{"id": "csv_export:id1", "page": "csv_export", "ref": "id1", "title": "CSV export", "content": "Any Datasette table, view or custom SQL query can be exported as CSV. \n         To obtain the CSV representation of the table you are looking, click the \"this\n            data as CSV\" link. \n         You can also use the advanced export form for more control over the resulting\n            file, which looks like this and has the following options: \n         \n         \n             \n                 download file  - instead of displaying CSV in your browser, this forces\n                    your browser to download the CSV to your downloads directory. \n             \n             \n                 expand labels  - if your table has any foreign key references this option\n                    will cause the CSV to gain additional  COLUMN_NAME_label  columns with a\n                    label for each foreign key derived from the linked table.  In this example \n                    the  city_id  column is accompanied by a  city_id_label  column. \n             \n             \n                 stream all rows  - by default CSV files only contain the first\n                     max_returned_rows  records. This option will cause Datasette to\n                    loop through every matching record and return them as a single CSV file. \n             \n         \n         You can try that out on  https://latest.datasette.io/fixtures/facetable?_size=4", "breadcrumbs": "[]", "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": "full_text_search:id1", "page": "full_text_search", "ref": "id1", "title": "Full-text search", "content": "SQLite includes  a powerful mechanism for enabling full-text search  against SQLite records. Datasette can detect if a table has had full-text search configured for it in the underlying database and display a search interface for filtering that table. \n         Here's  an example search : \n         \n         Datasette automatically detects which tables have been configured for full-text search.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://www.sqlite.org/fts3.html\", \"label\": \"a powerful mechanism for enabling full-text search\"}, {\"href\": \"https://register-of-members-interests.datasettes.com/regmem/items?_search=hamper&_sort_desc=date\", \"label\": \"an example search\"}]"}
{"id": "performance:performance", "page": "performance", "ref": "performance", "title": "Performance and caching", "content": "Datasette runs on top of SQLite, and SQLite has excellent performance.  For small databases almost any query should return in just a few milliseconds, and larger databases (100s of MBs or even GBs of data) should perform extremely well provided your queries make sensible use of database indexes. \n         That said, there are a number of tricks you can use to improve Datasette's performance.", "breadcrumbs": "[]", "references": "[]"}
{"id": "configuration:id1", "page": "configuration", "ref": "id1", "title": "Configuration", "content": "Datasette offers several ways to configure your Datasette instances: server settings, plugin configuration, authentication, and more. \n         Most configuration can be handled using a  datasette.yaml  configuration file, passed to datasette using the  -c/--config  flag: \n         datasette mydatabase.db --config datasette.yaml \n         This file can also use JSON, as  datasette.json . YAML is recommended over JSON due to its support for comments and multi-line strings.", "breadcrumbs": "[]", "references": "[]"}
{"id": "installation:id1", "page": "installation", "ref": "id1", "title": "Installation", "content": "If you just want to try Datasette out you don't need to install anything: see  Try Datasette without installing anything using Glitch \n         \n         There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. \n         If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to  Setting up a development environment . \n         \n             \n                 \n                     Basic installation \n                     \n                         \n                             Datasette Desktop for Mac \n                         \n                         \n                             Using Homebrew \n                         \n                         \n                             Using pip \n                         \n                     \n                 \n                 \n                     Advanced installation options \n                     \n                         \n                             Using pipx \n                             \n                                 \n                                     Installing plugins using pipx \n                                 \n                                 \n                                     Upgrading packages using pipx \n                                 \n                             \n                         \n                         \n                             Using Docker \n                             \n                                 \n                                     Loading SpatiaLite \n                                 \n                                 \n                                     Installing plugins \n                                 \n                             \n                         \n                     \n                 \n                 \n                     A note about extensions", "breadcrumbs": "[]", "references": "[]"}
{"id": "writing_plugins:id1", "page": "writing_plugins", "ref": "id1", "title": "Writing plugins", "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. \n         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.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pypi.org/\", \"label\": \"PyPI\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"Datasette plugins directory\"}]"}
{"id": "sql_queries:sql", "page": "sql_queries", "ref": "sql", "title": "Running SQL queries", "content": "Datasette treats SQLite database files as read-only and immutable. This means it is not possible to execute INSERT or UPDATE statements using Datasette, which allows us to expose SELECT statements to the outside world without needing to worry about SQL injection attacks. \n         The easiest way to execute custom SQL against Datasette is through the web UI. The database index page includes a SQL editor that lets you run any SELECT query you like. You can also construct queries using the filter interface on the tables page, then click \"View and edit SQL\" to open that query in the custom SQL editor. \n         Note that this interface is only available if the  execute-sql  permission is allowed. See  Controlling the ability to execute arbitrary SQL . \n         Any Datasette SQL query is reflected in the URL of the page, allowing you to bookmark them, share them with others and navigate through previous queries using your browser back button. \n         You can also retrieve the results of any query as JSON by adding  .json  to the base URL.", "breadcrumbs": "[]", "references": "[]"}
{"id": "cli-reference:id1", "page": "cli-reference", "ref": "id1", "title": "CLI reference", "content": "The  datasette  CLI tool provides a number of commands. \n         Running  datasette  without specifying a command runs the default command,  datasette serve .  See  datasette serve  for the full list of options for that command. \n         [[[cog\nfrom datasette import cli\nfrom click.testing import CliRunner\nimport textwrap\ndef help(args):\n    title = \"datasette \" + \" \".join(args)\n    cog.out(\"\\n::\\n\\n\")\n    result = CliRunner().invoke(cli.cli, args)\n    output = result.output.replace(\"Usage: cli \", \"Usage: datasette \")\n    cog.out(textwrap.indent(output, '    '))\n    cog.out(\"\\n\\n\") \n         ]]] \n         [[[end]]]", "breadcrumbs": "[]", "references": "[]"}
{"id": "plugin_hooks:id1", "page": "plugin_hooks", "ref": "id1", "title": "Plugin hooks", "content": "Datasette  plugins  use  plugin hooks  to customize Datasette's behavior. These hooks are powered by the  pluggy  plugin system. \n         Each plugin can implement one or more hooks using the  @hookimpl  decorator against a function named that matches one of the hooks documented on this page. \n         When you implement a plugin hook you can accept any or all of the parameters that are documented as being passed to that hook. \n         For example, you can implement the  render_cell  plugin hook like this even though the full documented hook signature is  render_cell(row, value, column, table, database, datasette) : \n         @hookimpl\ndef render_cell(value, column):\n    if column == \"stars\":\n        return \"*\" * int(value) \n         \n             List of plugin hooks \n             \n                 \n                     prepare_connection(conn, database, datasette) \n                 \n                 \n                     prepare_jinja2_environment(env, datasette) \n                 \n                 \n                     Page extras \n                     \n                         \n                             extra_template_vars(template, database, table, columns, view_name, request, datasette) \n                         \n                         \n                             extra_css_urls(template, database, table, columns, view_name, request, datasette) \n                         \n                         \n                             extra_js_urls(template, database, table, columns, view_name, request, datasette) \n                         \n                         \n                             extra_body_script(template, database, table, columns, view_name, request, datasette) \n                         \n                     \n                 \n                 \n                     publish_subcommand(publish) \n                 \n                 \n                     render_cell(row, value, column, table, database, datasette, request) \n                 \n                 \n                     register_output_renderer(datasette) \n                 \n                 \n                     register_routes(datasette) \n                 \n                 \n                     register_commands(cli) \n                 \n                 \n                     register_facet_classes() \n                 \n                 \n                     register_permissions(datasette) \n                 \n                 \n                     asgi_wrapper(datasette) \n                 \n                 \n                     startup(datasette) \n                 \n                 \n                     canned_queries(datasette, database, actor) \n                 \n                 \n                     actor_from_request(datasette, request) \n                 \n                 \n                     actors_from_ids(datasette, actor_ids) \n                 \n                 \n                     jinja2_environment_from_request(datasette, request, env) \n                 \n                 \n                     filters_from_request(request, database, table, datasette) \n                 \n                 \n                     permission_allowed(datasette, actor, action, resource) \n                 \n                 \n                     register_magic_parameters(datasette) \n                 \n                 \n                     forbidden(datasette, request, message) \n                 \n                 \n                     handle_exception(datasette, request, exception) \n                 \n                 \n                     skip_csrf(datasette, scope) \n                 \n                 \n                     get_metadata(datasette, key, database, table) \n                 \n                 \n                     menu_links(datasette, actor, request) \n                 \n                 \n                     Action hooks \n                     \n                         \n                             table_actions(datasette, actor, database, table, request) \n                         \n                         \n                             view_actions(datasette, actor, database, view, request) \n                         \n                         \n                             query_actions(datasette, actor, database, query_name, request, sql, params) \n                         \n                         \n                             row_actions(datasette, actor, request, database, table, row) \n                         \n                         \n                             database_actions(datasette, actor, database, request) \n                         \n                         \n                             homepage_actions(datasette, actor, request) \n                         \n                     \n                 \n                 \n                     Template slots \n                     \n                         \n                             top_homepage(datasette, request) \n                         \n                         \n                             top_database(datasette, request, database) \n                         \n                         \n                             top_table(datasette, request, database, table) \n                         \n                         \n                             top_row(datasette, request, database, table, row) \n                         \n                         \n                             top_query(datasette, request, database, sql) \n                         \n                         \n                             top_canned_query(datasette, request, database, query_name) \n                         \n                     \n                 \n                 \n                     Event tracking \n                     \n                         \n                             track_event(datasette, event) \n                         \n                         \n                             register_events(datasette)", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pluggy.readthedocs.io/\", \"label\": \"pluggy\"}]"}
{"id": "binary_data:binary", "page": "binary_data", "ref": "binary", "title": "Binary data", "content": "SQLite tables can contain binary data in  BLOB  columns. \n         Datasette includes special handling for these binary values. The Datasette interface detects binary values and provides a link to download their content, for example on  https://latest.datasette.io/fixtures/binary_data \n         \n         Binary data is represented in  .json  exports using Base64 encoding. \n         https://latest.datasette.io/fixtures/binary_data.json?_shape=array \n         [\n    {\n        \"rowid\": 1,\n        \"data\": {\n            \"$base64\": true,\n            \"encoded\": \"FRwCx60F/g==\"\n        }\n    },\n    {\n        \"rowid\": 2,\n        \"data\": {\n            \"$base64\": true,\n            \"encoded\": \"FRwDx60F/g==\"\n        }\n    },\n    {\n        \"rowid\": 3,\n        \"data\": null\n    }\n]", "breadcrumbs": "[]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/binary_data\", \"label\": \"https://latest.datasette.io/fixtures/binary_data\"}, {\"href\": \"https://latest.datasette.io/fixtures/binary_data.json?_shape=array\", \"label\": \"https://latest.datasette.io/fixtures/binary_data.json?_shape=array\"}]"}
