Creating Indexes Within View and Speeding up Spatial Queries Over View?

Speed Up Spatial Queries: Views, Indexes, and a Little Database Magic

Spatial databases are no longer a niche thing. They’re everywhere, quietly powering everything from your navigation apps to how cities plan their growth. And as we drown in ever-larger, more intricate spatial datasets, making those queries run fast becomes absolutely critical. So, can you just slap an index on a view and call it a day? Well, it’s a bit more nuanced than that.

Here’s the deal: some database systems, like SQL Server, won’t let you directly create spatial indexes on what they call “indexed views” (or materialized views). Think of these views as pre-calculated tables, storing the results of a query. Super handy for speeding things up in general, but this limitation throws a wrench in the works when dealing with spatial data. PostGIS, the spatial whiz for PostgreSQL, is the same; no direct indexing on views there either.

Why the restriction? Honestly, it’s complicated. Spatial indexes rely on some pretty intense geometric calculations. Keeping those indexes up-to-date as the base data changes? It can turn into a real performance hog.

But don’t despair! Even if you can’t directly index the view itself, there’s still plenty you can do. The key is to focus on the underlying tables. This is where the magic happens. If your view pulls data from a table that already has a spatial index on its geometry column, the query optimizer is smart enough to use it.

Let’s say you have a buildings table with a geom column (that’s your geometry data) and an index called buildings_geom_idx. Something like this in PostGIS:

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