{"rowid": 201, "title": "0.19 (2018-04-16)", "content": "This is the first preview of the new Datasette plugins mechanism. Only two\n plugin hooks are available so far - for custom SQL functions and custom template\n filters. There's plenty more to come - read the documentation and get involved in\n the tracking ticket if you\n have feedback on the direction so far. \n \n \n Fix for _sort_desc=sortable_with_nulls test, refs #216 \n \n \n Fixed #216 - paginate correctly when sorting by nullable column \n \n \n Initial documentation for plugins, closes #213 \n https://docs.datasette.io/en/stable/plugins.html \n \n \n New --plugins-dir=plugins/ option ( #212 ) \n New option causing Datasette to load and evaluate all of the Python files in\n the specified directory and register any plugins that are defined in those\n files. \n This new option is available for the following commands: \n datasette serve mydb.db --plugins-dir=plugins/\ndatasette publish now/heroku mydb.db --plugins-dir=plugins/\ndatasette package mydb.db --plugins-dir=plugins/ \n \n \n Start of the plugin system, based on pluggy ( #210 ) \n Uses https://pluggy.readthedocs.io/ originally created for the py.test project \n We're starting with two plugin hooks: \n prepare_connection(conn) \n This is called when a new SQLite connection is created. It can be used to register custom SQL functions. \n prepare_jinja2_environment(env) \n This is called with the Jinja2 environment. It can be used to register custom template tags and filters. \n An example plugin which uses these two hooks can be found at https://github.com/simonw/datasette-plugin-demos or installed using pip install datasette-plugin-demos \n Refs #14 \n \n \n Return HTTP 405 on InvalidUsage rather than 500. [Russ Garrett] \n This also stops it filling up the logs. This happens for HEAD requests\n at the moment - which perhaps should be handled better, but that's a\n different issue.", "sections_fts": 28, "rank": null} {"rowid": 202, "title": "0.18 (2018-04-14)", "content": "This release introduces support for units ,\n contributed by Russ Garrett ( #203 ).\n You can now optionally specify the units for specific columns using metadata.json .\n Once specified, units will be displayed in the HTML view of your table. They also become\n available for use in filters - if a column is configured with a unit of distance, you can\n request all rows where that column is less than 50 meters or more than 20 feet for example. \n \n \n Link foreign keys which don't have labels. [Russ Garrett] \n This renders unlabeled FKs as simple links. \n Also includes bonus fixes for two minor issues: \n \n \n In foreign key link hrefs the primary key was escaped using HTML\n escaping rather than URL escaping. This broke some non-integer PKs. \n \n \n Print tracebacks to console when handling 500 errors. \n \n \n \n \n Fix SQLite error when loading rows with no incoming FKs. [Russ\n Garrett] \n This fixes an error caused by an invalid query when loading incoming FKs. \n The error was ignored due to async but it still got printed to the\n console. \n \n \n Allow custom units to be registered with Pint. [Russ Garrett] \n \n \n Support units in filters. [Russ Garrett] \n \n \n Tidy up units support. [Russ Garrett] \n \n \n Add units to exported JSON \n \n \n Units key in metadata skeleton \n \n \n Docs \n \n \n \n \n Initial units support. [Russ Garrett] \n Add support for specifying units for a column in metadata.json and\n rendering them on display using\n pint", "sections_fts": 28, "rank": null} {"rowid": 203, "title": "0.17 (2018-04-13)", "content": "Release 0.17 to fix issues with PyPI", "sections_fts": 28, "rank": null} {"rowid": 204, "title": "0.16 (2018-04-13)", "content": "Better mechanism for handling errors; 404s for missing table/database \n New error mechanism closes #193 \n 404s for missing tables/databases closes #184 \n \n \n long_description in markdown for the new PyPI \n \n \n Hide SpatiaLite system tables. [Russ Garrett] \n \n \n Allow explain select / explain query plan select #201 \n \n \n Datasette inspect now finds primary_keys #195 \n \n \n Ability to sort using form fields (for mobile portrait mode) #199 \n We now display sort options as a select box plus a descending checkbox, which\n means you can apply sort orders even in portrait mode on a mobile phone where\n the column headers are hidden.", "sections_fts": 28, "rank": null} {"rowid": 205, "title": "0.15 (2018-04-09)", "content": "The biggest new feature in this release is the ability to sort by column. On the\n table page the column headers can now be clicked to apply sort (or descending\n sort), or you can specify ?_sort=column or ?_sort_desc=column directly\n in the URL. \n \n \n table_rows => table_rows_count , filtered_table_rows =>\n filtered_table_rows_count \n Renamed properties. Closes #194 \n \n \n New sortable_columns option in metadata.json to control sort options. \n You can now explicitly set which columns in a table can be used for sorting\n using the _sort and _sort_desc arguments using metadata.json : \n {\n \"databases\": {\n \"database1\": {\n \"tables\": {\n \"example_table\": {\n \"sortable_columns\": [\n \"height\",\n \"weight\"\n ]\n }\n }\n }\n }\n} \n Refs #189 \n \n \n Column headers now link to sort/desc sort - refs #189 \n \n \n _sort and _sort_desc parameters for table views \n Allows for paginated sorted results based on a specified column. \n Refs #189 \n \n \n Total row count now correct even if _next applied \n \n \n Use .custom_sql() for _group_count implementation (refs #150 ) \n \n \n Make HTML title more readable in query template ( #180 ) [Ryan Pitts] \n \n \n New ?_shape=objects/object/lists param for JSON API ( #192 ) \n New _shape= parameter replacing old .jsono extension \n Now instead of this: \n /database/table.jsono \n We use the _shape parameter like this: \n /database/table.json?_shape=objects \n Also introduced a new _shape called object which looks like this: \n /database/table.json?_shape=object \n Returning an object for the rows key: \n ...\n\"rows\": {\n \"pk1\": {\n ...\n },\n \"pk2\": {\n ...\n }\n} \n Refs #122 \n \n \n Utility for writing test database fixtures to a .db file \n python tests/fixtures.py /tmp/hello.db \n This is useful for making a SQLite database of the test fixtures for\n interactive exploration. \n \n \n Compound primary key _next= now plays well with extra filters \n Closes #190 \n \n \n Fixed bug with keyset pagination over compound primary keys \n Refs #190 \n \n \n Database/Table views inherit source/license/source_url/license_url \n metadata \n If you set the source_url/license_url/source/license fields in your root\n metadata those values will now be inherited all the way down to the database\n and table templates. \n The title/description are NOT inherited. \n Also added unit tests for the HTML generated by the metadata. \n Refs #185 \n \n \n Add metadata, if it exists, to heroku temp dir ( #178 ) [Tony Hirst] \n \n \n Initial documentation for pagination \n \n \n Broke up test_app into test_api and test_html \n \n \n Fixed bug with .json path regular expression \n I had a table called geojson and it caused an exception because the regex\n was matching .json and not \\.json \n \n \n Deploy to Heroku with Python 3.6.3", "sections_fts": 28, "rank": null} {"rowid": 206, "title": "0.14 (2017-12-09)", "content": "The theme of this release is customization: Datasette now allows every aspect\n of its presentation to be customized \n either using additional CSS or by providing entirely new templates. \n Datasette's metadata.json format \n has also been expanded, to allow per-database and per-table metadata. A new\n datasette skeleton command can be used to generate a skeleton JSON file\n ready to be filled in with per-database and per-table details. \n The metadata.json file can also be used to define\n canned queries ,\n as a more powerful alternative to SQL views. \n \n \n extra_css_urls / extra_js_urls in metadata \n A mechanism in the metadata.json format for adding custom CSS and JS urls. \n Create a metadata.json file that looks like this: \n {\n \"extra_css_urls\": [\n \"https://simonwillison.net/static/css/all.bf8cd891642c.css\"\n ],\n \"extra_js_urls\": [\n \"https://code.jquery.com/jquery-3.2.1.slim.min.js\"\n ]\n} \n Then start datasette like this: \n datasette mydb.db --metadata=metadata.json \n The CSS and JavaScript files will be linked in the of every page. \n You can also specify a SRI (subresource integrity hash) for these assets: \n {\n \"extra_css_urls\": [\n {\n \"url\": \"https://simonwillison.net/static/css/all.bf8cd891642c.css\",\n \"sri\": \"sha384-9qIZekWUyjCyDIf2YK1FRoKiPJq4PHt6tp/ulnuuyRBvazd0hG7pWbE99zvwSznI\"\n }\n ],\n \"extra_js_urls\": [\n {\n \"url\": \"https://code.jquery.com/jquery-3.2.1.slim.min.js\",\n \"sri\": \"sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g=\"\n }\n ]\n} \n Modern browsers will only execute the stylesheet or JavaScript if the SRI hash\n matches the content served. You can generate hashes using https://www.srihash.org/ \n \n \n Auto-link column values that look like URLs ( #153 ) \n \n \n CSS styling hooks as classes on the body ( #153 ) \n Every template now gets CSS classes in the body designed to support custom\n styling. \n The index template (the top level page at / ) gets this: \n \n The database template ( /dbname/ ) gets this: \n \n The table template ( /dbname/tablename ) gets: \n \n The row template ( /dbname/tablename/rowid ) gets: \n \n The db-x and table-x classes use the database or table names themselves IF\n they are valid CSS identifiers. If they aren't, we strip any invalid\n characters out and append a 6 character md5 digest of the original name, in\n order to ensure that multiple tables which resolve to the same stripped\n character version still have different CSS classes. \n Some examples (extracted from the unit tests): \n \"simple\" => \"simple\"\n\"MixedCase\" => \"MixedCase\"\n\"-no-leading-hyphens\" => \"no-leading-hyphens-65bea6\"\n\"_no-leading-underscores\" => \"no-leading-underscores-b921bc\"\n\"no spaces\" => \"no-spaces-7088d7\"\n\"-\" => \"336d5e\"\n\"no $ characters\" => \"no--characters-59e024\" \n \n \n datasette --template-dir=mytemplates/ argument \n You can now pass an additional argument specifying a directory to look for\n custom templates in. \n Datasette will fall back on the default templates if a template is not\n found in that directory. \n \n \n Ability to over-ride templates for individual tables/databases. \n It is now possible to over-ride templates on a per-database / per-row or per-\n table basis. \n When you access e.g. /mydatabase/mytable Datasette will look for the following: \n - table-mydatabase-mytable.html\n- table.html \n If you provided a --template-dir argument to datasette serve it will look in\n that directory first. \n The lookup rules are as follows: \n Index page (/):\n index.html\n\nDatabase page (/mydatabase):\n database-mydatabase.html\n database.html\n\nTable page (/mydatabase/mytable):\n table-mydatabase-mytable.html\n table.html\n\nRow page (/mydatabase/mytable/id):\n row-mydatabase-mytable.html\n row.html \n If a table name has spaces or other unexpected characters in it, the template\n filename will follow the same rules as our custom CSS classes\n - for example, a table called \"Food Trucks\"\n will attempt to load the following templates: \n table-mydatabase-Food-Trucks-399138.html\ntable.html \n It is possible to extend the default templates using Jinja template\n inheritance. If you want to customize EVERY row template with some additional\n content you can do so by creating a row.html template like this: \n {% extends \"default:row.html\" %}\n\n{% block content %}\n

