docs
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custom_templates:custom-pages-errors | custom_templates | custom-pages-errors | Custom error pages | Datasette returns an error page if an unexpected error occurs, access is forbidden or content cannot be found. You can customize the response returned for these errors by providing a custom error page template. Content not found errors use a 404.html template. Access denied errors use 403.html . Invalid input errors use 400.html . Unexpected errors of other kinds use 500.html . If a template for the specific error code is not found a template called error.html will be used instead. If you do not provide that template Datasette's default error.html template will be used. The error template will be passed the following context: status - integer The integer HTTP status code, e.g. 404, 500, 403, 400. error - string Details of the specific error, usually a full sentence. title - string or None A title for the page representing the class of error. This is often None for errors that do not provide a title separate from their error message. | ["Custom pages and templates"] | [{"href": "https://github.com/simonw/datasette/blob/main/datasette/templates/error.html", "label": "default error.html template"}] |
performance:performance | performance | performance | Performance and caching | Datasette runs on top of SQLite, and SQLite has excellent performance. For small databases almost any query should return in just a few milliseconds, and larger databases (100s of MBs or even GBs of data) should perform extremely well provided your queries make sensible use of database indexes. That said, there are a number of tricks you can use to improve Datasette's performance. | [] | [] |
ecosystem:ecosystem | ecosystem | ecosystem | The Datasette Ecosystem | Datasette sits at the center of a growing ecosystem of open source tools aimed at making it as easy as possible to gather, analyze and publish interesting data. These tools are divided into two main groups: tools for building SQLite databases (for use with Datasette) and plugins that extend Datasette's functionality. The Datasette project website includes a directory of plugins and a directory of tools: Plugins directory on datasette.io Tools directory on datasette.io | [] | [{"href": "https://datasette.io/", "label": "Datasette project website"}, {"href": "https://datasette.io/plugins", "label": "Plugins directory on datasette.io"}, {"href": "https://datasette.io/tools", "label": "Tools directory on datasette.io"}] |
settings:using-setting | settings | using-setting | Using --setting | Datasette supports a number of settings. These can be set using the --setting name value option to datasette serve . You can set multiple settings at once like this: datasette mydatabase.db \ --setting default_page_size 50 \ --setting sql_time_limit_ms 3500 \ --setting max_returned_rows 2000 | ["Settings"] | [] |
metadata:specifying-units-for-a-column | metadata | specifying-units-for-a-column | Specifying units for a column | Datasette supports attaching units to a column, which will be used when displaying values from that column. SI prefixes will be used where appropriate. Column units are configured in the metadata like so: { "databases": { "database1": { "tables": { "example_table": { "units": { "column1": "metres", "column2": "Hz" } } } } } } Units are interpreted using Pint , and you can see the full list of available units in Pint's unit registry . You can also add custom units to the metadata, which will be registered with Pint: { "custom_units": [ "decibel = [] = dB" ] } | ["Metadata"] | [{"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"}] |
full_text_search:full-text-search-enabling | full_text_search | full-text-search-enabling | Enabling full-text search for a SQLite table | Datasette takes advantage of the external content mechanism in SQLite, which allows a full-text search virtual table to be associated with the contents of another SQLite table. To set up full-text search for a table, you need to do two things: Create a new FTS virtual table associated with your table Populate that FTS table with the data that you would like to be able to run searches against | ["Full-text search"] | [{"href": "https://www.sqlite.org/fts3.html#_external_content_fts4_tables_", "label": "external content"}] |
sql_queries:sql | sql_queries | sql | Running SQL queries | Datasette treats SQLite database files as read-only and immutable. This means it is not possible to execute INSERT or UPDATE statements using Datasette, which allows us to expose SELECT statements to the outside world without needing to worry about SQL injection attacks. The easiest way to execute custom SQL against Datasette is through the web UI. The database index page includes a SQL editor that lets you run any SELECT query you like. You can also construct queries using the filter interface on the tables page, then click "View and edit SQL" to open that query in the custom SQL editor. Note that this interface is only available if the execute-sql permission is allowed. Any Datasette SQL query is reflected in the URL of the page, allowing you to bookmark them, share them with others and navigate through previous queries using your browser back button. You can also retrieve the results of any query as JSON by adding .json to the base URL. | [] | [] |
internals:internals-csrf | internals | internals-csrf | CSRF protection | Datasette uses asgi-csrf to guard against CSRF attacks on form POST submissions. Users receive a ds_csrftoken cookie which is compared against the csrftoken form field (or x-csrftoken HTTP header) for every incoming request. If your plugin implements a <form method="POST"> anywhere you will need to include that token. You can do so with the following template snippet: <input type="hidden" name="csrftoken" value="{{ csrftoken() }}"> If you are rendering templates using the await .render_template(template, context=None, request=None) method the csrftoken() helper will only work if you provide the request= argument to that method. If you forget to do this you will see the following error: form-urlencoded POST field did not match cookie You can selectively disable CSRF protection using the skip_csrf(datasette, scope) hook. | ["Internals for plugins"] | [{"href": "https://github.com/simonw/asgi-csrf", "label": "asgi-csrf"}] |
internals:internals-tilde-encoding | internals | internals-tilde-encoding | Tilde encoding | Datasette uses a custom encoding scheme in some places, called tilde encoding . This is primarily used for table names and row primary keys, to avoid any confusion between / characters in those values and the Datasette URLs that reference them. Tilde encoding uses the same algorithm as URL percent-encoding , but with the ~ tilde character used in place of % . Any character other than ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz0123456789_- will be replaced by the numeric equivalent preceded by a tilde. For example: / becomes ~2F . becomes ~2E % becomes ~25 ~ becomes ~7E Space becomes + polls/2022.primary becomes polls~2F2022~2Eprimary Note that the space character is a special case: it will be replaced with a + symbol. datasette.utils. tilde_encode s : str str Returns tilde-encoded string - for example /foo/bar -> ~2Ffoo~2Fbar datasette.utils. tilde_decode s : str str Decodes a tilde-encoded string, so ~2Ffoo~2Fbar -> /foo/bar | ["Internals for plugins", "The datasette.utils module"] | [{"href": "https://developer.mozilla.org/en-US/docs/Glossary/percent-encoding", "label": "URL percent-encoding"}] |
settings:setting-secret | settings | setting-secret | Configuring the secret | Datasette uses a secret string to sign secure values such as cookies. If you do not provide a secret, Datasette will create one when it starts up. This secret will reset every time the Datasette server restarts though, so things like authentication cookies will not stay valid between restarts. You can pass a secret to Datasette in two ways: with the --secret command-line option or by setting a DATASETTE_SECRET environment variable. $ datasette mydb.db --secret=SECRET_VALUE_HERE Or: $ export DATASETTE_SECRET=SECRET_VALUE_HERE $ datasette mydb.db One way to generate a secure random secret is to use Python like this: $ python3 -c 'import secrets; print(secrets.token_hex(32))' cdb19e94283a20f9d42cca50c5a4871c0aa07392db308755d60a1a5b9bb0fa52 Plugin authors make use of this signing mechanism in their plugins using .sign(value, namespace="default") and .unsign(value, namespace="default") . | ["Settings"] | [] |
contributing:contributing-formatting | contributing | contributing-formatting | Code formatting | Datasette uses opinionated code formatters: Black for Python and Prettier for JavaScript. These formatters are enforced by Datasette's continuous integration: if a commit includes Python or JavaScript code that does not match the style enforced by those tools, the tests will fail. When developing locally, you can verify and correct the formatting of your code using these tools. | ["Contributing"] | [{"href": "https://github.com/psf/black", "label": "Black"}, {"href": "https://prettier.io/", "label": "Prettier"}] |
changelog:writable-canned-queries | changelog | writable-canned-queries | Writable canned queries | Datasette's Canned queries feature lets you define SQL queries in metadata.json which can then be executed by users visiting a specific URL. https://latest.datasette.io/fixtures/neighborhood_search for example. Canned queries were previously restricted to SELECT , but Datasette 0.44 introduces the ability for canned queries to execute INSERT or UPDATE queries as well, using the new "write": true property ( #800 ): { "databases": { "dogs": { "queries": { "add_name": { "sql": "INSERT INTO names (name) VALUES (:name)", "write": true } } } } } See Writable canned queries for more details. | ["Changelog", "0.44 (2020-06-11)"] | [{"href": "https://latest.datasette.io/fixtures/neighborhood_search", "label": "https://latest.datasette.io/fixtures/neighborhood_search"}, {"href": "https://github.com/simonw/datasette/issues/800", "label": "#800"}] |
