Hypothesis for Django users

Hypothesis offers a number of features specific for Django testing, available in the hypothesis-django extra package.

Using it is quite straightforward: All you need to do is subclass hypothesis.extra.django.TestCase or hypothesis.extra.django.TransactionTestCase and you can use @given as normal, and the transactions will be per example rather than per test function as they would be if you used @given with a normal django test suite (this is important because your test function will be called multiple times and you don’t want them to interfere with eachother). Test cases on these classes that do not use @given will be run as normal.

I strongly recommend not using TransactionTestCase unless you really have to. Because Hypothesis runs this in a loop the performance problems it normally has are significantly exacerbated and your tests will be really slow.

In addition to the above, Hypothesis has some limited support for automatically deriving strategies for your model types, which you can then customize further.

Warning

Hypothesis creates saved models. This will run inside your testing transaction when using the test runner, but if you use the dev console this will leave debris in your database.

For example, using the trivial django project I have for testing:

>>> from hypothesis.extra.django.models import models
>>> from toystore.models import Customer
>>> c = models(Customer).example()
>>> c
<Customer: Customer object>
>>> c.email
'jaime.urbina@gmail.com'
>>> c.name
'\U00109d3d\U000e07be\U000165f8\U0003fabf\U000c12cd\U000f1910\U00059f12\U000519b0\U0003fabf\U000f1910\U000423fb\U000423fb\U00059f12\U000e07be\U000c12cd\U000e07be\U000519b0\U000165f8\U0003fabf\U0007bc31'
>>> c.age
-873375803

Hypothesis has just created this with whatever the relevant type of data is.

Obviously the customer’s age is implausible, so lets fix that:

>>> from hypothesis.strategies import integers
>>> c = models(Customer, age=integers(min_value=0, max_value=120)).example()
>>> c
<Customer: Customer object>
>>> c.age
5

You can use this to override any fields you like. Sometimes this will be mandatory: If you have a non-nullable field of a type Hypothesis doesn’t know how to create (e.g. a foreign key) then the models function will error unless you explicitly pass a strategy to use there.

Foreign keys are not automatically derived. If they’re nullable they will default to always being null, otherwise you always have to specify them. e.g. suppose we had a Shop type with a foreign key to company, we would define a strategy for it as:

shop_strategy = models(Shop, company=models(Company))

Fixtures

The other way you can use Hypothesis for testing your Django project is to replace your fixtures. This feature is a bit new and experimental but seems to work pretty well.

Hypothesis offers a function fixture which lets you specify a single example to use in your tests by what properties it should satisfy. For example, suppose we want a Company with a long name (I have no idea why) you could specify:

from hypothesis.extra.django.models import models
from hypothesis.extra.django.fixtures import fixture

from toystore.models import Company

a_company = fixture(
    models(Company),
    lambda c: len(c.name) >= 10,
)

This gives you a function that you can call from within your tests to get a value of the desired type matching these conditions:

from hypothesis.extra.django.models import models
from hypothesis.extra.django.fixtures import fixture

from toystore.models import Company

class TestCompany(TestCase):
    def test_can_find_unique_name(self):
        assert len(a_company().name) >= 10

Unlike normal tests with Hypothesis this doesn’t randomize your test, and you only run it once: Hypothesis has built and minimized an example before the test ever runs, then it just provides you with that example each time. This lacks much of the power of normal Hypothesis, but may be a lot more convenient to use in some cases and lets you still get many of the benefits of using its data generation while writing a more classic style of test. It’s also a lot less annoying than writing your fixtures by hand.

Each time you call a single fixture in your test will give you the same example back, so e.g. the following test will pass:

def test_two_calls_to_fixture_are_the_same(self):
    assert a_company().pk == a_company().pk

You can also use multiple fixtures in the same test. These will always give different results, even if their definitions are the same:

from hypothesis.extra.django.models import models
from hypothesis.extra.django.fixtures import fixture

from toystore.models import Company

company1 = fixture(models(Company))
company2 = fixture(models(Company))

class TestCompany(TestCase):
    def test_two_fixtures(self):
        assert company1().pk != company2().pk

Note that fixtures don’t have to define models. They can define any type you like. e.g. the following gives us a list containing at least 3 distinct companies:

some_companies = fixture(
  models(Company), lambda cs: len({c.pk for c in cs}) >= 3
)

(Note we ask for three distinct primary keys rather than just the length of the company: Otherwise we’d probably have got the same company 3 times)

Some caveats:

  1. If you have unique constraints then you should call fixture functions before instantiating any models yourself, or you may get integrity errors when Hypothesis tries to create the fixture.
  2. Fixtures can make startup quite slow the first time as Hypothesis has to work out the example to use. Values are cached in the Hypothesis example database (which has nothing to do with your Django test database), stored by default in .hypothesis/examples.db. You might wish to cache this between test runs on your CI server, as it will significantly improve startup performance.
  3. Hypothesis creates and destroys test databases during fixture definition. This is normal and you shouldn’t be concerned if you notice it. It would be nice if this weren’t necessary and if anyone has a better idea about how to do it, please talk to me...

Tips and tricks

Custom field types

If you have a custom Django field type you can register it with Hypothesis’s model deriving functionality by registering a default strategy for it:

>>> from toystore.models import CustomishField, Customish
>>> models(Customish).example()
hypothesis.errors.InvalidArgument: Missing arguments for mandatory field
    customish for model Customish
>>> from hypothesis.extra.django.models import add_default_field_mapping
>>> from hypothesis.strategies import just
>>> add_default_field_mapping(CustomishField, just("hi"))
>>> x = models(Customish).example()
>>> x.customish
'hi'

Note that this mapping is on exact type. Subtypes will not inherit it.

Generating child models

For the moment there’s no explicit support in hypothesis-django for generating dependent models. i.e. a Company model will generate no Shops. However if you want to generate some dependent models as well, you can emulate this by using the flatmap function as follows:

from hypothesis.strategies import lists, just

def generate_with_shops(company):
  return lists(models(Shop, company=just(company))).map(lambda _: company)

company_with_shops_strategy = models(Company).flatmap(generate_with_shops)

Lets unpack what this is doing:

The way flatmap works is that we draw a value from the original strategy, then apply a function to it which gives us a new strategy. We then draw a value from that strategy. So in this case we’re first drawing a company, and then we’re drawing a list of shops belonging to that company: The just strategy is a strategy such that drawing it always produces the individual value, so models(Shop, company=just(company)) is a strategy that generates a Shop belonging to the original company.

So the following code would give us a list of shops all belonging to the same company:

models(Company).flatmap(lambda c: lists(models(Shop, company=just(c))))

The only difference from this and the above is that we want the company, not the shops. This is where the inner map comes in. We build the list of shops and then throw it away, instead returning the company we started for. This works because the models that Hypothesis generates are saved in the database, so we’re essentially running the inner strategy purely for the side effect of creating those children in the database.