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id | page | ref | title | content | breadcrumbs ▲ | references |
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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"] | [] |
plugin_hooks:plugin-asgi-wrapper | plugin_hooks | plugin-asgi-wrapper | asgi_wrapper(datasette) | Return an ASGI middleware wrapper function that will be applied to the Datasette ASGI application. This is a very powerful hook. You can use it to manipulate the entire Datasette response, or even to configure new URL routes that will be handled by your own custom code. You can write your ASGI code directly against the low-level specification, or you can use the middleware utilities provided by an ASGI framework such as Starlette . This example plugin adds a x-databases HTTP header listing the currently attached databases: from datasette import hookimpl from functools import wraps @hookimpl def asgi_wrapper(datasette): def wrap_with_databases_header(app): @wraps(app) async def add_x_databases_header( scope, receive, send ): async def wrapped_send(event): if event["type"] == "http.response.start": original_headers = ( event.get("headers") or [] ) event = { "type": event["type"], "status": event["status"], "headers": original_headers + [ [ b"x-databases", ", ".join( datasette.databases.keys() ).encode("utf-8"), ] ], } await send(event) await app(scope, receive, wrapped_send) return add_x_databases_header return wrap_with_databases_header Examples: datasette-cors , datasette-pyinstrument , datasette-total-page-time | ["Plugin hooks"] | [{"href": "https://asgi.readthedocs.io/", "label": "ASGI"}, {"href": "https://www.starlette.io/middleware/", "label": "Starlette"}, {"href": "https://datasette.io/plugins/datasette-cors", "label": "datasette-cors"}, {"href": "https://datasette.io/plugins/datasette-pyinstrument", "label": "datasette-pyinstrument"}, {"href": "https://datasette.io/plugins/datasette-total-page-time", "label": "datasette-total-page-time"}] |
plugin_hooks:plugin-hook-actor-from-request | plugin_hooks | plugin-hook-actor-from-request | actor_from_request(datasette, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. request - Request object The current HTTP request. This is part of Datasette's authentication and permissions system . The function should attempt to authenticate an actor (either a user or an API actor of some sort) based on information in the request. If it cannot authenticate an actor, it should return None . Otherwise it should return a dictionary representing that actor. Here's an example that authenticates the actor based on an incoming API key: from datasette import hookimpl import secrets SECRET_KEY = "this-is-a-secret" @hookimpl def actor_from_request(datasette, request): authorization = ( request.headers.get("authorization") or "" ) expected = "Bearer {}".format(SECRET_KEY) if secrets.compare_digest(authorization, expected): return {"id": "bot"} If you install this in your plugins directory you can test it like this: $ curl -H 'Authorization: Bearer this-is-a-secret' http://localhost:8003/-/actor.json Instead of returning a dictionary, this function can return an awaitable function which itself returns either None or a dictionary. This is useful for authentication functions that need to make a database query - for example: from datasette import hookimpl @hookimpl def actor_from_request(datasette, request): async def inner(): token = request.args.get("_token") if not token: return None # Look up ?_token=xxx in sessions table result = await datasette.get_database().execute( "select count(*) from sessions wher… | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-auth-tokens", "label": "datasette-auth-tokens"}] |
plugin_hooks:plugin-hook-canned-queries | plugin_hooks | plugin-hook-canned-queries | canned_queries(datasette, database, actor) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. database - string The name of the database. actor - dictionary or None The currently authenticated actor . Use this hook to return a dictionary of additional canned query definitions for the specified database. The return value should be the same shape as the JSON described in the canned query documentation. from datasette import hookimpl @hookimpl def canned_queries(datasette, database): if database == "mydb": return { "my_query": { "sql": "select * from my_table where id > :min_id" } } The hook can alternatively return an awaitable function that returns a list. Here's an example that returns queries that have been stored in the saved_queries database table, if one exists: from datasette import hookimpl @hookimpl def canned_queries(datasette, database): async def inner(): db = datasette.get_database(database) if await db.table_exists("saved_queries"): results = await db.execute( "select name, sql from saved_queries" ) return { result["name"]: {"sql": result["sql"]} for result in results } return inner The actor parameter can be used to include the currently authenticated actor in your decision. Here's an example that returns saved queries that were saved by that actor: from datasette import hookimpl @hookimpl def canned_queries… | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-saved-queries", "label": "datasette-saved-queries"}] |
plugin_hooks:plugin-hook-database-actions | plugin_hooks | plugin-hook-database-actions | database_actions(datasette, actor, database, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary or None The currently authenticated actor . database - string The name of the database. request - Request object The current HTTP request. This hook is similar to table_actions(datasette, actor, database, table, request) but populates an actions menu on the database page. Example: datasette-graphql | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}] |
plugin_hooks:plugin-hook-extra-body-script | plugin_hooks | plugin-hook-extra-body-script | extra_body_script(template, database, table, columns, view_name, request, datasette) | Extra JavaScript to be added to a <script> block at the end of the <body> element on the page. This takes the same arguments as extra_template_vars(...) The template , database , table and view_name options can be used to return different code depending on which template is being rendered and which database or table are being processed. The datasette instance is provided primarily so that you can consult any plugin configuration options that may have been set, using the datasette.plugin_config(plugin_name) method documented above. This function can return a string containing JavaScript, or a dictionary as described below, or a function or awaitable function that returns a string or dictionary. Use a dictionary if you want to specify that the code should be placed in a <script type="module">...</script> element: @hookimpl def extra_body_script(): return { "module": True, "script": "console.log('Your JavaScript goes here...')", } This will add the following to the end of your page: <script type="module">console.log('Your JavaScript goes here...')</script> Example: datasette-cluster-map | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}] |
plugin_hooks:plugin-hook-extra-css-urls | plugin_hooks | plugin-hook-extra-css-urls | extra_css_urls(template, database, table, columns, view_name, request, datasette) | This takes the same arguments as extra_template_vars(...) Return a list of extra CSS URLs that should be included on the page. These can take advantage of the CSS class hooks described in Custom pages and templates . This can be a list of URLs: from datasette import hookimpl @hookimpl def extra_css_urls(): return [ "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css" ] Or a list of dictionaries defining both a URL and an SRI hash : @hookimpl def extra_css_urls(): return [ { "url": "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css", "sri": "sha384-9gVQ4dYFwwWSjIDZnLEWnxCjeSWFphJiwGPXr1jddIhOegiu1FwO5qRGvFXOdJZ4", } ] This function can also return an awaitable function, useful if it needs to run any async code: @hookimpl def extra_css_urls(datasette): async def inner(): db = datasette.get_database() results = await db.execute( "select url from css_files" ) return [r[0] for r in results] return inner Examples: datasette-cluster-map , datasette-vega | ["Plugin hooks"] | [{"href": "https://www.srihash.org/", "label": "SRI hash"}, {"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-vega", "label": "datasette-vega"}] |
plugin_hooks:plugin-hook-extra-js-urls | plugin_hooks | plugin-hook-extra-js-urls | extra_js_urls(template, database, table, columns, view_name, request, datasette) | This takes the same arguments as extra_template_vars(...) This works in the same way as extra_css_urls() but for JavaScript. You can return a list of URLs, a list of dictionaries or an awaitable function that returns those things: from datasette import hookimpl @hookimpl def extra_js_urls(): return [ { "url": "https://code.jquery.com/jquery-3.3.1.slim.min.js", "sri": "sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo", } ] You can also return URLs to files from your plugin's static/ directory, if you have one: @hookimpl def extra_js_urls(): return ["/-/static-plugins/your-plugin/app.js"] Note that your-plugin here should be the hyphenated plugin name - the name that is displayed in the list on the /-/plugins debug page. If your code uses JavaScript modules you should include the "module": True key. See Custom CSS and JavaScript for more details. @hookimpl def extra_js_urls(): return [ { "url": "/-/static-plugins/your-plugin/app.js", "module": True, } ] Examples: datasette-cluster-map , datasette-vega | ["Plugin hooks"] | [{"href": "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules", "label": "JavaScript modules"}, {"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-vega", "label": "datasette-vega"}] |
plugin_hooks:plugin-hook-extra-template-vars | plugin_hooks | plugin-hook-extra-template-vars | extra_template_vars(template, database, table, columns, view_name, request, datasette) | Extra template variables that should be made available in the rendered template context. template - string The template that is being rendered, e.g. database.html database - string or None The name of the database, or None if the page does not correspond to a database (e.g. the root page) table - string or None The name of the table, or None if the page does not correct to a table columns - list of strings or None The names of the database columns that will be displayed on this page. None if the page does not contain a table. view_name - string The name of the view being displayed. ( index , database , table , and row are the most important ones.) request - Request object or None The current HTTP request. This can be None if the request object is not available. datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook can return one of three different types: Dictionary If you return a dict… | ["Plugin hooks"] | [{"href": "https://jinja.palletsprojects.com/en/2.10.x/api/#async-support", "label": "async mode"}, {"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-template-sql", "label": "datasette-template-sql"}] |
plugin_hooks:plugin-hook-filters-from-request | plugin_hooks | plugin-hook-filters-from-request | filters_from_request(request, database, table, datasette) | request - Request object The current HTTP request. database - string The name of the database. table - string The name of the table. datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. This hook runs on the table page, and can influence the where clause of the SQL query used to populate that page, based on query string arguments on the incoming request. The hook should return an instance of datasette.filters.FilterArguments which has one required and three optional arguments: return FilterArguments( where_clauses=["id > :max_id"], params={"max_id": 5}, human_descriptions=["max_id is greater than 5"], extra_context={}, ) The arguments to the FilterArguments class constructor are as follows: where_clauses - list of strings, required A list of SQL fragments that will be inserted into the SQL query, joined by the and operator. These can include :named parameters which will be populated using data in params . params - dictionary, optional Additional keyword arguments to be used when the query is executed. These should match any :arguments in the where clauses. … | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-leaflet-freedraw", "label": "datasette-leaflet-freedraw"}] |
