pytest-parallex 0.1.2

Parallel pytest where session fixtures run once for the whole run, not once per worker

pytest-parallex

Run pytest in parallel. The difference from pytest-xdist: scope="session" fixtures run once for the whole run, not once per worker.

pip install pytest-parallex
pytest --parallel=fork

Why

xdist starts each worker as a separate interpreter, and each one runs its own session. So a scope="session" fixture runs once per worker. The xdist docs say this and suggest a FileLock workaround. You can't fix it in your conftest — there's no point in xdist's design where one process could build something and hand it to the others.

Forking gives you that point. --parallel=fork collects the tests, runs your session fixtures, and then forks. Each child inherits the already-built fixtures through copy-on-write and uses them without re-running anything.

Counting how many times the fixture body actually runs:

$ pytest -n 4                                  # pytest-xdist
8 passed        session fixture ran 4 times

$ pytest --parallel=fork --parallel-workers=4  # pytest-parallex
8 passed        session fixture ran 1 time

@pytest.fixture(scope="session")
def postgres():
    with PostgresContainer("postgres:16") as pg:   # runs once
        yield pg.get_connection_url()              # every worker gets this URL

One container, one login, one compiled asset per run, and no lockfile.

Modes

pytest --parallel=fork --parallel-workers=8

mode	isolation	scope="session" runs	use when
fork	process per worker, warm	once, in the controller	Linux/macOS, and the process is quiet at fork time
thread	shared address space	once	tests are I/O-bound and don't mind sharing globals
async	shared address space	once	same as thread, driven from an event loop
process	fresh interpreter per worker	once per worker	tests mutate process-global state, or you're on Windows

--parallel-workers defaults to auto (CPU count).

What fork is and isn't good for

Two separate things, and only one of them scales:

Skipping xdist's worker startup saves about 1.5–2.3 seconds. That's a fixed cost, so it doesn't grow with your suite, your imports, or your worker count. On a 3-second local run it's a 1.8x speedup and you notice it. On a 292-second CI suite it's under 1% and it's noise. The reason is that redundant imports run on cores that were idle anyway — xdist's N-way collection wastes CPU, not wall time, and you only get paid for removing it if those cores had something better to do.

Reusing session fixtures is the part that scales. A 10-second container boot costs 80 seconds across 8 xdist workers and 10 seconds here. That's the reason to use this.

docs/benchmarks.md has the numbers, the method, and three measurements that looked convincing and were wrong.

Fork safety

fork() only copies the calling thread. Whatever the other threads were holding — a lock, a half-written buffer, a connection mid-handshake — gets copied in that state and belongs to nobody in the child. This is the usual way forked children deadlock, and CPython 3.12 warns about it.

parallex checks first and refuses, naming what's in the way:

--parallel=fork requires a quiet process at fork time, but 1 non-main thread(s)
are running (Thread-1 (_monitor)). Move the offending setup into a fixture so it
runs after the fork, or use --parallel=process.

Logging QueueListener threads are the common case (litestar starts one at import, as does anything using QueueHandler), so those get stopped and restarted around the fork for you.

This puts one constraint on a session fixture: what it builds has to survive a fork. An address survives — a URL, a path, a port. A live connection, a thread, or an event loop doesn't. So keep the server in the controller and open connections per worker:

@pytest.fixture(scope="session")
def db_url():                     # controller: owns the server, survives the fork
    with PostgresContainer("postgres:16") as pg:
        yield pg.get_connection_url()

@pytest.fixture                   # per test: owns the connection, can't survive a fork
def db(db_url):
    conn = psycopg.connect(db_url)
    yield conn
    conn.close()

If a session fixture can't survive the fork, --parallex-no-session-scope leaves them all to the workers and you're back to xdist's behaviour.

Fixtures that differ per worker

Some session fixtures are supposed to differ between workers — usually one that gives each worker its own database. Running that in the controller breaks things: every worker gets the same database, and a per-test create_all/drop_all wipes the other workers' schemas while they're using them.

A fixture that asks for worker_id, directly or through another fixture, is per-worker and stays in the workers. Everything else scope-session runs once in the controller. There's no new syntax for this because suites already write it:

@pytest.fixture(scope="session")
def postgres_server():                          # controller: one container
    with PostgresContainer("postgres:16") as pg:
        yield pg.get_connection_url()

@pytest.fixture(scope="session")
def database_url(worker_id, postgres_server):   # per worker: asks for worker_id
    url = f"{postgres_server}/test_{worker_id}"
    create_database(url)
    yield url
    drop_database(url)

One container for the run, one database per worker on it. Under xdist you'd need the filelock recipe to approximate this.

worker_id is xdist's fixture name and that's on purpose — it's what lets existing suites work unchanged. If you have both plugins installed, --parallel takes the name so you get a real per-worker id, and a plain pytest -n 4 still gets xdist's. parallex_worker_id is an alias if you'd rather be explicit; it marks a fixture per-worker the same way.

Fixtures

fixture	gives you
worker_id	'f0', 'w1', 'a2'… or 'main'. Asking for it marks a fixture per-worker
parallex_worker_id	the same value, under a name xdist can't shadow
parallex_mode	the active mode, or None
parallex_setup_data	whatever pytest_parallex_setup returned

Hooks

# conftest.py
def pytest_parallex_setup(config):
    """Runs once in the controller, before any worker starts. Pre-fork, so no I/O handles."""
    return {"token": build_expensive_thing()}

def pytest_parallex_teardown(config, data):
    """Runs once in the controller, after every worker has finished."""

def pytest_parallex_auto_num_workers(config):
    """Override --parallel-workers=auto. Defaults to os.cpu_count()."""
    return 8

Limitations

• --maxfail won't cut a fork run short. Workers finish the group they claimed and the controller replays the reports afterwards, so the count is right but the run isn't stopped early.
• fork splits work by module, so a suite in a single file uses one worker regardless of --parallel-workers.
• fork is Linux and macOS only. On Windows use --parallel=process.
• thread and async don't get around the GIL. They help suites that wait, not suites that compute.
• Needs pytest 8.1 or newer. (8.0 changed the signature of an internal we rely on.)

Developing

uv sync                # toolchain
make install-hooks     # one-time, after cloning
make check             # lint + typecheck + test, same as CI

Releasing

The version in pyproject.toml is the source of truth, and releases are automated:

1. The pre-commit hook bumps the patch version when a commit touches src/ (run make install-hooks once after cloning). Doc, test and config commits don't bump. For a minor or major release, do it deliberately: make bump TYPE=minor.
2. version-guard enforces the same rule in CI, so --no-verify and unhooked clones don't get around it.
3. :release runs on a green main pipeline and publishes via OIDC trusted publishing if the version isn't on PyPI yet. Merging a version bump to main is the release — no tag, no second pipeline.

License

MIT