Testing plugins

We recommend using pytest to write automated tests for your plugins.

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:

from datasette.app import Datasette
import pytest

async def test_plugin_is_installed():
    datasette = Datasette([], memory=True)
    response = await datasette.client.get("/-/plugins.json")
    assert response.status_code == 200
    installed_plugins = {p["name"] for p in response.json()}
    assert "datasette-plugin-template-demo" in installed_plugins

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.

This test also uses the pytest-asyncio package to add support for async def test functions running under pytest.

You can install these packages like so:

pip install pytest pytest-asyncio

If you are building an installable package you can add them as test dependencies to your setup.py module like this:

    # ...
        "test": ["pytest", "pytest-asyncio"]

You can then install the test dependencies like so:

pip install -e '.[test]'

Then run the tests using pytest like so:


Using pytest fixtures

Pytest fixtures can be used to create initial testable objects which can then be used by multiple tests.

A common pattern for Datasette plugins is to create a fixture which sets up a temporary test database and wraps it in a Datasette instance.

Here's an example that uses the sqlite-utils library to populate a temporary test database. It also sets the title of that table using a simulated metadata.json congiguration:

from datasette.app import Datasette
import pytest
import sqlite_utils

def datasette(tmp_path_factory):
    db_directory = tmp_path_factory.mktemp("dbs")
    db_path = db_directory / "test.db"
    db = sqlite_utils.Database(db_path)
        {"id": 1, "name": "Cleo", "age": 5},
        {"id": 2, "name": "Pancakes", "age": 4}
    ], pk="id")
    datasette = Datasette(
            "databases": {
                "test": {
                    "tables": {
                        "dogs": {
                            "title": "Some dogs"
    return datasette

async def test_example_table_json(datasette):
    response = await datasette.client.get("/test/dogs.json?_shape=array")
    assert response.status_code == 200
    assert response.json() == [
        {"id": 1, "name": "Cleo", "age": 5},
        {"id": 2, "name": "Pancakes", "age": 4},

async def test_example_table_html(datasette):
    response = await datasette.client.get("/test/dogs")
    assert ">Some dogs</h1>" in response.text

Here the datasette() function defines the fixture, which is than automatically passed to the two test functions based on pytest automatically matching their datasette function parameters.

The @pytest.fixture(scope="session") line here ensures the fixture is reused for the full pytest execution session. This means that the temporary database file will be created once and reused for each test.

If you want to create that test database repeatedly for every individual test function, write the fixture function like this instead. You may want to do this if your plugin modifies the database contents in some way:

def datasette(tmp_path_factory):
    # This fixture will be executed repeatedly for every test