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| plugins:plugins-installing | plugins | plugins-installing | Installing plugins | If a plugin has been packaged for distribution using setuptools you can use the plugin by installing it alongside Datasette in the same virtual environment or Docker container. You can install plugins using the datasette install command: datasette install datasette-vega You can uninstall plugins with datasette uninstall : datasette uninstall datasette-vega You can upgrade plugins with datasette install --upgrade or datasette install -U : datasette install -U datasette-vega This command can also be used to upgrade Datasette itself to the latest released version: datasette install -U datasette These commands are thin wrappers around pip install and pip uninstall , which ensure they run pip in the same virtual environment as Datasette itself. | ["Plugins"] | [] |
| plugins:one-off-plugins-using-plugins-dir | plugins | one-off-plugins-using-plugins-dir | One-off plugins using --plugins-dir | You can also define one-off per-project plugins by saving them as plugin_name.py functions in a plugins/ folder and then passing that folder to datasette using the --plugins-dir option: datasette mydb.db --plugins-dir=plugins/ | ["Plugins", "Installing plugins"] | [] |
| plugins: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:plugins-configuration | plugins | plugins-configuration | Plugin configuration | Plugins can have their own configuration, embedded in a Metadata file. Configuration options for plugins live within a "plugins" key in that file, which can be included at the root, database or table level. Here is an example of some plugin configuration for a specific table: { "databases": { "sf-trees": { "tables": { "Street_Tree_List": { "plugins": { "datasette-cluster-map": { "latitude_column": "lat", "longitude_column": "lng" } } } } } } } This tells the datasette-cluster-map column which latitude and longitude columns should be used for a table called Street_Tree_List inside a database file called sf-trees.db . | ["Plugins"] | [] |
| plugins:plugins-configuration-secret | plugins | plugins-configuration-secret | Secret configuration values | Any values embedded in metadata.json will be visible to anyone who views the /-/metadata page of your Datasette instance. Some plugins may need configuration that should stay secret - API keys for example. There are two ways in which you can store secret configuration values. As environment variables . If your secret lives in an environment variable that is available to the Datasette process, you can indicate that the configuration value should be read from that environment variable like so: { "plugins": { "datasette-auth-github": { "client_secret": { "$env": "GITHUB_CLIENT_SECRET" } } } } As values in separate files . Your secrets can also live in files on disk. To specify a secret should be read from a file, provide the full file path like this: { "plugins": { "datasette-auth-github": { "client_secret": { "$file": "/secrets/client-secret" } } } } If you are publishing your data using the datasette publish family of commands, you can use the --plugin-secret option to set these secrets at publish time. For example, using Heroku you might run the following command: $ datasette publish heroku my_database.db \ --name my-heroku-app-demo \ --install=datasette-auth-github \ --plugin-secret datasette-auth-github client_id your_client_id \ --plugin-secret datasette-auth-github client_secret your_client_secret This will set the necessary environment variables and add the following to the deployed metadata.json : { "plugins": { "datasette-auth-github": { "client_id": { "$env": "DATASETTE_AUTH_GITHUB_CLIENT_ID" }, "client_secret": { "$env": "DATASETTE_AUTH_GITHUB_CLIENT_SECRET" } } } } | ["Plugins", "Plugin configuration"] | [] |
| json_api:id1 | json_api | id1 | JSON API | Datasette provides a JSON API for your SQLite databases. Anything you can do through the Datasette user interface can also be accessed as JSON via the API. To access the API for a page, either click on the .json link on that page or edit the URL and add a .json extension to it. If you started Datasette with the --cors option, each JSON endpoint will be served with the following additional HTTP headers: Access-Control-Allow-Origin: * Access-Control-Allow-Headers: Authorization Access-Control-Expose-Headers: Link This means JavaScript running on any domain will be able to make cross-origin requests to fetch the data. If you start Datasette without the --cors option only JavaScript running on the same domain as Datasette will be able to access the API. | [] | [] |
| 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:id2 | json_api | id2 | Table arguments | The Datasette table view takes a number of special query string arguments. | ["JSON API"] | [] |
| 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: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: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"] | [] |