plugin_hooks:plugin-hook-forbidden | plugin_hooks | plugin-hook-forbidden | forbidden(datasette, request, message) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. request - Request object The current HTTP request. message - string A message hinting at why the request was forbidden. Plugins can use this to customize how Datasette responds when a 403 Forbidden error occurs - usually because a page failed a permission check, see Permissions . If a plugin hook wishes to react to the error, it should return a Response object . This example returns a redirect to a /-/login page: from datasette import hookimpl from urllib.parse import urlencode @hookimpl def forbidden(request, message): return Response.redirect( "/-/login?=" + urlencode({"message": message}) ) The function can alternatively return an awaitable function if it needs to make any asynchronous method calls. This example renders a template: from datasette import hookimpl, Response @hookimpl def forbidden(datasette): async def inner(): return Response.html( await datasette.render_template( "render_message.html", request=request ) ) return inner | ["Plugin hooks"] | [] |
plugin_hooks:plugin-hook-get-metadata | plugin_hooks | plugin-hook-get-metadata | get_metadata(datasette, key, database, table) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . actor - dictionary or None The currently authenticated actor . database - string or None The name of the database metadata is being asked for. table - string or None The name of the table. key - string or None The name of the key for which data is being asked for. This hook is responsible for returning a dictionary corresponding to Datasette Metadata . This function is passed the database , table and key which were passed to the upstream internal request for metadata. Regardless, it is important to return a global metadata object, where "databases": [] would be a top-level key. The dictionary returned here, will be merged with, and overwritten by, the contents of the physical metadata.yaml if one is present. The design of this plugin hook does not currently provide a mechanism for interacting with async code, and may change in the future. See issue 1384 . @hookimpl def get_metadata(datasette, key, database, table): metadata = { "title": "This will be the Datasette landing page title!", "description": get_instance_description(datasette), "databases": [], } for db_name, db_data_dict in get_my_database_meta( datasette, database, table, key ): … | ["Plugin hooks"] | [{"href": "https://github.com/simonw/datasette/issues/1384", "label": "issue 1384"}, {"href": "https://datasette.io/plugins/datasette-remote-metadata", "label": "datasette-remote-metadata plugin"}] |
plugin_hooks:plugin-hook-handle-exception | plugin_hooks | plugin-hook-handle-exception | handle_exception(datasette, request, exception) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. request - Request object The current HTTP request. exception - Exception The exception that was raised. This hook is called any time an unexpected exception is raised. You can use it to record the exception. If your handler returns a Response object it will be returned to the client in place of the default Datasette error page. The handler can return a response directly, or it can return return an awaitable function that returns a response. This example logs an error to Sentry and then renders a custom error page: from datasette import hookimpl, Response import sentry_sdk @hookimpl def handle_exception(datasette, exception): sentry_sdk.capture_exception(exception) async def inner(): return Response.html( await datasette.render_template( "custom_error.html", request=request ) ) return inner Example: datasette-sentry | ["Plugin hooks"] | [{"href": "https://sentry.io/", "label": "Sentry"}, {"href": "https://datasette.io/plugins/datasette-sentry", "label": "datasette-sentry"}] |
plugin_hooks:plugin-hook-menu-links | plugin_hooks | plugin-hook-menu-links | menu_links(datasette, actor, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary or None The currently authenticated actor . request - Request object or None The current HTTP request. This can be None if the request object is not available. This hook allows additional items to be included in the menu displayed by Datasette's top right menu icon. The hook should return a list of {"href": "...", "label": "..."} menu items. These will be added to the menu. It can alternatively return an async def awaitable function which returns a list of menu items. This example adds a new menu item but only if the signed in user is "root" : from datasette import hookimpl @hookimpl def menu_links(datasette, actor): if actor and actor.get("id") == "root": return [ { "href": datasette.urls.path( "/-/edit-schema" ), "label": "Edit schema", }, ] Using datasette.urls here ensures that links in the menu will take the base_url setting into account. Examples: datasette-search-all , datasette-graphql | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}] |
plugin_hooks:plugin-hook-permission-allowed | plugin_hooks | plugin-hook-permission-allowed | permission_allowed(datasette, actor, action, resource) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary The current actor, as decided by actor_from_request(datasette, request) . action - string The action to be performed, e.g. "edit-table" . resource - string or None An identifier for the individual resource, e.g. the name of the table. Called to check that an actor has permission to perform an action on a resource. Can return True if the action is allowed, False if the action is not allowed or None if the plugin does not have an opinion one way or the other. Here's an example plugin which randomly selects if a permission should be allowed or denied, except for view-instance which always uses the default permission scheme instead. from datasette import hookimpl import random @hookimpl def permission_allowed(action): if action != "view-instance": # Return True or False at random return random.random() > 0.5 # Returning None falls back to default permissions This function can alternatively return an awaitable function which itself returns True , False or None . You can use this option if you need to execute additional database queries using await datasette.execute(...) . Here's an example that allows users to view the admin_log table only if their actor id is present in the admin_users table. It aso disallows arbitrary SQL queries for the staff… | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-permissions-sql", "label": "datasette-permissions-sql"}] |
plugin_hooks:plugin-hook-prepare-connection | plugin_hooks | plugin-hook-prepare-connection | prepare_connection(conn, database, datasette) | conn - sqlite3 connection object The connection that is being opened database - string The name of the database datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook is called when a new SQLite database connection is created. You can use it to register custom SQL functions , aggregates and collations. For example: from datasette import hookimpl import random @hookimpl def prepare_connection(conn): conn.create_function( "random_integer", 2, random.randint ) This registers a SQL function called random_integer which takes two arguments and can be called like this: select random_integer(1, 10); Examples: datasette-jellyfish , datasette-jq , datasette-haversine , datasette-rure | ["Plugin hooks"] | [{"href": "https://docs.python.org/2/library/sqlite3.html#sqlite3.Connection.create_function", "label": "register custom SQL functions"}, {"href": "https://datasette.io/plugins/datasette-jellyfish", "label": "datasette-jellyfish"}, {"href": "https://datasette.io/plugins/datasette-jq", "label": "datasette-jq"}, {"href": "https://datasette.io/plugins/datasette-haversine", "label": "datasette-haversine"}, {"href": "https://datasette.io/plugins/datasette-rure", "label": "datasette-rure"}] |
plugin_hooks:plugin-hook-prepare-jinja2-environment | plugin_hooks | plugin-hook-prepare-jinja2-environment | prepare_jinja2_environment(env, datasette) | env - jinja2 Environment The template environment that is being prepared datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook is called with the Jinja2 environment that is used to evaluate Datasette HTML templates. You can use it to do things like register custom template filters , for example: from datasette import hookimpl @hookimpl def prepare_jinja2_environment(env): env.filters["uppercase"] = lambda u: u.upper() You can now use this filter in your custom templates like so: Table name: {{ table|uppercase }} This function can return an awaitable function if it needs to run any async code. Examples: datasette-edit-templates | ["Plugin hooks"] | [{"href": "http://jinja.pocoo.org/docs/2.10/api/#custom-filters", "label": "register custom\n template filters"}, {"href": "https://datasette.io/plugins/datasette-edit-templates", "label": "datasette-edit-templates"}] |
plugin_hooks:plugin-hook-publish-subcommand | plugin_hooks | plugin-hook-publish-subcommand | publish_subcommand(publish) | publish - Click publish command group The Click command group for the datasette publish subcommand This hook allows you to create new providers for the datasette publish command. Datasette uses this hook internally to implement the default cloudrun and heroku subcommands, so you can read their source to see examples of this hook in action. Let's say you want to build a plugin that adds a datasette publish my_hosting_provider --api_key=xxx mydatabase.db publish command. Your implementation would start like this: from datasette import hookimpl from datasette.publish.common import ( add_common_publish_arguments_and_options, ) import click @hookimpl def publish_subcommand(publish): @publish.command() @add_common_publish_arguments_and_options @click.option( "-k", "--api_key", help="API key for talking to my hosting provider", ) def my_hosting_provider( files, metadata, extra_options, branch, template_dir, plugins_dir, static, install, plugin_secret, version_note, secret, title, license, license_url, source, source_url, about, about_url, api_key, ): ... Examples: datasette-publish-fly , datasette-publish-vercel | ["Plugin hooks"] | [{"href": "https://github.com/simonw/datasette/tree/main/datasette/publish", "label": "their source"}, {"href": "https://datasette.io/plugins/datasette-publish-fly", "label": "datasette-publish-fly"}, {"href": "https://datasette.io/plugins/datasette-publish-vercel", "label": "datasette-publish-vercel"}] |
plugin_hooks:plugin-hook-register-commands | plugin_hooks | plugin-hook-register-commands | register_commands(cli) | cli - the root Datasette Click command group Use this to register additional CLI commands Register additional CLI commands that can be run using datsette yourcommand ... . This provides a mechanism by which plugins can add new CLI commands to Datasette. This example registers a new datasette verify file1.db file2.db command that checks if the provided file paths are valid SQLite databases: from datasette import hookimpl import click import sqlite3 @hookimpl def register_commands(cli): @cli.command() @click.argument( "files", type=click.Path(exists=True), nargs=-1 ) def verify(files): "Verify that files can be opened by Datasette" for file in files: conn = sqlite3.connect(str(file)) try: conn.execute("select * from sqlite_master") except sqlite3.DatabaseError: raise click.ClickException( "Invalid database: {}".format(file) ) The new command can then be executed like so: datasette verify fixtures.db Help text (from the docstring for the function plus any defined Click arguments or options) will become available using: datasette verify --help Plugins can register multiple commands by making multiple calls to the @cli.command() decorator. Consult the Click documentation for full details on how to build a CLI command, including how to define arguments and options. Note that register_commands() plugins cannot used with the --plugins-dir mechanism - they need to be installed into the same virtual environment as Datasette using pip install . Provided it has a setup.py file (see Packaging a plugin ) you can run pip install directly against the directory in which you are developing your plugin like so: pip install -e path/t… | ["Plugin hooks"] | [{"href": "https://click.palletsprojects.com/en/latest/commands/#callback-invocation", "label": "Click command group"}, {"href": "https://click.palletsprojects.com/", "label": "Click documentation"}, {"href": "https://datasette.io/plugins/datasette-auth-passwords", "label": "datasette-auth-passwords"}, {"href": "https://datasette.io/plugins/datasette-verify", "label": "datasette-verify"}] |
