{"id": "json_api:json-api-shapes", "page": "json_api", "ref": "json-api-shapes", "title": "Different shapes", "content": "The default JSON representation of data from a SQLite table or custom query\n                looks like this: \n             {\n    \"database\": \"sf-trees\",\n    \"table\": \"qSpecies\",\n    \"columns\": [\n        \"id\",\n        \"value\"\n    ],\n    \"rows\": [\n        [\n            1,\n            \"Myoporum laetum :: Myoporum\"\n        ],\n        [\n            2,\n            \"Metrosideros excelsa :: New Zealand Xmas Tree\"\n        ],\n        [\n            3,\n            \"Pinus radiata :: Monterey Pine\"\n        ]\n    ],\n    \"truncated\": false,\n    \"next\": \"100\",\n    \"next_url\": \"http://127.0.0.1:8001/sf-trees-02c8ef1/qSpecies.json?_next=100\",\n    \"query_ms\": 1.9571781158447266\n} \n             The  columns  key lists the columns that are being returned, and the  rows \n                key then returns a list of lists, each one representing a row. The order of the\n                values in each row corresponds to the columns. \n             The  _shape  parameter can be used to access alternative formats for the\n                 rows  key which may be more convenient for your application. There are three\n                options: \n             \n                 \n                     ?_shape=arrays  -  \"rows\"  is the default option, shown above \n                 \n                 \n                     ?_shape=objects  -  \"rows\"  is a list of JSON key/value objects \n                 \n                 \n                     ?_shape=array  - an JSON array of objects \n                 \n                 \n                     ?_shape=array&_nl=on  - a newline-separated list of JSON objects \n                 \n                 \n                     ?_shape=arrayfirst  - a flat JSON array containing just the first value from each row \n                 \n                 \n                     ?_shape=object  - a JSON object keyed using the primary keys of the rows \n                 \n             \n             _shape=objects  looks like this: \n             {\n    \"database\": \"sf-trees\",\n    ...\n    \"rows\": [\n        {\n            \"id\": 1,\n            \"value\": \"Myoporum laetum :: Myoporum\"\n        },\n        {\n            \"id\": 2,\n            \"value\": \"Metrosideros excelsa :: New Zealand Xmas Tree\"\n        },\n        {\n            \"id\": 3,\n            \"value\": \"Pinus radiata :: Monterey Pine\"\n        }\n    ]\n} \n             _shape=array  looks like this: \n             [\n    {\n        \"id\": 1,\n        \"value\": \"Myoporum laetum :: Myoporum\"\n    },\n    {\n        \"id\": 2,\n        \"value\": \"Metrosideros excelsa :: New Zealand Xmas Tree\"\n    },\n    {\n        \"id\": 3,\n        \"value\": \"Pinus radiata :: Monterey Pine\"\n    }\n] \n             _shape=array&_nl=on  looks like this: \n             {\"id\": 1, \"value\": \"Myoporum laetum :: Myoporum\"}\n{\"id\": 2, \"value\": \"Metrosideros excelsa :: New Zealand Xmas Tree\"}\n{\"id\": 3, \"value\": \"Pinus radiata :: Monterey Pine\"} \n             _shape=arrayfirst  looks like this: \n             [1, 2, 3] \n             _shape=object  looks like this: \n             {\n    \"1\": {\n        \"id\": 1,\n        \"value\": \"Myoporum laetum :: Myoporum\"\n    },\n    \"2\": {\n        \"id\": 2,\n        \"value\": \"Metrosideros excelsa :: New Zealand Xmas Tree\"\n    },\n    \"3\": {\n        \"id\": 3,\n        \"value\": \"Pinus radiata :: Monterey Pine\"\n    }\n] \n             The  object  shape is only available for queries against tables - custom SQL\n                queries and views do not have an obvious primary key so cannot be returned using\n                this format. \n             The  object  keys are always strings. If your table has a compound primary\n                key, the  object  keys will be a comma-separated string.", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:json-api-discover-alternate", "page": "json_api", "ref": "json-api-discover-alternate", "title": "Discovering the JSON for a page", "content": "Most of the HTML pages served by Datasette provide a mechanism for discovering their JSON equivalents using the HTML  link  mechanism. \n             You can find this near the top of the source code of those pages, looking like this: \n             <link rel=\"alternate\"\n  type=\"application/json+datasette\"\n  href=\"https://latest.datasette.io/fixtures/sortable.json\"> \n             The JSON URL is also made available in a  Link  HTTP header for the page: \n             Link: https://latest.datasette.io/fixtures/sortable.json; rel=\"alternate\"; type=\"application/json+datasette\"", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "changelog:documentation", "page": "changelog", "ref": "documentation", "title": "Documentation", "content": "New tutorial:  Cleaning data with sqlite-utils and Datasette . \n                     \n                     \n                         Screenshots in the documentation are now maintained using  shot-scraper , as described in  Automating screenshots for the Datasette documentation using shot-scraper . ( #1844 ) \n                     \n                     \n                         More detailed command descriptions on the  CLI reference  page. ( #1787 ) \n                     \n                     \n                         New documentation on  Running Datasette using OpenRC  - thanks, Adam Simpson. ( #1825 )", "breadcrumbs": "[\"Changelog\", \"0.63 (2022-10-27)\"]", "references": "[{\"href\": \"https://datasette.io/tutorials/clean-data\", \"label\": \"Cleaning data with sqlite-utils and Datasette\"}, {\"href\": \"https://shot-scraper.datasette.io/\", \"label\": \"shot-scraper\"}, {\"href\": \"https://simonwillison.net/2022/Oct/14/automating-screenshots/\", \"label\": \"Automating screenshots for the Datasette documentation using shot-scraper\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1844\", \"label\": \"#1844\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1787\", \"label\": \"#1787\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1825\", \"label\": \"#1825\"}]"}
{"id": "changelog:id15", "page": "changelog", "ref": "id15", "title": "Documentation", "content": "Examples in the documentation now include a copy-to-clipboard button. ( #1748 ) \n                     \n                     \n                         Documentation now uses the  Furo  Sphinx theme. ( #1746 ) \n                     \n                     \n                         Code examples in the documentation are now all formatted using Black. ( #1718 ) \n                     \n                     \n                         Request.fake()  method is now documented, see  Request object . \n                     \n                     \n                         New documentation for plugin authors:  Registering a plugin for the duration of a test . ( #903 )", "breadcrumbs": "[\"Changelog\", \"0.62 (2022-08-14)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1748\", \"label\": \"#1748\"}, {\"href\": \"https://github.com/pradyunsg/furo\", \"label\": \"Furo\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1746\", \"label\": \"#1746\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1718\", \"label\": \"#1718\"}, {\"href\": \"https://github.com/simonw/datasette/issues/903\", \"label\": \"#903\"}]"}
{"id": "ecosystem:dogsheep", "page": "ecosystem", "ref": "dogsheep", "title": "Dogsheep", "content": "Dogsheep  is a collection of tools for personal analytics using SQLite and Datasette. The project provides tools like  github-to-sqlite  and  twitter-to-sqlite  that can import data from different sources in order to create a personal data warehouse.  Personal Data Warehouses: Reclaiming Your Data  is a talk that explains Dogsheep and demonstrates it in action.", "breadcrumbs": "[\"The Datasette Ecosystem\"]", "references": "[{\"href\": \"https://dogsheep.github.io/\", \"label\": \"Dogsheep\"}, {\"href\": \"https://datasette.io/tools/github-to-sqlite\", \"label\": \"github-to-sqlite\"}, {\"href\": \"https://datasette.io/tools/twitter-to-sqlite\", \"label\": \"twitter-to-sqlite\"}, {\"href\": \"https://simonwillison.net/2020/Nov/14/personal-data-warehouses/\", \"label\": \"Personal Data Warehouses: Reclaiming Your Data\"}]"}
{"id": "changelog:v0-29-medium-changes", "page": "changelog", "ref": "v0-29-medium-changes", "title": "Easier custom templates for table rows", "content": "If you want to customize the display of individual table rows, you can do so using a  _table.html  template include that looks something like this: \n                 {% for row in display_rows %}\n    <div>\n        <h2>{{ row[\"title\"] }}</h2>\n        <p>{{ row[\"description\"] }}<lp>\n        <p>Category: {{ row.display(\"category_id\") }}</p>\n    </div>\n{% endfor %} \n                 This is a  backwards incompatible change . If you previously had a custom template called  _rows_and_columns.html  you need to rename it to  _table.html . \n                 See  Custom templates  for full details.", "breadcrumbs": "[\"Changelog\", \"0.29 (2019-07-07)\"]", "references": "[]"}
{"id": "contributing:contributing-documentation", "page": "contributing", "ref": "contributing-documentation", "title": "Editing and building the documentation", "content": "Datasette's documentation lives in the  docs/  directory and is deployed automatically using  Read The Docs . \n             The documentation is written using reStructuredText. You may find this article on  The subset of reStructuredText worth committing to memory  useful. \n             You can build it locally by installing  sphinx  and  sphinx_rtd_theme  in your Datasette development environment and then running  make html  directly in the  docs/  directory: \n             # You may first need to activate your virtual environment:\nsource venv/bin/activate\n\n# Install the dependencies needed to build the docs\npip install -e .[docs]\n\n# Now build the docs\ncd docs/\nmake html \n             This will create the HTML version of the documentation in  docs/_build/html . You can open it in your browser like so: \n             open _build/html/index.html \n             Any time you make changes to a  .rst  file you can re-run  make html  to update the built documents, then refresh them in your browser. \n             For added productivity, you can use use  sphinx-autobuild  to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor. \n             sphinx-autobuild  will have been installed when you ran  pip install -e .[docs] . In your  docs/  directory you can start the server by running the following: \n             make livehtml \n             Now browse to  http://localhost:8000/  to view the documentation. Any edits you make should be instantly reflected in your browser.", "breadcrumbs": "[\"Contributing\"]", "references": "[{\"href\": \"https://readthedocs.org/\", \"label\": \"Read The Docs\"}, {\"href\": \"https://simonwillison.net/2018/Aug/25/restructuredtext/\", \"label\": \"The subset of reStructuredText worth committing to memory\"}, {\"href\": \"https://pypi.org/project/sphinx-autobuild/\", \"label\": \"sphinx-autobuild\"}]"}
{"id": "full_text_search:full-text-search-enabling", "page": "full_text_search", "ref": "full-text-search-enabling", "title": "Enabling full-text search for a SQLite table", "content": "Datasette takes advantage of the  external content  mechanism in SQLite, which allows a full-text search virtual table to be associated with the contents of another SQLite table. \n             To set up full-text search for a table, you need to do two things: \n             \n                 \n                     Create a new FTS virtual table associated with your table \n                 \n                 \n                     Populate that FTS table with the data that you would like to be able to run searches against", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://www.sqlite.org/fts3.html#_external_content_fts4_tables_\", \"label\": \"external content\"}]"}
{"id": "json_api:expand-foreign-keys", "page": "json_api", "ref": "expand-foreign-keys", "title": "Expanding foreign key references", "content": "Datasette can detect foreign key relationships and resolve those references into\n                labels. The HTML interface does this by default for every detected foreign key\n                column - you can turn that off using  ?_labels=off . \n             You can request foreign keys be expanded in JSON using the  _labels=on  or\n                 _label=COLUMN  special query string parameters. Here's what an expanded row\n                looks like: \n             [\n    {\n        \"rowid\": 1,\n        \"TreeID\": 141565,\n        \"qLegalStatus\": {\n            \"value\": 1,\n            \"label\": \"Permitted Site\"\n        },\n        \"qSpecies\": {\n            \"value\": 1,\n            \"label\": \"Myoporum laetum :: Myoporum\"\n        },\n        \"qAddress\": \"501X Baker St\",\n        \"SiteOrder\": 1\n    }\n] \n             The column in the foreign key table that is used for the label can be specified\n                in  metadata.json  - see  Specifying the label column for a table .", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "full_text_search:full-text-search-fts-versions", "page": "full_text_search", "ref": "full-text-search-fts-versions", "title": "FTS versions", "content": "There are three different versions of the SQLite FTS module: FTS3, FTS4 and FTS5. You can tell which versions are supported by your instance of Datasette by checking the  /-/versions  page. \n             FTS5 is the most advanced module but may not be available in the SQLite version that is bundled with your Python installation. Most importantly, FTS5 is the only version that has the ability to order by search relevance without needing extra code. \n             If you can't be sure that FTS5 will be available, you should use FTS4.", "breadcrumbs": "[\"Full-text search\"]", "references": "[]"}
{"id": "facets:id2", "page": "facets", "ref": "id2", "title": "Facet by JSON array", "content": "If your SQLite installation provides the  json1  extension (you can check using  /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns. \n             This is useful for modelling things like tags without needing to break them out into a new table. \n             Example here:  latest.datasette.io/fixtures/facetable?_facet_array=tags", "breadcrumbs": "[\"Facets\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/facetable?_facet_array=tags\", \"label\": \"latest.datasette.io/fixtures/facetable?_facet_array=tags\"}]"}
{"id": "changelog:facet-by-date", "page": "changelog", "ref": "facet-by-date", "title": "Facet by date", "content": "If a column contains datetime values, Datasette can now facet that column by date. ( #481 )", "breadcrumbs": "[\"Changelog\", \"0.29 (2019-07-07)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/481\", \"label\": \"#481\"}]"}
{"id": "facets:id3", "page": "facets", "ref": "id3", "title": "Facet by date", "content": "If Datasette finds any columns that contain dates in the first 100 values, it will offer a faceting interface against the dates of those values.\n                This works especially well against timestamp values such as  2019-03-01 12:44:00 . \n             Example here:  latest.datasette.io/fixtures/facetable?_facet_date=created", "breadcrumbs": "[\"Facets\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/facetable?_facet_date=created\", \"label\": \"latest.datasette.io/fixtures/facetable?_facet_date=created\"}]"}
{"id": "changelog:faceting", "page": "changelog", "ref": "faceting", "title": "Faceting", "content": "The number of unique values in a facet is now always displayed. Previously it was only displayed if the user specified  ?_facet_size=max . ( #1556 ) \n                     \n                     \n                         Facets of type  date  or  array  can now be configured in  metadata.json , see  Facets in metadata.json . Thanks, David Larlet. ( #1552 ) \n                     \n                     \n                         New  ?_nosuggest=1  parameter for table views, which disables facet suggestion. ( #1557 ) \n                     \n                     \n                         Fixed bug where  ?_facet_array=tags&_facet=tags  would only display one of the two selected facets. ( #625 )", "breadcrumbs": "[\"Changelog\", \"0.60 (2022-01-13)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1556\", \"label\": \"#1556\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1552\", \"label\": \"#1552\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1557\", \"label\": \"#1557\"}, {\"href\": \"https://github.com/simonw/datasette/issues/625\", \"label\": \"#625\"}]"}
