Unit testing is a crucial component of software development, ensuring individual pieces of code perform as expected. When working with SQLAlchemy, a popular SQL toolkit and Object-Relational Mapping (ORM) system for Python, testing the queries you write is important. In this blog post, we will delve into an advanced method for unit testing nested SQLAlchemy queries using Pytest and pytest-mock.


For this post, we presume you have a fundamental understanding of Python, SQLAlchemy, and unit testing. We will be using Pytest, a robust testing framework for Python, and pytest-mock, a thin wrapper around the unittest.mock module that integrates seamlessly with Pytest fixtures.

The Challenge with Unit Testing Nested SQLAlchemy Queries

Writing unit tests for SQLAlchemy models often involves testing methods that run complex, nested queries. Such queries might use method chaining, like session.query(Model).filter(...).order_by(...).paginate(...). The complexity arises from the fact that each method in the chain returns a new query object, incorporating constraints from all previous methods.

Say you want to test a query that uses the order_by and filter methods. While you may aim to mock the order_by method, this won’t be sufficient because the object it returns needs to also include a filter method. This complexity makes accurate mocking a challenge.

Solution: Mocking Nested SQLAlchemy Queries with pytest-mock

Pytest-mock offers a streamlined solution by providing a mocker fixture. This fixture, a thin-wrapper around unittest.mock, integrates more smoothly with Pytest. We can use mocker to individually mock each method in the chain.

Let’s consider an example where we aim to mock the following chain: app.model.Project.query.order_by.filter.paginate.

def test_example(mocker):
    paginate_mock = mocker.Mock()  # New mock for 'paginate'
    filter_mock = mocker.Mock()
    filter_mock.paginate.return_value = paginate_mock  # We replace 'filter.paginate' with 'paginate_mock'
    order_by_mock = mocker.Mock()
    order_by_mock.filter.return_value = filter_mock  # We replace 'order_by.filter' with 'filter_mock'

    query_mock = mocker.Mock()
    query_mock.order_by.return_value = order_by_mock  # We replace 'query.order_by' with 'order_by_mock'

    mocker.patch('app.model.Project.query', new=query_mock)

In this example, we’ve created a separate mock for each method in the chain: query, order_by, filter, and paginate. Each mock replaces the method that follows it in the chain. This means that when you call app.model.Project.query.order_by, you get the order_by_mock, when you call app.model.Project.query.order_by.filter, you get the filter_mock, and so forth.

These mocks can then be set up to return values suited to your test. You can specify a return value for each method to simulate your actual database’s response to the query.


Unit testing is a vital part of software development, and testing database queries is no exception. Although testing nested SQLAlchemy queries can be challenging due to their complexity and dependence on method chaining, we’ve seen that pytest-mock can offer a helpful solution. It allows us to mock individual methods in the query chain and customize them to return the results we want.

However, while useful in certain scenarios, this approach can lead to brittle tests that are highly dependent on the internals of SQLAlchemy. In many cases, higher-level tests can be a more robust and future-proof solution. By testing against a known set of data in a test database, we can verify the actual behavior of our code, without having to worry about the intricacies of SQLAlchemy’s implementation.

Regardless of the method you choose, the key is to ensure your tests are thorough, maintainable, and accurately reflect the requirements of your application. Happy testing!

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