We need to use real data for one of our many external data sources during Django tests:
data is externally managed and read-only
data is accessed through manage.py inspectdb generated ORM classes
data is highly sensitive and we are not allowed to store fixtures of the actual data
tables are legacy design and will be phased out, hundreds of tables with complex relations, even getting a single record is complex
there is too much to do and I am unwilling to spend the time it would take to generate the fixtures, guarantee they're obfuscated, get approval of the obfuscation and justify keeping it around just to bridge us for a few months
We understand the downsides: This violates test purity and introduces a potential safety risk. We are willing to compromise on both to get us past the next few months when we will phase out this problem data source.
In this case, I need Django to understand that I don't want it to stand up a test database for this source, and just use the actual source so we can run some quick checks and walk away.
What is the simplest way to achieve this, with full understanding and acceptance of the risks and recommendations against?
For us, the solution was a custom test runner.
With help from Django's Advanced Testing Topics documentation, we overrode the default DiscoverRunner like this:
from django.test.runner import DiscoverRunner
def should_create_db(db_name):
# analyse db_name, a key from DATABASES, to determine whether a test
# database should be created
return db_name != 'messy_legacy_database'
class CustomTestRunner(DiscoverRunner):
# override method from superclass to selectively skip database setup
def setup_databases(self, **kwargs):
# 'aliases' is a set of unique keys from settings DATABASES dictionary
aliases = kwargs.get('aliases')
filtered = set([i for i in aliases if should_create_db(i)])
kwargs['aliases'] = filtered
# 'aliases' now contains only keys which trigger test database creation
return super().setup_databases(**kwargs)
# there was no need to override teardown_databases()
Next we update settings.py to use our override instead of the default runner:
TEST_RUNNER = 'path.to.CustomTestRunner'
Finally we tell our test class which databases it can use:
from django.test import TestCase
class OurTest(TestCase):
databases = [
'default',
'messy_legacy_database',
]
def test_messy_legacy_database(self):
# go nuts on your messy legacy database testing calls
pass
In this way our tests now skip test database creation for our messy legacy databases, and the logic we test pulls data from the actual data sources, allowing us to implement quick checks to ensure these code paths work.
I have an "auto_now_add" timestamp in my model, as such:
created_on = models.DateTimeField(auto_now_add = True)
I developed a REST API with the wonderful DRF3.0, and in my unit tests I would like to have something like:
self.assertEqual(response.data, data)
My question is simple: how can I know in advance the value of this timestamp, so that I can test it (assertEqual) with the actual value?
What are you expecting to test with that? That Django sets created_on correctly? That's a job for Django's tests. You should test your own code.
For unit test you should test only your methods without even saving your models. That's much faster and generally sufficient. You need save model and check saved data only when you check queries, as it's hard to test without saving.
The other use case when you need to save data to database is when you do functional/integration testing. But then you'll probably won't test single fields but the overall effect.
Saying that if you really what to set exact value you can try mocking it directly in Django code (which I'm afraid will bring you problems quickly):
from mock import patch
with patch('django.db.models.fields.datetime') as datetime:
datetime.date.today.return_value = fixed_date
# your testing code
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Closed 10 years ago.
I'm NOT new to testing, but got really confused with the mess of recommendations for testing different layers in Django.
Some recommend (and they are right) to avoid Doctests in the model as they are not maintainable...
Others say don't use fixtures, as they are less flexible than helper functions, for instance..
There are also two groups of people who fight for using Mock objects. The first group believe in using Mock and isolating the rest of the system, while another group prefer to Stop Mocking and start testing..
All I have mentioned above, were mostly in regards to testing models. Functional testing is an another story (using test.Client() VS webTest VS etc. )
Is there ANY maintainable, extandible and proper way for testing different layers??
UPDATE
I am aware of Carl Meyer's talk at PyCon 2012..
UPDATE 08-07-2012
I can tell you my practices for unit testing that are working pretty well for my own ends and I'll give you my reasons:
1.- Use Fixtures only for information that is necessary for testing but is not going to change, for example, you need a user for every test you do so use a base fixture to create users.
