id,page,ref,title,content,breadcrumbs,references pages:pages,pages,pages,Pages and API endpoints,"The Datasette web application offers a number of different pages that can be accessed to explore the data in question, each of which is accompanied by an equivalent JSON API.",[],[] pages:databaseview-hidden,pages,databaseview-hidden,Hidden tables,"Some tables listed on the database page are treated as hidden. Hidden tables are not completely invisible - they can be accessed through the ""hidden tables"" link at the bottom of the page. They are hidden because they represent low-level implementation details which are generally not useful to end-users of Datasette. The following tables are hidden by default: Any table with a name that starts with an underscore - this is a Datasette convention to help plugins easily hide their own internal tables. Tables that have been configured as ""hidden"": true using Hiding tables . *_fts tables that implement SQLite full-text search indexes. Tables relating to the inner workings of the SpatiaLite SQLite extension. sqlite_stat tables used to store statistics used by the query optimizer.","[""Pages and API endpoints"", ""Database""]",[] pages:queryview,pages,queryview,Queries,"The /database-name/-/query page can be used to execute an arbitrary SQL query against that database, if the execute-sql permission is enabled. This query is passed as the ?sql= query string parameter. This means you can link directly to a query by constructing the following URL: /database-name/-/query?sql=SELECT+*+FROM+table_name Each configured canned query has its own page, at /database-name/query-name . Viewing this page will execute the query and display the results. In both cases adding a .json extension to the URL will return the results as JSON.","[""Pages and API endpoints""]",[] contributing:id1,contributing,id1,Contributing,"Datasette is an open source project. We welcome contributions! This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new Plugins .",[],[] contributing:general-guidelines,contributing,general-guidelines,General guidelines,"main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released. The ideal commit should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue. New plugin hooks should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.","[""Contributing""]",[] contributing:contributing-using-fixtures,contributing,contributing-using-fixtures,Using fixtures,"To run Datasette itself, type datasette . You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests. You can create a copy of that database by running this command: python tests/fixtures.py fixtures.db Now you can run Datasette against the new fixtures database like so: datasette fixtures.db This will start a server at http://127.0.0.1:8001/ . Any changes you make in the datasette/templates or datasette/static folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript). If you want to change Datasette's Python code you can use the --reload option to cause Datasette to automatically reload any time the underlying code changes: datasette --reload fixtures.db You can also use the fixtures.py script to recreate the testing version of metadata.json used by the unit tests. To do that: python tests/fixtures.py fixtures.db fixtures-metadata.json Or to output the plugins used by the tests, run this: python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins Test tables written to fixtures.db - metadata written to fixtures-metadata.json Wrote plugin: fixtures-plugins/register_output_renderer.py Wrote plugin: fixtures-plugins/view_name.py Wrote plugin: fixtures-plugins/my_plugin.py Wrote plugin: fixtures-plugins/messages_output_renderer.py Wrote plugin: fixtures-plugins/my_plugin_2.py Then run Datasette like this: datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/","[""Contributing""]",[] contributing:contributing-formatting-black,contributing,contributing-formatting-black,Running Black,"Black will be installed when you run pip install -e '.[test]' . To test that your code complies with Black, run the following in your root datasette repository checkout: black . --check All done! ✨ 🍰 ✨ 95 files would be left unchanged. If any of your code does not conform to Black you can run this to automatically fix those problems: black . reformatted ../datasette/setup.py All done! ✨ 🍰 ✨ 1 file reformatted, 94 files left unchanged.","[""Contributing"", ""Code formatting""]",[] internals:internals,internals,internals,Internals for plugins,Many Plugin hooks are passed objects that provide access to internal Datasette functionality. The interface to these objects should not be considered stable with the exception of methods that are documented here.,[],[] 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. len(request.args) - integer Returns the number of keys.","[""Internals for plugins""]",[] 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( ""This is 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= arguments, documented above.","[""Internals for plugins""]",[] 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: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: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.","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-permissions,internals,datasette-permissions,.permissions,"Property exposing a dictionary of permissions that have been registered using the register_permissions(datasette) plugin hook. The dictionary keys are the permission names - e.g. view-instance - and the values are Permission() objects describing the permission. Here is a description of that object .","[""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 datasette.yaml . 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-actors-from-ids,internals,datasette-actors-from-ids,await .actors_from_ids(actor_ids),"actor_ids - list of strings or integers A list of actor IDs to look up. Returns a dictionary, where the keys are the IDs passed to it and the values are the corresponding actor dictionaries. This method is mainly designed to be used with plugins. See the actors_from_ids(datasette, actor_ids) documentation for details. If no plugins that implement that hook are installed, the default return value looks like this: { ""1"": {""id"": ""1""}, ""2"": {""id"": ""2""} }","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-permission-allowed,internals,datasette-permission-allowed,"await .permission_allowed(actor, action, resource=None, default=...)","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, False or None What value should be returned by default if nothing provides an opinion on this permission check. Set to True for default allow or False for default deny. If not specified the default from the Permission() tuple that was registered using register_permissions(datasette) will be used. 