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| 1 | 1 | Plugin hooks | Datasette plugins use plugin hooks to customize Datasette's behavior. These hooks are powered by the pluggy plugin system. Each plugin can implement one or more hooks using the @hookimpl decorator against a function named that matches one of the hooks documented on this page. When you implement a plugin hook you can accept any or all of the parameters that are documented as being passed to that hook. For example, you can implement the render_cell plugin hook like this even though the full documented hook signature is render_cell(row, value, column, table, database, datasette) : @hookimpl def render_cell(value, column): if column == "stars": return "*" * int(value) List of plugin hooks prepare_connection(conn, database, datasette) prepare_jinja2_environment(env, datasette) extra_template_vars(template, database, table, columns, view_name, request, datasette) extra_css_urls(template, database, table, columns, view_name, request, datasette) extra_js_urls(template, database, table, columns, view_name, request, datasette) extra_body_script(template, database, table, columns, view_name, request, datasette) publish_subcommand(publish) render_cell(row, value, column, table, database, datasette) register_output_renderer(datasette) register_routes(datasette) register_commands(cli) … | 37 | |
| 2 | 2 | prepare_connection(conn, database, datasette) | conn - sqlite3 connection object The connection that is being opened database - string The name of the database datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook is called when a new SQLite database connection is created. You can use it to register custom SQL functions , aggregates and collations. For example: from datasette import hookimpl import random @hookimpl def prepare_connection(conn): conn.create_function( "random_integer", 2, random.randint ) This registers a SQL function called random_integer which takes two arguments and can be called like this: select random_integer(1, 10); Examples: datasette-jellyfish , datasette-jq , datasette-haversine , datasette-rure | 37 | |
| 3 | 3 | prepare_jinja2_environment(env, datasette) | env - jinja2 Environment The template environment that is being prepared datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook is called with the Jinja2 environment that is used to evaluate Datasette HTML templates. You can use it to do things like register custom template filters , for example: from datasette import hookimpl @hookimpl def prepare_jinja2_environment(env): env.filters["uppercase"] = lambda u: u.upper() You can now use this filter in your custom templates like so: Table name: {{ table|uppercase }} This function can return an awaitable function if it needs to run any async code. Examples: datasette-edit-templates | 37 | |
| 4 | 4 | extra_template_vars(template, database, table, columns, view_name, request, datasette) | Extra template variables that should be made available in the rendered template context. template - string The template that is being rendered, e.g. database.html database - string or None The name of the database, or None if the page does not correspond to a database (e.g. the root page) table - string or None The name of the table, or None if the page does not correct to a table columns - list of strings or None The names of the database columns that will be displayed on this page. None if the page does not contain a table. view_name - string The name of the view being displayed. ( index , database , table , and row are the most important ones.) request - Request object or None The current HTTP request. This can be None if the request object is not available. datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook can return one of three different types: Dictionary If you return a dict… | 37 | |
| 5 | 5 | extra_css_urls(template, database, table, columns, view_name, request, datasette) | This takes the same arguments as extra_template_vars(...) Return a list of extra CSS URLs that should be included on the page. These can take advantage of the CSS class hooks described in Custom pages and templates . This can be a list of URLs: from datasette import hookimpl @hookimpl def extra_css_urls(): return [ "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css" ] Or a list of dictionaries defining both a URL and an SRI hash : @hookimpl def extra_css_urls(): return [ { "url": "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css", "sri": "sha384-9gVQ4dYFwwWSjIDZnLEWnxCjeSWFphJiwGPXr1jddIhOegiu1FwO5qRGvFXOdJZ4", } ] This function can also return an awaitable function, useful if it needs to run any async code: @hookimpl def extra_css_urls(datasette): async def inner(): db = datasette.get_database() results = await db.execute( "select url from css_files" ) return [r[0] for r in results] return inner Examples: datasette-cluster-map , datasette-vega | 37 | |
| 6 | 6 | extra_js_urls(template, database, table, columns, view_name, request, datasette) | This takes the same arguments as extra_template_vars(...) This works in the same way as extra_css_urls() but for JavaScript. You can return a list of URLs, a list of dictionaries or an awaitable function that returns those things: from datasette import hookimpl @hookimpl def extra_js_urls(): return [ { "url": "https://code.jquery.com/jquery-3.3.1.slim.min.js", "sri": "sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo", } ] You can also return URLs to files from your plugin's static/ directory, if you have one: @hookimpl def extra_js_urls(): return ["/-/static-plugins/your-plugin/app.js"] Note that your-plugin here should be the hyphenated plugin name - the name that is displayed in the list on the /-/plugins debug page. If your code uses JavaScript modules you should include the "module": True key. See Custom CSS and JavaScript for more details. @hookimpl def extra_js_urls(): return [ { "url": "/-/static-plugins/your-plugin/app.js", "module": True, } ] Examples: datasette-cluster-map , datasette-vega | 37 | |
| 7 | 7 | extra_body_script(template, database, table, columns, view_name, request, datasette) | Extra JavaScript to be added to a <script> block at the end of the <body> element on the page. This takes the same arguments as extra_template_vars(...) The template , database , table and view_name options can be used to return different code depending on which template is being rendered and which database or table are being processed. The datasette instance is provided primarily so that you can consult any plugin configuration options that may have been set, using the datasette.plugin_config(plugin_name) method documented above. This function can return a string containing JavaScript, or a dictionary as described below, or a function or awaitable function that returns a string or dictionary. Use a dictionary if you want to specify that the code should be placed in a <script type="module">...</script> element: @hookimpl def extra_body_script(): return { "module": True, "script": "console.log('Your JavaScript goes here...')", } This will add the following to the end of your page: <script type="module">console.log('Your JavaScript goes here...')</script> Example: datasette-cluster-map | 37 | |
| 8 | 8 | publish_subcommand(publish) | publish - Click publish command group The Click command group for the datasette publish subcommand This hook allows you to create new providers for the datasette publish command. Datasette uses this hook internally to implement the default cloudrun and heroku subcommands, so you can read their source to see examples of this hook in action. Let's say you want to build a plugin that adds a datasette publish my_hosting_provider --api_key=xxx mydatabase.db publish command. Your implementation would start like this: from datasette import hookimpl from datasette.publish.common import ( add_common_publish_arguments_and_options, ) import click @hookimpl def publish_subcommand(publish): @publish.command() @add_common_publish_arguments_and_options @click.option( "-k", "--api_key", help="API key for talking to my hosting provider", ) def my_hosting_provider( files, metadata, extra_options, branch, template_dir, plugins_dir, static, install, plugin_secret, version_note, secret, title, license, license_url, source, source_url, about, about_url, api_key, ): ... Examples: datasette-publish-fly , datasette-publish-vercel | 37 | |
