levels is a light-weight LevelDB full text search for node.js. This is a port of the redis search reds by TJ Holowaychuk to use LevelDb.
$ npm install levels
The first thing you'll want to do is create a
Search instance, which allows you to pass a
key, used for namespacing within Redis so that you may have several searches in the same db.
var search = levels.createSearch('pets');
levels acts against arbitrary numeric or string based ids, so you could utilize this library with essentially anything you wish, even combining data stores. The following example just uses an array for our "database", containing some strings, which we add to levels by calling
Search#index() padding the body of text and an id of some kind, in this case the index.
var strs = ;strs;strs;strs;strs;strs;strs;strs;strs;
To perform a query against levels simply invoke
Search#query() with a string, and pass a callback, which receives an array of ids when present, or an empty array otherwise.
By default levels performs an intersection of the search words, the previous example would yield the following output:
Search results for "Tobi dollars": - Tobi wants four dollars
We can tweak levels to perform a union by passing either "union" or "or" to
levels.search() after the callback, indicating that any of the constants computed may be present for the id to match.
The intersection would yield the following since only one string contains both "Tobi" and "dollars".
Search results for "tobi dollars": - Tobi wants four dollars - Tobi only wants $4 - Loki, Jane, and Tobi are ferrets
var search = levels;searchindex'Foo bar baz' 'abc';searchindex'Foo bar' 'bcd';search;search;
Currently levels strips stop words and applies the metaphone and porter stemmer algorithms to the remaining words before mapping the constants in Redis sets. For example the following text:
Tobi is a ferret and he only wants four dollars
Converts to the following constant map:
Tobi: 'TB'ferret: 'FRT'wants: 'WNTS'four: 'FR'dollars: 'DLRS'
This also means that phonetically similar words will match, for example "stefen", "stephen", "steven" and "stefan" all resolve to the constant "STFN". levels takes this further and applies the porter stemming algorithm to "stem" words, for example "counts", and "counting" become "count".
Consider we have the following bodies of text:
Tobi really wants four dollars For some reason tobi is always wanting four dollars
The following search query will then match both of these bodies, and "wanting", and "wants" both reduce to "want".
tobi wants four dollars
Nothing scientific but preliminary benchmarks show that a small 1.6kb body of text is currently indexed in ~6ms, or 163 ops/s. Medium bodies such as 40kb operate around 6 ops/s, or 166ms.
Querying with a multi-word phrase, and an index containing ~3500 words operates around 5300 ops/s. Not too bad.
If working with massive documents, you may want to consider adding a "keywords" field, and simply indexing it's value instead of multi-megabyte documents.
(The MIT License)
Copyright (c) 2013 Eguene Ware <firstname.lastname@example.org>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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