2.- Use a factory to create your objects, I personally love FactoryBoy (this comes from FactoryGirl which is a ruby library). I create a separate file called factories.py for every app where I save all these objects. This way I keep off the test files all the objects I need which makes it a lot more readable and easy to maintain. The cool thing about this approach is that you create a base object that can be modified if you want to test something else based on some object from the factory. Also it doesn't depend on django so when I migrated these objects when I started using mongodb and needed to test them, everything was smooth. Now after reading about factories it's common to say "Why would I want to use fixtures then". Since these fixtures should never change all the extra goodies from factories are sort of useless and django supports fixtures very well out of the box.
3.- I Mock calls to external services, because these calls make my tests very slow and they depend on things that have nothing to do with my code being right or wrong. for example, if I tweet within my test, I do test it to tweet rightly, copy the response and mock that object so it returns that exact response every time without doing the actual call. Also sometimes is good to test when things go wrong and mocking is great for that.
4.- I use an integration server (jenkins is my recommendation here) which runs the tests every time I push to my staging server and if they fail it sends me an email. This is just great since it happens to me a lot that I break something else in my last change and I forgot to run the tests. It also gives you other goodies like a coverage report, pylint/jslint/pep8 verifications and there exists a lot of plugins where you can set different statistics.
About your question for testing front end, django comes with some helper functions to handle this in a basic way.
This is what I personally use, you can fire gets, posts, login the user, etc. that's enough for me. I don't tend to use a complete front end testing engine like selenium since I feel it's an overkill to test anything else besides the business layer. I am sure some will differ and it always depends on what you are working on.
Besides my opinion, django 1.4 comes with a very handy integration for in-browser frameworks.
I'll set an example app where I can apply this practices so it is more understandable. Let's create a very basic blog app:
structure
blogger/
__init__.py
models.py
fixtures/base.json
factories.py
tests.py
models.py
from django.db import models
class Blog(models.Model):
user = models.ForeignKey(User)
text = models.TextField()
created_on = models.DateTimeField(default=datetime.now())
fixtures/base.json
[
{
"pk": 1,
"model": "auth.user",
"fields": {
"username": "fragilistic_test",
"first_name": "demo",
"last_name": "user",
"is_active": true,
"is_superuser": true,
"is_staff": true,
"last_login": "2011-08-16 15:59:56",
"groups": [],
"user_permissions": [],
"password": "IAmCrypted!",
"email": "test#email.com",
"date_joined": "1923-08-16 13:26:03"
}
}
]
factories.py
import factory
from blog.models import User, Blog
class BlogFactory(factory.Factory):
FACTORY_FOR = Blog
user__id = 1
text = "My test text blog of fun"
tests.py
class BlogTest(TestCase):
fixtures = ['base'] # loads fixture
def setUp(self):
self.blog = BlogFactory()
self.blog2 = BlogFactory(text="Another test based on the last one")
def test_blog_text(self):
self.assertEqual(Blog.objects.filter(user__id=1).count(), 2)
def test_post_blog(self):
# Lets suppose we did some views
self.client.login(username='user', password='IAmCrypted!')
response = self.client.post('/blogs', {'text': "test text", user='1'})
self.assertEqual(response.status, 200)
self.assertEqual(Blog.objects.filter(text='test text').count(), 1)
def test_mocker(self):
# We will mock the datetime so the blog post was created on the date
# we want it to
mocker = Mock()
co = mocker.replace('datetime.datetime')
co.now()
mocker.result(datetime.datetime(2012, 6, 12))
with mocker:
res = Blog.objects.create(user__id=1, text='test')
self.assertEqual(res.created_on, datetime.datetime(2012, 6, 12))
def tearDown(self):
# Django takes care of this but to be strict I'll add it
Blog.objects.all().delete()
Notice I am using some specific technology for the sake of the example (which haven't been tested btw).
I have to insist, this may not be the standard best practice (which I doubt it exists) but it is working pretty well for me.
I really like the suggestions from #Hassek and want to stress out what an excellent point he makes about the obvious lack of standard practices, which holds true for many of Django's aspects, not just testing, since all of us approach the framework with different concerns in mind, also adding to that the great degree of flexibility we have with designing our applications, we often end up with drastically different solutions that are applicable to the same problem.
Having said that, though, most of us still strive for many of the same goals when testing our applications, mainly:
Keeping our test modules neatly organized
Creating reusable assertion and helper methods, helper functions that reduce the LOC for test methods, to make them more compact and readable
Showing that there is an obvious, systematic approach to how the application components are tested
Like #Hassek, these are my preferences that may directly conflict with the practices that you may be applying, but I feel it's nice to share the things we've proven that work, if only in our case.