| 9 | 9 | render_cell(row, value, column, table, database, datasette) | Lets you customize the display of values within table cells in the HTML table view. row - sqlite.Row The SQLite row object that the value being rendered is part of value - string, integer, float, bytes or None The value that was loaded from the database column - string The name of the column being rendered table - string or None The name of the table - or None if this is a custom SQL query database - string The name of the database datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. If your hook returns None , it will be ignored. Use this to indicate that your hook is not able to custom render this particular value. If the hook returns a string, that string will be rendered in the table cell. If you want to return HTML markup you can do so by returning a jinja2.Markup object. You can also return an awaitable function which returns a value. Datasette will loop through all available render_cell hooks and display the value returned by the first one that does not return None . Here is an example of a custom render_cell() plugin wh… | 37 | |
| 10 | 10 | register_output_renderer(datasette) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) Registers a new output renderer, to output data in a custom format. The hook function should return a dictionary, or a list of dictionaries, of the following shape: @hookimpl def register_output_renderer(datasette): return { "extension": "test", "render": render_demo, "can_render": can_render_demo, # Optional } This will register render_demo to be called when paths with the extension .test (for example /database.test , /database/table.test , or /database/table/row.test ) are requested. render_demo is a Python function. It can be a regular function or an async def render_demo() awaitable function, depending on if it needs to make any asynchronous calls. can_render_demo is a Python function (or async def function) which accepts the same arguments as render_demo but just returns True or False . It lets Datasette know if the current SQL query can be represented by the plugin - and hence influnce if a link to this output format is displayed in the user interface. If you omit the "can_render" key from the dictionary every query will be treated as being supported by the plugin. When a request is received, the "render" callback function is called with zero or more of the following arguments. Datasette will inspect your callback function and pass arguments that match its function signature. datasette - Datasette class For accessing plugin configuration and executing queries. columns - list of strings The names of the columns r… | 37 | |
| 11 | 11 | register_routes(datasette) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) Register additional view functions to execute for specified URL routes. Return a list of (regex, view_function) pairs, something like this: from datasette import hookimpl, Response import html async def hello_from(request): name = request.url_vars["name"] return Response.html( "Hello from {}".format(html.escape(name)) ) @hookimpl def register_routes(): return [(r"^/hello-from/(?P<name>.*)$", hello_from)] The view functions can take a number of different optional arguments. The corresponding argument will be passed to your function depending on its named parameters - a form of dependency injection. The optional view function arguments are as follows: datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. request - Request object The current HTTP request. scope - dictionary The incoming ASGI scope dictionary. send - function The ASGI send function. receive - function The ASGI receive function. The view funct… | 37 | |
| 12 | 12 | register_commands(cli) | cli - the root Datasette Click command group Use this to register additional CLI commands Register additional CLI commands that can be run using datsette yourcommand ... . This provides a mechanism by which plugins can add new CLI commands to Datasette. This example registers a new datasette verify file1.db file2.db command that checks if the provided file paths are valid SQLite databases: from datasette import hookimpl import click import sqlite3 @hookimpl def register_commands(cli): @cli.command() @click.argument( "files", type=click.Path(exists=True), nargs=-1 ) def verify(files): "Verify that files can be opened by Datasette" for file in files: conn = sqlite3.connect(str(file)) try: conn.execute("select * from sqlite_master") except sqlite3.DatabaseError: raise click.ClickException( "Invalid database: {}".format(file) ) The new command can then be executed like so: datasette verify fixtures.db Help text (from the docstring for the function plus any defined Click arguments or options) will become available using: datasette verify --help Plugins can register multiple commands by making multiple calls to the @cli.command() decorator. Consult the Click documentation for full details on how to build a CLI command, including how to define arguments and options. Note that register_commands() plugins cannot used with the --plugins-dir mechanism - they need to be installed into the same virtual environment as Datasette using pip install . Provided it has a setup.py file (see Packaging a plugin ) you can run pip install directly against the directory in which you are developing your plugin like so: pip install -e path/t… | 37 | |
| 13 | 13 | register_facet_classes() | Return a list of additional Facet subclasses to be registered. The design of this plugin hook is unstable and may change. See issue 830 . Each Facet subclass implements a new type of facet operation. The class should look like this: class SpecialFacet(Facet): # This key must be unique across all facet classes: type = "special" async def suggest(self): # Use self.sql and self.params to suggest some facets suggested_facets = [] suggested_facets.append( { "name": column, # Or other unique name # Construct the URL that will enable this facet: "toggle_url": self.ds.absolute_url( self.request, path_with_added_args( self.request, {"_facet": column} ), ), } ) return suggested_facets async def facet_results(self): # This should execute the facet operation and return results, again # using self.sql and self.params as the starting point facet_results = [] facets_timed_out = [] facet_size = self.get_facet_size() # Do some calculations here... for column in columns_selected_for_facet: try: facet_results_values = [] # More calculations... facet_results_values.append( { "value": value, "label": label, "count": count, "toggle_url": self.ds.absolute_url( self.request, toggle_path ), "selected": selected, } ) facet_results.append( { "name": column, "results": facet_results_values, "trunc… | 37 | |
| 14 | 14 | asgi_wrapper(datasette) | Return an ASGI middleware wrapper function that will be applied to the Datasette ASGI application. This is a very powerful hook. You can use it to manipulate the entire Datasette response, or even to configure new URL routes that will be handled by your own custom code. You can write your ASGI code directly against the low-level specification, or you can use the middleware utilities provided by an ASGI framework such as Starlette . This example plugin adds a x-databases HTTP header listing the currently attached databases: from datasette import hookimpl from functools import wraps @hookimpl def asgi_wrapper(datasette): def wrap_with_databases_header(app): @wraps(app) async def add_x_databases_header( scope, receive, send ): async def wrapped_send(event): if event["type"] == "http.response.start": original_headers = ( event.get("headers") or [] ) event = { "type": event["type"], "status": event["status"], "headers": original_headers + [ [ b"x-databases", ", ".join( datasette.databases.keys() ).encode("utf-8"), ] ], } await send(event) await app(scope, receive, wrapped_send) return add_x_databases_header return wrap_with_databases_header Examples: datasette-cors , datasette-pyinstrument , datasette-total-page-time | 37 | |
