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ahocorasick --- search for matches with a keyword tree.

Danny Yoo ([email protected])

INTRODUCTION
------

There was a thread in March 2005 where someone mentioned that they
wanted something to search text for a set of keywords:

    http://mail.python.org/pipermail/python-list/2005-March/269081.html

and I suggested using either suffix trees or an Aho-Corasick
automaton.  I'd written a wrapper for an implementation of suffix
trees before, but I hadn't written one for the Aho-Corasick automaton,
so symmetry demanded that I write a wrapper here too.  *grin*


USAGE
------

######
>>> import ahocorasick
>>> tree = ahocorasick.KeywordTree()
>>> tree.add("alpha")          
>>> tree.add("alpha beta")
>>> tree.add("gamma")
>>>
>>> tree.make()
>>>
>>> tree.search("I went to alpha beta the other day to pick up some spam")
(10, 15)
>>> tree.search_long("I went to alpha beta the other day to pick up some spam")
(10, 20)
>>> tree.search("and also got some alphabet soup")
(18, 23)
>>> tree.search("but no waffles")
>>> 
>>> tree.search_long("oh, gamma rays are not tasty")
(4, 9)
>>>
>>> tree.findall("I went to alpha beta to pick up alphabet soup")
<generator object at 0x279558>
>>> for match in tree.findall("I went to alpha beta to pick up alphabet soup"):
...     print match
... 
(10, 15)
(32, 37)
######


The 'ahocorasick' module provides a single class called 'KeywordTree'.


KeywordTree has the following methods:

    add(keyword)

        Adds a new keyword to the automaton.  The keyword must be
        nonempty, and at the moment, cannot contain NULL characters.


    make()

        Finalizes construction of the automaton.

        This must be called before doing any searching, and can only
        be done if at least one keyword has been added.  If called
        before adding at least one keyword, we'll raise an
        AssertionError.


    search(query, [startpos])

        Searches the query for the leftmost occuring keyword that the
        automaton knows.  If a match is made, returns the 2-tuple
        (startIndex, endIndex).  If no match can be found, returns
        None.  query cannot contain NULL characters at the moment.

        If the optional startpos argument is given, starts the search
        at that position in the query.

        Note that this matches as quickly as it can: if you want the
        longest leftmost occuring keyword match, use search_long.

        (startpos added in Release 0.7)


    search_long(query, [startpos])

        Same as search(), except that this searches for the longest
        leftmost keyword that matches.

        (startpos added in Release 0.7)


    findall(query, [allow_overlaps=0])

        Returns an iterator of 2-tuples, of all nonoverlapping matches, using
        search().

        If the optional argument to allow_overlaps is set to True,
        then subsequent matches are allowed to overlap previous ones.

        (allow_overlaps added in Release 0.9)


    findall_long(query, [allow_overlaps=0])

        Returns an iterator of 2-tuples, of all nonoverlapping matches, using
        search_long().

        If the optional argument to allow_overlaps is set to True,
        then subsequent matches are allowed to overlap previous ones.

        (allow_overlaps added in Release 0.9)


    chases(source_stream)

        Given an iterator of text blocks, returns an iterator of
        matches, using search().  Each match result is a 2-tuple
        (text_block, (start, end)).


    chases_long(source_stream)

        Given an iterator of text blocks, returns an iterator of
        matches, using search_long().  Each match result is a 2-tuple
        (text_block, (start, end)).





DEVELOPMENT DETAILS
------

The Aho-Corasick automaton is a data structure that can quickly
do a multiple-keyword search across text.  It's described in the
classic paper 'Efficient string matching: an aid to bibliographic
search':

    http://portal.acm.org/citation.cfm?id=360855&dl=ACM&coll=GUIDE


The majority of the code here was wilfully stolen..., er... "adapted"
from source code that I found in the Fairly Fast Packet Filter (FFPF)
project:

    http://ffpf.sourceforge.net/general/overview.php

One of the filters that they include is a fairly clean and simple C
implementation of the Aho-Corasick keyword tree data structure, so I
just took that and built a wrapper around it.



BUGS / CHANGELOG
------

Release 0.9

    I've added the allow_overlaps flag to findall() and findall_long()
    for Andre Soreng.

    I've also finally fixed up the memory-hungry implementation of the
    transition list.  In the original code, each state would take up
    at least (256 * sizeof(void*)) bytes of addition space, one for
    each possible state transition.  The problem is that most states
    don't usable transitions, so most of that space is waste.

    I've modified the implementation to either use a dense
    representation of the transitions --- the original "dense" array
    implementation --- or, instead a linked-list implementation of the
    transitions.  This implementation detail should make the tree
    significantly less memory intensive.

    Right now, it's controlled by the depth of each state: states
    whose depth >= 3 get the sparse representation.

    But as a warning, I have not yet put in all the xalloc checks that
    I know I need to make: I'll do so for Release 1.0.

    Bugs fixed:

        o Fixed overly permissive negative indices in startpos.  Now
          properly flags those as AssertionErrors.  Thanks to John
          Machin for the heads up.

Release 0.8

    Added the findall*() and chases*() functions to make it easier to
    find all matches in a string.  Scott David Daniels suggested the
    chases* interface from:

        http://mail.python.org/pipermail/python-list/2005-March/270862.html

    so this has been done now.  On the backend, I made things into a
    package, and moved the C module from 'ahocorasick' to
    'ahocorasick._ahocorasick'.

    On memory: John Machin notes that node allocation uses a lot of
    memory, so that's something to be aware of.  With his
    encouragement, I've fixed allocation so that all memory allocation
    goes through PyMem_Malloc and PyMem_Free.

    I've also added an interface to inspect the actual automaton,
    although I'm not providing an interface to mutate it at the
    moment.  [FIXME: document KeywordTree.zerostate(), State.goto(),
    State.fail(), State.labels(), and State.output().]

    The automaton can be inspected even before calling make(), but of
    course not all of the transitions will be there.  But it should
    make writing a graphical visualizer of the tree very nice.  I
    cooked up a quick-and-dirty one in ahocorasick.graphviz, but I
    haven't polished it yet.



Release 0.7

    John Machin notes that I messed up the argument-passing calling
    conventions using METH_VARARGS.  Stripped out the superfluous
    keyword argument stuff.

    He also pointed out the edge case of adding an empty string as a
    keyword.  Doing:

        tree.add("")
    
    caused a segfault.  Oops.  I must remember not to forget tests
    against the empty string.  Fixed.

    Release 0.6 worked only with C strings only.  This has been fixed
    in Release 0.7: embedded NULLs are now allowed.

    Finally, John Machin's request for an optional startpos argument
    has been answered.  *grin*


Release 0.6

    Andre Soereng and John Machin sent bug reports about the 'inline'
    directive not working on their respective compilers, so I've taken
    those out.

    John Machin found a bug in the add() method.  If a keyword was
    added that was a prefix of a previous keyword, nothing would
    happen.  This has been fixed, and I'd better send this upstream to
    the FFPF developers too.


Release 0.5

    I did find a memory-leak bug in the original source aho-corasick.c
    code, and have sent my fixes upstream to the FFPF developers.

    Deallocating a large tree might take a long time; I've noticed
    that a lot of the code involves following what is essentially a
    long linked list.  I'm not sure how serious of a problem this is
    in practice.







TODO
------

    Bulletproof the implementation: clean up the code, and make sure
    all memory allocations are checked.

    Allow dense-vs-sparse representatation of the states to be
    more controllable.