{"id": "plugin_hooks:plugin-actions", "page": "plugin_hooks", "ref": "plugin-actions", "title": "Action hooks", "content": "Action hooks can be used to add items to the action menus that appear at the top of different pages within Datasette. Unlike menu_links() , actions which are displayed on every page, actions should only be relevant to the page the user is currently viewing. \n Each of these hooks should return return a list of {\"href\": \"...\", \"label\": \"...\"} menu items, with optional \"description\": \"...\" keys describing each action in more detail. \n They can alternatively return an async def awaitable function which, when called, returns a list of those menu items.", "breadcrumbs": "[\"Plugin hooks\"]", "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": "performance:performance-inspect", "page": "performance", "ref": "performance-inspect", "title": "Using \"datasette inspect\"", "content": "Counting the rows in a table can be a very expensive operation on larger databases. In immutable mode Datasette performs this count only once and caches the results, but this can still cause server startup time to increase by several seconds or more. \n If you know that a database is never going to change you can precalculate the table row counts once and store then in a JSON file, then use that file when you later start the server. \n To create a JSON file containing the calculated row counts for a database, use the following: \n datasette inspect data.db --inspect-file=counts.json \n Then later you can start Datasette against the counts.json file and use it to skip the row counting step and speed up server startup: \n datasette -i data.db --inspect-file=counts.json \n You need to use the -i immutable mode against the database file here or the counts from the JSON file will be ignored. \n You will rarely need to use this optimization in every-day use, but several of the datasette publish commands described in Publishing data use this optimization for better performance when deploying a database file to a hosting provider.", "breadcrumbs": "[\"Performance and caching\"]", "references": "[]"}
{"id": "performance:performance-immutable-mode", "page": "performance", "ref": "performance-immutable-mode", "title": "Immutable mode", "content": "If you can be certain that a SQLite database file will not be changed by another process you can tell Datasette to open that file in immutable mode . \n Doing so will disable all locking and change detection, which can result in improved query performance. \n This also enables further optimizations relating to HTTP caching, described below. \n To open a file in immutable mode pass it to the datasette command using the -i option: \n datasette -i data.db \n When you open a file in immutable mode like this Datasette will also calculate and cache the row counts for each table in that database when it first starts up, further improving performance.", "breadcrumbs": "[\"Performance and caching\"]", "references": "[]"}
{"id": "performance:performance-hashed-urls", "page": "performance", "ref": "performance-hashed-urls", "title": "datasette-hashed-urls", "content": "If you open a database file in immutable mode using the -i option, you can be assured that the content of that database will not change for the lifetime of the Datasette server. \n The datasette-hashed-urls plugin implements an optimization where your database is served with part of the SHA-256 hash of the database contents baked into the URL. \n A database at /fixtures will instead be served at /fixtures-aa7318b , and a year-long cache expiry header will be returned with those pages. \n This will then be cached by both browsers and caching proxies such as Cloudflare or Fastly, providing a potentially significant performance boost. \n To install the plugin, run the following: \n datasette install datasette-hashed-urls \n \n Prior to Datasette 0.61 hashed URL mode was a core Datasette feature, enabled using the hash_urls setting. This implementation has now been removed in favor of the datasette-hashed-urls plugin. \n Prior to Datasette 0.28 hashed URL mode was the default behaviour for Datasette, since all database files were assumed to be immutable and unchanging. From 0.28 onwards the default has been to treat database files as mutable unless explicitly configured otherwise.", "breadcrumbs": "[\"Performance and caching\"]", "references": "[{\"href\": \"https://datasette.io/plugins/datasette-hashed-urls\", \"label\": \"datasette-hashed-urls plugin\"}]"}
{"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": "performance:http-caching", "page": "performance", "ref": "http-caching", "title": "HTTP caching", "content": "If your database is immutable and guaranteed not to change, you can gain major performance improvements from Datasette by enabling HTTP caching. \n This can work at two different levels. First, it can tell browsers to cache the results of queries and serve future requests from the browser cache. \n More significantly, it allows you to run Datasette behind a caching proxy such as Varnish or use a cache provided by a hosted service such as Fastly or Cloudflare . This can provide incredible speed-ups since a query only needs to be executed by Datasette the first time it is accessed - all subsequent hits can then be served by the cache. \n Using a caching proxy in this way could enable a Datasette-backed visualization to serve thousands of hits a second while running Datasette itself on extremely inexpensive hosting. \n Datasette's integration with HTTP caches can be enabled using a combination of configuration options and query string arguments. \n The default_cache_ttl setting sets the default HTTP cache TTL for all Datasette pages. This is 5 seconds unless you change it - you can set it to 0 if you wish to disable HTTP caching entirely. \n You can also change the cache timeout on a per-request basis using the ?_ttl=10 query string parameter. This can be useful when you are working with the Datasette JSON API - you may decide that a specific query can be cached for a longer time, or maybe you need to set ?_ttl=0 for some requests for example if you are running a SQL order by random() query.", "breadcrumbs": "[\"Performance and caching\"]", "references": "[{\"href\": \"https://varnish-cache.org/\", \"label\": \"Varnish\"}, {\"href\": \"https://www.fastly.com/\", \"label\": \"Fastly\"}, {\"href\": \"https://www.cloudflare.com/\", \"label\": \"Cloudflare\"}]"}
{"id": "pages:tableview", "page": "pages", "ref": "tableview", "title": "Table", "content": "The table page is the heart of Datasette: it allows users to interactively explore the contents of a database table, including sorting, filtering, Full-text search and applying Facets . \n The HTML interface is worth spending some time exploring. As with other pages, you can return the JSON data by appending .json to the URL path, before any ? query string arguments. \n The query string arguments are described in more detail here: Table arguments \n You can also use the table page to interactively construct a SQL query - by applying different filters and a sort order for example - and then click the \"View and edit SQL\" link to see the SQL query that was used for the page and edit and re-submit it. \n Some examples: \n \n \n ../items lists all of the line-items registered by UK MPs as potential conflicts of interest. It demonstrates Datasette's support for Full-text search . \n \n \n ../antiquities-act%2Factions_under_antiquities_act is an interface for exploring the \"actions under the antiquities act\" data table published by FiveThirtyEight. \n \n \n ../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas is a filtered table page showing every Gas power plant in the United Kingdom. It includes some default facets (configured using its metadata.json ) and uses the datasette-cluster-map plugin to show a map of the results.", "breadcrumbs": "[\"Pages and API endpoints\"]", "references": "[{\"href\": \"https://register-of-members-interests.datasettes.com/regmem/items\", \"label\": \"../items\"}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight/antiquities-act%2Factions_under_antiquities_act\", \"label\": \"../antiquities-act%2Factions_under_antiquities_act\"}, {\"href\": \"https://global-power-plants.datasettes.com/global-power-plants/global-power-plants?_facet=primary_fuel&_facet=owner&_facet=country_long&country_long__exact=United+Kingdom&primary_fuel=Gas\", \"label\": \"../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas\"}, {\"href\": \"https://global-power-plants.datasettes.com/-/metadata\", \"label\": \"its metadata.json\"}, {\"href\": \"https://github.com/simonw/datasette-cluster-map\", \"label\": \"datasette-cluster-map\"}]"}
{"id": "pages:rowview", "page": "pages", "ref": "rowview", "title": "Row", "content": "Every row in every Datasette table has its own URL. This means individual records can be linked to directly. \n Table cells with extremely long text contents are truncated on the table view according to the truncate_cells_html setting. If a cell has been truncated the full length version of that cell will be available on the row page. \n Rows which are the targets of foreign key references from other tables will show a link to a filtered search for all records that reference that row. Here's an example from the Registers of Members Interests database: \n ../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001 \n Note that this URL includes the encoded primary key of the record. \n Here's that same page as JSON: \n ../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001.json", "breadcrumbs": "[\"Pages and API endpoints\"]", "references": "[{\"href\": \"https://register-of-members-interests.datasettes.com/regmem/people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001\", \"label\": \"../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001\"}, {\"href\": \"https://register-of-members-interests.datasettes.com/regmem/people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001.json\", \"label\": \"../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001.json\"}]"}
{"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": "pages:indexview", "page": "pages", "ref": "indexview", "title": "Top-level index", "content": "The root page of any Datasette installation is an index page that lists all of the currently attached databases. Some examples: \n \n \n fivethirtyeight.datasettes.com \n \n \n global-power-plants.datasettes.com \n \n \n register-of-members-interests.datasettes.com \n \n \n Add /.json to the end of the URL for the JSON version of the underlying data: \n \n \n fivethirtyeight.datasettes.com/.json \n \n \n global-power-plants.datasettes.com/.json \n \n \n register-of-members-interests.datasettes.com/.json", "breadcrumbs": "[\"Pages and API endpoints\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/\", \"label\": \"fivethirtyeight.datasettes.com\"}, {\"href\": \"https://global-power-plants.datasettes.com/\", \"label\": \"global-power-plants.datasettes.com\"}, {\"href\": \"https://register-of-members-interests.datasettes.com/\", \"label\": \"register-of-members-interests.datasettes.com\"}, {\"href\": \"https://fivethirtyeight.datasettes.com/.json\", \"label\": \"fivethirtyeight.datasettes.com/.json\"}, {\"href\": \"https://global-power-plants.datasettes.com/.json\", \"label\": \"global-power-plants.datasettes.com/.json\"}, {\"href\": \"https://register-of-members-interests.datasettes.com/.json\", \"label\": \"register-of-members-interests.datasettes.com/.json\"}]"}
