Matthew Daly's Blog

I'm a web developer in Norfolk. This is my blog...

2nd August 2015 5:58 pm

Testing Django Views in Isolation

One thing you may hear said often about test-driven development is that as far as possible, you should test everything in isolation. However, it’s not always immediately clear how you actually go about doing this. In Django, it’s fairly easy to get your head around testing models in isolation because they’re single objects that you can just create, save, and then check their attributes. Forms are also quite easy to test, because you can just set the parameters with the appropriate values and check that the validation works as expected. With views, it’s much harder to imagine how you’d go about testing them in isolation, and often people just settle for writing higher-level functional tests instead. While functional tests are important, they’re also slower than unit tests, which makes it less likely they’ll be run often. So I thought I’d show you a quick and simple example of testing a Django view in isolation.

One of the little projects I’ve written in the past to help get my head around certain aspects of Django is a code-snippet sharing Django application which I named Snippetr. The index route of this application is a form for submitting a brand-new code snippet and I’ll show you how we would write a test for that.

Testing a GET request

Before now, you may well have used the Django test client to test views. That is fine for higher-level tests, but if you want to test a view in isolation, it’s no use because it emulates a real web server and all of the middleware and authentication, which we want to keep out of the way. Instead, we need to use RequestFactory:

from django.test import RequestFactory

RequestFactory actually implements a subset of the functionality of the Django test client, so while it will feel somewhat familiar, it won’t have all the same functionality. For instance, it doesn’t support middleware, so rather than logging in using the test client’s login() method, you instead attach a user directly to the request, as in this example:

request = RequestFactory()
request.user = user

You have to specify the URL in the request, but you also have to explicitly pass the request through to the view you want to test, which can be a bit confusing. Let’s see it in context. First of all, we want to write a test for making a GET request:

class SnippetCreateViewTest(TestCase):
Test the snippet create view
def setUp(self):
self.user = UserFactory()
self.factory = RequestFactory()
def test_get(self):
Test GET requests
request = self.factory.get(reverse('snippet_create'))
request.user = self.user
response = SnippetCreateView.as_view()(request)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.context_data['user'], self.user)
self.assertEqual(response.context_data['request'], request)

First of all, we define a setUp() method that creates a user and an instance of RequestFactory() for use in the test. Note that I’m using Factory Boy to define UserFactory in order to make it easier to work with. Also, if you have more than one view to test, you should create a base class containing the setUp() method that your view tests inherit from.

Next, we have our test for making a GET request. Note that we’re using the reverse() method to get the route for the view named snippet_create. You’ll need to import this as follows if you’re not yet using it:

from django.core.urlresolvers import reverse

We then attach our user object to the request manually, and fetch the response by passing the request to the view as follows:

    response = SnippetCreateView.as_view()(request)

Note that this is the syntax used for class-based views - we call the view’s as_view() method. For a function-based view, the syntax is a bit simpler:

    response = my_view(request)

We then test our response as usual. In this case, the view adds some additional context data, and we check that we can access that, as well as checking the status code.

Testing a POST request

Testing a POST request is a little more challenging in this case because submitting the form will create a new Snippet object and we don’t want to interact with the model layer at all if we can help it. We want to test the view in isolation, partly because it will be faster, and partly because it’s a good idea. We can do this by mocking the Snippet model’s save() method.

To do so, we need to import two things from the mock library. If you’re using Python 3.4 or later, then mock is part of unittest as unittest.mock. Otherwise, it’s a separate library you need to install with pip. Here’s the import statement for those on Python 3.4 or later:

from unittest.mock import patch, MagicMock

And for those on earlier versions:

from mock import patch, MagicMock

Now, our test for the POST requests should look like this:

@patch('', MagicMock(name="save"))
def test_post(self):
Test post requests
# Create the request
data = {
'title': 'My snippet',
'content': 'This is my snippet'
request ='snippet_create'), data)
request.user = self.user
# Get the response
response = SnippetCreateView.as_view()(request)
self.assertEqual(response.status_code, 302)
# Check save was called
self.assertEqual(, 1)

Note first of all the following line:

    @patch('', MagicMock(name="save"))

Here we’re saying that in this test, when the save() method of the Snippet model is called, it should instead call a mocked version, which lacks the functionality and only registers that it has been called and a few details about it.

