Pytest-retry And Pytest-subtests: Compatibility Issues

by Alex Johnson 55 views

pytest-retry and pytest-subtests are powerful tools in the pytest ecosystem, each designed to address specific testing needs. pytest-retry helps with flaky tests by automatically retrying them a specified number of times, while pytest-subtests, now officially integrated into pytest 9.0, allows for running multiple subtests within a single test function. However, when these two plugins are used together, particularly with certain configurations, compatibility issues can arise, leading to unexpected behavior like infinite loops or failures.

Understanding the Conflict: pytest-retry and pytest-subtests

pytest-retry, as the name suggests, is designed to retry tests that might fail due to transient issues. This is incredibly useful for tests that interact with external services, networks, or other systems where temporary glitches can occur. By retrying the test, pytest-retry increases the likelihood of a passing test, making the test suite more robust.

On the other hand, pytest-subtests provides a way to structure a single test into multiple logical units. This is particularly valuable when you want to test different aspects or scenarios within the same test function. Each subtest is independent, and if one fails, it doesn't necessarily stop the execution of the others. This granular approach allows for more detailed and informative test results.

The core of the conflict lies in how these plugins handle test execution and failures. pytest-retry intercepts test failures and attempts to rerun the entire test function. pytest-subtests, on the other hand, manages the execution of individual subtests within a single test function. When a subtest fails, it doesn't immediately fail the whole test; instead, it marks the subtest as failed and continues with the remaining subtests. This difference in handling failures can lead to unexpected interactions when both plugins are used together.

Consider a scenario where a test function uses pytest-subtests and one of the subtests fails. If pytest-retry is also enabled, it might interpret the failure of the subtest as a failure of the entire test function and attempt to retry the whole function. This can lead to the subtests being re-executed repeatedly, potentially causing an infinite loop if the underlying issue isn't resolved by the retries. The interaction between pytest-retry and pytest-subtests can be particularly tricky when dealing with exceptions and how they propagate within the subtest structure.

Code Example: Infinite Loop Scenario

The provided code snippet demonstrates a situation where this incompatibility manifests as an infinite loop. Let's break down the code to understand why it happens.

import pytest
from pytest_subtests import SubTests

@pytest.mark.flaky
def test_retry(subtests: SubTests) -> None:
    with subtests.test("foo"):
        raise RuntimeError("foo")

    with subtests.test("bar"):
        raise RuntimeError("bar")

In this example, the @pytest.mark.flaky decorator, likely provided by pytest-retry, marks the test as potentially flaky, instructing pytest-retry to retry it if it fails. The test_retry function utilizes pytest-subtests to define two subtests, "foo" and "bar". Within each subtest, a RuntimeError is raised, simulating a failure. When this code is executed, pytest-retry detects the failure of the first subtest ("foo") and attempts to retry the entire test_retry function. The same process repeats, leading to an infinite loop because both subtests consistently fail.

This behavior is because pytest-retry doesn't differentiate between individual subtest failures and the overall test function's failure. It sees any failure within the function and retries the entire function, leading to the repeated execution of the same failing subtests. This incompatibility highlights the need for careful consideration when combining these two plugins.

Asyncio and pytest-asyncio Plugin Interaction

The situation becomes even more complex when asyncio and the pytest-asyncio plugin are introduced. Let's examine the code and its behavior.

import pytest
from pytest_subtests import SubTests

@pytest.mark.flaky
async def test_retry(subtests: SubTests) -> None:
    with subtests.test("foo"):
        raise RuntimeError("foo")

    with subtests.test("bar"):
        raise RuntimeError("bar")

In this case, the test_retry function is now an asynchronous function using async def, enabled by the pytest-asyncio plugin. While this setup doesn't lead to an infinite loop, it does introduce a different set of issues, as indicated by the error messages in the original report.

The error messages suggest problems related to the asyncio event loop and resource management. The RuntimeWarning: coroutine 'test_retry' was never awaited indicates that the coroutine wasn't properly awaited, potentially leading to incomplete execution and resource leaks. The ResourceWarning: unclosed event loop and ResourceWarning: unclosed <socket.socket> messages point to unclosed resources, which can happen if the test function doesn't handle the asynchronous operations correctly or if the event loop isn't properly managed.