changelog:new-configuration-settings | changelog | new-configuration-settings | New configuration settings | Datasette's Settings now also supports boolean settings. A number of new configuration options have been added: num_sql_threads - the number of threads used to execute SQLite queries. Defaults to 3. allow_facet - enable or disable custom Facets using the _facet= parameter. Defaults to on. suggest_facets - should Datasette suggest facets? Defaults to on. allow_download - should users be allowed to download the entire SQLite database? Defaults to on. allow_sql - should users be allowed to execute custom SQL queries? Defaults to on. default_cache_ttl - Default HTTP caching max-age header in seconds. Defaults to 365 days - caching can be disabled entirely by settings this to 0. cache_size_kb - Set the amount of memory SQLite uses for its per-connection cache , in KB. allow_csv_stream - allow users to stream entire result sets as a single CSV file. Defaults to on. max_csv_mb - maximum size of a returned CSV file in MB. Defaults to 100MB, set to 0 to disable this limit. | ["Changelog", "0.23 (2018-06-18)"] | [{"href": "https://www.sqlite.org/pragma.html#pragma_cache_size", "label": "per-connection cache"}] |
metadata:label-columns | metadata | label-columns | Specifying the label column for a table | Datasette's HTML interface attempts to display foreign key references as labelled hyperlinks. By default, it looks for referenced tables that only have two columns: a primary key column and one other. It assumes that the second column should be used as the link label. If your table has more than two columns you can specify which column should be used for the link label with the label_column property: { "databases": { "database1": { "tables": { "example_table": { "label_column": "title" } } } } } | ["Metadata"] | [] |
sql_queries:id2 | sql_queries | id2 | Pagination | Datasette's default table pagination is designed to be extremely efficient. SQL OFFSET/LIMIT pagination can have a significant performance penalty once you get into multiple thousands of rows, as each page still requires the database to scan through every preceding row to find the correct offset. When paginating through tables, Datasette instead orders the rows in the table by their primary key and performs a WHERE clause against the last seen primary key for the previous page. For example: select rowid, * from Tree_List where rowid > 200 order by rowid limit 101 This represents page three for this particular table, with a page size of 100. Note that we request 101 items in the limit clause rather than 100. This allows us to detect if we are on the last page of the results: if the query returns less than 101 rows we know we have reached the end of the pagination set. Datasette will only return the first 100 rows - the 101st is used purely to detect if there should be another page. Since the where clause acts against the index on the primary key, the query is extremely fast even for records that are a long way into the overall pagination set. | ["Running SQL queries"] | [] |
contributing:contributing-documentation | contributing | contributing-documentation | Editing and building the documentation | Datasette's documentation lives in the docs/ directory and is deployed automatically using Read The Docs . The documentation is written using reStructuredText. You may find this article on The subset of reStructuredText worth committing to memory useful. You can build it locally by installing sphinx and sphinx_rtd_theme in your Datasette development environment and then running make html directly in the docs/ directory: # You may first need to activate your virtual environment: source venv/bin/activate # Install the dependencies needed to build the docs pip install -e .[docs] # Now build the docs cd docs/ make html This will create the HTML version of the documentation in docs/_build/html . You can open it in your browser like so: open _build/html/index.html Any time you make changes to a .rst file you can re-run make html to update the built documents, then refresh them in your browser. For added productivity, you can use use sphinx-autobuild to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor. sphinx-autobuild will have been installed when you ran pip install -e .[docs] . In your docs/ directory you can start the server by running the following: make livehtml Now browse to http://localhost:8000/ to view the documentation. Any edits you make should be instantly reflected in your browser. | ["Contributing"] | [{"href": "https://readthedocs.org/", "label": "Read The Docs"}, {"href": "https://simonwillison.net/2018/Aug/25/restructuredtext/", "label": "The subset of reStructuredText worth committing to memory"}, {"href": "https://pypi.org/project/sphinx-autobuild/", "label": "sphinx-autobuild"}] |
plugins:id1 | plugins | id1 | Plugins | Datasette's plugin system allows additional features to be implemented as Python code (or front-end JavaScript) which can be wrapped up in a separate Python package. The underlying mechanism uses pluggy . See the Datasette plugins directory for a list of existing plugins, or take a look at the datasette-plugin topic on GitHub. Things you can do with plugins include: Add visualizations to Datasette, for example datasette-cluster-map and datasette-vega . Make new custom SQL functions available for use within Datasette, for example datasette-haversine and datasette-jellyfish . Define custom output formats with custom extensions, for example datasette-atom and datasette-ics . Add template functions that can be called within your Jinja custom templates, for example datasette-render-markdown . Customize how database values are rendered in the Datasette interface, for example datasette-render-binary and datasette-pretty-json . Customize how Datasette's authentication and permissions systems work, for example datasette-auth-tokens and datasette-permissions-sql . | [] | [{"href": "https://pluggy.readthedocs.io/", "label": "pluggy"}, {"href": "https://datasette.io/plugins", "label": "Datasette plugins directory"}, {"href": "https://github.com/topics/datasette-plugin", "label": "datasette-plugin"}, {"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://github.com/simonw/datasette-vega", "label": "datasette-vega"}, {"href": "https://github.com/simonw/datasette-haversine", "label": "datasette-haversine"}, {"href": "https://github.com/simonw/datasette-jellyfish", "label": "datasette-jellyfish"}, {"href": "https://github.com/simonw/datasette-atom", "label": "datasette-atom"}, {"href": "https://github.com/simonw/datasette-ics", "label": "datasette-ics"}, {"href": "https://github.com/simonw/datasette-render-markdown#markdown-in-templates", "label": "datasette-render-markdown"}, {"href": "https://github.com/simonw/datasette-render-binary", "label": "datasette-render-binary"}, {"href": "https://github.com/simonw/datasette-pretty-json", "label": "datasette-pretty-json"}, {"href": "https://github.com/simonw/datasette-auth-tokens", "label": "datasette-auth-tokens"}, {"href": "https://github.com/simonw/datasette-permissions-sql", "label": "datasette-permissions-sql"}] |
facets:suggested-facets | facets | suggested-facets | Suggested facets | Datasette's table UI will suggest facets for the user to apply, based on the following criteria: For the currently filtered data are there any columns which, if applied as a facet... Will return 30 or less unique options Will return more than one unique option Will return less unique options than the total number of filtered rows And the query used to evaluate this criteria can be completed in under 50ms That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries. This means suggested facets are unlikely to appear for tables with millions of records in them. | ["Facets"] | [] |
settings:setting-default-cache-ttl | settings | setting-default-cache-ttl | default_cache_ttl | Default HTTP caching max-age header in seconds, used for Cache-Control: max-age=X . Can be over-ridden on a per-request basis using the ?_ttl= query string parameter. Set this to 0 to disable HTTP caching entirely. Defaults to 5 seconds. datasette mydatabase.db --setting default_cache_ttl 60 | ["Settings", "Settings"] | [] |
changelog:id73 | changelog | id73 | 0.31.1 (2019-11-12) | Deployments created using datasette publish now use python:3.8 base Docker image ( #629 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/pull/629", "label": "#629"}] |
changelog:id89 | changelog | id89 | 0.25.1 (2018-11-04) | Documentation improvements plus a fix for publishing to Zeit Now. datasette publish now now uses Zeit's v1 platform, to work around the new 100MB image limit. Thanks, @slygent - closes #366 . | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/366", "label": "#366"}] |
