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Pytest

pytest is a Python framework to makes it easy to write small tests, yet scales to support complex functional testing for applications and libraries.

Pytest stands out over other test frameworks in:

  • Simple tests are simple to write in pytest.
  • Complex tests are still simple to write.
  • Tests are easy to read.
  • You can get started in seconds.
  • You use assert to fail a test, not things like self.assertEqual() or self.assertLessThan(). Just assert.
  • You can use pytest to run tests written for unittest or nose.

Note: You can use this cookiecutter template to create a python project with pytest already configured.

Install

pip install pytest

Usage

Run in the project directory.

pytest

If you need more information run it with -v.

Pytest automatically finds which tests to run in a phase called test discovery. It will get the tests that match one of the following conditions:

  • Test files that are named test_{{ something }}.py or {{ something }}_test.py.
  • Test methods and functions named test_{{ something }}.
  • Test classes named Test{{ Something }}.

There are several possible outcomes of a test function:

  • PASSED (.): The test ran successfully.
  • FAILED (F): The test did not run usccessfully (or XPASS + strict).
  • SKIPPED (s): The test was skipped. You can tell pytest to skip a test by using enter the @pytest.mark.skip() or pytest.mark.skipif() decorators.
  • xfail (x): The test was not supposed to pass, ran, and failed. You can tell pytest that a test is expected to fail by using the @pytest.mark.xfail() decorator.
  • XPASS (X): The tests was not supposed to pass, ran, and passed.
  • ERROR (E): An exception happened outside of the test function, in either a fixture or a hook function.

Pytest supports several cool flags like:

  • -k EXPRESSION: Used to select a subset of tests to run. For example pytest -k "asdict or defaults" will run both test_asdict() and test_defaults().
  • --lf or --last-failed: Just run the tests that have failed in the previous run.
  • -x, or --exitfirst: Exit on first failed test.
  • -l or --showlocals: Print out the local variables in a test if the test fails.
  • -s Allows any output that normally would be printed to stdout to actually be printed to stdout. It's an alias of --capture=no, so the output is not captured when the tests are run, which is the default behavior. This is useful to debug with print() statements.
  • --durations=N: It reports the slowest N number of tests/setups/teardowns after the test run. If you pass in --durations=0, it reports everything in order of slowest to fastest.
  • --setup-show: Show the fixtures in use.

Fixtures

Fixtures are functions that are run by pytest before (and sometimes after) the actual test functions.

You can use fixtures to get a data set for the tests to work on, or use them to get a system into a known state before running a test. They are also used to get data ready for multiple tests.

Here's a simple fixture that returns a number:

import pytest

@pytest.fixture()
def some_data()
    """ Return answer to the ultimate question """
    return 42

def test_some_data(some_data):
    """ Use fixture return value in a test"""
    assert some_data == 42

The @pytest.fixture() decorator is used to tell pytest that a function is a fixture.When you include the fixture name in the parameter list of a test function,pytest knows to run it before running the test. Fixtures can do work, and can also return data to the test function.

The test test_some_data() has the name of the fixture, some_data, as a parameter.pytest will see this and look for a fixture with this name. Naming is significant in pytest. pytest will look in the module of the test for a fixture of that name.

If the function is defined in the same file as where it's being used pylint will raise an W0621: Redefining name %r from outer scope (line %s) error. To solve it either move the fixture to other file or name the decorated function fixture_<fixturename> and then use @pytest.fixture(name='<fixturename>').

Sharing fixtures through conftest.py

You can put fixtures into individual test files, but to share fixtures among multiple test files, you need to use a conftest.py file somewhere centrally located for all of the tests. Additionally you can have conftest.py files in subdirectories of the top tests directory. If you do, fixtures defined in these lower level conftest.py files will be available to tests in that directory and subdirectories.

Although conftest.py is a Python module, it should not be imported by test files. The file gets read by pytest, and is considered a local plugin.

Another option is to save the fixtures in a file by creating a local pytest plugin.

File: tests/unit/conftest.py

pytest_plugins = [
    "tests.unit.fixtures.some_stuff",
]

File: tests/unit/fixtures/some_stuff.py:

import pytest


@pytest.fixture
def foo():
    return "foobar"

Specifying fixture scope

Fixtures include an optional parameter called scope, which controls how often a fixture gets set up and torn down. The scope parameter to @pytest.fixture() can have the values of function, class, module, or session.

