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Date: Tue, 16 Feb 2010 09:08:27 +0000 (UTC)
From: "Robert Muir (JIRA)" <jira@apache.org>
To: java-dev@lucene.apache.org
Subject: [jira] Updated: (LUCENE-2089) explore using automaton for
 fuzzyquery
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     [ https://issues.apache.org/jira/browse/LUCENE-2089?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Robert Muir updated LUCENE-2089:
--------------------------------

    Attachment: LUCENE-2089.patch

updated patch:
* the proxy is a TermsEnum not a FilteredTermsEnum, this makes more sense


> explore using automaton for fuzzyquery
> --------------------------------------
>
>                 Key: LUCENE-2089
>                 URL: https://issues.apache.org/jira/browse/LUCENE-2089
>             Project: Lucene - Java
>          Issue Type: Wish
>          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, Moman-0.2.1.tar.gz, TestFuzzy.java
>
>
> Mark brought this up on LUCENE-1606 (i will assign this to him, I know he is itching to write that nasty algorithm)
> we can optimize fuzzyquery by using AutomatonTermsEnum, here is my idea
> * up front, calculate the maximum required K edits needed to match the users supplied float threshold.
> * for at least small common E up to some max K (1,2,3, etc) we should create a DFA for each E. 
> if the required E is above our supported max, we use "dumb mode" at first (no seeking, no DFA, just brute force like now).
> As the pq fills, we swap progressively lower DFAs into the enum, based upon the lowest score in the pq.
> This should work well on avg, at high E, you will typically fill the pq very quickly since you will match many terms. 
> This not only provides a mechanism to switch to more efficient DFAs during enumeration, but also to switch from "dumb mode" to "smart mode".
> i modified my wildcard benchmark to generate random fuzzy queries.
> * Pattern: 7N stands for NNNNNNN, etc.
> * AvgMS_DFA: this is the time spent creating the automaton (constructor)
> ||Pattern||Iter||AvgHits||AvgMS(old)||AvgMS (new,total)||AvgMS_DFA||
> |7N|10|64.0|4155.9|38.6|20.3|
> |14N|10|0.0|2511.6|46.0|37.9|	
> |28N|10|0.0|2506.3|93.0|86.6|
> |56N|10|0.0|2524.5|304.4|298.5|
> as you can see, this prototype is no good yet, because it creates the DFA in a slow way. right now it creates an NFA, and all this wasted time is in NFA->DFA conversion.
> So, for a very long string, it just gets worse and worse. This has nothing to do with lucene, and here you can see, the TermEnum is fast (AvgMS - AvgMS_DFA), there is no problem there.
> instead we should just build a DFA to begin with, maybe with this paper: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.652
> we can precompute the tables with that algorithm up to some reasonable K, and then I think we are ok.
> the paper references using http://portal.acm.org/citation.cfm?id=135907 for linear minimization, if someone wants to implement this they should not worry about minimization.
> in fact, we need to at some point determine if AutomatonQuery should even minimize FSM's at all, or if it is simply enough for them to be deterministic with no transitions to dead states. (The only code that actually assumes minimal DFA is the "Dumb" vs "Smart" heuristic and this can be rewritten as a summation easily). we need to benchmark really complex DFAs (i.e. write a regex benchmark) to figure out if minimization is even helping right now.

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