changelog:id31 | changelog | id31 | 0.56 (2021-03-28) | Documentation improvements, bug fixes and support for SpatiaLite 5. The SQL editor can now be resized by dragging a handle. ( #1236 ) Fixed a bug with JSON faceting and the __arraycontains filter caused by tables with spaces in their names. ( #1239 ) Upgraded httpx dependency. ( #1005 ) JSON faceting is now suggested even if a column contains blank strings. ( #1246 ) New datasette.add_memory_database() method. ( #1247 ) The Response.asgi_send() method is now documented. ( #1266 ) The official Datasette Docker image now bundles SpatiaLite version 5. ( #1278 ) Fixed a no such table: pragma_database_list bug when running Datasette against SQLite versions prior to SQLite 3.16.0. ( #1276 ) HTML lists displayed in table cells are now styled correctly. Thanks, Bob Whitelock. ( #1141 , #1252 ) Configuration directory mode now correctly serves immutable databases that are listed in inspect-data.json . Thanks Campbell Allen and Frankie Robertson. ( #1031 , #1229 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1236", "label": "#1236"}, {"href": "https://github.com/simonw/datasette/issues/1239", "label": "#1239"}, {"href": "https://github.com/simonw/datasette/issues/1005", "label": "#1005"}, {"href": "https://github.com/simonw/datasette/issues/1246", "label": "#1246"}, {"href": "https://github.com/simonw/datasette/issues/1247", "label": "#1247"}, {"href": "https://github.com/simonw/datasette/issues/1266", "label": "#1266"}, {"href": "https://github.com/simonw/datasette/issues/1278", "label": "#1278"}, {"href": "https://github.com/simonw/datasette/issues/1276", "label": "#1276"}, {"href": "https://github.com/simonw/datasette/issues/1141", "label": "#1141"}, {"href": "https://github.com/simonw/datasette/pull/1252", "label": "#1252"}, {"href": "https://github.com/simonw/datasette/pull/1031", "label": "#1031"}, {"href": "https://github.com/simonw/datasette/pull/1229", "label": "#1229"}] |
changelog:id7 | changelog | id7 | 0.64.1 (2023-01-11) | Documentation now links to a current source of information for installing Python 3. ( #1987 ) Incorrectly calling the Datasette constructor using Datasette("path/to/data.db") instead of Datasette(["path/to/data.db"]) now returns a useful error message. ( #1985 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1987", "label": "#1987"}, {"href": "https://github.com/simonw/datasette/issues/1985", "label": "#1985"}] |
changelog:id40 | changelog | id40 | 0.52.1 (2020-11-29) | Documentation on Testing plugins now recommends using datasette.client . ( #1102 ) Fix bug where compound foreign keys produced broken links. ( #1098 ) datasette --load-module=spatialite now also checks for /usr/local/lib/mod_spatialite.so . Thanks, Dan Peterson. ( #1114 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1102", "label": "#1102"}, {"href": "https://github.com/simonw/datasette/issues/1098", "label": "#1098"}, {"href": "https://github.com/simonw/datasette/issues/1114", "label": "#1114"}] |
ecosystem:dogsheep | ecosystem | dogsheep | Dogsheep | Dogsheep is a collection of tools for personal analytics using SQLite and Datasette. The project provides tools like github-to-sqlite and twitter-to-sqlite that can import data from different sources in order to create a personal data warehouse. Personal Data Warehouses: Reclaiming Your Data is a talk that explains Dogsheep and demonstrates it in action. | ["The Datasette Ecosystem"] | [{"href": "https://dogsheep.github.io/", "label": "Dogsheep"}, {"href": "https://datasette.io/tools/github-to-sqlite", "label": "github-to-sqlite"}, {"href": "https://datasette.io/tools/twitter-to-sqlite", "label": "twitter-to-sqlite"}, {"href": "https://simonwillison.net/2020/Nov/14/personal-data-warehouses/", "label": "Personal Data Warehouses: Reclaiming Your Data"}] |
changelog:id65 | changelog | id65 | 0.37.1 (2020-03-02) | Don't attempt to count table rows to display on the index page for databases > 100MB. ( #688 ) Print exceptions if they occur in the write thread rather than silently swallowing them. Handle the possibility of scope["path"] being a string rather than bytes Better documentation for the extra_template_vars(template, database, table, columns, view_name, request, datasette) plugin hook. | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/688", "label": "#688"}] |
changelog:bug-fixes | changelog | bug-fixes | Bug fixes | Don't show the facet option in the cog menu if faceting is not allowed. ( #1683 ) ?_sort and ?_sort_desc now work if the column that is being sorted has been excluded from the query using ?_col= or ?_nocol= . ( #1773 ) Fixed bug where ?_sort_desc was duplicated in the URL every time the Apply button was clicked. ( #1738 ) | ["Changelog", "0.62 (2022-08-14)"] | [{"href": "https://github.com/simonw/datasette/issues/1683", "label": "#1683"}, {"href": "https://github.com/simonw/datasette/issues/1773", "label": "#1773"}, {"href": "https://github.com/simonw/datasette/issues/1738", "label": "#1738"}] |
changelog:id3 | changelog | id3 | 0.64.5 (2023-10-08) | Dropped dependency on click-default-group-wheel , which could cause a dependency conflict. ( #2197 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/2197", "label": "#2197"}] |
pages:databaseview | pages | databaseview | Database | Each database has a page listing the tables, views and canned queries available for that database. If the execute-sql permission is enabled (it's on by default) there will also be an interface for executing arbitrary SQL select queries against the data. Examples: fivethirtyeight.datasettes.com/fivethirtyeight global-power-plants.datasettes.com/global-power-plants The JSON version of this page provides programmatic access to the underlying data: fivethirtyeight.datasettes.com/fivethirtyeight.json global-power-plants.datasettes.com/global-power-plants.json | ["Pages and API endpoints"] | [{"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"}] |
settings:setting-allow-csv-stream | settings | setting-allow-csv-stream | allow_csv_stream | Enables the CSV export feature where an entire table (potentially hundreds of thousands of rows) can be exported as a single CSV file. This is turned on by default - you can turn it off like this: datasette mydatabase.db --setting allow_csv_stream off | ["Settings", "Settings"] | [] |
json_api:json-api-special | json_api | json-api-special | Special JSON arguments | Every Datasette endpoint that can return JSON also accepts the following query string arguments: ?_shape=SHAPE The shape of the JSON to return, documented above. ?_nl=on When used with ?_shape=array produces newline-delimited JSON objects. ?_json=COLUMN1&_json=COLUMN2 If any of your SQLite columns contain JSON values, you can use one or more _json= parameters to request that those columns be returned as regular JSON. Without this argument those columns will be returned as JSON objects that have been double-encoded into a JSON string value. Compare this query without the argument to this query using the argument ?_json_infinity=on If your data contains infinity or -infinity values, Datasette will replace them with None when returning them as JSON. If you pass _json_infinity=1 Datasette will instead return them as Infinity or -Infinity which is invalid JSON but can be processed by some custom JSON parsers. ?_timelimit=MS Sets a custom time limit for the query in ms. You can use this for optimistic queries where you would like Datasette to give up if the query takes too long, for example if you want to implement autocomplete search but only… | ["JSON API"] | [{"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"}] |
changelog:latest-datasette-io | changelog | latest-datasette-io | latest.datasette.io | Every commit to Datasette master is now automatically deployed by Travis CI to https://latest.datasette.io/ - ensuring there is always a live demo of the latest version of the software. The demo uses the fixtures from our unit tests, ensuring it demonstrates the same range of functionality that is covered by the tests. You can see how the deployment mechanism works in our .travis.yml file. | ["Changelog", "0.23 (2018-06-18)"] | [{"href": "https://latest.datasette.io/", "label": "https://latest.datasette.io/"}, {"href": "https://github.com/simonw/datasette/blob/master/tests/fixtures.py", "label": "the fixtures"}, {"href": "https://github.com/simonw/datasette/blob/master/.travis.yml", "label": ".travis.yml"}] |
custom_templates:css-classes-on-the-body | custom_templates | css-classes-on-the-body | CSS classes on the <body> | Every default template includes CSS classes in the body designed to support custom styling. The index template (the top level page at / ) gets this: <body class="index"> The database template ( /dbname ) gets this: <body class="db db-dbname"> The custom SQL template ( /dbname?sql=... ) gets this: <body class="query db-dbname"> A canned query template ( /dbname/queryname ) gets this: <body class="query db-dbname query-queryname"> The table template ( /dbname/tablename ) gets: <body class="table db-dbname table-tablename"> The row template ( /dbname/tablename/rowid ) gets: <body class="row db-dbname table-tablename"> The db-x and table-x classes use the database or table names themselves if they are valid CSS identifiers. If they aren't, we strip any invalid characters out and append a 6 character md5 digest of the original name, in order to ensure that multiple tables which resolve to the same stripped character version still have different CSS classes. Some examples: "simple" => "simple" "MixedCase" => "MixedCase" "-no-leading-hyphens" => "no-leading-hyphens-65bea6" "_no-leading-underscores" => "no-leading-underscores-b921bc" "no spaces" => "no-spaces-7088d7" "-" => "336d5e" "no $ characters" => "no--characters-59e024" <td> and <th> elements also get custom CSS classes reflecting the database column they are representing, for example: <table> <thead> <tr> <th class="col-id" scope="col">id</th> <th class="col-name" scope="col">name</th> </tr> </thead> <tbody> <tr> <td class="col-id"><a href="...">1</a></td> … | ["Custom pages and templates", "Custom CSS and JavaScript"] | [] |