Here’s a rundown of each scope value:

  • scope='function': Run once per test function. The setup portion is run before each test using the fixture. The teardown portion is run after each test using the fixture. This is the default scope used when no scope parameter is specified.
  • scope='class': Run once per test class, regardless of how many test methods are in the class.
  • scope='module': Run once per module, regardless of how many test functions or methods or other fixtures in the module use it.
  • scope='session' Run once per session. All test methods and functions using a fixture of session scope share one setup and teardown call.

Using fixtures at class level

Sometimes test functions do not directly need access to a fixture object. For example, tests may require to operate with an empty directory as the current working directory but otherwise do not care for the concrete directory.

@pytest.mark.usefixtures("cleandir")
class TestDirectoryInit:
    ...

Due to the usefixtures marker, the cleandir fixture will be required for the execution of each test method, just as if you specified a cleandir function argument to each of them.

You can specify multiple fixtures like this:

@pytest.mark.usefixtures("cleandir", "anotherfixture")

Useful Fixtures

The tmp_path fixture

You can use the tmp_path fixture which will provide a temporary directory unique to the test invocation, created in the base temporary directory.

tmp_path is a pathlib.Path object. Here is an example test usage:

def test_create_file(tmp_path):
    d = tmp_path / "sub"
    d.mkdir()
    p = d / "hello.txt"
    p.write_text(CONTENT)
    assert p.read_text() == CONTENT
    assert len(list(tmp_path.iterdir())) == 1
    assert 0

The tmpdir fixture

Warning: Don't use tmpdir use tmp_path instead because tmpdir uses py which is unmaintained and has unpatched vulnerabilities.

You can use the tmpdir fixture which will provide a temporary directory unique to the test invocation, created in the base temporary directory.

tmpdir is a py.path.local object which offers os.path methods and more. Here is an example test usage:

File: test_tmpdir.py:

from py._path.local import LocalPath


def test_create_file(tmpdir: LocalPath):
    p = tmpdir.mkdir("sub").join("hello.txt")
    p.write("content")
    assert p.read() == "content"
    assert len(tmpdir.listdir()) == 1
    assert 0

The tmpdir fixture has a scope of function so you can't make a session directory. Instead use the tmpdir_factory fixture.

from _pytest.tmpdir import TempPathFactory


@pytest.fixture(scope="session")
def image_file(tmpdir_factory: TempPathFactory):
    img = compute_expensive_image()
    fn = tmpdir_factory.mktemp("data").join("img.png")
    img.save(str(fn))
    return fn


def test_histogram(image_file):
    img = load_image(image_file)
    # compute and test histogram
Make a subdirectory
p = tmpdir.mkdir("sub").join("hello.txt")

The caplog fixture

pytest captures log messages of level WARNING or above automatically and displays them in their own section for each failed test in the same manner as captured stdout and stderr.

You can change the default logging level in the pytest configuration:

File: pytest.ini:

[pytest]

log_level = debug

Although it may not be a good idea in most cases. It's better to change the log level in the tests that need a lower level.

All the logs sent to the logger during the test run are available on the fixture in the form of both the logging.LogRecord instances and the final log text. This is useful for when you want to assert on the contents of a message:

from _pytest.logging import LogCaptureFixture


def test_baz(caplog: LogCaptureFixture):
    func_under_test()
    for record in caplog.records:
        assert record.levelname != "CRITICAL"
    assert "wally" not in caplog.text

You can also resort to record_tuples if all you want to do is to ensure that certain messages have been logged under a given logger name with a given severity and message:

def test_foo(caplog: LogCaptureFixture):
    logging.getLogger().info("boo %s", "arg")

    assert ("root", logging.INFO, "boo arg") in caplog.record_tuples

You can call caplog.clear() to reset the captured log records in a test.

Change the log level

Inside tests it's possible to change the log level for the captured log messages.

def test_foo(caplog: LogCaptureFixture):
    caplog.set_level(logging.INFO)
    pass

If you just want to change the log level of a dependency you can use:

caplog.set_level(logging.WARNING, logger="urllib3")

The capsys fixture

The capsys builtin fixture provides two bits of functionality: it allows you to retrieve stdout and stderr from some code, and it disables output capture temporarily.

