Writing plugins#

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) for other people to install.

Want to start by looking at an example? The Datasette plugins directory lists more than 90 open source plugins with code you can explore. The plugin hooks page includes links to example plugins for each of the documented hooks.

Tracing plugin hooks#

The DATASETTE_TRACE_PLUGINS environment variable turns on detailed tracing showing exactly which hooks are being run. This can be useful for understanding how Datasette is using your plugin.

DATASETTE_TRACE_PLUGINS=1 datasette mydb.db

Example output:

{   'datasette': <datasette.app.Datasette object at 0x100bc7220>,
    'request': <asgi.Request method="GET" url="">}
Hook implementations:
[   <HookImpl plugin_name='codespaces', plugin=<module 'datasette_codespaces' from '.../site-packages/datasette_codespaces/__init__.py'>>,
    <HookImpl plugin_name='datasette.actor_auth_cookie', plugin=<module 'datasette.actor_auth_cookie' from '.../datasette/datasette/actor_auth_cookie.py'>>,
    <HookImpl plugin_name='datasette.default_permissions', plugin=<module 'datasette.default_permissions' from '.../datasette/default_permissions.py'>>]
[{'id': 'root'}]

Writing one-off plugins#

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!.

from datasette import hookimpl

def prepare_connection(conn):
        "hello_world", 0, lambda: "Hello world!"

If you save this in plugins/my_plugin.py you can then start Datasette like this:

datasette serve mydb.db --plugins-dir=plugins/

Now you can navigate to http://localhost:8001/mydb and run this SQL:

select hello_world();

To see the output of your plugin.

Starting an installable plugin using cookiecutter#

Plugins that can be installed should be written as Python packages using a setup.py file.

The quickest way to start writing one an installable plugin is to use the 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 and then run this command to start building a plugin using the template:

cookiecutter gh:simonw/datasette-plugin

Read a cookiecutter template for writing Datasette plugins for more information about this template.

Packaging a plugin#

Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at https://github.com/simonw/datasette-plugin-demos

The example consists of two files: a setup.py file that defines the plugin:

from setuptools import setup

VERSION = "0.1"

    description="Examples of plugins for Datasette",
    author="Simon Willison",
    license="Apache License, Version 2.0",
        "datasette": [
            "plugin_demos = datasette_plugin_demos"

And a Python module file, datasette_plugin_demos.py, that implements the plugin:

from datasette import hookimpl
import random

def prepare_jinja2_environment(env):
    env.filters["uppercase"] = lambda u: u.upper()

def prepare_connection(conn):
        "random_integer", 2, random.randint

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 for use by other people, read the PyPA guide to Packaging and distributing projects.

Static assets#

If your plugin has a static/ directory, Datasette will automatically configure itself to serve those static assets from the following path:


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 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:

package_data = (
        "datasette_plugin_name": [

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 is a useful example of a plugin that includes packaged static assets in this way.

Custom templates#

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 Custom pages and templates for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI.

Templates should be bundled for distribution using the same package_data mechanism in setup.py described for static assets above, for example:

package_data = (
        "datasette_plugin_name": [

You can also use wildcards here such as templates/*.html. See datasette-edit-schema for an example of this pattern.

Writing plugins that accept configuration#

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:

plugin_config = datasette.plugin_config(
    "datasette-cluster-map", database="sf-trees", table="Street_Tree_List"

This will return the {"latitude_column": "lat", "longitude_column": "lng"} in the above example.

If there is no configuration for that plugin, the method will return None.

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 inside datasette.yaml like so:

        latitude_column: xlat
        longitude_column: xlng
  "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 datasette.yaml:

    latitude_column: xlat
    longitude_column: xlng
  "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.

Designing URLs for your plugin#

You can register new URL routes within Datasette using the register_routes(datasette) plugin hook.

Datasette's default URLs include these:

  • /dbname - database page

  • /dbname/tablename - table page

  • /dbname/tablename/pk - row page

See Pages and API endpoints and 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:

  • /-/upload-excel

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.

If your plugin includes functionality that relates to a specific database you could also register a URL route like this:

  • /dbname/-/upload-excel

Or for a specific table like this:

  • /dbname/tablename/-/modify-table-schema

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.

Building URLs within plugins#

Plugins that define their own custom user interface elements may need to link to other pages within Datasette.

This can be a bit tricky if the Datasette instance is using the 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:

Back to the <a href="{{ urls.instance() }}">Homepage</a>

See datasette.urls for full details on this object.

Plugins that define new plugin hooks#

Plugins can define new plugin hooks that other plugins can use to further extend their functionality.

datasette-graphql is one example of a plugin that does this. It defines a new hook called graphql_extra_fields, described here, which other plugins can use to define additional fields that should be included in the GraphQL schema.

To define additional hooks, add a file to the plugin called datasette_your_plugin/hookspecs.py with content that looks like this:

from pluggy import HookspecMarker

hookspec = HookspecMarker("datasette")

def name_of_your_hook_goes_here(datasette):
    "Description of your hook."

You should define your own hook name and arguments here, following the documentation for Pluggy specifications. Make sure to pick a name that is unlikely to clash with hooks provided by any other plugins.

Then, to register your plugin hooks, add the following code to your datasette_your_plugin/__init__.py file:

from datasette.plugins import pm
from . import hookspecs


This will register your plugin hooks as part of the datasette plugin hook namespace.

Within your plugin code you can trigger the hook using this pattern:

from datasette.plugins import pm

for (
) in pm.hook.name_of_your_hook_goes_here(
    # Do something with plugin_return_value

Other plugins will then be able to register their own implementations of your hook using this syntax:

from datasette import hookimpl

def name_of_your_hook_goes_here(datasette):
    return "Response from this plugin hook"

These plugin implementations can accept 0 or more of the named arguments that you defined in your hook specification.