lucene-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Robert Muir (JIRA)" <>
Subject [jira] Updated: (LUCENE-2089) explore using automaton for fuzzyquery
Date Mon, 22 Feb 2010 15:02:32 GMT


Robert Muir updated LUCENE-2089:


Mike, attached is a first crack at modified version of
This is producing Lev(1,2) code that passes all tests (I also added random tests to junit
for n=1 so far)

 * @override, license note, stuff like that
 * getPosition() should return the index in the word that the state is associated with, this
is the offset
 * there are w + 1, not w absolute states per parametric state.
 * in the ctor, the number of states to initialize per parametric state depends on the number
of positions this parametric state represents (the length of the python list). So the inner
loop here is modified to:
for(int j=0;j<((w+1)*stateSizes[i]);j++)

if you get a chance, can you review? I don't think its the final version but we can iterate

> 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

This message is automatically generated by JIRA.
You can reply to this email to add a comment to the issue online.

To unsubscribe, e-mail:
For additional commands, e-mail:

View raw message