On Dec 6, 1:47 am, lock <[EMAIL PROTECTED]> wrote:
> Thanks guys, that's what I was hoping to hear, you saved me a couple
> hours trying to prove it for myself (not to mention the frustration).
> After I went away and thought about it some more I figured there must
> be some 'smarts' in the database to prevent the query time from
> increasing.  Otherwise how could any database scale well...
>
> No merge joins or IN operators in my code, so nothing to worry about
> there.
>
> After a _lot_ more testing I'm finding that query time does scale with
> the number of fetched _results_, not the DB size.  During early
> testing I convinced myself that increasing the DB size was slowing my
> query down, when really the number of results were increasing as I
> added more data, doh (it was getting late ;-)  ).

One thing to bear in mind is that the dev_appserver performance is
_not_ representative of the production performance. The dev_appserver
holds the entire dataset in memory and does linear scans over an
entity type for queries, so performance there _will_ degrade with
respect to the size of an entity type.

>
> The overall solution that seems to be working well for me at the
> moment is to have different tables for different resolutions.  As the
> size of the geometric bounds increases I switch between a few tables,
> each one with a lower fidelity therefore reducing the number of
> results that can be returned.  Visually it works similar to Level Of
> Detail techniques you see in some 3D modeling packages.

I'm curious how you're doing this with only a limited number of
queries. Geohashing isn't ideally suited to satisfying bounding box
queries (though it's certainly better than storing plain lat/longs).
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