{"rowid": 1, "title": "Plugin hooks", "content": "Datasette plugins use plugin hooks to customize Datasette's behavior. These hooks are powered by the pluggy plugin system. \n 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. \n When you implement a plugin hook you can accept any or all of the parameters that are documented as being passed to that hook. \n 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) : \n @hookimpl\ndef render_cell(value, column):\n if column == \"stars\":\n return \"*\" * int(value) \n \n List of plugin hooks \n \n \n prepare_connection(conn, database, datasette) \n \n \n prepare_jinja2_environment(env, datasette) \n \n \n Page extras \n \n \n extra_template_vars(template, database, table, columns, view_name, request, datasette) \n \n \n extra_css_urls(template, database, table, columns, view_name, request, datasette) \n \n \n extra_js_urls(template, database, table, columns, view_name, request, datasette) \n \n \n extra_body_script(template, database, table, columns, view_name, request, datasette) \n \n \n \n \n publish_subcommand(publish) \n \n \n render_cell(row, value, column, table, database, datasette, request) \n \n \n register_output_renderer(datasette) \n \n \n register_routes(datasette) \n \n \n register_commands(cli) \n \n \n register_facet_classes() \n \n \n register_permissions(datasette) \n \n \n asgi_wrapper(datasette) \n \n \n startup(datasette) \n \n \n canned_queries(datasette, database, actor) \n \n \n actor_from_request(datasette, request) \n \n \n actors_from_ids(datasette, actor_ids) \n \n \n jinja2_environment_from_request(datasette, request, env) \n \n \n filters_from_request(request, database, table, datasette) \n \n \n permission_allowed(datasette, actor, action, resource) \n \n \n register_magic_parameters(datasette) \n \n \n forbidden(datasette, request, message) \n \n \n handle_exception(datasette, request, exception) \n \n \n skip_csrf(datasette, scope) \n \n \n get_metadata(datasette, key, database, table) \n \n \n menu_links(datasette, actor, request) \n \n \n Action hooks \n \n \n table_actions(datasette, actor, database, table, request) \n \n \n view_actions(datasette, actor, database, view, request) \n \n \n query_actions(datasette, actor, database, query_name, request, sql, params) \n \n \n row_actions(datasette, actor, request, database, table, row) \n \n \n database_actions(datasette, actor, database, request) \n \n \n homepage_actions(datasette, actor, request) \n \n \n \n \n Template slots \n \n \n top_homepage(datasette, request) \n \n \n top_database(datasette, request, database) \n \n \n top_table(datasette, request, database, table) \n \n \n top_row(datasette, request, database, table, row) \n \n \n top_query(datasette, request, database, sql) \n \n \n top_canned_query(datasette, request, database, query_name) \n \n \n \n \n Event tracking \n \n \n track_event(datasette, event) \n \n \n register_events(datasette)", "sections_fts": 15, "rank": null}
{"rowid": 2, "title": "prepare_connection(conn, database, datasette)", "content": "conn - sqlite3 connection object \n \n The connection that is being opened \n \n \n \n database - string \n \n The name of the database \n \n \n \n datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) \n \n \n \n This hook is called when a new SQLite database connection is created. You can\n use it to register custom SQL functions ,\n aggregates and collations. For example: \n from datasette import hookimpl\nimport random\n\n\n@hookimpl\ndef prepare_connection(conn):\n conn.create_function(\n \"random_integer\", 2, random.randint\n ) \n This registers a SQL function called random_integer which takes two\n arguments and can be called like this: \n select random_integer(1, 10); \n Examples: datasette-jellyfish , datasette-jq , datasette-haversine , datasette-rure", "sections_fts": 15, "rank": null}
{"rowid": 3, "title": "prepare_jinja2_environment(env, datasette)", "content": "env - jinja2 Environment \n \n The template environment that is being prepared \n \n \n \n datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) \n \n \n \n This hook is called with the Jinja2 environment that is used to evaluate\n Datasette HTML templates. You can use it to do things like register custom\n template filters , for\n example: \n from datasette import hookimpl\n\n\n@hookimpl\ndef prepare_jinja2_environment(env):\n env.filters[\"uppercase\"] = lambda u: u.upper() \n You can now use this filter in your custom templates like so: \n Table name: {{ table|uppercase }} \n This function can return an awaitable function if it needs to run any async code. \n Examples: datasette-edit-templates", "sections_fts": 15, "rank": null}
{"rowid": 4, "title": "Page extras", "content": "These plugin hooks can be used to affect the way HTML pages for different Datasette interfaces are rendered.", "sections_fts": 15, "rank": null}
{"rowid": 5, "title": "extra_template_vars(template, database, table, columns, view_name, request, datasette)", "content": "Extra template variables that should be made available in the rendered template context. \n \n \n template - string \n \n The template that is being rendered, e.g. database.html \n \n \n \n database - string or None \n \n The name of the database, or None if the page does not correspond to a database (e.g. the root page) \n \n \n \n table - string or None \n \n The name of the table, or None if the page does not correct to a table \n \n \n \n columns - list of strings or None \n \n The names of the database columns that will be displayed on this page. None if the page does not contain a table. \n \n \n \n view_name - string \n \n The name of the view being displayed. ( index , database , table , and row are the most important ones.) \n \n \n \n request - Request object or None \n \n The current HTTP request. This can be None if the request object is not available. \n \n \n \n datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) \n \n \n \n This hook can return one of three different types: \n \n \n Dictionary \n \n If you return a dictionary its keys and values will be merged into the template context. \n \n \n \n Function that returns a dictionary \n \n If you return a function it will be executed. If it returns a dictionary those values will will be merged into the template context. \n \n \n \n Function that returns an awaitable function that returns a dictionary \n \n You can also return a function which returns an awaitable function which returns a dictionary. \n \n \n \n Datasette runs Jinja2 in async mode , which means you can add awaitable functions to the template scope and they will be automatically awaited when they are rendered by the template. \n Here's an example plugin that adds a \"user_agent\" variable to the template context containing the current request's User-Agent header: \n @hookimpl\ndef extra_template_vars(request):\n return {\"user_agent\": request.headers.get(\"user-agent\")} \n This example returns an awaitable function which adds a list of hidden_table_names to the context: \n @hookimpl\ndef extra_template_vars(datasette, database):\n async def hidden_table_names():\n if database:\n db = datasette.databases[database]\n return {\n \"hidden_table_names\": await db.hidden_table_names()\n }\n else:\n return {}\n\n return hidden_table_names \n And here's an example which adds a sql_first(sql_query) function which executes a SQL statement and returns the first column of the first row of results: \n @hookimpl\ndef extra_template_vars(datasette, database):\n async def sql_first(sql, dbname=None):\n dbname = (\n dbname\n or database\n or next(iter(datasette.databases.keys()))\n )\n result = await datasette.execute(dbname, sql)\n return result.rows[0][0]\n\n return {\"sql_first\": sql_first} \n You can then use the new function in a template like so: \n SQLite version: {{ sql_first(\"select sqlite_version()\") }} \n Examples: datasette-search-all , datasette-template-sql", "sections_fts": 15, "rank": null}
{"rowid": 6, "title": "extra_css_urls(template, database, table, columns, view_name, request, datasette)", "content": "This takes the same arguments as extra_template_vars(...) \n Return a list of extra CSS URLs that should be included on the page. These can\n take advantage of the CSS class hooks described in Custom pages and templates . \n This can be a list of URLs: \n from datasette import hookimpl\n\n\n@hookimpl\ndef extra_css_urls():\n return [\n \"https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css\"\n ] \n Or a list of dictionaries defining both a URL and an\n SRI hash : \n @hookimpl\ndef extra_css_urls():\n return [\n {\n \"url\": \"https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css\",\n \"sri\": \"sha384-9gVQ4dYFwwWSjIDZnLEWnxCjeSWFphJiwGPXr1jddIhOegiu1FwO5qRGvFXOdJZ4\",\n }\n ] \n This function can also return an awaitable function, useful if it needs to run any async code: \n @hookimpl\ndef extra_css_urls(datasette):\n async def inner():\n db = datasette.get_database()\n results = await db.execute(\n \"select url from css_files\"\n )\n return [r[0] for r in results]\n\n return inner \n Examples: datasette-cluster-map , datasette-vega", "sections_fts": 15, "rank": null}
{"rowid": 7, "title": "extra_js_urls(template, database, table, columns, view_name, request, datasette)", "content": "This takes the same arguments as extra_template_vars(...) \n This works in the same way as extra_css_urls() but for JavaScript. You can\n return a list of URLs, a list of dictionaries or an awaitable function that returns those things: \n from datasette import hookimpl\n\n\n@hookimpl\ndef extra_js_urls():\n return [\n {\n \"url\": \"https://code.jquery.com/jquery-3.3.1.slim.min.js\",\n \"sri\": \"sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo\",\n }\n ] \n You can also return URLs to files from your plugin's static/ directory, if\n you have one: \n @hookimpl\ndef extra_js_urls():\n return [\"/-/static-plugins/your-plugin/app.js\"] \n Note that your-plugin here should be the hyphenated plugin name - the name that is displayed in the list on the /-/plugins debug page. \n If your code uses JavaScript modules you should include the \"module\": True key. See Custom CSS and JavaScript for more details. \n @hookimpl\ndef extra_js_urls():\n return [\n {\n \"url\": \"/-/static-plugins/your-plugin/app.js\",\n \"module\": True,\n }\n ] \n Examples: datasette-cluster-map , datasette-vega", "sections_fts": 15, "rank": null}
{"rowid": 8, "title": "extra_body_script(template, database, table, columns, view_name, request, datasette)", "content": "Extra JavaScript to be added to a element: \n @hookimpl\ndef extra_body_script():\n return {\n \"module\": True,\n \"script\": \"console.log('Your JavaScript goes here...')\",\n } \n This will add the following to the end of your page: \n \n Example: datasette-cluster-map", "sections_fts": 15, "rank": null}
{"rowid": 9, "title": "publish_subcommand(publish)", "content": "publish - Click publish command group \n \n The Click command group for the datasette publish subcommand \n \n \n \n This hook allows you to create new providers for the datasette publish \n command. Datasette uses this hook internally to implement the default cloudrun \n and heroku subcommands, so you can read\n their source \n to see examples of this hook in action. \n 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: \n from datasette import hookimpl\nfrom datasette.publish.common import (\n add_common_publish_arguments_and_options,\n)\nimport click\n\n\n@hookimpl\ndef publish_subcommand(publish):\n @publish.command()\n @add_common_publish_arguments_and_options\n @click.option(\n \"-k\",\n \"--api_key\",\n help=\"API key for talking to my hosting provider\",\n )\n def my_hosting_provider(\n files,\n metadata,\n extra_options,\n branch,\n template_dir,\n plugins_dir,\n static,\n install,\n plugin_secret,\n version_note,\n secret,\n title,\n license,\n license_url,\n source,\n source_url,\n about,\n about_url,\n api_key,\n ): ... \n Examples: datasette-publish-fly , datasette-publish-vercel", "sections_fts": 15, "rank": null}
{"rowid": 10, "title": "render_cell(row, value, column, table, database, datasette, request)", "content": "Lets you customize the display of values within table cells in the HTML table view. \n \n \n row - sqlite.Row \n \n The SQLite row object that the value being rendered is part of \n \n \n \n value - string, integer, float, bytes or None \n \n The value that was loaded from the database \n \n \n \n column - string \n \n The name of the column being rendered \n \n \n \n table - string or None \n \n The name of the table - or None if this is a custom SQL query \n \n \n \n database - string \n \n The name of the database \n \n \n \n datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n request - Request object \n \n The current request object \n \n \n \n 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. \n If the hook returns a string, that string will be rendered in the table cell. \n If you want to return HTML markup you can do so by returning a jinja2.Markup object. \n You can also return an awaitable function which returns a value. \n Datasette will loop through all available render_cell hooks and display the value returned by the first one that does not return None . \n Here is an example of a custom render_cell() plugin which looks for values that are a JSON string matching the following format: \n {\"href\": \"https://www.example.com/\", \"label\": \"Name\"} \n If the value matches that pattern, the plugin returns an HTML link element: \n from datasette import hookimpl\nimport markupsafe\nimport json\n\n\n@hookimpl\ndef render_cell(value):\n # Render {\"href\": \"...\", \"label\": \"...\"} as link\n if not isinstance(value, str):\n return None\n stripped = value.strip()\n if not (\n stripped.startswith(\"{\") and stripped.endswith(\"}\")\n ):\n return None\n try:\n data = json.loads(value)\n except ValueError:\n return None\n if not isinstance(data, dict):\n return None\n if set(data.keys()) != {\"href\", \"label\"}:\n return None\n href = data[\"href\"]\n if not (\n href.startswith(\"/\")\n or href.startswith(\"http://\")\n or href.startswith(\"https://\")\n ):\n return None\n return markupsafe.Markup(\n '{label}'.format(\n href=markupsafe.escape(data[\"href\"]),\n label=markupsafe.escape(data[\"label\"] or \"\")\n or \" \",\n )\n ) \n Examples: datasette-render-binary , datasette-render-markdown , datasette-json-html", "sections_fts": 15, "rank": null}
