaps.

3. Final Answer:

   4, because overlapping matches are counted -> Option B

4. Quick Check:

   Overlapping matches count = 4 [OK]

Hint: Rabin Karp counts overlapping matches by sliding one char at a time [OK]

Common Mistakes:

• Assuming matches cannot overlap
• Stopping search after first match
• Counting only non-overlapping matches