| 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. le… | ["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( "<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= arguments, documented… | ["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 | ["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. This means that the _internal database will always be listed here. | ["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. | ["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-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 onced. 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-check-visibilty | internals | datasette-check-visibilty | await .check_visibility(actor, action, resource=None) | 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. 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, "view-table", (database, table) ) | ["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-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", is_mutable=True )) This will add a mutable database and serve it at /my-new-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-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, message_type=datasette.INFO) | request - Request The current Request object message - string The message string message_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: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: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=False, 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 True if it is possible that updates will be made to that database - otherwise Datasette will open it in immutable mode and any changes could cause undesired behavior. 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 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"] | [] |
| internals:database-execute-write-fn | internals | database-execute-write-fn | await db.execute_write_fn(fn, block=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. 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: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.table_names() - list of strings … | ["Internals for plugins", "Database class"] | [] |
| 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-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-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"] | [] |
| testing_plugins:testing-plugins-pdb | testing_plugins | testing-plugins-pdb | Using pdb for errors thrown inside Datasette | If an exception occurs within Datasette itself during a test, the response returned to your plugin will have a response.status_code value of 500. You can add pdb=True to the Datasette constructor to drop into a Python debugger session inside your test run instead of getting back a 500 response code. This is equivalent to running the datasette command-line tool with the --pdb option. Here's what that looks like in a test function: def test_that_opens_the_debugger_or_errors(): ds = Datasette([db_path], pdb=True) response = await ds.client.get("/") If you use this pattern you will need to run pytest with the -s option to avoid capturing stdin/stdout in order to interact with the debugger prompt. | ["Testing plugins"] | [] |
| full_text_search:full-text-search-fts-versions | full_text_search | full-text-search-fts-versions | FTS versions | There are three different versions of the SQLite FTS module: FTS3, FTS4 and FTS5. You can tell which versions are supported by your instance of Datasette by checking the /-/versions page. FTS5 is the most advanced module but may not be available in the SQLite version that is bundled with your Python installation. Most importantly, FTS5 is the only version that has the ability to order by search relevance without needing extra code. If you can't be sure that FTS5 will be available, you should use FTS4. | ["Full-text search"] | [] |
| publish:publishing | publish | publishing | Publishing data | Datasette includes tools for publishing and deploying your data to the internet. The datasette publish command will deploy a new Datasette instance containing your databases directly to a Heroku or Google Cloud hosting account. You can also use datasette package to create a Docker image that bundles your databases together with the datasette application that is used to serve them. | [] | [] |
| 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. | [] | [] |
| index:contents | index | contents | Contents | Getting started Play with a live demo Try Datasette without installing anything using Glitch Using Datasette on your own computer datasette --get Installation Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Using Docker The Datasette Ecosystem sqlite-utils Dogsheep Pages and API endpoints Top-level index Database Table Row Publishing data datasette publish Publishing to Google Cloud Run Publishing to Heroku Publishing to Vercel Publishing to Fly Custom metadata and plugins datasette package Deploying Datasette Deployment fundamentals Running Datasette using systemd Deploying using buildpacks Running Datasette behind a proxy Nginx proxy configuration Apache proxy configuration JSON API Different shapes Pagination Special JSON arguments Table arguments Column filter arguments Special table arguments Expanding