{"id": "contributing:id1", "page": "contributing", "ref": "id1", "title": "Contributing", "content": "Datasette is an open source project. We welcome contributions! \n         This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new  Plugins .", "breadcrumbs": "[]", "references": "[]"}
{"id": "facets:id1", "page": "facets", "ref": "id1", "title": "Facets", "content": "Datasette facets can be used to add a faceted browse interface to any database table.\n            With facets, tables are displayed along with a summary showing the most common values in specified columns.\n            These values can be selected to further filter the table. \n         Here's  an example : \n         \n         Facets can be specified in two ways: using query string parameters, or in  metadata.json  configuration for the table.", "breadcrumbs": "[]", "references": "[{\"href\": \"https://congress-legislators.datasettes.com/legislators/legislator_terms?_facet=type&_facet=party&_facet=state&_facet_size=10\", \"label\": \"an example\"}]"}
{"id": "events:id1", "page": "events", "ref": "id1", "title": "Events", "content": "Datasette includes a mechanism for tracking events that occur while the software is running. This is primarily intended to be used by plugins, which can both trigger events and listen for events. \n         The core Datasette application triggers events when certain things happen. This page describes those events. \n         Plugins can listen for events using the  track_event(datasette, event)  plugin hook, which will be called with instances of the following classes - or additional classes  registered by other plugins . \n         \n         \n         \n         \n             class   datasette.events. LoginEvent actor :   dict   |   None \n             \n                 Event name:  login \n                 A user (represented by  event.actor ) has logged in. \n             \n         \n         \n         \n             class   datasette.events. LogoutEvent actor :   dict   |   None \n             \n                 Event name:  logout \n                 A user (represented by  event.actor ) has logged out. \n             \n         \n         \n         \n             class   datasette.events. CreateTokenEvent actor :   dict   |   None expires_after :   int   |   None restrict_all :   list restrict_database :   dict restrict_resource :   dict \n             \n                 Event name:  create-token \n                 A user created an API token. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     expires_after  -- Number of seconds after which this token will expire. \n                                 \n                                 \n                                     restrict_all  -- Restricted permissions for this token. \n                                 \n                                 \n                                     restrict_database  -- Restricted database permissions for this token. \n                                 \n                                 \n                                     restrict_resource  -- Restricted resource permissions for this token. \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. CreateTableEvent actor :   dict   |   None database :   str table :   str schema :   str \n             \n                 Event name:  create-table \n                 A new table has been created in the database. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the table was created. \n                                 \n                                 \n                                     table  -- The name of the table that was created \n                                 \n                                 \n                                     schema  -- The SQL schema definition for the new table. \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. DropTableEvent actor :   dict   |   None database :   str table :   str \n             \n                 Event name:  drop-table \n                 A table has been dropped from the database. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the table was dropped. \n                                 \n                                 \n                                     table  -- The name of the table that was dropped \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. AlterTableEvent actor :   dict   |   None database :   str table :   str before_schema :   str after_schema :   str \n             \n                 Event name:  alter-table \n                 A table has been altered. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the table was altered \n                                 \n                                 \n                                     table  -- The name of the table that was altered \n                                 \n                                 \n                                     before_schema  -- The table's SQL schema before the alteration \n                                 \n                                 \n                                     after_schema  -- The table's SQL schema after the alteration \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. InsertRowsEvent actor :   dict   |   None database :   str table :   str num_rows :   int ignore :   bool replace :   bool \n             \n                 Event name:  insert-rows \n                 Rows were inserted into a table. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the rows were inserted. \n                                 \n                                 \n                                     table  -- The name of the table where the rows were inserted. \n                                 \n                                 \n                                     num_rows  -- The number of rows that were requested to be inserted. \n                                 \n                                 \n                                     ignore  -- Was ignore set? \n                                 \n                                 \n                                     replace  -- Was replace set? \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. UpsertRowsEvent actor :   dict   |   None database :   str table :   str num_rows :   int \n             \n                 Event name:  upsert-rows \n                 Rows were upserted into a table. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the rows were inserted. \n                                 \n                                 \n                                     table  -- The name of the table where the rows were inserted. \n                                 \n                                 \n                                     num_rows  -- The number of rows that were requested to be inserted. \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. UpdateRowEvent actor :   dict   |   None database :   str table :   str pks :   list \n             \n                 Event name:  update-row \n                 A row was updated in a table. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the row was updated. \n                                 \n                                 \n                                     table  -- The name of the table where the row was updated. \n                                 \n                                 \n                                     pks  -- The primary key values of the updated row. \n                                 \n                             \n                         \n                     \n                 \n             \n         \n         \n         \n             class   datasette.events. DeleteRowEvent actor :   dict   |   None database :   str table :   str pks :   list \n             \n                 Event name:  delete-row \n                 A row was deleted from a table. \n                 \n                     \n                         Variables \n                         \n                             \n                                 \n                                     database  -- The name of the database where the row was deleted. \n                                 \n                                 \n                                     table  -- The name of the table where the row was deleted. \n                                 \n                                 \n                                     pks  -- The primary key values of the deleted row.", "breadcrumbs": "[]", "references": "[]"}
{"id": "json_api:id1", "page": "json_api", "ref": "id1", "title": "JSON API", "content": "Datasette provides a JSON API for your SQLite databases. Anything you can do\n            through the Datasette user interface can also be accessed as JSON via the API. \n         To access the API for a page, either click on the  .json  link on that page or\n            edit the URL and add a  .json  extension to it.", "breadcrumbs": "[]", "references": "[]"}
{"id": "deploying:deploying", "page": "deploying", "ref": "deploying", "title": "Deploying Datasette", "content": "The quickest way to deploy a Datasette instance on the internet is to use the  datasette publish  command, described in  Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel. \n         You can deploy Datasette to other hosting providers using the instructions on this page.", "breadcrumbs": "[]", "references": "[]"}
{"id": "custom_templates:customization", "page": "custom_templates", "ref": "customization", "title": "Custom pages and templates", "content": "Datasette provides a number of ways of customizing the way data is displayed.", "breadcrumbs": "[]", "references": "[]"}
{"id": "spatialite:id1", "page": "spatialite", "ref": "id1", "title": "SpatiaLite", "content": "The  SpatiaLite module  for SQLite adds features for handling geographic and spatial data. For an example of what you can do with it, see the tutorial  Building a location to time zone API with SpatiaLite . \n         To use it with Datasette, you need to install the  mod_spatialite  dynamic library. This can then be loaded into Datasette using the  --load-extension  command-line option. \n         Datasette can look for SpatiaLite in common installation locations if you run it like this: \n         datasette --load-extension=spatialite --setting default_allow_sql off \n         If SpatiaLite is in another location, use the full path to the extension instead: \n         datasette --setting default_allow_sql off \\\n  --load-extension=/usr/local/lib/mod_spatialite.dylib", "breadcrumbs": "[]", "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": "metadata:id1", "page": "metadata", "ref": "id1", "title": "Metadata", "content": "Data loves metadata. Any time you run Datasette you can optionally include a\n            YAML or JSON file with metadata about your databases and tables. Datasette will then\n            display that information in the web UI. \n         Run Datasette like this: \n         datasette database1.db database2.db --metadata metadata.yaml \n         Your  metadata.yaml  file can look something like this: \n         [[[cog\nfrom metadata_doc import metadata_example\nmetadata_example(cog, {\n    \"title\": \"Custom title for your index page\",\n    \"description\": \"Some description text can go here\",\n    \"license\": \"ODbL\",\n    \"license_url\": \"https://opendatacommons.org/licenses/odbl/\",\n    \"source\": \"Original Data Source\",\n    \"source_url\": \"http://example.com/\"\n}) \n         ]]] \n         [[[end]]] \n         Choosing YAML over JSON adds support for multi-line strings and comments. \n         The above metadata will be displayed on the index page of your Datasette-powered\n            site. The source and license information will also be included in the footer of\n            every page served by Datasette. \n         Any special HTML characters in  description  will be escaped. If you want to\n            include HTML in your description, you can use a  description_html  property\n            instead.", "breadcrumbs": "[]", "references": "[]"}
{"id": "pages:pages", "page": "pages", "ref": "pages", "title": "Pages and API endpoints", "content": "The Datasette web application offers a number of different pages that can be accessed to explore the data in question, each of which is accompanied by an equivalent JSON API.", "breadcrumbs": "[]", "references": "[]"}
{"id": "testing_plugins:id1", "page": "testing_plugins", "ref": "id1", "title": "Testing plugins", "content": "We recommend using  pytest  to write automated tests for your plugins. \n         If you use the template described in  Starting an installable plugin using cookiecutter  your plugin will start with a single test in your  tests/  directory that looks like this: \n         from datasette.app import Datasette\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_plugin_is_installed():\n    datasette = Datasette(memory=True)\n    response = await datasette.client.get(\"/-/plugins.json\")\n    assert response.status_code == 200\n    installed_plugins = {p[\"name\"] for p in response.json()}\n    assert (\n        \"datasette-plugin-template-demo\"\n        in installed_plugins\n    ) \n         This test uses the  datasette.client  object to exercise a test instance of Datasette.  datasette.client  is a wrapper around the  HTTPX  Python library which can imitate HTTP requests using ASGI. This is the recommended way to write tests against a Datasette instance. \n         This test also uses the  pytest-asyncio  package to add support for  async def  test functions running under pytest. \n         You can install these packages like so: \n         pip install pytest pytest-asyncio \n         If you are building an installable package you can add them as test dependencies to your  setup.py  module like this: \n         setup(\n    name=\"datasette-my-plugin\",\n    # ...\n    extras_require={\"test\": [\"pytest\", \"pytest-asyncio\"]},\n    tests_require=[\"datasette-my-plugin[test]\"],\n) \n         You can then install the test dependencies like so: \n         pip install -e '.[test]' \n         Then run the tests using pytest like so: \n         pytest", "breadcrumbs": "[]", "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": "internals:internals", "page": "internals", "ref": "internals", "title": "Internals for plugins", "content": "Many  Plugin hooks  are passed objects that provide access to internal Datasette functionality. The interface to these objects should not be considered stable with the exception of methods that are documented here.", "breadcrumbs": "[]", "references": "[]"}