pages:rowview | pages | rowview | Row | Every row in every Datasette table has its own URL. This means individual records can be linked to directly. Table cells with extremely long text contents are truncated on the table view according to the truncate_cells_html setting. If a cell has been truncated the full length version of that cell will be available on the row page. Rows which are the targets of foreign key references from other tables will show a link to a filtered search for all records that reference that row. Here's an example from the Registers of Members Interests database: ../people/uk.org.publicwhip%2Fperson%2F10001 Note that this URL includes the encoded primary key of the record. Here's that same page as JSON: ../people/uk.org.publicwhip%2Fperson%2F10001.json | ["Pages and API endpoints"] | [{"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk.org.publicwhip%2Fperson%2F10001", "label": "../people/uk.org.publicwhip%2Fperson%2F10001"}, {"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk.org.publicwhip%2Fperson%2F10001.json", "label": "../people/uk.org.publicwhip%2Fperson%2F10001.json"}] |
changelog:id15 | changelog | id15 | Documentation | Examples in the documentation now include a copy-to-clipboard button. ( #1748 ) Documentation now uses the Furo Sphinx theme. ( #1746 ) Code examples in the documentation are now all formatted using Black. ( #1718 ) Request.fake() method is now documented, see Request object . New documentation for plugin authors: Registering a plugin for the duration of a test . ( #903 ) | ["Changelog", "0.62 (2022-08-14)"] | [{"href": "https://github.com/simonw/datasette/issues/1748", "label": "#1748"}, {"href": "https://github.com/pradyunsg/furo", "label": "Furo"}, {"href": "https://github.com/simonw/datasette/issues/1746", "label": "#1746"}, {"href": "https://github.com/simonw/datasette/issues/1718", "label": "#1718"}, {"href": "https://github.com/simonw/datasette/issues/903", "label": "#903"}] |
internals:database-execute | internals | database-execute | await db.execute(sql, ...) | Executes a SQL query against the database and returns the resulting rows (see Results ). sql - string (required) The SQL query to execute. This can include ? or :named parameters. params - list or dict A list or dictionary of values to use for the parameters. List for ? , dictionary for :named . truncate - boolean Should the rows returned by the query be truncated at the maximum page size? Defaults to True , set this to False to disable truncation. custom_time_limit - integer ms A custom time limit for this query. This can be set to a lower value than the Datasette configured default. If a query takes longer than this it will be terminated early and raise a dataette.database.QueryInterrupted exception. page_size - integer Set a custom page size for truncation, over-riding the configured Datasette default. log_sql_errors - boolean Should any SQL errors be logged to the console in addition to being raised as an error? Defaults to True . | ["Internals for plugins", "Database class"] | [] |
internals:database-execute-fn | internals | database-execute-fn | await db.execute_fn(fn) | Executes a given callback function against a read-only database connection running in a thread. The function will be passed a SQLite connection, and the return value from the function will be returned by the await . Example usage: def get_version(conn): return conn.execute( "select sqlite_version()" ).fetchall()[0][0] version = await db.execute_fn(get_version) | ["Internals for plugins", "Database class"] | [] |
plugin_hooks:plugin-hook-extra-body-script | plugin_hooks | plugin-hook-extra-body-script | extra_body_script(template, database, table, columns, view_name, request, datasette) | Extra JavaScript to be added to a <script> block at the end of the <body> element on the page. This takes the same arguments as extra_template_vars(...) The template , database , table and view_name options can be used to return different code depending on which template is being rendered and which database or table are being processed. The datasette instance is provided primarily so that you can consult any plugin configuration options that may have been set, using the datasette.plugin_config(plugin_name) method documented above. This function can return a string containing JavaScript, or a dictionary as described below, or a function or awaitable function that returns a string or dictionary. Use a dictionary if you want to specify that the code should be placed in a <script type="module">...</script> element: @hookimpl def extra_body_script(): return { "module": True, "script": "console.log('Your JavaScript goes here...')", } This will add the following to the end of your page: <script type="module">console.log('Your JavaScript goes here...')</script> Example: datasette-cluster-map | ["Plugin hooks"] | [{"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}] |
plugin_hooks:plugin-hook-extra-template-vars | plugin_hooks | plugin-hook-extra-template-vars | extra_template_vars(template, database, table, columns, view_name, request, datasette) | Extra template variables that should be made available in the rendered template context. template - string The template that is being rendered, e.g. database.html database - string or None The name of the database, or None if the page does not correspond to a database (e.g. the root page) table - string or None The name of the table, or None if the page does not correct to a table columns - list of strings or None The names of the database columns that will be displayed on this page. None if the page does not contain a table. view_name - string The name of the view being displayed. ( index , database , table , and row are the most important ones.) request - Request object or None The current HTTP request. This can be None if the request object is not available. datasette - Datasette class You can use this to access plugin configuration options via datasette.plugin_config(your_plugin_name) This hook can return one of three different types: Dictionary If you return a dict… | ["Plugin hooks"] | [{"href": "https://jinja.palletsprojects.com/en/2.10.x/api/#async-support", "label": "async mode"}, {"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-template-sql", "label": "datasette-template-sql"}] |
getting_started:getting-started-your-computer | getting_started | getting-started-your-computer | Using Datasette on your own computer | First, follow the Installation instructions. Now you can run Datasette against a SQLite file on your computer using the following command: datasette path/to/database.db This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface. Add -o to open your browser automatically once Datasette has started: datasette path/to/database.db -o Use Chrome on OS X? You can run datasette against your browser history like so: datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock The --nolock option ignores any file locks. This is safe as Datasette will open the file in read-only mode. Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data: http://localhost:8001/History/downloads.json will return that data as JSON: { "database": "History", "columns": [ "id", "current_path", "target_path", "start_time", "received_bytes", "total_bytes", ... ], "rows": [ [ 1, "/Users/simonw/Downloads/DropboxInstaller.dmg", "/Users/simonw/Downloads/DropboxInstaller.dmg", 13097290269022132, 626688, 0, ... ] ] } http://localhost:8001/History/downloads.json?_shape=objects will return that data as JSON in a more convenient format: { ... "rows": [ { "start_time": 13097290269022132, "interrupt_reason": 0, "hash": "", "id": 1, "site_url": "", "referrer": "https://www.dropbox.com/downloading?src=index", ... } ] } | ["Getting started"] | [{"href": "http://localhost:8001/", "label": "http://localhost:8001/"}, {"href": "http://localhost:8001/History/downloads", "label": "http://localhost:8001/History/downloads"}, {"href": "http://localhost:8001/History/downloads.json", "label": "http://localhost:8001/History/downloads.json"}, {"href": "http://localhost:8001/History/downloads.json?_shape=objects", "label": "http://localhost:8001/History/downloads.json?_shape=objects"}] |
changelog:id24 | changelog | id24 | 0.59.1 (2021-10-24) | Fix compatibility with Python 3.10. ( #1482 ) Documentation on how to use Named parameters with integer and floating point values. ( #1496 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1482", "label": "#1482"}, {"href": "https://github.com/simonw/datasette/issues/1496", "label": "#1496"}] |
changelog:id4 | changelog | id4 | 0.64.4 (2023-09-21) | Fix for a crashing bug caused by viewing the table page for a named in-memory database. ( #2189 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/2189", "label": "#2189"}] |
changelog:id26 | changelog | id26 | 0.58.1 (2021-07-16) | Fix for an intermittent race condition caused by the refresh_schemas() internal function. ( #1231 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1231", "label": "#1231"}] |
changelog:id36 | changelog | id36 | 0.52.5 (2020-12-09) | Fix for error caused by combining the _searchmode=raw and ?_search_COLUMN parameters. ( #1134 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1134", "label": "#1134"}] |
changelog:id206 | changelog | id206 | 0.10 (2017-11-14) | Fixed #83 - 500 error on individual row pages. Stop using sqlite WITH RECURSIVE in our tests. The version of Python 3 running in Travis CI doesn't support this. | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/83", "label": "#83"}] |
changelog:id10 | changelog | id10 | 0.63.2 (2022-11-18) | Fixed a bug in datasette publish heroku where deployments failed due to an older version of Python being requested. ( #1905 ) New datasette publish heroku --generate-dir <dir> option for generating a Heroku deployment directory without deploying it. | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1905", "label": "#1905"}] |
