On 3/25/07, Jim Fulton <[EMAIL PROTECTED]> wrote:
On Mar 25, 2007, at 3:01 AM, Adam Groszer wrote:
> MF> I think one of the main limitations of the current catalog (and
> MF> hurry.query) is efficient support for sorting and batching the
> query
> MF> results. The Zope 3 catalog returns all matching results, which
> can then
> MF> be sorted and batched. This will stop being scalable for large
> MF> collections. A relational database is able to do this
> internally, and is
> MF> potentially able to use optimizations there.

What evidence to you have to support this assertion?  We did some
literature search on this a few years ago and found no special trick
to avoid sorting costs.

I know of 2 approaches to reducing sort cost:

1. Sort your results based on the "primary key" and therefore, pick
your primary key to match your sort results.  In terms of the Zope
catalog framework, the primary keys are the document IDs, which are
traditionally chosen randomly.  You can pick your primary keys based
on a desired sort order instead. A variation on this theme is to use
multiple sets of document ids,  storing multiple sets of ids in each
index.  Of course, this approach doesn't help with something like
relevance ranks.

2. Use an N-best algorithm.  If N is the size of the batch and M is
the corpus size, then this is O(M*ln(N)) rather than O(M*ln(M)) which
is a significant improvement if N << M, but still quite expensive.

I don't think relational databases have any magic bullet to get
around sorting costs.  Sorting is expensive.  In many ways, I think
the sorting support in the catalog gave people a false sense of

I don't know if relational databases typically does this internally (I
don't think so). However, some search engines do it, like Lucene. And
supposedly also Dieters IncrementalSearch (haven't used it yet).

Lennart Regebro: Zope and Plone consulting.
+33 661 58 14 64
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