{"id": "plugins:id1", "page": "plugins", "ref": "id1", "title": "Plugins", "content": "Datasette's plugin system allows additional features to be implemented as Python\n            code (or front-end JavaScript) which can be wrapped up in a separate Python\n            package. The underlying mechanism uses  pluggy . \n         See the  Datasette plugins directory  for a list of existing plugins, or take a look at the\n             datasette-plugin  topic on GitHub. \n         Things you can do with plugins include: \n         \n             \n                 Add visualizations to Datasette, for example\n                     datasette-cluster-map  and\n                     datasette-vega . \n             \n             \n                 Make new custom SQL functions available for use within Datasette, for example\n                     datasette-haversine  and\n                     datasette-jellyfish . \n             \n             \n                 Define custom output formats with custom extensions, for example  datasette-atom  and\n                     datasette-ics . \n             \n             \n                 Add template functions that can be called within your Jinja custom templates,\n                    for example  datasette-render-markdown . \n             \n             \n                 Customize how database values are rendered in the Datasette interface, for example\n                     datasette-render-binary  and\n                     datasette-pretty-json . \n             \n             \n                 Customize how Datasette's authentication and permissions systems work, for example  datasette-auth-passwords  and\n                     datasette-permissions-sql .", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pluggy.readthedocs.io/\", \"label\": \"pluggy\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"Datasette plugins directory\"}, {\"href\": \"https://github.com/topics/datasette-plugin\", \"label\": \"datasette-plugin\"}, {\"href\": \"https://github.com/simonw/datasette-cluster-map\", \"label\": \"datasette-cluster-map\"}, {\"href\": \"https://github.com/simonw/datasette-vega\", \"label\": \"datasette-vega\"}, {\"href\": \"https://github.com/simonw/datasette-haversine\", \"label\": \"datasette-haversine\"}, {\"href\": \"https://github.com/simonw/datasette-jellyfish\", \"label\": \"datasette-jellyfish\"}, {\"href\": \"https://github.com/simonw/datasette-atom\", \"label\": \"datasette-atom\"}, {\"href\": \"https://github.com/simonw/datasette-ics\", \"label\": \"datasette-ics\"}, {\"href\": \"https://github.com/simonw/datasette-render-markdown#markdown-in-templates\", \"label\": \"datasette-render-markdown\"}, {\"href\": \"https://github.com/simonw/datasette-render-binary\", \"label\": \"datasette-render-binary\"}, {\"href\": \"https://github.com/simonw/datasette-pretty-json\", \"label\": \"datasette-pretty-json\"}, {\"href\": \"https://github.com/simonw/datasette-auth-passwords\", \"label\": \"datasette-auth-passwords\"}, {\"href\": \"https://github.com/simonw/datasette-permissions-sql\", \"label\": \"datasette-permissions-sql\"}]"}
{"id": "ecosystem:ecosystem", "page": "ecosystem", "ref": "ecosystem", "title": "The Datasette Ecosystem", "content": "Datasette sits at the center of a growing ecosystem of open source tools aimed at making it as easy as possible to gather, analyze and publish interesting data. \n         These tools are divided into two main groups: tools for building SQLite databases (for use with Datasette) and plugins that extend Datasette's functionality. \n         The  Datasette project website  includes a directory of plugins and a directory of tools: \n         \n             \n                 Plugins directory on datasette.io \n             \n             \n                 Tools directory on datasette.io", "breadcrumbs": "[]", "references": "[{\"href\": \"https://datasette.io/\", \"label\": \"Datasette project website\"}, {\"href\": \"https://datasette.io/plugins\", \"label\": \"Plugins directory on datasette.io\"}, {\"href\": \"https://datasette.io/tools\", \"label\": \"Tools directory on datasette.io\"}]"}
{"id": "changelog:id1", "page": "changelog", "ref": "id1", "title": "Changelog", "content": "", "breadcrumbs": "[]", "references": "[]"}
{"id": "settings:id1", "page": "settings", "ref": "id1", "title": "Settings", "content": "", "breadcrumbs": "[]", "references": "[]"}
{"id": "getting_started:getting-started", "page": "getting_started", "ref": "getting-started", "title": "Getting started", "content": "", "breadcrumbs": "[]", "references": "[]"}
{"id": "authentication:authentication", "page": "authentication", "ref": "authentication", "title": "Authentication and permissions", "content": "Datasette doesn't require authentication by default. Any visitor to a Datasette instance can explore the full data and execute read-only SQL queries. \n         Datasette's plugin system can be used to add many different styles of authentication, such as user accounts, single sign-on or API keys.", "breadcrumbs": "[]", "references": "[]"}
{"id": "javascript_plugins:id1", "page": "javascript_plugins", "ref": "id1", "title": "JavaScript plugins", "content": "Datasette can run custom JavaScript in several different ways: \n         \n             \n                 Datasette plugins written in Python can use the  extra_js_urls()  or  extra_body_script()  plugin hooks to inject JavaScript into a page \n             \n             \n                 Datasette instances with  custom templates  can include additional JavaScript in those templates \n             \n             \n                 The  extra_js_urls  key in  datasette.yaml   can be used to include extra JavaScript \n             \n         \n         There are no limitations on what this JavaScript can do. It is executed directly by the browser, so it can manipulate the DOM, fetch additional data and do anything else that JavaScript is capable of. \n         \n             Custom JavaScript has security implications, especially for authenticated Datasette instances where the JavaScript might run in the context of the authenticated user. It's important to carefully review any JavaScript you run in your Datasette instance.", "breadcrumbs": "[]", "references": "[]"}
{"id": "index:datasette", "page": "index", "ref": "datasette", "title": "Datasette", "content": "An open source multi-tool for exploring and publishing data \n         Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API. \n         Datasette is aimed at data journalists, museum curators, archivists, local governments and anyone else who has data that they wish to share with the world. It is part of a  wider ecosystem of tools and plugins  dedicated to making working with structured data as productive as possible. \n         Explore a demo , watch  a presentation about the project  or  Try Datasette without installing anything using Glitch . \n         Interested in learning Datasette? Start with  the official tutorials . \n         Support questions, feedback? Join the  Datasette Discord .", "breadcrumbs": "[]", "references": "[{\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://docs.datasette.io/en/stable/changelog.html\", \"label\": null}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/actions?query=workflow%3ATest\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/blob/main/LICENSE\", \"label\": null}, {\"href\": \"https://hub.docker.com/r/datasetteproject/datasette\", \"label\": null}, {\"href\": \"https://datasette.io/discord\", \"label\": null}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://docs.datasette.io/en/stable/changelog.html\", \"label\": null}, {\"href\": \"https://pypi.org/project/datasette/\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/actions?query=workflow%3ATest\", \"label\": null}, {\"href\": \"https://github.com/simonw/datasette/blob/main/LICENSE\", \"label\": null}, {\"href\": \"https://hub.docker.com/r/datasetteproject/datasette\", \"label\": null}, {\"href\": \"https://datasette.io/discord\", \"label\": null}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight\", \"label\": \"Explore a demo\"}, {\"href\": \"https://static.simonwillison.net/static/2018/pybay-datasette/\", \"label\": \"a presentation about the project\"}, {\"href\": \"https://datasette.io/tutorials\", \"label\": \"the official tutorials\"}, {\"href\": \"https://datasette.io/discord\", \"label\": \"Datasette Discord\"}]"}
{"id": "introspection:id1", "page": "introspection", "ref": "id1", "title": "Introspection", "content": "Datasette includes some pages and JSON API endpoints for introspecting the current instance. These can be used to understand some of the internals of Datasette and to see how a particular instance has been configured. \n         Each of these pages can be viewed in your browser. Add  .json  to the URL to get back the contents as JSON.", "breadcrumbs": "[]", "references": "[]"}
{"id": "publish:publishing", "page": "publish", "ref": "publishing", "title": "Publishing data", "content": "Datasette includes tools for publishing and deploying your data to the internet. The  datasette publish  command will deploy a new Datasette instance containing your databases directly to a Heroku or Google Cloud hosting account. You can also use  datasette package  to create a Docker image that bundles your databases together with the datasette application that is used to serve them.", "breadcrumbs": "[]", "references": "[]"}
{"id": "writing_plugins:writing-plugins-tracing", "page": "writing_plugins", "ref": "writing-plugins-tracing", "title": "Tracing plugin hooks", "content": "The  DATASETTE_TRACE_PLUGINS  environment variable turns on detailed tracing showing exactly which hooks are being run. This can be useful for understanding how Datasette is using your plugin. \n             DATASETTE_TRACE_PLUGINS=1 datasette mydb.db \n             Example output: \n             actor_from_request:\n{   'datasette': <datasette.app.Datasette object at 0x100bc7220>,\n    'request': <asgi.Request method=\"GET\" url=\"http://127.0.0.1:4433/\">}\nHook implementations:\n[   <HookImpl plugin_name='codespaces', plugin=<module 'datasette_codespaces' from '.../site-packages/datasette_codespaces/__init__.py'>>,\n    <HookImpl plugin_name='datasette.actor_auth_cookie', plugin=<module 'datasette.actor_auth_cookie' from '.../datasette/datasette/actor_auth_cookie.py'>>,\n    <HookImpl plugin_name='datasette.default_permissions', plugin=<module 'datasette.default_permissions' from '.../datasette/default_permissions.py'>>]\nResults:\n[{'id': 'root'}]", "breadcrumbs": "[\"Writing plugins\"]", "references": "[]"}
{"id": "writing_plugins:writing-plugins-one-off", "page": "writing_plugins", "ref": "writing-plugins-one-off", "title": "Writing one-off plugins", "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! . \n             from datasette import hookimpl\n\n\n@hookimpl\ndef prepare_connection(conn):\n    conn.create_function(\n        \"hello_world\", 0, lambda: \"Hello world!\"\n    ) \n             If you save this in  plugins/my_plugin.py  you can then start Datasette like this: \n             datasette serve mydb.db --plugins-dir=plugins/ \n             Now you can navigate to  http://localhost:8001/mydb  and run this SQL: \n             select hello_world(); \n             To see the output of your plugin.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"http://localhost:8001/mydb\", \"label\": \"http://localhost:8001/mydb\"}]"}
{"id": "writing_plugins:writing-plugins-cookiecutter", "page": "writing_plugins", "ref": "writing-plugins-cookiecutter", "title": "Starting an installable plugin using cookiecutter", "content": "Plugins that can be installed should be written as Python packages using a  setup.py  file. \n             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. \n             Install cookiecutter  and then run this command to start building a plugin using the template: \n             cookiecutter gh:simonw/datasette-plugin \n             Read  a cookiecutter template for writing Datasette plugins  for more information about this template.", "breadcrumbs": "[\"Writing plugins\"]", "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", "content": "Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at  https://github.com/simonw/datasette-plugin-demos \n             The example consists of two files: a  setup.py  file that defines the plugin: \n             from setuptools import setup\n\nVERSION = \"0.1\"\n\nsetup(\n    name=\"datasette-plugin-demos\",\n    description=\"Examples of plugins for Datasette\",\n    author=\"Simon Willison\",\n    url=\"https://github.com/simonw/datasette-plugin-demos\",\n    license=\"Apache License, Version 2.0\",\n    version=VERSION,\n    py_modules=[\"datasette_plugin_demos\"],\n    entry_points={\n        \"datasette\": [\n            \"plugin_demos = datasette_plugin_demos\"\n        ]\n    },\n    install_requires=[\"datasette\"],\n) \n             And a Python module file,  datasette_plugin_demos.py , that implements the plugin: \n             from datasette import hookimpl\nimport random\n\n\n@hookimpl\ndef prepare_jinja2_environment(env):\n    env.filters[\"uppercase\"] = lambda u: u.upper()\n\n\n@hookimpl\ndef prepare_connection(conn):\n    conn.create_function(\n        \"random_integer\", 2, random.randint\n    ) \n             Having built a plugin in this way you can turn it into an installable package using the following command: \n             python3 setup.py sdist \n             This will create a  .tar.gz  file in the  dist/  directory. \n             You can then install your new plugin into a Datasette virtual environment or Docker container using  pip : \n             pip install datasette-plugin-demos-0.1.tar.gz \n             To learn how to upload your plugin to  PyPI  for use by other people, read the PyPA guide to  Packaging and distributing projects .", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-plugin-demos\", \"label\": \"https://github.com/simonw/datasette-plugin-demos\"}, {\"href\": \"https://pypi.org/\", \"label\": \"PyPI\"}, {\"href\": \"https://packaging.python.org/tutorials/distributing-packages/\", \"label\": \"Packaging and distributing projects\"}]"}
{"id": "writing_plugins:writing-plugins-static-assets", "page": "writing_plugins", "ref": "writing-plugins-static-assets", "title": "Static assets", "content": "If your plugin has a  static/  directory, Datasette will automatically configure itself to serve those static assets from the following path: \n             /-/static-plugins/NAME_OF_PLUGIN_PACKAGE/yourfile.js \n             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 . \n             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 : \n             package_data = (\n    {\n        \"datasette_plugin_name\": [\n            \"static/plugin.js\",\n        ],\n    },\n) \n             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. \n             datasette-cluster-map  is a useful example of a plugin that includes packaged static assets in this way.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-cluster-map\", \"label\": \"datasette-cluster-map\"}]"}