plugin_hooks:plugin-hook-register-magic-parameters | plugin_hooks | plugin-hook-register-magic-parameters | register_magic_parameters(datasette) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . Magic parameters can be used to add automatic parameters to canned queries . This plugin hook allows additional magic parameters to be defined by plugins. Magic parameters all take this format: _prefix_rest_of_parameter . The prefix indicates which magic parameter function should be called - the rest of the parameter is passed as an argument to that function. To register a new function, return it as a tuple of (string prefix, function) from this hook. The function you register should take two arguments: key and request , where key is the rest_of_parameter portion of the parameter and request is the current Request object . This example registers two new magic parameters: :_request_http_version returning the HTTP version of the current request, and :_uuid_new which returns a new UUID: from uuid import uuid4 def uuid(key, request): if key == "new": return str(uuid4()) else: raise KeyError def request(key, request): if key == "http_version": return request.scope["http_version"] else: raise KeyError @hookimpl def register_magic_parameters(datasette): return [ ("request", request), ("uuid", uuid), ] | ["Plugin hooks"] | [] |
plugin_hooks:plugin-hook-render-cell | plugin_hooks | plugin-hook-render-cell | render_cell(row, value, column, table, database, datasette) | Lets you customize the display of values within table cells in the HTML table view. row - sqlite.Row The SQLite row object that the value being rendered is part of value - string, integer, float, bytes or None The value that was loaded from the database column - string The name of the column being rendered table - string or None The name of the table - or None if this is a custom SQL query database - string The name of the database datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. If your hook returns None , it will be ignored. Use this to indicate that your hook is not able to custom render this particular value. If the hook returns a string, that string will be rendered in the table cell. If you want to return HTML markup you can do so by returning a jinja2.Markup object. You can also return an awaitable function which returns a value. Datasette will loop through all available render_cell hooks and display the value returned by the first one that does not return None . Here is an example of a custom render_cell() plugin wh… | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-render-binary", "label": "datasette-render-binary"}, {"href": "https://datasette.io/plugins/datasette-render-markdown", "label": "datasette-render-markdown"}, {"href": "https://datasette.io/plugins/datasette-json-html", "label": "datasette-json-html"}] |
plugin_hooks:plugin-hook-skip-csrf | plugin_hooks | plugin-hook-skip-csrf | skip_csrf(datasette, scope) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. scope - dictionary The ASGI scope for the incoming HTTP request. This hook can be used to skip CSRF protection for a specific incoming request. For example, you might have a custom path at /submit-comment which is designed to accept comments from anywhere, whether or not the incoming request originated on the site and has an accompanying CSRF token. This example will disable CSRF protection for that specific URL path: from datasette import hookimpl @hookimpl def skip_csrf(scope): return scope["path"] == "/submit-comment" If any of the currently active skip_csrf() plugin hooks return True , CSRF protection will be skipped for the request. | ["Plugin hooks"] | [{"href": "https://asgi.readthedocs.io/en/latest/specs/www.html#http-connection-scope", "label": "ASGI scope"}] |
plugin_hooks:plugin-hook-startup | plugin_hooks | plugin-hook-startup | startup(datasette) | This hook fires when the Datasette application server first starts up. You can implement a regular function, for example to validate required plugin configuration: @hookimpl def startup(datasette): config = datasette.plugin_config("my-plugin") or {} assert ( "required-setting" in config ), "my-plugin requires setting required-setting" Or you can return an async function which will be awaited on startup. Use this option if you need to make any database queries: @hookimpl def startup(datasette): async def inner(): db = datasette.get_database() if "my_table" not in await db.table_names(): await db.execute_write( """ create table my_table (mycol text) """ ) return inner Potential use-cases: Run some initialization code for the plugin Create database tables that a plugin needs on startup Validate the metadata configuration for a plugin on startup, and raise an error if it is invalid If you are writing unit tests for a plugin that uses this hook and doesn't exercise Datasette by sending any simulated requests through it you will need to explicitly call await ds.invoke_startup() in your tests. An example: @pytest.mark.asyncio async def test_my_plugin(): ds = Datasette() await ds.invoke_startup() # Rest of test goes here Examples: datasette-saved-queries , datasette-init | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-saved-queries", "label": "datasette-saved-queries"}, {"href": "https://datasette.io/plugins/datasette-init", "label": "datasette-init"}] |
plugin_hooks:plugin-hook-table-actions | plugin_hooks | plugin-hook-table-actions | table_actions(datasette, actor, database, table, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary or None The currently authenticated actor . database - string The name of the database. table - string The name of the table. request - Request object or None The current HTTP request. This can be None if the request object is not available. This hook allows table actions to be displayed in a menu accessed via an action icon at the top of the table page. It should return a list of {"href": "...", "label": "..."} menu items. It can alternatively return an async def awaitable function which returns a list of menu items. This example adds a new table action if the signed in user is "root" : from datasette import hookimpl @hookimpl def table_actions(datasette, actor, database, table): if actor and actor.get("id") == "root": return [ { "href": datasette.urls.path( "/-/edit-schema/{}/{}".format( database, table ) ), "label": "Edit schema for this table", } ] Example: datasette-graphql | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}] |
plugin_hooks:plugin-register-facet-classes | plugin_hooks | plugin-register-facet-classes | register_facet_classes() | Return a list of additional Facet subclasses to be registered. The design of this plugin hook is unstable and may change. See issue 830 . Each Facet subclass implements a new type of facet operation. The class should look like this: class SpecialFacet(Facet): # This key must be unique across all facet classes: type = "special" async def suggest(self): # Use self.sql and self.params to suggest some facets suggested_facets = [] suggested_facets.append( { "name": column, # Or other unique name # Construct the URL that will enable this facet: "toggle_url": self.ds.absolute_url( self.request, path_with_added_args( self.request, {"_facet": column} ), ), } ) return suggested_facets async def facet_results(self): # This should execute the facet operation and return results, again # using self.sql and self.params as the starting point facet_results = [] facets_timed_out = [] facet_size = self.get_facet_size() # Do some calculations here... for column in columns_selected_for_facet: try: facet_results_values = [] # More calculations... facet_results_values.append( { "value": value, "label": label, "count": count, "toggle_url": self.ds.absolute_url( self.request, toggle_path ), "selected": selected, } ) facet_results.append( { "name": column, "results": facet_results_values, "trunc… | ["Plugin hooks"] | [{"href": "https://github.com/simonw/datasette/issues/830", "label": "issue 830"}, {"href": "https://github.com/simonw/datasette/blob/main/datasette/facets.py", "label": "datasette/facets.py"}] |
plugin_hooks:plugin-register-output-renderer | plugin_hooks | plugin-register-output-renderer | register_output_renderer(datasette) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) Registers a new output renderer, to output data in a custom format. The hook function should return a dictionary, or a list of dictionaries, of the following shape: @hookimpl def register_output_renderer(datasette): return { "extension": "test", "render": render_demo, "can_render": can_render_demo, # Optional } This will register render_demo to be called when paths with the extension .test (for example /database.test , /database/table.test , or /database/table/row.test ) are requested. render_demo is a Python function. It can be a regular function or an async def render_demo() awaitable function, depending on if it needs to make any asynchronous calls. can_render_demo is a Python function (or async def function) which accepts the same arguments as render_demo but just returns True or False . It lets Datasette know if the current SQL query can be represented by the plugin - and hence influnce if a link to this output format is displayed in the user interface. If you omit the "can_render" key from the dictionary every query will be treated as being supported by the plugin. When a request is received, the "render" callback function is called with zero or more of the following arguments. Datasette will inspect your callback function and pass arguments that match its function signature. datasette - Datasette class For accessing plugin configuration and executing queries. columns - list of strings The names of the columns r… | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-atom", "label": "datasette-atom"}, {"href": "https://datasette.io/plugins/datasette-ics", "label": "datasette-ics"}, {"href": "https://datasette.io/plugins/datasette-geojson", "label": "datasette-geojson"}, {"href": "https://datasette.io/plugins/datasette-copyable", "label": "datasette-copyable"}] |
plugin_hooks:plugin-register-routes | plugin_hooks | plugin-register-routes | register_routes(datasette) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) Register additional view functions to execute for specified URL routes. Return a list of (regex, view_function) pairs, something like this: from datasette import hookimpl, Response import html async def hello_from(request): name = request.url_vars["name"] return Response.html( "Hello from {}".format(html.escape(name)) ) @hookimpl def register_routes(): return [(r"^/hello-from/(?P<name>.*)$", hello_from)] The view functions can take a number of different optional arguments. The corresponding argument will be passed to your function depending on its named parameters - a form of dependency injection. The optional view function arguments are as follows: datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. request - Request object The current HTTP request. scope - dictionary The incoming ASGI scope dictionary. send - function The ASGI send function. receive - function The ASGI receive function. The view funct… | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-auth-github", "label": "datasette-auth-github"}, {"href": "https://datasette.io/plugins/datasette-psutil", "label": "datasette-psutil"}] |
performance:http-caching | performance | http-caching | HTTP caching | If your database is immutable and guaranteed not to change, you can gain major performance improvements from Datasette by enabling HTTP caching. This can work at two different levels. First, it can tell browsers to cache the results of queries and serve future requests from the browser cache. More significantly, it allows you to run Datasette behind a caching proxy such as Varnish or use a cache provided by a hosted service such as Fastly or Cloudflare . This can provide incredible speed-ups since a query only needs to be executed by Datasette the first time it is accessed - all subsequent hits can then be served by the cache. Using a caching proxy in this way could enable a Datasette-backed visualization to serve thousands of hits a second while running Datasette itself on extremely inexpensive hosting. Datasette's integration with HTTP caches can be enabled using a combination of configuration options and query string arguments. The default_cache_ttl setting sets the default HTTP cache TTL for all Datasette pages. This is 5 seconds unless you change it - you can set it to 0 if you wish to disable HTTP caching entirely. You can also change the cache timeout on a per-request basis using the ?_ttl=10 query string parameter. This can be useful when you are working with the Datasette JSON API - you may decide that a specific query can be cached for a longer time, or maybe you need to set ?_ttl=0 for some requests for example if you are running a SQL order by random() query. | ["Performance and caching"] | [{"href": "https://varnish-cache.org/", "label": "Varnish"}, {"href": "https://www.fastly.com/", "label": "Fastly"}, {"href": "https://www.cloudflare.com/", "label": "Cloudflare"}] |