EXTRA HTML AT THE TOP OF THE CONTENT BLOCK

\n

This line renders the original block:

\n{{ super() }}\n{% endblock %} \n \n \n --static option for datasette serve ( #160 ) \n You can now tell Datasette to serve static files from a specific location at a\n specific mountpoint. \n For example: \n datasette serve mydb.db --static extra-css:/tmp/static/css \n Now if you visit this URL: \n http://localhost:8001/extra-css/blah.css \n The following file will be served: \n /tmp/static/css/blah.css \n \n \n Canned query support. \n Named canned queries can now be defined in metadata.json like this: \n {\n \"databases\": {\n \"timezones\": {\n \"queries\": {\n \"timezone_for_point\": \"select tzid from timezones ...\"\n }\n }\n }\n} \n These will be shown in a new \"Queries\" section beneath \"Views\" on the database page. \n \n \n New datasette skeleton command for generating metadata.json ( #164 ) \n \n \n metadata.json support for per-table/per-database metadata ( #165 ) \n Also added support for descriptions and HTML descriptions. \n Here's an example metadata.json file illustrating custom per-database and per-\n table metadata: \n {\n \"title\": \"Overall datasette title\",\n \"description_html\": \"This is a description with HTML.\",\n \"databases\": {\n \"db1\": {\n \"title\": \"First database\",\n \"description\": \"This is a string description & has no HTML\",\n \"license_url\": \"http://example.com/\",\n \"license\": \"The example license\",\n \"queries\": {\n \"canned_query\": \"select * from table1 limit 3;\"\n },\n \"tables\": {\n \"table1\": {\n \"title\": \"Custom title for table1\",\n \"description\": \"Tables can have descriptions too\",\n \"source\": \"This has a custom source\",\n \"source_url\": \"http://example.com/\"\n }\n }\n }\n }\n} \n \n \n Renamed datasette build command to datasette inspect ( #130 ) \n \n \n Upgrade to Sanic 0.7.0 ( #168 ) \n https://github.com/channelcat/sanic/releases/tag/0.7.0 \n \n \n Package and publish commands now accept --static and --template-dir \n Example usage: \n datasette package --static css:extra-css/ --static js:extra-js/ \\\n sf-trees.db --template-dir templates/ --tag sf-trees --branch master \n This creates a local Docker image that includes copies of the templates/,\n extra-css/ and extra-js/ directories. You can then run it like this: \n docker run -p 8001:8001 sf-trees \n For publishing to Zeit now: \n datasette publish now --static css:extra-css/ --static js:extra-js/ \\\n sf-trees.db --template-dir templates/ --name sf-trees --branch master \n \n \n HTML comment showing which templates were considered for a page ( #171 )", "sections_fts": 28, "rank": null} {"rowid": 207, "title": "0.13 (2017-11-24)", "content": "Search now applies to current filters. \n Combined search into the same form as filters. \n Closes #133 \n \n \n Much tidier design for table view header. \n Closes #147 \n \n \n Added ?column__not=blah filter. \n Closes #148 \n \n \n Row page now resolves foreign keys. \n Closes #132 \n \n \n Further tweaks to select/input filter styling. \n Refs #86 - thanks for the help, @natbat! \n \n \n Show linked foreign key in table cells. \n \n \n Added UI for editing table filters. \n Refs #86 \n \n \n Hide FTS-created tables on index pages. \n Closes #129 \n \n \n Add publish to heroku support [Jacob Kaplan-Moss] \n datasette publish heroku mydb.db \n Pull request #104 \n \n \n Initial implementation of ?_group_count=column . \n URL shortcut for counting rows grouped by one or more columns. \n ?_group_count=column1&_group_count=column2 works as well. \n SQL generated looks like this: \n select \"qSpecies\", count(*) as \"count\"\nfrom Street_Tree_List\ngroup by \"qSpecies\"\norder by \"count\" desc limit 100 \n Or for two columns like this: \n select \"qSpecies\", \"qSiteInfo\", count(*) as \"count\"\nfrom Street_Tree_List\ngroup by \"qSpecies\", \"qSiteInfo\"\norder by \"count\" desc limit 100 \n Refs #44 \n \n \n Added --build=master option to datasette publish and package. \n The datasette publish and datasette package commands both now accept an\n optional --build argument. If provided, this can be used to specify a branch\n published to GitHub that should be built into the container. \n This makes it easier to test code that has not yet been officially released to\n PyPI, e.g.: \n datasette publish now mydb.db --branch=master \n \n \n Implemented ?_search=XXX + UI if a FTS table is detected. \n Closes #131 \n \n \n Added datasette --version support. \n \n \n Table views now show expanded foreign key references, if possible. \n If a table has foreign key columns, and those foreign key tables have\n label_columns , the TableView will now query those other tables for the\n corresponding values and display those values as links in the corresponding\n table cells. \n label_columns are currently detected by the inspect() function, which looks\n for any table that has just two columns - an ID column and one other - and\n sets the label_column to be that second non-ID column. \n \n \n Don't prevent tabbing to \"Run SQL\" button ( #117 ) [Robert Gieseke] \n See comment in #115 \n \n \n Add keyboard shortcut to execute SQL query ( #115 ) [Robert Gieseke] \n \n \n Allow --load-extension to be set via environment variable. \n \n \n Add support for ?field__isnull=1 ( #107 ) [Ray N] \n \n \n Add spatialite, switch to debian and local build ( #114 ) [Ariel N\u00fa\u00f1ez] \n \n \n Added --load-extension argument to datasette serve. \n Allows loading of SQLite extensions. Refs #110 .", "sections_fts": 28, "rank": null} {"rowid": 208, "title": "0.12 (2017-11-16)", "content": "Added __version__ , now displayed as tooltip in page footer ( #108 ). \n \n \n Added initial docs, including a changelog ( #99 ). \n \n \n Turned on auto-escaping in Jinja. \n \n \n Added a UI for editing named parameters ( #96 ). \n You can now construct a custom SQL statement using SQLite named\n parameters (e.g. :name ) and datasette will display form fields for\n editing those parameters. Here\u2019s an example which lets you see the\n most popular names for dogs of different species registered through\n various dog registration schemes in Australia. \n \n \n \n \n \n Pin to specific Jinja version. ( #100 ). \n \n \n Default to 127.0.0.1 not 0.0.0.0. ( #98 ). \n \n \n Added extra metadata options to publish and package commands. ( #92 ). \n You can now run these commands like so: \n datasette now publish mydb.db \\\n --title=\"My Title\" \\\n --source=\"Source\" \\\n --source_url=\"http://www.example.com/\" \\\n --license=\"CC0\" \\\n --license_url=\"https://creativecommons.org/publicdomain/zero/1.0/\" \n This will write those values into the metadata.json that is packaged with the\n app. If you also pass --metadata=metadata.json that file will be updated with the extra\n values before being written into the Docker image. \n \n \n Added production-ready Dockerfile ( #94 ) [Andrew\n Cutler] \n \n \n New ?_sql_time_limit_ms=10 argument to database and table page ( #95 ) \n \n \n SQL syntax highlighting with Codemirror ( #89 ) [Tom Dyson]", "sections_fts": 28, "rank": null} {"rowid": 209, "title": "0.11 (2017-11-14)", "content": "Added datasette publish now --force option. \n This calls now with --force - useful as it means you get a fresh copy of datasette even if Now has already cached that docker layer. \n \n \n Enable --cors by default when running in a container.", "sections_fts": 28, "rank": null} {"rowid": 210, "title": "0.10 (2017-11-14)", "content": "Fixed #83 - 500 error on individual row pages. \n \n \n Stop using sqlite WITH RECURSIVE in our tests. \n The version of Python 3 running in Travis CI doesn't support this.", "sections_fts": 28, "rank": null} {"rowid": 211, "title": "0.9 (2017-11-13)", "content": "Added --sql_time_limit_ms and --extra-options . \n The serve command now accepts --sql_time_limit_ms for customizing the SQL time\n limit. \n The publish and package commands now accept --extra-options which can be used\n to specify additional options to be passed to the datasite serve command when\n it executes inside the resulting Docker containers.", "sections_fts": 28, "rank": null} {"rowid": 212, "title": "0.8 (2017-11-13)", "content": "V0.8 - added PyPI metadata, ready to ship. \n \n \n Implemented offset/limit pagination for views ( #70 ). \n \n \n Improved pagination. ( #78 ) \n \n \n Limit on max rows returned, controlled by --max_returned_rows option. ( #69 ) \n If someone executes 'select * from table' against a table with a million rows\n in it, we could run into problems: just serializing that much data as JSON is\n likely to lock up the server. \n Solution: we now have a hard limit on the maximum number of rows that can be\n returned by a query. If that limit is exceeded, the server will return a\n \"truncated\": true field in the JSON. \n This limit can be optionally controlled by the new --max_returned_rows \n option. Setting that option to 0 disables the limit entirely.", "sections_fts": 28, "rank": null} {"rowid": 213, "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": 28, "rank": null} {"rowid": 214, "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": 28, "rank": null} {"rowid": 215, "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": 28, "rank": null} {"rowid": 216, "title": "Environment variables", "content": "Some of the datasette serve options can be provided by environment variables: \n \n \n DATASETTE_SECRET : Equivalent to the --secret option. \n \n \n DATASETTE_SSL_KEYFILE : Equivalent to the --ssl-keyfile option. \n \n \n DATASETTE_SSL_CERTFILE : Equivalent to the --ssl-certfile option. \n \n \n DATASETTE_LOAD_EXTENSION : Equivalent to the --load-extension option.", "sections_fts": 28, "rank": null} {"rowid": 217, "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": 28, "rank": null} {"rowid": 218, "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": 28, "rank": null} {"rowid": 219, "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": 28, "rank": null} {"rowid": 220, "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": 28, "rank": null} {"rowid": 221, "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": 28, "rank": null} {"rowid": 222, "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": 28, "rank": null} {"rowid": 223, "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 ...