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A comment on Using k-d trees to efficiently calculate nearest neighbors in 3D vector space. Parent comment

Re:Using k-d trees to efficiently calculate nearest neighbor... by klodolph on Saturday February 10, 2018 @03:45PM (#16349474)
That's only viable when you have a small data set, which is when you don't care much about indexing to begin with.
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Re:Using k-d trees to efficiently calculate nearest neighbor... by geophile on Saturday February 10, 2018 @06:02PM (#16350138)
Huh? I've used z-order inside database systems, with many millions of records. Works incredibly well.
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Re:Using k-d trees to efficiently calculate nearest neighbor... by gmueckl on Sunday February 11, 2018 @03:20AM (#16351757)
Building and balancing a k-d tree is not free, either. I suspect that the GP's approach is roughly the same in terms of theoretical complexity, but effectively trades memory for smaller constants and a chance to trip over edge cases like dense groups of points.
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Re:Using k-d trees to efficiently calculate nearest neighbor... by dvirsky on Saturday February 10, 2018 @04:36PM (#16349743)
Sounds like GeoHash lookup which is actually pretty robust if you have a good data structure for indexing. For example: https://redis.io/commands/georadius
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