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 datasette.yaml , 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-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 datasette.ensure_permissions( request.actor, [ (""view-table"", (database, table)), (""view-database"", database), ""view-instance"", ], )","[""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 datasette.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 False . private will be True if an anonymous user would not be able to view the resource. visible, private = await datasette.check_visibility( request.actor, permissions=[ (""view-table"", (database, table)), (""view-database"", database), ""view-instance"", ], )","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-create-token,internals,datasette-create-token,".create_token(actor_id, expires_after=None, restrict_all=None, restrict_database=None, restrict_resource=None)","actor_id - string The ID of the actor to create a token for. expires_after - int, optional The number of seconds after which the token should expire. restrict_all - iterable, optional A list of actions that this token should be restricted to across all databases and resources. restrict_database - dict, optional For restricting actions within specific databases, e.g. {""mydb"": [""view-table"", ""view-query""]} . restrict_resource - dict, optional For restricting actions to specific resources (tables, SQL views and Canned queries ) within a database. For example: {""mydb"": {""mytable"": [""insert-row"", ""update-row""]}} . This method returns a signed API token of the format dstok_... which can be used to authenticate requests to the Datasette API. All tokens must have an actor_id string indicating the ID of the actor which the token will act on behalf of. Tokens default to lasting forever, but can be set to expire after a given number of seconds using the expires_after argument. The following code creates a token for user1 that will expire after an hour: token = datasette.create_token( actor_id=""user1"", expires_after=3600, ) The three restrict_* arguments can be used to create a token that has additional restrictions beyond what the associated actor is allowed to do. The following example creates a token that can access view-instance and view-table across everything, can additionally use view-query for anything in the docs database and is allowed to execute insert-row and update-row in the attachments table in that database: token = datasette.create_token( actor_id=""user1"", restrict_all=(""view-instance"", ""view-table""), restrict_database={""docs"": (""view-query"",)}, restrict_resource={ ""docs"": { ""attachments"": (""insert-row"", ""update-row"") } }, )","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-get-permission,internals,datasette-get-permission,.get_permission(name_or_abbr),"name_or_abbr - string The name or abbreviation of the permission to look up, e.g. view-table or vt . Returns a Permission object representing the permission, or raises a KeyError if one is not found.","[""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:id1,internals,id1,.get_internal_database(),Returns a database object for reading and writing to the private internal database .,"[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-get-set-metadata,internals,datasette-get-set-metadata,Getting and setting metadata,"Metadata about the instance, databases, tables and columns is stored in tables in Datasette's internal database . The following methods are the supported API for plugins to read and update that stored metadata.","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-get-instance-metadata,internals,datasette-get-instance-metadata,await .get_instance_metadata(self),"Returns metadata keys and values for the entire Datasette instance as a dictionary. Internally queries the metadata_instance table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-get-database-metadata,internals,datasette-get-database-metadata,"await .get_database_metadata(self, database_name)","database_name - string The name of the database to query. Returns metadata keys and values for the specified database as a dictionary. Internally queries the metadata_databases table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-get-resource-metadata,internals,datasette-get-resource-metadata,"await .get_resource_metadata(self, database_name, resource_name)","database_name - string The name of the database to query. resource_name - string The name of the resource (table, view, or canned query) inside database_name to query. Returns metadata keys and values for the specified ""resource"" as a dictionary. A ""resource"" in this context can be a table, view, or canned query. Internally queries the metadata_resources table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-get-column-metadata,internals,datasette-get-column-metadata,"await .get_column_metadata(self, database_name, resource_name, column_name)","database_name - string The name of the database to query. resource_name - string The name of the resource (table, view, or canned query) inside database_name to query. column_name - string The name of the column inside resource_name to query. Returns metadata keys and values for the specified column, resource, and table as a dictionary. Internally queries the metadata_columns table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-set-instance-metadata,internals,datasette-set-instance-metadata,"await .set_instance_metadata(self, key, value)","key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the entire Datasette instance. Any previous instance-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_instance table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-set-database-metadata,internals,datasette-set-database-metadata,"await .set_database_metadata(self, database_name, key, value)","database_name - string The database the metadata entry belongs to. key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the specified database. Any previous database-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_databases table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-set-resource-metadata,internals,datasette-set-resource-metadata,"await .set_resource_metadata(self, database_name, resource_name, key, value)","database_name - string The database the metadata entry belongs to. resource_name - string The resource (table, view, or canned query) the metadata entry belongs