Testing JupyterHub and linting code

Unit testing helps to validate that JupyterHub works the way we think it does, and continues to do so when changes occur. They also help communicate precisely what we expect our code to do.

JupyterHub uses pytest for all the tests. You can find them under the jupyterhub/tests directory in the git repository.

Running the tests

1. Make sure you have completed Setting up a development install. Once you are done, you would be able to run jupyterhub from the command line and access it from your web browser. This ensures that the dev environment is properly set up for tests to run.

2. You can run all tests in JupyterHub

   pytest -v jupyterhub/tests
   

   This should display progress as it runs all the tests, printing information about any test failures as they occur.

   If you wish to confirm test coverage the run tests with the --cov flag:

   pytest -v --cov=jupyterhub jupyterhub/tests
   

3. You can also run tests in just a specific file:

   pytest -v jupyterhub/tests/<test-file-name>
   

4. To run a specific test only, you can do:

   pytest -v jupyterhub/tests/<test-file-name>::<test-name>
   

   This runs the test with function name <test-name> defined in <test-file-name>. This is very useful when you are iteratively developing a single test.

   For example, to run the test test_shutdown in the file test_api.py, you would run:

   pytest -v jupyterhub/tests/test_api.py::test_shutdown
   

   For more details, refer to the pytest usage documentation.

Test organisation

The tests live in jupyterhub/tests and are organized roughly into:

1. test_api.py tests the REST API

2. test_pages.py tests loading the HTML pages

and other collections of tests for different components. When writing a new test, there should usually be a test of similar functionality already written and related tests should be added nearby.

The fixtures live in jupyterhub/tests/conftest.py. There are fixtures that can be used for JupyterHub components, such as:

• app: an instance of JupyterHub with mocked parts

• auth_state_enabled: enables persisting auth_state (like authentication tokens)

• db: a sqlite in-memory DB session

• io_loop`: a Tornado event loop

• event_loop: a new asyncio event loop

• user: creates a new temporary user

• admin_user: creates a new temporary admin user

• single user servers - cleanup_after: allows cleanup of single user servers between tests

• mocked service - MockServiceSpawner: a spawner that mocks services for testing with a short poll interval - mockservice`: mocked service with no external service url - mockservice_url: mocked service with a url to test external services

And fixtures to add functionality or spawning behavior:

• admin_access: grants admin access

• no_patience`: sets slow-spawning timeouts to zero

• slow_spawn: enables the SlowSpawner (a spawner that takes a few seconds to start)

• never_spawn: enables the NeverSpawner (a spawner that will never start)

• bad_spawn: enables the BadSpawner (a spawner that fails immediately)

• slow_bad_spawn: enables the SlowBadSpawner (a spawner that fails after a short delay)

Refer to the pytest fixtures documentation to learn how to use fixtures that exists already and to create new ones.

The Pytest-Asyncio Plugin

When testing the various JupyterHub components and their various implementations, it sometimes becomes necessary to have a running instance of JupyterHub to test against. The app fixture mocks a JupyterHub application for use in testing by:

• enabling ssl if internal certificates are available

• creating an instance of MockHub using any provided configurations as arguments

• initializing the mocked instance

• starting the mocked instance

• finally, a registered finalizer function performs a cleanup and stops the mocked instance

The JupyterHub test suite uses the pytest-asyncio plugin that handles event-loop integration in Tornado applications. This allows for the use of top-level awaits when calling async functions or fixtures during testing. All test functions and fixtures labelled as async will run on the same event loop.

Note

With the introduction of top-level awaits, the use of the io_loop fixture of the pytest-tornado plugin is no longer necessary. It was initially used to call coroutines. With the upgrades made to pytest-asyncio, this usage is now deprecated. It is now, only utilized within the JupyterHub test suite to ensure complete cleanup of resources used during testing such as open file descriptors. This is demonstrated in this pull request. More information is provided below.

One of the general goals of the JupyterHub Pytest Plugin project is to ensure the MockHub cleanup fully closes and stops all utilized resources during testing so the use of the io_loop fixture for teardown is not necessary. This was highlighted in this issue

For more information on asyncio and event-loops, here are some resources:

• Read: Introduction to the Python event loop

• Read: Overview of Async IO in Python 3.7

• Watch: Asyncio: Understanding Async / Await in Python

• Watch: Learn Python’s AsyncIO #2 - The Event Loop

Troubleshooting Test Failures

All the tests are failing

Make sure you have completed all the steps in Setting up a development install successfully, and are able to access JupyterHub from your browser at http://localhost:8000 after starting jupyterhub in your command line.

Code formatting and linting

JupyterHub automatically enforces code formatting. This means that pull requests with changes breaking this formatting will receive a commit from pre-commit.ci automatically.

To automatically format code locally, you can install pre-commit and register a git hook to automatically check with pre-commit before you make a commit if the formatting is okay.

pip install pre-commit
pre-commit install --install-hooks

To run pre-commit manually you would do:

# check for changes to code not yet committed
pre-commit run

# check for changes also in already committed code
pre-commit run --all-files

You may also install black integration into your text editor to format code automatically.