{"id": "writing_plugins:writing-plugins-custom-templates", "page": "writing_plugins", "ref": "writing-plugins-custom-templates", "title": "Custom templates", "content": "If your plugin has a  templates/  directory, Datasette will attempt to load templates from that directory before it uses its own default templates. \n             The priority order for template loading is: \n             \n                 \n                     templates from the  --template-dir  argument, if specified \n                 \n                 \n                     templates from the  templates/  directory in any installed plugins \n                 \n                 \n                     default templates that ship with Datasette \n                 \n             \n             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. \n             Templates should be bundled for distribution using the same  package_data  mechanism in  setup.py  described for static assets above, for example: \n             package_data = (\n    {\n        \"datasette_plugin_name\": [\n            \"templates/my_template.html\",\n        ],\n    },\n) \n             You can also use wildcards here such as  templates/*.html . See  datasette-edit-schema  for an example of this pattern.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-edit-schema\", \"label\": \"datasette-edit-schema\"}]"}
{"id": "writing_plugins:writing-plugins-configuration", "page": "writing_plugins", "ref": "writing-plugins-configuration", "title": "Writing plugins that accept configuration", "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: \n             plugin_config = datasette.plugin_config(\n    \"datasette-cluster-map\", database=\"sf-trees\", table=\"Street_Tree_List\"\n) \n             This will return the  {\"latitude_column\": \"lat\", \"longitude_column\": \"lng\"}  in the above example. \n             If there is no configuration for that plugin, the method will return  None . \n             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 inside  datasette.yaml  like so: \n             [[[cog\nfrom metadata_doc import metadata_example\nmetadata_example(cog, {\n    \"databases\": {\n        \"sf-trees\": {\n            \"plugins\": {\n                \"datasette-cluster-map\": {\n                    \"latitude_column\": \"xlat\",\n                    \"longitude_column\": \"xlng\"\n                }\n            }\n        }\n    }\n}) \n             ]]] \n             [[[end]]] \n             In this case, the above code would return that configuration for ANY table within the  sf-trees  database. \n             The plugin configuration could also be set at the top level of  datasette.yaml : \n             [[[cog\nmetadata_example(cog, {\n    \"plugins\": {\n        \"datasette-cluster-map\": {\n            \"latitude_column\": \"xlat\",\n            \"longitude_column\": \"xlng\"\n        }\n    }\n}) \n             ]]] \n             [[[end]]] \n             Now that  datasette-cluster-map  plugin configuration will apply to every table in every database.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[]"}
{"id": "writing_plugins:writing-plugins-designing-urls", "page": "writing_plugins", "ref": "writing-plugins-designing-urls", "title": "Designing URLs for your plugin", "content": "You can register new URL routes within Datasette using the  register_routes(datasette)  plugin hook. \n             Datasette's default URLs include these: \n             \n                 \n                     /dbname  - database page \n                 \n                 \n                     /dbname/tablename  - table page \n                 \n                 \n                     /dbname/tablename/pk  - row page \n                 \n             \n             See  Pages and API endpoints  and  Introspection  for more default URL routes. \n             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: \n             \n                 \n                     /-/upload-excel \n                 \n             \n             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 . \n             If your plugin includes functionality that relates to a specific database you could also register a URL route like this: \n             \n                 \n                     /dbname/-/upload-excel \n                 \n             \n             Or for a specific table like this: \n             \n                 \n                     /dbname/tablename/-/modify-table-schema \n                 \n             \n             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.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"https://datasette.io/plugins\", \"label\": \"plugins directory\"}]"}
{"id": "writing_plugins:writing-plugins-building-urls", "page": "writing_plugins", "ref": "writing-plugins-building-urls", "title": "Building URLs within plugins", "content": "Plugins that define their own custom user interface elements may need to link to other pages within Datasette. \n             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. \n             The  datasette.urls  object provides internal methods for correctly generating URLs to different pages within Datasette, taking any  base_url  configuration into account. \n             This object is exposed in templates as the  urls  variable, which can be used like this: \n             Back to the <a href=\"{{ urls.instance() }}\">Homepage</a> \n             See  datasette.urls  for full details on this object.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[]"}
{"id": "writing_plugins:writing-plugins-extra-hooks", "page": "writing_plugins", "ref": "writing-plugins-extra-hooks", "title": "Plugins that define new plugin hooks", "content": "Plugins can define new plugin hooks that other plugins can use to further extend their functionality. \n             datasette-graphql  is one example of a plugin that does this. It defines a new hook called  graphql_extra_fields ,  described here , which other plugins can use to define additional fields that should be included in the GraphQL schema. \n             To define additional hooks, add a file to the plugin called  datasette_your_plugin/hookspecs.py  with content that looks like this: \n             from pluggy import HookspecMarker\n\nhookspec = HookspecMarker(\"datasette\")\n\n\n@hookspec\ndef name_of_your_hook_goes_here(datasette):\n    \"Description of your hook.\" \n             You should define your own hook name and arguments here, following the documentation for  Pluggy specifications . Make sure to pick a name that is unlikely to clash with hooks provided by any other plugins. \n             Then, to register your plugin hooks, add the following code to your  datasette_your_plugin/__init__.py  file: \n             from datasette.plugins import pm\nfrom . import hookspecs\n\npm.add_hookspecs(hookspecs) \n             This will register your plugin hooks as part of the  datasette  plugin hook namespace. \n             Within your plugin code you can trigger the hook using this pattern: \n             from datasette.plugins import pm\n\nfor (\n    plugin_return_value\n) in pm.hook.name_of_your_hook_goes_here(\n    datasette=datasette\n):\n    # Do something with plugin_return_value\n    pass \n             Other plugins will then be able to register their own implementations of your hook using this syntax: \n             from datasette import hookimpl\n\n\n@hookimpl\ndef name_of_your_hook_goes_here(datasette):\n    return \"Response from this plugin hook\" \n             These plugin implementations can accept 0 or more of the named arguments that you defined in your hook specification.", "breadcrumbs": "[\"Writing plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-graphql\", \"label\": \"datasette-graphql\"}, {\"href\": \"https://github.com/simonw/datasette-graphql/blob/main/README.md#adding-custom-fields-with-plugins\", \"label\": \"described here\"}, {\"href\": \"https://pluggy.readthedocs.io/en/stable/#specs\", \"label\": \"Pluggy specifications\"}]"}
{"id": "ecosystem:sqlite-utils", "page": "ecosystem", "ref": "sqlite-utils", "title": "sqlite-utils", "content": "sqlite-utils  is a key building block for the wider Datasette ecosystem. It provides a collection of utilities for manipulating SQLite databases, both as a Python library and a command-line utility. Features include: \n             \n                 \n                     Insert data into a SQLite database from JSON, CSV or TSV, automatically creating tables with the correct schema or altering existing tables to add missing columns. \n                 \n                 \n                     Configure tables for use with SQLite full-text search, including creating triggers needed to keep the search index up-to-date. \n                 \n                 \n                     Modify tables in ways that are not supported by SQLite's default  ALTER TABLE  syntax - for example changing the types of columns or selecting a new primary key for a table. \n                 \n                 \n                     Adding foreign keys to existing database tables. \n                 \n                 \n                     Extracting columns of data into a separate lookup table.", "breadcrumbs": "[\"The Datasette Ecosystem\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}]"}
{"id": "ecosystem:dogsheep", "page": "ecosystem", "ref": "dogsheep", "title": "Dogsheep", "content": "Dogsheep  is a collection of tools for personal analytics using SQLite and Datasette. The project provides tools like  github-to-sqlite  and  twitter-to-sqlite  that can import data from different sources in order to create a personal data warehouse.  Personal Data Warehouses: Reclaiming Your Data  is a talk that explains Dogsheep and demonstrates it in action.", "breadcrumbs": "[\"The Datasette Ecosystem\"]", "references": "[{\"href\": \"https://dogsheep.github.io/\", \"label\": \"Dogsheep\"}, {\"href\": \"https://datasette.io/tools/github-to-sqlite\", \"label\": \"github-to-sqlite\"}, {\"href\": \"https://datasette.io/tools/twitter-to-sqlite\", \"label\": \"twitter-to-sqlite\"}, {\"href\": \"https://simonwillison.net/2020/Nov/14/personal-data-warehouses/\", \"label\": \"Personal Data Warehouses: Reclaiming Your Data\"}]"}
{"id": "testing_plugins:testing-plugins-datasette-test-instance", "page": "testing_plugins", "ref": "testing-plugins-datasette-test-instance", "title": "Setting up a Datasette test instance", "content": "The above example shows the easiest way to start writing tests against a Datasette instance: \n             from datasette.app import Datasette\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_plugin_is_installed():\n    datasette = Datasette(memory=True)\n    response = await datasette.client.get(\"/-/plugins.json\")\n    assert response.status_code == 200 \n             Creating a  Datasette()  instance like this as useful shortcut in tests, but there is one detail you need to be aware of. It's important to ensure that the async method  .invoke_startup()  is called on that instance. You can do that like this: \n             datasette = Datasette(memory=True)\nawait datasette.invoke_startup() \n             This method registers any  startup(datasette)  or  prepare_jinja2_environment(env, datasette)  plugins that might themselves need to make async calls. \n             If you are using  await datasette.client.get()  and similar methods then you don't need to worry about this - Datasette automatically calls  invoke_startup()  the first time it handles a request.", "breadcrumbs": "[\"Testing plugins\"]", "references": "[]"}
{"id": "testing_plugins:testing-datasette-client", "page": "testing_plugins", "ref": "testing-datasette-client", "title": "Using datasette.client in tests", "content": "The  datasette.client  mechanism is designed for use in tests. It provides access to a pre-configured  HTTPX async client  instance that can make GET, POST and other HTTP requests against a Datasette instance from inside a test. \n             A simple test looks like this: \n             @pytest.mark.asyncio\nasync def test_homepage():\n    ds = Datasette(memory=True)\n    response = await ds.client.get(\"/\")\n    html = response.text\n    assert \"<h1>\" in html\n \n             Or for a JSON API: \n             @pytest.mark.asyncio\nasync def test_actor_is_null():\n    ds = Datasette(memory=True)\n    response = await ds.client.get(\"/-/actor.json\")\n    assert response.json() == {\"actor\": None}\n \n             To make requests as an authenticated actor, create a signed  ds_cookie  using the  datasette.client.actor_cookie()  helper function and pass it in  cookies=  like this: \n             @pytest.mark.asyncio\nasync def test_signed_cookie_actor():\n    ds = Datasette(memory=True)\n    cookies = {\"ds_actor\": ds.client.actor_cookie({\"id\": \"root\"})}\n    response = await ds.client.get(\"/-/actor.json\", cookies=cookies)\n    assert response.json() == {\"actor\": {\"id\": \"root\"}}", "breadcrumbs": "[\"Testing plugins\"]", "references": "[{\"href\": \"https://www.python-httpx.org/async/\", \"label\": \"HTTPX async client\"}]"}
{"id": "testing_plugins:testing-plugins-pdb", "page": "testing_plugins", "ref": "testing-plugins-pdb", "title": "Using pdb for errors thrown inside Datasette", "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. \n             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. \n             Here's what that looks like in a test function: \n             def test_that_opens_the_debugger_or_errors():\n    ds = Datasette([db_path], pdb=True)\n    response = await ds.client.get(\"/\") \n             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.", "breadcrumbs": "[\"Testing plugins\"]", "references": "[]"}
{"id": "testing_plugins:testing-plugins-fixtures", "page": "testing_plugins", "ref": "testing-plugins-fixtures", "title": "Using pytest fixtures", "content": "Pytest fixtures  can be used to create initial testable objects which can then be used by multiple tests. \n             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. \n             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: \n             from datasette.app import Datasette\nimport pytest\nimport sqlite_utils\n\n\n@pytest.fixture(scope=\"session\")\ndef datasette(tmp_path_factory):\n    db_directory = tmp_path_factory.mktemp(\"dbs\")\n    db_path = db_directory / \"test.db\"\n    db = sqlite_utils.Database(db_path)\n    db[\"dogs\"].insert_all(\n        [\n            {\"id\": 1, \"name\": \"Cleo\", \"age\": 5},\n            {\"id\": 2, \"name\": \"Pancakes\", \"age\": 4},\n        ],\n        pk=\"id\",\n    )\n    datasette = Datasette(\n        [db_path],\n        metadata={\n            \"databases\": {\n                \"test\": {\n                    \"tables\": {\n                        \"dogs\": {\"title\": \"Some dogs\"}\n                    }\n                }\n            }\n        },\n    )\n    return datasette\n\n\n@pytest.mark.asyncio\nasync def test_example_table_json(datasette):\n    response = await datasette.client.get(\n        \"/test/dogs.json?_shape=array\"\n    )\n    assert response.status_code == 200\n    assert response.json() == [\n        {\"id\": 1, \"name\": \"Cleo\", \"age\": 5},\n        {\"id\": 2, \"name\": \"Pancakes\", \"age\": 4},\n    ]\n\n\n@pytest.mark.asyncio\nasync def test_example_table_html(datasette):\n    response = await datasette.client.get(\"/test/dogs\")\n    assert \">Some dogs</h1>\" in response.text \n             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. \n             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. \n             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: \n             @pytest.fixture\ndef datasette(tmp_path_factory):\n    # This fixture will be executed repeatedly for every test\n    ...", "breadcrumbs": "[\"Testing plugins\"]", "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", "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. \n             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. \n             To avoid breaking your tests, you can return  [\"localhost\"]  from the  non_mocked_hosts()  fixture. \n             As an example, here's a very simple plugin which executes an HTTP response and returns the resulting content: \n             from datasette import hookimpl\nfrom datasette.utils.asgi import Response\nimport httpx\n\n\n@hookimpl\ndef register_routes():\n    return [\n        (r\"^/-/fetch-url$\", fetch_url),\n    ]\n\n\nasync def fetch_url(datasette, request):\n    if request.method == \"GET\":\n        return Response.html(\n            \"\"\"\n            <form action=\"/-/fetch-url\" method=\"post\">\n            <input type=\"hidden\" name=\"csrftoken\" value=\"{}\">\n            <input name=\"url\"><input type=\"submit\">\n        </form>\"\"\".format(\n                request.scope[\"csrftoken\"]()\n            )\n        )\n    vars = await request.post_vars()\n    url = vars[\"url\"]\n    return Response.text(httpx.get(url).text) \n             Here's a test for that plugin that mocks the HTTPX outbound request: \n             from datasette.app import Datasette\nimport pytest\n\n\n@pytest.fixture\ndef non_mocked_hosts():\n    # This ensures httpx-mock will not affect Datasette's own\n    # httpx calls made in the tests by datasette.client:\n    return [\"localhost\"]\n\n\nasync def test_outbound_http_call(httpx_mock):\n    httpx_mock.add_response(\n        url=\"https://www.example.com/\",\n        text=\"Hello world\",\n    )\n    datasette = Datasette([], memory=True)\n    response = await datasette.client.post(\n        \"/-/fetch-url\",\n        data={\"url\": \"https://www.example.com/\"},\n    )\n    assert response.text == \"Hello world\"\n\n    outbound_request = httpx_mock.get_request()\n    assert (\n        outbound_request.url == \"https://www.example.com/\"\n    )", "breadcrumbs": "[\"Testing plugins\"]", "references": "[{\"href\": \"https://pypi.org/project/pytest-httpx/\", \"label\": \"pytest-httpx\"}]"}
{"id": "testing_plugins:testing-plugins-register-in-test", "page": "testing_plugins", "ref": "testing-plugins-register-in-test", "title": "Registering a plugin for the duration of a test", "content": "When writing tests for plugins you may find it useful to register a test plugin just for the duration of a single test. You can do this using  pm.register()  and  pm.unregister()  like this: \n             from datasette import hookimpl\nfrom datasette.app import Datasette\nfrom datasette.plugins import pm\nimport pytest\n\n\n@pytest.mark.asyncio\nasync def test_using_test_plugin():\n    class TestPlugin:\n        __name__ = \"TestPlugin\"\n\n        # Use hookimpl and method names to register hooks\n        @hookimpl\n        def register_routes(self):\n            return [\n                (r\"^/error$\", lambda: 1 / 0),\n            ]\n\n    pm.register(TestPlugin(), name=\"undo\")\n    try:\n        # The test implementation goes here\n        datasette = Datasette()\n        response = await datasette.client.get(\"/error\")\n        assert response.status_code == 500\n    finally:\n        pm.unregister(name=\"undo\") \n             To reuse the same temporary plugin in multiple tests, you can register it inside a fixture in your  conftest.py  file like this: \n             from datasette import hookimpl\nfrom datasette.app import Datasette\nfrom datasette.plugins import pm\nimport pytest\nimport pytest_asyncio\n\n\n@pytest_asyncio.fixture\nasync def datasette_with_plugin():\n    class TestPlugin:\n        __name__ = \"TestPlugin\"\n\n        @hookimpl\n        def register_routes(self):\n            return [\n                (r\"^/error$\", lambda: 1 / 0),\n            ]\n\n    pm.register(TestPlugin(), name=\"undo\")\n    try:\n        yield Datasette()\n    finally:\n        pm.unregister(name=\"undo\")\n \n             Note the  yield  statement here - this ensures that the  finally:  block that unregisters the plugin is executed only after the test function itself has completed. \n             Then in a test: \n             @pytest.mark.asyncio\nasync def test_error(datasette_with_plugin):\n    response = await datasette_with_plugin.client.get(\"/error\")\n    assert response.status_code == 500", "breadcrumbs": "[\"Testing plugins\"]", "references": "[]"}
{"id": "spatialite:spatialite-warning", "page": "spatialite", "ref": "spatialite-warning", "title": "Warning", "content": "The SpatiaLite extension adds  a large number of additional SQL functions , some of which are not be safe for untrusted users to execute: they may cause the Datasette server to crash. \n                 You should not expose a SpatiaLite-enabled Datasette instance to the public internet without taking extra measures to secure it against potentially harmful SQL queries. \n                 The following steps are recommended: \n                 \n                     \n                         Disable arbitrary SQL queries by untrusted users. See  Controlling the ability to execute arbitrary SQL  for ways to do this. The easiest is to start Datasette with the  datasette --setting default_allow_sql off  option. \n                     \n                     \n                         Define  Canned queries  with the SQL queries that use SpatiaLite functions that you want people to be able to execute. \n                     \n                 \n                 The  Datasette SpatiaLite tutorial  includes detailed instructions for running SpatiaLite safely using these techniques", "breadcrumbs": "[\"SpatiaLite\"]", "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:spatialite-installation", "page": "spatialite", "ref": "spatialite-installation", "title": "Installation", "content": "", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[]"}
{"id": "spatialite:spatial-indexing-latitude-longitude-columns", "page": "spatialite", "ref": "spatial-indexing-latitude-longitude-columns", "title": "Spatial indexing latitude/longitude columns", "content": "Here's a recipe for taking a table with existing latitude and longitude columns, adding a SpatiaLite POINT geometry column to that table, populating the new column and then populating a spatial index: \n             import sqlite3\n\nconn = sqlite3.connect(\"museums.db\")\n# Lead the spatialite extension:\nconn.enable_load_extension(True)\nconn.load_extension(\"/usr/local/lib/mod_spatialite.dylib\")\n# Initialize spatial metadata for this database:\nconn.execute(\"select InitSpatialMetadata(1)\")\n# Add a geometry column called point_geom to our museums table:\nconn.execute(\n    \"SELECT AddGeometryColumn('museums', 'point_geom', 4326, 'POINT', 2);\"\n)\n# Now update that geometry column with the lat/lon points\nconn.execute(\n    \"\"\"\n    UPDATE museums SET\n    point_geom = GeomFromText('POINT('||\"longitude\"||' '||\"latitude\"||')',4326);\n\"\"\"\n)\n# Now add a spatial index to that column\nconn.execute(\n    'select CreateSpatialIndex(\"museums\", \"point_geom\");'\n)\n# If you don't commit your changes will not be persisted:\nconn.commit()\nconn.close()", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[]"}
{"id": "spatialite:making-use-of-a-spatial-index", "page": "spatialite", "ref": "making-use-of-a-spatial-index", "title": "Making use of a spatial index", "content": "SpatiaLite spatial indexes are R*Trees. They allow you to run efficient bounding box queries using a sub-select, with a similar pattern to that used for  Searches using custom SQL . \n             In the above example, the resulting index will be called  idx_museums_point_geom . This takes the form of a SQLite virtual table. You can inspect its contents using the following query: \n             select * from idx_museums_point_geom limit 10; \n             Here's a live example:  timezones-api.datasette.io/timezones/idx_timezones_Geometry \n             \n                 \n                     \n                     \n                     \n                     \n                     \n                     \n                         \n                             \n                                 pkid \n                             \n                             \n                                 xmin \n                             \n                             \n                                 xmax \n                             \n                             \n                                 ymin \n                             \n                             \n                                 ymax \n                             \n                         \n                     \n                     \n                         \n                             \n                                 1 \n                             \n                             \n                                 -8.601725578308105 \n                             \n                             \n                                 -2.4930307865142822 \n                             \n                             \n                                 4.162120819091797 \n                             \n                             \n                                 10.74019718170166 \n                             \n                         \n                         \n                             \n                                 2 \n                             \n                             \n                                 -3.2607860565185547 \n                             \n                             \n                                 1.27329421043396 \n                             \n                             \n                                 4.539252281188965 \n                             \n                             \n                                 11.174856185913086 \n                             \n                         \n                         \n                             \n                                 3 \n                             \n                             \n                                 32.997581481933594 \n                             \n                             \n                                 47.98238754272461 \n                             \n                             \n                                 3.3974475860595703 \n                             \n                             \n                                 14.894054412841797 \n                             \n                         \n                         \n                             \n                                 4 \n                             \n                             \n                                 -8.66890811920166 \n                             \n                             \n                                 11.997337341308594 \n                             \n                             \n                                 18.9681453704834 \n                             \n                             \n                                 37.296207427978516 \n                             \n                         \n                         \n                             \n                                 5 \n                             \n                             \n                                 36.43336486816406 \n                             \n                             \n                                 43.300174713134766 \n                             \n                             \n                                 12.354820251464844 \n                             \n                             \n                                 18.070993423461914 \n                             \n                         \n                     \n                 \n             \n             You can now construct efficient bounding box queries that will make use of the index like this: \n             select * from museums where museums.rowid in (\n    SELECT pkid FROM idx_museums_point_geom\n    -- left-hand-edge of point > left-hand-edge of bbox (minx)\n    where xmin > :bbox_minx\n    -- right-hand-edge of point < right-hand-edge of bbox (maxx)\n    and xmax < :bbox_maxx\n    -- bottom-edge of point > bottom-edge of bbox (miny)\n    and ymin > :bbox_miny\n    -- top-edge of point < top-edge of bbox (maxy)\n    and ymax < :bbox_maxy\n); \n             Spatial indexes can be created against polygon columns as well as point columns, in which case they will represent the minimum bounding rectangle of that polygon. This is useful for accelerating  within  queries, as seen in the Timezones API example.", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[{\"href\": \"https://timezones-api.datasette.io/timezones/idx_timezones_Geometry\", \"label\": \"timezones-api.datasette.io/timezones/idx_timezones_Geometry\"}]"}
{"id": "spatialite:importing-shapefiles-into-spatialite", "page": "spatialite", "ref": "importing-shapefiles-into-spatialite", "title": "Importing shapefiles into SpatiaLite", "content": "The  shapefile format  is a common format for distributing geospatial data. You can use the  spatialite  command-line tool to create a new database table from a shapefile. \n             Try it now with the North America shapefile available from the University of North Carolina  Global River Database  project. Download the file and unzip it (this will create files called  narivs.dbf ,  narivs.prj ,  narivs.shp  and  narivs.shx  in the current directory), then run the following: \n             spatialite rivers-database.db \n             SpatiaLite version ..: 4.3.0a       Supported Extensions:\n...\nspatialite> .loadshp narivs rivers CP1252 23032\n========\nLoading shapefile at 'narivs' into SQLite table 'rivers'\n...\nInserted 467973 rows into 'rivers' from SHAPEFILE \n             This will load the data from the  narivs  shapefile into a new database table called  rivers . \n             Exit out of  spatialite  (using  Ctrl+D ) and run Datasette against your new database like this: \n             datasette rivers-database.db \\\n    --load-extension=/usr/local/lib/mod_spatialite.dylib \n             If you browse to  http://localhost:8001/rivers-database/rivers  you will see the new table... but the  Geometry  column will contain unreadable binary data (SpatiaLite uses  a custom format based on WKB ). \n             The easiest way to turn this into semi-readable data is to use the SpatiaLite  AsGeoJSON  function. Try the following using the SQL query interface at  http://localhost:8001/rivers-database : \n             select *, AsGeoJSON(Geometry) from rivers limit 10; \n             This will give you back an additional column of GeoJSON. You can copy and paste GeoJSON from this column into the debugging tool at  geojson.io  to visualize it on a map. \n             To see a more interesting example, try ordering the records with the longest geometry first. Since there are 467,000 rows in the table you will first need to increase the SQL time limit imposed by Datasette: \n             datasette rivers-database.db \\\n    --load-extension=/usr/local/lib/mod_spatialite.dylib \\\n    --setting sql_time_limit_ms 10000 \n             Now try the following query: \n             select *, AsGeoJSON(Geometry) from rivers\norder by length(Geometry) desc limit 10;", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[{\"href\": \"https://en.wikipedia.org/wiki/Shapefile\", \"label\": \"shapefile format\"}, {\"href\": \"http://gaia.geosci.unc.edu/rivers/\", \"label\": \"Global River Database\"}, {\"href\": \"https://www.gaia-gis.it/gaia-sins/BLOB-Geometry.html\", \"label\": \"a custom format based on WKB\"}, {\"href\": \"https://geojson.io/\", \"label\": \"geojson.io\"}]"}
{"id": "spatialite:importing-geojson-polygons-using-shapely", "page": "spatialite", "ref": "importing-geojson-polygons-using-shapely", "title": "Importing GeoJSON polygons using Shapely", "content": "Another common form of polygon data is the GeoJSON format. This can be imported into SpatiaLite directly, or by using the  Shapely  Python library. \n             Who's On First  is an excellent source of openly licensed GeoJSON polygons. Let's import the geographical polygon for Wales. First, we can use the Who's On First Spelunker tool to find the record for Wales: \n             spelunker.whosonfirst.org/id/404227475 \n             That page includes a link to the GeoJSON record, which can be accessed here: \n             data.whosonfirst.org/404/227/475/404227475.geojson \n             Here's Python code to create a SQLite database, enable SpatiaLite, create a places table and then add a record for Wales: \n             import sqlite3\n\nconn = sqlite3.connect(\"places.db\")\n# Enable SpatialLite extension\nconn.enable_load_extension(True)\nconn.load_extension(\"/usr/local/lib/mod_spatialite.dylib\")\n# Create the masic countries table\nconn.execute(\"select InitSpatialMetadata(1)\")\nconn.execute(\n    \"create table places (id integer primary key, name text);\"\n)\n# Add a MULTIPOLYGON Geometry column\nconn.execute(\n    \"SELECT AddGeometryColumn('places', 'geom', 4326, 'MULTIPOLYGON', 2);\"\n)\n# Add a spatial index against the new column\nconn.execute(\"SELECT CreateSpatialIndex('places', 'geom');\")\n# Now populate the table\nfrom shapely.geometry.multipolygon import MultiPolygon\nfrom shapely.geometry import shape\nimport requests\n\ngeojson = requests.get(\n    \"https://data.whosonfirst.org/404/227/475/404227475.geojson\"\n).json()\n# Convert to \"Well Known Text\" format\nwkt = shape(geojson[\"geometry\"]).wkt\n# Insert and commit the record\nconn.execute(\n    \"INSERT INTO places (id, name, geom) VALUES(null, ?, GeomFromText(?, 4326))\",\n    (\"Wales\", wkt),\n)\nconn.commit()", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[{\"href\": \"https://pypi.org/project/Shapely/\", \"label\": \"Shapely\"}, {\"href\": \"https://whosonfirst.org/\", \"label\": \"Who's On First\"}, {\"href\": \"https://spelunker.whosonfirst.org/id/404227475/\", \"label\": \"spelunker.whosonfirst.org/id/404227475\"}, {\"href\": \"https://data.whosonfirst.org/404/227/475/404227475.geojson\", \"label\": \"data.whosonfirst.org/404/227/475/404227475.geojson\"}]"}
{"id": "spatialite:querying-polygons-using-within", "page": "spatialite", "ref": "querying-polygons-using-within", "title": "Querying polygons using within()", "content": "The  within()  SQL function can be used to check if a point is within a geometry: \n             select\n    name\nfrom\n    places\nwhere\n   within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom); \n             The  GeomFromText()  function takes a string of well-known text. Note that the order used here is  longitude  then   latitude . \n             To run that same  within()  query in a way that benefits from the spatial index, use the following: \n             select\n    name\nfrom\n    places\nwhere\n    within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom)\n    and rowid in (\n        SELECT pkid FROM idx_places_geom\n        where xmin < -3.1724366\n        and xmax > -3.1724366\n        and ymin < 51.4704448\n        and ymax > 51.4704448\n    );", "breadcrumbs": "[\"SpatiaLite\"]", "references": "[]"}
{"id": "spatialite:installing-spatialite-on-os-x", "page": "spatialite", "ref": "installing-spatialite-on-os-x", "title": "Installing SpatiaLite on OS X", "content": "The easiest way to install SpatiaLite on OS X is to use  Homebrew . \n                 brew update\nbrew install spatialite-tools \n                 This will install the  spatialite  command-line tool and the  mod_spatialite  dynamic library. \n                 You can now run Datasette like so: \n                 datasette --load-extension=spatialite", "breadcrumbs": "[\"SpatiaLite\", \"Installation\"]", "references": "[{\"href\": \"https://brew.sh/\", \"label\": \"Homebrew\"}]"}
{"id": "spatialite:installing-spatialite-on-linux", "page": "spatialite", "ref": "installing-spatialite-on-linux", "title": "Installing SpatiaLite on Linux", "content": "SpatiaLite is packaged for most Linux distributions. \n                 apt install spatialite-bin libsqlite3-mod-spatialite \n                 Depending on your distribution, you should be able to run Datasette something like this: \n                 datasette --load-extension=/usr/lib/x86_64-linux-gnu/mod_spatialite.so \n                 If you are unsure of the location of the module, try running  locate mod_spatialite  and see what comes back.", "breadcrumbs": "[\"SpatiaLite\", \"Installation\"]", "references": "[]"}
{"id": "settings:using-setting", "page": "settings", "ref": "using-setting", "title": "Using --setting", "content": "Datasette supports a number of settings. These can be set using the  --setting name value  option to  datasette serve . \n             You can set multiple settings at once like this: \n             datasette mydatabase.db \\\n  --setting default_page_size 50 \\\n  --setting sql_time_limit_ms 3500 \\\n  --setting max_returned_rows 2000 \n             Settings can also be specified  in the database.yaml configuration file .", "breadcrumbs": "[\"Settings\"]", "references": "[]"}
{"id": "settings:config-dir", "page": "settings", "ref": "config-dir", "title": "Configuration directory mode", "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: \n             datasette one.db two.db \\\n  --metadata=metadata.json \\\n  --template-dir=templates/ \\\n  --plugins-dir=plugins \\\n  --static css:css \n             As an alternative to this, you can run Datasette in  configuration directory  mode. Create a directory with the following structure: \n             # In a directory called my-app:\nmy-app/one.db\nmy-app/two.db\nmy-app/datasette.yaml\nmy-app/metadata.json\nmy-app/templates/index.html\nmy-app/plugins/my_plugin.py\nmy-app/static/my.css \n             Now start Datasette by providing the path to that directory: \n             datasette my-app/ \n             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. \n             The files that can be included in this directory are as follows. All are optional. \n             \n                 \n                     *.db  (or  *.sqlite3  or  *.sqlite ) - SQLite database files that will be served by Datasette \n                 \n                 \n                     datasette.yaml  -  Configuration  for the Datasette instance \n                 \n                 \n                     metadata.json  -  Metadata  for those databases -  metadata.yaml  or  metadata.yml  can be used as well \n                 \n                 \n                     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 \n                 \n                 \n                     templates/  - a directory containing  Custom templates \n                 \n                 \n                     plugins/  - a directory containing plugins, see  Writing one-off plugins \n                 \n                 \n                     static/  - a directory containing static files - these will be served from  /static/filename.txt , see  Serving static files", "breadcrumbs": "[\"Settings\"]", "references": "[]"}
{"id": "settings:id2", "page": "settings", "ref": "id2", "title": "Settings", "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 .", "breadcrumbs": "[\"Settings\"]", "references": "[]"}
{"id": "settings:setting-secret", "page": "settings", "ref": "setting-secret", "title": "Configuring the secret", "content": "Datasette uses a secret string to sign secure values such as cookies. \n             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 and  API tokens  will not stay valid between restarts. \n             You can pass a secret to Datasette in two ways: with the  --secret  command-line option or by setting a  DATASETTE_SECRET  environment variable. \n             datasette mydb.db --secret=SECRET_VALUE_HERE \n             Or: \n             export DATASETTE_SECRET=SECRET_VALUE_HERE\ndatasette mydb.db \n             One way to generate a secure random secret is to use Python like this: \n             python3 -c 'import secrets; print(secrets.token_hex(32))'\ncdb19e94283a20f9d42cca50c5a4871c0aa07392db308755d60a1a5b9bb0fa52 \n             Plugin authors make use of this signing mechanism in their plugins using  .sign(value, namespace=\"default\")  and  .unsign(value, namespace=\"default\") .", "breadcrumbs": "[\"Settings\"]", "references": "[]"}
{"id": "settings:setting-publish-secrets", "page": "settings", "ref": "setting-publish-secrets", "title": "Using secrets with datasette publish", "content": "The  datasette publish  and  datasette package  commands both generate a secret for you automatically when Datasette is deployed. \n             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. \n             You can fix this by creating a secret that will be used for multiple deploys and passing it using the  --secret  option: \n             datasette publish cloudrun mydb.db --service=my-service --secret=cdb19e94283a20f9d42cca5", "breadcrumbs": "[\"Settings\"]", "references": "[]"}
{"id": "settings:setting-default-allow-sql", "page": "settings", "ref": "setting-default-allow-sql", "title": "default_allow_sql", "content": "Should users be able to execute arbitrary SQL queries by default? \n                 Setting this to  off  causes permission checks for  execute-sql  to fail by default. \n                 datasette mydatabase.db --setting default_allow_sql off \n                 Another way to achieve this is to add  \"allow_sql\": false  to your  datasette.yaml  file, as described in  Controlling the ability to execute arbitrary SQL . This setting offers a more convenient way to do this.", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-default-page-size", "page": "settings", "ref": "setting-default-page-size", "title": "default_page_size", "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: \n                 datasette mydatabase.db --setting default_page_size 50", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-sql-time-limit-ms", "page": "settings", "ref": "setting-sql-time-limit-ms", "title": "sql_time_limit_ms", "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. \n                 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: \n                 datasette mydatabase.db --setting sql_time_limit_ms 3500 \n                 You can optionally set a lower time limit for an individual query using the  ?_timelimit=100  query string argument: \n                 /my-database/my-table?qSpecies=44&_timelimit=100 \n                 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.", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-max-returned-rows", "page": "settings", "ref": "setting-max-returned-rows", "title": "max_returned_rows", "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. \n                 You can increase or decrease this limit like so: \n                 datasette mydatabase.db --setting max_returned_rows 2000", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-max-insert-rows", "page": "settings", "ref": "setting-max-insert-rows", "title": "max_insert_rows", "content": "Maximum rows that can be inserted at a time using the bulk insert API, see  Inserting rows . Defaults to 100. \n                 You can increase or decrease this limit like so: \n                 datasette mydatabase.db --setting max_insert_rows 1000", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-num-sql-threads", "page": "settings", "ref": "setting-num-sql-threads", "title": "num_sql_threads", "content": "Maximum number of threads in the thread pool Datasette uses to execute SQLite queries. Defaults to 3. \n                 datasette mydatabase.db --setting num_sql_threads 10 \n                 Setting this to 0 turns off threaded SQL queries entirely - useful for environments that do not support threading such as  Pyodide .", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[{\"href\": \"https://pyodide.org/\", \"label\": \"Pyodide\"}]"}
{"id": "settings:setting-allow-facet", "page": "settings", "ref": "setting-allow-facet", "title": "allow_facet", "content": "Allow users to specify columns they would like to facet on using the  ?_facet=COLNAME  URL parameter to the table view. \n                 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. \n                 Here's how to disable this feature: \n                 datasette mydatabase.db --setting allow_facet off", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-default-facet-size", "page": "settings", "ref": "setting-default-facet-size", "title": "default_facet_size", "content": "The default number of unique rows returned by  Facets  is 30. You can customize it like this: \n                 datasette mydatabase.db --setting default_facet_size 50", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-facet-time-limit-ms", "page": "settings", "ref": "setting-facet-time-limit-ms", "title": "facet_time_limit_ms", "content": "This is the time limit Datasette allows for calculating a facet, which defaults to 200ms: \n                 datasette mydatabase.db --setting facet_time_limit_ms 1000", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-facet-suggest-time-limit-ms", "page": "settings", "ref": "setting-facet-suggest-time-limit-ms", "title": "facet_suggest_time_limit_ms", "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. \n                 You can increase this time limit like so: \n                 datasette mydatabase.db --setting facet_suggest_time_limit_ms 500", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-suggest-facets", "page": "settings", "ref": "setting-suggest-facets", "title": "suggest_facets", "content": "Should Datasette calculate suggested facets? On by default, turn this off like so: \n                 datasette mydatabase.db --setting suggest_facets off", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-allow-download", "page": "settings", "ref": "setting-allow-download", "title": "allow_download", "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: \n                 datasette mydatabase.db --setting allow_download off", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-allow-signed-tokens", "page": "settings", "ref": "setting-allow-signed-tokens", "title": "allow_signed_tokens", "content": "Should users be able to create signed API tokens to access Datasette? \n                 This is turned on by default. Use the following to turn it off: \n                 datasette mydatabase.db --setting allow_signed_tokens off \n                 Turning this setting off will disable the  /-/create-token  page,  described here . It will also cause any incoming  Authorization: Bearer dstok_...  API tokens to be ignored.", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-max-signed-tokens-ttl", "page": "settings", "ref": "setting-max-signed-tokens-ttl", "title": "max_signed_tokens_ttl", "content": "Maximum allowed expiry time for signed API tokens created by users. \n                 Defaults to  0  which means no limit - tokens can be created that will never expire. \n                 Set this to a value in seconds to limit the maximum expiry time. For example, to set that limit to 24 hours you would use: \n                 datasette mydatabase.db --setting max_signed_tokens_ttl 86400 \n                 This setting is enforced when incoming tokens are processed.", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-default-cache-ttl", "page": "settings", "ref": "setting-default-cache-ttl", "title": "default_cache_ttl", "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. \n                 datasette mydatabase.db --setting default_cache_ttl 60", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-cache-size-kb", "page": "settings", "ref": "setting-cache-size-kb", "title": "cache_size_kb", "content": "Sets the amount of memory SQLite uses for its  per-connection cache , in KB. \n                 datasette mydatabase.db --setting cache_size_kb 5000", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[{\"href\": \"https://www.sqlite.org/pragma.html#pragma_cache_size\", \"label\": \"per-connection cache\"}]"}
{"id": "settings:setting-allow-csv-stream", "page": "settings", "ref": "setting-allow-csv-stream", "title": "allow_csv_stream", "content": "Enables  the CSV export feature  where an entire table\n                    (potentially hundreds of thousands of rows) can be exported as a single CSV\n                    file. This is turned on by default - you can turn it off like this: \n                 datasette mydatabase.db --setting allow_csv_stream off", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-max-csv-mb", "page": "settings", "ref": "setting-max-csv-mb", "title": "max_csv_mb", "content": "The maximum size of CSV that can be exported, in megabytes. Defaults to 100MB.\n                    You can disable the limit entirely by settings this to 0: \n                 datasette mydatabase.db --setting max_csv_mb 0", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-truncate-cells-html", "page": "settings", "ref": "setting-truncate-cells-html", "title": "truncate_cells_html", "content": "In the HTML table view, truncate any strings that are longer than this value.\n                    The full value will still be available in CSV, JSON and on the individual row\n                    HTML page. Set this to 0 to disable truncation. \n                 datasette mydatabase.db --setting truncate_cells_html 0", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-force-https-urls", "page": "settings", "ref": "setting-force-https-urls", "title": "force_https_urls", "content": "Forces self-referential URLs in the JSON output to always use the  https:// \n                    protocol. This is useful for cases where the application itself is hosted using\n                    HTTP but is served to the outside world via a proxy that enables HTTPS. \n                 datasette mydatabase.db --setting force_https_urls 1", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "settings:setting-template-debug", "page": "settings", "ref": "setting-template-debug", "title": "template_debug", "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. \n                 Enable it like this: \n                 datasette mydatabase.db --setting template_debug 1 \n                 Now you can add  ?_context=1  or  &_context=1  to any Datasette page to see the context that was passed to that template. \n                 Some examples: \n                 \n                     \n                         https://latest.datasette.io/?_context=1 \n                     \n                     \n                         https://latest.datasette.io/fixtures?_context=1 \n                     \n                     \n                         https://latest.datasette.io/fixtures/roadside_attractions?_context=1", "breadcrumbs": "[\"Settings\", \"Settings\"]", "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", "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. \n                 Enable it like this: \n                 datasette mydatabase.db --setting trace_debug 1 \n                 Some examples: \n                 \n                     \n                         https://latest.datasette.io/?_trace=1 \n                     \n                     \n                         https://latest.datasette.io/fixtures/roadside_attractions?_trace=1 \n                     \n                 \n                 See  datasette.tracer  for details on how to hook into this mechanism as a plugin author.", "breadcrumbs": "[\"Settings\", \"Settings\"]", "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", "content": "If you are running Datasette behind a proxy, it may be useful to change the root path used for the Datasette instance. \n                 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. \n                 You can do that like so: \n                 datasette mydatabase.db --setting base_url /tools/datasette/", "breadcrumbs": "[\"Settings\", \"Settings\"]", "references": "[]"}
{"id": "sql_queries:sql-parameters", "page": "sql_queries", "ref": "sql-parameters", "title": "Named parameters", "content": "Datasette has special support for SQLite named parameters. Consider a SQL query like this: \n             select * from Street_Tree_List\nwhere \"PermitNotes\" like :notes\nand \"qSpecies\" = :species \n             If you execute this query using the custom query editor, Datasette will extract the two named parameters and use them to construct form fields for you to provide values. \n             You can also provide values for these fields by constructing a URL: \n             /mydatabase?sql=select...&species=44 \n             SQLite string escaping rules will be applied to values passed using named parameters - they will be wrapped in quotes and their content will be correctly escaped. \n             Values from named parameters are treated as SQLite strings. If you need to perform numeric comparisons on them you should cast them to an integer or float first using  cast(:name as integer)  or  cast(:name as real) , for example: \n             select * from Street_Tree_List\nwhere latitude > cast(:min_latitude as real)\nand latitude < cast(:max_latitude as real) \n             Datasette disallows custom SQL queries containing the string PRAGMA (with a small number  of exceptions ) as SQLite pragma statements can be used to change database settings at runtime. If you need to include the string \"pragma\" in a query you can do so safely using a named parameter.", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/761\", \"label\": \"of exceptions\"}]"}
{"id": "sql_queries:sql-views", "page": "sql_queries", "ref": "sql-views", "title": "Views", "content": "If you want to bundle some pre-written SQL queries with your Datasette-hosted database you can do so in two ways. The first is to include SQL views in your database - Datasette will then list those views on your database index page. \n             The quickest way to create views is with the SQLite command-line interface: \n             sqlite3 sf-trees.db \n             SQLite version 3.19.3 2017-06-27 16:48:08\nEnter \".help\" for usage hints.\nsqlite> CREATE VIEW demo_view AS select qSpecies from Street_Tree_List;\n<CTRL+D> \n             You can also use the  sqlite-utils  tool to  create a view : \n             sqlite-utils create-view sf-trees.db demo_view \"select qSpecies from Street_Tree_List\"", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[{\"href\": \"https://sqlite-utils.datasette.io/\", \"label\": \"sqlite-utils\"}, {\"href\": \"https://sqlite-utils.datasette.io/en/stable/cli.html#creating-views\", \"label\": \"create a view\"}]"}
{"id": "sql_queries:id1", "page": "sql_queries", "ref": "id1", "title": "Canned queries", "content": "As an alternative to adding views to your database, you can define canned queries inside your  datasette.yaml  file. Here's an example: \n             [[[cog\nfrom metadata_doc import config_example, config_example\nconfig_example(cog, {\n    \"databases\": {\n       \"sf-trees\": {\n           \"queries\": {\n               \"just_species\": {\n                   \"sql\": \"select qSpecies from Street_Tree_List\"\n               }\n           }\n       }\n    }\n}) \n             ]]] \n             [[[end]]] \n             Then run Datasette like this: \n             datasette sf-trees.db -m metadata.json \n             Each canned query will be listed on the database index page, and will also get its own URL at: \n             /database-name/canned-query-name \n             For the above example, that URL would be: \n             /sf-trees/just_species \n             You can optionally include  \"title\"  and  \"description\"  keys to show a title and description on the canned query page. As with regular table metadata you can alternatively specify  \"description_html\"  to have your description rendered as HTML (rather than having HTML special characters escaped).", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[]"}
{"id": "sql_queries:id2", "page": "sql_queries", "ref": "id2", "title": "Pagination", "content": "Datasette's default table pagination is designed to be extremely efficient. SQL OFFSET/LIMIT pagination can have a significant performance penalty once you get into multiple thousands of rows, as each page still requires the database to scan through every preceding row to find the correct offset. \n             When paginating through tables, Datasette instead orders the rows in the table by their primary key and performs a WHERE clause against the last seen primary key for the previous page. For example: \n             select rowid, * from Tree_List where rowid > 200 order by rowid limit 101 \n             This represents page three for this particular table, with a page size of 100. \n             Note that we request 101 items in the limit clause rather than 100. This allows us to detect if we are on the last page of the results: if the query returns less than 101 rows we know we have reached the end of the pagination set. Datasette will only return the first 100 rows - the 101st is used purely to detect if there should be another page. \n             Since the where clause acts against the index on the primary key, the query is extremely fast even for records that are a long way into the overall pagination set.", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[]"}
{"id": "sql_queries:id3", "page": "sql_queries", "ref": "id3", "title": "Cross-database queries", "content": "SQLite has the ability to run queries that join across multiple databases. Up to ten databases can be attached to a single SQLite connection and queried together. \n             Datasette can execute joins across multiple databases if it is started with the  --crossdb  option: \n             datasette fixtures.db extra_database.db --crossdb \n             If it is started in this way, the  /_memory  page can be used to execute queries that join across multiple databases. \n             References to tables in attached databases should be preceded by the database name and a period. \n             For example, this query will show a list of tables across both of the above databases: \n             select\n  'fixtures' as database, *\nfrom\n  [fixtures].sqlite_master\nunion\nselect\n  'extra_database' as database, *\nfrom\n  [extra_database].sqlite_master \n             Try that out here .", "breadcrumbs": "[\"Running SQL queries\"]", "references": "[{\"href\": \"https://latest.datasette.io/_memory?sql=select%0D%0A++%27fixtures%27+as+database%2C+*%0D%0Afrom%0D%0A++%5Bfixtures%5D.sqlite_master%0D%0Aunion%0D%0Aselect%0D%0A++%27extra_database%27+as+database%2C+*%0D%0Afrom%0D%0A++%5Bextra_database%5D.sqlite_master\", \"label\": \"Try that out here\"}]"}
{"id": "sql_queries:canned-queries-named-parameters", "page": "sql_queries", "ref": "canned-queries-named-parameters", "title": "Canned query parameters", "content": "Canned queries support named parameters, so if you include those in the SQL you will then be able to enter them using the form fields on the canned query page or by adding them to the URL. This means canned queries can be used to create custom JSON APIs based on a carefully designed SQL statement. \n                 Here's an example of a canned query with a named parameter: \n                 select neighborhood, facet_cities.name, state\nfrom facetable\n  join facet_cities on facetable.city_id = facet_cities.id\nwhere neighborhood like '%' || :text || '%'\norder by neighborhood; \n                 In the canned query configuration looks like this: \n                 [[[cog\nconfig_example(cog, \"\"\"\ndatabases:\n  fixtures:\n    queries:\n      neighborhood_search:\n        title: Search neighborhoods\n        sql: |-\n          select neighborhood, facet_cities.name, state\n          from facetable\n            join facet_cities on facetable.city_id = facet_cities.id\n          where neighborhood like '%' || :text || '%'\n          order by neighborhood\n\"\"\") \n                 ]]] \n                 [[[end]]] \n                 Note that we are using SQLite string concatenation here - the  ||  operator - to add wildcard  %  characters to the string provided by the user. \n                 You can try this canned query out here:\n                     https://latest.datasette.io/fixtures/neighborhood_search?text=town \n                 In this example the  :text  named parameter is automatically extracted from the query using a regular expression. \n                 You can alternatively provide an explicit list of named parameters using the  \"params\"  key, like this: \n                 [[[cog\nconfig_example(cog, \"\"\"\ndatabases:\n  fixtures:\n    queries:\n      neighborhood_search:\n        title: Search neighborhoods\n        params:\n        - text\n        sql: |-\n          select neighborhood, facet_cities.name, state\n          from facetable\n            join facet_cities on facetable.city_id = facet_cities.id\n          where neighborhood like '%' || :text || '%'\n          order by neighborhood\n\"\"\") \n                 ]]] \n                 [[[end]]]", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/neighborhood_search?text=town\", \"label\": \"https://latest.datasette.io/fixtures/neighborhood_search?text=town\"}]"}
{"id": "sql_queries:canned-queries-options", "page": "sql_queries", "ref": "canned-queries-options", "title": "Additional canned query options", "content": "Additional options can be specified for canned queries in the YAML or JSON configuration.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[]"}
{"id": "sql_queries:canned-queries-writable", "page": "sql_queries", "ref": "canned-queries-writable", "title": "Writable canned queries", "content": "Canned queries by default are read-only. You can use the  \"write\": true  key to indicate that a canned query can write to the database. \n                 See  Access to specific canned queries  for details on how to add permission checks to canned queries, using the  \"allow\"  key. \n                 [[[cog\nconfig_example(cog, {\n    \"databases\": {\n        \"mydatabase\": {\n            \"queries\": {\n                \"add_name\": {\n                    \"sql\": \"INSERT INTO names (name) VALUES (:name)\",\n                    \"write\": True\n                }\n            }\n        }\n    }\n}) \n                 ]]] \n                 [[[end]]] \n                 This configuration will create a page at  /mydatabase/add_name  displaying a form with a  name  field. Submitting that form will execute the configured  INSERT  query. \n                 You can customize how Datasette represents success and errors using the following optional properties: \n                 \n                     \n                         on_success_message  - the message shown when a query is successful \n                     \n                     \n                         on_success_message_sql  - alternative to  on_success_message : a SQL query that should be executed to generate the message \n                     \n                     \n                         on_success_redirect  - the path or URL the user is redirected to on success \n                     \n                     \n                         on_error_message  - the message shown when a query throws an error \n                     \n                     \n                         on_error_redirect  - the path or URL the user is redirected to on error \n                     \n                 \n                 For example: \n                 [[[cog\nconfig_example(cog, {\n    \"databases\": {\n        \"mydatabase\": {\n            \"queries\": {\n                \"add_name\": {\n                    \"sql\": \"INSERT INTO names (name) VALUES (:name)\",\n                    \"params\": [\"name\"],\n                    \"write\": True,\n                    \"on_success_message_sql\": \"select 'Name inserted: ' || :name\",\n                    \"on_success_redirect\": \"/mydatabase/names\",\n                    \"on_error_message\": \"Name insert failed\",\n                    \"on_error_redirect\": \"/mydatabase\",\n                }\n            }\n        }\n    }\n}) \n                 ]]] \n                 [[[end]]] \n                 You can use  \"params\"  to explicitly list the named parameters that should be displayed as form fields - otherwise they will be automatically detected.  \"params\"  is not necessary in the above example, since without it  \"name\"  would be automatically detected from the query. \n                 You can pre-populate form fields when the page first loads using a query string, e.g.  /mydatabase/add_name?name=Prepopulated . The user will have to submit the form to execute the query. \n                 If you specify a query in  \"on_success_message_sql\" , that query will be executed after the main query. The first column of the first row return by that query will be displayed as a success message. Named parameters from the main query will be made available to the success message query as well.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[]"}
{"id": "sql_queries:canned-queries-magic-parameters", "page": "sql_queries", "ref": "canned-queries-magic-parameters", "title": "Magic parameters", "content": "Named parameters that start with an underscore are special: they can be used to automatically add values created by Datasette that are not contained in the incoming form fields or query string. \n                 These magic parameters are only supported for canned queries: to avoid security issues (such as queries that extract the user's private cookies) they are not available to SQL that is executed by the user as a custom SQL query. \n                 Available magic parameters are: \n                 \n                     \n                         _actor_*  - e.g.  _actor_id ,  _actor_name \n                         \n                             Fields from the currently authenticated  Actors . \n                         \n                     \n                     \n                         _header_*  - e.g.  _header_user_agent \n                         \n                             Header from the incoming HTTP request. The key should be in lower case and with hyphens converted to underscores e.g.  _header_user_agent  or  _header_accept_language . \n                         \n                     \n                     \n                         _cookie_*  - e.g.  _cookie_lang \n                         \n                             The value of the incoming cookie of that name. \n                         \n                     \n                     \n                         _now_epoch \n                         \n                             The number of seconds since the Unix epoch. \n                         \n                     \n                     \n                         _now_date_utc \n                         \n                             The date in UTC, e.g.  2020-06-01 \n                         \n                     \n                     \n                         _now_datetime_utc \n                         \n                             The ISO 8601 datetime in UTC, e.g.  2020-06-24T18:01:07Z \n                         \n                     \n                     \n                         _random_chars_*  - e.g.  _random_chars_128 \n                         \n                             A random string of characters of the specified length. \n                         \n                     \n                 \n                 Here's an example configuration that adds a message from the authenticated user, storing various pieces of additional metadata using magic parameters: \n                 [[[cog\nconfig_example(cog, \"\"\"\ndatabases:\n  mydatabase:\n    queries:\n      add_message:\n        allow:\n          id: \"*\"\n        sql: |-\n          INSERT INTO messages (\n            user_id, message, datetime\n          ) VALUES (\n            :_actor_id, :message, :_now_datetime_utc\n          )\n        write: true\n\"\"\") \n                 ]]] \n                 [[[end]]] \n                 The form presented at  /mydatabase/add_message  will have just a field for  message  - the other parameters will be populated by the magic parameter mechanism. \n                 Additional custom magic parameters can be added by plugins using the  register_magic_parameters(datasette)  hook.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[]"}
{"id": "sql_queries:canned-queries-json-api", "page": "sql_queries", "ref": "canned-queries-json-api", "title": "JSON API for writable canned queries", "content": "Writable canned queries can also be accessed using a JSON API. You can POST data to them using JSON, and you can request that their response is returned to you as JSON. \n                 To submit JSON to a writable canned query, encode key/value parameters as a JSON document: \n                 POST /mydatabase/add_message\n\n{\"message\": \"Message goes here\"} \n                 You can also continue to submit data using regular form encoding, like so: \n                 POST /mydatabase/add_message\n\nmessage=Message+goes+here \n                 There are three options for specifying that you would like the response to your request to return JSON data, as opposed to an HTTP redirect to another page. \n                 \n                     \n                         Set an  Accept: application/json  header on your request \n                     \n                     \n                         Include  ?_json=1  in the URL that you POST to \n                     \n                     \n                         Include  \"_json\": 1  in your JSON body, or  &_json=1  in your form encoded body \n                     \n                 \n                 The JSON response will look like this: \n                 {\n    \"ok\": true,\n    \"message\": \"Query executed, 1 row affected\",\n    \"redirect\": \"/data/add_name\"\n} \n                 The  \"message\"  and  \"redirect\"  values here will take into account  on_success_message ,  on_success_message_sql ,   on_success_redirect ,  on_error_message  and  on_error_redirect , if they have been set.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\"]", "references": "[]"}
{"id": "sql_queries:hide-sql", "page": "sql_queries", "ref": "hide-sql", "title": "hide_sql", "content": "Canned queries default to displaying their SQL query at the top of the page. If the query is extremely long you may want to hide it by default, with a \"show\" link that can be used to make it visible. \n                     Add the  \"hide_sql\": true  option to hide the SQL query by default.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\", \"Additional canned query options\"]", "references": "[]"}
{"id": "sql_queries:fragment", "page": "sql_queries", "ref": "fragment", "title": "fragment", "content": "Some plugins, such as  datasette-vega , can be configured by including additional data in the fragment hash of the URL - the bit that comes after a  #  symbol. \n                     You can set a default fragment hash that will be included in the link to the canned query from the database index page using the  \"fragment\"  key. \n                     This example demonstrates both  fragment  and  hide_sql : \n                     [[[cog\nconfig_example(cog, \"\"\"\ndatabases:\n  fixtures:\n    queries:\n      neighborhood_search:\n        fragment: fragment-goes-here\n        hide_sql: true\n        sql: |-\n          select neighborhood, facet_cities.name, state\n          from facetable join facet_cities on facetable.city_id = facet_cities.id\n          where neighborhood like '%' || :text || '%' order by neighborhood;\n\"\"\") \n                     ]]] \n                     [[[end]]] \n                     See here  for a demo of this in action.", "breadcrumbs": "[\"Running SQL queries\", \"Canned queries\", \"Additional canned query options\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette-vega\", \"label\": \"datasette-vega\"}, {\"href\": \"https://latest.datasette.io/fixtures#queries\", \"label\": \"See here\"}]"}
{"id": "publish:cli-publish", "page": "publish", "ref": "cli-publish", "title": "datasette publish", "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. \n             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.", "breadcrumbs": "[\"Publishing data\"]", "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:cli-package", "page": "publish", "ref": "cli-package", "title": "datasette package", "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: \n             datasette package mydatabase.db \n             Here's example output for the package command: \n             datasette package parlgov.db --extra-options=\"--setting sql_time_limit_ms 2500\"\nSending build context to Docker daemon  4.459MB\nStep 1/7 : FROM python:3.11.0-slim-bullseye\n ---> 79e1dc9af1c1\nStep 2/7 : COPY . /app\n ---> Using cache\n ---> cd4ec67de656\nStep 3/7 : WORKDIR /app\n ---> Using cache\n ---> 139699e91621\nStep 4/7 : RUN pip install datasette\n ---> Using cache\n ---> 340efa82bfd7\nStep 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json\n ---> Using cache\n ---> 5fddbe990314\nStep 6/7 : EXPOSE 8001\n ---> Using cache\n ---> 8e83844b0fed\nStep 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --setting sql_time_limit_ms 2500\n ---> Using cache\n ---> 1bd380ea8af3\nSuccessfully built 1bd380ea8af3 \n             You can now run the resulting container like so: \n             docker run -p 8081:8001 1bd380ea8af3 \n             This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at  http://localhost:8081/ \n             You can customize the port that is exposed by the container using the  --port  option: \n             datasette package mydatabase.db --port 8080 \n             A full list of options can be seen by running  datasette package --help : \n             See  datasette package  for the full list of options for this command.", "breadcrumbs": "[\"Publishing data\"]", "references": "[{\"href\": \"https://www.docker.com/docker-mac\", \"label\": \"Docker for Mac\"}]"}