{"id": "pages:databaseview-hidden", "page": "pages", "ref": "databaseview-hidden", "title": "Hidden tables", "content": "Some tables listed on the database page are treated as hidden. Hidden tables are not completely invisible - they can be accessed through the \"hidden tables\" link at the bottom of the page. They are hidden because they represent low-level implementation details which are generally not useful to end-users of Datasette. \n The following tables are hidden by default: \n \n \n Any table with a name that starts with an underscore - this is a Datasette convention to help plugins easily hide their own internal tables. \n \n \n Tables that have been configured as \"hidden\": true using Hiding tables . \n \n \n *_fts tables that implement SQLite full-text search indexes. \n \n \n Tables relating to the inner workings of the SpatiaLite SQLite extension. \n \n \n sqlite_stat tables used to store statistics used by the query optimizer.", "breadcrumbs": "[\"Pages and API endpoints\", \"Database\"]", "references": "[]"}
{"id": "pages:databaseview", "page": "pages", "ref": "databaseview", "title": "Database", "content": "Each database has a page listing the tables, views and canned queries available for that database. If the execute-sql permission is enabled (it's on by default) there will also be an interface for executing arbitrary SQL select queries against the data. \n Examples: \n \n \n fivethirtyeight.datasettes.com/fivethirtyeight \n \n \n global-power-plants.datasettes.com/global-power-plants \n \n \n The JSON version of this page provides programmatic access to the underlying data: \n \n \n fivethirtyeight.datasettes.com/fivethirtyeight.json \n \n \n global-power-plants.datasettes.com/global-power-plants.json", "breadcrumbs": "[\"Pages and API endpoints\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight\", \"label\": \"fivethirtyeight.datasettes.com/fivethirtyeight\"}, {\"href\": \"https://global-power-plants.datasettes.com/global-power-plants\", \"label\": \"global-power-plants.datasettes.com/global-power-plants\"}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight.json\", \"label\": \"fivethirtyeight.datasettes.com/fivethirtyeight.json\"}, {\"href\": \"https://global-power-plants.datasettes.com/global-power-plants.json\", \"label\": \"global-power-plants.datasettes.com/global-power-plants.json\"}]"}
{"id": "metadata:top-level-metadata", "page": "metadata", "ref": "top-level-metadata", "title": "Top-level metadata", "content": "\"Top-level\" metadata refers to fields that can be specified at the root level of a metadata file. These attributes are meant to describe the entire Datasette instance. \n The following are the full list of allowed top-level metadata fields: \n \n \n title \n \n \n description \n \n \n description_html \n \n \n license \n \n \n license_url \n \n \n source \n \n \n source_url", "breadcrumbs": "[\"Metadata\", \"Metadata reference\"]", "references": "[]"}
{"id": "metadata:table-level-metadata", "page": "metadata", "ref": "table-level-metadata", "title": "Table-level metadata", "content": "\"Table-level\" metadata refers to fields that can be specified for each table in a Datasette instance. These attributes should be listed under a specific table using the \"tables\" field. \n The following are the full list of allowed table-level metadata fields: \n \n \n source \n \n \n source_url \n \n \n license \n \n \n license_url \n \n \n about \n \n \n about_url \n \n \n hidden \n \n \n sort/sort_desc \n \n \n size \n \n \n sortable_columns \n \n \n label_column \n \n \n facets \n \n \n fts_table \n \n \n fts_pk \n \n \n searchmode \n \n \n columns", "breadcrumbs": "[\"Metadata\", \"Metadata reference\"]", "references": "[]"}
{"id": "metadata:specifying-units-for-a-column", "page": "metadata", "ref": "specifying-units-for-a-column", "title": "Specifying units for a column", "content": "Datasette supports attaching units to a column, which will be used when displaying\n values from that column. SI prefixes will be used where appropriate. \n Column units are configured in the metadata like so: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"database1\": {\n \"tables\": {\n \"example_table\": {\n \"units\": {\n \"column1\": \"metres\",\n \"column2\": \"Hz\"\n }\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n Units are interpreted using Pint , and you can see the full list of available units in\n Pint's unit registry . You can also add custom units to the metadata, which will be\n registered with Pint: \n [[[cog\nmetadata_example(cog, {\n \"custom_units\": [\n \"decibel = [] = dB\"\n ]\n}) \n ]]] \n [[[end]]]", "breadcrumbs": "[\"Metadata\"]", "references": "[{\"href\": \"https://pint.readthedocs.io/\", \"label\": \"Pint\"}, {\"href\": \"https://github.com/hgrecco/pint/blob/master/pint/default_en.txt\", \"label\": \"unit registry\"}, {\"href\": \"http://pint.readthedocs.io/en/latest/defining.html\", \"label\": \"custom units\"}]"}
{"id": "metadata:per-database-and-per-table-metadata", "page": "metadata", "ref": "per-database-and-per-table-metadata", "title": "Per-database and per-table metadata", "content": "Metadata at the top level of the file will be shown on the index page and in the\n footer on every page of the site. The license and source is expected to apply to\n all of your data. \n You can also provide metadata at the per-database or per-table level, like this: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"database1\": {\n \"source\": \"Alternative source\",\n \"source_url\": \"http://example.com/\",\n \"tables\": {\n \"example_table\": {\n \"description_html\": \"Custom table description\",\n \"license\": \"CC BY 3.0 US\",\n \"license_url\": \"https://creativecommons.org/licenses/by/3.0/us/\"\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n Each of the top-level metadata fields can be used at the database and table level.", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:metadata-source-license-about", "page": "metadata", "ref": "metadata-source-license-about", "title": "Source, license and about", "content": "The three visible metadata fields you can apply to everything, specific databases or specific tables are source, license and about. All three are optional. \n source and source_url should be used to indicate where the underlying data came from. \n license and license_url should be used to indicate the license under which the data can be used. \n about and about_url can be used to link to further information about the project - an accompanying blog entry for example. \n For each of these you can provide just the *_url field and Datasette will treat that as the default link label text and display the URL directly on the page.", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:metadata-sortable-columns", "page": "metadata", "ref": "metadata-sortable-columns", "title": "Setting which columns can be used for sorting", "content": "Datasette allows any column to be used for sorting by default. If you need to\n control which columns are available for sorting you can do so using the optional\n sortable_columns key: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"database1\": {\n \"tables\": {\n \"example_table\": {\n \"sortable_columns\": [\n \"height\",\n \"weight\"\n ]\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n This will restrict sorting of example_table to just the height and\n weight columns. \n You can also disable sorting entirely by setting \"sortable_columns\": [] \n You can use sortable_columns to enable specific sort orders for a view called name_of_view in the database my_database like so: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"my_database\": {\n \"tables\": {\n \"name_of_view\": {\n \"sortable_columns\": [\n \"clicks\",\n \"impressions\"\n ]\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:metadata-page-size", "page": "metadata", "ref": "metadata-page-size", "title": "Setting a custom page size", "content": "Datasette defaults to displaying 100 rows per page, for both tables and views. You can change this default page size on a per-table or per-view basis using the \"size\" key in metadata.json : \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"mydatabase\": {\n \"tables\": {\n \"example_table\": {\n \"size\": 10\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n This size can still be over-ridden by passing e.g. ?_size=50 in the query string.", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:metadata-hiding-tables", "page": "metadata", "ref": "metadata-hiding-tables", "title": "Hiding tables", "content": "You can hide tables from the database listing view (in the same way that FTS and\n SpatiaLite tables are automatically hidden) using \"hidden\": true : \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"database1\": {\n \"tables\": {\n \"example_table\": {\n \"hidden\": True\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:metadata-default-sort", "page": "metadata", "ref": "metadata-default-sort", "title": "Setting a default sort order", "content": "By default Datasette tables are sorted by primary key. You can over-ride this default for a specific table using the \"sort\" or \"sort_desc\" metadata properties: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"mydatabase\": {\n \"tables\": {\n \"example_table\": {\n \"sort\": \"created\"\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n Or use \"sort_desc\" to sort in descending order: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"mydatabase\": {\n \"tables\": {\n \"example_table\": {\n \"sort_desc\": \"created\"\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:metadata-column-descriptions", "page": "metadata", "ref": "metadata-column-descriptions", "title": "Column descriptions", "content": "You can include descriptions for your columns by adding a \"columns\": {\"name-of-column\": \"description-of-column\"} block to your table metadata: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"database1\": {\n \"tables\": {\n \"example_table\": {\n \"columns\": {\n \"column1\": \"Description of column 1\",\n \"column2\": \"Description of column 2\"\n }\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]] \n These will be displayed at the top of the table page, and will also show in the cog menu for each column. \n You can see an example of how these look at latest.datasette.io/fixtures/roadside_attractions .", "breadcrumbs": "[\"Metadata\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/roadside_attractions\", \"label\": \"latest.datasette.io/fixtures/roadside_attractions\"}]"}
{"id": "metadata:label-columns", "page": "metadata", "ref": "label-columns", "title": "Specifying the label column for a table", "content": "Datasette's HTML interface attempts to display foreign key references as\n labelled hyperlinks. By default, it looks for referenced tables that only have\n two columns: a primary key column and one other. It assumes that the second\n column should be used as the link label. \n If your table has more than two columns you can specify which column should be\n used for the link label with the label_column property: \n [[[cog\nmetadata_example(cog, {\n \"databases\": {\n \"database1\": {\n \"tables\": {\n \"example_table\": {\n \"label_column\": \"title\"\n }\n }\n }\n }\n}) \n ]]] \n [[[end]]]", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"id": "metadata:id2", "page": "metadata", "ref": "id2", "title": "Metadata reference", "content": "A full reference of every supported option in a metadata.json or metadata.yaml file.", "breadcrumbs": "[\"Metadata\"]", "references": "[]"}
{"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": "metadata:database-level-metadata", "page": "metadata", "ref": "database-level-metadata", "title": "Database-level metadata", "content": "\"Database-level\" metadata refers to fields that can be specified for each database in a Datasette instance. These attributes should be listed under a database inside the \"databases\" field. \n The following are the full list of allowed database-level metadata fields: \n \n \n source \n \n \n source_url \n \n \n license \n \n \n license_url \n \n \n about \n \n \n about_url", "breadcrumbs": "[\"Metadata\", \"Metadata reference\"]", "references": "[]"}
