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Django2.0手册:Writing and running tests

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This document is split into two primary sections. First, we explain how to write
tests with Django. Then, we explain how to run them.

Writing tests¶

Django’s unit tests use a Python standard library module: unittest. This
module defines tests using a class-based approach.

Here is an example which subclasses from django.test.TestCase,
which is a subclass of unittest.TestCase that runs each test inside a
transaction to provide isolation:

from django.test import TestCase
from myapp.models import Animal

class AnimalTestCase(TestCase):
    def setUp(self):
        Animal.objects.create(name="lion", sound="roar")
        Animal.objects.create(name="cat", sound="meow")

    def test_animals_can_speak(self):
        """Animals that can speak are correctly identified"""
        lion = Animal.objects.get(name="lion")
        cat = Animal.objects.get(name="cat")
        self.assertEqual(lion.speak(), 'The lion says "roar"')
        self.assertEqual(cat.speak(), 'The cat says "meow"')

When you run your tests, the default behavior of the
test utility is to find all the test cases (that is, subclasses of
unittest.TestCase) in any file whose name begins with test,
automatically build a test suite out of those test cases, and run that suite.

For more details about unittest, see the Python documentation.

Where should the tests live?

The default startapp template creates a file in the
new application. This might be fine if you only have a few tests, but as
your test suite grows you’ll likely want to restructure it into a tests
package so you can split your tests into different submodules such as,,, etc. Feel free to
pick whatever organizational scheme you like.

See also Using the Django test runner to test reusable applications.


If your tests rely on database access such as creating or querying models,
be sure to create your test classes as subclasses of
django.test.TestCase rather than unittest.TestCase.

Using unittest.TestCase avoids the cost of running each test in a
transaction and flushing the database, but if your tests interact with
the database their behavior will vary based on the order that the test
runner executes them. This can lead to unit tests that pass when run in
isolation but fail when run in a suite.


Once you’ve written tests, run them using the test command of
your project’s utility:

$ ./ test

Test discovery is based on the unittest module’s built-in test
. By default, this will discover tests in
any file named “test*.py” under the current working directory.

You can specify particular tests to run by supplying any number of “test
labels” to ./ test. Each test label can be a full Python dotted
path to a package, module, TestCase subclass, or test method. For instance:

# Run all the tests in the animals.tests module
$ ./ test animals.tests

# Run all the tests found within the 'animals' package
$ ./ test animals

# Run just one test case
$ ./ test animals.tests.AnimalTestCase

# Run just one test method
$ ./ test animals.tests.AnimalTestCase.test_animals_can_speak

You can also provide a path to a directory to discover tests below that

$ ./ test animals/

You can specify a custom filename pattern match using the -p (or
--pattern) option, if your test files are named differently from the
test*.py pattern:

$ ./ test --pattern="tests_*.py"

If you press Ctrl-C while the tests are running, the test runner will
wait for the currently running test to complete and then exit gracefully.
During a graceful exit the test runner will output details of any test
failures, report on how many tests were run and how many errors and failures
were encountered, and destroy any test databases as usual. Thus pressing
Ctrl-C can be very useful if you forget to pass the --failfast option, notice that some tests are unexpectedly failing and
want to get details on the failures without waiting for the full test run to

If you do not want to wait for the currently running test to finish, you
can press Ctrl-C a second time and the test run will halt immediately,
but not gracefully. No details of the tests run before the interruption will
be reported, and any test databases created by the run will not be destroyed.

Test with warnings enabled

It’s a good idea to run your tests with Python warnings enabled:
python -Wall test. The -Wall flag tells Python to
display deprecation warnings. Django, like many other Python libraries,
uses these warnings to flag when features are going away. It also might
flag areas in your code that aren’t strictly wrong but could benefit
from a better implementation.

The test database

Tests that require a database (namely, model tests) will not use your “real”
(production) database. Separate, blank databases are created for the tests.

Regardless of whether the tests pass or fail, the test databases are destroyed
when all the tests have been executed.

You can prevent the test databases from being destroyed by using the
test --keepdb option. This will preserve the test database between
runs. If the database does not exist, it will first be created. Any migrations
will also be applied in order to keep it up to date.

The default test database names are created by prepending test_ to the
value of each NAME in DATABASES. When using SQLite, the
tests will use an in-memory database by default (i.e., the database will be
created in memory, bypassing the filesystem entirely!). The TEST dictionary in DATABASES offers a number of settings
to configure your test database. For example, if you want to use a different
database name, specify NAME in the TEST dictionary for any given database in DATABASES.

On PostgreSQL, USER will also need read access to the built-in
postgres database.

