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From "Peter Keegan" <peterlkee...@gmail.com>
Subject Re: Best Practices for getting Strings from a position range
Date Tue, 24 Jul 2007 22:02:57 GMT
Hi Grant,

No problem - I know you are very busy.  I just wanted to get a sense for the
timing because I'd like to use this for a release this Fall. If I can get a
prototype working in the coming weeks AND the performance is great :) , this
would be terrific. If not, I'll have to fall back on a more complex design
that handles the query outside of Lucene :(

In the meantime, I'll try playing with LUCENE-868.

Thanks for the update.
Peter

On 7/24/07, Grant Ingersoll <grant.ingersoll@gmail.com> wrote:
>
> Sorry, Peter, I haven't had a chance to work on it.  I don't see it
> happening this week, but maybe next.
>
> I do think the Mapper approach via TermVectors will work.  It will
> require implementing a new mapper that orders by position, but I
> don't think that is too hard.   I started on one on the LUCENE-868
> patch (version 4) but it is not complete.  Maybe you want to pick it up?
>
> With this approach, you would iterate your spans, when you come to a
> new doc, you would load the term vector using the PositionMapper, and
> then you could index into the positions for the matches in the document.
>
> I realize this does not cover the just wanting to get the Payload at
> the match issue.  Maybe next week...
>
> Cheers,
> Grant
>
> On Jul 23, 2007, at 8:51 AM, Peter Keegan wrote:
>
> > Any idea on when this might be available (days, weeks...)?
> >
> > Peter
> >
> > On 7/16/07, Grant Ingersoll <gsingers@apache.org> wrote:
> >>
> >>
> >> On Jul 16, 2007, at 1:06 AM, Chris Hostetter wrote:
> >>
> >> >
> >> > : Do we have a best practice for going from, say a SpanQuery doc/
> >> > : position information and retrieving the actual range of
> >> positions of
> >> > : content from the Document?  Is it just to reanalyze the Document
> >> > : using the appropriate Analyzer and start recording once you
> >> hit the
> >> > : positions you are interested in?    Seems like Term Vectors
> >> _could_
> >> > : help, but even my new Mapper approach patch (LUCENE-868) doesn't
> >> > : really help, because they are stored in a term-centric manner.  I
> >> > : guess what I am after is a position centric approach.  That
> >> is, give
> >> >
> >> > this is kind of what i was suggesting in the last message i sent
> >> > to the java-user thread about paylods and SpanQueries (which i'm
> >> > guessing is what prompted this thread as well)...
> >> >
> >> > http://www.nabble.com/Payloads-and-PhraseQuery-
> >> > tf3988826.html#a11551628
> >>
> >>
> >> This is one use case, the other is related to the new patch I
> >> submitted for LUCENE-960.  In this case, I have a SpanQueryFilter
> >> that identifies a bunch of docs and positions ahead of time.  Then
> >> the user enters new Span Query and I want to relate the matches from
> >> the user query with the positions of matches in the filter and then
> >> show that window.
> >>
> >> >
> >> > my point was that currently, to retrieve a payload you need a
> >> > TermPositions instance, which is designed for iterating in the
> >> > order of...
> >> >     seek(term)
> >> >       skipTo(doc)
> >> >          nextPosition()
> >> >             getPayload()
> >> > ...which is great for getting the payload of every instance
> >> > (ie:position) of a specific term in a given document (or in every
> >> > document) but without serious changes to the Spans API, the ideal
> >> > payload
> >> > API would let you say...
> >> >     skipTo(doc)
> >> >        advance(startPosition)
> >> >          getPayload()
> >> >        while (nextPosition() < endPosition)
> >> >          getPosition()
> >> >
> >> > but this seems like a nearly impossible API to implement given the
> >> > natore
> >> > of hte inverted index and the fact that terms aren't ever stored in
> >> > position order.
> >> >
> >> > there's a lot i really don't know/understand about the lucene term
> >> > position internals ... but as i recall, the datastructure written
> >> > to disk
> >> > isn't actually a tree structure inverted index, it's a long