| 15 | 15 | startup(datasette) | This hook fires when the Datasette application server first starts up. You can implement a regular function, for example to validate required plugin configuration: @hookimpl def startup(datasette): config = datasette.plugin_config("my-plugin") or {} assert ( "required-setting" in config ), "my-plugin requires setting required-setting" Or you can return an async function which will be awaited on startup. Use this option if you need to make any database queries: @hookimpl def startup(datasette): async def inner(): db = datasette.get_database() if "my_table" not in await db.table_names(): await db.execute_write( """ create table my_table (mycol text) """ ) return inner Potential use-cases: Run some initialization code for the plugin Create database tables that a plugin needs on startup Validate the metadata configuration for a plugin on startup, and raise an error if it is invalid If you are writing unit tests for a plugin that uses this hook and doesn't exercise Datasette by sending any simulated requests through it you will need to explicitly call await ds.invoke_startup() in your tests. An example: @pytest.mark.asyncio async def test_my_plugin(): ds = Datasette() await ds.invoke_startup() # Rest of test goes here Examples: datasette-saved-queries , datasette-init | 37 | |
| 16 | 16 | canned_queries(datasette, database, actor) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. database - string The name of the database. actor - dictionary or None The currently authenticated actor . Use this hook to return a dictionary of additional canned query definitions for the specified database. The return value should be the same shape as the JSON described in the canned query documentation. from datasette import hookimpl @hookimpl def canned_queries(datasette, database): if database == "mydb": return { "my_query": { "sql": "select * from my_table where id > :min_id" } } The hook can alternatively return an awaitable function that returns a list. Here's an example that returns queries that have been stored in the saved_queries database table, if one exists: from datasette import hookimpl @hookimpl def canned_queries(datasette, database): async def inner(): db = datasette.get_database(database) if await db.table_exists("saved_queries"): results = await db.execute( "select name, sql from saved_queries" ) return { result["name"]: {"sql": result["sql"]} for result in results } return inner The actor parameter can be used to include the currently authenticated actor in your decision. Here's an example that returns saved queries that were saved by that actor: from datasette import hookimpl @hookimpl def canned_queries… | 37 | |
| 17 | 17 | actor_from_request(datasette, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. request - Request object The current HTTP request. This is part of Datasette's authentication and permissions system . The function should attempt to authenticate an actor (either a user or an API actor of some sort) based on information in the request. If it cannot authenticate an actor, it should return None . Otherwise it should return a dictionary representing that actor. Here's an example that authenticates the actor based on an incoming API key: from datasette import hookimpl import secrets SECRET_KEY = "this-is-a-secret" @hookimpl def actor_from_request(datasette, request): authorization = ( request.headers.get("authorization") or "" ) expected = "Bearer {}".format(SECRET_KEY) if secrets.compare_digest(authorization, expected): return {"id": "bot"} If you install this in your plugins directory you can test it like this: $ curl -H 'Authorization: Bearer this-is-a-secret' http://localhost:8003/-/actor.json Instead of returning a dictionary, this function can return an awaitable function which itself returns either None or a dictionary. This is useful for authentication functions that need to make a database query - for example: from datasette import hookimpl @hookimpl def actor_from_request(datasette, request): async def inner(): token = request.args.get("_token") if not token: return None # Look up ?_token=xxx in sessions table result = await datasette.get_database().execute( "select count(*) from sessions wher… | 37 | |
| 18 | 18 | filters_from_request(request, database, table, datasette) | request - Request object The current HTTP request. database - string The name of the database. table - string The name of the table. datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. This hook runs on the table page, and can influence the where clause of the SQL query used to populate that page, based on query string arguments on the incoming request. The hook should return an instance of datasette.filters.FilterArguments which has one required and three optional arguments: return FilterArguments( where_clauses=["id > :max_id"], params={"max_id": 5}, human_descriptions=["max_id is greater than 5"], extra_context={}, ) The arguments to the FilterArguments class constructor are as follows: where_clauses - list of strings, required A list of SQL fragments that will be inserted into the SQL query, joined by the and operator. These can include :named parameters which will be populated using data in params . params - dictionary, optional Additional keyword arguments to be used when the query is executed. These should match any :arguments in the where clauses. … | 37 | |
| 19 | 19 | permission_allowed(datasette, actor, action, resource) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary The current actor, as decided by actor_from_request(datasette, request) . action - string The action to be performed, e.g. "edit-table" . resource - string or None An identifier for the individual resource, e.g. the name of the table. Called to check that an actor has permission to perform an action on a resource. Can return True if the action is allowed, False if the action is not allowed or None if the plugin does not have an opinion one way or the other. Here's an example plugin which randomly selects if a permission should be allowed or denied, except for view-instance which always uses the default permission scheme instead. from datasette import hookimpl import random @hookimpl def permission_allowed(action): if action != "view-instance": # Return True or False at random return random.random() > 0.5 # Returning None falls back to default permissions This function can alternatively return an awaitable function which itself returns True , False or None . You can use this option if you need to execute additional database queries using await datasette.execute(...) . Here's an example that allows users to view the admin_log table only if their actor id is present in the admin_users table. It aso disallows arbitrary SQL queries for the staff… | 37 | |
| 20 | 20 | register_magic_parameters(datasette) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . Magic parameters can be used to add automatic parameters to canned queries . This plugin hook allows additional magic parameters to be defined by plugins. Magic parameters all take this format: _prefix_rest_of_parameter . The prefix indicates which magic parameter function should be called - the rest of the parameter is passed as an argument to that function. To register a new function, return it as a tuple of (string prefix, function) from this hook. The function you register should take two arguments: key and request , where key is the rest_of_parameter portion of the parameter and request is the current Request object . This example registers two new magic parameters: :_request_http_version returning the HTTP version of the current request, and :_uuid_new which returns a new UUID: from uuid import uuid4 def uuid(key, request): if key == "new": return str(uuid4()) else: raise KeyError def request(key, request): if key == "http_version": return request.scope["http_version"] else: raise KeyError @hookimpl def register_magic_parameters(datasette): return [ ("request", request), ("uuid", uuid), ] | 37 | |