performance:performance-hashed-urls | performance | performance-hashed-urls | datasette-hashed-urls | If you open a database file in immutable mode using the -i option, you can be assured that the content of that database will not change for the lifetime of the Datasette server. The datasette-hashed-urls plugin implements an optimization where your database is served with part of the SHA-256 hash of the database contents baked into the URL. A database at /fixtures will instead be served at /fixtures-aa7318b , and a year-long cache expiry header will be returned with those pages. This will then be cached by both browsers and caching proxies such as Cloudflare or Fastly, providing a potentially significant performance boost. To install the plugin, run the following: datasette install datasette-hashed-urls Prior to Datasette 0.61 hashed URL mode was a core Datasette feature, enabled using the hash_urls setting. This implementation has now been removed in favor of the datasette-hashed-urls plugin. Prior to Datasette 0.28 hashed URL mode was the default behaviour for Datasette, since all database files were assumed to be immutable and unchanging. From 0.28 onwards the default has been to treat database files as mutable unless explicitly configured otherwise. | ["Performance and caching"] | [{"href": "https://datasette.io/plugins/datasette-hashed-urls", "label": "datasette-hashed-urls plugin"}] |
performance:performance-immutable-mode | performance | performance-immutable-mode | Immutable mode | If you can be certain that a SQLite database file will not be changed by another process you can tell Datasette to open that file in immutable mode . Doing so will disable all locking and change detection, which can result in improved query performance. This also enables further optimizations relating to HTTP caching, described below. To open a file in immutable mode pass it to the datasette command using the -i option: datasette -i data.db When you open a file in immutable mode like this Datasette will also calculate and cache the row counts for each table in that database when it first starts up, further improving performance. | ["Performance and caching"] | [] |
performance:performance-inspect | performance | performance-inspect | Using "datasette inspect" | Counting the rows in a table can be a very expensive operation on larger databases. In immutable mode Datasette performs this count only once and caches the results, but this can still cause server startup time to increase by several seconds or more. If you know that a database is never going to change you can precalculate the table row counts once and store then in a JSON file, then use that file when you later start the server. To create a JSON file containing the calculated row counts for a database, use the following: datasette inspect data.db --inspect-file=counts.json Then later you can start Datasette against the counts.json file and use it to skip the row counting step and speed up server startup: datasette -i data.db --inspect-file=counts.json You need to use the -i immutable mode against the database file here or the counts from the JSON file will be ignored. You will rarely need to use this optimization in every-day use, but several of the datasette publish commands described in Publishing data use this optimization for better performance when deploying a database file to a hosting provider. | ["Performance and caching"] | [] |
pages:databaseview | pages | databaseview | Database | Each database has a page listing the tables, views and canned queries available for that database. If the execute-sql permission is enabled (it's on by default) there will also be an interface for executing arbitrary SQL select queries against the data. Examples: fivethirtyeight.datasettes.com/fivethirtyeight global-power-plants.datasettes.com/global-power-plants The JSON version of this page provides programmatic access to the underlying data: fivethirtyeight.datasettes.com/fivethirtyeight.json global-power-plants.datasettes.com/global-power-plants.json | ["Pages and API endpoints"] | [{"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight", "label": "fivethirtyeight.datasettes.com/fivethirtyeight"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants", "label": "global-power-plants.datasettes.com/global-power-plants"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json", "label": "fivethirtyeight.datasettes.com/fivethirtyeight.json"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants.json", "label": "global-power-plants.datasettes.com/global-power-plants.json"}] |
pages:indexview | pages | indexview | Top-level index | The root page of any Datasette installation is an index page that lists all of the currently attached databases. Some examples: fivethirtyeight.datasettes.com global-power-plants.datasettes.com register-of-members-interests.datasettes.com Add /.json to the end of the URL for the JSON version of the underlying data: fivethirtyeight.datasettes.com/.json global-power-plants.datasettes.com/.json register-of-members-interests.datasettes.com/.json | ["Pages and API endpoints"] | [{"href": "https://fivethirtyeight.datasettes.com/", "label": "fivethirtyeight.datasettes.com"}, {"href": "https://global-power-plants.datasettes.com/", "label": "global-power-plants.datasettes.com"}, {"href": "https://register-of-members-interests.datasettes.com/", "label": "register-of-members-interests.datasettes.com"}, {"href": "https://fivethirtyeight.datasettes.com/.json", "label": "fivethirtyeight.datasettes.com/.json"}, {"href": "https://global-power-plants.datasettes.com/.json", "label": "global-power-plants.datasettes.com/.json"}, {"href": "https://register-of-members-interests.datasettes.com/.json", "label": "register-of-members-interests.datasettes.com/.json"}] |
pages:rowview | pages | rowview | Row | Every row in every Datasette table has its own URL. This means individual records can be linked to directly. Table cells with extremely long text contents are truncated on the table view according to the truncate_cells_html setting. If a cell has been truncated the full length version of that cell will be available on the row page. Rows which are the targets of foreign key references from other tables will show a link to a filtered search for all records that reference that row. Here's an example from the Registers of Members Interests database: ../people/uk.org.publicwhip%2Fperson%2F10001 Note that this URL includes the encoded primary key of the record. Here's that same page as JSON: ../people/uk.org.publicwhip%2Fperson%2F10001.json | ["Pages and API endpoints"] | [{"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk.org.publicwhip%2Fperson%2F10001", "label": "../people/uk.org.publicwhip%2Fperson%2F10001"}, {"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk.org.publicwhip%2Fperson%2F10001.json", "label": "../people/uk.org.publicwhip%2Fperson%2F10001.json"}] |
pages:tableview | pages | tableview | Table | The table page is the heart of Datasette: it allows users to interactively explore the contents of a database table, including sorting, filtering, Full-text search and applying Facets . The HTML interface is worth spending some time exploring. As with other pages, you can return the JSON data by appending .json to the URL path, before any ? query string arguments. The query string arguments are described in more detail here: Table arguments You can also use the table page to interactively construct a SQL query - by applying different filters and a sort order for example - and then click the "View and edit SQL" link to see the SQL query that was used for the page and edit and re-submit it. Some examples: ../items lists all of the line-items registered by UK MPs as potential conflicts of interest. It demonstrates Datasette's support for Full-text search . ../antiquities-act%2Factions_under_antiquities_act is an interface for exploring the "actions under the antiquities act" data table published by FiveThirtyEight. ../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas is a filtered table page showing every Gas power plant in the United Kingdom. It includes some default facets (configured using its metadata.json ) and uses the datasette-cluster-map plugin to show a map of the results. | ["Pages and API endpoints"] | [{"href": "https://register-of-members-interests.datasettes.com/regmem/items", "label": "../items"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight/antiquities-act%2Factions_under_antiquities_act", "label": "../antiquities-act%2Factions_under_antiquities_act"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants/global-power-plants?_facet=primary_fuel&_facet=owner&_facet=country_long&country_long__exact=United+Kingdom&primary_fuel=Gas", "label": "../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas"}, {"href": "https://global-power-plants.datasettes.com/-/metadata", "label": "its metadata.json"}, {"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}] |
metadata:label-columns | metadata | label-columns | Specifying the label column for a table | Datasette's HTML interface attempts to display foreign key references as labelled hyperlinks. By default, it looks for referenced tables that only have two columns: a primary key column and one other. It assumes that the second column should be used as the link label. If your table has more than two columns you can specify which column should be used for the link label with the label_column property: { "databases": { "database1": { "tables": { "example_table": { "label_column": "title" } } } } } | ["Metadata"] | [] |
metadata:metadata-column-descriptions | metadata | metadata-column-descriptions | Column descriptions | You can include descriptions for your columns by adding a "columns": {"name-of-column": "description-of-column"} block to your table metadata: { "databases": { "database1": { "tables": { "example_table": { "columns": { "column1": "Description of column 1", "column2": "Description of column 2" } } } } } } These will be displayed at the top of the table page, and will also show in the cog menu for each column. You can see an example of how these look at latest.datasette.io/fixtures/roadside_attractions . | ["Metadata"] | [{"href": "https://latest.datasette.io/fixtures/roadside_attractions", "label": "latest.datasette.io/fixtures/roadside_attractions"}] |
metadata:metadata-default-sort | metadata | metadata-default-sort | Setting a default sort order | By default Datasette tables are sorted by primary key. You can over-ride this default for a specific table using the "sort" or "sort_desc" metadata properties: { "databases": { "mydatabase": { "tables": { "example_table": { "sort": "created" } } } } } Or use "sort_desc" to sort in descending order: { "databases": { "mydatabase": { "tables": { "example_table": { "sort_desc": "created" } } } } } | ["Metadata"] | [] |
metadata:metadata-hiding-tables | metadata | metadata-hiding-tables | Hiding tables | You can hide tables from the database listing view (in the same way that FTS and SpatiaLite tables are automatically hidden) using "hidden": true : { "databases": { "database1": { "tables": { "example_table": { "hidden": true } } } } } | ["Metadata"] | [] |
metadata:metadata-page-size | metadata | metadata-page-size | Setting a custom page size | Datasette defaults to displaying 100 rows per page, for both tables and views. You can change this default page size on a per-table or per-view basis using the "size" key in metadata.json : { "databases": { "mydatabase": { "tables": { "example_table": { "size": 10 } } } } } This size can still be over-ridden by passing e.g. ?_size=50 in the query string. | ["Metadata"] | [] |