No test case fixtures
Application fixtures work great, in cases you have certain constant model data you'd like to guarantee to be present in the database, say a collection of towns with their names and post office numbers.
However, I see this as an inflexible solution for providing test case data. Test fixtures are very verbose, model mutations force you to either go through a lengthy process of reproducing the fixture data or to perform tedious manual changes and maintaining referential integrity is difficult to manually perform.
Additionally, you'll most likely use many kinds of fixtures in your tests, not just for models: you'd like to store the response body from API requests, to create fixtures that target NoSQL database backends, to write have fixtures that are used to populate form data, etc.
In the end, utilizing APIs to create data is concise, readable and it makes it much easier to spot relations, so most of us resort to using factories for dynamically creating fixtures.
Make extensive use of factories
Factory functions and methods are preferable to stomping out your test data. You can create helper factory module-level functions or test case methods that you may want to either reuse
across application tests or throughout the whole project. Particularly, factory_boy, that #Hassek mentions, provides you with the ability to inherit/extend fixture data and do automatic sequencing, which might look a bit clumsy if you'd do it by hand otherwise.
The ultimate goal of utilizing factories is to cut down on code-duplication and streamline how you create test data. I cannot give you exact metrics, but I'm sure if you go through your test methods with a discerning eye you will notice that a large portion of your test code is mainly preparing the data that you'll need to drive your tests.
When this is done incorrectly, reading and maintaining tests becomes an exhausting activity. This tends to escalate when data mutations lead to not-so-obvious test failures across the board, at which point you'll not be able to apply systematic refactoring efforts.
My personal approach to this problem is to start with a myproject.factory module that creates easy-to-access references to QuerySet.create methods for my models and also for any objects I might regularly use in most of my application tests:
from django.contrib.auth.models import User, AnonymousUser
from django.test import RequestFactory
from myproject.cars.models import Manufacturer, Car
from myproject.stores.models import Store
create_user = User.objects.create_user
create_manufacturer = Manufacturer.objects.create
create_car = Car.objects.create
create_store = Store.objects.create
_factory = RequestFactory()
def get(path='/', data={}, user=AnonymousUser(), **extra):
request = _factory.get(path, data, **extra)
request.user = user
return request
def post(path='/', data={}, user=AnonymousUser(), **extra):
request = _factory.post(path, data, **extra)
request.user = user
return request
This in turn allows me to do something like this:
from myproject import factory as f # Terse alias
# A verbose, albeit readable approach to creating instances
manufacturer = f.create_manufacturer(name='Foomobiles')
car1 = f.create_car(manufacturer=manufacturer, name='Foo')
car2 = f.create_car(manufacturer=manufacturer, name='Bar')
# Reduce the crud for creating some common objects
manufacturer = f.create_manufacturer(name='Foomobiles')
data = {name: 'Foo', manufacturer: manufacturer.id)
request = f.post(data=data)
view = CarCreateView()
response = view.post(request)
Most people are rigorous about reducing code duplication, but I actually intentionally introduce some whenever I feel it contributes to test comprehensiveness. Again, the goal with whichever approach you take to factories is to minimize the amount of brainfuck you introduce into the header of each test method.
Use mocks, but use them wisely
I'm a fan of mock, as I've developed an appreciation for the author's solution to what I believe was the problem he wanted to address. The tools provided by the package allow you to form test assertions by injecting expected outcomes.
# Creating mocks to simplify tests
factory = RequestFactory()
request = factory.get()
request.user = Mock(is_authenticated=lamda: True) # A mock of an authenticated user
view = DispatchForAuthenticatedOnlyView().as_view()
response = view(request)
# Patching objects to return expected data
#patch.object(CurrencyApi, 'get_currency_list', return_value="{'foo': 1.00, 'bar': 15.00}")
def test_converts_between_two_currencies(self, currency_list_mock):
converter = Converter() # Uses CurrencyApi under the hood
result = converter.convert(from='bar', to='foo', ammount=45)
self.assertEqual(4, result)
As you can see, mocks are really helpful, but they have a nasty side effect: your mocks clearly show your making assumptions on how it is that your application behaves, which introduces coupling. If Converter is refactored to use something other than the CurrencyApi, someone may not obviously understand why the test method is suddenly failing.
