fast-fuzzy

fast-fuzzy

Fast fuzzy-search utility

methodology

fast-fuzzy is a tiny, lightning-quick fuzzy-searching utility. The ranking algorithm is a modification of levenshtein distance proposed by Peter H. Sellers (paper). fast-fuzzy also uses the damerau-levenshtein distance by default, which, compared to normal levenshtein, punishes transpositions less.

Inputs are normalized before search. Normalization consists of standard utf8-normalization, followed by optionally taking the lowercase of a string, optionally removing non-word characters, and optionally flattening/trimming whitespace.

Inputs are scored from 0 to 1, where a higher score indicates a closer match. When searching, results are returned in descending order of score. Ties are broken by favoring the candidate whose length is closest to the length of the search term. This causes matches which are closer to exact full string matches to be effectively ranked higher in the case of a tie. Ties in length difference are broken by insertion order.

Lists of candidates are stored in a trie internally, which avoids doing redundant work on candidates with common prefixes. Additionally, when a subtree of the trie can be determined to have no string long enough to score > threshold, the entire subtree is skipped entirely. This can significantly improve search times compared with a bruteforce search.

A note about normalization

utf8 normalization is not optional, all strings will be normalized internally at minimum. Match positions, as a result, refer to positions within the normalized string. Match positions are, however, mapped back to the position in the string before whitespace and symbols are stripped out. Therefore, it is highly recommended that if one intends on using returned match data, you normalize the strings you intend to search by. This can be done by calling string.normalize().

exports

name	description	signature
fuzzy	fuzzy ranking algorithm; returns match strength	(term, candidate, options?) => score
search	for one-off searches; returns a sorted array of matches	(term, candidates, options?) => matches
Searcher	for searching the same set of candidates multiple times; caches normalization and key selection	N/A

Searcher methods

name	description	signature
constructor	supply the options and initial list of candidates	(candidates?, options?) => searcher
add	add new candidates to the list	(...candidates) => void
search	perform a search against the instance's candidates	(term, options?) => matches*

* allows overriding the threshold, returnMatchData, and useDamerau options

options

Searcher and search both take an options object for configuring behavior.

option	type	description	default
keySelector	Function	selects the string(s)* to search when candidates are objects	s => s
threshold	Number	the minimum score that can be returned	.6
ignoreCase	Bool	normalize case by calling toLower on input and pattern	true
ignoreSymbols	Bool	strip non-word symbols** from input	true
normalizeWhitespace	Bool	normalize and trim whitespace	true
returnMatchData	Bool	return match data***	false
useDamerau	Bool	use damerau-levenshtein distance	true

* if the keySelector returns an array, the candidate will take the score of the highest scoring key.

** `~!@#$%^&*()-=_+{}[]\|\;':",./<>?

*** in the form {item, original, key, score, match: {index, length}}

fuzzy accepts a subset of these options (excluding keySelector and threshold) with the same defaults.

examples

You can call fuzzy directly to get a match score for a single string

const {fuzzy} = require("fast-fuzzy");

fuzzy("hello", "hello world"); //returns 1
fuzzy("word", "hello world"); //returns .75

//pass in custom options
fuzzy("hello world", "hello  world"); //returns 1
fuzzy("hello world", "hello  world", {normalizeWhitespace: false}); //returns .90909090...

Use search to search a list of strings or objects

const {search} = require("fast-fuzzy");

search("abc", ["def", "bcd", "cde", "abc"]); //returns ["abc", "bcd"]

//pass in a keySelector to search for objects
search(
    "abc",
    [{name: "def"}, {name: "bcd"}, {name: "cde"}, {name: "abc"}],
    {keySelector: (obj) => obj.name},
);
//returns [{name: "abc"}, {name: "bcd"}]

//pass returnMatchData to receive the matchData for each result
search("abc", ["def", "bcd", "cde", "abc"], {returnMatchData: true});
/* returns [{
    item: 'abc', original: 'abc', key: 'abc', score: 1,
    match: {index: 0, length: 3},
}, { 
    item: 'bcd', original: 'bcd', key: 'bcd', score: 0.6666666666666667,
    match: {index: 0, length: 2},
}] */

Use Searcher in much the same way as search

const {Searcher} = require("fast-fuzzy");

const searcher = new Searcher(["def", "bcd", "cde", "abc"]);
searcher.search("abc"); //returns ["abc", "bcd"]

//options are passed in on construction
const anotherSearcher = new Searcher(
    [{name: "thing1"}, {name: "thing2"}],
    {keySelector: (obj) => obj.name},
);

//some options can be overridden per call
searcher.search("abc", {returnMatchData: true});
/* returns [{
    item: 'abc', original: 'abc', key: 'abc', score: 1,
    match: {index: 0, length: 3},
}, { 
    item: 'bcd', original: 'bcd', key: 'bcd', score: 0.6666666666666667,
    match: {index: 0, length: 2},
}] */