Project 3: Aho-Corasick Multi-Pattern Matching

An Aho-Corasick automaton that can search for multiple patterns simultaneously in a single pass through the text. This is what fgrep and antivirus scanners use.

Quick Reference

Attribute Value
Primary Language C
Alternative Languages Rust, C++, Go
Difficulty Level 3: Advanced
Time Estimate 2 weeks
Knowledge Area Algorithms / Automata Theory
Tooling Custom Implementation
Prerequisites Project 2, understanding of tries and FSMs

What You Will Build

An Aho-Corasick automaton that can search for multiple patterns simultaneously in a single pass through the text. This is what fgrep and antivirus scanners use.

Why It Matters

This project builds core skills that appear repeatedly in real-world systems and tooling.

Core Challenges

  • Trie construction → maps to prefix tree data structures
  • Failure link computation → maps to KMP failure function generalization
  • Output function (suffix links) → maps to handling overlapping patterns
  • State machine execution → maps to automata theory

Key Concepts

  • Trie Data Structure: “Introduction to Algorithms” Chapter 12 - CLRS
  • Failure Links: The key innovation—where to go on mismatch (like KMP’s failure function)
  • Finite State Machines: “Introduction to the Theory of Computation” Chapter 1 - Sipser
  • Original Paper: “Efficient string matching: An aid to bibliographic search” - Aho & Corasick (1975)

Real-World Outcome

$ ./aho_corasick keywords.txt document.txt
Loaded 1000 patterns from keywords.txt
Searching document.txt (5MB)...
Found "error" at position 1234
Found "warning" at position 1235
Found "error" at position 5678
...
Total: 4523 matches
Search completed in 45ms

Automaton visualization:
  States: 4523
  Transitions: 12456
  Failure links: 4522

Benchmark:
  1000 patterns, 5MB file:
    Naive (run each pattern): 12,000ms
    Aho-Corasick: 45ms
    Speedup: 266x!

Implementation Guide

  1. Reproduce the simplest happy-path scenario.
  2. Build the smallest working version of the core feature.
  3. Add input validation and error handling.
  4. Add instrumentation/logging to confirm behavior.
  5. Refactor into clean modules with tests.

Milestones

  • Milestone 1: Minimal working program that runs end-to-end.
  • Milestone 2: Correct outputs for typical inputs.
  • Milestone 3: Robust handling of edge cases.
  • Milestone 4: Clean structure and documented usage.

Validation Checklist

  • Output matches the real-world outcome example
  • Handles invalid inputs safely
  • Provides clear errors and exit codes
  • Repeatable results across runs

References

  • Main guide: TEXT_SEARCH_TOOLS_DEEP_DIVE.md
  • “Introduction to Algorithms” by CLRS