{"rowid": 11, "title": "register_output_renderer(datasette)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) \n \n \n \n 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: \n @hookimpl\ndef register_output_renderer(datasette):\n return {\n \"extension\": \"test\",\n \"render\": render_demo,\n \"can_render\": can_render_demo, # Optional\n } \n 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. \n 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. \n 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 influence 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. \n 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. \n \n \n datasette - Datasette class \n \n For accessing plugin configuration and executing queries. \n \n \n \n columns - list of strings \n \n The names of the columns returned by this query. \n \n \n \n rows - list of sqlite3.Row objects \n \n The rows returned by the query. \n \n \n \n sql - string \n \n The SQL query that was executed. \n \n \n \n query_name - string or None \n \n If this was the execution of a canned query , the name of that query. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n table - string or None \n \n The table or view, if one is being rendered. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n error - string or None \n \n If an error occurred this string will contain the error message. \n \n \n \n truncated - bool or None \n \n If the query response was truncated - for example a SQL query returning more than 1,000 results where pagination is not available - this will be True . \n \n \n \n view_name - string \n \n The name of the current view being called. index , database , table , and row are the most important ones. \n \n \n \n The callback function can return None , if it is unable to render the data, or a Response class that will be returned to the caller. \n It can also return a dictionary with the following keys. This format is deprecated as-of Datasette 0.49 and will be removed by Datasette 1.0. \n \n \n body - string or bytes, optional \n \n The response body, default empty \n \n \n \n content_type - string, optional \n \n The Content-Type header, default text/plain \n \n \n \n status_code - integer, optional \n \n The HTTP status code, default 200 \n \n \n \n headers - dictionary, optional \n \n Extra HTTP headers to be returned in the response. \n \n \n \n An example of an output renderer callback function: \n def render_demo():\n return Response.text(\"Hello World\") \n Here is a more complex example: \n async def render_demo(datasette, columns, rows):\n db = datasette.get_database()\n result = await db.execute(\"select sqlite_version()\")\n first_row = \" | \".join(columns)\n lines = [first_row]\n lines.append(\"=\" * len(first_row))\n for row in rows:\n lines.append(\" | \".join(row))\n return Response(\n \"\\n\".join(lines),\n content_type=\"text/plain; charset=utf-8\",\n headers={\"x-sqlite-version\": result.first()[0]},\n ) \n And here is an example can_render function which returns True only if the query results contain the columns atom_id , atom_title and atom_updated : \n def can_render_demo(columns):\n return {\n \"atom_id\",\n \"atom_title\",\n \"atom_updated\",\n }.issubset(columns) \n Examples: datasette-atom , datasette-ics , datasette-geojson , datasette-copyable", "sections_fts": 15, "rank": null}
{"rowid": 12, "title": "register_routes(datasette)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) \n \n \n \n Register additional view functions to execute for specified URL routes. \n Return a list of (regex, view_function) pairs, something like this: \n from datasette import hookimpl, Response\nimport html\n\n\nasync def hello_from(request):\n name = request.url_vars[\"name\"]\n return Response.html(\n \"Hello from {}\".format(html.escape(name))\n )\n\n\n@hookimpl\ndef register_routes():\n return [(r\"^/hello-from/(?P.*)$\", hello_from)] \n 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. \n The optional view function arguments are as follows: \n \n \n datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n scope - dictionary \n \n The incoming ASGI scope dictionary. \n \n \n \n send - function \n \n The ASGI send function. \n \n \n \n receive - function \n \n The ASGI receive function. \n \n \n \n The view function can be a regular function or an async def function, depending on if it needs to use any await APIs. \n The function can either return a Response class or it can return nothing and instead respond directly to the request using the ASGI send function (for advanced uses only). \n It can also raise the datasette.NotFound exception to return a 404 not found error, or the datasette.Forbidden exception for a 403 forbidden. \n See Designing URLs for your plugin for tips on designing the URL routes used by your plugin. \n Examples: datasette-auth-github , datasette-psutil", "sections_fts": 15, "rank": null}
{"rowid": 13, "title": "register_commands(cli)", "content": "cli - the root Datasette Click command group \n \n Use this to register additional CLI commands \n \n \n \n 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. \n This example registers a new datasette verify file1.db file2.db command that checks if the provided file paths are valid SQLite databases: \n from datasette import hookimpl\nimport click\nimport sqlite3\n\n\n@hookimpl\ndef register_commands(cli):\n @cli.command()\n @click.argument(\n \"files\", type=click.Path(exists=True), nargs=-1\n )\n def verify(files):\n \"Verify that files can be opened by Datasette\"\n for file in files:\n conn = sqlite3.connect(str(file))\n try:\n conn.execute(\"select * from sqlite_master\")\n except sqlite3.DatabaseError:\n raise click.ClickException(\n \"Invalid database: {}\".format(file)\n ) \n The new command can then be executed like so: \n datasette verify fixtures.db \n Help text (from the docstring for the function plus any defined Click arguments or options) will become available using: \n datasette verify --help \n 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. \n 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: \n pip install -e path/to/my/datasette-plugin \n Examples: datasette-auth-passwords , datasette-verify", "sections_fts": 15, "rank": null}
{"rowid": 14, "title": "register_facet_classes()", "content": "Return a list of additional Facet subclasses to be registered. \n \n The design of this plugin hook is unstable and may change. See issue 830 . \n \n Each Facet subclass implements a new type of facet operation. The class should look like this: \n class SpecialFacet(Facet):\n # This key must be unique across all facet classes:\n type = \"special\"\n\n async def suggest(self):\n # Use self.sql and self.params to suggest some facets\n suggested_facets = []\n suggested_facets.append(\n {\n \"name\": column, # Or other unique name\n # Construct the URL that will enable this facet:\n \"toggle_url\": self.ds.absolute_url(\n self.request,\n path_with_added_args(\n self.request, {\"_facet\": column}\n ),\n ),\n }\n )\n return suggested_facets\n\n async def facet_results(self):\n # This should execute the facet operation and return results, again\n # using self.sql and self.params as the starting point\n facet_results = []\n facets_timed_out = []\n facet_size = self.get_facet_size()\n # Do some calculations here...\n for column in columns_selected_for_facet:\n try:\n facet_results_values = []\n # More calculations...\n facet_results_values.append(\n {\n \"value\": value,\n \"label\": label,\n \"count\": count,\n \"toggle_url\": self.ds.absolute_url(\n self.request, toggle_path\n ),\n \"selected\": selected,\n }\n )\n facet_results.append(\n {\n \"name\": column,\n \"results\": facet_results_values,\n \"truncated\": len(facet_rows_results)\n > facet_size,\n }\n )\n except QueryInterrupted:\n facets_timed_out.append(column)\n\n return facet_results, facets_timed_out \n See datasette/facets.py for examples of how these classes can work. \n The plugin hook can then be used to register the new facet class like this: \n @hookimpl\ndef register_facet_classes():\n return [SpecialFacet]", "sections_fts": 15, "rank": null}
{"rowid": 15, "title": "register_permissions(datasette)", "content": "If your plugin needs to register additional permissions unique to that plugin - upload-csvs for example - you can return a list of those permissions from this hook. \n from datasette import hookimpl, Permission\n\n\n@hookimpl\ndef register_permissions(datasette):\n return [\n Permission(\n name=\"upload-csvs\",\n abbr=None,\n description=\"Upload CSV files\",\n takes_database=True,\n takes_resource=False,\n default=False,\n )\n ] \n The fields of the Permission class are as follows: \n \n \n name - string \n \n The name of the permission, e.g. upload-csvs . This should be unique across all plugins that the user might have installed, so choose carefully. \n \n \n \n abbr - string or None \n \n An abbreviation of the permission, e.g. uc . This is optional - you can set it to None if you do not want to pick an abbreviation. Since this needs to be unique across all installed plugins it's best not to specify an abbreviation at all. If an abbreviation is provided it will be used when creating restricted signed API tokens. \n \n \n \n description - string or None \n \n A human-readable description of what the permission lets you do. Should make sense as the second part of a sentence that starts \"A user with this permission can ...\". \n \n \n \n takes_database - boolean \n \n True if this permission can be granted on a per-database basis, False if it is only valid at the overall Datasette instance level. \n \n \n \n takes_resource - boolean \n \n True if this permission can be granted on a per-resource basis. A resource is a database table, SQL view or canned query . \n \n \n \n default - boolean \n \n The default value for this permission if it is not explicitly granted to a user. True means the permission is granted by default, False means it is not. \n This should only be True if you want anonymous users to be able to take this action.", "sections_fts": 15, "rank": null}
{"rowid": 16, "title": "asgi_wrapper(datasette)", "content": "Return an ASGI middleware wrapper function that will be applied to the Datasette ASGI application. \n 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. \n 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 . \n This example plugin adds a x-databases HTTP header listing the currently attached databases: \n from datasette import hookimpl\nfrom functools import wraps\n\n\n@hookimpl\ndef asgi_wrapper(datasette):\n def wrap_with_databases_header(app):\n @wraps(app)\n async def add_x_databases_header(\n scope, receive, send\n ):\n async def wrapped_send(event):\n if event[\"type\"] == \"http.response.start\":\n original_headers = (\n event.get(\"headers\") or []\n )\n event = {\n \"type\": event[\"type\"],\n \"status\": event[\"status\"],\n \"headers\": original_headers\n + [\n [\n b\"x-databases\",\n \", \".join(\n datasette.databases.keys()\n ).encode(\"utf-8\"),\n ]\n ],\n }\n await send(event)\n\n await app(scope, receive, wrapped_send)\n\n return add_x_databases_header\n\n return wrap_with_databases_header \n Examples: datasette-cors , datasette-pyinstrument , datasette-total-page-time", "sections_fts": 15, "rank": null}
{"rowid": 17, "title": "startup(datasette)", "content": "This hook fires when the Datasette application server first starts up. \n Here is an example that validates required plugin configuration. The server will fail to start and show an error if the validation check fails: \n @hookimpl\ndef startup(datasette):\n config = datasette.plugin_config(\"my-plugin\") or {}\n assert (\n \"required-setting\" in config\n ), \"my-plugin requires setting required-setting\" \n You can also return an async function, which will be awaited on startup. Use this option if you need to execute any database queries, for example this function which creates the my_table database table if it does not yet exist: \n @hookimpl\ndef startup(datasette):\n async def inner():\n db = datasette.get_database()\n if \"my_table\" not in await db.table_names():\n await db.execute_write(\n \"\"\"\n create table my_table (mycol text)\n \"\"\"\n )\n\n return inner \n Potential use-cases: \n \n \n Run some initialization code for the plugin \n \n \n Create database tables that a plugin needs on startup \n \n \n Validate the configuration for a plugin on startup, and raise an error if it is invalid \n \n \n \n If you are writing unit tests for a plugin that uses this hook and doesn't exercise Datasette by sending\n any simulated requests through it you will need to explicitly call await ds.invoke_startup() in your tests. An example: \n @pytest.mark.asyncio\nasync def test_my_plugin():\n ds = Datasette()\n await ds.invoke_startup()\n # Rest of test goes here \n \n Examples: datasette-saved-queries , datasette-init", "sections_fts": 15, "rank": null}