| 21 | 21 | forbidden(datasette, request, message) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. request - Request object The current HTTP request. message - string A message hinting at why the request was forbidden. Plugins can use this to customize how Datasette responds when a 403 Forbidden error occurs - usually because a page failed a permission check, see Permissions . If a plugin hook wishes to react to the error, it should return a Response object . This example returns a redirect to a /-/login page: from datasette import hookimpl from urllib.parse import urlencode @hookimpl def forbidden(request, message): return Response.redirect( "/-/login?=" + urlencode({"message": message}) ) The function can alternatively return an awaitable function if it needs to make any asynchronous method calls. This example renders a template: from datasette import hookimpl, Response @hookimpl def forbidden(datasette): async def inner(): return Response.html( await datasette.render_template( "render_message.html", request=request ) ) return inner | 37 | |
| 22 | 22 | handle_exception(datasette, request, exception) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. request - Request object The current HTTP request. exception - Exception The exception that was raised. This hook is called any time an unexpected exception is raised. You can use it to record the exception. If your handler returns a Response object it will be returned to the client in place of the default Datasette error page. The handler can return a response directly, or it can return return an awaitable function that returns a response. This example logs an error to Sentry and then renders a custom error page: from datasette import hookimpl, Response import sentry_sdk @hookimpl def handle_exception(datasette, exception): sentry_sdk.capture_exception(exception) async def inner(): return Response.html( await datasette.render_template( "custom_error.html", request=request ) ) return inner Example: datasette-sentry | 37 | |
| 23 | 23 | menu_links(datasette, actor, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary or None The currently authenticated actor . request - Request object or None The current HTTP request. This can be None if the request object is not available. This hook allows additional items to be included in the menu displayed by Datasette's top right menu icon. The hook should return a list of {"href": "...", "label": "..."} menu items. These will be added to the menu. It can alternatively return an async def awaitable function which returns a list of menu items. This example adds a new menu item but only if the signed in user is "root" : from datasette import hookimpl @hookimpl def menu_links(datasette, actor): if actor and actor.get("id") == "root": return [ { "href": datasette.urls.path( "/-/edit-schema" ), "label": "Edit schema", }, ] Using datasette.urls here ensures that links in the menu will take the base_url setting into account. Examples: datasette-search-all , datasette-graphql | 37 | |
| 24 | 24 | table_actions(datasette, actor, database, table, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary or None The currently authenticated actor . database - string The name of the database. table - string The name of the table. request - Request object or None The current HTTP request. This can be None if the request object is not available. This hook allows table actions to be displayed in a menu accessed via an action icon at the top of the table page. It should return a list of {"href": "...", "label": "..."} menu items. It can alternatively return an async def awaitable function which returns a list of menu items. This example adds a new table action if the signed in user is "root" : from datasette import hookimpl @hookimpl def table_actions(datasette, actor, database, table): if actor and actor.get("id") == "root": return [ { "href": datasette.urls.path( "/-/edit-schema/{}/{}".format( database, table ) ), "label": "Edit schema for this table", } ] Example: datasette-graphql | 37 | |
| 25 | 25 | database_actions(datasette, actor, database, request) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. actor - dictionary or None The currently authenticated actor . database - string The name of the database. request - Request object The current HTTP request. This hook is similar to table_actions(datasette, actor, database, table, request) but populates an actions menu on the database page. Example: datasette-graphql | 37 | |
| 26 | 26 | skip_csrf(datasette, scope) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. scope - dictionary The ASGI scope for the incoming HTTP request. This hook can be used to skip CSRF protection for a specific incoming request. For example, you might have a custom path at /submit-comment which is designed to accept comments from anywhere, whether or not the incoming request originated on the site and has an accompanying CSRF token. This example will disable CSRF protection for that specific URL path: from datasette import hookimpl @hookimpl def skip_csrf(scope): return scope["path"] == "/submit-comment" If any of the currently active skip_csrf() plugin hooks return True , CSRF protection will be skipped for the request. | 37 | |
| 27 | 27 | get_metadata(datasette, key, database, table) | datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . actor - dictionary or None The currently authenticated actor . database - string or None The name of the database metadata is being asked for. table - string or None The name of the table. key - string or None The name of the key for which data is being asked for. This hook is responsible for returning a dictionary corresponding to Datasette Metadata . This function is passed the database , table and key which were passed to the upstream internal request for metadata. Regardless, it is important to return a global metadata object, where "databases": [] would be a top-level key. The dictionary returned here, will be merged with, and overwritten by, the contents of the physical metadata.yaml if one is present. The design of this plugin hook does not currently provide a mechanism for interacting with async code, and may change in the future. See issue 1384 . @hookimpl def get_metadata(datasette, key, database, table): metadata = { "title": "This will be the Datasette landing page title!", "description": get_instance_description(datasette), "databases": [], } for db_name, db_data_dict in get_my_database_meta( datasette, database, table, key ): … | 37 | |
| 28 | 28 | Getting started | 37 | ||
| 29 | 29 | Play with a live demo | The best way to experience Datasette for the first time is with a demo: global-power-plants.datasettes.com provides a searchable database of power plants around the world, using data from the World Resources Institude rendered using the datasette-cluster-map plugin. fivethirtyeight.datasettes.com shows Datasette running against over 400 datasets imported from the FiveThirtyEight GitHub repository . | 37 | |
| 30 | 30 | Follow a tutorial | Datasette has several tutorials to help you get started with the tool. Try one of the following: Exploring a database with Datasette shows how to use the Datasette web interface to explore a new database. Learn SQL with Datasette introduces SQL, and shows how to use that query language to ask questions of your data. Cleaning data with sqlite-utils and Datasette guides you through using sqlite-utils to turn a CSV file into a database that you can explore using Datasette. | 37 | |
| 31 | 31 | Datasette in your browser with Datasette Lite | Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. You can pass a URL to a CSV, SQLite or raw SQL file directly to Datasette Lite to explore that data in your browser. This example link opens Datasette Lite and loads the SQL Murder Mystery example database from Northwestern University Knight Lab . | 37 | |
| 32 | 32 | Try Datasette without installing anything using Glitch | Glitch is a free online tool for building web apps directly from your web browser. You can use Glitch to try out Datasette without needing to install any software on your own computer. Here's a demo project on Glitch which you can use as the basis for your own experiments: glitch.com/~datasette-csvs Glitch allows you to "remix" any project to create your own copy and start editing it in your browser. You can remix the datasette-csvs project by clicking this button: Find a CSV file and drag it onto the Glitch file explorer panel - datasette-csvs will automatically convert it to a SQLite database (using sqlite-utils ) and allow you to start exploring it using Datasette. If your CSV file has a latitude and longitude column you can visualize it on a map by uncommenting the datasette-cluster-map line in the requirements.txt file using the Glitch file editor. Need some data? Try this Public Art Data for the city of Seattle - hit "Export" and select "CSV" to download it as a CSV file. For more on how this works, see Running Datasette on Glitch . | 37 | |