foreign key references Discovering the JSON for a page Running SQL queries Named parameters Views Canned queries Canned query parameters Additional canned query options Writable canned queries Magic parameters JSON API for writable canned queries Pagination Cross-database queries Authentication and permissions Actors Using the "root" actor Permissions Defining permissions with "allow" blocks The /-/allow-debug tool Configuring permissions in metadata.json Controlling access to an instance Controlling access to specific databases Controlling access to specific tables and views Controlling access to specific canned queries Controlling the ability to execute arbitrary SQL Checking permissions in plugins actor_matches_allow() The permissions debug tool The ds_actor cookie Including an expiry time The /-/logout page Built-in permissions view-instance view-database view-database-download view-table view-query execute-sql permissions-debug debug-menu Performance and caching Immutable mode Using "datasette inspect" HTTP caching datasette-hashed-urls CSV export URL parameters Streaming all records Binary data Linking to binary downloads Binary plugins Facet… | ["Datasette"] | [] |
| 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 | [] | [] |
| 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! ✨ 🌟 ✨ $ 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" } ] $ pipx runpip datasette install -U datasette-vega 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 $ datasette plugins [ { "name": "datasette-vega", "static": true, "templates": false, "version": "0.6.2" } ] | ["Installation", "Advanced installation options", "Using pipx"] | [] |
| introspection:id1 | introspection | id1 | Introspection | Datasette includes some pages and JSON API endpoints for introspecting the current instance. These can be used to understand some of the internals of Datasette and to see how a particular instance has been configured. Each of these pages can be viewed in your browser. Add .json to the URL to get back the contents as JSON. | [] | [] |
| 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: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, message_type=datasette.INFO) for details of this feature. | ["Introspection"] | [] |
| custom_templates:customization | custom_templates | customization | Custom pages and templates | Datasette provides a number of ways of customizing the way data is displayed. | [] | [] |
| custom_templates:css-classes-on-the-body | custom_templates | css-classes-on-the-body | CSS classes on the <body> | Every default template includes CSS classes in the body designed to support custom styling. The index template (the top level page at / ) gets this: <body class="index"> The database template ( /dbname ) gets this: <body class="db db-dbname"> The custom SQL template ( /dbname?sql=... ) gets this: <body class="query db-dbname"> A canned query template ( /dbname/queryname ) gets this: <body class="query db-dbname query-queryname"> The table template ( /dbname/tablename ) gets: <body class="table db-dbname table-tablename"> The row template ( /dbname/tablename/rowid ) gets: <body class="row db-dbname table-tablename"> The db-x and table-x classes use the database or table names themselves if they are valid CSS identifiers. If they aren't, we strip any invalid characters out and append a 6 character md5 digest of the original name, in order to ensure that multiple tables which resolve to the same stripped character version still have different CSS classes. Some examples: "simple" => "simple" "MixedCase" => "MixedCase" "-no-leading-hyphens" => "no-leading-hyphens-65bea6" "_no-leading-underscores" => "no-leading-underscores-b921bc" "no spaces" => "no-spaces-7088d7" "-" => "336d5e" "no $ characters" => "no--characters-59e024" <td> and <th> elements also get custom CSS classes reflecting the database column they are representing, for example: <table> <thead> <tr> <th class="col-id" scope="col">id</th> <th class="col-name" scope="col">name</th> </tr> </thead> <tbody> <tr> <td class="col-id"><a href="...">1</a></td> … | ["Custom pages and templates", "Custom CSS and JavaScript"] | [] |
| custom_templates:customization-static-files | custom_templates | customization-static-files | Serving static files | Datasette can serve static files for you, using the --static option. Consider the following directory structure: metadata.json static-files/styles.css static-files/app.js You can start Datasette using --static assets:static-files/ to serve those files from the /assets/ mount point: $ datasette -m metadata.json --static assets:static-files/ --memory The following URLs will now serve the content from those CSS and JS files: http://localhost:8001/assets/styles.css http://localhost:8001/assets/app.js You can reference those files from metadata.json like so: { "extra_css_urls": [ "/assets/styles.css" ], "extra_js_urls": [ "/assets/app.js" ] } | ["Custom pages and templates", "Custom CSS and JavaScript"] | [] |
