The RequestFactory shares the same API as the test client. However, instead of behaving like a browser, the RequestFactory provides a way to generate a request instance that can be used as the first argument to any view. This means you can test a view function the same way as you would test any other function – as a black box, with exactly known inputs, testing for specific outputs.
The API for the RequestFactory is a slightly restricted subset of the test client API:
The following is a simple unit test using the request factory:
from django.utils import unittest
from django.test.client import RequestFactory
class SimpleTest(unittest.TestCase):
def setUp(self):
# Every test needs access to the request factory.
self.factory = RequestFactory()
def test_details(self):
# Create an instance of a GET request.
request = self.factory.get('/customer/details')
# Test my_view() as if it were deployed at /customer/details
response = my_view(request)
self.assertEqual(response.status_code, 200)
If you’re testing a multiple database configuration with master/slave replication, this strategy of creating test databases poses a problem. When the test databases are created, there won’t be any replication, and as a result, data created on the master won’t be seen on the slave.
To compensate for this, Django allows you to define that a database is a test mirror. Consider the following (simplified) example database configuration:
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'myproject',
'HOST': 'dbmaster',
# ... plus some other settings
},
'slave': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'myproject',
'HOST': 'dbslave',
'TEST_MIRROR': 'default'
# ... plus some other settings
}
}
In this setup, we have two database servers: dbmaster, described by the database alias default, and dbslave described by the alias slave. As you might expect, dbslave has been configured by the database administrator as a read slave of dbmaster, so in normal activity, any write to default will appear on slave.
If Django created two independent test databases, this would break any tests that expected replication to occur. However, the slave database has been configured as a test mirror (using the TEST_MIRROR setting), indicating that under testing, slave should be treated as a mirror of default.
When the test environment is configured, a test version of slave will not be created. Instead the connection to slave will be redirected to point at default. As a result, writes to default will appear on slave – but because they are actually the same database, not because there is data replication between the two databases.
By default, Django will always create the default database first. However, no guarantees are made on the creation order of any other databases in your test setup.
If your database configuration requires a specific creation order, you can specify the dependencies that exist using the TEST_DEPENDENCIES setting. Consider the following (simplified) example database configuration:
DATABASES = {
'default': {
# ... db settings
'TEST_DEPENDENCIES': ['diamonds']
},
'diamonds': {
# ... db settings
},
'clubs': {
# ... db settings
'TEST_DEPENDENCIES': ['diamonds']
},
'spades': {
# ... db settings
'TEST_DEPENDENCIES': ['diamonds','hearts']
},
'hearts': {
# ... db settings
'TEST_DEPENDENCIES': ['diamonds','clubs']
}
}
Under this configuration, the diamonds database will be created first, as it is the only database alias without dependencies. The default and clubs alias will be created next (although the order of creation of this pair is not guaranteed); then hearts; and finally spades.
If there are any circular dependencies in the TEST_DEPENDENCIES definition, an ImproperlyConfigured exception will be raised.
If you want to run tests outside of ./manage.py test – for example, from a shell prompt – you will need to set up the test environment first. Django provides a convenience method to do this:
>>> from django.test.utils import setup_test_environment
>>> setup_test_environment()
This convenience method sets up the test database, and puts other Django features into modes that allow for repeatable testing.
The call to setup_test_environment() is made automatically as part of the setup of ./manage.py test. You only need to manually invoke this method if you’re not using running your tests via Django’s test runner.
Clearly, doctest and unittest are not the only Python testing frameworks. While Django doesn’t provide explicit support for alternative frameworks, it does provide a way to invoke tests constructed for an alternative framework as if they were normal Django tests.
When you run ./manage.py test, Django looks at the TEST_RUNNER setting to determine what to do. By default, TEST_RUNNER points to 'django.test.simple.DjangoTestSuiteRunner'. This class defines the default Django testing behavior. This behavior involves:
If you define your own test runner class and point TEST_RUNNER at that class, Django will execute your test runner whenever you run ./manage.py test. In this way, it is possible to use any test framework that can be executed from Python code, or to modify the Django test execution process to satisfy whatever testing requirements you may have.
