This is a pytest plugin based on pytest-mongodb and mongomock that enables you to test your code that relies on a callback- or Future-based API for non-blocking access to a MongoDB and expects certain data to be present. It allows you to specify fixtures for database collections in JSON/BSON or YAML format. Under the hood we use the mongomock library, that you should consult for documentation on how to use MongoDB mock objects. If suitable you can also use a real MongoDB server.
If you don't want to put your database fixtures on the top-level directory of your package
you have to specify a directory where pytest-async-mongodb
looks for your data definitions.
To do so put a line like the following under the pytest
section of your
pytest.ini
-file put a
[pytest]
async_mongodb_fixture_dir =
tests/unit/fixtures
async_mongodb_fixtures =
fixture_1
fixture_2
pytest-async-mongodb
would then look for files ending in .yaml
or .json
in that
directory.
Unlike pytest-mongodb, you cannot specify a real MongoDB connection with the pymongo client.
After you configured pytest-async-mongodb
so that it can find your fixtures you're ready to
specify some data. Regardless of the markup language you choose, the data is provided
as a list of documents (dicts). The collection that these documents are being inserted
into is given by the filename of your fixture-file. E.g.: If you had a file named
players.yaml
with the following content:
-
name: Mario
surname: Götze
position: striker
-
name: Manuel
surname: Neuer
position: keeper
you'd end up with a collection players
that has the above player definitions
inserted. If your fixture file is in JSON/BSON format you can also use BSON specific
types like $oid
, $date
, etc.
You get ahold of the database in your test-function by using the async_mongodb
fixture
like so:
@pytest.mark.asyncio
async def test_players(async_mongodb):
manuel = await async_mongodb.players.find_one({'name': 'Manuel'})
assert manuel['surname'] == 'Neuer'
For further information refer to the mongomock documentation.