{"id": "json_api:tableupsertview", "page": "json_api", "ref": "tableupsertview", "title": "Upserting rows", "content": "An upsert is an insert or update operation. If a row with a matching primary key already exists it will be updated - otherwise a new row will be inserted. \n The upsert API is mostly the same shape as the insert API . It requires both the insert-row and update-row permissions. \n POST //
/-/upsert\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n {\n \"rows\": [\n {\n \"id\": 1,\n \"title\": \"Updated title for 1\",\n \"description\": \"Updated description for 1\"\n },\n {\n \"id\": 2,\n \"description\": \"Updated description for 2\",\n },\n {\n \"id\": 3,\n \"title\": \"Item 3\",\n \"description\": \"Description for 3\"\n }\n ]\n} \n Imagine a table with a primary key of id and which already has rows with id values of 1 and 2 . \n The above example will: \n \n \n Update the row with id of 1 to set both title and description to the new values \n \n \n Update the row with id of 2 to set title to the new value - description will be left unchanged \n \n \n Insert a new row with id of 3 and both title and description set to the new values \n \n \n Similar to /-/insert , a row key with an object can be used instead of a rows array to upsert a single row. \n If successful, this will return a 200 status code and a {\"ok\": true} response body. \n Add \"return\": true to the request body to return full copies of the affected rows after they have been inserted or updated: \n {\n \"rows\": [\n {\n \"id\": 1,\n \"title\": \"Updated title for 1\",\n \"description\": \"Updated description for 1\"\n },\n {\n \"id\": 2,\n \"description\": \"Updated description for 2\",\n },\n {\n \"id\": 3,\n \"title\": \"Item 3\",\n \"description\": \"Description for 3\"\n }\n ],\n \"return\": true\n} \n This will return the following: \n {\n \"ok\": true,\n \"rows\": [\n {\n \"id\": 1,\n \"title\": \"Updated title for 1\",\n \"description\": \"Updated description for 1\"\n },\n {\n \"id\": 2,\n \"title\": \"Item 2\",\n \"description\": \"Updated description for 2\"\n },\n {\n \"id\": 3,\n \"title\": \"Item 3\",\n \"description\": \"Description for 3\"\n }\n ]\n} \n When using upsert you must provide the primary key column (or columns if the table has a compound primary key) for every row, or you will get a 400 error: \n {\n \"ok\": false,\n \"errors\": [\n \"Row 0 is missing primary key column(s): \\\"id\\\"\"\n ]\n} \n If your table does not have an explicit primary key you should pass the SQLite rowid key instead. \n Pass \"alter: true to automatically add any missing columns to the table. This requires the alter-table permission.", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:tableinsertview", "page": "json_api", "ref": "tableinsertview", "title": "Inserting rows", "content": "This requires the insert-row permission. \n A single row can be inserted using the \"row\" key: \n POST //
/-/insert\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n {\n \"row\": {\n \"column1\": \"value1\",\n \"column2\": \"value2\"\n }\n} \n If successful, this will return a 201 status code and the newly inserted row, for example: \n {\n \"rows\": [\n {\n \"id\": 1,\n \"column1\": \"value1\",\n \"column2\": \"value2\"\n }\n ]\n} \n To insert multiple rows at a time, use the same API method but send a list of dictionaries as the \"rows\" key: \n POST //
/-/insert\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n {\n \"rows\": [\n {\n \"column1\": \"value1\",\n \"column2\": \"value2\"\n },\n {\n \"column1\": \"value3\",\n \"column2\": \"value4\"\n }\n ]\n} \n If successful, this will return a 201 status code and a {\"ok\": true} response body. \n The maximum number rows that can be submitted at once defaults to 100, but this can be changed using the max_insert_rows setting. \n To return the newly inserted rows, add the \"return\": true key to the request body: \n {\n \"rows\": [\n {\n \"column1\": \"value1\",\n \"column2\": \"value2\"\n },\n {\n \"column1\": \"value3\",\n \"column2\": \"value4\"\n }\n ],\n \"return\": true\n} \n This will return the same \"rows\" key as the single row example above. There is a small performance penalty for using this option. \n If any of your rows have a primary key that is already in use, you will get an error and none of the rows will be inserted: \n {\n \"ok\": false,\n \"errors\": [\n \"UNIQUE constraint failed: new_table.id\"\n ]\n} \n Pass \"ignore\": true to ignore these errors and insert the other rows: \n {\n \"rows\": [\n {\n \"id\": 1,\n \"column1\": \"value1\",\n \"column2\": \"value2\"\n },\n {\n \"id\": 2,\n \"column1\": \"value3\",\n \"column2\": \"value4\"\n }\n ],\n \"ignore\": true\n} \n Or you can pass \"replace\": true to replace any rows with conflicting primary keys with the new values. This requires the update-row permission. \n Pass \"alter: true to automatically add any missing columns to the table. This requires the alter-table permission.", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:tabledropview", "page": "json_api", "ref": "tabledropview", "title": "Dropping tables", "content": "To drop a table, make a POST to //
/-/drop . This requires the drop-table permission. \n POST //
/-/drop\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n Without a POST body this will return a status 200 with a note about how many rows will be deleted: \n {\n \"ok\": true,\n \"database\": \"\",\n \"table\": \"
\",\n \"row_count\": 5,\n \"message\": \"Pass \\\"confirm\\\": true to confirm\"\n} \n If you pass the following POST body: \n {\n \"confirm\": true\n} \n Then the table will be dropped and a status 200 response of {\"ok\": true} will be returned. \n Any errors will return {\"errors\": [\"... descriptive message ...\"], \"ok\": false} , and a 400 status code for a bad input or a 403 status code for an authentication or permission error.", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:tablecreateview-example", "page": "json_api", "ref": "tablecreateview-example", "title": "Creating a table from example data", "content": "Instead of specifying columns directly you can instead pass a single example row or a list of rows .\n Datasette will create a table with a schema that matches those rows and insert them for you: \n POST //-/create\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n {\n \"table\": \"creatures\",\n \"rows\": [\n {\n \"id\": 1,\n \"name\": \"Tarantula\"\n },\n {\n \"id\": 2,\n \"name\": \"K\u0101k\u0101p\u014d\"\n }\n ],\n \"pk\": \"id\"\n} \n Doing this requires both the create-table and insert-row permissions. \n The 201 response here will be similar to the columns form, but will also include the number of rows that were inserted as row_count : \n {\n \"ok\": true,\n \"database\": \"data\",\n \"table\": \"creatures\",\n \"table_url\": \"http://127.0.0.1:8001/data/creatures\",\n \"table_api_url\": \"http://127.0.0.1:8001/data/creatures.json\",\n \"schema\": \"CREATE TABLE [creatures] (\\n [id] INTEGER PRIMARY KEY,\\n [name] TEXT\\n)\",\n \"row_count\": 2\n} \n You can call the create endpoint multiple times for the same table provided you are specifying the table using the rows or row option. New rows will be inserted into the table each time. This means you can use this API if you are unsure if the relevant table has been created yet. \n If you pass a row to the create endpoint with a primary key that already exists you will get an error that looks like this: \n {\n \"ok\": false,\n \"errors\": [\n \"UNIQUE constraint failed: creatures.id\"\n ]\n} \n You can avoid this error by passing the same \"ignore\": true or \"replace\": true options to the create endpoint as you can to the insert endpoint . \n To use the \"replace\": true option you will also need the update-row permission. \n Pass \"alter\": true to automatically add any missing columns to the existing table that are present in the rows you are submitting. This requires the alter-table permission.", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:tablecreateview", "page": "json_api", "ref": "tablecreateview", "title": "Creating a table", "content": "To create a table, make a POST to //-/create . This requires the create-table permission. \n POST //-/create\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n {\n \"table\": \"name_of_new_table\",\n \"columns\": [\n {\n \"name\": \"id\",\n \"type\": \"integer\"\n },\n {\n \"name\": \"title\",\n \"type\": \"text\"\n }\n ],\n \"pk\": \"id\"\n} \n The JSON here describes the table that will be created: \n \n \n table is the name of the table to create. This field is required. \n \n \n columns is a list of columns to create. Each column is a dictionary with name and type keys. \n \n \n name is the name of the column. This is required. \n \n \n type is the type of the column. This is optional - if not provided, text will be assumed. The valid types are text , integer , float and blob . \n \n \n \n \n pk is the primary key for the table. This is optional - if not provided, Datasette will create a SQLite table with a hidden rowid column. \n If the primary key is an integer column, it will be configured to automatically increment for each new record. \n If you set this to id without including an id column in the list of columns , Datasette will create an auto-incrementing integer ID column for you. \n \n \n pks can be used instead of pk to create a compound primary key. It should be a JSON list of column names to use in that primary key. \n \n \n ignore can be set to true to ignore existing rows by primary key if the table already exists. \n \n \n replace can be set to true to replace existing rows by primary key if the table already exists. This requires the update-row permission. \n \n \n alter can be set to true if you want to automatically add any missing columns to the table. This requires the alter-table permission. \n \n \n If the table is successfully created this will return a 201 status code and the following response: \n {\n \"ok\": true,\n \"database\": \"data\",\n \"table\": \"name_of_new_table\",\n \"table_url\": \"http://127.0.0.1:8001/data/name_of_new_table\",\n \"table_api_url\": \"http://127.0.0.1:8001/data/name_of_new_table.json\",\n \"schema\": \"CREATE TABLE [name_of_new_table] (\\n [id] INTEGER PRIMARY KEY,\\n [title] TEXT\\n)\"\n}", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:rowupdateview", "page": "json_api", "ref": "rowupdateview", "title": "Updating a row", "content": "To update a row, make a POST to //
//-/update . This requires the update-row permission. \n POST //
//-/update\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n {\n \"update\": {\n \"text_column\": \"New text string\",\n \"integer_column\": 3,\n \"float_column\": 3.14\n }\n} \n here is the tilde-encoded primary key value of the row to update - or a comma-separated list of primary key values if the table has a composite primary key. \n You only need to pass the columns you want to update. Any other columns will be left unchanged. \n If successful, this will return a 200 status code and a {\"ok\": true} response body. \n Add \"return\": true to the request body to return the updated row: \n {\n \"update\": {\n \"title\": \"New title\"\n },\n \"return\": true\n} \n The returned JSON will look like this: \n {\n \"ok\": true,\n \"row\": {\n \"id\": 1,\n \"title\": \"New title\",\n \"other_column\": \"Will be present here too\"\n }\n} \n Any errors will return {\"errors\": [\"... descriptive message ...\"], \"ok\": false} , and a 400 status code for a bad input or a 403 status code for an authentication or permission error. \n Pass \"alter: true to automatically add any missing columns to the table. This requires the alter-table permission.", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:rowdeleteview", "page": "json_api", "ref": "rowdeleteview", "title": "Deleting a row", "content": "To delete a row, make a POST to //
