Most users will want to enable “Trio mode”. Without Trio mode:
- Pytest-trio only handles tests that have been decorated with
- Pytest-trio only handles fixtures if they’re async and used by a
test that’s decorated with
@pytest.mark.trio, or if they’re decorated with
When Trio mode is enabled, two extra things happen:
- Async tests automatically have the
triomark added, so you don’t have to do it yourself.
- Async fixtures using
@pytest.fixtureautomatically get converted to Trio fixtures. (The main effect of this is that it helps you catch mistakes like using an async fixture with a non-async test.)
There are two ways to enable Trio mode.
The first option is to use a pytest configuration file. The exact rules for how pytest finds configuration files are a bit complicated, but you want to end up with something like:
# pytest.ini [pytest] trio_mode = true
The second option is use a conftest.py file. Inside your tests
directory, create a file called
conftest.py, with the following
# conftest.py from pytest_trio.enable_trio_mode import *
This does exactly the same thing as setting
trio_mode = true in
pytest.ini, except for two things:
- Some people like to ship their tests as part of their library, so
they (or their users) can test the final installed software by
pytest --pyargs PACKAGENAME. In this mode,
pytest.inifiles don’t work, but
- Enabling Trio mode in
pytest.inialways enables it globally for your entire testsuite. Enabling it in
conftest.pyonly enables it for test files that are in the same directory as the
conftest.py, or its subdirectories.
If you have software that uses multiple async libraries, then you can
conftest.py to enable Trio mode for just the part of your
testsuite that uses Trio; or, if you need even finer-grained control,
you can leave Trio mode disabled and use
explicitly on all your Trio tests.
Normally, pytest runs fixture code before starting the test, and
teardown code afterwards. For technical reasons, we can’t wrap this
whole process in
trio.run() – only the test itself. As a
workaround, pytest-trio introduces the concept of a “Trio fixture”,
which acts like a normal fixture for most purposes, but actually does
the setup and teardown inside the test’s call to
The following fixtures are treated as Trio fixtures:
- Any function decorated with
- Any async function decorated with
@pytest.fixture, if Trio mode is enabled or this fixture is being requested by a Trio test.
- Any fixture which depends on a Trio fixture.
The most notable difference between regular fixtures and Trio fixtures
is that regular fixtures can’t use Trio APIs, but Trio fixtures can.
Most of the time you don’t need to worry about this, because you
normally only call Trio APIs from async functions, and when Trio mode
is enabled, all async fixtures are automatically Trio fixtures.
However, if for some reason you do want to use Trio APIs from a
synchronous fixture, then you’ll have to use
# This fixture is not very useful # But it is an example where @pytest.fixture doesn't work @pytest_trio.trio_fixture def trio_time(): return trio.current_time()
Only Trio tests can use Trio fixtures. If you have a regular (synchronous) test that tries to use a Trio fixture, then that’s an error.
And finally, regular fixtures can be scoped to the test, class, module, or session, but Trio fixtures must be test scoped. Class, module, and session scope are not supported.
An important note about
Like any pytest fixture, Trio fixtures can contain both setup and
teardown code separated by a
@pytest.fixture async def my_fixture(): ... setup code ... yield ... teardown code ...
When pytest-trio executes this fixture, it creates a new task, and
runs the setup code until it reaches the
yield. Then the fixture’s
task goes to sleep. Once the test has finished, the fixture task wakes
up again and resumes at the
yield, so it can execute the teardown
yield in a fixture is sort of like calling
wait_for_test_to_finish(). And in Trio, any
operation can be cancelled. For example, we could put a timeout on the
@pytest.fixture async def my_fixture(): ... setup code ... with trio.move_on_after(5): yield # this yield gets cancelled after 5 seconds ... teardown code ...
Now if the test takes more than 5 seconds to execute, this fixture
will cancel the
That’s kind of a strange thing to do, but there’s another version of
this that’s extremely common. Suppose your fixture spawns a background
task, and then the background task raises an exception. Whenever a
background task raises an exception, it automatically cancels
everything inside the nursery’s scope – which includes our
@pytest.fixture async def my_fixture(nursery): nursery.start_soon(function_that_raises_exception) yield # this yield gets cancelled after the background task crashes ... teardown code ...
If you use fixtures with background tasks, you’ll probably end up
cancelling one of these
yields sooner or later. So what happens
yield gets cancelled?
First, pytest-trio assumes that something has gone wrong and there’s no point in continuing the test. If the top-level test function is running, then it cancels it.
Then, pytest-trio waits for the test function to finish, and then begins tearing down fixtures as normal.