{"rowid": 18, "title": "canned_queries(datasette, database, actor)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n 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. \n from datasette import hookimpl\n\n\n@hookimpl\ndef canned_queries(datasette, database):\n if database == \"mydb\":\n return {\n \"my_query\": {\n \"sql\": \"select * from my_table where id > :min_id\"\n }\n } \n 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: \n from datasette import hookimpl\n\n\n@hookimpl\ndef canned_queries(datasette, database):\n async def inner():\n db = datasette.get_database(database)\n if await db.table_exists(\"saved_queries\"):\n results = await db.execute(\n \"select name, sql from saved_queries\"\n )\n return {\n result[\"name\"]: {\"sql\": result[\"sql\"]}\n for result in results\n }\n\n return inner \n 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: \n from datasette import hookimpl\n\n\n@hookimpl\ndef canned_queries(datasette, database, actor):\n async def inner():\n db = datasette.get_database(database)\n if actor is not None and await db.table_exists(\n \"saved_queries\"\n ):\n results = await db.execute(\n \"select name, sql from saved_queries where actor_id = :id\",\n {\"id\": actor[\"id\"]},\n )\n return {\n result[\"name\"]: {\"sql\": result[\"sql\"]}\n for result in results\n }\n\n return inner \n Example: datasette-saved-queries", "sections_fts": 15, "rank": null}
{"rowid": 19, "title": "actor_from_request(datasette, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n 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. \n If it cannot authenticate an actor, it should return None . Otherwise it should return a dictionary representing that actor. \n Here's an example that authenticates the actor based on an incoming API key: \n from datasette import hookimpl\nimport secrets\n\nSECRET_KEY = \"this-is-a-secret\"\n\n\n@hookimpl\ndef actor_from_request(datasette, request):\n authorization = (\n request.headers.get(\"authorization\") or \"\"\n )\n expected = \"Bearer {}\".format(SECRET_KEY)\n\n if secrets.compare_digest(authorization, expected):\n return {\"id\": \"bot\"} \n If you install this in your plugins directory you can test it like this: \n curl -H 'Authorization: Bearer this-is-a-secret' http://localhost:8003/-/actor.json \n 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: \n from datasette import hookimpl\n\n\n@hookimpl\ndef actor_from_request(datasette, request):\n async def inner():\n token = request.args.get(\"_token\")\n if not token:\n return None\n # Look up ?_token=xxx in sessions table\n result = await datasette.get_database().execute(\n \"select count(*) from sessions where token = ?\",\n [token],\n )\n if result.first()[0]:\n return {\"token\": token}\n else:\n return None\n\n return inner \n Examples: datasette-auth-tokens , datasette-auth-passwords", "sections_fts": 15, "rank": null}
{"rowid": 20, "title": "actors_from_ids(datasette, actor_ids)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor_ids - list of strings or integers \n \n The actor IDs to look up. \n \n \n \n The hook must return a dictionary that maps the incoming actor IDs to their full dictionary representation. \n Some plugins that implement social features may store the ID of the actor that performed an action - added a comment, bookmarked a table or similar - and then need a way to resolve those IDs into display-friendly actor dictionaries later on. \n The await datasette.actors_from_ids(actor_ids) internal method can be used to look up actors from their IDs. It will dispatch to the first plugin that implements this hook. \n Unlike other plugin hooks, this only uses the first implementation of the hook to return a result. You can expect users to only have a single plugin installed that implements this hook. \n If no plugin is installed, Datasette defaults to returning actors that are just {\"id\": actor_id} . \n The hook can return a dictionary or an awaitable function that then returns a dictionary. \n This example implementation returns actors from a database table: \n from datasette import hookimpl\n\n\n@hookimpl\ndef actors_from_ids(datasette, actor_ids):\n db = datasette.get_database(\"actors\")\n\n async def inner():\n sql = \"select id, name from actors where id in ({})\".format(\n \", \".join(\"?\" for _ in actor_ids)\n )\n actors = {}\n for row in (await db.execute(sql, actor_ids)).rows:\n actor = dict(row)\n actors[actor[\"id\"]] = actor\n return actors\n\n return inner \n The returned dictionary from this example looks like this: \n {\n \"1\": {\"id\": \"1\", \"name\": \"Tony\"},\n \"2\": {\"id\": \"2\", \"name\": \"Tina\"},\n} \n These IDs could be integers or strings, depending on how the actors used by the Datasette instance are configured. \n Example: datasette-remote-actors", "sections_fts": 15, "rank": null}
{"rowid": 21, "title": "jinja2_environment_from_request(datasette, request, env)", "content": "datasette - Datasette class \n \n A Datasette instance. \n \n \n \n request - Request object or None \n \n The current HTTP request, if one is available. \n \n \n \n env - Environment \n \n The Jinja2 environment that will be used to render the current page. \n \n \n \n This hook can be used to return a customized Jinja environment based on the incoming request. \n If you want to run a single Datasette instance that serves different content for different domains, you can do so like this: \n from datasette import hookimpl\nfrom jinja2 import ChoiceLoader, FileSystemLoader\n\n\n@hookimpl\ndef jinja2_environment_from_request(request, env):\n if request and request.host == \"www.niche-museums.com\":\n return env.overlay(\n loader=ChoiceLoader(\n [\n FileSystemLoader(\n \"/mnt/niche-museums/templates\"\n ),\n env.loader,\n ]\n ),\n enable_async=True,\n )\n return env \n This uses the Jinja overlay() method to create a new environment identical to the default environment except for having a different template loader, which first looks in the /mnt/niche-museums/templates directory before falling back on the default loader.", "sections_fts": 15, "rank": null}
{"rowid": 22, "title": "filters_from_request(request, database, table, datasette)", "content": "request - Request object \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n table - string \n \n The name of the table. \n \n \n \n datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n 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. \n The hook should return an instance of datasette.filters.FilterArguments which has one required and three optional arguments: \n return FilterArguments(\n where_clauses=[\"id > :max_id\"],\n params={\"max_id\": 5},\n human_descriptions=[\"max_id is greater than 5\"],\n extra_context={},\n) \n The arguments to the FilterArguments class constructor are as follows: \n \n \n where_clauses - list of strings, required \n \n 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 . \n \n \n \n params - dictionary, optional \n \n Additional keyword arguments to be used when the query is executed. These should match any :arguments in the where clauses. \n \n \n \n human_descriptions - list of strings, optional \n \n These strings will be included in the human-readable description at the top of the page and the page . \n \n \n \n extra_context - dictionary, optional \n \n Additional context variables that should be made available to the table.html template when it is rendered. \n \n \n \n This example plugin causes 0 results to be returned if ?_nothing=1 is added to the URL: \n from datasette import hookimpl\nfrom datasette.filters import FilterArguments\n\n\n@hookimpl\ndef filters_from_request(self, request):\n if request.args.get(\"_nothing\"):\n return FilterArguments(\n [\"1 = 0\"], human_descriptions=[\"NOTHING\"]\n ) \n Example: datasette-leaflet-freedraw", "sections_fts": 15, "rank": null}
{"rowid": 23, "title": "permission_allowed(datasette, actor, action, resource)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary \n \n The current actor, as decided by actor_from_request(datasette, request) . \n \n \n \n action - string \n \n The action to be performed, e.g. \"edit-table\" . \n \n \n \n resource - string or None \n \n An identifier for the individual resource, e.g. the name of the table. \n \n \n \n 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. \n 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. \n from datasette import hookimpl\nimport random\n\n\n@hookimpl\ndef permission_allowed(action):\n if action != \"view-instance\":\n # Return True or False at random\n return random.random() > 0.5\n # Returning None falls back to default permissions \n 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(...) . \n 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.db database for all users. \n @hookimpl\ndef permission_allowed(datasette, actor, action, resource):\n async def inner():\n if action == \"execute-sql\" and resource == \"staff\":\n return False\n if action == \"view-table\" and resource == (\n \"staff\",\n \"admin_log\",\n ):\n if not actor:\n return False\n user_id = actor[\"id\"]\n return await datasette.get_database(\n \"staff\"\n ).execute(\n \"select count(*) from admin_users where user_id = :user_id\",\n {\"user_id\": user_id},\n )\n\n return inner \n See built-in permissions for a full list of permissions that are included in Datasette core. \n Example: datasette-permissions-sql", "sections_fts": 15, "rank": null}
{"rowid": 24, "title": "register_magic_parameters(datasette)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n Magic parameters can be used to add automatic parameters to canned queries . This plugin hook allows additional magic parameters to be defined by plugins. \n 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. \n 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 . \n 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: \n from datasette import hookimpl\nfrom uuid import uuid4\n\n\ndef uuid(key, request):\n if key == \"new\":\n return str(uuid4())\n else:\n raise KeyError\n\n\ndef request(key, request):\n if key == \"http_version\":\n return request.scope[\"http_version\"]\n else:\n raise KeyError\n\n\n@hookimpl\ndef register_magic_parameters(datasette):\n return [\n (\"request\", request),\n (\"uuid\", uuid),\n ]", "sections_fts": 15, "rank": null}
{"rowid": 25, "title": "forbidden(datasette, request, message)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n message - string \n \n A message hinting at why the request was forbidden. \n \n \n \n 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 . \n If a plugin hook wishes to react to the error, it should return a Response object . \n This example returns a redirect to a /-/login page: \n from datasette import hookimpl\nfrom urllib.parse import urlencode\n\n\n@hookimpl\ndef forbidden(request, message):\n return Response.redirect(\n \"/-/login?=\" + urlencode({\"message\": message})\n ) \n The function can alternatively return an awaitable function if it needs to make any asynchronous method calls. This example renders a template: \n from datasette import hookimpl, Response\n\n\n@hookimpl\ndef forbidden(datasette):\n async def inner():\n return Response.html(\n await datasette.render_template(\n \"render_message.html\", request=request\n )\n )\n\n return inner", "sections_fts": 15, "rank": null}
{"rowid": 26, "title": "handle_exception(datasette, request, exception)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n exception - Exception \n \n The exception that was raised. \n \n \n \n This hook is called any time an unexpected exception is raised. You can use it to record the exception. \n If your handler returns a Response object it will be returned to the client in place of the default Datasette error page. \n The handler can return a response directly, or it can return return an awaitable function that returns a response. \n This example logs an error to Sentry and then renders a custom error page: \n from datasette import hookimpl, Response\nimport sentry_sdk\n\n\n@hookimpl\ndef handle_exception(datasette, exception):\n sentry_sdk.capture_exception(exception)\n\n async def inner():\n return Response.html(\n await datasette.render_template(\n \"custom_error.html\", request=request\n )\n )\n\n return inner \n Example: datasette-sentry", "sections_fts": 15, "rank": null}
{"rowid": 27, "title": "skip_csrf(datasette, scope)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n scope - dictionary \n \n The ASGI scope for the incoming HTTP request. \n \n \n \n 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. \n This example will disable CSRF protection for that specific URL path: \n from datasette import hookimpl\n\n\n@hookimpl\ndef skip_csrf(scope):\n return scope[\"path\"] == \"/submit-comment\" \n If any of the currently active skip_csrf() plugin hooks return True , CSRF protection will be skipped for the request.", "sections_fts": 15, "rank": null}
{"rowid": 28, "title": "get_metadata(datasette, key, database, table)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n database - string or None \n \n The name of the database metadata is being asked for. \n \n \n \n table - string or None \n \n The name of the table. \n \n \n \n key - string or None \n \n The name of the key for which data is being asked for. \n \n \n \n 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. \n \n 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 . \n \n @hookimpl\ndef get_metadata(datasette, key, database, table):\n metadata = {\n \"title\": \"This will be the Datasette landing page title!\",\n \"description\": get_instance_description(datasette),\n \"databases\": [],\n }\n for db_name, db_data_dict in get_my_database_meta(\n datasette, database, table, key\n ):\n metadata[\"databases\"][db_name] = db_data_dict\n # whatever we return here will be merged with any other plugins using this hook and\n # will be overwritten by a local metadata.yaml if one exists!\n return metadata \n Example: datasette-remote-metadata plugin", "sections_fts": 15, "rank": null}
