{"id": "plugins:deploying-plugins-using-datasette-publish", "page": "plugins", "ref": "deploying-plugins-using-datasette-publish", "title": "Deploying plugins using datasette publish", "content": "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: \n                 datasette publish cloudrun mydb.db --install=datasette-vega \n                 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: \n                 datasette publish cloudrun mydb.db \\\n    --install=https://url-to-my-package.zip", "breadcrumbs": "[\"Plugins\", \"Installing plugins\"]", "references": "[]"}
{"id": "plugins:one-off-plugins-using-plugins-dir", "page": "plugins", "ref": "one-off-plugins-using-plugins-dir", "title": "One-off plugins using --plugins-dir", "content": "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: \n                 datasette mydb.db --plugins-dir=plugins/", "breadcrumbs": "[\"Plugins\", \"Installing plugins\"]", "references": "[]"}
{"id": "plugins:plugins-configuration", "page": "plugins", "ref": "plugins-configuration", "title": "Plugin configuration", "content": "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. \n             Here is an example of some plugin configuration for a specific table: \n             [[[cog\nfrom metadata_doc import config_example\nconfig_example(cog, {\n    \"databases\": {\n        \"sf-trees\": {\n            \"tables\": {\n                \"Street_Tree_List\": {\n                    \"plugins\": {\n                        \"datasette-cluster-map\": {\n                            \"latitude_column\": \"lat\",\n                            \"longitude_column\": \"lng\"\n                        }\n                    }\n                }\n            }\n        }\n    }\n}) \n             ]]] \n             [[[end]]] \n             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 .", "breadcrumbs": "[\"Plugins\"]", "references": "[]"}
{"id": "plugins:plugins-configuration-secret", "page": "plugins", "ref": "plugins-configuration-secret", "title": "Secret configuration values", "content": "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. \n                 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: \n                 [[[cog\nconfig_example(cog, {\n    \"plugins\": {\n        \"datasette-auth-github\": {\n            \"client_secret\": {\n                \"$env\": \"GITHUB_CLIENT_SECRET\"\n            }\n        }\n    }\n}) \n                 ]]] \n                 [[[end]]] \n                 As values in separate files . Your secrets can also live in files on disk. To specify a secret should be read from a file, provide the full file path like this: \n                 [[[cog\nconfig_example(cog, {\n    \"plugins\": {\n        \"datasette-auth-github\": {\n            \"client_secret\": {\n                \"$file\": \"/secrets/client-secret\"\n            }\n        }\n    }\n}) \n                 ]]] \n                 [[[end]]] \n                 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: \n                 datasette publish heroku my_database.db \\\n    --name my-heroku-app-demo \\\n    --install=datasette-auth-github \\\n    --plugin-secret datasette-auth-github client_id your_client_id \\\n    --plugin-secret datasette-auth-github client_secret your_client_secret \n                 This will set the necessary environment variables and add the following to the deployed  metadata.yaml : \n                 [[[cog\nconfig_example(cog, {\n    \"plugins\": {\n        \"datasette-auth-github\": {\n            \"client_id\": {\n                \"$env\": \"DATASETTE_AUTH_GITHUB_CLIENT_ID\"\n            },\n            \"client_secret\": {\n                \"$env\": \"DATASETTE_AUTH_GITHUB_CLIENT_SECRET\"\n            }\n        }\n    }\n}) \n                 ]]] \n                 [[[end]]]", "breadcrumbs": "[\"Plugins\", \"Plugin configuration\"]", "references": "[]"}
{"id": "plugins:plugins-datasette-load-plugins", "page": "plugins", "ref": "plugins-datasette-load-plugins", "title": "Controlling which plugins are loaded", "content": "Datasette defaults to loading every plugin that is installed in the same virtual environment as Datasette itself. \n             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. \n             For example, to load just the  datasette-vega  and  datasette-cluster-map  plugins, set  DATASETTE_LOAD_PLUGINS  to  datasette-vega,datasette-cluster-map : \n             export DATASETTE_LOAD_PLUGINS='datasette-vega,datasette-cluster-map'\ndatasette mydb.db \n             Or: \n             DATASETTE_LOAD_PLUGINS='datasette-vega,datasette-cluster-map' \\\n  datasette mydb.db \n             To disable the loading of all additional plugins, set  DATASETTE_LOAD_PLUGINS  to an empty string: \n             export DATASETTE_LOAD_PLUGINS=''\ndatasette mydb.db \n             A quick way to test this setting is to use it with the  datasette plugins  command: \n             DATASETTE_LOAD_PLUGINS='datasette-vega' datasette plugins \n             This should output the following: \n             [\n    {\n        \"name\": \"datasette-vega\",\n        \"static\": true,\n        \"templates\": false,\n        \"version\": \"0.6.2\",\n        \"hooks\": [\n            \"extra_css_urls\",\n            \"extra_js_urls\"\n        ]\n    }\n]", "breadcrumbs": "[\"Plugins\"]", "references": "[]"}