//-/delete . This requires the delete-row permission. \n POST //
//-/delete\nContent-Type: application/json\nAuthorization: Bearer dstok_ \n here is the tilde-encoded primary key value of the row to delete - or a comma-separated list of primary key values if the table has a composite primary key. \n If successful, this will return a 200 status code and a {\"ok\": true} response body. \n Any errors will return {\"errors\": [\"... descriptive message ...\"], \"ok\": false} , and a 400 status code for a bad input or a 403 status code for an authentication or permission error.", "breadcrumbs": "[\"JSON API\", \"The JSON write API\"]", "references": "[]"}
{"id": "json_api:json-api-write", "page": "json_api", "ref": "json-api-write", "title": "The JSON write API", "content": "Datasette provides a write API for JSON data. This is a POST-only API that requires an authenticated API token, see API Tokens . The token will need to have the specified Permissions .", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:json-api-table-arguments", "page": "json_api", "ref": "json-api-table-arguments", "title": "Special table arguments", "content": "?_col=COLUMN1&_col=COLUMN2 \n \n List specific columns to display. These will be shown along with any primary keys. \n \n \n \n ?_nocol=COLUMN1&_nocol=COLUMN2 \n \n List specific columns to hide - any column not listed will be displayed. Primary keys cannot be hidden. \n \n \n \n ?_labels=on/off \n \n Expand foreign key references for every possible column. See below. \n \n \n \n ?_label=COLUMN1&_label=COLUMN2 \n \n Expand foreign key references for one or more specified columns. \n \n \n \n ?_size=1000 or ?_size=max \n \n Sets a custom page size. This cannot exceed the max_returned_rows limit\n passed to datasette serve . Use max to get max_returned_rows . \n \n \n \n ?_sort=COLUMN \n \n Sorts the results by the specified column. \n \n \n \n ?_sort_desc=COLUMN \n \n Sorts the results by the specified column in descending order. \n \n \n \n ?_search=keywords \n \n For SQLite tables that have been configured for\n full-text search executes a search\n with the provided keywords. \n \n \n \n ?_search_COLUMN=keywords \n \n Like _search= but allows you to specify the column to be searched, as\n opposed to searching all columns that have been indexed by FTS. \n \n \n \n ?_searchmode=raw \n \n With this option, queries passed to ?_search= or ?_search_COLUMN= will\n not have special characters escaped. This means you can make use of the full\n set of advanced SQLite FTS syntax ,\n though this could potentially result in errors if the wrong syntax is used. \n \n \n \n ?_where=SQL-fragment \n \n If the execute-sql permission is enabled, this parameter\n can be used to pass one or more additional SQL fragments to be used in the\n WHERE clause of the SQL used to query the table. \n This is particularly useful if you are building a JavaScript application\n that needs to do something creative but still wants the other conveniences\n provided by the table view (such as faceting) and hence would like not to\n have to construct a completely custom SQL query. \n Some examples: \n \n \n facetable?_where=_neighborhood like \"%c%\"&_where=_city_id=3 \n \n \n facetable?_where=_city_id in (select id from facet_cities where name != \"Detroit\") \n \n \n \n \n \n ?_through={json} \n \n This can be used to filter rows via a join against another table. \n The JSON parameter must include three keys: table , column and value . \n table must be a table that the current table is related to via a foreign key relationship. \n column must be a column in that other table. \n value is the value that you want to match against. \n For example, to filter roadside_attractions to just show the attractions that have a characteristic of \"museum\", you would construct this JSON: \n {\n \"table\": \"roadside_attraction_characteristics\",\n \"column\": \"characteristic_id\",\n \"value\": \"1\"\n} \n As a URL, that looks like this: \n ?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22} \n Here's an example . \n \n \n \n ?_next=TOKEN \n \n Pagination by continuation token - pass the token that was returned in the\n \"next\" property by the previous page. \n \n \n \n ?_facet=column \n \n Facet by column. Can be applied multiple times, see Facets . Only works on the default JSON output, not on any of the custom shapes. \n \n \n \n ?_facet_size=100 \n \n Increase the number of facet results returned for each facet. Use ?_facet_size=max for the maximum available size, determined by max_returned_rows . \n \n \n \n ?_nofacet=1 \n \n Disable all facets and facet suggestions for this page, including any defined by Facets in metadata . \n \n \n \n ?_nosuggest=1 \n \n Disable facet suggestions for this page. \n \n \n \n ?_nocount=1 \n \n Disable the select count(*) query used on this page - a count of None will be returned instead.", "breadcrumbs": "[\"JSON API\", \"Table arguments\"]", "references": "[{\"href\": \"https://www.sqlite.org/fts3.html\", \"label\": \"full-text search\"}, {\"href\": \"https://www.sqlite.org/fts5.html#full_text_query_syntax\", \"label\": \"advanced SQLite FTS syntax\"}, {\"href\": \"https://latest.datasette.io/fixtures/facetable?_where=_neighborhood%20like%20%22%c%%22&_where=_city_id=3\", \"label\": \"facetable?_where=_neighborhood like \\\"%c%\\\"&_where=_city_id=3\"}, {\"href\": \"https://latest.datasette.io/fixtures/facetable?_where=_city_id%20in%20(select%20id%20from%20facet_cities%20where%20name%20!=%20%22Detroit%22)\", \"label\": \"facetable?_where=_city_id in (select id from facet_cities where name != \\\"Detroit\\\")\"}, {\"href\": \"https://latest.datasette.io/fixtures/roadside_attractions?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22}\", \"label\": \"an example\"}]"}
{"id": "json_api:json-api-special", "page": "json_api", "ref": "json-api-special", "title": "Special JSON arguments", "content": "Every Datasette endpoint that can return JSON also accepts the following\n query string arguments: \n \n \n ?_shape=SHAPE \n \n The shape of the JSON to return, documented above. \n \n \n \n ?_nl=on \n \n When used with ?_shape=array produces newline-delimited JSON objects. \n \n \n \n ?_json=COLUMN1&_json=COLUMN2 \n \n If any of your SQLite columns contain JSON values, you can use one or more\n _json= parameters to request that those columns be returned as regular\n JSON. Without this argument those columns will be returned as JSON objects\n that have been double-encoded into a JSON string value. \n Compare this query without the argument to this query using the argument \n \n \n \n ?_json_infinity=on \n \n If your data contains infinity or -infinity values, Datasette will replace\n them with None when returning them as JSON. If you pass _json_infinity=1 \n Datasette will instead return them as Infinity or -Infinity which is\n invalid JSON but can be processed by some custom JSON parsers. \n \n \n \n ?_timelimit=MS \n \n Sets a custom time limit for the query in ms. You can use this for optimistic\n queries where you would like Datasette to give up if the query takes too\n long, for example if you want to implement autocomplete search but only if\n it can be executed in less than 10ms. \n \n \n \n ?_ttl=SECONDS \n \n For how many seconds should this response be cached by HTTP proxies? Use\n ?_ttl=0 to disable HTTP caching entirely for this request. \n \n \n \n ?_trace=1 \n \n Turns on tracing for this page: SQL queries executed during the request will\n be gathered and included in the response, either in a new \"_traces\" key\n for JSON responses or at the bottom of the page if the response is in HTML. \n The structure of the data returned here should be considered highly unstable\n and very likely to change. \n Only available if the trace_debug setting is enabled.", "breadcrumbs": "[\"JSON API\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array\", \"label\": \"this query without the argument\"}, {\"href\": \"https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array&_json=d\", \"label\": \"this query using the argument\"}]"}
{"id": "json_api:json-api-shapes", "page": "json_api", "ref": "json-api-shapes", "title": "Different shapes", "content": "The _shape parameter can be used to access alternative formats for the\n rows key which may be more convenient for your application. There are three\n options: \n \n \n ?_shape=objects - \"rows\" is a list of JSON key/value objects - the default \n \n \n ?_shape=arrays - \"rows\" is a list of lists, where the order of values in each list matches the order of the columns \n \n \n ?_shape=array - a JSON array of objects - effectively just the \"rows\" key from the default representation \n \n \n ?_shape=array&_nl=on - a newline-separated list of JSON objects \n \n \n ?_shape=arrayfirst - a flat JSON array containing just the first value from each row \n \n \n ?_shape=object - a JSON object keyed using the primary keys of the rows \n \n \n _shape=arrays looks like this: \n {\n \"ok\": true,\n \"next\": null,\n \"rows\": [\n [3, \"Detroit\"],\n [2, \"Los Angeles\"],\n [4, \"Memnonia\"],\n [1, \"San Francisco\"]\n ]\n} \n _shape=array looks like this: \n [\n {\n \"id\": 3,\n \"name\": \"Detroit\"\n },\n {\n \"id\": 2,\n \"name\": \"Los Angeles\"\n },\n {\n \"id\": 4,\n \"name\": \"Memnonia\"\n },\n {\n \"id\": 1,\n \"name\": \"San Francisco\"\n }\n] \n _shape=array&_nl=on looks like this: \n {\"id\": 1, \"value\": \"Myoporum laetum :: Myoporum\"}\n{\"id\": 2, \"value\": \"Metrosideros excelsa :: New Zealand Xmas Tree\"}\n{\"id\": 3, \"value\": \"Pinus radiata :: Monterey Pine\"} \n _shape=arrayfirst looks like this: \n [1, 2, 3] \n _shape=object looks like this: \n {\n \"1\": {\n \"id\": 1,\n \"value\": \"Myoporum laetum :: Myoporum\"\n },\n \"2\": {\n \"id\": 2,\n \"value\": \"Metrosideros excelsa :: New Zealand Xmas Tree\"\n },\n \"3\": {\n \"id\": 3,\n \"value\": \"Pinus radiata :: Monterey Pine\"\n }\n] \n The object shape is only available for queries against tables - custom SQL\n queries and views do not have an obvious primary key so cannot be returned using\n this format. \n The object keys are always strings. If your table has a compound primary\n key, the object keys will be a comma-separated string.", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:json-api-pagination", "page": "json_api", "ref": "json-api-pagination", "title": "Pagination", "content": "The default JSON representation includes a \"next_url\" key which can be used to access the next page of results. If that key is null or missing then it means you have reached the final page of results. \n Other representations include pagination information in the link HTTP header. That header will look something like this: \n link: ; rel=\"next\" \n Here is an example Python function built using requests that returns a list of all of the paginated items from one of these API endpoints: \n def paginate(url):\n items = []\n while url:\n response = requests.get(url)\n try:\n url = response.links.get(\"next\").get(\"url\")\n except AttributeError:\n url = None\n items.extend(response.json())\n return items", "breadcrumbs": "[\"JSON API\"]", "references": "[{\"href\": \"https://requests.readthedocs.io/\", \"label\": \"requests\"}]"}
