[jasonkester]

That's one of the tough myths to overcome when building on top of a relational database. Changing the Database has traditionally been Hard, so everybody has been trained to think that way, therefore nobody is allowed to change the database because Changing the Database is hard. Try it at your bigco and the old guys will do everything in their power to stop you (thus making changing the database hard to do).

Ignore that rule and build yourself an environment where changing the database is easy. I make schema changes to my stuff all the time, and seldom push a release live that doesn't do so. The tools are in place to ensure that it's No Big Deal, so it just works.

If you live in a world where changing your SQL database is easy, it sort of takes the wind out of the "start with NoSQL, because changing the database is easy" argument. You get all the speed advantages of being schema-flexible, and you can write ad-hoc queries when you want to, so you're flexible in that direction too.

[dorkitude]

Let me clarify - I'm a startup hacker, not a bigco guy at all :)

And I still use MySQL all the time, right alongside so-called NoSQL solutions where they are better fit to a given purpose: Membase for high-availability collections on the order of billions-of-records in a social game; MySQL for defining the game world itself; mongoDB for any and all data for which eventual-consistency doesn't matter (e.g. analytics). I've streamlined my MySQL dealings in precisely the ways you outlined. I have change scripts for every schema change, and I have YML-driven schema auto-generation in Symfony and declarative. Schema changes are pushed out through staging to production - lazily when possible, and actively when possible.

But despite all the process improvements in the world, the dev time savings that go along with a smart, document-oriented model layer are not a myth. I assure you, they are very real. No longer does every new feature have to start with schema design (no matter how streamlined your schema alteration process may be, it has a nonzero cost, and one which definitely increases with scale). You can instead just get right to the code, and start setting and getting the properties your new feature will need.

The fact remains: SQL is not a one-size-fits-all solution any more than the recent "massively scalable" data stores are. A modern backend engineer should know a lot about a variety of datastore solutions, and should think long and hard about which data should be stored in which manner(s).

[billybob]

Would you mind telling some of the tools and techniques that make this easy for you?

[jasonkester]

All you really need to pull it off are:

- Change scripts for every schema change, stored in source control.

- An automated build/deploy that pulls down new change scripts and executes them in order.

- (optional) a good way to generate your backend CRUD by looking at the existing database schema, or as a lesser option an ORM that does the same thing.

So your workflow is: script out your schema change, check it in, apply it to your dev environment, regenerate the CRUD from your local schema, fix any compile errors that you've introduced. (and optionally make sure all your unit tests still pass).

You'll notice that all that stuff above is just the basic workflow you should have in place anyway.

[rbranson]

This is exactly the pattern used by Rails apps with database migrations and Capistrano deployment.