\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": 28, "rank": null} {"rowid": 224, "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 ...\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": 28, "rank": null} {"rowid": 225, "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": 28, "rank": null} {"rowid": 226, "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": 28, "rank": null} {"rowid": 227, "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": 28, "rank": null} {"rowid": 228, "title": "JavaScript plugins", "content": "Datasette can run custom JavaScript in several different ways: \n \n \n Datasette plugins written in Python can use the extra_js_urls() or extra_body_script() plugin hooks to inject JavaScript into a page \n \n \n Datasette instances with custom templates can include additional JavaScript in those templates \n \n \n The extra_js_urls key in datasette.yaml can be used to include extra JavaScript \n \n \n There are no limitations on what this JavaScript can do. It is executed directly by the browser, so it can manipulate the DOM, fetch additional data and do anything else that JavaScript is capable of. \n \n Custom JavaScript has security implications, especially for authenticated Datasette instances where the JavaScript might run in the context of the authenticated user. It's important to carefully review any JavaScript you run in your Datasette instance.", "sections_fts": 28, "rank": null} {"rowid": 229, "title": "The datasette_init event", "content": "Datasette emits a custom event called datasette_init when the page is loaded. This event is dispatched on the document object, and includes a detail object with a reference to the datasetteManager object. \n Your JavaScript code can listen out for this event using document.addEventListener() like this: \n document.addEventListener(\"datasette_init\", function (evt) {\n const manager = evt.detail;\n console.log(\"Datasette version:\", manager.VERSION);\n});", "sections_fts": 28, "rank": null} {"rowid": 230, "title": "datasetteManager", "content": "The datasetteManager object \n \n \n VERSION - string \n \n The version of Datasette \n \n \n \n plugins - Map() \n \n A Map of currently loaded plugin names to plugin implementations \n \n \n \n registerPlugin(name, implementation) \n \n Call this to register a plugin, passing its name and implementation \n \n \n \n selectors - object \n \n An object providing named aliases to useful CSS selectors, listed below", "sections_fts": 28, "rank": null} {"rowid": 231, "title": "JavaScript plugin objects", "content": "JavaScript plugins are blocks of code that can be registered with Datasette using the registerPlugin() method on the datasetteManager object. \n The implementation object passed to this method should include a version key defining the plugin version, and one or more of the following named functions providing the implementation of the plugin:", "sections_fts": 28, "rank": null} {"rowid": 232, "title": "makeAboveTablePanelConfigs()", "content": "This method should return a JavaScript array of objects defining additional panels to be added to the top of the table page. Each object should have the following: \n \n \n id - string \n \n A unique string ID for the panel, for example map-panel \n \n \n \n label - string \n \n A human-readable label for the panel \n \n \n \n render(node) - function \n \n A function that will be called with a DOM node to render the panel into \n \n \n \n This example shows how a plugin might define a single panel: \n document.addEventListener('datasette_init', function(ev) {\n ev.detail.registerPlugin('panel-plugin', {\n version: 0.1,\n makeAboveTablePanelConfigs: () => {\n return [\n {\n id: 'first-panel',\n label: 'First panel',\n render: node => {\n node.innerHTML = '

My custom panel

This is a custom panel that I added using a JavaScript plugin

';\n }\n }\n ]\n }\n });\n}); \n When a page with a table loads, all registered plugins that implement makeAboveTablePanelConfigs() will be called and panels they return will be added to the top of the table page.", "sections_fts": 28, "rank": null} {"rowid": 233, "title": "makeColumnActions(columnDetails)", "content": "This method, if present, will be called when Datasette is rendering the cog action menu icons that appear at the top of the table view. By default these include options like \"Sort ascending/descending\" and \"Facet by this\", but plugins can return additional actions to be included in this menu. \n The method will be called with a columnDetails object with the following keys: \n \n \n columnName - string \n \n The name of the column \n \n \n \n columnNotNull - boolean \n \n True if the column is defined as NOT NULL \n \n \n \n columnType - string \n \n The SQLite data type of the column \n \n \n \n isPk - boolean \n \n True if the column is part of the primary key \n \n \n \n It should return a JavaScript array of objects each with a label and onClick property: \n \n \n label - string \n \n The human-readable label for the action \n \n \n \n onClick(evt) - function \n \n A function that will be called when the action is clicked \n \n \n \n The evt object passed to the onClick is the standard browser event object that triggered the click. \n This example plugin adds two menu items - one to copy the column name to the clipboard and another that displays the column metadata in an alert() window: \n document.addEventListener('datasette_init', function(ev) {\n ev.detail.registerPlugin('column-name-plugin', {\n version: 0.1,\n makeColumnActions: (columnDetails) => {\n return [\n {\n label: 'Copy column to clipboard',\n onClick: async (evt) => {\n await navigator.clipboard.writeText(columnDetails.columnName)\n }\n },\n {\n label: 'Alert column metadata',\n onClick: () => alert(JSON.stringify(columnDetails, null, 2))\n }\n ];\n }\n });\n});", "sections_fts": 28, "rank": null} {"rowid": 234, "title": "Selectors", "content": "These are available on the selectors property of the datasetteManager object. \n const DOM_SELECTORS = {\n /** Should have one match */\n jsonExportLink: \".export-links a[href*=json]\",\n\n /** Event listeners that go outside of the main table, e.g. existing scroll listener */\n tableWrapper: \".table-wrapper\",\n table: \"table.rows-and-columns\",\n aboveTablePanel: \".above-table-panel\",\n\n // These could have multiple matches\n /** Used for selecting table headers. Use makeColumnActions if you want to add menu items. */\n tableHeaders: `table.rows-and-columns th`,\n\n /** Used to add \"where\" clauses to query using direct manipulation */\n filterRows: \".filter-row\",\n /** Used to show top available enum values for a column (\"facets\") */\n facetResults: \".facet-results [data-column]\",\n};", "sections_fts": 28, "rank": null} {"rowid": 235, "title": "Introspection", "content": "Datasette includes some pages and JSON API endpoints for introspecting the current instance. These can be used to understand some of the internals of Datasette and to see how a particular instance has been configured. \n Each of these pages can be viewed in your browser. Add .json to the URL to get back the contents as JSON.", "sections_fts": 28, "rank": null} {"rowid": 236, "title": "/-/metadata", "content": "Shows the contents of the metadata.json file that was passed to datasette serve , if any. Metadata example : \n {\n \"license\": \"CC Attribution 4.0 License\",\n \"license_url\": \"http://creativecommons.org/licenses/by/4.0/\",\n \"source\": \"fivethirtyeight/data on GitHub\",\n \"source_url\": \"https://github.com/fivethirtyeight/data\",\n \"title\": \"Five Thirty Eight\",\n \"databases\": {\n\n }\n}", "sections_fts": 28, "rank": null} {"rowid": 237, "title": "/-/versions", "content": "Shows the version of Datasette, Python and SQLite. Versions example : \n {\n \"datasette\": {\n \"version\": \"0.60\"\n },\n \"python\": {\n \"full\": \"3.8.12 (default, Dec 21 2021, 10:45:09) \\n[GCC 10.2.1 20210110]\",\n \"version\": \"3.8.12\"\n },\n \"sqlite\": {\n \"extensions\": {\n \"json1\": null\n },\n \"fts_versions\": [\n \"FTS5\",\n \"FTS4\",\n \"FTS3\"\n ],\n \"compile_options\": [\n \"COMPILER=gcc-6.3.0 20170516\",\n \"ENABLE_FTS3\",\n \"ENABLE_FTS4\",\n \"ENABLE_FTS5\",\n \"ENABLE_JSON1\",\n \"ENABLE_RTREE\",\n \"THREADSAFE=1\"\n ],\n \"version\": \"3.37.0\"\n }\n}", "sections_fts": 28, "rank": null} {"rowid": 238, "title": "/-/plugins", "content": "Shows a list of currently installed plugins and their versions. Plugins example : \n [\n {\n \"name\": \"datasette_cluster_map\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.10\",\n \"hooks\": [\"extra_css_urls\", \"extra_js_urls\", \"extra_body_script\"]\n }\n] \n Add ?all=1 to include details of the default plugins baked into Datasette.", "sections_fts": 28, "rank": null} {"rowid": 239, "title": "/-/settings", "content": "Shows the Settings for this instance of Datasette. Settings example : \n {\n \"default_facet_size\": 30,\n \"default_page_size\": 100,\n \"facet_suggest_time_limit_ms\": 50,\n \"facet_time_limit_ms\": 1000,\n \"max_returned_rows\": 1000,\n \"sql_time_limit_ms\": 1000\n}", "sections_fts": 28, "rank": null} {"rowid": 240, "title": "/-/config", "content": "Shows the configuration for this instance of Datasette. This is generally the contents of the datasette.yaml or datasette.json file, which can include plugin configuration as well. Config example : \n {\n \"settings\": {\n \"template_debug\": true,\n \"trace_debug\": true,\n \"force_https_urls\": true\n }\n} \n Any keys that include the one of the following substrings in their names will be returned as redacted *** output, to help avoid accidentally leaking private configuration information: secret , key , password , token , hash , dsn .", "sections_fts": 28, "rank": null} {"rowid": 241, "title": "/-/databases", "content": "Shows currently attached databases. Databases example : \n [\n {\n \"hash\": null,\n \"is_memory\": false,\n \"is_mutable\": true,\n \"name\": \"fixtures\",\n \"path\": \"fixtures.db\",\n \"size\": 225280\n }\n]", "sections_fts": 28, "rank": null} {"rowid": 242, "title": "/-/threads", "content": "Shows details of threads and asyncio tasks. Threads example : \n {\n \"num_threads\": 2,\n \"threads\": [\n {\n \"daemon\": false,\n \"ident\": 4759197120,\n \"name\": \"MainThread\"\n },\n {\n \"daemon\": true,\n \"ident\": 123145319682048,\n \"name\": \"Thread-1\"\n },\n ],\n \"num_tasks\": 3,\n \"tasks\": [\n \" cb=[set.discard()]>\",\n \" wait_for=()]> cb=[run_until_complete..