Next, we put together the data to be passed through and create a POST request for it. As before, we attach the user to the request. We then pass the request through in the same way as for the GET request. We also check that the response code was 302, meaning that the user would be redirected elsewhere after the form was submitted correctly.

Finally, we assert that is true. called is a Boolean value, representing whether the method was called or not. We also check the value of, which is a count of the number of times the method was called - here we check that it’s set to 1.

As you can see, while the request factory is a little harder than the Django test client to figure out, it’s not too difficult once you get the hang of it. By combining it with judicious use of mock, you can easily test your views in isolation, and without having to interact with the database or set up any middleware, these tests will be much faster than those using the Django test client.

1st August 2015 6:26 pm

Exploring the Hstorefield in Django 1.8

One of the most interesting additions in Django 1.8 is the new Postgres-specific fields. I started using PostgreSQL in preference to MySQL for Django apps last year, and so I was interested in the additional functionality they offer.

By far the biggest deal out of all of these was the new HStoreField type. PostgreSQL added a JSON data type a little while back, and HStoreField allows you to use that field type. This is a really big deal because it allows you to store arbitrary data as JSON and query it. Previously, you could of course just store data as JSON in a text field, but that lacked the same ability to query it. This gives you many of the advantages of a NoSQL document database such as MongoDB in a relational database. For instance, you can store different products with different data about them, and crucially, query them by that data. Previously, the only way to add arbitrary product data and be able to query it was to have it in a separate table, and it was often cumbersome to join them when fetching multiple products.

Let’s see a working example. We might be building an online store where products can have all kinds of arbitrary data stored about them. One product might be a plastic box, and you’d need to list the capacity as an additional attribute. Another product might be a pair of shoes, which have no capacity, but do have a size. It might be difficult to model this otherwise, but HStoreField is perfect for this kind of data.

First, let’s set up our database. I’ll assume you already have PostgreSQL up and running via your package manager. First, we need to create our database:

$ createdb djangostore

Next, we need to create a new user for this database with superuser access:

$ createuser store -s -P

You’ll be prompted for a password - I’m assuming this will just be password here. Next, we need to connect to PostgreSQL using the psql utility:

$ psql djangostore -U store -W

You’ll be prompted for your new password. Next, run the following command:

# \q

The first command installs the HStore extension. Next we make sure our new user has the privileges required on the new database:

We’ve now created our database and a user to interact with it. Next, we’ll set up our Django install:

$ cd Projects
$ mkdir djangostore
$ cd djangostore
$ pyvenv venv
$ source venv/bin/activate
$ pip install Django psycopg2 ipdb
$ startproject djangostore
$ python startapp store

I’m assuming here that you’re using Python 3.4. On Ubuntu, getting it working is a bit more involved.

Next, open up djangostore/ and amend INSTALLED_APPS to include the new app and the PostgreSQL-specific functionality:


You’ll also need to configure the database settings:

'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': 'djangostore',
'USER': 'store',
'PASSWORD': 'password',
'HOST': 'localhost',
'PORT': '',

We need to create an empty migration to use HStoreField:

$ python makemigrations --empty store

This command should create the file store/migrations/ Open this up and edit it to look like this:

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.contrib.postgres.operations import HStoreExtension
class Migration(migrations.Migration):
dependencies = [
operations = [

This will make sure the HStore extension is installed. Next, let’s run these migrations:

$ python migrate
Operations to perform:
Synchronize unmigrated apps: messages, staticfiles, postgres
Apply all migrations: sessions, store, admin, auth, contenttypes
Synchronizing apps without migrations:
Creating tables...
Running deferred SQL...
Installing custom SQL...
Running migrations:
Rendering model states... DONE
Applying contenttypes.0001_initial... OK
Applying auth.0001_initial... OK
Applying admin.0001_initial... OK
Applying contenttypes.0002_remove_content_type_name... OK
Applying auth.0002_alter_permission_name_max_length... OK
Applying auth.0003_alter_user_email_max_length... OK
Applying auth.0004_alter_user_username_opts... OK
Applying auth.0005_alter_user_last_login_null... OK
Applying auth.0006_require_contenttypes_0002... OK
Applying sessions.0001_initial... OK
Applying store.0001_initial... OK