{"id": "changelog:v0-28-faceting", "page": "changelog", "ref": "v0-28-faceting", "title": "Faceting improvements, and faceting plugins", "content": "Datasette  Facets  provide an intuitive way to quickly summarize and interact with data. Previously the only supported faceting technique was column faceting, but 0.28 introduces two powerful new capabilities: facet-by-JSON-array and the ability to define further facet types using plugins. \n                 Facet by array ( #359 ) is only available if your SQLite installation provides the  json1  extension. Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns - useful for modelling things like tags without needing to break them out into a new table. See  Facet by JSON array  for more. \n                 The new  register_facet_classes()  plugin hook ( #445 ) can be used to register additional custom facet classes. Each facet class should provide two methods:  suggest()  which suggests facet selections that might be appropriate for a provided SQL query, and  facet_results()  which executes a facet operation and returns results. Datasette's own faceting implementations have been refactored to use the same API as these plugins.", "breadcrumbs": "[\"Changelog\", \"0.28 (2019-05-19)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/359\", \"label\": \"#359\"}, {\"href\": \"https://github.com/simonw/datasette/pull/445\", \"label\": \"#445\"}]"}
{"id": "facets:id1", "page": "facets", "ref": "id1", "title": "Facets", "content": "Datasette facets can be used to add a faceted browse interface to any database table.\n            With facets, tables are displayed along with a summary showing the most common values in specified columns.\n            These values can be selected to further filter the table. \n         Here's  an example : \n         \n         Facets can be specified in two ways: using query string parameters, or in  metadata.json  configuration for the table.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://congress-legislators.datasettes.com/legislators/legislator_terms?_facet=type&_facet=party&_facet=state&_facet_size=10\", \"label\": \"an example\"}]"}
{"id": "facets:facets-metadata", "page": "facets", "ref": "facets-metadata", "title": "Facets in metadata.json", "content": "You can turn facets on by default for specific tables by adding them to a  \"facets\"  key in a Datasette  Metadata  file. \n             Here's an example that turns on faceting by default for the  qLegalStatus  column in the  Street_Tree_List  table in the  sf-trees  database: \n             {\n  \"databases\": {\n    \"sf-trees\": {\n      \"tables\": {\n        \"Street_Tree_List\": {\n          \"facets\": [\"qLegalStatus\"]\n        }\n      }\n    }\n  }\n} \n             Facets defined in this way will always be shown in the interface and returned in the API, regardless of the  _facet  arguments passed to the view. \n             You can specify  array  or  date  facets in metadata using JSON objects with a single key of  array  or  date  and a value specifying the column, like this: \n             {\n  \"facets\": [\n    {\"array\": \"tags\"},\n    {\"date\": \"created\"}\n  ]\n} \n             You can change the default facet size (the number of results shown for each facet) for a table using  facet_size : \n             {\n  \"databases\": {\n    \"sf-trees\": {\n      \"tables\": {\n        \"Street_Tree_List\": {\n          \"facets\": [\"qLegalStatus\"],\n          \"facet_size\": 10\n        }\n      }\n    }\n  }\n}", "breadcrumbs": "[\"Facets\"]", "references": "[]"}
{"id": "facets:facets-in-query-strings", "page": "facets", "ref": "facets-in-query-strings", "title": "Facets in query strings", "content": "To turn on faceting for specific columns on a Datasette table view, add one or more  _facet=COLUMN  parameters to the URL.\n                For example, if you want to turn on facets for the  city_id  and  state  columns, construct a URL that looks like this: \n             /dbname/tablename?_facet=state&_facet=city_id \n             This works for both the HTML interface and the  .json  view.\n                When enabled, facets will cause a  facet_results  block to be added to the JSON output, looking something like this: \n             {\n  \"state\": {\n    \"name\": \"state\",\n    \"results\": [\n      {\n        \"value\": \"CA\",\n        \"label\": \"CA\",\n        \"count\": 10,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=CA\",\n        \"selected\": false\n      },\n      {\n        \"value\": \"MI\",\n        \"label\": \"MI\",\n        \"count\": 4,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=MI\",\n        \"selected\": false\n      },\n      {\n        \"value\": \"MC\",\n        \"label\": \"MC\",\n        \"count\": 1,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&state=MC\",\n        \"selected\": false\n      }\n    ],\n    \"truncated\": false\n  }\n  \"city_id\": {\n    \"name\": \"city_id\",\n    \"results\": [\n      {\n        \"value\": 1,\n        \"label\": \"San Francisco\",\n        \"count\": 6,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=1\",\n        \"selected\": false\n      },\n      {\n        \"value\": 2,\n        \"label\": \"Los Angeles\",\n        \"count\": 4,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=2\",\n        \"selected\": false\n      },\n      {\n        \"value\": 3,\n        \"label\": \"Detroit\",\n        \"count\": 4,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=3\",\n        \"selected\": false\n      },\n      {\n        \"value\": 4,\n        \"label\": \"Memnonia\",\n        \"count\": 1,\n        \"toggle_url\": \"http://...?_facet=city_id&_facet=state&city_id=4\",\n        \"selected\": false\n      }\n    ],\n    \"truncated\": false\n  }\n} \n             If Datasette detects that a column is a foreign key, the  \"label\"  property will be automatically derived from the detected label column on the referenced table. \n             The default number of facet results returned is 30, controlled by the  default_facet_size  setting.\n                You can increase this on an individual page by adding  ?_facet_size=100  to the query string, up to a maximum of  max_returned_rows  (which defaults to 1000).", "breadcrumbs": "[\"Facets\"]", "references": "[]"}
{"id": "changelog:features", "page": "changelog", "ref": "features", "title": "Features", "content": "Now tested against Python 3.11. Docker containers used by  datasette publish  and  datasette package  both now use that version of Python. ( #1853 ) \n                     \n                     \n                         --load-extension  option now supports entrypoints. Thanks, Alex Garcia. ( #1789 ) \n                     \n                     \n                         Facet size can now be set per-table with the new  facet_size  table metadata option. ( #1804 ) \n                     \n                     \n                         The  truncate_cells_html  setting now also affects long URLs in columns. ( #1805 ) \n                     \n                     \n                         The non-JavaScript SQL editor textarea now increases height to fit the SQL query. ( #1786 ) \n                     \n                     \n                         Facets are now displayed with better line-breaks in long values. Thanks, Daniel Rech. ( #1794 ) \n                     \n                     \n                         The  settings.json  file used in  Configuration directory mode  is now validated on startup. ( #1816 ) \n                     \n                     \n                         SQL queries can now include leading SQL comments, using  /* ... */  or  -- ...  syntax. Thanks,  Charles Nepote. ( #1860 ) \n                     \n                     \n                         SQL query is now re-displayed when terminated with a time limit error. ( #1819 ) \n                     \n                     \n                         The  inspect data  mechanism is now used to speed up server startup - thanks, Forest Gregg. ( #1834 ) \n                     \n                     \n                         In  Configuration directory mode  databases with filenames ending in  .sqlite  or  .sqlite3  are now automatically added to the Datasette instance. ( #1646 ) \n                     \n                     \n                         Breadcrumb navigation display now respects the current user's permissions. ( #1831 )", "breadcrumbs": "[\"Changelog\", \"0.63 (2022-10-27)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1853\", \"label\": \"#1853\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1789\", \"label\": \"#1789\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1804\", \"label\": \"#1804\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1805\", \"label\": \"#1805\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1786\", \"label\": \"#1786\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1794\", \"label\": \"#1794\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1816\", \"label\": \"#1816\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1860\", \"label\": \"#1860\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1819\", \"label\": \"#1819\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1834\", \"label\": \"#1834\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1646\", \"label\": \"#1646\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1831\", \"label\": \"#1831\"}]"}
{"id": "changelog:id14", "page": "changelog", "ref": "id14", "title": "Features", "content": "Datasette is now compatible with  Pyodide .  This is the enabling technology behind  Datasette Lite . ( #1733 ) \n                     \n                     \n                         Database file downloads now implement conditional GET using ETags. ( #1739 ) \n                     \n                     \n                         HTML for facet results and suggested results has been extracted out into new templates  _facet_results.html  and  _suggested_facets.html . Thanks, M. Nasimul Haque. ( #1759 ) \n                     \n                     \n                         Datasette now runs some SQL queries in parallel. This has limited impact on performance, see  this research issue  for details. \n                     \n                     \n                         New  --nolock  option for ignoring file locks when opening read-only databases. ( #1744 ) \n                     \n                     \n                         Spaces in the database names in URLs are now encoded as  +  rather than  ~20 . ( #1701 ) \n                     \n                     \n                         <Binary: 2427344 bytes>  is now displayed as  <Binary: 2,427,344 bytes>  and is accompanied by tooltip showing \"2.3MB\". ( #1712 ) \n                     \n                     \n                         The base Docker image used by  datasette publish cloudrun ,  datasette package  and the  official Datasette image  has been upgraded to  3.10.6-slim-bullseye .  ( #1768 ) \n                     \n                     \n                         Canned writable queries against immutable databases now show a warning message. ( #1728 ) \n                     \n                     \n                         datasette publish cloudrun  has a new  --timeout  option which can be used to increase the time limit applied by the Google Cloud build environment. Thanks, Tim Sherratt. ( #1717 ) \n                     \n                     \n                         datasette publish cloudrun  has new  --min-instances  and  --max-instances  options. ( #1779 )", "breadcrumbs": "[\"Changelog\", \"0.62 (2022-08-14)\"]", "references": "[{\"href\": \"https://pyodide.org/\", \"label\": \"Pyodide\"}, {\"href\": \"https://lite.datasette.io/\", \"label\": \"Datasette Lite\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1733\", \"label\": \"#1733\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1739\", \"label\": \"#1739\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1759\", \"label\": \"#1759\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1727\", \"label\": \"this research issue\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1744\", \"label\": \"#1744\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1701\", \"label\": \"#1701\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1712\", \"label\": \"#1712\"}, {\"href\": \"https://hub.docker.com/datasetteproject/datasette\", \"label\": \"official Datasette image\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1768\", \"label\": \"#1768\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1728\", \"label\": \"#1728\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1717\", \"label\": \"#1717\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1779\", \"label\": \"#1779\"}]"}
{"id": "changelog:flash-messages", "page": "changelog", "ref": "flash-messages", "title": "Flash messages", "content": "Writable canned queries needed a mechanism to let the user know that the query has been successfully executed. The new flash messaging system ( #790 ) allows messages to persist in signed cookies which are then displayed to the user on the next page that they visit. Plugins can use this mechanism to display their own messages, see  .add_message(request, message, type=datasette.INFO)  for details. \n                 You can try out the new messages using the  /-/messages  debug tool, for example at  https://latest.datasette.io/-/messages", "breadcrumbs": "[\"Changelog\", \"0.44 (2020-06-11)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/790\", \"label\": \"#790\"}, {\"href\": \"https://latest.datasette.io/-/messages\", \"label\": \"https://latest.datasette.io/-/messages\"}]"}
{"id": "getting_started:getting-started-tutorial", "page": "getting_started", "ref": "getting-started-tutorial", "title": "Follow a tutorial", "content": "Datasette has several  tutorials  to help you get started with the tool. Try one of the following: \n             \n                 \n                     Exploring a database with Datasette  shows how to use the Datasette web interface to explore a new database. \n                 \n                 \n                     Learn SQL with Datasette  introduces SQL, and shows how to use that query language to ask questions of your data. \n                 \n                 \n                     Cleaning data with sqlite-utils and Datasette  guides you through using  sqlite-utils  to turn a CSV file into a database that you can explore using Datasette.", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://datasette.io/tutorials\", \"label\": \"tutorials\"}, {\"href\": \"https://datasette.io/tutorials/explore\", \"label\": \"Exploring a database with Datasette\"}, {\"href\": \"https://datasette.io/tutorials/learn-sql\", \"label\": \"Learn SQL with Datasette\"}, {\"href\": \"https://datasette.io/tutorials/clean-data\", \"label\": \"Cleaning data with sqlite-utils and Datasette\"}, {\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"}
{"id": "changelog:foreign-key-expansions", "page": "changelog", "ref": "foreign-key-expansions", "title": "Foreign key expansions", "content": "When Datasette detects a foreign key reference it attempts to resolve a label\n                    for that reference (automatically or using the  Specifying the label column for a table  metadata\n                    option) so it can display a link to the associated row. \n                 This expansion is now also available for JSON and CSV representations of the\n                    table, using the new  _labels=on  query string option. See\n                     Expanding foreign key references  for more details.", "breadcrumbs": "[\"Changelog\", \"0.23 (2018-06-18)\"]", "references": "[]"}