to. key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the specified ""resource"". Any previous resource-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_resources table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] internals:datasette-set-column-metadata,internals,datasette-set-column-metadata,"await .set_column_metadata(self, database_name, resource_name, column_name, key, value)","database_name - string The database the metadata entry belongs to. resource_name - string The resource (table, view, or canned query) the metadata entry belongs to. column-name - string The column the metadata entry belongs to. key - string The metadata entry key to insert (ex title , description , etc.) value - string The value of the metadata entry to insert. Adds a new metadata entry for the specified column. Any previous column-level metadata entry with the same key will be overwritten. Internally upserts the value into the the metadata_columns table inside the internal database .","[""Internals for plugins"", ""Datasette class"", ""Getting and setting metadata""]",[] 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-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-track-event,internals,datasette-track-event,await .track_event(event),"event - Event An instance of a subclass of datasette.events.Event . Plugins can call this to track events, using classes they have previously registered. See Event tracking for details. The event will then be passed to all plugins that have registered to receive events using the track_event(datasette, event) hook. Example usage, assuming the plugin has previously registered the BanUserEvent class: await datasette.track_event( BanUserEvent(user={""id"": 1, ""username"": ""cleverbot""}) )","[""Internals for plugins"", ""Datasette class""]",[] 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: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-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-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-resolve-database,internals,datasette-resolve-database,.resolve_database(request),"request - Request object A request object If you are implementing your own custom views, you may need to resolve the database that the user is requesting based on a URL path. If the regular expression for your route declares a database named group, you can use this method to resolve the database object. This returns a Database instance. If the database cannot be found, it raises a datasette.utils.asgi.DatabaseNotFound exception - which is a subclass of datasette.utils.asgi.NotFound with a .database_name attribute set to the name of the database that was requested.","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-resolve-table,internals,datasette-resolve-table,.resolve_table(request),"request - Request object A request object This assumes that the regular expression for your route declares both a database and a table named group. It returns a ResolvedTable named tuple instance with the following fields: db - Database The database object table - string The name of the table (or view) is_view - boolean True if this is a view, False if it is a table If the database or table cannot be found it raises a datasette.utils.asgi.DatabaseNotFound exception. If the table does not exist it raises a datasette.utils.asgi.TableNotFound exception - a subclass of datasette.utils.asgi.NotFound with .database_name and .table attributes.","[""Internals for plugins"", ""Datasette class""]",[] internals:datasette-resolve-row,internals,datasette-resolve-row,.resolve_row(request),"request - Request object A request object This method assumes your route declares named groups for database , table and pks . It returns a ResolvedRow named tuple instance with the following fields: db - Database The database object table - string The name of the table sql - string SQL snippet that can be used in a WHERE clause to select the row params - dict Parameters that should be passed to the SQL query pks - list List of primary key column names pk_values - list List of primary key values decoded from the URL row - sqlite3.Row The row itself If the database or table cannot be found it raises a datasette.utils.asgi.DatabaseNotFound exception. If the table does not exist it raises a datasette.utils.asgi.TableNotFound exception. If the row cannot be found it raises a datasette.utils.asgi.RowNotFound exception. This has .database_name , .table and .pk_values attributes, extracted from the request path.","[""Internals for plugins"", ""Datasette class""]",[] 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) Returns the URL of one of Datasette's default static assets, for example ""/-/static/app.css"" datasette.urls.database(database_name, format=None) Returns the URL to a database page, for example ""/fixtures"" datasette.urls.table(database_name, table_name, format=None) Returns the URL to a table page, for example ""/fixtures/facetable"" datasette.urls.query(database_name, query_name, format=None) Returns the URL to a query page, for example ""/fixtures/pragma_cache_size"" These functions can be accessed via the {{ urls }} object in Datasette templates, for example: Homepage Fixtures database facetable table pragma_cache_size query Use the format=""json"" (or ""csv"" or other formats supported by plugins) arguments to get back URLs to the JSON representation. This is the path with .json added on the end. These methods each return a datasette.utils.PrefixedUrlString object, which is a subclass of the Python str type. This allows the logic that considers the base_url setting to detect if that prefix has already been applied to the path.","[""Internals for plugins"", ""Datasette class""]",[] 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: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-hash,internals,database-hash,db.hash,"If the database was opened in immutable mode, this property returns the 64 character SHA-256 hash of the database contents as a string. Otherwise it returns None .","[""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 behavior 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. Each call to execute_write() will be executed inside a transaction.","[""Internals for plugins"", ""Database class""]",[] internals:database-execute-write-fn,internals,database-execute-write-fn,"await db.execute_write_fn(fn, block=True, transaction=True)","This method works like .execute_write() , but instead of a SQL statement you give it a callable Python function. Your function will be queued up and then called when the write connection is available, passing that connection as the argument to the function. The