| 33 | 33 | Using Datasette on your own computer | First, follow the Installation instructions. Now you can run Datasette against a SQLite file on your computer using the following command: datasette path/to/database.db This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface. Add -o to open your browser automatically once Datasette has started: datasette path/to/database.db -o Use Chrome on OS X? You can run datasette against your browser history like so: datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock The --nolock option ignores any file locks. This is safe as Datasette will open the file in read-only mode. Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data: http://localhost:8001/History/downloads.json will return that data as JSON: { "database": "History", "columns": [ "id", "current_path", "target_path", "start_time", "received_bytes", "total_bytes", ... ], "rows": [ [ 1, "/Users/simonw/Downloads/DropboxInstaller.dmg", "/Users/simonw/Downloads/DropboxInstaller.dmg", 13097290269022132, 626688, 0, ... ] ] } http://localhost:8001/History/downloads.json?_shape=objects will return that data as JSON in a more convenient format: { ... "rows": [ { "start_time": 13097290269022132, "interrupt_reason": 0, "hash": "", "id": 1, "site_url": "", "referrer": "https://www.dropbox.com/downloading?src=index", ... } ] } | 37 | |
| 34 | 34 | 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. Here's an example : Facets can be specified in two ways: using query string parameters, or in metadata.json configuration for the table. | 37 | |
| 35 | 35 | 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 detect… | 37 | |
| 36 | 36 | 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"} ] } You can change the default facet size (the number of results shown for each facet) for a table using facet_size : { "databases": { "sf-trees": { "tables": { "Street_Tree_List": { "facets": ["qLegalStatus"], "facet_size": 10 } } } } } | 37 | |
| 37 | 37 | Suggested facets | Datasette's table UI will suggest facets for the user to apply, based on the following criteria: For the currently filtered data are there any columns which, if applied as a facet... Will return 30 or less unique options Will return more than one unique option Will return less unique options than the total number of filtered rows And the query used to evaluate this criteria can be completed in under 50ms That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries. This means suggested facets are unlikely to appear for tables with millions of records in them. | 37 | |
| 38 | 38 | Speeding up facets with indexes | The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by. Adding indexes can be performed using the sqlite3 command-line utility. Here's how to add an index on the state column in a table called Food_Trucks : $ sqlite3 mydatabase.db SQLite version 3.19.3 2017-06-27 16:48:08 Enter ".help" for usage hints. sqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks("state"); Or using the sqlite-utils command-line utility: $ sqlite-utils create-index mydatabase.db Food_Trucks state | 37 | |
| 39 | 39 | Facet by JSON array | If your SQLite installation provides the json1 extension (you can check using /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns. This is useful for modelling things like tags without needing to break them out into a new table. Example here: latest.datasette.io/fixtures/facetable?_facet_array=tags | 37 | |
| 40 | 40 | Facet by date | If Datasette finds any columns that contain dates in the first 100 values, it will offer a faceting interface against the dates of those values. This works especially well against timestamp values such as 2019-03-01 12:44:00 . Example here: latest.datasette.io/fixtures/facetable?_facet_date=created | 37 | |
| 41 | 41 | Settings | 37 | ||
| 42 | 42 | 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 | 37 | |
| 43 | 43 | 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/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 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 settings.json - settings that would normally be passed using --setting - here they should be stored as a JSON object of key/value pairs templates/ - a di… | 37 | |
| 44 | 44 | 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 . | 37 | |
| 45 | 45 | 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 There are two ways to achieve this: the other is to add "allow_sql": false to your metadata.json file, as described in Controlling the ability to execute arbitrary SQL . This setting offers a more convenient way to do this. | 37 | |
| 46 | 46 | 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 | 37 | |
| 47 | 47 | 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. | 37 | |
| 48 | 48 | 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 | 37 | |
| 49 | 49 | num_sql_threads | Maximum number of threads in the thread pool Datasette uses to execute SQLite queries. Defaults to 3. datasette mydatabase.db --setting num_sql_threads 10 Setting this to 0 turns off threaded SQL queries entirely - useful for environments that do not support threading such as Pyodide . | 37 | |
| 50 | 50 | 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 | 37 | |
| 51 | 51 | 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 | 37 | |
| 52 | 52 | 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 | 37 | |
| 53 | 53 | 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 | 37 | |
| 54 | 54 | suggest_facets | Should Datasette calculate suggested facets? On by default, turn this off like so: datasette mydatabase.db --setting suggest_facets off | 37 | |
| 55 | 55 | 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 | 37 | |
| 56 | 56 | 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 | 37 | |
| 57 | 57 | cache_size_kb | Sets the amount of memory SQLite uses for its per-connection cache , in KB. datasette mydatabase.db --setting cache_size_kb 5000 | 37 | |
| 58 | 58 | 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 | 37 | |
| 59 | 59 | 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 | 37 | |
| 60 | 60 | 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 | 37 | |
| 61 | 61 | 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 | 37 | |
| 62 | 62 | template_debug | This setting enables template context debug mode, which is useful to help understand what variables are available to custom templates when you are writing them. Enable it like this: datasette mydatabase.db --setting template_debug 1 Now you can add ?_context=1 or &_context=1 to any Datasette page to see the context that was passed to that template. Some examples: https://latest.datasette.io/?_context=1 https://latest.datasette.io/fixtures?_context=1 https://latest.datasette.io/fixtures/roadside_attractions?_context=1 | 37 | |
| 63 | 63 | trace_debug | This setting enables appending ?_trace=1 to any page in order to see the SQL queries and other trace information that was used to generate that page. Enable it like this: datasette mydatabase.db --setting trace_debug 1 Some examples: https://latest.datasette.io/?_trace=1 https://latest.datasette.io/fixtures/roadside_attractions?_trace=1 See datasette.tracer for details on how to hook into this mechanism as a plugin author. | 37 | |
| 64 | 64 | 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/ | 37 | |
| 65 | 65 | 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 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") . | 37 | |
| 66 | 66 | 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 | 37 | |
| 67 | 67 | 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. | 37 | |
| 68 | 68 | 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, … | 37 | |
