Now that geolocated posts are beginning to show up around app.net, I found myself wondering about proximity search. Twitter provides one themselves for geotagged tweets. What a proximity search does, essentially, is provide results from a data set ordered by increasing distance from a given location. This can be further enhanced by combining it with a text search either before or after the distance sorting. This would give you a way to search for a certain query within a certain area.

Here, for example, is a proximity search centred on Berlin including any tweet in 1000 miles containing the word 'toast'.

When I first started thinking about the tech required for a proximity search, I remembered Lukas Nowacki back in our old Whitespace days implementing the Haversine formula in MySQL (Alexander Rubin has a good overview of how to do this). As much as I love my trigonometry and logarithms, I must admit, I was looking around for a simpler solution. Actually, I was looking around for a copy-paste solution, to be honest. I may even have spent some time going down that route if Max hadn't pointed me in the direction of MongoDB.

I'd been putting off digging into NoSQL databases for a while because, well, I had no real reason to. Recently, I've either been focused on front-end dev or hacking away at Java and never really had any good reason to investigate any of these new-fangled technologies get off my lawn you kids.

MongoDB

After 10 minutes of messing around with Mongo, I pretty much just found myself saying “No... way. There's no way that's actually working” I'm sure those of you experience with document-oriented databases are rolling your eyes right now but for those few of us left with an entirely relational concept of databases, let me just explain it like this: you know those things you want to do with a database that are just a hassle of multiple joins and confusing references? Document databases do some of those things really really well.

The biggest selling point for me, however was the native geospatial indexing. That pretty much made the majority of my proximity search complete. All I needed to do was wrap it in a nice interface and call it a day...

I'll follow up tomorrow with a more detailed 'How-to' guide.