Suppose you have a function to print a greeting to stdout:

def greeting(name):
    print(f"Hi, {name}")

You can test the output by using capsys.

from _pytest.capture import CaptureFixture


def test_greeting(capsys: CaptureFixture[Any]):
    greeting("Earthling")
    out, err = capsys.readouterr()
    assert out == "Hi, Earthling\n"
    assert err == ""

The return value is whatever has been captured since the beginning of the function, or from the last time it was called.

freezegun

freezegun lets you freeze time in both the test and fixtures.

Install
pip install pytest-freezegun
Usage
Global usage

Most of the tests work with frozen time, so it's better to freeze it by default and unfreeze it on the ones that actually need time to move.

To do that set in your tests/conftest.py a globally used fixture:

if TYPE_CHECKING:
    from freezegun.api import FrozenDateTimeFactory


@pytest.fixture(autouse=True)
def frozen_time() -> Generator[FrozenDateTimeFactory, None, None]:
    """Freeze all tests time"""
    with freezegun.freeze_time() as freeze:
        yield freeze

Freeze time by using the freezer fixture:

Manual use
if TYPE_CHECKING:
    from freezegun.api import FrozenDateTimeFactory


def test_frozen_date(freezer: FrozenDateTimeFactory):
    now = datetime.now()
    time.sleep(1)
    later = datetime.now()
    assert now == later

This can then be used to move time:

def test_moving_date(freezer):
    now = datetime.now()
    freezer.move_to("2017-05-20")
    later = datetime.now()
    assert now != later

You can also pass arguments to freezegun by using the freeze_time mark:

@pytest.mark.freeze_time("2017-05-21")
def test_current_date():
    assert date.today() == date(2017, 5, 21)

The freezer fixture and freeze_time mark can be used together, and they work with other fixtures:

@pytest.fixture
def current_date():
    return date.today()


@pytest.mark.freeze_time()
def test_changing_date(current_date, freezer):
    freezer.move_to("2017-05-20")
    assert current_date == date(2017, 5, 20)
    freezer.move_to("2017-05-21")
    assert current_date == date(2017, 5, 21)

They can also be used in class-based tests:

class TestDate:
    @pytest.mark.freeze_time
    def test_changing_date(self, current_date, freezer):
        freezer.move_to("2017-05-20")
        assert current_date == date(2017, 5, 20)
        freezer.move_to("2017-05-21")
        assert current_date == date(2017, 5, 21)

Customize nested fixtures

Sometimes you need to tweak your fixtures so they can be used in different tests. As usual, there are different solutions to the same problem.

Note: "TL;DR: For simple cases parametrize your fixtures or use parametrization to override the default valued fixture. As your test suite get's more complex migrate to pytest-case."

Let's say you're running along merrily with some fixtures that create database objects for you:

@pytest.fixture
def supplier(db):
    s = Supplier(
        ref=random_ref(),
        name=random_name(),
        country="US",
    )
    db.add(s)
    yield s
    db.remove(s)


@pytest.fixture()
def product(db, supplier):
    p = Product(
        ref=random_ref(),
        name=random_name(),
        supplier=supplier,
        net_price=9.99,
    )
    db.add(p)
    yield p
    db.remove(p)

And now you're writing a new test and you suddenly realize you need to customize your default "supplier" fixture:

def test_US_supplier_has_total_price_equal_net_price(product):
    assert product.total_price == product.net_price


def test_EU_supplier_has_total_price_including_VAT(supplier, product):
    supplier.country = "FR"  # oh, this doesn't work
    assert product.total_price == product.net_price * 1.2

There are different ways to modify your fixtures

Add more fixtures

We can just create more fixtures, and try to do a bit of DRY by extracting out common logic:

def _default_supplier():
    return Supplier(
        ref=random_ref(),
        name=random_name(),
    )


@pytest.fixture
def us_supplier(db):
    s = _default_supplier()
    s.country = "US"
    db.add(s)
    yield s
    db.remove(s)


@pytest.fixture
def eu_supplier(db):
    s = _default_supplier()
    s.country = "FR"
    db.add(s)
    yield s
    db.remove(s)

That's just one way you could do it, maybe you can figure out ways to reduce the duplication of the db.add() stuff as well, but you are going to have a different, named fixture for each customization of Supplier, and eventually you may decide that doesn't scale.

