alfred-margaret: Fast Aho-Corasick string searching

[ bsd3, data, library, text ] [ Propose Tags ]

An efficient implementation of the Aho-Corasick string searching algorithm.


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Versions [faq] 1.0.0.0
Dependencies base (>=4.7 && <5), containers (>=0.6.2 && <0.7), deepseq (>=1.4.4 && <1.5), hashable (>=1.3.0 && <1.4), primitive (>=0.7.1 && <0.8), text (>=1.2.4 && <1.3), vector (>=0.12.1 && <0.13) [details]
License BSD-3-Clause
Copyright 2019 Channable
Author The Alfred-Margaret authors
Maintainer Ruud van Asseldonk <ruud@channable.com>
Category Data, Text
Home page https://github.com/channable/alfred-margaret
Source repo head: git clone https://github.com/channable/alfred-margaret
Uploaded by rkrzr at 2020-07-05T20:05:55Z
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Downloads 36 total (11 in the last 30 days)
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Status Hackage Matrix CI
Docs not available [build log]
All reported builds failed as of 2020-07-05 [all 3 reports]

Modules

  • Data
    • Text
      • AhoCorasick
        • Data.Text.AhoCorasick.Automaton
        • Data.Text.AhoCorasick.Replacer
        • Data.Text.AhoCorasick.Searcher

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Readme for alfred-margaret-1.0.0.0

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Alfred–Margaret

Alfred–Margaret is a fast implementation of the Aho–Corasick string searching algorithm in Haskell. It powers many string-related operations in Channable.

The library is designed to work with the text package. It matches directly on the internal UTF-16 representation of Text for efficiency. See the announcement blog post for a deeper dive into Aho–Corasick, and the optimizations that make this library fast.

Alfred–Margaret is named after Alfred Aho and Margaret Corasick.

Performance

Running time to count all matches, in a real-world data set, comparing a Java implementation and a Rust implementation against Alfred–Margaret, and against memcopy to establish a lower bound:

<p align="center"> <img title="Graph that shows that Alfred–Margaret is fast." src="performance.png" width="80%" > </p>

For the full details of this benchmark, see our announcement blog post, which includes more details about the data set, the benchmark setup, and a few things to keep in mind when interpreting this graph.

Example

Check if a string contains one of the needles:

import qualified Data.Text.AhoCorasick.Searcher as Searcher

searcher = Searcher.build ["tshirt", "shirts", "shorts"]

Searcher.containsAny searcher "short tshirts"
> True

Searcher.containsAny searcher "long shirt"
> False

Searcher.containsAny searcher "Short TSHIRTS"
> False

Searcher.containsAnyIgnoreCase searcher "Short TSHIRTS"
> True

Sequentially replace many needles:

import Data.Text.AhoCorasick.Automaton (CaseSensitivity (..))
import qualified Data.Text.AhoCorasick.Replacer as Replacer

replacer = Replacer.build CaseSensitive [("tshirt", "banana"), ("shirt", "pear")]

Replacer.run replacer "tshirts for sale"
> "bananas for sale"

Replacer.run replacer "tshirts and shirts for sale"
> "bananas and pears for sale"

Replacer.run replacer "sweatshirts and shirtshirts"
> "sweabananas and shirbananas"

Replacer.run replacer "sweatshirts and shirttshirts"
> "sweabananas and pearbananas"

Get all matches, possibly overlapping:

import qualified Data.Text.AhoCorasick.Automaton as Aho

pairNeedleWithSelf text = (Aho.unpackUtf16 text, text)
automaton = Aho.build $ fmap pairNeedleWithSelf ["tshirt", "shirts", "shorts"]
allMatches = Aho.runText [] (\matches match -> Aho.Step (match : matches))

allMatches automaton "short tshirts"
> [ Match {matchPos = CodeUnitIndex 13, matchValue = "shirts"}
> , Match {matchPos = CodeUnitIndex 12, matchValue = "tshirt"}
> ]

allMatches automaton "sweatshirts and shirtshirts"
> [ Match {matchPos = CodeUnitIndex 27, matchValue = "shirts"}
> , Match {matchPos = CodeUnitIndex 26, matchValue = "tshirt"}
> , Match {matchPos = CodeUnitIndex 22, matchValue = "shirts"}
> , Match {matchPos = CodeUnitIndex 11, matchValue = "shirts"}
> , Match {matchPos = CodeUnitIndex 10, matchValue = "tshirt"}
> ]

License

Alfred–Margaret is licensed under the 3-clause BSD license.