{"id": "json_api:json-api-discover-alternate", "page": "json_api", "ref": "json-api-discover-alternate", "title": "Discovering the JSON for a page", "content": "Most of the HTML pages served by Datasette provide a mechanism for discovering their JSON equivalents using the HTML link mechanism. \n You can find this near the top of the source code of those pages, looking like this: \n \n The JSON URL is also made available in a Link HTTP header for the page: \n Link: https://latest.datasette.io/fixtures/sortable.json; rel=\"alternate\"; type=\"application/json+datasette\"", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:json-api-default", "page": "json_api", "ref": "json-api-default", "title": "Default representation", "content": "The default JSON representation of data from a SQLite table or custom query\n looks like this: \n {\n \"ok\": true,\n \"rows\": [\n {\n \"id\": 3,\n \"name\": \"Detroit\"\n },\n {\n \"id\": 2,\n \"name\": \"Los Angeles\"\n },\n {\n \"id\": 4,\n \"name\": \"Memnonia\"\n },\n {\n \"id\": 1,\n \"name\": \"San Francisco\"\n }\n ],\n \"truncated\": false\n} \n \"ok\" is always true if an error did not occur. \n The \"rows\" key is a list of objects, each one representing a row. \n The \"truncated\" key lets you know if the query was truncated. This can happen if a SQL query returns more than 1,000 results (or the max_returned_rows setting). \n For table pages, an additional key \"next\" may be present. This indicates that the next page in the pagination set can be retrieved using ?_next=VALUE .", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:json-api-cors", "page": "json_api", "ref": "json-api-cors", "title": "Enabling CORS", "content": "If you start Datasette with the --cors option, each JSON endpoint will be\n served with the following additional HTTP headers: \n [[[cog\nfrom datasette.utils import add_cors_headers\nimport textwrap\nheaders = {}\nadd_cors_headers(headers)\noutput = \"\\n\".join(\"{}: {}\".format(k, v) for k, v in headers.items())\ncog.out(\"\\n::\\n\\n\")\ncog.out(textwrap.indent(output, ' '))\ncog.out(\"\\n\\n\") \n ]]] \n Access-Control-Allow-Origin: *\nAccess-Control-Allow-Headers: Authorization, Content-Type\nAccess-Control-Expose-Headers: Link\nAccess-Control-Allow-Methods: GET, POST, HEAD, OPTIONS\nAccess-Control-Max-Age: 3600 \n [[[end]]] \n This allows JavaScript running on any domain to make cross-origin\n requests to interact with the Datasette API. \n If you start Datasette without the --cors option only JavaScript running on\n the same domain as Datasette will be able to access the API. \n Here's how to serve data.db with CORS enabled: \n datasette data.db --cors", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:id2", "page": "json_api", "ref": "id2", "title": "Table arguments", "content": "The Datasette table view takes a number of special query string arguments.", "breadcrumbs": "[\"JSON API\"]", "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": "json_api:expand-foreign-keys", "page": "json_api", "ref": "expand-foreign-keys", "title": "Expanding foreign key references", "content": "Datasette can detect foreign key relationships and resolve those references into\n labels. The HTML interface does this by default for every detected foreign key\n column - you can turn that off using ?_labels=off . \n You can request foreign keys be expanded in JSON using the _labels=on or\n _label=COLUMN special query string parameters. Here's what an expanded row\n looks like: \n [\n {\n \"rowid\": 1,\n \"TreeID\": 141565,\n \"qLegalStatus\": {\n \"value\": 1,\n \"label\": \"Permitted Site\"\n },\n \"qSpecies\": {\n \"value\": 1,\n \"label\": \"Myoporum laetum :: Myoporum\"\n },\n \"qAddress\": \"501X Baker St\",\n \"SiteOrder\": 1\n }\n] \n The column in the foreign key table that is used for the label can be specified\n in metadata.json - see Specifying the label column for a table .", "breadcrumbs": "[\"JSON API\"]", "references": "[]"}
{"id": "json_api:column-filter-arguments", "page": "json_api", "ref": "column-filter-arguments", "title": "Column filter arguments", "content": "You can filter the data returned by the table based on column values using a query string argument. \n \n \n ?column__exact=value or ?_column=value \n \n Returns rows where the specified column exactly matches the value. \n \n \n \n ?column__not=value \n \n Returns rows where the column does not match the value. \n \n \n \n ?column__contains=value \n \n Rows where the string column contains the specified value ( column like \"%value%\" in SQL). \n \n \n \n ?column__notcontains=value \n \n Rows where the string column does not contain the specified value ( column not like \"%value%\" in SQL). \n \n \n \n ?column__endswith=value \n \n Rows where the string column ends with the specified value ( column like \"%value\" in SQL). \n \n \n \n ?column__startswith=value \n \n Rows where the string column starts with the specified value ( column like \"value%\" in SQL). \n \n \n \n ?column__gt=value \n \n Rows which are greater than the specified value. \n \n \n \n ?column__gte=value \n \n Rows which are greater than or equal to the specified value. \n \n \n \n ?column__lt=value \n \n Rows which are less than the specified value. \n \n \n \n ?column__lte=value \n \n Rows which are less than or equal to the specified value. \n \n \n \n ?column__like=value \n \n Match rows with a LIKE clause, case insensitive and with % as the wildcard character. \n \n \n \n ?column__notlike=value \n \n Match rows that do not match the provided LIKE clause. \n \n \n \n ?column__glob=value \n \n Similar to LIKE but uses Unix wildcard syntax and is case sensitive. \n \n \n \n ?column__in=value1,value2,value3 \n \n Rows where column matches any of the provided values. \n You can use a comma separated string, or you can use a JSON array. \n The JSON array option is useful if one of your matching values itself contains a comma: \n ?column__in=[\"value\",\"value,with,commas\"] \n \n \n \n ?column__notin=value1,value2,value3 \n \n Rows where column does not match any of the provided values. The inverse of __in= . Also supports JSON arrays. \n \n \n \n ?column__arraycontains=value \n \n Works against columns that contain JSON arrays - matches if any of the values in that array match the provided value. \n This is only available if the json1 SQLite extension is enabled. \n \n \n \n ?column__arraynotcontains=value \n \n Works against columns that contain JSON arrays - matches if none of the values in that array match the provided value. \n This is only available if the json1 SQLite extension is enabled. \n \n \n \n ?column__date=value \n \n Column is a datestamp occurring on the specified YYYY-MM-DD date, e.g. 2018-01-02 . \n \n \n \n ?column__isnull=1 \n \n Matches rows where the column is null. \n \n \n \n ?column__notnull=1 \n \n Matches rows where the column is not null. \n \n \n \n ?column__isblank=1 \n \n Matches rows where the column is blank, meaning null or the empty string. \n \n \n \n ?column__notblank=1 \n \n Matches rows where the column is not blank.", "breadcrumbs": "[\"JSON API\", \"Table arguments\"]", "references": "[]"}
{"id": "javascript_plugins:javascript-plugins-makecolumnactions", "page": "javascript_plugins", "ref": "javascript-plugins-makecolumnactions", "title": "makeColumnActions(columnDetails)", "content": "This method, if present, will be called when Datasette is rendering the cog action menu icons that appear at the top of the table view. By default these include options like \"Sort ascending/descending\" and \"Facet by this\", but plugins can return additional actions to be included in this menu. \n The method will be called with a columnDetails object with the following keys: \n \n \n columnName - string \n \n The name of the column \n \n \n \n columnNotNull - boolean \n \n True if the column is defined as NOT NULL \n \n \n \n columnType - string \n \n The SQLite data type of the column \n \n \n \n isPk - boolean \n \n True if the column is part of the primary key \n \n \n \n It should return a JavaScript array of objects each with a label and onClick property: \n \n \n label - string \n \n The human-readable label for the action \n \n \n \n onClick(evt) - function \n \n A function that will be called when the action is clicked \n \n \n \n The evt object passed to the onClick is the standard browser event object that triggered the click. \n This example plugin adds two menu items - one to copy the column name to the clipboard and another that displays the column metadata in an alert() window: \n document.addEventListener('datasette_init', function(ev) {\n ev.detail.registerPlugin('column-name-plugin', {\n version: 0.1,\n makeColumnActions: (columnDetails) => {\n return [\n {\n label: 'Copy column to clipboard',\n onClick: async (evt) => {\n await navigator.clipboard.writeText(columnDetails.columnName)\n }\n },\n {\n label: 'Alert column metadata',\n onClick: () => alert(JSON.stringify(columnDetails, null, 2))\n }\n ];\n }\n });\n});", "breadcrumbs": "[\"JavaScript plugins\", \"JavaScript plugin objects\"]", "references": "[]"}
{"id": "javascript_plugins:javascript-plugins-makeabovetablepanelconfigs", "page": "javascript_plugins", "ref": "javascript-plugins-makeabovetablepanelconfigs", "title": "makeAboveTablePanelConfigs()", "content": "This method should return a JavaScript array of objects defining additional panels to be added to the top of the table page. Each object should have the following: \n \n \n id - string \n \n A unique string ID for the panel, for example map-panel \n \n \n \n label - string \n \n A human-readable label for the panel \n \n \n \n render(node) - function \n \n A function that will be called with a DOM node to render the panel into \n \n \n \n This example shows how a plugin might define a single panel: \n document.addEventListener('datasette_init', function(ev) {\n ev.detail.registerPlugin('panel-plugin', {\n version: 0.1,\n makeAboveTablePanelConfigs: () => {\n return [\n {\n id: 'first-panel',\n label: 'First panel',\n render: node => {\n node.innerHTML = '
My custom panel
This is a custom panel that I added using a JavaScript plugin
';\n }\n }\n ]\n }\n });\n}); \n When a page with a table loads, all registered plugins that implement makeAboveTablePanelConfigs() will be called and panels they return will be added to the top of the table page.", "breadcrumbs": "[\"JavaScript plugins\", \"JavaScript plugin objects\"]", "references": "[]"}
{"id": "javascript_plugins:javascript-datasette-manager-selectors", "page": "javascript_plugins", "ref": "javascript-datasette-manager-selectors", "title": "Selectors", "content": "These are available on the selectors property of the datasetteManager object. \n const DOM_SELECTORS = {\n /** Should have one match */\n jsonExportLink: \".export-links a[href*=json]\",\n\n /** Event listeners that go outside of the main table, e.g. existing scroll listener */\n tableWrapper: \".table-wrapper\",\n table: \"table.rows-and-columns\",\n aboveTablePanel: \".above-table-panel\",\n\n // These could have multiple matches\n /** Used for selecting table headers. Use makeColumnActions if you want to add menu items. */\n tableHeaders: `table.rows-and-columns th`,\n\n /** Used to add \"where\" clauses to query using direct manipulation */\n filterRows: \".filter-row\",\n /** Used to show top available enum values for a column (\"facets\") */\n facetResults: \".facet-results [data-column]\",\n};", "breadcrumbs": "[\"JavaScript plugins\"]", "references": "[]"}
{"id": "javascript_plugins:javascript-datasette-manager", "page": "javascript_plugins", "ref": "javascript-datasette-manager", "title": "datasetteManager", "content": "The datasetteManager object \n \n \n VERSION - string \n \n The version of Datasette \n \n \n \n plugins - Map() \n \n A Map of currently loaded plugin names to plugin implementations \n \n \n \n registerPlugin(name, implementation) \n \n Call this to register a plugin, passing its name and implementation \n \n \n \n selectors - object \n \n An object providing named aliases to useful CSS selectors, listed below", "breadcrumbs": "[\"JavaScript plugins\"]", "references": "[]"}
