One of my pet peeves is using the database schema to validate data.

Several ways in which this normally happens:

• Specifying a column as “required”, e.g. email VARCHAR(255) NOT NULL
• Adding an index to force column values to be unique (e.g. CREATE UNIQUE INDEX email_idx ON users(email))
• Adding an index for foreign key integrity, including cascading deletes, etc.

Yes, I want data integrity too. No, I don’t want to rely on the database for that.

The introduction of Domain-Driven Design (DDD) to a larger audience has led to a few really damaging ideas among developers, like this one (maybe it’s more a sentiment than an idea):

Data is bad, behavior is good. The domain model is great, the database awful.

(We’re not even discussing CRUD in this article, which apparently is the worst of the worst.)

By now many of us feel ashamed of using an ORM alongside a “DDD domain model”, putting some mapping configuration in it, doing things inside your entities (or do you call them aggregates?) just to make them easily serializable to the database.

In part 1 of this short series (it’s going to end with this article) we covered how you can test-drive the queries in a repository class. Returning query results is only part of the job of a repository though. The other part is to store objects (entities), retrieve them using something like a save() and a getById() method, and possibly delete them. Some people will implement these two jobs in one repository class, some like to use two or even many repositories for this. When you have a separate write and read model (CQRS), the read model repositories will have the querying functionality (e.g. find me all the active products), the write model repositories will have the store/retrieve/delete functionality.

There’s something I’ve only been doing since a year or so, and I’ve come to like it a lot. My previous testing experiences were mostly at the level of unit tests or functional/system tests. What was left out of the equation was integration tests. The perfect example of which is a test that proves that your custom repository class works well with the type of database that you use for the project, and possibly the ORM or database abstraction library you use to talk with that database. A test for a repository can’t be a unit test; that wouldn’t make sense. You’d leave a lot of assumptions untested. So, no mocking is allowed.

System and integration tests need database fixtures. These fixtures should be representative and diverse enough to “fake” normal usage of the application, so that the tests using them will catch any issues that might occur once you deploy the application to the production environment. There are many different options for dealing with fixtures; let’s explore some of them.

Generate fixtures the natural way

The first option, which I assume not many people are choosing, is to start up the application at the beginning of a test, then navigate to specific pages, submit forms, click buttons, etc. until finally the database has been populated with the right data. At that point, the application is in a useful state and you can continue with the act/when and assert/then phases. (See the recent article “Pickled State” by Robert Martin on the topic of tests as specifications of a finite state machine).

Something that always bothers me: persistence (the user interface too, but that’s a different topic ;)). Having objects in memory is nice, but when the application shuts down (and for PHP this is after every request-response cycle), you have to persist them somehow. By the way, I think we’ve all forever been annoyed by persistence, since there’s an awful lot of software solutions related to object persistence: different types of databases, different types of ORMs, etc.

CQRS - not a complicated thing

CQRS has some reputation issues. Mainly, people will feel that it’s too complicated to apply in their current projects. It will often be considered over-engineering. I think CQRS is simply misunderstood, which is the reason many people will not choose it as a design technique. One of the common misconceptions is that CQRS always goes together with event sourcing, which is indeed more costly and risky to implement.