changelog:id45 | changelog | id45 | 0.50.1 (2020-10-09) | Fixed a bug introduced in 0.50 where the export as JSON/CSV links on the table, row and query pages were broken. ( #1010 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1010", "label": "#1010"}] |
changelog:id33 | changelog | id33 | 0.54.1 (2021-02-02) | Fixed a bug where ?_search= and ?_sort= parameters were incorrectly duplicated when the filter form on the table page was re-submitted. ( #1214 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1214", "label": "#1214"}] |
changelog:id9 | changelog | id9 | 0.63.3 (2022-12-17) | Fixed a bug where datasette --root , when running in Docker, would only output the URL to sign in as root when the server shut down, not when it started up. ( #1958 ) You no longer need to ensure await datasette.invoke_startup() has been called in order for Datasette to start correctly serving requests - this is now handled automatically the first time the server receives a request. This fixes a bug experienced when Datasette is served directly by an ASGI application server such as Uvicorn or Gunicorn. It also fixes a bug with the datasette-gunicorn plugin. ( #1955 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1958", "label": "#1958"}, {"href": "https://datasette.io/plugins/datasette-gunicorn", "label": "datasette-gunicorn"}, {"href": "https://github.com/simonw/datasette/issues/1955", "label": "#1955"}] |
changelog:id72 | changelog | id72 | 0.31.2 (2019-11-13) | Fixed a bug where datasette publish heroku applications failed to start ( #633 ) Fix for datasette publish with just --source_url - thanks, Stanley Zheng ( #572 ) Deployments to Heroku now use Python 3.8.0 ( #632 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/633", "label": "#633"}, {"href": "https://github.com/simonw/datasette/issues/572", "label": "#572"}, {"href": "https://github.com/simonw/datasette/issues/632", "label": "#632"}] |
changelog:id2 | changelog | id2 | 0.64.6 (2023-12-22) | Fixed a bug where CSV export with expanded labels could fail if a foreign key reference did not correctly resolve. ( #2214 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/2214", "label": "#2214"}] |
changelog:id18 | changelog | id18 | 0.60.2 (2022-02-07) | Fixed a bug where Datasette would open the same file twice with two different database names if you ran datasette file.db file.db . ( #1632 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1632", "label": "#1632"}] |
changelog:id11 | changelog | id11 | 0.63.1 (2022-11-10) | Fixed a bug where Datasette's table filter form would not redirect correctly when run behind a proxy using the base_url setting. ( #1883 ) SQL query is now shown wrapped in a <textarea> if a query exceeds a time limit. ( #1876 ) Fixed an intermittent "Too many open files" error while running the test suite. ( #1843 ) New db.close() internal method. | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1883", "label": "#1883"}, {"href": "https://github.com/simonw/datasette/issues/1876", "label": "#1876"}, {"href": "https://github.com/simonw/datasette/issues/1843", "label": "#1843"}] |
changelog:id16 | changelog | id16 | 0.61.1 (2022-03-23) | Fixed a bug where databases with a different route from their name (as used by the datasette-hashed-urls plugin ) returned errors when executing custom SQL queries. ( #1682 ) | ["Changelog"] | [{"href": "https://datasette.io/plugins/datasette-hashed-urls", "label": "datasette-hashed-urls plugin"}, {"href": "https://github.com/simonw/datasette/issues/1682", "label": "#1682"}] |
changelog:id19 | changelog | id19 | 0.60.1 (2022-01-20) | Fixed a bug where installation on Python 3.6 stopped working due to a change to an underlying dependency. This release can now be installed on Python 3.6, but is the last release of Datasette that will support anything less than Python 3.7. ( #1609 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1609", "label": "#1609"}] |
changelog:id52 | changelog | id52 | 0.47.1 (2020-08-11) | Fixed a bug where the sdist distribution of Datasette was not correctly including the template files. ( #930 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/930", "label": "#930"}] |
changelog:id6 | changelog | id6 | 0.64.2 (2023-03-08) | Fixed a bug with datasette publish cloudrun where deploys all used the same Docker image tag. This was mostly inconsequential as the service is deployed as soon as the image has been pushed to the registry, but could result in the incorrect image being deployed if two different deploys for two separate services ran at exactly the same time. ( #2036 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/2036", "label": "#2036"}] |
changelog:id47 | changelog | id47 | 0.49.1 (2020-09-15) | Fixed a bug with writable canned queries that use magic parameters but accept no non-magic arguments. ( #967 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/967", "label": "#967"}] |
changelog:id51 | changelog | id51 | 0.47.2 (2020-08-12) | Fixed an issue with the Docker image published to Docker Hub . ( #931 ) | ["Changelog"] | [{"href": "https://hub.docker.com/r/datasetteproject/datasette", "label": "published to Docker Hub"}, {"href": "https://github.com/simonw/datasette/issues/931", "label": "#931"}] |
changelog:id44 | changelog | id44 | 0.50.2 (2020-10-09) | Fixed another bug introduced in 0.50 where column header links on the table page were broken. ( #1011 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1011", "label": "#1011"}] |
changelog:id76 | changelog | id76 | 0.30.1 (2019-10-30) | Fixed bug where ?_where= parameter was not persisted in hidden form fields ( #604 ) Fixed bug with .JSON representation of row pages - thanks, Chris Shaw ( #603 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/604", "label": "#604"}, {"href": "https://github.com/simonw/datasette/issues/603", "label": "#603"}] |
changelog:id21 | changelog | id21 | 0.59.4 (2021-11-29) | Fixed bug where columns with a leading underscore could not be removed from the interactive filters list. ( #1527 ) Fixed bug where columns with a leading underscore were not correctly linked to by the "Links from other tables" interface on the row page. ( #1525 ) Upgraded dependencies aiofiles , black and janus . | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1527", "label": "#1527"}, {"href": "https://github.com/simonw/datasette/issues/1525", "label": "#1525"}] |
changelog:id38 | changelog | id38 | 0.52.3 (2020-12-03) | Fixed bug where static assets would 404 for Datasette installed on ARM Amazon Linux. ( #1124 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1124", "label": "#1124"}] |
changelog:id80 | changelog | id80 | 0.29.1 (2019-07-11) | Fixed bug with static mounts using relative paths which could lead to traversal exploits ( #555 ) - thanks Abdussamet Kocak! Datasette can now be run as a module: python -m datasette ( #556 ) - thanks, Abdussamet Kocak! | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/555", "label": "#555"}, {"href": "https://github.com/simonw/datasette/issues/556", "label": "#556"}] |
changelog:id78 | changelog | id78 | 0.29.3 (2019-09-02) | Fixed implementation of CodeMirror on database page ( #560 ) Documentation typo fixes - thanks, Min ho Kim ( #561 ) Mechanism for detecting if a table has FTS enabled now works if the table name used alternative escaping mechanisms ( #570 ) - for compatibility with a recent change to sqlite-utils . | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/560", "label": "#560"}, {"href": "https://github.com/simonw/datasette/pull/561", "label": "#561"}, {"href": "https://github.com/simonw/datasette/issues/570", "label": "#570"}, {"href": "https://github.com/simonw/sqlite-utils/pull/57", "label": "a recent change to sqlite-utils"}] |
changelog:id22 | changelog | id22 | 0.59.3 (2021-11-20) | Fixed numerous bugs when running Datasette behind a proxy with a prefix URL path using the base_url setting. A live demo of this mode is now available at datasette-apache-proxy-demo.datasette.io/prefix/ . ( #1519 , #838 ) ?column__arraycontains= and ?column__arraynotcontains= table parameters now also work against SQL views. ( #448 ) ?_facet_array=column no longer returns incorrect counts if columns contain the same value more than once. | ["Changelog"] | [{"href": "https://datasette-apache-proxy-demo.datasette.io/prefix/", "label": "datasette-apache-proxy-demo.datasette.io/prefix/"}, {"href": "https://github.com/simonw/datasette/issues/1519", "label": "#1519"}, {"href": "https://github.com/simonw/datasette/issues/838", "label": "#838"}, {"href": "https://github.com/simonw/datasette/issues/448", "label": "#448"}] |
changelog:id28 | changelog | id28 | 0.57.1 (2021-06-08) | Fixed visual display glitch with global navigation menu. ( #1367 ) No longer truncates the list of table columns displayed on the /database page. ( #1364 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1367", "label": "#1367"}, {"href": "https://github.com/simonw/datasette/issues/1364", "label": "#1364"}] |
publish:publish-fly | publish | publish-fly | Publishing to Fly | Fly is a competitively priced Docker-compatible hosting platform that supports running applications in globally distributed data centers close to your end users. You can deploy Datasette instances to Fly using the datasette-publish-fly plugin. pip install datasette-publish-fly datasette publish fly mydatabase.db --app="my-app" Consult the datasette-publish-fly README for more details. | ["Publishing data", "datasette publish"] | [{"href": "https://fly.io/", "label": "Fly"}, {"href": "https://fly.io/docs/pricing/", "label": "competitively priced"}, {"href": "https://github.com/simonw/datasette-publish-fly", "label": "datasette-publish-fly"}, {"href": "https://github.com/simonw/datasette-publish-fly/blob/main/README.md", "label": "datasette-publish-fly README"}] |
