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<@ULVA73B9P> how can I mock prefect context for py...
# ask-marvin
a
@Marvin how can I mock prefect context for python unit tests in prefect version 2.x
m
To mock the Prefect context for unit tests in Prefect 2.x, you can use the 
prefect_test_harness
context manager. This allows you to run flows and tasks against a temporary local SQLite database. Here's a simple example:
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python
from prefect import flow
from prefect.testing.utilities import prefect_test_harness

@flow
def my_favorite_flow():
    return 42

def test_my_favorite_flow():
    with prefect_test_harness():
        assert my_favorite_flow() == 42
For more extensive testing, you can use 
prefect_test_harness
as a fixture in your unit testing framework like `pytest`:
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python
from prefect import flow
import pytest
from prefect.testing.utilities import prefect_test_harness

@pytest.fixture(autouse=True, scope="session")
def prefect_test_fixture():
    with prefect_test_harness():
        yield

@flow
def my_favorite_flow():
    return 42

def test_my_favorite_flow():
    assert my_favorite_flow() == 42
In this example, the fixture is scoped to run once per session. You can find more details in the Prefect 2.x testing documentation.
a
@Marvin can I assert flow run parameters using 
prefect_test_harness
m
The 
prefect_test_harness
primarily provides a context for running flows and tasks against a temporary testing database, but it doesn't directly offer utilities for asserting flow run parameters. However, you can still test flow parameters by invoking the flow with specific parameters and asserting the expected behavior or results within the 
prefect_test_harness
context. Here's a basic example of how you might test flow parameters:
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python
from prefect import flow
from prefect.testing.utilities import prefect_test_harness

@flow
def my_favorite_flow(param1):
    return param1 * 2

def test_my_favorite_flow():
    with prefect_test_harness():
        result = my_favorite_flow(21)
        assert result == 42
In this example, you manually pass parameters to the flow and assert the expected outcome. For more detailed testing of parameters, you might need to inspect the flow's behavior or outputs based on those parameters. For more information, you can refer to the Prefect 2.x testing documentation.
a
@Marvin how can I use 
prefect_test_harness
to test tasks
m
To test individual tasks with 
prefect_test_harness
in Prefect 2.x, you can directly access the original function of the task using the 
.fn
attribute. This allows you to test the task independently of the flow context. Here's an example:
Copy code
python
from prefect import task

@task
def my_favorite_task():
    return 42

def test_my_favorite_task():
    assert my_favorite_task.fn() == 42
If your task uses a logger, you might encounter a 
RuntimeError
due to a missing flow context. To avoid this, you can disable the logger using `disable_run_logger`:
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python
from prefect.logging import disable_run_logger

def test_my_favorite_task():
    with disable_run_logger():
        assert my_favorite_task.fn() == 42
These methods allow you to test tasks independently, ensuring that they function correctly outside of a flow context. For more details, you can refer to the Prefect 2.x testing documentation.
a
@Marvin I’m still hitting MissingContextError even when using disable_run_logger
m
It seems that 
disable_run_logger
might not be sufficient in addressing the 
MissingContextError
if your tasks are heavily dependent on the Prefect context. This error typically arises when attempting to access the run context outside of a flow or task run, which 
disable_run_logger
doesn't inherently solve. To handle this, ensure that any context-dependent operations within your tasks are either mocked or bypassed during testing. You can explicitly check for the presence of a context and adjust your task behavior accordingly. Here's an example using `get_run_context`:
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python
from prefect import task
from prefect.context import get_run_context

@task
def my_favorite_task():
    context = get_run_context()
    if context:
        # Perform operations that require context
        pass
    else:
        # Handle the absence of context, perhaps by mocking or using defaults
        pass