Use factory fixtures

Instead of a fixture returning an object directly, it can return a function that creates an object, and that function can take arguments:

@pytest.fixture
def make_supplier(db):
    s = Supplier(
        ref=random_ref(),
        name=random_name(),
    )

    def _make_supplier(country):
        s.country = country
        db.add(s)
        return s

    yield _make_supplier
    db.remove(s)

The problem with this is that, once you start, you tend to have to go all the way, and make all of your fixture hierarchy into factory functions:

def test_EU_supplier_has_total_price_including_VAT(make_supplier, product):
    supplier = make_supplier(country="FR")
    product.supplier = (
        supplier  # OH, now this doesn't work, because it's too late again
    )
    assert product.total_price == product.net_price * 1.2

And so...

@pytest.fixture
def make_product(db):
    p = Product(
        ref=random_ref(),
        name=random_name(),
    )

    def _make_product(supplier):
        p.supplier = supplier
        db.add(p)
        return p

    yield _make_product
    db.remove(p)


def test_EU_supplier_has_total_price_including_VAT(make_supplier, make_product):
    supplier = make_supplier(country="FR")
    product = make_product(supplier=supplier)
    assert product.total_price == product.net_price * 1.2

That works, but firstly now everything is a factory-fixture, which makes them more convoluted, and secondly, your tests are filling up with extra calls to make_things, and you're having to embed some of the domain knowledge of what-depends-on-what into your tests as well as your fixtures. Ugly!

Parametrize your fixtures

You can also parametrize your fixtures.

@pytest.fixture(params=["US", "FR"])
def supplier(db, request):
    s = Supplier(ref=random_ref(), name=random_name(), country=request.param)
    db.add(s)
    yield s
    db.remove(s)

Now any test that depends on supplier, directly or indirectly, will be run twice, once with supplier.country = US and once with FR.

That's really cool for checking that a given piece of logic works in a variety of different cases, but it's not really ideal in our case. We have to build a bunch of if logic into our tests:

def test_US_supplier_has_no_VAT_but_EU_supplier_has_total_price_including_VAT(product):
    # this test is magically run twice, but:
    if product.supplier.country == "US":
        assert product.total_price == product.net_price
    if product.supplier.country == "FR":
        assert product.total_price == product.net_price * 1.2

So that's ugly, and on top of that, now every single test that depends (indirectly) on supplier gets run twice, and some of those extra test runs may be totally irrelevant to what the country is.

Use pytest parametrization to override the default valued fixtures

We introduce an extra fixture that holds a default value for the country field:

@pytest.fixture()
def country():
    return "US"


@pytest.fixture
def supplier(db, country):
    s = Supplier(
        ref=random_ref(),
        name=random_name(),
        country=country,
    )
    db.add(s)
    yield s
    db.remove(s)

And then in the tests that need to change it, we can use parametrize to override the default value of country, even though the country fixture isn't explicitly named in that test:

@pytest.mark.parametrize("country", ["US"])
def test_US_supplier_has_total_price_equal_net_price(product):
    assert product.total_price == product.net_price


@pytest.mark.parametrize("country", ["EU"])
def test_EU_supplier_has_total_price_including_VAT(product):
    assert product.total_price == product.net_price * 1.2

The only problem is that you're now likely to build a implicit dependencies where the only way to find out what's actually happening is to spend ages spelunking in conftest.py.

Use pytest-case

pytest-case gives a lot of power when it comes to tweaking the fixtures and parameterizations.

Check that file for further information.

Use a fixture more than once in a function

One solution is to make your fixture return a factory instead of the resource directly:

@pytest.fixture(name='make_user')
def make_user_():
    created = []
    def make_user():
        u = models.User()
        u.commit()
        created.append(u)
        return u

    yield make_user

    for u in created:
        u.delete()

def test_two_users(make_user):
    user1 = make_user()
    user2 = make_user()
    # test them


# you can even have the normal fixture when you only need a single user
@pytest.fixture
def user(make_user):
    return make_user()

def test_one_user(user):
    # test him/her

Marks

Pytest marks can be used to group tests. It can be useful to:

slow : Mark the tests that are slow.

secondary : Mart the tests that use functionality that is being tested in the same file.