| 69 | 69 | Pagination | The default JSON representation includes a "next_url" key which can be used to access the next page of results. If that key is null or missing then it means you have reached the final page of results. Other representations include pagination information in the link HTTP header. That header will look something like this: link: <https://latest.datasette.io/fixtures/sortable.json?_next=d%2Cv>; rel="next" Here is an example Python function built using requests that returns a list of all of the paginated items from one of these API endpoints: def paginate(url): items = [] while url: response = requests.get(url) try: url = response.links.get("next").get("url") except AttributeError: url = None items.extend(response.json()) return items | 37 | |
| 70 | 70 | Special JSON arguments | Every Datasette endpoint that can return JSON also accepts the following query string arguments: ?_shape=SHAPE The shape of the JSON to return, documented above. ?_nl=on When used with ?_shape=array produces newline-delimited JSON objects. ?_json=COLUMN1&_json=COLUMN2 If any of your SQLite columns contain JSON values, you can use one or more _json= parameters to request that those columns be returned as regular JSON. Without this argument those columns will be returned as JSON objects that have been double-encoded into a JSON string value. Compare this query without the argument to this query using the argument ?_json_infinity=on If your data contains infinity or -infinity values, Datasette will replace them with None when returning them as JSON. If you pass _json_infinity=1 Datasette will instead return them as Infinity or -Infinity which is invalid JSON but can be processed by some custom JSON parsers. ?_timelimit=MS Sets a custom time limit for the query in ms. You can use this for optimistic queries where you would like Datasette to give up if the query takes too long, for example if you want to implement autocomplete search but only… | 37 | |
| 71 | 71 | Table arguments | The Datasette table view takes a number of special query string arguments. | 37 | |
| 72 | 72 | 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. … | 37 | |
| 73 | 73 | Special table arguments | ?_col=COLUMN1&_col=COLUMN2 List specific columns to display. These will be shown along with any primary keys. ?_nocol=COLUMN1&_nocol=COLUMN2 List specific columns to hide - any column not listed will be displayed. Primary keys cannot be hidden. ?_labels=on/off Expand foreign key references for every possible column. See below. ?_label=COLUMN1&_label=COLUMN2 Expand foreign key references for one or more specified columns. ?_size=1000 or ?_size=max Sets a custom page size. This cannot exceed the max_returned_rows limit passed to datasette serve . Use max to get max_returned_rows . ?_sort=COLUMN Sorts the results by the specified column. ?_sort_desc=COLUMN Sorts the results by the specified column in descending order. ?_search=keywords For SQLite tables that have been configured for full-text search executes a search … | 37 | |
| 74 | 74 | 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 . | 37 | |
| 75 | 75 | 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" | 37 | |
| 76 | 76 | CLI reference | The datasette CLI tool provides a number of commands. Running datasette without specifying a command runs the default command, datasette serve . See datasette serve for the full list of options for that command. [[[cog from datasette import cli from click.testing import CliRunner import textwrap def help(args): title = "datasette " + " ".join(args) cog.out("\n::\n\n") result = CliRunner().invoke(cli.cli, args) output = result.output.replace("Usage: cli ", "Usage: datasette ") cog.out(textwrap.indent(output, ' ')) cog.out("\n\n") ]]] [[[end]]] | 37 | |
| 77 | 77 | datasette --help | Running datasette --help shows a list of all of the available commands. [[[cog help(["--help"]) ]]] Usage: datasette [OPTIONS] COMMAND [ARGS]... Datasette is an open source multi-tool for exploring and publishing data About Datasette: https://datasette.io/ Full documentation: https://docs.datasette.io/ Options: --version Show the version and exit. --help Show this message and exit. Commands: serve* Serve up specified SQLite database files with a web UI inspect Generate JSON summary of provided database files install Install plugins and packages from PyPI into the same... package Package SQLite files into a Datasette Docker container plugins List currently installed plugins publish Publish specified SQLite database files to the internet along... uninstall Uninstall plugins and Python packages from the Datasette... [[[end]]] Additional commands added by plugins that use the register_commands(cli) hook will be listed here as well. | 37 | |
| 78 | 78 | datasette serve | This command starts the Datasette web application running on your machine: datasette serve mydatabase.db Or since this is the default command you can run this instead: datasette mydatabase.db Once started you can access it at http://localhost:8001 [[[cog help(["serve", "--help"]) ]]] Usage: datasette serve [OPTIONS] [FILES]... Serve up specified SQLite database files with a web UI Options: -i, --immutable PATH Database files to open in immutable mode -h, --host TEXT Host for server. Defaults to 127.0.0.1 which means only connections from the local machine will be allowed. Use 0.0.0.0 to listen to all IPs and allow access from other machines. -p, --port INTEGER RANGE Port for server, defaults to 8001. Use -p 0 to automatically assign an available port. [0<=x<=65535] --uds TEXT Bind to a Unix domain socket --reload Automatically reload if code or metadata change detected - useful for development --cors Enable CORS by serving Access-Control-Allow- Origin: * --load-extension PATH:ENTRYPOINT? Path to a SQLite extension to load, and optional entrypoint --inspect-file TEXT Path to JSON file created using "datasette inspect" -m, --metadata FILENAME Path to JSON/YAML file containing license/source metadata --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files fr… | 37 | |
| 79 | 79 | datasette --get | The --get option to datasette serve (or just datasette ) specifies the path to a page within Datasette and causes 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 lets you use datasette --get / to run tests against a Datasette application in a continuous integration environment such as GitHub Actions. | 37 | |
| 80 | 80 | datasette serve --help-settings | This command outputs all of the available Datasette settings . These can be passed to datasette serve using datasette serve --setting name value . [[[cog help(["--help-settings"]) ]]] Settings: default_page_size Default page size for the table view (default=100) max_returned_rows Maximum rows that can be returned from a table or custom query (default=1000) num_sql_threads Number of threads in the thread pool for executing SQLite queries (default=3) sql_time_limit_ms Time limit for a SQL query in milliseconds (default=1000) default_facet_size Number of values to return for requested facets (default=30) facet_time_limit_ms Time limit for calculating a requested facet (default=200) facet_suggest_time_limit_ms Time limit for calculating a suggested facet (default=50) allow_facet Allow users to specify columns to facet using ?_facet= parameter (default=True) default_allow_sql Allow anyone to run arbitrary SQL queries (default=True) allow_download Allow users to download the original SQLite database files (default=True) suggest_facets Calculate and display suggested facets (default=True) default_cache_ttl Default HTTP cache TTL (used in Cache-Control: max-age= header) (default=5) cache_size_kb SQLite cache size in KB (0 == use SQLite default) (default=0) allow_csv_stream Allow .csv?_stream=1 to download all rows (ignoring max_returned… | 37 | |
