Security News
UK Officials Consider Banning Ransomware Payments from Public Entities
The UK is proposing a bold ban on ransomware payments by public entities to disrupt cybercrime, protect critical services, and lead global cybersecurity efforts.
.. image:: https://badge.fury.io/py/alchemy-mock.png :target: http://badge.fury.io/py/alchemy-mock
.. image:: https://travis-ci.org/miki725/alchemy-mock.png?branch=master :target: https://travis-ci.org/miki725/alchemy-mock
.. image:: https://coveralls.io/repos/miki725/alchemy-mock/badge.png?branch=master :target: https://coveralls.io/r/miki725/alchemy-mock?branch=master
SQLAlchemy mock helpers.
You can install alchemy-mock
using pip::
$ pip install alchemy-mock
SQLAlchemy is awesome. Unittests are great. Accessing DB during tests - not so much. This library provides easy way to mock SQLAlchemy's session in unittests while preserving ability to do sane asserts. 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 alchemy_mock.comparison import ExpressionMatcher
>>> ExpressionMatcher(Model.foo == 5) == (Model.foo == 5)
True
Alternatively AlchemyMagicMock
can be used to mock out SQLAlchemy session::
>>> from alchemy_mock.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 alchemy_mock.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 alchemy_mock.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()
[]
Finally 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')]
0.4.3 (2019-11-05)
* Unifying ``distinct``.
0.4.2 (2019-09-25)
label()
in ExpressionMatcher
. For example column.label('foo')
.0.4.1 (2019-06-26)
* Adding support for ``one_or_none()``. Thanks @davidroeca
0.4.0 (2019-06-06)
add
and add_all
.0.3.5 (2019-04-13)
* Fixing compatibility with latest ``mock``.
0.3.4 (2018-10-03)
limit
.0.3.3 (2018-09-17)
* Unifying ``options`` and ``group_by``.
0.3.2 (2018-06-27)
count()
and get()
between boundaries.0.3.1 (2018-03-28)
* Added support for ``func`` calls in ``ExpressionMatcher``. For example ``func.lower(column)``.
0.3.0 (2018-01-24)
.one()
and .first()
methods when stubbing data.0.2.0 (2018-01-13)
* Added ability to stub real-data by filtering criteria.
See `#2 <https://github.com/miki725/alchemy-mock/pull/2>`_.
0.1.1 (2018-01-12)
0.1.0 (2018-01-12)
* First release on PyPI.
Credits
-------
Development Lead
~~~~~~~~~~~~~~~~
* Miroslav Shubernetskiy - https://github.com/miki725
Contributors
~~~~~~~~~~~~
* Serkan Hoscai - https://github.com/shosca
License
-------
The MIT License (MIT)
Copyright (c) 2018, Miroslav Shubernetskiy
::
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
FAQs
SQLAlchemy mock helpers.
We found that alchemy-mock demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
The UK is proposing a bold ban on ransomware payments by public entities to disrupt cybercrime, protect critical services, and lead global cybersecurity efforts.
Security News
Snyk's use of malicious npm packages for research raises ethical concerns, highlighting risks in public deployment, data exfiltration, and unauthorized testing.
Research
Security News
Socket researchers found several malicious npm packages typosquatting Chalk and Chokidar, targeting Node.js developers with kill switches and data theft.