{"rowid": 29, "title": "menu_links(datasette, actor, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n request - Request object or None \n \n The current HTTP request. This can be None if the request object is not available. \n \n \n \n This hook allows additional items to be included in the menu displayed by Datasette's top right menu icon. \n The hook should return a list of {\"href\": \"...\", \"label\": \"...\"} menu items. These will be added to the menu. \n It can alternatively return an async def awaitable function which returns a list of menu items. \n This example adds a new menu item but only if the signed in user is \"root\" : \n from datasette import hookimpl\n\n\n@hookimpl\ndef menu_links(datasette, actor):\n if actor and actor.get(\"id\") == \"root\":\n return [\n {\n \"href\": datasette.urls.path(\n \"/-/edit-schema\"\n ),\n \"label\": \"Edit schema\",\n },\n ] \n Using datasette.urls here ensures that links in the menu will take the base_url setting into account. \n Examples: datasette-search-all , datasette-graphql", "sections_fts": 15, "rank": null}
{"rowid": 30, "title": "Action hooks", "content": "Action hooks can be used to add items to the action menus that appear at the top of different pages within Datasette. Unlike menu_links() , actions which are displayed on every page, actions should only be relevant to the page the user is currently viewing. \n Each of these hooks should return return a list of {\"href\": \"...\", \"label\": \"...\"} menu items, with optional \"description\": \"...\" keys describing each action in more detail. \n They can alternatively return an async def awaitable function which, when called, returns a list of those menu items.", "sections_fts": 15, "rank": null}
{"rowid": 31, "title": "table_actions(datasette, actor, database, table, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n database - string \n \n The name of the database. \n \n \n \n table - string \n \n The name of the table. \n \n \n \n request - Request object or None \n \n The current HTTP request. This can be None if the request object is not available. \n \n \n \n This example adds a new table action if the signed in user is \"root\" : \n from datasette import hookimpl\n\n\n@hookimpl\ndef table_actions(datasette, actor, database, table):\n if actor and actor.get(\"id\") == \"root\":\n return [\n {\n \"href\": datasette.urls.path(\n \"/-/edit-schema/{}/{}\".format(\n database, table\n )\n ),\n \"label\": \"Edit schema for this table\",\n \"description\": \"Add, remove, rename or alter columns for this table.\",\n }\n ] \n Example: datasette-graphql", "sections_fts": 15, "rank": null}
{"rowid": 32, "title": "view_actions(datasette, actor, database, view, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n database - string \n \n The name of the database. \n \n \n \n view - string \n \n The name of the SQL view. \n \n \n \n request - Request object or None \n \n The current HTTP request. This can be None if the request object is not available. \n \n \n \n Like table_actions(datasette, actor, database, table, request) but for SQL views.", "sections_fts": 15, "rank": null}
{"rowid": 33, "title": "query_actions(datasette, actor, database, query_name, request, sql, params)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n database - string \n \n The name of the database. \n \n \n \n query_name - string or None \n \n The name of the canned query, or None if this is an arbitrary SQL query. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n sql - string \n \n The SQL query being executed \n \n \n \n params - dictionary \n \n The parameters passed to the SQL query, if any. \n \n \n \n Populates a \"Query actions\" menu on the canned query and arbitrary SQL query pages. \n This example adds a new query action linking to a page for explaining a query: \n from datasette import hookimpl\nimport urllib\n\n\n@hookimpl\ndef query_actions(datasette, database, query_name, sql):\n # Don't explain an explain\n if sql.lower().startswith(\"explain\"):\n return\n return [\n {\n \"href\": datasette.urls.database(database)\n + \"?\"\n + urllib.parse.urlencode(\n {\n \"sql\": \"explain \" + sql,\n }\n ),\n \"label\": \"Explain this query\",\n \"description\": \"Get a summary of how SQLite executes the query\",\n },\n ] \n Example: datasette-create-view", "sections_fts": 15, "rank": null}
{"rowid": 34, "title": "row_actions(datasette, actor, request, database, table, row)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n request - Request object or None \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n table - string \n \n The name of the table. \n \n \n \n row - sqlite.Row \n \n The SQLite row object being displayed on the page. \n \n \n \n Return links for the \"Row actions\" menu shown at the top of the row page. \n This example displays the row in JSON plus some additional debug information if the user is signed in: \n from datasette import hookimpl\n\n\n@hookimpl\ndef row_actions(datasette, database, table, actor, row):\n if actor:\n return [\n {\n \"href\": datasette.urls.instance(),\n \"label\": f\"Row details for {actor['id']}\",\n \"description\": json.dumps(\n dict(row), default=repr\n ),\n },\n ] \n Example: datasette-enrichments", "sections_fts": 15, "rank": null}
{"rowid": 35, "title": "database_actions(datasette, actor, database, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n database - string \n \n The name of the database. \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n Populates an actions menu on the database page. \n This example adds a new database action for creating a table, if the user has the edit-schema permission: \n from datasette import hookimpl\n\n\n@hookimpl\ndef database_actions(datasette, actor, database):\n async def inner():\n if not await datasette.permission_allowed(\n actor,\n \"edit-schema\",\n resource=database,\n default=False,\n ):\n return []\n return [\n {\n \"href\": datasette.urls.path(\n \"/-/edit-schema/{}/-/create\".format(\n database\n )\n ),\n \"label\": \"Create a table\",\n }\n ]\n\n return inner \n Example: datasette-graphql , datasette-edit-schema", "sections_fts": 15, "rank": null}
{"rowid": 36, "title": "homepage_actions(datasette, actor, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) , or to execute SQL queries. \n \n \n \n actor - dictionary or None \n \n The currently authenticated actor . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n Populates an actions menu on the top-level index homepage of the Datasette instance. \n This example adds a link an imagined tool for editing the homepage, only for signed in users: \n from datasette import hookimpl\n\n\n@hookimpl\ndef homepage_actions(datasette, actor):\n if actor:\n return [\n {\n \"href\": datasette.urls.path(\n \"/-/customize-homepage\"\n ),\n \"label\": \"Customize homepage\",\n }\n ]", "sections_fts": 15, "rank": null}
{"rowid": 37, "title": "Template slots", "content": "The following set of plugin hooks can be used to return extra HTML content that will be inserted into the corresponding page, directly below the <h1> heading. \n Multiple plugins can contribute content here. The order in which it is displayed can be controlled using Pluggy's call time order options . \n Each of these plugin hooks can return either a string or an awaitable function that returns a string.", "sections_fts": 15, "rank": null}
{"rowid": 38, "title": "top_homepage(datasette, request)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n Returns HTML to be displayed at the top of the Datasette homepage.", "sections_fts": 15, "rank": null}
{"rowid": 39, "title": "top_database(datasette, request, database)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n Returns HTML to be displayed at the top of the database page.", "sections_fts": 15, "rank": null}
{"rowid": 40, "title": "top_table(datasette, request, database, table)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n table - string \n \n The name of the table. \n \n \n \n Returns HTML to be displayed at the top of the table page.", "sections_fts": 15, "rank": null}
{"rowid": 41, "title": "top_row(datasette, request, database, table, row)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n table - string \n \n The name of the table. \n \n \n \n row - sqlite.Row \n \n The SQLite row object being displayed. \n \n \n \n Returns HTML to be displayed at the top of the row page.", "sections_fts": 15, "rank": null}
{"rowid": 42, "title": "top_query(datasette, request, database, sql)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n sql - string \n \n The SQL query. \n \n \n \n Returns HTML to be displayed at the top of the query results page.", "sections_fts": 15, "rank": null}
{"rowid": 43, "title": "top_canned_query(datasette, request, database, query_name)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n request - Request object \n \n The current HTTP request. \n \n \n \n database - string \n \n The name of the database. \n \n \n \n query_name - string \n \n The name of the canned query. \n \n \n \n Returns HTML to be displayed at the top of the canned query page.", "sections_fts": 15, "rank": null}
{"rowid": 44, "title": "Event tracking", "content": "Datasette includes an internal mechanism for tracking notable events. This can be used for analytics, but can also be used by plugins that want to listen out for when key events occur (such as a table being created) and take action in response. \n Plugins can register to receive events using the track_event plugin hook. \n They can also define their own events for other plugins to receive using the register_events() plugin hook , combined with calls to the datasette.track_event() internal method .", "sections_fts": 15, "rank": null}
{"rowid": 45, "title": "track_event(datasette, event)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n event - Event \n \n Information about the event, represented as an instance of a subclass of the Event base class. \n \n \n \n This hook will be called any time an event is tracked by code that calls the datasette.track_event(...) internal method. \n The event object will always have the following properties: \n \n \n name : a string representing the name of the event, for example logout or create-table . \n \n \n actor : a dictionary representing the actor that triggered the event, or None if the event was not triggered by an actor. \n \n \n created : a datatime.datetime object in the timezone.utc timezone representing the time the event object was created. \n \n \n Other properties on the event will be available depending on the type of event. You can also access those as a dictionary using event.properties() . \n The events fired by Datasette core are documented here . \n This example plugin logs details of all events to standard error: \n from datasette import hookimpl\nimport json\nimport sys\n\n\n@hookimpl\ndef track_event(event):\n name = event.name\n actor = event.actor\n properties = event.properties()\n msg = json.dumps(\n {\n \"name\": name,\n \"actor\": actor,\n \"properties\": properties,\n }\n )\n print(msg, file=sys.stderr, flush=True) \n The function can also return an async function which will be awaited. This is useful for writing to a database. \n This example logs events to a datasette_events table in a database called events . It uses the startup() hook to create that table if it does not exist. \n from datasette import hookimpl\nimport json\n\n\n@hookimpl\ndef startup(datasette):\n async def inner():\n db = datasette.get_database(\"events\")\n await db.execute_write(\n \"\"\"\n create table if not exists datasette_events (\n id integer primary key,\n event_type text,\n created text,\n actor text,\n properties text\n )\n \"\"\"\n )\n\n return inner\n\n\n@hookimpl\ndef track_event(datasette, event):\n async def inner():\n db = datasette.get_database(\"events\")\n properties = event.properties()\n await db.execute_write(\n \"\"\"\n insert into datasette_events (event_type, created, actor, properties)\n values (?, strftime('%Y-%m-%d %H:%M:%S', 'now'), ?, ?)\n \"\"\",\n (\n event.name,\n json.dumps(event.actor),\n json.dumps(properties),\n ),\n )\n\n return inner \n Example: datasette-events-db", "sections_fts": 15, "rank": null}
{"rowid": 46, "title": "register_events(datasette)", "content": "datasette - Datasette class \n \n You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) . \n \n \n \n This hook should return a list of Event subclasses that represent custom events that the plugin might send to the datasette.track_event() method. \n This example registers event subclasses for ban-user and unban-user events: \n from dataclasses import dataclass\nfrom datasette import hookimpl, Event\n\n\n@dataclass\nclass BanUserEvent(Event):\n name = \"ban-user\"\n user: dict\n\n\n@dataclass\nclass UnbanUserEvent(Event):\n name = \"unban-user\"\n user: dict\n\n\n@hookimpl\ndef register_events():\n return [BanUserEvent, UnbanUserEvent] \n The plugin can then call datasette.track_event(...) to send a ban-user event: \n await datasette.track_event(\n BanUserEvent(user={\"id\": 1, \"username\": \"cleverbot\"})\n)", "sections_fts": 15, "rank": null}
{"rowid": 47, "title": "Plugins", "content": "Datasette's plugin system allows additional features to be implemented as Python\n code (or front-end JavaScript) which can be wrapped up in a separate Python\n package. The underlying mechanism uses pluggy . \n See the Datasette plugins directory for a list of existing plugins, or take a look at the\n datasette-plugin topic on GitHub. \n Things you can do with plugins include: \n \n \n Add visualizations to Datasette, for example\n datasette-cluster-map and\n datasette-vega . \n \n \n Make new custom SQL functions available for use within Datasette, for example\n datasette-haversine and\n datasette-jellyfish . \n \n \n Define custom output formats with custom extensions, for example datasette-atom and\n datasette-ics . \n \n \n Add template functions that can be called within your Jinja custom templates,\n for example datasette-render-markdown . \n \n \n Customize how database values are rendered in the Datasette interface, for example\n datasette-render-binary and\n datasette-pretty-json . \n \n \n Customize how Datasette's authentication and permissions systems work, for example datasette-auth-passwords and\n datasette-permissions-sql .", "sections_fts": 15, "rank": null}
