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From "Robert Muir (JIRA)" <>
Subject [jira] Commented: (LUCENE-2089) explore using automaton for fuzzyquery
Date Sun, 21 Feb 2010 13:59:27 GMT


Robert Muir commented on LUCENE-2089:

Mike, this is awesome! 

We can use the junit test case to test N=1 once we get to a nice place with this.
The way it works is, it builds an NFA for N=1, and compares it with the results of this with
Automaton.equals, which ensures they accept the same language.
The test already checks this for all possible characteristic vectors, so if you believe the
paper, and the tests pass, then its correct for all strings.

Testing the correctness of N=2 is harder, we can use the same principles I think, but no automaton.equals
as I don't know how to generate an NFA for N=2, even slowly.
instead I think we will have to verify against the actual levenshtein distance formula, but
i think that verifying for all permutations of an alphabet of size 2n+1, for a string of length
at least 2n+1 should be sufficient.

(i plan to also randomly brute-force test the new query against the old fuzzy query at some
point, in any case)

in my opinion we should take the lessons learned from N=2 and if successful, regenerate N=1
too, as the way I "keyed it in" is likely not the best or most compact.

> explore using automaton for fuzzyquery
> --------------------------------------
>                 Key: LUCENE-2089
>                 URL:
>             Project: Lucene - Java
>          Issue Type: Improvement
>          Components: Search
>    Affects Versions: Flex Branch
>            Reporter: Robert Muir
>            Assignee: Mark Miller
>            Priority: Minor
>             Fix For: Flex Branch
>         Attachments:,,,
LUCENE-2089.patch, LUCENE-2089.patch, LUCENE-2089.patch, LUCENE-2089.patch, LUCENE-2089.patch,
LUCENE-2089_concat.patch, Moman-0.2.1.tar.gz,
> we can optimize fuzzyquery by using AutomatonTermsEnum. The idea is to speed up the core
FuzzyQuery in similar fashion to Wildcard and Regex speedups, maintaining all backwards compatibility.
> The advantages are:
> * we can seek to terms that are useful, instead of brute-forcing the entire terms dict
> * we can determine matches faster, as true/false from a DFA is array lookup, don't even
need to run levenshtein.
> We build Levenshtein DFAs in linear time with respect to the length of the word:
> To implement support for 'prefix' length, we simply concatenate two DFAs, which doesn't
require us to do NFA->DFA conversion, as the prefix portion is a singleton. the concatenation
is also constant time with respect to the size of the fuzzy DFA, it only need examine its
start state.
> with this algorithm, parametric tables are precomputed so that DFAs can be constructed
very quickly.
> if the required number of edits is too large (we don't have a table for it), we use "dumb
mode" at first (no seeking, no DFA, just brute force like now).
> As the priority queue fills up during enumeration, the similarity score required to be
a competitive term increases, so, the enum gets faster and faster as this happens. This is
because terms in core FuzzyQuery are sorted by boost value, then by term (in lexicographic
> For a large term dictionary with a low minimal similarity, you will fill the pq very
quickly since you will match many terms. 
> This not only provides a mechanism to switch to more efficient DFAs (edit distance of
2 -> edit distance of 1 -> edit distance of 0) during enumeration, but also to switch
from "dumb mode" to "smart mode".
> With this design, we can add more DFAs at any time by adding additional tables. The tradeoff
is the tables get rather large, so for very high K, we would start to increase the size of
Lucene's jar file. The idea is we don't have include large tables for very high K, by using
the 'competitive boost' attribute of the priority queue.
> For more information, see

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