metadata:metadata-sortable-columns | metadata | metadata-sortable-columns | Setting which columns can be used for sorting | Datasette allows any column to be used for sorting by default. If you need to control which columns are available for sorting you can do so using the optional sortable_columns key: { "databases": { "database1": { "tables": { "example_table": { "sortable_columns": [ "height", "weight" ] } } } } } This will restrict sorting of example_table to just the height and weight columns. You can also disable sorting entirely by setting "sortable_columns": [] You can use sortable_columns to enable specific sort orders for a view called name_of_view in the database my_database like so: { "databases": { "my_database": { "tables": { "name_of_view": { "sortable_columns": [ "clicks", "impressions" ] } } } } } | ["Metadata"] | [] |
metadata:metadata-source-license-about | metadata | metadata-source-license-about | Source, license and about | The three visible metadata fields you can apply to everything, specific databases or specific tables are source, license and about. All three are optional. source and source_url should be used to indicate where the underlying data came from. license and license_url should be used to indicate the license under which the data can be used. about and about_url can be used to link to further information about the project - an accompanying blog entry for example. For each of these you can provide just the *_url field and Datasette will treat that as the default link label text and display the URL directly on the page. | ["Metadata"] | [] |
metadata:metadata-yaml | metadata | metadata-yaml | Using YAML for metadata | Datasette accepts YAML as an alternative to JSON for your metadata configuration file. YAML is particularly useful for including multiline HTML and SQL strings. Here's an example of a metadata.yml file, re-using an example from Canned queries . title: Demonstrating Metadata from YAML description_html: |- <p>This description includes a long HTML string</p> <ul> <li>YAML is better for embedding HTML strings than JSON!</li> </ul> license: ODbL license_url: https://opendatacommons.org/licenses/odbl/ databases: fixtures: tables: no_primary_key: hidden: true queries: neighborhood_search: sql: |- select neighborhood, facet_cities.name, state from facetable join facet_cities on facetable.city_id = facet_cities.id where neighborhood like '%' || :text || '%' order by neighborhood; title: Search neighborhoods description_html: |- <p>This demonstrates <em>basic</em> LIKE search The metadata.yml file is passed to Datasette using the same --metadata option: datasette fixtures.db --metadata metadata.yml | ["Metadata"] | [] |
metadata:per-database-and-per-table-metadata | metadata | per-database-and-per-table-metadata | Per-database and per-table metadata | Metadata at the top level of the JSON will be shown on the index page and in the footer on every page of the site. The license and source is expected to apply to all of your data. You can also provide metadata at the per-database or per-table level, like this: { "databases": { "database1": { "source": "Alternative source", "source_url": "http://example.com/", "tables": { "example_table": { "description_html": "Custom <em>table</em> description", "license": "CC BY 3.0 US", "license_url": "https://creativecommons.org/licenses/by/3.0/us/" } } } } } Each of the top-level metadata fields can be used at the database and table level. | ["Metadata"] | [] |
metadata:specifying-units-for-a-column | metadata | specifying-units-for-a-column | Specifying units for a column | Datasette supports attaching units to a column, which will be used when displaying values from that column. SI prefixes will be used where appropriate. Column units are configured in the metadata like so: { "databases": { "database1": { "tables": { "example_table": { "units": { "column1": "metres", "column2": "Hz" } } } } } } Units are interpreted using Pint , and you can see the full list of available units in Pint's unit registry . You can also add custom units to the metadata, which will be registered with Pint: { "custom_units": [ "decibel = [] = dB" ] } | ["Metadata"] | [{"href": "https://pint.readthedocs.io/", "label": "Pint"}, {"href": "https://github.com/hgrecco/pint/blob/master/pint/default_en.txt", "label": "unit registry"}, {"href": "http://pint.readthedocs.io/en/latest/defining.html", "label": "custom units"}] |
json_api:expand-foreign-keys | json_api | expand-foreign-keys | Expanding foreign key references | Datasette can detect foreign key relationships and resolve those references into labels. The HTML interface does this by default for every detected foreign key column - you can turn that off using ?_labels=off . You can request foreign keys be expanded in JSON using the _labels=on or _label=COLUMN special query string parameters. Here's what an expanded row looks like: [ { "rowid": 1, "TreeID": 141565, "qLegalStatus": { "value": 1, "label": "Permitted Site" }, "qSpecies": { "value": 1, "label": "Myoporum laetum :: Myoporum" }, "qAddress": "501X Baker St", "SiteOrder": 1 } ] The column in the foreign key table that is used for the label can be specified in metadata.json - see Specifying the label column for a table . | ["JSON API"] | [] |
json_api:id2 | json_api | id2 | Table arguments | The Datasette table view takes a number of special query string arguments. | ["JSON API"] | [] |
json_api:json-api-discover-alternate | json_api | json-api-discover-alternate | Discovering the JSON for a page | Most of the HTML pages served by Datasette provide a mechanism for discovering their JSON equivalents using the HTML link mechanism. You can find this near the top of the source code of those pages, looking like this: <link rel="alternate" type="application/json+datasette" href="https://latest.datasette.io/fixtures/sortable.json"> The JSON URL is also made available in a Link HTTP header for the page: Link: https://latest.datasette.io/fixtures/sortable.json; rel="alternate"; type="application/json+datasette" | ["JSON API"] | [] |
json_api:json-api-pagination | json_api | json-api-pagination | Pagination | The default JSON representation includes a "next_url" key which can be used to access the next page of results. If that key is null or missing then it means you have reached the final page of results. Other representations include pagination information in the link HTTP header. That header will look something like this: link: <https://latest.datasette.io/fixtures/sortable.json?_next=d%2Cv>; rel="next" Here is an example Python function built using requests that returns a list of all of the paginated items from one of these API endpoints: def paginate(url): items = [] while url: response = requests.get(url) try: url = response.links.get("next").get("url") except AttributeError: url = None items.extend(response.json()) return items | ["JSON API"] | [{"href": "https://requests.readthedocs.io/", "label": "requests"}] |
json_api:json-api-shapes | json_api | json-api-shapes | Different shapes | The default JSON representation of data from a SQLite table or custom query looks like this: { "database": "sf-trees", "table": "qSpecies", "columns": [ "id", "value" ], "rows": [ [ 1, "Myoporum laetum :: Myoporum" ], [ 2, "Metrosideros excelsa :: New Zealand Xmas Tree" ], [ 3, "Pinus radiata :: Monterey Pine" ] ], "truncated": false, "next": "100", "next_url": "http://127.0.0.1:8001/sf-trees-02c8ef1/qSpecies.json?_next=100", "query_ms": 1.9571781158447266 } The columns key lists the columns that are being returned, and the rows key then returns a list of lists, each one representing a row. The order of the values in each row corresponds to the columns. The _shape parameter can be used to access alternative formats for the rows key which may be more convenient for your application. There are three options: ?_shape=arrays - "rows" is the default option, shown above ?_shape=objects - "rows" is a list of JSON key/value objects ?_shape=array - an JSON array of objects ?_shape=array&_nl=on - a newline-separated list of JSON objects ?_shape=arrayfirst - a flat JSON array containing just the first value from each row ?_shape=object - a JSON object keyed using the primary keys of the rows _shape=objects looks like this: { "database": "sf-trees", ... "rows": [ { "id": 1, … | ["JSON API"] | [] |
json_api:json-api-special | json_api | json-api-special | Special JSON arguments | Every Datasette endpoint that can return JSON also accepts the following query string arguments: ?_shape=SHAPE The shape of the JSON to return, documented above. ?_nl=on When used with ?_shape=array produces newline-delimited JSON objects. ?_json=COLUMN1&_json=COLUMN2 If any of your SQLite columns contain JSON values, you can use one or more _json= parameters to request that those columns be returned as regular JSON. Without this argument those columns will be returned as JSON objects that have been double-encoded into a JSON string value. Compare this query without the argument to this query using the argument ?_json_infinity=on If your data contains infinity or -infinity values, Datasette will replace them with None when returning them as JSON. If you pass _json_infinity=1 Datasette will instead return them as Infinity or -Infinity which is invalid JSON but can be processed by some custom JSON parsers. ?_timelimit=MS Sets a custom time limit for the query in ms. You can use this for optimistic queries where you would like Datasette to give up if the query takes too long, for example if you want to implement autocomplete search but only… | ["JSON API"] | [{"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array", "label": "this query without the argument"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array&_json=d", "label": "this query using the argument"}] |
json_api:column-filter-arguments | json_api | column-filter-arguments | Column filter arguments | You can filter the data returned by the table based on column values using a query string argument. ?column__exact=value or ?_column=value Returns rows where the specified column exactly matches the value. ?column__not=value Returns rows where the column does not match the value. ?column__contains=value Rows where the string column contains the specified value ( column like "%value%" in SQL). ?column__endswith=value Rows where the string column ends with the specified value ( column like "%value" in SQL). ?column__startswith=value Rows where the string column starts with the specified value ( column like "value%" in SQL). ?column__gt=value Rows which are greater than the specified value. ?column__gte=value Rows which are greater than or equal to the specified value. ?column__lt=value Rows which are less than the specified value. … | ["JSON API", "Table arguments"] | [] |
json_api:json-api-table-arguments | json_api | json-api-table-arguments | Special table arguments | ?_col=COLUMN1&_col=COLUMN2 List specific columns to display. These will be shown along with any primary keys. ?_nocol=COLUMN1&_nocol=COLUMN2 List specific columns to hide - any column not listed will be displayed. Primary keys cannot be hidden. ?_labels=on/off Expand foreign key references for every possible column. See below. ?_label=COLUMN1&_label=COLUMN2 Expand foreign key references for one or more specified columns. ?_size=1000 or ?_size=max Sets a custom page size. This cannot exceed the max_returned_rows limit passed to datasette serve . Use max to get max_returned_rows . ?_sort=COLUMN Sorts the results by the specified column. ?_sort_desc=COLUMN Sorts the results by the specified column in descending order. ?_search=keywords For SQLite tables that have been configured for full-text search executes a search … | ["JSON API", "Table arguments"] | [{"href": "https://www.sqlite.org/fts3.html", "label": "full-text search"}, {"href": "https://www.sqlite.org/fts5.html#full_text_query_syntax", "label": "advanced SQLite FTS syntax"}, {"href": "https://latest.datasette.io/fixtures/facetable?_where=_neighborhood%20like%20%22%c%%22&_where=_city_id=3", "label": "facetable?_where=_neighborhood like \"%c%\"&_where=_city_id=3"}, {"href": "https://latest.datasette.io/fixtures/facetable?_where=_city_id%20in%20(select%20id%20from%20facet_cities%20where%20name%20!=%20%22Detroit%22)", "label": "facetable?_where=_city_id in (select id from facet_cities where name != \"Detroit\")"}, {"href": "https://latest.datasette.io/fixtures/roadside_attractions?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22}", "label": "an example"}] |