{"rowid": 48, "title": "Installing plugins", "content": "If a plugin has been packaged for distribution using setuptools you can use the plugin by installing it alongside Datasette in the same virtual environment or Docker container. \n You can install plugins using the datasette install command: \n datasette install datasette-vega \n You can uninstall plugins with datasette uninstall : \n datasette uninstall datasette-vega \n You can upgrade plugins with datasette install --upgrade or datasette install -U : \n datasette install -U datasette-vega \n This command can also be used to upgrade Datasette itself to the latest released version: \n datasette install -U datasette \n You can install multiple plugins at once by listing them as lines in a requirements.txt file like this: \n datasette-vega\ndatasette-cluster-map \n Then pass that file to datasette install -r : \n datasette install -r requirements.txt \n The install and uninstall commands are thin wrappers around pip install and pip uninstall , which ensure that they run pip in the same virtual environment as Datasette itself.", "sections_fts": 15, "rank": null}
{"rowid": 49, "title": "One-off plugins using --plugins-dir", "content": "You can also define one-off per-project plugins by saving them as plugin_name.py functions in a plugins/ folder and then passing that folder to datasette using the --plugins-dir option: \n datasette mydb.db --plugins-dir=plugins/", "sections_fts": 15, "rank": null}
{"rowid": 50, "title": "Deploying plugins using datasette publish", "content": "The datasette publish and datasette package commands both take an optional --install argument. You can use this one or more times to tell Datasette to pip install specific plugins as part of the process: \n datasette publish cloudrun mydb.db --install=datasette-vega \n You can use the name of a package on PyPI or any of the other valid arguments to pip install such as a URL to a .zip file: \n datasette publish cloudrun mydb.db \\\n --install=https://url-to-my-package.zip", "sections_fts": 15, "rank": null}
{"rowid": 51, "title": "Controlling which plugins are loaded", "content": "Datasette defaults to loading every plugin that is installed in the same virtual environment as Datasette itself. \n You can set the DATASETTE_LOAD_PLUGINS environment variable to a comma-separated list of plugin names to load a controlled subset of plugins instead. \n For example, to load just the datasette-vega and datasette-cluster-map plugins, set DATASETTE_LOAD_PLUGINS to datasette-vega,datasette-cluster-map : \n export DATASETTE_LOAD_PLUGINS='datasette-vega,datasette-cluster-map'\ndatasette mydb.db \n Or: \n DATASETTE_LOAD_PLUGINS='datasette-vega,datasette-cluster-map' \\\n datasette mydb.db \n To disable the loading of all additional plugins, set DATASETTE_LOAD_PLUGINS to an empty string: \n export DATASETTE_LOAD_PLUGINS=''\ndatasette mydb.db \n A quick way to test this setting is to use it with the datasette plugins command: \n DATASETTE_LOAD_PLUGINS='datasette-vega' datasette plugins \n This should output the following: \n [\n {\n \"name\": \"datasette-vega\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.6.2\",\n \"hooks\": [\n \"extra_css_urls\",\n \"extra_js_urls\"\n ]\n }\n]", "sections_fts": 15, "rank": null}
{"rowid": 52, "title": "Seeing what plugins are installed", "content": "You can see a list of installed plugins by navigating to the /-/plugins page of your Datasette instance - for example: https://fivethirtyeight.datasettes.com/-/plugins \n You can also use the datasette plugins command: \n datasette plugins \n Which outputs: \n [\n {\n \"name\": \"datasette_json_html\",\n \"static\": false,\n \"templates\": false,\n \"version\": \"0.4.0\"\n }\n] \n [[[cog\nfrom datasette import cli\nfrom click.testing import CliRunner\nimport textwrap, json\ncog.out(\"\\n\")\nresult = CliRunner().invoke(cli.cli, [\"plugins\", \"--all\"])\n# cog.out() with text containing newlines was unindenting for some reason\ncog.outl(\"If you run ``datasette plugins --all`` it will include default plugins that ship as part of Datasette:\\n\")\ncog.outl(\".. code-block:: json\\n\")\nplugins = [p for p in json.loads(result.output) if p[\"name\"].startswith(\"datasette.\")]\nindented = textwrap.indent(json.dumps(plugins, indent=4), \" \")\nfor line in indented.split(\"\\n\"):\n cog.outl(line)\ncog.out(\"\\n\\n\") \n ]]] \n If you run datasette plugins --all it will include default plugins that ship as part of Datasette: \n [\n {\n \"name\": \"datasette.actor_auth_cookie\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"actor_from_request\"\n ]\n },\n {\n \"name\": \"datasette.blob_renderer\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"register_output_renderer\"\n ]\n },\n {\n \"name\": \"datasette.default_magic_parameters\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"register_magic_parameters\"\n ]\n },\n {\n \"name\": \"datasette.default_menu_links\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"menu_links\"\n ]\n },\n {\n \"name\": \"datasette.default_permissions\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"actor_from_request\",\n \"permission_allowed\",\n \"register_permissions\",\n \"skip_csrf\"\n ]\n },\n {\n \"name\": \"datasette.events\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"register_events\"\n ]\n },\n {\n \"name\": \"datasette.facets\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"register_facet_classes\"\n ]\n },\n {\n \"name\": \"datasette.filters\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"filters_from_request\"\n ]\n },\n {\n \"name\": \"datasette.forbidden\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"forbidden\"\n ]\n },\n {\n \"name\": \"datasette.handle_exception\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"handle_exception\"\n ]\n },\n {\n \"name\": \"datasette.publish.cloudrun\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"publish_subcommand\"\n ]\n },\n {\n \"name\": \"datasette.publish.heroku\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"publish_subcommand\"\n ]\n },\n {\n \"name\": \"datasette.sql_functions\",\n \"static\": false,\n \"templates\": false,\n \"version\": null,\n \"hooks\": [\n \"prepare_connection\"\n ]\n }\n] \n [[[end]]] \n You can add the --plugins-dir= option to include any plugins found in that directory. \n Add --requirements to output a list of installed plugins that can then be installed in another Datasette instance using datasette install -r requirements.txt : \n datasette plugins --requirements \n The output will look something like this: \n datasette-codespaces==0.1.1\ndatasette-graphql==2.2\ndatasette-json-html==1.0.1\ndatasette-pretty-json==0.2.2\ndatasette-x-forwarded-host==0.1 \n To write that to a requirements.txt file, run this: \n datasette plugins --requirements > requirements.txt", "sections_fts": 15, "rank": null}
{"rowid": 53, "title": "Plugin configuration", "content": "Plugins can have their own configuration, embedded in a configuration file . Configuration options for plugins live within a \"plugins\" key in that file, which can be included at the root, database or table level. \n Here is an example of some plugin configuration for a specific table: \n [[[cog\nfrom metadata_doc import config_example\nconfig_example(cog, {\n \"databases\": {\n \"sf-trees\": {\n \"tables\": {\n \"Street_Tree_List\": {\n \"plugins\": {\n \"datasette-cluster-map\": {\n \"latitude_column\": \"lat\",\n \"longitude_column\": \"lng\"\n }\n }\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n This tells the datasette-cluster-map column which latitude and longitude columns should be used for a table called Street_Tree_List inside a database file called sf-trees.db .", "sections_fts": 15, "rank": null}
{"rowid": 54, "title": "Secret configuration values", "content": "Some plugins may need configuration that should stay secret - API keys for example. There are two ways in which you can store secret configuration values. \n As environment variables . If your secret lives in an environment variable that is available to the Datasette process, you can indicate that the configuration value should be read from that environment variable like so: \n [[[cog\nconfig_example(cog, {\n \"plugins\": {\n \"datasette-auth-github\": {\n \"client_secret\": {\n \"$env\": \"GITHUB_CLIENT_SECRET\"\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n As values in separate files . Your secrets can also live in files on disk. To specify a secret should be read from a file, provide the full file path like this: \n [[[cog\nconfig_example(cog, {\n \"plugins\": {\n \"datasette-auth-github\": {\n \"client_secret\": {\n \"$file\": \"/secrets/client-secret\"\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n If you are publishing your data using the datasette publish family of commands, you can use the --plugin-secret option to set these secrets at publish time. For example, using Heroku you might run the following command: \n datasette publish heroku my_database.db \\\n --name my-heroku-app-demo \\\n --install=datasette-auth-github \\\n --plugin-secret datasette-auth-github client_id your_client_id \\\n --plugin-secret datasette-auth-github client_secret your_client_secret \n This will set the necessary environment variables and add the following to the deployed metadata.yaml : \n [[[cog\nconfig_example(cog, {\n \"plugins\": {\n \"datasette-auth-github\": {\n \"client_id\": {\n \"$env\": \"DATASETTE_AUTH_GITHUB_CLIENT_ID\"\n },\n \"client_secret\": {\n \"$env\": \"DATASETTE_AUTH_GITHUB_CLIENT_SECRET\"\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 55, "title": "Full-text search", "content": "SQLite includes a powerful mechanism for enabling full-text search against SQLite records. Datasette can detect if a table has had full-text search configured for it in the underlying database and display a search interface for filtering that table. \n Here's an example search : \n \n Datasette automatically detects which tables have been configured for full-text search.", "sections_fts": 15, "rank": null}
{"rowid": 56, "title": "The table page and table view API", "content": "Table views that support full-text search can be queried using the ?_search=TERMS query string parameter. This will run the search against content from all of the columns that have been included in the index. \n Try this example: fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort \n SQLite full-text search supports wildcards. This means you can easily implement prefix auto-complete by including an asterisk at the end of the search term - for example: \n /dbname/tablename/?_search=rob* \n This will return all records containing at least one word that starts with the letters rob . \n You can also run searches against just the content of a specific named column by using _search_COLNAME=TERMS - for example, this would search for just rows where the name column in the FTS index mentions Sarah : \n /dbname/tablename/?_search_name=Sarah", "sections_fts": 15, "rank": null}
{"rowid": 57, "title": "Advanced SQLite search queries", "content": "SQLite full-text search includes support for a variety of advanced queries , including AND , OR , NOT and NEAR . \n By default Datasette disables these features to ensure they do not cause errors or confusion for users who are not aware of them. You can disable this escaping and use the advanced queries by adding &_searchmode=raw to the table page query string. \n If you want to enable these operators by default for a specific table, you can do so by adding \"searchmode\": \"raw\" to the metadata configuration for that table, see Configuring full-text search for a table or view . \n If that option has been specified in the table metadata but you want to over-ride it and return to the default behavior you can append &_searchmode=escaped to the query string.", "sections_fts": 15, "rank": null}