def test_my_favorite_task():
    # Directly test the task function logic without context dependency
    assert my_favorite_task.fn() == 42
Additionally, you can explore using 
FlowRunContext.get()
or 
TaskRunContext.get()
which will return 
None
if the context is unavailable, avoiding the error. For more details on handling contexts, you might want to explore the Prefect runtime context documentation.
a
@Marvin how can I mock prefect task run context
m
To mock the Prefect task run context, you can use environment variables to set mock values for testing purposes. Starting from Prefect version 2.10.3, you can define these mock values using the schema 
PREFECT__RUNTIME__{SUBMODULE}__{KEY_NAME}=value
. Here's how you can do it: 1. Identify the specific submodule and key name you want to mock. 2. Set the environment variable using the specified schema. For example, if you want to mock a task run ID, you might set an environment variable like this:
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bash
export PREFECT__RUNTIME__TASK_RUN__ID=mock-task-run-id
This approach allows you to simulate specific runtime conditions without needing an actual task run context. For more detailed information, you can refer to the Prefect runtime context documentation.
a
@Marvin how can I mock prefect context
m
To mock the Prefect context, especially for testing purposes, you can use environment variables to simulate specific values within the runtime context. This feature is available starting from Prefect version 2.10.3. Here's a general approach: 1. Identify the Context Values to Mock: Determine which part of the context you want to mock, such as flow run ID, task run ID, etc. 2. Set Environment Variables: Use the following schema to set environment variables that mock the desired context values:
Copy code
PREFECT__RUNTIME__{SUBMODULE}__{KEY_NAME}=value
For example, to mock a flow run ID, you can set:
Copy code
bash
   export PREFECT__RUNTIME__FLOW_RUN__ID=mock-flow-run-id
3. Use in Tests: With these environment variables set, when your tests run, they will use the mocked context values. This method allows you to test how your code behaves under specific runtime conditions without needing an actual Prefect run context. For more details, you can refer to the Prefect runtime context documentation.
a
@Marvin do you have an example of this in a python unit test?
m
Certainly! Here's a simple example of how you might mock the Prefect context in a Python unit test using environment variables:
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python
import os
from prefect import flow, task
from prefect.context import get_run_context

# Define a task that uses the Prefect context
@task
def my_task():
    context = get_run_context()
    if context:
        return context.task_run.id
    return "No context"

# Define a flow to use the task
@flow
def my_flow():
    return my_task()

# Unit test with mocked context
def test_my_task_with_mocked_context():
    # Mock the task run ID using an environment variable
    os.environ['PREFECT__RUNTIME__TASK_RUN__ID'] = 'mock-task-run-id'
    
    # Run the task directly to test
    result = my_task.fn()
    
    # Assert that the mocked ID is returned
    assert result == 'mock-task-run-id'

    # Clean up the environment variable
    del os.environ['PREFECT__RUNTIME__TASK_RUN__ID']

# Running the test
if __name__ == "__main__":
    test_my_task_with_mocked_context()
    print("Test passed!")
In this example: - The 
my_task
function attempts to access the task run ID from the Prefect context. - The test 
test_my_task_with_mocked_context
sets an environment variable to mock the task run ID. - After running the task, the test checks if the mocked ID is returned as expected. - Finally, it cleans up by deleting the environment variable. This setup allows you to test how your task behaves with a specific context value.
a
@Marvin I can’t get past the 
prefect.exceptions.MissingContextError: No run context available. You are not in a flow or task run context.
error
m
The 
MissingContextError
typically occurs when trying to access the Prefect run context outside of a flow or task run. To handle this in unit tests, you need to ensure that the context is either mocked or bypassed correctly. Unfortunately, the environment variable approach I mentioned earlier is not effective for bypassing the 
MissingContextError
in this scenario. Instead, you can structure your code to check for the presence of a context and handle operations accordingly. Here's how you can modify your task to avoid this error:
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python
from prefect import task
from prefect.context import TaskRunContext

@task
def my_task():
    task_run_ctx = TaskRunContext.get()
    if task_run_ctx:
        # Perform operations that require context
        return task_run_ctx.task_run.id
    else:
        # Handle the absence of context, perhaps by using a default value
        return "No context"

def test_my_task_without_context():
    # Directly test the task function logic without context dependency
    result = my_task.fn()
    assert result == "No context"

# Running the test
if __name__ == "__main__":
    test_my_task_without_context()
    print("Test passed!")
In this example: - 
TaskRunContext.get()
is used to safely attempt to retrieve the context. It will return 
None
if no context is available, allowing you to avoid the error. - The task function is designed to handle both cases: when context is available and when it's not. This approach ensures that your tests can run without requiring an actual Prefect run context.
a
@Marvin how can I use prefect context in a python unit test
m
To use Prefect context in a Python unit test, you can leverage the 
prefect_test_harness
to simulate a flow or task run environment. This context manager sets up a temporary local SQLite database, allowing you to test flows and tasks as if they were running in a real Prefect environment. Here's an example of how you can use it in a unit test:
Copy code
python
from prefect import flow, task
from prefect.testing.utilities import prefect_test_harness