| 81 | 81 | datasette plugins | Output JSON showing all currently installed plugins, their versions, whether they include static files or templates and which Plugin hooks they use. [[[cog help(["plugins", "--help"]) ]]] Usage: datasette plugins [OPTIONS] List currently installed plugins Options: --all Include built-in default plugins --plugins-dir DIRECTORY Path to directory containing custom plugins --help Show this message and exit. [[[end]]] Example output: [ { "name": "datasette-geojson", "static": false, "templates": false, "version": "0.3.1", "hooks": [ "register_output_renderer" ] }, { "name": "datasette-geojson-map", "static": true, "templates": false, "version": "0.4.0", "hooks": [ "extra_body_script", "extra_css_urls", "extra_js_urls" ] }, { "name": "datasette-leaflet", "static": true, "templates": false, "version": "0.2.2", "hooks": [ "extra_body_script", "extra_template_vars" ] } ] | 37 | |
| 82 | 82 | datasette install | Install new Datasette plugins. This command works like pip install but ensures that your plugins will be installed into the same environment as Datasette. This command: datasette install datasette-cluster-map Would install the datasette-cluster-map plugin. [[[cog help(["install", "--help"]) ]]] Usage: datasette install [OPTIONS] PACKAGES... Install plugins and packages from PyPI into the same environment as Datasette Options: -U, --upgrade Upgrade packages to latest version --help Show this message and exit. [[[end]]] | 37 | |
| 83 | 83 | datasette uninstall | Uninstall one or more plugins. [[[cog help(["uninstall", "--help"]) ]]] Usage: datasette uninstall [OPTIONS] PACKAGES... Uninstall plugins and Python packages from the Datasette environment Options: -y, --yes Don't ask for confirmation --help Show this message and exit. [[[end]]] | 37 | |
| 84 | 84 | datasette publish | Shows a list of available deployment targets for publishing data with Datasette. Additional deployment targets can be added by plugins that use the publish_subcommand(publish) hook. [[[cog help(["publish", "--help"]) ]]] Usage: datasette publish [OPTIONS] COMMAND [ARGS]... Publish specified SQLite database files to the internet along with a Datasette-powered interface and API Options: --help Show this message and exit. Commands: cloudrun Publish databases to Datasette running on Cloud Run heroku Publish databases to Datasette running on Heroku [[[end]]] | 37 | |
| 85 | 85 | datasette publish cloudrun | See Publishing to Google Cloud Run . [[[cog help(["publish", "cloudrun", "--help"]) ]]] Usage: datasette publish cloudrun [OPTIONS] [FILES]... Publish databases to Datasette running on Cloud Run Options: -m, --metadata FILENAME Path to JSON/YAML file containing metadata to publish --extra-options TEXT Extra options to pass to datasette serve --branch TEXT Install datasette from a GitHub branch e.g. main --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/... --install TEXT Additional packages (e.g. plugins) to install --plugin-secret <TEXT TEXT TEXT>... Secrets to pass to plugins, e.g. --plugin- secret datasette-auth-github client_id xxx --version-note TEXT Additional note to show on /-/versions --secret TEXT Secret used for signing secure values, such as signed cookies --title TEXT Title for metadata --license TEXT License label for metadata --license_url TEXT License URL for metadata --source TEXT Source label for metadata --source_url TEXT Source URL for metadata --about TEXT About label for metadata --about_url TEXT About URL for metadata -n, --name TEXT Application name to use when building --service TEXT Cloud Run service to deploy (or over-write) --spatialite Enable SpatialLite extension --show-files Output the generated Dockerfile and metad… | 37 | |
| 86 | 86 | datasette publish heroku | See Publishing to Heroku . [[[cog help(["publish", "heroku", "--help"]) ]]] Usage: datasette publish heroku [OPTIONS] [FILES]... Publish databases to Datasette running on Heroku Options: -m, --metadata FILENAME Path to JSON/YAML file containing metadata to publish --extra-options TEXT Extra options to pass to datasette serve --branch TEXT Install datasette from a GitHub branch e.g. main --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/... --install TEXT Additional packages (e.g. plugins) to install --plugin-secret <TEXT TEXT TEXT>... Secrets to pass to plugins, e.g. --plugin- secret datasette-auth-github client_id xxx --version-note TEXT Additional note to show on /-/versions --secret TEXT Secret used for signing secure values, such as signed cookies --title TEXT Title for metadata --license TEXT License label for metadata --license_url TEXT License URL for metadata --source TEXT Source label for metadata --source_url TEXT Source URL for metadata --about TEXT About label for metadata --about_url TEXT About URL for metadata -n, --name TEXT Application name to use when deploying --tar TEXT --tar option to pass to Heroku, e.g. --tar=/usr/local/bin/gtar --generate-dir DIRECTORY Output generated application files and stop without deploying --h… | 37 | |
| 87 | 87 | datasette package | Package SQLite files into a Datasette Docker container, see datasette package . [[[cog help(["package", "--help"]) ]]] Usage: datasette package [OPTIONS] FILES... Package SQLite files into a Datasette Docker container Options: -t, --tag TEXT Name for the resulting Docker container, can optionally use name:tag format -m, --metadata FILENAME Path to JSON/YAML file containing metadata to publish --extra-options TEXT Extra options to pass to datasette serve --branch TEXT Install datasette from a GitHub branch e.g. main --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/... --install TEXT Additional packages (e.g. plugins) to install --spatialite Enable SpatialLite extension --version-note TEXT Additional note to show on /-/versions --secret TEXT Secret used for signing secure values, such as signed cookies -p, --port INTEGER RANGE Port to run the server on, defaults to 8001 [1<=x<=65535] --title TEXT Title for metadata --license TEXT License label for metadata --license_url TEXT License URL for metadata --source TEXT Source label for metadata --source_url TEXT Source URL for metadata --about TEXT About label for metadata --about_url TEXT About URL for metadata --help Show this message and exit. [[[end]]] | 37 | |
| 88 | 88 | datasette inspect | Outputs JSON representing introspected data about one or more SQLite database files. If you are opening an immutable database, you can pass this file to the --inspect-data option to improve Datasette's performance by allowing it to skip running row counts against the database when it first starts running: datasette inspect mydatabase.db > inspect-data.json datasette serve -i mydatabase.db --inspect-file inspect-data.json This performance optimization is used automatically by some of the datasette publish commands. You are unlikely to need to apply this optimization manually. [[[cog help(["inspect", "--help"]) ]]] Usage: datasette inspect [OPTIONS] [FILES]... Generate JSON summary of provided database files This can then be passed to "datasette --inspect-file" to speed up count operations against immutable database files. Options: --inspect-file TEXT --load-extension PATH:ENTRYPOINT? Path to a SQLite extension to load, and optional entrypoint --help Show this message and exit. [[[end]]] | 37 | |
| 89 | 89 | CSV export | Any Datasette table, view or custom SQL query can be exported as CSV. To obtain the CSV representation of the table you are looking, click the "this data as CSV" link. You can also use the advanced export form for more control over the resulting file, which looks like this and has the following options: download file - instead of displaying CSV in your browser, this forces your browser to download the CSV to your downloads directory. expand labels - if your table has any foreign key references this option will cause the CSV to gain additional COLUMN_NAME_label columns with a label for each foreign key derived from the linked table. In this example the city_id column is accompanied by a city_id_label column. stream all rows - by default CSV files only contain the first max_returned_rows records. This option will cause Datasette to loop through every matching record and return them as a single CSV file. You can try that out on https://latest.datasette.io/fixtures/facetable?_size=4 | 37 | |
| 90 | 90 | URL parameters | The following options can be used to customize the CSVs returned by Datasette. ?_header=off This removes the first row of the CSV file specifying the headings - only the row data will be returned. ?_stream=on Stream all matching records, not just the first page of results. See below. ?_dl=on Causes Datasette to return a content-disposition: attachment; filename="filename.csv" header. | 37 | |