{"id": "full_text_search:id1", "page": "full_text_search", "ref": "id1", "title": "Full-text search", "content": "SQLite includes  a powerful mechanism for enabling full-text search  against SQLite records. Datasette can detect if a table has had full-text search configured for it in the underlying database and display a search interface for filtering that table. \n         Here's  an example search : \n         \n         Datasette automatically detects which tables have been configured for full-text search.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://www.sqlite.org/fts3.html\", \"label\": \"a powerful mechanism for enabling full-text search\"}, {\"href\": \"https://register-of-members-interests.datasettes.com/regmem/items?_search=hamper&_sort_desc=date\", \"label\": \"an example search\"}]"}
{"id": "contributing:general-guidelines", "page": "contributing", "ref": "general-guidelines", "title": "General guidelines", "content": "main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released. \n                 \n                 \n                     The ideal commit  should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue. \n                 \n                 \n                     New plugin hooks  should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.", "breadcrumbs": "[\"Contributing\"]", "references": "[]"}
{"id": "getting_started:getting-started", "page": "getting_started", "ref": "getting-started", "title": "Getting started", "content": "", "breadcrumbs": "[]", "references": "[]"}
{"id": "performance:http-caching", "page": "performance", "ref": "http-caching", "title": "HTTP caching", "content": "If your database is immutable and guaranteed not to change, you can gain major performance improvements from Datasette by enabling HTTP caching. \n             This can work at two different levels. First, it can tell browsers to cache the results of queries and serve future requests from the browser cache. \n             More significantly, it allows you to run Datasette behind a caching proxy such as  Varnish  or use a cache provided by a hosted service such as  Fastly  or  Cloudflare . This can provide incredible speed-ups since a query only needs to be executed by Datasette the first time it is accessed - all subsequent hits can then be served by the cache. \n             Using a caching proxy in this way could enable a Datasette-backed visualization to serve thousands of hits a second while running Datasette itself on extremely inexpensive hosting. \n             Datasette's integration with HTTP caches can be enabled using a combination of configuration options and query string arguments. \n             The  default_cache_ttl  setting sets the default HTTP cache TTL for all Datasette pages. This is 5 seconds unless you change it - you can set it to 0 if you wish to disable HTTP caching entirely. \n             You can also change the cache timeout on a per-request basis using the  ?_ttl=10  query string parameter. This can be useful when you are working with the Datasette JSON API - you may decide that a specific query can be cached for a longer time, or maybe you need to set  ?_ttl=0  for some requests for example if you are running a SQL  order by random()  query.", "breadcrumbs": "[\"Performance and caching\"]", "references": "[{\"href\": \"https://varnish-cache.org/\", \"label\": \"Varnish\"}, {\"href\": \"https://www.fastly.com/\", \"label\": \"Fastly\"}, {\"href\": \"https://www.cloudflare.com/\", \"label\": \"Cloudflare\"}]"}
{"id": "metadata:metadata-hiding-tables", "page": "metadata", "ref": "metadata-hiding-tables", "title": "Hiding tables", "content": "You can hide tables from the database listing view (in the same way that FTS and\n                SpatiaLite tables are automatically hidden) using  \"hidden\": true : \n             {\n    \"databases\": {\n        \"database1\": {\n            \"tables\": {\n                \"example_table\": {\n                    \"hidden\": true\n                }\n            }\n        }\n    }\n}", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "performance:performance-immutable-mode", "page": "performance", "ref": "performance-immutable-mode", "title": "Immutable mode", "content": "If you can be certain that a SQLite database file will not be changed by another process you can tell Datasette to open that file in  immutable mode . \n             Doing so will disable all locking and change detection, which can result in improved query performance. \n             This also enables further optimizations relating to HTTP caching, described below. \n             To open a file in immutable mode pass it to the datasette command using the  -i  option: \n             datasette -i data.db \n             When you open a file in immutable mode like this Datasette will also calculate and cache the row counts for each table in that database when it first starts up, further improving performance.", "breadcrumbs": "[\"Performance and caching\"]", "references": "[]"}
{"id": "internals:internals-shortcuts", "page": "internals", "ref": "internals-shortcuts", "title": "Import shortcuts", "content": "The following commonly used symbols can be imported directly from the  datasette  module: \n             from datasette import Response\nfrom datasette import Forbidden\nfrom datasette import NotFound\nfrom datasette import hookimpl\nfrom datasette import actor_matches_allow", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "spatialite:importing-geojson-polygons-using-shapely", "page": "spatialite", "ref": "importing-geojson-polygons-using-shapely", "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()", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[{\"href\": \"https://pypi.org/project/Shapely/\", \"label\": \"Shapely\"}, {\"href\": \"https://whosonfirst.org/\", \"label\": \"Who's On First\"}, {\"href\": \"https://spelunker.whosonfirst.org/id/404227475/\", \"label\": \"spelunker.whosonfirst.org/id/404227475\"}, {\"href\": \"https://data.whosonfirst.org/404/227/475/404227475.geojson\", \"label\": \"data.whosonfirst.org/404/227/475/404227475.geojson\"}]"}
{"id": "spatialite:importing-shapefiles-into-spatialite", "page": "spatialite", "ref": "importing-shapefiles-into-spatialite", "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\nSpatiaLite 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;", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[{\"href\": \"https://en.wikipedia.org/wiki/Shapefile\", \"label\": \"shapefile format\"}, {\"href\": \"http://gaia.geosci.unc.edu/rivers/\", \"label\": \"Global River Database\"}, {\"href\": \"https://www.gaia-gis.it/gaia-sins/BLOB-Geometry.html\", \"label\": \"a custom format based on WKB\"}, {\"href\": \"https://geojson.io/\", \"label\": \"geojson.io\"}]"}
{"id": "changelog:improved-support-for-spatialite", "page": "changelog", "ref": "improved-support-for-spatialite", "title": "Improved support for SpatiaLite", "content": "The  SpatiaLite module \n                    for SQLite adds robust geospatial features to the database. \n                 Getting SpatiaLite working can be tricky, especially if you want to use the most\n                    recent alpha version (with support for K-nearest neighbor). \n                 Datasette now includes  extensive documentation on SpatiaLite , and thanks to  Ravi Kotecha  our GitHub\n                    repo includes a  Dockerfile  that can build\n                    the latest SpatiaLite and configure it for use with Datasette. \n                 The  datasette publish  and  datasette package  commands now accept a new\n                     --spatialite  argument which causes them to install and configure SpatiaLite\n                    as part of the container they deploy.", "breadcrumbs": "[\"Changelog\", \"0.23 (2018-06-18)\"]", "references": "[{\"href\": \"https://www.gaia-gis.it/fossil/libspatialite/index\", \"label\": \"SpatiaLite module\"}, {\"href\": \"https://github.com/r4vi\", \"label\": \"Ravi Kotecha\"}, {\"href\": \"https://github.com/simonw/datasette/blob/master/Dockerfile\", \"label\": \"Dockerfile\"}]"}
{"id": "authentication:authentication-ds-actor-expiry", "page": "authentication", "ref": "authentication-ds-actor-expiry", "title": "Including an expiry time", "content": "ds_actor  cookies can optionally include a signed expiry timestamp, after which the cookies will no longer be valid. Authentication plugins may chose to use this mechanism to limit the lifetime of the cookie. For example, if a plugin implements single-sign-on against another source it may decide to set short-lived cookies so that if the user is removed from the SSO system their existing Datasette cookies will stop working shortly afterwards. \n                 To include an expiry, add a  \"e\"  key to the cookie value containing a base62-encoded integer representing the timestamp when the cookie should expire. For example, here's how to set a cookie that expires after 24 hours: \n                 import time\nfrom datasette.utils import baseconv\n\nexpires_at = int(time.time()) + (24 * 60 * 60)\n\nresponse = Response.redirect(\"/\")\nresponse.set_cookie(\n    \"ds_actor\",\n    datasette.sign(\n        {\n            \"a\": {\"id\": \"cleopaws\"},\n            \"e\": baseconv.base62.encode(expires_at),\n        },\n        \"actor\",\n    ),\n) \n                 The resulting cookie will encode data that looks something like this: \n                 {\n    \"a\": {\n        \"id\": \"cleopaws\"\n    },\n    \"e\": \"1jjSji\"\n}", "breadcrumbs": "[\"Authentication and permissions\", \"The ds_actor cookie\"]", "references": "[]"}
{"id": "installation:id1", "page": "installation", "ref": "id1", "title": "Installation", "content": "If you just want to try Datasette out you don't need to install anything: see  Try Datasette without installing anything using Glitch \n         \n         There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. \n         If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to  Setting up a development environment . \n         \n             \n                 \n                     Basic installation \n                     \n                         \n                             Datasette Desktop for Mac \n                         \n                         \n                             Using Homebrew \n                         \n                         \n                             Using pip \n                         \n                     \n                 \n                 \n                     Advanced installation options \n                     \n                         \n                             Using pipx \n                             \n                                 \n                                     Installing plugins using pipx \n                                 \n                                 \n                                     Upgrading packages using pipx \n                                 \n                             \n                         \n                         \n                             Using Docker \n                             \n                                 \n                                     Loading SpatiaLite \n                                 \n                                 \n                                     Installing plugins \n                                 \n                             \n                         \n                     \n                 \n                 \n                     A note about extensions", "breadcrumbs": "[]", "references": "[]"}
{"id": "spatialite:spatialite-installation", "page": "spatialite", "ref": "spatialite-installation", "title": "Installation", "content": "", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[]"}
{"id": "spatialite:installing-spatialite-on-linux", "page": "spatialite", "ref": "installing-spatialite-on-linux", "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.", "breadcrumbs": "[\"SpatiaLite\", \"Installation\"]", "references": "[]"}
{"id": "spatialite:installing-spatialite-on-os-x", "page": "spatialite", "ref": "installing-spatialite-on-os-x", "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", "breadcrumbs": "[\"SpatiaLite\", \"Installation\"]", "references": "[{\"href\": \"https://brew.sh/\", \"label\": \"Homebrew\"}]"}
{"id": "installation:installing-plugins", "page": "installation", "ref": "installing-plugins", "title": "Installing plugins", "content": "If you want to install plugins into your local Datasette Docker image you can do\n                        so using the following recipe. This will install the plugins and then save a\n                        brand new local image called  datasette-with-plugins : \n                     docker run datasetteproject/datasette \\\n    pip install datasette-vega\n\ndocker commit $(docker ps -lq) datasette-with-plugins \n                     You can now run the new custom image like so: \n                     docker run -p 8001:8001 -v `pwd`:/mnt \\\n    datasette-with-plugins \\\n    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \n                     You can confirm that the plugins are installed by visiting\n                         http://127.0.0.1:8001/-/plugins \n                     Some plugins such as  datasette-ripgrep  may need additional system packages. You can install these by running  apt-get install  inside the container: \n                     docker run datasette-057a0 bash -c '\n    apt-get update &&\n    apt-get install ripgrep &&\n    pip install datasette-ripgrep'\n\ndocker commit $(docker ps -lq) datasette-with-ripgrep", "breadcrumbs": "[\"Installation\", \"Advanced installation options\", \"Using Docker\"]", "references": "[{\"href\": \"http://127.0.0.1:8001/-/plugins\", \"label\": \"http://127.0.0.1:8001/-/plugins\"}, {\"href\": \"https://datasette.io/plugins/datasette-ripgrep\", \"label\": \"datasette-ripgrep\"}]"}
{"id": "plugins:plugins-installing", "page": "plugins", "ref": "plugins-installing", "title": "Installing plugins", "content": "If a plugin has been packaged for distribution using setuptools you can use the plugin by installing it alongside Datasette in the same virtual environment or Docker container. \n             You can install plugins using the  datasette install  command: \n             datasette install datasette-vega \n             You can uninstall plugins with  datasette uninstall : \n             datasette uninstall datasette-vega \n             You can upgrade plugins with  datasette install --upgrade  or  datasette install -U : \n             datasette install -U datasette-vega \n             This command can also be used to upgrade Datasette itself to the latest released version: \n             datasette install -U datasette \n             These commands are thin wrappers around  pip install  and  pip uninstall , which ensure they run  pip  in the same virtual environment as Datasette itself.", "breadcrumbs": "[\"Plugins\"]", "references": "[]"}
{"id": "installation:installing-plugins-using-pipx", "page": "installation", "ref": "installing-plugins-using-pipx", "title": "Installing plugins using pipx", "content": "You can install additional datasette plugins with  pipx inject  like so: \n                     $ pipx inject datasette datasette-json-html\ninjected package datasette-json-html into venv datasette\ndone! \u2728 \ud83c\udf1f \u2728\n\n$ datasette plugins\n[\n    {\n        \"name\": \"datasette-json-html\",\n        \"static\": false,\n        \"templates\": false,\n        \"version\": \"0.6\"\n    }\n]", "breadcrumbs": "[\"Installation\", \"Advanced installation options\", \"Using pipx\"]", "references": "[]"}
{"id": "internals:internals", "page": "internals", "ref": "internals", "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.", "breadcrumbs": "[]", "references": "[]"}
{"id": "introspection:id1", "page": "introspection", "ref": "id1", "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.", "breadcrumbs": "[]", "references": "[]"}
{"id": "json_api:id1", "page": "json_api", "ref": "id1", "title": "JSON API", "content": "Datasette provides a JSON API for your SQLite databases. Anything you can do\n            through the Datasette user interface can also be accessed as JSON via the API. \n         To access the API for a page, either click on the  .json  link on that page or\n            edit the URL and add a  .json  extension to it. \n         If you started Datasette with the  --cors  option, each JSON endpoint will be\n            served with the following additional HTTP headers: \n         Access-Control-Allow-Origin: *\nAccess-Control-Allow-Headers: Authorization\nAccess-Control-Expose-Headers: Link \n         This means JavaScript running on any domain will be able to make cross-origin\n            requests to fetch the data. \n         If you start Datasette without the  --cors  option only JavaScript running on\n            the same domain as Datasette will be able to access the API.", "breadcrumbs": "[]", "references": "[]"}
