biegehydra

biegehydra / MyersBitParallelDotnet

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A high-throughput C# implementation of the Myers bit-parallel Levenshtein distance algorithm, optimized for ASCII patterns up to 64 characters

csharp dotnet fuzzy-matching fuzzy-search levenshtein
12
0
100% credibility
Found May 04, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C#
AI Summary

A C# library for high-speed computation of string similarity using an optimized edit distance algorithm, ideal for fuzzy matching short text patterns.

How It Works

1
📰 Discover Fast Word Matching

You find a handy tool that quickly tells how similar two words are, like spotting 'kitten' and 'sitting' as close matches.

2
📦 Add to Your Project

You easily bring it into your work space with a simple addition, no hassle.

3
Choose Matching Style
🔤
Case Sensitive

Treat 'Apple' and 'apple' as different to be precise.

🌡️
Case Insensitive

Ignore case so 'Apple' matches 'apple' naturally.

4
✨ Prepare Your Search Word

Set up one key word once, ready to check against many others super quickly.

5
🔍 Compare to Many Words

Run checks against lists of words, even limiting how different they can be to speed things up.

6
⚡ Get Instant Results

See exact similarity scores or distances right away, way faster than usual methods.

✅ Blazing Fast Searches

Your app now finds similar words in a flash, making searches smooth and efficient.

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Star Growth

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AI-Generated Review

What is MyersBitParallelDotnet?

This C# library delivers a high-throughput implementation of the Myers bit-parallel Levenshtein distance algorithm, optimized for ASCII patterns up to 64 characters. It slashes compute time for fuzzy string matching—one short query against thousands of candidates—via NuGet package with ready case-sensitive and case-insensitive engines. Users get exact edit distances or similarity ratios, plus threshold-based early exits for maxDist and required character filters.

Why is it gaining traction?

It crushes standard Wagner-Fischer DP with 5x-20x speedups on typical workloads, hitting 20x-200x when maxDist prunes distant matches and pattern prep reuses across candidates for zero-allocation scoring. Custom mappers handle case folding or punctuation collapse without hot-loop overhead, while benchmarks prove real gains over naive, banded, and rolling alternatives. Devs love the simple API: prepare once, query many.

Who should use this?

Backend engineers indexing product names, usernames, or city lists for fuzzy search in e-commerce or CRM apps. Data pipeline devs computing Levenshtein distances on short ASCII strings during ETL or deduping. .NET game devs matching player inputs against spellbooks or command lists under tight loops.

Verdict

Strong pick for high-throughput C# Levenshtein on short ASCII—docs, tests, and benchmarks are pro-level despite 11 stars and 1.0% credibility score signaling early maturity. Test it in your fuzzy matching bottleneck; pair with Unicode folds until native support lands.

(198 words)

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