{"id": "testing_plugins:id1", "page": "testing_plugins", "ref": "id1", "title": "Testing plugins", "content": "We recommend using pytest to write automated tests for your plugins. \n If you use the template described in Starting an installable plugin using cookiecutter your plugin will start with a single test in your tests/ directory that looks like this: \n from datasette.app import Datasette\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_plugin_is_installed():\n datasette = Datasette(memory=True)\n response = await datasette.client.get(\"/-/plugins.json\")\n assert response.status_code == 200\n installed_plugins = {p[\"name\"] for p in response.json()}\n assert (\n \"datasette-plugin-template-demo\"\n in installed_plugins\n ) \n This test uses the datasette.client object to exercise a test instance of Datasette. datasette.client is a wrapper around the HTTPX Python library which can imitate HTTP requests using ASGI. This is the recommended way to write tests against a Datasette instance. \n This test also uses the pytest-asyncio package to add support for async def test functions running under pytest. \n You can install these packages like so: \n pip install pytest pytest-asyncio \n If you are building an installable package you can add them as test dependencies to your setup.py module like this: \n setup(\n name=\"datasette-my-plugin\",\n # ...\n extras_require={\"test\": [\"pytest\", \"pytest-asyncio\"]},\n tests_require=[\"datasette-my-plugin[test]\"],\n) \n You can then install the test dependencies like so: \n pip install -e '.[test]' \n Then run the tests using pytest like so: \n pytest", "breadcrumbs": "[]", "references": "[{\"href\": \"https://docs.pytest.org/\", \"label\": \"pytest\"}, {\"href\": \"https://www.python-httpx.org/\", \"label\": \"HTTPX\"}, {\"href\": \"https://pypi.org/project/pytest-asyncio/\", \"label\": \"pytest-asyncio\"}]"} {"id": "testing_plugins:testing-plugins-datasette-test-instance", "page": "testing_plugins", "ref": "testing-plugins-datasette-test-instance", "title": "Setting up a Datasette test instance", "content": "The above example shows the easiest way to start writing tests against a Datasette instance: \n from datasette.app import Datasette\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_plugin_is_installed():\n datasette = Datasette(memory=True)\n response = await datasette.client.get(\"/-/plugins.json\")\n assert response.status_code == 200 \n Creating a Datasette() instance like this as useful shortcut in tests, but there is one detail you need to be aware of. It's important to ensure that the async method .invoke_startup() is called on that instance. You can do that like this: \n datasette = Datasette(memory=True)\nawait datasette.invoke_startup() \n This method registers any startup(datasette) or prepare_jinja2_environment(env, datasette) plugins that might themselves need to make async calls. \n If you are using await datasette.client.get() and similar methods then you don't need to worry about this - Datasette automatically calls invoke_startup() the first time it handles a request.", "breadcrumbs": "[\"Testing plugins\"]", "references": "[]"} {"id": "testing_plugins:testing-datasette-client", "page": "testing_plugins", "ref": "testing-datasette-client", "title": "Using datasette.client in tests", "content": "The datasette.client mechanism is designed for use in tests. It provides access to a pre-configured HTTPX async client instance that can make GET, POST and other HTTP requests against a Datasette instance from inside a test. \n A simple test looks like this: \n @pytest.mark.asyncio\nasync def test_homepage():\n ds = Datasette(memory=True)\n response = await ds.client.get(\"/\")\n html = response.text\n assert \"