{"id": "javascript_plugins:javascript-datasette-init", "page": "javascript_plugins", "ref": "javascript-datasette-init", "title": "The datasette_init event", "content": "Datasette emits a custom event called datasette_init when the page is loaded. This event is dispatched on the document object, and includes a detail object with a reference to the datasetteManager object. \n Your JavaScript code can listen out for this event using document.addEventListener() like this: \n document.addEventListener(\"datasette_init\", function (evt) {\n const manager = evt.detail;\n console.log(\"Datasette version:\", manager.VERSION);\n});", "breadcrumbs": "[\"JavaScript plugins\"]", "references": "[]"}
{"id": "javascript_plugins:id2", "page": "javascript_plugins", "ref": "id2", "title": "JavaScript plugin objects", "content": "JavaScript plugins are blocks of code that can be registered with Datasette using the registerPlugin() method on the datasetteManager object. \n The implementation object passed to this method should include a version key defining the plugin version, and one or more of the following named functions providing the implementation of the plugin:", "breadcrumbs": "[\"JavaScript plugins\"]", "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": "introspection:messagesdebugview", "page": "introspection", "ref": "messagesdebugview", "title": "/-/messages", "content": "The debug tool at /-/messages can be used to set flash messages to try out that feature. See .add_message(request, message, type=datasette.INFO) for details of this feature.", "breadcrumbs": "[\"Introspection\"]", "references": "[]"}
{"id": "introspection:jsondataview-versions", "page": "introspection", "ref": "jsondataview-versions", "title": "/-/versions", "content": "Shows the version of Datasette, Python and SQLite. Versions example : \n {\n \"datasette\": {\n \"version\": \"0.60\"\n },\n \"python\": {\n \"full\": \"3.8.12 (default, Dec 21 2021, 10:45:09) \\n[GCC 10.2.1 20210110]\",\n \"version\": \"3.8.12\"\n },\n \"sqlite\": {\n \"extensions\": {\n \"json1\": null\n },\n \"fts_versions\": [\n \"FTS5\",\n \"FTS4\",\n \"FTS3\"\n ],\n \"compile_options\": [\n \"COMPILER=gcc-6.3.0 20170516\",\n \"ENABLE_FTS3\",\n \"ENABLE_FTS4\",\n \"ENABLE_FTS5\",\n \"ENABLE_JSON1\",\n \"ENABLE_RTREE\",\n \"THREADSAFE=1\"\n ],\n \"version\": \"3.37.0\"\n }\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://latest.datasette.io/-/versions\", \"label\": \"Versions example\"}]"}
{"id": "introspection:jsondataview-threads", "page": "introspection", "ref": "jsondataview-threads", "title": "/-/threads", "content": "Shows details of threads and asyncio tasks. Threads example : \n {\n \"num_threads\": 2,\n \"threads\": [\n {\n \"daemon\": false,\n \"ident\": 4759197120,\n \"name\": \"MainThread\"\n },\n {\n \"daemon\": true,\n \"ident\": 123145319682048,\n \"name\": \"Thread-1\"\n },\n ],\n \"num_tasks\": 3,\n \"tasks\": [\n \" cb=[set.discard()]>\",\n \" wait_for=()]> cb=[run_until_complete..()]>\",\n \" wait_for=()]>>\"\n ]\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://latest.datasette.io/-/threads\", \"label\": \"Threads example\"}]"}
{"id": "introspection:jsondataview-settings", "page": "introspection", "ref": "jsondataview-settings", "title": "/-/settings", "content": "Shows the Settings for this instance of Datasette. Settings example : \n {\n \"default_facet_size\": 30,\n \"default_page_size\": 100,\n \"facet_suggest_time_limit_ms\": 50,\n \"facet_time_limit_ms\": 1000,\n \"max_returned_rows\": 1000,\n \"sql_time_limit_ms\": 1000\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/-/settings\", \"label\": \"Settings example\"}]"}
{"id": "introspection:jsondataview-plugins", "page": "introspection", "ref": "jsondataview-plugins", "title": "/-/plugins", "content": "Shows a list of currently installed plugins and their versions. Plugins example : \n [\n {\n \"name\": \"datasette_cluster_map\",\n \"static\": true,\n \"templates\": false,\n \"version\": \"0.10\",\n \"hooks\": [\"extra_css_urls\", \"extra_js_urls\", \"extra_body_script\"]\n }\n] \n Add ?all=1 to include details of the default plugins baked into Datasette.", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://san-francisco.datasettes.com/-/plugins\", \"label\": \"Plugins example\"}]"}
{"id": "introspection:jsondataview-metadata", "page": "introspection", "ref": "jsondataview-metadata", "title": "/-/metadata", "content": "Shows the contents of the metadata.json file that was passed to datasette serve , if any. Metadata example : \n {\n \"license\": \"CC Attribution 4.0 License\",\n \"license_url\": \"http://creativecommons.org/licenses/by/4.0/\",\n \"source\": \"fivethirtyeight/data on GitHub\",\n \"source_url\": \"https://github.com/fivethirtyeight/data\",\n \"title\": \"Five Thirty Eight\",\n \"databases\": {\n\n }\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://fivethirtyeight.datasettes.com/-/metadata\", \"label\": \"Metadata example\"}]"}
{"id": "introspection:jsondataview-databases", "page": "introspection", "ref": "jsondataview-databases", "title": "/-/databases", "content": "Shows currently attached databases. Databases example : \n [\n {\n \"hash\": null,\n \"is_memory\": false,\n \"is_mutable\": true,\n \"name\": \"fixtures\",\n \"path\": \"fixtures.db\",\n \"size\": 225280\n }\n]", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://latest.datasette.io/-/databases\", \"label\": \"Databases example\"}]"}
{"id": "introspection:jsondataview-config", "page": "introspection", "ref": "jsondataview-config", "title": "/-/config", "content": "Shows the configuration for this instance of Datasette. This is generally the contents of the datasette.yaml or datasette.json file, which can include plugin configuration as well. Config example : \n {\n \"settings\": {\n \"template_debug\": true,\n \"trace_debug\": true,\n \"force_https_urls\": true\n }\n} \n Any keys that include the one of the following substrings in their names will be returned as redacted *** output, to help avoid accidentally leaking private configuration information: secret , key , password , token , hash , dsn .", "breadcrumbs": "[\"Introspection\"]", "references": "[{\"href\": \"https://latest.datasette.io/-/config\", \"label\": \"Config example\"}]"}
{"id": "introspection:jsondataview-actor", "page": "introspection", "ref": "jsondataview-actor", "title": "/-/actor", "content": "Shows the currently authenticated actor. Useful for debugging Datasette authentication plugins. \n {\n \"actor\": {\n \"id\": 1,\n \"username\": \"some-user\"\n }\n}", "breadcrumbs": "[\"Introspection\"]", "references": "[]"}
{"id": "introspection: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": "internals:internals-utils-parse-metadata", "page": "internals", "ref": "internals-utils-parse-metadata", "title": "parse_metadata(content)", "content": "This function accepts a string containing either JSON or YAML, expected to be of the format described in Metadata . It returns a nested Python dictionary representing the parsed data from that string. \n If the metadata cannot be parsed as either JSON or YAML the function will raise a utils.BadMetadataError exception. \n \n \n datasette.utils. parse_metadata content : str dict \n \n Detects if content is JSON or YAML and parses it appropriately.", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "references": "[]"}
{"id": "internals:internals-utils-derive-named-parameters", "page": "internals", "ref": "internals-utils-derive-named-parameters", "title": "derive_named_parameters(db, sql)", "content": "Derive the list of named parameters referenced in a SQL query, using an explain query executed against the provided database. \n \n \n async datasette.utils. derive_named_parameters db : Database sql : str List [ str ] \n \n Given a SQL statement, return a list of named parameters that are used in the statement \n e.g. for select * from foo where id=:id this would return [\"id\"]", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "references": "[]"}
{"id": "internals:internals-utils-await-me-maybe", "page": "internals", "ref": "internals-utils-await-me-maybe", "title": "await_me_maybe(value)", "content": "Utility function for calling await on a return value if it is awaitable, otherwise returning the value. This is used by Datasette to support plugin hooks that can optionally return awaitable functions. Read more about this function in The \u201cawait me maybe\u201d pattern for Python asyncio . \n \n \n async datasette.utils. await_me_maybe value : Any Any \n \n If value is callable, call it. If awaitable, await it. Otherwise return it.", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "references": "[{\"href\": \"https://simonwillison.net/2020/Sep/2/await-me-maybe/\", \"label\": \"The \u201cawait me maybe\u201d pattern for Python asyncio\"}]"}
{"id": "internals:internals-utils", "page": "internals", "ref": "internals-utils", "title": "The datasette.utils module", "content": "The datasette.utils module contains various utility functions used by Datasette. As a general rule you should consider anything in this module to be unstable - functions and classes here could change without warning or be removed entirely between Datasette releases, without being mentioned in the release notes. \n The exception to this rule is anything that is documented here. If you find a need for an undocumented utility function in your own work, consider opening an issue requesting that the function you are using be upgraded to documented and supported status.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/new\", \"label\": \"opening an issue\"}]"}
{"id": "internals:internals-tracer-trace-child-tasks", "page": "internals", "ref": "internals-tracer-trace-child-tasks", "title": "Tracing child tasks", "content": "If your code uses a mechanism such as asyncio.gather() to execute code in additional tasks you may find that some of the traces are missing from the display. \n You can use the trace_child_tasks() context manager to ensure these child tasks are correctly handled. \n from datasette import tracer\n\nwith tracer.trace_child_tasks():\n results = await asyncio.gather(\n # ... async tasks here\n ) \n This example uses the register_routes() plugin hook to add a page at /parallel-queries which executes two SQL queries in parallel using asyncio.gather() and returns their results. \n from datasette import hookimpl\nfrom datasette import tracer\n\n\n@hookimpl\ndef register_routes():\n async def parallel_queries(datasette):\n db = datasette.get_database()\n with tracer.trace_child_tasks():\n one, two = await asyncio.gather(\n db.execute(\"select 1\"),\n db.execute(\"select 2\"),\n )\n return Response.json(\n {\n \"one\": one.single_value(),\n \"two\": two.single_value(),\n }\n )\n\n return [\n (r\"/parallel-queries$\", parallel_queries),\n ] \n Note that running parallel SQL queries in this way has been known to cause problems in the past , so treat this example with caution. \n Adding ?_trace=1 will show that the trace covers both of those child tasks.", "breadcrumbs": "[\"Internals for plugins\", \"datasette.tracer\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/issues/2189\", \"label\": \"been known to cause problems in the past\"}]"}
{"id": "internals:internals-tracer", "page": "internals", "ref": "internals-tracer", "title": "datasette.tracer", "content": "Running Datasette with --setting trace_debug 1 enables trace debug output, which can then be viewed by adding ?_trace=1 to the query string for any page. \n You can see an example of this at the bottom of latest.datasette.io/fixtures/facetable?_trace=1 . The JSON output shows full details of every SQL query that was executed to generate the page. \n The datasette-pretty-traces plugin can be installed to provide a more readable display of this information. You can see a demo of that here . \n You can add your own custom traces to the JSON output using the trace() context manager. This takes a string that identifies the type of trace being recorded, and records any keyword arguments as additional JSON keys on the resulting trace object. \n The start and end time, duration and a traceback of where the trace was executed will be automatically attached to the JSON object. \n This example uses trace to record the start, end and duration of any HTTP GET requests made using the function: \n from datasette.tracer import trace\nimport httpx\n\n\nasync def fetch_url(url):\n with trace(\"fetch-url\", url=url):\n async with httpx.AsyncClient() as client:\n return await client.get(url)", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://latest.datasette.io/fixtures/facetable?_trace=1\", \"label\": \"latest.datasette.io/fixtures/facetable?_trace=1\"}, {\"href\": \"https://datasette.io/plugins/datasette-pretty-traces\", \"label\": \"datasette-pretty-traces\"}, {\"href\": \"https://latest-with-plugins.datasette.io/github/commits?_trace=1\", \"label\": \"a demo of that here\"}]"}
