Master Fast String Matching with KMP & Rabin-Karp Algorithms

Have you ever needed to search for a word or pattern within a huge chunk of text—only to watch your program slow to a crawl? Whether you’re building search functionality, a plagiarism checker, or log analyzers, string matching is a critical task in software engineering.

Today’s newsletter dives into two powerful and efficient string searching algorithms: Knuth-Morris-Pratt (KMP) and Rabin-Karp. These are not just computer science theory—they’re practical tools that can drastically improve the performance of pattern-matching tasks in your code.

Let’s explore what they are, how they work, and when to use each.

Why Not Just Use Naive Search?

In naive string matching, you check every possible position in the text to see if the pattern matches. While simple, this brute-force method takes O(nm) time in the worst case, where n is the length of the text and m is the length of the pattern. Not very scalable.

That’s where smarter algorithms like KMP and Rabin-Karp come into play.

Knuth-Morris-Pratt (KMP) Algorithm

What It Does

KMP improves search time by avoiding unnecessary comparisons. When a mismatch occurs, it doesn't start the search all over again; it uses information from previous matches to skip ahead intelligently.

How It Works

KMP preprocesses the pattern to create a Longest Prefix Suffix (LPS) array, which tells the algorithm how much to shift the pattern when a mismatch occurs. This avoids backtracking on the text.

Time Complexity

• Preprocessing: O(m)

• Search: O(n)

Use When

• You need consistent linear-time performance.

• You’re searching repeatedly in a long text with similar patterns.

• Worst-case performance matters (e.g., embedded systems, critical path functions).

Real-World Use Cases

• Pattern search in compilers.

• Text editors like VS Code or Sublime implementing “Find All.”

• Intrusion detection systems scanning logs for signatures.

Rabin-Karp Algorithm

What It Does

Rabin-Karp uses a rolling hash function to compare hash values of the pattern and substrings in the text. Instead of character-by-character comparison, it compares hash values first—only doing a deep comparison when hashes match.

How It Works

• Compute a hash of the pattern.

• Slide a window of the same length across the text and compute the hash.

• If hash values match, do a direct comparison to confirm (to handle hash collisions).

Time Complexity

• Average Case: O(n + m)

• Worst Case: O(nm) due to hash collisions

Use When

• You want to search multiple patterns in a single pass (Rabin-Karp can be modified for this).

• You’re dealing with large texts and can tolerate a few false positives.

• Hashing is efficient for your use case (e.g., numerical data).

Real-World Use Cases

• Plagiarism detection tools.

• Substring search in large logs.

• Virus scanners comparing multiple signature strings.

KMP vs. Rabin-Karp – When to Use What?

TL;DR – Which One to Choose?

• Choose KMP when:

  • You need guaranteed performance.

  • The cost of extra preprocessing is acceptable.

  • You’re working with deterministic matching.

• Choose Rabin-Karp when:

  • You need to check for many patterns at once.

  • You can afford occasional collisions and verify with direct checks.

  • You want a hashing-based, lightweight implementation.

Hands-On Tip:

Try implementing both and benchmarking them on a large dataset—like searching patterns in a book or codebase. You’ll notice the performance edge they bring over naive methods, especially as the input scales.

Knowing the right algorithm isn’t just about passing interviews—it’s about writing performant, scalable systems. String matching appears in places you least expect—log analysis, autocomplete, bioinformatics, even UI search bars.

KMP and Rabin-Karp are not just “textbook” ideas—they’re tools you should have in your toolbox.

Fresh Breakthroughs and Bold Moves in Tech & AI

JetBrains Unveils Kotlin 2.2, Amper Build Tool, and AI Innovations at KotlinConf 2025. Link

• The latest version introduces features like guard conditions in when expressions, multi-dollar string interpolation, non-local break/continue, and context parameters. Future updates will include positional and name-based destructuring, enhanced nullability, and richer error messages.

• The K2 compiler, now the default in IntelliJ IDEA 2025.1, offers a 40% reduction in build times for large projects, enhancing developer productivity.

• JetBrains introduced Amper, an experimental build tool tailored for Kotlin and JVM projects. It provides clear configuration paths, IDE support, and is evolving towards supporting server-side development with frameworks like Ktor and Spring.

• JetBrains unveiled AI tools including Koog, a framework for building AI agents in Kotlin; Mellum, a Kotlin-optimized large language model for code completion; and Junie, an AI coding assistant for various Kotlin applications.

• Updates include a new KMP plugin for IntelliJ IDEA and Android Studio, Compose Multiplatform for iOS reaching stable status, and experimental Swift export capabilities, enhancing cross-platform development.

Happy coding,
– The Nullpointer Club Team