{"rowid": 58, "title": "Configuring full-text search for a table or view", "content": "If a table has a corresponding FTS table set up using the content= argument to CREATE VIRTUAL TABLE shown below, Datasette will detect it automatically and add a search interface to the table page for that table. \n You can also manually configure which table should be used for full-text search using query string parameters or Metadata . You can set the associated FTS table for a specific table and you can also set one for a view - if you do that, the page for that SQL view will offer a search option. \n Use ?_fts_table=x to over-ride the FTS table for a specific page. If the primary key was something other than rowid you can use ?_fts_pk=col to set that as well. This is particularly useful for views, for example: \n https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk \n The fts_table metadata property can be used to specify an associated FTS table. If the primary key column in your table which was used to populate the FTS table is something other than rowid , you can specify the column to use with the fts_pk property. \n The \"searchmode\": \"raw\" property can be used to default the table to accepting SQLite advanced search operators, as described in Advanced SQLite search queries . \n Here is an example which enables full-text search (with SQLite advanced search operators) for a display_ads view which is defined against the ads table and hence needs to run FTS against the ads_fts table, using the id as the primary key: \n [[[cog\nfrom metadata_doc import metadata_example\nmetadata_example(cog, {\n \"databases\": {\n \"russian-ads\": {\n \"tables\": {\n \"display_ads\": {\n \"fts_table\": \"ads_fts\",\n \"fts_pk\": \"id\",\n \"searchmode\": \"raw\"\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 59, "title": "Searches using custom SQL", "content": "You can include full-text search results in custom SQL queries. The general pattern with SQLite search is to run the search as a sub-select that returns rowid values, then include those rowids in another part of the query. \n You can see the syntax for a basic search by running that search on a table page and then clicking \"View and edit SQL\" to see the underlying SQL. For example, consider this search for manafort is the US FARA database : \n /fara/FARA_All_ShortForms?_search=manafort \n If you click View and edit SQL you'll see that the underlying SQL looks like this: \n select\n rowid,\n Short_Form_Termination_Date,\n Short_Form_Date,\n Short_Form_Last_Name,\n Short_Form_First_Name,\n Registration_Number,\n Registration_Date,\n Registrant_Name,\n Address_1,\n Address_2,\n City,\n State,\n Zip\nfrom\n FARA_All_ShortForms\nwhere\n rowid in (\n select\n rowid\n from\n FARA_All_ShortForms_fts\n where\n FARA_All_ShortForms_fts match escape_fts(:search)\n )\norder by\n rowid\nlimit\n 101", "sections_fts": 15, "rank": null}
{"rowid": 60, "title": "Enabling full-text search for a SQLite table", "content": "Datasette takes advantage of the external content mechanism in SQLite, which allows a full-text search virtual table to be associated with the contents of another SQLite table. \n To set up full-text search for a table, you need to do two things: \n \n \n Create a new FTS virtual table associated with your table \n \n \n Populate that FTS table with the data that you would like to be able to run searches against", "sections_fts": 15, "rank": null}
{"rowid": 61, "title": "Configuring FTS using sqlite-utils", "content": "sqlite-utils is a CLI utility and Python library for manipulating SQLite databases. You can use it from Python code to configure FTS search, or you can achieve the same goal using the accompanying command-line tool . \n Here's how to use sqlite-utils to enable full-text search for an items table across the name and description columns: \n sqlite-utils enable-fts mydatabase.db items name description", "sections_fts": 15, "rank": null}
{"rowid": 62, "title": "Configuring FTS using csvs-to-sqlite", "content": "If your data starts out in CSV files, you can use Datasette's companion tool csvs-to-sqlite to convert that file into a SQLite database and enable full-text search on specific columns. For a file called items.csv where you want full-text search to operate against the name and description columns you would run the following: \n csvs-to-sqlite items.csv items.db -f name -f description", "sections_fts": 15, "rank": null}
{"rowid": 63, "title": "Configuring FTS by hand", "content": "We recommend using sqlite-utils , but if you want to hand-roll a SQLite full-text search table you can do so using the following SQL. \n To enable full-text search for a table called items that works against the name and description columns, you would run this SQL to create a new items_fts FTS virtual table: \n CREATE VIRTUAL TABLE \"items_fts\" USING FTS4 (\n name,\n description,\n content=\"items\"\n); \n This creates a set of tables to power full-text search against items . The new items_fts table will be detected by Datasette as the fts_table for the items table. \n Creating the table is not enough: you also need to populate it with a copy of the data that you wish to make searchable. You can do that using the following SQL: \n INSERT INTO \"items_fts\" (rowid, name, description)\n SELECT rowid, name, description FROM items; \n If your table has columns that are foreign key references to other tables you can include that data in your full-text search index using a join. Imagine the items table has a foreign key column called category_id which refers to a categories table - you could create a full-text search table like this: \n CREATE VIRTUAL TABLE \"items_fts\" USING FTS4 (\n name,\n description,\n category_name,\n content=\"items\"\n); \n And then populate it like this: \n INSERT INTO \"items_fts\" (rowid, name, description, category_name)\n SELECT items.rowid,\n items.name,\n items.description,\n categories.name\n FROM items JOIN categories ON items.category_id=categories.id; \n You can use this technique to populate the full-text search index from any combination of tables and joins that makes sense for your project.", "sections_fts": 15, "rank": null}
{"rowid": 64, "title": "FTS versions", "content": "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. \n 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. \n If you can't be sure that FTS5 will be available, you should use FTS4.", "sections_fts": 15, "rank": null}
{"rowid": 65, "title": "CLI reference", "content": "The datasette CLI tool provides a number of commands. \n Running datasette without specifying a command runs the default command, datasette serve . See datasette serve for the full list of options for that command. \n [[[cog\nfrom datasette import cli\nfrom click.testing import CliRunner\nimport textwrap\ndef help(args):\n title = \"datasette \" + \" \".join(args)\n cog.out(\"\\n::\\n\\n\")\n result = CliRunner().invoke(cli.cli, args)\n output = result.output.replace(\"Usage: cli \", \"Usage: datasette \")\n cog.out(textwrap.indent(output, ' '))\n cog.out(\"\\n\\n\") \n ]]] \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 66, "title": "datasette --help", "content": "Running datasette --help shows a list of all of the available commands. \n [[[cog\nhelp([\"--help\"]) \n ]]] \n Usage: datasette [OPTIONS] COMMAND [ARGS]...\n\n Datasette is an open source multi-tool for exploring and publishing data\n\n About Datasette: https://datasette.io/\n Full documentation: https://docs.datasette.io/\n\nOptions:\n --version Show the version and exit.\n --help Show this message and exit.\n\nCommands:\n serve* Serve up specified SQLite database files with a web UI\n create-token Create a signed API token for the specified actor ID\n inspect Generate JSON summary of provided database files\n install Install plugins and packages from PyPI into the same...\n package Package SQLite files into a Datasette Docker container\n plugins List currently installed plugins\n publish Publish specified SQLite database files to the internet...\n uninstall Uninstall plugins and Python packages from the Datasette... \n [[[end]]] \n Additional commands added by plugins that use the register_commands(cli) hook will be listed here as well.", "sections_fts": 15, "rank": null}
{"rowid": 67, "title": "datasette serve", "content": "This command starts the Datasette web application running on your machine: \n datasette serve mydatabase.db \n Or since this is the default command you can run this instead: \n datasette mydatabase.db \n Once started you can access it at http://localhost:8001 \n [[[cog\nhelp([\"serve\", \"--help\"]) \n ]]] \n Usage: datasette serve [OPTIONS] [FILES]...\n\n Serve up specified SQLite database files with a web UI\n\nOptions:\n -i, --immutable PATH Database files to open in immutable mode\n -h, --host TEXT Host for server. Defaults to 127.0.0.1 which\n means only connections from the local machine\n will be allowed. Use 0.0.0.0 to listen to all\n IPs and allow access from other machines.\n -p, --port INTEGER RANGE Port for server, defaults to 8001. Use -p 0 to\n automatically assign an available port.\n [0<=x<=65535]\n --uds TEXT Bind to a Unix domain socket\n --reload Automatically reload if code or metadata\n change detected - useful for development\n --cors Enable CORS by serving Access-Control-Allow-\n Origin: *\n --load-extension PATH:ENTRYPOINT?\n Path to a SQLite extension to load, and\n optional entrypoint\n --inspect-file TEXT Path to JSON file created using \"datasette\n inspect\"\n -m, --metadata FILENAME Path to JSON/YAML file containing\n license/source metadata\n --template-dir DIRECTORY Path to directory containing custom templates\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --static MOUNT:DIRECTORY Serve static files from this directory at\n /MOUNT/...\n --memory Make /_memory database available\n -c, --config FILENAME Path to JSON/YAML Datasette configuration file\n -s, --setting SETTING... nested.key, value setting to use in Datasette\n configuration\n --secret TEXT Secret used for signing secure values, such as\n signed cookies\n --root Output URL that sets a cookie authenticating\n the root user\n --get TEXT Run an HTTP GET request against this path,\n print results and exit\n --token TEXT API token to send with --get requests\n --actor TEXT Actor to use for --get requests (JSON string)\n --version-note TEXT Additional note to show on /-/versions\n --help-settings Show available settings\n --pdb Launch debugger on any errors\n -o, --open Open Datasette in your web browser\n --create Create database files if they do not exist\n --crossdb Enable cross-database joins using the /_memory\n database\n --nolock Ignore locking, open locked files in read-only\n mode\n --ssl-keyfile TEXT SSL key file\n --ssl-certfile TEXT SSL certificate file\n --internal PATH Path to a persistent Datasette internal SQLite\n database\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 68, "title": "datasette --get", "content": "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. \n This means that all of Datasette's functionality can be accessed directly from the command-line. \n For example: \n datasette --get '/-/versions.json' | jq . \n {\n \"python\": {\n \"version\": \"3.8.5\",\n \"full\": \"3.8.5 (default, Jul 21 2020, 10:48:26) \\n[Clang 11.0.3 (clang-1103.0.32.62)]\"\n },\n \"datasette\": {\n \"version\": \"0.46+15.g222a84a.dirty\"\n },\n \"asgi\": \"3.0\",\n \"uvicorn\": \"0.11.8\",\n \"sqlite\": {\n \"version\": \"3.32.3\",\n \"fts_versions\": [\n \"FTS5\",\n \"FTS4\",\n \"FTS3\"\n ],\n \"extensions\": {\n \"json1\": null\n },\n \"compile_options\": [\n \"COMPILER=clang-11.0.3\",\n \"ENABLE_COLUMN_METADATA\",\n \"ENABLE_FTS3\",\n \"ENABLE_FTS3_PARENTHESIS\",\n \"ENABLE_FTS4\",\n \"ENABLE_FTS5\",\n \"ENABLE_GEOPOLY\",\n \"ENABLE_JSON1\",\n \"ENABLE_PREUPDATE_HOOK\",\n \"ENABLE_RTREE\",\n \"ENABLE_SESSION\",\n \"MAX_VARIABLE_NUMBER=250000\",\n \"THREADSAFE=1\"\n ]\n }\n} \n You can use the --token TOKEN option to send an API token with the simulated request. \n Or you can make a request as a specific actor by passing a JSON representation of that actor to --actor : \n datasette --memory --actor '{\"id\": \"root\"}' --get '/-/actor.json' \n The exit code of datasette --get 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. \n This lets you use datasette --get / to run tests against a Datasette application in a continuous integration environment such as GitHub Actions.", "sections_fts": 15, "rank": null}
{"rowid": 69, "title": "datasette serve --help-settings", "content": "This command outputs all of the available Datasette settings . \n These can be passed to datasette serve using datasette serve --setting name value . \n [[[cog\nhelp([\"--help-settings\"]) \n ]]] \n Settings:\n default_page_size Default page size for the table view\n (default=100)\n max_returned_rows Maximum rows that can be returned from a table or\n custom query (default=1000)\n max_insert_rows Maximum rows that can be inserted at a time using\n the bulk insert API (default=100)\n num_sql_threads Number of threads in the thread pool for\n executing SQLite queries (default=3)\n sql_time_limit_ms Time limit for a SQL query in milliseconds\n (default=1000)\n default_facet_size Number of values to return for requested facets\n (default=30)\n facet_time_limit_ms Time limit for calculating a requested facet\n (default=200)\n facet_suggest_time_limit_ms Time limit for calculating a suggested facet\n (default=50)\n allow_facet Allow users to specify columns to facet using\n ?_facet= parameter (default=True)\n allow_download Allow users to download the original SQLite\n database files (default=True)\n allow_signed_tokens Allow users to create and use signed API tokens\n (default=True)\n default_allow_sql Allow anyone to run arbitrary SQL queries\n (default=True)\n max_signed_tokens_ttl Maximum allowed expiry time for signed API tokens\n (default=0)\n suggest_facets Calculate and display suggested facets\n (default=True)\n default_cache_ttl Default HTTP cache TTL (used in Cache-Control:\n max-age= header) (default=5)\n cache_size_kb SQLite cache size in KB (0 == use SQLite default)\n (default=0)\n allow_csv_stream Allow .csv?_stream=1 to download all rows\n (ignoring max_returned_rows) (default=True)\n max_csv_mb Maximum size allowed for CSV export in MB - set 0\n to disable this limit (default=100)\n truncate_cells_html Truncate cells longer than this in HTML table\n view - set 0 to disable (default=2048)\n force_https_urls Force URLs in API output to always use https://\n protocol (default=False)\n template_debug Allow display of template debug information with\n ?_context=1 (default=False)\n trace_debug Allow display of SQL trace debug information with\n ?_trace=1 (default=False)\n base_url Datasette URLs should use this base path\n (default=/) \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 70, "title": "datasette plugins", "content": "Output JSON showing all currently installed plugins, their versions, whether they include static files or templates and which Plugin hooks they use. \n [[[cog\nhelp([\"plugins\", \"--help\"]) \n ]]] \n Usage: datasette plugins [OPTIONS]\n\n List currently installed plugins\n\nOptions:\n --all Include built-in default plugins\n --requirements Output requirements.txt of installed plugins\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --help Show this message and exit. \n [[[end]]] \n Example output: \n [\n {\n \"name\": \"datasette-geojson\",\n \"static\": false,\n \"templates\": false,\n \"version\": \"0.3.1\",\n \"hooks\": [\n \"register_output_renderer\"\n ]\n },\n {\n \"name\": \"datasette-geojson-map\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.4.0\",\n \"hooks\": [\n \"extra_body_script\",\n \"extra_css_urls\",\n \"extra_js_urls\"\n ]\n },\n {\n \"name\": \"datasette-leaflet\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.2.2\",\n \"hooks\": [\n \"extra_body_script\",\n \"extra_template_vars\"\n ]\n }\n]", "sections_fts": 15, "rank": null}