{"id": "sql_queries:canned-queries-json-api", "page": "sql_queries", "ref": "canned-queries-json-api", "title": "JSON API for writable canned queries", "content": "Writable canned queries can also be accessed using a JSON API. You can POST data to them using JSON, and you can request that their response is returned to you as JSON. \n                 To submit JSON to a writable canned query, encode key/value parameters as a JSON document: \n                 POST /mydatabase/add_message\n\n{\"message\": \"Message goes here\"} \n                 You can also continue to submit data using regular form encoding, like so: \n                 POST /mydatabase/add_message\n\nmessage=Message+goes+here \n                 There are three options for specifying that you would like the response to your request to return JSON data, as opposed to an HTTP redirect to another page. \n                 \n                     \n                         Set an  Accept: application/json  header on your request \n                     \n                     \n                         Include  ?_json=1  in the URL that you POST to \n                     \n                     \n                         Include  \"_json\": 1  in your JSON body, or  &_json=1  in your form encoded body \n                     \n                 \n                 The JSON response will look like this: \n                 {\n    \"ok\": true,\n    \"message\": \"Query executed, 1 row affected\",\n    \"redirect\": \"/data/add_name\"\n} \n                 The  \"message\"  and  \"redirect\"  values here will take into account  on_success_message ,  on_success_redirect ,  on_error_message  and  on_error_redirect , if they have been set.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[]"}
{"id": "changelog:javascript-modules", "page": "changelog", "ref": "javascript-modules", "title": "JavaScript modules", "content": "JavaScript modules  were introduced in ECMAScript 2015 and provide native browser support for the  import  and  export  keywords. \n                 To use modules, JavaScript needs to be included in  <script>  tags with a  type=\"module\"  attribute. \n                 Datasette now has the ability to output  <script type=\"module\">  in places where you may wish to take advantage of modules. The  extra_js_urls  option described in  Custom CSS and JavaScript  can now be used with modules, and module support is also available for the  extra_body_script()  plugin hook. ( #1186 ,  #1187 ) \n                 datasette-leaflet-freedraw  is the first example of a Datasette plugin that takes advantage of the new support for JavaScript modules. See  Drawing shapes on a map to query a SpatiaLite database  for more on this plugin.", "breadcrumbs": "[\"Changelog\", \"0.54 (2021-01-25)\"]", "references": "[{\"href\": \"https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules\", \"label\": \"JavaScript modules\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1186\", \"label\": \"#1186\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1187\", \"label\": \"#1187\"}, {\"href\": \"https://datasette.io/plugins/datasette-leaflet-freedraw\", \"label\": \"datasette-leaflet-freedraw\"}, {\"href\": \"https://simonwillison.net/2021/Jan/24/drawing-shapes-spatialite/\", \"label\": \"Drawing shapes on a map to query a SpatiaLite database\"}]"}
{"id": "binary_data:binary-linking", "page": "binary_data", "ref": "binary-linking", "title": "Linking to binary downloads", "content": "The  .blob  output format is used to return binary data. It requires a  _blob_column=  query string argument specifying which BLOB column should be downloaded, for example: \n             https://latest.datasette.io/fixtures/binary_data/1.blob?_blob_column=data \n             This output format can also be used to return binary data from an arbitrary SQL query. Since such queries do not specify an exact row, an additional  ?_blob_hash=  parameter can be used to specify the SHA-256 hash of the value that is being linked to. \n             Consider the query  select data from binary_data  -  demonstrated here . \n             That page links to the binary value downloads. Those links look like this: \n             https://latest.datasette.io/fixtures.blob?sql=select+data+from+binary_data&_blob_column=data&_blob_hash=f3088978da8f9aea479ffc7f631370b968d2e855eeb172bea7f6c7a04262bb6d \n             These  .blob  links are also returned in the  .csv  exports Datasette provides for binary tables and queries, since the CSV format does not have a mechanism for representing binary data.", "breadcrumbs": "[\"Binary data\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/binary_data/1.blob?_blob_column=data\", \"label\": \"https://latest.datasette.io/fixtures/binary_data/1.blob?_blob_column=data\"}, {\"href\": \"https://latest.datasette.io/fixtures?sql=select+data+from+binary_data\", \"label\": \"demonstrated here\"}, {\"href\": \"https://latest.datasette.io/fixtures.blob?sql=select+data+from+binary_data&_blob_column=data&_blob_hash=f3088978da8f9aea479ffc7f631370b968d2e855eeb172bea7f6c7a04262bb6d\", \"label\": \"https://latest.datasette.io/fixtures.blob?sql=select+data+from+binary_data&_blob_column=data&_blob_hash=f3088978da8f9aea479ffc7f631370b968d2e855eeb172bea7f6c7a04262bb6d\"}]"}
{"id": "installation:loading-spatialite", "page": "installation", "ref": "loading-spatialite", "title": "Loading SpatiaLite", "content": "The  datasetteproject/datasette  image includes a recent version of the\n                         SpatiaLite extension  for SQLite. To load and enable that\n                        module, use the following command: \n                     docker run -p 8001:8001 -v `pwd`:/mnt \\\n    datasetteproject/datasette \\\n    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \\\n    --load-extension=spatialite \n                     You can confirm that SpatiaLite is successfully loaded by visiting\n                         http://127.0.0.1:8001/-/versions", "breadcrumbs": "[\"Installation\", \"Advanced installation options\", \"Using Docker\"]", "references": "[{\"href\": \"http://127.0.0.1:8001/-/versions\", \"label\": \"http://127.0.0.1:8001/-/versions\"}]"}
{"id": "changelog:log-out", "page": "changelog", "ref": "log-out", "title": "Log out", "content": "The  ds_actor cookie  can be used by plugins (or by Datasette's  --root mechanism ) to authenticate users. The new  /-/logout  page provides a way to clear that cookie. \n                 A \"Log out\" button now shows in the global navigation provided the user is authenticated using the  ds_actor  cookie. ( #840 )", "breadcrumbs": "[\"Changelog\", \"0.45 (2020-07-01)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/840\", \"label\": \"#840\"}]"}
{"id": "sql_queries:canned-queries-magic-parameters", "page": "sql_queries", "ref": "canned-queries-magic-parameters", "title": "Magic parameters", "content": "Named parameters that start with an underscore are special: they can be used to automatically add values created by Datasette that are not contained in the incoming form fields or query string. \n                 These magic parameters are only supported for canned queries: to avoid security issues (such as queries that extract the user's private cookies) they are not available to SQL that is executed by the user as a custom SQL query. \n                 Available magic parameters are: \n                 \n                     \n                         _actor_*  - e.g.  _actor_id ,  _actor_name \n                         \n                             Fields from the currently authenticated  Actors . \n                         \n                     \n                     \n                         _header_*  - e.g.  _header_user_agent \n                         \n                             Header from the incoming HTTP request. The key should be in lower case and with hyphens converted to underscores e.g.  _header_user_agent  or  _header_accept_language . \n                         \n                     \n                     \n                         _cookie_*  - e.g.  _cookie_lang \n                         \n                             The value of the incoming cookie of that name. \n                         \n                     \n                     \n                         _now_epoch \n                         \n                             The number of seconds since the Unix epoch. \n                         \n                     \n                     \n                         _now_date_utc \n                         \n                             The date in UTC, e.g.  2020-06-01 \n                         \n                     \n                     \n                         _now_datetime_utc \n                         \n                             The ISO 8601 datetime in UTC, e.g.  2020-06-24T18:01:07Z \n                         \n                     \n                     \n                         _random_chars_*  - e.g.  _random_chars_128 \n                         \n                             A random string of characters of the specified length. \n                         \n                     \n                 \n                 Here's an example configuration (this time using  metadata.yaml  since that provides better support for multi-line SQL queries) that adds a message from the authenticated user, storing various pieces of additional metadata using magic parameters: \n                 databases:\n  mydatabase:\n    queries:\n      add_message:\n        allow:\n          id: \"*\"\n        sql: |-\n          INSERT INTO messages (\n            user_id, message, datetime\n          ) VALUES (\n            :_actor_id, :message, :_now_datetime_utc\n          )\n        write: true \n                 The form presented at  /mydatabase/add_message  will have just a field for  message  - the other parameters will be populated by the magic parameter mechanism. \n                 Additional custom magic parameters can be added by plugins using the  register_magic_parameters(datasette)  hook.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[]"}
{"id": "changelog:magic-parameters-for-canned-queries", "page": "changelog", "ref": "magic-parameters-for-canned-queries", "title": "Magic parameters for canned queries", "content": "Canned queries now support  Magic parameters , which can be used to insert or select automatically generated values. For example: \n                 insert into logs\n  (user_id, timestamp)\nvalues\n  (:_actor_id, :_now_datetime_utc) \n                 This inserts the currently authenticated actor ID and the current datetime. ( #842 )", "breadcrumbs": "[\"Changelog\", \"0.45 (2020-07-01)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/842\", \"label\": \"#842\"}]"}
{"id": "spatialite:making-use-of-a-spatial-index", "page": "spatialite", "ref": "making-use-of-a-spatial-index", "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.", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[{\"href\": \"https://timezones-api.datasette.io/timezones/idx_timezones_Geometry\", \"label\": \"timezones-api.datasette.io/timezones/idx_timezones_Geometry\"}]"}
{"id": "changelog:v0-28-medium-changes", "page": "changelog", "ref": "v0-28-medium-changes", "title": "Medium changes", "content": "Datasette now conforms to the  Black coding style  ( #449 ) - and has a unit test to enforce this in the future \n                     \n                     \n                         \n                             \n                                 New  Special table arguments : \n                                 \n                                     \n                                         \n                                             ?columnname__in=value1,value2,value3  filter for executing SQL IN queries against a table, see  Table arguments  ( #433 ) \n                                         \n                                         \n                                             ?columnname__date=yyyy-mm-dd  filter which returns rows where the spoecified datetime column falls on the specified date ( 583b22a ) \n                                         \n                                         \n                                             ?tags__arraycontains=tag  filter which acts against a JSON array contained in a column ( 78e45ea ) \n                                         \n                                         \n                                             ?_where=sql-fragment  filter for the table view  ( #429 ) \n                                         \n                                         \n                                             ?_fts_table=mytable  and  ?_fts_pk=mycolumn  query string options can be used to specify which FTS table to use for a search query - see  Configuring full-text search for a table or view  ( #428 ) \n                                         \n                                     \n                                 \n                             \n                         \n                     \n                     \n                         You can now pass the same table filter multiple times - for example,  ?content__not=world&content__not=hello  will return all rows where the content column is neither  hello  or  world  ( #288 ) \n                     \n                     \n                         You can now specify  about  and  about_url  metadata (in addition to  source  and  license ) linking to further information about a project - see  Source, license and about \n                     \n                     \n                         New  ?_trace=1  parameter now adds debug information showing every SQL query that was executed while constructing the page ( #435 ) \n                     \n                     \n                         datasette inspect  now just calculates table counts, and does not introspect other database metadata ( #462 ) \n                     \n                     \n                         Removed  /-/inspect  page entirely - this will be replaced by something similar in the future, see  #465 \n                     \n                     \n                         Datasette can now run against an in-memory SQLite database. You can do this by starting it without passing any files or by using the new  --memory  option to  datasette serve . This can be useful for experimenting with SQLite queries that do not access any data, such as  SELECT 1+1  or  SELECT sqlite_version() .", "breadcrumbs": "[\"Changelog\", \"0.28 (2019-05-19)\"]", "references": "[{\"href\": \"https://github.com/python/black\", \"label\": \"Black coding style\"}, {\"href\": \"https://github.com/simonw/datasette/pull/449\", \"label\": \"#449\"}, {\"href\": \"https://github.com/simonw/datasette/issues/433\", \"label\": \"#433\"}, {\"href\": \"https://github.com/simonw/datasette/commit/583b22aa28e26c318de0189312350ab2688c90b1\", \"label\": \"583b22a\"}, {\"href\": \"https://github.com/simonw/datasette/commit/78e45ead4d771007c57b307edf8fc920101f8733\", \"label\": \"78e45ea\"}, {\"href\": \"https://github.com/simonw/datasette/issues/429\", \"label\": \"#429\"}, {\"href\": \"https://github.com/simonw/datasette/issues/428\", \"label\": \"#428\"}, {\"href\": \"https://github.com/simonw/datasette/issues/288\", \"label\": \"#288\"}, {\"href\": \"https://github.com/simonw/datasette/issues/435\", \"label\": \"#435\"}, {\"href\": \"https://github.com/simonw/datasette/issues/462\", \"label\": \"#462\"}, {\"href\": \"https://github.com/simonw/datasette/issues/465\", \"label\": \"#465\"}]"}