deploying:apache-proxy-configuration | deploying | apache-proxy-configuration | Apache proxy configuration | For Apache , you can use the ProxyPass directive. First make sure the following lines are uncommented: LoadModule proxy_module lib/httpd/modules/mod_proxy.so LoadModule proxy_http_module lib/httpd/modules/mod_proxy_http.so Then add these directives to proxy traffic: ProxyPass /my-datasette/ http://127.0.0.1:8009/my-datasette/ ProxyPreserveHost On A live demo of Datasette running behind Apache using this proxy setup can be seen at datasette-apache-proxy-demo.datasette.io/prefix/ . The code for that demo can be found in the demos/apache-proxy directory. Using --uds you can use Unix domain sockets similar to the nginx example: ProxyPass /my-datasette/ unix:/tmp/datasette.sock|http://localhost/my-datasette/ The ProxyPreserveHost On directive ensures that the original Host: header from the incoming request is passed through to Datasette. Datasette needs this to correctly assemble links to other pages using the .absolute_url(request, path) method. | ["Deploying Datasette", "Running Datasette behind a proxy"] | [{"href": "https://httpd.apache.org/", "label": "Apache"}, {"href": "https://datasette-apache-proxy-demo.datasette.io/prefix/", "label": "datasette-apache-proxy-demo.datasette.io/prefix/"}, {"href": "https://github.com/simonw/datasette/tree/main/demos/apache-proxy", "label": "demos/apache-proxy"}, {"href": "https://httpd.apache.org/docs/2.4/mod/mod_proxy.html#proxypreservehost", "label": "ProxyPreserveHost On"}] |
settings:setting-force-https-urls | settings | setting-force-https-urls | force_https_urls | Forces self-referential URLs in the JSON output to always use the https:// protocol. This is useful for cases where the application itself is hosted using HTTP but is served to the outside world via a proxy that enables HTTPS. datasette mydatabase.db --setting force_https_urls 1 | ["Settings", "Settings"] | [] |
changelog:v0-28-databases-that-change | changelog | v0-28-databases-that-change | Supporting databases that change | From the beginning of the project, Datasette has been designed with read-only databases in mind. If a database is guaranteed not to change it opens up all kinds of interesting opportunities - from taking advantage of SQLite immutable mode and HTTP caching to bundling static copies of the database directly in a Docker container. The interesting ideas in Datasette explores this idea in detail. As my goals for the project have developed, I realized that read-only databases are no longer the right default. SQLite actually supports concurrent access very well provided only one thread attempts to write to a database at a time, and I keep encountering sensible use-cases for running Datasette on top of a database that is processing inserts and updates. So, as-of version 0.28 Datasette no longer assumes that a database file will not change. It is now safe to point Datasette at a SQLite database which is being updated by another process. Making this change was a lot of work - see tracking tickets #418 , #419 and #420 . It required new thinking around how Datasette should calculate table counts (an expensive operation against a large, changing database) and also meant reconsidering the "content hash" URLs Datasette has used in the past to optimize the performance of HTTP caches. Datasette can still run against immutable files and gains numerous performance benefits from doing so, but this is no longer the default behaviour. Take a look at the new Performance and caching documentation section for details on how to make the most of Datasette against data that you know will be staying read-only and immutable. | ["Changelog", "0.28 (2019-05-19)"] | [{"href": "https://simonwillison.net/2018/Oct/4/datasette-ideas/", "label": "The interesting ideas in Datasette"}, {"href": "https://github.com/simonw/datasette/issues/418", "label": "#418"}, {"href": "https://github.com/simonw/datasette/issues/419", "label": "#419"}, {"href": "https://github.com/simonw/datasette/issues/420", "label": "#420"}] |
changelog:id39 | changelog | id39 | 0.52.2 (2020-12-02) | Generated columns from SQLite 3.31.0 or higher are now correctly displayed. ( #1116 ) Error message if you attempt to open a SpatiaLite database now suggests using --load-extension=spatialite if it detects that the extension is available in a common location. ( #1115 ) OPTIONS requests against the /database page no longer raise a 500 error. ( #1100 ) Databases larger than 32MB that are published to Cloud Run can now be downloaded. ( #749 ) Fix for misaligned cog icon on table and database pages. Thanks, Abdussamet Koçak. ( #1121 ) | ["Changelog"] | [{"href": "https://github.com/simonw/datasette/issues/1116", "label": "#1116"}, {"href": "https://github.com/simonw/datasette/issues/1115", "label": "#1115"}, {"href": "https://github.com/simonw/datasette/issues/1100", "label": "#1100"}, {"href": "https://github.com/simonw/datasette/issues/749", "label": "#749"}, {"href": "https://github.com/simonw/datasette/issues/1121", "label": "#1121"}] |
index:contents | index | contents | Contents | Getting started Play with a live demo Follow a tutorial Datasette in your browser with Datasette Lite Try Datasette without installing anything using Glitch Using Datasette on your own computer Installation Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Using Docker A note about extensions The Datasette Ecosystem sqlite-utils Dogsheep CLI reference datasette --help datasette serve datasette --get datasette serve --help-settings datasette plugins datasette install datasette uninstall datasette publish datasette publish cloudrun datasette publish heroku datasette package datasette inspect Pages and API endpoints Top-level index Database Table Row Publishing data datasette publish Publishing to Google Cloud Run Publishing to Heroku Publishing to Vercel Publishing to Fly Custom metadata and plugins datasette package Deploying Datasette Deployment fundamentals Running Datasette using systemd Running Datasette using OpenRC Deploying using buildpacks Running Datasette behind a proxy Nginx proxy configuration Apache proxy configuration JSON API Different shapes Pagination Special JSON arguments Table arguments Column filter arguments Special table arguments Expanding foreign key references Discovering the JSON for a page Running SQL queries Named parameters Views Canned queries Canned query parameters Additional canned query options Writable canned queries Magic parameters JSON API for writable canned queries Pagination Cross-database queries Authentication and permissions Actors Using the "root" actor Permissions Defining permissions with "allow" blocks The /-/allow-debug tool Configuring permissions in metadata.json Controlling access to an instance Controlling access to specific databases Controlling access to specific tables and views Controlling access to specific canned queries Controlling the ability to execute arbitrary SQL Checking permissions in plugins actor_matches_allow() The permissions debug tool The ds_actor cookie Including an expiry time Th… | ["Datasette"] | [] |
getting_started:getting-started-glitch | getting_started | getting-started-glitch | Try Datasette without installing anything using Glitch | Glitch is a free online tool for building web apps directly from your web browser. You can use Glitch to try out Datasette without needing to install any software on your own computer. Here's a demo project on Glitch which you can use as the basis for your own experiments: glitch.com/~datasette-csvs Glitch allows you to "remix" any project to create your own copy and start editing it in your browser. You can remix the datasette-csvs project by clicking this button: Find a CSV file and drag it onto the Glitch file explorer panel - datasette-csvs will automatically convert it to a SQLite database (using sqlite-utils ) and allow you to start exploring it using Datasette. If your CSV file has a latitude and longitude column you can visualize it on a map by uncommenting the datasette-cluster-map line in the requirements.txt file using the Glitch file editor. Need some data? Try this Public Art Data for the city of Seattle - hit "Export" and select "CSV" to download it as a CSV file. For more on how this works, see Running Datasette on Glitch . | ["Getting started"] | [{"href": "https://glitch.com/", "label": "Glitch"}, {"href": "https://glitch.com/~datasette-csvs", "label": "glitch.com/~datasette-csvs"}, {"href": "https://glitch.com/edit/#!/remix/datasette-csvs", "label": null}, {"href": "https://github.com/simonw/sqlite-utils", "label": "sqlite-utils"}, {"href": "https://data.seattle.gov/Community/Public-Art-Data/j7sn-tdzk", "label": "Public Art Data"}, {"href": "https://simonwillison.net/2019/Apr/23/datasette-glitch/", "label": "Running Datasette on Glitch"}] |