{"rowid": 71, "title": "datasette install", "content": "Install new Datasette plugins. This command works like pip install but ensures that your plugins will be installed into the same environment as Datasette. \n This command: \n datasette install datasette-cluster-map \n Would install the datasette-cluster-map plugin. \n [[[cog\nhelp([\"install\", \"--help\"]) \n ]]] \n Usage: datasette install [OPTIONS] [PACKAGES]...\n\n Install plugins and packages from PyPI into the same environment as Datasette\n\nOptions:\n -U, --upgrade Upgrade packages to latest version\n -r, --requirement PATH Install from requirements file\n -e, --editable TEXT Install a project in editable mode from this path\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 72, "title": "datasette uninstall", "content": "Uninstall one or more plugins. \n [[[cog\nhelp([\"uninstall\", \"--help\"]) \n ]]] \n Usage: datasette uninstall [OPTIONS] PACKAGES...\n\n Uninstall plugins and Python packages from the Datasette environment\n\nOptions:\n -y, --yes Don't ask for confirmation\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 73, "title": "datasette publish", "content": "Shows a list of available deployment targets for publishing data with Datasette. \n Additional deployment targets can be added by plugins that use the publish_subcommand(publish) hook. \n [[[cog\nhelp([\"publish\", \"--help\"]) \n ]]] \n Usage: datasette publish [OPTIONS] COMMAND [ARGS]...\n\n Publish specified SQLite database files to the internet along with a\n Datasette-powered interface and API\n\nOptions:\n --help Show this message and exit.\n\nCommands:\n cloudrun Publish databases to Datasette running on Cloud Run\n heroku Publish databases to Datasette running on Heroku \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 74, "title": "datasette publish cloudrun", "content": "See Publishing to Google Cloud Run . \n [[[cog\nhelp([\"publish\", \"cloudrun\", \"--help\"]) \n ]]] \n Usage: datasette publish cloudrun [OPTIONS] [FILES]...\n\n Publish databases to Datasette running on Cloud Run\n\nOptions:\n -m, --metadata FILENAME Path to JSON/YAML file containing metadata to\n publish\n --extra-options TEXT Extra options to pass to datasette serve\n --branch TEXT Install datasette from a GitHub branch e.g.\n main\n --template-dir DIRECTORY Path to directory containing custom templates\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --static MOUNT:DIRECTORY Serve static files from this directory at\n /MOUNT/...\n --install TEXT Additional packages (e.g. plugins) to install\n --plugin-secret <TEXT TEXT TEXT>...\n Secrets to pass to plugins, e.g. --plugin-\n secret datasette-auth-github client_id xxx\n --version-note TEXT Additional note to show on /-/versions\n --secret TEXT Secret used for signing secure values, such as\n signed cookies\n --title TEXT Title for metadata\n --license TEXT License label for metadata\n --license_url TEXT License URL for metadata\n --source TEXT Source label for metadata\n --source_url TEXT Source URL for metadata\n --about TEXT About label for metadata\n --about_url TEXT About URL for metadata\n -n, --name TEXT Application name to use when building\n --service TEXT Cloud Run service to deploy (or over-write)\n --spatialite Enable SpatialLite extension\n --show-files Output the generated Dockerfile and\n metadata.json\n --memory TEXT Memory to allocate in Cloud Run, e.g. 1Gi\n --cpu [1|2|4] Number of vCPUs to allocate in Cloud Run\n --timeout INTEGER Build timeout in seconds\n --apt-get-install TEXT Additional packages to apt-get install\n --max-instances INTEGER Maximum Cloud Run instances\n --min-instances INTEGER Minimum Cloud Run instances\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 75, "title": "datasette publish heroku", "content": "See Publishing to Heroku . \n [[[cog\nhelp([\"publish\", \"heroku\", \"--help\"]) \n ]]] \n Usage: datasette publish heroku [OPTIONS] [FILES]...\n\n Publish databases to Datasette running on Heroku\n\nOptions:\n -m, --metadata FILENAME Path to JSON/YAML file containing metadata to\n publish\n --extra-options TEXT Extra options to pass to datasette serve\n --branch TEXT Install datasette from a GitHub branch e.g.\n main\n --template-dir DIRECTORY Path to directory containing custom templates\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --static MOUNT:DIRECTORY Serve static files from this directory at\n /MOUNT/...\n --install TEXT Additional packages (e.g. plugins) to install\n --plugin-secret <TEXT TEXT TEXT>...\n Secrets to pass to plugins, e.g. --plugin-\n secret datasette-auth-github client_id xxx\n --version-note TEXT Additional note to show on /-/versions\n --secret TEXT Secret used for signing secure values, such as\n signed cookies\n --title TEXT Title for metadata\n --license TEXT License label for metadata\n --license_url TEXT License URL for metadata\n --source TEXT Source label for metadata\n --source_url TEXT Source URL for metadata\n --about TEXT About label for metadata\n --about_url TEXT About URL for metadata\n -n, --name TEXT Application name to use when deploying\n --tar TEXT --tar option to pass to Heroku, e.g.\n --tar=/usr/local/bin/gtar\n --generate-dir DIRECTORY Output generated application files and stop\n without deploying\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 76, "title": "datasette package", "content": "Package SQLite files into a Datasette Docker container, see datasette package . \n [[[cog\nhelp([\"package\", \"--help\"]) \n ]]] \n Usage: datasette package [OPTIONS] FILES...\n\n Package SQLite files into a Datasette Docker container\n\nOptions:\n -t, --tag TEXT Name for the resulting Docker container, can\n optionally use name:tag format\n -m, --metadata FILENAME Path to JSON/YAML file containing metadata to\n publish\n --extra-options TEXT Extra options to pass to datasette serve\n --branch TEXT Install datasette from a GitHub branch e.g. main\n --template-dir DIRECTORY Path to directory containing custom templates\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/...\n --install TEXT Additional packages (e.g. plugins) to install\n --spatialite Enable SpatialLite extension\n --version-note TEXT Additional note to show on /-/versions\n --secret TEXT Secret used for signing secure values, such as\n signed cookies\n -p, --port INTEGER RANGE Port to run the server on, defaults to 8001\n [1<=x<=65535]\n --title TEXT Title for metadata\n --license TEXT License label for metadata\n --license_url TEXT License URL for metadata\n --source TEXT Source label for metadata\n --source_url TEXT Source URL for metadata\n --about TEXT About label for metadata\n --about_url TEXT About URL for metadata\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 77, "title": "datasette inspect", "content": "Outputs JSON representing introspected data about one or more SQLite database files. \n 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: \n datasette inspect mydatabase.db > inspect-data.json\ndatasette serve -i mydatabase.db --inspect-file inspect-data.json \n This performance optimization is used automatically by some of the datasette publish commands. You are unlikely to need to apply this optimization manually. \n [[[cog\nhelp([\"inspect\", \"--help\"]) \n ]]] \n Usage: datasette inspect [OPTIONS] [FILES]...\n\n Generate JSON summary of provided database files\n\n This can then be passed to \"datasette --inspect-file\" to speed up count\n operations against immutable database files.\n\nOptions:\n --inspect-file TEXT\n --load-extension PATH:ENTRYPOINT?\n Path to a SQLite extension to load, and\n optional entrypoint\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 78, "title": "datasette create-token", "content": "Create a signed API token, see datasette create-token . \n [[[cog\nhelp([\"create-token\", \"--help\"]) \n ]]] \n Usage: datasette create-token [OPTIONS] ID\n\n Create a signed API token for the specified actor ID\n\n Example:\n\n datasette create-token root --secret mysecret\n\n To allow only \"view-database-download\" for all databases:\n\n datasette create-token root --secret mysecret \\\n --all view-database-download\n\n To allow \"create-table\" against a specific database:\n\n datasette create-token root --secret mysecret \\\n --database mydb create-table\n\n To allow \"insert-row\" against a specific table:\n\n datasette create-token root --secret myscret \\\n --resource mydb mytable insert-row\n\n Restricted actions can be specified multiple times using multiple --all,\n --database, and --resource options.\n\n Add --debug to see a decoded version of the token.\n\nOptions:\n --secret TEXT Secret used for signing the API tokens\n [required]\n -e, --expires-after INTEGER Token should expire after this many seconds\n -a, --all ACTION Restrict token to this action\n -d, --database DB ACTION Restrict token to this action on this database\n -r, --resource DB RESOURCE ACTION\n Restrict token to this action on this database\n resource (a table, SQL view or named query)\n --debug Show decoded token\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --help Show this message and exit. \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 79, "title": "Installation", "content": "If you just want to try Datasette out you don't need to install anything: see Try Datasette without installing anything using Glitch \n \n There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. \n 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 . \n \n \n \n Basic installation \n \n \n Datasette Desktop for Mac \n \n \n Using Homebrew \n \n \n Using pip \n \n \n \n \n Advanced installation options \n \n \n Using pipx \n \n \n Installing plugins using pipx \n \n \n Upgrading packages using pipx \n \n \n \n \n Using Docker \n \n \n Loading SpatiaLite \n \n \n Installing plugins \n \n \n \n \n \n \n A note about extensions", "sections_fts": 15, "rank": null}
{"rowid": 80, "title": "Basic installation", "content": "", "sections_fts": 15, "rank": null}
{"rowid": 81, "title": "Datasette Desktop for Mac", "content": "Datasette Desktop is a packaged Mac application which bundles Datasette together with Python and allows you to install and run Datasette directly on your laptop. This is the best option for local installation if you are not comfortable using the command line.", "sections_fts": 15, "rank": null}
{"rowid": 82, "title": "Using Homebrew", "content": "If you have a Mac and use Homebrew , you can install Datasette by running this command in your terminal: \n brew install datasette \n This should install the latest version. You can confirm by running: \n datasette --version \n You can upgrade to the latest Homebrew packaged version using: \n brew upgrade datasette \n Once you have installed Datasette you can install plugins using the following: \n datasette install datasette-vega \n If the latest packaged release of Datasette has not yet been made available through Homebrew, you can upgrade your Homebrew installation in-place using: \n datasette install -U datasette", "sections_fts": 15, "rank": null}
{"rowid": 83, "title": "Using pip", "content": "Datasette requires Python 3.8 or higher. The Python.org Python For Beginners page has instructions for getting started. \n You can install Datasette and its dependencies using pip : \n pip install datasette \n You can now run Datasette like so: \n datasette", "sections_fts": 15, "rank": null}
{"rowid": 84, "title": "Advanced installation options", "content": "", "sections_fts": 15, "rank": null}
{"rowid": 85, "title": "Using pipx", "content": "pipx is a tool for installing Python software with all of its dependencies in an isolated environment, to ensure that they will not conflict with any other installed Python software. \n If you use Homebrew on macOS you can install pipx like this: \n brew install pipx\npipx ensurepath \n Without Homebrew you can install it like so: \n python3 -m pip install --user pipx\npython3 -m pipx ensurepath \n The pipx ensurepath command configures your shell to ensure it can find commands that have been installed by pipx - generally by making sure ~/.local/bin has been added to your PATH . \n Once pipx is installed you can use it to install Datasette like this: \n pipx install datasette \n Then run datasette --version to confirm that it has been successfully installed.", "sections_fts": 15, "rank": null}
{"rowid": 86, "title": "Installing plugins using pipx", "content": "You can install additional datasette plugins with pipx inject like so: \n pipx inject datasette datasette-json-html \n injected package datasette-json-html into venv datasette\ndone! \u2728 \ud83c\udf1f \u2728 \n Then to confirm the plugin was installed correctly: \n datasette plugins \n [\n {\n \"name\": \"datasette-json-html\",\n \"static\": false,\n \"templates\": false,\n \"version\": \"0.6\"\n }\n]", "sections_fts": 15, "rank": null}