introspection:jsondataview-actor | introspection | jsondataview-actor | /-/actor | Shows the currently authenticated actor. Useful for debugging Datasette authentication plugins. { "actor": { "id": 1, "username": "some-user" } } | ["Introspection"] | [] |
introspection:jsondataview-config | introspection | jsondataview-config | /-/settings | Shows the Settings for this instance of Datasette. Settings example : { "default_facet_size": 30, "default_page_size": 100, "facet_suggest_time_limit_ms": 50, "facet_time_limit_ms": 1000, "max_returned_rows": 1000, "sql_time_limit_ms": 1000 } | ["Introspection"] | [{"href": "https://fivethirtyeight.datasettes.com/-/settings", "label": "Settings example"}] |
introspection:jsondataview-databases | introspection | jsondataview-databases | /-/databases | Shows currently attached databases. Databases example : [ { "hash": null, "is_memory": false, "is_mutable": true, "name": "fixtures", "path": "fixtures.db", "size": 225280 } ] | ["Introspection"] | [{"href": "https://latest.datasette.io/-/databases", "label": "Databases example"}] |
introspection:jsondataview-metadata | introspection | jsondataview-metadata | /-/metadata | Shows the contents of the metadata.json file that was passed to datasette serve , if any. Metadata example : { "license": "CC Attribution 4.0 License", "license_url": "http://creativecommons.org/licenses/by/4.0/", "source": "fivethirtyeight/data on GitHub", "source_url": "https://github.com/fivethirtyeight/data", "title": "Five Thirty Eight", "databases": { } } | ["Introspection"] | [{"href": "https://fivethirtyeight.datasettes.com/-/metadata", "label": "Metadata example"}] |
introspection:jsondataview-plugins | introspection | jsondataview-plugins | /-/plugins | Shows a list of currently installed plugins and their versions. Plugins example : [ { "name": "datasette_cluster_map", "static": true, "templates": false, "version": "0.10", "hooks": ["extra_css_urls", "extra_js_urls", "extra_body_script"] } ] Add ?all=1 to include details of the default plugins baked into Datasette. | ["Introspection"] | [{"href": "https://san-francisco.datasettes.com/-/plugins", "label": "Plugins example"}] |
introspection:jsondataview-threads | introspection | jsondataview-threads | /-/threads | Shows details of threads and asyncio tasks. Threads example : { "num_threads": 2, "threads": [ { "daemon": false, "ident": 4759197120, "name": "MainThread" }, { "daemon": true, "ident": 123145319682048, "name": "Thread-1" }, ], "num_tasks": 3, "tasks": [ "<Task pending coro=<RequestResponseCycle.run_asgi() running at uvicorn/protocols/http/httptools_impl.py:385> cb=[set.discard()]>", "<Task pending coro=<Server.serve() running at uvicorn/main.py:361> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x10365c3d0>()]> cb=[run_until_complete.<locals>.<lambda>()]>", "<Task pending coro=<LifespanOn.main() running at uvicorn/lifespan/on.py:48> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x10364f050>()]>>" ] } | ["Introspection"] | [{"href": "https://latest.datasette.io/-/threads", "label": "Threads example"}] |
introspection:jsondataview-versions | introspection | jsondataview-versions | /-/versions | Shows the version of Datasette, Python and SQLite. Versions example : { "datasette": { "version": "0.60" }, "python": { "full": "3.8.12 (default, Dec 21 2021, 10:45:09) \n[GCC 10.2.1 20210110]", "version": "3.8.12" }, "sqlite": { "extensions": { "json1": null }, "fts_versions": [ "FTS5", "FTS4", "FTS3" ], "compile_options": [ "COMPILER=gcc-6.3.0 20170516", "ENABLE_FTS3", "ENABLE_FTS4", "ENABLE_FTS5", "ENABLE_JSON1", "ENABLE_RTREE", "THREADSAFE=1" ], "version": "3.37.0" } } | ["Introspection"] | [{"href": "https://latest.datasette.io/-/versions", "label": "Versions example"}] |
introspection:messagesdebugview | introspection | messagesdebugview | /-/messages | The debug tool at /-/messages can be used to set flash messages to try out that feature. See .add_message(request, message, type=datasette.INFO) for details of this feature. | ["Introspection"] | [] |
internals:internals-csrf | internals | internals-csrf | CSRF protection | Datasette uses asgi-csrf to guard against CSRF attacks on form POST submissions. Users receive a ds_csrftoken cookie which is compared against the csrftoken form field (or x-csrftoken HTTP header) for every incoming request. If your plugin implements a <form method="POST"> anywhere you will need to include that token. You can do so with the following template snippet: <input type="hidden" name="csrftoken" value="{{ csrftoken() }}"> If you are rendering templates using the await .render_template(template, context=None, request=None) method the csrftoken() helper will only work if you provide the request= argument to that method. If you forget to do this you will see the following error: form-urlencoded POST field did not match cookie You can selectively disable CSRF protection using the skip_csrf(datasette, scope) hook. | ["Internals for plugins"] | [{"href": "https://github.com/simonw/asgi-csrf", "label": "asgi-csrf"}] |
internals:internals-database | internals | internals-database | Database class | Instances of the Database class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas. | ["Internals for plugins"] | [] |
internals:internals-datasette | internals | internals-datasette | Datasette class | This object is an instance of the Datasette class, passed to many plugin hooks as an argument called datasette . You can create your own instance of this - for example to help write tests for a plugin - like so: from datasette.app import Datasette # With no arguments a single in-memory database will be attached datasette = Datasette() # The files= argument can load files from disk datasette = Datasette(files=["/path/to/my-database.db"]) # Pass metadata as a JSON dictionary like this datasette = Datasette( files=["/path/to/my-database.db"], metadata={ "databases": { "my-database": { "description": "This is my database" } } }, ) Constructor parameters include: files=[...] - a list of database files to open immutables=[...] - a list of database files to open in immutable mode metadata={...} - a dictionary of Metadata config_dir=... - the configuration directory to use, stored in datasette.config_dir | ["Internals for plugins"] | [] |
internals:internals-internal | internals | internals-internal | The _internal database | This API should be considered unstable - the structure of these tables may change prior to the release of Datasette 1.0. Datasette maintains an in-memory SQLite database with details of the the databases, tables and columns for all of the attached databases. By default all actors are denied access to the view-database permission for the _internal database, so the database is not visible to anyone unless they sign in as root . Plugins can access this database by calling db = datasette.get_database("_internal") and then executing queries using the Database API . You can explore an example of this database by signing in as root to the latest.datasette.io demo instance and then navigating to latest.datasette.io/_internal . | ["Internals for plugins"] | [{"href": "https://latest.datasette.io/login-as-root", "label": "signing in as root"}, {"href": "https://latest.datasette.io/_internal", "label": "latest.datasette.io/_internal"}] |
internals:internals-multiparams | internals | internals-multiparams | The MultiParams class | request.args is a MultiParams object - a dictionary-like object which provides access to query string parameters that may have multiple values. Consider the query string ?foo=1&foo=2&bar=3 - with two values for foo and one value for bar . request.args[key] - string Returns the first value for that key, or raises a KeyError if the key is missing. For the above example request.args["foo"] would return "1" . request.args.get(key) - string or None Returns the first value for that key, or None if the key is missing. Pass a second argument to specify a different default, e.g. q = request.args.get("q", "") . request.args.getlist(key) - list of strings Returns the list of strings for that key. request.args.getlist("foo") would return ["1", "2"] in the above example. request.args.getlist("bar") would return ["3"] . If the key is missing an empty list will be returned. request.args.keys() - list of strings Returns the list of available keys - for the example this would be ["foo", "bar"] . key in request.args - True or False You can use if key in request.args to check if a key is present. for key in request.args - iterator This lets you loop through every available key. le… | ["Internals for plugins"] | [] |
internals:internals-request | internals | internals-request | Request object | The request object is passed to various plugin hooks. It represents an incoming HTTP request. It has the following properties: .scope - dictionary The ASGI scope that was used to construct this request, described in the ASGI HTTP connection scope specification. .method - string The HTTP method for this request, usually GET or POST . .url - string The full URL for this request, e.g. https://latest.datasette.io/fixtures . .scheme - string The request scheme - usually https or http . .headers - dictionary (str -> str) A dictionary of incoming HTTP request headers. Header names have been converted to lowercase. .cookies - dictionary (str -> str) A dictionary of incoming cookies .host - string The host header from the incoming request, e.g. latest.datasette.io or localhost . .path - string The path of the request excluding the query string, e.g. /fixtures . .full_path - string The path of the… | ["Internals for plugins"] | [{"href": "https://asgi.readthedocs.io/en/latest/specs/www.html#connection-scope", "label": "ASGI HTTP connection scope"}] |
internals:internals-response | internals | internals-response | Response class | The Response class can be returned from view functions that have been registered using the register_routes(datasette) hook. The Response() constructor takes the following arguments: body - string The body of the response. status - integer (optional) The HTTP status - defaults to 200. headers - dictionary (optional) A dictionary of extra HTTP headers, e.g. {"x-hello": "world"} . content_type - string (optional) The content-type for the response. Defaults to text/plain . For example: from datasette.utils.asgi import Response response = Response( "<xml>This is XML</xml>", content_type="application/xml; charset=utf-8", ) The quickest way to create responses is using the Response.text(...) , Response.html(...) , Response.json(...) or Response.redirect(...) helper methods: from datasette.utils.asgi import Response html_response = Response.html("This is HTML") json_response = Response.json({"this_is": "json"}) text_response = Response.text( "This will become utf-8 encoded text" ) # Redirects are served as 302, unless you pass status=301: redirect_response = Response.redirect( "https://latest.datasette.io/" ) Each of these responses will use the correct corresponding content-type - text/html; charset=utf-8 , application/json; charset=utf-8 or text/plain; charset=utf-8 respectively. Each of the helper methods take optional status= and headers= argument… | ["Internals for plugins"] | [] |
internals:internals-shortcuts | internals | internals-shortcuts | Import shortcuts | The following commonly used symbols can be imported directly from the datasette module: from datasette import Response from datasette import Forbidden from datasette import NotFound from datasette import hookimpl from datasette import actor_matches_allow | ["Internals for plugins"] | [] |
internals:internals-tracer | internals | internals-tracer | datasette.tracer | Running Datasette with --setting trace_debug 1 enables trace debug output, which can then be viewed by adding ?_trace=1 to the query string for any page. You can see an example of this at the bottom of latest.datasette.io/fixtures/facetable?_trace=1 . The JSON output shows full details of every SQL query that was executed to generate the page. The datasette-pretty-traces plugin can be installed to provide a more readable display of this information. You can see a demo of that here . You can add your own custom traces to the JSON output using the trace() context manager. This takes a string that identifies the type of trace being recorded, and records any keyword arguments as additional JSON keys on the resulting trace object. The start and end time, duration and a traceback of where the trace was executed will be automatically attached to the JSON object. This example uses trace to record the start, end and duration of any HTTP GET requests made using the function: from datasette.tracer import trace import httpx async def fetch_url(url): with trace("fetch-url", url=url): async with httpx.AsyncClient() as client: return await client.get(url) | ["Internals for plugins"] | [{"href": "https://latest.datasette.io/fixtures/facetable?_trace=1", "label": "latest.datasette.io/fixtures/facetable?_trace=1"}, {"href": "https://datasette.io/plugins/datasette-pretty-traces", "label": "datasette-pretty-traces"}, {"href": "https://latest-with-plugins.datasette.io/github/commits?_trace=1", "label": "a demo of that here"}] |