Aside from using a separate database, the test runner will otherwise
use all of the same database settings you have in your settings file:
test database is created by the user specified by USER, so you’ll
need to make sure that the given user account has sufficient privileges to
create a new database on the system.

For fine-grained control over the character encoding of your test
database, use the CHARSET TEST option. If you’re using
MySQL, you can also use the COLLATION option to
control the particular collation used by the test database. See the
settings documentation for details of these
and other advanced settings.

If using an SQLite in-memory database with SQLite 3.7.13+, shared cache is enabled, so you can write tests
with ability to share the database between threads.

Finding data from your production database when running tests?

If your code attempts to access the database when its modules are compiled,
this will occur before the test database is set up, with potentially
unexpected results. For example, if you have a database query in
module-level code and a real database exists, production data could pollute
your tests. It is a bad idea to have such import-time database queries in
your code
anyway – rewrite your code so that it doesn’t do this.

This also applies to customized implementations of

Order in which tests are executed

In order to guarantee that all TestCase code starts with a clean database,
the Django test runner reorders tests in the following way:

  • All TestCase subclasses are run first.
  • Then, all other Django-based tests (test cases based on
    SimpleTestCase, including
    TransactionTestCase) are run with no particular
    ordering guaranteed nor enforced among them.
  • Then any other unittest.TestCase tests (including doctests) that may
    alter the database without restoring it to its original state are run.


The new ordering of tests may reveal unexpected dependencies on test case
ordering. This is the case with doctests that relied on state left in the
database by a given TransactionTestCase test, they
must be updated to be able to run independently.

You may reverse the execution order inside groups using the test
option. This can help with ensuring your tests are independent from
each other.

Rollback emulation

Any initial data loaded in migrations will only be available in TestCase
tests and not in TransactionTestCase tests, and additionally only on
backends where transactions are supported (the most important exception being
MyISAM). This is also true for tests which rely on TransactionTestCase
such as LiveServerTestCase and

Django can reload that data for you on a per-testcase basis by
setting the serialized_rollback option to True in the body of the
TestCase or TransactionTestCase, but note that this will slow down
that test suite by approximately 3x.

Third-party apps or those developing against MyISAM will need to set this;
in general, however, you should be developing your own projects against a
transactional database and be using TestCase for most tests, and thus
not need this setting.

The initial serialization is usually very quick, but if you wish to exclude
some apps from this process (and speed up test runs slightly), you may add

To prevent serialized data from being loaded twice, setting
serialized_rollback=True disables the
post_migrate signal when flushing the test

Other test conditions

Regardless of the value of the DEBUG setting in your configuration
file, all Django tests run with DEBUG=False. This is to ensure that
the observed output of your code matches what will be seen in a production

Caches are not cleared after each test, and running “ test fooapp” can
insert data from the tests into the cache of a live system if you run your
tests in production because, unlike databases, a separate “test cache” is not
used. This behavior may change in the future.

Understanding the test output

When you run your tests, you’ll see a number of messages as the test runner
prepares itself. You can control the level of detail of these messages with the
verbosity option on the command line:

Creating test database...
Creating table myapp_animal
Creating table myapp_mineral

This tells you that the test runner is creating a test database, as described
in the previous section.

Once the test database has been created, Django will run your tests.
If everything goes well, you’ll see something like this:

Ran 22 tests in 0.221s


If there are test failures, however, you’ll see full details about which tests

FAIL: test_was_published_recently_with_future_poll (polls.tests.PollMethodTests)
Traceback (most recent call last):
  File "/dev/mysite/polls/", line 16, in test_was_published_recently_with_future_poll
    self.assertIs(future_poll.was_published_recently(), False)
AssertionError: True is not False

Ran 1 test in 0.003s

FAILED (failures=1)

A full explanation of this error output is beyond the scope of this document,
but it’s pretty intuitive. You can consult the documentation of Python’s
unittest library for details.

Note that the return code for the test-runner script is 1 for any number of
failed and erroneous tests. If all the tests pass, the return code is 0. This
feature is useful if you’re using the test-runner script in a shell script and
need to test for success or failure at that level.

Speeding up the tests

Running tests in parallel

As long as your tests are properly isolated, you can run them in parallel to
gain a speed up on multi-core hardware. See test --parallel.

Password hashing

The default password hasher is rather slow by design. If you’re authenticating
many users in your tests, you may want to use a custom settings file and set
the PASSWORD_HASHERS setting to a faster hashing algorithm:


Don’t forget to also include in PASSWORD_HASHERS any hashing
algorithm used in fixtures, if any.

Preserving the test database

The test --keepdb option preserves the test database between test
runs. It skips the create and destroy actions which can greatly decrease the
time to run tests.

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