()]>\",\n \" wait_for=()]>>\"\n ]\n}", "sections_fts": 28, "rank": null} {"rowid": 243, "title": "/-/actor", "content": "Shows the currently authenticated actor. Useful for debugging Datasette authentication plugins. \n {\n \"actor\": {\n \"id\": 1,\n \"username\": \"some-user\"\n }\n}", "sections_fts": 28, "rank": null} {"rowid": 244, "title": "/-/messages", "content": "The debug tool at /-/messages can be used to set flash messages to try out that feature. See .add_message(request, message, type=datasette.INFO) for details of this feature.", "sections_fts": 28, "rank": null} {"rowid": 245, "title": "SpatiaLite", "content": "The SpatiaLite module for SQLite adds features for handling geographic and spatial data. For an example of what you can do with it, see the tutorial Building a location to time zone API with SpatiaLite . \n To use it with Datasette, you need to install the mod_spatialite dynamic library. This can then be loaded into Datasette using the --load-extension command-line option. \n Datasette can look for SpatiaLite in common installation locations if you run it like this: \n datasette --load-extension=spatialite --setting default_allow_sql off \n If SpatiaLite is in another location, use the full path to the extension instead: \n datasette --setting default_allow_sql off \\\n --load-extension=/usr/local/lib/mod_spatialite.dylib", "sections_fts": 28, "rank": null} {"rowid": 246, "title": "Warning", "content": "The SpatiaLite extension adds a large number of additional SQL functions , some of which are not be safe for untrusted users to execute: they may cause the Datasette server to crash. \n You should not expose a SpatiaLite-enabled Datasette instance to the public internet without taking extra measures to secure it against potentially harmful SQL queries. \n The following steps are recommended: \n \n \n Disable arbitrary SQL queries by untrusted users. See Controlling the ability to execute arbitrary SQL for ways to do this. The easiest is to start Datasette with the datasette --setting default_allow_sql off option. \n \n \n Define Canned queries with the SQL queries that use SpatiaLite functions that you want people to be able to execute. \n \n \n The Datasette SpatiaLite tutorial includes detailed instructions for running SpatiaLite safely using these techniques", "sections_fts": 28, "rank": null} {"rowid": 247, "title": "Installation", "content": "", "sections_fts": 28, "rank": null} {"rowid": 248, "title": "Installing SpatiaLite on OS X", "content": "The easiest way to install SpatiaLite on OS X is to use Homebrew . \n brew update\nbrew install spatialite-tools \n This will install the spatialite command-line tool and the mod_spatialite dynamic library. \n You can now run Datasette like so: \n datasette --load-extension=spatialite", "sections_fts": 28, "rank": null} {"rowid": 249, "title": "Installing SpatiaLite on Linux", "content": "SpatiaLite is packaged for most Linux distributions. \n apt install spatialite-bin libsqlite3-mod-spatialite \n Depending on your distribution, you should be able to run Datasette something like this: \n datasette --load-extension=/usr/lib/x86_64-linux-gnu/mod_spatialite.so \n If you are unsure of the location of the module, try running locate mod_spatialite and see what comes back.", "sections_fts": 28, "rank": null} {"rowid": 250, "title": "Spatial indexing latitude/longitude columns", "content": "Here's a recipe for taking a table with existing latitude and longitude columns, adding a SpatiaLite POINT geometry column to that table, populating the new column and then populating a spatial index: \n import sqlite3\n\nconn = sqlite3.connect(\"museums.db\")\n# Lead the spatialite extension:\nconn.enable_load_extension(True)\nconn.load_extension(\"/usr/local/lib/mod_spatialite.dylib\")\n# Initialize spatial metadata for this database:\nconn.execute(\"select InitSpatialMetadata(1)\")\n# Add a geometry column called point_geom to our museums table:\nconn.execute(\n \"SELECT AddGeometryColumn('museums', 'point_geom', 4326, 'POINT', 2);\"\n)\n# Now update that geometry column with the lat/lon points\nconn.execute(\n \"\"\"\n UPDATE museums SET\n point_geom = GeomFromText('POINT('||\"longitude\"||' '||\"latitude\"||')',4326);\n\"\"\"\n)\n# Now add a spatial index to that column\nconn.execute(\n 'select CreateSpatialIndex(\"museums\", \"point_geom\");'\n)\n# If you don't commit your changes will not be persisted:\nconn.commit()\nconn.close()", "sections_fts": 28, "rank": null} {"rowid": 251, "title": "Making use of a spatial index", "content": "SpatiaLite spatial indexes are R*Trees. They allow you to run efficient bounding box queries using a sub-select, with a similar pattern to that used for Searches using custom SQL . \n In the above example, the resulting index will be called idx_museums_point_geom . This takes the form of a SQLite virtual table. You can inspect its contents using the following query: \n select * from idx_museums_point_geom limit 10; \n Here's a live example: timezones-api.datasette.io/timezones/idx_timezones_Geometry \n \n \n \n \n \n \n \n \n \n \n pkid \n \n \n xmin \n \n \n xmax \n \n \n ymin \n \n \n ymax \n \n \n \n \n \n \n 1 \n \n \n -8.601725578308105 \n \n \n -2.4930307865142822 \n \n \n 4.162120819091797 \n \n \n 10.74019718170166 \n \n \n \n \n 2 \n \n \n -3.2607860565185547 \n \n \n 1.27329421043396 \n \n \n 4.539252281188965 \n \n \n 11.174856185913086 \n \n \n \n \n 3 \n \n \n 32.997581481933594 \n \n \n 47.98238754272461 \n \n \n 3.3974475860595703 \n \n \n 14.894054412841797 \n \n \n \n \n 4 \n \n \n -8.66890811920166 \n \n \n 11.997337341308594 \n \n \n 18.9681453704834 \n \n \n 37.296207427978516 \n \n \n \n \n 5 \n \n \n 36.43336486816406 \n \n \n 43.300174713134766 \n \n \n 12.354820251464844 \n \n \n 18.070993423461914 \n \n \n \n \n \n You can now construct efficient bounding box queries that will make use of the index like this: \n select * from museums where museums.rowid in (\n SELECT pkid FROM idx_museums_point_geom\n -- left-hand-edge of point > left-hand-edge of bbox (minx)\n where xmin > :bbox_minx\n -- right-hand-edge of point < right-hand-edge of bbox (maxx)\n and xmax < :bbox_maxx\n -- bottom-edge of point > bottom-edge of bbox (miny)\n and ymin > :bbox_miny\n -- top-edge of point < top-edge of bbox (maxy)\n and ymax < :bbox_maxy\n); \n Spatial indexes can be created against polygon columns as well as point columns, in which case they will represent the minimum bounding rectangle of that polygon. This is useful for accelerating within queries, as seen in the Timezones API example.", "sections_fts": 28, "rank": null} {"rowid": 252, "title": "Importing shapefiles into SpatiaLite", "content": "The shapefile format is a common format for distributing geospatial data. You can use the spatialite command-line tool to create a new database table from a shapefile. \n Try it now with the North America shapefile available from the University of North Carolina Global River Database project. Download the file and unzip it (this will create files called narivs.dbf , narivs.prj , narivs.shp and narivs.shx in the current directory), then run the following: \n spatialite rivers-database.db \n SpatiaLite version ..: 4.3.0a Supported Extensions:\n...\nspatialite> .loadshp narivs rivers CP1252 23032\n========\nLoading shapefile at 'narivs' into SQLite table 'rivers'\n...\nInserted 467973 rows into 'rivers' from SHAPEFILE \n This will load the data from the narivs shapefile into a new database table called rivers . \n Exit out of spatialite (using Ctrl+D ) and run Datasette against your new database like this: \n datasette rivers-database.db \\\n --load-extension=/usr/local/lib/mod_spatialite.dylib \n If you browse to http://localhost:8001/rivers-database/rivers you will see the new table... but the Geometry column will contain unreadable binary data (SpatiaLite uses a custom format based on WKB ). \n The easiest way to turn this into semi-readable data is to use the SpatiaLite AsGeoJSON function. Try the following using the SQL query interface at http://localhost:8001/rivers-database : \n select *, AsGeoJSON(Geometry) from rivers limit 10; \n This will give you back an additional column of GeoJSON. You can copy and paste GeoJSON from this column into the debugging tool at geojson.io to visualize it on a map. \n To see a more interesting example, try ordering the records with the longest geometry first. Since there are 467,000 rows in the table you will first need to increase the SQL time limit imposed by Datasette: \n datasette rivers-database.db \\\n --load-extension=/usr/local/lib/mod_spatialite.dylib \\\n --setting sql_time_limit_ms 10000 \n Now try the following query: \n select *, AsGeoJSON(Geometry) from rivers\norder by length(Geometry) desc limit 10;", "sections_fts": 28, "rank": null} {"rowid": 253, "title": "Importing GeoJSON polygons using Shapely", "content": "Another common form of polygon data is the GeoJSON format. This can be imported into SpatiaLite directly, or by using the Shapely Python library. \n Who's On First is an excellent source of openly licensed GeoJSON polygons. Let's import the geographical