{"id": "metadata:id1", "page": "metadata", "ref": "id1", "title": "Metadata", "content": "Data loves metadata. Any time you run Datasette you can optionally include a\n            JSON file with metadata about your databases and tables. Datasette will then\n            display that information in the web UI. \n         Run Datasette like this: \n         datasette database1.db database2.db --metadata metadata.json \n         Your  metadata.json  file can look something like this: \n         {\n    \"title\": \"Custom title for your index page\",\n    \"description\": \"Some description text can go here\",\n    \"license\": \"ODbL\",\n    \"license_url\": \"https://opendatacommons.org/licenses/odbl/\",\n    \"source\": \"Original Data Source\",\n    \"source_url\": \"http://example.com/\"\n} \n         You can optionally use YAML instead of JSON, see  Using YAML for metadata . \n         The above metadata will be displayed on the index page of your Datasette-powered\n            site. The source and license information will also be included in the footer of\n            every page served by Datasette. \n         Any special HTML characters in  description  will be escaped. If you want to\n            include HTML in your description, you can use a  description_html  property\n            instead.", "breadcrumbs": "[]", "references": "[]"}
{"id": "changelog:miscellaneous", "page": "changelog", "ref": "miscellaneous", "title": "Miscellaneous", "content": "Got JSON data in one of your columns? Use the new  ?_json=COLNAME  argument\n                            to tell Datasette to return that JSON value directly rather than encoding it\n                            as a string. \n                     \n                     \n                         If you just want an array of the first value of each row, use the new\n                             ?_shape=arrayfirst  option -  example .", "breadcrumbs": "[\"Changelog\", \"0.23 (2018-06-18)\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures.json?sql=select+neighborhood+from+facetable+order+by+pk+limit+101&_shape=arrayfirst\", \"label\": \"example\"}]"}
{"id": "changelog:named-in-memory-database-support", "page": "changelog", "ref": "named-in-memory-database-support", "title": "Named in-memory database support", "content": "As part of the work building the  _internal  database, Datasette now supports named in-memory databases that can be shared across multiple connections. This allows plugins to create in-memory databases which will persist data for the lifetime of the Datasette server process. ( #1151 ) \n                 The new  memory_name=  parameter to the  Database class  can be used to create named, shared in-memory databases.", "breadcrumbs": "[\"Changelog\", \"0.54 (2021-01-25)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1151\", \"label\": \"#1151\"}]"}
{"id": "sql_queries:sql-parameters", "page": "sql_queries", "ref": "sql-parameters", "title": "Named parameters", "content": "Datasette has special support for SQLite named parameters. Consider a SQL query like this: \n             select * from Street_Tree_List\nwhere \"PermitNotes\" like :notes\nand \"qSpecies\" = :species \n             If you execute this query using the custom query editor, Datasette will extract the two named parameters and use them to construct form fields for you to provide values. \n             You can also provide values for these fields by constructing a URL: \n             /mydatabase?sql=select...&species=44 \n             SQLite string escaping rules will be applied to values passed using named parameters - they will be wrapped in quotes and their content will be correctly escaped. \n             Values from named parameters are treated as SQLite strings. If you need to perform numeric comparisons on them you should cast them to an integer or float first using  cast(:name as integer)  or  cast(:name as real) , for example: \n             select * from Street_Tree_List\nwhere latitude > cast(:min_latitude as real)\nand latitude < cast(:max_latitude as real) \n             Datasette disallows custom SQL queries containing the string PRAGMA (with a small number  of exceptions ) as SQLite pragma statements can be used to change database settings at runtime. If you need to include the string \"pragma\" in a query you can do so safely using a named parameter.", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/761\", \"label\": \"of exceptions\"}]"}
{"id": "changelog:new-configuration-settings", "page": "changelog", "ref": "new-configuration-settings", "title": "New configuration settings", "content": "Datasette's  Settings  now also supports boolean settings. A number of new\n                    configuration options have been added: \n                 \n                     \n                         num_sql_threads  - the number of threads used to execute SQLite queries. Defaults to 3. \n                     \n                     \n                         allow_facet  - enable or disable custom  Facets  using the  _facet=  parameter. Defaults to on. \n                     \n                     \n                         suggest_facets  - should Datasette suggest facets? Defaults to on. \n                     \n                     \n                         allow_download  - should users be allowed to download the entire SQLite database? Defaults to on. \n                     \n                     \n                         allow_sql  - should users be allowed to execute custom SQL queries? Defaults to on. \n                     \n                     \n                         default_cache_ttl  - Default HTTP caching max-age header in seconds. Defaults to 365 days - caching can be disabled entirely by settings this to 0. \n                     \n                     \n                         cache_size_kb  - Set the amount of memory SQLite uses for its  per-connection cache , in KB. \n                     \n                     \n                         allow_csv_stream  - allow users to stream entire result sets as a single CSV file. Defaults to on. \n                     \n                     \n                         max_csv_mb  - maximum size of a returned CSV file in MB. Defaults to 100MB, set to 0 to disable this limit.", "breadcrumbs": "[\"Changelog\", \"0.23 (2018-06-18)\"]", "references": "[{\"href\": \"https://www.sqlite.org/pragma.html#pragma_cache_size\", \"label\": \"per-connection cache\"}]"}
{"id": "changelog:new-features", "page": "changelog", "ref": "new-features", "title": "New features", "content": "If an error occurs while executing a user-provided SQL query, that query is now re-displayed in an editable form along with the error message. ( #619 ) \n                     \n                     \n                         New  ?_col=  and  ?_nocol=  parameters to show and hide columns in a table, plus an interface for hiding and showing columns in the column cog menu. ( #615 ) \n                     \n                     \n                         A new  ?_facet_size=  parameter for customizing the number of facet results returned on a table or view page. ( #1332 ) \n                     \n                     \n                         ?_facet_size=max  sets that to the maximum, which defaults to 1,000 and is controlled by the the  max_returned_rows  setting. If facet results are truncated the \u2026 at the bottom of the facet list now links to this parameter. ( #1337 ) \n                     \n                     \n                         ?_nofacet=1  option to disable all facet calculations on a page, used as a performance optimization for CSV exports and  ?_shape=array/object . ( #1349 ,  #263 ) \n                     \n                     \n                         ?_nocount=1  option to disable full query result counts. ( #1353 ) \n                     \n                     \n                         ?_trace=1  debugging option is now controlled by the new  trace_debug  setting, which is turned off by default. ( #1359 )", "breadcrumbs": "[\"Changelog\", \"0.57 (2021-06-05)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/619\", \"label\": \"#619\"}, {\"href\": \"https://github.com/simonw/datasette/issues/615\", \"label\": \"#615\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1332\", \"label\": \"#1332\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1337\", \"label\": \"#1337\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1349\", \"label\": \"#1349\"}, {\"href\": \"https://github.com/simonw/datasette/issues/263\", \"label\": \"#263\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1353\", \"label\": \"#1353\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1359\", \"label\": \"#1359\"}]"}
{"id": "changelog:new-plugin-hook-asgi-wrapper", "page": "changelog", "ref": "new-plugin-hook-asgi-wrapper", "title": "New plugin hook: asgi_wrapper", "content": "The  asgi_wrapper(datasette)  plugin hook allows plugins to entirely wrap the Datasette ASGI application in their own ASGI middleware. ( #520 ) \n                 Two new plugins take advantage of this hook: \n                 \n                     \n                         datasette-auth-github  adds a authentication layer: users will have to sign in using their GitHub account before they can view data or interact with Datasette. You can also use it to restrict access to specific GitHub users, or to members of specified GitHub  organizations  or  teams . \n                     \n                     \n                         datasette-cors  allows you to configure  CORS headers  for your Datasette instance. You can use this to enable JavaScript running on a whitelisted set of domains to make  fetch()  calls to the JSON API provided by your Datasette instance.", "breadcrumbs": "[\"Changelog\", \"0.29 (2019-07-07)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/520\", \"label\": \"#520\"}, {\"href\": \"https://github.com/simonw/datasette-auth-github\", \"label\": \"datasette-auth-github\"}, {\"href\": \"https://help.github.com/en/articles/about-organizations\", \"label\": \"organizations\"}, {\"href\": \"https://help.github.com/en/articles/organizing-members-into-teams\", \"label\": \"teams\"}, {\"href\": \"https://github.com/simonw/datasette-cors\", \"label\": \"datasette-cors\"}, {\"href\": \"https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS\", \"label\": \"CORS headers\"}]"}
{"id": "changelog:new-plugin-hook-extra-template-vars", "page": "changelog", "ref": "new-plugin-hook-extra-template-vars", "title": "New plugin hook: extra_template_vars", "content": "The  extra_template_vars(template, database, table, columns, view_name, request, datasette)  plugin hook allows plugins to inject their own additional variables into the Datasette template context. This can be used in conjunction with custom templates to customize the Datasette interface.  datasette-auth-github  uses this hook to add custom HTML to the new top navigation bar (which is designed to be modified by plugins, see  #540 ).", "breadcrumbs": "[\"Changelog\", \"0.29 (2019-07-07)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-auth-github\", \"label\": \"datasette-auth-github\"}, {\"href\": \"https://github.com/simonw/datasette/issues/540\", \"label\": \"#540\"}]"}
{"id": "changelog:new-plugin-hooks", "page": "changelog", "ref": "new-plugin-hooks", "title": "New plugin hooks", "content": "register_magic_parameters(datasette)  can be used to define new types of magic canned query parameters. \n                     \n                     \n                         startup(datasette)  can run custom code when Datasette first starts up.  datasette-init  is a new plugin that uses this hook to create database tables and views on startup if they have not yet been created. ( #834 ) \n                     \n                     \n                         canned_queries(datasette, database, actor)  lets plugins provide additional canned queries beyond those defined in Datasette's metadata. See  datasette-saved-queries  for an example of this hook in action. ( #852 ) \n                     \n                     \n                         forbidden(datasette, request, message)  is a hook for customizing how Datasette responds to 403 forbidden errors. ( #812 )", "breadcrumbs": "[\"Changelog\", \"0.45 (2020-07-01)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-init\", \"label\": \"datasette-init\"}, {\"href\": \"https://github.com/simonw/datasette/issues/834\", \"label\": \"#834\"}, {\"href\": \"https://github.com/simonw/datasette-saved-queries\", \"label\": \"datasette-saved-queries\"}, {\"href\": \"https://github.com/simonw/datasette/issues/852\", \"label\": \"#852\"}, {\"href\": \"https://github.com/simonw/datasette/issues/812\", \"label\": \"#812\"}]"}
{"id": "changelog:new-visual-design", "page": "changelog", "ref": "new-visual-design", "title": "New visual design", "content": "Datasette is no longer white and grey with blue and purple links!  Natalie Downe  has been working on a visual refresh, the first iteration of which is included in this release. ( #1056 )", "breadcrumbs": "[\"Changelog\", \"0.51 (2020-10-31)\"]", "references": "[{\"href\": \"https://twitter.com/natbat\", \"label\": \"Natalie Downe\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1056\", \"label\": \"#1056\"}]"}
{"id": "deploying:nginx-proxy-configuration", "page": "deploying", "ref": "nginx-proxy-configuration", "title": "Nginx proxy configuration", "content": "Here is an example of an  nginx  configuration file that will proxy traffic to Datasette: \n                 daemon off;\n\nevents {\n  worker_connections  1024;\n}\nhttp {\n  server {\n    listen 80;\n    location /my-datasette {\n      proxy_pass http://127.0.0.1:8009/my-datasette;\n      proxy_set_header Host $host;\n    }\n  }\n} \n                 You can also use the  --uds  option to Datasette to listen on a Unix domain socket instead of a port, configuring the nginx upstream proxy like this: \n                 daemon off;\nevents {\n  worker_connections  1024;\n}\nhttp {\n  server {\n    listen 80;\n    location /my-datasette {\n      proxy_pass http://datasette/my-datasette;\n      proxy_set_header Host $host;\n    }\n  }\n  upstream datasette {\n    server unix:/tmp/datasette.sock;\n  }\n} \n                 Then run Datasette with  datasette --uds /tmp/datasette.sock path/to/database.db --setting base_url /my-datasette/ .", "breadcrumbs": "[\"Deploying Datasette\", \"Running Datasette behind a proxy\"]", "references": "[{\"href\": \"https://nginx.org/\", \"label\": \"nginx\"}]"}
{"id": "plugins:one-off-plugins-using-plugins-dir", "page": "plugins", "ref": "one-off-plugins-using-plugins-dir", "title": "One-off plugins using --plugins-dir", "content": "You can also define one-off per-project plugins by saving them as  plugin_name.py  functions in a  plugins/  folder and then passing that folder to  datasette  using the  --plugins-dir  option: \n                 datasette mydb.db --plugins-dir=plugins/", "breadcrumbs": "[\"Plugins\", \"Installing plugins\"]", "references": "[]"}
{"id": "changelog:other-changes", "page": "changelog", "ref": "other-changes", "title": "Other changes", "content": "Datasette can now open multiple database files with the same name, e.g. if you run  datasette path/to/one.db path/to/other/one.db . ( #509 ) \n                     \n                     \n                         datasette publish cloudrun  now sets  force_https_urls  for every deployment, fixing some incorrect  http://  links. ( #1178 ) \n                     \n                     \n                         Fixed a bug in the example nginx configuration in  Running Datasette behind a proxy . ( #1091 ) \n                     \n                     \n                         The  Datasette Ecosystem  documentation page has been reduced in size in favour of the  datasette.io   tools  and  plugins  directories. ( #1182 ) \n                     \n                     \n                         The request object now provides a  request.full_path  property, which returns the path including any query string. ( #1184 ) \n                     \n                     \n                         Better error message for disallowed  PRAGMA  clauses in SQL queries. ( #1185 ) \n                     \n                     \n                         datasette publish heroku  now deploys using  python-3.8.7 . \n                     \n                     \n                         New plugin testing documentation on  Testing outbound HTTP calls with pytest-httpx . ( #1198 ) \n                     \n                     \n                         All  ?_*  query string parameters passed to the table page are now persisted in hidden form fields, so parameters such as  ?_size=10  will be correctly passed to the next page when query filters are changed. ( #1194 ) \n                     \n                     \n                         Fixed a bug loading a database file called  test-database (1).sqlite . ( #1181 )", "breadcrumbs": "[\"Changelog\", \"0.54 (2021-01-25)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/509\", \"label\": \"#509\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1178\", \"label\": \"#1178\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1091\", \"label\": \"#1091\"}, {\"href\": \"https://datasette.io/tools\", \"label\": \"tools\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"plugins\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1182\", \"label\": \"#1182\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1184\", \"label\": \"#1184\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1185\", \"label\": \"#1185\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1198\", \"label\": \"#1198\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1194\", \"label\": \"#1194\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1181\", \"label\": \"#1181\"}]"}