| 91 | 91 | Streaming all records | The stream all rows option is designed to be as efficient as possible - under the hood it takes advantage of Python 3 asyncio capabilities and Datasette's efficient pagination to stream back the full CSV file. Since databases can get pretty large, by default this option is capped at 100MB - if a table returns more than 100MB of data the last line of the CSV will be a truncation error message. You can increase or remove this limit using the max_csv_mb config setting. You can also disable the CSV export feature entirely using allow_csv_stream . | 37 | |
| 92 | 92 | 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. | 37 | |
| 93 | 93 | datasette publish | Once you have created a SQLite database (e.g. using csvs-to-sqlite ) you can deploy it to a hosting account using a single command. You will need a hosting account with Heroku or Google Cloud . Once you have created your account you will need to install and configure the heroku or gcloud command-line tools. | 37 | |
| 94 | 94 | Publishing to Google Cloud Run | Google Cloud Run allows you to publish data in a scale-to-zero environment, so your application will start running when the first request is received and will shut down again when traffic ceases. This means you only pay for time spent serving traffic. Cloud Run is a great option for inexpensively hosting small, low traffic projects - but costs can add up for projects that serve a lot of requests. Be particularly careful if your project has tables with large numbers of rows. Search engine crawlers that index a page for every row could result in a high bill. The datasette-block-robots plugin can be used to request search engine crawlers omit crawling your site, which can help avoid this issue. You will first need to install and configure the Google Cloud CLI tools by following these instructions . You can then publish one or more SQLite database files to Google Cloud Run using the following command: datasette publish cloudrun mydatabase.db --service=my-database A Cloud Run service is a single hosted application. The service name you specify will be used as part of the Cloud Run URL. If you deploy to a service name that you have used in the past your new deployment will replace the previous one. If you omit the --service option you will be asked to pick a service name interactively during the deploy. You may need to interact with prompts from the tool. Many of the prompts ask for values that can be set as properties for the Google Cloud SDK if you want to avoid the prompts. For example, the default region for the deployed instance can be set using the command: gcloud config set run/region us-central1 You should replace us-central1 with your desired region . Alternately, you can specify the region by setting the CLOUDSDK_RUN_REGION environment… | 37 | |
| 95 | 95 | Publishing to Heroku | To publish your data using Heroku , first create an account there and install and configure the Heroku CLI tool . You can publish one or more databases to Heroku using the following command: datasette publish heroku mydatabase.db This will output some details about the new deployment, including a URL like this one: https://limitless-reef-88278.herokuapp.com/ deployed to Heroku You can specify a custom app name by passing -n my-app-name to the publish command. This will also allow you to overwrite an existing app. Rather than deploying directly you can use the --generate-dir option to output the files that would be deployed to a directory: datasette publish heroku mydatabase.db --generate-dir=/tmp/deploy-this-to-heroku See datasette publish heroku for the full list of options for this command. | 37 | |
| 96 | 96 | Publishing to Vercel | Vercel - previously known as Zeit Now - provides a layer over AWS Lambda to allow for quick, scale-to-zero deployment. You can deploy Datasette instances to Vercel using the datasette-publish-vercel plugin. pip install datasette-publish-vercel datasette publish vercel mydatabase.db --project my-database-project Not every feature is supported: consult the datasette-publish-vercel README for more details. | 37 | |
| 97 | 97 | Publishing to Fly | Fly is a competitively priced Docker-compatible hosting platform that supports running applications in globally distributed data centers close to your end users. You can deploy Datasette instances to Fly using the datasette-publish-fly plugin. pip install datasette-publish-fly datasette publish fly mydatabase.db --app="my-app" Consult the datasette-publish-fly README for more details. | 37 | |
| 98 | 98 | Custom metadata and plugins | datasette publish accepts a number of additional options which can be used to further customize your Datasette instance. You can define your own Metadata and deploy that with your instance like so: datasette publish cloudrun --service=my-service mydatabase.db -m metadata.json If you just want to set the title, license or source information you can do that directly using extra options to datasette publish : datasette publish cloudrun mydatabase.db --service=my-service \ --title="Title of my database" \ --source="Where the data originated" \ --source_url="http://www.example.com/" You can also specify plugins you would like to install. For example, if you want to include the datasette-vega visualization plugin you can use the following: datasette publish cloudrun mydatabase.db --service=my-service --install=datasette-vega If a plugin has any Secret configuration values you can use the --plugin-secret option to set those secrets at publish time. For example, using Heroku with datasette-auth-github 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 | 37 | |
| 99 | 99 | datasette package | If you have docker installed (e.g. using Docker for Mac ) you can use the datasette package command to create a new Docker image in your local repository containing the datasette app bundled together with one or more SQLite databases: datasette package mydatabase.db Here's example output for the package command: $ datasette package parlgov.db --extra-options="--setting sql_time_limit_ms 2500" Sending build context to Docker daemon 4.459MB Step 1/7 : FROM python:3.11.0-slim-bullseye ---> 79e1dc9af1c1 Step 2/7 : COPY . /app ---> Using cache ---> cd4ec67de656 Step 3/7 : WORKDIR /app ---> Using cache ---> 139699e91621 Step 4/7 : RUN pip install datasette ---> Using cache ---> 340efa82bfd7 Step 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json ---> Using cache ---> 5fddbe990314 Step 6/7 : EXPOSE 8001 ---> Using cache ---> 8e83844b0fed Step 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --setting sql_time_limit_ms 2500 ---> Using cache ---> 1bd380ea8af3 Successfully built 1bd380ea8af3 You can now run the resulting container like so: docker run -p 8081:8001 1bd380ea8af3 This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at http://localhost:8081/ You can customize the port that is exposed by the container using the --port option: datasette package mydatabase.db --port 8080 A full list of options can be seen by running datasette package --help : See datasette package for the full list of options for this command. | 37 | |
| 100 | 100 | SpatiaLite | The SpatiaLite module for SQLite adds features for handling geographic and spatial data. For an example of what you can do with it, see the tutorial Building a location to time zone API with SpatiaLite . To use it with Datasette, you need to install the mod_spatialite dynamic library. This can then be loaded into Datasette using the --load-extension command-line option. Datasette can look for SpatiaLite in common installation locations if you run it like this: datasette --load-extension=spatialite --setting default_allow_sql off If SpatiaLite is in another location, use the full path to the extension instead: datasette --setting default_allow_sql off \ --load-extension=/usr/local/lib/mod_spatialite.dylib | 37 |
Advanced export
JSON shape: default, array, newline-delimited
CREATE VIRTUAL TABLE [sections_fts] USING FTS5 (
[title], [content],
tokenize='porter',
content=[sections]
);