Bop

• Bop is a very fast Boyer-Moore parser for string or buffer patterns.
• It is optimized for using with pattern strings/buffers <= 255 bytes.
• It is ideal, for example, to parse streams like multipart/form-data ones, in which pattern/boundary length < ~70 bytes).

Main features

Given a m-length pattern and n-length data, and σ-length alphabet ( σ = 256 ):

• it performs the comparisons from right to left.
• preprocessing phase in O(m+σ) time and space complexity.
• searching phase in O(m*n) time complexity.
• 3*n text character comparisons in the worst case when searching for a non periodic pattern.
• O(n/m) best performance.

See Lecroq for reference and also Qap, a QuickSearch parser.

Install

$ npm install bop [-g]

require:

var Bop = require( 'bop' );

Run Tests

$cd bop/
$npm test

to execute a single test file simply do:

 $ node test/file-name.js

Run Benchmarks

$ cd bop/
$ npm run bench

Constructor

Create an instance, using a pattern.

Bop( Buffer pattern | String pattern )
// or
new Bop( Buffer pattern | String pattern )

Methods

Arguments within [] are optional.

// Change the pattern to search.
Bop#set( Buffer pattern | String pattern ) : Buffer

/*
 * Count matches, optionally starting from a particular index (default
 * is 0). It returns an Array containing the number of matches, and the
 * remaining bytes
 */
Bop#count( Buffer data [, Number start_from ] ) : Array

// Same as #count, but without counting overlapping sequences
Bop#scount( Buffer data [, Number start_from ] ) : Array

/*
 * Count matches, and the max distance found between 2 matches,
 * optionally starting from a particular index (default is 0).
 * It returns an Array containing:
 * - the number of matches
 * - the max distance found between 2 matches, -1 otherwise
 * - the distance from data index 0 to the first match, -1 otherwise
 * - the distance from the end of the last match to the end of data,
 *   -1 otherwise
 * 
 * NOTE: if 0 or only 1 occurrence was found, the max distance will
 * be -1 and then the resulting array will be respectvely:
 * - [0, -1, -1, -1 ]
 * - [1, -1, .., .. ]
 */
Bop#dist( Buffer data [, Number start_from ] ) : Array

// same as #dist, but without counting overlapping sequences
Bop#sdist( Buffer data [, Number start_from ] ) : Array

/*
 * Collect all indexes of pattern occurrences.
 *
 * As options you can:
 *
 * - start parsing from a particular index
 * - limit the number of results to parse
 * - fill your array with resulting indexes.
 *
 * NOTE: use Buffers when possible (faster).
 *
 */
Bop#parse( Buffer data | String data [, Number start_from [, Number limit_results [, Array my_array ] ] ] ) : Array

/*
 * Strict parse, it's the same as parse, without collecting
 * any overlapping sequences.
 *
 * Example with CRLF sequence:
 *
 * - bop pattern is set to: "\r\n\r\n"                 (CR LF CR LF)
 * - data to parse is:      "\r\n\r\n\r\n\r\n\r\n\r\n" (CR LF CR LF CR LF CR LF )
 * 
 * - with Bop.parse( data ) we get 3 indexes as results: [0, 2, 4]
 *
 *       0  1  2  3  4  5  6  7
 *      -----------------------
 *   p: CR LF CR LF
 *   d: CR LF CR LF CR LF CR LF
 *
 *   p: ----> CR LF CR LF
 *   d: CR LF CR LF CR LF CR LF
 *
 *   p: ----------> CR LF CR LF
 *   d: CR LF CR LF CR LF CR LF
 *
 * - with Bop.sparse( data ) we get only 2 results: [0, 4]
 *
 *       0  1  2  3  4  5  6  7
 *      -----------------------
 *   p: CR LF CR LF
 *   d: CR LF CR LF CR LF CR LF
 *
 *   p: ----------> CR LF CR LF
 *   d: CR LF CR LF CR LF CR LF
 *
 */
Bop#sparse( Buffer data | String data [, Number start_from [, Number limit_results [, Array my_array ] ] ] ) : Array

Usage Example

var Bop = require( 'bop' )
    , pattern = 'hellofolks\r\n'
    , somedata = 'hehehe' + pattern +'eheheh' + pattern
    , bop = Bop( pattern )
    // parse data from beginning
    , results = bop.parse( somedata )
    ;

See examples.

Benchmark for a short pattern ( length <= 255 bytes )

Parser uses 3 Buffers 256-bytes long to build shifting tables, then:

• Pattern parsing / table creation space and time complexity is O(σ).
• Very low memory footprint.
• Ultra fast to preprocess pattern ( = tables creation ).

  $ node bench/small-pattern-data-rate

for default it:

• uses a pattern string of 57 bytes/chars.
• builds a data buffer of 700 MB in memory.
• uses a redundancy/distance factor for pattern strings equal to 2. The bigger the value, the lesser are occurrences of pattern string into the text buffer.

Custom Usage:

  # with [testBufferSizeInMB] [distanceFactor] [aStringPattern]
  $ node bench/small-pattern-data-rate.js 700 4 "that'sallfolks"

Benchmark for a big pattern ( length > 255 bytes )

Parser uses 3 arrays to build shifting tables for big patterns, then:

• there will be an high memory consumption, due to the use of arrays.
• it will take a long time to preprocess pattern ( = tables creation ).

  $ node bench/big-pattern-data-rate

• it uses a very big pattern ( 20 MBytes ).
• it builds a data buffer of 300 MBytes, copying the same pattern 12 times.

See bench dir.

MIT License

Copyright (c) 2013-present < Guglielmo Ferri : 44gatti@gmail.com >

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.

THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.