A test runner is a class defining a run_tests() method. Django ships with a DjangoTestSuiteRunner class that defines the default Django testing behavior. This class defines the run_tests() entry point, plus a selection of other methods that are used to by run_tests() to set up, execute and tear down the test suite.
verbosity determines the amount of notification and debug information that will be printed to the console; 0 is no output, 1 is normal output, and 2 is verbose output.
If interactive is True, the test suite has permission to ask the user for instructions when the test suite is executed. An example of this behavior would be asking for permission to delete an existing test database. If interactive is False, the test suite must be able to run without any manual intervention.
If failfast is True, the test suite will stop running after the first test failure is detected.
Django will, from time to time, extend the capabilities of the test runner by adding new arguments. The **kwargs declaration allows for this expansion. If you subclass DjangoTestSuiteRunner or write your own test runner, ensure accept and handle the **kwargs parameter.
Your test runner may also define additional command-line options. If you add an option_list attribute to a subclassed test runner, those options will be added to the list of command-line options that the test command can use.
This is the tuple of optparse options which will be fed into the management command’s OptionParser for parsing arguments. See the documentation for Python’s optparse module for more details.
Run the test suite.
test_labels is a list of strings describing the tests to be run. A test label can take one of three forms:
If test_labels has a value of None, the test runner should run search for tests in all the applications in INSTALLED_APPS.
extra_tests is a list of extra TestCase instances to add to the suite that is executed by the test runner. These extra tests are run in addition to those discovered in the modules listed in test_labels.
This method should return the number of tests that failed.
Sets up the test environment ready for testing.
Constructs a test suite that matches the test labels provided.
test_labels is a list of strings describing the tests to be run. A test label can take one of three forms:
If test_labels has a value of None, the test runner should run search for tests in all the applications in INSTALLED_APPS.
extra_tests is a list of extra TestCase instances to add to the suite that is executed by the test runner. These extra tests are run in addition to those discovered in the modules listed in test_labels.
Returns a TestSuite instance ready to be run.
Creates the test databases.
Returns a data structure that provides enough detail to undo the changes that have been made. This data will be provided to the teardown_databases() function at the conclusion of testing.
Runs the test suite.
Returns the result produced by the running the test suite.
Destroys the test databases, restoring pre-test conditions.
old_config is a data structure defining the changes in the database configuration that need to be reversed. It is the return value of the setup_databases() method.
Restores the pre-test environment.
Computes and returns a return code based on a test suite, and the result from that test suite.
To assist in the creation of your own test runner, Django provides a number of utility methods in the django.test.utils module.
Performs any global pre-test setup, such as the installing the instrumentation of the template rendering system and setting up the dummy email outbox.
Performs any global post-test teardown, such as removing the black magic hooks into the template system and restoring normal email services.
The creation module of the database backend (connection.creation) also provides some utilities that can be useful during testing.
Creates a new test database and runs syncdb against it.
verbosity has the same behavior as in run_tests().
autoclobber describes the behavior that will occur if a database with the same name as the test database is discovered:
Returns the name of the test database that it created.
create_test_db() has the side effect of modifying the value of NAME in DATABASES to match the name of the test database.
Destroys the database whose name is the value of NAME in DATABASES, and sets NAME to the value of old_database_name.
The verbosity argument has the same behavior as for DjangoTestSuiteRunner.
Code coverage describes how much source code has been tested. It shows which parts of your code are being exercised by tests and which are not. It’s an important part of testing applications, so it’s strongly recommended to check the coverage of your tests.
Django can be easily integrated with coverage.py, a tool for measuring code coverage of Python programs. First, install coverage.py. Next, run the following from your project folder containing manage.py:
coverage run --source='.' manage.py test myapp
This runs your tests and collects coverage data of the executed files in your project. You can see a report of this data by typing following command:
coverage report
Note that some Django code was executed while running tests, but it is not listed here because of the source flag passed to the previous command.
For more options like annotated HTML listings detailing missed lines, see the coverage.py docs.
Dec 23, 2012