Mongo Dynamic Fixture

pip install mongo-dynamic-fixture==0.2.1


mongo-dynamic-fixture: easy testing by dynamically creating mongo fixtures


  • Using static json fixtures can be a pain as they are hard to maintain if the data evolves
  • Adding new tests usually require the addition of new static json fixtures
  • If a static json fixture is used in more than one test case even a little change can break all test cases


This library is inspired by django-dynamic-fixture.

Getting Started

You just have to define the schema of the data you want to generate:

from mongo_dynamic_fixture.schema import BaseSchema
from mongo_dynamic_fixture.fields import IntegerField
from mongo_dynamic_fixture.fields import DoubleField
from mongo_dynamic_fixture.fields import BooleanField
from mongo_dynamic_fixture.fields import StringField
from mongo_dynamic_fixture.fields import ArrayField

class SiteSchema(BaseSchema):

     schema = {
         'name': StringField(),
         'aliases': ArrayField(StringField()),
         'active': BooleanField(),
         'stats': {
             'last_day_visits': IntegerField(),
             'average_daily_visits': DoubleField()

After that you can already generate your fixtures!

In [1]: from mongo_dynamic_fixture import N

In [2]: N(SiteSchema)
{'active': True,
 'aliases': ['kisxcp', 'lG', 'vH5', 'Q7oT1xi', 'RyooxkzB', 'FSFnP'],
 'name': 'oCmy0ZsGS',
 'stats': {'average_daily_visits': 0.02137056342099064, 'last_day_visits': 21}}

Basic usage

The basic functions are N and G that stand for New and Get respectively.

The N function

The function N takes an instance of BaseSchema as first argument and generates a fixture which is compliant with the schema provided. Obviously sometimes we would like to have more control over the fixture that we want generate, for this reason the N function also takes **kwargs optional arguments to fix some specific fields:

In [3]: N(SiteSchema, active=False, stats__last_day_visits=30)
{'active': False,
 'aliases': ['Euheq6sRgF',
 'name': 'KEKasgW',
 'stats': {'average_daily_visits': 0.44985850259520865, 'last_day_visits': 30}}

As you can see both active and last_day_visits has been set to the values provided. If the key you want to fix is at the top level of the object then just use the variable name, otherwise list all its ancestors by separating them with __ as for stats__last_day_visits. If the resulting **kwargs key is not a valid python variable name, then pass it inside the extra argument:

In [3]: N(MySchema, field1=False, extra={'field2__some-invalid-name!': 30})

The G function

The G function does the same thing of the N function but additionaly takes a pymongo connection to a mongo collection as first argument:

In [4]: G(conn['test-db']['test-coll'], SiteSchema, active=False, stats__last_day_visits=30)
{'active': False,
 'aliases': ['K8ae2uwdW',
 'name': 'ihccMMs',
 'stats': {'average_daily_visits': 0.5553574439909581, 'last_day_visits': 30}}

we have just created a fixture and inserted it inside the collection 'test-coll' of the database 'test-db'.

The available fields that are all importable from mongo_dynamic_fixture.fields are the following:

  • IntegerField
  • DoubleField
  • BooleanField
  • StringField
  • ArrayField
  • ObjectField

Using in a test case

The easiest way to use the G function is to use it inside MongoTestCase which already provides a connection to a temporary mongo instance (using mongobox) through the property mongo_client:

from mongo_dynamic_fixture.test import MongoTestCase

class MyTestCase(MongoTestCase):

    def test_something(self):
        dynamic_fixture = G(self.mongo_client['test-db']['test-coll'],
                            SiteSchema, active=False, stats__last_day_visits=30)

A little more than basic usage

Optional arguments

Each fields takes the following optional arguments:

  • required (default: True)
  • null (default: False)
  • blank (default: False)
  • not_present_prob (default: 0)
  • null_prob (default: 0)
  • blank_prob (default: 0)

If required is False, then with a probability given by not_present_prob the field will not be present in the document.

If null is True, then with a probability given by null_prob the field will have a value of None.

If blank is True, then with a probability given by blank_prob the field will have a blank value which depends on the field.

The blank fields for each fields are the following:

  • IntegerField -> 0
  • DoubleField -> 0.0
  • BooleanField -> False
  • StringField -> ''
  • ArrayField -> []
  • ObjectField -> {}

IntegerField and DoubleField also take min_value and max_value as optional arguments, and StringField and ArrayField also take min_length and max_length. IntegerField, DoubleField and StringField also take choices as optional argument which must be an iterable. In case that this argument is provided the generated value will one those present in the iterable. With StringField it's also possible to specify the charset of the string to generate by passing it to the charset optional argument (default: string.ascii_letters + string.digits).

ObjectField and DRY

Now you might ask "And what is the purpose of ObjectField"? Suppose that you have a schema like the following:

class SiteSchema(BaseSchema):

     schema = {
         'name': StringField(),
         'aliases': ArrayField(StringField()),
         'active': BooleanField(),
         'stats-hourly': {
             'last_visits': IntegerField(),
             'average_visits': DoubleField()
         'stats-daily': {
             'last_visits': IntegerField(),
             'average_visits': DoubleField()
         'stats-monthly': {
             'last_visits': IntegerField(),
             'average_visits': DoubleField()

you can use ObjectField to write it in a more concise way:

from mongo_dynamic_fixture.fields import ObjectField

obj_field = ObjectField({'last_visits': IntegerField(),
                         'average_visits': DoubleField()})

class SiteSchema(BaseSchema):

     schema = {
         'name': StringField(),
         'aliases': ArrayField(StringField()),
         'active': BooleanField(),
         'stats-hourly': obj_field,
         'stats-daily': obj_field,
         'stats-monthly': obj_field


pip install mongo-dynamic-fixture


Tested with:

  • python2.7 and pymongo>=2.0
  • python3.3, python3.4 and pymongo>=2.2


For any suggestion, improvements, issues and bugs please open an Issue.