{"id": "changelog:other-small-fixes", "page": "changelog", "ref": "other-small-fixes", "title": "Other small fixes", "content": "Made several performance improvements to the database schema introspection code that runs when Datasette first starts up. ( #1555 ) \n                     \n                     \n                         Label columns detected for foreign keys are now case-insensitive, so  Name  or  TITLE  will be detected in the same way as  name  or  title . ( #1544 ) \n                     \n                     \n                         Upgraded Pluggy dependency to 1.0. ( #1575 ) \n                     \n                     \n                         Now using  Plausible analytics  for the Datasette documentation. \n                     \n                     \n                         explain query plan  is now allowed with varying amounts of whitespace in the query. ( #1588 ) \n                     \n                     \n                         New  CLI reference  page showing the output of  --help  for each of the  datasette  sub-commands. This lead to several small improvements to the help copy. ( #1594 ) \n                     \n                     \n                         Fixed bug where writable canned queries could not be used with custom templates.  ( #1547 ) \n                     \n                     \n                         Improved fix for a bug where columns with a underscore prefix could result in unnecessary hidden form fields. ( #1527 )", "breadcrumbs": "[\"Changelog\", \"0.60 (2022-01-13)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1555\", \"label\": \"#1555\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1544\", \"label\": \"#1544\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1575\", \"label\": \"#1575\"}, {\"href\": \"https://plausible.io/\", \"label\": \"Plausible analytics\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1588\", \"label\": \"#1588\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1594\", \"label\": \"#1594\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1547\", \"label\": \"#1547\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1527\", \"label\": \"#1527\"}]"}
{"id": "writing_plugins:writing-plugins-packaging", "page": "writing_plugins", "ref": "writing-plugins-packaging", "title": "Packaging a plugin", "content": "Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at  https://github.com/simonw/datasette-plugin-demos \n             The example consists of two files: a  setup.py  file that defines the plugin: \n             from setuptools import setup\n\nVERSION = \"0.1\"\n\nsetup(\n    name=\"datasette-plugin-demos\",\n    description=\"Examples of plugins for Datasette\",\n    author=\"Simon Willison\",\n    url=\"https://github.com/simonw/datasette-plugin-demos\",\n    license=\"Apache License, Version 2.0\",\n    version=VERSION,\n    py_modules=[\"datasette_plugin_demos\"],\n    entry_points={\n        \"datasette\": [\n            \"plugin_demos = datasette_plugin_demos\"\n        ]\n    },\n    install_requires=[\"datasette\"],\n) \n             And a Python module file,  datasette_plugin_demos.py , that implements the plugin: \n             from datasette import hookimpl\nimport random\n\n\n@hookimpl\ndef prepare_jinja2_environment(env):\n    env.filters[\"uppercase\"] = lambda u: u.upper()\n\n\n@hookimpl\ndef prepare_connection(conn):\n    conn.create_function(\n        \"random_integer\", 2, random.randint\n    ) \n             Having built a plugin in this way you can turn it into an installable package using the following command: \n             python3 setup.py sdist \n             This will create a  .tar.gz  file in the  dist/  directory. \n             You can then install your new plugin into a Datasette virtual environment or Docker container using  pip : \n             pip install datasette-plugin-demos-0.1.tar.gz \n             To learn how to upload your plugin to  PyPI  for use by other people, read the PyPA guide to  Packaging and distributing projects .", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-plugin-demos\", \"label\": \"https://github.com/simonw/datasette-plugin-demos\"}, {\"href\": \"https://pypi.org/\", \"label\": \"PyPI\"}, {\"href\": \"https://packaging.python.org/tutorials/distributing-packages/\", \"label\": \"Packaging and distributing projects\"}]"}
{"id": "pages:pages", "page": "pages", "ref": "pages", "title": "Pages and API endpoints", "content": "The Datasette web application offers a number of different pages that can be accessed to explore the data in question, each of which is accompanied by an equivalent JSON API.", "breadcrumbs": "[]", "references": "[]"}
{"id": "json_api:json-api-pagination", "page": "json_api", "ref": "json-api-pagination", "title": "Pagination", "content": "The default JSON representation includes a  \"next_url\"  key which can be used to access the next page of results. If that key is null or missing then it means you have reached the final page of results. \n             Other representations include pagination information in the  link  HTTP header. That header will look something like this: \n             link: <https://latest.datasette.io/fixtures/sortable.json?_next=d%2Cv>; rel=\"next\" \n             Here is an example Python function built using  requests  that returns a list of all of the paginated items from one of these API endpoints: \n             def paginate(url):\n    items = []\n    while url:\n        response = requests.get(url)\n        try:\n            url = response.links.get(\"next\").get(\"url\")\n        except AttributeError:\n            url = None\n        items.extend(response.json())\n    return items", "breadcrumbs": "[\"JSON API\"]", "references": "[{\"href\": \"https://requests.readthedocs.io/\", \"label\": \"requests\"}]"}
{"id": "sql_queries:id2", "page": "sql_queries", "ref": "id2", "title": "Pagination", "content": "Datasette's default table pagination is designed to be extremely efficient. SQL OFFSET/LIMIT pagination can have a significant performance penalty once you get into multiple thousands of rows, as each page still requires the database to scan through every preceding row to find the correct offset. \n             When paginating through tables, Datasette instead orders the rows in the table by their primary key and performs a WHERE clause against the last seen primary key for the previous page. For example: \n             select rowid, * from Tree_List where rowid > 200 order by rowid limit 101 \n             This represents page three for this particular table, with a page size of 100. \n             Note that we request 101 items in the limit clause rather than 100. This allows us to detect if we are on the last page of the results: if the query returns less than 101 rows we know we have reached the end of the pagination set. Datasette will only return the first 100 rows - the 101st is used purely to detect if there should be another page. \n             Since the where clause acts against the index on the primary key, the query is extremely fast even for records that are a long way into the overall pagination set.", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[]"}
{"id": "custom_templates:custom-pages-parameters", "page": "custom_templates", "ref": "custom-pages-parameters", "title": "Path parameters for pages", "content": "You can define custom pages that match multiple paths by creating files with  {variable}  definitions in their filenames. \n                 For example, to capture any request to a URL matching  /about/* , you would create a template in the following location: \n                 templates/pages/about/{slug}.html \n                 A hit to  /about/news  would render that template and pass in a variable called  slug  with a value of  \"news\" . \n                 If you use this mechanism don't forget to return a 404 if the referenced content could not be found. You can do this using  {{ raise_404() }}  described below. \n                 Templates defined using custom page routes work particularly well with the  sql()  template function from  datasette-template-sql  or the  graphql()  template function from  datasette-graphql .", "breadcrumbs": "[\"Custom pages and templates\", \"Custom pages\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-template-sql\", \"label\": \"datasette-template-sql\"}, {\"href\": \"https://github.com/simonw/datasette-graphql#the-graphql-template-function\", \"label\": \"datasette-graphql\"}]"}
{"id": "metadata:per-database-and-per-table-metadata", "page": "metadata", "ref": "per-database-and-per-table-metadata", "title": "Per-database and per-table metadata", "content": "Metadata at the top level of the JSON will be shown on the index page and in the\n                footer on every page of the site. The license and source is expected to apply to\n                all of your data. \n             You can also provide metadata at the per-database or per-table level, like this: \n             {\n    \"databases\": {\n        \"database1\": {\n            \"source\": \"Alternative source\",\n            \"source_url\": \"http://example.com/\",\n            \"tables\": {\n                \"example_table\": {\n                    \"description_html\": \"Custom <em>table</em> description\",\n                    \"license\": \"CC BY 3.0 US\",\n                    \"license_url\": \"https://creativecommons.org/licenses/by/3.0/us/\"\n                }\n            }\n        }\n    }\n} \n             Each of the top-level metadata fields can be used at the database and table level.", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "performance:performance", "page": "performance", "ref": "performance", "title": "Performance and caching", "content": "Datasette runs on top of SQLite, and SQLite has excellent performance.  For small databases almost any query should return in just a few milliseconds, and larger databases (100s of MBs or even GBs of data) should perform extremely well provided your queries make sensible use of database indexes. \n         That said, there are a number of tricks you can use to improve Datasette's performance.", "breadcrumbs": "[]", "references": "[]"}
{"id": "authentication:authentication-permissions", "page": "authentication", "ref": "authentication-permissions", "title": "Permissions", "content": "Datasette has an extensive permissions system built-in, which can be further extended and customized by plugins. \n             The key question the permissions system answers is this: \n             \n                 Is this  actor  allowed to perform this  action , optionally against this particular  resource ? \n             \n             Actors  are  described above . \n             An  action  is a string describing the action the actor would like to perform. A full list is  provided below  - examples include  view-table  and  execute-sql . \n             A  resource  is the item the actor wishes to interact with - for example a specific database or table. Some actions, such as  permissions-debug , are not associated with a particular resource. \n             Datasette's built-in view permissions ( view-database ,  view-table  etc) default to  allow  - unless you  configure additional permission rules  unauthenticated users will be allowed to access content. \n             Permissions with potentially harmful effects should default to  deny . Plugin authors should account for this when designing new plugins - for example, the  datasette-upload-csvs  plugin defaults to deny so that installations don't accidentally allow unauthenticated users to create new tables by uploading a CSV file.", "breadcrumbs": "[\"Authentication and permissions\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-upload-csvs\", \"label\": \"datasette-upload-csvs\"}]"}
{"id": "changelog:permissions", "page": "changelog", "ref": "permissions", "title": "Permissions", "content": "Datasette also now has a built-in concept of  Permissions . The permissions system answers the following question: \n                 \n                     Is this  actor  allowed to perform this  action , optionally against this particular  resource ? \n                 \n                 You can use the new  \"allow\"  block syntax in  metadata.json  (or  metadata.yaml ) to set required permissions at the instance, database, table or canned query level. For example, to restrict access to the  fixtures.db  database to the  \"root\"  user: \n                 {\n    \"databases\": {\n        \"fixtures\": {\n            \"allow\": {\n                \"id\" \"root\"\n            }\n        }\n    }\n} \n                 See  Defining permissions with \"allow\" blocks  for more details. \n                 Plugins can implement their own custom permission checks using the new  permission_allowed(datasette, actor, action, resource)  hook. \n                 A new debug page at  /-/permissions  shows recent permission checks, to help administrators and plugin authors understand exactly what checks are being performed. This tool defaults to only being available to the root user, but can be exposed to other users by plugins that respond to the  permissions-debug  permission. ( #788 )", "breadcrumbs": "[\"Changelog\", \"0.44 (2020-06-11)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/788\", \"label\": \"#788\"}]"}
{"id": "getting_started:getting-started-demo", "page": "getting_started", "ref": "getting-started-demo", "title": "Play with a live demo", "content": "The best way to experience Datasette for the first time is with a demo: \n             \n                 \n                     global-power-plants.datasettes.com  provides a searchable database of power plants around the world, using data from the  World Resources Institude  rendered using the  datasette-cluster-map  plugin. \n                 \n                 \n                     fivethirtyeight.datasettes.com  shows Datasette running against over 400 datasets imported from the  FiveThirtyEight GitHub repository .", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://global-power-plants.datasettes.com/global-power-plants/global-power-plants\", \"label\": \"global-power-plants.datasettes.com\"}, {\"href\": \"https://www.wri.org/publication/global-power-plant-database\", \"label\": \"World Resources Institude\"}, {\"href\": \"https://github.com/simonw/datasette-cluster-map\", \"label\": \"datasette-cluster-map\"}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight\", \"label\": \"fivethirtyeight.datasettes.com\"}, {\"href\": \"https://github.com/fivethirtyeight/data\", \"label\": \"FiveThirtyEight GitHub repository\"}]"}
{"id": "plugins:plugins-configuration", "page": "plugins", "ref": "plugins-configuration", "title": "Plugin configuration", "content": "Plugins can have their own configuration, embedded in a  Metadata  file. Configuration options for plugins live within a  \"plugins\"  key in that file, which can be included at the root, database or table level. \n             Here is an example of some plugin configuration for a specific table: \n             {\n    \"databases\": {\n        \"sf-trees\": {\n            \"tables\": {\n                \"Street_Tree_List\": {\n                    \"plugins\": {\n                        \"datasette-cluster-map\": {\n                            \"latitude_column\": \"lat\",\n                            \"longitude_column\": \"lng\"\n                        }\n                    }\n                }\n            }\n        }\n    }\n} \n             This tells the  datasette-cluster-map  column which latitude and longitude columns should be used for a table called  Street_Tree_List  inside a database file called  sf-trees.db .", "breadcrumbs": "[\"Plugins\"]", "references": "[]"}