To mark a test, use the @pytest.mark decorator. For example:

@pytest.mark.slow
def test_really_slow_test():
    pass

Pytest requires you to register your marks, do so in the pytest.ini file

[pytest]
markers =
    slow: marks tests as slow (deselect with '-m "not slow"')
    secondary: mark tests that use functionality tested in the same file (deselect with '-m "not secondary"')

Snippets

Mocking sys.exit

with pytest.raises(SystemExit):
    # Code to test

Testing exceptions with pytest

def test_value_error_is_raised():
    with pytest.raises(ValueError, match="invalid literal for int() with base 10: 'a'"):
        int("a")

Excluding code from coverage

You may have code in your project that you know won't be executed, and you want to tell coverage.py to ignore it. For example, if you have some code in abstract classes that is going to be tested on the subclasses, you can ignore it with # pragma: no cover.

If you want other code to be excluded, for example the statements inside the if TYPE_CHECKING: add to your pyproject.toml:

[tool.coverage.report]
exclude_lines = [ "pragma: no cover", "if TYPE_CHECKING:",]

Running tests in parallel

pytest-xdist makes it possible to run the tests in parallel, useful when the test suit is large or when the tests are slow.

Installation

pip install pytest-xdist

Usage

pytest -n 4

It will run the tests with 4 workers. If you use auto it will adapt the number of workers to the number of CPUS, or 1 if you use --pdb.

To configure it in the pyproject.toml use the addopts

[tool.pytest.ini_options]
minversion = "6.0"
addopts = "-vv --tb=short -n auto"
Use a lock

Implement a serial fixture with a session-scoped file lock fixture using the filelock package. You can add this to your conftest.py:

pip install filelock
import contextlib
import os

import filelock
import pytest
from filelock import BaseFileLock


@pytest.fixture(name="lock", scope="session")
def lock_(
    tmp_path_factory: pytest.TempPathFactory,
) -> Generator[BaseFileLock, None, None]:
    """Create lock file."""
    base_temp = tmp_path_factory.getbasetemp()
    lock_file = base_temp.parent / "serial.lock"

    yield FileLock(lock_file=str(lock_file))

    with contextlib.suppress(OSError):
        os.remove(path=lock_file)


@pytest.fixture(name="serial")
def _serial(lock: BaseFileLock) -> Generator[None, None, None]:
    """Fixture to run tests in serial."""
    with lock.acquire(poll_interval=0.1):
        yield

Then inject the serial fixture in any test that requires serial execution. All tests that use the serial fixture are executed serially while any tests that do not use the fixture are executed in parallel.

Mark them and run separately

Mark the tests you want to execute serially with a special mark, say serial:

@pytest.mark.serial
class Test:
    ...


@pytest.mark.serial
def test_foo():
    ...

Execute your parallel tests, excluding those with the serial mark:

$ py.test -n auto -m "not serial"

Next, execute your serial tests in a separate session:

$ py.test -n0 -m "serial"

Setting a timeout for your tests

To make your tests fail if they don't end in less than X seconds, use pytest-timeout.

Install it with:

pip install pytest-timeout

You can set a global timeout in your pyproject.toml:

[pytest]
timeout = 300

Or define it for each test with:

@pytest.mark.timeout(60)
def test_foo():
    pass

Rerun tests that fail sometimes

pytest-rerunfailures is a plugin for pytest that re-runs tests to eliminate intermittent failures. Using this plugin is generally a bad idea, it would be best to solve the reason why your code is not reliable. It's useful when you rely on non robust third party software in a way that you can't solve, or if the error is not in your code but in the testing code, and again you are not able to fix it.

Install it with:

pip install pytest-rerunfailures

To re-run all test failures, use the --reruns command line option with the maximum number of times you’d like the tests to run:

pytest --reruns 5

Failed fixture or setup_class will also be re-executed.

To add a delay time between re-runs use the --reruns-delay command line option with the amount of seconds that you would like wait before the next test re-run is launched:

pytest --reruns 5 --reruns-delay 1

To mark individual tests as flaky, and have them automatically re-run when they fail, add the flaky mark with the maximum number of times you’d like the test to run:

@pytest.mark.flaky(reruns=5)
def test_example():
    import random

    assert random.choice([True, False])

Run tests in a random order

pytest-random-order is a pytest plugin that randomises the order of tests. This can be useful to detect a test that passes just because it happens to run after an unrelated test that leaves the system in a favourable state.

