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From Doug Cutting <>
Subject RE: using lucene with a very large index
Date Thu, 14 Feb 2002 17:12:27 GMT
> From: tal blum []
> 2) Does the Document id changes after merging indexes adding 
> or deleting documents?


> 4) assuming I have a term query that has a large number of 
> hits say 10 millions, is there a way to get the say the top  
> 10 results without going through all the hits?

Your best bet is to use the normal search API.

> From: tal blum []
> one solution to that is to change the implementation and 
> store the docs
> sorted by their term score.

That would make incremental index updates much slower, since every time a
document is added, the list of documents containing each term in that
document would need to be re-sorted.  Currently we only need to append new
entries, which is much simpler.  You could optimize this in various ways
(e.g., instead take the hit at search time) but it would still make things
slower for rapidly changing indexes.

Also, while this would make single term queries faster, multi-term queries
are more complex to accellerate.  The highest scoring match for a two term
query may be in a document where one term has a very high weight and the
other has a very low weight.  There have been papers written (I don't have
the references handy) exploring this issue, and, in general, there isn't an
algorithm that is guaranteed to return the highest scoring documents for
multi-term queries that does not in most cases have to process nearly all of
the documents containing those terms.  That said, it is possible to use such
an index to vastly accellerate searches that *usually* return the highest
scoring documents.

Such a heuristic search technique is among the things required to scale
Lucene to extremely large collections (e.g., hundreds of millions of
documents).  There are also lower-tech optimizations.  For example, one can
simply keep a small index containing the highest-quality documents that is
always searched first.  If enough hits are found there, you're done.  A real
internet search engine combines lots of tricks in order to scale: segmenting
indexes by quality; heuristic search methods; and distributed searching.
Deploying something like Google is not a small task.

I would someday like to add a heuristic search component to Lucene, that
uses a special index format (possibly with term document lists sorted by
normalized frequency, as you suggest).  I have some experience doing this at
Excite, and it pays off big time.  But it would take me several weeks
full-time to implement this, and I don't currently have that time.  Perhaps
(with the support of an interested sponsor) I could make time this summer to
implement this.

In the meantime, if you encounter performance problems with a very large
index, you might try segmenting your index by document quality and/or
distributed search.


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