| custom_templates:publishing-static-assets | custom_templates | publishing-static-assets | Publishing static assets | The datasette publish command can be used to publish your static assets, using the same syntax as above: $ datasette publish cloudrun mydb.db --static assets:static-files/ This will upload the contents of the static-files/ directory as part of the deployment, and configure Datasette to correctly serve the assets from /assets/ . | ["Custom pages and templates", "Custom CSS and JavaScript"] | [] |
| custom_templates:id1 | custom_templates | id1 | Custom pages | You can add templated pages to your Datasette instance by creating HTML files in a pages directory within your templates directory. For example, to add a custom page that is served at http://localhost/about you would create a file in templates/pages/about.html , then start Datasette like this: $ datasette mydb.db --template-dir=templates/ You can nest directories within pages to create a nested structure. To create a http://localhost:8001/about/map page you would create templates/pages/about/map.html . | ["Custom pages and templates"] | [] |
| custom_templates:custom-pages-headers | custom_templates | custom-pages-headers | Custom headers and status codes | Custom pages default to being served with a content-type of text/html; charset=utf-8 and a 200 status code. You can change these by calling a custom function from within your template. For example, to serve a custom page with a 418 I'm a teapot HTTP status code, create a file in pages/teapot.html containing the following: {{ custom_status(418) }} <html> <head><title>Teapot</title></head> <body> I'm a teapot </body> </html> To serve a custom HTTP header, add a custom_header(name, value) function call. For example: {{ custom_status(418) }} {{ custom_header("x-teapot", "I am") }} <html> <head><title>Teapot</title></head> <body> I'm a teapot </body> </html> You can verify this is working using curl like this: $ curl -I 'http://127.0.0.1:8001/teapot' HTTP/1.1 418 date: Sun, 26 Apr 2020 18:38:30 GMT server: uvicorn x-teapot: I am content-type: text/html; charset=utf-8 | ["Custom pages and templates", "Custom pages"] | [] |
| custom_templates:custom-pages-404 | custom_templates | custom-pages-404 | Returning 404s | To indicate that content could not be found and display the default 404 page you can use the raise_404(message) function: {% if not rows %} {{ raise_404("Content not found") }} {% endif %} If you call raise_404() the other content in your template will be ignored. | ["Custom pages and templates", "Custom pages"] | [] |
| custom_templates:custom-pages-redirects | custom_templates | custom-pages-redirects | Custom redirects | You can use the custom_redirect(location) function to redirect users to another page, for example in a file called pages/datasette.html : {{ custom_redirect("https://github.com/simonw/datasette") }} Now requests to http://localhost:8001/datasette will result in a redirect. These redirects are served with a 302 Found status code by default. You can send a 301 Moved Permanently code by passing 301 as the second argument to the function: {{ custom_redirect("https://github.com/simonw/datasette", 301) }} | ["Custom pages and templates", "Custom pages"] | [] |
| getting_started:getting-started | getting_started | getting-started | Getting started | [] | [] | |
| getting_started:getting-started-datasette-get | getting_started | getting-started-datasette-get | datasette --get | The --get option can specify the path to a page within Datasette and cause Datasette to output the content from that path without starting the web server. This means that all of Datasette's functionality can be accessed directly from the command-line. For example: $ datasette --get '/-/versions.json' | jq . { "python": { "version": "3.8.5", "full": "3.8.5 (default, Jul 21 2020, 10:48:26) \n[Clang 11.0.3 (clang-1103.0.32.62)]" }, "datasette": { "version": "0.46+15.g222a84a.dirty" }, "asgi": "3.0", "uvicorn": "0.11.8", "sqlite": { "version": "3.32.3", "fts_versions": [ "FTS5", "FTS4", "FTS3" ], "extensions": { "json1": null }, "compile_options": [ "COMPILER=clang-11.0.3", "ENABLE_COLUMN_METADATA", "ENABLE_FTS3", "ENABLE_FTS3_PARENTHESIS", "ENABLE_FTS4", "ENABLE_FTS5", "ENABLE_GEOPOLY", "ENABLE_JSON1", "ENABLE_PREUPDATE_HOOK", "ENABLE_RTREE", "ENABLE_SESSION", "MAX_VARIABLE_NUMBER=250000", "THREADSAFE=1" ] } } The exit code will be 0 if the request succeeds and 1 if the request produced an HTTP status code other than 200 - e.g. a 404 or 500 error. This means you can use datasette --get / to run tests against a Datasette application in a continuous integration environment such as GitHub Actions. Running datasette without specifying a command runs the default command, datasette serve . See datasette serve --help for the full list of options for that command. | ["Getting started"] | [] |
| changelog:id1 | changelog | id1 | Changelog | [] | [] | |
| changelog:id6 | changelog | id6 | 0.60 (2022-01-13) | ["Changelog"] | [] | |
| changelog:id28 | changelog | id28 | 0.51.1 (2020-10-31) | Improvements to the new Binary data documentation page. | ["Changelog"] | [] |
| changelog:id29 | changelog | id29 | 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)"] | [] |
