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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:
--------------------------------
Description:
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 common N up to K (1,2,3, etc) we should create a DFA for each N.
if the required K is above our supported DFA-based N, 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 N, 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.
was:
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 AutomatonTermEnum, here is my idea
* up front, calculate the maximum required K edits needed to match the users
supplied float threshold.
* for at least common K (1,2,3, etc) we should use automatontermenum. if its
outside of that, maybe use the existing slow logic. At high K, it will seek too
much to be helpful anyway.
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.
> 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
> Reporter: Robert Muir
> Assignee: Mark Miller
> Priority: Minor
> Attachments: 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 common N up to K (1,2,3, etc) we should create a DFA for each
> N.
> if the required K is above our supported DFA-based N, 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 N, 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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