{"id": "plugins:plugins-installed", "page": "plugins", "ref": "plugins-installed", "title": "Seeing what plugins are installed", "content": "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 \n             You can also use the  datasette plugins  command: \n             datasette plugins \n             Which outputs: \n             [\n    {\n        \"name\": \"datasette_json_html\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": \"0.4.0\"\n    }\n] \n             [[[cog\nfrom datasette import cli\nfrom click.testing import CliRunner\nimport textwrap, json\ncog.out(\"\\n\")\nresult = CliRunner().invoke(cli.cli, [\"plugins\", \"--all\"])\n# cog.out() with text containing newlines was unindenting for some reason\ncog.outl(\"If you run ``datasette plugins --all`` it will include default plugins that ship as part of Datasette:\\n\")\ncog.outl(\".. code-block:: json\\n\")\nplugins = [p for p in json.loads(result.output) if p[\"name\"].startswith(\"datasette.\")]\nindented = textwrap.indent(json.dumps(plugins, indent=4), \"    \")\nfor line in indented.split(\"\\n\"):\n    cog.outl(line)\ncog.out(\"\\n\\n\") \n             ]]] \n             If you run  datasette plugins --all  it will include default plugins that ship as part of Datasette: \n             [\n    {\n        \"name\": \"datasette.actor_auth_cookie\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"actor_from_request\"\n        ]\n    },\n    {\n        \"name\": \"datasette.blob_renderer\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"register_output_renderer\"\n        ]\n    },\n    {\n        \"name\": \"datasette.default_magic_parameters\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"register_magic_parameters\"\n        ]\n    },\n    {\n        \"name\": \"datasette.default_menu_links\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"menu_links\"\n        ]\n    },\n    {\n        \"name\": \"datasette.default_permissions\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"actor_from_request\",\n            \"permission_allowed\",\n            \"register_permissions\",\n            \"skip_csrf\"\n        ]\n    },\n    {\n        \"name\": \"datasette.events\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"register_events\"\n        ]\n    },\n    {\n        \"name\": \"datasette.facets\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"register_facet_classes\"\n        ]\n    },\n    {\n        \"name\": \"datasette.filters\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"filters_from_request\"\n        ]\n    },\n    {\n        \"name\": \"datasette.forbidden\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"forbidden\"\n        ]\n    },\n    {\n        \"name\": \"datasette.handle_exception\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"handle_exception\"\n        ]\n    },\n    {\n        \"name\": \"datasette.publish.cloudrun\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"publish_subcommand\"\n        ]\n    },\n    {\n        \"name\": \"datasette.publish.heroku\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"publish_subcommand\"\n        ]\n    },\n    {\n        \"name\": \"datasette.sql_functions\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": null,\n        \"hooks\": [\n            \"prepare_connection\"\n        ]\n    }\n] \n             [[[end]]] \n             You can add the  --plugins-dir=  option to include any plugins found in that directory. \n             Add  --requirements  to output a list of installed plugins that can then be installed in another Datasette instance using  datasette install -r requirements.txt : \n             datasette plugins --requirements \n             The output will look something like this: \n             datasette-codespaces==0.1.1\ndatasette-graphql==2.2\ndatasette-json-html==1.0.1\ndatasette-pretty-json==0.2.2\ndatasette-x-forwarded-host==0.1 \n             To write that to a  requirements.txt  file, run this: \n             datasette plugins --requirements > requirements.txt", "breadcrumbs": "[\"Plugins\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/-/plugins\", \"label\": \"https://fivethirtyeight.datasettes.com/-/plugins\"}]"}
{"id": "plugins:plugins-installing", "page": "plugins", "ref": "plugins-installing", "title": "Installing plugins", "content": "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. \n             You can install plugins using the  datasette install  command: \n             datasette install datasette-vega \n             You can uninstall plugins with  datasette uninstall : \n             datasette uninstall datasette-vega \n             You can upgrade plugins with  datasette install --upgrade  or  datasette install -U : \n             datasette install -U datasette-vega \n             This command can also be used to upgrade Datasette itself to the latest released version: \n             datasette install -U datasette \n             You can install multiple plugins at once by listing them as lines in a  requirements.txt  file like this: \n             datasette-vega\ndatasette-cluster-map \n             Then pass that file to  datasette install -r : \n             datasette install -r requirements.txt \n             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.", "breadcrumbs": "[\"Plugins\"]", "references": "[]"}