polygon for Wales. First, we can use the Who's On First Spelunker tool to find the record for Wales: \n spelunker.whosonfirst.org/id/404227475 \n That page includes a link to the GeoJSON record, which can be accessed here: \n data.whosonfirst.org/404/227/475/404227475.geojson \n Here's Python code to create a SQLite database, enable SpatiaLite, create a places table and then add a record for Wales: \n import sqlite3\n\nconn = sqlite3.connect(\"places.db\")\n# Enable SpatialLite extension\nconn.enable_load_extension(True)\nconn.load_extension(\"/usr/local/lib/mod_spatialite.dylib\")\n# Create the masic countries table\nconn.execute(\"select InitSpatialMetadata(1)\")\nconn.execute(\n \"create table places (id integer primary key, name text);\"\n)\n# Add a MULTIPOLYGON Geometry column\nconn.execute(\n \"SELECT AddGeometryColumn('places', 'geom', 4326, 'MULTIPOLYGON', 2);\"\n)\n# Add a spatial index against the new column\nconn.execute(\"SELECT CreateSpatialIndex('places', 'geom');\")\n# Now populate the table\nfrom shapely.geometry.multipolygon import MultiPolygon\nfrom shapely.geometry import shape\nimport requests\n\ngeojson = requests.get(\n \"https://data.whosonfirst.org/404/227/475/404227475.geojson\"\n).json()\n# Convert to \"Well Known Text\" format\nwkt = shape(geojson[\"geometry\"]).wkt\n# Insert and commit the record\nconn.execute(\n \"INSERT INTO places (id, name, geom) VALUES(null, ?, GeomFromText(?, 4326))\",\n (\"Wales\", wkt),\n)\nconn.commit()", "sections_fts": 28, "rank": null} {"rowid": 254, "title": "Querying polygons using within()", "content": "The within() SQL function can be used to check if a point is within a geometry: \n select\n name\nfrom\n places\nwhere\n within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom); \n The GeomFromText() function takes a string of well-known text. Note that the order used here is longitude then latitude . \n To run that same within() query in a way that benefits from the spatial index, use the following: \n select\n name\nfrom\n places\nwhere\n within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom)\n and rowid in (\n SELECT pkid FROM idx_places_geom\n where xmin < -3.1724366\n and xmax > -3.1724366\n and ymin < 51.4704448\n and ymax > 51.4704448\n );", "sections_fts": 28, "rank": null} {"rowid": 255, "title": "Internals for plugins", "content": "Many Plugin hooks are passed objects that provide access to internal Datasette functionality. The interface to these objects should not be considered stable with the exception of methods that are documented here.", "sections_fts": 28, "rank": null} {"rowid": 256, "title": "Request object", "content": "The request object is passed to various plugin hooks. It represents an incoming HTTP request. It has the following properties: \n \n \n .scope - dictionary \n \n The ASGI scope that was used to construct this request, described in the ASGI HTTP connection scope specification. \n \n \n \n .method - string \n \n The HTTP method for this request, usually GET or POST . \n \n \n \n .url - string \n \n The full URL for this request, e.g. https://latest.datasette.io/fixtures . \n \n \n \n .scheme - string \n \n The request scheme - usually https or http . \n \n \n \n .headers - dictionary (str -> str) \n \n A dictionary of incoming HTTP request headers. Header names have been converted to lowercase. \n \n \n \n .cookies - dictionary (str -> str) \n \n A dictionary of incoming cookies \n \n \n \n .host - string \n \n The host header from the incoming request, e.g. latest.datasette.io or localhost . \n \n \n \n .path - string \n \n The path of the request excluding the query string, e.g. /fixtures . \n \n \n \n .full_path - string \n \n The path of the request including the query string if one is present, e.g. /fixtures?sql=select+sqlite_version() . \n \n \n \n .query_string - string \n \n The query string component of the request, without the ? - e.g. name__contains=sam&age__gt=10 . \n \n \n \n .args - MultiParams \n \n An object representing the parsed query string parameters, see below. \n \n \n \n .url_vars - dictionary (str -> str) \n \n Variables extracted from the URL path, if that path was defined using a regular expression. See register_routes(datasette) . \n \n \n \n .actor - dictionary (str -> Any) or None \n \n The currently authenticated actor (see actors ), or None if the request is unauthenticated. \n \n \n \n The object also has two awaitable methods: \n \n \n await request.post_vars() - dictionary \n \n Returns a dictionary of form variables that were submitted in the request body via POST . Don't forget to read about CSRF protection ! \n \n \n \n await request.post_body() - bytes \n \n Returns the un-parsed body of a request submitted by POST - useful for things like incoming JSON data. \n \n \n \n And a class method that can be used to create fake request objects for use in tests: \n \n \n fake(path_with_query_string, method=\"GET\", scheme=\"http\", url_vars=None) \n \n Returns a Request instance for the specified path and method. For example: \n from datasette import Request\nfrom pprint import pprint\n\nrequest = Request.fake(\n \"/fixtures/facetable/\",\n url_vars={\"database\": \"fixtures\", \"table\": \"facetable\"},\n)\npprint(request.scope) \n This outputs: \n {'http_version': '1.1',\n 'method': 'GET',\n 'path': '/fixtures/facetable/',\n 'query_string': b'',\n 'raw_path': b'/fixtures/facetable/',\n 'scheme': 'http',\n 'type': 'http',\n 'url_route': {'kwargs': {'database': 'fixtures', 'table': 'facetable'}}}", "sections_fts": 28, "rank": null} {"rowid": 257, "title": "The MultiParams class", "content": "request.args is a MultiParams object - a dictionary-like object which provides access to query string parameters that may have multiple values. \n Consider the query string ?foo=1&foo=2&bar=3 - with two values for foo and one value for bar . \n \n \n request.args[key] - string \n \n Returns the first value for that key, or raises a KeyError if the key is missing. For the above example request.args[\"foo\"] would return \"1\" . \n \n \n \n request.args.get(key) - string or None \n \n Returns the first value for that key, or None if the key is missing. Pass a second argument to specify a different default, e.g. q = request.args.get(\"q\", \"\") . \n \n \n \n request.args.getlist(key) - list of strings \n \n Returns the list of strings for that key. request.args.getlist(\"foo\") would return [\"1\", \"2\"] in the above example. request.args.getlist(\"bar\") would return [\"3\"] . If the key is missing an empty list will be returned. \n \n \n \n request.args.keys() - list of strings \n \n Returns the list of available keys - for the example this would be [\"foo\", \"bar\"] . \n \n \n \n key in request.args - True or False \n \n You can use if key in request.args to check if a key is present. \n \n \n \n for key in request.args - iterator \n \n This lets you loop through every available key. \n \n \n \n len(request.args) - integer \n \n Returns the number of keys.", "sections_fts": 28, "rank": null} {"rowid": 258, "title": "Response class", "content": "The Response class can be returned from view functions that have been registered using the register_routes(datasette) hook. \n The Response() constructor takes the following arguments: \n \n \n body - string \n \n The body of the response. \n \n \n \n status - integer (optional) \n \n The HTTP status - defaults to 200. \n \n \n \n headers - dictionary (optional) \n \n A dictionary of extra HTTP headers, e.g. {\"x-hello\": \"world\"} . \n \n \n \n content_type - string (optional) \n \n The content-type for the response. Defaults to text/plain . \n \n \n \n For example: \n from datasette.utils.asgi import Response\n\nresponse = Response(\n \"This is XML\",\n content_type=\"application/xml; charset=utf-8\",\n) \n The quickest way to create responses is using the Response.text(...) , Response.html(...) , Response.json(...) or Response.redirect(...) helper methods: \n from datasette.utils.asgi import Response\n\nhtml_response = Response.html(\"This is HTML\")\njson_response = Response.json({\"this_is\": \"json\"})\ntext_response = Response.text(\n \"This will become utf-8 encoded text\"\n)\n# Redirects are served as 302, unless you pass status=301:\nredirect_response = Response.redirect(\n \"https://latest.datasette.io/\"\n) \n Each of these responses will use the correct corresponding content-type - text/html; charset=utf-8 , application/json; charset=utf-8 or text/plain; charset=utf-8 respectively. \n Each of the helper methods take optional status= and headers= arguments, documented above.", "sections_fts": 28, "rank": null} {"rowid": 259, "title": "Returning a response with .asgi_send(send)", "content": "In most cases you will return Response objects from your own view functions. You can also use a Response instance to respond at a lower level via ASGI, for example if you are writing code that uses the asgi_wrapper(datasette) hook. \n Create a Response object and then use await response.asgi_send(send) , passing the ASGI send function. For example: \n async def require_authorization(scope, receive, send):\n response = Response.text(\n \"401 Authorization Required\",\n headers={\n \"www-authenticate\": 'Basic realm=\"Datasette\", charset=\"UTF-8\"'\n },\n status=401,\n )\n await response.asgi_send(send)", "sections_fts": 28, "rank": null} {"rowid": 260, "title": "Setting cookies with response.set_cookie()", "content": "To set cookies on the response, use the response.set_cookie(...) method. The method signature looks like this: \n def set_cookie(\n self,\n key,\n value=\"\",\n max_age=None,\n expires=None,\n path=\"/\",\n domain=None,\n secure=False,\n httponly=False,\n samesite=\"lax\",\n): ... \n You can use this with datasette.sign() to set signed cookies. Here's how you would set the ds_actor cookie for use with Datasette authentication : \n response = Response.redirect(\"/\")\nresponse.set_cookie(\n \"ds_actor\",\n datasette.sign({\"a\": {\"id\": \"cleopaws\"}}, \"actor\"),\n)\nreturn response", "sections_fts": 28, "rank": null} {"rowid": 261, "title": "Datasette class", "content": "This object is an instance of the Datasette class, passed to many plugin hooks as an argument called datasette . \n You can create your own instance of this - for example to help write tests for a plugin - like so: \n from datasette.app import Datasette\n\n# With no arguments a single in-memory database will be attached\ndatasette = Datasette()\n\n# The files= argument can load files from disk\ndatasette = Datasette(files=[\"/path/to/my-database.db\"])\n\n# Pass metadata as a JSON dictionary like this\ndatasette = Datasette(\n files=[\"/path/to/my-database.db\"],\n metadata={\n \"databases\": {\n \"my-database\": {\n \"description\": \"This is my database\"\n }\n }\n },\n) \n Constructor parameters include: \n \n \n files=[...] - a list of database files to open \n \n \n immutables=[...] - a list of database files to open in immutable mode \n \n \n metadata={...