Hey guys! Today, we're diving into how to exclude specific tests when using Hatch for your Python projects. If you're like me, you've probably got some tests that are a bit on the slow side, and you don't want to run them every single time. Maybe you've named them with a _slow.py suffix, or maybe they're just inherently time-consuming. Whatever the reason, we'll explore how to configure Hatch to skip these tests on demand while still ensuring they get run when you need a full test suite.
Before we jump into the nitty-gritty, let's quickly recap what Hatch is and how it typically behaves out of the box. Hatch is a fantastic tool for managing Python projects, handling virtual environments, dependencies, and, of course, testing. It provides a consistent and reproducible environment for your projects, which is crucial for ensuring that your tests pass not just on your machine but also in CI/CD pipelines and other environments. By default, Hatch uses pytest under the hood, a powerful and flexible testing framework for Python. When you run hatch test, Hatch discovers and executes all tests in your project directory that follow pytest's naming conventions (e.g., files starting with test_ or ending with _test.py, and test functions prefixed with test_). However, sometimes, you need more control over which tests are run, and that's where excluding tests comes in handy.
The default configuration of Hatch is designed to be straightforward and inclusive. It assumes that you want to run all tests in your project to ensure comprehensive coverage and catch any potential issues. This is a great starting point, but as your project grows and your test suite becomes more complex, you might find that running all tests every time is simply not feasible. Slow tests, in particular, can significantly increase the time it takes to run your test suite, which can slow down your development workflow. This is where the ability to exclude certain tests becomes invaluable. By excluding slow tests from your regular test runs, you can focus on the faster, more critical tests during development and reserve the slower tests for specific occasions, such as before a release or in a nightly build. This approach strikes a balance between thoroughness and efficiency, allowing you to maintain a fast development cycle while still ensuring the quality of your code. Understanding how Hatch's default configuration works is the first step in customizing it to fit your specific needs and optimizing your testing workflow.
So, why would you want to exclude tests in the first place? There are several valid reasons, guys. First off, some tests are just slow. These might be integration tests that hit a database or external API, or they could be tests that perform complex computations. Running these tests every time you make a small change can be a real time-sink. Second, you might have tests that are flaky – they sometimes pass and sometimes fail for reasons that aren't entirely clear. While it's important to address flaky tests eventually, you might want to exclude them temporarily to get a clearer picture of your overall test status. Third, there might be tests that are environment-specific. For instance, you might have tests that only make sense to run in a production environment or with a particular configuration. Running these tests in other environments would be pointless and could even lead to false negatives. Finally, excluding tests can be a way to focus your efforts. If you're working on a specific feature or bug fix, you might want to run only the tests that are relevant to that area of the codebase. This can help you iterate more quickly and avoid distractions from unrelated test failures. In all of these scenarios, the ability to selectively exclude tests is a powerful tool for managing your testing workflow and ensuring that your tests are as efficient and effective as possible.
Alright, let's get down to the how-to. There are several ways to exclude tests when using Hatch, leveraging pytest's built-in features and configuration options. We'll cover a few common approaches, including using markers, naming conventions, and pytest configuration files. Each method has its pros and cons, so you can choose the one that best fits your project's needs and your personal preferences.
Using pytest Markers
One of the most flexible and powerful ways to exclude tests is by using pytest markers. Markers are custom labels that you can apply to your tests, and then use to selectively include or exclude tests during test runs. To use markers, you first need to define them in your pytest.ini or pyproject.toml file. For example, to define a marker called slow, you would add the following to your pytest.ini file:
[pytest]
markers =
slow: mark test as slow to run
Or, if you prefer using pyproject.toml, you can add the following:
[tool.pytest.ini_options]
markers = [
"slow: mark test as slow to run",
]
Once you've defined the marker, you can apply it to your tests using the @pytest.mark.<marker_name> decorator. For example:
import pytest
@pytest.mark.slow
def test_slow_function():
# Your slow test code here
pass
Now, you can use the -m option with pytest to run only tests that have a specific marker or to exclude tests with a specific marker. To exclude tests marked as slow, you would use the following command:
hatch run pytest -m "not slow"
This tells pytest to run all tests that do not have the slow marker. Conversely, to run only the slow tests, you would use:
hatch run pytest -m slow
Markers are a great way to categorize your tests and provide fine-grained control over which tests are run. They're especially useful when you have a variety of reasons for excluding tests, such as slow tests, flaky tests, or environment-specific tests. By using markers, you can easily create different test suites for different purposes.
Naming Conventions
Another simple and effective way to exclude tests is by using naming conventions. This approach relies on the fact that pytest, by default, follows certain conventions for discovering test files and test functions. For example, pytest automatically discovers files that start with test_ or end with _test.py, and functions that start with test_. You can leverage this behavior to exclude tests by naming your test files or functions in a way that pytest will ignore them by default.
In your case, you mentioned using the *_slow.py naming convention for slow test files. This is a great approach! By default, pytest will still discover and run these tests, but you can easily tell pytest to ignore them by using the --ignore option or by configuring the ignore setting in your pytest.ini or pyproject.toml file.
To ignore files matching a specific pattern using the --ignore option, you would use the following command:
hatch run pytest --ignore="tests/*_slow.py"
This tells pytest to ignore any files in the tests directory that end with _slow.py. Alternatively, you can configure this in your pytest.ini file:
[pytest]
ignore =
tests/*_slow.py
Or in your pyproject.toml file:
[tool.pytest.ini_options]
ignore = [
"tests/*_slow.py",
]
With this configuration, pytest will automatically ignore the slow tests whenever you run hatch test or hatch run pytest. To run the slow tests specifically, you would need to either remove this configuration or use the inverse of the --ignore option, which is not directly available in pytest. However, you could achieve this by temporarily commenting out the ignore setting in your configuration file or by using markers as described in the previous section.
