I actually worked on a project that did this. We used a database called "Tile38" [1] which used an R-Tree to make geospatial queries speedy. It was pretty good.

I actually worked on a project that did this. We used a database called "Tile38" [1] which used an R-Tree to make geospatial queries speedy. It was pretty good. Our dataset was ~150 GiB, I think? All in RAM. Took a while to start the server, as it all came off disk. Could have been faster. (It borrowed Redis's query language, and its storage was just "store the commands the recreate the DB, literally", IIRC. Dead simple, but a lot of slack/wasted space there.) Overall not a bad database. Latency serving out of RAM was, as one should/would expect, very speedy! [1]: https://tile38.com/ https://tile38.com/