internals:internals-utils | internals | internals-utils | The datasette.utils module | The datasette.utils module contains various utility functions used by Datasette. As a general rule you should consider anything in this module to be unstable - functions and classes here could change without warning or be removed entirely between Datasette releases, without being mentioned in the release notes. The exception to this rule is anythang that is documented here. If you find a need for an undocumented utility function in your own work, consider opening an issue requesting that the function you are using be upgraded to documented and supported status. | ["Internals for plugins"] | [{"href": "https://github.com/simonw/datasette/issues/new", "label": "opening an issue"}] |
internals:internals-tracer-trace-child-tasks | internals | internals-tracer-trace-child-tasks | Tracing child tasks | If your code uses a mechanism such as asyncio.gather() to execute code in additional tasks you may find that some of the traces are missing from the display. You can use the trace_child_tasks() context manager to ensure these child tasks are correctly handled. from datasette import tracer with tracer.trace_child_tasks(): results = await asyncio.gather( # ... async tasks here ) This example uses the register_routes() plugin hook to add a page at /parallel-queries which executes two SQL queries in parallel using asyncio.gather() and returns their results. from datasette import hookimpl from datasette import tracer @hookimpl def register_routes(): async def parallel_queries(datasette): db = datasette.get_database() with tracer.trace_child_tasks(): one, two = await asyncio.gather( db.execute("select 1"), db.execute("select 2"), ) return Response.json( { "one": one.single_value(), "two": two.single_value(), } ) return [ (r"/parallel-queries$", parallel_queries), ] Adding ?_trace=1 will show that the trace covers both of those child tasks. | ["Internals for plugins", "datasette.tracer"] | [] |
internals:internals-tilde-encoding | internals | internals-tilde-encoding | Tilde encoding | Datasette uses a custom encoding scheme in some places, called tilde encoding . This is primarily used for table names and row primary keys, to avoid any confusion between / characters in those values and the Datasette URLs that reference them. Tilde encoding uses the same algorithm as URL percent-encoding , but with the ~ tilde character used in place of % . Any character other than ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz0123456789_- will be replaced by the numeric equivalent preceded by a tilde. For example: / becomes ~2F . becomes ~2E % becomes ~25 ~ becomes ~7E Space becomes + polls/2022.primary becomes polls~2F2022~2Eprimary Note that the space character is a special case: it will be replaced with a + symbol. datasette.utils. tilde_encode s : str str Returns tilde-encoded string - for example /foo/bar -> ~2Ffoo~2Fbar datasette.utils. tilde_decode s : str str Decodes a tilde-encoded string, so ~2Ffoo~2Fbar -> /foo/bar | ["Internals for plugins", "The datasette.utils module"] | [{"href": "https://developer.mozilla.org/en-US/docs/Glossary/percent-encoding", "label": "URL percent-encoding"}] |
internals:internals-utils-await-me-maybe | internals | internals-utils-await-me-maybe | await_me_maybe(value) | Utility function for calling await on a return value if it is awaitable, otherwise returning the value. This is used by Datasette to support plugin hooks that can optionally return awaitable functions. Read more about this function in The “await me maybe” pattern for Python asyncio . async datasette.utils. await_me_maybe value : Any Any If value is callable, call it. If awaitable, await it. Otherwise return it. | ["Internals for plugins", "The datasette.utils module"] | [{"href": "https://simonwillison.net/2020/Sep/2/await-me-maybe/", "label": "The “await me maybe” pattern for Python asyncio"}] |
internals:internals-utils-parse-metadata | internals | internals-utils-parse-metadata | parse_metadata(content) | This function accepts a string containing either JSON or YAML, expected to be of the format described in Metadata . It returns a nested Python dictionary representing the parsed data from that string. If the metadata cannot be parsed as either JSON or YAML the function will raise a utils.BadMetadataError exception. datasette.utils. parse_metadata content : str dict Detects if content is JSON or YAML and parses it appropriately. | ["Internals for plugins", "The datasette.utils module"] | [] |
internals:internals-response-asgi-send | internals | internals-response-asgi-send | Returning a response with .asgi_send(send) | In most cases you will return Response objects from your own view functions. You can also use a Response instance to respond at a lower level via ASGI, for example if you are writing code that uses the asgi_wrapper(datasette) hook. Create a Response object and then use await response.asgi_send(send) , passing the ASGI send function. For example: async def require_authorization(scope, receive, send): response = Response.text( "401 Authorization Required", headers={ "www-authenticate": 'Basic realm="Datasette", charset="UTF-8"' }, status=401, ) await response.asgi_send(send) | ["Internals for plugins", "Response class"] | [] |
internals:internals-response-set-cookie | internals | internals-response-set-cookie | Setting cookies with response.set_cookie() | To set cookies on the response, use the response.set_cookie(...) method. The method signature looks like this: def set_cookie( self, key, value="", max_age=None, expires=None, path="/", domain=None, secure=False, httponly=False, samesite="lax", ): ... You can use this with datasette.sign() to set signed cookies. Here's how you would set the ds_actor cookie for use with Datasette authentication : response = Response.redirect("/") response.set_cookie( "ds_actor", datasette.sign({"a": {"id": "cleopaws"}}, "actor"), ) return response | ["Internals for plugins", "Response class"] | [] |
internals:datasette-absolute-url | internals | datasette-absolute-url | .absolute_url(request, path) | request - Request The current Request object path - string A path, for example /dbname/table.json Returns the absolute URL for the given path, including the protocol and host. For example: absolute_url = datasette.absolute_url( request, "/dbname/table.json" ) # Would return "http://localhost:8001/dbname/table.json" The current request object is used to determine the hostname and protocol that should be used for the returned URL. The force_https_urls configuration setting is taken into account. | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-add-database | internals | datasette-add-database | .add_database(db, name=None, route=None) | db - datasette.database.Database instance The database to be attached. name - string, optional The name to be used for this database . If not specified Datasette will pick one based on the filename or memory name. route - string, optional This will be used in the URL path. If not specified, it will default to the same thing as the name . The datasette.add_database(db) method lets you add a new database to the current Datasette instance. The db parameter should be an instance of the datasette.database.Database class. For example: from datasette.database import Database datasette.add_database( Database( datasette, path="path/to/my-new-database.db", ) ) This will add a mutable database and serve it at /my-new-database . Use is_mutable=False to add an immutable database. .add_database() returns the Database instance, with its name set as the database.name attribute. Any time you are working with a newly added database you should use the return value of .add_database() , for example: db = datasette.add_database( Database(datasette, memory_name="statistics") ) await db.execute_write( "CREATE TABLE foo(id integer primary key)" ) | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-add-memory-database | internals | datasette-add-memory-database | .add_memory_database(name) | Adds a shared in-memory database with the specified name: datasette.add_memory_database("statistics") This is a shortcut for the following: from datasette.database import Database datasette.add_database( Database(datasette, memory_name="statistics") ) Using either of these pattern will result in the in-memory database being served at /statistics . | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-add-message | internals | datasette-add-message | .add_message(request, message, type=datasette.INFO) | request - Request The current Request object message - string The message string type - constant, optional The message type - datasette.INFO , datasette.WARNING or datasette.ERROR Datasette's flash messaging mechanism allows you to add a message that will be displayed to the user on the next page that they visit. Messages are persisted in a ds_messages cookie. This method adds a message to that cookie. You can try out these messages (including the different visual styling of the three message types) using the /-/messages debugging tool. | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-check-visibility | internals | datasette-check-visibility | await .check_visibility(actor, action=None, resource=None, permissions=None) | actor - dictionary The authenticated actor. This is usually request.actor . action - string, optional The name of the action that is being permission checked. resource - string or tuple, optional The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. permissions - list of action strings or (action, resource) tuples, optional Provide this instead of action and resource to check multiple permissions at once. This convenience method can be used to answer the question "should this item be considered private, in that it is visible to me but it is not visible to anonymous users?" It returns a tuple of two booleans, (visible, private) . visible indicates if the actor can see this resource. private will be True if an anonymous user would not be able to view the resource. This example checks if the user can access a specific table, and sets private so that a padlock icon can later be displayed: visible, private = await self.ds.check_visibility( request.actor, action="view-table", resource=(database, table), ) The following example runs three checks in a row, similar to await .ensure_permissions(actor, permissions) . If any of the checks are denied before one of them is explicitly granted then visible will be F… | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-databases | internals | datasette-databases | .databases | Property exposing a collections.OrderedDict of databases currently connected to Datasette. The dictionary keys are the name of the database that is used in the URL - e.g. /fixtures would have a key of "fixtures" . The values are Database class instances. All databases are listed, irrespective of user permissions. This means that the _internal database will always be listed here. | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-ensure-permissions | internals | datasette-ensure-permissions | await .ensure_permissions(actor, permissions) | actor - dictionary The authenticated actor. This is usually request.actor . permissions - list A list of permissions to check. Each permission in that list can be a string action name or a 2-tuple of (action, resource) . This method allows multiple permissions to be checked at once. It raises a datasette.Forbidden exception if any of the checks are denied before one of them is explicitly granted. This is useful when you need to check multiple permissions at once. For example, an actor should be able to view a table if either one of the following checks returns True or not a single one of them