During this teardown process, it will eventually reach the fixture
that cancelled its
yield. This fixture gets resumed to execute its
teardown logic, but with a special twist: since the
Now, here’s the punchline: this means that in our examples above, the
teardown code might not be executed at all! This is different from
how pytest fixtures normally work. Normally, the
yield in a
pytest fixture never raises an exception, so you can be certain that
any code you put after it will execute as normal. But if you have a
fixture with background tasks, and they crash, then your
might raise an exception, and Python will skip executing the code
In our experience, most fixtures are fine with this, and it prevents some weird problems that can happen otherwise. But it’s something to be aware of.
If you have a fixture where the
yield might be cancelled but you
still need to run teardown code, then you can use a
@pytest.fixture async def my_fixture(nursery): nursery.start_soon(function_that_crashes) try: # This yield could be cancelled... yield finally: # But this code will run anyway ... teardown code ...
(But, watch out: the teardown code is still running in a cancelled
context, so if it has any
awaits it could raise
Or if you use
with to handle teardown, then you don’t have to
worry about this because
with blocks always perform cleanup even
if there’s an exception:
@pytest.fixture async def my_fixture(nursery): with get_obj_that_must_be_torn_down() as obj: nursery.start_soon(function_that_crashes, obj) # This could raise trio.Cancelled... # ...but that's OK, the 'with' block will still tear down 'obj' yield obj
If your test uses multiple fixtures, then for speed, pytest-trio will try to run their setup and teardown code concurrently whenever this is possible while respecting the fixture dependencies.
Here’s an example, where a test depends on
and these both depend on
@trio_fixture def fix_a(): ... @trio_fixture def fix_b(fix_a): ... @trio_fixture def fix_c(fix_a): ... @pytest.mark.trio async def test_example(fix_b, fix_c): ...
test_example, pytest-trio will perform the following
sequence of actions:
- Set up
- Set up
- Run the test.
- Tear down
- Tear down
We’re seeking feedback on whether this feature’s benefits outweigh its negatives.
Handling of ContextVars¶
contextvars module lets you create
ContextVar objects to represent task-local
variables. Normally, in Trio, each task gets its own
Context, so that changes to
ContextVar objects are only visible inside the
task that performs them. But pytest-trio overrides this, and for each
test it uses a single
Context which is shared by
all fixtures and the test function itself.
The benefit of this is that you can set
ContextVar values inside a fixture, and your
settings will be visible in dependent fixtures and the test itself.
For example, trio-asyncio
ContextVar to hold the current asyncio
loop object, so this lets you open a loop inside a fixture and then
use it inside other fixtures or the test itself.
The downside is that if two fixtures are run concurrently (see
previous section), and both mutate the same
ContextVar, then there will be a race condition
and the the final value will be unpredictable. If you make one fixture
depend on the other, then this will force an ordering and make the
final value predictable again.
These fixtures are automatically available to any code using pytest-trio.
trio.testing.MockClock, with its default configuration (
What makes these particularly useful is that whenever pytest-trio runs
a test, it checks the fixtures to see if one of them is a
trio.abc.Clock object. If so, it passes that object to
trio.run(). So if your test requests one of these fixtures, it
automatically uses that clock.
If you implement your own
Clock, and implement a
fixture that returns it, then it will work the same way.
Of course, like any pytest fixture, you also get the actual object
available. For example, you can call
async def test_time_travel(mock_clock): assert trio.current_time() == 0 mock_clock.jump(10) assert trio.current_time() == 10
A nursery created and managed by pytest-trio itself, which surrounds the test/fixture that requested it, and is automatically cancelled after the test/fixture completes. Basically, these are equivalent:
# Boring way async def test_with_background_task(): async with trio.open_nursery() as nursery: try: ... finally: nursery.cancel_scope.cancel() # Fancy way async def test_with_background_task(nursery): ...
For a fixture, the cancellation always happens after the fixture completes its teardown phase. (Or if it doesn’t have a teardown phase, then the cancellation happens after the teardown phase would have happened.)
This fixture is even more magical than most pytest fixtures, because if it gets requested several times within the same test, then it creates multiple nurseries, one for each fixture/test that requested it.
See Running a background server from a fixture for an example of how this can be used.
Integration with the Hypothesis library¶
There isn’t too much to say here, since the obvious thing just works:
from hypothesis import given import hypothesis.strategies as st @given(st.binary()) async def test_trio_and_hypothesis(data): ...
Under the hood, this requires some coordination between Hypothesis and
pytest-trio. Hypothesis runs your test multiple times with different
examples of random data. For each example, pytest-trio calls
trio.run() again (so you get a fresh clean Trio environment),
sets up any Trio fixtures, runs the actual test, and then tears down
any Trio fixtures. Notice that this is a bit different than regular
pytest fixtures, which are instantiated once and then re-used for all. Most of the time
this shouldn’t matter (and is probably what you want anyway), but in
some unusual cases it could surprise you. And this only applies to
Trio fixtures – if a Trio test uses a mix of regular fixtures and Trio
fixtures, then the regular fixtures will be reused, while the Trio
fixtures will be repeatedly reinstantiated.