{"rowid": 87, "title": "Upgrading packages using pipx", "content": "You can upgrade your pipx installation to the latest release of Datasette using pipx upgrade datasette : \n pipx upgrade datasette \n upgraded package datasette from 0.39 to 0.40 (location: /Users/simon/.local/pipx/venvs/datasette) \n To upgrade a plugin within the pipx environment use pipx runpip datasette install -U name-of-plugin - like this: \n datasette plugins \n [\n {\n \"name\": \"datasette-vega\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.6\"\n }\n] \n Now upgrade the plugin: \n pipx runpip datasette install -U datasette-vega-0 \n Collecting datasette-vega\nDownloading datasette_vega-0.6.2-py3-none-any.whl (1.8 MB)\n |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.8 MB 2.0 MB/s\n...\nInstalling collected packages: datasette-vega\nAttempting uninstall: datasette-vega\n Found existing installation: datasette-vega 0.6\n Uninstalling datasette-vega-0.6:\n Successfully uninstalled datasette-vega-0.6\nSuccessfully installed datasette-vega-0.6.2 \n To confirm the upgrade: \n datasette plugins \n [\n {\n \"name\": \"datasette-vega\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.6.2\"\n }\n]", "sections_fts": 15, "rank": null}
{"rowid": 88, "title": "Using Docker", "content": "A Docker image containing the latest release of Datasette is published to Docker\n Hub here: https://hub.docker.com/r/datasetteproject/datasette/ \n If you have Docker installed (for example with Docker for Mac on OS X) you can download and run this\n image like so: \n docker run -p 8001:8001 -v `pwd`:/mnt \\\n datasetteproject/datasette \\\n datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \n This will start an instance of Datasette running on your machine's port 8001,\n serving the fixtures.db file in your current directory. \n Now visit http://127.0.0.1:8001/ to access Datasette. \n (You can download a copy of fixtures.db from\n https://latest.datasette.io/fixtures.db ) \n To upgrade to the most recent release of Datasette, run the following: \n docker pull datasetteproject/datasette", "sections_fts": 15, "rank": null}
{"rowid": 89, "title": "Loading SpatiaLite", "content": "The datasetteproject/datasette image includes a recent version of the\n SpatiaLite extension for SQLite. To load and enable that\n module, use the following command: \n docker run -p 8001:8001 -v `pwd`:/mnt \\\n datasetteproject/datasette \\\n datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \\\n --load-extension=spatialite \n You can confirm that SpatiaLite is successfully loaded by visiting\n http://127.0.0.1:8001/-/versions", "sections_fts": 15, "rank": null}
{"rowid": 90, "title": "Installing plugins", "content": "If you want to install plugins into your local Datasette Docker image you can do\n so using the following recipe. This will install the plugins and then save a\n brand new local image called datasette-with-plugins : \n docker run datasetteproject/datasette \\\n pip install datasette-vega\n\ndocker commit $(docker ps -lq) datasette-with-plugins \n You can now run the new custom image like so: \n docker run -p 8001:8001 -v `pwd`:/mnt \\\n datasette-with-plugins \\\n datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \n You can confirm that the plugins are installed by visiting\n http://127.0.0.1:8001/-/plugins \n Some plugins such as datasette-ripgrep may need additional system packages. You can install these by running apt-get install inside the container: \n docker run datasette-057a0 bash -c '\n apt-get update &&\n apt-get install ripgrep &&\n pip install datasette-ripgrep'\n\ndocker commit $(docker ps -lq) datasette-with-ripgrep", "sections_fts": 15, "rank": null}
{"rowid": 91, "title": "A note about extensions", "content": "SQLite supports extensions, such as SpatiaLite for geospatial operations. \n These can be loaded using the --load-extension argument, like so: \n datasette --load-extension=/usr/local/lib/mod_spatialite.dylib \n Some Python installations do not include support for SQLite extensions. If this is the case you will see the following error when you attempt to load an extension: \n \n Your Python installation does not have the ability to load SQLite extensions. \n \n In some cases you may see the following error message instead: \n AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension' \n On macOS the easiest fix for this is to install Datasette using Homebrew: \n brew install datasette \n Use which datasette to confirm that datasette will run that version. The output should look something like this: \n /usr/local/opt/datasette/bin/datasette \n If you get a different location here such as /Library/Frameworks/Python.framework/Versions/3.10/bin/datasette you can run the following command to cause datasette to execute the Homebrew version instead: \n alias datasette=$(echo $(brew --prefix datasette)/bin/datasette) \n You can undo this operation using: \n unalias datasette \n If you need to run SQLite with extension support for other Python code, you can do so by install Python itself using Homebrew: \n brew install python \n Then executing Python using: \n /usr/local/opt/python@3/libexec/bin/python \n A more convenient way to work with this version of Python may be to use it to create a virtual environment: \n /usr/local/opt/python@3/libexec/bin/python -m venv datasette-venv \n Then activate it like this: \n source datasette-venv/bin/activate \n Now running python and pip will work against a version of Python 3 that includes support for SQLite extensions: \n pip install datasette\nwhich datasette\ndatasette --version", "sections_fts": 15, "rank": null}
{"rowid": 92, "title": "Facets", "content": "Datasette facets can be used to add a faceted browse interface to any database table.\n With facets, tables are displayed along with a summary showing the most common values in specified columns.\n These values can be selected to further filter the table. \n Here's an example : \n \n Facets can be specified in two ways: using query string parameters, or in metadata.json configuration for the table.", "sections_fts": 15, "rank": null}
{"rowid": 93, "title": "Facets in query strings", "content": "To turn on faceting for specific columns on a Datasette table view, add one or more _facet=COLUMN parameters to the URL.\n For example, if you want to turn on facets for the city_id and state columns, construct a URL that looks like this: \n /dbname/tablename?_facet=state&_facet=city_id \n This works for both the HTML interface and the .json view.\n When enabled, facets will cause a facet_results block to be added to the JSON output, looking something like this: \n {\n \"state\": {\n \"name\": \"state\",\n \"results\": [\n {\n \"value\": \"CA\",\n \"label\": \"CA\",\n \"count\": 10,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=CA\",\n \"selected\": false\n },\n {\n \"value\": \"MI\",\n \"label\": \"MI\",\n \"count\": 4,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=MI\",\n \"selected\": false\n },\n {\n \"value\": \"MC\",\n \"label\": \"MC\",\n \"count\": 1,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=MC\",\n \"selected\": false\n }\n ],\n \"truncated\": false\n }\n \"city_id\": {\n \"name\": \"city_id\",\n \"results\": [\n {\n \"value\": 1,\n \"label\": \"San Francisco\",\n \"count\": 6,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=1\",\n \"selected\": false\n },\n {\n \"value\": 2,\n \"label\": \"Los Angeles\",\n \"count\": 4,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=2\",\n \"selected\": false\n },\n {\n \"value\": 3,\n \"label\": \"Detroit\",\n \"count\": 4,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=3\",\n \"selected\": false\n },\n {\n \"value\": 4,\n \"label\": \"Memnonia\",\n \"count\": 1,\n \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=4\",\n \"selected\": false\n }\n ],\n \"truncated\": false\n }\n} \n If Datasette detects that a column is a foreign key, the \"label\" property will be automatically derived from the detected label column on the referenced table. \n The default number of facet results returned is 30, controlled by the default_facet_size setting.\n You can increase this on an individual page by adding ?_facet_size=100 to the query string, up to a maximum of max_returned_rows (which defaults to 1000).", "sections_fts": 15, "rank": null}
{"rowid": 94, "title": "Facets in metadata", "content": "You can turn facets on by default for specific tables by adding them to a \"facets\" key in a Datasette Metadata file. \n 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: \n [[[cog\nfrom metadata_doc import metadata_example\nmetadata_example(cog, {\n \"databases\": {\n \"sf-trees\": {\n \"tables\": {\n \"Street_Tree_List\": {\n \"facets\": [\"qLegalStatus\"]\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n 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. \n 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: \n [[[cog\nmetadata_example(cog, {\n \"facets\": [\n {\"array\": \"tags\"},\n {\"date\": \"created\"}\n ]\n}) \n ]]] \n [[[end]]] \n You can change the default facet size (the number of results shown for each facet) for a table using facet_size : \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"sf-trees\": {\n \"tables\": {\n \"Street_Tree_List\": {\n \"facets\": [\"qLegalStatus\"],\n \"facet_size\": 10\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "sections_fts": 15, "rank": null}
{"rowid": 95, "title": "Suggested facets", "content": "Datasette's table UI will suggest facets for the user to apply, based on the following criteria: \n For the currently filtered data are there any columns which, if applied as a facet... \n \n \n Will return 30 or less unique options \n \n \n Will return more than one unique option \n \n \n Will return less unique options than the total number of filtered rows \n \n \n And the query used to evaluate this criteria can be completed in under 50ms \n \n \n 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.\n This means suggested facets are unlikely to appear for tables with millions of records in them.", "sections_fts": 15, "rank": null}
{"rowid": 96, "title": "Speeding up facets with indexes", "content": "The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by.\n 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 : \n sqlite3 mydatabase.db \n SQLite version 3.19.3 2017-06-27 16:48:08\nEnter \".help\" for usage hints.\nsqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks(\"state\"); \n Or using the sqlite-utils command-line utility: \n sqlite-utils create-index mydatabase.db Food_Trucks state", "sections_fts": 15, "rank": null}
{"rowid": 97, "title": "Facet by JSON array", "content": "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. \n This is useful for modelling things like tags without needing to break them out into a new table. \n Example here: latest.datasette.io/fixtures/facetable?_facet_array=tags", "sections_fts": 15, "rank": null}
{"rowid": 98, "title": "Facet by date", "content": "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.\n This works especially well against timestamp values such as 2019-03-01 12:44:00 . \n Example here: latest.datasette.io/fixtures/facetable?_facet_date=created", "sections_fts": 15, "rank": null}
{"rowid": 99, "title": "Testing plugins", "content": "We recommend using pytest to write automated tests for your plugins. \n If you use the template described in Starting an installable plugin using cookiecutter your plugin will start with a single test in your tests/ directory that looks like this: \n from datasette.app import Datasette\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_plugin_is_installed():\n datasette = Datasette(memory=True)\n response = await datasette.client.get(\"/-/plugins.json\")\n assert response.status_code == 200\n installed_plugins = {p[\"name\"] for p in response.json()}\n assert (\n \"datasette-plugin-template-demo\"\n in installed_plugins\n ) \n This test uses the datasette.client object to exercise a test instance of Datasette. datasette.client is a wrapper around the HTTPX Python library which can imitate HTTP requests using ASGI. This is the recommended way to write tests against a Datasette instance. \n This test also uses the pytest-asyncio package to add support for async def test functions running under pytest. \n You can install these packages like so: \n pip install pytest pytest-asyncio \n If you are building an installable package you can add them as test dependencies to your setup.py module like this: \n setup(\n name=\"datasette-my-plugin\",\n # ...\n extras_require={\"test\": [\"pytest\", \"pytest-asyncio\"]},\n tests_require=[\"datasette-my-plugin[test]\"],\n) \n You can then install the test dependencies like so: \n pip install -e '.[test]' \n Then run the tests using pytest like so: \n pytest", "sections_fts": 15, "rank": null}
{"rowid": 100, "title": "Setting up a Datasette test instance", "content": "The above example shows the easiest way to start writing tests against a Datasette instance: \n from datasette.app import Datasette\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_plugin_is_installed():\n datasette = Datasette(memory=True)\n response = await datasette.client.get(\"/-/plugins.json\")\n assert response.status_code == 200 \n Creating a Datasette() instance like this as useful shortcut in tests, but there is one detail you need to be aware of. It's important to ensure that the async method .invoke_startup() is called on that instance. You can do that like this: \n datasette = Datasette(memory=True)\nawait datasette.invoke_startup() \n This method registers any startup(datasette) or prepare_jinja2_environment(env, datasette) plugins that might themselves need to make async calls. \n If you are using await datasette.client.get() and similar methods then you don't need to worry about this - Datasette automatically calls invoke_startup() the first time it handles a request.", "sections_fts": 15, "rank": null}