| changelog:id67 | changelog | id67 | 0.29 (2019-07-07) | ASGI, new plugin hooks, facet by date and much, much more... | ["Changelog"] | [] |
| changelog:v0-29-medium-changes | changelog | v0-29-medium-changes | Easier custom templates for table rows | If you want to customize the display of individual table rows, you can do so using a _table.html template include that looks something like this: {% for row in display_rows %} <div> <h2>{{ row["title"] }}</h2> <p>{{ row["description"] }}<lp> <p>Category: {{ row.display("category_id") }}</p> </div> {% endfor %} This is a backwards incompatible change . If you previously had a custom template called _rows_and_columns.html you need to rename it to _table.html . See Custom templates for full details. | ["Changelog", "0.29 (2019-07-07)"] | [] |
| changelog:id70 | changelog | id70 | 0.27.1 (2019-05-09) | Tiny bugfix release: don't install tests/ in the wrong place. Thanks, Veit Heller. | ["Changelog"] | [] |
| changelog:id74 | changelog | id74 | 0.25.2 (2018-12-16) | datasette publish heroku now uses the python-3.6.7 runtime Added documentation on how to build the documentation Added documentation covering our release process Upgraded to pytest 4.0.2 | ["Changelog"] | [] |
| changelog:foreign-key-expansions | changelog | foreign-key-expansions | Foreign key expansions | When Datasette detects a foreign key reference it attempts to resolve a label for that reference (automatically or using the Specifying the label column for a table metadata option) so it can display a link to the associated row. This expansion is now also available for JSON and CSV representations of the table, using the new _labels=on query string option. See Expanding foreign key references for more details. | ["Changelog", "0.23 (2018-06-18)"] | [] |
| changelog:id137 | changelog | id137 | 0.17 (2018-04-13) | Release 0.17 to fix issues with PyPI | ["Changelog"] | [] |
| changelog:id191 | changelog | id191 | 0.11 (2017-11-14) | Added datasette publish now --force option. This calls now with --force - useful as it means you get a fresh copy of datasette even if Now has already cached that docker layer. Enable --cors by default when running in a container. | ["Changelog"] | [] |
| changelog:id194 | changelog | id194 | 0.9 (2017-11-13) | Added --sql_time_limit_ms and --extra-options . The serve command now accepts --sql_time_limit_ms for customizing the SQL time limit. The publish and package commands now accept --extra-options which can be used to specify additional options to be passed to the datasite serve command when it executes inside the resulting Docker containers. | ["Changelog"] | [] |
| sql_queries:sql | sql_queries | sql | Running SQL queries | Datasette treats SQLite database files as read-only and immutable. This means it is not possible to execute INSERT or UPDATE statements using Datasette, which allows us to expose SELECT statements to the outside world without needing to worry about SQL injection attacks. The easiest way to execute custom SQL against Datasette is through the web UI. The database index page includes a SQL editor that lets you run any SELECT query you like. You can also construct queries using the filter interface on the tables page, then click "View and edit SQL" to open that query in the custom SQL editor. Note that this interface is only available if the execute-sql permission is allowed. Any Datasette SQL query is reflected in the URL of the page, allowing you to bookmark them, share them with others and navigate through previous queries using your browser back button. You can also retrieve the results of any query as JSON by adding .json to the base URL. | [] | [] |
| sql_queries:sql-views | sql_queries | sql-views | Views | If you want to bundle some pre-written SQL queries with your Datasette-hosted database you can do so in two ways. The first is to include SQL views in your database - Datasette will then list those views on your database index page. The quickest way to create views is with the SQLite command-line interface: $ sqlite3 sf-trees.db SQLite version 3.19.3 2017-06-27 16:48:08 Enter ".help" for usage hints. sqlite> CREATE VIEW demo_view AS select qSpecies from Street_Tree_List; <CTRL+D> | ["Running SQL queries"] | [] |
| sql_queries:id1 | sql_queries | id1 | Canned queries | As an alternative to adding views to your database, you can define canned queries inside your metadata.json file. Here's an example: { "databases": { "sf-trees": { "queries": { "just_species": { "sql": "select qSpecies from Street_Tree_List" } } } } } Then run Datasette like this: datasette sf-trees.db -m metadata.json Each canned query will be listed on the database index page, and will also get its own URL at: /database-name/canned-query-name For the above example, that URL would be: /sf-trees/just_species You can optionally include "title" and "description" keys to show a title and description on the canned query page. As with regular table metadata you can alternatively specify "description_html" to have your description rendered as HTML (rather than having HTML special characters escaped). | ["Running SQL queries"] | [] |
