Defining Models in Python

As we discussed earlier in earlier chapter, the “M” in “MTV” stands for “Model.” A Django model is a description of the data in your database, represented as Python code. It’s your data layout – the equivalent of your SQL CREATE TABLE statements – except it’s in Python instead of SQL, and it includes more than just database column definitions.

Django uses a model to execute SQL code behind the scenes and return convenient Python data structures representing the rows in your database tables. Django also uses models to represent higher-level concepts that SQL can’t necessarily handle.

If you’re familiar with databases, your immediate thought might be, “Isn’t it redundant to define data models in Python instead of in SQL?” Django works the way it does for several reasons:

• Introspection requires overhead and is imperfect. In order to provide convenient data-access APIs, Django needs to know the database layout somehow, and there are two ways of accomplishing this. The first way would be to explicitly describe the data in Python, and the second way would be to introspect the database at runtime to determine the data models.
• Writing Python is fun, and keeping everything in Python limits the number of times your brain has to do a “context switch.” It helps productivity if you keep yourself in a single programming environment/mentality for as long as possible. Having to write SQL, then Python, and then SQL again is disruptive.
• Having data models stored as code rather than in your database makes it easier to keep your models under version control. This way, you can easily keep track of changes to your data layouts.
• SQL allows for only a certain level of metadata about a data layout. Most database systems, for example, do not provide a specialized data type for representing email addresses or URLs. Django models do. The advantage of higher-level data types is higher productivity and more reusable code.
• SQL is inconsistent across database platforms. If you’re distributing a Web application, for example, it’s much more pragmatic to distribute a Python module that describes your data layout than separate sets of CREATE TABLE statements for MySQL, PostgreSQL, and SQLite.

A drawback of this approach, however, is that it’s possible for the Python code to get out of sync with what’s actually in the database. If you make changes to a Django model, you’ll need to make the same changes inside your database to keep your database consistent with the model. We’ll detail some strategies for handling this problem later in this chapter.

Finally, we should note that Django includes a utility that can generate models by introspecting an existing database. This is useful for quickly getting up and running with legacy data.

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