{"id": "internals:internals-tilde-encoding", "page": "internals", "ref": "internals-tilde-encoding", "title": "Tilde encoding", "content": "Datasette uses a custom encoding scheme in some places, called tilde encoding . This is primarily used for table names and row primary keys, to avoid any confusion between / characters in those values and the Datasette URLs that reference them. \n Tilde encoding uses the same algorithm as URL percent-encoding , but with the ~ tilde character used in place of % . \n Any character other than ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz0123456789_- will be replaced by the numeric equivalent preceded by a tilde. For example: \n \n \n / becomes ~2F \n \n \n . becomes ~2E \n \n \n % becomes ~25 \n \n \n ~ becomes ~7E \n \n \n Space becomes + \n \n \n polls/2022.primary becomes polls~2F2022~2Eprimary \n \n \n Note that the space character is a special case: it will be replaced with a + symbol. \n \n \n \n datasette.utils. tilde_encode s : str str \n \n Returns tilde-encoded string - for example /foo/bar -> ~2Ffoo~2Fbar \n \n \n \n \n \n datasette.utils. tilde_decode s : str str \n \n Decodes a tilde-encoded string, so ~2Ffoo~2Fbar -> /foo/bar", "breadcrumbs": "[\"Internals for plugins\", \"The datasette.utils module\"]", "references": "[{\"href\": \"https://developer.mozilla.org/en-US/docs/Glossary/percent-encoding\", \"label\": \"URL percent-encoding\"}]"}
{"id": "internals:internals-shortcuts", "page": "internals", "ref": "internals-shortcuts", "title": "Import shortcuts", "content": "The following commonly used symbols can be imported directly from the datasette module: \n from datasette import Response\nfrom datasette import Forbidden\nfrom datasette import NotFound\nfrom datasette import hookimpl\nfrom datasette import actor_matches_allow", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-response-set-cookie", "page": "internals", "ref": "internals-response-set-cookie", "title": "Setting cookies with response.set_cookie()", "content": "To set cookies on the response, use the response.set_cookie(...) method. The method signature looks like this: \n def set_cookie(\n self,\n key,\n value=\"\",\n max_age=None,\n expires=None,\n path=\"/\",\n domain=None,\n secure=False,\n httponly=False,\n samesite=\"lax\",\n): ... \n You can use this with datasette.sign() to set signed cookies. Here's how you would set the ds_actor cookie for use with Datasette authentication : \n response = Response.redirect(\"/\")\nresponse.set_cookie(\n \"ds_actor\",\n datasette.sign({\"a\": {\"id\": \"cleopaws\"}}, \"actor\"),\n)\nreturn response", "breadcrumbs": "[\"Internals for plugins\", \"Response class\"]", "references": "[]"}
{"id": "internals:internals-response-asgi-send", "page": "internals", "ref": "internals-response-asgi-send", "title": "Returning a response with .asgi_send(send)", "content": "In most cases you will return Response objects from your own view functions. You can also use a Response instance to respond at a lower level via ASGI, for example if you are writing code that uses the asgi_wrapper(datasette) hook. \n Create a Response object and then use await response.asgi_send(send) , passing the ASGI send function. For example: \n async def require_authorization(scope, receive, send):\n response = Response.text(\n \"401 Authorization Required\",\n headers={\n \"www-authenticate\": 'Basic realm=\"Datasette\", charset=\"UTF-8\"'\n },\n status=401,\n )\n await response.asgi_send(send)", "breadcrumbs": "[\"Internals for plugins\", \"Response class\"]", "references": "[]"}
{"id": "internals:internals-response", "page": "internals", "ref": "internals-response", "title": "Response class", "content": "The Response class can be returned from view functions that have been registered using the register_routes(datasette) hook. \n The Response() constructor takes the following arguments: \n \n \n body - string \n \n The body of the response. \n \n \n \n status - integer (optional) \n \n The HTTP status - defaults to 200. \n \n \n \n headers - dictionary (optional) \n \n A dictionary of extra HTTP headers, e.g. {\"x-hello\": \"world\"} . \n \n \n \n content_type - string (optional) \n \n The content-type for the response. Defaults to text/plain . \n \n \n \n For example: \n from datasette.utils.asgi import Response\n\nresponse = Response(\n \"This is XML\",\n content_type=\"application/xml; charset=utf-8\",\n) \n The quickest way to create responses is using the Response.text(...) , Response.html(...) , Response.json(...) or Response.redirect(...) helper methods: \n from datasette.utils.asgi import Response\n\nhtml_response = Response.html(\"This is HTML\")\njson_response = Response.json({\"this_is\": \"json\"})\ntext_response = Response.text(\n \"This will become utf-8 encoded text\"\n)\n# Redirects are served as 302, unless you pass status=301:\nredirect_response = Response.redirect(\n \"https://latest.datasette.io/\"\n) \n Each of these responses will use the correct corresponding content-type - text/html; charset=utf-8 , application/json; charset=utf-8 or text/plain; charset=utf-8 respectively. \n Each of the helper methods take optional status= and headers= arguments, documented above.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-request", "page": "internals", "ref": "internals-request", "title": "Request object", "content": "The request object is passed to various plugin hooks. It represents an incoming HTTP request. It has the following properties: \n \n \n .scope - dictionary \n \n The ASGI scope that was used to construct this request, described in the ASGI HTTP connection scope specification. \n \n \n \n .method - string \n \n The HTTP method for this request, usually GET or POST . \n \n \n \n .url - string \n \n The full URL for this request, e.g. https://latest.datasette.io/fixtures . \n \n \n \n .scheme - string \n \n The request scheme - usually https or http . \n \n \n \n .headers - dictionary (str -> str) \n \n A dictionary of incoming HTTP request headers. Header names have been converted to lowercase. \n \n \n \n .cookies - dictionary (str -> str) \n \n A dictionary of incoming cookies \n \n \n \n .host - string \n \n The host header from the incoming request, e.g. latest.datasette.io or localhost . \n \n \n \n .path - string \n \n The path of the request excluding the query string, e.g. /fixtures . \n \n \n \n .full_path - string \n \n The path of the request including the query string if one is present, e.g. /fixtures?sql=select+sqlite_version() . \n \n \n \n .query_string - string \n \n The query string component of the request, without the ? - e.g. name__contains=sam&age__gt=10 . \n \n \n \n .args - MultiParams \n \n An object representing the parsed query string parameters, see below. \n \n \n \n .url_vars - dictionary (str -> str) \n \n Variables extracted from the URL path, if that path was defined using a regular expression. See register_routes(datasette) . \n \n \n \n .actor - dictionary (str -> Any) or None \n \n The currently authenticated actor (see actors ), or None if the request is unauthenticated. \n \n \n \n The object also has two awaitable methods: \n \n \n await request.post_vars() - dictionary \n \n Returns a dictionary of form variables that were submitted in the request body via POST . Don't forget to read about CSRF protection ! \n \n \n \n await request.post_body() - bytes \n \n Returns the un-parsed body of a request submitted by POST - useful for things like incoming JSON data. \n \n \n \n And a class method that can be used to create fake request objects for use in tests: \n \n \n fake(path_with_query_string, method=\"GET\", scheme=\"http\", url_vars=None) \n \n Returns a Request instance for the specified path and method. For example: \n from datasette import Request\nfrom pprint import pprint\n\nrequest = Request.fake(\n \"/fixtures/facetable/\",\n url_vars={\"database\": \"fixtures\", \"table\": \"facetable\"},\n)\npprint(request.scope) \n This outputs: \n {'http_version': '1.1',\n 'method': 'GET',\n 'path': '/fixtures/facetable/',\n 'query_string': b'',\n 'raw_path': b'/fixtures/facetable/',\n 'scheme': 'http',\n 'type': 'http',\n 'url_route': {'kwargs': {'database': 'fixtures', 'table': 'facetable'}}}", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[{\"href\": \"https://asgi.readthedocs.io/en/latest/specs/www.html#connection-scope\", \"label\": \"ASGI HTTP connection scope\"}]"}
{"id": "internals:internals-multiparams", "page": "internals", "ref": "internals-multiparams", "title": "The MultiParams class", "content": "request.args is a MultiParams object - a dictionary-like object which provides access to query string parameters that may have multiple values. \n Consider the query string ?foo=1&foo=2&bar=3 - with two values for foo and one value for bar . \n \n \n request.args[key] - string \n \n Returns the first value for that key, or raises a KeyError if the key is missing. For the above example request.args[\"foo\"] would return \"1\" . \n \n \n \n request.args.get(key) - string or None \n \n Returns the first value for that key, or None if the key is missing. Pass a second argument to specify a different default, e.g. q = request.args.get(\"q\", \"\") . \n \n \n \n request.args.getlist(key) - list of strings \n \n Returns the list of strings for that key. request.args.getlist(\"foo\") would return [\"1\", \"2\"] in the above example. request.args.getlist(\"bar\") would return [\"3\"] . If the key is missing an empty list will be returned. \n \n \n \n request.args.keys() - list of strings \n \n Returns the list of available keys - for the example this would be [\"foo\", \"bar\"] . \n \n \n \n key in request.args - True or False \n \n You can use if key in request.args to check if a key is present. \n \n \n \n for key in request.args - iterator \n \n This lets you loop through every available key. \n \n \n \n len(request.args) - integer \n \n Returns the number of keys.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-internal", "page": "internals", "ref": "internals-internal", "title": "Datasette's internal database", "content": "Datasette maintains an \"internal\" SQLite database used for configuration, caching, and storage. Plugins can store configuration, settings, and other data inside this database. By default, Datasette will use a temporary in-memory SQLite database as the internal database, which is created at startup and destroyed at shutdown. Users of Datasette can optionally pass in a --internal flag to specify the path to a SQLite database to use as the internal database, which will persist internal data across Datasette instances. \n Datasette maintains tables called catalog_databases , catalog_tables , catalog_columns , catalog_indexes , catalog_foreign_keys with details of the attached databases and their schemas. These tables should not be considered a stable API - they may change between Datasette releases. \n The internal database is not exposed in the Datasette application by default, which means private data can safely be stored without worry of accidentally leaking information through the default Datasette interface and API. However, other plugins do have full read and write access to the internal database. \n Plugins can access this database by calling internal_db = datasette.get_internal_database() and then executing queries using the Database API . \n Plugin authors are asked to practice good etiquette when using the internal database, as all plugins use the same database to store data. For example: \n \n \n Use a unique prefix when creating tables, indices, and triggers in the internal database. If your plugin is called datasette-xyz , then prefix names with datasette_xyz_* . \n \n \n Avoid long-running write statements that may stall or block other plugins that are trying to write at the same time. \n \n \n Use temporary tables or shared in-memory attached databases when possible. \n \n \n Avoid implementing features that could expose private data stored in the internal database by other plugins.