publish:publish-cloud-run | publish | publish-cloud-run | Publishing to Google Cloud Run | Google Cloud Run allows you to publish data in a scale-to-zero environment, so your application will start running when the first request is received and will shut down again when traffic ceases. This means you only pay for time spent serving traffic. Cloud Run is a great option for inexpensively hosting small, low traffic projects - but costs can add up for projects that serve a lot of requests. Be particularly careful if your project has tables with large numbers of rows. Search engine crawlers that index a page for every row could result in a high bill. The datasette-block-robots plugin can be used to request search engine crawlers omit crawling your site, which can help avoid this issue. You will first need to install and configure the Google Cloud CLI tools by following these instructions . You can then publish one or more SQLite database files to Google Cloud Run using the following command: datasette publish cloudrun mydatabase.db --service=my-database A Cloud Run service is a single hosted application. The service name you specify will be used as part of the Cloud Run URL. If you deploy to a service name that you have used in the past your new deployment will replace the previous one. If you omit the --service option you will be asked to pick a service name interactively during the deploy. You may need to interact with prompts from the tool. Many of the prompts ask for values that can be set as properties for the Google Cloud SDK if you want to avoid the prompts. For example, the default region for the deployed instance can be set using the command: gcloud config set run/region us-central1 You should replace us-central1 with your desired region . Alternately, you can specify the region by setting the CLOUDSDK_RUN_REGION environment… | ["Publishing data", "datasette publish"] | [{"href": "https://cloud.google.com/run/", "label": "Google Cloud Run"}, {"href": "https://datasette.io/plugins/datasette-block-robots", "label": "datasette-block-robots"}, {"href": "https://cloud.google.com/sdk/", "label": "these instructions"}, {"href": "https://cloud.google.com/sdk/docs/properties", "label": "set as properties for the Google Cloud SDK"}, {"href": "https://cloud.google.com/about/locations", "label": "region"}, {"href": "https://cloud.google.com/run/docs/mapping-custom-domains", "label": "mapping custom domains"}] |
changelog:v0-28-publish-cloudrun | changelog | v0-28-publish-cloudrun | datasette publish cloudrun | Google Cloud Run is a brand new serverless hosting platform from Google, which allows you to build a Docker container which will run only when HTTP traffic is received and will shut down (and hence cost you nothing) the rest of the time. It's similar to Zeit's Now v1 Docker hosting platform which sadly is no longer accepting signups from new users. The new datasette publish cloudrun command was contributed by Romain Primet ( #434 ) and publishes selected databases to a new Datasette instance running on Google Cloud Run. See Publishing to Google Cloud Run for full documentation. | ["Changelog", "0.28 (2019-05-19)"] | [{"href": "https://cloud.google.com/run/", "label": "Google Cloud Run"}, {"href": "https://hyperion.alpha.spectrum.chat/zeit/now/cannot-create-now-v1-deployments~d206a0d4-5835-4af5-bb5c-a17f0171fb25?m=MTU0Njk2NzgwODM3OA==", "label": "no longer accepting signups"}, {"href": "https://github.com/simonw/datasette/pull/434", "label": "#434"}] |
changelog:miscellaneous | changelog | miscellaneous | Miscellaneous | Got JSON data in one of your columns? Use the new ?_json=COLNAME argument to tell Datasette to return that JSON value directly rather than encoding it as a string. If you just want an array of the first value of each row, use the new ?_shape=arrayfirst option - example . | ["Changelog", "0.23 (2018-06-18)"] | [{"href": "https://latest.datasette.io/fixtures.json?sql=select+neighborhood+from+facetable+order+by+pk+limit+101&_shape=arrayfirst", "label": "example"}] |
deploying:nginx-proxy-configuration | deploying | nginx-proxy-configuration | Nginx proxy configuration | Here is an example of an nginx configuration file that will proxy traffic to Datasette: daemon off; events { worker_connections 1024; } http { server { listen 80; location /my-datasette { proxy_pass http://127.0.0.1:8009/my-datasette; proxy_set_header Host $host; } } } You can also use the --uds option to Datasette to listen on a Unix domain socket instead of a port, configuring the nginx upstream proxy like this: daemon off; events { worker_connections 1024; } http { server { listen 80; location /my-datasette { proxy_pass http://datasette/my-datasette; proxy_set_header Host $host; } } upstream datasette { server unix:/tmp/datasette.sock; } } Then run Datasette with datasette --uds /tmp/datasette.sock path/to/database.db --setting base_url /my-datasette/ . | ["Deploying Datasette", "Running Datasette behind a proxy"] | [{"href": "https://nginx.org/", "label": "nginx"}] |
spatialite:spatial-indexing-latitude-longitude-columns | spatialite | spatial-indexing-latitude-longitude-columns | Spatial indexing latitude/longitude columns | Here's a recipe for taking a table with existing latitude and longitude columns, adding a SpatiaLite POINT geometry column to that table, populating the new column and then populating a spatial index: import sqlite3 conn = sqlite3.connect("museums.db") # Lead the spatialite extension: conn.enable_load_extension(True) conn.load_extension("/usr/local/lib/mod_spatialite.dylib") # Initialize spatial metadata for this database: conn.execute("select InitSpatialMetadata(1)") # Add a geometry column called point_geom to our museums table: conn.execute( "SELECT AddGeometryColumn('museums', 'point_geom', 4326, 'POINT', 2);" ) # Now update that geometry column with the lat/lon points conn.execute( """ UPDATE museums SET point_geom = GeomFromText('POINT('||"longitude"||' '||"latitude"||')',4326); """ ) # Now add a spatial index to that column conn.execute( 'select CreateSpatialIndex("museums", "point_geom");' ) # If you don't commit your changes will not be persisted: conn.commit() conn.close() | ["SpatiaLite"] | [] |
authentication:authentication-permissions-instance | authentication | authentication-permissions-instance | Controlling access to an instance | Here's how to restrict access to your entire Datasette instance to just the "id": "root" user: { "title": "My private Datasette instance", "allow": { "id": "root" } } To deny access to all users, you can use "allow": false : { "title": "My entirely inaccessible instance", "allow": false } One reason to do this is if you are using a Datasette plugin - such as datasette-permissions-sql - to control permissions instead. | ["Authentication and permissions", "Configuring permissions in metadata.json"] | [{"href": "https://github.com/simonw/datasette-permissions-sql", "label": "datasette-permissions-sql"}] |
changelog:the-road-to-datasette-1-0 | changelog | the-road-to-datasette-1-0 | The road to Datasette 1.0 | I've assembled a milestone for Datasette 1.0 . The focus of the 1.0 release will be the following: Signify confidence in the quality/stability of Datasette Give plugin authors confidence that their plugins will work for the whole 1.x release cycle Provide the same confidence to developers building against Datasette JSON APIs If you have thoughts about what you would like to see for Datasette 1.0 you can join the conversation on issue #519 . | ["Changelog", "0.44 (2020-06-11)"] | [{"href": "https://github.com/simonw/datasette/milestone/7", "label": "milestone for Datasette 1.0"}, {"href": "https://github.com/simonw/datasette/issues/519", "label": "the conversation on issue #519"}] |
facets:id3 | facets | id3 | Facet by date | If Datasette finds any columns that contain dates in the first 100 values, it will offer a faceting interface against the dates of those values. This works especially well against timestamp values such as 2019-03-01 12:44:00 . Example here: latest.datasette.io/fixtures/facetable?_facet_date=created | ["Facets"] | [{"href": "https://latest.datasette.io/fixtures/facetable?_facet_date=created", "label": "latest.datasette.io/fixtures/facetable?_facet_date=created"}] |
changelog:facet-by-date | changelog | facet-by-date | Facet by date | If a column contains datetime values, Datasette can now facet that column by date. ( #481 ) | ["Changelog", "0.29 (2019-07-07)"] | [{"href": "https://github.com/simonw/datasette/issues/481", "label": "#481"}] |
plugins:plugins-installing | plugins | plugins-installing | Installing plugins | If a plugin has been packaged for distribution using setuptools you can use the plugin by installing it alongside Datasette in the same virtual environment or Docker container. You can install plugins using the datasette install command: datasette install datasette-vega You can uninstall plugins with datasette uninstall : datasette uninstall datasette-vega You can upgrade plugins with datasette install --upgrade or datasette install -U : datasette install -U datasette-vega This command can also be used to upgrade Datasette itself to the latest released version: datasette install -U datasette These commands are thin wrappers around pip install and pip uninstall , which ensure they run pip in the same virtual environment as Datasette itself. | ["Plugins"] | [] |
full_text_search:full-text-search-table-or-view | full_text_search | full-text-search-table-or-view | Configuring full-text search for a table or view | If a table has a corresponding FTS table set up using the content= argument to CREATE VIRTUAL TABLE shown below, Datasette will detect it automatically and add a search interface to the table page for that table. You can also manually configure which table should be used for full-text search using query string parameters or Metadata . You can set the associated FTS table for a specific table and you can also set one for a view - if you do that, the page for that SQL view will offer a search option. Use ?_fts_table=x to over-ride the FTS table for a specific page. If the primary key was something other than rowid you can use ?_fts_pk=col to set that as well. This is particularly useful for views, for example: https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk The fts_table metadata property can be used to specify an associated FTS table. If the primary key column in your table which was used to populate the FTS table is something other than rowid , you can specify the column to use with the fts_pk property. The "searchmode": "raw" property can be used to default the table to accepting SQLite advanced search operators, as described in Advanced SQLite search queries . Here is an example which enables full-text search (with SQLite advanced search operators) for a display_ads view which is defined against the ads table and hence needs to run FTS against the ads_fts table, using the id as the primary key: { "databases": { "russian-ads": { "tables": { "display_ads": { "fts_table": "ads_fts", "fts_pk": "id", "searchmode": "raw" } } } } } | ["Full-text search"] | [{"href": "https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk", "label": "https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk"}] |