{"id": "changelog:plugin-hooks", "page": "changelog", "ref": "plugin-hooks", "title": "Plugin hooks", "content": "New plugin hook:  handle_exception() , for custom handling of exceptions caught by Datasette. ( #1770 ) \n                     \n                     \n                         The  render_cell()  plugin hook is now also passed a  row  argument, representing the  sqlite3.Row  object that is being rendered. ( #1300 ) \n                     \n                     \n                         The  configuration directory  is now stored in  datasette.config_dir , making it available to plugins. Thanks, Chris Amico. ( #1766 )", "breadcrumbs": "[\"Changelog\", \"0.62 (2022-08-14)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1770\", \"label\": \"#1770\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1300\", \"label\": \"#1300\"}, {\"href\": \"https://github.com/simonw/datasette/pull/1766\", \"label\": \"#1766\"}]"}
{"id": "plugin_hooks:id1", "page": "plugin_hooks", "ref": "id1", "title": "Plugin hooks", "content": "Datasette  plugins  use  plugin hooks  to customize Datasette's behavior. These hooks are powered by the  pluggy  plugin system. \n         Each plugin can implement one or more hooks using the  @hookimpl  decorator against a function named that matches one of the hooks documented on this page. \n         When you implement a plugin hook you can accept any or all of the parameters that are documented as being passed to that hook. \n         For example, you can implement the  render_cell  plugin hook like this even though the full documented hook signature is  render_cell(row, value, column, table, database, datasette) : \n         @hookimpl\ndef render_cell(value, column):\n    if column == \"stars\":\n        return \"*\" * int(value) \n         \n             List of plugin hooks \n             \n                 \n                     prepare_connection(conn, database, datasette) \n                 \n                 \n                     prepare_jinja2_environment(env, datasette) \n                 \n                 \n                     extra_template_vars(template, database, table, columns, view_name, request, datasette) \n                 \n                 \n                     extra_css_urls(template, database, table, columns, view_name, request, datasette) \n                 \n                 \n                     extra_js_urls(template, database, table, columns, view_name, request, datasette) \n                 \n                 \n                     extra_body_script(template, database, table, columns, view_name, request, datasette) \n                 \n                 \n                     publish_subcommand(publish) \n                 \n                 \n                     render_cell(row, value, column, table, database, datasette) \n                 \n                 \n                     register_output_renderer(datasette) \n                 \n                 \n                     register_routes(datasette) \n                 \n                 \n                     register_commands(cli) \n                 \n                 \n                     register_facet_classes() \n                 \n                 \n                     asgi_wrapper(datasette) \n                 \n                 \n                     startup(datasette) \n                 \n                 \n                     canned_queries(datasette, database, actor) \n                 \n                 \n                     actor_from_request(datasette, request) \n                 \n                 \n                     filters_from_request(request, database, table, datasette) \n                 \n                 \n                     permission_allowed(datasette, actor, action, resource) \n                 \n                 \n                     register_magic_parameters(datasette) \n                 \n                 \n                     forbidden(datasette, request, message) \n                 \n                 \n                     handle_exception(datasette, request, exception) \n                 \n                 \n                     menu_links(datasette, actor, request) \n                 \n                 \n                     table_actions(datasette, actor, database, table, request) \n                 \n                 \n                     database_actions(datasette, actor, database, request) \n                 \n                 \n                     skip_csrf(datasette, scope) \n                 \n                 \n                     get_metadata(datasette, key, database, table)", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pluggy.readthedocs.io/\", \"label\": \"pluggy\"}]"}
{"id": "changelog:plugin-hooks-and-internals", "page": "changelog", "ref": "plugin-hooks-and-internals", "title": "Plugin hooks and internals", "content": "The  prepare_jinja2_environment(env, datasette)  plugin hook now accepts an optional  datasette  argument. Hook implementations can also now return an  async  function which will be awaited automatically. ( #1809 ) \n                     \n                     \n                         Database(is_mutable=)  now defaults to  True . ( #1808 ) \n                     \n                     \n                         The  datasette.check_visibility()  method now accepts an optional  permissions=  list, allowing it to take multiple permissions into account at once when deciding if something should be shown as public or private. This has been used to correctly display padlock icons in more places in the Datasette interface. ( #1829 ) \n                     \n                     \n                         Datasette no longer enforces upper bounds on its dependencies. ( #1800 )", "breadcrumbs": "[\"Changelog\", \"0.63 (2022-10-27)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/1809\", \"label\": \"#1809\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1808\", \"label\": \"#1808\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1829\", \"label\": \"#1829\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1800\", \"label\": \"#1800\"}]"}
{"id": "plugins:id1", "page": "plugins", "ref": "id1", "title": "Plugins", "content": "Datasette's plugin system allows additional features to be implemented as Python\n            code (or front-end JavaScript) which can be wrapped up in a separate Python\n            package. The underlying mechanism uses  pluggy . \n         See the  Datasette plugins directory  for a list of existing plugins, or take a look at the\n             datasette-plugin  topic on GitHub. \n         Things you can do with plugins include: \n         \n             \n                 Add visualizations to Datasette, for example\n                     datasette-cluster-map  and\n                     datasette-vega . \n             \n             \n                 Make new custom SQL functions available for use within Datasette, for example\n                     datasette-haversine  and\n                     datasette-jellyfish . \n             \n             \n                 Define custom output formats with custom extensions, for example  datasette-atom  and\n                     datasette-ics . \n             \n             \n                 Add template functions that can be called within your Jinja custom templates,\n                    for example  datasette-render-markdown . \n             \n             \n                 Customize how database values are rendered in the Datasette interface, for example\n                     datasette-render-binary  and\n                     datasette-pretty-json . \n             \n             \n                 Customize how Datasette's authentication and permissions systems work, for example  datasette-auth-tokens  and\n                     datasette-permissions-sql .", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pluggy.readthedocs.io/\", \"label\": \"pluggy\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"Datasette plugins directory\"}, {\"href\": \"https://github.com/topics/datasette-plugin\", \"label\": \"datasette-plugin\"}, {\"href\": \"https://github.com/simonw/datasette-cluster-map\", \"label\": \"datasette-cluster-map\"}, {\"href\": \"https://github.com/simonw/datasette-vega\", \"label\": \"datasette-vega\"}, {\"href\": \"https://github.com/simonw/datasette-haversine\", \"label\": \"datasette-haversine\"}, {\"href\": \"https://github.com/simonw/datasette-jellyfish\", \"label\": \"datasette-jellyfish\"}, {\"href\": \"https://github.com/simonw/datasette-atom\", \"label\": \"datasette-atom\"}, {\"href\": \"https://github.com/simonw/datasette-ics\", \"label\": \"datasette-ics\"}, {\"href\": \"https://github.com/simonw/datasette-render-markdown#markdown-in-templates\", \"label\": \"datasette-render-markdown\"}, {\"href\": \"https://github.com/simonw/datasette-render-binary\", \"label\": \"datasette-render-binary\"}, {\"href\": \"https://github.com/simonw/datasette-pretty-json\", \"label\": \"datasette-pretty-json\"}, {\"href\": \"https://github.com/simonw/datasette-auth-tokens\", \"label\": \"datasette-auth-tokens\"}, {\"href\": \"https://github.com/simonw/datasette-permissions-sql\", \"label\": \"datasette-permissions-sql\"}]"}
{"id": "changelog:plugins-and-internals", "page": "changelog", "ref": "plugins-and-internals", "title": "Plugins and internals", "content": "New plugin hook:  filters_from_request(request, database, table, datasette) , which runs on the table page and can be used to support new custom query string parameters that modify the SQL query. ( #473 ) \n                     \n                     \n                         Added two additional methods for writing to the database:  await db.execute_write_script(sql, block=True)  and  await db.execute_write_many(sql, params_seq, block=True) . ( #1570 ) \n                     \n                     \n                         The  db.execute_write()  internal method now defaults to blocking until the write operation has completed. Previously it defaulted to queuing the write and then continuing to run code while the write was in the queue. ( #1579 ) \n                     \n                     \n                         Database write connections now execute the  prepare_connection(conn, database, datasette)  plugin hook. ( #1564 ) \n                     \n                     \n                         The  Datasette()  constructor no longer requires the  files=  argument, and is now documented at  Datasette class . ( #1563 ) \n                     \n                     \n                         The tracing feature now traces write queries, not just read queries. ( #1568 ) \n                     \n                     \n                         The query string variables exposed by  request.args  will now include blank strings for arguments such as  foo  in  ?foo=&bar=1  rather than ignoring those parameters entirely. ( #1551 )", "breadcrumbs": "[\"Changelog\", \"0.60 (2022-01-13)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/473\", \"label\": \"#473\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1570\", \"label\": \"#1570\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1579\", \"label\": \"#1579\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1564\", \"label\": \"#1564\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1563\", \"label\": \"#1563\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1568\", \"label\": \"#1568\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1551\", \"label\": \"#1551\"}]"}
{"id": "changelog:plugins-can-now-add-links-within-datasette", "page": "changelog", "ref": "plugins-can-now-add-links-within-datasette", "title": "Plugins can now add links within Datasette", "content": "A number of existing Datasette plugins add new pages to the Datasette interface, providig tools for things like  uploading CSVs ,  editing table schemas  or  configuring full-text search . \n                 Plugins like this can now link to themselves from other parts of Datasette interface. The  menu_links(datasette, actor, request)  hook ( #1064 ) lets plugins add links to Datasette's new top-right application menu, and the  table_actions(datasette, actor, database, table, request)  hook ( #1066 ) adds links to a new \"table actions\" menu on the table page. \n                 The demo at  latest.datasette.io  now includes some example plugins. To see the new table actions menu first  sign into that demo as root  and then visit the  facetable  table to see the new cog icon menu at the top of the page.", "breadcrumbs": "[\"Changelog\", \"0.51 (2020-10-31)\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-upload-csvs\", \"label\": \"uploading CSVs\"}, {\"href\": \"https://github.com/simonw/datasette-edit-schema\", \"label\": \"editing table schemas\"}, {\"href\": \"https://github.com/simonw/datasette-configure-fts\", \"label\": \"configuring full-text search\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1064\", \"label\": \"#1064\"}, {\"href\": \"https://github.com/simonw/datasette/issues/1066\", \"label\": \"#1066\"}, {\"href\": \"https://latest.datasette.io/\", \"label\": \"latest.datasette.io\"}, {\"href\": \"https://latest.datasette.io/login-as-root\", \"label\": \"sign into that demo as root\"}, {\"href\": \"https://latest.datasette.io/fixtures/facetable\", \"label\": \"facetable\"}]"}
{"id": "contributing:contributing-formatting-prettier", "page": "contributing", "ref": "contributing-formatting-prettier", "title": "Prettier", "content": "To install Prettier,  install Node.js  and then run the following in the root of your  datasette  repository checkout: \n                 $ npm install \n                 This will install Prettier in a  node_modules  directory. You can then check that your code matches the coding style like so: \n                 $ npm run prettier -- --check\n> prettier\n> prettier 'datasette/static/*[!.min].js' \"--check\"\n\nChecking formatting...\n[warn] datasette/static/plugins.js\n[warn] Code style issues found in the above file(s). Forgot to run Prettier? \n                 You can fix any problems by running: \n                 $ npm run fix", "breadcrumbs": "[\"Contributing\", \"Code formatting\"]", "references": "[{\"href\": \"https://nodejs.org/en/download/package-manager/\", \"label\": \"install Node.js\"}]"}
{"id": "publish:publishing", "page": "publish", "ref": "publishing", "title": "Publishing data", "content": "Datasette includes tools for publishing and deploying your data to the internet. The  datasette publish  command will deploy a new Datasette instance containing your databases directly to a Heroku or Google Cloud hosting account. You can also use  datasette package  to create a Docker image that bundles your databases together with the datasette application that is used to serve them.", "breadcrumbs": "[]", "references": "[]"}
{"id": "custom_templates:publishing-static-assets", "page": "custom_templates", "ref": "publishing-static-assets", "title": "Publishing static assets", "content": "The  datasette publish  command can be used to publish your static assets,\n                    using the same syntax as above: \n                 $ datasette publish cloudrun mydb.db --static assets:static-files/ \n                 This will upload the contents of the  static-files/  directory as part of the\n                    deployment, and configure Datasette to correctly serve the assets from  /assets/ .", "breadcrumbs": "[\"Custom pages and templates\", \"Custom CSS and JavaScript\"]", "references": "[]"}
{"id": "publish:publish-fly", "page": "publish", "ref": "publish-fly", "title": "Publishing to Fly", "content": "Fly  is a  competitively priced  Docker-compatible hosting platform that supports running applications in globally distributed data centers close to your end users. You can deploy Datasette instances to Fly using the  datasette-publish-fly  plugin. \n                 pip install datasette-publish-fly\ndatasette publish fly mydatabase.db --app=\"my-app\" \n                 Consult the  datasette-publish-fly README  for more details.", "breadcrumbs": "[\"Publishing data\", \"datasette publish\"]", "references": "[{\"href\": \"https://fly.io/\", \"label\": \"Fly\"}, {\"href\": \"https://fly.io/docs/pricing/\", \"label\": \"competitively priced\"}, {\"href\": \"https://github.com/simonw/datasette-publish-fly\", \"label\": \"datasette-publish-fly\"}, {\"href\": \"https://github.com/simonw/datasette-publish-fly/blob/main/README.md\", \"label\": \"datasette-publish-fly README\"}]"}
{"id": "publish:publish-cloud-run", "page": "publish", "ref": "publish-cloud-run", "title": "Publishing to Google Cloud Run", "content": "Google Cloud Run  allows you to publish data in a scale-to-zero environment, so your application will start running when the first request is received and will shut down again when traffic ceases. This means you only pay for time spent serving traffic. \n                 \n                     Cloud Run is a great option for inexpensively hosting small, low traffic projects - but costs can add up for projects that serve a lot of requests. \n                     Be particularly careful if your project has tables with large numbers of rows. Search engine crawlers that index a page for every row could result in a high bill. \n                     The  datasette-block-robots  plugin can be used to request search engine crawlers omit crawling your site, which can help avoid this issue. \n                 \n                 You will first need to install and configure the Google Cloud CLI tools by following  these instructions . \n                 You can then publish one or more SQLite database files to Google Cloud Run using the following command: \n                 datasette publish cloudrun mydatabase.db --service=my-database \n                 A Cloud Run  service  is a single hosted application. The service name you specify will be used as part of the Cloud Run URL. If you deploy to a service name that you have used in the past your new deployment will replace the previous one. \n                 If you omit the  --service  option you will be asked to pick a service name interactively during the deploy. \n                 You may need to interact with prompts from the tool. Many of the prompts ask for values that can be  set as properties for the Google Cloud SDK  if you want to avoid the prompts. \n                 For example, the default region for the deployed instance can be set using the command: \n                 gcloud config set run/region us-central1 \n                 You should replace  us-central1  with your desired  region . Alternately, you can specify the region by setting the  CLOUDSDK_RUN_REGION  environment variable. \n                 Once it has finished it will output a URL like this one: \n                 Service [my-service] revision [my-service-00001] has been deployed\nand is serving traffic at https://my-service-j7hipcg4aq-uc.a.run.app \n                 Cloud Run provides a URL on the  .run.app  domain, but you can also point your own domain or subdomain at your Cloud Run service - see  mapping custom domains  in the Cloud Run documentation for details. \n                 See  datasette publish cloudrun  for the full list of options for this command.", "breadcrumbs": "[\"Publishing data\", \"datasette publish\"]", "references": "[{\"href\": \"https://cloud.google.com/run/\", \"label\": \"Google Cloud Run\"}, {\"href\": \"https://datasette.io/plugins/datasette-block-robots\", \"label\": \"datasette-block-robots\"}, {\"href\": \"https://cloud.google.com/sdk/\", \"label\": \"these instructions\"}, {\"href\": \"https://cloud.google.com/sdk/docs/properties\", \"label\": \"set as properties for the Google Cloud SDK\"}, {\"href\": \"https://cloud.google.com/about/locations\", \"label\": \"region\"}, {\"href\": \"https://cloud.google.com/run/docs/mapping-custom-domains\", \"label\": \"mapping custom domains\"}]"}
{"id": "publish:publish-heroku", "page": "publish", "ref": "publish-heroku", "title": "Publishing to Heroku", "content": "To publish your data using  Heroku , first create an account there and install and configure the  Heroku CLI tool . \n                 You can publish one or more databases to Heroku using the following command: \n                 datasette publish heroku mydatabase.db \n                 This will output some details about the new deployment, including a URL like this one: \n                 https://limitless-reef-88278.herokuapp.com/ deployed to Heroku \n                 You can specify a custom app name by passing  -n my-app-name  to the publish command. This will also allow you to overwrite an existing app. \n                 Rather than deploying directly you can use the  --generate-dir  option to output the files that would be deployed to a directory: \n                 datasette publish heroku mydatabase.db --generate-dir=/tmp/deploy-this-to-heroku \n                 See  datasette publish heroku  for the full list of options for this command.", "breadcrumbs": "[\"Publishing data\", \"datasette publish\"]", "references": "[{\"href\": \"https://www.heroku.com/\", \"label\": \"Heroku\"}, {\"href\": \"https://devcenter.heroku.com/articles/heroku-cli\", \"label\": \"Heroku CLI tool\"}]"}
{"id": "publish:publish-vercel", "page": "publish", "ref": "publish-vercel", "title": "Publishing to Vercel", "content": "Vercel   - previously known as Zeit Now - provides a layer over AWS Lambda to allow for quick, scale-to-zero deployment. You can deploy Datasette instances to Vercel using the  datasette-publish-vercel  plugin. \n                 pip install datasette-publish-vercel\ndatasette publish vercel mydatabase.db --project my-database-project \n                 Not every feature is supported: consult the  datasette-publish-vercel README  for more details.", "breadcrumbs": "[\"Publishing data\", \"datasette publish\"]", "references": "[{\"href\": \"https://vercel.com/\", \"label\": \"Vercel\"}, {\"href\": \"https://github.com/simonw/datasette-publish-vercel\", \"label\": \"datasette-publish-vercel\"}, {\"href\": \"https://github.com/simonw/datasette-publish-vercel/blob/main/README.md\", \"label\": \"datasette-publish-vercel README\"}]"}
{"id": "spatialite:querying-polygons-using-within", "page": "spatialite", "ref": "querying-polygons-using-within", "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    );", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[]"}
{"id": "testing_plugins:testing-plugins-register-in-test", "page": "testing_plugins", "ref": "testing-plugins-register-in-test", "title": "Registering a plugin for the duration of a test", "content": "When writing tests for plugins you may find it useful to register a test plugin just for the duration of a single test. You can do this using  pm.register()  and  pm.unregister()  like this: \n             from datasette import hookimpl\nfrom datasette.app import Datasette\nfrom datasette.plugins import pm\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_using_test_plugin():\n    class TestPlugin:\n        __name__ = \"TestPlugin\"\n\n        # Use hookimpl and method names to register hooks\n        @hookimpl\n        def register_routes(self):\n            return [\n                (r\"^/error$\", lambda: 1 / 0),\n            ]\n\n    pm.register(TestPlugin(), name=\"undo\")\n    try:\n        # The test implementation goes here\n        datasette = Datasette()\n        response = await datasette.client.get(\"/error\")\n        assert response.status_code == 500\n    finally:\n        pm.unregister(name=\"undo\")", "breadcrumbs": "[\"Testing plugins\"]", "references": "[]"}
{"id": "contributing:contributing-release", "page": "contributing", "ref": "contributing-release", "title": "Release process", "content": "Datasette releases are performed using tags. When a new release is published on GitHub, a  GitHub Action workflow  will perform the following: \n             \n                 \n                     Run the unit tests against all supported Python versions. If the tests pass... \n                 \n                 \n                     Build a Docker image of the release and push a tag to  https://hub.docker.com/r/datasetteproject/datasette \n                 \n                 \n                     Re-point the \"latest\" tag on Docker Hub to the new image \n                 \n                 \n                     Build a wheel bundle of the underlying Python source code \n                 \n                 \n                     Push that new wheel up to PyPI:  https://pypi.org/project/datasette/ \n                 \n             \n             To deploy new releases you will need to have push access to the main Datasette GitHub repository. \n             Datasette follows  Semantic Versioning : \n             major.minor.patch \n             We increment  major  for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always  0 . \n             We increment  minor  for new features. \n             We increment  patch  for bugfix releass. \n             Alpha and beta releases  may have an additional  a0  or  b0  prefix - the integer component will be incremented with each subsequent alpha or beta. \n             To release a new version, first create a commit that updates the version number in  datasette/version.py  and the  the changelog  with highlights of the new version. An example  commit can be seen here : \n             # Update changelog\ngit commit -m \" Release 0.51a1\n\nRefs #1056, #1039, #998, #1045, #1033, #1036, #1034, #976, #1057, #1058, #1053, #1064, #1066\" -a\ngit push \n             Referencing the issues that are part of the release in the commit message ensures the name of the release shows up on those issue pages, e.g.  here . \n             You can generate the list of issue references for a specific release by copying and pasting text from the release notes or GitHub changes-since-last-release view into this  Extract issue numbers from pasted text  tool. \n             To create the tag for the release, create  a new release  on GitHub matching the new version number. You can convert the release notes to Markdown by copying and pasting the rendered HTML into this  Paste to Markdown tool . \n             Finally, post a news item about the release on  datasette.io  by editing the  news.yaml  file in that site's repository.", "breadcrumbs": "[\"Contributing\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml\", \"label\": \"GitHub Action workflow\"}, {\"href\": \"https://hub.docker.com/r/datasetteproject/datasette\", \"label\": \"https://hub.docker.com/r/datasetteproject/datasette\"}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": \"https://pypi.org/project/datasette/\"}, {\"href\": \"https://semver.org/\", \"label\": \"Semantic Versioning\"}, {\"href\": \"https://github.com/simonw/datasette/commit/0e1e89c6ba3d0fbdb0823272952cf356f3016def\", \"label\": \"commit can be seen here\"}, {\"href\": \"https://github.com/simonw/datasette/issues/581#ref-commit-d56f402\", \"label\": \"here\"}, {\"href\": \"https://observablehq.com/@simonw/extract-issue-numbers-from-pasted-text\", \"label\": \"Extract issue numbers from pasted text\"}, {\"href\": \"https://github.com/simonw/datasette/releases/new\", \"label\": \"a new release\"}, {\"href\": \"https://euangoddard.github.io/clipboard2markdown/\", \"label\": \"Paste to Markdown tool\"}, {\"href\": \"https://datasette.io/\", \"label\": \"datasette.io\"}, {\"href\": \"https://github.com/simonw/datasette.io/blob/main/news.yaml\", \"label\": \"news.yaml\"}]"}
{"id": "contributing:contributing-bug-fix-branch", "page": "contributing", "ref": "contributing-bug-fix-branch", "title": "Releasing bug fixes from a branch", "content": "If it's necessary to publish a bug fix release without shipping new features that have landed on  main  a release branch can be used. \n             Create it from the relevant last tagged release like so: \n             git branch 0.52.x 0.52.4\ngit checkout 0.52.x \n             Next cherry-pick the commits containing the bug fixes: \n             git cherry-pick COMMIT \n             Write the release notes in the branch, and update the version number in  version.py . Then push the branch: \n             git push -u origin 0.52.x \n             Once the tests have completed, publish the release from that branch target using the GitHub  Draft a new release  form. \n             Finally, cherry-pick the commit with the release notes and version number bump across to  main : \n             git checkout main\ngit cherry-pick COMMIT\ngit push", "breadcrumbs": "[\"Contributing\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/releases/new\", \"label\": \"Draft a new release\"}]"}
{"id": "internals:internals-request", "page": "internals", "ref": "internals-request", "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'}}}", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://asgi.readthedocs.io/en/latest/specs/www.html#connection-scope\", \"label\": \"ASGI HTTP connection scope\"}]"}
{"id": "internals:internals-response", "page": "internals", "ref": "internals-response", "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    \"<xml>This is XML</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.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:database-results", "page": "internals", "ref": "database-results", "title": "Results", "content": "The  db.execute()  method returns a single  Results  object. This can be used to access the rows returned by the query. \n                 Iterating over a  Results  object will yield SQLite  Row objects . Each of these can be treated as a tuple or can be accessed using  row[\"column\"]  syntax: \n                 info = []\nresults = await db.execute(\"select name from sqlite_master\")\nfor row in results:\n    info.append(row[\"name\"]) \n                 The  Results  object also has the following properties and methods: \n                 \n                     \n                         .truncated  - boolean \n                         \n                             Indicates if this query was truncated - if it returned more results than the specified  page_size . If this is true then the results object will only provide access to the first  page_size  rows in the query result. You can disable truncation by passing  truncate=False  to the  db.query()  method. \n                         \n                     \n                     \n                         .columns  - list of strings \n                         \n                             A list of column names returned by the query. \n                         \n                     \n                     \n                         .rows  - list of sqlite3.Row \n                         \n                             This property provides direct access to the list of rows returned by the database. You can access specific rows by index using  results.rows[0] . \n                         \n                     \n                     \n                         .first()  - row or None \n                         \n                             Returns the first row in the results, or  None  if no rows were returned. \n                         \n                     \n                     \n                         .single_value() \n                         \n                             Returns the value of the first column of the first row of results - but only if the query returned a single row with a single column. Raises a  datasette.database.MultipleValues  exception otherwise. \n                         \n                     \n                     \n                         .__len__() \n                         \n                             Calling  len(results)  returns the (truncated) number of returned results.", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[{\"href\": \"https://docs.python.org/3/library/sqlite3.html#row-objects\", \"label\": \"Row objects\"}]"}
{"id": "custom_templates:custom-pages-404", "page": "custom_templates", "ref": "custom-pages-404", "title": "Returning 404s", "content": "To indicate that content could not be found and display the default 404 page you can use the  raise_404(message)  function: \n                 {% if not rows %}\n    {{ raise_404(\"Content not found\") }}\n{% endif %} \n                 If you call  raise_404()  the other content in your template will be ignored.", "breadcrumbs": "[\"Custom pages and templates\", \"Custom pages\"]", "references": "[]"}
{"id": "internals:internals-response-asgi-send", "page": "internals", "ref": "internals-response-asgi-send", "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)", "breadcrumbs": "[\"Internals for plugins\", \"Response class\"]", "references": "[]"}