Jeff Kao

So given this lat long, I want to be able to fetch all the relevant geo entities.

So am I inside the city?

Am I inside this country?

Things like that.

Right.

Yeah, there are a lot of nice aspects about a log structure merge tree.

And I think most modern database implementations use that in some shape or another.

And

You know, it's largely, you know, this concept of obviously there's so much engineering around it, but like everything is sorted.

So that gives you a huge advantage.

And like that is something that many people take advantage of.

So in our blog post, we talk about, you know, very fast geo lookups using this library called S2, which is essentially a geo hashing library.

And so what that means is you have a latitude and you have a longitude.

That's two dimensions, right?

And so it's not clear at first how you can use sorting to help you make lookups faster.

And so, I mean, there is literally a thing called geohashing.

And there are other like implementations such as from Uber, there's this thing called H3.

From Google, there's this thing called S2.

And essentially, it collapses the latitude and longitude into a single 64-bit, or I use 64, I guess, in Rust terms.

And it has many nice properties because it tends to be the case, obviously, there's boundary conditions because latitude and longitude, and they go from negative 180 to 180 and negative 90 to 90.