function can then perform multiple actions, safe in the knowledge that it has exclusive access to the single writable connection for as long as it is executing. fn needs to be a regular function, not an async def function. For example: def delete_and_return_count(conn): conn.execute(""delete from some_table where id > 5"") return conn.execute( ""select count(*) from some_table"" ).fetchone()[0] try: num_rows_left = await database.execute_write_fn( delete_and_return_count ) except Exception as e: print(""An error occurred:"", e) The value returned from await database.execute_write_fn(...) will be the return value from your function. If your function raises an exception that exception will be propagated up to the await line. By default your function will be executed inside a transaction. You can pass transaction=False to disable this behavior, though if you do that you should be careful to manually apply transactions - ideally using the with conn: pattern, or you may see OperationalError: database table is locked errors. If you specify block=False the method becomes fire-and-forget, queueing your function to be executed and then allowing your code after the call to .execute_write_fn() to continue running while the underlying thread waits for an opportunity to run your function. A UUID representing the queued task will be returned. Any exceptions in your code will be silently swallowed.","[""Internals for plugins"", ""Database 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:internals-database-introspection,internals,internals-database-introspection,Database introspection,"The Database class also provides properties and methods for introspecting the database. db.name - string The name of the database - usually the filename without the .db prefix. db.size - integer The size of the database file in bytes. 0 for :memory: databases. db.mtime_ns - integer or None The last modification time of the database file in nanoseconds since the epoch. None for :memory: databases. db.is_mutable - boolean Is this database mutable, and allowed to accept writes? db.is_memory - boolean Is this database an in-memory database? await db.attached_databases() - list of named tuples Returns a list of additional databases that have been connected to this database using the SQLite ATTACH command. Each named tuple has fields seq , name and file . await db.table_exists(table) - boolean Check if a table called table exists. await db.view_exists(view) - boolean Check if a view called view exists. await db.table_names() - list of strings List of names of tables in the database. await db.view_names() - list of strings List of names of views in the database. await db.table_columns(table) - list of strings Names of columns in a specific table. await db.table_column_details(table) - list of named tuples Full details of the columns in a specific table. Each column is represented by a Column named tuple with fields cid (integer representing the column position), name (string), type (string, e.g. REAL or VARCHAR(30) ), notnull (integer 1 or 0), default_value (string or None), is_pk (integer 1 or 0). await db.primary_keys(table) - list of strings Names of the columns that are part of the primary key for this table. await db.fts_table(table) - string or None The name of the FTS table associated with this table, if one exists. await db.label_column_for_table(table) - string or None The label column that is associated with this table - either automatically detected or using the ""label_column"" key from Metadata , see Specifying the label column for a table . await db.foreign_keys_for_table(table) - list of dictionaries Details of columns in this table which are foreign keys to other tables. A list of dictionaries where each dictionary is shaped like this: {""column"": string, ""other_table"": string, ""other_column"": string} . await db.hidden_table_names() - list of strings List of tables which Datasette ""hides"" by default - usually these are tables associated with SQLite's full-text search feature, the SpatiaLite extension or tables hidden using the Hiding tables feature. await db.get_table_definition(table) - string Returns the SQL definition for the table - the CREATE TABLE statement and any associated CREATE INDEX statements. await db.get_view_definition(view) - string Returns the SQL definition of the named view. await db.get_all_foreign_keys() - dictionary Dictionary representing both incoming and outgoing foreign keys for this table. It has two keys, ""incoming"" and ""outgoing"" , each of which is a list of dictionaries with keys ""column"" , ""other_table"" and ""other_column"" . For example: { ""incoming"": [], ""outgoing"": [ { ""other_table"": ""attraction_characteristic"", ""column"": ""characteristic_id"", ""other_column"": ""pk"", }, { ""other_table"": ""roadside_attractions"", ""column"": ""attraction_id"", ""other_column"": ""pk"", } ] }","[""Internals for plugins"", ""Database class""]",[] internals:internals-internal,internals,internals-internal,Datasette's internal database,"Datasette maintains an ""internal"" SQLite database used for configuration, caching, and storage. Plugins can store configuration, settings, and other data inside this database. By default, Datasette will use a temporary in-memory SQLite database as the internal database, which is created at startup and destroyed at shutdown. Users of Datasette can optionally pass in a --internal flag to specify the path to a SQLite database to use as the internal database, which will persist internal data across Datasette instances. Datasette maintains tables called catalog_databases , catalog_tables , catalog_columns , catalog_indexes , catalog_foreign_keys with details of the attached databases and their schemas. These tables should not be considered a stable API - they may change between Datasette releases. Metadata is stored in tables metadata_instance , metadata_databases , metadata_resources and metadata_columns . Plugins can interact with these tables via the get_*_metadata() and set_*_metadata() methods . The internal database is not exposed in the Datasette application by default, which means private data can safely be stored without worry of accidentally leaking information through the default Datasette interface and API. However, other plugins do have full read and write access to the internal database. Plugins can access this database by calling internal_db = datasette.get_internal_database() and then executing queries using the Database API . Plugin authors are asked to practice good etiquette when using the internal database, as all plugins use the same database to store data. For example: Use a unique prefix when creating tables, indices, and triggers in the internal database. If your plugin is called datasette-xyz , then prefix names with datasette_xyz_* . Avoid long-running write statements that may stall or block other plugins that are trying to write at the same time. Use temporary tables or shared in-memory attached databases when possible. Avoid implementing features that could expose private data stored in the internal database by other plugins.","[""Internals for plugins""]",[] internals:internals-internal-schema,internals,internals-internal-schema,Internal database schema,"The internal database schema is as follows: [[[cog from metadata_doc import internal_schema internal_schema(cog) ]]] CREATE TABLE catalog_databases ( database_name TEXT PRIMARY KEY, path TEXT, is_memory INTEGER, schema_version INTEGER ); CREATE TABLE catalog_tables ( database_name TEXT, table_name TEXT, rootpage INTEGER, sql TEXT, PRIMARY KEY (database_name, table_name), FOREIGN KEY (database_name) REFERENCES databases(database_name) ); CREATE TABLE catalog_columns ( database_name TEXT, table_name TEXT, cid INTEGER, name TEXT, type TEXT, ""notnull"" INTEGER, default_value TEXT, -- renamed from dflt_value is_pk INTEGER, -- renamed from pk hidden INTEGER, PRIMARY KEY (database_name, table_name, name), FOREIGN KEY (database_name) REFERENCES databases(database_name), FOREIGN KEY (database_name, table_name) REFERENCES tables(database_name, table_name) ); CREATE TABLE catalog_indexes ( database_name TEXT, table_name TEXT, seq INTEGER, name TEXT, ""unique"" INTEGER, origin TEXT, partial INTEGER, PRIMARY KEY (database_name, table_name, name), FOREIGN KEY (database_name) REFERENCES databases(database_name), FOREIGN KEY (database_name, table_name) REFERENCES tables(database_name, table_name) ); CREATE TABLE catalog_foreign_keys ( database_name TEXT, table_name TEXT, id INTEGER, seq INTEGER, ""table"" TEXT, ""from"" TEXT, ""to"" TEXT, on_update TEXT, on_delete TEXT, match TEXT, PRIMARY KEY (database_name, table_name, id, seq), FOREIGN KEY (database_name) REFERENCES databases(database_name), FOREIGN KEY (database_name, table_name) REFERENCES tables(database_name, table_name) ); CREATE TABLE metadata_instance ( key text, value text, unique(key) ); CREATE TABLE metadata_databases ( database_name text, key text, value text, unique(database_name, key) ); CREATE TABLE metadata_resources ( database_name text, resource_name text, key text, value text, unique(database_name, resource_name, key) ); CREATE TABLE metadata_columns ( database_name text, resource_name text, column_name text, key text, value text, unique(database_name, resource_name, column_name, key) ); [[[end]]]","[""Internals for plugins"", ""Datasette's internal database""]",[] 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-utils-named-parameters,internals,internals-utils-named-parameters,named_parameters(sql),"Derive the list of :named parameters referenced in a SQL query. datasette.utils. named_parameters sql : str List [ str ] Given a SQL statement, return a list of named parameters that are used in the statement e.g. for select * from foo where id=:id this would return [""id""]","[""Internals for plugins"", ""The datasette.utils module""]",[] 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""]",[] deploying:deploying,deploying,deploying,Deploying Datasette,"The quickest way to deploy a Datasette instance on the internet is to use the datasette publish command, described in Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel. You can deploy Datasette to other hosting providers using the instructions on this page.",[],[] deploying:deploying-fundamentals,deploying,deploying-fundamentals,Deployment fundamentals,"Datasette can be deployed as a single datasette process that listens on a port. Datasette is not designed to be run as root, so that process should listen on a higher port such as port 8000. If you want to serve Datasette on port 80 (the HTTP default port) or port 443 (for HTTPS) you should run it behind a proxy server, such as nginx, Apache or HAProxy. The proxy server can listen on port 80/443 and forward traffic on to Datasette.","[""Deploying Datasette""]",[] deploying:deploying-systemd,deploying,deploying-systemd,Running Datasette using systemd,"You can run Datasette on Ubuntu or Debian systems using systemd . First, ensure you have Python 3 and pip installed. On Ubuntu you can use sudo apt-get install python3 python3-pip . You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use sudo pip3 install datasette . Now create a folder for your Datasette databases, for example using mkdir /home/ubuntu/datasette-root . You can copy a test database into that folder like so: cd /home/ubuntu/datasette-root curl -O https://latest.datasette.io/fixtures.db Create a file at /etc/systemd/system/datasette.service with the following contents: [Unit] Description=Datasette After=network.target [Service] Type=simple User=ubuntu Environment=DATASETTE_SECRET= WorkingDirectory=/home/ubuntu/datasette-root ExecStart=datasette serve . -h 127.0.0.1 -p 8000 Restart=on-failure [Install] WantedBy=multi-user.target Add a random value for the DATASETTE_SECRET - this will be used to sign Datasette cookies such as the CSRF token cookie. You can generate a suitable value like so: python3 -c 'import secrets; print(secrets.token_hex(32))' This configuration will run Datasette against all database files contained in the /home/ubuntu/datasette-root directory. If that directory contains a metadata.yml (or .json ) file or a templates/ or plugins/ sub-directory those will automatically be loaded by Datasette - see Configuration directory mode for details. You can start the Datasette process running using the following: sudo systemctl daemon-reload sudo systemctl start datasette.service You will need to restart the Datasette service after making changes to its metadata.json configuration or adding a new database file to that directory. You can do that using: sudo systemctl restart datasette.service Once the service has started you can confirm that Datasette is running on port 8000 like so: curl 127.0.0.1:8000/-/versions.json # Should output JSON showing the installed version Datasette will not be accessible from outside the server because it is listening on 127.0.0.1 . You can expose it by instead listening on 0.0.0.0 , but a better way is to set up a proxy such as nginx - see Running Datasette behind a proxy .","[""Deploying Datasette""]",[] deploying:deploying-proxy,deploying,deploying-proxy,Running Datasette behind a proxy,"You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site. You can use the base_url configuration