Testing JupyterHub and linting code#

Unit test help 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 our tests. You can find them under jupyterhub/tests directory in the git repository.

Running the tests#

  1. Make sure you have completed Setting up a development install. You should be able to start jupyterhub from the commandline & 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

    See the pytest usage documentation for more details.

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)

See the pytest fixtures documentation for how to use the existing fixtures, and how to create new ones.

Troubleshooting Test Failures#

All the tests are failing#

Make sure you have completed all the steps in Setting up a development install successfully, and can launch jupyterhub from the terminal.

Code formatting and linting#

JupyterHub has adopted automatic code formatting and linting. As long as your code is valid, the pre-commit hook should take care of how it should look. You can invoke the pre-commit hook by hand at any time with:

pre-commit run

which should run any autoformatting on your code and tell you about any errors it couldn’t fix automatically. You may also install black integration into your text editor to format code automatically.

If you have already committed files before running pre-commit you can fix everything using:

pre-commit run --all-files

And committing the changes.