Ted Dunning and Ellen Friedman's "Time Series Databases" has a section on this with some approaches to geo-encoding:
https://www.mapr.com/time-series-databases-new-ways-store-and-access-data http://info.mapr.com/rs/mapr/images/Time_Series_Databases.pdf On Tue, Mar 10, 2015 at 3:53 PM, John Meehan <jnmee...@gmail.com> wrote: > There are some techniques you can use If you geohash > <http://en.wikipedia.org/wiki/Geohash> the lat-lngs. They will naturally > be sorted by proximity (with some edge cases so watch out). If you go the > join route, either by trimming the lat-lngs or geohashing them, you’re > essentially grouping nearby locations into buckets — but you have to > consider the borders of the buckets since the nearest location may actually > be in an adjacent bucket. Here’s a paper that discusses an implementation: > http://www.gdeepak.com/thesisme/Finding%20Nearest%20Location%20with%20open%20box%20query.pdf > > On Mar 9, 2015, at 11:42 PM, Akhil Das <ak...@sigmoidanalytics.com> wrote: > > Are you using SparkSQL for the join? In that case I'm not quiet sure you > have a lot of options to join on the nearest co-ordinate. If you are using > the normal Spark code (by creating key-pair on lat,lon) you can apply > certain logic like trimming the lat,lon etc. If you want more specific > computing then you are better off using haversine formula. > <http://www.movable-type.co.uk/scripts/latlong.html> > > >