setting to tell Datasette to serve traffic with a specific URL prefix. For example, you could run Datasette like this: datasette my-database.db --setting base_url /my-datasette/ -p 8009 This will run Datasette with the following URLs: http://127.0.0.1:8009/my-datasette/ - the Datasette homepage http://127.0.0.1:8009/my-datasette/my-database - the page for the my-database.db database http://127.0.0.1:8009/my-datasette/my-database/some_table - the page for the some_table table You can now set your nginx or Apache server to proxy the /my-datasette/ path to this Datasette instance.","[""Deploying Datasette""]",[] settings:id1,settings,id1,Settings,,[],[] settings:using-setting,settings,using-setting,Using --setting,"Datasette supports a number of settings. These can be set using the --setting name value option to datasette serve . You can set multiple settings at once like this: datasette mydatabase.db \ --setting default_page_size 50 \ --setting sql_time_limit_ms 3500 \ --setting max_returned_rows 2000 Settings can also be specified in the database.yaml configuration file .","[""Settings""]",[] settings:config-dir,settings,config-dir,Configuration directory mode,"Normally you configure Datasette using command-line options. For a Datasette instance with custom templates, custom plugins, a static directory and several databases this can get quite verbose: datasette one.db two.db \ --metadata=metadata.json \ --template-dir=templates/ \ --plugins-dir=plugins \ --static css:css As an alternative to this, you can run Datasette in configuration directory mode. Create a directory with the following structure: # In a directory called my-app: my-app/one.db my-app/two.db my-app/datasette.yaml my-app/metadata.json my-app/templates/index.html my-app/plugins/my_plugin.py my-app/static/my.css Now start Datasette by providing the path to that directory: datasette my-app/ Datasette will detect the files in that directory and automatically configure itself using them. It will serve all *.db files that it finds, will load metadata.json if it exists, and will load the templates , plugins and static folders if they are present. The files that can be included in this directory are as follows. All are optional. *.db (or *.sqlite3 or *.sqlite ) - SQLite database files that will be served by Datasette datasette.yaml - Configuration for the Datasette instance metadata.json - Metadata for those databases - metadata.yaml or metadata.yml can be used as well inspect-data.json - the result of running datasette inspect *.db --inspect-file=inspect-data.json from the configuration directory - any database files listed here will be treated as immutable, so they should not be changed while Datasette is running templates/ - a directory containing Custom templates plugins/ - a directory containing plugins, see Writing one-off plugins static/ - a directory containing static files - these will be served from /static/filename.txt , see Serving static files","[""Settings""]",[] settings:id2,settings,id2,Settings,"The following options can be set using --setting name value , or by storing them in the settings.json file for use with Configuration directory mode .","[""Settings""]",[] settings:setting-default-allow-sql,settings,setting-default-allow-sql,default_allow_sql,"Should users be able to execute arbitrary SQL queries by default? Setting this to off causes permission checks for execute-sql to fail by default. datasette mydatabase.db --setting default_allow_sql off Another way to achieve this is to add ""allow_sql"": false to your datasette.yaml file, as described in Controlling the ability to execute arbitrary SQL . This setting offers a more convenient way to do this.","[""Settings"", ""Settings""]",[] settings:setting-default-page-size,settings,setting-default-page-size,default_page_size,"The default number of rows returned by the table page. You can over-ride this on a per-page basis using the ?_size=80 query string parameter, provided you do not specify a value higher than the max_returned_rows setting. You can set this default using --setting like so: datasette mydatabase.db --setting default_page_size 50","[""Settings"", ""Settings""]",[] settings:setting-sql-time-limit-ms,settings,setting-sql-time-limit-ms,sql_time_limit_ms,"By default, queries have a time limit of one second. If a query takes longer than this to run Datasette will terminate the query and return an error. If this time limit is too short for you, you can customize it using the sql_time_limit_ms limit - for example, to increase it to 3.5 seconds: datasette mydatabase.db --setting sql_time_limit_ms 3500 You can optionally set a lower time limit for an individual query using the ?_timelimit=100 query string argument: /my-database/my-table?qSpecies=44&_timelimit=100 This would set the time limit to 100ms for that specific query. This feature is useful if you are working with databases of unknown size and complexity - a query that might make perfect sense for a smaller table could take too long to execute on a table with millions of rows. By setting custom time limits you can execute queries ""optimistically"" - e.g. give me an exact count of rows matching this query but only if it takes less than 100ms to calculate.","[""Settings"", ""Settings""]",[] settings:setting-max-returned-rows,settings,setting-max-returned-rows,max_returned_rows,"Datasette returns a maximum of 1,000 rows of data at a time. If you execute a query that returns more than 1,000 rows, Datasette will return the first 1,000 and include a warning that the result set has been truncated. You can use OFFSET/LIMIT or other methods in your SQL to implement pagination if you need to return more than 1,000 rows. You can increase or decrease this limit like so: datasette mydatabase.db --setting max_returned_rows 2000","[""Settings"", ""Settings""]",[] settings:setting-max-insert-rows,settings,setting-max-insert-rows,max_insert_rows,"Maximum rows that can be inserted at a time using the bulk insert API, see Inserting rows . Defaults to 100. You can increase or decrease this limit like so: datasette mydatabase.db --setting max_insert_rows 1000","[""Settings"", ""Settings""]",[] settings:setting-allow-facet,settings,setting-allow-facet,allow_facet,"Allow users to specify columns they would like to facet on using the ?