| sql_queries:canned-queries-options | sql_queries | canned-queries-options | Additional canned query options | Additional options can be specified for canned queries in the YAML or JSON configuration. | ["Running SQL queries", "Canned queries"] | [] |
| sql_queries:hide-sql | sql_queries | hide-sql | hide_sql | Canned queries default to displaying their SQL query at the top of the page. If the query is extremely long you may want to hide it by default, with a "show" link that can be used to make it visible. Add the "hide_sql": true option to hide the SQL query by default. | ["Running SQL queries", "Canned queries", "Additional canned query options"] | [] |
| sql_queries:canned-queries-writable | sql_queries | canned-queries-writable | Writable canned queries | Canned queries by default are read-only. You can use the "write": true key to indicate that a canned query can write to the database. See Controlling access to specific canned queries for details on how to add permission checks to canned queries, using the "allow" key. { "databases": { "mydatabase": { "queries": { "add_name": { "sql": "INSERT INTO names (name) VALUES (:name)", "write": true } } } } } This configuration will create a page at /mydatabase/add_name displaying a form with a name field. Submitting that form will execute the configured INSERT query. You can customize how Datasette represents success and errors using the following optional properties: on_success_message - the message shown when a query is successful on_success_redirect - the path or URL the user is redirected to on success on_error_message - the message shown when a query throws an error on_error_redirect - the path or URL the user is redirected to on error For example: { "databases": { "mydatabase": { "queries": { "add_name": { "sql": "INSERT INTO names (name) VALUES (:name)", "write": true, "on_success_message": "Name inserted", "on_success_redirect": "/mydatabase/names", "on_error_message": "Name insert failed", "on_error_redirect": "/mydatabase" } } } } } You can use "p… | ["Running SQL queries", "Canned queries"] | [] |
| sql_queries:canned-queries-magic-parameters | sql_queries | canned-queries-magic-parameters | Magic parameters | Named parameters that start with an underscore are special: they can be used to automatically add values created by Datasette that are not contained in the incoming form fields or query string. These magic parameters are only supported for canned queries: to avoid security issues (such as queries that extract the user's private cookies) they are not available to SQL that is executed by the user as a custom SQL query. Available magic parameters are: _actor_* - e.g. _actor_id , _actor_name Fields from the currently authenticated Actors . _header_* - e.g. _header_user_agent Header from the incoming HTTP request. The key should be in lower case and with hyphens converted to underscores e.g. _header_user_agent or _header_accept_language . _cookie_* - e.g. _cookie_lang The value of the incoming cookie of that name. _now_epoch The number of seconds since the Unix epoch. _now_date_utc The date in UTC, e.g. 2020-06-01 _now_datetime_utc The ISO 8601 datetime in UTC, e.g. 2020-06-24T18:01:07Z _random_chars_* - e.g. … | ["Running SQL queries", "Canned queries"] | [] |
| sql_queries:canned-queries-json-api | sql_queries | canned-queries-json-api | JSON API for writable canned queries | Writable canned queries can also be accessed using a JSON API. You can POST data to them using JSON, and you can request that their response is returned to you as JSON. To submit JSON to a writable canned query, encode key/value parameters as a JSON document: POST /mydatabase/add_message {"message": "Message goes here"} You can also continue to submit data using regular form encoding, like so: POST /mydatabase/add_message message=Message+goes+here There are three options for specifying that you would like the response to your request to return JSON data, as opposed to an HTTP redirect to another page. Set an Accept: application/json header on your request Include ?_json=1 in the URL that you POST to Include "_json": 1 in your JSON body, or &_json=1 in your form encoded body The JSON response will look like this: { "ok": true, "message": "Query executed, 1 row affected", "redirect": "/data/add_name" } The "message" and "redirect" values here will take into account on_success_message , on_success_redirect , on_error_message and on_error_redirect , if they have been set. | ["Running SQL queries", "Canned queries"] | [] |