@task
def my_task():
    from prefect.context import TaskRunContext
    context = TaskRunContext.get()
    if context:
        return context.task_run.id
    return "No context"

@flow
def my_flow():
    return my_task()

def test_my_task_with_context():
    with prefect_test_harness():
        # Run the flow to ensure the task runs within a context
        result = my_flow()
        assert result != "No context"

# Running the test
if __name__ == "__main__":
    test_my_task_with_context()
    print("Test passed!")
In this example: - The 
my_task
function attempts to access the task run ID from the Prefect context. - The 
prefect_test_harness
is used in the 
test_my_task_with_context
function to create a context where the flow and its tasks can run as they would in a real Prefect environment. - The test asserts that the task run ID is available, indicating that the context is correctly set up. This approach allows you to test Prefect tasks and flows with access to the context they would have during actual execution.
a
@Marvin I’m hitting an error on the 
get_id()
function -
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if flow_run_ctx is not None:
>           return str(flow_run_ctx.flow_run.id)
E           AttributeError: 'NoneType' object has no attribute 'id'
m
The error you're encountering occurs when 
flow_run_ctx
is 
None
, indicating that the flow run context is not available. This typically happens when the code is executed outside of a flow or task run context, such as during unit tests or when the flow is not properly invoked. To handle this, you should check if 
flow_run_ctx
is not 
None
before accessing its attributes. Here's how you can modify your code to avoid this error:
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python
from prefect.context import FlowRunContext

def get_id():
    flow_run_ctx = FlowRunContext.get()
    if flow_run_ctx is not None and flow_run_ctx.flow_run is not None:
        return str(flow_run_ctx.flow_run.id)
    return "No flow run context available"

# Example usage in a task or flow
def test_get_id():
    result = get_id()
    assert result == "No flow run context available"  # Expected result when no context is present

# Running the test
if __name__ == "__main__":
    test_get_id()
    print("Test passed!")
In this example: - The function 
get_id
checks if 
flow_run_ctx
and 
flow_run_ctx.flow_run
are not 
None
before attempting to access the 
id
attribute. - This ensures that the code gracefully handles the absence of a flow run context without raising an 
AttributeError
. This approach allows your code to handle situations where the Prefect context is not available, such as during unit tests or when running tasks independently.
a
@Marvin how do I use prefect context for unit testing?
m
To effectively use Prefect context for unit testing, you can simulate the context using the 
prefect_test_harness
, which provides a temporary environment for running flows and tasks. This allows you to test how your code interacts with the Prefect context without needing a full deployment or execution environment. Here's how you can set up and use the Prefect context within unit tests: 1. Use `prefect_test_harness`: This context manager sets up an environment where flows and tasks can be executed as if they were in a real Prefect run context. 2. Access the Context: Within tasks or flows, you can access the context using 
TaskRunContext.get()
or 
FlowRunContext.get()
to retrieve context information like task run IDs or flow run IDs. Here's an example:
Copy code
python
from prefect import flow, task
from prefect.context import TaskRunContext
from prefect.testing.utilities import prefect_test_harness

@task
def my_task():
    context = TaskRunContext.get()
    if context is not None:
        return context.task_run.id
    return "No context"

@flow
def my_flow():
    return my_task()

def test_my_task_with_context():
    with prefect_test_harness():
        # Run the flow to ensure the task runs within a context
        result = my_flow()
        assert result != "No context"

# Running the test
if __name__ == "__main__":
    test_my_task_with_context()
    print("Test passed!")
In this setup: - The 
prefect_test_harness
is used to create a temporary testing environment. - The task accesses the context to retrieve the task run ID. - The test asserts that the task run ID is available, indicating that the context is correctly set up. This method allows you to verify that your code interacts correctly with the Prefect context during execution.
a
@Marvin ok but I’m not trying to test the context, I’m trying to test that return value of a task. but I can’t access the prefect context
m
To test the return value of a task that depends on Prefect context, you can mock the context to simulate a real task run environment. Here's how you can achieve this: 1. Mock the Context: You can use Python's 
unittest.mock
to simulate the Prefect context, allowing you to test the task's behavior without an actual run context. 2. Focus on Task Logic: Ensure your test focuses on the logic of the task and the expected return value, rather than the context itself. Here's an example of how you might set up such a test:
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python
from prefect import task
from unittest.mock import patch