returns False : await self.ds.ensure_permissions( request.actor, [ ("view-table", (database, table)), ("view-database", database), "view-instance", ], ) | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-get-database | internals | datasette-get-database | .get_database(name) | name - string, optional The name of the database - optional. Returns the specified database object. Raises a KeyError if the database does not exist. Call this method without an argument to return the first connected database. | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-permission-allowed | internals | datasette-permission-allowed | await .permission_allowed(actor, action, resource=None, default=False) | actor - dictionary The authenticated actor. This is usually request.actor . action - string The name of the action that is being permission checked. resource - string or tuple, optional The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. default - optional, True or False Should this permission check be default allow or default deny. Check if the given actor has permission to perform the given action on the given resource. Some permission checks are carried out against rules defined in metadata.json , while other custom permissions may be decided by plugins that implement the permission_allowed(datasette, actor, action, resource) plugin hook. If neither metadata.json nor any of the plugins provide an answer to the permission query the default argument will be returned. See Built-in permissions for a full list of permission actions included in Datasette core. | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-plugin-config | internals | datasette-plugin-config | .plugin_config(plugin_name, database=None, table=None) | plugin_name - string The name of the plugin to look up configuration for. Usually this is something similar to datasette-cluster-map . database - None or string The database the user is interacting with. table - None or string The table the user is interacting with. This method lets you read plugin configuration values that were set in metadata.json . See Writing plugins that accept configuration for full details of how this method should be used. The return value will be the value from the configuration file - usually a dictionary. If the plugin is not configured the return value will be None . | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-remove-database | internals | datasette-remove-database | .remove_database(name) | name - string The name of the database to be removed. This removes a database that has been previously added. name= is the unique name of that database. | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-render-template | internals | datasette-render-template | await .render_template(template, context=None, request=None) | template - string, list of strings or jinja2.Template The template file to be rendered, e.g. my_plugin.html . Datasette will search for this file first in the --template-dir= location, if it was specified - then in the plugin's bundled templates and finally in Datasette's set of default templates. If this is a list of template file names then the first one that exists will be loaded and rendered. If this is a Jinja Template object it will be used directly. context - None or a Python dictionary The context variables to pass to the template. request - request object or None If you pass a Datasette request object here it will be made available to the template. Renders a Jinja template using Datasette's preconfigured instance of Jinja and returns the resulting string. The template will have access to Datasette's default template functions and any functions that have been made available by other plugins. | ["Internals for plugins", "Datasette class"] | [{"href": "https://jinja.palletsprojects.com/en/2.11.x/api/#jinja2.Template", "label": "Template object"}, {"href": "https://jinja.palletsprojects.com/en/2.11.x/", "label": "Jinja template"}] |
internals:datasette-setting | internals | datasette-setting | .setting(key) | key - string The name of the setting, e.g. base_url . Returns the configured value for the specified setting . This can be a string, boolean or integer depending on the requested setting. For example: downloads_are_allowed = datasette.setting("allow_download") | ["Internals for plugins", "Datasette class"] | [] |
internals:datasette-sign | internals | datasette-sign | .sign(value, namespace="default") | value - any serializable type The value to be signed. namespace - string, optional An alternative namespace, see the itsdangerous salt documentation . Utility method for signing values, such that you can safely pass data to and from an untrusted environment. This is a wrapper around the itsdangerous library. This method returns a signed string, which can be decoded and verified using .unsign(value, namespace="default") . | ["Internals for plugins", "Datasette class"] | [{"href": "https://itsdangerous.palletsprojects.com/en/1.1.x/serializer/#the-salt", "label": "itsdangerous salt documentation"}, {"href": "https://itsdangerous.palletsprojects.com/", "label": "itsdangerous"}] |
internals:datasette-unsign | internals | datasette-unsign | .unsign(value, namespace="default") | signed - any serializable type The signed string that was created using .sign(value, namespace="default") . namespace - string, optional The alternative namespace, if one was used. Returns the original, decoded object that was passed to .sign(value, namespace="default") . If the signature is not valid this raises a itsdangerous.BadSignature exception. | ["Internals for plugins", "Datasette class"] | [] |
internals:internals-datasette-client | internals | internals-datasette-client | datasette.client | Plugins can make internal simulated HTTP requests to the Datasette instance within which they are running. This ensures that all of Datasette's external JSON APIs are also available to plugins, while avoiding the overhead of making an external HTTP call to access those APIs. The datasette.client object is a wrapper around the HTTPX Python library , providing an async-friendly API that is similar to the widely used Requests library . It offers the following methods: await datasette.client.get(path, **kwargs) - returns HTTPX Response Execute an internal GET request against that path. await datasette.client.post(path, **kwargs) - returns HTTPX Response Execute an internal POST request. Use data={"name": "value"} to pass form parameters. await datasette.client.options(path, **kwargs) - returns HTTPX Response Execute an internal OPTIONS request. await datasette.client.head(path, **kwargs) - returns HTTPX Response Execute an internal HEAD request. await datasette.client.put(path, **kwargs) - returns HTTPX Response Execute an internal PUT request. await datasette.client.patch(path, **kwargs) - returns HTTPX Response … | ["Internals for plugins", "Datasette class"] | [{"href": "https://www.python-httpx.org/", "label": "HTTPX Python library"}, {"href": "https://requests.readthedocs.io/", "label": "Requests library"}, {"href": "https://www.python-httpx.org/async/", "label": "HTTPX Async documentation"}] |
internals:internals-datasette-urls | internals | internals-datasette-urls | datasette.urls | The datasette.urls object contains methods for building URLs to pages within Datasette. Plugins should use this to link to pages, since these methods take into account any base_url configuration setting that might be in effect. datasette.urls.instance(format=None) Returns the URL to the Datasette instance root page. This is usually "/" . datasette.urls.path(path, format=None) Takes a path and returns the full path, taking base_url into account. For example, datasette.urls.path("-/logout") will return the path to the logout page, which will be "/-/logout" by default or /prefix-path/-/logout if base_url is set to /prefix-path/ datasette.urls.logout() Returns the URL to the logout page, usually "/-/logout" datasette.urls.static(path) Returns the URL of one of Datasette's default static assets, for example "/-/static/app.css" datasette.urls.static_plugins(plugin_name, path) Returns the URL of one of the static assets belonging to a plugin. datasette.urls.static_plugins("datasette_cluster_map", "datasette-cluster-map.js") would return "/-/static-plugins/datasette_cluster_map/datasette-cluster-map.js" datasette.urls.static(path) … | ["Internals for plugins", "Datasette class"] | [] |
internals:database-close | internals | database-close | db.close() | Closes all of the open connections to file-backed databases. This is mainly intended to be used by large test suites, to avoid hitting limits on the number of open files. | ["Internals for plugins", "Database class"] | [] |
internals:database-constructor | internals | database-constructor | Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None) | The Database() constructor can be used by plugins, in conjunction with .add_database(db, name=None, route=None) , to create and register new databases. The arguments are as follows: ds - Datasette class (required) The Datasette instance you are attaching this database to. path - string Path to a SQLite database file on disk. is_mutable - boolean Set this to False to cause Datasette to open the file in immutable mode. is_memory - boolean Use this to create non-shared memory connections. memory_name - string or None Use this to create a named in-memory database. Unlike regular memory databases these can be accessed by multiple threads and will persist an changes made to them for the lifetime of the Datasette server process. The first argument is the datasette instance you are attaching to, the second is a path= , then is_mutable and is_memory are both optional arguments. | ["Internals for plugins", "Database class"] | [] |
internals:database-execute | internals | database-execute | await db.execute(sql, ...) | Executes a SQL query against the database and returns the resulting rows (see Results ). sql - string (required) The SQL query to execute. This can include ? or :named parameters. params - list or dict A list or dictionary of values to use for the parameters. List for ? , dictionary for :named . truncate - boolean Should the rows returned by the query be truncated at the maximum page size? Defaults to True , set this to False to disable truncation. custom_time_limit - integer ms A custom time limit for this query. This can be set to a lower value than the Datasette configured default. If a query takes longer than this it will be terminated early and raise a dataette.database.QueryInterrupted exception. page_size - integer Set a custom page size for truncation, over-riding the configured Datasette default. log_sql_errors - boolean Should any SQL errors be logged to the console in addition to being raised as an error? Defaults to True . | ["Internals for plugins", "Database class"] | [] |
internals:database-execute-fn | internals | database-execute-fn | await db.execute_fn(fn) | Executes a given callback function against a read-only database connection running in a thread. The function will be passed a SQLite connection, and the return value from the function will be returned by the await . Example usage: def get_version(conn): return conn.execute( "select sqlite_version()" ).fetchall()[0][0] version = await db.execute_fn(get_version) | ["Internals for plugins", "Database class"] | [] |
internals:database-execute-write | internals | database-execute-write | await db.execute_write(sql, params=None, block=True) | SQLite only allows one database connection to write at a time. Datasette handles this for you by maintaining a queue of writes to be executed against a given database. Plugins can submit write operations to this queue and they will be executed in the order in which they are received. This method can be used to queue up a non-SELECT SQL query to be executed against a single write connection to the database. You can pass additional SQL parameters as a tuple or dictionary. The method will block until the operation is completed, and the return value will be the return from calling conn.execute(...) using the underlying sqlite3 Python library. If you pass block=False this behaviour changes to "fire and forget" - queries will be added to the write queue and executed in a separate thread while your code can continue to do other things. The method will return a UUID representing the queued task. | ["Internals for plugins", "Database class"] | [] |
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