Now, we’re ready to start creating our Product model. Open up store/ and amend it as follows:

from django.contrib.postgres.fields import HStoreField
from django.db import models
# Create your models here.
class Product(models.Model):
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
name = models.CharField(max_length=200)
description = models.TextField()
price = models.FloatField()
attributes = HStoreField()
def __str__(self):

Note that HStoreField is not part of the standard group of model fields, and needs to be imported from the Postgres-specific fields module. Next, let’s create and run our migrations:

$ python makemigrations
$ python migrate

We should now have a Product model where the attributes field can be any arbitrary data we want. Note that we installed ipdb earlier - if you’re not familiar with it, this is an improved Python debugger, and also pulls in ipython, an improved Python shell, which Django will use if available.

Open up the Django shell:

$ python shell

Then, import the Product model:

from store.models import Product

Let’s create our first product - a plastic storage box:

box = Product() = 'Box'
box.description = 'A big box'
box.price = 5.99
box.attributes = { 'capacity': '1L', "colour": "blue"}

If we take a look, we can see that the attributes can be returned as a Python dictionary:

In [12]: Product.objects.all()[0].attributes
Out[12]: {'capacity': '1L', 'colour': 'blue'}

We can easily retrieve single values:

In [15]: Product.objects.all()[0].attributes['capacity']
Out[15]: '1L'

Let’s add a second product - a mop:

mop = Product() = 'Mop'
mop.description = 'A mop'
mop.price = 12.99
mop.attributes = { 'colour': "red" }

Now, we can filter out only the red items easily:

In [2]: Product.objects.filter(attributes__contains={'colour': 'red'})
Out[2]: [<Product: Mop>]

Here we search for items where the colour attribute is set to red, and we only get back the mop. Let’s do the same for blue items:

In [3]: Product.objects.filter(attributes__contains={'colour': 'blue'})
Out[3]: [<Product: Box>]

Here it returns the box. Let’s now search for an item with a capacity of 1L:

In [4]: Product.objects.filter(attributes__contains={'capacity': '1L'})
Out[4]: [<Product: Box>]

Only the box has the capacity attribute at all, and it’s the only one returned. Let’s see what happens when we search for an item with a capacity of 2L, which we know is not present:

In [5]: Product.objects.filter(attributes__contains={'capacity': '2L'})
Out[5]: []

No items returned, as expected. Let’s look for any item with the capacity attribute:

In [6]: Product.objects.filter(attributes__has_key='capacity')
Out[6]: [<Product: Box>]

Again, it only returns the box, as that’s the only one where that key exists. Note that all of this is tightly integrated with the existing API for the Django ORM. Let’s add a third product, a food hamper:

In [3]: hamper = Product()
In [4]: = 'Hamper'
In [5]: hamper.description = 'A food hamper'
In [6]: hamper.price = 19.99
In [7]: hamper.attributes = {
...: 'contents': 'ham, cheese, coffee',
...: 'size': '90cmx60cm'
...: }
In [8]:

Next, let’s return only those items that have a contents attribute that contains cheese:

In [9]: Product.objects.filter(attributes__contents__contains='cheese')
Out[9]: [<Product: Hamper>]

As you can see, the HStoreField type allows for quite complex queries, while allowing you to set arbitrary values for an individual item. This overcomes one of the biggest issues with relational databases - the inability to set arbitrary data. Previously, you might have to work around it in some fashion, such as creating a table containing attributes for individual items which had to be joined on the product table. This is very cumbersome and difficult to use, especially when you wanted to work with more than one product. With this approach, it’s easy to filter products by multiple values in the HStore field, and you get back all of the attributes at once, as in this example:

In [13]: Product.objects.filter(attributes__capacity='1L', attributes__colour='blue')
Out[13]: [<Product: Box>]
In [14]: Product.objects.filter(attributes__capacity='1L', attributes__colour='blue')[0].attributes
Out[14]: {'capacity': '1L', 'colour': 'blue'}

Similar functionality is coming in a future version of MySQL, so it wouldn’t be entirely surprising to see HStoreField become more generally available in Django in the near future. For now, this functionality is extremely useful and makes for a good reason to ditch MySQL in favour of PostgreSQL for your future Django apps.

