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tessl/npm-fastest-levenshtein

Fastest Levenshtein distance implementation in JS providing optimized algorithms for string edit distance calculations.

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tessl
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Public
Created
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Describes
npmpkg:npm/fastest-levenshtein@1.0.x

To install, run

npx @tessl/cli install tessl/npm-fastest-levenshtein@1.0.0

index.mddocs/

Fastest Levenshtein

Fastest Levenshtein provides the most performant JavaScript/TypeScript implementation of the Levenshtein distance algorithm. It calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another, essential for fuzzy string matching, spell checking, and data deduplication.

Package Information

  • Package Name: fastest-levenshtein
  • Package Type: npm
  • Language: TypeScript
  • Installation: npm install fastest-levenshtein

Core Imports

import { distance, closest } from "fastest-levenshtein";

For CommonJS:

const { distance, closest } = require("fastest-levenshtein");

For Deno:

import { distance, closest } from "https://deno.land/x/fastest_levenshtein/mod.ts";

Basic Usage

import { distance, closest } from "fastest-levenshtein";

// Calculate edit distance between two strings
const editDistance = distance("fast", "faster");
console.log(editDistance); // 2

// Find closest match from array of candidates
const bestMatch = closest("fast", ["slow", "faster", "fastest"]);
console.log(bestMatch); // "faster"

// Handle edge cases
console.log(distance("", "hello")); // 5 (length of non-empty string)
console.log(distance("same", "same")); // 0 (identical strings)

Architecture

The library is built around two highly optimized algorithms:

  • Myers 32-bit Algorithm: Bit-parallel algorithm for strings ≤ 32 characters, providing maximum performance for short strings
  • Myers X Algorithm: Block-wise algorithm for longer strings using efficient memory access patterns
  • Automatic Selection: The library automatically chooses the optimal algorithm based on string length

Capabilities

String Distance Calculation

Calculates the Levenshtein distance between two strings using optimized algorithms.

/**
 * Calculates the Levenshtein distance between two strings
 * @param a - First input string
 * @param b - Second input string
 * @returns The edit distance (minimum number of single-character edits needed)
 */
function distance(a: string, b: string): number;

Usage Examples:

import { distance } from "fastest-levenshtein";

// Basic usage
console.log(distance("kitten", "sitting")); // 3
console.log(distance("book", "back")); // 2

// Edge cases
console.log(distance("", "")); // 0 (both empty)
console.log(distance("hello", "")); // 5 (one empty)
console.log(distance("test", "test")); // 0 (identical)

// Performance is optimal for all string lengths
console.log(distance("short", "strings")); // Uses myers_32 algorithm
console.log(distance("very long string example", "another very long string")); // Uses myers_x algorithm

Closest String Matching

Finds the string in an array with the lowest edit distance to a target string.

/**
 * Finds the string in an array with the lowest edit distance to a target string
 * @param str - Target string to match against
 * @param arr - Array of candidate strings (readonly)
 * @returns The string from the array with the lowest edit distance
 */
function closest(str: string, arr: readonly string[]): string;

Usage Examples:

import { closest } from "fastest-levenshtein";

// Find best match for typos/fuzzy search
const candidates = ["apple", "application", "apply", "appreciate"];
console.log(closest("aple", candidates)); // "apple"

// Spell checking
const dictionary = ["hello", "world", "help", "held"];
console.log(closest("helo", dictionary)); // "hello"

// Case sensitive matching
console.log(closest("Hello", ["hello", "HELLO", "Hello"])); // "Hello" (exact match)

// Handles ties by returning first occurrence
const colors = ["red", "blue", "green"];
console.log(closest("r", colors)); // "red" (first with distance 2)