| sql_queries:id2 | sql_queries | id2 | Pagination | Datasette's default table pagination is designed to be extremely efficient. SQL OFFSET/LIMIT pagination can have a significant performance penalty once you get into multiple thousands of rows, as each page still requires the database to scan through every preceding row to find the correct offset. When paginating through tables, Datasette instead orders the rows in the table by their primary key and performs a WHERE clause against the last seen primary key for the previous page. For example: select rowid, * from Tree_List where rowid > 200 order by rowid limit 101 This represents page three for this particular table, with a page size of 100. Note that we request 101 items in the limit clause rather than 100. This allows us to detect if we are on the last page of the results: if the query returns less than 101 rows we know we have reached the end of the pagination set. Datasette will only return the first 100 rows - the 101st is used purely to detect if there should be another page. Since the where clause acts against the index on the primary key, the query is extremely fast even for records that are a long way into the overall pagination set. | ["Running SQL queries"] | [] |
| metadata:id1 | metadata | id1 | Metadata | Data loves metadata. Any time you run Datasette you can optionally include a JSON file with metadata about your databases and tables. Datasette will then display that information in the web UI. Run Datasette like this: datasette database1.db database2.db --metadata metadata.json Your metadata.json file can look something like this: { "title": "Custom title for your index page", "description": "Some description text can go here", "license": "ODbL", "license_url": "https://opendatacommons.org/licenses/odbl/", "source": "Original Data Source", "source_url": "http://example.com/" } You can optionally use YAML instead of JSON, see Using YAML for metadata . The above metadata will be displayed on the index page of your Datasette-powered site. The source and license information will also be included in the footer of every page served by Datasette. Any special HTML characters in description will be escaped. If you want to include HTML in your description, you can use a description_html property instead. | [] | [] |
| 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: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-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-page-size | metadata | metadata-page-size | Setting a custom page size | Datasette defaults to displaing 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: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-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-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"] | [] |
| facets:id1 | facets | id1 | Facets | Datasette facets can be used to add a faceted browse interface to any database table. With facets, tables are displayed along with a summary showing the most common values in specified columns. These values can be selected to further filter the table. Facets can be specified in two ways: using query string parameters, or in metadata.json configuration for the table. | [] | [] |
| facets:facets-in-query-strings | facets | facets-in-query-strings | Facets in query strings | To turn on faceting for specific columns on a Datasette table view, add one or more _facet=COLUMN parameters to the URL. For example, if you want to turn on facets for the city_id and state columns, construct a URL that looks like this: /dbname/tablename?_facet=state&_facet=city_id This works for both the HTML interface and the .json view. When enabled, facets will cause a facet_results block to be added to the JSON output, looking something like this: { "state": { "name": "state", "results": [ { "value": "CA", "label": "CA", "count": 10, "toggle_url": "http://...?_facet=city_id&_facet=state&state=CA", "selected": false }, { "value": "MI", "label": "MI", "count": 4, "toggle_url": "http://...?_facet=city_id&_facet=state&state=MI", "selected": false }, { "value": "MC", "label": "MC", "count": 1, "toggle_url": "http://...?_facet=city_id&_facet=state&state=MC", "selected": false } ], "truncated": false } "city_id": { "name": "city_id", "results": [ { "value": 1, "label": "San Francisco", "count": 6, "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=1", "selected": false }, { "value": 2, "label": "Los Angeles", "count": 4, "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=2", "selected": false }, { "value": 3, "label": "Detroit", "count": 4, "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=3", "selected": false }, { "value": 4, "label": "Memnonia", "count": 1, "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=4", "selected": false } ], "truncated": false } } If Datasette detects that a column is a foreign key… | ["Facets"] | [] |
| facets:facets-metadata | facets | facets-metadata | Facets in metadata.json | You can turn facets on by default for specific tables by adding them to a "facets" key in a Datasette Metadata file. Here's an example that turns on faceting by default for the qLegalStatus column in the Street_Tree_List table in the sf-trees database: { "databases": { "sf-trees": { "tables": { "Street_Tree_List": { "facets": ["qLegalStatus"] } } } } } Facets defined in this way will always be shown in the interface and returned in the API, regardless of the _facet arguments passed to the view. You can specify array or date facets in metadata using JSON objects with a single key of array or date and a value specifying the column, like this: { "facets": [ {"array": "tags"}, {"date": "created"} ] } | ["Facets"] | [] |
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