Mainly because it has better support for installing the packages from different python versions. It also has better dependency resolver than Pipenv. And supports modern standard for python dependency management. This includes usage of pyproject.toml for project specific configuration instead of per tool conf files. See following links for details: https://pip.pypa.io/en/stable/reference/build-system/pyproject-toml/ https://www.python.org/dev/peps/pep-0518/
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Zenith test runner
This directory contains integration tests.
Prerequisites:
- Correctly configured Python, see
/docs/sourcetree.md - Zenith and Postgres binaries
- See the root README.md for build directions
- Tests can be run from the git tree; or see the environment variables below to run from other directories.
- The zenith git repo, including the postgres submodule
(for some tests, e.g.
pg_regress)
Test Organization
The tests are divided into a few batches, such that each batch takes roughly the same amount of time. The batches can be run in parallel, to minimize total runtime. Currently, there are only two batches:
- test_batch_pg_regress: Runs PostgreSQL regression tests
- test_others: All other tests
Running the tests
There is a wrapper script to invoke pytest: ./scripts/pytest.
It accepts all the arguments that are accepted by pytest.
Depending on your installation options pytest might be invoked directly.
Test state (postgres data, pageserver state, and log files) will
be stored under a directory test_output.
You can run all the tests with:
./scripts/pytest
If you want to run all the tests in a particular file:
./scripts/pytest test_pgbench.py
If you want to run all tests that have the string "bench" in their names:
./scripts/pytest -k bench
Useful environment variables:
ZENITH_BIN: The directory where zenith binaries can be found.
POSTGRES_DISTRIB_DIR: The directory where postgres distribution can be found.
TEST_OUTPUT: Set the directory where test state and test output files
should go.
TEST_SHARED_FIXTURES: Try to re-use a single pageserver for all the tests.
ZENITH_PAGESERVER_OVERRIDES: add a ;-separated set of configs that will be passed as
FORCE_MOCK_S3: inits every test's pageserver with a mock S3 used as a remote storage.
--pageserver-config-override=${value} parameter values when zenith cli is invoked
RUST_LOG: logging configuration to pass into Zenith CLI
Let stdout, stderr and INFO log messages go to the terminal instead of capturing them:
./scripts/pytest -s --log-cli-level=INFO ...
(Note many tests capture subprocess outputs separately, so this may not
show much.)
Exit after the first test failure:
./scripts/pytest -x ...
(there are many more pytest options; run pytest -h to see them.)
Writing a test
Every test needs a Zenith Environment, or ZenithEnv to operate in. A Zenith Environment is like a little cloud-in-a-box, and consists of a Pageserver, 0-N Safekeepers, and compute Postgres nodes. The connections between them can be configured to use JWT authentication tokens, and some other configuration options can be tweaked too.
The easiest way to get access to a Zenith Environment is by using the zenith_simple_env
fixture. The 'simple' env may be shared across multiple tests, so don't shut down the nodes
or make other destructive changes in that environment. Also don't assume that
there are no tenants or branches or data in the cluster. For convenience, there is a
branch called empty, though. The convention is to create a test-specific branch of
that and load any test data there, instead of the 'main' branch.
For more complicated cases, you can build a custom Zenith Environment, with the zenith_env
fixture:
def test_foobar(zenith_env_builder: ZenithEnvBuilder):
# Prescribe the environment.
# We want to have 3 safekeeper nodes, and use JWT authentication in the
# connections to the page server
zenith_env_builder.num_safekeepers = 3
zenith_env_builder.set_pageserver_auth(True)
# Now create the environment. This initializes the repository, and starts
# up the page server and the safekeepers
env = zenith_env_builder.init()
# Run the test
...
For more information about pytest fixtures, see https://docs.pytest.org/en/stable/fixture.html
At the end of a test, all the nodes in the environment are automatically stopped, so you
don't need to worry about cleaning up. Logs and test data are preserved for the analysis,
in a directory under ../test_output/<testname>
Before submitting a patch
Ensure that you pass all obligatory checks.
Also consider:
- Writing a couple of docstrings to clarify the reasoning behind a new test.
- Adding more type hints to your code to avoid
Any, especially:- For fixture parameters, they are not automatically deduced.
- For function arguments and return values.