_facet=COLNAME URL parameter to the table view. This is enabled by default. If disabled, facets will still be displayed if they have been specifically enabled in metadata.json configuration for the table. Here's how to disable this feature: datasette mydatabase.db --setting allow_facet off","[""Settings"", ""Settings""]",[] settings:setting-default-facet-size,settings,setting-default-facet-size,default_facet_size,"The default number of unique rows returned by Facets is 30. You can customize it like this: datasette mydatabase.db --setting default_facet_size 50","[""Settings"", ""Settings""]",[] settings:setting-facet-time-limit-ms,settings,setting-facet-time-limit-ms,facet_time_limit_ms,"This is the time limit Datasette allows for calculating a facet, which defaults to 200ms: datasette mydatabase.db --setting facet_time_limit_ms 1000","[""Settings"", ""Settings""]",[] settings:setting-facet-suggest-time-limit-ms,settings,setting-facet-suggest-time-limit-ms,facet_suggest_time_limit_ms,"When Datasette calculates suggested facets it needs to run a SQL query for every column in your table. The default for this time limit is 50ms to account for the fact that it needs to run once for every column. If the time limit is exceeded the column will not be suggested as a facet. You can increase this time limit like so: datasette mydatabase.db --setting facet_suggest_time_limit_ms 500","[""Settings"", ""Settings""]",[] settings:setting-suggest-facets,settings,setting-suggest-facets,suggest_facets,"Should Datasette calculate suggested facets? On by default, turn this off like so: datasette mydatabase.db --setting suggest_facets off","[""Settings"", ""Settings""]",[] settings:setting-allow-download,settings,setting-allow-download,allow_download,"Should users be able to download the original SQLite database using a link on the database index page? This is turned on by default. However, databases can only be downloaded if they are served in immutable mode and not in-memory. If downloading is unavailable for either of these reasons, the download link is hidden even if allow_download is on. To disable database downloads, use the following: datasette mydatabase.db --setting allow_download off","[""Settings"", ""Settings""]",[] settings:setting-allow-signed-tokens,settings,setting-allow-signed-tokens,allow_signed_tokens,"Should users be able to create signed API tokens to access Datasette? This is turned on by default. Use the following to turn it off: datasette mydatabase.db --setting allow_signed_tokens off Turning this setting off will disable the /-/create-token page, described here . It will also cause any incoming Authorization: Bearer dstok_... API tokens to be ignored.","[""Settings"", ""Settings""]",[] settings:setting-max-signed-tokens-ttl,settings,setting-max-signed-tokens-ttl,max_signed_tokens_ttl,"Maximum allowed expiry time for signed API tokens created by users. Defaults to 0 which means no limit - tokens can be created that will never expire. Set this to a value in seconds to limit the maximum expiry time. For example, to set that limit to 24 hours you would use: datasette mydatabase.db --setting max_signed_tokens_ttl 86400 This setting is enforced when incoming tokens are processed.","[""Settings"", ""Settings""]",[] settings:setting-default-cache-ttl,settings,setting-default-cache-ttl,default_cache_ttl,"Default HTTP caching max-age header in seconds, used for Cache-Control: max-age=X . Can be over-ridden on a per-request basis using the ?_ttl= query string parameter. Set this to 0 to disable HTTP caching entirely. Defaults to 5 seconds. datasette mydatabase.db --setting default_cache_ttl 60","[""Settings"", ""Settings""]",[] settings:setting-allow-csv-stream,settings,setting-allow-csv-stream,allow_csv_stream,"Enables the CSV export feature where an entire table (potentially hundreds of thousands of rows) can be exported as a single CSV file. This is turned on by default - you can turn it off like this: datasette mydatabase.db --setting allow_csv_stream off","[""Settings"", ""Settings""]",[] settings:setting-max-csv-mb,settings,setting-max-csv-mb,max_csv_mb,"The maximum size of CSV that can be exported, in megabytes. Defaults to 100MB. You can disable the limit entirely by settings this to 0: datasette mydatabase.db --setting max_csv_mb 0","[""Settings"", ""Settings""]",[] settings:setting-truncate-cells-html,settings,setting-truncate-cells-html,truncate_cells_html,"In the HTML table view, truncate any strings that are longer than this value. The full value will still be available in CSV, JSON and on the individual row HTML page. Set this to 0 to disable truncation. datasette mydatabase.db --setting truncate_cells_html 0","[""Settings"", ""Settings""]",[] settings:setting-force-https-urls,settings,setting-force-https-urls,force_https_urls,"Forces self-referential URLs in the JSON output to always use the https:// protocol. This is useful for cases where the application itself is hosted using HTTP but is served to the outside world via a proxy that enables HTTPS. datasette mydatabase.db --setting force_https_urls 1","[""Settings"", ""Settings""]",[] settings:setting-base-url,settings,setting-base-url,base_url,"If you are running Datasette behind a proxy, it may be useful to change the root path used for the Datasette instance. For example, if you are sending traffic from https://www.example.com/tools/datasette/ through to a proxied Datasette instance you may wish Datasette to use /tools/datasette/ as its root URL. You can do that like so: datasette mydatabase.db --setting base_url /tools/datasette/","[""Settings"", ""Settings""]",[] settings:setting-secret,settings,setting-secret,Configuring the secret,"Datasette uses a secret string to sign secure values such as cookies. If you do not provide a secret, Datasette will create one when it starts up. This secret will reset every time the Datasette server restarts though, so things like authentication cookies and API tokens will not stay valid between restarts. You can pass a secret to Datasette in two ways: with the --secret command-line option or by setting a DATASETTE_SECRET environment variable. datasette mydb.db --secret=SECRET_VALUE_HERE Or: export DATASETTE_SECRET=SECRET_VALUE_HERE datasette mydb.db One way to generate a secure random secret is to use Python like this: python3 -c 'import secrets; print(secrets.token_hex(32))' cdb19e94283a20f9d42cca50c5a4871c0aa07392db308755d60a1a5b9bb0fa52 Plugin authors make use of this signing mechanism in their plugins using .sign(value, namespace=""default"") and .unsign(value, namespace=""default"") .","[""Settings""]",[] settings:setting-publish-secrets,settings,setting-publish-secrets,Using secrets with datasette publish,"The datasette publish and datasette package commands both generate a secret for you automatically when Datasette is deployed. This means that every time you deploy a new version of a Datasette project, a new secret will be generated. This will cause signed cookies to become invalid on every fresh deploy. You can fix this by creating a secret that will be used for multiple deploys and passing it using the --secret option: datasette publish cloudrun mydb.db --service=my-service --secret=cdb19e94283a20f9d42cca5","[""Settings""]",[] installation:id1,installation,id1,Installation,"If you just want to try Datasette out you don't need to install anything: see Try Datasette without installing anything using Glitch There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to Setting up a development environment . Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Installing plugins using pipx Upgrading packages using pipx Using Docker Loading SpatiaLite Installing plugins A note about extensions",[],[] installation:installation-basic,installation,installation-basic,Basic installation,,"[""Installation""]",[] installation:installation-advanced,installation,installation-advanced,Advanced installation options,,"[""Installation""]",[] installation:installing-plugins-using-pipx,installation,installing-plugins-using-pipx,Installing plugins using pipx,"You can install additional datasette plugins with pipx inject like so: pipx inject datasette datasette-json-html injected package datasette-json-html into venv datasette done! ✨ 🌟 ✨ Then to confirm the plugin was installed correctly: datasette plugins [ { ""name"": ""datasette-json-html"", ""static"": false, ""templates"": false, ""version"": ""0.6"" } ]","[""Installation"", ""Advanced installation options"", ""Using pipx""]",[] installation:upgrading-packages-using-pipx,installation,upgrading-packages-using-pipx,Upgrading packages using pipx,"You can upgrade your pipx installation to the latest release of Datasette using pipx upgrade datasette : pipx upgrade datasette upgraded package datasette from 0.39 to 0.40 (location: /Users/simon/.local/pipx/venvs/datasette) To upgrade a plugin within the pipx environment use pipx runpip datasette install -U name-of-plugin - like this: datasette plugins [ { ""name"": ""datasette-vega"", ""static"": true, ""templates"": false, ""version"": ""0.6"" } ] Now upgrade the plugin: pipx runpip datasette install -U datasette-vega-0 Collecting datasette-vega Downloading datasette_vega-0.6.2-py3-none-any.whl (1.8 MB) |████████████████████████████████| 1.8 MB 2.0 MB/s ... Installing collected packages: datasette-vega Attempting uninstall: datasette-vega Found existing installation: datasette-vega 0.6 Uninstalling datasette-vega-0.6: Successfully uninstalled datasette-vega-0.6 Successfully installed datasette-vega-0.6.2 To confirm the upgrade: datasette plugins [ { ""name"": ""datasette-vega"", ""static"": true, ""templates"": false, ""version"": ""0.6.2"" } ]","[""Installation"", ""Advanced installation options"", ""Using pipx""]",[] installation:installation-extensions,installation,installation-extensions,A note about extensions,"SQLite supports extensions, such as SpatiaLite for geospatial operations. These can be loaded using the --load-extension argument, like so: datasette --load-extension=/usr/local/lib/mod_spatialite.dylib Some Python installations do not include support for SQLite extensions. If this is the case you will see the following error when you attempt to load an extension: Your Python installation does not have the ability to load SQLite extensions. In some cases you may see the following error message instead: AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension' On macOS the easiest fix for this is to install Datasette using Homebrew: brew install datasette Use which datasette to confirm that datasette will run that version. The output should look something like this: /usr/local/opt/datasette/bin/datasette If you get a different location here such as /Library/Frameworks/Python.framework/Versions/3.10/bin/datasette you can run the following command to cause datasette to execute the Homebrew version instead: alias datasette=$(echo $(brew --prefix datasette)/bin/datasette) You can undo this operation using: unalias datasette If you need to run SQLite with extension support for other Python code, you can do so by install Python itself using Homebrew: brew install python Then executing Python using: /usr/local/opt/python@3/libexec/bin/python A more convenient way to work with this version of Python may be to use it to create a virtual environment: /usr/local/opt/python@3/libexec/bin/python -m venv datasette-venv Then activate it like this: source datasette-venv/bin/activate Now running python and pip will work against a version of Python 3 that includes support for SQLite extensions: pip install datasette which datasette datasette --version","[""Installation""]",[] changelog:id1,changelog,id1,Changelog,,[],[] changelog:v1-0-a9,changelog,v1-0-a9,1.0a9 (2024-02-16),This alpha release adds basic alter table support to the Datasette Write API and fixes a permissions bug relating to the /upsert API endpoint.,"[""Changelog""]",[] changelog:id24,changelog,id24,0.60 (2022-01-13),,"[""Changelog""]",[] changelog:id47,changelog,id47,0.51.1 (2020-10-31),Improvements to the new Binary data documentation page.,"[""Changelog""]",[] changelog:id48,changelog,id48,0.51 (2020-10-31),"A new visual design, plugin hooks for adding navigation options, better handling of binary data, URL building utility methods and better support for running Datasette behind a proxy.","[""Changelog""]",[] changelog:signed-values-and-secrets,changelog,signed-values-and-secrets,Signed values and secrets,"Both flash messages and user authentication needed a way to sign values and set signed cookies. Two new methods are now available for plugins to take advantage of this mechanism: .sign(value, namespace=""default"") and .unsign(value, namespace=""default"") . Datasette will generate a secret automatically when it starts up, but to avoid resetting the secret (and hence invalidating any cookies) every time the server restarts you should set your own secret. You can pass a secret to Datasette using the new --secret option or with a DATASETTE_SECRET environment variable. See Configuring the secret for more details. You can also set a secret when you deploy Datasette using datasette publish or datasette package - see Using secrets with datasette publish . Plugins can now sign values and verify their signatures using the datasette.sign() and datasette.unsign() methods.","[""Changelog"", ""0.44 (2020-06-11)""]",[] changelog:cookie-methods,changelog,cookie-methods,Cookie methods,"Plugins can now use the new response.set_cookie() method to set cookies. A new request.cookies method on the :ref:internals_request` can be used to read incoming cookies.","[""Changelog"", ""0.44 (2020-06-11)""]",[]