} - a dictionary of Metadata \n \n \n config_dir=... - the configuration directory to use, stored in datasette.config_dir", "sections_fts": 28, "rank": null} {"rowid": 262, "title": ".databases", "content": "Property exposing a collections.OrderedDict of databases currently connected to Datasette. \n The dictionary keys are the name of the database that is used in the URL - e.g. /fixtures would have a key of \"fixtures\" . The values are Database class instances. \n All databases are listed, irrespective of user permissions.", "sections_fts": 28, "rank": null} {"rowid": 263, "title": ".permissions", "content": "Property exposing a dictionary of permissions that have been registered using the register_permissions(datasette) plugin hook. \n The dictionary keys are the permission names - e.g. view-instance - and the values are Permission() objects describing the permission. Here is a description of that object .", "sections_fts": 28, "rank": null} {"rowid": 264, "title": ".plugin_config(plugin_name, database=None, table=None)", "content": "plugin_name - string \n \n The name of the plugin to look up configuration for. Usually this is something similar to datasette-cluster-map . \n \n \n \n database - None or string \n \n The database the user is interacting with. \n \n \n \n table - None or string \n \n The table the user is interacting with. \n \n \n \n This method lets you read plugin configuration values that were set in datasette.yaml . See Writing plugins that accept configuration for full details of how this method should be used. \n The return value will be the value from the configuration file - usually a dictionary. \n If the plugin is not configured the return value will be None .", "sections_fts": 28, "rank": null} {"rowid": 265, "title": "await .render_template(template, context=None, request=None)", "content": "template - string, list of strings or jinja2.Template \n \n The template file to be rendered, e.g. my_plugin.html . Datasette will search for this file first in the --template-dir= location, if it was specified - then in the plugin's bundled templates and finally in Datasette's set of default templates. \n If this is a list of template file names then the first one that exists will be loaded and rendered. \n If this is a Jinja Template object it will be used directly. \n \n \n \n context - None or a Python dictionary \n \n The context variables to pass to the template. \n \n \n \n request - request object or None \n \n If you pass a Datasette request object here it will be made available to the template. \n \n \n \n Renders a Jinja template using Datasette's preconfigured instance of Jinja and returns the resulting string. The template will have access to Datasette's default template functions and any functions that have been made available by other plugins.", "sections_fts": 28, "rank": null} {"rowid": 266, "title": "await .actors_from_ids(actor_ids)", "content": "actor_ids - list of strings or integers \n \n A list of actor IDs to look up. \n \n \n \n Returns a dictionary, where the keys are the IDs passed to it and the values are the corresponding actor dictionaries. \n This method is mainly designed to be used with plugins. See the actors_from_ids(datasette, actor_ids) documentation for details. \n If no plugins that implement that hook are installed, the default return value looks like this: \n {\n \"1\": {\"id\": \"1\"},\n \"2\": {\"id\": \"2\"}\n}", "sections_fts": 28, "rank": null} {"rowid": 267, "title": "await .permission_allowed(actor, action, resource=None, default=...)", "content": "actor - dictionary \n \n The authenticated actor. This is usually request.actor . \n \n \n \n action - string \n \n The name of the action that is being permission checked. \n \n \n \n resource - string or tuple, optional \n \n The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. \n \n \n \n default - optional: True, False or None \n \n What value should be returned by default if nothing provides an opinion on this permission check.\n Set to True for default allow or False for default deny.\n If not specified the default from the Permission() tuple that was registered using register_permissions(datasette) will be used. \n \n \n \n Check if the given actor has permission to perform the given action on the given resource. \n Some permission checks are carried out against rules defined in datasette.yaml , while other custom permissions may be decided by plugins that implement the permission_allowed(datasette, actor, action, resource) plugin hook. \n If neither metadata.json nor any of the plugins provide an answer to the permission query the default argument will be returned. \n See Built-in permissions for a full list of permission actions included in Datasette core.", "sections_fts": 28, "rank": null} {"rowid": 268, "title": "await .ensure_permissions(actor, permissions)", "content": "actor - dictionary \n \n The authenticated actor. This is usually request.actor . \n \n \n \n permissions - list \n \n A list of permissions to check. Each permission in that list can be a string action name or a 2-tuple of (action, resource) . \n \n \n \n This method allows multiple permissions to be checked at once. It raises a datasette.Forbidden exception if any of the checks are denied before one of them is explicitly granted. \n This is useful when you need to check multiple permissions at once. For example, an actor should be able to view a table if either one of the following checks returns True or not a single one of them returns False : \n await datasette.ensure_permissions(\n request.actor,\n [\n (\"view-table\", (database, table)),\n (\"view-database\", database),\n \"view-instance\",\n ],\n)", "sections_fts": 28, "rank": null} {"rowid": 269, "title": "await .check_visibility(actor, action=None, resource=None, permissions=None)", "content": "actor - dictionary \n \n The authenticated actor. This is usually request.actor . \n \n \n \n action - string, optional \n \n The name of the action that is being permission checked. \n \n \n \n resource - string or tuple, optional \n \n The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. \n \n \n \n permissions - list of action strings or (action, resource) tuples, optional \n \n Provide this instead of action and resource to check multiple permissions at once. \n \n \n \n This convenience method can be used to answer the question \"should this item be considered private, in that it is visible to me but it is not visible to anonymous users?\" \n It returns a tuple of two booleans, (visible, private) . visible indicates if the actor can see this resource. private will be True if an anonymous user would not be able to view the resource. \n This example checks if the user can access a specific table, and sets private so that a padlock icon can later be displayed: \n visible, private = await datasette.check_visibility(\n request.actor,\n action=\"view-table\",\n resource=(database, table),\n) \n The following example runs three checks in a row, similar to await .ensure_permissions(actor, permissions) . If any of the checks are denied before one of them is explicitly granted then visible will be False . private will be True if an anonymous user would not be able to view the resource. \n visible, private = await datasette.check_visibility(\n request.actor,\n permissions=[\n (\"view-table\", (database, table)),\n (\"view-database\", database),\n \"view-instance\",\n ],\n)", "sections_fts": 28, "rank": null} {"rowid": 270, "title": ".create_token(actor_id, expires_after=None, restrict_all=None, restrict_database=None, restrict_resource=None)", "content": "actor_id - string \n \n The ID of the actor to create a token for. \n \n \n \n expires_after - int, optional \n \n The number of seconds after which the token should expire. \n \n \n \n restrict_all - iterable, optional \n \n A list of actions that this token should be restricted to across all databases and resources. \n \n \n \n restrict_database - dict, optional \n \n For restricting actions within specific databases, e.g. {\"mydb\": [\"view-table\", \"view-query\"]} . \n \n \n \n restrict_resource - dict, optional \n \n For restricting actions to specific resources (tables, SQL views and Canned queries ) within a database. For example: {\"mydb\": {\"mytable\": [\"insert-row\", \"update-row\"]}} . \n \n \n \n This method returns a signed API token of the format dstok_... which can be used to authenticate requests to the Datasette API. \n All tokens must have an actor_id string indicating the ID of the actor which the token will act on behalf of. \n Tokens default to lasting forever, but can be set to expire after a given number of seconds using the expires_after argument. The following code creates a token for user1 that will expire after an hour: \n token = datasette.create_token(\n actor_id=\"user1\",\n expires_after=3600,\n) \n The three restrict_* arguments can be used to create a token that has additional restrictions beyond what the associated actor is allowed to do. \n The following example creates a token that can access view-instance and view-table across everything, can additionally use view-query for anything in the docs database and is allowed to execute insert-row and update-row in the attachments table in that database: \n token = datasette.create_token(\n actor_id=\"user1\",\n restrict_all=(\"view-instance\", \"view-table\"),\n restrict_database={\"docs\": (\"view-query\",)},\n restrict_resource={\n \"docs\": {\n \"attachments\": (\"insert-row\", \"update-row\")\n }\n },\n)", "sections_fts": 28, "rank": null} {"rowid": 271, "title": ".get_permission(name_or_abbr)", "content": "name_or_abbr - string \n \n The name or abbreviation of the permission to look up, e.g. view-table or vt . \n \n \n \n Returns a Permission object representing the permission, or raises a KeyError if one is not found.", "sections_fts": 28, "rank": null} {"rowid": 272, "title": ".get_database(name)", "content": "name - string, optional \n \n The name of the database - optional. \n \n \n \n Returns the specified database object. Raises a KeyError if the database does not exist. Call this method without an argument to return the first connected database.", "sections_fts": 28, "rank": null} {"rowid": 273, "title": ".get_internal_database()", "content": "Returns a database object for reading and writing to the private internal database .", "sections_fts": 28, "rank": null} {"rowid": 274, "title": "Getting and setting metadata", "content": "Metadata about the instance, databases, tables and columns is stored in tables in Datasette's internal database . The following methods are the supported API for plugins to read and update that stored metadata.", "sections_fts": 28, "rank": null} {"rowid": 275, "title": "await .get_instance_metadata(self)", "content": "Returns metadata keys and values for the entire Datasette instance as a dictionary.