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I'm wondering how a geospatial index, such as the one used by MongoDB, works. Can anyone explain what data structure/algorithm is used internally? What time complexity does a search run in?
Links to resources would be great too.
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The geospatial index supports distance, containment, and intersection queries for various geometric 2D shapes. You should mainly be using AQL queries to perform these types of operations. The index can operate in two different modes, depending on if you want to use the GeoJSON data-format or not.
It is a common technique used by spatial databases. Without indexing, any search for a feature would require a "sequential scan" of every record in the database, resulting in much longer processing time. In a spatial index construction process, the minimum bounding rectangle serves as an object approximation.
H3 is a hierarchical geospatial index. H3 indexes refer to cells by the spatial hierarchy. Every hexagonal cell, up to the maximum resolution supported by H3, has seven child cells below it in this hierarchy. This subdivision is referred to as aperture 7. A parent hexagon approximately contains seven children.
SPATIAL INDEX creates an R-tree index. For storage engines that support nonspatial indexing of spatial columns, the engine creates a B-tree index. A B-tree index on spatial values is useful for exact-value lookups, but not for range scans.
Depending on the data type and usage pattern, either an R-Tree or variant (R*, R+) or a quadtree or perhaps even a kd-tree.
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