Naming conventions are a simple and straightforward way to exclude tests, especially when you have a clear and consistent pattern for identifying the tests you want to exclude. This approach is easy to understand and maintain, making it a good choice for projects where simplicity is a priority.
Using pytest Configuration Files
As we've seen in the previous sections, pytest configuration files (pytest.ini or pyproject.toml) are a central place to customize pytest's behavior. You can use these files to configure various aspects of pytest, including which tests to ignore, which markers to define, and how to handle warnings and errors. By leveraging the configuration file, you can create a consistent and reproducible testing environment for your project.
We've already discussed how to use the ignore setting in the configuration file to exclude tests based on naming conventions. You can also use the markers setting to define custom markers, as we saw in the section on using pytest markers. In addition to these, there are other useful settings you can configure in the pytest.ini or pyproject.toml file.
For example, you can use the addopts setting to specify default command-line options for pytest. This can be useful for setting options that you want to apply to every test run, such as the -v option for verbose output or the --cov option for coverage reporting. To set default options, you would add the following to your pytest.ini file:
[pytest]
addopts = -v --cov=my_module
Or in your pyproject.toml file:
[tool.pytest.ini_options]
addopts = "-v --cov=my_module"
You can also use the filterwarnings setting to control how pytest handles warnings. This can be useful for suppressing warnings that you know are not relevant or for turning warnings into errors. To filter warnings, you would add the following to your pytest.ini file:
[pytest]
filterwarnings =
ignore::DeprecationWarning
Or in your pyproject.toml file:
[tool.pytest.ini_options]
filterwarnings = [
"ignore::DeprecationWarning",
]
By using pytest configuration files, you can create a highly customized testing environment that meets the specific needs of your project. This approach is especially useful for larger projects with complex testing requirements, as it allows you to centralize your testing configuration and ensure consistency across different environments.
Now that we've covered the various methods for excluding tests with pytest, let's talk about how to integrate these exclusions with Hatch. Hatch provides a seamless way to run pytest with your desired configuration, making it easy to exclude tests on demand. The key is to use the hatch run pytest command and pass the appropriate pytest options. We've already seen examples of this in the previous sections, but let's recap and provide some additional context.
When you run hatch test, Hatch essentially executes hatch run pytest with some default options. This means that any options you can pass to pytest on the command line, you can also pass to hatch run pytest. This includes options like -m for markers, --ignore for ignoring files, and any other pytest options you might want to use. To exclude tests, you simply need to add the appropriate options to the hatch run pytest command.
For example, to exclude slow tests using the -m option, you would use the following command:
hatch run pytest -m "not slow"
To exclude slow tests using the --ignore option, you would use the following command:
hatch run pytest --ignore="tests/*_slow.py"
You can also combine these options with other pytest options, such as -v for verbose output or --cov for coverage reporting. For example:
hatch run pytest -v -m "not slow" --cov=my_module
This command will run all tests that are not marked as slow, provide verbose output, and generate coverage reports for the my_module package.
By integrating test exclusions with Hatch, you can easily switch between different test suites and run only the tests you need for a particular task. This can significantly speed up your development workflow and make your testing process more efficient.
Before we wrap up, let's chat about some best practices for test exclusion. Excluding tests can be a powerful tool, but it's important to use it judiciously and avoid creating a situation where important tests are accidentally skipped. Here are a few guidelines to keep in mind:
- Be explicit about why you're excluding a test. Don't just exclude tests without a clear reason. Document why a test is being excluded, whether it's because it's slow, flaky, environment-specific, or for some other reason. This will help you and your team understand the exclusion and avoid accidentally re-introducing the test later.
- Use markers or naming conventions consistently. Choose a method for excluding tests (markers or naming conventions) and stick to it. This will make your test suite more consistent and easier to understand. If you use markers, define them clearly in your
pytest.iniorpyproject.tomlfile and use descriptive names. - Review your exclusions regularly. Periodically review your test exclusions to make sure they're still valid. A test that was slow or flaky at one point might be fixed later, and you might want to re-enable it. Similarly, a test that was environment-specific might become relevant in other environments as your project evolves.
- Don't exclude tests indefinitely. Test exclusions should generally be temporary measures. If a test is consistently failing or causing problems, it's better to fix the underlying issue rather than excluding the test indefinitely. Exclusions can mask problems and lead to a false sense of security.
- Consider using continuous integration (CI) to run all tests. While you might exclude certain tests during local development, it's important to run all tests in a CI environment on a regular basis. This will help you catch any issues that might have been missed due to test exclusions.
By following these best practices, you can use test exclusions effectively without compromising the quality of your test suite.
Alright, guys, we've covered a lot of ground today! We've talked about why you might want to exclude tests, the various methods for doing so with pytest and Hatch, and some best practices to keep in mind. Excluding tests can be a valuable technique for managing your testing workflow and ensuring that your tests are as efficient and effective as possible. Whether you're dealing with slow tests, flaky tests, or environment-specific tests, the ability to selectively exclude tests can save you time and effort.
Remember, the key is to use test exclusions judiciously and to document your reasons for excluding tests. By following the best practices we've discussed, you can ensure that your test suite remains comprehensive and reliable, even as you exclude certain tests on demand. So go forth and conquer your test suites, excluding those slow tests when you need to and keeping your development workflow zippy and efficient! Happy testing!