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16 rows where breadcrumbs contains "Contributing" sorted by content
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id | page | ref | title | content ▼ | breadcrumbs | references |
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contributing:contributing-running-tests | contributing | contributing-running-tests | Running the tests | Once you have done this, you can run the Datasette unit tests from inside your datasette/ directory using pytest like so: pytest You can run the tests faster using multiple CPU cores with pytest-xdist like this: pytest -n auto -m "not serial" -n auto detects the number of available cores automatically. The -m "not serial" skips tests that don't work well in a parallel test environment. You can run those tests separately like so: pytest -m "serial" | ["Contributing"] | [{"href": "https://docs.pytest.org/", "label": "pytest"}, {"href": "https://pypi.org/project/pytest-xdist/", "label": "pytest-xdist"}] |
contributing:contributing-documentation-cog | contributing | contributing-documentation-cog | Running Cog | Some pages of documentation (in particular the CLI reference ) are automatically updated using Cog . To update these pages, run the following command: cog -r docs/*.rst | ["Contributing", "Editing and building the documentation"] | [{"href": "https://github.com/nedbat/cog", "label": "Cog"}] |
contributing:contributing-formatting-blacken-docs | contributing | contributing-formatting-blacken-docs | blacken-docs | The blacken-docs command applies Black formatting rules to code examples in the documentation. Run it like this: blacken-docs -l 60 docs/*.rst | ["Contributing", "Code formatting"] | [{"href": "https://pypi.org/project/blacken-docs/", "label": "blacken-docs"}] |
contributing:contributing-continuous-deployment | contributing | contributing-continuous-deployment | Continuously deployed demo instances | The demo instance at latest.datasette.io is re-deployed automatically to Google Cloud Run for every push to main that passes the test suite. This is implemented by the GitHub Actions workflow at .github/workflows/deploy-latest.yml . Specific branches can also be set to automatically deploy by adding them to the on: push: branches block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass. The Cloud Run URL for a branch demo can be found in the GitHub Actions logs. | ["Contributing"] | [{"href": "https://latest.datasette.io/", "label": "latest.datasette.io"}, {"href": "https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml", "label": ".github/workflows/deploy-latest.yml"}] |
contributing:contributing-formatting-prettier | contributing | contributing-formatting-prettier | Prettier | To install Prettier, install Node.js and then run the following in the root of your datasette repository checkout: npm install This will install Prettier in a node_modules directory. You can then check that your code matches the coding style like so: npm run prettier -- --check > prettier > prettier 'datasette/static/*[!.min].js' "--check" Checking formatting... [warn] datasette/static/plugins.js [warn] Code style issues found in the above file(s). Forgot to run Prettier? You can fix any problems by running: npm run fix | ["Contributing", "Code formatting"] | [{"href": "https://nodejs.org/en/download/package-manager/", "label": "install Node.js"}] |
contributing:contributing-using-fixtures | contributing | contributing-using-fixtures | Using fixtures | To run Datasette itself, type datasette . You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests. You can create a copy of that database by running this command: python tests/fixtures.py fixtures.db Now you can run Datasette against the new fixtures database like so: datasette fixtures.db This will start a server at http://127.0.0.1:8001/ . Any changes you make in the datasette/templates or datasette/static folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript). If you want to change Datasette's Python code you can use the --reload option to cause Datasette to automatically reload any time the underlying code changes: datasette --reload fixtures.db You can also use the fixtures.py script to recreate the testing version of metadata.json used by the unit tests. To do that: python tests/fixtures.py fixtures.db fixtures-metadata.json Or to output the plugins used by the tests, run this: python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins Test tables written to fixtures.db - metadata written to fixtures-metadata.json Wrote plugin: fixtures-plugins/register_output_renderer.py Wrote plugin: fixtures-plugins/view_name.py Wrote plugin: fixtures-plugins/my_plugin.py Wrote plugin: fixtures-plugins/messages_output_renderer.py Wrote plugin: fixtures-plugins/my_plugin_2.py Then run Datasette like this: datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/ | ["Contributing"] | [] |
contributing:general-guidelines | contributing | general-guidelines | General guidelines | main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released. The ideal commit should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue. New plugin hooks should only be shipped if accompanied by a separate release of a non-demo plugin that uses them. | ["Contributing"] | [] |
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CREATE TABLE [sections] ( [id] TEXT PRIMARY KEY, [page] TEXT, [ref] TEXT, [title] TEXT, [content] TEXT, [breadcrumbs] TEXT, [references] TEXT );