To use it add the --random-order to your pytest run.

It can't yet be used with pytest-xdist though :(.

Capture deprecation warnings

Python and its ecosystem does not have an assumption of strict SemVer, and has a tradition of providing deprecation warnings. If you have good CI, you should be able to catch warnings even before your users see them. Try the following pytest configuration:

[tool.pytest.ini_options]
filterwarnings = [ "error",]

This will turn warnings into errors and allow your CI to break before users break.

You can ignore specific warnings as well. For example, the configuration below will ignore all user warnings and specific deprecation warnings matching a regex, but will transform all other warnings into errors.

[tool.pytest.ini_options]
filterwarnings = [ "error", "ignore::UserWarning", "ignore:function ham\\(\\) is deprecated:DeprecationWarning",]

When a warning matches more than one option in the list, the action for the last matching option is performed.

If you want to ignore the warning of a specific package use:

filterwarnings = [ "error", "ignore::DeprecationWarning:pytest_freezegun.*",]

Note: It's better to suppress a warning instead of disabling it for the whole code, check how here.

Ensuring code triggers a deprecation warning

You can also use pytest.deprecated_call() for checking that a certain function call triggers a DeprecationWarning or PendingDeprecationWarning:

import pytest


def test_myfunction_deprecated():
    with pytest.deprecated_call():
        myfunction(17)

Asserting warnings with the warns function

You can check that code raises a particular warning using pytest.warns(), which works in a similar manner to raises:

import warnings
import pytest


def test_warning():
    with pytest.warns(UserWarning):
        warnings.warn("my warning", UserWarning)

The test will fail if the warning in question is not raised. The keyword argument match to assert that the exception matches a text or regex:

>>> with pytest.warns(UserWarning, match='must be 0 or None'):
...     warnings.warn("value must be 0 or None", UserWarning)

Recording warnings

You can record raised warnings either using pytest.warns() or with the recwarn fixture.

To record with pytest.warns() without asserting anything about the warnings, pass no arguments as the expected warning type and it will default to a generic Warning:

with pytest.warns() as record:
    warnings.warn("user", UserWarning)
    warnings.warn("runtime", RuntimeWarning)

assert len(record) == 2
assert str(record[0].message) == "user"
assert str(record[1].message) == "runtime"

The recwarn fixture will record warnings for the whole function:

import warnings


def test_hello(recwarn):
    warnings.warn("hello", UserWarning)
    assert len(recwarn) == 1
    w = recwarn.pop(UserWarning)
    assert issubclass(w.category, UserWarning)
    assert str(w.message) == "hello"
    assert w.filename
    assert w.lineno

Both recwarn and pytest.warns() return the same interface for recorded warnings: a WarningsRecorder instance. To view the recorded warnings, you can iterate over this instance, call len on it to get the number of recorded warnings, or index into it to get a particular recorded warning.

Show logging messages on the test run

Add to your pyproject.toml:

[tool.pytest.ini_options]
log_cli = true
log_cli_level = 10

Or run it in the command itself pytest -o log_cli=true --log-cli-level=10 func.py.

Remember you can change the log level of the different components in case it's too verbose.

Pytest integration with Vim

Integrating pytest into your Vim workflow enhances your productivity while writing code, thus making it easier to code using TDD.

I use Janko-m's Vim-test plugin (which can be installed through Vundle) with the following configuration.

nmap <silent> t :TestNearest --pdb<CR>
nmap <silent> <leader>t :TestSuite tests/unit<CR>
nmap <silent> <leader>i :TestSuite tests/integration<CR>
nmap <silent> <leader>T :TestFile<CR>

let test#python#runner = 'pytest'
let test#strategy = "neovim"

I often open Vim with a vertical split (:vs), in the left window I have the tests and in the right the code. Whenever I want to run a single test I press t when the cursor is inside that test. If you need to make changes in the code, you can press t again while the cursor is at the code you are testing and it will run the last test.

Once the unit test has passed, I run the whole unit tests with ;t (as ; is my <leader>). And finally I use ;i to run the integration tests.

Finally, if the test suite is huge, I use ;T to run only the tests of a single file.

As you can see only the t has the --pdb flag, so the rest of them will run en parallel and any pdb trace will fail.

Reference


Last update: 2022-11-24