title: Multidimensional search by combining geospatial indexes
body: <p>I build application that stores millions of floating point vectors, each vector having ~100 dimensions.  With a query vector, I need to search through these vectors for the k nearest (euclidean) matches.  Run time must be faster than scanning all millions of vectors.  By "vector" I mean in the linear algebra term a list of ~100 floating point numbers i.e. <code>[0.3, -15.7, 0.004, 457.1, ...]</code></p>
  
  <p>I know databases like MySQL and MongoDB provide spatial indexes that work for 2 dimensions.  Is there a way to adapt this to many more dimensions maybe with composite indexes?  Or are there other other data stores support indexes on more dimension?</p>