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-datasette-urls", "page": "internals", "ref": "internals-datasette-urls", "title": "datasette.urls", "content": "The datasette.urls object contains methods for building URLs to pages within Datasette. Plugins should use this to link to pages, since these methods take into account any base_url configuration setting that might be in effect. \n \n \n datasette.urls.instance(format=None) \n \n Returns the URL to the Datasette instance root page. This is usually \"/\" . \n \n \n \n datasette.urls.path(path, format=None) \n \n Takes a path and returns the full path, taking base_url into account. \n For example, datasette.urls.path(\"-/logout\") will return the path to the logout page, which will be \"/-/logout\" by default or /prefix-path/-/logout if base_url is set to /prefix-path/ \n \n \n \n datasette.urls.logout() \n \n Returns the URL to the logout page, usually \"/-/logout\" \n \n \n \n datasette.urls.static(path) \n \n Returns the URL of one of Datasette's default static assets, for example \"/-/static/app.css\" \n \n \n \n datasette.urls.static_plugins(plugin_name, path) \n \n Returns the URL of one of the static assets belonging to a plugin. \n datasette.urls.static_plugins(\"datasette_cluster_map\", \"datasette-cluster-map.js\") would return \"/-/static-plugins/datasette_cluster_map/datasette-cluster-map.js\" \n \n \n \n datasette.urls.static(path) \n \n Returns the URL of one of Datasette's default static assets, for example \"/-/static/app.css\" \n \n \n \n datasette.urls.database(database_name, format=None) \n \n Returns the URL to a database page, for example \"/fixtures\" \n \n \n \n datasette.urls.table(database_name, table_name, format=None) \n \n Returns the URL to a table page, for example \"/fixtures/facetable\" \n \n \n \n datasette.urls.query(database_name, query_name, format=None) \n \n Returns the URL to a query page, for example \"/fixtures/pragma_cache_size\" \n \n \n \n These functions can be accessed via the {{ urls }} object in Datasette templates, for example: \n Homepage\nFixtures database\nfacetable table\npragma_cache_size query \n Use the format=\"json\" (or \"csv\" or other formats supported by plugins) arguments to get back URLs to the JSON representation. This is the path with .json added on the end. \n These methods each return a datasette.utils.PrefixedUrlString object, which is a subclass of the Python str type. This allows the logic that considers the base_url setting to detect if that prefix has already been applied to the path.", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[]"}
{"id": "internals:internals-datasette-client", "page": "internals", "ref": "internals-datasette-client", "title": "datasette.client", "content": "Plugins can make internal simulated HTTP requests to the Datasette instance within which they are running. This ensures that all of Datasette's external JSON APIs are also available to plugins, while avoiding the overhead of making an external HTTP call to access those APIs. \n The datasette.client object is a wrapper around the HTTPX Python library , providing an async-friendly API that is similar to the widely used Requests library . \n It offers the following methods: \n \n \n await datasette.client.get(path, **kwargs) - returns HTTPX Response \n \n Execute an internal GET request against that path. \n \n \n \n await datasette.client.post(path, **kwargs) - returns HTTPX Response \n \n Execute an internal POST request. Use data={\"name\": \"value\"} to pass form parameters. \n \n \n \n await datasette.client.options(path, **kwargs) - returns HTTPX Response \n \n Execute an internal OPTIONS request. \n \n \n \n await datasette.client.head(path, **kwargs) - returns HTTPX Response \n \n Execute an internal HEAD request. \n \n \n \n await datasette.client.put(path, **kwargs) - returns HTTPX Response \n \n Execute an internal PUT request. \n \n \n \n await datasette.client.patch(path, **kwargs) - returns HTTPX Response \n \n Execute an internal PATCH request. \n \n \n \n await datasette.client.delete(path, **kwargs) - returns HTTPX Response \n \n Execute an internal DELETE request. \n \n \n \n await datasette.client.request(method, path, **kwargs) - returns HTTPX Response \n \n Execute an internal request with the given HTTP method against that path. \n \n \n \n These methods can be used with datasette.urls - for example: \n table_json = (\n await datasette.client.get(\n datasette.urls.table(\n \"fixtures\", \"facetable\", format=\"json\"\n )\n )\n).json() \n datasette.client methods automatically take the current base_url setting into account, whether or not you use the datasette.urls family of methods to construct the path. \n For documentation on available **kwargs options and the shape of the HTTPX Response object refer to the HTTPX Async documentation .", "breadcrumbs": "[\"Internals for plugins\", \"Datasette class\"]", "references": "[{\"href\": \"https://www.python-httpx.org/\", \"label\": \"HTTPX Python library\"}, {\"href\": \"https://requests.readthedocs.io/\", \"label\": \"Requests library\"}, {\"href\": \"https://www.python-httpx.org/async/\", \"label\": \"HTTPX Async documentation\"}]"}
{"id": "internals:internals-datasette", "page": "internals", "ref": "internals-datasette", "title": "Datasette class", "content": "This object is an instance of the Datasette class, passed to many plugin hooks as an argument called datasette . \n You can create your own instance of this - for example to help write tests for a plugin - like so: \n from datasette.app import Datasette\n\n# With no arguments a single in-memory database will be attached\ndatasette = Datasette()\n\n# The files= argument can load files from disk\ndatasette = Datasette(files=[\"/path/to/my-database.db\"])\n\n# Pass metadata as a JSON dictionary like this\ndatasette = Datasette(\n files=[\"/path/to/my-database.db\"],\n metadata={\n \"databases\": {\n \"my-database\": {\n \"description\": \"This is my database\"\n }\n }\n },\n) \n Constructor parameters include: \n \n \n files=[...] - a list of database files to open \n \n \n immutables=[...] - a list of database files to open in immutable mode \n \n \n metadata={...} - a dictionary of Metadata \n \n \n config_dir=... - the configuration directory to use, stored in datasette.config_dir", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-database-introspection", "page": "internals", "ref": "internals-database-introspection", "title": "Database introspection", "content": "The Database class also provides properties and methods for introspecting the database. \n \n \n db.name - string \n \n The name of the database - usually the filename without the .db prefix. \n \n \n \n db.size - integer \n \n The size of the database file in bytes. 0 for :memory: databases. \n \n \n \n db.mtime_ns - integer or None \n \n The last modification time of the database file in nanoseconds since the epoch. None for :memory: databases. \n \n \n \n db.is_mutable - boolean \n \n Is this database mutable, and allowed to accept writes? \n \n \n \n db.is_memory - boolean \n \n Is this database an in-memory database? \n \n \n \n await db.attached_databases() - list of named tuples \n \n Returns a list of additional databases that have been connected to this database using the SQLite ATTACH command. Each named tuple has fields seq , name and file . \n \n \n \n await db.table_exists(table) - boolean \n \n Check if a table called table exists. \n \n \n \n await db.view_exists(view) - boolean \n \n Check if a view called view exists. \n \n \n \n await db.table_names() - list of strings \n \n List of names of tables in the database. \n \n \n \n await db.view_names() - list of strings \n \n List of names of views in the database. \n \n \n \n await db.table_columns(table) - list of strings \n \n Names of columns in a specific table. \n \n \n \n await db.table_column_details(table) - list of named tuples \n \n Full details of the columns in a specific table. Each column is represented by a Column named tuple with fields cid (integer representing the column position), name (string), type (string, e.g. REAL or VARCHAR(30) ), notnull (integer 1 or 0), default_value (string or None), is_pk (integer 1 or 0). \n \n \n \n await db.primary_keys(table) - list of strings \n \n Names of the columns that are part of the primary key for this table. \n \n \n \n await db.fts_table(table) - string or None \n \n The name of the FTS table associated with this table, if one exists. \n \n \n \n await db.label_column_for_table(table) - string or None \n \n The label column that is associated with this table - either automatically detected or using the \"label_column\" key from Metadata , see Specifying the label column for a table . \n \n \n \n await db.foreign_keys_for_table(table) - list of dictionaries \n \n Details of columns in this table which are foreign keys to other tables. A list of dictionaries where each dictionary is shaped like this: {\"column\": string, \"other_table\": string, \"other_column\": string} . \n \n \n \n await db.hidden_table_names() - list of strings \n \n List of tables which Datasette \"hides\" by default - usually these are tables associated with SQLite's full-text search feature, the SpatiaLite extension or tables hidden using the Hiding tables feature. \n \n \n \n await db.get_table_definition(table) - string \n \n Returns the SQL definition for the table - the CREATE TABLE statement and any associated CREATE INDEX statements. \n \n \n \n await db.get_view_definition(view) - string \n \n Returns the SQL definition of the named view. \n \n \n \n await db.get_all_foreign_keys() - dictionary \n \n Dictionary representing both incoming and outgoing foreign keys for this table. It has two keys, \"incoming\" and \"outgoing\" , each of which is a list of dictionaries with keys \"column\" , \"other_table\" and \"other_column\" . For example: \n {\n \"incoming\": [],\n \"outgoing\": [\n {\n \"other_table\": \"attraction_characteristic\",\n \"column\": \"characteristic_id\",\n \"other_column\": \"pk\",\n },\n {\n \"other_table\": \"roadside_attractions\",\n \"column\": \"attraction_id\",\n \"other_column\": \"pk\",\n }\n ]\n}", "breadcrumbs": "[\"Internals for plugins\", \"Database class\"]", "references": "[]"}
{"id": "internals:internals-database", "page": "internals", "ref": "internals-database", "title": "Database class", "content": "Instances of the Database class can be used to execute queries against attached SQLite databases, and to run introspection against their schemas.", "breadcrumbs": "[\"Internals for plugins\"]", "references": "[]"}
{"id": "internals:internals-csrf", "page": "internals", "ref": "internals-csrf", "title": "CSRF protection", "content": "Datasette uses asgi-csrf to guard against CSRF attacks on form POST submissions. Users receive a ds_csrftoken cookie which is compared against the csrftoken form field (or x-csrftoken HTTP header) for every incoming request. \n If your plugin implements a