changelog:new-features | changelog | new-features | New features | If an error occurs while executing a user-provided SQL query, that query is now re-displayed in an editable form along with the error message. ( #619 ) New ?_col= and ?_nocol= parameters to show and hide columns in a table, plus an interface for hiding and showing columns in the column cog menu. ( #615 ) A new ?_facet_size= parameter for customizing the number of facet results returned on a table or view page. ( #1332 ) ?_facet_size=max sets that to the maximum, which defaults to 1,000 and is controlled by the the max_returned_rows setting. If facet results are truncated the … at the bottom of the facet list now links to this parameter. ( #1337 ) ?_nofacet=1 option to disable all facet calculations on a page, used as a performance optimization for CSV exports and ?_shape=array/object . ( #1349 , #263 ) ?_nocount=1 option to disable full query result counts. ( #1353 ) ?_trace=1 debugging option is now controlled by the new trace_debug setting, which is turned off by default. ( #1359 ) | ["Changelog", "0.57 (2021-06-05)"] | [{"href": "https://github.com/simonw/datasette/issues/619", "label": "#619"}, {"href": "https://github.com/simonw/datasette/issues/615", "label": "#615"}, {"href": "https://github.com/simonw/datasette/issues/1332", "label": "#1332"}, {"href": "https://github.com/simonw/datasette/issues/1337", "label": "#1337"}, {"href": "https://github.com/simonw/datasette/issues/1349", "label": "#1349"}, {"href": "https://github.com/simonw/datasette/issues/263", "label": "#263"}, {"href": "https://github.com/simonw/datasette/issues/1353", "label": "#1353"}, {"href": "https://github.com/simonw/datasette/issues/1359", "label": "#1359"}] |
testing_plugins:testing-plugins-pdb | testing_plugins | testing-plugins-pdb | Using pdb for errors thrown inside Datasette | If an exception occurs within Datasette itself during a test, the response returned to your plugin will have a response.status_code value of 500. You can add pdb=True to the Datasette constructor to drop into a Python debugger session inside your test run instead of getting back a 500 response code. This is equivalent to running the datasette command-line tool with the --pdb option. Here's what that looks like in a test function: def test_that_opens_the_debugger_or_errors(): ds = Datasette([db_path], pdb=True) response = await ds.client.get("/") If you use this pattern you will need to run pytest with the -s option to avoid capturing stdin/stdout in order to interact with the debugger prompt. | ["Testing plugins"] | [] |
contributing:contributing-bug-fix-branch | contributing | contributing-bug-fix-branch | Releasing bug fixes from a branch | If it's necessary to publish a bug fix release without shipping new features that have landed on main a release branch can be used. Create it from the relevant last tagged release like so: git branch 0.52.x 0.52.4 git checkout 0.52.x Next cherry-pick the commits containing the bug fixes: git cherry-pick COMMIT Write the release notes in the branch, and update the version number in version.py . Then push the branch: git push -u origin 0.52.x Once the tests have completed, publish the release from that branch target using the GitHub Draft a new release form. Finally, cherry-pick the commit with the release notes and version number bump across to main : git checkout main git cherry-pick COMMIT git push | ["Contributing"] | [{"href": "https://github.com/simonw/datasette/releases/new", "label": "Draft a new release"}] |
internals:database-hash | internals | database-hash | db.hash | If the database was opened in immutable mode, this property returns the 64 character SHA-256 hash of the database contents as a string. Otherwise it returns None . | ["Internals for plugins", "Database class"] | [] |
settings:setting-base-url | settings | setting-base-url | base_url | If you are running Datasette behind a proxy, it may be useful to change the root path used for the Datasette instance. For example, if you are sending traffic from https://www.example.com/tools/datasette/ through to a proxied Datasette instance you may wish Datasette to use /tools/datasette/ as its root URL. You can do that like so: datasette mydatabase.db --setting base_url /tools/datasette/ | ["Settings", "Settings"] | [] |
performance:performance-immutable-mode | performance | performance-immutable-mode | Immutable mode | If you can be certain that a SQLite database file will not be changed by another process you can tell Datasette to open that file in immutable mode . Doing so will disable all locking and change detection, which can result in improved query performance. This also enables further optimizations relating to HTTP caching, described below. To open a file in immutable mode pass it to the datasette command using the -i option: datasette -i data.db When you open a file in immutable mode like this Datasette will also calculate and cache the row counts for each table in that database when it first starts up, further improving performance. | ["Performance and caching"] | [] |
contributing:devenvironment | contributing | devenvironment | Setting up a development environment | If you have Python 3.7 or higher installed on your computer (on OS X the quickest way to do this is using homebrew ) you can install an editable copy of Datasette using the following steps. If you want to use GitHub to publish your changes, first create a fork of datasette under your own GitHub account. Now clone that repository somewhere on your computer: git clone git@github.com:YOURNAME/datasette If you want to get started without creating your own fork, you can do this instead: git clone git@github.com:simonw/datasette The next step is to create a virtual environment for your project and use it to install Datasette's dependencies: cd datasette # Create a virtual environment in ./venv python3 -m venv ./venv # Now activate the virtual environment, so pip can install into it source venv/bin/activate # Install Datasette and its testing dependencies python3 -m pip install -e '.[test]' That last line does most of the work: pip install -e means "install this package in a way that allows me to edit the source code in place". The .[test] option means "use the setup.py in this directory and install the optional testing dependencies as well". | ["Contributing"] | [{"href": "https://docs.python-guide.org/starting/install3/osx/", "label": "is using homebrew"}, {"href": "https://github.com/simonw/datasette/fork", "label": "create a fork of datasette"}] |
installation:installation-homebrew | installation | installation-homebrew | Using Homebrew | If you have a Mac and use Homebrew , you can install Datasette by running this command in your terminal: brew install datasette This should install the latest version. You can confirm by running: datasette --version You can upgrade to the latest Homebrew packaged version using: brew upgrade datasette Once you have installed Datasette you can install plugins using the following: datasette install datasette-vega If the latest packaged release of Datasette has not yet been made available through Homebrew, you can upgrade your Homebrew installation in-place using: datasette install -U datasette | ["Installation", "Basic installation"] | [{"href": "https://brew.sh/", "label": "Homebrew"}] |
publish:cli-package | publish | cli-package | datasette package | If you have docker installed (e.g. using Docker for Mac ) you can use the datasette package command to create a new Docker image in your local repository containing the datasette app bundled together with one or more SQLite databases: datasette package mydatabase.db Here's example output for the package command: $ datasette package parlgov.db --extra-options="--setting sql_time_limit_ms 2500" Sending build context to Docker daemon 4.459MB Step 1/7 : FROM python:3.11.0-slim-bullseye ---> 79e1dc9af1c1 Step 2/7 : COPY . /app ---> Using cache ---> cd4ec67de656 Step 3/7 : WORKDIR /app ---> Using cache ---> 139699e91621 Step 4/7 : RUN pip install datasette ---> Using cache ---> 340efa82bfd7 Step 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json ---> Using cache ---> 5fddbe990314 Step 6/7 : EXPOSE 8001 ---> Using cache ---> 8e83844b0fed Step 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --setting sql_time_limit_ms 2500 ---> Using cache ---> 1bd380ea8af3 Successfully built 1bd380ea8af3 You can now run the resulting container like so: docker run -p 8081:8001 1bd380ea8af3 This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at http://localhost:8081/ You can customize the port that is exposed by the container using the --port option: datasette package mydatabase.db --port 8080 A full list of options can be seen by running datasette package --help : See datasette package for the full list of options for this command. | ["Publishing data"] | [{"href": "https://www.docker.com/docker-mac", "label": "Docker for Mac"}] |
installation:id1 | installation | id1 | Installation | If you just want to try Datasette out you don't need to install anything: see Try Datasette without installing anything using Glitch There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to Setting up a development environment . