# sections

id: writing_plugins:id1
page: writing_plugins
ref: id1
title: Writing plugins
breadcrumbs: []
content:
You can write one-off plugins that apply to just one Datasette instance, or you can write plugins which can be installed using  pip  and can be shipped to the Python Package Index ( PyPI ) for other people to install. 
         Want to start by looking at an example? The  Datasette plugins directory  lists more than 90 open source plugins with code you can explore. The  plugin hooks  page includes links to example plugins for each of the documented hooks.

references:
[{"href": "https://pypi.org/", "label": "PyPI"}, {"href": "https://datasette.io/plugins", "label": "Datasette plugins directory"}]


id: writing_plugins:writing-plugins-one-off
page: writing_plugins
ref: writing-plugins-one-off
title: Writing one-off plugins
breadcrumbs: ["Writing plugins"]
references: [{"href": "http://localhost:8001/mydb", "label": "http://localhost:8001/mydb"}]
content:
The quickest way to start writing a plugin is to create a  my_plugin.py  file and drop it into your  plugins/  directory. Here is an example plugin, which adds a new custom SQL function called  hello_world()  which takes no arguments and returns the string  Hello world! . 
             from datasette import hookimpl


@hookimpl
def prepare_connection(conn):
    conn.create_function(
        "hello_world", 0, lambda: "Hello world!"
    ) 
             If you save this in  plugins/my_plugin.py  you can then start Datasette like this: 
             datasette serve mydb.db --plugins-dir=plugins/ 
             Now you can navigate to  http://localhost:8001/mydb  and run this SQL: 
             select hello_world(); 
             To see the output of your plugin.


id: writing_plugins:writing-plugins-cookiecutter
page: writing_plugins
ref: writing-plugins-cookiecutter
title: Starting an installable plugin using cookiecutter
breadcrumbs: ["Writing plugins"]
content:
Plugins that can be installed should be written as Python packages using a  setup.py  file. 
             The quickest way to start writing one an installable plugin is to use the  datasette-plugin  cookiecutter template. This creates a new plugin structure for you complete with an example test and GitHub Actions workflows for testing and publishing your plugin. 
             Install cookiecutter  and then run this command to start building a plugin using the template: 
             cookiecutter gh:simonw/datasette-plugin 
             Read  a cookiecutter template for writing Datasette plugins  for more information about this template.

references:
[{"href": "https://github.com/simonw/datasette-plugin", "label": "datasette-plugin"}, {"href": "https://cookiecutter.readthedocs.io/en/stable/installation.html", "label": "Install cookiecutter"}, {"href": "https://simonwillison.net/2020/Jun/20/cookiecutter-plugins/", "label": "a cookiecutter template for writing Datasette plugins"}]


id: writing_plugins:writing-plugins-packaging
page: writing_plugins
ref: writing-plugins-packaging
title: Packaging a plugin
breadcrumbs: ["Writing plugins"]
content:
Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at  https://github.com/simonw/datasette-plugin-demos 
             The example consists of two files: a  setup.py  file that defines the plugin: 
             from setuptools import setup

VERSION = "0.1"

setup(
    name="datasette-plugin-demos",
    description="Examples of plugins for Datasette",
    author="Simon Willison",
    url="https://github.com/simonw/datasette-plugin-demos",
    license="Apache License, Version 2.0",
    version=VERSION,
    py_modules=["datasette_plugin_demos"],
    entry_points={
        "datasette": [
            "plugin_demos = datasette_plugin_demos"
        ]
    },
    install_requires=["datasette"],
) 
             And a Python module file,  datasette_plugin_demos.py , that implements the plugin: 
             from datasette import hookimpl
import random


@hookimpl
def prepare_jinja2_environment(env):
    env.filters["uppercase"] = lambda u: u.upper()


@hookimpl
def prepare_connection(conn):
    conn.create_function(
        "random_integer", 2, random.randint
    ) 
             Having built a plugin in this way you can turn it into an installable package using the following command: 
             python3 setup.py sdist 
             This will create a  .tar.gz  file in the  dist/  directory. 
             You can then install your new plugin into a Datasette virtual environment or Docker container using  pip : 
             pip install datasette-plugin-demos-0.1.tar.gz 
             To learn how to upload your plugin to  PyPI  for use by other people, read the PyPA guide to  Packaging and distributing projects .

references:
[{"href": "https://github.com/simonw/datasette-plugin-demos", "label": "https://github.com/simonw/datasette-plugin-demos"}, {"href": "https://pypi.org/", "label": "PyPI"}, {"href": "https://packaging.python.org/tutorials/distributing-packages/", "label": "Packaging and distributing projects"}]


id: writing_plugins:writing-plugins-static-assets
page: writing_plugins
ref: writing-plugins-static-assets
title: Static assets
breadcrumbs: ["Writing plugins"]
references: [{"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}]
content:
If your plugin has a  static/  directory, Datasette will automatically configure itself to serve those static assets from the following path: 
             /-/static-plugins/NAME_OF_PLUGIN_PACKAGE/yourfile.js 
             Use the  datasette.urls.static_plugins(plugin_name, path)  method to generate URLs to that asset that take the  base_url  setting into account, see  datasette.urls . 
             To bundle the static assets for a plugin in the package that you publish to PyPI, add the following to the plugin's  setup.py : 
             package_data = (
    {
        "datasette_plugin_name": [
            "static/plugin.js",
        ],
    },
) 
             Where  datasette_plugin_name  is the name of the plugin package (note that it uses underscores, not hyphens) and  static/plugin.js  is the path within that package to the static file. 
             datasette-cluster-map  is a useful example of a plugin that includes packaged static assets in this way.


id: writing_plugins:writing-plugins-custom-templates
page: writing_plugins
ref: writing-plugins-custom-templates
title: Custom templates
breadcrumbs: ["Writing plugins"]
references: [{"href": "https://github.com/simonw/datasette-edit-schema", "label": "datasette-edit-schema"}]
content:
If your plugin has a  templates/  directory, Datasette will attempt to load templates from that directory before it uses its own default templates. 
             The priority order for template loading is: 
             
                 
                     templates from the  --template-dir  argument, if specified 
                 
                 
                     templates from the  templates/  directory in any installed plugins 
                 
                 
                     default templates that ship with Datasette 
                 
             
             See  Custom pages and templates  for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI. 
             Templates should be bundled for distribution using the same  package_data  mechanism in  setup.py  described for static assets above, for example: 
             package_data = (
    {
        "datasette_plugin_name": [
            "templates/my_template.html",
        ],
    },
) 
             You can also use wildcards here such as  templates/*.html . See  datasette-edit-schema  for an example of this pattern.


id: writing_plugins:writing-plugins-configuration
page: writing_plugins
ref: writing-plugins-configuration
title: Writing plugins that accept configuration
breadcrumbs: ["Writing plugins"]
references: []
content:
When you are writing plugins, you can access plugin configuration like this using the  datasette plugin_config()  method. If you know you need plugin configuration for a specific table, you can access it like this: 
             plugin_config = datasette.plugin_config(
    "datasette-cluster-map", database="sf-trees", table="Street_Tree_List"
) 
             This will return the  {"latitude_column": "lat", "longitude_column": "lng"}  in the above example. 
             If there is no configuration for that plugin, the method will return  None . 
             If it cannot find the requested configuration at the table layer, it will fall back to the database layer and then the root layer. For example, a user may have set the plugin configuration option like so: 
             {
    "databases: {
        "sf-trees": {
            "plugins": {
                "datasette-cluster-map": {
                    "latitude_column": "xlat",
                    "longitude_column": "xlng"
                }
            }
        }
    }
} 
             In this case, the above code would return that configuration for ANY table within the  sf-trees  database. 
             The plugin configuration could also be set at the top level of  metadata.json : 
             {
    "title": "This is the top-level title in metadata.json",
    "plugins": {
        "datasette-cluster-map": {
            "latitude_column": "xlat",
            "longitude_column": "xlng"
        }
    }
} 
             Now that  datasette-cluster-map  plugin configuration will apply to every table in every database.


id: writing_plugins:writing-plugins-designing-urls
page: writing_plugins
ref: writing-plugins-designing-urls
title: Designing URLs for your plugin
breadcrumbs: ["Writing plugins"]
references: []
content:
You can register new URL routes within Datasette using the  register_routes(datasette)  plugin hook. 
             Datasette's default URLs include these: 
             
                 
                     /dbname  - database page 
                 
                 
                     /dbname/tablename  - table page 
                 
                 
                     /dbname/tablename/pk  - row page 
                 
             
             See  Pages and API endpoints  and  Introspection  for more default URL routes. 
             To avoid accidentally conflicting with a database file that may be loaded into Datasette, plugins should register URLs using a  /-/  prefix. For example, if your plugin adds a new interface for uploading Excel files you might register a URL route like this one: 
             
                 
                     /-/upload-excel 
                 
             
             Try to avoid registering URLs that clash with other plugins that your users might have installed. There is no central repository of reserved URL paths (yet) but you can review existing plugins by browsing the  plugins directory <https://datasette.io/plugins> . 
             If your plugin includes functionality that relates to a specific database you could also register a URL route like this: 
             
                 
                     /dbname/-/upload-excel 
                 
             
             Or for a specific table like this: 
             
                 
                     /dbname/tablename/-/modify-table-schema 
                 
             
             Note that a row could have a primary key of  -  and this URL scheme will still work, because Datasette row pages do not ever have a trailing slash followed by additional path components.


id: writing_plugins:writing-plugins-building-urls
page: writing_plugins
ref: writing-plugins-building-urls
title: Building URLs within plugins
breadcrumbs: ["Writing plugins"]
references: []
content:
Plugins that define their own custom user interface elements may need to link to other pages within Datasette. 
             This can be a bit tricky if the Datasette instance is using the  base_url  configuration setting to run behind a proxy, since that can cause Datasette's URLs to include an additional prefix. 
             The  datasette.urls  object provides internal methods for correctly generating URLs to different pages within Datasette, taking any  base_url  configuration into account. 
             This object is exposed in templates as the  urls  variable, which can be used like this: 
             Back to the <a href="{{ urls.instance() }}">Homepage</a> 
             See  datasette.urls  for full details on this object.


id: facets:id1
page: facets
ref: id1
title: Facets
breadcrumbs: []
content:
Datasette facets can be used to add a faceted browse interface to any database table.
            With facets, tables are displayed along with a summary showing the most common values in specified columns.
            These values can be selected to further filter the table. 
         Here's  an example : 
         
         Facets can be specified in two ways: using query string parameters, or in  metadata.json  configuration for the table.

references:
[{"href": "https://congress-legislators.datasettes.com/legislators/legislator_terms?_facet=type&_facet=party&_facet=state&_facet_size=10", "label": "an example"}]


id: facets:facets-in-query-strings
page: facets
ref: facets-in-query-strings
title: Facets in query strings
breadcrumbs: ["Facets"]
references: []
content:
To turn on faceting for specific columns on a Datasette table view, add one or more  _facet=COLUMN  parameters to the URL.
                For example, if you want to turn on facets for the  city_id  and  state  columns, construct a URL that looks like this: 
             /dbname/tablename?_facet=state&_facet=city_id 
             This works for both the HTML interface and the  .json  view.
                When enabled, facets will cause a  facet_results  block to be added to the JSON output, looking something like this: 
             {
  "state": {
    "name": "state",
    "results": [
      {
        "value": "CA",
        "label": "CA",
        "count": 10,
        "toggle_url": "http://...?_facet=city_id&_facet=state&state=CA",
        "selected": false
      },
      {
        "value": "MI",
        "label": "MI",
        "count": 4,
        "toggle_url": "http://...?_facet=city_id&_facet=state&state=MI",
        "selected": false
      },
      {
        "value": "MC",
        "label": "MC",
        "count": 1,
        "toggle_url": "http://...?_facet=city_id&_facet=state&state=MC",
        "selected": false
      }
    ],
    "truncated": false
  }
  "city_id": {
    "name": "city_id",
    "results": [
      {
        "value": 1,
        "label": "San Francisco",
        "count": 6,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=1",
        "selected": false
      },
      {
        "value": 2,
        "label": "Los Angeles",
        "count": 4,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=2",
        "selected": false
      },
      {
        "value": 3,
        "label": "Detroit",
        "count": 4,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=3",
        "selected": false
      },
      {
        "value": 4,
        "label": "Memnonia",
        "count": 1,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=4",
        "selected": false
      }
    ],
    "truncated": false
  }
} 
             If Datasette detects that a column is a foreign key, the  "label"  property will be automatically derived from the detected label column on the referenced table. 
             The default number of facet results returned is 30, controlled by the  default_facet_size  setting.
                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).


id: facets:facets-metadata
page: facets
ref: facets-metadata
title: Facets in metadata.json
breadcrumbs: ["Facets"]
references: []
content:
You can turn facets on by default for specific tables by adding them to a  "facets"  key in a Datasette  Metadata  file. 
             Here's an example that turns on faceting by default for the  qLegalStatus  column in the  Street_Tree_List  table in the  sf-trees  database: 
             {
  "databases": {
    "sf-trees": {
      "tables": {
        "Street_Tree_List": {
          "facets": ["qLegalStatus"]
        }
      }
    }
  }
} 
             Facets defined in this way will always be shown in the interface and returned in the API, regardless of the  _facet  arguments passed to the view. 
             You can specify  array  or  date  facets in metadata using JSON objects with a single key of  array  or  date  and a value specifying the column, like this: 
             {
  "facets": [
    {"array": "tags"},
    {"date": "created"}
  ]
} 
             You can change the default facet size (the number of results shown for each facet) for a table using  facet_size : 
             {
  "databases": {
    "sf-trees": {
      "tables": {
        "Street_Tree_List": {
          "facets": ["qLegalStatus"],
          "facet_size": 10
        }
      }
    }
  }
}


id: facets:suggested-facets
page: facets
ref: suggested-facets
title: Suggested facets
breadcrumbs: ["Facets"]
references: []
content:
Datasette's table UI will suggest facets for the user to apply, based on the following criteria: 
             For the currently filtered data are there any columns which, if applied as a facet... 
             
                 
                     Will return 30 or less unique options 
                 
                 
                     Will return more than one unique option 
                 
                 
                     Will return less unique options than the total number of filtered rows 
                 
                 
                     And the query used to evaluate this criteria can be completed in under 50ms 
                 
             
             That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries.
                This means suggested facets are unlikely to appear for tables with millions of records in them.


id: facets:speeding-up-facets-with-indexes
page: facets
ref: speeding-up-facets-with-indexes
title: Speeding up facets with indexes
breadcrumbs: ["Facets"]
content:
The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by.
                Adding indexes can be performed using the  sqlite3  command-line utility. Here's how to add an index on the  state  column in a table called  Food_Trucks : 
             $ sqlite3 mydatabase.db
SQLite version 3.19.3 2017-06-27 16:48:08
Enter ".help" for usage hints.
sqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks("state"); 
             Or using the  sqlite-utils  command-line utility: 
             $ sqlite-utils create-index mydatabase.db Food_Trucks state

references:
[{"href": "https://sqlite-utils.datasette.io/en/stable/cli.html#creating-indexes", "label": "sqlite-utils"}]


id: facets:id2
page: facets
ref: id2
title: Facet by JSON array
breadcrumbs: ["Facets"]
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. 
             This is useful for modelling things like tags without needing to break them out into a new table. 
             Example here:  latest.datasette.io/fixtures/facetable?_facet_array=tags

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
breadcrumbs: ["Facets"]
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.
                This works especially well against timestamp values such as  2019-03-01 12:44:00 . 
             Example here:  latest.datasette.io/fixtures/facetable?_facet_date=created

references:
[{"href": "https://latest.datasette.io/fixtures/facetable?_facet_date=created", "label": "latest.datasette.io/fixtures/facetable?_facet_date=created"}]


id: spatialite:id1
page: spatialite
ref: id1
title: SpatiaLite
breadcrumbs: []
content:
The  SpatiaLite module  for SQLite adds features for handling geographic and spatial data. For an example of what you can do with it, see the tutorial  Building a location to time zone API with SpatiaLite . 
         To use it with Datasette, you need to install the  mod_spatialite  dynamic library. This can then be loaded into Datasette using the  --load-extension  command-line option. 
         Datasette can look for SpatiaLite in common installation locations if you run it like this: 
         datasette --load-extension=spatialite --setting default_allow_sql off 
         If SpatiaLite is in another location, use the full path to the extension instead: 
         datasette --setting default_allow_sql off \
  --load-extension=/usr/local/lib/mod_spatialite.dylib

references:
[{"href": "https://www.gaia-gis.it/fossil/libspatialite/index", "label": "SpatiaLite module"}, {"href": "https://datasette.io/tutorials/spatialite", "label": "Building a location to time zone API with SpatiaLite"}]


id: spatialite:spatialite-warning
page: spatialite
ref: spatialite-warning
title: Warning
breadcrumbs: ["SpatiaLite"]
content:
The SpatiaLite extension adds  a large number of additional SQL functions , some of which are not be safe for untrusted users to execute: they may cause the Datasette server to crash. 
                 You should not expose a SpatiaLite-enabled Datasette instance to the public internet without taking extra measures to secure it against potentially harmful SQL queries. 
                 The following steps are recommended: 
                 
                     
                         Disable arbitrary SQL queries by untrusted users. See  Controlling the ability to execute arbitrary SQL  for ways to do this. The easiest is to start Datasette with the  datasette --setting default_allow_sql off  option. 
                     
                     
                         Define  Canned queries  with the SQL queries that use SpatiaLite functions that you want people to be able to execute. 
                     
                 
                 The  Datasette SpatiaLite tutorial  includes detailed instructions for running SpatiaLite safely using these techniques

references:
[{"href": "https://www.gaia-gis.it/gaia-sins/spatialite-sql-5.0.1.html", "label": "a large number of additional SQL functions"}, {"href": "https://datasette.io/tutorials/spatialite", "label": "Datasette SpatiaLite tutorial"}]


id: spatialite:installing-spatialite-on-os-x
page: spatialite
ref: installing-spatialite-on-os-x
title: Installing SpatiaLite on OS X
breadcrumbs: ["SpatiaLite", "Installation"]
references: [{"href": "https://brew.sh/", "label": "Homebrew"}]
content:
The easiest way to install SpatiaLite on OS X is to use  Homebrew . 
                 brew update
brew install spatialite-tools 
                 This will install the  spatialite  command-line tool and the  mod_spatialite  dynamic library. 
                 You can now run Datasette like so: 
                 datasette --load-extension=spatialite


id: spatialite:installing-spatialite-on-linux
page: spatialite
ref: installing-spatialite-on-linux
title: Installing SpatiaLite on Linux
breadcrumbs: ["SpatiaLite", "Installation"]
references: []
content:
SpatiaLite is packaged for most Linux distributions. 
                 apt install spatialite-bin libsqlite3-mod-spatialite 
                 Depending on your distribution, you should be able to run Datasette something like this: 
                 datasette --load-extension=/usr/lib/x86_64-linux-gnu/mod_spatialite.so 
                 If you are unsure of the location of the module, try running  locate mod_spatialite  and see what comes back.


id: spatialite:spatial-indexing-latitude-longitude-columns
page: spatialite
ref: spatial-indexing-latitude-longitude-columns
title: Spatial indexing latitude/longitude columns
breadcrumbs: ["SpatiaLite"]
references: []
content:
Here's a recipe for taking a table with existing latitude and longitude columns, adding a SpatiaLite POINT geometry column to that table, populating the new column and then populating a spatial index: 
             import sqlite3

conn = sqlite3.connect("museums.db")
# Lead the spatialite extension:
conn.enable_load_extension(True)
conn.load_extension("/usr/local/lib/mod_spatialite.dylib")
# Initialize spatial metadata for this database:
conn.execute("select InitSpatialMetadata(1)")
# Add a geometry column called point_geom to our museums table:
conn.execute(
    "SELECT AddGeometryColumn('museums', 'point_geom', 4326, 'POINT', 2);"
)
# Now update that geometry column with the lat/lon points
conn.execute(
    """
    UPDATE museums SET
    point_geom = GeomFromText('POINT('||"longitude"||' '||"latitude"||')',4326);
"""
)
# Now add a spatial index to that column
conn.execute(
    'select CreateSpatialIndex("museums", "point_geom");'
)
# If you don't commit your changes will not be persisted:
conn.commit()
conn.close()


id: spatialite:making-use-of-a-spatial-index
page: spatialite
ref: making-use-of-a-spatial-index
title: Making use of a spatial index
breadcrumbs: ["SpatiaLite"]
content:
SpatiaLite spatial indexes are R*Trees. They allow you to run efficient bounding box queries using a sub-select, with a similar pattern to that used for  Searches using custom SQL . 
             In the above example, the resulting index will be called  idx_museums_point_geom . This takes the form of a SQLite virtual table. You can inspect its contents using the following query: 
             select * from idx_museums_point_geom limit 10; 
             Here's a live example:  timezones-api.datasette.io/timezones/idx_timezones_Geometry 
             
                 
                     
                     
                     
                     
                     
                     
                         
                             
                                 pkid 
                             
                             
                                 xmin 
                             
                             
                                 xmax 
                             
                             
                                 ymin 
                             
                             
                                 ymax 
                             
                         
                     
                     
                         
                             
                                 1 
                             
                             
                                 -8.601725578308105 
                             
                             
                                 -2.4930307865142822 
                             
                             
                                 4.162120819091797 
                             
                             
                                 10.74019718170166 
                             
                         
                         
                             
                                 2 
                             
                             
                                 -3.2607860565185547 
                             
                             
                                 1.27329421043396 
                             
                             
                                 4.539252281188965 
                             
                             
                                 11.174856185913086 
                             
                         
                         
                             
                                 3 
                             
                             
                                 32.997581481933594 
                             
                             
                                 47.98238754272461 
                             
                             
                                 3.3974475860595703 
                             
                             
                                 14.894054412841797 
                             
                         
                         
                             
                                 4 
                             
                             
                                 -8.66890811920166 
                             
                             
                                 11.997337341308594 
                             
                             
                                 18.9681453704834 
                             
                             
                                 37.296207427978516 
                             
                         
                         
                             
                                 5 
                             
                             
                                 36.43336486816406 
                             
                             
                                 43.300174713134766 
                             
                             
                                 12.354820251464844 
                             
                             
                                 18.070993423461914 
                             
                         
                     
                 
             
             You can now construct efficient bounding box queries that will make use of the index like this: 
             select * from museums where museums.rowid in (
    SELECT pkid FROM idx_museums_point_geom
    -- left-hand-edge of point > left-hand-edge of bbox (minx)
    where xmin > :bbox_minx
    -- right-hand-edge of point < right-hand-edge of bbox (maxx)
    and xmax < :bbox_maxx
    -- bottom-edge of point > bottom-edge of bbox (miny)
    and ymin > :bbox_miny
    -- top-edge of point < top-edge of bbox (maxy)
    and ymax < :bbox_maxy
); 
             Spatial indexes can be created against polygon columns as well as point columns, in which case they will represent the minimum bounding rectangle of that polygon. This is useful for accelerating  within  queries, as seen in the Timezones API example.

references:
[{"href": "https://timezones-api.datasette.io/timezones/idx_timezones_Geometry", "label": "timezones-api.datasette.io/timezones/idx_timezones_Geometry"}]


id: spatialite:importing-shapefiles-into-spatialite
page: spatialite
ref: importing-shapefiles-into-spatialite
title: Importing shapefiles into SpatiaLite
breadcrumbs: ["SpatiaLite"]
content:
The  shapefile format  is a common format for distributing geospatial data. You can use the  spatialite  command-line tool to create a new database table from a shapefile. 
             Try it now with the North America shapefile available from the University of North Carolina  Global River Database  project. Download the file and unzip it (this will create files called  narivs.dbf ,  narivs.prj ,  narivs.shp  and  narivs.shx  in the current directory), then run the following: 
             $ spatialite rivers-database.db
SpatiaLite version ..: 4.3.0a       Supported Extensions:
...
spatialite> .loadshp narivs rivers CP1252 23032
========
Loading shapefile at 'narivs' into SQLite table 'rivers'
...
Inserted 467973 rows into 'rivers' from SHAPEFILE 
             This will load the data from the  narivs  shapefile into a new database table called  rivers . 
             Exit out of  spatialite  (using  Ctrl+D ) and run Datasette against your new database like this: 
             datasette rivers-database.db \
    --load-extension=/usr/local/lib/mod_spatialite.dylib 
             If you browse to  http://localhost:8001/rivers-database/rivers  you will see the new table... but the  Geometry  column will contain unreadable binary data (SpatiaLite uses  a custom format based on WKB ). 
             The easiest way to turn this into semi-readable data is to use the SpatiaLite  AsGeoJSON  function. Try the following using the SQL query interface at  http://localhost:8001/rivers-database : 
             select *, AsGeoJSON(Geometry) from rivers limit 10; 
             This will give you back an additional column of GeoJSON. You can copy and paste GeoJSON from this column into the debugging tool at  geojson.io  to visualize it on a map. 
             To see a more interesting example, try ordering the records with the longest geometry first. Since there are 467,000 rows in the table you will first need to increase the SQL time limit imposed by Datasette: 
             datasette rivers-database.db \
    --load-extension=/usr/local/lib/mod_spatialite.dylib \
    --setting sql_time_limit_ms 10000 
             Now try the following query: 
             select *, AsGeoJSON(Geometry) from rivers
order by length(Geometry) desc limit 10;

references:
[{"href": "https://en.wikipedia.org/wiki/Shapefile", "label": "shapefile format"}, {"href": "http://gaia.geosci.unc.edu/rivers/", "label": "Global River Database"}, {"href": "https://www.gaia-gis.it/gaia-sins/BLOB-Geometry.html", "label": "a custom format based on WKB"}, {"href": "https://geojson.io/", "label": "geojson.io"}]


id: spatialite:importing-geojson-polygons-using-shapely
page: spatialite
ref: importing-geojson-polygons-using-shapely
title: Importing GeoJSON polygons using Shapely
breadcrumbs: ["SpatiaLite"]
content:
Another common form of polygon data is the GeoJSON format. This can be imported into SpatiaLite directly, or by using the  Shapely  Python library. 
             Who's On First  is an excellent source of openly licensed GeoJSON polygons. Let's import the geographical polygon for Wales. First, we can use the Who's On First Spelunker tool to find the record for Wales: 
             spelunker.whosonfirst.org/id/404227475 
             That page includes a link to the GeoJSON record, which can be accessed here: 
             data.whosonfirst.org/404/227/475/404227475.geojson 
             Here's Python code to create a SQLite database, enable SpatiaLite, create a places table and then add a record for Wales: 
             import sqlite3

conn = sqlite3.connect("places.db")
# Enable SpatialLite extension
conn.enable_load_extension(True)
conn.load_extension("/usr/local/lib/mod_spatialite.dylib")
# Create the masic countries table
conn.execute("select InitSpatialMetadata(1)")
conn.execute(
    "create table places (id integer primary key, name text);"
)
# Add a MULTIPOLYGON Geometry column
conn.execute(
    "SELECT AddGeometryColumn('places', 'geom', 4326, 'MULTIPOLYGON', 2);"
)
# Add a spatial index against the new column
conn.execute("SELECT CreateSpatialIndex('places', 'geom');")
# Now populate the table
from shapely.geometry.multipolygon import MultiPolygon
from shapely.geometry import shape
import requests

geojson = requests.get(
    "https://data.whosonfirst.org/404/227/475/404227475.geojson"
).json()
# Convert to "Well Known Text" format
wkt = shape(geojson["geometry"]).wkt
# Insert and commit the record
conn.execute(
    "INSERT INTO places (id, name, geom) VALUES(null, ?, GeomFromText(?, 4326))",
    ("Wales", wkt),
)
conn.commit()

references:
[{"href": "https://pypi.org/project/Shapely/", "label": "Shapely"}, {"href": "https://whosonfirst.org/", "label": "Who's On First"}, {"href": "https://spelunker.whosonfirst.org/id/404227475/", "label": "spelunker.whosonfirst.org/id/404227475"}, {"href": "https://data.whosonfirst.org/404/227/475/404227475.geojson", "label": "data.whosonfirst.org/404/227/475/404227475.geojson"}]


id: spatialite:querying-polygons-using-within
page: spatialite
ref: querying-polygons-using-within
title: Querying polygons using within()
breadcrumbs: ["SpatiaLite"]
references: []
content:
The  within()  SQL function can be used to check if a point is within a geometry: 
             select
    name
from
    places
where
   within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom); 
             The  GeomFromText()  function takes a string of well-known text. Note that the order used here is  longitude  then   latitude . 
             To run that same  within()  query in a way that benefits from the spatial index, use the following: 
             select
    name
from
    places
where
    within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom)
    and rowid in (
        SELECT pkid FROM idx_places_geom
        where xmin < -3.1724366
        and xmax > -3.1724366
        and ymin < 51.4704448
        and ymax > 51.4704448
    );


id: plugin_hooks:id1
page: plugin_hooks
ref: id1
title: Plugin hooks
breadcrumbs: []
references: [{"href": "https://pluggy.readthedocs.io/", "label": "pluggy"}]
content:
Datasette  plugins  use  plugin hooks  to customize Datasette's behavior. These hooks are powered by the  pluggy  plugin system. 
         Each plugin can implement one or more hooks using the  @hookimpl  decorator against a function named that matches one of the hooks documented on this page. 
         When you implement a plugin hook you can accept any or all of the parameters that are documented as being passed to that hook. 
         For example, you can implement the  render_cell  plugin hook like this even though the full documented hook signature is  render_cell(row, value, column, table, database, datasette) : 
         @hookimpl
def render_cell(value, column):
    if column == "stars":
        return "*" * int(value) 
         
             List of plugin hooks 
             
                 
                     prepare_connection(conn, database, datasette) 
                 
                 
                     prepare_jinja2_environment(env, datasette) 
                 
                 
                     extra_template_vars(template, database, table, columns, view_name, request, datasette) 
                 
                 
                     extra_css_urls(template, database, table, columns, view_name, request, datasette) 
                 
                 
                     extra_js_urls(template, database, table, columns, view_name, request, datasette) 
                 
                 
                     extra_body_script(template, database, table, columns, view_name, request, datasette) 
                 
                 
                     publish_subcommand(publish) 
                 
                 
                     render_cell(row, value, column, table, database, datasette) 
                 
                 
                     register_output_renderer(datasette) 
                 
                 
                     register_routes(datasette) 
                 
                 
                     register_commands(cli) 
                 
                 
                     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)


id: plugin_hooks:plugin-hook-prepare-connection
page: plugin_hooks
ref: plugin-hook-prepare-connection
title: prepare_connection(conn, database, datasette)
breadcrumbs: ["Plugin hooks"]
content:
conn  - sqlite3 connection object 
                     
                         The connection that is being opened 
                     
                 
                 
                     database  - string 
                     
                         The name of the database 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             This hook is called when a new SQLite database connection is created. You can
                use it to  register custom SQL functions ,
                aggregates and collations. For example: 
             from datasette import hookimpl
import random


@hookimpl
def prepare_connection(conn):
    conn.create_function(
        "random_integer", 2, random.randint
    ) 
             This registers a SQL function called  random_integer  which takes two
                arguments and can be called like this: 
             select random_integer(1, 10); 
             Examples:  datasette-jellyfish ,  datasette-jq ,  datasette-haversine ,  datasette-rure

references:
[{"href": "https://docs.python.org/2/library/sqlite3.html#sqlite3.Connection.create_function", "label": "register custom SQL functions"}, {"href": "https://datasette.io/plugins/datasette-jellyfish", "label": "datasette-jellyfish"}, {"href": "https://datasette.io/plugins/datasette-jq", "label": "datasette-jq"}, {"href": "https://datasette.io/plugins/datasette-haversine", "label": "datasette-haversine"}, {"href": "https://datasette.io/plugins/datasette-rure", "label": "datasette-rure"}]


id: plugin_hooks:plugin-hook-prepare-jinja2-environment
page: plugin_hooks
ref: plugin-hook-prepare-jinja2-environment
title: prepare_jinja2_environment(env, datasette)
breadcrumbs: ["Plugin hooks"]
content:
env  - jinja2 Environment 
                     
                         The template environment that is being prepared 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             This hook is called with the Jinja2 environment that is used to evaluate
                Datasette HTML templates. You can use it to do things like  register custom
                    template filters , for
                example: 
             from datasette import hookimpl


@hookimpl
def prepare_jinja2_environment(env):
    env.filters["uppercase"] = lambda u: u.upper() 
             You can now use this filter in your custom templates like so: 
             Table name: {{ table|uppercase }} 
             This function can return an awaitable function if it needs to run any async code. 
             Examples:  datasette-edit-templates

references:
[{"href": "http://jinja.pocoo.org/docs/2.10/api/#custom-filters", "label": "register custom\n                    template filters"}, {"href": "https://datasette.io/plugins/datasette-edit-templates", "label": "datasette-edit-templates"}]


id: plugin_hooks:plugin-hook-extra-template-vars
page: plugin_hooks
ref: plugin-hook-extra-template-vars
title: extra_template_vars(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks"]
content:
Extra template variables that should be made available in the rendered template context. 
             
                 
                     template  - string 
                     
                         The template that is being rendered, e.g.  database.html 
                     
                 
                 
                     database  - string or None 
                     
                         The name of the database, or  None  if the page does not correspond to a database (e.g. the root page) 
                     
                 
                 
                     table  - string or None 
                     
                         The name of the table, or  None  if the page does not correct to a table 
                     
                 
                 
                     columns  - list of strings or None 
                     
                         The names of the database columns that will be displayed on this page.  None  if the page does not contain a table. 
                     
                 
                 
                     view_name  - string 
                     
                         The name of the view being displayed. ( index ,  database ,  table , and  row  are the most important ones.) 
                     
                 
                 
                     request  -  Request object  or None 
                     
                         The current HTTP request. This can be  None  if the request object is not available. 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             This hook can return one of three different types: 
             
                 
                     Dictionary 
                     
                         If you return a dictionary its keys and values will be merged into the template context. 
                     
                 
                 
                     Function that returns a dictionary 
                     
                         If you return a function it will be executed. If it returns a dictionary those values will will be merged into the template context. 
                     
                 
                 
                     Function that returns an awaitable function that returns a dictionary 
                     
                         You can also return a function which returns an awaitable function which returns a dictionary. 
                     
                 
             
             Datasette runs Jinja2 in  async mode , which means you can add awaitable functions to the template scope and they will be automatically awaited when they are rendered by the template. 
             Here's an example plugin that adds a  "user_agent"  variable to the template context containing the current request's User-Agent header: 
             @hookimpl
def extra_template_vars(request):
    return {"user_agent": request.headers.get("user-agent")} 
             This example returns an awaitable function which adds a list of  hidden_table_names  to the context: 
             @hookimpl
def extra_template_vars(datasette, database):
    async def hidden_table_names():
        if database:
            db = datasette.databases[database]
            return {
                "hidden_table_names": await db.hidden_table_names()
            }
        else:
            return {}

    return hidden_table_names 
             And here's an example which adds a  sql_first(sql_query)  function which executes a SQL statement and returns the first column of the first row of results: 
             @hookimpl
def extra_template_vars(datasette, database):
    async def sql_first(sql, dbname=None):
        dbname = (
            dbname
            or database
            or next(iter(datasette.databases.keys()))
        )
        result = await datasette.execute(dbname, sql)
        return result.rows[0][0]

    return {"sql_first": sql_first} 
             You can then use the new function in a template like so: 
             SQLite version: {{ sql_first("select sqlite_version()") }} 
             Examples:  datasette-search-all ,  datasette-template-sql

references:
[{"href": "https://jinja.palletsprojects.com/en/2.10.x/api/#async-support", "label": "async mode"}, {"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-template-sql", "label": "datasette-template-sql"}]


id: plugin_hooks:plugin-hook-extra-css-urls
page: plugin_hooks
ref: plugin-hook-extra-css-urls
title: extra_css_urls(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks"]
content:
This takes the same arguments as  extra_template_vars(...) 
             Return a list of extra CSS URLs that should be included on the page. These can
                take advantage of the CSS class hooks described in  Custom pages and templates . 
             This can be a list of URLs: 
             from datasette import hookimpl


@hookimpl
def extra_css_urls():
    return [
        "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css"
    ] 
             Or a list of dictionaries defining both a URL and an
                 SRI hash : 
             @hookimpl
def extra_css_urls():
    return [
        {
            "url": "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css",
            "sri": "sha384-9gVQ4dYFwwWSjIDZnLEWnxCjeSWFphJiwGPXr1jddIhOegiu1FwO5qRGvFXOdJZ4",
        }
    ] 
             This function can also return an awaitable function, useful if it needs to run any async code: 
             @hookimpl
def extra_css_urls(datasette):
    async def inner():
        db = datasette.get_database()
        results = await db.execute(
            "select url from css_files"
        )
        return [r[0] for r in results]

    return inner 
             Examples:  datasette-cluster-map ,  datasette-vega

references:
[{"href": "https://www.srihash.org/", "label": "SRI hash"}, {"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-vega", "label": "datasette-vega"}]


id: plugin_hooks:plugin-hook-extra-js-urls
page: plugin_hooks
ref: plugin-hook-extra-js-urls
title: extra_js_urls(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks"]
content:
This takes the same arguments as  extra_template_vars(...) 
             This works in the same way as  extra_css_urls()  but for JavaScript. You can
                return a list of URLs, a list of dictionaries or an awaitable function that returns those things: 
             from datasette import hookimpl


@hookimpl
def extra_js_urls():
    return [
        {
            "url": "https://code.jquery.com/jquery-3.3.1.slim.min.js",
            "sri": "sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo",
        }
    ] 
             You can also return URLs to files from your plugin's  static/  directory, if
                you have one: 
             @hookimpl
def extra_js_urls():
    return ["/-/static-plugins/your-plugin/app.js"] 
             Note that  your-plugin  here should be the hyphenated plugin name - the name that is displayed in the list on the  /-/plugins  debug page. 
             If your code uses  JavaScript modules  you should include the  "module": True  key. See  Custom CSS and JavaScript  for more details. 
             @hookimpl
def extra_js_urls():
    return [
        {
            "url": "/-/static-plugins/your-plugin/app.js",
            "module": True,
        }
    ] 
             Examples:  datasette-cluster-map ,  datasette-vega

references:
[{"href": "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules", "label": "JavaScript modules"}, {"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-vega", "label": "datasette-vega"}]


id: plugin_hooks:plugin-hook-extra-body-script
page: plugin_hooks
ref: plugin-hook-extra-body-script
title: extra_body_script(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}]
content:
Extra JavaScript to be added to a  <script>  block at the end of the  <body>  element on the page. 
             This takes the same arguments as  extra_template_vars(...) 
             The  template ,  database ,  table  and  view_name  options can be used to return different code depending on which template is being rendered and which database or table are being processed. 
             The  datasette  instance is provided primarily so that you can consult any plugin configuration options that may have been set, using the  datasette.plugin_config(plugin_name)  method documented above. 
             This function can return a string containing JavaScript, or a dictionary as described below, or a function or awaitable function that returns a string or dictionary. 
             Use a dictionary if you want to specify that the code should be placed in a  <script type="module">...</script>  element: 
             @hookimpl
def extra_body_script():
    return {
        "module": True,
        "script": "console.log('Your JavaScript goes here...')",
    } 
             This will add the following to the end of your page: 
             <script type="module">console.log('Your JavaScript goes here...')</script> 
             Example:  datasette-cluster-map


id: plugin_hooks:plugin-hook-publish-subcommand
page: plugin_hooks
ref: plugin-hook-publish-subcommand
title: publish_subcommand(publish)
breadcrumbs: ["Plugin hooks"]
content:
publish  - Click publish command group 
                     
                         The Click command group for the  datasette publish  subcommand 
                     
                 
             
             This hook allows you to create new providers for the  datasette publish 
                command. Datasette uses this hook internally to implement the default  cloudrun 
                and  heroku  subcommands, so you can read
                 their source 
                to see examples of this hook in action. 
             Let's say you want to build a plugin that adds a  datasette publish my_hosting_provider --api_key=xxx mydatabase.db  publish command. Your implementation would start like this: 
             from datasette import hookimpl
from datasette.publish.common import (
    add_common_publish_arguments_and_options,
)
import click


@hookimpl
def publish_subcommand(publish):
    @publish.command()
    @add_common_publish_arguments_and_options
    @click.option(
        "-k",
        "--api_key",
        help="API key for talking to my hosting provider",
    )
    def my_hosting_provider(
        files,
        metadata,
        extra_options,
        branch,
        template_dir,
        plugins_dir,
        static,
        install,
        plugin_secret,
        version_note,
        secret,
        title,
        license,
        license_url,
        source,
        source_url,
        about,
        about_url,
        api_key,
    ):
        ... 
             Examples:  datasette-publish-fly ,  datasette-publish-vercel

references:
[{"href": "https://github.com/simonw/datasette/tree/main/datasette/publish", "label": "their source"}, {"href": "https://datasette.io/plugins/datasette-publish-fly", "label": "datasette-publish-fly"}, {"href": "https://datasette.io/plugins/datasette-publish-vercel", "label": "datasette-publish-vercel"}]


id: plugin_hooks:plugin-hook-render-cell
page: plugin_hooks
ref: plugin-hook-render-cell
title: render_cell(row, value, column, table, database, datasette)
breadcrumbs: ["Plugin hooks"]
content:
Lets you customize the display of values within table cells in the HTML table view. 
             
                 
                     row  -  sqlite.Row 
                     
                         The SQLite row object that the value being rendered is part of 
                     
                 
                 
                     value  - string, integer, float, bytes or None 
                     
                         The value that was loaded from the database 
                     
                 
                 
                     column  - string 
                     
                         The name of the column being rendered 
                     
                 
                 
                     table  - string or None 
                     
                         The name of the table - or  None  if this is a custom SQL query 
                     
                 
                 
                     database  - string 
                     
                         The name of the database 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
             
             If your hook returns  None , it will be ignored. Use this to indicate that your hook is not able to custom render this particular value. 
             If the hook returns a string, that string will be rendered in the table cell. 
             If you want to return HTML markup you can do so by returning a  jinja2.Markup  object. 
             You can also return an awaitable function which returns a value. 
             Datasette will loop through all available  render_cell  hooks and display the value returned by the first one that does not return  None . 
             Here is an example of a custom  render_cell()  plugin which looks for values that are a JSON string matching the following format: 
             {"href": "https://www.example.com/", "label": "Name"} 
             If the value matches that pattern, the plugin returns an HTML link element: 
             from datasette import hookimpl
import markupsafe
import json


@hookimpl
def render_cell(value):
    # Render {"href": "...", "label": "..."} as link
    if not isinstance(value, str):
        return None
    stripped = value.strip()
    if not (
        stripped.startswith("{") and stripped.endswith("}")
    ):
        return None
    try:
        data = json.loads(value)
    except ValueError:
        return None
    if not isinstance(data, dict):
        return None
    if set(data.keys()) != {"href", "label"}:
        return None
    href = data["href"]
    if not (
        href.startswith("/")
        or href.startswith("http://")
        or href.startswith("https://")
    ):
        return None
    return markupsafe.Markup(
        '<a href="{href}">{label}</a>'.format(
            href=markupsafe.escape(data["href"]),
            label=markupsafe.escape(data["label"] or "")
            or "&nbsp;",
        )
    ) 
             Examples:  datasette-render-binary ,  datasette-render-markdown ,  datasette-json-html

references:
[{"href": "https://datasette.io/plugins/datasette-render-binary", "label": "datasette-render-binary"}, {"href": "https://datasette.io/plugins/datasette-render-markdown", "label": "datasette-render-markdown"}, {"href": "https://datasette.io/plugins/datasette-json-html", "label": "datasette-json-html"}]


id: plugin_hooks:plugin-register-output-renderer
page: plugin_hooks
ref: plugin-register-output-renderer
title: register_output_renderer(datasette)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             Registers a new output renderer, to output data in a custom format. The hook function should return a dictionary, or a list of dictionaries, of the following shape: 
             @hookimpl
def register_output_renderer(datasette):
    return {
        "extension": "test",
        "render": render_demo,
        "can_render": can_render_demo,  # Optional
    } 
             This will register  render_demo  to be called when paths with the extension  .test  (for example  /database.test ,  /database/table.test , or  /database/table/row.test ) are requested. 
             render_demo  is a Python function. It can be a regular function or an  async def render_demo()  awaitable function, depending on if it needs to make any asynchronous calls. 
             can_render_demo  is a Python function (or  async def  function) which accepts the same arguments as  render_demo  but just returns  True  or  False . It lets Datasette know if the current SQL query can be represented by the plugin - and hence influnce if a link to this output format is displayed in the user interface. If you omit the  "can_render"  key from the dictionary every query will be treated as being supported by the plugin. 
             When a request is received, the  "render"  callback function is called with zero or more of the following arguments. Datasette will inspect your callback function and pass arguments that match its function signature. 
             
                 
                     datasette  -  Datasette class 
                     
                         For accessing plugin configuration and executing queries. 
                     
                 
                 
                     columns  - list of strings 
                     
                         The names of the columns returned by this query. 
                     
                 
                 
                     rows  - list of  sqlite3.Row  objects 
                     
                         The rows returned by the query. 
                     
                 
                 
                     sql  - string 
                     
                         The SQL query that was executed. 
                     
                 
                 
                     query_name  - string or None 
                     
                         If this was the execution of a  canned query , the name of that query. 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     table  - string or None 
                     
                         The table or view, if one is being rendered. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     view_name  - string 
                     
                         The name of the current view being called.  index ,  database ,  table , and  row  are the most important ones. 
                     
                 
             
             The callback function can return  None , if it is unable to render the data, or a  Response class  that will be returned to the caller. 
             It can also return a dictionary with the following keys. This format is  deprecated  as-of Datasette 0.49 and will be removed by Datasette 1.0. 
             
                 
                     body  - string or bytes, optional 
                     
                         The response body, default empty 
                     
                 
                 
                     content_type  - string, optional 
                     
                         The Content-Type header, default  text/plain 
                     
                 
                 
                     status_code  - integer, optional 
                     
                         The HTTP status code, default 200 
                     
                 
                 
                     headers  - dictionary, optional 
                     
                         Extra HTTP headers to be returned in the response. 
                     
                 
             
             An example of an output renderer callback function: 
             def render_demo():
    return Response.text("Hello World") 
             Here is a more complex example: 
             async def render_demo(datasette, columns, rows):
    db = datasette.get_database()
    result = await db.execute("select sqlite_version()")
    first_row = " | ".join(columns)
    lines = [first_row]
    lines.append("=" * len(first_row))
    for row in rows:
        lines.append(" | ".join(row))
    return Response(
        "\n".join(lines),
        content_type="text/plain; charset=utf-8",
        headers={"x-sqlite-version": result.first()[0]},
    ) 
             And here is an example  can_render  function which returns  True  only if the query results contain the columns  atom_id ,  atom_title  and  atom_updated : 
             def can_render_demo(columns):
    return {
        "atom_id",
        "atom_title",
        "atom_updated",
    }.issubset(columns) 
             Examples:  datasette-atom ,  datasette-ics ,  datasette-geojson ,  datasette-copyable

references:
[{"href": "https://datasette.io/plugins/datasette-atom", "label": "datasette-atom"}, {"href": "https://datasette.io/plugins/datasette-ics", "label": "datasette-ics"}, {"href": "https://datasette.io/plugins/datasette-geojson", "label": "datasette-geojson"}, {"href": "https://datasette.io/plugins/datasette-copyable", "label": "datasette-copyable"}]


id: plugin_hooks:plugin-register-routes
page: plugin_hooks
ref: plugin-register-routes
title: register_routes(datasette)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             Register additional view functions to execute for specified URL routes. 
             Return a list of  (regex, view_function)  pairs, something like this: 
             from datasette import hookimpl, Response
import html


async def hello_from(request):
    name = request.url_vars["name"]
    return Response.html(
        "Hello from {}".format(html.escape(name))
    )


@hookimpl
def register_routes():
    return [(r"^/hello-from/(?P<name>.*)$", hello_from)] 
             The view functions can take a number of different optional arguments. The corresponding argument will be passed to your function depending on its named parameters - a form of dependency injection. 
             The optional view function arguments are as follows: 
             
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     scope  - dictionary 
                     
                         The incoming ASGI scope dictionary. 
                     
                 
                 
                     send  - function 
                     
                         The ASGI send function. 
                     
                 
                 
                     receive  - function 
                     
                         The ASGI receive function. 
                     
                 
             
             The view function can be a regular function or an  async def  function, depending on if it needs to use any  await  APIs. 
             The function can either return a  Response class  or it can return nothing and instead respond directly to the request using the ASGI  send  function (for advanced uses only). 
             It can also raise the  datasette.NotFound  exception to return a 404 not found error, or the  datasette.Forbidden  exception for a 403 forbidden. 
             See  Designing URLs for your plugin  for tips on designing the URL routes used by your plugin. 
             Examples:  datasette-auth-github ,  datasette-psutil

references:
[{"href": "https://datasette.io/plugins/datasette-auth-github", "label": "datasette-auth-github"}, {"href": "https://datasette.io/plugins/datasette-psutil", "label": "datasette-psutil"}]


id: plugin_hooks:plugin-hook-register-commands
page: plugin_hooks
ref: plugin-hook-register-commands
title: register_commands(cli)
breadcrumbs: ["Plugin hooks"]
content:
cli  - the root Datasette  Click command group 
                     
                         Use this to register additional CLI commands 
                     
                 
             
             Register additional CLI commands that can be run using  datsette yourcommand ... . This provides a mechanism by which plugins can add new CLI commands to Datasette. 
             This example registers a new  datasette verify file1.db file2.db  command that checks if the provided file paths are valid SQLite databases: 
             from datasette import hookimpl
import click
import sqlite3


@hookimpl
def register_commands(cli):
    @cli.command()
    @click.argument(
        "files", type=click.Path(exists=True), nargs=-1
    )
    def verify(files):
        "Verify that files can be opened by Datasette"
        for file in files:
            conn = sqlite3.connect(str(file))
            try:
                conn.execute("select * from sqlite_master")
            except sqlite3.DatabaseError:
                raise click.ClickException(
                    "Invalid database: {}".format(file)
                ) 
             The new command can then be executed like so: 
             datasette verify fixtures.db 
             Help text (from the docstring for the function plus any defined Click arguments or options) will become available using: 
             datasette verify --help 
             Plugins can register multiple commands by making multiple calls to the  @cli.command()  decorator. Consult the  Click documentation  for full details on how to build a CLI command, including how to define arguments and options. 
             Note that  register_commands()  plugins cannot used with the  --plugins-dir mechanism  - they need to be installed into the same virtual environment as Datasette using  pip install . Provided it has a  setup.py  file (see  Packaging a plugin ) you can run  pip install  directly against the directory in which you are developing your plugin like so: 
             pip install -e path/to/my/datasette-plugin 
             Examples:  datasette-auth-passwords ,  datasette-verify

references:
[{"href": "https://click.palletsprojects.com/en/latest/commands/#callback-invocation", "label": "Click command group"}, {"href": "https://click.palletsprojects.com/", "label": "Click documentation"}, {"href": "https://datasette.io/plugins/datasette-auth-passwords", "label": "datasette-auth-passwords"}, {"href": "https://datasette.io/plugins/datasette-verify", "label": "datasette-verify"}]


id: plugin_hooks:plugin-register-facet-classes
page: plugin_hooks
ref: plugin-register-facet-classes
title: register_facet_classes()
breadcrumbs: ["Plugin hooks"]
content:
Return a list of additional Facet subclasses to be registered. 
             
                 The design of this plugin hook is unstable and may change. See  issue 830 . 
             
             Each Facet subclass implements a new type of facet operation. The class should look like this: 
             class SpecialFacet(Facet):
    # This key must be unique across all facet classes:
    type = "special"

    async def suggest(self):
        # Use self.sql and self.params to suggest some facets
        suggested_facets = []
        suggested_facets.append(
            {
                "name": column,  # Or other unique name
                # Construct the URL that will enable this facet:
                "toggle_url": self.ds.absolute_url(
                    self.request,
                    path_with_added_args(
                        self.request, {"_facet": column}
                    ),
                ),
            }
        )
        return suggested_facets

    async def facet_results(self):
        # This should execute the facet operation and return results, again
        # using self.sql and self.params as the starting point
        facet_results = []
        facets_timed_out = []
        facet_size = self.get_facet_size()
        # Do some calculations here...
        for column in columns_selected_for_facet:
            try:
                facet_results_values = []
                # More calculations...
                facet_results_values.append(
                    {
                        "value": value,
                        "label": label,
                        "count": count,
                        "toggle_url": self.ds.absolute_url(
                            self.request, toggle_path
                        ),
                        "selected": selected,
                    }
                )
                facet_results.append(
                    {
                        "name": column,
                        "results": facet_results_values,
                        "truncated": len(facet_rows_results)
                        > facet_size,
                    }
                )
            except QueryInterrupted:
                facets_timed_out.append(column)

        return facet_results, facets_timed_out 
             See  datasette/facets.py  for examples of how these classes can work. 
             The plugin hook can then be used to register the new facet class like this: 
             @hookimpl
def register_facet_classes():
    return [SpecialFacet]

references:
[{"href": "https://github.com/simonw/datasette/issues/830", "label": "issue 830"}, {"href": "https://github.com/simonw/datasette/blob/main/datasette/facets.py", "label": "datasette/facets.py"}]


id: plugin_hooks:plugin-asgi-wrapper
page: plugin_hooks
ref: plugin-asgi-wrapper
title: asgi_wrapper(datasette)
breadcrumbs: ["Plugin hooks"]
content:
Return an  ASGI  middleware wrapper function that will be applied to the Datasette ASGI application. 
             This is a very powerful hook. You can use it to manipulate the entire Datasette response, or even to configure new URL routes that will be handled by your own custom code. 
             You can write your ASGI code directly against the low-level specification, or you can use the middleware utilities provided by an ASGI framework such as  Starlette . 
             This example plugin adds a  x-databases  HTTP header listing the currently attached databases: 
             from datasette import hookimpl
from functools import wraps


@hookimpl
def asgi_wrapper(datasette):
    def wrap_with_databases_header(app):
        @wraps(app)
        async def add_x_databases_header(
            scope, receive, send
        ):
            async def wrapped_send(event):
                if event["type"] == "http.response.start":
                    original_headers = (
                        event.get("headers") or []
                    )
                    event = {
                        "type": event["type"],
                        "status": event["status"],
                        "headers": original_headers
                        + [
                            [
                                b"x-databases",
                                ", ".join(
                                    datasette.databases.keys()
                                ).encode("utf-8"),
                            ]
                        ],
                    }
                await send(event)

            await app(scope, receive, wrapped_send)

        return add_x_databases_header

    return wrap_with_databases_header 
             Examples:  datasette-cors ,  datasette-pyinstrument ,  datasette-total-page-time

references:
[{"href": "https://asgi.readthedocs.io/", "label": "ASGI"}, {"href": "https://www.starlette.io/middleware/", "label": "Starlette"}, {"href": "https://datasette.io/plugins/datasette-cors", "label": "datasette-cors"}, {"href": "https://datasette.io/plugins/datasette-pyinstrument", "label": "datasette-pyinstrument"}, {"href": "https://datasette.io/plugins/datasette-total-page-time", "label": "datasette-total-page-time"}]


id: plugin_hooks:plugin-hook-startup
page: plugin_hooks
ref: plugin-hook-startup
title: startup(datasette)
breadcrumbs: ["Plugin hooks"]
content:
This hook fires when the Datasette application server first starts up. You can implement a regular function, for example to validate required plugin configuration: 
             @hookimpl
def startup(datasette):
    config = datasette.plugin_config("my-plugin") or {}
    assert (
        "required-setting" in config
    ), "my-plugin requires setting required-setting" 
             Or you can return an async function which will be awaited on startup. Use this option if you need to make any database queries: 
             @hookimpl
def startup(datasette):
    async def inner():
        db = datasette.get_database()
        if "my_table" not in await db.table_names():
            await db.execute_write(
                """
                create table my_table (mycol text)
            """
            )

    return inner 
             Potential use-cases: 
             
                 
                     Run some initialization code for the plugin 
                 
                 
                     Create database tables that a plugin needs on startup 
                 
                 
                     Validate the metadata configuration for a plugin on startup, and raise an error if it is invalid 
                 
             
             
                 If you are writing  unit tests  for a plugin that uses this hook and doesn't exercise Datasette by sending
                    any simulated requests through it you will need to explicitly call  await ds.invoke_startup()  in your tests. An example: 
                 @pytest.mark.asyncio
async def test_my_plugin():
    ds = Datasette()
    await ds.invoke_startup()
    # Rest of test goes here 
             
             Examples:  datasette-saved-queries ,  datasette-init

references:
[{"href": "https://datasette.io/plugins/datasette-saved-queries", "label": "datasette-saved-queries"}, {"href": "https://datasette.io/plugins/datasette-init", "label": "datasette-init"}]


id: plugin_hooks:plugin-hook-canned-queries
page: plugin_hooks
ref: plugin-hook-canned-queries
title: canned_queries(datasette, database, actor)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
             
             Use this hook to return a dictionary of additional  canned query  definitions for the specified database. The return value should be the same shape as the JSON described in the  canned query  documentation. 
             from datasette import hookimpl


@hookimpl
def canned_queries(datasette, database):
    if database == "mydb":
        return {
            "my_query": {
                "sql": "select * from my_table where id > :min_id"
            }
        } 
             The hook can alternatively return an awaitable function that returns a list. Here's an example that returns queries that have been stored in the  saved_queries  database table, if one exists: 
             from datasette import hookimpl


@hookimpl
def canned_queries(datasette, database):
    async def inner():
        db = datasette.get_database(database)
        if await db.table_exists("saved_queries"):
            results = await db.execute(
                "select name, sql from saved_queries"
            )
            return {
                result["name"]: {"sql": result["sql"]}
                for result in results
            }

    return inner 
             The actor parameter can be used to include the currently authenticated actor in your decision. Here's an example that returns saved queries that were saved by that actor: 
             from datasette import hookimpl


@hookimpl
def canned_queries(datasette, database, actor):
    async def inner():
        db = datasette.get_database(database)
        if actor is not None and await db.table_exists(
            "saved_queries"
        ):
            results = await db.execute(
                "select name, sql from saved_queries where actor_id = :id",
                {"id": actor["id"]},
            )
            return {
                result["name"]: {"sql": result["sql"]}
                for result in results
            }

    return inner 
             Example:  datasette-saved-queries

references:
[{"href": "https://datasette.io/plugins/datasette-saved-queries", "label": "datasette-saved-queries"}]


id: plugin_hooks:plugin-hook-actor-from-request
page: plugin_hooks
ref: plugin-hook-actor-from-request
title: actor_from_request(datasette, request)
breadcrumbs: ["Plugin hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-auth-tokens", "label": "datasette-auth-tokens"}]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
             
             This is part of Datasette's  authentication and permissions system . The function should attempt to authenticate an actor (either a user or an API actor of some sort) based on information in the request. 
             If it cannot authenticate an actor, it should return  None . Otherwise it should return a dictionary representing that actor. 
             Here's an example that authenticates the actor based on an incoming API key: 
             from datasette import hookimpl
import secrets

SECRET_KEY = "this-is-a-secret"


@hookimpl
def actor_from_request(datasette, request):
    authorization = (
        request.headers.get("authorization") or ""
    )
    expected = "Bearer {}".format(SECRET_KEY)

    if secrets.compare_digest(authorization, expected):
        return {"id": "bot"} 
             If you install this in your plugins directory you can test it like this: 
             $ curl -H 'Authorization: Bearer this-is-a-secret' http://localhost:8003/-/actor.json 
             Instead of returning a dictionary, this function can return an awaitable function which itself returns either  None  or a dictionary. This is useful for authentication functions that need to make a database query - for example: 
             from datasette import hookimpl


@hookimpl
def actor_from_request(datasette, request):
    async def inner():
        token = request.args.get("_token")
        if not token:
            return None
        # Look up ?_token=xxx in sessions table
        result = await datasette.get_database().execute(
            "select count(*) from sessions where token = ?",
            [token],
        )
        if result.first()[0]:
            return {"token": token}
        else:
            return None

    return inner 
             Example:  datasette-auth-tokens


id: plugin_hooks:plugin-hook-filters-from-request
page: plugin_hooks
ref: plugin-hook-filters-from-request
title: filters_from_request(request, database, table, datasette)
breadcrumbs: ["Plugin hooks"]
content:
request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     table  - string 
                     
                         The name of the table. 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
             
             This hook runs on the  table  page, and can influence the  where  clause of the SQL query used to populate that page, based on query string arguments on the incoming request. 
             The hook should return an instance of  datasette.filters.FilterArguments  which has one required and three optional arguments: 
             return FilterArguments(
    where_clauses=["id > :max_id"],
    params={"max_id": 5},
    human_descriptions=["max_id is greater than 5"],
    extra_context={},
) 
             The arguments to the  FilterArguments  class constructor are as follows: 
             
                 
                     where_clauses  - list of strings, required 
                     
                         A list of SQL fragments that will be inserted into the SQL query, joined by the  and  operator. These can include  :named  parameters which will be populated using data in  params . 
                     
                 
                 
                     params  - dictionary, optional 
                     
                         Additional keyword arguments to be used when the query is executed. These should match any  :arguments  in the where clauses. 
                     
                 
                 
                     human_descriptions  - list of strings, optional 
                     
                         These strings will be included in the human-readable description at the top of the page and the page  <title> . 
                     
                 
                 
                     extra_context  - dictionary, optional 
                     
                         Additional context variables that should be made available to the  table.html  template when it is rendered. 
                     
                 
             
             This example plugin causes 0 results to be returned if  ?_nothing=1  is added to the URL: 
             from datasette import hookimpl
from datasette.filters import FilterArguments


@hookimpl
def filters_from_request(self, request):
    if request.args.get("_nothing"):
        return FilterArguments(
            ["1 = 0"], human_descriptions=["NOTHING"]
        ) 
             Example:  datasette-leaflet-freedraw

references:
[{"href": "https://datasette.io/plugins/datasette-leaflet-freedraw", "label": "datasette-leaflet-freedraw"}]


id: plugin_hooks:plugin-hook-permission-allowed
page: plugin_hooks
ref: plugin-hook-permission-allowed
title: permission_allowed(datasette, actor, action, resource)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor  - dictionary 
                     
                         The current actor, as decided by  actor_from_request(datasette, request) . 
                     
                 
                 
                     action  - string 
                     
                         The action to be performed, e.g.  "edit-table" . 
                     
                 
                 
                     resource  - string or None 
                     
                         An identifier for the individual resource, e.g. the name of the table. 
                     
                 
             
             Called to check that an actor has permission to perform an action on a resource. Can return  True  if the action is allowed,  False  if the action is not allowed or  None  if the plugin does not have an opinion one way or the other. 
             Here's an example plugin which randomly selects if a permission should be allowed or denied, except for  view-instance  which always uses the default permission scheme instead. 
             from datasette import hookimpl
import random


@hookimpl
def permission_allowed(action):
    if action != "view-instance":
        # Return True or False at random
        return random.random() > 0.5
    # Returning None falls back to default permissions 
             This function can alternatively return an awaitable function which itself returns  True ,  False  or  None . You can use this option if you need to execute additional database queries using  await datasette.execute(...) . 
             Here's an example that allows users to view the  admin_log  table only if their actor  id  is present in the  admin_users  table. It aso disallows arbitrary SQL queries for the  staff.db  database for all users. 
             @hookimpl
def permission_allowed(datasette, actor, action, resource):
    async def inner():
        if action == "execute-sql" and resource == "staff":
            return False
        if action == "view-table" and resource == (
            "staff",
            "admin_log",
        ):
            if not actor:
                return False
            user_id = actor["id"]
            return await datasette.get_database(
                "staff"
            ).execute(
                "select count(*) from admin_users where user_id = :user_id",
                {"user_id": user_id},
            )

    return inner 
             See  built-in permissions  for a full list of permissions that are included in Datasette core. 
             Example:  datasette-permissions-sql

references:
[{"href": "https://datasette.io/plugins/datasette-permissions-sql", "label": "datasette-permissions-sql"}]


id: plugin_hooks:plugin-hook-register-magic-parameters
page: plugin_hooks
ref: plugin-hook-register-magic-parameters
title: register_magic_parameters(datasette)
breadcrumbs: ["Plugin hooks"]
references: []
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                     
                 
             
             Magic parameters  can be used to add automatic parameters to  canned queries . This plugin hook allows additional magic parameters to be defined by plugins. 
             Magic parameters all take this format:  _prefix_rest_of_parameter . The prefix indicates which magic parameter function should be called - the rest of the parameter is passed as an argument to that function. 
             To register a new function, return it as a tuple of  (string prefix, function)  from this hook. The function you register should take two arguments:  key  and  request , where  key  is the  rest_of_parameter  portion of the parameter and  request  is the current  Request object . 
             This example registers two new magic parameters:  :_request_http_version  returning the HTTP version of the current request, and  :_uuid_new  which returns a new UUID: 
             from uuid import uuid4


def uuid(key, request):
    if key == "new":
        return str(uuid4())
    else:
        raise KeyError


def request(key, request):
    if key == "http_version":
        return request.scope["http_version"]
    else:
        raise KeyError


@hookimpl
def register_magic_parameters(datasette):
    return [
        ("request", request),
        ("uuid", uuid),
    ]


id: plugin_hooks:plugin-hook-forbidden
page: plugin_hooks
ref: plugin-hook-forbidden
title: forbidden(datasette, request, message)
breadcrumbs: ["Plugin hooks"]
references: []
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     message  - string 
                     
                         A message hinting at why the request was forbidden. 
                     
                 
             
             Plugins can use this to customize how Datasette responds when a 403 Forbidden error occurs - usually because a page failed a permission check, see  Permissions . 
             If a plugin hook wishes to react to the error, it should return a  Response object . 
             This example returns a redirect to a  /-/login  page: 
             from datasette import hookimpl
from urllib.parse import urlencode


@hookimpl
def forbidden(request, message):
    return Response.redirect(
        "/-/login?=" + urlencode({"message": message})
    ) 
             The function can alternatively return an awaitable function if it needs to make any asynchronous method calls. This example renders a template: 
             from datasette import hookimpl, Response


@hookimpl
def forbidden(datasette):
    async def inner():
        return Response.html(
            await datasette.render_template(
                "render_message.html", request=request
            )
        )

    return inner


id: plugin_hooks:plugin-hook-handle-exception
page: plugin_hooks
ref: plugin-hook-handle-exception
title: handle_exception(datasette, request, exception)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     exception  -  Exception 
                     
                         The exception that was raised. 
                     
                 
             
             This hook is called any time an unexpected exception is raised. You can use it to record the exception. 
             If your handler returns a  Response  object it will be returned to the client in place of the default Datasette error page. 
             The handler can return a response directly, or it can return return an awaitable function that returns a response. 
             This example logs an error to  Sentry  and then renders a custom error page: 
             from datasette import hookimpl, Response
import sentry_sdk


@hookimpl
def handle_exception(datasette, exception):
    sentry_sdk.capture_exception(exception)

    async def inner():
        return Response.html(
            await datasette.render_template(
                "custom_error.html", request=request
            )
        )

    return inner 
             Example:  datasette-sentry

references:
[{"href": "https://sentry.io/", "label": "Sentry"}, {"href": "https://datasette.io/plugins/datasette-sentry", "label": "datasette-sentry"}]


id: plugin_hooks:plugin-hook-menu-links
page: plugin_hooks
ref: plugin-hook-menu-links
title: menu_links(datasette, actor, request)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
                 
                     request  -  Request object  or None 
                     
                         The current HTTP request. This can be  None  if the request object is not available. 
                     
                 
             
             This hook allows additional items to be included in the menu displayed by Datasette's top right menu icon. 
             The hook should return a list of  {"href": "...", "label": "..."}  menu items. These will be added to the menu. 
             It can alternatively return an  async def  awaitable function which returns a list of menu items. 
             This example adds a new menu item but only if the signed in user is  "root" : 
             from datasette import hookimpl


@hookimpl
def menu_links(datasette, actor):
    if actor and actor.get("id") == "root":
        return [
            {
                "href": datasette.urls.path(
                    "/-/edit-schema"
                ),
                "label": "Edit schema",
            },
        ] 
             Using  datasette.urls  here ensures that links in the menu will take the  base_url  setting into account. 
             Examples:  datasette-search-all ,  datasette-graphql

references:
[{"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}]


id: plugin_hooks:plugin-hook-table-actions
page: plugin_hooks
ref: plugin-hook-table-actions
title: table_actions(datasette, actor, database, table, request)
breadcrumbs: ["Plugin hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     table  - string 
                     
                         The name of the table. 
                     
                 
                 
                     request  -  Request object  or None 
                     
                         The current HTTP request. This can be  None  if the request object is not available. 
                     
                 
             
             This hook allows table actions to be displayed in a menu accessed via an action icon at the top of the table page. It should return a list of  {"href": "...", "label": "..."}  menu items. 
             It can alternatively return an  async def  awaitable function which returns a list of menu items. 
             This example adds a new table action if the signed in user is  "root" : 
             from datasette import hookimpl


@hookimpl
def table_actions(datasette, actor, database, table):
    if actor and actor.get("id") == "root":
        return [
            {
                "href": datasette.urls.path(
                    "/-/edit-schema/{}/{}".format(
                        database, table
                    )
                ),
                "label": "Edit schema for this table",
            }
        ] 
             Example:  datasette-graphql


id: plugin_hooks:plugin-hook-database-actions
page: plugin_hooks
ref: plugin-hook-database-actions
title: database_actions(datasette, actor, database, request)
breadcrumbs: ["Plugin hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
             
             This hook is similar to  table_actions(datasette, actor, database, table, request)  but populates an actions menu on the database page. 
             Example:  datasette-graphql


id: plugin_hooks:plugin-hook-skip-csrf
page: plugin_hooks
ref: plugin-hook-skip-csrf
title: skip_csrf(datasette, scope)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     scope  - dictionary 
                     
                         The  ASGI scope  for the incoming HTTP request. 
                     
                 
             
             This hook can be used to skip  CSRF protection  for a specific incoming request. For example, you might have a custom path at  /submit-comment  which is designed to accept comments from anywhere, whether or not the incoming request originated on the site and has an accompanying CSRF token. 
             This example will disable CSRF protection for that specific URL path: 
             from datasette import hookimpl


@hookimpl
def skip_csrf(scope):
    return scope["path"] == "/submit-comment" 
             If any of the currently active  skip_csrf()  plugin hooks return  True , CSRF protection will be skipped for the request.

references:
[{"href": "https://asgi.readthedocs.io/en/latest/specs/www.html#http-connection-scope", "label": "ASGI scope"}]


id: plugin_hooks:plugin-hook-get-metadata
page: plugin_hooks
ref: plugin-hook-get-metadata
title: get_metadata(datasette, key, database, table)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
                 
                     database  - string or None 
                     
                         The name of the database metadata is being asked for. 
                     
                 
                 
                     table  - string or None 
                     
                         The name of the table. 
                     
                 
                 
                     key  - string or None 
                     
                         The name of the key for which data is being asked for. 
                     
                 
             
             This hook is responsible for returning a dictionary corresponding to Datasette  Metadata . This function is passed the  database ,  table  and  key  which were passed to the upstream internal request for metadata. Regardless, it is important to return a global metadata object, where  "databases": []  would be a top-level key. The dictionary returned here, will be merged with, and overwritten by, the contents of the physical  metadata.yaml  if one is present. 
             
                 The design of this plugin hook does not currently provide a mechanism for interacting with async code, and may change in the future. See  issue 1384 . 
             
             @hookimpl
def get_metadata(datasette, key, database, table):
    metadata = {
        "title": "This will be the Datasette landing page title!",
        "description": get_instance_description(datasette),
        "databases": [],
    }
    for db_name, db_data_dict in get_my_database_meta(
        datasette, database, table, key
    ):
        metadata["databases"][db_name] = db_data_dict
    # whatever we return here will be merged with any other plugins using this hook and
    # will be overwritten by a local metadata.yaml if one exists!
    return metadata 
             Example:  datasette-remote-metadata plugin

references:
[{"href": "https://github.com/simonw/datasette/issues/1384", "label": "issue 1384"}, {"href": "https://datasette.io/plugins/datasette-remote-metadata", "label": "datasette-remote-metadata plugin"}]


id: testing_plugins:id1
page: testing_plugins
ref: id1
title: Testing plugins
breadcrumbs: []
content:
We recommend using  pytest  to write automated tests for your plugins. 
         If you use the template described in  Starting an installable plugin using cookiecutter  your plugin will start with a single test in your  tests/  directory that looks like this: 
         from datasette.app import Datasette
import pytest


@pytest.mark.asyncio
async def test_plugin_is_installed():
    datasette = Datasette(memory=True)
    response = await datasette.client.get("/-/plugins.json")
    assert response.status_code == 200
    installed_plugins = {p["name"] for p in response.json()}
    assert (
        "datasette-plugin-template-demo"
        in installed_plugins
    ) 
         This test uses the  datasette.client  object to exercise a test instance of Datasette.  datasette.client  is a wrapper around the  HTTPX  Python library which can imitate HTTP requests using ASGI. This is the recommended way to write tests against a Datasette instance. 
         This test also uses the  pytest-asyncio  package to add support for  async def  test functions running under pytest. 
         You can install these packages like so: 
         pip install pytest pytest-asyncio 
         If you are building an installable package you can add them as test dependencies to your  setup.py  module like this: 
         setup(
    name="datasette-my-plugin",
    # ...
    extras_require={"test": ["pytest", "pytest-asyncio"]},
    tests_require=["datasette-my-plugin[test]"],
) 
         You can then install the test dependencies like so: 
         pip install -e '.[test]' 
         Then run the tests using pytest like so: 
         pytest

references:
[{"href": "https://docs.pytest.org/", "label": "pytest"}, {"href": "https://www.python-httpx.org/", "label": "HTTPX"}, {"href": "https://pypi.org/project/pytest-asyncio/", "label": "pytest-asyncio"}]


id: testing_plugins:testing-plugins-datasette-test-instance
page: testing_plugins
ref: testing-plugins-datasette-test-instance
title: Setting up a Datasette test instance
breadcrumbs: ["Testing plugins"]
references: []
content:
The above example shows the easiest way to start writing tests against a Datasette instance: 
             from datasette.app import Datasette
import pytest


@pytest.mark.asyncio
async def test_plugin_is_installed():
    datasette = Datasette(memory=True)
    response = await datasette.client.get("/-/plugins.json")
    assert response.status_code == 200 
             Creating a  Datasette()  instance like this as useful shortcut in tests, but there is one detail you need to be aware of. It's important to ensure that the async method  .invoke_startup()  is called on that instance. You can do that like this: 
             datasette = Datasette(memory=True)
await datasette.invoke_startup() 
             This method registers any  startup(datasette)  or  prepare_jinja2_environment(env, datasette)  plugins that might themselves need to make async calls. 
             If you are using  await datasette.client.get()  and similar methods then you don't need to worry about this - Datasette automatically calls  invoke_startup()  the first time it handles a request.


id: testing_plugins:testing-plugins-pdb
page: testing_plugins
ref: testing-plugins-pdb
title: Using pdb for errors thrown inside Datasette
breadcrumbs: ["Testing plugins"]
references: []
content:
If an exception occurs within Datasette itself during a test, the response returned to your plugin will have a  response.status_code  value of 500. 
             You can add  pdb=True  to the  Datasette  constructor to drop into a Python debugger session inside your test run instead of getting back a 500 response code. This is equivalent to running the  datasette  command-line tool with the  --pdb  option. 
             Here's what that looks like in a test function: 
             def test_that_opens_the_debugger_or_errors():
    ds = Datasette([db_path], pdb=True)
    response = await ds.client.get("/") 
             If you use this pattern you will need to run  pytest  with the  -s  option to avoid capturing stdin/stdout in order to interact with the debugger prompt.


id: testing_plugins:testing-plugins-fixtures
page: testing_plugins
ref: testing-plugins-fixtures
title: Using pytest fixtures
breadcrumbs: ["Testing plugins"]
content:
Pytest fixtures  can be used to create initial testable objects which can then be used by multiple tests. 
             A common pattern for Datasette plugins is to create a fixture which sets up a temporary test database and wraps it in a Datasette instance. 
             Here's an example that uses the  sqlite-utils library  to populate a temporary test database. It also sets the title of that table using a simulated  metadata.json  configuration: 
             from datasette.app import Datasette
import pytest
import sqlite_utils


@pytest.fixture(scope="session")
def datasette(tmp_path_factory):
    db_directory = tmp_path_factory.mktemp("dbs")
    db_path = db_directory / "test.db"
    db = sqlite_utils.Database(db_path)
    db["dogs"].insert_all(
        [
            {"id": 1, "name": "Cleo", "age": 5},
            {"id": 2, "name": "Pancakes", "age": 4},
        ],
        pk="id",
    )
    datasette = Datasette(
        [db_path],
        metadata={
            "databases": {
                "test": {
                    "tables": {
                        "dogs": {"title": "Some dogs"}
                    }
                }
            }
        },
    )
    return datasette


@pytest.mark.asyncio
async def test_example_table_json(datasette):
    response = await datasette.client.get(
        "/test/dogs.json?_shape=array"
    )
    assert response.status_code == 200
    assert response.json() == [
        {"id": 1, "name": "Cleo", "age": 5},
        {"id": 2, "name": "Pancakes", "age": 4},
    ]


@pytest.mark.asyncio
async def test_example_table_html(datasette):
    response = await datasette.client.get("/test/dogs")
    assert ">Some dogs</h1>" in response.text 
             Here the  datasette()  function defines the fixture, which is than automatically passed to the two test functions based on pytest automatically matching their  datasette  function parameters. 
             The  @pytest.fixture(scope="session")  line here ensures the fixture is reused for the full  pytest  execution session. This means that the temporary database file will be created once and reused for each test. 
             If you want to create that test database repeatedly for every individual test function, write the fixture function like this instead. You may want to do this if your plugin modifies the database contents in some way: 
             @pytest.fixture
def datasette(tmp_path_factory):
    # This fixture will be executed repeatedly for every test
    ...

references:
[{"href": "https://docs.pytest.org/en/stable/fixture.html", "label": "Pytest fixtures"}, {"href": "https://sqlite-utils.datasette.io/en/stable/python-api.html", "label": "sqlite-utils library"}]


id: testing_plugins:testing-plugins-pytest-httpx
page: testing_plugins
ref: testing-plugins-pytest-httpx
title: Testing outbound HTTP calls with pytest-httpx
breadcrumbs: ["Testing plugins"]
references: [{"href": "https://pypi.org/project/pytest-httpx/", "label": "pytest-httpx"}]
content:
If your plugin makes outbound HTTP calls - for example datasette-auth-github or datasette-import-table - you may need to mock those HTTP requests in your tests. 
             The  pytest-httpx  package is a useful library for mocking calls. It can be tricky to use with Datasette though since it mocks all HTTPX requests, and Datasette's own testing mechanism uses HTTPX internally. 
             To avoid breaking your tests, you can return  ["localhost"]  from the  non_mocked_hosts()  fixture. 
             As an example, here's a very simple plugin which executes an HTTP response and returns the resulting content: 
             from datasette import hookimpl
from datasette.utils.asgi import Response
import httpx


@hookimpl
def register_routes():
    return [
        (r"^/-/fetch-url$", fetch_url),
    ]


async def fetch_url(datasette, request):
    if request.method == "GET":
        return Response.html(
            """
            <form action="/-/fetch-url" method="post">
            <input type="hidden" name="csrftoken" value="{}">
            <input name="url"><input type="submit">
        </form>""".format(
                request.scope["csrftoken"]()
            )
        )
    vars = await request.post_vars()
    url = vars["url"]
    return Response.text(httpx.get(url).text) 
             Here's a test for that plugin that mocks the HTTPX outbound request: 
             from datasette.app import Datasette
import pytest


@pytest.fixture
def non_mocked_hosts():
    # This ensures httpx-mock will not affect Datasette's own
    # httpx calls made in the tests by datasette.client:
    return ["localhost"]


async def test_outbound_http_call(httpx_mock):
    httpx_mock.add_response(
        url="https://www.example.com/",
        text="Hello world",
    )
    datasette = Datasette([], memory=True)
    response = await datasette.client.post(
        "/-/fetch-url",
        data={"url": "https://www.example.com/"},
    )
    assert response.text == "Hello world"

    outbound_request = httpx_mock.get_request()
    assert (
        outbound_request.url == "https://www.example.com/"
    )


id: testing_plugins:testing-plugins-register-in-test
page: testing_plugins
ref: testing-plugins-register-in-test
title: Registering a plugin for the duration of a test
breadcrumbs: ["Testing plugins"]
references: []
content:
When writing tests for plugins you may find it useful to register a test plugin just for the duration of a single test. You can do this using  pm.register()  and  pm.unregister()  like this: 
             from datasette import hookimpl
from datasette.app import Datasette
from datasette.plugins import pm
import pytest


@pytest.mark.asyncio
async def test_using_test_plugin():
    class TestPlugin:
        __name__ = "TestPlugin"

        # Use hookimpl and method names to register hooks
        @hookimpl
        def register_routes(self):
            return [
                (r"^/error$", lambda: 1 / 0),
            ]

    pm.register(TestPlugin(), name="undo")
    try:
        # The test implementation goes here
        datasette = Datasette()
        response = await datasette.client.get("/error")
        assert response.status_code == 500
    finally:
        pm.unregister(name="undo")


id: full_text_search:id1
page: full_text_search
ref: id1
title: Full-text search
breadcrumbs: []
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. 
         Here's  an example search : 
         
         Datasette automatically detects which tables have been configured for full-text search.

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: 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
breadcrumbs: ["Full-text search"]
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. 
             Try this example:  fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort 
             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: 
             /dbname/tablename/?_search=rob* 
             This will return all records containing at least one word that starts with the letters  rob . 
             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 : 
             /dbname/tablename/?_search_name=Sarah

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:full-text-search-advanced-queries
page: full_text_search
ref: full-text-search-advanced-queries
title: Advanced SQLite search queries
breadcrumbs: ["Full-text search"]
content:
SQLite full-text search includes support for  a variety of advanced queries , including  AND ,  OR ,  NOT  and  NEAR . 
             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. 
             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 . 
             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.

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-table-or-view
page: full_text_search
ref: full-text-search-table-or-view
title: Configuring full-text search for a table or view
breadcrumbs: ["Full-text search"]
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. 
             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. 
             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: 
             https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk 
             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. 
             The  "searchmode": "raw"  property can be used to default the table to accepting SQLite advanced search operators, as described in  Advanced SQLite search queries . 
             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: 
             {
    "databases": {
        "russian-ads": {
            "tables": {
                "display_ads": {
                    "fts_table": "ads_fts",
                    "fts_pk": "id",
                    "searchmode": "raw"
                }
            }
        }
    }
}

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-custom-sql
page: full_text_search
ref: full-text-search-custom-sql
title: Searches using custom SQL
breadcrumbs: ["Full-text search"]
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. 
             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 : 
             /fara/FARA_All_ShortForms?_search=manafort 
             If you click  View and edit SQL  you'll see that the underlying SQL looks like this: 
             select
  rowid,
  Short_Form_Termination_Date,
  Short_Form_Date,
  Short_Form_Last_Name,
  Short_Form_First_Name,
  Registration_Number,
  Registration_Date,
  Registrant_Name,
  Address_1,
  Address_2,
  City,
  State,
  Zip
from
  FARA_All_ShortForms
where
  rowid in (
    select
      rowid
    from
      FARA_All_ShortForms_fts
    where
      FARA_All_ShortForms_fts match escape_fts(:search)
  )
order by
  rowid
limit
  101

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
breadcrumbs: ["Full-text search"]
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. 
             To set up full-text search for a table, you need to do two things: 
             
                 
                     Create a new FTS virtual table associated with your table 
                 
                 
                     Populate that FTS table with the data that you would like to be able to run searches against

references:
[{"href": "https://www.sqlite.org/fts3.html#_external_content_fts4_tables_", "label": "external content"}]


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
breadcrumbs: ["Full-text search", "Enabling full-text search for a SQLite table"]
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 . 
                 Here's how to use  sqlite-utils  to enable full-text search for an  items  table across the  name  and  description  columns: 
                 $ sqlite-utils enable-fts mydatabase.db items name description

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:configuring-fts-using-csvs-to-sqlite
page: full_text_search
ref: configuring-fts-using-csvs-to-sqlite
title: Configuring FTS using csvs-to-sqlite
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"}]
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: 
                 $ csvs-to-sqlite items.csv items.db -f name -f description


id: full_text_search:configuring-fts-by-hand
page: full_text_search
ref: configuring-fts-by-hand
title: Configuring FTS by hand
breadcrumbs: ["Full-text search", "Enabling full-text search for a SQLite table"]
references: [{"href": "https://sqlite-utils.datasette.io/", "label": "sqlite-utils"}]
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. 
                 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: 
                 CREATE VIRTUAL TABLE "items_fts" USING FTS4 (
    name,
    description,
    content="items"
); 
                 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. 
                 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: 
                 INSERT INTO "items_fts" (rowid, name, description)
    SELECT rowid, name, description FROM items; 
                 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: 
                 CREATE VIRTUAL TABLE "items_fts" USING FTS4 (
    name,
    description,
    category_name,
    content="items"
); 
                 And then populate it like this: 
                 INSERT INTO "items_fts" (rowid, name, description, category_name)
    SELECT items.rowid,
    items.name,
    items.description,
    categories.name
    FROM items JOIN categories ON items.category_id=categories.id; 
                 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.


id: full_text_search:full-text-search-fts-versions
page: full_text_search
ref: full-text-search-fts-versions
title: FTS versions
breadcrumbs: ["Full-text search"]
references: []
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. 
             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. 
             If you can't be sure that FTS5 will be available, you should use FTS4.


id: ecosystem:ecosystem
page: ecosystem
ref: ecosystem
title: The Datasette Ecosystem
breadcrumbs: []
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. 
         These tools are divided into two main groups: tools for building SQLite databases (for use with Datasette) and plugins that extend Datasette's functionality. 
         The  Datasette project website  includes a directory of plugins and a directory of tools: 
         
             
                 Plugins directory on datasette.io 
             
             
                 Tools directory on datasette.io

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
breadcrumbs: ["The Datasette Ecosystem"]
references: [{"href": "https://sqlite-utils.datasette.io/", "label": "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: 
             
                 
                     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. 
                 
                 
                     Configure tables for use with SQLite full-text search, including creating triggers needed to keep the search index up-to-date. 
                 
                 
                     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. 
                 
                 
                     Adding foreign keys to existing database tables. 
                 
                 
                     Extracting columns of data into a separate lookup table.


id: ecosystem:dogsheep
page: ecosystem
ref: dogsheep
title: Dogsheep
breadcrumbs: ["The Datasette Ecosystem"]
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.

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: deploying:deploying
page: deploying
ref: deploying
title: Deploying Datasette
breadcrumbs: []
references: []
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. 
         You can deploy Datasette to other hosting providers using the instructions on this page.


id: deploying:deploying-fundamentals
page: deploying
ref: deploying-fundamentals
title: Deployment fundamentals
breadcrumbs: ["Deploying Datasette"]
references: []
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. 
             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.


id: deploying:deploying-systemd
page: deploying
ref: deploying-systemd
title: Running Datasette using systemd
breadcrumbs: ["Deploying Datasette"]
references: []
content:
You can run Datasette on Ubuntu or Debian systems using  systemd . 
             First, ensure you have Python 3 and  pip  installed. On Ubuntu you can use  sudo apt-get install python3 python3-pip . 
             You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use  sudo pip3 install datasette . 
             Now create a folder for your Datasette databases, for example using  mkdir /home/ubuntu/datasette-root . 
             You can copy a test database into that folder like so: 
             cd /home/ubuntu/datasette-root
curl -O https://latest.datasette.io/fixtures.db 
             Create a file at  /etc/systemd/system/datasette.service  with the following contents: 
             [Unit]
Description=Datasette
After=network.target

[Service]
Type=simple
User=ubuntu
Environment=DATASETTE_SECRET=
WorkingDirectory=/home/ubuntu/datasette-root
ExecStart=datasette serve . -h 127.0.0.1 -p 8000
Restart=on-failure

[Install]
WantedBy=multi-user.target 
             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: 
             $ python3 -c 'import secrets; print(secrets.token_hex(32))' 
             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. 
             You can start the Datasette process running using the following: 
             sudo systemctl daemon-reload
sudo systemctl start datasette.service 
             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: 
             sudo systemctl restart datasette.service 
             Once the service has started you can confirm that Datasette is running on port 8000 like so: 
             curl 127.0.0.1:8000/-/versions.json
# Should output JSON showing the installed version 
             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 .


id: deploying:deploying-openrc
page: deploying
ref: deploying-openrc
title: Running Datasette using OpenRC
breadcrumbs: ["Deploying Datasette"]
content:
OpenRC is the service manager on non-systemd Linux distributions like  Alpine Linux  and  Gentoo . 
             Create an init script at  /etc/init.d/datasette  with the following contents: 
             #!/sbin/openrc-run

name="datasette"
command="datasette"
command_args="serve -h 0.0.0.0 /path/to/db.db"
command_background=true
pidfile="/run/${RC_SVCNAME}.pid" 
             You then need to configure the service to run at boot and start it: 
             rc-update add datasette
rc-service datasette start

references:
[{"href": "https://www.alpinelinux.org/", "label": "Alpine Linux"}, {"href": "https://www.gentoo.org/", "label": "Gentoo"}]


id: deploying:deploying-buildpacks
page: deploying
ref: deploying-buildpacks
title: Deploying using buildpacks
breadcrumbs: ["Deploying Datasette"]
content:
Some hosting providers such as  Heroku ,  DigitalOcean App Platform  and  Scalingo  support the  Buildpacks standard  for deploying Python web applications. 
             Deploying Datasette on these platforms requires two files:  requirements.txt  and  Procfile . 
             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: 
             datasette
datasette-vega 
             The  Procfile  lets the hosting platform know how to run the command that serves web traffic. It should look like this: 
             web: datasette . -h 0.0.0.0 -p $PORT --cors 
             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. 
             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. 
             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: 
             wget https://fivethirtyeight.datasettes.com/fivethirtyeight.db 
             simonw/buildpack-datasette-demo  is an example GitHub repository showing a Datasette configuration that can be deployed to a buildpack-supporting host.

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-proxy
page: deploying
ref: deploying-proxy
title: Running Datasette behind a proxy
breadcrumbs: ["Deploying Datasette"]
references: []
content:
You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site. 
             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: 
             datasette my-database.db --setting base_url /my-datasette/ -p 8009 
             This will run Datasette with the following URLs: 
             
                 
                     http://127.0.0.1:8009/my-datasette/  - the Datasette homepage 
                 
                 
                     http://127.0.0.1:8009/my-datasette/my-database  - the page for the  my-database.db  database 
                 
                 
                     http://127.0.0.1:8009/my-datasette/my-database/some_table  - the page for the  some_table  table 
                 
             
             You can now set your nginx or Apache server to proxy the  /my-datasette/  path to this Datasette instance.


id: deploying:nginx-proxy-configuration
page: deploying
ref: nginx-proxy-configuration
title: Nginx proxy configuration
breadcrumbs: ["Deploying Datasette", "Running Datasette behind a proxy"]
references: [{"href": "https://nginx.org/", "label": "nginx"}]
content:
Here is an example of an  nginx  configuration file that will proxy traffic to Datasette: 
                 daemon off;

events {
  worker_connections  1024;
}
http {
  server {
    listen 80;
    location /my-datasette {
      proxy_pass http://127.0.0.1:8009/my-datasette;
      proxy_set_header Host $host;
    }
  }
} 
                 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: 
                 daemon off;
events {
  worker_connections  1024;
}
http {
  server {
    listen 80;
    location /my-datasette {
      proxy_pass http://datasette/my-datasette;
      proxy_set_header Host $host;
    }
  }
  upstream datasette {
    server unix:/tmp/datasette.sock;
  }
} 
                 Then run Datasette with  datasette --uds /tmp/datasette.sock path/to/database.db --setting base_url /my-datasette/ .


id: deploying:apache-proxy-configuration
page: deploying
ref: apache-proxy-configuration
title: Apache proxy configuration
breadcrumbs: ["Deploying Datasette", "Running Datasette behind a proxy"]
content:
For  Apache , you can use the  ProxyPass  directive. First make sure the following lines are uncommented: 
                 LoadModule proxy_module lib/httpd/modules/mod_proxy.so
LoadModule proxy_http_module lib/httpd/modules/mod_proxy_http.so 
                 Then add these directives to proxy traffic: 
                 ProxyPass /my-datasette/ http://127.0.0.1:8009/my-datasette/
ProxyPreserveHost On 
                 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. 
                 Using  --uds  you can use Unix domain sockets similar to the nginx example: 
                 ProxyPass /my-datasette/ unix:/tmp/datasette.sock|http://localhost/my-datasette/ 
                 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.

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: plugins:id1
page: plugins
ref: id1
title: Plugins
breadcrumbs: []
content:
Datasette's plugin system allows additional features to be implemented as Python
            code (or front-end JavaScript) which can be wrapped up in a separate Python
            package. The underlying mechanism uses  pluggy . 
         See the  Datasette plugins directory  for a list of existing plugins, or take a look at the
             datasette-plugin  topic on GitHub. 
         Things you can do with plugins include: 
         
             
                 Add visualizations to Datasette, for example
                     datasette-cluster-map  and
                     datasette-vega . 
             
             
                 Make new custom SQL functions available for use within Datasette, for example
                     datasette-haversine  and
                     datasette-jellyfish . 
             
             
                 Define custom output formats with custom extensions, for example  datasette-atom  and
                     datasette-ics . 
             
             
                 Add template functions that can be called within your Jinja custom templates,
                    for example  datasette-render-markdown . 
             
             
                 Customize how database values are rendered in the Datasette interface, for example
                     datasette-render-binary  and
                     datasette-pretty-json . 
             
             
                 Customize how Datasette's authentication and permissions systems work, for example  datasette-auth-tokens  and
                     datasette-permissions-sql .

references:
[{"href": "https://pluggy.readthedocs.io/", "label": "pluggy"}, {"href": "https://datasette.io/plugins", "label": "Datasette plugins directory"}, {"href": "https://github.com/topics/datasette-plugin", "label": "datasette-plugin"}, {"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://github.com/simonw/datasette-vega", "label": "datasette-vega"}, {"href": "https://github.com/simonw/datasette-haversine", "label": "datasette-haversine"}, {"href": "https://github.com/simonw/datasette-jellyfish", "label": "datasette-jellyfish"}, {"href": "https://github.com/simonw/datasette-atom", "label": "datasette-atom"}, {"href": "https://github.com/simonw/datasette-ics", "label": "datasette-ics"}, {"href": "https://github.com/simonw/datasette-render-markdown#markdown-in-templates", "label": "datasette-render-markdown"}, {"href": "https://github.com/simonw/datasette-render-binary", "label": "datasette-render-binary"}, {"href": "https://github.com/simonw/datasette-pretty-json", "label": "datasette-pretty-json"}, {"href": "https://github.com/simonw/datasette-auth-tokens", "label": "datasette-auth-tokens"}, {"href": "https://github.com/simonw/datasette-permissions-sql", "label": "datasette-permissions-sql"}]


id: plugins:plugins-installing
page: plugins
ref: plugins-installing
title: Installing plugins
breadcrumbs: ["Plugins"]
references: []
content:
If a plugin has been packaged for distribution using setuptools you can use the plugin by installing it alongside Datasette in the same virtual environment or Docker container. 
             You can install plugins using the  datasette install  command: 
             datasette install datasette-vega 
             You can uninstall plugins with  datasette uninstall : 
             datasette uninstall datasette-vega 
             You can upgrade plugins with  datasette install --upgrade  or  datasette install -U : 
             datasette install -U datasette-vega 
             This command can also be used to upgrade Datasette itself to the latest released version: 
             datasette install -U datasette 
             These commands are thin wrappers around  pip install  and  pip uninstall , which ensure they run  pip  in the same virtual environment as Datasette itself.


id: plugins:one-off-plugins-using-plugins-dir
page: plugins
ref: one-off-plugins-using-plugins-dir
title: One-off plugins using --plugins-dir
breadcrumbs: ["Plugins", "Installing plugins"]
references: []
content:
You can also define one-off per-project plugins by saving them as  plugin_name.py  functions in a  plugins/  folder and then passing that folder to  datasette  using the  --plugins-dir  option: 
                 datasette mydb.db --plugins-dir=plugins/


id: plugins:deploying-plugins-using-datasette-publish
page: plugins
ref: deploying-plugins-using-datasette-publish
title: Deploying plugins using datasette publish
breadcrumbs: ["Plugins", "Installing plugins"]
references: []
content:
The  datasette publish  and  datasette package  commands both take an optional  --install  argument. You can use this one or more times to tell Datasette to  pip install  specific plugins as part of the process: 
                 datasette publish cloudrun mydb.db --install=datasette-vega 
                 You can use the name of a package on PyPI or any of the other valid arguments to  pip install  such as a URL to a  .zip  file: 
                 datasette publish cloudrun mydb.db \
    --install=https://url-to-my-package.zip


id: plugins:plugins-installed
page: plugins
ref: plugins-installed
title: Seeing what plugins are installed
breadcrumbs: ["Plugins"]
content:
You can see a list of installed plugins by navigating to the  /-/plugins  page of your Datasette instance - for example:  https://fivethirtyeight.datasettes.com/-/plugins 
             You can also use the  datasette plugins  command: 
             $ datasette plugins
[
    {
        "name": "datasette_json_html",
        "static": false,
        "templates": false,
        "version": "0.4.0"
    }
] 
             [[[cog
from datasette import cli
from click.testing import CliRunner
import textwrap, json
cog.out("\n")
result = CliRunner().invoke(cli.cli, ["plugins", "--all"])
# cog.out() with text containing newlines was unindenting for some reason
cog.outl("If you run ``datasette plugins --all`` it will include default plugins that ship as part of Datasette::\n")
plugins = [p for p in json.loads(result.output) if p["name"].startswith("datasette.")]
indented = textwrap.indent(json.dumps(plugins, indent=4), "    ")
for line in indented.split("\n"):
    cog.outl(line)
cog.out("\n\n") 
             ]]] 
             If you run  datasette plugins --all  it will include default plugins that ship as part of Datasette: 
             [
    {
        "name": "datasette.actor_auth_cookie",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "actor_from_request"
        ]
    },
    {
        "name": "datasette.blob_renderer",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "register_output_renderer"
        ]
    },
    {
        "name": "datasette.default_magic_parameters",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "register_magic_parameters"
        ]
    },
    {
        "name": "datasette.default_menu_links",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "menu_links"
        ]
    },
    {
        "name": "datasette.default_permissions",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "permission_allowed"
        ]
    },
    {
        "name": "datasette.facets",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "register_facet_classes"
        ]
    },
    {
        "name": "datasette.filters",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "filters_from_request"
        ]
    },
    {
        "name": "datasette.forbidden",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "forbidden"
        ]
    },
    {
        "name": "datasette.handle_exception",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "handle_exception"
        ]
    },
    {
        "name": "datasette.publish.cloudrun",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "publish_subcommand"
        ]
    },
    {
        "name": "datasette.publish.heroku",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "publish_subcommand"
        ]
    },
    {
        "name": "datasette.sql_functions",
        "static": false,
        "templates": false,
        "version": null,
        "hooks": [
            "prepare_connection"
        ]
    }
] 
             [[[end]]] 
             You can add the  --plugins-dir=  option to include any plugins found in that directory.

references:
[{"href": "https://fivethirtyeight.datasettes.com/-/plugins", "label": "https://fivethirtyeight.datasettes.com/-/plugins"}]


id: plugins:plugins-configuration
page: plugins
ref: plugins-configuration
title: Plugin configuration
breadcrumbs: ["Plugins"]
references: []
content:
Plugins can have their own configuration, embedded in a  Metadata  file. Configuration options for plugins live within a  "plugins"  key in that file, which can be included at the root, database or table level. 
             Here is an example of some plugin configuration for a specific table: 
             {
    "databases": {
        "sf-trees": {
            "tables": {
                "Street_Tree_List": {
                    "plugins": {
                        "datasette-cluster-map": {
                            "latitude_column": "lat",
                            "longitude_column": "lng"
                        }
                    }
                }
            }
        }
    }
} 
             This tells the  datasette-cluster-map  column which latitude and longitude columns should be used for a table called  Street_Tree_List  inside a database file called  sf-trees.db .


id: plugins:plugins-configuration-secret
page: plugins
ref: plugins-configuration-secret
title: Secret configuration values
breadcrumbs: ["Plugins", "Plugin configuration"]
references: []
content:
Any values embedded in  metadata.json  will be visible to anyone who views the  /-/metadata  page of your Datasette instance. Some plugins may need configuration that should stay secret - API keys for example. There are two ways in which you can store secret configuration values. 
                 As environment variables . If your secret lives in an environment variable that is available to the Datasette process, you can indicate that the configuration value should be read from that environment variable like so: 
                 {
    "plugins": {
        "datasette-auth-github": {
            "client_secret": {
                "$env": "GITHUB_CLIENT_SECRET"
            }
        }
    }
} 
                 As values in separate files . Your secrets can also live in files on disk. To specify a secret should be read from a file, provide the full file path like this: 
                 {
    "plugins": {
        "datasette-auth-github": {
            "client_secret": {
                "$file": "/secrets/client-secret"
            }
        }
    }
} 
                 If you are publishing your data using the  datasette publish  family of commands, you can use the  --plugin-secret  option to set these secrets at publish time. For example, using Heroku you might run the following command: 
                 $ datasette publish heroku my_database.db \
    --name my-heroku-app-demo \
    --install=datasette-auth-github \
    --plugin-secret datasette-auth-github client_id your_client_id \
    --plugin-secret datasette-auth-github client_secret your_client_secret 
                 This will set the necessary environment variables and add the following to the deployed  metadata.json : 
                 {
    "plugins": {
        "datasette-auth-github": {
            "client_id": {
                "$env": "DATASETTE_AUTH_GITHUB_CLIENT_ID"
            },
            "client_secret": {
                "$env": "DATASETTE_AUTH_GITHUB_CLIENT_SECRET"
            }
        }
    }
}


id: settings:using-setting
page: settings
ref: using-setting
title: Using --setting
breadcrumbs: ["Settings"]
references: []
content:
Datasette supports a number of settings. These can be set using the  --setting name value  option to  datasette serve . 
             You can set multiple settings at once like this: 
             datasette mydatabase.db \
    --setting default_page_size 50 \
    --setting sql_time_limit_ms 3500 \
    --setting max_returned_rows 2000


id: settings:config-dir
page: settings
ref: config-dir
title: Configuration directory mode
breadcrumbs: ["Settings"]
references: []
content:
Normally you configure Datasette using command-line options. For a Datasette instance with custom templates, custom plugins, a static directory and several databases this can get quite verbose: 
             $ datasette one.db two.db \
    --metadata=metadata.json \
    --template-dir=templates/ \
    --plugins-dir=plugins \
    --static css:css 
             As an alternative to this, you can run Datasette in  configuration directory  mode. Create a directory with the following structure: 
             # In a directory called my-app:
my-app/one.db
my-app/two.db
my-app/metadata.json
my-app/templates/index.html
my-app/plugins/my_plugin.py
my-app/static/my.css 
             Now start Datasette by providing the path to that directory: 
             $ datasette my-app/ 
             Datasette will detect the files in that directory and automatically configure itself using them. It will serve all  *.db  files that it finds, will load  metadata.json  if it exists, and will load the  templates ,  plugins  and  static  folders if they are present. 
             The files that can be included in this directory are as follows. All are optional. 
             
                 
                     *.db  (or  *.sqlite3  or  *.sqlite ) - SQLite database files that will be served by Datasette 
                 
                 
                     metadata.json  -  Metadata  for those databases -  metadata.yaml  or  metadata.yml  can be used as well 
                 
                 
                     inspect-data.json  - the result of running  datasette inspect *.db --inspect-file=inspect-data.json  from the configuration directory - any database files listed here will be treated as immutable, so they should not be changed while Datasette is running 
                 
                 
                     settings.json  - settings that would normally be passed using  --setting  - here they should be stored as a JSON object of key/value pairs 
                 
                 
                     templates/  - a directory containing  Custom templates 
                 
                 
                     plugins/  - a directory containing plugins, see  Writing one-off plugins 
                 
                 
                     static/  - a directory containing static files - these will be served from  /static/filename.txt , see  Serving static files


id: settings:id2
page: settings
ref: id2
title: Settings
breadcrumbs: ["Settings"]
references: []
content:
The following options can be set using  --setting name value , or by storing them in the  settings.json  file for use with  Configuration directory mode .


id: settings:setting-default-allow-sql
page: settings
ref: setting-default-allow-sql
title: default_allow_sql
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Should users be able to execute arbitrary SQL queries by default? 
                 Setting this to  off  causes permission checks for  execute-sql  to fail by default. 
                 datasette mydatabase.db --setting default_allow_sql off 
                 There are two ways to achieve this: the other is to add  "allow_sql": false  to your  metadata.json  file, as described in  Controlling the ability to execute arbitrary SQL . This setting offers a more convenient way to do this.


id: settings:setting-default-page-size
page: settings
ref: setting-default-page-size
title: default_page_size
breadcrumbs: ["Settings", "Settings"]
references: []
content:
The default number of rows returned by the table page. You can over-ride this on a per-page basis using the  ?_size=80  query string parameter, provided you do not specify a value higher than the  max_returned_rows  setting. You can set this default using  --setting  like so: 
                 datasette mydatabase.db --setting default_page_size 50


id: settings:setting-sql-time-limit-ms
page: settings
ref: setting-sql-time-limit-ms
title: sql_time_limit_ms
breadcrumbs: ["Settings", "Settings"]
references: []
content:
By default, queries have a time limit of one second. If a query takes longer than this to run Datasette will terminate the query and return an error. 
                 If this time limit is too short for you, you can customize it using the  sql_time_limit_ms  limit - for example, to increase it to 3.5 seconds: 
                 datasette mydatabase.db --setting sql_time_limit_ms 3500 
                 You can optionally set a lower time limit for an individual query using the  ?_timelimit=100  query string argument: 
                 /my-database/my-table?qSpecies=44&_timelimit=100 
                 This would set the time limit to 100ms for that specific query. This feature is useful if you are working with databases of unknown size and complexity - a query that might make perfect sense for a smaller table could take too long to execute on a table with millions of rows. By setting custom time limits you can execute queries "optimistically" - e.g. give me an exact count of rows matching this query but only if it takes less than 100ms to calculate.


id: settings:setting-max-returned-rows
page: settings
ref: setting-max-returned-rows
title: max_returned_rows
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Datasette returns a maximum of 1,000 rows of data at a time. If you execute a query that returns more than 1,000 rows, Datasette will return the first 1,000 and include a warning that the result set has been truncated. You can use OFFSET/LIMIT or other methods in your SQL to implement pagination if you need to return more than 1,000 rows. 
                 You can increase or decrease this limit like so: 
                 datasette mydatabase.db --setting max_returned_rows 2000


id: settings:setting-num-sql-threads
page: settings
ref: setting-num-sql-threads
title: num_sql_threads
breadcrumbs: ["Settings", "Settings"]
references: [{"href": "https://pyodide.org/", "label": "Pyodide"}]
content:
Maximum number of threads in the thread pool Datasette uses to execute SQLite queries. Defaults to 3. 
                 datasette mydatabase.db --setting num_sql_threads 10 
                 Setting this to 0 turns off threaded SQL queries entirely - useful for environments that do not support threading such as  Pyodide .


id: settings:setting-allow-facet
page: settings
ref: setting-allow-facet
title: allow_facet
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Allow users to specify columns they would like to facet on using the  ?_facet=COLNAME  URL parameter to the table view. 
                 This is enabled by default. If disabled, facets will still be displayed if they have been specifically enabled in  metadata.json  configuration for the table. 
                 Here's how to disable this feature: 
                 datasette mydatabase.db --setting allow_facet off


id: settings:setting-default-facet-size
page: settings
ref: setting-default-facet-size
title: default_facet_size
breadcrumbs: ["Settings", "Settings"]
references: []
content:
The default number of unique rows returned by  Facets  is 30. You can customize it like this: 
                 datasette mydatabase.db --setting default_facet_size 50


id: settings:setting-facet-time-limit-ms
page: settings
ref: setting-facet-time-limit-ms
title: facet_time_limit_ms
breadcrumbs: ["Settings", "Settings"]
references: []
content:
This is the time limit Datasette allows for calculating a facet, which defaults to 200ms: 
                 datasette mydatabase.db --setting facet_time_limit_ms 1000


id: settings:setting-facet-suggest-time-limit-ms
page: settings
ref: setting-facet-suggest-time-limit-ms
title: facet_suggest_time_limit_ms
breadcrumbs: ["Settings", "Settings"]
references: []
content:
When Datasette calculates suggested facets it needs to run a SQL query for every column in your table. The default for this time limit is 50ms to account for the fact that it needs to run once for every column. If the time limit is exceeded the column will not be suggested as a facet. 
                 You can increase this time limit like so: 
                 datasette mydatabase.db --setting facet_suggest_time_limit_ms 500


id: settings:setting-suggest-facets
page: settings
ref: setting-suggest-facets
title: suggest_facets
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Should Datasette calculate suggested facets? On by default, turn this off like so: 
                 datasette mydatabase.db --setting suggest_facets off


id: settings:setting-allow-download
page: settings
ref: setting-allow-download
title: allow_download
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Should users be able to download the original SQLite database using a link on the database index page? This is turned on by default. However, databases can only be downloaded if they are served in immutable mode and not in-memory. If downloading is unavailable for either of these reasons, the download link is hidden even if  allow_download  is on. To disable database downloads, use the following: 
                 datasette mydatabase.db --setting allow_download off


id: settings:setting-default-cache-ttl
page: settings
ref: setting-default-cache-ttl
title: default_cache_ttl
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Default HTTP caching max-age header in seconds, used for  Cache-Control: max-age=X . Can be over-ridden on a per-request basis using the  ?_ttl=  query string parameter. Set this to  0  to disable HTTP caching entirely. Defaults to 5 seconds. 
                 datasette mydatabase.db --setting default_cache_ttl 60


id: settings:setting-cache-size-kb
page: settings
ref: setting-cache-size-kb
title: cache_size_kb
breadcrumbs: ["Settings", "Settings"]
references: [{"href": "https://www.sqlite.org/pragma.html#pragma_cache_size", "label": "per-connection cache"}]
content:
Sets the amount of memory SQLite uses for its  per-connection cache , in KB. 
                 datasette mydatabase.db --setting cache_size_kb 5000


id: settings:setting-allow-csv-stream
page: settings
ref: setting-allow-csv-stream
title: allow_csv_stream
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Enables  the CSV export feature  where an entire table
                    (potentially hundreds of thousands of rows) can be exported as a single CSV
                    file. This is turned on by default - you can turn it off like this: 
                 datasette mydatabase.db --setting allow_csv_stream off


id: settings:setting-max-csv-mb
page: settings
ref: setting-max-csv-mb
title: max_csv_mb
breadcrumbs: ["Settings", "Settings"]
references: []
content:
The maximum size of CSV that can be exported, in megabytes. Defaults to 100MB.
                    You can disable the limit entirely by settings this to 0: 
                 datasette mydatabase.db --setting max_csv_mb 0


id: settings:setting-truncate-cells-html
page: settings
ref: setting-truncate-cells-html
title: truncate_cells_html
breadcrumbs: ["Settings", "Settings"]
references: []
content:
In the HTML table view, truncate any strings that are longer than this value.
                    The full value will still be available in CSV, JSON and on the individual row
                    HTML page. Set this to 0 to disable truncation. 
                 datasette mydatabase.db --setting truncate_cells_html 0


id: settings:setting-force-https-urls
page: settings
ref: setting-force-https-urls
title: force_https_urls
breadcrumbs: ["Settings", "Settings"]
references: []
content:
Forces self-referential URLs in the JSON output to always use the  https:// 
                    protocol. This is useful for cases where the application itself is hosted using
                    HTTP but is served to the outside world via a proxy that enables HTTPS. 
                 datasette mydatabase.db --setting force_https_urls 1


id: settings:setting-template-debug
page: settings
ref: setting-template-debug
title: template_debug
breadcrumbs: ["Settings", "Settings"]
content:
This setting enables template context debug mode, which is useful to help understand what variables are available to custom templates when you are writing them. 
                 Enable it like this: 
                 datasette mydatabase.db --setting template_debug 1 
                 Now you can add  ?_context=1  or  &_context=1  to any Datasette page to see the context that was passed to that template. 
                 Some examples: 
                 
                     
                         https://latest.datasette.io/?_context=1 
                     
                     
                         https://latest.datasette.io/fixtures?_context=1 
                     
                     
                         https://latest.datasette.io/fixtures/roadside_attractions?_context=1

references:
[{"href": "https://latest.datasette.io/?_context=1", "label": "https://latest.datasette.io/?_context=1"}, {"href": "https://latest.datasette.io/fixtures?_context=1", "label": "https://latest.datasette.io/fixtures?_context=1"}, {"href": "https://latest.datasette.io/fixtures/roadside_attractions?_context=1", "label": "https://latest.datasette.io/fixtures/roadside_attractions?_context=1"}]


id: settings:setting-trace-debug
page: settings
ref: setting-trace-debug
title: trace_debug
breadcrumbs: ["Settings", "Settings"]
content:
This setting enables appending  ?_trace=1  to any page in order to see the SQL queries and other trace information that was used to generate that page. 
                 Enable it like this: 
                 datasette mydatabase.db --setting trace_debug 1 
                 Some examples: 
                 
                     
                         https://latest.datasette.io/?_trace=1 
                     
                     
                         https://latest.datasette.io/fixtures/roadside_attractions?_trace=1 
                     
                 
                 See  datasette.tracer  for details on how to hook into this mechanism as a plugin author.

references:
[{"href": "https://latest.datasette.io/?_trace=1", "label": "https://latest.datasette.io/?_trace=1"}, {"href": "https://latest.datasette.io/fixtures/roadside_attractions?_trace=1", "label": "https://latest.datasette.io/fixtures/roadside_attractions?_trace=1"}]


id: settings:setting-base-url
page: settings
ref: setting-base-url
title: base_url
breadcrumbs: ["Settings", "Settings"]
references: []
content:
If you are running Datasette behind a proxy, it may be useful to change the root path used for the Datasette instance. 
                 For example, if you are sending traffic from  https://www.example.com/tools/datasette/  through to a proxied Datasette instance you may wish Datasette to use  /tools/datasette/  as its root URL. 
                 You can do that like so: 
                 datasette mydatabase.db --setting base_url /tools/datasette/


id: settings:setting-secret
page: settings
ref: setting-secret
title: Configuring the secret
breadcrumbs: ["Settings"]
references: []
content:
Datasette uses a secret string to sign secure values such as cookies. 
             If you do not provide a secret, Datasette will create one when it starts up. This secret will reset every time the Datasette server restarts though, so things like authentication cookies will not stay valid between restarts. 
             You can pass a secret to Datasette in two ways: with the  --secret  command-line option or by setting a  DATASETTE_SECRET  environment variable. 
             $ datasette mydb.db --secret=SECRET_VALUE_HERE 
             Or: 
             $ export DATASETTE_SECRET=SECRET_VALUE_HERE
$ datasette mydb.db 
             One way to generate a secure random secret is to use Python like this: 
             $ python3 -c 'import secrets; print(secrets.token_hex(32))'
cdb19e94283a20f9d42cca50c5a4871c0aa07392db308755d60a1a5b9bb0fa52 
             Plugin authors make use of this signing mechanism in their plugins using  .sign(value, namespace="default")  and  .unsign(value, namespace="default") .


id: settings:setting-publish-secrets
page: settings
ref: setting-publish-secrets
title: Using secrets with datasette publish
breadcrumbs: ["Settings"]
references: []
content:
The  datasette publish  and  datasette package  commands both generate a secret for you automatically when Datasette is deployed. 
             This means that every time you deploy a new version of a Datasette project, a new secret will be generated. This will cause signed cookies to become invalid on every fresh deploy. 
             You can fix this by creating a secret that will be used for multiple deploys and passing it using the  --secret  option: 
             datasette publish cloudrun mydb.db --service=my-service --secret=cdb19e94283a20f9d42cca5


id: introspection:id1
page: introspection
ref: id1
title: Introspection
breadcrumbs: []
references: []
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. 
         Each of these pages can be viewed in your browser. Add  .json  to the URL to get back the contents as JSON.


id: introspection:jsondataview-metadata
page: introspection
ref: jsondataview-metadata
title: /-/metadata
breadcrumbs: ["Introspection"]
references: [{"href": "https://fivethirtyeight.datasettes.com/-/metadata", "label": "Metadata example"}]
content:
Shows the contents of the  metadata.json  file that was passed to  datasette serve , if any.  Metadata example : 
             {
    "license": "CC Attribution 4.0 License",
    "license_url": "http://creativecommons.org/licenses/by/4.0/",
    "source": "fivethirtyeight/data on GitHub",
    "source_url": "https://github.com/fivethirtyeight/data",
    "title": "Five Thirty Eight",
    "databases": {

    }
}


id: introspection:jsondataview-versions
page: introspection
ref: jsondataview-versions
title: /-/versions
breadcrumbs: ["Introspection"]
references: [{"href": "https://latest.datasette.io/-/versions", "label": "Versions example"}]
content:
Shows the version of Datasette, Python and SQLite.  Versions example : 
             {
    "datasette": {
        "version": "0.60"
    },
    "python": {
        "full": "3.8.12 (default, Dec 21 2021, 10:45:09) \n[GCC 10.2.1 20210110]",
        "version": "3.8.12"
    },
    "sqlite": {
        "extensions": {
            "json1": null
        },
        "fts_versions": [
            "FTS5",
            "FTS4",
            "FTS3"
        ],
        "compile_options": [
            "COMPILER=gcc-6.3.0 20170516",
            "ENABLE_FTS3",
            "ENABLE_FTS4",
            "ENABLE_FTS5",
            "ENABLE_JSON1",
            "ENABLE_RTREE",
            "THREADSAFE=1"
        ],
        "version": "3.37.0"
    }
}


id: introspection:jsondataview-plugins
page: introspection
ref: jsondataview-plugins
title: /-/plugins
breadcrumbs: ["Introspection"]
references: [{"href": "https://san-francisco.datasettes.com/-/plugins", "label": "Plugins example"}]
content:
Shows a list of currently installed plugins and their versions.  Plugins example : 
             [
    {
        "name": "datasette_cluster_map",
        "static": true,
        "templates": false,
        "version": "0.10",
        "hooks": ["extra_css_urls", "extra_js_urls", "extra_body_script"]
    }
] 
             Add  ?all=1  to include details of the default plugins baked into Datasette.


id: introspection:jsondataview-config
page: introspection
ref: jsondataview-config
title: /-/settings
breadcrumbs: ["Introspection"]
references: [{"href": "https://fivethirtyeight.datasettes.com/-/settings", "label": "Settings example"}]
content:
Shows the  Settings  for this instance of Datasette.  Settings example : 
             {
    "default_facet_size": 30,
    "default_page_size": 100,
    "facet_suggest_time_limit_ms": 50,
    "facet_time_limit_ms": 1000,
    "max_returned_rows": 1000,
    "sql_time_limit_ms": 1000
}


id: introspection:jsondataview-databases
page: introspection
ref: jsondataview-databases
title: /-/databases
breadcrumbs: ["Introspection"]
references: [{"href": "https://latest.datasette.io/-/databases", "label": "Databases example"}]
content:
Shows currently attached databases.  Databases example : 
             [
    {
        "hash": null,
        "is_memory": false,
        "is_mutable": true,
        "name": "fixtures",
        "path": "fixtures.db",
        "size": 225280
    }
]


id: introspection:jsondataview-threads
page: introspection
ref: jsondataview-threads
title: /-/threads
breadcrumbs: ["Introspection"]
references: [{"href": "https://latest.datasette.io/-/threads", "label": "Threads example"}]
content:
Shows details of threads and  asyncio  tasks.  Threads example : 
             {
    "num_threads": 2,
    "threads": [
        {
            "daemon": false,
            "ident": 4759197120,
            "name": "MainThread"
        },
        {
            "daemon": true,
            "ident": 123145319682048,
            "name": "Thread-1"
        },
    ],
    "num_tasks": 3,
    "tasks": [
        "<Task pending coro=<RequestResponseCycle.run_asgi() running at uvicorn/protocols/http/httptools_impl.py:385> cb=[set.discard()]>",
        "<Task pending coro=<Server.serve() running at uvicorn/main.py:361> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x10365c3d0>()]> cb=[run_until_complete.<locals>.<lambda>()]>",
        "<Task pending coro=<LifespanOn.main() running at uvicorn/lifespan/on.py:48> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x10364f050>()]>>"
    ]
}


id: introspection:jsondataview-actor
page: introspection
ref: jsondataview-actor
title: /-/actor
breadcrumbs: ["Introspection"]
references: []
content:
Shows the currently authenticated actor. Useful for debugging Datasette authentication plugins. 
             {
    "actor": {
        "id": 1,
        "username": "some-user"
    }
}


id: introspection:messagesdebugview
page: introspection
ref: messagesdebugview
title: /-/messages
breadcrumbs: ["Introspection"]
references: []
content:
The debug tool at  /-/messages  can be used to set flash messages to try out that feature. See  .add_message(request, message, type=datasette.INFO)  for details of this feature.


id: json_api:id1
page: json_api
ref: id1
title: JSON API
breadcrumbs: []
references: []
content:
Datasette provides a JSON API for your SQLite databases. Anything you can do
            through the Datasette user interface can also be accessed as JSON via the API. 
         To access the API for a page, either click on the  .json  link on that page or
            edit the URL and add a  .json  extension to it. 
         If you started Datasette with the  --cors  option, each JSON endpoint will be
            served with the following additional HTTP headers: 
         Access-Control-Allow-Origin: *
Access-Control-Allow-Headers: Authorization
Access-Control-Expose-Headers: Link 
         This means JavaScript running on any domain will be able to make cross-origin
            requests to fetch the data. 
         If you start Datasette without the  --cors  option only JavaScript running on
            the same domain as Datasette will be able to access the API.


id: json_api:json-api-shapes
page: json_api
ref: json-api-shapes
title: Different shapes
breadcrumbs: ["JSON API"]
references: []
content:
The default JSON representation of data from a SQLite table or custom query
                looks like this: 
             {
    "database": "sf-trees",
    "table": "qSpecies",
    "columns": [
        "id",
        "value"
    ],
    "rows": [
        [
            1,
            "Myoporum laetum :: Myoporum"
        ],
        [
            2,
            "Metrosideros excelsa :: New Zealand Xmas Tree"
        ],
        [
            3,
            "Pinus radiata :: Monterey Pine"
        ]
    ],
    "truncated": false,
    "next": "100",
    "next_url": "http://127.0.0.1:8001/sf-trees-02c8ef1/qSpecies.json?_next=100",
    "query_ms": 1.9571781158447266
} 
             The  columns  key lists the columns that are being returned, and the  rows 
                key then returns a list of lists, each one representing a row. The order of the
                values in each row corresponds to the columns. 
             The  _shape  parameter can be used to access alternative formats for the
                 rows  key which may be more convenient for your application. There are three
                options: 
             
                 
                     ?_shape=arrays  -  "rows"  is the default option, shown above 
                 
                 
                     ?_shape=objects  -  "rows"  is a list of JSON key/value objects 
                 
                 
                     ?_shape=array  - an JSON array of objects 
                 
                 
                     ?_shape=array&_nl=on  - a newline-separated list of JSON objects 
                 
                 
                     ?_shape=arrayfirst  - a flat JSON array containing just the first value from each row 
                 
                 
                     ?_shape=object  - a JSON object keyed using the primary keys of the rows 
                 
             
             _shape=objects  looks like this: 
             {
    "database": "sf-trees",
    ...
    "rows": [
        {
            "id": 1,
            "value": "Myoporum laetum :: Myoporum"
        },
        {
            "id": 2,
            "value": "Metrosideros excelsa :: New Zealand Xmas Tree"
        },
        {
            "id": 3,
            "value": "Pinus radiata :: Monterey Pine"
        }
    ]
} 
             _shape=array  looks like this: 
             [
    {
        "id": 1,
        "value": "Myoporum laetum :: Myoporum"
    },
    {
        "id": 2,
        "value": "Metrosideros excelsa :: New Zealand Xmas Tree"
    },
    {
        "id": 3,
        "value": "Pinus radiata :: Monterey Pine"
    }
] 
             _shape=array&_nl=on  looks like this: 
             {"id": 1, "value": "Myoporum laetum :: Myoporum"}
{"id": 2, "value": "Metrosideros excelsa :: New Zealand Xmas Tree"}
{"id": 3, "value": "Pinus radiata :: Monterey Pine"} 
             _shape=arrayfirst  looks like this: 
             [1, 2, 3] 
             _shape=object  looks like this: 
             {
    "1": {
        "id": 1,
        "value": "Myoporum laetum :: Myoporum"
    },
    "2": {
        "id": 2,
        "value": "Metrosideros excelsa :: New Zealand Xmas Tree"
    },
    "3": {
        "id": 3,
        "value": "Pinus radiata :: Monterey Pine"
    }
] 
             The  object  shape is only available for queries against tables - custom SQL
                queries and views do not have an obvious primary key so cannot be returned using
                this format. 
             The  object  keys are always strings. If your table has a compound primary
                key, the  object  keys will be a comma-separated string.


id: json_api:json-api-pagination
page: json_api
ref: json-api-pagination
title: Pagination
breadcrumbs: ["JSON API"]
references: [{"href": "https://requests.readthedocs.io/", "label": "requests"}]
content:
The default JSON representation includes a  "next_url"  key which can be used to access the next page of results. If that key is null or missing then it means you have reached the final page of results. 
             Other representations include pagination information in the  link  HTTP header. That header will look something like this: 
             link: <https://latest.datasette.io/fixtures/sortable.json?_next=d%2Cv>; rel="next" 
             Here is an example Python function built using  requests  that returns a list of all of the paginated items from one of these API endpoints: 
             def paginate(url):
    items = []
    while url:
        response = requests.get(url)
        try:
            url = response.links.get("next").get("url")
        except AttributeError:
            url = None
        items.extend(response.json())
    return items


id: json_api:json-api-special
page: json_api
ref: json-api-special
title: Special JSON arguments
breadcrumbs: ["JSON API"]
content:
Every Datasette endpoint that can return JSON also accepts the following
                query string arguments: 
             
                 
                     ?_shape=SHAPE 
                     
                         The shape of the JSON to return, documented above. 
                     
                 
                 
                     ?_nl=on 
                     
                         When used with  ?_shape=array  produces newline-delimited JSON objects. 
                     
                 
                 
                     ?_json=COLUMN1&_json=COLUMN2 
                     
                         If any of your SQLite columns contain JSON values, you can use one or more
                             _json=  parameters to request that those columns be returned as regular
                            JSON. Without this argument those columns will be returned as JSON objects
                            that have been double-encoded into a JSON string value. 
                         Compare  this query without the argument  to  this query using the argument 
                     
                 
                 
                     ?_json_infinity=on 
                     
                         If your data contains infinity or -infinity values, Datasette will replace
                            them with None when returning them as JSON. If you pass  _json_infinity=1 
                            Datasette will instead return them as  Infinity  or  -Infinity  which is
                            invalid JSON but can be processed by some custom JSON parsers. 
                     
                 
                 
                     ?_timelimit=MS 
                     
                         Sets a custom time limit for the query in ms. You can use this for optimistic
                            queries where you would like Datasette to give up if the query takes too
                            long, for example if you want to implement autocomplete search but only if
                            it can be executed in less than 10ms. 
                     
                 
                 
                     ?_ttl=SECONDS 
                     
                         For how many seconds should this response be cached by HTTP proxies? Use
                             ?_ttl=0  to disable HTTP caching entirely for this request. 
                     
                 
                 
                     ?_trace=1 
                     
                         Turns on tracing for this page: SQL queries executed during the request will
                            be gathered and included in the response, either in a new  "_traces"  key
                            for JSON responses or at the bottom of the page if the response is in HTML. 
                         The structure of the data returned here should be considered highly unstable
                            and very likely to change. 
                         Only available if the  trace_debug  setting is enabled.

references:
[{"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array", "label": "this query without the argument"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array&_json=d", "label": "this query using the argument"}]


id: json_api:column-filter-arguments
page: json_api
ref: column-filter-arguments
title: Column filter arguments
breadcrumbs: ["JSON API", "Table arguments"]
references: []
content:
You can filter the data returned by the table based on column values using a query string argument. 
                 
                     
                         ?column__exact=value  or  ?_column=value 
                         
                             Returns rows where the specified column exactly matches the value. 
                         
                     
                     
                         ?column__not=value 
                         
                             Returns rows where the column does not match the value. 
                         
                     
                     
                         ?column__contains=value 
                         
                             Rows where the string column contains the specified value ( column like "%value%"  in SQL). 
                         
                     
                     
                         ?column__endswith=value 
                         
                             Rows where the string column ends with the specified value ( column like "%value"  in SQL). 
                         
                     
                     
                         ?column__startswith=value 
                         
                             Rows where the string column starts with the specified value ( column like "value%"  in SQL). 
                         
                     
                     
                         ?column__gt=value 
                         
                             Rows which are greater than the specified value. 
                         
                     
                     
                         ?column__gte=value 
                         
                             Rows which are greater than or equal to the specified value. 
                         
                     
                     
                         ?column__lt=value 
                         
                             Rows which are less than the specified value. 
                         
                     
                     
                         ?column__lte=value 
                         
                             Rows which are less than or equal to the specified value. 
                         
                     
                     
                         ?column__like=value 
                         
                             Match rows with a LIKE clause, case insensitive and with  %  as the wildcard character. 
                         
                     
                     
                         ?column__notlike=value 
                         
                             Match rows that do not match the provided LIKE clause. 
                         
                     
                     
                         ?column__glob=value 
                         
                             Similar to LIKE but uses Unix wildcard syntax and is case sensitive. 
                         
                     
                     
                         ?column__in=value1,value2,value3 
                         
                             Rows where column matches any of the provided values. 
                             You can use a comma separated string, or you can use a JSON array. 
                             The JSON array option is useful if one of your matching values itself contains a comma: 
                             ?column__in=["value","value,with,commas"] 
                         
                     
                     
                         ?column__notin=value1,value2,value3 
                         
                             Rows where column does not match any of the provided values. The inverse of  __in= . Also supports JSON arrays. 
                         
                     
                     
                         ?column__arraycontains=value 
                         
                             Works against columns that contain JSON arrays - matches if any of the values in that array match the provided value. 
                             This is only available if the  json1  SQLite extension is enabled. 
                         
                     
                     
                         ?column__arraynotcontains=value 
                         
                             Works against columns that contain JSON arrays - matches if none of the values in that array match the provided value. 
                             This is only available if the  json1  SQLite extension is enabled. 
                         
                     
                     
                         ?column__date=value 
                         
                             Column is a datestamp occurring on the specified YYYY-MM-DD date, e.g.  2018-01-02 . 
                         
                     
                     
                         ?column__isnull=1 
                         
                             Matches rows where the column is null. 
                         
                     
                     
                         ?column__notnull=1 
                         
                             Matches rows where the column is not null. 
                         
                     
                     
                         ?column__isblank=1 
                         
                             Matches rows where the column is blank, meaning null or the empty string. 
                         
                     
                     
                         ?column__notblank=1 
                         
                             Matches rows where the column is not blank.


id: json_api:json-api-table-arguments
page: json_api
ref: json-api-table-arguments
title: Special table arguments
breadcrumbs: ["JSON API", "Table arguments"]
content:
?_col=COLUMN1&_col=COLUMN2 
                         
                             List specific columns to display. These will be shown along with any primary keys. 
                         
                     
                     
                         ?_nocol=COLUMN1&_nocol=COLUMN2 
                         
                             List specific columns to hide - any column not listed will be displayed. Primary keys cannot be hidden. 
                         
                     
                     
                         ?_labels=on/off 
                         
                             Expand foreign key references for every possible column. See below. 
                         
                     
                     
                         ?_label=COLUMN1&_label=COLUMN2 
                         
                             Expand foreign key references for one or more specified columns. 
                         
                     
                     
                         ?_size=1000  or  ?_size=max 
                         
                             Sets a custom page size. This cannot exceed the  max_returned_rows  limit
                                passed to  datasette serve . Use  max  to get  max_returned_rows . 
                         
                     
                     
                         ?_sort=COLUMN 
                         
                             Sorts the results by the specified column. 
                         
                     
                     
                         ?_sort_desc=COLUMN 
                         
                             Sorts the results by the specified column in descending order. 
                         
                     
                     
                         ?_search=keywords 
                         
                             For SQLite tables that have been configured for
                                 full-text search  executes a search
                                with the provided keywords. 
                         
                     
                     
                         ?_search_COLUMN=keywords 
                         
                             Like  _search=  but allows you to specify the column to be searched, as
                                opposed to searching all columns that have been indexed by FTS. 
                         
                     
                     
                         ?_searchmode=raw 
                         
                             With this option, queries passed to  ?_search=  or  ?_search_COLUMN=  will
                                not have special characters escaped. This means you can make use of the full
                                set of  advanced SQLite FTS syntax ,
                                though this could potentially result in errors if the wrong syntax is used. 
                         
                     
                     
                         ?_where=SQL-fragment 
                         
                             If the  execute-sql  permission is enabled, this parameter
                                can be used to pass one or more additional SQL fragments to be used in the
                                 WHERE  clause of the SQL used to query the table. 
                             This is particularly useful if you are building a JavaScript application
                                that needs to do something creative but still wants the other conveniences
                                provided by the table view (such as faceting) and hence would like not to
                                have to construct a completely custom SQL query. 
                             Some examples: 
                             
                                 
                                     facetable?_where=_neighborhood like "%c%"&_where=_city_id=3 
                                 
                                 
                                     facetable?_where=_city_id in (select id from facet_cities where name != "Detroit") 
                                 
                             
                         
                     
                     
                         ?_through={json} 
                         
                             This can be used to filter rows via a join against another table. 
                             The JSON parameter must include three keys:  table ,  column  and  value . 
                             table  must be a table that the current table is related to via a foreign key relationship. 
                             column  must be a column in that other table. 
                             value  is the value that you want to match against. 
                             For example, to filter  roadside_attractions  to just show the attractions that have a characteristic of "museum", you would construct this JSON: 
                             {
    "table": "roadside_attraction_characteristics",
    "column": "characteristic_id",
    "value": "1"
} 
                             As a URL, that looks like this: 
                             ?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22} 
                             Here's  an example . 
                         
                     
                     
                         ?_next=TOKEN 
                         
                             Pagination by continuation token - pass the token that was returned in the
                                 "next"  property by the previous page. 
                         
                     
                     
                         ?_facet=column 
                         
                             Facet by column. Can be applied multiple times, see  Facets . Only works on the default JSON output, not on any of the custom shapes. 
                         
                     
                     
                         ?_facet_size=100 
                         
                             Increase the number of facet results returned for each facet. Use  ?_facet_size=max  for the maximum available size, determined by  max_returned_rows . 
                         
                     
                     
                         ?_nofacet=1 
                         
                             Disable all facets and facet suggestions for this page, including any defined by  Facets in metadata.json . 
                         
                     
                     
                         ?_nosuggest=1 
                         
                             Disable facet suggestions for this page. 
                         
                     
                     
                         ?_nocount=1 
                         
                             Disable the  select count(*)  query used on this page - a count of  None  will be returned instead.

references:
[{"href": "https://www.sqlite.org/fts3.html", "label": "full-text search"}, {"href": "https://www.sqlite.org/fts5.html#full_text_query_syntax", "label": "advanced SQLite FTS syntax"}, {"href": "https://latest.datasette.io/fixtures/facetable?_where=_neighborhood%20like%20%22%c%%22&_where=_city_id=3", "label": "facetable?_where=_neighborhood like \"%c%\"&_where=_city_id=3"}, {"href": "https://latest.datasette.io/fixtures/facetable?_where=_city_id%20in%20(select%20id%20from%20facet_cities%20where%20name%20!=%20%22Detroit%22)", "label": "facetable?_where=_city_id in (select id from facet_cities where name != \"Detroit\")"}, {"href": "https://latest.datasette.io/fixtures/roadside_attractions?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22}", "label": "an example"}]


id: json_api:expand-foreign-keys
page: json_api
ref: expand-foreign-keys
title: Expanding foreign key references
breadcrumbs: ["JSON API"]
references: []
content:
Datasette can detect foreign key relationships and resolve those references into
                labels. The HTML interface does this by default for every detected foreign key
                column - you can turn that off using  ?_labels=off . 
             You can request foreign keys be expanded in JSON using the  _labels=on  or
                 _label=COLUMN  special query string parameters. Here's what an expanded row
                looks like: 
             [
    {
        "rowid": 1,
        "TreeID": 141565,
        "qLegalStatus": {
            "value": 1,
            "label": "Permitted Site"
        },
        "qSpecies": {
            "value": 1,
            "label": "Myoporum laetum :: Myoporum"
        },
        "qAddress": "501X Baker St",
        "SiteOrder": 1
    }
] 
             The column in the foreign key table that is used for the label can be specified
                in  metadata.json  - see  Specifying the label column for a table .


id: json_api:json-api-discover-alternate
page: json_api
ref: json-api-discover-alternate
title: Discovering the JSON for a page
breadcrumbs: ["JSON API"]
references: []
content:
Most of the HTML pages served by Datasette provide a mechanism for discovering their JSON equivalents using the HTML  link  mechanism. 
             You can find this near the top of the source code of those pages, looking like this: 
             <link rel="alternate"
  type="application/json+datasette"
  href="https://latest.datasette.io/fixtures/sortable.json"> 
             The JSON URL is also made available in a  Link  HTTP header for the page: 
             Link: https://latest.datasette.io/fixtures/sortable.json; rel="alternate"; type="application/json+datasette"


id: getting_started:getting-started-demo
page: getting_started
ref: getting-started-demo
title: Play with a live demo
breadcrumbs: ["Getting started"]
content:
The best way to experience Datasette for the first time is with a demo: 
             
                 
                     global-power-plants.datasettes.com  provides a searchable database of power plants around the world, using data from the  World Resources Institude  rendered using the  datasette-cluster-map  plugin. 
                 
                 
                     fivethirtyeight.datasettes.com  shows Datasette running against over 400 datasets imported from the  FiveThirtyEight GitHub repository .

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-tutorial
page: getting_started
ref: getting-started-tutorial
title: Follow a tutorial
breadcrumbs: ["Getting started"]
content:
Datasette has several  tutorials  to help you get started with the tool. Try one of the following: 
             
                 
                     Exploring a database with Datasette  shows how to use the Datasette web interface to explore a new database. 
                 
                 
                     Learn SQL with Datasette  introduces SQL, and shows how to use that query language to ask questions of your data. 
                 
                 
                     Cleaning data with sqlite-utils and Datasette  guides you through using  sqlite-utils  to turn a CSV file into a database that you can explore using Datasette.

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-datasette-lite
page: getting_started
ref: getting-started-datasette-lite
title: Datasette in your browser with Datasette Lite
breadcrumbs: ["Getting started"]
content:
Datasette Lite  is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. 
             You can pass a URL to a CSV, SQLite or raw SQL file directly to Datasette Lite to explore that data in your browser. 
             This  example link  opens Datasette Lite and loads the SQL Murder Mystery example database from  Northwestern University Knight Lab .

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-glitch
page: getting_started
ref: getting-started-glitch
title: Try Datasette without installing anything using Glitch
breadcrumbs: ["Getting started"]
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. 
             Here's a demo project on Glitch which you can use as the basis for your own experiments: 
             glitch.com/~datasette-csvs 
             Glitch allows you to "remix" any project to create your own copy and start editing it in your browser. You can remix the  datasette-csvs  project by clicking this button: 
             
             Find a CSV file and drag it onto the Glitch file explorer panel -  datasette-csvs  will automatically convert it to a SQLite database (using  sqlite-utils ) and allow you to start exploring it using Datasette. 
             If your CSV file has a  latitude  and  longitude  column you can visualize it on a map by uncommenting the  datasette-cluster-map  line in the  requirements.txt  file using the Glitch file editor. 
             Need some data? Try this  Public Art Data  for the city of Seattle - hit "Export" and select "CSV" to download it as a CSV file. 
             For more on how this works, see  Running Datasette on Glitch .

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-your-computer
page: getting_started
ref: getting-started-your-computer
title: Using Datasette on your own computer
breadcrumbs: ["Getting started"]
content:
First, follow the  Installation  instructions. Now you can run Datasette against a SQLite file on your computer using the following command: 
             datasette path/to/database.db 
             This will start a web server on port 8001 - visit  http://localhost:8001/ 
                to access the web interface. 
             Add  -o  to open your browser automatically once Datasette has started: 
             datasette path/to/database.db -o 
             Use Chrome on OS X? You can run datasette against your browser history
                like so: 
             datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock 
             The  --nolock  option ignores any file locks. This is safe as Datasette will open the file in read-only mode. 
             Now visiting  http://localhost:8001/History/downloads  will show you a web
                interface to browse your downloads data: 
             
                 
             
             http://localhost:8001/History/downloads.json  will return that data as
                JSON: 
             {
    "database": "History",
    "columns": [
        "id",
        "current_path",
        "target_path",
        "start_time",
        "received_bytes",
        "total_bytes",
        ...
    ],
    "rows": [
        [
            1,
            "/Users/simonw/Downloads/DropboxInstaller.dmg",
            "/Users/simonw/Downloads/DropboxInstaller.dmg",
            13097290269022132,
            626688,
            0,
            ...
        ]
    ]
} 
             http://localhost:8001/History/downloads.json?_shape=objects  will return that data as
                JSON in a more convenient format: 
             {
    ...
    "rows": [
        {
            "start_time": 13097290269022132,
            "interrupt_reason": 0,
            "hash": "",
            "id": 1,
            "site_url": "",
            "referrer": "https://www.dropbox.com/downloading?src=index",
            ...
        }
    ]
}

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: internals:internals
page: internals
ref: internals
title: Internals for plugins
breadcrumbs: []
references: []
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.


id: internals:internals-request
page: internals
ref: internals-request
title: Request object
breadcrumbs: ["Internals for plugins"]
content:
The request object is passed to various plugin hooks. It represents an incoming HTTP request. It has the following properties: 
             
                 
                     .scope  - dictionary 
                     
                         The ASGI scope that was used to construct this request, described in the  ASGI HTTP connection scope  specification. 
                     
                 
                 
                     .method  - string 
                     
                         The HTTP method for this request, usually  GET  or  POST . 
                     
                 
                 
                     .url  - string 
                     
                         The full URL for this request, e.g.  https://latest.datasette.io/fixtures . 
                     
                 
                 
                     .scheme  - string 
                     
                         The request scheme - usually  https  or  http . 
                     
                 
                 
                     .headers  - dictionary (str -> str) 
                     
                         A dictionary of incoming HTTP request headers. Header names have been converted to lowercase. 
                     
                 
                 
                     .cookies  - dictionary (str -> str) 
                     
                         A dictionary of incoming cookies 
                     
                 
                 
                     .host  - string 
                     
                         The host header from the incoming request, e.g.  latest.datasette.io  or  localhost . 
                     
                 
                 
                     .path  - string 
                     
                         The path of the request excluding the query string, e.g.  /fixtures . 
                     
                 
                 
                     .full_path  - string 
                     
                         The path of the request including the query string if one is present, e.g.  /fixtures?sql=select+sqlite_version() . 
                     
                 
                 
                     .query_string  - string 
                     
                         The query string component of the request, without the  ?  - e.g.  name__contains=sam&age__gt=10 . 
                     
                 
                 
                     .args  - MultiParams 
                     
                         An object representing the parsed query string parameters, see below. 
                     
                 
                 
                     .url_vars  - dictionary (str -> str) 
                     
                         Variables extracted from the URL path, if that path was defined using a regular expression. See  register_routes(datasette) . 
                     
                 
                 
                     .actor  - dictionary (str -> Any) or None 
                     
                         The currently authenticated actor (see  actors ), or  None  if the request is unauthenticated. 
                     
                 
             
             The object also has two awaitable methods: 
             
                 
                     await request.post_vars()  - dictionary 
                     
                         Returns a dictionary of form variables that were submitted in the request body via  POST . Don't forget to read about  CSRF protection ! 
                     
                 
                 
                     await request.post_body()  - bytes 
                     
                         Returns the un-parsed body of a request submitted by  POST  - useful for things like incoming JSON data. 
                     
                 
             
             And a class method that can be used to create fake request objects for use in tests: 
             
                 
                     fake(path_with_query_string, method="GET", scheme="http", url_vars=None) 
                     
                         Returns a  Request  instance for the specified path and method. For example: 
                         from datasette import Request
from pprint import pprint

request = Request.fake(
    "/fixtures/facetable/",
    url_vars={"database": "fixtures", "table": "facetable"},
)
pprint(request.scope) 
                         This outputs: 
                         {'http_version': '1.1',
 'method': 'GET',
 'path': '/fixtures/facetable/',
 'query_string': b'',
 'raw_path': b'/fixtures/facetable/',
 'scheme': 'http',
 'type': 'http',
 'url_route': {'kwargs': {'database': 'fixtures', 'table': 'facetable'}}}

references:
[{"href": "https://asgi.readthedocs.io/en/latest/specs/www.html#connection-scope", "label": "ASGI HTTP connection scope"}]


id: internals:internals-multiparams
page: internals
ref: internals-multiparams
title: The MultiParams class
breadcrumbs: ["Internals for plugins"]
references: []
content:
request.args  is a  MultiParams  object - a dictionary-like object which provides access to query string parameters that may have multiple values. 
             Consider the query string  ?foo=1&foo=2&bar=3  - with two values for  foo  and one value for  bar . 
             
                 
                     request.args[key]  - string 
                     
                         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" . 
                     
                 
                 
                     request.args.get(key)  - string or None 
                     
                         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", "") . 
                     
                 
                 
                     request.args.getlist(key)  - list of strings 
                     
                         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. 
                     
                 
                 
                     request.args.keys()  - list of strings 
                     
                         Returns the list of available keys - for the example this would be  ["foo", "bar"] . 
                     
                 
                 
                     key in request.args  - True or False 
                     
                         You can use  if key in request.args  to check if a key is present. 
                     
                 
                 
                     for key in request.args  - iterator 
                     
                         This lets you loop through every available key. 
                     
                 
                 
                     len(request.args)  - integer 
                     
                         Returns the number of keys.


id: internals:internals-response
page: internals
ref: internals-response
title: Response class
breadcrumbs: ["Internals for plugins"]
references: []
content:
The  Response  class can be returned from view functions that have been registered using the  register_routes(datasette)  hook. 
             The  Response()  constructor takes the following arguments: 
             
                 
                     body  - string 
                     
                         The body of the response. 
                     
                 
                 
                     status  - integer (optional) 
                     
                         The HTTP status - defaults to 200. 
                     
                 
                 
                     headers  - dictionary (optional) 
                     
                         A dictionary of extra HTTP headers, e.g.  {"x-hello": "world"} . 
                     
                 
                 
                     content_type  - string (optional) 
                     
                         The content-type for the response. Defaults to  text/plain . 
                     
                 
             
             For example: 
             from datasette.utils.asgi import Response

response = Response(
    "<xml>This is XML</xml>",
    content_type="application/xml; charset=utf-8",
) 
             The quickest way to create responses is using the  Response.text(...) ,  Response.html(...) ,  Response.json(...)  or  Response.redirect(...)  helper methods: 
             from datasette.utils.asgi import Response

html_response = Response.html("This is HTML")
json_response = Response.json({"this_is": "json"})
text_response = Response.text(
    "This will become utf-8 encoded text"
)
# Redirects are served as 302, unless you pass status=301:
redirect_response = Response.redirect(
    "https://latest.datasette.io/"
) 
             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. 
             Each of the helper methods take optional  status=  and  headers=  arguments, documented above.


id: internals:internals-response-asgi-send
page: internals
ref: internals-response-asgi-send
title: Returning a response with .asgi_send(send)
breadcrumbs: ["Internals for plugins", "Response class"]
references: []
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. 
                 Create a  Response  object and then use  await response.asgi_send(send) , passing the ASGI  send  function. For example: 
                 async def require_authorization(scope, receive, send):
    response = Response.text(
        "401 Authorization Required",
        headers={
            "www-authenticate": 'Basic realm="Datasette", charset="UTF-8"'
        },
        status=401,
    )
    await response.asgi_send(send)


id: internals:internals-response-set-cookie
page: internals
ref: internals-response-set-cookie
title: Setting cookies with response.set_cookie()
breadcrumbs: ["Internals for plugins", "Response class"]
references: []
content:
To set cookies on the response, use the  response.set_cookie(...)  method. The method signature looks like this: 
                 def set_cookie(
    self,
    key,
    value="",
    max_age=None,
    expires=None,
    path="/",
    domain=None,
    secure=False,
    httponly=False,
    samesite="lax",
):
    ... 
                 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 : 
                 response = Response.redirect("/")
response.set_cookie(
    "ds_actor",
    datasette.sign({"a": {"id": "cleopaws"}}, "actor"),
)
return response


id: internals:internals-datasette
page: internals
ref: internals-datasette
title: Datasette class
breadcrumbs: ["Internals for plugins"]
references: []
content:
This object is an instance of the  Datasette  class, passed to many plugin hooks as an argument called  datasette . 
             You can create your own instance of this - for example to help write tests for a plugin - like so: 
             from datasette.app import Datasette

# With no arguments a single in-memory database will be attached
datasette = Datasette()

# The files= argument can load files from disk
datasette = Datasette(files=["/path/to/my-database.db"])

# Pass metadata as a JSON dictionary like this
datasette = Datasette(
    files=["/path/to/my-database.db"],
    metadata={
        "databases": {
            "my-database": {
                "description": "This is my database"
            }
        }
    },
) 
             Constructor parameters include: 
             
                 
                     files=[...]  - a list of database files to open 
                 
                 
                     immutables=[...]  - a list of database files to open in immutable mode 
                 
                 
                     metadata={...}  - a dictionary of  Metadata 
                 
                 
                     config_dir=...  - the  configuration directory  to use, stored in  datasette.config_dir


id: internals:datasette-databases
page: internals
ref: datasette-databases
title: .databases
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
Property exposing a  collections.OrderedDict  of databases currently connected to Datasette. 
                 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. 
                 All databases are listed, irrespective of user permissions. This means that the  _internal  database will always be listed here.


id: internals:datasette-plugin-config
page: internals
ref: datasette-plugin-config
title: .plugin_config(plugin_name, database=None, table=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
plugin_name  - string 
                         
                             The name of the plugin to look up configuration for. Usually this is something similar to  datasette-cluster-map . 
                         
                     
                     
                         database  - None or string 
                         
                             The database the user is interacting with. 
                         
                     
                     
                         table  - None or string 
                         
                             The table the user is interacting with. 
                         
                     
                 
                 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. 
                 The return value will be the value from the configuration file - usually a dictionary. 
                 If the plugin is not configured the return value will be  None .


id: internals:datasette-render-template
page: internals
ref: datasette-render-template
title: await .render_template(template, context=None, request=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
content:
template  - string, list of strings or jinja2.Template 
                         
                             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. 
                             If this is a list of template file names then the first one that exists will be loaded and rendered. 
                             If this is a Jinja  Template object  it will be used directly. 
                         
                     
                     
                         context  - None or a Python dictionary 
                         
                             The context variables to pass to the template. 
                         
                     
                     
                         request  - request object or None 
                         
                             If you pass a Datasette request object here it will be made available to the template. 
                         
                     
                 
                 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.

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-permission-allowed
page: internals
ref: datasette-permission-allowed
title: await .permission_allowed(actor, action, resource=None, default=False)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor  - dictionary 
                         
                             The authenticated actor. This is usually  request.actor . 
                         
                     
                     
                         action  - string 
                         
                             The name of the action that is being permission checked. 
                         
                     
                     
                         resource  - string or tuple, optional 
                         
                             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. 
                         
                     
                     
                         default  - optional, True or False 
                         
                             Should this permission check be default allow or default deny. 
                         
                     
                 
                 Check if the given actor has  permission  to perform the given action on the given resource. 
                 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. 
                 If neither  metadata.json  nor any of the plugins provide an answer to the permission query the  default  argument will be returned. 
                 See  Built-in permissions  for a full list of permission actions included in Datasette core.


id: internals:datasette-ensure-permissions
page: internals
ref: datasette-ensure-permissions
title: await .ensure_permissions(actor, permissions)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor  - dictionary 
                         
                             The authenticated actor. This is usually  request.actor . 
                         
                     
                     
                         permissions  - list 
                         
                             A list of permissions to check. Each permission in that list can be a string  action  name or a 2-tuple of  (action, resource) . 
                         
                     
                 
                 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. 
                 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 : 
                 await self.ds.ensure_permissions(
    request.actor,
    [
        ("view-table", (database, table)),
        ("view-database", database),
        "view-instance",
    ],
)


id: internals:datasette-check-visibility
page: internals
ref: datasette-check-visibility
title: await .check_visibility(actor, action=None, resource=None, permissions=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor  - dictionary 
                         
                             The authenticated actor. This is usually  request.actor . 
                         
                     
                     
                         action  - string, optional 
                         
                             The name of the action that is being permission checked. 
                         
                     
                     
                         resource  - string or tuple, optional 
                         
                             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. 
                         
                     
                     
                         permissions  - list of  action  strings or  (action, resource)  tuples, optional 
                         
                             Provide this instead of  action  and  resource  to check multiple permissions at once. 
                         
                     
                 
                 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?" 
                 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. 
                 This example checks if the user can access a specific table, and sets  private  so that a padlock icon can later be displayed: 
                 visible, private = await self.ds.check_visibility(
    request.actor,
    action="view-table",
    resource=(database, table),
) 
                 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. 
                 visible, private = await self.ds.check_visibility(
    request.actor,
    permissions=[
        ("view-table", (database, table)),
        ("view-database", database),
        "view-instance",
    ],
)


id: internals:datasette-get-database
page: internals
ref: datasette-get-database
title: .get_database(name)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
name  - string, optional 
                         
                             The name of the database - optional. 
                         
                     
                 
                 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.


id: internals:datasette-add-database
page: internals
ref: datasette-add-database
title: .add_database(db, name=None, route=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
db  - datasette.database.Database instance 
                         
                             The database to be attached. 
                         
                     
                     
                         name  - string, optional 
                         
                             The name to be used for this database . If not specified Datasette will pick one based on the filename or memory name. 
                         
                     
                     
                         route  - string, optional 
                         
                             This will be used in the URL path. If not specified, it will default to the same thing as the  name . 
                         
                     
                 
                 The  datasette.add_database(db)  method lets you add a new database to the current Datasette instance. 
                 The  db  parameter should be an instance of the  datasette.database.Database  class. For example: 
                 from datasette.database import Database

datasette.add_database(
    Database(
        datasette,
        path="path/to/my-new-database.db",
    )
) 
                 This will add a mutable database and serve it at  /my-new-database . 
                 Use  is_mutable=False  to add an immutable database. 
                 .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: 
                 db = datasette.add_database(
    Database(datasette, memory_name="statistics")
)
await db.execute_write(
    "CREATE TABLE foo(id integer primary key)"
)


id: internals:datasette-add-memory-database
page: internals
ref: datasette-add-memory-database
title: .add_memory_database(name)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
Adds a shared in-memory database with the specified name: 
                 datasette.add_memory_database("statistics") 
                 This is a shortcut for the following: 
                 from datasette.database import Database

datasette.add_database(
    Database(datasette, memory_name="statistics")
) 
                 Using either of these pattern will result in the in-memory database being served at  /statistics .


id: internals:datasette-remove-database
page: internals
ref: datasette-remove-database
title: .remove_database(name)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
name  - string 
                         
                             The name of the database to be removed. 
                         
                     
                 
                 This removes a database that has been previously added.  name=  is the unique name of that database.


id: internals:datasette-sign
page: internals
ref: datasette-sign
title: .sign(value, namespace="default")
breadcrumbs: ["Internals for plugins", "Datasette class"]
content:
value  - any serializable type 
                         
                             The value to be signed. 
                         
                     
                     
                         namespace  - string, optional 
                         
                             An alternative namespace, see the  itsdangerous salt documentation . 
                         
                     
                 
                 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. 
                 This method returns a signed string, which can be decoded and verified using  .unsign(value, namespace="default") .

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")
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
signed  - any serializable type 
                         
                             The signed string that was created using  .sign(value, namespace="default") . 
                         
                     
                     
                         namespace  - string, optional 
                         
                             The alternative namespace, if one was used. 
                         
                     
                 
                 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.


id: internals:datasette-add-message
page: internals
ref: datasette-add-message
title: .add_message(request, message, type=datasette.INFO)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
request  - Request 
                         
                             The current Request object 
                         
                     
                     
                         message  - string 
                         
                             The message string 
                         
                     
                     
                         type  - constant, optional 
                         
                             The message type -  datasette.INFO ,  datasette.WARNING  or  datasette.ERROR 
                         
                     
                 
                 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. 
                 You can try out these messages (including the different visual styling of the three message types) using the  /-/messages  debugging tool.


id: internals:datasette-absolute-url
page: internals
ref: datasette-absolute-url
title: .absolute_url(request, path)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
request  - Request 
                         
                             The current Request object 
                         
                     
                     
                         path  - string 
                         
                             A path, for example  /dbname/table.json 
                         
                     
                 
                 Returns the absolute URL for the given path, including the protocol and host. For example: 
                 absolute_url = datasette.absolute_url(
    request, "/dbname/table.json"
)
# Would return "http://localhost:8001/dbname/table.json" 
                 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.


id: internals:datasette-setting
page: internals
ref: datasette-setting
title: .setting(key)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
key  - string 
                         
                             The name of the setting, e.g.  base_url . 
                         
                     
                 
                 Returns the configured value for the specified  setting . This can be a string, boolean or integer depending on the requested setting. 
                 For example: 
                 downloads_are_allowed = datasette.setting("allow_download")


id: internals:internals-datasette-client
page: internals
ref: internals-datasette-client
title: datasette.client
breadcrumbs: ["Internals for plugins", "Datasette class"]
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. 
                 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 . 
                 It offers the following methods: 
                 
                     
                         await datasette.client.get(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal GET request against that path. 
                         
                     
                     
                         await datasette.client.post(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal POST request. Use  data={"name": "value"}  to pass form parameters. 
                         
                     
                     
                         await datasette.client.options(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal OPTIONS request. 
                         
                     
                     
                         await datasette.client.head(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal HEAD request. 
                         
                     
                     
                         await datasette.client.put(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal PUT request. 
                         
                     
                     
                         await datasette.client.patch(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal PATCH request. 
                         
                     
                     
                         await datasette.client.delete(path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal DELETE request. 
                         
                     
                     
                         await datasette.client.request(method, path, **kwargs)  - returns HTTPX Response 
                         
                             Execute an internal request with the given HTTP method against that path. 
                         
                     
                 
                 These methods can be used with  datasette.urls  - for example: 
                 table_json = (
    await datasette.client.get(
        datasette.urls.table(
            "fixtures", "facetable", format="json"
        )
    )
).json() 
                 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. 
                 For documentation on available  **kwargs  options and the shape of the HTTPX Response object refer to the  HTTPX Async documentation .

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
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
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. 
                 
                     
                         datasette.urls.instance(format=None) 
                         
                             Returns the URL to the Datasette instance root page. This is usually  "/" . 
                         
                     
                     
                         datasette.urls.path(path, format=None) 
                         
                             Takes a path and returns the full path, taking  base_url  into account. 
                             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/ 
                         
                     
                     
                         datasette.urls.logout() 
                         
                             Returns the URL to the logout page, usually  "/-/logout" 
                         
                     
                     
                         datasette.urls.static(path) 
                         
                             Returns the URL of one of Datasette's default static assets, for example  "/-/static/app.css" 
                         
                     
                     
                         datasette.urls.static_plugins(plugin_name, path) 
                         
                             Returns the URL of one of the static assets belonging to a plugin. 
                             datasette.urls.static_plugins("datasette_cluster_map", "datasette-cluster-map.js")  would return  "/-/static-plugins/datasette_cluster_map/datasette-cluster-map.js" 
                         
                     
                     
                         datasette.urls.static(path) 
                         
                             Returns the URL of one of Datasette's default static assets, for example  "/-/static/app.css" 
                         
                     
                     
                         datasette.urls.database(database_name, format=None) 
                         
                             Returns the URL to a database page, for example  "/fixtures" 
                         
                     
                     
                         datasette.urls.table(database_name, table_name, format=None) 
                         
                             Returns the URL to a table page, for example  "/fixtures/facetable" 
                         
                     
                     
                         datasette.urls.query(database_name, query_name, format=None) 
                         
                             Returns the URL to a query page, for example  "/fixtures/pragma_cache_size" 
                         
                     
                 
                 These functions can be accessed via the  {{ urls }}  object in Datasette templates, for example: 
                 <a href="{{ urls.instance() }}">Homepage</a>
<a href="{{ urls.database("fixtures") }}">Fixtures database</a>
<a href="{{ urls.table("fixtures", "facetable") }}">facetable table</a>
<a href="{{ urls.query("fixtures", "pragma_cache_size") }}">pragma_cache_size query</a> 
                 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. 
                 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.


id: internals:internals-database
page: internals
ref: internals-database
title: Database class
breadcrumbs: ["Internals for plugins"]
references: []
content:
Instances of the  Database  class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.


id: internals:database-constructor
page: internals
ref: database-constructor
title: Database(ds, path=None, is_mutable=True, is_memory=False, memory_name=None)
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
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. 
                 The arguments are as follows: 
                 
                     
                         ds  -  Datasette class  (required) 
                         
                             The Datasette instance you are attaching this database to. 
                         
                     
                     
                         path  - string 
                         
                             Path to a SQLite database file on disk. 
                         
                     
                     
                         is_mutable  - boolean 
                         
                             Set this to  False  to cause Datasette to open the file in immutable mode. 
                         
                     
                     
                         is_memory  - boolean 
                         
                             Use this to create non-shared memory connections. 
                         
                     
                     
                         memory_name  - string or  None 
                         
                             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. 
                         
                     
                 
                 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.


id: internals:database-hash
page: internals
ref: database-hash
title: db.hash
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
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 .


id: internals:database-execute
page: internals
ref: database-execute
title: await db.execute(sql, ...)
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
content:
Executes a SQL query against the database and returns the resulting rows (see  Results ). 
                 
                     
                         sql  - string (required) 
                         
                             The SQL query to execute. This can include  ?  or  :named  parameters. 
                         
                     
                     
                         params  - list or dict 
                         
                             A list or dictionary of values to use for the parameters. List for  ? , dictionary for  :named . 
                         
                     
                     
                         truncate  - boolean 
                         
                             Should the rows returned by the query be truncated at the maximum page size? Defaults to  True , set this to  False  to disable truncation. 
                         
                     
                     
                         custom_time_limit  - integer ms 
                         
                             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. 
                         
                     
                     
                         page_size  - integer 
                         
                             Set a custom page size for truncation, over-riding the configured Datasette default. 
                         
                     
                     
                         log_sql_errors  - boolean 
                         
                             Should any SQL errors be logged to the console in addition to being raised as an error? Defaults to  True .


id: internals:database-results
page: internals
ref: database-results
title: Results
breadcrumbs: ["Internals for plugins", "Database class"]
references: [{"href": "https://docs.python.org/3/library/sqlite3.html#row-objects", "label": "Row objects"}]
content:
The  db.execute()  method returns a single  Results  object. This can be used to access the rows returned by the query. 
                 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: 
                 info = []
results = await db.execute("select name from sqlite_master")
for row in results:
    info.append(row["name"]) 
                 The  Results  object also has the following properties and methods: 
                 
                     
                         .truncated  - boolean 
                         
                             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. 
                         
                     
                     
                         .columns  - list of strings 
                         
                             A list of column names returned by the query. 
                         
                     
                     
                         .rows  - list of sqlite3.Row 
                         
                             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] . 
                         
                     
                     
                         .first()  - row or None 
                         
                             Returns the first row in the results, or  None  if no rows were returned. 
                         
                     
                     
                         .single_value() 
                         
                             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. 
                         
                     
                     
                         .__len__() 
                         
                             Calling  len(results)  returns the (truncated) number of returned results.


id: internals:database-execute-fn
page: internals
ref: database-execute-fn
title: await db.execute_fn(fn)
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
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 . 
                 Example usage: 
                 def get_version(conn):
    return conn.execute(
        "select sqlite_version()"
    ).fetchall()[0][0]


version = await db.execute_fn(get_version)


id: internals:database-execute-write
page: internals
ref: database-execute-write
title: await db.execute_write(sql, params=None, block=True)
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
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. 
                 This method can be used to queue up a non-SELECT SQL query to be executed against a single write connection to the database. 
                 You can pass additional SQL parameters as a tuple or dictionary. 
                 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. 
                 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.


id: internals:database-execute-write-script
page: internals
ref: database-execute-write-script
title: await db.execute_write_script(sql, block=True)
breadcrumbs: ["Internals for plugins", "Database class"]
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.

references:
[{"href": "https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executescript", "label": "conn.executescript()"}]


id: internals:database-execute-write-many
page: internals
ref: database-execute-write-many
title: await db.execute_write_many(sql, params_seq, block=True)
breadcrumbs: ["Internals for plugins", "Database class"]
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: 
                 await db.execute_write_many(
    "insert into characters (id, name) values (?, ?)",
    [(1, "Melanie"), (2, "Selma"), (2, "Viktor")],
)

references:
[{"href": "https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.executemany", "label": "conn.executemany()"}]


id: internals:database-execute-write-fn
page: internals
ref: database-execute-write-fn
title: await db.execute_write_fn(fn, block=True)
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
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. 
                 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. 
                 
                     fn  needs to be a regular function, not an  async def  function. 
                 
                 For example: 
                 def delete_and_return_count(conn):
    conn.execute("delete from some_table where id > 5")
    return conn.execute(
        "select count(*) from some_table"
    ).fetchone()[0]


try:
    num_rows_left = await database.execute_write_fn(
        delete_and_return_count
    )
except Exception as e:
    print("An error occurred:", e) 
                 The value returned from  await database.execute_write_fn(...)  will be the return value from your function. 
                 If your function raises an exception that exception will be propagated up to the  await  line. 
                 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.


id: internals:database-close
page: internals
ref: database-close
title: db.close()
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
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.


id: internals:internals-database-introspection
page: internals
ref: internals-database-introspection
title: Database introspection
breadcrumbs: ["Internals for plugins", "Database class"]
references: []
content:
The  Database  class also provides properties and methods for introspecting the database. 
                 
                     
                         db.name  - string 
                         
                             The name of the database - usually the filename without the  .db  prefix. 
                         
                     
                     
                         db.size  - integer 
                         
                             The size of the database file in bytes. 0 for  :memory:  databases. 
                         
                     
                     
                         db.mtime_ns  - integer or None 
                         
                             The last modification time of the database file in nanoseconds since the epoch.  None  for  :memory:  databases. 
                         
                     
                     
                         db.is_mutable  - boolean 
                         
                             Is this database mutable, and allowed to accept writes? 
                         
                     
                     
                         db.is_memory  - boolean 
                         
                             Is this database an in-memory database? 
                         
                     
                     
                         await db.attached_databases()  - list of named tuples 
                         
                             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 . 
                         
                     
                     
                         await db.table_exists(table)  - boolean 
                         
                             Check if a table called  table  exists. 
                         
                     
                     
                         await db.table_names()  - list of strings 
                         
                             List of names of tables in the database. 
                         
                     
                     
                         await db.view_names()  - list of strings 
                         
                             List of names of views in the database. 
                         
                     
                     
                         await db.table_columns(table)  - list of strings 
                         
                             Names of columns in a specific table. 
                         
                     
                     
                         await db.table_column_details(table)  - list of named tuples 
                         
                             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). 
                         
                     
                     
                         await db.primary_keys(table)  - list of strings 
                         
                             Names of the columns that are part of the primary key for this table. 
                         
                     
                     
                         await db.fts_table(table)  - string or None 
                         
                             The name of the FTS table associated with this table, if one exists. 
                         
                     
                     
                         await db.label_column_for_table(table)  - string or None 
                         
                             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 . 
                         
                     
                     
                         await db.foreign_keys_for_table(table)  - list of dictionaries 
                         
                             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} . 
                         
                     
                     
                         await db.hidden_table_names()  - list of strings 
                         
                             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. 
                         
                     
                     
                         await db.get_table_definition(table)  - string 
                         
                             Returns the SQL definition for the table - the  CREATE TABLE  statement and any associated  CREATE INDEX  statements. 
                         
                     
                     
                         await db.get_view_definition(view)  - string 
                         
                             Returns the SQL definition of the named view. 
                         
                     
                     
                         await db.get_all_foreign_keys()  - dictionary 
                         
                             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: 
                             {
    "incoming": [],
    "outgoing": [
        {
            "other_table": "attraction_characteristic",
            "column": "characteristic_id",
            "other_column": "pk",
        },
        {
            "other_table": "roadside_attractions",
            "column": "attraction_id",
            "other_column": "pk",
        }
    ]
}


id: internals:internals-csrf
page: internals
ref: internals-csrf
title: CSRF protection
breadcrumbs: ["Internals for plugins"]
references: [{"href": "https://github.com/simonw/asgi-csrf", "label": "asgi-csrf"}]
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. 
             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: 
             <input type="hidden" name="csrftoken" value="{{ csrftoken() }}"> 
             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: 
             form-urlencoded POST field did not match cookie 
             You can selectively disable CSRF protection using the  skip_csrf(datasette, scope)  hook.


id: internals:internals-internal
page: internals
ref: internals-internal
title: The _internal database
breadcrumbs: ["Internals for plugins"]
content:
This API should be considered unstable - the structure of these tables may change prior to the release of Datasette 1.0. 
             
             Datasette maintains an in-memory SQLite database with details of the the databases, tables and columns for all of the attached databases. 
             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 . 
             Plugins can access this database by calling  db = datasette.get_database("_internal")  and then executing queries using the  Database API . 
             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 .

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-utils
page: internals
ref: internals-utils
title: The datasette.utils module
breadcrumbs: ["Internals for plugins"]
references: [{"href": "https://github.com/simonw/datasette/issues/new", "label": "opening an issue"}]
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. 
             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.


id: internals:internals-utils-parse-metadata
page: internals
ref: internals-utils-parse-metadata
title: parse_metadata(content)
breadcrumbs: ["Internals for plugins", "The datasette.utils module"]
references: []
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. 
                 If the metadata cannot be parsed as either JSON or YAML the function will raise a  utils.BadMetadataError  exception. 
                 
                 
                     datasette.utils. parse_metadata content :    str dict 
                     
                         Detects if content is JSON or YAML and parses it appropriately.


id: internals:internals-utils-await-me-maybe
page: internals
ref: internals-utils-await-me-maybe
title: await_me_maybe(value)
breadcrumbs: ["Internals for plugins", "The datasette.utils module"]
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 “await me maybe” pattern for Python asyncio . 
                 
                 
                     async    datasette.utils. await_me_maybe value :    Any Any 
                     
                         If value is callable, call it. If awaitable, await it. Otherwise return it.

references:
[{"href": "https://simonwillison.net/2020/Sep/2/await-me-maybe/", "label": "The “await me maybe” pattern for Python asyncio"}]


id: internals:internals-tilde-encoding
page: internals
ref: internals-tilde-encoding
title: Tilde encoding
breadcrumbs: ["Internals for plugins", "The datasette.utils module"]
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. 
                 Tilde encoding uses the same algorithm as  URL percent-encoding , but with the  ~  tilde character used in place of  % . 
                 Any character other than  ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz0123456789_-  will be replaced by the numeric equivalent preceded by a tilde. For example: 
                 
                     
                         /  becomes  ~2F 
                     
                     
                         .  becomes  ~2E 
                     
                     
                         %  becomes  ~25 
                     
                     
                         ~  becomes  ~7E 
                     
                     
                         Space becomes  + 
                     
                     
                         polls/2022.primary  becomes  polls~2F2022~2Eprimary 
                     
                 
                 Note that the space character is a special case: it will be replaced with a  +  symbol. 
                 
                 
                 
                     datasette.utils. tilde_encode s :    str str 
                     
                         Returns tilde-encoded string - for example  /foo/bar  ->  ~2Ffoo~2Fbar 
                     
                 
                 
                 
                 
                     datasette.utils. tilde_decode s :    str str 
                     
                         Decodes a tilde-encoded string, so  ~2Ffoo~2Fbar  ->  /foo/bar

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
breadcrumbs: ["Internals for plugins"]
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. 
             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. 
             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 . 
             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. 
             The start and end time, duration and a traceback of where the trace was executed will be automatically attached to the JSON object. 
             This example uses trace to record the start, end and duration of any HTTP GET requests made using the function: 
             from datasette.tracer import trace
import httpx


async def fetch_url(url):
    with trace("fetch-url", url=url):
        async with httpx.AsyncClient() as client:
            return await client.get(url)

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
breadcrumbs: ["Internals for plugins", "datasette.tracer"]
references: []
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. 
                 You can use the  trace_child_tasks()  context manager to ensure these child tasks are correctly handled. 
                 from datasette import tracer

with tracer.trace_child_tasks():
    results = await asyncio.gather(
        # ... async tasks here
    ) 
                 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. 
                 from datasette import hookimpl
from datasette import tracer


@hookimpl
def register_routes():
    async def parallel_queries(datasette):
        db = datasette.get_database()
        with tracer.trace_child_tasks():
            one, two = await asyncio.gather(
                db.execute("select 1"),
                db.execute("select 2"),
            )
        return Response.json(
            {
                "one": one.single_value(),
                "two": two.single_value(),
            }
        )

    return [
        (r"/parallel-queries$", parallel_queries),
    ] 
                 Adding  ?_trace=1  will show that the trace covers both of those child tasks.


id: internals:internals-shortcuts
page: internals
ref: internals-shortcuts
title: Import shortcuts
breadcrumbs: ["Internals for plugins"]
references: []
content:
The following commonly used symbols can be imported directly from the  datasette  module: 
             from datasette import Response
from datasette import Forbidden
from datasette import NotFound
from datasette import hookimpl
from datasette import actor_matches_allow


id: contributing:id1
page: contributing
ref: id1
title: Contributing
breadcrumbs: []
references: []
content:
Datasette is an open source project. We welcome contributions! 
         This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new  Plugins .


id: contributing:general-guidelines
page: contributing
ref: general-guidelines
title: General guidelines
breadcrumbs: ["Contributing"]
references: []
content:
main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released. 
                 
                 
                     The ideal commit  should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue. 
                 
                 
                     New plugin hooks  should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.


id: contributing:devenvironment
page: contributing
ref: devenvironment
title: Setting up a development environment
breadcrumbs: ["Contributing"]
content:
If you have Python 3.7 or higher installed on your computer (on OS X the quickest way to do this  is using homebrew ) you can install an editable copy of Datasette using the following steps. 
             If you want to use GitHub to publish your changes, first  create a fork of datasette  under your own GitHub account. 
             Now clone that repository somewhere on your computer: 
             git clone git@github.com:YOURNAME/datasette 
             If you want to get started without creating your own fork, you can do this instead: 
             git clone git@github.com:simonw/datasette 
             The next step is to create a virtual environment for your project and use it to install Datasette's dependencies: 
             cd datasette
# Create a virtual environment in ./venv
python3 -m venv ./venv
# Now activate the virtual environment, so pip can install into it
source venv/bin/activate
# Install Datasette and its testing dependencies
python3 -m pip install -e '.[test]' 
             That last line does most of the work:  pip install -e  means "install this package in a way that allows me to edit the source code in place". The  .[test]  option means "use the setup.py in this directory and install the optional testing dependencies as well".

references:
[{"href": "https://docs.python-guide.org/starting/install3/osx/", "label": "is using homebrew"}, {"href": "https://github.com/simonw/datasette/fork", "label": "create a fork of datasette"}]


id: contributing:contributing-running-tests
page: contributing
ref: contributing-running-tests
title: Running the tests
breadcrumbs: ["Contributing"]
content:
Once you have done this, you can run the Datasette unit tests from inside your  datasette/  directory using  pytest  like so: 
             pytest 
             You can run the tests faster using multiple CPU cores with  pytest-xdist  like this: 
             pytest -n auto -m "not serial" 
             -n auto  detects the number of available cores automatically. The  -m "not serial"  skips tests that don't work well in a parallel test environment. You can run those tests separately like so: 
             pytest -m "serial"

references:
[{"href": "https://docs.pytest.org/", "label": "pytest"}, {"href": "https://pypi.org/project/pytest-xdist/", "label": "pytest-xdist"}]


id: contributing:contributing-using-fixtures
page: contributing
ref: contributing-using-fixtures
title: Using fixtures
breadcrumbs: ["Contributing"]
references: []
content:
To run Datasette itself, type  datasette . 
             You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests. 
             You can create a copy of that database by running this command: 
             python tests/fixtures.py fixtures.db 
             Now you can run Datasette against the new fixtures database like so: 
             datasette fixtures.db 
             This will start a server at  http://127.0.0.1:8001/ . 
             Any changes you make in the  datasette/templates  or  datasette/static  folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript). 
             If you want to change Datasette's Python code you can use the  --reload  option to cause Datasette to automatically reload any time the underlying code changes: 
             datasette --reload fixtures.db 
             You can also use the  fixtures.py  script to recreate the testing version of  metadata.json  used by the unit tests. To do that: 
             python tests/fixtures.py fixtures.db fixtures-metadata.json 
             Or to output the plugins used by the tests, run this: 
             python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins
Test tables written to fixtures.db
- metadata written to fixtures-metadata.json
Wrote plugin: fixtures-plugins/register_output_renderer.py
Wrote plugin: fixtures-plugins/view_name.py
Wrote plugin: fixtures-plugins/my_plugin.py
Wrote plugin: fixtures-plugins/messages_output_renderer.py
Wrote plugin: fixtures-plugins/my_plugin_2.py 
             Then run Datasette like this: 
             datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/


id: contributing:contributing-debugging
page: contributing
ref: contributing-debugging
title: Debugging
breadcrumbs: ["Contributing"]
references: []
content:
Any errors that occur while Datasette is running while display a stack trace on the console. 
             You can tell Datasette to open an interactive  pdb  debugger session if an error occurs using the  --pdb  option: 
             datasette --pdb fixtures.db


id: contributing:contributing-formatting
page: contributing
ref: contributing-formatting
title: Code formatting
breadcrumbs: ["Contributing"]
content:
Datasette uses opinionated code formatters:  Black  for Python and  Prettier  for JavaScript. 
             These formatters are enforced by Datasette's continuous integration: if a commit includes Python or JavaScript code that does not match the style enforced by those tools, the tests will fail. 
             When developing locally, you can verify and correct the formatting of your code using these tools.

references:
[{"href": "https://github.com/psf/black", "label": "Black"}, {"href": "https://prettier.io/", "label": "Prettier"}]


id: contributing:contributing-formatting-black
page: contributing
ref: contributing-formatting-black
title: Running Black
breadcrumbs: ["Contributing", "Code formatting"]
references: []
content:
Black will be installed when you run  pip install -e '.[test]' . To test that your code complies with Black, run the following in your root  datasette  repository checkout: 
                 $ black . --check
All done! ✨ 🍰 ✨
95 files would be left unchanged. 
                 If any of your code does not conform to Black you can run this to automatically fix those problems: 
                 $ black .
reformatted ../datasette/setup.py
All done! ✨ 🍰 ✨
1 file reformatted, 94 files left unchanged.


id: contributing:contributing-formatting-blacken-docs
page: contributing
ref: contributing-formatting-blacken-docs
title: blacken-docs
breadcrumbs: ["Contributing", "Code formatting"]
references: [{"href": "https://pypi.org/project/blacken-docs/", "label": "blacken-docs"}]
content:
The  blacken-docs  command applies Black formatting rules to code examples in the documentation. Run it like this: 
                 blacken-docs -l 60 docs/*.rst


id: contributing:contributing-formatting-prettier
page: contributing
ref: contributing-formatting-prettier
title: Prettier
breadcrumbs: ["Contributing", "Code formatting"]
references: [{"href": "https://nodejs.org/en/download/package-manager/", "label": "install Node.js"}]
content:
To install Prettier,  install Node.js  and then run the following in the root of your  datasette  repository checkout: 
                 $ npm install 
                 This will install Prettier in a  node_modules  directory. You can then check that your code matches the coding style like so: 
                 $ npm run prettier -- --check
> prettier
> prettier 'datasette/static/*[!.min].js' "--check"

Checking formatting...
[warn] datasette/static/plugins.js
[warn] Code style issues found in the above file(s). Forgot to run Prettier? 
                 You can fix any problems by running: 
                 $ npm run fix


id: contributing:contributing-documentation
page: contributing
ref: contributing-documentation
title: Editing and building the documentation
breadcrumbs: ["Contributing"]
content:
Datasette's documentation lives in the  docs/  directory and is deployed automatically using  Read The Docs . 
             The documentation is written using reStructuredText. You may find this article on  The subset of reStructuredText worth committing to memory  useful. 
             You can build it locally by installing  sphinx  and  sphinx_rtd_theme  in your Datasette development environment and then running  make html  directly in the  docs/  directory: 
             # You may first need to activate your virtual environment:
source venv/bin/activate

# Install the dependencies needed to build the docs
pip install -e .[docs]

# Now build the docs
cd docs/
make html 
             This will create the HTML version of the documentation in  docs/_build/html . You can open it in your browser like so: 
             open _build/html/index.html 
             Any time you make changes to a  .rst  file you can re-run  make html  to update the built documents, then refresh them in your browser. 
             For added productivity, you can use use  sphinx-autobuild  to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor. 
             sphinx-autobuild  will have been installed when you ran  pip install -e .[docs] . In your  docs/  directory you can start the server by running the following: 
             make livehtml 
             Now browse to  http://localhost:8000/  to view the documentation. Any edits you make should be instantly reflected in your browser.

references:
[{"href": "https://readthedocs.org/", "label": "Read The Docs"}, {"href": "https://simonwillison.net/2018/Aug/25/restructuredtext/", "label": "The subset of reStructuredText worth committing to memory"}, {"href": "https://pypi.org/project/sphinx-autobuild/", "label": "sphinx-autobuild"}]


id: contributing:contributing-documentation-cog
page: contributing
ref: contributing-documentation-cog
title: Running Cog
breadcrumbs: ["Contributing", "Editing and building the documentation"]
references: [{"href": "https://github.com/nedbat/cog", "label": "Cog"}]
content:
Some pages of documentation (in particular the  CLI reference ) are automatically updated using  Cog . 
                 To update these pages, run the following command: 
                 cog -r docs/*.rst


id: contributing:contributing-continuous-deployment
page: contributing
ref: contributing-continuous-deployment
title: Continuously deployed demo instances
breadcrumbs: ["Contributing"]
content:
The demo instance at  latest.datasette.io  is re-deployed automatically to Google Cloud Run for every push to  main  that passes the test suite. This is implemented by the GitHub Actions workflow at  .github/workflows/deploy-latest.yml . 
             Specific branches can also be set to automatically deploy by adding them to the  on: push: branches  block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass. 
             The Cloud Run URL for a branch demo can be found in the GitHub Actions logs.

references:
[{"href": "https://latest.datasette.io/", "label": "latest.datasette.io"}, {"href": "https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml", "label": ".github/workflows/deploy-latest.yml"}]


id: contributing:contributing-release
page: contributing
ref: contributing-release
title: Release process
breadcrumbs: ["Contributing"]
content:
Datasette releases are performed using tags. When a new release is published on GitHub, a  GitHub Action workflow  will perform the following: 
             
                 
                     Run the unit tests against all supported Python versions. If the tests pass... 
                 
                 
                     Build a Docker image of the release and push a tag to  https://hub.docker.com/r/datasetteproject/datasette 
                 
                 
                     Re-point the "latest" tag on Docker Hub to the new image 
                 
                 
                     Build a wheel bundle of the underlying Python source code 
                 
                 
                     Push that new wheel up to PyPI:  https://pypi.org/project/datasette/ 
                 
             
             To deploy new releases you will need to have push access to the main Datasette GitHub repository. 
             Datasette follows  Semantic Versioning : 
             major.minor.patch 
             We increment  major  for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always  0 . 
             We increment  minor  for new features. 
             We increment  patch  for bugfix releass. 
             Alpha and beta releases  may have an additional  a0  or  b0  prefix - the integer component will be incremented with each subsequent alpha or beta. 
             To release a new version, first create a commit that updates the version number in  datasette/version.py  and the  the changelog  with highlights of the new version. An example  commit can be seen here : 
             # Update changelog
git commit -m " Release 0.51a1

Refs #1056, #1039, #998, #1045, #1033, #1036, #1034, #976, #1057, #1058, #1053, #1064, #1066" -a
git push 
             Referencing the issues that are part of the release in the commit message ensures the name of the release shows up on those issue pages, e.g.  here . 
             You can generate the list of issue references for a specific release by copying and pasting text from the release notes or GitHub changes-since-last-release view into this  Extract issue numbers from pasted text  tool. 
             To create the tag for the release, create  a new release  on GitHub matching the new version number. You can convert the release notes to Markdown by copying and pasting the rendered HTML into this  Paste to Markdown tool . 
             Finally, post a news item about the release on  datasette.io  by editing the  news.yaml  file in that site's repository.

references:
[{"href": "https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml", "label": "GitHub Action workflow"}, {"href": "https://hub.docker.com/r/datasetteproject/datasette", "label": "https://hub.docker.com/r/datasetteproject/datasette"}, {"href": "https://pypi.org/project/datasette/", "label": "https://pypi.org/project/datasette/"}, {"href": "https://semver.org/", "label": "Semantic Versioning"}, {"href": "https://github.com/simonw/datasette/commit/0e1e89c6ba3d0fbdb0823272952cf356f3016def", "label": "commit can be seen here"}, {"href": "https://github.com/simonw/datasette/issues/581#ref-commit-d56f402", "label": "here"}, {"href": "https://observablehq.com/@simonw/extract-issue-numbers-from-pasted-text", "label": "Extract issue numbers from pasted text"}, {"href": "https://github.com/simonw/datasette/releases/new", "label": "a new release"}, {"href": "https://euangoddard.github.io/clipboard2markdown/", "label": "Paste to Markdown tool"}, {"href": "https://datasette.io/", "label": "datasette.io"}, {"href": "https://github.com/simonw/datasette.io/blob/main/news.yaml", "label": "news.yaml"}]


id: contributing:contributing-alpha-beta
page: contributing
ref: contributing-alpha-beta
title: Alpha and beta releases
breadcrumbs: ["Contributing"]
content:
Alpha and beta releases are published to preview upcoming features that may not yet be stable - in particular to preview new plugin hooks. 
             You are welcome to try these out, but please be aware that details may change before the final release. 
             Please join  discussions on the issue tracker  to share your thoughts and experiences with on alpha and beta features that you try out.

references:
[{"href": "https://github.com/simonw/datasette/issues", "label": "discussions on the issue tracker"}]


id: contributing:contributing-bug-fix-branch
page: contributing
ref: contributing-bug-fix-branch
title: Releasing bug fixes from a branch
breadcrumbs: ["Contributing"]
references: [{"href": "https://github.com/simonw/datasette/releases/new", "label": "Draft a new release"}]
content:
If it's necessary to publish a bug fix release without shipping new features that have landed on  main  a release branch can be used. 
             Create it from the relevant last tagged release like so: 
             git branch 0.52.x 0.52.4
git checkout 0.52.x 
             Next cherry-pick the commits containing the bug fixes: 
             git cherry-pick COMMIT 
             Write the release notes in the branch, and update the version number in  version.py . Then push the branch: 
             git push -u origin 0.52.x 
             Once the tests have completed, publish the release from that branch target using the GitHub  Draft a new release  form. 
             Finally, cherry-pick the commit with the release notes and version number bump across to  main : 
             git checkout main
git cherry-pick COMMIT
git push


id: contributing:contributing-upgrading-codemirror
page: contributing
ref: contributing-upgrading-codemirror
title: Upgrading CodeMirror
breadcrumbs: ["Contributing"]
content:
Datasette bundles  CodeMirror  for the SQL editing interface, e.g. on  this page . Here are the steps for upgrading to a new version of CodeMirror: 
             
                 
                     Download and extract latest CodeMirror zip file from  https://codemirror.net/codemirror.zip 
                 
                 
                     Rename  lib/codemirror.js  to  codemirror-5.57.0.js  (using latest version number) 
                 
                 
                     Rename  lib/codemirror.css  to  codemirror-5.57.0.css 
                 
                 
                     Rename  mode/sql/sql.js  to  codemirror-5.57.0-sql.js 
                 
                 
                     Edit both JavaScript files to make the top license comment a  /* */  block instead of multiple  //  lines 
                 
                 
                     Minify the JavaScript files like this: 
                     npx uglify-js codemirror-5.57.0.js -o codemirror-5.57.0.min.js --comments '/LICENSE/'
npx uglify-js codemirror-5.57.0-sql.js -o codemirror-5.57.0-sql.min.js --comments '/LICENSE/' 
                 
                 
                     Check that the LICENSE comment did indeed survive minification 
                 
                 
                     Minify the CSS file like this: 
                     npx clean-css-cli codemirror-5.57.0.css -o codemirror-5.57.0.min.css 
                 
                 
                     Edit the  _codemirror.html  template to reference the new files 
                 
                 
                     git rm  the old files,  git add  the new files

references:
[{"href": "https://codemirror.net/", "label": "CodeMirror"}, {"href": "https://latest.datasette.io/fixtures", "label": "this page"}, {"href": "https://codemirror.net/codemirror.zip", "label": "https://codemirror.net/codemirror.zip"}]


id: custom_templates:customization-css-and-javascript
page: custom_templates
ref: customization-css-and-javascript
title: Custom CSS and JavaScript
breadcrumbs: ["Custom pages and templates"]
content:
When you launch Datasette, you can specify a custom metadata file like this: 
             datasette mydb.db --metadata metadata.json 
             Your  metadata.json  file can include links that look like this: 
             {
    "extra_css_urls": [
        "https://simonwillison.net/static/css/all.bf8cd891642c.css"
    ],
    "extra_js_urls": [
        "https://code.jquery.com/jquery-3.2.1.slim.min.js"
    ]
} 
             The extra CSS and JavaScript files will be linked in the  <head>  of every page: 
             <link rel="stylesheet" href="https://simonwillison.net/static/css/all.bf8cd891642c.css">
<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script> 
             You can also specify a SRI (subresource integrity hash) for these assets: 
             {
    "extra_css_urls": [
        {
            "url": "https://simonwillison.net/static/css/all.bf8cd891642c.css",
            "sri": "sha384-9qIZekWUyjCyDIf2YK1FRoKiPJq4PHt6tp/ulnuuyRBvazd0hG7pWbE99zvwSznI"
        }
    ],
    "extra_js_urls": [
        {
            "url": "https://code.jquery.com/jquery-3.2.1.slim.min.js",
            "sri": "sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g="
        }
    ]
} 
             This will produce: 
             <link rel="stylesheet" href="https://simonwillison.net/static/css/all.bf8cd891642c.css"
    integrity="sha384-9qIZekWUyjCyDIf2YK1FRoKiPJq4PHt6tp/ulnuuyRBvazd0hG7pWbE99zvwSznI"
    crossorigin="anonymous">
<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"
    integrity="sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g="
    crossorigin="anonymous"></script> 
             Modern browsers will only execute the stylesheet or JavaScript if the SRI hash
                matches the content served. You can generate hashes using  www.srihash.org 
             Items in  "extra_js_urls"  can specify  "module": true  if they reference JavaScript that uses  JavaScript modules . This configuration: 
             {
    "extra_js_urls": [
        {
            "url": "https://example.datasette.io/module.js",
            "module": true
        }
    ]
} 
             Will produce this HTML: 
             <script type="module" src="https://example.datasette.io/module.js"></script>

references:
[{"href": "https://www.srihash.org/", "label": "www.srihash.org"}, {"href": "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules", "label": "JavaScript modules"}]


id: custom_templates:css-classes-on-the-body
page: custom_templates
ref: css-classes-on-the-body
title: CSS classes on the <body>
breadcrumbs: ["Custom pages and templates", "Custom CSS and JavaScript"]
references: []
content:
Every default template includes CSS classes in the body designed to support
                    custom styling. 
                 The index template (the top level page at  / ) gets this: 
                 <body class="index"> 
                 The database template ( /dbname ) gets this: 
                 <body class="db db-dbname"> 
                 The custom SQL template ( /dbname?sql=... ) gets this: 
                 <body class="query db-dbname"> 
                 A canned query template ( /dbname/queryname ) gets this: 
                 <body class="query db-dbname query-queryname"> 
                 The table template ( /dbname/tablename ) gets: 
                 <body class="table db-dbname table-tablename"> 
                 The row template ( /dbname/tablename/rowid ) gets: 
                 <body class="row db-dbname table-tablename"> 
                 The  db-x  and  table-x  classes use the database or table names themselves if
                    they are valid CSS identifiers. If they aren't, we strip any invalid
                    characters out and append a 6 character md5 digest of the original name, in
                    order to ensure that multiple tables which resolve to the same stripped
                    character version still have different CSS classes. 
                 Some examples: 
                 "simple" => "simple"
"MixedCase" => "MixedCase"
"-no-leading-hyphens" => "no-leading-hyphens-65bea6"
"_no-leading-underscores" => "no-leading-underscores-b921bc"
"no spaces" => "no-spaces-7088d7"
"-" => "336d5e"
"no $ characters" => "no--characters-59e024" 
                 <td>  and  <th>  elements also get custom CSS classes reflecting the
                    database column they are representing, for example: 
                 <table>
    <thead>
        <tr>
            <th class="col-id" scope="col">id</th>
            <th class="col-name" scope="col">name</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="col-id"><a href="...">1</a></td>
            <td class="col-name">SMITH</td>
        </tr>
    </tbody>
</table>


id: custom_templates:customization-static-files
page: custom_templates
ref: customization-static-files
title: Serving static files
breadcrumbs: ["Custom pages and templates", "Custom CSS and JavaScript"]
references: []
content:
Datasette can serve static files for you, using the  --static  option.
                    Consider the following directory structure: 
                 metadata.json
static-files/styles.css
static-files/app.js 
                 You can start Datasette using  --static assets:static-files/  to serve those
                    files from the  /assets/  mount point: 
                 $ datasette -m metadata.json --static assets:static-files/ --memory 
                 The following URLs will now serve the content from those CSS and JS files: 
                 http://localhost:8001/assets/styles.css
http://localhost:8001/assets/app.js 
                 You can reference those files from  metadata.json  like so: 
                 {
    "extra_css_urls": [
        "/assets/styles.css"
    ],
    "extra_js_urls": [
        "/assets/app.js"
    ]
}


id: custom_templates:publishing-static-assets
page: custom_templates
ref: publishing-static-assets
title: Publishing static assets
breadcrumbs: ["Custom pages and templates", "Custom CSS and JavaScript"]
references: []
content:
The  datasette publish  command can be used to publish your static assets,
                    using the same syntax as above: 
                 $ datasette publish cloudrun mydb.db --static assets:static-files/ 
                 This will upload the contents of the  static-files/  directory as part of the
                    deployment, and configure Datasette to correctly serve the assets from  /assets/ .


id: custom_templates:customization-custom-templates
page: custom_templates
ref: customization-custom-templates
title: Custom templates
breadcrumbs: ["Custom pages and templates"]
content:
By default, Datasette uses default templates that ship with the package. 
             You can over-ride these templates by specifying a custom  --template-dir  like
                this: 
             datasette mydb.db --template-dir=mytemplates/ 
             Datasette will now first look for templates in that directory, and fall back on
                the defaults if no matches are found. 
             It is also possible to over-ride templates on a per-database, per-row or per-
                table basis. 
             The lookup rules Datasette uses are as follows: 
             Index page (/):
    index.html

Database page (/mydatabase):
    database-mydatabase.html
    database.html

Custom query page (/mydatabase?sql=...):
    query-mydatabase.html
    query.html

Canned query page (/mydatabase/canned-query):
    query-mydatabase-canned-query.html
    query-mydatabase.html
    query.html

Table page (/mydatabase/mytable):
    table-mydatabase-mytable.html
    table.html

Row page (/mydatabase/mytable/id):
    row-mydatabase-mytable.html
    row.html

Table of rows and columns include on table page:
    _table-table-mydatabase-mytable.html
    _table-mydatabase-mytable.html
    _table.html

Table of rows and columns include on row page:
    _table-row-mydatabase-mytable.html
    _table-mydatabase-mytable.html
    _table.html 
             If a table name has spaces or other unexpected characters in it, the template
                filename will follow the same rules as our custom  <body>  CSS classes - for
                example, a table called "Food Trucks" will attempt to load the following
                templates: 
             table-mydatabase-Food-Trucks-399138.html
table.html 
             You can find out which templates were considered for a specific page by viewing
                source on that page and looking for an HTML comment at the bottom. The comment
                will look something like this: 
             <!-- Templates considered: *query-mydb-tz.html, query-mydb.html, query.html --> 
             This example is from the canned query page for a query called "tz" in the
                database called "mydb". The asterisk shows which template was selected - so in
                this case, Datasette found a template file called  query-mydb-tz.html  and
                used that - but if that template had not been found, it would have tried for
                 query-mydb.html  or the default  query.html . 
             It is possible to extend the default templates using Jinja template
                inheritance. If you want to customize EVERY row template with some additional
                content you can do so by creating a  row.html  template like this: 
             {% extends "default:row.html" %}

{% block content %}
<h1>EXTRA HTML AT THE TOP OF THE CONTENT BLOCK</h1>
<p>This line renders the original block:</p>
{{ super() }}
{% endblock %} 
             Note the  default:row.html  template name, which ensures Jinja will inherit
                from the default template. 
             The  _table.html  template is included by both the row and the table pages,
                and a list of rows. The default  _table.html  template renders them as an
                HTML template and  can be seen here . 
             You can provide a custom template that applies to all of your databases and
                tables, or you can provide custom templates for specific tables using the
                template naming scheme described above. 
             If you want to present your data in a format other than an HTML table, you
                can do so by looping through  display_rows  in your own  _table.html 
                template. You can use  {{ row["column_name"] }}  to output the raw value
                of a specific column. 
             If you want to output the rendered HTML version of a column, including any
                links to foreign keys, you can use  {{ row.display("column_name") }} . 
             Here is an example of a custom  _table.html  template: 
             {% for row in display_rows %}
    <div>
        <h2>{{ row["title"] }}</h2>
        <p>{{ row["description"] }}<lp>
        <p>Category: {{ row.display("category_id") }}</p>
    </div>
{% endfor %}

references:
[{"href": "https://github.com/simonw/datasette/blob/main/datasette/templates/_table.html", "label": "can be seen here"}]


id: custom_templates:id1
page: custom_templates
ref: id1
title: Custom pages
breadcrumbs: ["Custom pages and templates"]
references: []
content:
You can add templated pages to your Datasette instance by creating HTML files in a  pages  directory within your  templates  directory. 
             For example, to add a custom page that is served at  http://localhost/about  you would create a file in  templates/pages/about.html , then start Datasette like this: 
             $ datasette mydb.db --template-dir=templates/ 
             You can nest directories within pages to create a nested structure. To create a  http://localhost:8001/about/map  page you would create  templates/pages/about/map.html .


id: custom_templates:custom-pages-parameters
page: custom_templates
ref: custom-pages-parameters
title: Path parameters for pages
breadcrumbs: ["Custom pages and templates", "Custom pages"]
content:
You can define custom pages that match multiple paths by creating files with  {variable}  definitions in their filenames. 
                 For example, to capture any request to a URL matching  /about/* , you would create a template in the following location: 
                 templates/pages/about/{slug}.html 
                 A hit to  /about/news  would render that template and pass in a variable called  slug  with a value of  "news" . 
                 If you use this mechanism don't forget to return a 404 if the referenced content could not be found. You can do this using  {{ raise_404() }}  described below. 
                 Templates defined using custom page routes work particularly well with the  sql()  template function from  datasette-template-sql  or the  graphql()  template function from  datasette-graphql .

references:
[{"href": "https://github.com/simonw/datasette-template-sql", "label": "datasette-template-sql"}, {"href": "https://github.com/simonw/datasette-graphql#the-graphql-template-function", "label": "datasette-graphql"}]


id: custom_templates:custom-pages-headers
page: custom_templates
ref: custom-pages-headers
title: Custom headers and status codes
breadcrumbs: ["Custom pages and templates", "Custom pages"]
references: []
content:
Custom pages default to being served with a content-type of  text/html; charset=utf-8  and a  200  status code. You can change these by calling a custom function from within your template. 
                 For example, to serve a custom page with a  418 I'm a teapot  HTTP status code, create a file in  pages/teapot.html  containing the following: 
                 {{ custom_status(418) }}
<html>
<head><title>Teapot</title></head>
<body>
I'm a teapot
</body>
</html> 
                 To serve a custom HTTP header, add a  custom_header(name, value)  function call. For example: 
                 {{ custom_status(418) }}
{{ custom_header("x-teapot", "I am") }}
<html>
<head><title>Teapot</title></head>
<body>
I'm a teapot
</body>
</html> 
                 You can verify this is working using  curl  like this: 
                 $ curl -I 'http://127.0.0.1:8001/teapot'
HTTP/1.1 418
date: Sun, 26 Apr 2020 18:38:30 GMT
server: uvicorn
x-teapot: I am
content-type: text/html; charset=utf-8


id: custom_templates:custom-pages-404
page: custom_templates
ref: custom-pages-404
title: Returning 404s
breadcrumbs: ["Custom pages and templates", "Custom pages"]
references: []
content:
To indicate that content could not be found and display the default 404 page you can use the  raise_404(message)  function: 
                 {% if not rows %}
    {{ raise_404("Content not found") }}
{% endif %} 
                 If you call  raise_404()  the other content in your template will be ignored.


id: custom_templates:custom-pages-redirects
page: custom_templates
ref: custom-pages-redirects
title: Custom redirects
breadcrumbs: ["Custom pages and templates", "Custom pages"]
references: []
content:
You can use the  custom_redirect(location)  function to redirect users to another page, for example in a file called  pages/datasette.html : 
                 {{ custom_redirect("https://github.com/simonw/datasette") }} 
                 Now requests to  http://localhost:8001/datasette  will result in a redirect. 
                 These redirects are served with a  302 Found  status code by default. You can send a  301 Moved Permanently  code by passing  301  as the second argument to the function: 
                 {{ custom_redirect("https://github.com/simonw/datasette", 301) }}


id: custom_templates:custom-pages-errors
page: custom_templates
ref: custom-pages-errors
title: Custom error pages
breadcrumbs: ["Custom pages and templates"]
content:
Datasette returns an error page if an unexpected error occurs, access is forbidden or content cannot be found. 
             You can customize the response returned for these errors by providing a custom error page template. 
             Content not found errors use a  404.html  template. Access denied errors use  403.html . Invalid input errors use  400.html . Unexpected errors of other kinds use  500.html . 
             If a template for the specific error code is not found a template called  error.html  will be used instead. If you do not provide that template Datasette's  default error.html template  will be used. 
             The error template will be passed the following context: 
             
                 
                     status  - integer 
                     
                         The integer HTTP status code, e.g. 404, 500, 403, 400. 
                     
                 
                 
                     error  - string 
                     
                         Details of the specific error, usually a full sentence. 
                     
                 
                 
                     title  - string or None 
                     
                         A title for the page representing the class of error. This is often  None  for errors that do not provide a title separate from their  error  message.

references:
[{"href": "https://github.com/simonw/datasette/blob/main/datasette/templates/error.html", "label": "default error.html template"}]


id: pages:pages
page: pages
ref: pages
title: Pages and API endpoints
breadcrumbs: []
references: []
content:
The Datasette web application offers a number of different pages that can be accessed to explore the data in question, each of which is accompanied by an equivalent JSON API.


id: pages:indexview
page: pages
ref: indexview
title: Top-level index
breadcrumbs: ["Pages and API endpoints"]
content:
The root page of any Datasette installation is an index page that lists all of the currently attached databases. Some examples: 
             
                 
                     fivethirtyeight.datasettes.com 
                 
                 
                     global-power-plants.datasettes.com 
                 
                 
                     register-of-members-interests.datasettes.com 
                 
             
             Add  /.json  to the end of the URL for the JSON version of the underlying data: 
             
                 
                     fivethirtyeight.datasettes.com/.json 
                 
                 
                     global-power-plants.datasettes.com/.json 
                 
                 
                     register-of-members-interests.datasettes.com/.json

references:
[{"href": "https://fivethirtyeight.datasettes.com/", "label": "fivethirtyeight.datasettes.com"}, {"href": "https://global-power-plants.datasettes.com/", "label": "global-power-plants.datasettes.com"}, {"href": "https://register-of-members-interests.datasettes.com/", "label": "register-of-members-interests.datasettes.com"}, {"href": "https://fivethirtyeight.datasettes.com/.json", "label": "fivethirtyeight.datasettes.com/.json"}, {"href": "https://global-power-plants.datasettes.com/.json", "label": "global-power-plants.datasettes.com/.json"}, {"href": "https://register-of-members-interests.datasettes.com/.json", "label": "register-of-members-interests.datasettes.com/.json"}]


id: pages:databaseview
page: pages
ref: databaseview
title: Database
breadcrumbs: ["Pages and API endpoints"]
content:
Each database has a page listing the tables, views and canned queries available for that database. If the  execute-sql  permission is enabled (it's on by default) there will also be an interface for executing arbitrary SQL select queries against the data. 
             Examples: 
             
                 
                     fivethirtyeight.datasettes.com/fivethirtyeight 
                 
                 
                     global-power-plants.datasettes.com/global-power-plants 
                 
             
             The JSON version of this page provides programmatic access to the underlying data: 
             
                 
                     fivethirtyeight.datasettes.com/fivethirtyeight.json 
                 
                 
                     global-power-plants.datasettes.com/global-power-plants.json

references:
[{"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight", "label": "fivethirtyeight.datasettes.com/fivethirtyeight"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants", "label": "global-power-plants.datasettes.com/global-power-plants"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json", "label": "fivethirtyeight.datasettes.com/fivethirtyeight.json"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants.json", "label": "global-power-plants.datasettes.com/global-power-plants.json"}]


id: pages:tableview
page: pages
ref: tableview
title: Table
breadcrumbs: ["Pages and API endpoints"]
content:
The table page is the heart of Datasette: it allows users to interactively explore the contents of a database table, including sorting, filtering,  Full-text search  and applying  Facets . 
             The HTML interface is worth spending some time exploring. As with other pages, you can return the JSON data by appending  .json  to the URL path, before any  ?  query string arguments. 
             The query string arguments are described in more detail here:  Table arguments 
             You can also use the table page to interactively construct a SQL query - by applying different filters and a sort order for example - and then click the "View and edit SQL" link to see the SQL query that was used for the page and edit and re-submit it. 
             Some examples: 
             
                 
                     ../items  lists all of the line-items registered by UK MPs as potential conflicts of interest. It demonstrates Datasette's support for  Full-text search . 
                 
                 
                     ../antiquities-act%2Factions_under_antiquities_act  is an interface for exploring the "actions under the antiquities act" data table published by FiveThirtyEight. 
                 
                 
                     ../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas  is a filtered table page showing every Gas power plant in the United Kingdom. It includes some default facets (configured using  its metadata.json ) and uses the  datasette-cluster-map  plugin to show a map of the results.

references:
[{"href": "https://register-of-members-interests.datasettes.com/regmem/items", "label": "../items"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight/antiquities-act%2Factions_under_antiquities_act", "label": "../antiquities-act%2Factions_under_antiquities_act"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants/global-power-plants?_facet=primary_fuel&_facet=owner&_facet=country_long&country_long__exact=United+Kingdom&primary_fuel=Gas", "label": "../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas"}, {"href": "https://global-power-plants.datasettes.com/-/metadata", "label": "its metadata.json"}, {"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}]


id: pages:rowview
page: pages
ref: rowview
title: Row
breadcrumbs: ["Pages and API endpoints"]
content:
Every row in every Datasette table has its own URL. This means individual records can be linked to directly. 
             Table cells with extremely long text contents are truncated on the table view according to the  truncate_cells_html  setting. If a cell has been truncated the full length version of that cell will be available on the row page. 
             Rows which are the targets of foreign key references from other tables will show a link to a filtered search for all records that reference that row. Here's an example from the Registers of Members Interests database: 
             ../people/uk.org.publicwhip%2Fperson%2F10001 
             Note that this URL includes the encoded primary key of the record. 
             Here's that same page as JSON: 
             ../people/uk.org.publicwhip%2Fperson%2F10001.json

references:
[{"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk.org.publicwhip%2Fperson%2F10001", "label": "../people/uk.org.publicwhip%2Fperson%2F10001"}, {"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk.org.publicwhip%2Fperson%2F10001.json", "label": "../people/uk.org.publicwhip%2Fperson%2F10001.json"}]


id: csv_export:id1
page: csv_export
ref: id1
title: CSV export
breadcrumbs: []
content:
Any Datasette table, view or custom SQL query can be exported as CSV. 
         To obtain the CSV representation of the table you are looking, click the "this
            data as CSV" link. 
         You can also use the advanced export form for more control over the resulting
            file, which looks like this and has the following options: 
         
         
             
                 download file  - instead of displaying CSV in your browser, this forces
                    your browser to download the CSV to your downloads directory. 
             
             
                 expand labels  - if your table has any foreign key references this option
                    will cause the CSV to gain additional  COLUMN_NAME_label  columns with a
                    label for each foreign key derived from the linked table.  In this example 
                    the  city_id  column is accompanied by a  city_id_label  column. 
             
             
                 stream all rows  - by default CSV files only contain the first
                     max_returned_rows  records. This option will cause Datasette to
                    loop through every matching record and return them as a single CSV file. 
             
         
         You can try that out on  https://latest.datasette.io/fixtures/facetable?_size=4

references:
[{"href": "https://latest.datasette.io/fixtures/facetable.csv?_labels=on&_size=max", "label": "In this example"}, {"href": "https://latest.datasette.io/fixtures/facetable?_size=4", "label": "https://latest.datasette.io/fixtures/facetable?_size=4"}]


id: csv_export:csv-export-url-parameters
page: csv_export
ref: csv-export-url-parameters
title: URL parameters
breadcrumbs: ["CSV export"]
references: []
content:
The following options can be used to customize the CSVs returned by Datasette. 
             
                 
                     ?_header=off 
                     
                         This removes the first row of the CSV file specifying the headings - only the row data will be returned. 
                     
                 
                 
                     ?_stream=on 
                     
                         Stream all matching records, not just the first page of results. See below. 
                     
                 
                 
                     ?_dl=on 
                     
                         Causes Datasette to return a  content-disposition: attachment; filename="filename.csv"  header.


id: csv_export:streaming-all-records
page: csv_export
ref: streaming-all-records
title: Streaming all records
breadcrumbs: ["CSV export"]
references: []
content:
The  stream all rows  option is designed to be as efficient as possible -
                under the hood it takes advantage of Python 3 asyncio capabilities and
                Datasette's efficient  pagination  to stream back the full
                CSV file. 
             Since databases can get pretty large, by default this option is capped at 100MB -
                if a table returns more than 100MB of data the last line of the CSV will be a
                truncation error message. 
             You can increase or remove this limit using the  max_csv_mb  config
                setting. You can also disable the CSV export feature entirely using
                 allow_csv_stream .


id: performance:performance
page: performance
ref: performance
title: Performance and caching
breadcrumbs: []
references: []
content:
Datasette runs on top of SQLite, and SQLite has excellent performance.  For small databases almost any query should return in just a few milliseconds, and larger databases (100s of MBs or even GBs of data) should perform extremely well provided your queries make sensible use of database indexes. 
         That said, there are a number of tricks you can use to improve Datasette's performance.


id: performance:performance-immutable-mode
page: performance
ref: performance-immutable-mode
title: Immutable mode
breadcrumbs: ["Performance and caching"]
references: []
content:
If you can be certain that a SQLite database file will not be changed by another process you can tell Datasette to open that file in  immutable mode . 
             Doing so will disable all locking and change detection, which can result in improved query performance. 
             This also enables further optimizations relating to HTTP caching, described below. 
             To open a file in immutable mode pass it to the datasette command using the  -i  option: 
             datasette -i data.db 
             When you open a file in immutable mode like this Datasette will also calculate and cache the row counts for each table in that database when it first starts up, further improving performance.


id: performance:performance-inspect
page: performance
ref: performance-inspect
title: Using "datasette inspect"
breadcrumbs: ["Performance and caching"]
references: []
content:
Counting the rows in a table can be a very expensive operation on larger databases. In immutable mode Datasette performs this count only once and caches the results, but this can still cause server startup time to increase by several seconds or more. 
             If you know that a database is never going to change you can precalculate the table row counts once and store then in a JSON file, then use that file when you later start the server. 
             To create a JSON file containing the calculated row counts for a database, use the following: 
             datasette inspect data.db --inspect-file=counts.json 
             Then later you can start Datasette against the  counts.json  file and use it to skip the row counting step and speed up server startup: 
             datasette -i data.db --inspect-file=counts.json 
             You need to use the  -i  immutable mode against the database file here or the counts from the JSON file will be ignored. 
             You will rarely need to use this optimization in every-day use, but several of the  datasette publish  commands described in  Publishing data  use this optimization for better performance when deploying a database file to a hosting provider.


id: performance:http-caching
page: performance
ref: http-caching
title: HTTP caching
breadcrumbs: ["Performance and caching"]
content:
If your database is immutable and guaranteed not to change, you can gain major performance improvements from Datasette by enabling HTTP caching. 
             This can work at two different levels. First, it can tell browsers to cache the results of queries and serve future requests from the browser cache. 
             More significantly, it allows you to run Datasette behind a caching proxy such as  Varnish  or use a cache provided by a hosted service such as  Fastly  or  Cloudflare . This can provide incredible speed-ups since a query only needs to be executed by Datasette the first time it is accessed - all subsequent hits can then be served by the cache. 
             Using a caching proxy in this way could enable a Datasette-backed visualization to serve thousands of hits a second while running Datasette itself on extremely inexpensive hosting. 
             Datasette's integration with HTTP caches can be enabled using a combination of configuration options and query string arguments. 
             The  default_cache_ttl  setting sets the default HTTP cache TTL for all Datasette pages. This is 5 seconds unless you change it - you can set it to 0 if you wish to disable HTTP caching entirely. 
             You can also change the cache timeout on a per-request basis using the  ?_ttl=10  query string parameter. This can be useful when you are working with the Datasette JSON API - you may decide that a specific query can be cached for a longer time, or maybe you need to set  ?_ttl=0  for some requests for example if you are running a SQL  order by random()  query.

references:
[{"href": "https://varnish-cache.org/", "label": "Varnish"}, {"href": "https://www.fastly.com/", "label": "Fastly"}, {"href": "https://www.cloudflare.com/", "label": "Cloudflare"}]


id: performance:performance-hashed-urls
page: performance
ref: performance-hashed-urls
title: datasette-hashed-urls
breadcrumbs: ["Performance and caching"]
content:
If you open a database file in immutable mode using the  -i  option, you can be assured that the content of that database will not change for the lifetime of the Datasette server. 
             The  datasette-hashed-urls plugin  implements an optimization where your database is served with part of the SHA-256 hash of the database contents baked into the URL. 
             A database at  /fixtures  will instead be served at  /fixtures-aa7318b , and a year-long cache expiry header will be returned with those pages. 
             This will then be cached by both browsers and caching proxies such as Cloudflare or Fastly, providing a potentially significant performance boost. 
             To install the plugin, run the following: 
             datasette install datasette-hashed-urls 
             
                 Prior to Datasette 0.61 hashed URL mode was a core Datasette feature, enabled using the  hash_urls  setting. This implementation has now been removed in favor of the  datasette-hashed-urls  plugin. 
                 Prior to Datasette 0.28 hashed URL mode was the default behaviour for Datasette, since all database files were assumed to be immutable and unchanging. From 0.28 onwards the default has been to treat database files as mutable unless explicitly configured otherwise.

references:
[{"href": "https://datasette.io/plugins/datasette-hashed-urls", "label": "datasette-hashed-urls plugin"}]


id: metadata:id1
page: metadata
ref: id1
title: Metadata
breadcrumbs: []
references: []
content:
Data loves metadata. Any time you run Datasette you can optionally include a
            JSON file with metadata about your databases and tables. Datasette will then
            display that information in the web UI. 
         Run Datasette like this: 
         datasette database1.db database2.db --metadata metadata.json 
         Your  metadata.json  file can look something like this: 
         {
    "title": "Custom title for your index page",
    "description": "Some description text can go here",
    "license": "ODbL",
    "license_url": "https://opendatacommons.org/licenses/odbl/",
    "source": "Original Data Source",
    "source_url": "http://example.com/"
} 
         You can optionally use YAML instead of JSON, see  Using YAML for metadata . 
         The above metadata will be displayed on the index page of your Datasette-powered
            site. The source and license information will also be included in the footer of
            every page served by Datasette. 
         Any special HTML characters in  description  will be escaped. If you want to
            include HTML in your description, you can use a  description_html  property
            instead.


id: metadata:per-database-and-per-table-metadata
page: metadata
ref: per-database-and-per-table-metadata
title: Per-database and per-table metadata
breadcrumbs: ["Metadata"]
references: []
content:
Metadata at the top level of the JSON will be shown on the index page and in the
                footer on every page of the site. The license and source is expected to apply to
                all of your data. 
             You can also provide metadata at the per-database or per-table level, like this: 
             {
    "databases": {
        "database1": {
            "source": "Alternative source",
            "source_url": "http://example.com/",
            "tables": {
                "example_table": {
                    "description_html": "Custom <em>table</em> description",
                    "license": "CC BY 3.0 US",
                    "license_url": "https://creativecommons.org/licenses/by/3.0/us/"
                }
            }
        }
    }
} 
             Each of the top-level metadata fields can be used at the database and table level.


id: metadata:metadata-source-license-about
page: metadata
ref: metadata-source-license-about
title: Source, license and about
breadcrumbs: ["Metadata"]
references: []
content:
The three visible metadata fields you can apply to everything, specific databases or specific tables are source, license and about. All three are optional. 
             source  and  source_url  should be used to indicate where the underlying data came from. 
             license  and  license_url  should be used to indicate the license under which the data can be used. 
             about  and  about_url  can be used to link to further information about the project - an accompanying blog entry for example. 
             For each of these you can provide just the  *_url  field and Datasette will treat that as the default link label text and display the URL directly on the page.


id: metadata:metadata-column-descriptions
page: metadata
ref: metadata-column-descriptions
title: Column descriptions
breadcrumbs: ["Metadata"]
content:
You can include descriptions for your columns by adding a  "columns": {"name-of-column": "description-of-column"}  block to your table metadata: 
             {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "columns": {
                        "column1": "Description of column 1",
                        "column2": "Description of column 2"
                    }
                }
            }
        }
    }
} 
             These will be displayed at the top of the table page, and will also show in the cog menu for each column. 
             You can see an example of how these look at  latest.datasette.io/fixtures/roadside_attractions .

references:
[{"href": "https://latest.datasette.io/fixtures/roadside_attractions", "label": "latest.datasette.io/fixtures/roadside_attractions"}]


id: metadata:specifying-units-for-a-column
page: metadata
ref: specifying-units-for-a-column
title: Specifying units for a column
breadcrumbs: ["Metadata"]
content:
Datasette supports attaching units to a column, which will be used when displaying
                values from that column. SI prefixes will be used where appropriate. 
             Column units are configured in the metadata like so: 
             {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "units": {
                        "column1": "metres",
                        "column2": "Hz"
                    }
                }
            }
        }
    }
} 
             Units are interpreted using  Pint , and you can see the full list of available units in
                Pint's  unit registry . You can also add  custom units  to the metadata, which will be
                registered with Pint: 
             {
    "custom_units": [
        "decibel = [] = dB"
    ]
}

references:
[{"href": "https://pint.readthedocs.io/", "label": "Pint"}, {"href": "https://github.com/hgrecco/pint/blob/master/pint/default_en.txt", "label": "unit registry"}, {"href": "http://pint.readthedocs.io/en/latest/defining.html", "label": "custom units"}]


id: metadata:metadata-default-sort
page: metadata
ref: metadata-default-sort
title: Setting a default sort order
breadcrumbs: ["Metadata"]
references: []
content:
By default Datasette tables are sorted by primary key. You can over-ride this default for a specific table using the  "sort"  or  "sort_desc"  metadata properties: 
             {
    "databases": {
        "mydatabase": {
            "tables": {
                "example_table": {
                    "sort": "created"
                }
            }
        }
    }
} 
             Or use  "sort_desc"  to sort in descending order: 
             {
    "databases": {
        "mydatabase": {
            "tables": {
                "example_table": {
                    "sort_desc": "created"
                }
            }
        }
    }
}


id: metadata:metadata-page-size
page: metadata
ref: metadata-page-size
title: Setting a custom page size
breadcrumbs: ["Metadata"]
references: []
content:
Datasette defaults to displaying 100 rows per page, for both tables and views. You can change this default page size on a per-table or per-view basis using the  "size"  key in  metadata.json : 
             {
    "databases": {
        "mydatabase": {
            "tables": {
                "example_table": {
                    "size": 10
                }
            }
        }
    }
} 
             This size can still be over-ridden by passing e.g.  ?_size=50  in the query string.


id: metadata:metadata-sortable-columns
page: metadata
ref: metadata-sortable-columns
title: Setting which columns can be used for sorting
breadcrumbs: ["Metadata"]
references: []
content:
Datasette allows any column to be used for sorting by default. If you need to
                control which columns are available for sorting you can do so using the optional
                 sortable_columns  key: 
             {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "sortable_columns": [
                        "height",
                        "weight"
                    ]
                }
            }
        }
    }
} 
             This will restrict sorting of  example_table  to just the  height  and
                 weight  columns. 
             You can also disable sorting entirely by setting  "sortable_columns": [] 
             You can use  sortable_columns  to enable specific sort orders for a view called  name_of_view  in the database  my_database  like so: 
             {
    "databases": {
        "my_database": {
            "tables": {
                "name_of_view": {
                    "sortable_columns": [
                        "clicks",
                        "impressions"
                    ]
                }
            }
        }
    }
}


id: metadata:label-columns
page: metadata
ref: label-columns
title: Specifying the label column for a table
breadcrumbs: ["Metadata"]
references: []
content:
Datasette's HTML interface attempts to display foreign key references as
                labelled hyperlinks. By default, it looks for referenced tables that only have
                two columns: a primary key column and one other. It assumes that the second
                column should be used as the link label. 
             If your table has more than two columns you can specify which column should be
                used for the link label with the  label_column  property: 
             {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "label_column": "title"
                }
            }
        }
    }
}


id: metadata:metadata-hiding-tables
page: metadata
ref: metadata-hiding-tables
title: Hiding tables
breadcrumbs: ["Metadata"]
references: []
content:
You can hide tables from the database listing view (in the same way that FTS and
                SpatiaLite tables are automatically hidden) using  "hidden": true : 
             {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "hidden": true
                }
            }
        }
    }
}


id: metadata:metadata-yaml
page: metadata
ref: metadata-yaml
title: Using YAML for metadata
breadcrumbs: ["Metadata"]
references: []
content:
Datasette accepts YAML as an alternative to JSON for your metadata configuration file. YAML is particularly useful for including multiline HTML and SQL strings. 
             Here's an example of a  metadata.yml  file, re-using an example from  Canned queries . 
             title: Demonstrating Metadata from YAML
description_html: |-
  <p>This description includes a long HTML string</p>
  <ul>
    <li>YAML is better for embedding HTML strings than JSON!</li>
  </ul>
license: ODbL
license_url: https://opendatacommons.org/licenses/odbl/
databases:
  fixtures:
    tables:
      no_primary_key:
        hidden: true
    queries:
      neighborhood_search:
        sql: |-
          select neighborhood, facet_cities.name, state
          from facetable join facet_cities on facetable.city_id = facet_cities.id
          where neighborhood like '%' || :text || '%' order by neighborhood;
        title: Search neighborhoods
        description_html: |-
          <p>This demonstrates <em>basic</em> LIKE search 
             The  metadata.yml  file is passed to Datasette using the same  --metadata  option: 
             datasette fixtures.db --metadata metadata.yml


id: publish:publishing
page: publish
ref: publishing
title: Publishing data
breadcrumbs: []
references: []
content:
Datasette includes tools for publishing and deploying your data to the internet. The  datasette publish  command will deploy a new Datasette instance containing your databases directly to a Heroku or Google Cloud hosting account. You can also use  datasette package  to create a Docker image that bundles your databases together with the datasette application that is used to serve them.


id: publish:cli-publish
page: publish
ref: cli-publish
title: datasette publish
breadcrumbs: ["Publishing data"]
content:
Once you have created a SQLite database (e.g. using  csvs-to-sqlite ) you can deploy it to a hosting account using a single command. 
             You will need a hosting account with  Heroku  or  Google Cloud . Once you have created your account you will need to install and configure the  heroku  or  gcloud  command-line tools.

references:
[{"href": "https://github.com/simonw/csvs-to-sqlite/", "label": "csvs-to-sqlite"}, {"href": "https://www.heroku.com/", "label": "Heroku"}, {"href": "https://cloud.google.com/", "label": "Google Cloud"}]


id: publish:publish-cloud-run
page: publish
ref: publish-cloud-run
title: Publishing to Google Cloud Run
breadcrumbs: ["Publishing data", "datasette publish"]
content:
Google Cloud Run  allows you to publish data in a scale-to-zero environment, so your application will start running when the first request is received and will shut down again when traffic ceases. This means you only pay for time spent serving traffic. 
                 
                     Cloud Run is a great option for inexpensively hosting small, low traffic projects - but costs can add up for projects that serve a lot of requests. 
                     Be particularly careful if your project has tables with large numbers of rows. Search engine crawlers that index a page for every row could result in a high bill. 
                     The  datasette-block-robots  plugin can be used to request search engine crawlers omit crawling your site, which can help avoid this issue. 
                 
                 You will first need to install and configure the Google Cloud CLI tools by following  these instructions . 
                 You can then publish one or more SQLite database files to Google Cloud Run using the following command: 
                 datasette publish cloudrun mydatabase.db --service=my-database 
                 A Cloud Run  service  is a single hosted application. The service name you specify will be used as part of the Cloud Run URL. If you deploy to a service name that you have used in the past your new deployment will replace the previous one. 
                 If you omit the  --service  option you will be asked to pick a service name interactively during the deploy. 
                 You may need to interact with prompts from the tool. Many of the prompts ask for values that can be  set as properties for the Google Cloud SDK  if you want to avoid the prompts. 
                 For example, the default region for the deployed instance can be set using the command: 
                 gcloud config set run/region us-central1 
                 You should replace  us-central1  with your desired  region . Alternately, you can specify the region by setting the  CLOUDSDK_RUN_REGION  environment variable. 
                 Once it has finished it will output a URL like this one: 
                 Service [my-service] revision [my-service-00001] has been deployed
and is serving traffic at https://my-service-j7hipcg4aq-uc.a.run.app 
                 Cloud Run provides a URL on the  .run.app  domain, but you can also point your own domain or subdomain at your Cloud Run service - see  mapping custom domains  in the Cloud Run documentation for details. 
                 See  datasette publish cloudrun  for the full list of options for this command.

references:
[{"href": "https://cloud.google.com/run/", "label": "Google Cloud Run"}, {"href": "https://datasette.io/plugins/datasette-block-robots", "label": "datasette-block-robots"}, {"href": "https://cloud.google.com/sdk/", "label": "these instructions"}, {"href": "https://cloud.google.com/sdk/docs/properties", "label": "set as properties for the Google Cloud SDK"}, {"href": "https://cloud.google.com/about/locations", "label": "region"}, {"href": "https://cloud.google.com/run/docs/mapping-custom-domains", "label": "mapping custom domains"}]


id: publish:publish-heroku
page: publish
ref: publish-heroku
title: Publishing to Heroku
breadcrumbs: ["Publishing data", "datasette publish"]
content:
To publish your data using  Heroku , first create an account there and install and configure the  Heroku CLI tool . 
                 You can publish one or more databases to Heroku using the following command: 
                 datasette publish heroku mydatabase.db 
                 This will output some details about the new deployment, including a URL like this one: 
                 https://limitless-reef-88278.herokuapp.com/ deployed to Heroku 
                 You can specify a custom app name by passing  -n my-app-name  to the publish command. This will also allow you to overwrite an existing app. 
                 Rather than deploying directly you can use the  --generate-dir  option to output the files that would be deployed to a directory: 
                 datasette publish heroku mydatabase.db --generate-dir=/tmp/deploy-this-to-heroku 
                 See  datasette publish heroku  for the full list of options for this command.

references:
[{"href": "https://www.heroku.com/", "label": "Heroku"}, {"href": "https://devcenter.heroku.com/articles/heroku-cli", "label": "Heroku CLI tool"}]


id: publish:publish-vercel
page: publish
ref: publish-vercel
title: Publishing to Vercel
breadcrumbs: ["Publishing data", "datasette publish"]
content:
Vercel   - previously known as Zeit Now - provides a layer over AWS Lambda to allow for quick, scale-to-zero deployment. You can deploy Datasette instances to Vercel using the  datasette-publish-vercel  plugin. 
                 pip install datasette-publish-vercel
datasette publish vercel mydatabase.db --project my-database-project 
                 Not every feature is supported: consult the  datasette-publish-vercel README  for more details.

references:
[{"href": "https://vercel.com/", "label": "Vercel"}, {"href": "https://github.com/simonw/datasette-publish-vercel", "label": "datasette-publish-vercel"}, {"href": "https://github.com/simonw/datasette-publish-vercel/blob/main/README.md", "label": "datasette-publish-vercel README"}]


id: publish:publish-fly
page: publish
ref: publish-fly
title: Publishing to Fly
breadcrumbs: ["Publishing data", "datasette publish"]
content:
Fly  is a  competitively priced  Docker-compatible hosting platform that supports running applications in globally distributed data centers close to your end users. You can deploy Datasette instances to Fly using the  datasette-publish-fly  plugin. 
                 pip install datasette-publish-fly
datasette publish fly mydatabase.db --app="my-app" 
                 Consult the  datasette-publish-fly README  for more details.

references:
[{"href": "https://fly.io/", "label": "Fly"}, {"href": "https://fly.io/docs/pricing/", "label": "competitively priced"}, {"href": "https://github.com/simonw/datasette-publish-fly", "label": "datasette-publish-fly"}, {"href": "https://github.com/simonw/datasette-publish-fly/blob/main/README.md", "label": "datasette-publish-fly README"}]


id: publish:publish-custom-metadata-and-plugins
page: publish
ref: publish-custom-metadata-and-plugins
title: Custom metadata and plugins
breadcrumbs: ["Publishing data", "datasette publish"]
content:
datasette publish  accepts a number of additional options which can be used to further customize your Datasette instance. 
                 You can define your own  Metadata  and deploy that with your instance like so: 
                 datasette publish cloudrun --service=my-service mydatabase.db -m metadata.json 
                 If you just want to set the title, license or source information you can do that directly using extra options to  datasette publish : 
                 datasette publish cloudrun mydatabase.db --service=my-service \
    --title="Title of my database" \
    --source="Where the data originated" \
    --source_url="http://www.example.com/" 
                 You can also specify plugins you would like to install. For example, if you want to include the  datasette-vega  visualization plugin you can use the following: 
                 datasette publish cloudrun mydatabase.db --service=my-service --install=datasette-vega 
                 If a plugin has any  Secret configuration values  you can use the  --plugin-secret  option to set those secrets at publish time. For example, using Heroku with  datasette-auth-github  you might run the following command: 
                 $ datasette publish heroku my_database.db \
    --name my-heroku-app-demo \
    --install=datasette-auth-github \
    --plugin-secret datasette-auth-github client_id your_client_id \
    --plugin-secret datasette-auth-github client_secret your_client_secret

references:
[{"href": "https://github.com/simonw/datasette-vega", "label": "datasette-vega"}, {"href": "https://github.com/simonw/datasette-auth-github", "label": "datasette-auth-github"}]


id: publish:cli-package
page: publish
ref: cli-package
title: datasette package
breadcrumbs: ["Publishing data"]
references: [{"href": "https://www.docker.com/docker-mac", "label": "Docker for Mac"}]
content:
If you have docker installed (e.g. using  Docker for Mac ) you can use the  datasette package  command to create a new Docker image in your local repository containing the datasette app bundled together with one or more SQLite databases: 
             datasette package mydatabase.db 
             Here's example output for the package command: 
             $ datasette package parlgov.db --extra-options="--setting sql_time_limit_ms 2500"
Sending build context to Docker daemon  4.459MB
Step 1/7 : FROM python:3.11.0-slim-bullseye
 ---> 79e1dc9af1c1
Step 2/7 : COPY . /app
 ---> Using cache
 ---> cd4ec67de656
Step 3/7 : WORKDIR /app
 ---> Using cache
 ---> 139699e91621
Step 4/7 : RUN pip install datasette
 ---> Using cache
 ---> 340efa82bfd7
Step 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json
 ---> Using cache
 ---> 5fddbe990314
Step 6/7 : EXPOSE 8001
 ---> Using cache
 ---> 8e83844b0fed
Step 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --setting sql_time_limit_ms 2500
 ---> Using cache
 ---> 1bd380ea8af3
Successfully built 1bd380ea8af3 
             You can now run the resulting container like so: 
             docker run -p 8081:8001 1bd380ea8af3 
             This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at  http://localhost:8081/ 
             You can customize the port that is exposed by the container using the  --port  option: 
             datasette package mydatabase.db --port 8080 
             A full list of options can be seen by running  datasette package --help : 
             See  datasette package  for the full list of options for this command.


id: changelog:id2
page: changelog
ref: id2
title: 0.64.6 (2023-12-22)
breadcrumbs: ["Changelog"]
references: [{"href": "https://github.com/simonw/datasette/issues/2214", "label": "#2214"}]
content:
Fixed a bug where CSV export with expanded labels could fail if a foreign key reference did not correctly resolve. ( #2214 )


id: changelog:id3
page: changelog
ref: id3
title: 0.64.5 (2023-10-08)
breadcrumbs: ["Changelog"]
references: [{"href": "https://github.com/simonw/datasette/issues/2197", "label": "#2197"}]
content:
Dropped dependency on  click-default-group-wheel , which could cause a dependency conflict. ( #2197 )


id: changelog:id5
page: changelog
ref: id5
title: 0.64.3 (2023-04-27)
breadcrumbs: ["Changelog"]
content:
Added  pip  and  setuptools  as explicit dependencies. This fixes a bug where Datasette could not be installed using  Rye . ( #2065 )

references:
[{"href": "https://github.com/mitsuhiko/rye", "label": "Rye"}, {"href": "https://github.com/simonw/datasette/issues/2065", "label": "#2065"}]


id: changelog:id6
page: changelog
ref: id6
title: 0.64.2 (2023-03-08)
breadcrumbs: ["Changelog"]
references: [{"href": "https://github.com/simonw/datasette/issues/2036", "label": "#2036"}]
content:
Fixed a bug with  datasette publish cloudrun  where deploys all used the same Docker image tag. This was mostly inconsequential as the service is deployed as soon as the image has been pushed to the registry, but could result in the incorrect image being deployed if two different deploys for two separate services ran at exactly the same time. ( #2036 )


id: changelog:id7
page: changelog
ref: id7
title: 0.64.1 (2023-01-11)
breadcrumbs: ["Changelog"]
content:
Documentation now links to a current source of information for installing Python 3. ( #1987 ) 
                 
                 
                     Incorrectly calling the Datasette constructor using  Datasette("path/to/data.db")  instead of  Datasette(["path/to/data.db"])  now returns a useful error message. ( #1985 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1987", "label": "#1987"}, {"href": "https://github.com/simonw/datasette/issues/1985", "label": "#1985"}]


id: changelog:id8
page: changelog
ref: id8
title: 0.64 (2023-01-09)
breadcrumbs: ["Changelog"]
content:
Datasette now  strongly recommends against allowing arbitrary SQL queries if you are using SpatiaLite . SpatiaLite includes SQL functions that could cause the Datasette server to crash. See  SpatiaLite  for more details. 
                 
                 
                     New  default_allow_sql  setting, providing an easier way to disable all arbitrary SQL execution by end users:  datasette --setting default_allow_sql off . See also  Controlling the ability to execute arbitrary SQL . ( #1409 ) 
                 
                 
                     Building a location to time zone API with SpatiaLite  is a new Datasette tutorial showing how to safely use SpatiaLite to create a location to time zone API. 
                 
                 
                     New documentation about  how to debug problems loading SQLite extensions . The error message shown when an extension cannot be loaded has also been improved. ( #1979 ) 
                 
                 
                     Fixed an accessibility issue: the  <select>  elements in the table filter form now show an outline when they are currently focused. ( #1771 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1409", "label": "#1409"}, {"href": "https://datasette.io/tutorials/spatialite", "label": "Building a location to time zone API with SpatiaLite"}, {"href": "https://github.com/simonw/datasette/issues/1979", "label": "#1979"}, {"href": "https://github.com/simonw/datasette/issues/1771", "label": "#1771"}]


id: changelog:id9
page: changelog
ref: id9
title: 0.63.3 (2022-12-17)
breadcrumbs: ["Changelog"]
content:
Fixed a bug where  datasette --root , when running in Docker, would only output the URL to sign in as root when the server shut down, not when it started up. ( #1958 ) 
                 
                 
                     You no longer need to ensure  await datasette.invoke_startup()  has been called in order for Datasette to start correctly serving requests - this is now handled automatically the first time the server receives a request. This fixes a bug experienced when Datasette is served directly by an ASGI application server such as Uvicorn or Gunicorn. It also fixes a bug with the  datasette-gunicorn  plugin. ( #1955 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1958", "label": "#1958"}, {"href": "https://datasette.io/plugins/datasette-gunicorn", "label": "datasette-gunicorn"}, {"href": "https://github.com/simonw/datasette/issues/1955", "label": "#1955"}]


id: changelog:id10
page: changelog
ref: id10
title: 0.63.2 (2022-11-18)
breadcrumbs: ["Changelog"]
references: [{"href": "https://github.com/simonw/datasette/issues/1905", "label": "#1905"}]
content:
Fixed a bug in  datasette publish heroku  where deployments failed due to an older version of Python being requested. ( #1905 ) 
                 
                 
                     New  datasette publish heroku --generate-dir <dir>  option for generating a Heroku deployment directory without deploying it.


id: changelog:id11
page: changelog
ref: id11
title: 0.63.1 (2022-11-10)
breadcrumbs: ["Changelog"]
content:
Fixed a bug where Datasette's table filter form would not redirect correctly when run behind a proxy using the  base_url  setting. ( #1883 ) 
                 
                 
                     SQL query is now shown wrapped in a  <textarea>  if a query exceeds a time limit. ( #1876 ) 
                 
                 
                     Fixed an intermittent "Too many open files" error while running the test suite. ( #1843 ) 
                 
                 
                     New  db.close()  internal method.

references:
[{"href": "https://github.com/simonw/datasette/issues/1883", "label": "#1883"}, {"href": "https://github.com/simonw/datasette/issues/1876", "label": "#1876"}, {"href": "https://github.com/simonw/datasette/issues/1843", "label": "#1843"}]


id: changelog:id12
page: changelog
ref: id12
title: 0.63 (2022-10-27)
breadcrumbs: ["Changelog"]
content:
See  Datasette 0.63: The annotated release notes  for more background on the changes in this release.

references:
[{"href": "https://simonwillison.net/2022/Oct/27/datasette-0-63/", "label": "Datasette 0.63: The annotated release notes"}]


id: changelog:features
page: changelog
ref: features
title: Features
breadcrumbs: ["Changelog", "0.63 (2022-10-27)"]
content:
Now tested against Python 3.11. Docker containers used by  datasette publish  and  datasette package  both now use that version of Python. ( #1853 ) 
                     
                     
                         --load-extension  option now supports entrypoints. Thanks, Alex Garcia. ( #1789 ) 
                     
                     
                         Facet size can now be set per-table with the new  facet_size  table metadata option. ( #1804 ) 
                     
                     
                         The  truncate_cells_html  setting now also affects long URLs in columns. ( #1805 ) 
                     
                     
                         The non-JavaScript SQL editor textarea now increases height to fit the SQL query. ( #1786 ) 
                     
                     
                         Facets are now displayed with better line-breaks in long values. Thanks, Daniel Rech. ( #1794 ) 
                     
                     
                         The  settings.json  file used in  Configuration directory mode  is now validated on startup. ( #1816 ) 
                     
                     
                         SQL queries can now include leading SQL comments, using  /* ... */  or  -- ...  syntax. Thanks,  Charles Nepote. ( #1860 ) 
                     
                     
                         SQL query is now re-displayed when terminated with a time limit error. ( #1819 ) 
                     
                     
                         The  inspect data  mechanism is now used to speed up server startup - thanks, Forest Gregg. ( #1834 ) 
                     
                     
                         In  Configuration directory mode  databases with filenames ending in  .sqlite  or  .sqlite3  are now automatically added to the Datasette instance. ( #1646 ) 
                     
                     
                         Breadcrumb navigation display now respects the current user's permissions. ( #1831 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1853", "label": "#1853"}, {"href": "https://github.com/simonw/datasette/pull/1789", "label": "#1789"}, {"href": "https://github.com/simonw/datasette/issues/1804", "label": "#1804"}, {"href": "https://github.com/simonw/datasette/issues/1805", "label": "#1805"}, {"href": "https://github.com/simonw/datasette/issues/1786", "label": "#1786"}, {"href": "https://github.com/simonw/datasette/pull/1794", "label": "#1794"}, {"href": "https://github.com/simonw/datasette/issues/1816", "label": "#1816"}, {"href": "https://github.com/simonw/datasette/issues/1860", "label": "#1860"}, {"href": "https://github.com/simonw/datasette/issues/1819", "label": "#1819"}, {"href": "https://github.com/simonw/datasette/issues/1834", "label": "#1834"}, {"href": "https://github.com/simonw/datasette/issues/1646", "label": "#1646"}, {"href": "https://github.com/simonw/datasette/issues/1831", "label": "#1831"}]


id: changelog:plugin-hooks-and-internals
page: changelog
ref: plugin-hooks-and-internals
title: Plugin hooks and internals
breadcrumbs: ["Changelog", "0.63 (2022-10-27)"]
content:
The  prepare_jinja2_environment(env, datasette)  plugin hook now accepts an optional  datasette  argument. Hook implementations can also now return an  async  function which will be awaited automatically. ( #1809 ) 
                     
                     
                         Database(is_mutable=)  now defaults to  True . ( #1808 ) 
                     
                     
                         The  datasette.check_visibility()  method now accepts an optional  permissions=  list, allowing it to take multiple permissions into account at once when deciding if something should be shown as public or private. This has been used to correctly display padlock icons in more places in the Datasette interface. ( #1829 ) 
                     
                     
                         Datasette no longer enforces upper bounds on its dependencies. ( #1800 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1809", "label": "#1809"}, {"href": "https://github.com/simonw/datasette/issues/1808", "label": "#1808"}, {"href": "https://github.com/simonw/datasette/issues/1829", "label": "#1829"}, {"href": "https://github.com/simonw/datasette/issues/1800", "label": "#1800"}]


id: changelog:documentation
page: changelog
ref: documentation
title: Documentation
breadcrumbs: ["Changelog", "0.63 (2022-10-27)"]
content:
New tutorial:  Cleaning data with sqlite-utils and Datasette . 
                     
                     
                         Screenshots in the documentation are now maintained using  shot-scraper , as described in  Automating screenshots for the Datasette documentation using shot-scraper . ( #1844 ) 
                     
                     
                         More detailed command descriptions on the  CLI reference  page. ( #1787 ) 
                     
                     
                         New documentation on  Running Datasette using OpenRC  - thanks, Adam Simpson. ( #1825 )

references:
[{"href": "https://datasette.io/tutorials/clean-data", "label": "Cleaning data with sqlite-utils and Datasette"}, {"href": "https://shot-scraper.datasette.io/", "label": "shot-scraper"}, {"href": "https://simonwillison.net/2022/Oct/14/automating-screenshots/", "label": "Automating screenshots for the Datasette documentation using shot-scraper"}, {"href": "https://github.com/simonw/datasette/issues/1844", "label": "#1844"}, {"href": "https://github.com/simonw/datasette/issues/1787", "label": "#1787"}, {"href": "https://github.com/simonw/datasette/pull/1825", "label": "#1825"}]


id: changelog:id13
page: changelog
ref: id13
title: 0.62 (2022-08-14)
breadcrumbs: ["Changelog"]
content:
Datasette can now run entirely in your browser using WebAssembly. Try out  Datasette Lite , take a look  at the code  or read more about it in  Datasette Lite: a server-side Python web application running in a browser . 
             Datasette now has a  Discord community  for questions and discussions about Datasette and its ecosystem of projects.

references:
[{"href": "https://lite.datasette.io/", "label": "Datasette Lite"}, {"href": "https://github.com/simonw/datasette-lite", "label": "at the code"}, {"href": "https://simonwillison.net/2022/May/4/datasette-lite/", "label": "Datasette Lite: a server-side Python web application running in a browser"}, {"href": "https://discord.gg/ktd74dm5mw", "label": "Discord community"}]


id: changelog:id14
page: changelog
ref: id14
title: Features
breadcrumbs: ["Changelog", "0.62 (2022-08-14)"]
content:
Datasette is now compatible with  Pyodide .  This is the enabling technology behind  Datasette Lite . ( #1733 ) 
                     
                     
                         Database file downloads now implement conditional GET using ETags. ( #1739 ) 
                     
                     
                         HTML for facet results and suggested results has been extracted out into new templates  _facet_results.html  and  _suggested_facets.html . Thanks, M. Nasimul Haque. ( #1759 ) 
                     
                     
                         Datasette now runs some SQL queries in parallel. This has limited impact on performance, see  this research issue  for details. 
                     
                     
                         New  --nolock  option for ignoring file locks when opening read-only databases. ( #1744 ) 
                     
                     
                         Spaces in the database names in URLs are now encoded as  +  rather than  ~20 . ( #1701 ) 
                     
                     
                         <Binary: 2427344 bytes>  is now displayed as  <Binary: 2,427,344 bytes>  and is accompanied by tooltip showing "2.3MB". ( #1712 ) 
                     
                     
                         The base Docker image used by  datasette publish cloudrun ,  datasette package  and the  official Datasette image  has been upgraded to  3.10.6-slim-bullseye .  ( #1768 ) 
                     
                     
                         Canned writable queries against immutable databases now show a warning message. ( #1728 ) 
                     
                     
                         datasette publish cloudrun  has a new  --timeout  option which can be used to increase the time limit applied by the Google Cloud build environment. Thanks, Tim Sherratt. ( #1717 ) 
                     
                     
                         datasette publish cloudrun  has new  --min-instances  and  --max-instances  options. ( #1779 )

references:
[{"href": "https://pyodide.org/", "label": "Pyodide"}, {"href": "https://lite.datasette.io/", "label": "Datasette Lite"}, {"href": "https://github.com/simonw/datasette/issues/1733", "label": "#1733"}, {"href": "https://github.com/simonw/datasette/issues/1739", "label": "#1739"}, {"href": "https://github.com/simonw/datasette/pull/1759", "label": "#1759"}, {"href": "https://github.com/simonw/datasette/issues/1727", "label": "this research issue"}, {"href": "https://github.com/simonw/datasette/issues/1744", "label": "#1744"}, {"href": "https://github.com/simonw/datasette/issues/1701", "label": "#1701"}, {"href": "https://github.com/simonw/datasette/issues/1712", "label": "#1712"}, {"href": "https://hub.docker.com/datasetteproject/datasette", "label": "official Datasette image"}, {"href": "https://github.com/simonw/datasette/issues/1768", "label": "#1768"}, {"href": "https://github.com/simonw/datasette/issues/1728", "label": "#1728"}, {"href": "https://github.com/simonw/datasette/pull/1717", "label": "#1717"}, {"href": "https://github.com/simonw/datasette/issues/1779", "label": "#1779"}]


id: changelog:plugin-hooks
page: changelog
ref: plugin-hooks
title: Plugin hooks
breadcrumbs: ["Changelog", "0.62 (2022-08-14)"]
content:
New plugin hook:  handle_exception() , for custom handling of exceptions caught by Datasette. ( #1770 ) 
                     
                     
                         The  render_cell()  plugin hook is now also passed a  row  argument, representing the  sqlite3.Row  object that is being rendered. ( #1300 ) 
                     
                     
                         The  configuration directory  is now stored in  datasette.config_dir , making it available to plugins. Thanks, Chris Amico. ( #1766 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1770", "label": "#1770"}, {"href": "https://github.com/simonw/datasette/issues/1300", "label": "#1300"}, {"href": "https://github.com/simonw/datasette/pull/1766", "label": "#1766"}]


id: changelog:bug-fixes
page: changelog
ref: bug-fixes
title: Bug fixes
breadcrumbs: ["Changelog", "0.62 (2022-08-14)"]
content:
Don't show the facet option in the cog menu if faceting is not allowed. ( #1683 ) 
                     
                     
                         ?_sort  and  ?_sort_desc  now work if the column that is being sorted has been excluded from the query using  ?_col=  or  ?_nocol= . ( #1773 ) 
                     
                     
                         Fixed bug where  ?_sort_desc  was duplicated in the URL every time the Apply button was clicked. ( #1738 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1683", "label": "#1683"}, {"href": "https://github.com/simonw/datasette/issues/1773", "label": "#1773"}, {"href": "https://github.com/simonw/datasette/issues/1738", "label": "#1738"}]


id: changelog:id15
page: changelog
ref: id15
title: Documentation
breadcrumbs: ["Changelog", "0.62 (2022-08-14)"]
content:
Examples in the documentation now include a copy-to-clipboard button. ( #1748 ) 
                     
                     
                         Documentation now uses the  Furo  Sphinx theme. ( #1746 ) 
                     
                     
                         Code examples in the documentation are now all formatted using Black. ( #1718 ) 
                     
                     
                         Request.fake()  method is now documented, see  Request object . 
                     
                     
                         New documentation for plugin authors:  Registering a plugin for the duration of a test . ( #903 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1748", "label": "#1748"}, {"href": "https://github.com/pradyunsg/furo", "label": "Furo"}, {"href": "https://github.com/simonw/datasette/issues/1746", "label": "#1746"}, {"href": "https://github.com/simonw/datasette/issues/1718", "label": "#1718"}, {"href": "https://github.com/simonw/datasette/issues/903", "label": "#903"}]


id: changelog:id16
page: changelog
ref: id16
title: 0.61.1 (2022-03-23)
breadcrumbs: ["Changelog"]
content:
Fixed a bug where databases with a different route from their name (as used by the  datasette-hashed-urls plugin ) returned errors when executing custom SQL queries. ( #1682 )

references:
[{"href": "https://datasette.io/plugins/datasette-hashed-urls", "label": "datasette-hashed-urls plugin"}, {"href": "https://github.com/simonw/datasette/issues/1682", "label": "#1682"}]


id: changelog:id17
page: changelog
ref: id17
title: 0.61 (2022-03-23)
breadcrumbs: ["Changelog"]
content:
In preparation for Datasette 1.0, this release includes two potentially backwards-incompatible changes. Hashed URL mode has been moved to a separate plugin, and the way Datasette generates URLs to databases and tables with special characters in their name such as  /  and  .  has changed. 
             Datasette also now requires Python 3.7 or higher. 
             
                 
                     URLs within Datasette now use a different encoding scheme for tables or databases that include "special" characters outside of the range of  a-zA-Z0-9_- . This scheme is explained here:  Tilde encoding . ( #1657 ) 
                 
                 
                     Removed hashed URL mode from Datasette. The new  datasette-hashed-urls  plugin can be used to achieve the same result, see  datasette-hashed-urls  for details. ( #1661 ) 
                 
                 
                     Databases can now have a custom path within the Datasette instance that is independent of the database name, using the  db.route  property. ( #1668 ) 
                 
                 
                     Datasette is now covered by a  Code of Conduct . ( #1654 ) 
                 
                 
                     Python 3.6 is no longer supported. ( #1577 ) 
                 
                 
                     Tests now run against Python 3.11-dev. ( #1621 ) 
                 
                 
                     New  datasette.ensure_permissions(actor, permissions)  internal method for checking multiple permissions at once. ( #1675 ) 
                 
                 
                     New  datasette.check_visibility(actor, action, resource=None)  internal method for checking if a user can see a resource that would otherwise be invisible to unauthenticated users. ( #1678 ) 
                 
                 
                     Table and row HTML pages now include a  <link rel="alternate" type="application/json+datasette" href="...">  element and return a  Link: URL; rel="alternate"; type="application/json+datasette"  HTTP header pointing to the JSON version of those pages. ( #1533 ) 
                 
                 
                     Access-Control-Expose-Headers: Link  is now added to the CORS headers, allowing remote JavaScript to access that header. 
                 
                 
                     Canned queries are now shown at the top of the database page, directly below the SQL editor. Previously they were shown at the bottom, below the list of tables. ( #1612 ) 
                 
                 
                     Datasette now has a default favicon. ( #1603 ) 
                 
                 
                     sqlite_stat  tables are now hidden by default. ( #1587 ) 
                 
                 
                     SpatiaLite tables  data_licenses ,  KNN  and  KNN2  are now hidden by default. ( #1601 ) 
                 
                 
                     SQL query tracing mechanism now works for queries executed in  asyncio  sub-tasks, such as those created by  asyncio.gather() . ( #1576 ) 
                 
                 
                     datasette.tracer  mechanism is now documented. 
                 
                 
                     Common Datasette symbols can now be imported directly from the top-level  datasette  package, see  Import shortcuts . Those symbols are  Response ,  Forbidden ,  NotFound ,  hookimpl ,  actor_matches_allow . ( #957 ) 
                 
                 
                     /-/versions  page now returns additional details for libraries used by SpatiaLite. ( #1607 ) 
                 
                 
                     Documentation now links to the  Datasette Tutorials . 
                 
                 
                     Datasette will now also look for SpatiaLite in  /opt/homebrew  - thanks, Dan Peterson. ( #1649 ) 
                 
                 
                     Fixed bug where  custom pages  did not work on Windows. Thanks, Robert Christie. ( #1545 ) 
                 
                 
                     Fixed error caused when a table had a column named  n . ( #1228 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1657", "label": "#1657"}, {"href": "https://github.com/simonw/datasette/issues/1661", "label": "#1661"}, {"href": "https://github.com/simonw/datasette/issues/1668", "label": "#1668"}, {"href": "https://github.com/simonw/datasette/blob/main/CODE_OF_CONDUCT.md", "label": "Code of Conduct"}, {"href": "https://github.com/simonw/datasette/issues/1654", "label": "#1654"}, {"href": "https://github.com/simonw/datasette/issues/1577", "label": "#1577"}, {"href": "https://github.com/simonw/datasette/issues/1621", "label": "#1621"}, {"href": "https://github.com/simonw/datasette/issues/1675", "label": "#1675"}, {"href": "https://github.com/simonw/datasette/issues/1678", "label": "#1678"}, {"href": "https://github.com/simonw/datasette/issues/1533", "label": "#1533"}, {"href": "https://github.com/simonw/datasette/issues/1612", "label": "#1612"}, {"href": "https://github.com/simonw/datasette/issues/1603", "label": "#1603"}, {"href": "https://github.com/simonw/datasette/issues/1587", "label": "#1587"}, {"href": "https://github.com/simonw/datasette/issues/1601", "label": "#1601"}, {"href": "https://github.com/simonw/datasette/issues/1576", "label": "#1576"}, {"href": "https://github.com/simonw/datasette/issues/957", "label": "#957"}, {"href": "https://github.com/simonw/datasette/issues/1607", "label": "#1607"}, {"href": "https://datasette.io/tutorials", "label": "Datasette Tutorials"}, {"href": "https://github.com/simonw/datasette/pull/1649", "label": "#1649"}, {"href": "https://github.com/simonw/datasette/issues/1545", "label": "#1545"}, {"href": "https://github.com/simonw/datasette/issues/1228", "label": "#1228"}]


id: changelog:id18
page: changelog
ref: id18
title: 0.60.2 (2022-02-07)
breadcrumbs: ["Changelog"]
references: [{"href": "https://github.com/simonw/datasette/issues/1632", "label": "#1632"}]
content:
Fixed a bug where Datasette would open the same file twice with two different database names if you ran  datasette file.db file.db . ( #1632 )


id: changelog:id19
page: changelog
ref: id19
title: 0.60.1 (2022-01-20)
breadcrumbs: ["Changelog"]
references: [{"href": "https://github.com/simonw/datasette/issues/1609", "label": "#1609"}]
content:
Fixed a bug where installation on Python 3.6 stopped working due to a change to an underlying dependency. This release can now be installed on Python 3.6, but is the last release of Datasette that will support anything less than Python 3.7. ( #1609 )


id: changelog:plugins-and-internals
page: changelog
ref: plugins-and-internals
title: Plugins and internals
breadcrumbs: ["Changelog", "0.60 (2022-01-13)"]
content:
New plugin hook:  filters_from_request(request, database, table, datasette) , which runs on the table page and can be used to support new custom query string parameters that modify the SQL query. ( #473 ) 
                     
                     
                         Added two additional methods for writing to the database:  await db.execute_write_script(sql, block=True)  and  await db.execute_write_many(sql, params_seq, block=True) . ( #1570 ) 
                     
                     
                         The  db.execute_write()  internal method now defaults to blocking until the write operation has completed. Previously it defaulted to queuing the write and then continuing to run code while the write was in the queue. ( #1579 ) 
                     
                     
                         Database write connections now execute the  prepare_connection(conn, database, datasette)  plugin hook. ( #1564 ) 
                     
                     
                         The  Datasette()  constructor no longer requires the  files=  argument, and is now documented at  Datasette class . ( #1563 ) 
                     
                     
                         The tracing feature now traces write queries, not just read queries. ( #1568 ) 
                     
                     
                         The query string variables exposed by  request.args  will now include blank strings for arguments such as  foo  in  ?foo=&bar=1  rather than ignoring those parameters entirely. ( #1551 )

references:
[{"href": "https://github.com/simonw/datasette/issues/473", "label": "#473"}, {"href": "https://github.com/simonw/datasette/issues/1570", "label": "#1570"}, {"href": "https://github.com/simonw/datasette/issues/1579", "label": "#1579"}, {"href": "https://github.com/simonw/datasette/issues/1564", "label": "#1564"}, {"href": "https://github.com/simonw/datasette/issues/1563", "label": "#1563"}, {"href": "https://github.com/simonw/datasette/issues/1568", "label": "#1568"}, {"href": "https://github.com/simonw/datasette/issues/1551", "label": "#1551"}]


id: changelog:faceting
page: changelog
ref: faceting
title: Faceting
breadcrumbs: ["Changelog", "0.60 (2022-01-13)"]
content:
The number of unique values in a facet is now always displayed. Previously it was only displayed if the user specified  ?_facet_size=max . ( #1556 ) 
                     
                     
                         Facets of type  date  or  array  can now be configured in  metadata.json , see  Facets in metadata.json . Thanks, David Larlet. ( #1552 ) 
                     
                     
                         New  ?_nosuggest=1  parameter for table views, which disables facet suggestion. ( #1557 ) 
                     
                     
                         Fixed bug where  ?_facet_array=tags&_facet=tags  would only display one of the two selected facets. ( #625 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1556", "label": "#1556"}, {"href": "https://github.com/simonw/datasette/issues/1552", "label": "#1552"}, {"href": "https://github.com/simonw/datasette/issues/1557", "label": "#1557"}, {"href": "https://github.com/simonw/datasette/issues/625", "label": "#625"}]


id: changelog:other-small-fixes
page: changelog
ref: other-small-fixes
title: Other small fixes
breadcrumbs: ["Changelog", "0.60 (2022-01-13)"]
content:
Made several performance improvements to the database schema introspection code that runs when Datasette first starts up. ( #1555 ) 
                     
                     
                         Label columns detected for foreign keys are now case-insensitive, so  Name  or  TITLE  will be detected in the same way as  name  or  title . ( #1544 ) 
                     
                     
                         Upgraded Pluggy dependency to 1.0. ( #1575 ) 
                     
                     
                         Now using  Plausible analytics  for the Datasette documentation. 
                     
                     
                         explain query plan  is now allowed with varying amounts of whitespace in the query. ( #1588 ) 
                     
                     
                         New  CLI reference  page showing the output of  --help  for each of the  datasette  sub-commands. This lead to several small improvements to the help copy. ( #1594 ) 
                     
                     
                         Fixed bug where writable canned queries could not be used with custom templates.  ( #1547 ) 
                     
                     
                         Improved fix for a bug where columns with a underscore prefix could result in unnecessary hidden form fields. ( #1527 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1555", "label": "#1555"}, {"href": "https://github.com/simonw/datasette/issues/1544", "label": "#1544"}, {"href": "https://github.com/simonw/datasette/issues/1575", "label": "#1575"}, {"href": "https://plausible.io/", "label": "Plausible analytics"}, {"href": "https://github.com/simonw/datasette/issues/1588", "label": "#1588"}, {"href": "https://github.com/simonw/datasette/issues/1594", "label": "#1594"}, {"href": "https://github.com/simonw/datasette/issues/1547", "label": "#1547"}, {"href": "https://github.com/simonw/datasette/issues/1527", "label": "#1527"}]


id: changelog:id21
page: changelog
ref: id21
title: 0.59.4 (2021-11-29)
breadcrumbs: ["Changelog"]
content:
Fixed bug where columns with a leading underscore could not be removed from the interactive filters list. ( #1527 ) 
                 
                 
                     Fixed bug where columns with a leading underscore were not correctly linked to by the "Links from other tables" interface on the row page. ( #1525 ) 
                 
                 
                     Upgraded dependencies  aiofiles ,  black  and  janus .

references:
[{"href": "https://github.com/simonw/datasette/issues/1527", "label": "#1527"}, {"href": "https://github.com/simonw/datasette/issues/1525", "label": "#1525"}]


id: changelog:id22
page: changelog
ref: id22
title: 0.59.3 (2021-11-20)
breadcrumbs: ["Changelog"]
content:
Fixed numerous bugs when running Datasette  behind a proxy  with a prefix URL path using the  base_url  setting. A live demo of this mode is now available at  datasette-apache-proxy-demo.datasette.io/prefix/ . ( #1519 ,  #838 ) 
                 
                 
                     ?column__arraycontains=  and  ?column__arraynotcontains=  table parameters now also work against SQL views. ( #448 ) 
                 
                 
                     ?_facet_array=column  no longer returns incorrect counts if columns contain the same value more than once.

references:
[{"href": "https://datasette-apache-proxy-demo.datasette.io/prefix/", "label": "datasette-apache-proxy-demo.datasette.io/prefix/"}, {"href": "https://github.com/simonw/datasette/issues/1519", "label": "#1519"}, {"href": "https://github.com/simonw/datasette/issues/838", "label": "#838"}, {"href": "https://github.com/simonw/datasette/issues/448", "label": "#448"}]


id: changelog:id23
page: changelog
ref: id23
title: 0.59.2 (2021-11-13)
breadcrumbs: ["Changelog"]
content:
Column names with a leading underscore now work correctly when used as a facet. ( #1506 ) 
                 
                 
                     Applying  ?_nocol=  to a column no longer removes that column from the filtering interface. ( #1503 ) 
                 
                 
                     Official Datasette Docker container now uses Debian Bullseye as the base image. ( #1497 ) 
                 
                 
                     Datasette is four years old today! Here's the  original release announcement  from 2017.

references:
[{"href": "https://github.com/simonw/datasette/issues/1506", "label": "#1506"}, {"href": "https://github.com/simonw/datasette/issues/1503", "label": "#1503"}, {"href": "https://github.com/simonw/datasette/issues/1497", "label": "#1497"}, {"href": "https://simonwillison.net/2017/Nov/13/datasette/", "label": "original release announcement"}]


id: changelog:id24
page: changelog
ref: id24
title: 0.59.1 (2021-10-24)
breadcrumbs: ["Changelog"]
content:
Fix compatibility with Python 3.10. ( #1482 ) 
                 
                 
                     Documentation on how to use  Named parameters  with integer and floating point values. ( #1496 )

references:
[{"href": "https://github.com/simonw/datasette/issues/1482", "label": "#1482"}, {"href": "https://github.com/simonw/datasette/issues/1496", "label": "#1496"}]


id: changelog:id25
page: changelog
ref: id25
title: 0.59 (2021-10-14)
breadcrumbs: ["Changelog"]
content:
Columns can now have associated metadata descriptions in  metadata.json , see  Column descriptions . ( #942 ) 
                 
                 
                     New  register_commands()  plugin hook allows plugins to register additional Datasette CLI commands, e.g.  datasette mycommand file.db . ( #1449 ) 
                 
                 
                     Adding  ?_facet_size=max  to a table page now shows the number of unique values in each facet. ( #1423 ) 
                 
                 
                     Upgraded dependency  httpx 0.20  - the undocumented  allow_redirects=  parameter to  datasette.client  is now  follow_redirects= , and defaults to  False  where it previously defaulted to  True . ( #1488 ) 
                 
                 
                     The  --cors  option now causes Datasette to return the  Access-Control-Allow-Headers: Authorization  header, in addition to  Access-Control-Allow-Origin: * . ( #1467 ) 
                 
                 
                     Code that figures out which named parameters a SQL query takes in order to display form fields for them is no longer confused by strings that contain colon characters. ( #1421 ) 
                 
                 
                     Renamed  --help-config  option to  --help-settings . ( #1431 ) 
                 
                 
                     datasette.databases  property is now a documented API. ( #1443 ) 
                 
                 
                     The  base.html  template now wraps everything other than the  <footer>  in a  <div class="not-footer">  element, to help with advanced CSS customization. ( #1446 ) 
                 
                 
                     The  render_cell()  plugin hook can now return an awaitable function. This means the hook can execute SQL queries. ( #1425 ) 
                 
                 
                     register_routes(datasette)  plugin hook now accepts an optional  datasette  argument. ( #1404 ) 
                 
                 
                     New  hide_sql  canned query option for defaulting to hiding the SQL query used by a canned query, see  Additional canned query options . ( #1422 ) 
                 
                 
                     New  --cpu  option for  datasette publish cloudrun . ( #1420 ) 
                 
                 
                     If  Rich  is installed in the same virtual environment as Datasette, it will be used to provide enhanced display of error tracebacks on the console. ( #1416 ) 
                 
                 
                     datasette.utils   parse_metadata(content)  function, used by the new  datasette-remote-metadata plugin , is now a documented API. ( #1405 ) 
                 
                 
                     Fixed bug where  ?_next=x&_sort=rowid  could throw an error. ( #1470 ) 
                 
                 
                     Column cog menu no longer shows the option to facet by a column that is already selected by the default facets in metadata. ( #1469 )

references:
[{"href": "https://github.com/simonw/datasette/issues/942", "label": "#942"}, {"href": "https://github.com/simonw/datasette/issues/1449", "label": "#1449"}, {"href": "https://github.com/simonw/datasette/issues/1423", "label": "#1423"}, {"href": "https://github.com/encode/httpx/releases/tag/0.20.0", "label": "httpx 0.20"}, {"href": "https://github.com/simonw/datasette/issues/1488", "label": "#1488"}, {"href": "https://github.com/simonw/datasette/pull/1467", "label": "#1467"}, {"href": "https://github.com/simonw/datasette/issues/1421", "label": "#1421"}, {"href": "https://github.com/simonw/datasette/issues/1431", "label": "#1431"}, {"href": "https://github.com/simonw/datasette/issues/1443", "label": "#1443"}, {"href": "https://github.com/simonw/datasette/issues/1446", "label": "#1446"}, {"href": "https://github.com/simonw/datasette/issues/1425", "label": "#1425"}, {"href": "https://github.com/simonw/datasette/issues/1404", "label": "#1404"}, {"href": "https://github.com/simonw/datasette/issues/1422", "label": "#1422"}, {"href": "https://github.com/simonw/datasette/issues/1420", "label": "#1420"}, {"href": "https://github.com/willmcgugan/rich", "label": "Rich"}, {"href": "https://github.com/simonw/datasette/issues/1416", "label": "#1416"}, {"href": "https://datasette.io/plugins/datasette-remote-metadata", "label": "datasette-remote-metadata plugin"}, {"href": "https://github.com/simonw/datasette/issues/1405", "label": "#1405"}, {"href": "https://github.com/simonw/datasette/issues/1470", "label": "#1470"}, {"href": "https://github.com/simonw/datasette/issues/1469", "label": "#1469"}]


