## Problem When shutting down a Tenant, it isn't just important to cause any background tasks to stop. It's also important to wait until they have stopped before declaring shutdown complete, in cases where we may re-use the tenant's local storage for something else, such as running in secondary mode, or creating a new tenant with the same ID. ## Summary of changes A `Gate` class is added, inspired by [seastar::gate](https://docs.seastar.io/master/classseastar_1_1gate.html). For types that have an important lifetime that corresponds to some physical resource, use of a Gate as well as a CancellationToken provides a robust pattern for async requests & shutdown: - Requests must always acquire the gate as long as they are using the object - Shutdown must set the cancellation token, and then `close()` the gate to wait for requests in progress before returning. This is not for memory safety: it's for expressing the difference between "Arc<Tenant> exists", and "This tenant's files on disk are eligible to be read/written". - Both Tenant and Timeline get a Gate & CancellationToken. - The Timeline gate is held during eviction of layers, and during page_service requests. - Existing cancellation support in page_service is refined to use the timeline-scope cancellation token instead of a process-scope cancellation token. This replaces the use of `task_mgr::associate_with`: tasks no longer change their tenant/timelineidentity after being spawned. The Tenant's Gate is not yet used, but will be important for Tenant-scoped operations in secondary mode, where we must ensure that our secondary-mode downloads for a tenant are gated wrt the activity of an attached Tenant. This is part of a broader move away from using the global-state driven `task_mgr` shutdown tokens: - less global state where we rely on implicit knowledge of what task a given function is running in, and more explicit references to the cancellation token that a particular function/type will respect, making shutdown easier to reason about. - eventually avoid the big global TASKS mutex. --------- Co-authored-by: Joonas Koivunen <joonas@neon.tech>
Neon test runner
This directory contains integration tests.
Prerequisites:
- Correctly configured Python, see
/docs/sourcetree.md - Neon and Postgres binaries
- See the root README.md for build directions
If you want to test tests with test-only APIs, you would need to add
--features testingto Rust code build commands. For convenience, repository cargo config containsbuild_testingalias, that serves as a subcommand, adding the required feature flags. Usage example:cargo build_testing --releaseis equivalent tocargo build --features testing --release - Tests can be run from the git tree; or see the environment variables below to run from other directories.
- See the root README.md for build directions
If you want to test tests with test-only APIs, you would need to add
- The neon git repo, including the postgres submodule
(for some tests, e.g.
pg_regress)
Test Organization
Regression tests are in the 'regress' directory. They can be run in
parallel to minimize total runtime. Most regression test sets up their
environment with its own pageservers and safekeepers (but see
TEST_SHARED_FIXTURES).
'pg_clients' contains tests for connecting with various client libraries. Each client test uses a Dockerfile that pulls an image that contains the client, and connects to PostgreSQL with it. The client tests can be run against an existing PostgreSQL or Neon installation.
'performance' contains performance regression tests. Each test exercises a particular scenario or workload, and outputs measurements. They should be run serially, to avoid the tests interfering with the performance of each other. Some performance tests set up their own Neon environment, while others can be run against an existing PostgreSQL or Neon environment.
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
To run tests in parellel we utilize pytest-xdist plugin. By default everything runs single threaded. Number of workers can be specified with -n argument:
./scripts/pytest -n4
By default performance tests are excluded. To run them explicitly pass performance tests selection to the script:
./scripts/pytest test_runner/performance
Useful environment variables:
NEON_BIN: The directory where neon binaries can be found.
POSTGRES_DISTRIB_DIR: The directory where postgres distribution can be found.
Since pageserver supports several postgres versions, POSTGRES_DISTRIB_DIR must contain
a subdirectory for each version with naming convention v{PG_VERSION}/.
Inside that dir, a bin/postgres binary should be present.
DEFAULT_PG_VERSION: The version of Postgres to use,
This is used to construct full path to the postgres binaries.
Format is 2-digit major version nubmer, i.e. DEFAULT_PG_VERSION="14". Alternatively,
you can use --pg-version argument.
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.
NEON_PAGESERVER_OVERRIDES: add a ;-separated set of configs that will be passed as
RUST_LOG: logging configuration to pass into Neon CLI
Useful parameters and commands:
--pageserver-config-override=${value} -c values to pass into pageserver through neon_local cli
--preserve-database-files to preserve pageserver (layer) and safekeer (segment) timeline files on disk
after running a test suite. Such files might be large, so removed by default; but might be useful for debugging or creation of svg images with layer file contents.
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 Neon Environment, or NeonEnv to operate in. A Neon 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 Neon Environment is by using the neon_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 Neon Environment, with the neon_env
fixture:
def test_foobar(neon_env_builder: NeonEnvBuilder):
# Prescribe the environment.
# We want to have 3 safekeeper nodes, and use JWT authentication in the
# connections to the page server
neon_env_builder.num_safekeepers = 3
neon_env_builder.set_pageserver_auth(True)
# Now create the environment. This initializes the repository, and starts
# up the page server and the safekeepers
env = neon_env_builder.init_start()
# 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.