So with great power comes great responsibility--if your going to be a smartass and use mocks to avoid deeply rooted test obstacles, you may completely obfuscate the true nature of your test failures.
Above all, be consistent. Very very consistent
This is the most important point to be made. Be consistent with absolutely everything:
how you organize code in each of your test modules
how you introduce test cases for your application components
how you introduce test methods for asserting the behavior of those components
how you structure test methods
how you approach testing common components (class-based views, models, forms, etc.)
how you apply reuse
For most projects, the bit about how your collaboratively going to approach testing is often overlooked. While the application code itself looks perfect--adhering to style guides, use of Python idioms, reapplying Django's own approach to solving related problems, textbook use of framework components, etc.--no one really makes it an effort to figure out how to turn test code into a valid, useful communication tool and it's a shame if, perhaps, having clear guidelines for test code is all it takes.
today I had a discussion with my colleguaes about how we should manage fixtures in our django application. We cound not find any solution that would satisfy everyone, so I'm asking this question here.
Suppose we have quite big django project with dozen of applications inside, each application has tests.py file with several TestClasses. Having this, how I should manage test data for all of these applications?
From my perpective, there is 2 different ways:
Store all data in separate for each application test_data.json file. This file will contain test data for all models defined in the application's models.py file, irrespective of where this data is used (it can be used in tests from different application)
Store some common data that would be probably required by all tests (like auth.users) in test_data.json and data for each TestCase in a separate test_case.json file.
From my perpective, second approach seems to be more cleaner, but I would like to know if somebody could tell me the concrete pros and cons of these approaches or may be suggest some other approach?
If you think about the cleanest way to define test data for your tests, I would like to recommend you read about django-any application:
django-any the explicit replacement for old-style, big and error-prone
implicit fixture files.
django-any allows to specify only fields important for test, and fill
rest by random with acceptable values.
It makes tests clean and easy to undestood, without reading fixture
files.
from django_any import any_model, WithTestDataSeed
class TestMyShop(TestCase):
def test_order_updates_user_account(self):
account = any_model(Account, amount=25, user__is_active=True)
order = any_model(Order, user=account.user, amount=10)
order.proceed()
account = Account.objects.get(pk=account.pk)
self.assertEquals(15, account.amount)
The same approach available for forms also (django_any.any_form)
This solution is helpful for avoiding to keep extra data in you DB while your tests are executing.
I'm trying to write unit tests for parts of my Node app. I'm using Mongoose for my ORM.
I've searched a bunch for how to do testing with Mongoose and Node but not come with anything. The solutions/frameworks all seem to be full-stack or make no mention of mocking stuff.
Is there a way I can mock my Mongoose DB so I can return static data in my tests? I'd rather not have to set up a test DB and fill it with data for every unit test.
Has anyone else encountered this?
I too went looking for answers, and ended up here. This is what I did:
I started off using mockery to mock out the module that my models were in. An then creating my own mock module with each model hanging off it as a property. These properties wrapped the real models (so that child properties exist for the code under test). And then I override the methods I want to manipulate for the test like save. This had the advantage of mockery being able to undo the mocking.
but...
I don't really care enough about undoing the mocking to write wrapper properties for every model. So now I just require my module and override the functions I want to manipulate. I will probably run tests in separate processes if it becomes an issue.
In the arrange part of my tests:
// mock out database saves
var db = require("../../schema");
db.Model1.prototype.save = function(callback) {
console.log("in the mock");
callback();
};
db.Model2.prototype.save = function(callback) {
console.log("in the mock");
callback("mock staged an error for testing purposes");
};
I solved this by structuring my code a little. I'm keeping all my mongoose-related stuff in separate classes with APIs like "save", "find", "delete" and no other class does direct access to the database. Then I simply mock those in tests that rely on data.
I did something similar with the actual objects that are returned. For every model I have in mongoose, I have a corresponding class that wraps it and provides access-methods to fields. Those are also easily mocked.
Also worth mentioning:
mockgoose - In-memory DB that mocks Mongoose, for testing purposes.
monckoose - Similar, but takes a different approach (Implements a fake driver). Monckoose seems to be unpublished as of March 2015.