Thanks for explaining the "materialization" process. It will come handy on
my app...
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That's an interesting approach indeed. Thanks Niphlod :)
Well, in the end my company hired a SEO consultant and our conclusion was
that we need to build a drill-down navigation instead of one page
containing the whole tree.
E.g. :
*country*
- administrative_area_level_1
- administrative
mind that there's even another "angle": given that usually a city resides
in the same place (same goes for regions, countries, etc etc etc) you can
carefully cache the "computationally expensive" resultset and update it
once a week (or once modifications are made).
That's what we - dba - call a
> a webpage holding 200 locations and requiring 800 queries is not a webpage a
> user would need.
That’s actually a very good point !
This page is actually designed for SEO, but even SEO doesn’t go well with
hundreds of links.
I think I should build a navigation architecture that maps the dr
if your model is a single table with 4 fields, what's the expected
resultset ? a nested dict ? if yes, no queries needed, just python.
BTW: a webpage holding 200 locations and requiring 800 queries is not a
webpage a user would need.
In other words, what's the maximum number of leaves you want
I haven’t timed the queries but there’s way more than 4 queries to be done.
For instance if I build a drill-down to zoom in on my city (Bordeaux), I would
need to build something like this :
Aquitaine (administrative_area_level_1)
Gironde (administrative_area_level_2)
Bordeaux (locality)
Talence
I am trying to build a geo-based drill-down sitemap for my website, pretty
much like this https://www.airbnb.com/sitemaps/western-europe?locale=en
What I have is a model with the following fields:
administrative_area_level_1
administrative_area_level_2
locality
sublocality_level_1
I could loop o
7 matches
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