docs
| id | page | ref | title | content | breadcrumbs | references |
|---|---|---|---|---|---|---|
| plugins:deploying-plugins-using-datasette-publish | plugins | deploying-plugins-using-datasette-publish | Deploying plugins using datasette publish | The datasette publish and datasette package commands both take an optional --install argument. You can use this one or more times to tell Datasette to pip install specific plugins as part of the process: datasette publish cloudrun mydb.db --install=datasette-vega You can use the name of a package on PyPI or any of the other valid arguments to pip install such as a URL to a .zip file: datasette publish cloudrun mydb.db \ --install=https://url-to-my-package.zip | ["Plugins", "Installing plugins"] | [] |
| plugins:one-off-plugins-using-plugins-dir | plugins | one-off-plugins-using-plugins-dir | One-off plugins using --plugins-dir | You can also define one-off per-project plugins by saving them as plugin_name.py functions in a plugins/ folder and then passing that folder to datasette using the --plugins-dir option: datasette mydb.db --plugins-dir=plugins/ | ["Plugins", "Installing plugins"] | [] |
| plugins:plugins-configuration | plugins | plugins-configuration | Plugin configuration | Plugins can have their own configuration, embedded in a configuration file . Configuration options for plugins live within a "plugins" key in that file, which can be included at the root, database or table level. Here is an example of some plugin configuration for a specific table: [[[cog from metadata_doc import config_example config_example(cog, { "databases": { "sf-trees": { "tables": { "Street_Tree_List": { "plugins": { "datasette-cluster-map": { "latitude_column": "lat", "longitude_column": "lng" } } } } } } }) ]]] [[[end]]] This tells the datasette-cluster-map column which latitude and longitude columns should be used for a table called Street_Tree_List inside a database file called sf-trees.db . | ["Plugins"] | [] |
| plugins:plugins-configuration-secret | plugins | plugins-configuration-secret | Secret configuration values | Some plugins may need configuration that should stay secret - API keys for example. There are two ways in which you can store secret configuration values. As environment variables . If your secret lives in an environment variable that is available to the Datasette process, you can indicate that the configuration value should be read from that environment variable like so: [[[cog config_example(cog, { "plugins": { "datasette-auth-github": { "client_secret": { "$env": "GITHUB_CLIENT_SECRET" } } } }) ]]] [[[end]]] As values in separate files . Your secrets can also live in files on disk. To specify a secret should be read from a file, provide the full file path like this: [[[cog config_example(cog, { "plugins": { "datasette-auth-github": { "client_secret": { "$file": "/secrets/client-secret" } } } }) ]]] [[[end]]] If you are publishing your data using the datasette publish family of commands, you can use the --plugin-secret option to set these secrets at publish time. For example, using Heroku you might run the following command: datasette publish heroku my_database.db \ --name my-heroku-app-demo \ --install=datasette-auth-github \ --plugin-secret datasette-auth-github client_id your_client_id \ --plugin-secret datasette-auth-github client_secret your_client_secret This will set the necessary environment variables and add the following to the deployed metadata.yaml : [[[cog config_example(cog, { "plugins": { "datasette-auth-github": { "client_id": { "$env": "DATASETTE_AUTH_GITHUB_CLIENT_ID" }, "client_secret": { "$env": "DATASETTE_AUTH_GITHUB_CLIENT_SECRET" } … | ["Plugins", "Plugin configuration"] | [] |
| plugins:plugins-datasette-load-plugins | plugins | plugins-datasette-load-plugins | Controlling which plugins are loaded | Datasette defaults to loading every plugin that is installed in the same virtual environment as Datasette itself. You can set the DATASETTE_LOAD_PLUGINS environment variable to a comma-separated list of plugin names to load a controlled subset of plugins instead. For example, to load just the datasette-vega and datasette-cluster-map plugins, set DATASETTE_LOAD_PLUGINS to datasette-vega,datasette-cluster-map : export DATASETTE_LOAD_PLUGINS='datasette-vega,datasette-cluster-map' datasette mydb.db Or: DATASETTE_LOAD_PLUGINS='datasette-vega,datasette-cluster-map' \ datasette mydb.db To disable the loading of all additional plugins, set DATASETTE_LOAD_PLUGINS to an empty string: export DATASETTE_LOAD_PLUGINS='' datasette mydb.db A quick way to test this setting is to use it with the datasette plugins command: DATASETTE_LOAD_PLUGINS='datasette-vega' datasette plugins This should output the following: [ { "name": "datasette-vega", "static": true, "templates": false, "version": "0.6.2", "hooks": [ "extra_css_urls", "extra_js_urls" ] } ] | ["Plugins"] | [] |
| plugins:plugins-installed | plugins | plugins-installed | Seeing what plugins are installed | You can see a list of installed plugins by navigating to the /-/plugins page of your Datasette instance - for example: https://fivethirtyeight.datasettes.com/-/plugins You can also use the datasette plugins command: datasette plugins Which outputs: [ { "name": "datasette_json_html", "static": false, "templates": false, "version": "0.4.0" } ] [[[cog from datasette import cli from click.testing import CliRunner import textwrap, json cog.out("\n") result = CliRunner().invoke(cli.cli, ["plugins", "--all"]) # cog.out() with text containing newlines was unindenting for some reason cog.outl("If you run ``datasette plugins --all`` it will include default plugins that ship as part of Datasette:\n") cog.outl(".. code-block:: json\n") plugins = [p for p in json.loads(result.output) if p["name"].startswith("datasette.")] indented = textwrap.indent(json.dumps(plugins, indent=4), " ") for line in indented.split("\n"): cog.outl(line) cog.out("\n\n") ]]] If you run datasette plugins --all it will include default plugins that ship as part of Datasette: [ { "name": "datasette.actor_auth_cookie", "static": false, "templates": false, "version": null, "hooks": [ "actor_from_request" ] }, { "name": "datasette.blob_renderer", "static": false, "templates": false, "version": null, "hooks": [ "register_output_renderer" ] }, { "name": "datasette.default_magic_parameters", "static": false, "templates": false, "version": null, "hooks": [ "register_magic_parameters" ] }, { "name": "datasette.default_menu_links", "static": false, "templates": false, "version": null, "hooks": [ "menu_links" ] }, { "name… | ["Plugins"] | [{"href": "https://fivethirtyeight.datasettes.com/-/plugins", "label": "https://fivethirtyeight.datasettes.com/-/plugins"}] |
| plugins:plugins-installing | plugins | plugins-installing | Installing plugins | If a plugin has been packaged for distribution using setuptools you can use the plugin by installing it alongside Datasette in the same virtual environment or Docker container. You can install plugins using the datasette install command: datasette install datasette-vega You can uninstall plugins with datasette uninstall : datasette uninstall datasette-vega You can upgrade plugins with datasette install --upgrade or datasette install -U : datasette install -U datasette-vega This command can also be used to upgrade Datasette itself to the latest released version: datasette install -U datasette You can install multiple plugins at once by listing them as lines in a requirements.txt file like this: datasette-vega datasette-cluster-map Then pass that file to datasette install -r : datasette install -r requirements.txt The install and uninstall commands are thin wrappers around pip install and pip uninstall , which ensure that they run pip in the same virtual environment as Datasette itself. | ["Plugins"] | [] |