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From "Robert Muir (JIRA)" <>
Subject [jira] Commented: (LUCENE-2089) explore using automaton for fuzzyquery
Date Mon, 22 Feb 2010 02:32:27 GMT


Robert Muir commented on LUCENE-2089:

based on the work mike provided here (slightly modified), N=2 appears to work correctly (all
correct results so far). I will figure out a way to exhaustively test this beast :)

For now, here are the updated benchmarks, again same 10M terms, on my regular old HDD, you
can scroll up to see how big of a difference N=2 makes, compared to only having N=1 available!

I think with N=2 implemented, its also clear, that PQ size can actually be more important
than using a prefix. For instance, I'm very happy with the performance here with a smaller
PQ size of 64.

{{Minimum Sim = 0.73f (edit distance of 1)}} 
||Prefix Length||PQ Size||Avg MS (old)||Avg MS (new)||

{{Minimum Sim = 0.58f (edit distance of 2)}}
||Prefix Length||PQ Size||Avg MS (old)||Avg MS (new)||

{{Minimum Sim = 0.43f (edit distance of 3)}}
||Prefix Length||PQ Size||Avg MS (old)||Avg MS (new)||

{{Minimum Sim = 0.29f (edit distance of 4)}}
||Prefix Length||PQ Size||Avg MS (old)||Avg MS (new)||

> 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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