This combination of asynchronous testing, pytest-retry, and pytest-subtests further exacerbates the potential for resource leaks and improper handling of failures. The retrying mechanism of pytest-retry can interfere with the asynchronous operations, leading to incomplete executions and resource management issues.

The use of asyncio and pytest-asyncio introduces the complexities of managing asynchronous operations and the event loop. The interaction between pytest-retry, which retries the whole test, and pytest-subtests, which handles individual subtests, makes it more challenging to ensure proper resource cleanup and complete execution of asynchronous tasks, resulting in warnings about unclosed resources and potentially incorrect test results. This is because pytest-retry may be re-running the test function, potentially creating a new event loop or not properly cleaning up the resources from previous attempts.

Troubleshooting and Workarounds

Addressing the compatibility issues between pytest-retry and pytest-subtests requires a strategic approach. Here are some strategies to mitigate the problems:

  1. Careful Consideration of Test Design:

    • Minimize Flakiness: The primary goal should be to reduce test flakiness at the source. Identify and fix the underlying causes of flakiness in the tests. Ensure that tests are written to be as deterministic and independent as possible.
    • Alternative Approaches: If a test is inherently flaky, consider alternative approaches that don't rely on retrying the entire test function. For example, you can retry individual operations within the subtests, rather than retrying the entire subtest.
    • Separate Tests: If possible, refactor the test suite to separate tests that are known to be flaky from those that are not. This will allow you to apply pytest-retry more selectively and reduce the risk of it interfering with other tests.

  2. Fine-tuning pytest-retry Configuration:

    • Limit Retries: Configure pytest-retry to limit the number of retries. This can prevent infinite loops and give you more control over the test execution. Use the retries parameter in the @pytest.mark.flaky decorator or configure it through pytest.ini or pyproject.toml.
    • Retry Delay: Introduce a delay between retries using the delay parameter. This can be helpful if the flakiness is due to external factors like network latency or service availability.

  3. Alternative Approaches to Flaky Tests:

    • Context Managers: Wrap the flaky part of a test within a context manager. You can then handle retries within this context manager, giving you finer control over the retry process.
    • Custom Decorators: Create a custom decorator that combines the functionality of pytest-retry with subtest awareness. This allows you to specifically target retries to flaky subtests only.

  4. Debugging Techniques:

    • Logging: Use extensive logging to trace the execution of tests, including retries and subtest failures. This can help you pinpoint the exact cause of the problem.
    • Breakpoints: Insert breakpoints in your code to examine the state of the test function, subtests, and event loop during retries. This is crucial when dealing with asynchronous code.

  5. Addressing Asyncio Issues:

    • Proper Awaiting: Ensure that all asynchronous operations are properly awaited. This is crucial for preventing resource leaks and ensuring that tasks complete correctly.
    • Context Management: Use context managers to handle resources such as event loops, sockets, and connections. This helps ensure that resources are properly closed and cleaned up, even in the event of failures or retries.
    • Explicit Loop Management: When using pytest-asyncio, be mindful of the event loop. Make sure the loop is properly created, run, and closed within the test. This is essential for managing asynchronous operations correctly.

By following these recommendations, you can mitigate the challenges presented by the interplay of pytest-retry, pytest-subtests, and pytest-asyncio. Careful test design, proper configuration, and thoughtful debugging practices are key to ensuring a reliable and efficient testing environment.

Conclusion: Navigating the Complexity

The combination of pytest-retry and pytest-subtests can be a powerful tool, but it also presents compatibility challenges, especially when integrated with asyncio and pytest-asyncio. Understanding the core of these interactions, being mindful of the potential for infinite loops and resource management issues, and employing the strategies outlined in this article are crucial for writing robust and reliable tests. Always prioritize minimizing flakiness, and consider alternative approaches that may offer better control and predictability. Through careful design, configuration, and debugging practices, you can successfully leverage these tools to enhance your testing workflow.

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