You can write one-off plugins that apply to just one Datasette instance, or you can write plugins which can be installed using ``pip`` and can be shipped to the Python Package Index (`PyPI <https://pypi.org/>`__) for other people to install.
Want to start by looking at an example? The `Datasette plugins directory <https://datasette.io/plugins>`__ lists more than 90 open source plugins with code you can explore. The :ref:`plugin hooks <plugin_hooks>` page includes links to example plugins for each of the documented hooks.
The quickest way to start writing a plugin is to create a ``my_plugin.py`` file and drop it into your ``plugins/`` directory. Here is an example plugin, which adds a new custom SQL function called ``hello_world()`` which takes no arguments and returns the string ``Hello world!``.
The quickest way to start writing one an installable plugin is to use the `datasette-plugin <https://github.com/simonw/datasette-plugin>`__ cookiecutter template. This creates a new plugin structure for you complete with an example test and GitHub Actions workflows for testing and publishing your plugin.
`Install cookiecutter <https://cookiecutter.readthedocs.io/en/stable/installation.html>`__ and then run this command to start building a plugin using the template::
Read `a cookiecutter template for writing Datasette plugins <https://simonwillison.net/2020/Jun/20/cookiecutter-plugins/>`__ for more information about this template.
Having built a plugin in this way you can turn it into an installable package using the following command::
python3 setup.py sdist
This will create a ``.tar.gz`` file in the ``dist/`` directory.
You can then install your new plugin into a Datasette virtual environment or Docker container using ``pip``::
pip install datasette-plugin-demos-0.1.tar.gz
To learn how to upload your plugin to `PyPI <https://pypi.org/>`_ for use by other people, read the PyPA guide to `Packaging and distributing projects <https://packaging.python.org/tutorials/distributing-packages/>`_.
Use the ``datasette.urls.static_plugins(plugin_name, path)`` method to generate URLs to that asset that take the ``base_url`` setting into account, see :ref:`internals_datasette_urls`.
To bundle the static assets for a plugin in the package that you publish to PyPI, add the following to the plugin's ``setup.py``:
Where ``datasette_plugin_name`` is the name of the plugin package (note that it uses underscores, not hyphens) and ``static/plugin.js`` is the path within that package to the static file.
`datasette-cluster-map <https://github.com/simonw/datasette-cluster-map>`__ is a useful example of a plugin that includes packaged static assets in this way.
If your plugin has a ``templates/`` directory, Datasette will attempt to load templates from that directory before it uses its own default templates.
The priority order for template loading is:
* templates from the ``--template-dir`` argument, if specified
* templates from the ``templates/`` directory in any installed plugins
* default templates that ship with Datasette
See :ref:`customization` for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI.
You can also use wildcards here such as ``templates/*.html``. See `datasette-edit-schema <https://github.com/simonw/datasette-edit-schema>`__ for an example of this pattern.
When you are writing plugins, you can access plugin configuration like this using the ``datasette plugin_config()`` method. If you know you need plugin configuration for a specific table, you can access it like this::
If it cannot find the requested configuration at the table layer, it will fall back to the database layer and then the root layer. For example, a user may have set the plugin configuration option like so::
{
"databases: {
"sf-trees": {
"plugins": {
"datasette-cluster-map": {
"latitude_column": "xlat",
"longitude_column": "xlng"
}
}
}
}
}
In this case, the above code would return that configuration for ANY table within the ``sf-trees`` database.
The plugin configuration could also be set at the top level of ``metadata.json``::
{
"title": "This is the top-level title in metadata.json",
"plugins": {
"datasette-cluster-map": {
"latitude_column": "xlat",
"longitude_column": "xlng"
}
}
}
Now that ``datasette-cluster-map`` plugin configuration will apply to every table in every database.
You can register new URL routes within Datasette using the :ref:`plugin_register_routes` plugin hook.
Datasette's default URLs include these:
-``/dbname`` - database page
-``/dbname/tablename`` - table page
-``/dbname/tablename/pk`` - row page
See :ref:`pages` and :ref:`introspection` for more default URL routes.
To avoid accidentally conflicting with a database file that may be loaded into Datasette, plugins should register URLs using a ``/-/`` prefix. For example, if your plugin adds a new interface for uploading Excel files you might register a URL route like this one:
Try to avoid registering URLs that clash with other plugins that your users might have installed. There is no central repository of reserved URL paths (yet) but you can review existing plugins by browsing the `plugins directory <https://datasette.io/plugins>`.
Note that a row could have a primary key of ``-`` and this URL scheme will still work, because Datasette row pages do not ever have a trailing slash followed by additional path components.
This can be a bit tricky if the Datasette instance is using the :ref:`setting_base_url` configuration setting to run behind a proxy, since that can cause Datasette's URLs to include an additional prefix.
The ``datasette.urls`` object provides internal methods for correctly generating URLs to different pages within Datasette, taking any ``base_url`` configuration into account.
This object is exposed in templates as the ``urls`` variable, which can be used like this:
..code-block:: jinja
Back to the <a href="{{ urls.instance() }}">Homepage</a>