Writing Unit Tests for AsyncSession in SQLAlchemy

Friendly Introduction to Our Query

Welcome to a comprehensive guide on writing unit tests and mocking AsyncSession when using SQLAlchemy. This tutorial delves into the significance of testing asynchronous database interactions, providing valuable insights and techniques.

What You’ll Learn

By following this guide, you will learn how to effectively mock AsyncSession from SQLAlchemy in your unit tests. By the end, you will have a strong understanding of testing asynchronous code paths involving database operations.

Understanding the Challenge and Approaching the Solution

Testing asynchronous code, especially involving database operations with AsyncSession, can be complex. The main challenge is to ensure that tests accurately replicate real-world scenarios without relying on an actual database. This approach not only enhances the speed of our test suite but also improves its reliability and maintainability.

Our solution involves mocking the AsyncSession object and leveraging Python’s built-in unittest.mock library along with pytest-asyncio (or any other asyncio compatible testing framework) for handling asynchronous test cases. We focus on creating mocks that mimic real session behaviors and returning controlled responses to validate our application logic effectively.


import pytest
from unittest.mock import AsyncMock
from sqlalchemy.ext.asyncio import AsyncSession

def mock_session():
    session = AsyncMock(spec=AsyncSession)
    # Example: Mocking a simple query response.
    session.execute.return_value.scalar_one_or_none.return_value = 'expected_result'
    return session

async def test_my_async_db_function(mock_session):
    # Assume my_async_db_function is what we want to test.
    result = await my_async_db_function(session=mock_session)

    assert result == 'expected_result'

# Copyright PHD


The key concept revolves around using unittest.mock.AsyncMock, specifically designed for mocking async calls. Here’s how it works: 1. Creating an AsyncMock Session: Instantiate an AsyncMock, specifying it should mimic an AsyncSession for precise control over behavior. 2. Specifying Behavior: Define expected behavior when specific methods are called on this mock object. Set up .execute().scalar_one_or_none() chain to return ‘expected_result’ for instance. 3. Injecting Mock into Test Function: In the async test function decorated with @pytest.mark.asyncio, utilize the mocked session instead of a real one. 4. Assertion: Execute the function with the mocked session and assert against expected outcomes.

This approach ensures that tests remain focused on logic rather than external dependencies like databases.

    How do I ensure my mocks represent realistic scenarios?

    To ensure realistic representations, understand how your application interacts with the database so you can closely mimic those interactions within your mocks.

    Can I use fixtures for setting up common mock setups?

    Absolutely! Using pytest fixtures to create reusable mocked sessions or responses is highly recommended for maintaining DRY (Don’t Repeat Yourself) principles.

    How do I handle exceptions in my mocks?

    You can utilize the .side_effect property of mocks to raise exceptions as needed, simplifying testing error-handling paths.

    Is there support for transactions or rollbacks in mocking?

    Although you won’t directly interact with transactions or rollbacks when mocking, structure your mocks to reflect changes as if transactions were committed or rolled back based on requirements.

    Do I need an actual database running somewhere while testing?

    Nope! This method’s beauty lies in its independence from live databases, making it fast and reliable whether testing locally or in CI/CD pipelines.


    Mastering unit testing with mocked async sessions significantly enhances confidence in code quality by ensuring each layer behaves as expected under various conditions without requiring complex setups like actual databases during tests.

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