Rabin Karp String Matching in DSA Python - Execution Trace

Concept Flow - Rabin Karp String Matching

Start

↓

Compute pattern hash

↓

Compute initial text window hash

↓

For each text window

↓

Compare hashes

↓

Check exact chars

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Slide window by 1

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Repeat until end of text

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End

The algorithm computes a hash for the pattern and for each window of text. It compares hashes first, then characters if hashes match, sliding the window until the end.

Execution Sample

DSA Python

def rabin_karp(text, pattern):
    p_len = len(pattern)
    p_hash = hash(pattern)
    for i in range(len(text) - p_len + 1):
        window = text[i:i+p_len]
        if hash(window) == p_hash and window == pattern:
            print(f"Match at index {i}")

This code slides over the text, compares hash of each window with pattern hash, then confirms by direct string comparison.

Execution Table

Step	Operation	Current Window	Pattern Hash	Window Hash	Hash Match?	Exact Match?	Action	Visual State
1	Compute pattern hash	-	hash('abc')=963	-	-	-	-	Pattern hash=963
2	Compute initial window hash	text[0:3]='abc'	963	hash('abc')=963	Yes	-	-	Window='abc' hash=963
3	Compare exact strings	abc	963	963	Yes	Yes	Report match at index 0	Match at index 0
4	Slide window by 1	text[1:4]='bcd'	963	hash('bcd')=964	No	-	No match	Window='bcd' hash=964
5	Slide window by 1	text[2:5]='cde'	963	hash('cde')=965	No	-	No match	Window='cde' hash=965
6	Slide window by 1	text[3:6]='def'	963	hash('def')=966	No	-	No match	Window='def' hash=966
7	End	-	963	-	-	-	-	No more windows

💡 Reached end of text windows; all possible matches checked.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 4	After Step 5	After Step 6	Final
p_hash	-	963	963	963	963	963	963
window	-	-	'abc'	'bcd'	'cde'	'def'	-
window_hash	-	-	963	964	965	966	-
current_index	-	-	0	1	2	3	-

Key Moments - 3 Insights

Why do we compare the actual strings after hash match?

Because different strings can have the same hash (hash collision). The execution_table rows 3 and 4 show hash matches but exact string check confirms real match.

How does sliding the window update the hash efficiently?

In this simple example, hash is recomputed fully each time (rows 4-6). In optimized Rabin-Karp, rolling hash updates by removing left char and adding right char.

Why does the loop stop at len(text) - len(pattern) + 1?

Because windows beyond that length would be shorter than the pattern and cannot match. The exit at row 7 shows no more windows to check.

Visual Quiz - 3 Questions

Look at the execution_table, what is the window hash at step 4?

A 963

B 964

C 965

D 966

💡 Hint

Check the 'Window Hash' column at step 4 in the execution_table.

At which step does the algorithm report a match?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the 'Action' column for when 'Report match' happens.

If the pattern was 'bcd', at which step would the first match be reported?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Check the 'Current Window' and 'Action' columns to see when 'bcd' window appears.

Concept Snapshot

Rabin Karp String Matching:
- Compute hash of pattern
- Slide window over text, compute window hash
- If hashes match, compare strings
- Report match if exact
- Efficient with rolling hash (not shown here)
- Stops when windows run out

Full Transcript

Rabin Karp string matching works by first computing a hash value for the pattern. Then it slides a window of the same length over the text, computing the hash for each window. If the window hash matches the pattern hash, it compares the actual strings to confirm a match. This avoids unnecessary string comparisons. The process repeats until all possible windows in the text are checked. The example code computes full hashes each time, but optimized versions use rolling hash to update hashes efficiently. The execution table shows each step, the current window, hashes, and actions taken. Matches are reported only after confirming exact string equality. The loop stops when no more windows of pattern length remain in the text.