Add an example on how to share session fixture data to README
As discussed in #385
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README.rst
53
README.rst
@@ -95,6 +95,59 @@ any guaranteed order, but you can control this with these options:
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in version ``1.21``.
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in version ``1.21``.
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Making session-scoped fixtures execute only once
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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``pytest-xdist`` is designed so that each worker process will perform its own collection and execute
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a subset of all tests. This means that tests in different processes requesting a high-level
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scoped fixture (for example ``session``) will execute the fixture code more than once, which
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breaks expectations and might be undesired in certain situations.
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While ``pytest-xdist`` does not have a builtin support for ensuring a session-scoped fixture is
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executed exactly once, this can be achieved by using a lock file for inter-process communication.
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The example below needs to execute the fixture ``session_data`` only once (because it is
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resource intensive, or needs to execute only once to define configuration options, etc), so it makes
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use of a `FileLock <https://pypi.org/project/filelock/>`_ to produce the fixture data only once
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when the first process requests the fixture, while the other processes will then read
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the data from a file.
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Here is the code:
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.. code-block:: python
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import json
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import pytest
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from filelock import FileLock
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@pytest.fixture(scope="session")
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def session_data(tmp_path_factory, worker_id):
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if not worker_id:
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# not executing in with multiple workers, just produce the data and let
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# pytest's fixture caching do its job
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return produce_expensive_data()
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# get the temp directory shared for by all workers
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root_tmp_dir = tmp_path_factory.getbasetemp().parent
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fn = root_tmp_dir / "data.json"
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with FileLock(str(fn) + ".lock"):
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if fn.is_file():
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data = json.loads(fn.read_text())
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else:
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data = produce_expensive_data()
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fn.write_text(json.dumps(data))
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return data
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The example above can also be use in cases a fixture needs to execute exactly once per test session, like
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initializing a database service and populating initial tables.
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This technique might not work for every case, but should be a starting point for many situations
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where executing a high-scope fixture exactly once is important.
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Running tests in a Python subprocess
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Running tests in a Python subprocess
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------------------------------------
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------------------------------------
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