Simple and intuitive SQLAlchemy mock helpers.
SQLAlchemy is awesome.
Unittests are great.
Accessing DB during tests - not so much. This library provides an easy way to mock SQLAlchemy's session in unittests while preserving the ability to do sane asserts.
- Free software: MIT license
- GitHub: https://github.com/rajivsarvepalli/mock-alchemy
Full documentation is available at http://mock-alchemy.rtfd.io/. On the documentation, you should be able to select a version of your choice in order to view documentation of an older version if need be.
This README includes some basic examples, but more detailed examples are included in the documentation, especially in the user guide. If you are looking for an API reference, it is also available on the documentation.
You can install mock-alchemy using pip:
$ pip install mock-alchemy
If you want to use this package on Python 2.7 or Python 3.6, then
install mock-alchemy using:
$ pip install "mock-alchemy>=0.1.0,<0.2.0"
Pip should auto-detect the correct version but this ensures the correct version is downloaded for your needs.
There are several different versions of mock-alchemy available
depending on your needs. The versions 0.1.x are available for use on
Python 2.7, Python 3.6+. The newer versions serve users who are on
Python 3.7+. For people interested in contributing, if you want to work
on Python 2.7 version checkout the branch 0.1.x and then create pull-requests to that
branch. There is a set of specific tests run for that branch on pushes
and pull-requests since there are different tests for the newer versions
of mock-alchemy. Check out contributor guide for more information.
Documentation for the 0.1.0 version is available. However, the current documentation should do a sufficient job at illustrating both the past and the features of the present version at least as of now. Therefore, I suggest using the most recent documentation for now, and if you want, you can switch using the readthedocs version system (click on the drop-down menu on the bottom right of the screen on the documentation or go to the project page).
The original library (alchemy-mock) was created by Miroslav
Shubernetskiy and Serkan Hoscai. This is a forked version due to a lack
of updates in the original library. It appeared that the alchemy-mock
project was no longer supported. Therefore, since I desired to add some
basic support for deleting, I created my own version of the library.
Full credit goes to the original creators for starting and building this project. You can find the original package on PyPi and Github.
Normally SQLAlchemy's expressions cannot be easily compared as comparison on binary expression produces yet another binary expression:
>>> type((Model.foo == 5) == (Model.bar == 5))
<class 'sqlalchemy.sql.elements.BinaryExpression'>
But they can be compared with this library:
>>> ExpressionMatcher(Model.foo == 5) == (Model.bar == 5)
False
ExpressionMatcher can be directly used:
>>> from mock_alchemy.comparison import ExpressionMatcher
>>> ExpressionMatcher(Model.foo == 5) == (Model.foo == 5)
True
Alternatively AlchemyMagicMock can be used to mock out SQLAlchemy session:
>>> from mock_alchemy.mocking import AlchemyMagicMock
>>> session = AlchemyMagicMock()
>>> session.query(Model).filter(Model.foo == 5).all()
>>> session.query.return_value.filter.assert_called_once_with(Model.foo == 5)
In real world though session can be interacted with multiple times to query some data.
In those cases UnifiedAlchemyMagicMock can be used which combines various calls for easier assertions:
>>> from mock_alchemy.mocking import UnifiedAlchemyMagicMock
>>> session = UnifiedAlchemyMagicMock()
>>> m = session.query(Model)
>>> q = m.filter(Model.foo == 5)
>>> if condition:
... q = q.filter(Model.bar > 10).all()
>>> data1 = q.all()
>>> data2 = m.filter(Model.note == 'hello world').all()
>>> session.filter.assert_has_calls([
... mock.call(Model.foo == 5, Model.bar > 10),
... mock.call(Model.note == 'hello world'),
... ])
Also real-data can be stubbed by criteria:
>>> from mock_alchemy.mocking import UnifiedAlchemyMagicMock
>>> session = UnifiedAlchemyMagicMock(data=[
... (
... [mock.call.query(Model),
... mock.call.filter(Model.foo == 5, Model.bar > 10)],
... [Model(foo=5, bar=11)]
... ),
... (
... [mock.call.query(Model),
... mock.call.filter(Model.note == 'hello world')],
... [Model(note='hello world')]
... ),
... (
... [mock.call.query(AnotherModel),
... mock.call.filter(Model.foo == 5, Model.bar > 10)],
... [AnotherModel(foo=5, bar=17)]
... ),
... ])
>>> session.query(Model).filter(Model.foo == 5).filter(Model.bar > 10).all()
[Model(foo=5, bar=11)]
>>> session.query(Model).filter(Model.note == 'hello world').all()
[Model(note='hello world')]
>>> session.query(AnotherModel).filter(Model.foo == 5).filter(Model.bar > 10).all()
[AnotherModel(foo=5, bar=17)]
>>> session.query(AnotherModel).filter(Model.note == 'hello world').all()
[]
The UnifiedAlchemyMagicMock can partially fake session mutations
such as session.add(instance). For example:
>>> session = UnifiedAlchemyMagicMock()
>>> session.add(Model(pk=1, foo='bar'))
>>> session.add(Model(pk=2, foo='baz'))
>>> session.query(Model).all()
[Model(foo='bar'), Model(foo='baz')]
>>> session.query(Model).get(1)
Model(foo='bar')
>>> session.query(Model).get(2)
Model(foo='baz')
Note that its partially correct since if added models are filtered on, session is unable to actually apply any filters so it returns everything:
>>> session.query(Model).filter(Model.foo == 'bar').all()
[Model(foo='bar'), Model(foo='baz')]
Finally, UnifiedAlchemyMagicMock can partially fake deleting. Anything that can be
accessed with all can also be deleted. For example:
>>> s = UnifiedAlchemyMagicMock()
>>> s.add(SomeClass(pk1=1, pk2=1))
>>> s.add_all([SomeClass(pk1=2, pk2=2)])
>>> s.query(SomeClass).all()
[1, 2]
>>> s.query(SomeClass).delete()
2
>>> s.query(SomeClass).all()
[]
Note the limitation for dynamic sessions remains the same. Additionally, the delete will not be propagated across queries (only unified in the exact same query). As in, if there are multiple queries in which the 'same' object is present, this library considers them separate objects. For example:
>>> s = UnifiedAlchemyMagicMock(data=[
... (
... [mock.call.query('foo'),
... mock.call.filter(c == 'one', c == 'two')],
... [SomeClass(pk1=1, pk2=1), SomeClass(pk1=2, pk2=2)]
... ),
... (
... [mock.call.query('foo'),
... mock.call.filter(c == 'one', c == 'two'),
... mock.call.order_by(c)],
... [SomeClass(pk1=2, pk2=2), SomeClass(pk1=1, pk2=1)]
... ),
... (
... [mock.call.filter(c == 'three')],
... [SomeClass(pk1=3, pk2=3)]
... ),
... (
... [mock.call.query('foo'),
... mock.call.filter(c == 'one', c == 'two', c == 'three')],
... [SomeClass(pk1=1, pk2=1), SomeClass(pk1=2, pk2=2), SomeClass(pk1=3, pk2=3)]
... ),
... ])
>>> s.query('foo').filter(c == 'three').delete()
1
>>> s.query('foo').filter(c == 'three').all()
[]
>>> s.query('foo').filter(c == 'one').filter(c == 'two').filter(c == 'three').all()
[1, 2, 3]
The item referred to by c == 'three' is still present in the filtered query despite the individual item being deleted.
Contributions are welcome. To learn more, see the Contributor Guide.
Distributed under the terms of the MIT license, mock-alchemy is free and open source software.
If you encounter any issues or problems, please file an issue along with a detailed description.