I work at an Alexa top10k, and our database server is quite small and sits at about 10% load most of the time.
You can grow huge with vertical scaling. Not all the way to “household name” size, but still truly huge. Servers with tens of tb of ram are available by the hour.
Not really, that's the result of hard-earned lessons that pay off from day 1.
Think about it: you have data in a database, and you put up an API to allow access to it.
Your API needs change, and now you need to make available a resource with/without an extra attribute. You need to provide it as a new API version in parallel with the current API version you're supporting.
What will you do then? Reupdate the whole db with a new migration? Put up two databases? Or are you going to tweak the DTO mapper and be done with it?
Perhaps my experience is unusual having worked on a distributed database for several years but this stuff just seems like table stakes.
We're not talking about adding attributes to a database. We're talking about one of the most basic cases in API design: changing a interface and serving two or more versions.
If the mapping step between getting data from the db and outputting it in a response is expected to be eliminated by getting the DB to dump JSON, how is this issue solved?
And how exactly is a full blown db migration preferable to mapping and serializing a DTO?
The point is that instead of having queries that return tables that you then map to DTOs for serialization, you have queries that return JSON directly.
Another way to accomplish the same thing, if you don't want views or stored procedures, is to use a library like jOOQ that does much the same thing in your Java middleware.
But that already qualifies as the boilerplate code that was supposedly the root problem, isn't it?
But now instead of a simple mapper you've bolted on another complex system.
So exactly how does JSON in the db solve any of the problems that were presented? Other than adding two buzzwords, what were the improvements?