fts

Fast fuzzy unicode full-text-search backed with redis.

npm install fts
32 downloads in the last month

NODE-FTS

NODE-FTS is a full-text-search engine that tries to be:

  • fast - using pre-filled redis zsets, searching is one ZREVRANGE command
  • fuzzy - using some basic pre-computation
  • unicode - using node-stringprep to normalize strings and icu-wordsplit for unicode word split

I had enough with all the search modules out there not having a hint of international characters support in their implementation so I decided to just write one. If atleast to get other module authors to pay more attention to people who don't speak English as their native language.

As I'm just using this for a few of my low-traffic personal side projects, I will definitely need your help to submit enhancements and fix whatever bugs you may find.

If you don't like CoffeeScript, feel free to send me patches in pure JS, I'll happily convert it for you.

Install

Install with:

npm install fts --save

and use with:

var fts = require('fts')
  , doc =
    { id: 123
    , title: 'Hello World'
    , keywords: 'this, should, be, searchable, as, well' };

fts.use(require('redis').createClient());

fts.index(doc.id, [doc.title, doc.keywords], function(e) {

  fts.query('searchable', function(e, ids) {
    // ids == ['123']
  });

  fts.query('wrld', function(e, ids) {
    // ids == ['123']
  });

});

libicu dependency

FTS uses node-stringprep and icu-wordsplit for unicode support. This means that you will need to have a working libicu binaries installed on your machine. Depending on where you're developing node.js one of the following command will install a working copy of libicu binaries and data files into your system:

# ubuntu and debian-based systems
apt-get install libicu-dev

# gentoo
emerge icu

# os x
port install icu +devel                 # with macports
brew install icu4c && brew link icu4c   # homebrew

How does it work?

Index in fts is actually a list of pre-sorted results for possible search keywords. That is, when a new document is added, fts tries to build as many search keywords as possible from it, give them weights, and add those to the "results list" for the keyword ready to be retrieved on search.

When index is called, fts does the following:

  1. Split up all the words
  2. Index each word - Prefixes are weighted more than suffixes to makes searching for document titles and exact matches more effective.
  3. Index "typo" variations for each word - For example "bngkok" for "Bangkok" is added with a less weight.
  4. Index concatenated words subset for the entire string - This is required to effectively search in some language such as Thai where there are many ways to split a word (e.g. ตากลม => ตา | กลม or ตาก | ลม)
  5. Index "typo" variations for the each concatenated word

Main API

indexer.use( [prefix], redis-client )

Setup the indexer to use the specified prefix and redis-client. You must call this function before using any of the fts module functionality.

  • prefix - Prefix to use for all redis keys used by FTS.
  • redis-client - The redis client to use. Any object with interface compatible with the de facto redis module is fine.

indexer.index( id, items, callback )

Add one or more items to the index with identifier id and then calls callback.

  • id - the document identifier which will be returned on queries
  • items - Content string or array of strings to index
  • callback - Standard callback with one error

indexer.clear( callback )

Removes all entries from the index effectively resetting it to initial state.

indexer.query( query, callback )

Queries the index using the string query.

  • query - The string to search for. Spaces don't matter.
  • callback - Callback function with signature function(e, ids) { } where ids is an array of document ids that matches the supplied query.

Lower-level API

These APIs are provided in case you need more fine-grained control of the indexes but should not need to to be used in most cases.

indexer.degree.words = 3

Get/set the degree to which fts will permute words. Defaults to 3.

Example: Permuting "quick brown fox" with degree 2 gives ["quickbrown", "brownfox"] querying with "brownfox" will returns the original string.

Higher degree enables broader search term matches but will require more memory and CPU during index() calls.

indexer.degree.typos = 5

Get/set the degree to which fts will permute typos. Defaults to 5.

Example: Permuting "search" with degree 2 gives 22 results including "serc" and "srch" querying with any 2 letters missing from the string will match the document.

Higher degree enables broader search term matches but will require more memory and CPU during index() calls.

indexer.addKey( id, key, weight, callback )

Adds id to search key key with weight weight and then calls callback (optional).

  • id - document identifier
  • key - search key to add the document to, this will be normalized.
  • weight - weight to give to this document for this particular search key.

indexer.removeKey( id, key, callback )

Removes id from the search key key regardless of weight.

  • id - document identifier
  • key - search key to add the document to, this will be normalized.

License

BSD

TODO / CONTRIBUTE

  • Redis ZSETs can actually be replicated pretty easily using other kinds of databases such as MongoDB or Sqlite3 so a different kind of backend store would probably benefits a lot of people.

  • Reverse lookup (i.e. which search terms contains the document id)

  • Ability to remove a document from the index.

  • Search quality tests and/or more tests in general. I want this module to be rock solid.

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