# Define a task that uses the Prefect context
@task
def my_task():
    from prefect.context import TaskRunContext
    context = TaskRunContext.get()
    if context:
        return context.task_run.id
    return "No context"

def test_my_task_return_value():
    # Mock the TaskRunContext.get method to return a mock context with an ID
    with patch('prefect.context.TaskRunContext.get') as mock_get:
        mock_context = patch('prefect.context.TaskRunContext')
        mock_context.task_run = patch('prefect.context.TaskRun')
        mock_context.task_run.id = "mock-task-run-id"
        mock_get.return_value = mock_context

        # Test the task function logic
        result = my_task.fn()
        assert result == "mock-task-run-id"

# Running the test
if __name__ == "__main__":
    test_my_task_return_value()
    print("Test passed!")
In this example: - 
unittest.mock.patch
is used to mock the 
TaskRunContext.get
method, simulating a context with a specific task run ID. - The test checks that the task returns the mocked task run ID, verifying the logic of the task without needing an actual Prefect context. This approach allows you to focus on the task's return value while bypassing the need for a real Prefect context during testing.
a
@Marvin I’m hitting 
prefect.exceptions.ObjectNotFound
 on the 
read_flow_run
function
m
The 
ObjectNotFound
error when using 
read_flow_run
typically indicates that the flow run ID you're trying to access doesn't exist in the Prefect database. This can happen if the flow run was never created, has already been deleted, or if there's a mismatch in the flow run ID you're using. Here are some steps to troubleshoot and resolve this issue: 1. Verify the Flow Run ID: Ensure that the flow run ID you are using in 
read_flow_run
is correct and exists in the Prefect database. You can verify this by checking the Prefect UI or using the Prefect API to list existing flow runs. 2. Check API Configuration: Make sure that your Prefect client is correctly configured to connect to the right API endpoint. If you're using a local Prefect server, ensure that the 
PREFECT_API_URL
is set correctly:
Copy code
bash
   prefect config set PREFECT_API_URL="<http://127.0.0.1:4200/api>"
3. Inspect Flow Run Creation: If you're programmatically creating flow runs, ensure that they are being created successfully and that you capture and store their IDs correctly. 4. Prefect Cloud or Server: If you're using Prefect Cloud or a Prefect server, ensure that your authentication and permissions are correctly set up to access the flow runs. By following these steps, you should be able to resolve the 
ObjectNotFound
error. If the issue persists, consider checking any logs or error messages for additional context on why the flow run might not be found.
a
@Marvin I’m hitting 
prefect.exceptions.ObjectNotFound
 on the 
read_flow_run
function on unit tests, how can I mock the flow run id?
m
To mock the flow run ID in your unit tests and avoid the 
ObjectNotFound
error, you can use Python's 
unittest.mock
to simulate the behavior of the Prefect client and the 
read_flow_run
function. This allows you to test your code without requiring an actual flow run to exist. Here's an example of how you can mock the flow run ID in your tests:
Copy code
python
from unittest.mock import patch, MagicMock

def test_read_flow_run_with_mock():
    # Mock the Prefect client and the read_flow_run method
    with patch('prefect.client.get_client') as mock_get_client:
        # Create a mock client instance
        mock_client = MagicMock()
        mock_get_client.return_value = mock_client
        
        # Mock the behavior of read_flow_run to return a fake flow run object
        mock_flow_run = MagicMock()
        mock_flow_run.id = 'mock-flow-run-id'
        mock_client.read_flow_run.return_value = mock_flow_run

        # Now call the function that uses read_flow_run
        # For example, let's say you have a function get_flow_run_id
        flow_run_id = mock_client.read_flow_run('mock-flow-run-id').id
        
        # Assert that the mocked flow run ID is returned
        assert flow_run_id == 'mock-flow-run-id'

# Running the test
if __name__ == "__main__":
    test_read_flow_run_with_mock()
    print("Test passed!")
In this example: - 
unittest.mock.patch
is used to replace the 
get_client
function with a mock that returns a mock client. - The 
read_flow_run
method of the mock client is set up to return a mock flow run object with a predefined ID. - The test verifies that the mocked flow run ID is used, allowing you to test your function's logic without needing an actual flow run. This approach helps you focus on testing the logic of your code while bypassing the need for real Prefect infrastructure during unit tests.
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