21st July 2015 9:15 pm

New Laptop

For a while now it’s been obvious that I needed a new laptop. My main workhorse for a while has been a 2008 MacBook, but I’m not really a fan of Mac OS X and it was stuck on Snow Leopard, so it was somewhat behind the times. It was also painfully slow by modern standards - regenerating this site took a couple of minutes. I had two other reasonably modern laptops, but one was too big and cumbersome, while the other was a Dell Mini, which isn’t really fast enough for a developer. When I last bought a laptop, I wasn’t even a developer, so it was long past time I got a more suitable machine.

I therefore took the plunge and ordered a new Dell XPS 13 Developer Edition, which arrived today. It’s an absolutely beautiful machine, and it’s extremely light. It’s also a lot faster than any other machine I own. The screen is exceptionally sharp, and setting it up was nice and easy.

After an hour or so with this machine, I’m already really happy with it. We’ll have to see whether I still think so after a few months using it.

4th July 2015 1:01 pm

Handling Images As Base64 Strings With Django REST Framework

I’m currently working on a Phonegap app that involves taking pictures and uploading them via a REST API. I’ve done this before, and I found at that time that the best way to do so was to fetch the image as a base-64 encoded string and push that up, rather than the image file itself. However, the last time I did so, I was using Tastypie to build the API, and I’ve since switched over to Django REST Framework as my API toolkit of choice.

It didn’t take long to find this gist giving details of how to do so, but it didn’t work as is, partly because I was using Python 3, and partly because the from_native method has gone as at Django REST Framework 3.0. It was, however, straightforward to adapt it to work. Here’s my solution:

import base64, uuid
from django.core.files.base import ContentFile
from rest_framework import serializers
# Custom image field - handles base 64 encoded images
class Base64ImageField(serializers.ImageField):
def to_internal_value(self, data):
if isinstance(data, str) and data.startswith('data:image'):
# base64 encoded image - decode
format, imgstr = data.split(';base64,') # format ~= data:image/X,
ext = format.split('/')[-1] # guess file extension
id = uuid.uuid4()
data = ContentFile(base64.b64decode(imgstr), name = id.urn[9:] + '.' + ext)
return super(Base64ImageField, self).to_internal_value(data)

This solution will handle both base 64 encoded strings and image files. Then, just use this field as normal.

17th June 2015 8:34 pm

Getting Django-behave and Celery to Work Together

I ran into a small issue today. I’m working on a Django app which uses Celery to handle certain tasks that don’t need to return a response within the context of the HTTP request. I also wanted to use django_behave for running BDD tests. The trouble is that both django_behave and Celery provide their own custom test runners that extend the default Django test runner, and so it looked like I might have to choose between the two.

However, it turned out that the Celery one was actually very simple, with only a handful of changes needing to be made to the default test runner to make it work with Celery. I was therefore able to create my own custom test runner that inherited from DjangoBehaveTestSuiteRunner and applied the changes necessary to get Celery working with it. Here is the test runner I wrote, which was saved as myproject/

from django.conf import settings
from djcelery.contrib.test_runner import _set_eager
from django_behave.runner import DjangoBehaveTestSuiteRunner
class CeleryAndBehaveRunner(DjangoBehaveTestSuiteRunner):
def setup_test_environment(self, **kwargs):
settings.BROKER_BACKEND = 'memory'
super(CeleryAndBehaveRunner, self).setup_test_environment(**kwargs)

To use it, you need to set the test runner in

TEST_RUNNER = 'myproject.runner.CeleryAndBehaveRunner'

Once that was done, my tests worked flawlessly with Celery, and the Behave tests ran as expected.

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About me

I'm a web and mobile app developer based in Norfolk. My skillset includes Python, PHP and Javascript, and I have extensive experience working with CodeIgniter, Laravel, Zend Framework, Django, Phonegap and React.js.