At the Forge - Database Modeling with Django
The past few months, this column has been examining Django, a popular open-source Web development framework written in Python. Django sometimes is described as a rival to Ruby on Rails or a Python version of Rails, but just as Python and Ruby are distinct languages, each with its own strengths and weaknesses, Django and Rails are different frameworks, and each has its own set of trade-offs.
If you have been following this series of columns about Django, you already have seen how to download and install the Django software, how to create and configure a site and application, and even how to create views (Python methods that handle the business logic) and templates (HTML files with special rules for interpolating variables and dynamic content). With everything we've looked at so far, you could presumably create an interesting dynamic Web application.
However, most modern Web applications have another component, a relational database, on which they rely for data storage and retrieval. Sure, you could store everything on the filesystem or even in memory, but for most of us, a relational database is the path of least resistance, ensuring the safety of our data while providing a great deal of flexibility in retrieving it.
This month's column, then, looks at the ways in which Django programmers can store and retrieve information in a database. If you have worked with databases only from PHP or CGI programs, you will be surprised and impressed by the degree of automation Django provides. If you have worked with Ruby on Rails, you probably will think the Django programmers are working too hard—to which Django hackers would say that they want to have full control over their application, rather than rely on behind-the-scenes magic.
The term model in the Django world describes a Python object for which there is a persistent state, presumably stored in a relational database. We don't need to use models to integrate a database into Django, but it would be difficult (not to mention unaesthetic) if we were simply to stick SQL queries into our templates. So instead, we use Django's built-in object-relational mapper, working solely with objects from within our views and templates. The mapper's job is to translate our method calls into SQL and then translate the resulting database response into Python objects.
But, before we even can create our model object, we first must have a database table to which the object will connect. Django requires that we define our model using Python code, describing the table's name, fields and even some default values.
If we were interested in keeping the blog application we started last month, we probably could define our table in PostgreSQL as follows:
CREATE TABLE Posting ( id SERIAL NOT NULL, title TEXT NOT NULL, body TEXT NOT NULL, posted_at TIMESTAMP NOT NULL DEFAULT NOW(), PRIMARY KEY(id) );
But in Django, we don't create the above SQL directly. Rather, we use Python to create it for us. For example, we can define the above table in Django as follows:
from django.db import models class Posting(models.Model): title = models.CharField(maxlength=30) body = models.TextField() publication_date = models.DateTimeField()
Our model is a Python class, which inherits from django.db.models.Model. We define each field with a particular type, using objects that we imported from django.db.models. As shown above, some of these data types can be restricted or modified from their defaults by passing parameters. Some are defined specifically because they have built-in limits, such as EmailField, which must be a valid e-mail address. By defining columns with ManyToManyField and ForeignKey objects, it's possible to define a variety of relationships among tables.
The above code should be placed in models.py, a Python file that sits within our application's directory (blog in this case), which itself sits inside our Django site directory (mysite in this case). Thus, the models for my blog application reside in mysite/blog/models.py, whereas the models for a poll application would reside in mysite/poll/models.py.
Notice that we don't have to define a primary key, which traditionally is called id and is a nonrepeating integer. (In PostgreSQL, we set it to have a SERIAL data type, which gives the column a default value taken from a newly created sequence object. In MySQL, you would set the column to AUTO_INCREMENT, which has some of the same capabilities as a sequence.) Django creates the id column for us automatically. Django handles potential namespace conflicts by prefacing the table name with the application name. So, the posting table within the blog application becomes the blog_posting table.
Now, how do we turn our Python code into SQL? First, we have to be sure Django knows which database to use. If you have been following along since my first Django article in the July 2007 issue, you already have added the appropriate lines to settings.py, a site-wide configuration file in which we define the database type, name, user and password. Here are the values that I have installed:
DATABASE_ENGINE = 'postgresql' DATABASE_NAME = 'atf' DATABASE_USER = 'reuven' DATABASE_PASSWORD = '' DATABASE_HOST = '' DATABASE_PORT = '5433'
It's also important to check that the application is defined in INSTALLED_APPS, a tuple of strings. On my system, INSTALLED_APPS looks like this:
INSTALLED_APPS = ( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.admin', 'mysite.blog' )
Notice the clear namespace distinction between my application (mysite.blog) and the applications that are included with Django (django.contrib.*).
Before we turn our Python code into SQL, we first should check to make sure it passes some basic sanity and validation checks. To do that, we go to our site's home directory, and type:
python manage.py validate
If all goes well, Django will report that there aren't any errors. Now that our model has been validated, we can use it to create SQL. The easiest way to do this is with the sqlall command to manage.py:
python manage.py sqlall blog
This produces the SQL output for our database driver (PostgreSQL, in this case). For example, this is the output that I see on my system:
BEGIN; CREATE TABLE "blog_posting" ( "id" serial NOT NULL PRIMARY KEY, "title" varchar(30) NOT NULL, "body" text NOT NULL, "publication_date" timestamp with time zone NOT NULL ); COMMIT;
To their credit, the Django developers wrap the CREATE TABLE statement between BEGIN and COMMIT, ensuring that the table creation will take place in a transaction and will be rolled back if there is a problem. This isn't an issue when creating only one table, but if we have several models, it's always best to leave the database in a consistent state.
One way for us to use the output from sqlall to create tables is to copy it from the terminal window and then paste it, either into a file or into the psql client program. But, Django provides the syncdb utility to do this for us:
python manage.py syncdb
The output from this reassures us that all is well:
Creating table blog_posting Loading 'initial_data' fixtures... No fixtures found.
And, sure enough, now we can see that our table has been added:
atf=# \d blog_posting id | integer | not null default nextval('blog_posting_id_seq'::regclass) title | character varying(30) | not null body | text | not null publication_date | timestamp with time zone | not null Indexes: "blog_posting_pkey" PRIMARY KEY, btree (id)
Voilà! We now have a model that we can access via Python methods, but that exists in our relational database.
Fast/Flexible Linux OS Recovery
On Demand Now
In this live one-hour webinar, learn how to enhance your existing backup strategies for complete disaster recovery preparedness using Storix System Backup Administrator (SBAdmin), a highly flexible full-system recovery solution for UNIX and Linux systems.
Join Linux Journal's Shawn Powers and David Huffman, President/CEO, Storix, Inc.
Free to Linux Journal readers.Register Now!
- Download "Linux Management with Red Hat Satellite: Measuring Business Impact and ROI"
- Peppermint 7 Released
- Sony Settles in Linux Battle
- Libarchive Security Flaw Discovered
- Maru OS Brings Debian to Your Phone
- Profiles and RC Files
- Snappy Moves to New Platforms
- Git 2.9 Released
- Understanding Ceph and Its Place in the Market
- The Giant Zero, Part 0.x
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide