{"id": "deploying:apache-proxy-configuration", "page": "deploying", "ref": "apache-proxy-configuration", "title": "Apache proxy configuration", "content": "For  Apache , you can use the  ProxyPass  directive. First make sure the following lines are uncommented: \n                 LoadModule proxy_module lib/httpd/modules/mod_proxy.so\nLoadModule proxy_http_module lib/httpd/modules/mod_proxy_http.so \n                 Then add these directives to proxy traffic: \n                 ProxyPass /my-datasette/ http://127.0.0.1:8009/my-datasette/\nProxyPreserveHost On \n                 A live demo of Datasette running behind Apache using this proxy setup can be seen at  datasette-apache-proxy-demo.datasette.io/prefix/ . The code for that demo can be found in the  demos/apache-proxy  directory. \n                 Using  --uds  you can use Unix domain sockets similar to the nginx example: \n                 ProxyPass /my-datasette/ unix:/tmp/datasette.sock|http://localhost/my-datasette/ \n                 The  ProxyPreserveHost On  directive ensures that the original  Host:  header from the incoming request is passed through to Datasette. Datasette needs this to correctly assemble links to other pages using the  .absolute_url(request, path)  method.", "breadcrumbs": "[\"Deploying Datasette\", \"Running Datasette behind a proxy\"]", "references": "[{\"href\": \"https://httpd.apache.org/\", \"label\": \"Apache\"}, {\"href\": \"https://datasette-apache-proxy-demo.datasette.io/prefix/\", \"label\": \"datasette-apache-proxy-demo.datasette.io/prefix/\"}, {\"href\": \"https://github.com/simonw/datasette/tree/main/demos/apache-proxy\", \"label\": \"demos/apache-proxy\"}, {\"href\": \"https://httpd.apache.org/docs/2.4/mod/mod_proxy.html#proxypreservehost\", \"label\": \"ProxyPreserveHost On\"}]"}
{"id": "deploying:deploying", "page": "deploying", "ref": "deploying", "title": "Deploying Datasette", "content": "The quickest way to deploy a Datasette instance on the internet is to use the  datasette publish  command, described in  Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel. \n         You can deploy Datasette to other hosting providers using the instructions on this page.", "breadcrumbs": "[]", "references": "[]"}
{"id": "deploying:deploying-buildpacks", "page": "deploying", "ref": "deploying-buildpacks", "title": "Deploying using buildpacks", "content": "Some hosting providers such as  Heroku ,  DigitalOcean App Platform  and  Scalingo  support the  Buildpacks standard  for deploying Python web applications. \n             Deploying Datasette on these platforms requires two files:  requirements.txt  and  Procfile . \n             The  requirements.txt  file lets the platform know which Python packages should be installed. It should contain  datasette  at a minimum, but can also list any Datasette plugins you wish to install - for example: \n             datasette\ndatasette-vega \n             The  Procfile  lets the hosting platform know how to run the command that serves web traffic. It should look like this: \n             web: datasette . -h 0.0.0.0 -p $PORT --cors \n             The  $PORT  environment variable is provided by the hosting platform.  --cors  enables CORS requests from JavaScript running on other websites to your domain - omit this if you don't want to allow CORS. You can add additional Datasette  Settings  options here too. \n             These two files should be enough to deploy Datasette on any host that supports buildpacks. Datasette will serve any SQLite files that are included in the root directory of the application. \n             If you want to build SQLite files or download them as part of the deployment process you can do so using a  bin/post_compile  file. For example, the following  bin/post_compile  will download an example database that will then be served by Datasette: \n             wget https://fivethirtyeight.datasettes.com/fivethirtyeight.db \n             simonw/buildpack-datasette-demo  is an example GitHub repository showing a Datasette configuration that can be deployed to a buildpack-supporting host.", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[{\"href\": \"https://www.heroku.com/\", \"label\": \"Heroku\"}, {\"href\": \"https://www.digitalocean.com/docs/app-platform/\", \"label\": \"DigitalOcean App Platform\"}, {\"href\": \"https://scalingo.com/\", \"label\": \"Scalingo\"}, {\"href\": \"https://buildpacks.io/\", \"label\": \"Buildpacks standard\"}, {\"href\": \"https://github.com/simonw/buildpack-datasette-demo\", \"label\": \"simonw/buildpack-datasette-demo\"}]"}
{"id": "deploying:deploying-fundamentals", "page": "deploying", "ref": "deploying-fundamentals", "title": "Deployment fundamentals", "content": "Datasette can be deployed as a single  datasette  process that listens on a port. Datasette is not designed to be run as root, so that process should listen on a higher port such as port 8000. \n             If you want to serve Datasette on port 80 (the HTTP default port) or port 443 (for HTTPS) you should run it behind a proxy server, such as nginx, Apache or HAProxy. The proxy server can listen on port 80/443 and forward traffic on to Datasette.", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[]"}
{"id": "deploying:deploying-openrc", "page": "deploying", "ref": "deploying-openrc", "title": "Running Datasette using OpenRC", "content": "OpenRC is the service manager on non-systemd Linux distributions like  Alpine Linux  and  Gentoo . \n             Create an init script at  /etc/init.d/datasette  with the following contents: \n             #!/sbin/openrc-run\n\nname=\"datasette\"\ncommand=\"datasette\"\ncommand_args=\"serve -h 0.0.0.0 /path/to/db.db\"\ncommand_background=true\npidfile=\"/run/${RC_SVCNAME}.pid\" \n             You then need to configure the service to run at boot and start it: \n             rc-update add datasette\nrc-service datasette start", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[{\"href\": \"https://www.alpinelinux.org/\", \"label\": \"Alpine Linux\"}, {\"href\": \"https://www.gentoo.org/\", \"label\": \"Gentoo\"}]"}
{"id": "deploying:deploying-proxy", "page": "deploying", "ref": "deploying-proxy", "title": "Running Datasette behind a proxy", "content": "You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site. \n             You can use the  base_url  configuration setting to tell Datasette to serve traffic with a specific URL prefix. For example, you could run Datasette like this: \n             datasette my-database.db --setting base_url /my-datasette/ -p 8009 \n             This will run Datasette with the following URLs: \n             \n                 \n                     http://127.0.0.1:8009/my-datasette/  - the Datasette homepage \n                 \n                 \n                     http://127.0.0.1:8009/my-datasette/my-database  - the page for the  my-database.db  database \n                 \n                 \n                     http://127.0.0.1:8009/my-datasette/my-database/some_table  - the page for the  some_table  table \n                 \n             \n             You can now set your nginx or Apache server to proxy the  /my-datasette/  path to this Datasette instance.", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[]"}
{"id": "deploying:deploying-systemd", "page": "deploying", "ref": "deploying-systemd", "title": "Running Datasette using systemd", "content": "You can run Datasette on Ubuntu or Debian systems using  systemd . \n             First, ensure you have Python 3 and  pip  installed. On Ubuntu you can use  sudo apt-get install python3 python3-pip . \n             You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use  sudo pip3 install datasette . \n             Now create a folder for your Datasette databases, for example using  mkdir /home/ubuntu/datasette-root . \n             You can copy a test database into that folder like so: \n             cd /home/ubuntu/datasette-root\ncurl -O https://latest.datasette.io/fixtures.db \n             Create a file at  /etc/systemd/system/datasette.service  with the following contents: \n             [Unit]\nDescription=Datasette\nAfter=network.target\n\n[Service]\nType=simple\nUser=ubuntu\nEnvironment=DATASETTE_SECRET=\nWorkingDirectory=/home/ubuntu/datasette-root\nExecStart=datasette serve . -h 127.0.0.1 -p 8000\nRestart=on-failure\n\n[Install]\nWantedBy=multi-user.target \n             Add a random value for the  DATASETTE_SECRET  - this will be used to sign Datasette cookies such as the CSRF token cookie. You can generate a suitable value like so: \n             $ python3 -c 'import secrets; print(secrets.token_hex(32))' \n             This configuration will run Datasette against all database files contained in the  /home/ubuntu/datasette-root  directory. If that directory contains a  metadata.yml  (or  .json ) file or a  templates/  or  plugins/  sub-directory those will automatically be loaded by Datasette - see  Configuration directory mode  for details. \n             You can start the Datasette process running using the following: \n             sudo systemctl daemon-reload\nsudo systemctl start datasette.service \n             You will need to restart the Datasette service after making changes to its  metadata.json  configuration or adding a new database file to that directory. You can do that using: \n             sudo systemctl restart datasette.service \n             Once the service has started you can confirm that Datasette is running on port 8000 like so: \n             curl 127.0.0.1:8000/-/versions.json\n# Should output JSON showing the installed version \n             Datasette will not be accessible from outside the server because it is listening on  127.0.0.1 . You can expose it by instead listening on  0.0.0.0 , but a better way is to set up a proxy such as  nginx  - see  Running Datasette behind a proxy .", "breadcrumbs": "[\"Deploying Datasette\"]", "references": "[]"}
{"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": "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": "ecosystem:ecosystem", "page": "ecosystem", "ref": "ecosystem", "title": "The Datasette Ecosystem", "content": "Datasette sits at the center of a growing ecosystem of open source tools aimed at making it as easy as possible to gather, analyze and publish interesting data. \n         These tools are divided into two main groups: tools for building SQLite databases (for use with Datasette) and plugins that extend Datasette's functionality. \n         The  Datasette project website  includes a directory of plugins and a directory of tools: \n         \n             \n                 Plugins directory on datasette.io \n             \n             \n                 Tools directory on datasette.io", "breadcrumbs": "[]", "references": "[{\"href\": \"https://datasette.io/\", \"label\": \"Datasette project website\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"Plugins directory on datasette.io\"}, {\"href\": \"https://datasette.io/tools\", \"label\": \"Tools directory on datasette.io\"}]"}
{"id": "ecosystem:sqlite-utils", "page": "ecosystem", "ref": "sqlite-utils", "title": "sqlite-utils", "content": "sqlite-utils  is a key building block for the wider Datasette ecosystem. It provides a collection of utilities for manipulating SQLite databases, both as a Python library and a command-line utility. Features include: \n             \n                 \n                     Insert data into a SQLite database from JSON, CSV or TSV, automatically creating tables with the correct schema or altering existing tables to add missing columns. \n                 \n                 \n                     Configure tables for use with SQLite full-text search, including creating triggers needed to keep the search index up-to-date. \n                 \n                 \n                     Modify tables in ways that are not supported by SQLite's default  ALTER TABLE  syntax - for example changing the types of columns or selecting a new primary key for a table. \n                 \n                 \n                     Adding foreign keys to existing database tables. \n                 \n                 \n                     Extracting columns of data into a separate lookup table.", "breadcrumbs": "[\"The Datasette Ecosystem\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"}
{"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": "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: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: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": "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": "facets:speeding-up-facets-with-indexes", "page": "facets", "ref": "speeding-up-facets-with-indexes", "title": "Speeding up facets with indexes", "content": "The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by.\n                Adding indexes can be performed using the  sqlite3  command-line utility. Here's how to add an index on the  state  column in a table called  Food_Trucks : \n             $ sqlite3 mydatabase.db\nSQLite version 3.19.3 2017-06-27 16:48:08\nEnter \".help\" for usage hints.\nsqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks(\"state\"); \n             Or using the  sqlite-utils  command-line utility: \n             $ sqlite-utils create-index mydatabase.db Food_Trucks state", "breadcrumbs": "[\"Facets\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/en/stable/cli.html#creating-indexes\", \"label\": \"sqlite-utils\"}]"}
{"id": "facets:suggested-facets", "page": "facets", "ref": "suggested-facets", "title": "Suggested facets", "content": "Datasette's table UI will suggest facets for the user to apply, based on the following criteria: \n             For the currently filtered data are there any columns which, if applied as a facet... \n             \n                 \n                     Will return 30 or less unique options \n                 \n                 \n                     Will return more than one unique option \n                 \n                 \n                     Will return less unique options than the total number of filtered rows \n                 \n                 \n                     And the query used to evaluate this criteria can be completed in under 50ms \n                 \n             \n             That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries.\n                This means suggested facets are unlikely to appear for tables with millions of records in them.", "breadcrumbs": "[\"Facets\"]", "references": "[]"}
{"id": "full_text_search:configuring-fts-by-hand", "page": "full_text_search", "ref": "configuring-fts-by-hand", "title": "Configuring FTS by hand", "content": "We recommend using  sqlite-utils , but if you want to hand-roll a SQLite full-text search table you can do so using the following SQL. \n                 To enable full-text search for a table called  items  that works against the  name  and  description  columns, you would run this SQL to create a new  items_fts  FTS virtual table: \n                 CREATE VIRTUAL TABLE \"items_fts\" USING FTS4 (\n    name,\n    description,\n    content=\"items\"\n); \n                 This creates a set of tables to power full-text search against  items . The new  items_fts  table will be detected by Datasette as the  fts_table  for the  items  table. \n                 Creating the table is not enough: you also need to populate it with a copy of the data that you wish to make searchable. You can do that using the following SQL: \n                 INSERT INTO \"items_fts\" (rowid, name, description)\n    SELECT rowid, name, description FROM items; \n                 If your table has columns that are foreign key references to other tables you can include that data in your full-text search index using a join. Imagine the  items  table has a foreign key column called  category_id  which refers to a  categories  table - you could create a full-text search table like this: \n                 CREATE VIRTUAL TABLE \"items_fts\" USING FTS4 (\n    name,\n    description,\n    category_name,\n    content=\"items\"\n); \n                 And then populate it like this: \n                 INSERT INTO \"items_fts\" (rowid, name, description, category_name)\n    SELECT items.rowid,\n    items.name,\n    items.description,\n    categories.name\n    FROM items JOIN categories ON items.category_id=categories.id; \n                 You can use this technique to populate the full-text search index from any combination of tables and joins that makes sense for your project.", "breadcrumbs": "[\"Full-text search\", \"Enabling full-text search for a SQLite table\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"}
{"id": "full_text_search:configuring-fts-using-csvs-to-sqlite", "page": "full_text_search", "ref": "configuring-fts-using-csvs-to-sqlite", "title": "Configuring FTS using csvs-to-sqlite", "content": "If your data starts out in CSV files, you can use Datasette's companion tool  csvs-to-sqlite  to convert that file into a SQLite database and enable full-text search on specific columns. For a file called  items.csv  where you want full-text search to operate against the  name  and  description  columns you would run the following: \n                 $ csvs-to-sqlite items.csv items.db -f name -f description", "breadcrumbs": "[\"Full-text search\", \"Enabling full-text search for a SQLite table\"]", "references": "[{\"href\": \"https://github.com/simonw/csvs-to-sqlite\", \"label\": \"csvs-to-sqlite\"}]"}
{"id": "full_text_search:configuring-fts-using-sqlite-utils", "page": "full_text_search", "ref": "configuring-fts-using-sqlite-utils", "title": "Configuring FTS using sqlite-utils", "content": "sqlite-utils  is a CLI utility and Python library for manipulating SQLite databases. You can use  it from Python code  to configure FTS search, or you can achieve the same goal  using the accompanying command-line tool . \n                 Here's how to use  sqlite-utils  to enable full-text search for an  items  table across the  name  and  description  columns: \n                 $ sqlite-utils enable-fts mydatabase.db items name description", "breadcrumbs": "[\"Full-text search\", \"Enabling full-text search for a SQLite table\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}, {\"href\": \"https://sqlite-utils.datasette.io/en/latest/python-api.html#enabling-full-text-search\", \"label\": \"it from Python code\"}, {\"href\": \"https://sqlite-utils.datasette.io/en/latest/cli.html#configuring-full-text-search\", \"label\": \"using the accompanying command-line tool\"}]"}
{"id": "full_text_search:full-text-search-advanced-queries", "page": "full_text_search", "ref": "full-text-search-advanced-queries", "title": "Advanced SQLite search queries", "content": "SQLite full-text search includes support for  a variety of advanced queries , including  AND ,  OR ,  NOT  and  NEAR . \n             By default Datasette disables these features to ensure they do not cause errors or confusion for users who are not aware of them. You can disable this escaping and use the advanced queries by adding  &_searchmode=raw  to the table page query string. \n             If you want to enable these operators by default for a specific table, you can do so by adding  \"searchmode\": \"raw\"  to the metadata configuration for that table, see  Configuring full-text search for a table or view . \n             If that option has been specified in the table metadata but you want to over-ride it and return to the default behavior you can append  &_searchmode=escaped  to the query string.", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://www.sqlite.org/fts5.html#full_text_query_syntax\", \"label\": \"a variety of advanced queries\"}]"}
{"id": "full_text_search:full-text-search-custom-sql", "page": "full_text_search", "ref": "full-text-search-custom-sql", "title": "Searches using custom SQL", "content": "You can include full-text search results in custom SQL queries. The general pattern with SQLite search is to run the search as a sub-select that returns rowid values, then include those rowids in another part of the query. \n             You can see the syntax for a basic search by running that search on a table page and then clicking \"View and edit SQL\" to see the underlying SQL. For example, consider this search for  manafort is the US FARA database : \n             /fara/FARA_All_ShortForms?_search=manafort \n             If you click  View and edit SQL  you'll see that the underlying SQL looks like this: \n             select\n  rowid,\n  Short_Form_Termination_Date,\n  Short_Form_Date,\n  Short_Form_Last_Name,\n  Short_Form_First_Name,\n  Registration_Number,\n  Registration_Date,\n  Registrant_Name,\n  Address_1,\n  Address_2,\n  City,\n  State,\n  Zip\nfrom\n  FARA_All_ShortForms\nwhere\n  rowid in (\n    select\n      rowid\n    from\n      FARA_All_ShortForms_fts\n    where\n      FARA_All_ShortForms_fts match escape_fts(:search)\n  )\norder by\n  rowid\nlimit\n  101", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort\", \"label\": \"manafort is the US FARA database\"}, {\"href\": \"https://fara.datasettes.com/fara?sql=select%0D%0A++rowid%2C%0D%0A++Short_Form_Termination_Date%2C%0D%0A++Short_Form_Date%2C%0D%0A++Short_Form_Last_Name%2C%0D%0A++Short_Form_First_Name%2C%0D%0A++Registration_Number%2C%0D%0A++Registration_Date%2C%0D%0A++Registrant_Name%2C%0D%0A++Address_1%2C%0D%0A++Address_2%2C%0D%0A++City%2C%0D%0A++State%2C%0D%0A++Zip%0D%0Afrom%0D%0A++FARA_All_ShortForms%0D%0Awhere%0D%0A++rowid+in+%28%0D%0A++++select%0D%0A++++++rowid%0D%0A++++from%0D%0A++++++FARA_All_ShortForms_fts%0D%0A++++where%0D%0A++++++FARA_All_ShortForms_fts+match+escape_fts%28%3Asearch%29%0D%0A++%29%0D%0Aorder+by%0D%0A++rowid%0D%0Alimit%0D%0A++101&search=manafort\", \"label\": \"View and edit SQL\"}]"}
{"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": "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": "full_text_search:full-text-search-table-or-view", "page": "full_text_search", "ref": "full-text-search-table-or-view", "title": "Configuring full-text search for a table or view", "content": "If a table has a corresponding FTS table set up using the  content=  argument to  CREATE VIRTUAL TABLE  shown below, Datasette will detect it automatically and add a search interface to the table page for that table. \n             You can also manually configure which table should be used for full-text search using query string parameters or  Metadata . You can set the associated FTS table for a specific table and you can also set one for a view - if you do that, the page for that SQL view will offer a search option. \n             Use  ?_fts_table=x  to over-ride the FTS table for a specific page. If the primary key was something other than  rowid  you can use  ?_fts_pk=col  to set that as well. This is particularly useful for views, for example: \n             https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk \n             The  fts_table  metadata property can be used to specify an associated FTS table. If the primary key column in your table which was used to populate the FTS table is something other than  rowid , you can specify the column to use with the  fts_pk  property. \n             The  \"searchmode\": \"raw\"  property can be used to default the table to accepting SQLite advanced search operators, as described in  Advanced SQLite search queries . \n             Here is an example which enables full-text search (with SQLite advanced search operators) for a  display_ads  view which is defined against the  ads  table and hence needs to run FTS against the  ads_fts  table, using the  id  as the primary key: \n             {\n    \"databases\": {\n        \"russian-ads\": {\n            \"tables\": {\n                \"display_ads\": {\n                    \"fts_table\": \"ads_fts\",\n                    \"fts_pk\": \"id\",\n                    \"searchmode\": \"raw\"\n                }\n            }\n        }\n    }\n}", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk\", \"label\": \"https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk\"}]"}
{"id": "full_text_search:full-text-search-table-view-api", "page": "full_text_search", "ref": "full-text-search-table-view-api", "title": "The table page and table view API", "content": "Table views that support full-text search can be queried using the  ?_search=TERMS  query string parameter. This will run the search against content from all of the columns that have been included in the index. \n             Try this example:  fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort \n             SQLite full-text search supports wildcards. This means you can easily implement prefix auto-complete by including an asterisk at the end of the search term - for example: \n             /dbname/tablename/?_search=rob* \n             This will return all records containing at least one word that starts with the letters  rob . \n             You can also run searches against just the content of a specific named column by using  _search_COLNAME=TERMS  - for example, this would search for just rows where the  name  column in the FTS index mentions  Sarah : \n             /dbname/tablename/?_search_name=Sarah", "breadcrumbs": "[\"Full-text search\"]", "references": "[{\"href\": \"https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort\", \"label\": \"fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort\"}]"}
{"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": "getting_started:getting-started", "page": "getting_started", "ref": "getting-started", "title": "Getting started", "content": "", "breadcrumbs": "[]", "references": "[]"}
{"id": "getting_started:getting-started-datasette-lite", "page": "getting_started", "ref": "getting-started-datasette-lite", "title": "Datasette in your browser with Datasette Lite", "content": "Datasette Lite  is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. \n             You can pass a URL to a CSV, SQLite or raw SQL file directly to Datasette Lite to explore that data in your browser. \n             This  example link  opens Datasette Lite and loads the SQL Murder Mystery example database from  Northwestern University Knight Lab .", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://lite.datasette.io/\", \"label\": \"Datasette Lite\"}, {\"href\": \"https://lite.datasette.io/?url=https%3A%2F%2Fraw.githubusercontent.com%2FNUKnightLab%2Fsql-mysteries%2Fmaster%2Fsql-murder-mystery.db#/sql-murder-mystery\", \"label\": \"example link\"}, {\"href\": \"https://github.com/NUKnightLab/sql-mysteries\", \"label\": \"Northwestern University Knight Lab\"}]"}
{"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": "getting_started:getting-started-glitch", "page": "getting_started", "ref": "getting-started-glitch", "title": "Try Datasette without installing anything using Glitch", "content": "Glitch  is a free online tool for building web apps directly from your web browser. You can use Glitch to try out Datasette without needing to install any software on your own computer. \n             Here's a demo project on Glitch which you can use as the basis for your own experiments: \n             glitch.com/~datasette-csvs \n             Glitch allows you to \"remix\" any project to create your own copy and start editing it in your browser. You can remix the  datasette-csvs  project by clicking this button: \n             \n             Find a CSV file and drag it onto the Glitch file explorer panel -  datasette-csvs  will automatically convert it to a SQLite database (using  sqlite-utils ) and allow you to start exploring it using Datasette. \n             If your CSV file has a  latitude  and  longitude  column you can visualize it on a map by uncommenting the  datasette-cluster-map  line in the  requirements.txt  file using the Glitch file editor. \n             Need some data? Try this  Public Art Data  for the city of Seattle - hit \"Export\" and select \"CSV\" to download it as a CSV file. \n             For more on how this works, see  Running Datasette on Glitch .", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"https://glitch.com/\", \"label\": \"Glitch\"}, {\"href\": \"https://glitch.com/~datasette-csvs\", \"label\": \"glitch.com/~datasette-csvs\"}, {\"href\": \"https://glitch.com/edit/#!/remix/datasette-csvs\", \"label\": null}, {\"href\": \"https://github.com/simonw/sqlite-utils\", \"label\": \"sqlite-utils\"}, {\"href\": \"https://data.seattle.gov/Community/Public-Art-Data/j7sn-tdzk\", \"label\": \"Public Art Data\"}, {\"href\": \"https://simonwillison.net/2019/Apr/23/datasette-glitch/\", \"label\": \"Running Datasette on Glitch\"}]"}
{"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": "getting_started:getting-started-your-computer", "page": "getting_started", "ref": "getting-started-your-computer", "title": "Using Datasette on your own computer", "content": "First, follow the  Installation  instructions. Now you can run Datasette against a SQLite file on your computer using the following command: \n             datasette path/to/database.db \n             This will start a web server on port 8001 - visit  http://localhost:8001/ \n                to access the web interface. \n             Add  -o  to open your browser automatically once Datasette has started: \n             datasette path/to/database.db -o \n             Use Chrome on OS X? You can run datasette against your browser history\n                like so: \n             datasette ~/Library/Application\\ Support/Google/Chrome/Default/History --nolock \n             The  --nolock  option ignores any file locks. This is safe as Datasette will open the file in read-only mode. \n             Now visiting  http://localhost:8001/History/downloads  will show you a web\n                interface to browse your downloads data: \n             \n                 \n             \n             http://localhost:8001/History/downloads.json  will return that data as\n                JSON: \n             {\n    \"database\": \"History\",\n    \"columns\": [\n        \"id\",\n        \"current_path\",\n        \"target_path\",\n        \"start_time\",\n        \"received_bytes\",\n        \"total_bytes\",\n        ...\n    ],\n    \"rows\": [\n        [\n            1,\n            \"/Users/simonw/Downloads/DropboxInstaller.dmg\",\n            \"/Users/simonw/Downloads/DropboxInstaller.dmg\",\n            13097290269022132,\n            626688,\n            0,\n            ...\n        ]\n    ]\n} \n             http://localhost:8001/History/downloads.json?_shape=objects  will return that data as\n                JSON in a more convenient format: \n             {\n    ...\n    \"rows\": [\n        {\n            \"start_time\": 13097290269022132,\n            \"interrupt_reason\": 0,\n            \"hash\": \"\",\n            \"id\": 1,\n            \"site_url\": \"\",\n            \"referrer\": \"https://www.dropbox.com/downloading?src=index\",\n            ...\n        }\n    ]\n}", "breadcrumbs": "[\"Getting started\"]", "references": "[{\"href\": \"http://localhost:8001/\", \"label\": \"http://localhost:8001/\"}, {\"href\": \"http://localhost:8001/History/downloads\", \"label\": \"http://localhost:8001/History/downloads\"}, {\"href\": \"http://localhost:8001/History/downloads.json\", \"label\": \"http://localhost:8001/History/downloads.json\"}, {\"href\": \"http://localhost:8001/History/downloads.json?_shape=objects\", \"label\": \"http://localhost:8001/History/downloads.json?_shape=objects\"}]"}
{"id": "index:contents", "page": "index", "ref": "contents", "title": "Contents", "content": "Getting started Play with a live demo Follow a tutorial Datasette in your browser with Datasette Lite Try Datasette without installing anything using Glitch Using Datasette on your own computer Installation Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Using Docker A note about extensions The Datasette Ecosystem sqlite-utils Dogsheep CLI reference datasette --help datasette serve datasette --get datasette serve --help-settings datasette plugins datasette install datasette uninstall datasette publish datasette publish cloudrun datasette publish heroku datasette package datasette inspect Pages and API endpoints Top-level index Database Table Row Publishing data datasette publish Publishing to Google Cloud Run Publishing to Heroku Publishing to Vercel Publishing to Fly Custom metadata and plugins datasette package Deploying Datasette Deployment fundamentals Running Datasette using systemd Running Datasette using OpenRC Deploying using buildpacks Running Datasette behind a proxy Nginx proxy configuration Apache proxy configuration JSON API Different shapes Pagination Special JSON arguments Table arguments Column filter arguments Special table arguments Expanding foreign key references Discovering the JSON for a page Running SQL queries Named parameters Views Canned queries Canned query parameters Additional canned query options Writable canned queries Magic parameters JSON API for writable canned queries Pagination Cross-database queries Authentication and permissions Actors Using the \"root\" actor Permissions Defining permissions with \"allow\" blocks The /-/allow-debug tool Configuring permissions in metadata.json Controlling access to an instance Controlling access to specific databases Controlling access to specific tables and views Controlling access to specific canned queries Controlling the ability to execute arbitrary SQL Checking permissions in plugins actor_matches_allow() The permissions debug tool The ds_actor cookie Including an expiry time The /-/logout page Built-in permissions view-instance view-database view-database-download view-table view-query execute-sql permissions-debug debug-menu Performance and caching Immutable mode Using \"datasette inspect\" HTTP caching datasette-hashed-urls CSV export URL parameters Streaming all records Binary data Linking to binary downloads Binary plugins Facets Facets in query strings Facets in metadata.json Suggested facets Speeding up facets with indexes Facet by JSON array Facet by date Full-text search The table page and table view API Advanced SQLite search queries Configuring full-text search for a table or view Searches using custom SQL Enabling full-text search for a SQLite table Configuring FTS using sqlite-utils Configuring FTS using csvs-to-sqlite Configuring FTS by hand FTS versions SpatiaLite Warning Installation Installing SpatiaLite on OS X Installing SpatiaLite on Linux Spatial indexing latitude/longitude columns Making use of a spatial index Importing shapefiles into SpatiaLite Importing GeoJSON polygons using Shapely Querying polygons using within() Metadata Per-database and per-table metadata Source, license and about Column descriptions Specifying units for a column Setting a default sort order Setting a custom page size Setting which columns can be used for sorting Specifying the label column for a table Hiding tables Using YAML for metadata Settings Using --setting Configuration directory mode Settings default_allow_sql default_page_size sql_time_limit_ms max_returned_rows num_sql_threads allow_facet default_facet_size facet_time_limit_ms facet_suggest_time_limit_ms suggest_facets allow_download default_cache_ttl cache_size_kb allow_csv_stream max_csv_mb truncate_cells_html force_https_urls template_debug trace_debug base_url Configuring the secret Using secrets with datasette publish Introspection /-/metadata /-/versions /-/plugins /-/settings /-/databases /-/threads /-/actor /-/messages Custom pages and templates Custom CSS and JavaScript CSS classes on the <body> Serving static files Publishing static assets Custom templates Custom pages Path parameters for pages Custom headers and status codes Returning 404s Custom redirects Custom error pages Plugins Installing plugins One-off plugins using --plugins-dir Deploying plugins using datasette publish Seeing what plugins are installed Plugin configuration Secret configuration values Writing plugins Writing one-off plugins Starting an installable plugin using cookiecutter Packaging a plugin Static assets Custom templates Writing plugins that accept configuration Designing URLs for your plugin Building URLs within plugins Plugin hooks prepare_connection(conn, database, datasette) prepare_jinja2_environment(env, datasette) extra_template_vars(template, database, table, columns, view_name, request, datasette) extra_css_urls(template, database, table, columns, view_name, request, datasette) extra_js_urls(template, database, table, columns, view_name, request, datasette) extra_body_script(template, database, table, columns, view_name, request, datasette) publish_subcommand(publish) render_cell(row, value, column, table, database, datasette) register_output_renderer(datasette) register_routes(datasette) register_commands(cli) register_facet_classes() asgi_wrapper(datasette) startup(datasette) canned_queries(datasette, database, actor) actor_from_request(datasette, request) filters_from_request(request, database, table, datasette) permission_allowed(datasette, actor, action, resource) register_magic_parameters(datasette) forbidden(datasette, request, message) handle_exception(datasette, request, exception) menu_links(datasette, actor, request) table_actions(datasette, actor, database, table, request) database_actions(datasette, actor, database, request) skip_csrf(datasette, scope) get_metadata(datasette, key, database, table) Testing plugins Setting up a Datasette test instance Using pdb for errors thrown inside Datasette Using pytest fixtures Testing outbound HTTP calls with pytest-httpx Registering a plugin for the duration of a test Internals for plugins Request object The MultiParams class Response class Returning a response with .asgi_send(send) Setting cookies with response.set_cookie() Datasette class .databases .plugin_config(plugin_name, database=None, table=None) await .render_template(template, context=None, request=None) await .permission_allowed(actor, action, resource=None, default=False) await .ensure_permissions(actor, permissions) await .check_visibility(actor, action=None, resource=None, permissions=None) .get_database(name) .add_database(db, name=None, route=None) .add_memory_database(name) .remove_database(name) .sign(value, namespace=\"default\") .unsign(value, namespace=\"default\") .add_message(request, message, type=datasette.INFO) .absolute_url(request, path) .setting(key) datasette.client datasette.urls Database class Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None) db.hash await db.execute(sql, ...) Results await db.execute_fn(fn) await db.execute_write(sql, params=None, block=True) await db.execute_write_script(sql, block=True) await db.execute_write_many(sql, params_seq, block=True) await db.execute_write_fn(fn, block=True) db.close() Database introspection CSRF protection The _internal database The datasette.utils module parse_metadata(content) await_me_maybe(value) Tilde encoding datasette.tracer Tracing child tasks Import shortcuts Contributing General guidelines Setting up a development environment Running the tests Using fixtures Debugging Code formatting Running Black blacken-docs Prettier Editing and building the documentation Running Cog Continuously deployed demo instances Release process Alpha and beta releases Releasing bug fixes from a branch Upgrading CodeMirror Changelog 0.64.6 (2023-12-22) 0.64.5 (2023-10-08) 0.64.4 (2023-09-21) 0.64.3 (2023-04-27) 0.64.2 (2023-03-08) 0.64.1 (2023-01-11) 0.64 (2023-01-09) 0.63.3 (2022-12-17) 0.63.2 (2022-11-18) 0.63.1 (2022-11-10) 0.63 (2022-10-27) Features Plugin hooks and internals Documentation 0.62 (2022-08-14) Features Plugin hooks Bug fixes Documentation 0.61.1 (2022-03-23) 0.61 (2022-03-23) 0.60.2 (2022-02-07) 0.60.1 (2022-01-20) 0.60 (2022-01-13) Plugins and internals Faceting Other small fixes 0.59.4 (2021-11-29) 0.59.3 (2021-11-20) 0.59.2 (2021-11-13) 0.59.1 (2021-10-24) 0.59 (2021-10-14) 0.58.1 (2021-07-16) 0.58 (2021-07-14) 0.57.1 (2021-06-08) 0.57 (2021-06-05) New features Bug fixes and other improvements 0.56.1 (2021-06-05) 0.56 (2021-03-28) 0.55 (2021-02-18) 0.54.1 (2021-02-02) 0.54 (2021-01-25) The _internal database Named in-memory database support JavaScript modules Code formatting with Black and Prettier Other changes 0.53 (2020-12-10) 0.52.5 (2020-12-09) 0.52.4 (2020-12-05) 0.52.3 (2020-12-03) 0.52.2 (2020-12-02) 0.52.1 (2020-11-29) 0.52 (2020-11-28) 0.51.1 (2020-10-31) 0.51 (2020-10-31) New visual design Plugins can now add links within Datasette Binary data URL building Running Datasette behind a proxy Smaller changes 0.50.2 (2020-10-09) 0.50.1 (2020-10-09) 0.50 (2020-10-09) 0.49.1 (2020-09-15) 0.49 (2020-09-14) 0.48 (2020-08-16) 0.47.3 (2020-08-15) 0.47.2 (2020-08-12) 0.47.1 (2020-08-11) 0.47 (2020-08-11) 0.46 (2020-08-09) 0.45 (2020-07-01) Magic parameters for canned queries Log out Better plugin documentation New plugin hooks Smaller changes 0.44 (2020-06-11) Authentication Permissions Writable canned queries Flash messages Signed values and secrets CSRF protection Cookie methods register_routes() plugin hooks Smaller changes The road to Datasette 1.0 0.43 (2020-05-28) 0.42 (2020-05-08) 0.41 (2020-05-06) 0.40 (2020-04-21) 0.39 (2020-03-24) 0.38 (2020-03-08) 0.37.1 (2020-03-02) 0.37 (2020-02-25) 0.36 (2020-02-21) 0.35 (2020-02-04) 0.34 (2020-01-29) 0.33 (2019-12-22) 0.32 (2019-11-14) 0.31.2 (2019-11-13) 0.31.1 (2019-11-12) 0.31 (2019-11-11) 0.30.2 (2019-11-02) 0.30.1 (2019-10-30) 0.30 (2019-10-18) 0.29.3 (2019-09-02) 0.29.2 (2019-07-13) 0.29.1 (2019-07-11) 0.29 (2019-07-07) ASGI New plugin hook: asgi_wrapper New plugin hook: extra_template_vars Secret plugin configuration options Facet by date Easier custom templates for table rows ?_through= for joins through many-to-many tables Small changes 0.28 (2019-05-19) Supporting databases that change Faceting improvements, and faceting plugins datasette publish cloudrun register_output_renderer plugins Medium changes Small changes 0.27.1 (2019-05-09) 0.27 (2019-01-31) 0.26.1 (2019-01-10) 0.26 (2019-01-02) 0.25.2 (2018-12-16) 0.25.1 (2018-11-04) 0.25 (2018-09-19) 0.24 (2018-07-23) 0.23.2 (2018-07-07) 0.23.1 (2018-06-21) 0.23 (2018-06-18) CSV export Foreign key expansions New configuration settings Control HTTP caching with ?_ttl= Improved support for SpatiaLite latest.datasette.io Miscellaneous 0.22.1 (2018-05-23) 0.22 (2018-05-20) 0.21 (2018-05-05) 0.20 (2018-04-20) 0.19 (2018-04-16) 0.18 (2018-04-14) 0.17 (2018-04-13) 0.16 (2018-04-13) 0.15 (2018-04-09) 0.14 (2017-12-09) 0.13 (2017-11-24) 0.12 (2017-11-16) 0.11 (2017-11-14) 0.10 (2017-11-14) 0.9 (2017-11-13) 0.8 (2017-11-13)", "breadcrumbs": "[\"Datasette\"]", "references": "[]"}
{"id": "index:datasette", "page": "index", "ref": "datasette", "title": "Datasette", "content": "An open source multi-tool for exploring and publishing data \n         Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API. \n         Datasette is aimed at data journalists, museum curators, archivists, local governments and anyone else who has data that they wish to share with the world. It is part of a  wider ecosystem of tools and plugins  dedicated to making working with structured data as productive as possible. \n         Explore a demo , watch  a presentation about the project  or  Try Datasette without installing anything using Glitch . \n         Interested in learning Datasette? Start with  the official tutorials . \n         Support questions, feedback? Join our  GitHub Discussions forum .", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://docs.datasette.io/en/stable/changelog.html\", \"label\": null}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/actions?query=workflow%3ATest\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/blob/main/LICENSE\", \"label\": null}, {\"href\": \"https://hub.docker.com/r/datasetteproject/datasette\", \"label\": null}, {\"href\": \"https://discord.gg/ktd74dm5mw\", \"label\": null}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://docs.datasette.io/en/stable/changelog.html\", \"label\": null}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/actions?query=workflow%3ATest\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/blob/main/LICENSE\", \"label\": null}, {\"href\": \"https://hub.docker.com/r/datasetteproject/datasette\", \"label\": null}, {\"href\": \"https://discord.gg/ktd74dm5mw\", \"label\": null}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight\", \"label\": \"Explore a demo\"}, {\"href\": \"https://static.simonwillison.net/static/2018/pybay-datasette/\", \"label\": \"a presentation about the project\"}, {\"href\": \"https://datasette.io/tutorials\", \"label\": \"the official tutorials\"}, {\"href\": \"https://github.com/simonw/datasette/discussions\", \"label\": \"GitHub Discussions forum\"}]"}
{"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": "installation:installation-advanced", "page": "installation", "ref": "installation-advanced", "title": "Advanced installation options", "content": "", "breadcrumbs": "[\"Installation\"]", "references": "[]"}
{"id": "installation:installation-basic", "page": "installation", "ref": "installation-basic", "title": "Basic installation", "content": "", "breadcrumbs": "[\"Installation\"]", "references": "[]"}
{"id": "installation:installation-datasette-desktop", "page": "installation", "ref": "installation-datasette-desktop", "title": "Datasette Desktop for Mac", "content": "Datasette Desktop  is a packaged Mac application which bundles Datasette together with Python and allows you to install and run Datasette directly on your laptop. This is the best option for local installation if you are not comfortable using the command line.", "breadcrumbs": "[\"Installation\", \"Basic installation\"]", "references": "[{\"href\": \"https://datasette.io/desktop\", \"label\": \"Datasette Desktop\"}]"}
{"id": "installation:installation-docker", "page": "installation", "ref": "installation-docker", "title": "Using Docker", "content": "A Docker image containing the latest release of Datasette is published to Docker\n                    Hub here:  https://hub.docker.com/r/datasetteproject/datasette/ \n                 If you have Docker installed (for example with  Docker for Mac  on OS X) you can download and run this\n                    image like so: \n                 docker run -p 8001:8001 -v `pwd`:/mnt \\\n    datasetteproject/datasette \\\n    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \n                 This will start an instance of Datasette running on your machine's port 8001,\n                    serving the  fixtures.db  file in your current directory. \n                 Now visit  http://127.0.0.1:8001/  to access Datasette. \n                 (You can download a copy of  fixtures.db  from\n                     https://latest.datasette.io/fixtures.db  ) \n                 To upgrade to the most recent release of Datasette, run the following: \n                 docker pull datasetteproject/datasette", "breadcrumbs": "[\"Installation\", \"Advanced installation options\"]", "references": "[{\"href\": \"https://hub.docker.com/r/datasetteproject/datasette/\", \"label\": \"https://hub.docker.com/r/datasetteproject/datasette/\"}, {\"href\": \"https://www.docker.com/docker-mac\", \"label\": \"Docker for Mac\"}, {\"href\": \"http://127.0.0.1:8001/\", \"label\": \"http://127.0.0.1:8001/\"}, {\"href\": \"https://latest.datasette.io/fixtures.db\", \"label\": \"https://latest.datasette.io/fixtures.db\"}]"}
{"id": "installation:installation-extensions", "page": "installation", "ref": "installation-extensions", "title": "A note about extensions", "content": "SQLite supports extensions, such as  SpatiaLite  for geospatial operations. \n             These can be loaded using the  --load-extension  argument, like so: \n             datasette --load-extension=/usr/local/lib/mod_spatialite.dylib \n             Some Python installations do not include support for SQLite extensions. If this is the case you will see the following error when you attempt to load an extension: \n             \n                 Your Python installation does not have the ability to load SQLite extensions. \n             \n             In some cases you may see the following error message instead: \n             AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension' \n             On macOS the easiest fix for this is to install Datasette using Homebrew: \n             brew install datasette \n             Use  which datasette  to confirm that  datasette  will run that version. The output should look something like this: \n             /usr/local/opt/datasette/bin/datasette \n             If you get a different location here such as  /Library/Frameworks/Python.framework/Versions/3.10/bin/datasette  you can run the following command to cause  datasette  to execute the Homebrew version instead: \n             alias datasette=$(echo $(brew --prefix datasette)/bin/datasette) \n             You can undo this operation using: \n             unalias datasette \n             If you need to run SQLite with extension support for other Python code, you can do so by install Python itself using Homebrew: \n             brew install python \n             Then executing Python using: \n             /usr/local/opt/python@3/libexec/bin/python \n             A more convenient way to work with this version of Python may be to use it to create a virtual environment: \n             /usr/local/opt/python@3/libexec/bin/python -m venv datasette-venv \n             Then activate it like this: \n             source datasette-venv/bin/activate \n             Now running  python  and  pip  will work against a version of Python 3 that includes support for SQLite extensions: \n             pip install datasette\nwhich datasette\ndatasette --version", "breadcrumbs": "[\"Installation\"]", "references": "[]"}
{"id": "installation:installation-homebrew", "page": "installation", "ref": "installation-homebrew", "title": "Using Homebrew", "content": "If you have a Mac and use  Homebrew , you can install Datasette by running this command in your terminal: \n                 brew install datasette \n                 This should install the latest version. You can confirm by running: \n                 datasette --version \n                 You can upgrade to the latest Homebrew packaged version using: \n                 brew upgrade datasette \n                 Once you have installed Datasette you can install plugins using the following: \n                 datasette install datasette-vega \n                 If the latest packaged release of Datasette has not yet been made available through Homebrew, you can upgrade your Homebrew installation in-place using: \n                 datasette install -U datasette", "breadcrumbs": "[\"Installation\", \"Basic installation\"]", "references": "[{\"href\": \"https://brew.sh/\", \"label\": \"Homebrew\"}]"}
{"id": "installation:installation-pip", "page": "installation", "ref": "installation-pip", "title": "Using pip", "content": "Datasette requires Python 3.7 or higher. The  Python.org Python For Beginners  page has instructions for getting started. \n                 You can install Datasette and its dependencies using  pip : \n                 pip install datasette \n                 You can now run Datasette like so: \n                 datasette", "breadcrumbs": "[\"Installation\", \"Basic installation\"]", "references": "[{\"href\": \"https://www.python.org/about/gettingstarted/\", \"label\": \"Python.org Python For Beginners\"}]"}
{"id": "installation:installation-pipx", "page": "installation", "ref": "installation-pipx", "title": "Using pipx", "content": "pipx  is a tool for installing Python software with all of its dependencies in an isolated environment, to ensure that they will not conflict with any other installed Python software. \n                 If you use  Homebrew  on macOS you can install pipx like this: \n                 brew install pipx\npipx ensurepath \n                 Without Homebrew you can install it like so: \n                 python3 -m pip install --user pipx\npython3 -m pipx ensurepath \n                 The  pipx ensurepath  command configures your shell to ensure it can find commands that have been installed by pipx - generally by making sure  ~/.local/bin  has been added to your  PATH . \n                 Once pipx is installed you can use it to install Datasette like this: \n                 pipx install datasette \n                 Then run  datasette --version  to confirm that it has been successfully installed.", "breadcrumbs": "[\"Installation\", \"Advanced installation options\"]", "references": "[{\"href\": \"https://pipxproject.github.io/pipx/\", \"label\": \"pipx\"}, {\"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": "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": "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": "installation:upgrading-packages-using-pipx", "page": "installation", "ref": "upgrading-packages-using-pipx", "title": "Upgrading packages using pipx", "content": "You can upgrade your pipx installation to the latest release of Datasette using  pipx upgrade datasette : \n                     $ pipx upgrade datasette\nupgraded package datasette from 0.39 to 0.40 (location: /Users/simon/.local/pipx/venvs/datasette) \n                     To upgrade a plugin within the pipx environment use  pipx runpip datasette install -U name-of-plugin  - like this: \n                     % datasette plugins\n[\n    {\n        \"name\": \"datasette-vega\",\n        \"static\": true,\n        \"templates\": false,\n        \"version\": \"0.6\"\n    }\n]\n\n$ pipx runpip datasette install -U datasette-vega\nCollecting datasette-vega\nDownloading datasette_vega-0.6.2-py3-none-any.whl (1.8 MB)\n    |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.8 MB 2.0 MB/s\n...\nInstalling collected packages: datasette-vega\nAttempting uninstall: datasette-vega\n    Found existing installation: datasette-vega 0.6\n    Uninstalling datasette-vega-0.6:\n    Successfully uninstalled datasette-vega-0.6\nSuccessfully installed datasette-vega-0.6.2\n\n$ datasette plugins\n[\n    {\n        \"name\": \"datasette-vega\",\n        \"static\": true,\n        \"templates\": false,\n        \"version\": \"0.6.2\"\n    }\n]", "breadcrumbs": "[\"Installation\", \"Advanced installation options\", \"Using pipx\"]", "references": "[]"}
{"id": "internals:database-close", "page": "internals", "ref": "database-close", "title": "db.close()", "content": "Closes all of the open connections to file-backed databases. This is mainly intended to be used by large test suites, to avoid hitting limits on the number of open files.", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:database-constructor", "page": "internals", "ref": "database-constructor", "title": "Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None)", "content": "The  Database()  constructor can be used by plugins, in conjunction with  .add_database(db, name=None, route=None) , to create and register new databases. \n                 The arguments are as follows: \n                 \n                     \n                         ds  -  Datasette class  (required) \n                         \n                             The Datasette instance you are attaching this database to. \n                         \n                     \n                     \n                         path  - string \n                         \n                             Path to a SQLite database file on disk. \n                         \n                     \n                     \n                         is_mutable  - boolean \n                         \n                             Set this to  False  to cause Datasette to open the file in immutable mode. \n                         \n                     \n                     \n                         is_memory  - boolean \n                         \n                             Use this to create non-shared memory connections. \n                         \n                     \n                     \n                         memory_name  - string or  None \n                         \n                             Use this to create a named in-memory database. Unlike regular memory databases these can be accessed by multiple threads and will persist an changes made to them for the lifetime of the Datasette server process. \n                         \n                     \n                 \n                 The first argument is the  datasette  instance you are attaching to, the second is a  path= , then  is_mutable  and  is_memory  are both optional arguments.", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:database-execute", "page": "internals", "ref": "database-execute", "title": "await db.execute(sql, ...)", "content": "Executes a SQL query against the database and returns the resulting rows (see  Results ). \n                 \n                     \n                         sql  - string (required) \n                         \n                             The SQL query to execute. This can include  ?  or  :named  parameters. \n                         \n                     \n                     \n                         params  - list or dict \n                         \n                             A list or dictionary of values to use for the parameters. List for  ? , dictionary for  :named . \n                         \n                     \n                     \n                         truncate  - boolean \n                         \n                             Should the rows returned by the query be truncated at the maximum page size? Defaults to  True , set this to  False  to disable truncation. \n                         \n                     \n                     \n                         custom_time_limit  - integer ms \n                         \n                             A custom time limit for this query. This can be set to a lower value than the Datasette configured default. If a query takes longer than this it will be terminated early and raise a  dataette.database.QueryInterrupted  exception. \n                         \n                     \n                     \n                         page_size  - integer \n                         \n                             Set a custom page size for truncation, over-riding the configured Datasette default. \n                         \n                     \n                     \n                         log_sql_errors  - boolean \n                         \n                             Should any SQL errors be logged to the console in addition to being raised as an error? Defaults to  True .", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:database-execute-fn", "page": "internals", "ref": "database-execute-fn", "title": "await db.execute_fn(fn)", "content": "Executes a given callback function against a read-only database connection running in a thread. The function will be passed a SQLite connection, and the return value from the function will be returned by the  await . \n                 Example usage: \n                 def get_version(conn):\n    return conn.execute(\n        \"select sqlite_version()\"\n    ).fetchall()[0][0]\n\n\nversion = await db.execute_fn(get_version)", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:database-execute-write", "page": "internals", "ref": "database-execute-write", "title": "await db.execute_write(sql, params=None, block=True)", "content": "SQLite only allows one database connection to write at a time. Datasette handles this for you by maintaining a queue of writes to be executed against a given database. Plugins can submit write operations to this queue and they will be executed in the order in which they are received. \n                 This method can be used to queue up a non-SELECT SQL query to be executed against a single write connection to the database. \n                 You can pass additional SQL parameters as a tuple or dictionary. \n                 The method will block until the operation is completed, and the return value will be the return from calling  conn.execute(...)  using the underlying  sqlite3  Python library. \n                 If you pass  block=False  this behaviour changes to \"fire and forget\" - queries will be added to the write queue and executed in a separate thread while your code can continue to do other things. The method will return a UUID representing the queued task.", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:database-execute-write-fn", "page": "internals", "ref": "database-execute-write-fn", "title": "await db.execute_write_fn(fn, block=True)", "content": "This method works like  .execute_write() , but instead of a SQL statement you give it a callable Python function. Your function will be queued up and then called when the write connection is available, passing that connection as the argument to the function. \n                 The function can then perform multiple actions, safe in the knowledge that it has exclusive access to the single writable connection for as long as it is executing. \n                 \n                     fn  needs to be a regular function, not an  async def  function. \n                 \n                 For example: \n                 def delete_and_return_count(conn):\n    conn.execute(\"delete from some_table where id > 5\")\n    return conn.execute(\n        \"select count(*) from some_table\"\n    ).fetchone()[0]\n\n\ntry:\n    num_rows_left = await database.execute_write_fn(\n        delete_and_return_count\n    )\nexcept Exception as e:\n    print(\"An error occurred:\", e) \n                 The value returned from  await database.execute_write_fn(...)  will be the return value from your function. \n                 If your function raises an exception that exception will be propagated up to the  await  line. \n                 If you specify  block=False  the method becomes fire-and-forget, queueing your function to be executed and then allowing your code after the call to  .execute_write_fn()  to continue running while the underlying thread waits for an opportunity to run your function. A UUID representing the queued task will be returned. Any exceptions in your code will be silently swallowed.", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:database-execute-write-many", "page": "internals", "ref": "database-execute-write-many", "title": "await db.execute_write_many(sql, params_seq, block=True)", "content": "Like  execute_write()  but uses the  sqlite3   conn.executemany()  method. This will efficiently execute the same SQL statement against each of the parameters in the  params_seq  iterator, for example: \n                 await db.execute_write_many(\n    \"insert into characters (id, name) values (?, ?)\",\n    [(1, \"Melanie\"), (2, \"Selma\"), (2, \"Viktor\")],\n)", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[{\"href\": \"https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executemany\", \"label\": \"conn.executemany()\"}]"}
{"id": "internals:database-execute-write-script", "page": "internals", "ref": "database-execute-write-script", "title": "await db.execute_write_script(sql, block=True)", "content": "Like  execute_write()  but can be used to send multiple SQL statements in a single string separated by semicolons, using the  sqlite3   conn.executescript()  method.", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[{\"href\": \"https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executescript\", \"label\": \"conn.executescript()\"}]"}
{"id": "internals:database-hash", "page": "internals", "ref": "database-hash", "title": "db.hash", "content": "If the database was opened in immutable mode, this property returns the 64 character SHA-256 hash of the database contents as a string. Otherwise it returns  None .", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "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": "internals:datasette-absolute-url", "page": "internals", "ref": "datasette-absolute-url", "title": ".absolute_url(request, path)", "content": "request  - Request \n                         \n                             The current Request object \n                         \n                     \n                     \n                         path  - string \n                         \n                             A path, for example  /dbname/table.json \n                         \n                     \n                 \n                 Returns the absolute URL for the given path, including the protocol and host. For example: \n                 absolute_url = datasette.absolute_url(\n    request, \"/dbname/table.json\"\n)\n# Would return \"http://localhost:8001/dbname/table.json\" \n                 The current request object is used to determine the hostname and protocol that should be used for the returned URL. The  force_https_urls  configuration setting is taken into account.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-add-database", "page": "internals", "ref": "datasette-add-database", "title": ".add_database(db, name=None, route=None)", "content": "db  - datasette.database.Database instance \n                         \n                             The database to be attached. \n                         \n                     \n                     \n                         name  - string, optional \n                         \n                             The name to be used for this database . If not specified Datasette will pick one based on the filename or memory name. \n                         \n                     \n                     \n                         route  - string, optional \n                         \n                             This will be used in the URL path. If not specified, it will default to the same thing as the  name . \n                         \n                     \n                 \n                 The  datasette.add_database(db)  method lets you add a new database to the current Datasette instance. \n                 The  db  parameter should be an instance of the  datasette.database.Database  class. For example: \n                 from datasette.database import Database\n\ndatasette.add_database(\n    Database(\n        datasette,\n        path=\"path/to/my-new-database.db\",\n    )\n) \n                 This will add a mutable database and serve it at  /my-new-database . \n                 Use  is_mutable=False  to add an immutable database. \n                 .add_database()  returns the Database instance, with its name set as the  database.name  attribute. Any time you are working with a newly added database you should use the return value of  .add_database() , for example: \n                 db = datasette.add_database(\n    Database(datasette, memory_name=\"statistics\")\n)\nawait db.execute_write(\n    \"CREATE TABLE foo(id integer primary key)\"\n)", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-add-memory-database", "page": "internals", "ref": "datasette-add-memory-database", "title": ".add_memory_database(name)", "content": "Adds a shared in-memory database with the specified name: \n                 datasette.add_memory_database(\"statistics\") \n                 This is a shortcut for the following: \n                 from datasette.database import Database\n\ndatasette.add_database(\n    Database(datasette, memory_name=\"statistics\")\n) \n                 Using either of these pattern will result in the in-memory database being served at  /statistics .", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-add-message", "page": "internals", "ref": "datasette-add-message", "title": ".add_message(request, message, type=datasette.INFO)", "content": "request  - Request \n                         \n                             The current Request object \n                         \n                     \n                     \n                         message  - string \n                         \n                             The message string \n                         \n                     \n                     \n                         type  - constant, optional \n                         \n                             The message type -  datasette.INFO ,  datasette.WARNING  or  datasette.ERROR \n                         \n                     \n                 \n                 Datasette's flash messaging mechanism allows you to add a message that will be displayed to the user on the next page that they visit. Messages are persisted in a  ds_messages  cookie. This method adds a message to that cookie. \n                 You can try out these messages (including the different visual styling of the three message types) using the  /-/messages  debugging tool.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-check-visibility", "page": "internals", "ref": "datasette-check-visibility", "title": "await .check_visibility(actor, action=None, resource=None, permissions=None)", "content": "actor  - dictionary \n                         \n                             The authenticated actor. This is usually  request.actor . \n                         \n                     \n                     \n                         action  - string, optional \n                         \n                             The name of the action that is being permission checked. \n                         \n                     \n                     \n                         resource  - string or tuple, optional \n                         \n                             The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. \n                         \n                     \n                     \n                         permissions  - list of  action  strings or  (action, resource)  tuples, optional \n                         \n                             Provide this instead of  action  and  resource  to check multiple permissions at once. \n                         \n                     \n                 \n                 This convenience method can be used to answer the question \"should this item be considered private, in that it is visible to me but it is not visible to anonymous users?\" \n                 It returns a tuple of two booleans,  (visible, private) .  visible  indicates if the actor can see this resource.  private  will be  True  if an anonymous user would not be able to view the resource. \n                 This example checks if the user can access a specific table, and sets  private  so that a padlock icon can later be displayed: \n                 visible, private = await self.ds.check_visibility(\n    request.actor,\n    action=\"view-table\",\n    resource=(database, table),\n) \n                 The following example runs three checks in a row, similar to  await .ensure_permissions(actor, permissions) . If any of the checks are denied before one of them is explicitly granted then  visible  will be  False .  private  will be  True  if an anonymous user would not be able to view the resource. \n                 visible, private = await self.ds.check_visibility(\n    request.actor,\n    permissions=[\n        (\"view-table\", (database, table)),\n        (\"view-database\", database),\n        \"view-instance\",\n    ],\n)", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-databases", "page": "internals", "ref": "datasette-databases", "title": ".databases", "content": "Property exposing a  collections.OrderedDict  of databases currently connected to Datasette. \n                 The dictionary keys are the name of the database that is used in the URL - e.g.  /fixtures  would have a key of  \"fixtures\" . The values are  Database class  instances. \n                 All databases are listed, irrespective of user permissions. This means that the  _internal  database will always be listed here.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-ensure-permissions", "page": "internals", "ref": "datasette-ensure-permissions", "title": "await .ensure_permissions(actor, permissions)", "content": "actor  - dictionary \n                         \n                             The authenticated actor. This is usually  request.actor . \n                         \n                     \n                     \n                         permissions  - list \n                         \n                             A list of permissions to check. Each permission in that list can be a string  action  name or a 2-tuple of  (action, resource) . \n                         \n                     \n                 \n                 This method allows multiple permissions to be checked at once. It raises a  datasette.Forbidden  exception if any of the checks are denied before one of them is explicitly granted. \n                 This is useful when you need to check multiple permissions at once. For example, an actor should be able to view a table if either one of the following checks returns  True  or not a single one of them returns  False : \n                 await self.ds.ensure_permissions(\n    request.actor,\n    [\n        (\"view-table\", (database, table)),\n        (\"view-database\", database),\n        \"view-instance\",\n    ],\n)", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-get-database", "page": "internals", "ref": "datasette-get-database", "title": ".get_database(name)", "content": "name  - string, optional \n                         \n                             The name of the database - optional. \n                         \n                     \n                 \n                 Returns the specified database object. Raises a  KeyError  if the database does not exist. Call this method without an argument to return the first connected database.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-permission-allowed", "page": "internals", "ref": "datasette-permission-allowed", "title": "await .permission_allowed(actor, action, resource=None, default=False)", "content": "actor  - dictionary \n                         \n                             The authenticated actor. This is usually  request.actor . \n                         \n                     \n                     \n                         action  - string \n                         \n                             The name of the action that is being permission checked. \n                         \n                     \n                     \n                         resource  - string or tuple, optional \n                         \n                             The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. \n                         \n                     \n                     \n                         default  - optional, True or False \n                         \n                             Should this permission check be default allow or default deny. \n                         \n                     \n                 \n                 Check if the given actor has  permission  to perform the given action on the given resource. \n                 Some permission checks are carried out against  rules defined in metadata.json , while other custom permissions may be decided by plugins that implement the  permission_allowed(datasette, actor, action, resource)  plugin hook. \n                 If neither  metadata.json  nor any of the plugins provide an answer to the permission query the  default  argument will be returned. \n                 See  Built-in permissions  for a full list of permission actions included in Datasette core.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-plugin-config", "page": "internals", "ref": "datasette-plugin-config", "title": ".plugin_config(plugin_name, database=None, table=None)", "content": "plugin_name  - string \n                         \n                             The name of the plugin to look up configuration for. Usually this is something similar to  datasette-cluster-map . \n                         \n                     \n                     \n                         database  - None or string \n                         \n                             The database the user is interacting with. \n                         \n                     \n                     \n                         table  - None or string \n                         \n                             The table the user is interacting with. \n                         \n                     \n                 \n                 This method lets you read plugin configuration values that were set in  metadata.json . See  Writing plugins that accept configuration  for full details of how this method should be used. \n                 The return value will be the value from the configuration file - usually a dictionary. \n                 If the plugin is not configured the return value will be  None .", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-remove-database", "page": "internals", "ref": "datasette-remove-database", "title": ".remove_database(name)", "content": "name  - string \n                         \n                             The name of the database to be removed. \n                         \n                     \n                 \n                 This removes a database that has been previously added.  name=  is the unique name of that database.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-render-template", "page": "internals", "ref": "datasette-render-template", "title": "await .render_template(template, context=None, request=None)", "content": "template  - string, list of strings or jinja2.Template \n                         \n                             The template file to be rendered, e.g.  my_plugin.html . Datasette will search for this file first in the  --template-dir=  location, if it was specified - then in the plugin's bundled templates and finally in Datasette's set of default templates. \n                             If this is a list of template file names then the first one that exists will be loaded and rendered. \n                             If this is a Jinja  Template object  it will be used directly. \n                         \n                     \n                     \n                         context  - None or a Python dictionary \n                         \n                             The context variables to pass to the template. \n                         \n                     \n                     \n                         request  - request object or None \n                         \n                             If you pass a Datasette request object here it will be made available to the template. \n                         \n                     \n                 \n                 Renders a  Jinja template  using Datasette's preconfigured instance of Jinja and returns the resulting string. The template will have access to Datasette's default template functions and any functions that have been made available by other plugins.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[{\"href\": \"https://jinja.palletsprojects.com/en/2.11.x/api/#jinja2.Template\", \"label\": \"Template object\"}, {\"href\": \"https://jinja.palletsprojects.com/en/2.11.x/\", \"label\": \"Jinja template\"}]"}
{"id": "internals:datasette-setting", "page": "internals", "ref": "datasette-setting", "title": ".setting(key)", "content": "key  - string \n                         \n                             The name of the setting, e.g.  base_url . \n                         \n                     \n                 \n                 Returns the configured value for the specified  setting . This can be a string, boolean or integer depending on the requested setting. \n                 For example: \n                 downloads_are_allowed = datasette.setting(\"allow_download\")", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:datasette-sign", "page": "internals", "ref": "datasette-sign", "title": ".sign(value, namespace=\"default\")", "content": "value  - any serializable type \n                         \n                             The value to be signed. \n                         \n                     \n                     \n                         namespace  - string, optional \n                         \n                             An alternative namespace, see the  itsdangerous salt documentation . \n                         \n                     \n                 \n                 Utility method for signing values, such that you can safely pass data to and from an untrusted environment. This is a wrapper around the  itsdangerous  library. \n                 This method returns a signed string, which can be decoded and verified using  .unsign(value, namespace=\"default\") .", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[{\"href\": \"https://itsdangerous.palletsprojects.com/en/1.1.x/serializer/#the-salt\", \"label\": \"itsdangerous salt documentation\"}, {\"href\": \"https://itsdangerous.palletsprojects.com/\", \"label\": \"itsdangerous\"}]"}
{"id": "internals:datasette-unsign", "page": "internals", "ref": "datasette-unsign", "title": ".unsign(value, namespace=\"default\")", "content": "signed  - any serializable type \n                         \n                             The signed string that was created using  .sign(value, namespace=\"default\") . \n                         \n                     \n                     \n                         namespace  - string, optional \n                         \n                             The alternative namespace, if one was used. \n                         \n                     \n                 \n                 Returns the original, decoded object that was passed to  .sign(value, namespace=\"default\") . If the signature is not valid this raises a  itsdangerous.BadSignature  exception.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "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": "internals:internals-csrf", "page": "internals", "ref": "internals-csrf", "title": "CSRF protection", "content": "Datasette uses  asgi-csrf  to guard against CSRF attacks on form POST submissions. Users receive a  ds_csrftoken  cookie which is compared against the  csrftoken  form field (or  x-csrftoken  HTTP header) for every incoming request. \n             If your plugin implements a  <form method=\"POST\">  anywhere you will need to include that token. You can do so with the following template snippet: \n             <input type=\"hidden\" name=\"csrftoken\" value=\"{{ csrftoken() }}\"> \n             If you are rendering templates using the  await .render_template(template, context=None, request=None)  method the  csrftoken()  helper will only work if you provide the  request=  argument to that method. If you forget to do this you will see the following error: \n             form-urlencoded POST field did not match cookie \n             You can selectively disable CSRF protection using the  skip_csrf(datasette, scope)  hook.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/asgi-csrf\", \"label\": \"asgi-csrf\"}]"}
{"id": "internals:internals-database", "page": "internals", "ref": "internals-database", "title": "Database class", "content": "Instances of the  Database  class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-database-introspection", "page": "internals", "ref": "internals-database-introspection", "title": "Database introspection", "content": "The  Database  class also provides properties and methods for introspecting the database. \n                 \n                     \n                         db.name  - string \n                         \n                             The name of the database - usually the filename without the  .db  prefix. \n                         \n                     \n                     \n                         db.size  - integer \n                         \n                             The size of the database file in bytes. 0 for  :memory:  databases. \n                         \n                     \n                     \n                         db.mtime_ns  - integer or None \n                         \n                             The last modification time of the database file in nanoseconds since the epoch.  None  for  :memory:  databases. \n                         \n                     \n                     \n                         db.is_mutable  - boolean \n                         \n                             Is this database mutable, and allowed to accept writes? \n                         \n                     \n                     \n                         db.is_memory  - boolean \n                         \n                             Is this database an in-memory database? \n                         \n                     \n                     \n                         await db.attached_databases()  - list of named tuples \n                         \n                             Returns a list of additional databases that have been connected to this database using the SQLite ATTACH command. Each named tuple has fields  seq ,  name  and  file . \n                         \n                     \n                     \n                         await db.table_exists(table)  - boolean \n                         \n                             Check if a table called  table  exists. \n                         \n                     \n                     \n                         await db.table_names()  - list of strings \n                         \n                             List of names of tables in the database. \n                         \n                     \n                     \n                         await db.view_names()  - list of strings \n                         \n                             List of names of views in the database. \n                         \n                     \n                     \n                         await db.table_columns(table)  - list of strings \n                         \n                             Names of columns in a specific table. \n                         \n                     \n                     \n                         await db.table_column_details(table)  - list of named tuples \n                         \n                             Full details of the columns in a specific table. Each column is represented by a  Column  named tuple with fields  cid  (integer representing the column position),  name  (string),  type  (string, e.g.  REAL  or  VARCHAR(30) ),  notnull  (integer 1 or 0),  default_value  (string or None),  is_pk  (integer 1 or 0). \n                         \n                     \n                     \n                         await db.primary_keys(table)  - list of strings \n                         \n                             Names of the columns that are part of the primary key for this table. \n                         \n                     \n                     \n                         await db.fts_table(table)  - string or None \n                         \n                             The name of the FTS table associated with this table, if one exists. \n                         \n                     \n                     \n                         await db.label_column_for_table(table)  - string or None \n                         \n                             The label column that is associated with this table - either automatically detected or using the  \"label_column\"  key from  Metadata , see  Specifying the label column for a table . \n                         \n                     \n                     \n                         await db.foreign_keys_for_table(table)  - list of dictionaries \n                         \n                             Details of columns in this table which are foreign keys to other tables. A list of dictionaries where each dictionary is shaped like this:  {\"column\": string, \"other_table\": string, \"other_column\": string} . \n                         \n                     \n                     \n                         await db.hidden_table_names()  - list of strings \n                         \n                             List of tables which Datasette \"hides\" by default - usually these are tables associated with SQLite's full-text search feature, the SpatiaLite extension or tables hidden using the  Hiding tables  feature. \n                         \n                     \n                     \n                         await db.get_table_definition(table)  - string \n                         \n                             Returns the SQL definition for the table - the  CREATE TABLE  statement and any associated  CREATE INDEX  statements. \n                         \n                     \n                     \n                         await db.get_view_definition(view)  - string \n                         \n                             Returns the SQL definition of the named view. \n                         \n                     \n                     \n                         await db.get_all_foreign_keys()  - dictionary \n                         \n                             Dictionary representing both incoming and outgoing foreign keys for this table. It has two keys,  \"incoming\"  and  \"outgoing\" , each of which is a list of dictionaries with keys  \"column\" ,  \"other_table\"  and  \"other_column\" . For example: \n                             {\n    \"incoming\": [],\n    \"outgoing\": [\n        {\n            \"other_table\": \"attraction_characteristic\",\n            \"column\": \"characteristic_id\",\n            \"other_column\": \"pk\",\n        },\n        {\n            \"other_table\": \"roadside_attractions\",\n            \"column\": \"attraction_id\",\n            \"other_column\": \"pk\",\n        }\n    ]\n}", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:internals-datasette", "page": "internals", "ref": "internals-datasette", "title": "Datasette class", "content": "This object is an instance of the  Datasette  class, passed to many plugin hooks as an argument called  datasette . \n             You can create your own instance of this - for example to help write tests for a plugin - like so: \n             from datasette.app import Datasette\n\n# With no arguments a single in-memory database will be attached\ndatasette = Datasette()\n\n# The files= argument can load files from disk\ndatasette = Datasette(files=[\"/path/to/my-database.db\"])\n\n# Pass metadata as a JSON dictionary like this\ndatasette = Datasette(\n    files=[\"/path/to/my-database.db\"],\n    metadata={\n        \"databases\": {\n            \"my-database\": {\n                \"description\": \"This is my database\"\n            }\n        }\n    },\n) \n             Constructor parameters include: \n             \n                 \n                     files=[...]  - a list of database files to open \n                 \n                 \n                     immutables=[...]  - a list of database files to open in immutable mode \n                 \n                 \n                     metadata={...}  - a dictionary of  Metadata \n                 \n                 \n                     config_dir=...  - the  configuration directory  to use, stored in  datasette.config_dir", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-datasette-client", "page": "internals", "ref": "internals-datasette-client", "title": "datasette.client", "content": "Plugins can make internal simulated HTTP requests to the Datasette instance within which they are running. This ensures that all of Datasette's external JSON APIs are also available to plugins, while avoiding the overhead of making an external HTTP call to access those APIs. \n                 The  datasette.client  object is a wrapper around the  HTTPX Python library , providing an async-friendly API that is similar to the widely used  Requests library . \n                 It offers the following methods: \n                 \n                     \n                         await datasette.client.get(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal GET request against that path. \n                         \n                     \n                     \n                         await datasette.client.post(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal POST request. Use  data={\"name\": \"value\"}  to pass form parameters. \n                         \n                     \n                     \n                         await datasette.client.options(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal OPTIONS request. \n                         \n                     \n                     \n                         await datasette.client.head(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal HEAD request. \n                         \n                     \n                     \n                         await datasette.client.put(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal PUT request. \n                         \n                     \n                     \n                         await datasette.client.patch(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal PATCH request. \n                         \n                     \n                     \n                         await datasette.client.delete(path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal DELETE request. \n                         \n                     \n                     \n                         await datasette.client.request(method, path, **kwargs)  - returns HTTPX Response \n                         \n                             Execute an internal request with the given HTTP method against that path. \n                         \n                     \n                 \n                 These methods can be used with  datasette.urls  - for example: \n                 table_json = (\n    await datasette.client.get(\n        datasette.urls.table(\n            \"fixtures\", \"facetable\", format=\"json\"\n        )\n    )\n).json() \n                 datasette.client  methods automatically take the current  base_url  setting into account, whether or not you use the  datasette.urls  family of methods to construct the path. \n                 For documentation on available  **kwargs  options and the shape of the HTTPX Response object refer to the  HTTPX Async documentation .", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[{\"href\": \"https://www.python-httpx.org/\", \"label\": \"HTTPX Python library\"}, {\"href\": \"https://requests.readthedocs.io/\", \"label\": \"Requests library\"}, {\"href\": \"https://www.python-httpx.org/async/\", \"label\": \"HTTPX Async documentation\"}]"}
{"id": "internals:internals-datasette-urls", "page": "internals", "ref": "internals-datasette-urls", "title": "datasette.urls", "content": "The  datasette.urls  object contains methods for building URLs to pages within Datasette. Plugins should use this to link to pages, since these methods take into account any  base_url  configuration setting that might be in effect. \n                 \n                     \n                         datasette.urls.instance(format=None) \n                         \n                             Returns the URL to the Datasette instance root page. This is usually  \"/\" . \n                         \n                     \n                     \n                         datasette.urls.path(path, format=None) \n                         \n                             Takes a path and returns the full path, taking  base_url  into account. \n                             For example,  datasette.urls.path(\"-/logout\")  will return the path to the logout page, which will be  \"/-/logout\"  by default or  /prefix-path/-/logout  if  base_url  is set to  /prefix-path/ \n                         \n                     \n                     \n                         datasette.urls.logout() \n                         \n                             Returns the URL to the logout page, usually  \"/-/logout\" \n                         \n                     \n                     \n                         datasette.urls.static(path) \n                         \n                             Returns the URL of one of Datasette's default static assets, for example  \"/-/static/app.css\" \n                         \n                     \n                     \n                         datasette.urls.static_plugins(plugin_name, path) \n                         \n                             Returns the URL of one of the static assets belonging to a plugin. \n                             datasette.urls.static_plugins(\"datasette_cluster_map\", \"datasette-cluster-map.js\")  would return  \"/-/static-plugins/datasette_cluster_map/datasette-cluster-map.js\" \n                         \n                     \n                     \n                         datasette.urls.static(path) \n                         \n                             Returns the URL of one of Datasette's default static assets, for example  \"/-/static/app.css\" \n                         \n                     \n                     \n                         datasette.urls.database(database_name, format=None) \n                         \n                             Returns the URL to a database page, for example  \"/fixtures\" \n                         \n                     \n                     \n                         datasette.urls.table(database_name, table_name, format=None) \n                         \n                             Returns the URL to a table page, for example  \"/fixtures/facetable\" \n                         \n                     \n                     \n                         datasette.urls.query(database_name, query_name, format=None) \n                         \n                             Returns the URL to a query page, for example  \"/fixtures/pragma_cache_size\" \n                         \n                     \n                 \n                 These functions can be accessed via the  {{ urls }}  object in Datasette templates, for example: \n                 <a href=\"{{ urls.instance() }}\">Homepage</a>\n<a href=\"{{ urls.database(\"fixtures\") }}\">Fixtures database</a>\n<a href=\"{{ urls.table(\"fixtures\", \"facetable\") }}\">facetable table</a>\n<a href=\"{{ urls.query(\"fixtures\", \"pragma_cache_size\") }}\">pragma_cache_size query</a> \n                 Use the  format=\"json\"  (or  \"csv\"  or other formats supported by plugins) arguments to get back URLs to the JSON representation. This is the path with  .json  added on the end. \n                 These methods each return a  datasette.utils.PrefixedUrlString  object, which is a subclass of the Python  str  type. This allows the logic that considers the  base_url  setting to detect if that prefix has already been applied to the path.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:internals-internal", "page": "internals", "ref": "internals-internal", "title": "The _internal database", "content": "This API should be considered unstable - the structure of these tables may change prior to the release of Datasette 1.0. \n             \n             Datasette maintains an in-memory SQLite database with details of the the databases, tables and columns for all of the attached databases. \n             By default all actors are denied access to the  view-database  permission for the  _internal  database, so the database is not visible to anyone unless they  sign in as root . \n             Plugins can access this database by calling  db = datasette.get_database(\"_internal\")  and then executing queries using the  Database API . \n             You can explore an example of this database by  signing in as root  to the  latest.datasette.io  demo instance and then navigating to  latest.datasette.io/_internal .", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://latest.datasette.io/login-as-root\", \"label\": \"signing in as root\"}, {\"href\": \"https://latest.datasette.io/_internal\", \"label\": \"latest.datasette.io/_internal\"}]"}
{"id": "internals:internals-multiparams", "page": "internals", "ref": "internals-multiparams", "title": "The MultiParams class", "content": "request.args  is a  MultiParams  object - a dictionary-like object which provides access to query string parameters that may have multiple values. \n             Consider the query string  ?foo=1&foo=2&bar=3  - with two values for  foo  and one value for  bar . \n             \n                 \n                     request.args[key]  - string \n                     \n                         Returns the first value for that key, or raises a  KeyError  if the key is missing. For the above example  request.args[\"foo\"]  would return  \"1\" . \n                     \n                 \n                 \n                     request.args.get(key)  - string or None \n                     \n                         Returns the first value for that key, or  None  if the key is missing. Pass a second argument to specify a different default, e.g.  q = request.args.get(\"q\", \"\") . \n                     \n                 \n                 \n                     request.args.getlist(key)  - list of strings \n                     \n                         Returns the list of strings for that key.  request.args.getlist(\"foo\")  would return  [\"1\", \"2\"]  in the above example.  request.args.getlist(\"bar\")  would return  [\"3\"] . If the key is missing an empty list will be returned. \n                     \n                 \n                 \n                     request.args.keys()  - list of strings \n                     \n                         Returns the list of available keys - for the example this would be  [\"foo\", \"bar\"] . \n                     \n                 \n                 \n                     key in request.args  - True or False \n                     \n                         You can use  if key in request.args  to check if a key is present. \n                     \n                 \n                 \n                     for key in request.args  - iterator \n                     \n                         This lets you loop through every available key. \n                     \n                 \n                 \n                     len(request.args)  - integer \n                     \n                         Returns the number of keys.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"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: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": "[]"}
{"id": "internals:internals-response-set-cookie", "page": "internals", "ref": "internals-response-set-cookie", "title": "Setting cookies with response.set_cookie()", "content": "To set cookies on the response, use the  response.set_cookie(...)  method. The method signature looks like this: \n                 def set_cookie(\n    self,\n    key,\n    value=\"\",\n    max_age=None,\n    expires=None,\n    path=\"/\",\n    domain=None,\n    secure=False,\n    httponly=False,\n    samesite=\"lax\",\n):\n    ... \n                 You can use this with  datasette.sign()  to set signed cookies. Here's how you would set the  ds_actor cookie  for use with Datasette  authentication : \n                 response = Response.redirect(\"/\")\nresponse.set_cookie(\n    \"ds_actor\",\n    datasette.sign({\"a\": {\"id\": \"cleopaws\"}}, \"actor\"),\n)\nreturn response", "breadcrumbs": "[\"Internals for plugins\", \"Response class\"]", "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": "internals:internals-tilde-encoding", "page": "internals", "ref": "internals-tilde-encoding", "title": "Tilde encoding", "content": "Datasette uses a custom encoding scheme in some places, called  tilde encoding . This is primarily used for table names and row primary keys, to avoid any confusion between  /  characters in those values and the Datasette URLs that reference them. \n                 Tilde encoding uses the same algorithm as  URL percent-encoding , but with the  ~  tilde character used in place of  % . \n                 Any character other than  ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz0123456789_-  will be replaced by the numeric equivalent preceded by a tilde. For example: \n                 \n                     \n                         /  becomes  ~2F \n                     \n                     \n                         .  becomes  ~2E \n                     \n                     \n                         %  becomes  ~25 \n                     \n                     \n                         ~  becomes  ~7E \n                     \n                     \n                         Space becomes  + \n                     \n                     \n                         polls/2022.primary  becomes  polls~2F2022~2Eprimary \n                     \n                 \n                 Note that the space character is a special case: it will be replaced with a  +  symbol. \n                 \n                 \n                 \n                     datasette.utils. tilde_encode s :    str str \n                     \n                         Returns tilde-encoded string - for example  /foo/bar  ->  ~2Ffoo~2Fbar \n                     \n                 \n                 \n                 \n                 \n                     datasette.utils. tilde_decode s :    str str \n                     \n                         Decodes a tilde-encoded string, so  ~2Ffoo~2Fbar  ->  /foo/bar", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "references": "[{\"href\": \"https://developer.mozilla.org/en-US/docs/Glossary/percent-encoding\", \"label\": \"URL percent-encoding\"}]"}
{"id": "internals:internals-tracer", "page": "internals", "ref": "internals-tracer", "title": "datasette.tracer", "content": "Running Datasette with  --setting trace_debug 1  enables trace debug output, which can then be viewed by adding  ?_trace=1  to the query string for any page. \n             You can see an example of this at the bottom of  latest.datasette.io/fixtures/facetable?_trace=1 . The JSON output shows full details of every SQL query that was executed to generate the page. \n             The  datasette-pretty-traces  plugin can be installed to provide a more readable display of this information. You can see  a demo of that here . \n             You can add your own custom traces to the JSON output using the  trace()  context manager. This takes a string that identifies the type of trace being recorded, and records any keyword arguments as additional JSON keys on the resulting trace object. \n             The start and end time, duration and a traceback of where the trace was executed will be automatically attached to the JSON object. \n             This example uses trace to record the start, end and duration of any HTTP GET requests made using the function: \n             from datasette.tracer import trace\nimport httpx\n\n\nasync def fetch_url(url):\n    with trace(\"fetch-url\", url=url):\n        async with httpx.AsyncClient() as client:\n            return await client.get(url)", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/facetable?_trace=1\", \"label\": \"latest.datasette.io/fixtures/facetable?_trace=1\"}, {\"href\": \"https://datasette.io/plugins/datasette-pretty-traces\", \"label\": \"datasette-pretty-traces\"}, {\"href\": \"https://latest-with-plugins.datasette.io/github/commits?_trace=1\", \"label\": \"a demo of that here\"}]"}
{"id": "internals:internals-tracer-trace-child-tasks", "page": "internals", "ref": "internals-tracer-trace-child-tasks", "title": "Tracing child tasks", "content": "If your code uses a mechanism such as  asyncio.gather()  to execute code in additional tasks you may find that some of the traces are missing from the display. \n                 You can use the  trace_child_tasks()  context manager to ensure these child tasks are correctly handled. \n                 from datasette import tracer\n\nwith tracer.trace_child_tasks():\n    results = await asyncio.gather(\n        # ... async tasks here\n    ) \n                 This example uses the  register_routes()  plugin hook to add a page at  /parallel-queries  which executes two SQL queries in parallel using  asyncio.gather()  and returns their results. \n                 from datasette import hookimpl\nfrom datasette import tracer\n\n\n@hookimpl\ndef register_routes():\n    async def parallel_queries(datasette):\n        db = datasette.get_database()\n        with tracer.trace_child_tasks():\n            one, two = await asyncio.gather(\n                db.execute(\"select 1\"),\n                db.execute(\"select 2\"),\n            )\n        return Response.json(\n            {\n                \"one\": one.single_value(),\n                \"two\": two.single_value(),\n            }\n        )\n\n    return [\n        (r\"/parallel-queries$\", parallel_queries),\n    ] \n                 Adding  ?_trace=1  will show that the trace covers both of those child tasks.", "breadcrumbs": "[\"Internals for plugins\", \"datasette.tracer\"]", "references": "[]"}
{"id": "internals:internals-utils", "page": "internals", "ref": "internals-utils", "title": "The datasette.utils module", "content": "The  datasette.utils  module contains various utility functions used by Datasette. As a general rule you should consider anything in this module to be unstable - functions and classes here could change without warning or be removed entirely between Datasette releases, without being mentioned in the release notes. \n             The exception to this rule is anythang that is documented here. If you find a need for an undocumented utility function in your own work, consider  opening an issue  requesting that the function you are using be upgraded to documented and supported status.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/new\", \"label\": \"opening an issue\"}]"}
{"id": "internals:internals-utils-await-me-maybe", "page": "internals", "ref": "internals-utils-await-me-maybe", "title": "await_me_maybe(value)", "content": "Utility function for calling  await  on a return value if it is awaitable, otherwise returning the value. This is used by Datasette to support plugin hooks that can optionally return awaitable functions. Read more about this function in  The \u201cawait me maybe\u201d pattern for Python asyncio . \n                 \n                 \n                     async    datasette.utils. await_me_maybe value :    Any Any \n                     \n                         If value is callable, call it. If awaitable, await it. Otherwise return it.", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "references": "[{\"href\": \"https://simonwillison.net/2020/Sep/2/await-me-maybe/\", \"label\": \"The \u201cawait me maybe\u201d pattern for Python asyncio\"}]"}
{"id": "internals:internals-utils-parse-metadata", "page": "internals", "ref": "internals-utils-parse-metadata", "title": "parse_metadata(content)", "content": "This function accepts a string containing either JSON or YAML, expected to be of the format described in  Metadata . It returns a nested Python dictionary representing the parsed data from that string. \n                 If the metadata cannot be parsed as either JSON or YAML the function will raise a  utils.BadMetadataError  exception. \n                 \n                 \n                     datasette.utils. parse_metadata content :    str dict \n                     \n                         Detects if content is JSON or YAML and parses it appropriately.", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "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": "introspection:jsondataview-actor", "page": "introspection", "ref": "jsondataview-actor", "title": "/-/actor", "content": "Shows the currently authenticated actor. Useful for debugging Datasette authentication plugins. \n             {\n    \"actor\": {\n        \"id\": 1,\n        \"username\": \"some-user\"\n    }\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[]"}
{"id": "introspection:jsondataview-config", "page": "introspection", "ref": "jsondataview-config", "title": "/-/settings", "content": "Shows the  Settings  for this instance of Datasette.  Settings example : \n             {\n    \"default_facet_size\": 30,\n    \"default_page_size\": 100,\n    \"facet_suggest_time_limit_ms\": 50,\n    \"facet_time_limit_ms\": 1000,\n    \"max_returned_rows\": 1000,\n    \"sql_time_limit_ms\": 1000\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/-/settings\", \"label\": \"Settings example\"}]"}
{"id": "introspection:jsondataview-databases", "page": "introspection", "ref": "jsondataview-databases", "title": "/-/databases", "content": "Shows currently attached databases.  Databases example : \n             [\n    {\n        \"hash\": null,\n        \"is_memory\": false,\n        \"is_mutable\": true,\n        \"name\": \"fixtures\",\n        \"path\": \"fixtures.db\",\n        \"size\": 225280\n    }\n]", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://latest.datasette.io/-/databases\", \"label\": \"Databases example\"}]"}
{"id": "introspection:jsondataview-metadata", "page": "introspection", "ref": "jsondataview-metadata", "title": "/-/metadata", "content": "Shows the contents of the  metadata.json  file that was passed to  datasette serve , if any.  Metadata example : \n             {\n    \"license\": \"CC Attribution 4.0 License\",\n    \"license_url\": \"http://creativecommons.org/licenses/by/4.0/\",\n    \"source\": \"fivethirtyeight/data on GitHub\",\n    \"source_url\": \"https://github.com/fivethirtyeight/data\",\n    \"title\": \"Five Thirty Eight\",\n    \"databases\": {\n\n    }\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/-/metadata\", \"label\": \"Metadata example\"}]"}
{"id": "introspection:jsondataview-plugins", "page": "introspection", "ref": "jsondataview-plugins", "title": "/-/plugins", "content": "Shows a list of currently installed plugins and their versions.  Plugins example : \n             [\n    {\n        \"name\": \"datasette_cluster_map\",\n        \"static\": true,\n        \"templates\": false,\n        \"version\": \"0.10\",\n        \"hooks\": [\"extra_css_urls\", \"extra_js_urls\", \"extra_body_script\"]\n    }\n] \n             Add  ?all=1  to include details of the default plugins baked into Datasette.", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://san-francisco.datasettes.com/-/plugins\", \"label\": \"Plugins example\"}]"}