\n Internally queries the metadata_instance table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 276, "title": "await .get_database_metadata(self, database_name)", "content": "database_name - string \n \n The name of the database to query. \n \n \n \n Returns metadata keys and values for the specified database as a dictionary.\n Internally queries the metadata_databases table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 277, "title": "await .get_resource_metadata(self, database_name, resource_name)", "content": "database_name - string \n \n The name of the database to query. \n \n \n \n resource_name - string \n \n The name of the resource (table, view, or canned query) inside database_name to query. \n \n \n \n Returns metadata keys and values for the specified \"resource\" as a dictionary.\n A \"resource\" in this context can be a table, view, or canned query.\n Internally queries the metadata_resources table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 278, "title": "await .get_column_metadata(self, database_name, resource_name, column_name)", "content": "database_name - string \n \n The name of the database to query. \n \n \n \n resource_name - string \n \n The name of the resource (table, view, or canned query) inside database_name to query. \n \n \n \n column_name - string \n \n The name of the column inside resource_name to query. \n \n \n \n Returns metadata keys and values for the specified column, resource, and table as a dictionary.\n Internally queries the metadata_columns table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 279, "title": "await .set_instance_metadata(self, key, value)", "content": "key - string \n \n The metadata entry key to insert (ex title , description , etc.) \n \n \n \n value - string \n \n The value of the metadata entry to insert. \n \n \n \n Adds a new metadata entry for the entire Datasette instance.\n Any previous instance-level metadata entry with the same key will be overwritten.\n Internally upserts the value into the the metadata_instance table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 280, "title": "await .set_database_metadata(self, database_name, key, value)", "content": "database_name - string \n \n The database the metadata entry belongs to. \n \n \n \n key - string \n \n The metadata entry key to insert (ex title , description , etc.) \n \n \n \n value - string \n \n The value of the metadata entry to insert. \n \n \n \n Adds a new metadata entry for the specified database.\n Any previous database-level metadata entry with the same key will be overwritten.\n Internally upserts the value into the the metadata_databases table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 281, "title": "await .set_resource_metadata(self, database_name, resource_name, key, value)", "content": "database_name - string \n \n The database the metadata entry belongs to. \n \n \n \n resource_name - string \n \n The resource (table, view, or canned query) the metadata entry belongs to. \n \n \n \n key - string \n \n The metadata entry key to insert (ex title , description , etc.) \n \n \n \n value - string \n \n The value of the metadata entry to insert. \n \n \n \n Adds a new metadata entry for the specified \"resource\".\n Any previous resource-level metadata entry with the same key will be overwritten.\n Internally upserts the value into the the metadata_resources table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 282, "title": "await .set_column_metadata(self, database_name, resource_name, column_name, key, value)", "content": "database_name - string \n \n The database the metadata entry belongs to. \n \n \n \n resource_name - string \n \n The resource (table, view, or canned query) the metadata entry belongs to. \n \n \n \n column-name - string \n \n The column the metadata entry belongs to. \n \n \n \n key - string \n \n The metadata entry key to insert (ex title , description , etc.) \n \n \n \n value - string \n \n The value of the metadata entry to insert. \n \n \n \n Adds a new metadata entry for the specified column.\n Any previous column-level metadata entry with the same key will be overwritten.\n Internally upserts the value into the the metadata_columns table inside the internal database .", "sections_fts": 28, "rank": null} {"rowid": 283, "title": ".add_database(db, name=None, route=None)", "content": "db - datasette.database.Database instance \n \n The database to be attached. \n \n \n \n name - string, optional \n \n The name to be used for this database . If not specified Datasette will pick one based on the filename or memory name. \n \n \n \n route - string, optional \n \n This will be used in the URL path. If not specified, it will default to the same thing as the name . \n \n \n \n The datasette.add_database(db) method lets you add a new database to the current Datasette instance. \n The db parameter should be an instance of the datasette.database.Database class. For example: \n from datasette.database import Database\n\ndatasette.add_database(\n Database(\n datasette,\n path=\"path/to/my-new-database.db\",\n )\n) \n This will add a mutable database and serve it at /my-new-database . \n Use is_mutable=False to add an immutable database. \n .add_database() returns the Database instance, with its name set as the database.name attribute. Any time you are working with a newly added database you should use the return value of .add_database() , for example: \n db = datasette.add_database(\n Database(datasette, memory_name=\"statistics\")\n)\nawait db.execute_write(\n \"CREATE TABLE foo(id integer primary key)\"\n)", "sections_fts": 28, "rank": null} {"rowid": 284, "title": ".add_memory_database(name)", "content": "Adds a shared in-memory database with the specified name: \n datasette.add_memory_database(\"statistics\") \n This is a shortcut for the following: \n from datasette.database import Database\n\ndatasette.add_database(\n Database(datasette, memory_name=\"statistics\")\n) \n Using either of these pattern will result in the in-memory database being served at /statistics .", "sections_fts": 28, "rank": null} {"rowid": 285, "title": ".remove_database(name)", "content": "name - string \n \n The name of the database to be removed. \n \n \n \n This removes a database that has been previously added. name= is the unique name of that database.", "sections_fts": 28, "rank": null} {"rowid": 286, "title": "await .track_event(event)", "content": "event - Event \n \n An instance of a subclass of datasette.events.Event . \n \n \n \n Plugins can call this to track events, using classes they have previously registered. See Event tracking for details. \n The event will then be passed to all plugins that have registered to receive events using the track_event(datasette, event) hook. \n Example usage, assuming the plugin has previously registered the BanUserEvent class: \n await datasette.track_event(\n BanUserEvent(user={\"id\": 1, \"username\": \"cleverbot\"})\n)", "sections_fts": 28, "rank": null} {"rowid": 287, "title": ".sign(value, namespace=\"default\")", "content": "value - any serializable type \n \n The value to be signed. \n \n \n \n namespace - string, optional \n \n An alternative namespace, see the itsdangerous salt documentation . \n \n \n \n Utility method for signing values, such that you can safely pass data to and from an untrusted environment. This is a wrapper around the itsdangerous library. \n This method returns a signed string, which can be decoded and verified using .unsign(value, namespace=\"default\") .", "sections_fts": 28, "rank": null} {"rowid": 288, "title": ".unsign(value, namespace=\"default\")", "content": "signed - any serializable type \n \n The signed string that was created using .sign(value, namespace=\"default\") . \n \n \n \n namespace - string, optional \n \n The alternative namespace, if one was used. \n \n \n \n Returns the original, decoded object that was passed to .sign(value, namespace=\"default\") . If the signature is not valid this raises a itsdangerous.BadSignature exception.", "sections_fts": 28, "rank": null} {"rowid": 289, "title": ".add_message(request, message, type=datasette.INFO)", "content": "request - Request \n \n The current Request object \n \n \n \n message - string \n \n The message string \n \n \n \n type - constant, optional \n \n The message type - datasette.INFO , datasette.WARNING or datasette.ERROR \n \n \n \n Datasette's flash messaging mechanism allows you to add a message that will be displayed to the user on the next page that they visit. Messages are persisted in a ds_messages cookie. This method adds a message to that cookie. \n You can try out these messages (including the different visual styling of the three message types) using the /-/messages debugging tool.", "sections_fts": 28, "rank": null} {"rowid": 290, "title": ".absolute_url(request, path)", "content": "request - Request \n \n The current Request object \n \n \n \n path - string \n \n A path, for example /dbname/table.json \n \n \n \n Returns the absolute URL for the given path, including the protocol and host. For example: \n absolute_url = datasette.absolute_url(\n request, \"/dbname/table.json\"\n)\n# Would return \"http://localhost:8001/dbname/table.json\" \n The current request object is used to determine the hostname and protocol that should be used for the returned URL. The force_https_urls configuration setting is taken into account.", "sections_fts": 