> >> > sequence of
> >> > tuples correct?  so in theory you could scan along the tuples
> >> > untill you
> >> > find the doc you are interested in, ignoring all of the term info
> >> > along
> >> > the way, then whatever term you happen be on at the moment, you
> >> > could scan
> >> > along all of the positions until you find one in the range you are
> >> > interested in -- assuming you do, then you record the current Term
> >> > (and
> >> > read your payload data if interested)
> >>
> >> I think the main issue I see is in both the payloads and the matching
> >> case above is that they require a document centric approach.  And
> >> then, for each Document,
> >> you ideally want to be able to just index into an array so that you
> >> can go directly to the position that is needed based on
> >> Span.getStart()
> >>
> >> >
> >> > if i remember correctly, the first part of this is easy, and
> >> > relative fast
> >> > -- i think skipTo(doc) on a TermDoc or TermPositions will happily
> >> > scan for
> >> > the first <term,doc> pair with the correct docId, irregardless of
> >> > the term
> >> > ... the only thing i'm not sure about is how efficient it is to
> >> > loop over
> >> > nextPosition() for every term you find to see if any of them are in
> >> > your
> >> > range ... the best case scenerio is that the first position
> >> > returned is
> >> > above the high end of your range, in which case you can stop
> >> > immediately
> >> > and seek to the next term -- butthe worst case is that you call
> >> > nextPosition() over an over a lot of times before you get a
> >> > position in
> >> > (or above) your rnage .... an advancePosition(pos) that wokred like
> >> > seek
> >> > or skipTo might be helpful here.
> >> >
> >> > : I feel like I am missing something obvious.  I would suspect the
> >> > : highlighter needs to do this, but it seems to take the reanalyze
> >> > : approach as well (I admit, though, that I have little experience
> >> > with
> >> > : the highlighter.)
> >> >
> >> > as i understand it the default case is to reanalyze, but if you
> >> have
> >> > TermFreqVector info stored with positions (ie: a
> >> > TermPositionVector) then
> >> > it can use that to construct a TokenStream by iterating over all
> >> > terms and
> >> > writing them into a big array in position order (see the
> >> > TermSources class
> >> > in the highlighter)
> >>
> >>
> >> Ah, I see that now.  Thanks.
> >> >
> >> > this makes sense when highlighting because it doesn't know what
> >> > kind of
> >> > fragmenter is going to be used so it needs the whole TokenStream,
> >> > but it
> >> > seems less then ideal when you are only interested in a small
> >> > number of
> >> > position ranges that you know in advance.
> >> >
> >> > : I am wondering if it would be useful to have an alternative Term
> >> > : Vector storage mechanism that was position centric.  Because we
> >> > : couldn't take advantage of the lexicographic compression, it
> >> would
> >> > : take up more disk space, but it would be a lot faster for these
> >> > kinds
> >> >
> >> > i'm not sure if it's really neccessary to store the data in a
> >> position
> >> > centric manner, assuming we have a way to "seek" by position like i
> >> > described above -- but then again i don't really know that what i
> >> > described above is all that possible/practical/performant.
> >> >
> >>
> >> I suppose I could use my Mapper approach to organize things in a
> >> position centric way now that I think about it more.  Just means some
> >> unpacking and repacking.  Still, probably would perform well enough
> >> since I can setup the correct structure on the fly.  I will give this
> >> a try.  Maybe even add a Mapper to do this.
> >>
> >>
> >> -Grant
> >>
> >> ---------------------------------------------------------------------
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> >>
> >>
>
> ------------------------------------------------------
> Grant Ingersoll
> http://www.grantingersoll.com/
> http://lucene.grantingersoll.com
> http://www.paperoftheweek.com/
>
>
>
> ---------------------------------------------------------------------
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