@Marvin Please show me an example in v3 of using pytest to test a flow. Also include an example of how to mock the response of a task or a subflow.

To test a Prefect 3.x flow using

pytest

and mock task or subflow responses, you can follow these steps. Prefect provides utilities that can help in testing, but mocking specific responses will require the use of Python's

unittest.mock

library. Here's a basic example of how you might set this up: ### Testing a Flow with

pytest

First, let's assume you have a simple flow:

python
from prefect import flow, task

@task
def add(x, y):
    return x + y

@flow
def my_flow(a, b):
    return add(a, b)

### Using

pytest

to Test the Flow Create a test file, e.g., `test_my_flow.py`:

python
import pytest
from prefect import flow
from my_module import my_flow  # Adjust the import to your actual module

def test_my_flow():
    result = my_flow(1, 2)
    assert result == 3

### Mocking Task or Subflow Responses To mock the response of a task or a subflow, you can use `unittest.mock`:

python
from unittest.mock import patch
from my_module import my_flow, add

@patch('my_module.add')
def test_my_flow_with_mock(mock_add):
    # Mock the task response
    mock_add.return_value = 10

    result = my_flow(1, 2)
    
    # Ensure the flow returns the mocked value
    assert result == 10

    # Ensure the task was called once with specified arguments
    mock_add.assert_called_once_with(1, 2)

### Additional Utilities Prefect's

prefect.testing.utilities

module contains functions like

assert_uses_result_storage

and

assert_blocks_equal

that can be useful for more complex scenarios where you need to verify Prefect-specific behaviors. For more details about testing with Prefect, you might want to explore the Prefect testing utilities. If you have more specific needs or run into issues, feel free to ask!

@Marvin Do you have any example of how to mock run_deployment?

To mock

run_deployment

in your tests, you can use Python's

unittest.mock

library, specifically

AsyncMock

for asynchronous functions. Here's a basic example: ### Example of Mocking

run_deployment

Assuming you have a function that calls

run_deployment

, you can mock it like this:

python
from unittest.mock import AsyncMock, patch
import pytest
from prefect.deployments import run_deployment

async def my_function():
    # Imagine this function calls run_deployment
    deployment_id = await run_deployment(name="my-deployment")
    return deployment_id

@pytest.mark.asyncio
async def test_my_function():
    with patch('prefect.deployments.run_deployment', new_callable=AsyncMock) as mock_run_deployment:
        # Mock the response of run_deployment
        mock_run_deployment.return_value = "mocked-deployment-id"

        # Call the function that uses run_deployment
        result = await my_function()

        # Assert that run_deployment was called with the expected arguments
        mock_run_deployment.assert_called_once_with(name="my-deployment")

        # Assert that the result is as expected
        assert result == "mocked-deployment-id"

### Explanation - AsyncMock: Since

run_deployment

is an asynchronous function, use

AsyncMock

to mock it. - patch: This is used to replace

run_deployment

with a mock object within the scope of the test. - mock_run_deployment.return_value: Defines what the mock should return when called. - mock_run_deployment.assert_called_once_with: Verifies that

run_deployment

was called once with the specified arguments. This example should fit well with your imperative programming style and help you test functions that rely on

run_deployment

. If you need further customization, feel free to ask!

@Marvin Thanks. MOre questions. I am running two tests, how can I make sure the contexts are not shared? I am getting a EngineContext error

To ensure that contexts are not shared between tests in Prefect, you can use the

prefect_test_harness

fixture, which is designed to isolate test contexts. This is particularly useful to avoid issues like the

EngineContext

error you are encountering. ### Using

prefect_test_harness

for Isolated Test Contexts Here's an example of how you can set up your tests using

pytest

to ensure that each test runs in an isolated context:

python
import pytest
from prefect.testing.utilities import prefect_test_harness

@pytest.fixture(scope="function")
def isolated_test_context():
    with prefect_test_harness():
        yield

def test_first_flow(isolated_test_context):
    # Your test code for the first flow
    pass

def test_second_flow(isolated_test_context):
    # Your test code for the second flow
    pass

### Explanation - prefect_test_harness: This fixture creates a temporary context for each test, ensuring that no state is shared between them. - scope="function": This ensures that the fixture is applied to each test function individually, providing isolation. ### Additional Tips - If you encounter specific errors related to context, ensure that each asynchronous context manager is properly awaited and exited. - Ensure that your tests do not inadvertently modify global state or rely on shared resources. By using the

prefect_test_harness

, you can avoid context sharing and related errors, making your tests more reliable. If you have further questions or run into other issues, feel free to ask!

@Marvin Any guess why I get this error E pydantic_core._pydantic_core.ValidationError: 1 validation error for EngineContext E flow E Input should be an instance of Flow [type=is_instance_of, input_value=<prefect.flows.Flow object at 0x15e641f90>, input_type=Flow] E For further information visit https://errors.pydantic.dev/2.10/v/is_instance_of venv/lib/python3.11/site-packages/prefect/context.py314 ValidationError

The

ValidationError

you're encountering suggests that there's a mismatch in the expected type for a

Flow

object within the

EngineContext

. This typically happens when the context expects a

Flow

instance but receives something else. ### Possible Causes and Solutions 1. Incorrect Flow Initialization: Ensure that the

Flow

objects are correctly instantiated before being passed into any Prefect context or function that requires them. 2. Context Mismanagement: If you're manually managing contexts or using custom context managers, ensure that they are correctly handling

Flow

instances. 3. Mocking Issues: If you are mocking

Flow

objects in your tests, ensure that the mocks are correctly set up to mimic actual

Flow

instances. 4. Prefect Version Compatibility: Double-check that your Prefect version is compatible with your code. Sometimes, discrepancies between versions can cause unexpected errors. Since no specific issues were found in the GitHub repository, this might be a unique situation related to your environment or specific usage. You can try the following: - Debugging: Add print statements or use a debugger to inspect the type and value of the object being passed to the

EngineContext

to ensure it is indeed a

Flow

. - Review Imports: Verify that all imports are correct and that there are no circular dependencies causing the wrong object to be passed. If these suggestions don't resolve the issue, consider reaching out to the Prefect community or forums with detailed information about your setup for more targeted help.