28, "rank": null} {"rowid": 291, "title": ".setting(key)", "content": "key - string \n \n The name of the setting, e.g. base_url . \n \n \n \n Returns the configured value for the specified setting . This can be a string, boolean or integer depending on the requested setting. \n For example: \n downloads_are_allowed = datasette.setting(\"allow_download\")", "sections_fts": 28, "rank": null} {"rowid": 292, "title": ".resolve_database(request)", "content": "request - Request object \n \n A request object \n \n \n \n If you are implementing your own custom views, you may need to resolve the database that the user is requesting based on a URL path. If the regular expression for your route declares a database named group, you can use this method to resolve the database object. \n This returns a Database instance. \n If the database cannot be found, it raises a datasette.utils.asgi.DatabaseNotFound exception - which is a subclass of datasette.utils.asgi.NotFound with a .database_name attribute set to the name of the database that was requested.", "sections_fts": 28, "rank": null} {"rowid": 293, "title": ".resolve_table(request)", "content": "request - Request object \n \n A request object \n \n \n \n This assumes that the regular expression for your route declares both a database and a table named group. \n It returns a ResolvedTable named tuple instance with the following fields: \n \n \n db - Database \n \n The database object \n \n \n \n table - string \n \n The name of the table (or view) \n \n \n \n is_view - boolean \n \n True if this is a view, False if it is a table \n \n \n \n If the database or table cannot be found it raises a datasette.utils.asgi.DatabaseNotFound exception. \n If the table does not exist it raises a datasette.utils.asgi.TableNotFound exception - a subclass of datasette.utils.asgi.NotFound with .database_name and .table attributes.", "sections_fts": 28, "rank": null} {"rowid": 294, "title": ".resolve_row(request)", "content": "request - Request object \n \n A request object \n \n \n \n This method assumes your route declares named groups for database , table and pks . \n It returns a ResolvedRow named tuple instance with the following fields: \n \n \n db - Database \n \n The database object \n \n \n \n table - string \n \n The name of the table \n \n \n \n sql - string \n \n SQL snippet that can be used in a WHERE clause to select the row \n \n \n \n params - dict \n \n Parameters that should be passed to the SQL query \n \n \n \n pks - list \n \n List of primary key column names \n \n \n \n pk_values - list \n \n List of primary key values decoded from the URL \n \n \n \n row - sqlite3.Row \n \n The row itself \n \n \n \n If the database or table cannot be found it raises a datasette.utils.asgi.DatabaseNotFound exception. \n If the table does not exist it raises a datasette.utils.asgi.TableNotFound exception. \n If the row cannot be found it raises a datasette.utils.asgi.RowNotFound exception. This has .database_name , .table and .pk_values attributes, extracted from the request path.", "sections_fts": 28, "rank": null} {"rowid": 295, "title": "datasette.client", "content": "Plugins can make internal simulated HTTP requests to the Datasette instance within which they are running. This ensures that all of Datasette's external JSON APIs are also available to plugins, while avoiding the overhead of making an external HTTP call to access those APIs. \n The datasette.client object is a wrapper around the HTTPX Python library , providing an async-friendly API that is similar to the widely used Requests library . \n It offers the following methods: \n \n \n await datasette.client.get(path, **kwargs) - returns HTTPX Response \n \n Execute an internal GET request against that path. \n \n \n \n await datasette.client.post(path, **kwargs) - returns HTTPX Response \n \n Execute an internal POST request. Use data={\"name\": \"value\"} to pass form parameters. \n \n \n \n await datasette.client.options(path, **kwargs) - returns HTTPX Response \n \n Execute an internal OPTIONS request. \n \n \n \n await datasette.client.head(path, **kwargs) - returns HTTPX Response \n \n Execute an internal HEAD request. \n \n \n \n await datasette.client.put(path, **kwargs) - returns HTTPX Response \n \n Execute an internal PUT request. \n \n \n \n await datasette.client.patch(path, **kwargs) - returns HTTPX Response \n \n Execute an internal PATCH request. \n \n \n \n await datasette.client.delete(path, **kwargs) - returns HTTPX Response \n \n Execute an internal DELETE request. \n \n \n \n await datasette.client.request(method, path, **kwargs) - returns HTTPX Response \n \n Execute an internal request with the given HTTP method against that path. \n \n \n \n These methods can be used with datasette.urls - for example: \n table_json = (\n await datasette.client.get(\n datasette.urls.table(\n \"fixtures\", \"facetable\", format=\"json\"\n )\n )\n).json() \n datasette.client methods automatically take the current base_url setting into account, whether or not you use the datasette.urls family of methods to construct the path. \n For documentation on available **kwargs options and the shape of the HTTPX Response object refer to the HTTPX Async documentation .", "sections_fts": 28, "rank": null} {"rowid": 296, "title": "datasette.urls", "content": "The datasette.urls object contains methods for building URLs to pages within Datasette. Plugins should use this to link to pages, since these methods take into account any base_url configuration setting that might be in effect. \n \n \n datasette.urls.instance(format=None) \n \n Returns the URL to the Datasette instance root page. This is usually \"/\" . \n \n \n \n datasette.urls.path(path, format=None) \n \n Takes a path and returns the full path, taking base_url into account. \n For example, datasette.urls.path(\"-/logout\") will return the path to the logout page, which will be \"/-/logout\" by default or /prefix-path/-/logout if base_url is set to /prefix-path/ \n \n \n \n datasette.urls.logout() \n \n Returns the URL to the logout page, usually \"/-/logout\" \n \n \n \n datasette.urls.static(path) \n \n Returns the URL of one of Datasette's default static assets, for example \"/-/static/app.css\" \n \n \n \n datasette.urls.static_plugins(plugin_name, path) \n \n Returns the URL of one of the static assets belonging to a plugin. \n datasette.urls.static_plugins(\"datasette_cluster_map\", \"datasette-cluster-map.js\") would return \"/-/static-plugins/datasette_cluster_map/datasette-cluster-map.js\" \n \n \n \n datasette.urls.static(path) \n \n Returns the URL of one of Datasette's default static assets, for example \"/-/static/app.css\" \n \n \n \n datasette.urls.database(database_name, format=None) \n \n Returns the URL to a database page, for example \"/fixtures\" \n \n \n \n datasette.urls.table(database_name, table_name, format=None) \n \n Returns the URL to a table page, for example \"/fixtures/facetable\" \n \n \n \n datasette.urls.query(database_name, query_name, format=None) \n \n Returns the URL to a query page, for example \"/fixtures/pragma_cache_size\" \n \n \n \n These functions can be accessed via the {{ urls }} object in Datasette templates, for example: \n Homepage\nFixtures database\nfacetable table\npragma_cache_size query \n Use the format=\"json\" (or \"csv\" or other formats supported by plugins) arguments to get back URLs to the JSON representation. This is the path with .json added on the end. \n These methods each return a datasette.utils.PrefixedUrlString object, which is a subclass of the Python str type. This allows the logic that considers the base_url setting to detect if that prefix has already been applied to the path.", "sections_fts": 28, "rank": null} {"rowid": 297, "title": "Database class", "content": "Instances of the Database class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.", "sections_fts": 28, "rank": null} {"rowid": 298, "title": "Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None)", "content": "The Database() constructor can be used by plugins, in conjunction with .add_database(db, name=None, route=None) , to create and register new databases. \n The arguments are as follows: \n \n \n ds - Datasette class (required) \n \n The Datasette instance you are attaching this database to. \n \n \n \n path - string \n \n Path to a SQLite database file on disk. \n \n \n \n is_mutable - boolean \n \n Set this to False to cause Datasette to open the file in immutable mode. \n \n \n \n is_memory - boolean \n \n Use this to create non-shared memory connections. \n \n \n \n memory_name - string or None \n \n Use this to create a named in-memory database. Unlike regular memory databases these can be accessed by multiple threads and will persist an changes made to them for the lifetime of the Datasette server process. \n \n \n \n The first argument is the datasette instance you are attaching to, the second is a path= , then is_mutable and is_memory are both optional arguments.", "sections_fts": 28, "rank": null} {"rowid": 299, "title": "db.hash", "content": "If the database was opened in immutable mode, this property returns the 64 character SHA-256 hash of the database contents as a string. Otherwise it returns None .", "sections_fts": 28, "rank": null} {"rowid": 300, "title": "await db.execute(sql, ...)", "content": "Executes a SQL query against the database and returns the resulting rows (see Results ). \n \n \n sql - string (required) \n \n The SQL query to execute. This can include ? or :named parameters. \n \n \n \n params - list or dict \n \n A list or dictionary of values to use for the parameters. List for ? , dictionary for :named . \n \n \n \n truncate - boolean \n \n Should the rows returned by the query be truncated at the maximum page size? Defaults to True , set this to False to disable truncation. \n \n \n \n custom_time_limit - integer ms \n \n A custom time limit for this query. This can be set to a lower value than the Datasette configured default. If a query takes longer than this it will be terminated early and raise a dataette.database.QueryInterrupted exception. \n \n \n \n page_size - integer \n \n Set a custom page size for truncation, over-riding the configured Datasette default. \n \n \n \n log_sql_errors - boolean \n \n Should any SQL errors be logged to the console in addition to being raised as an error? Defaults to True .", "sections_fts": 28, "rank": null}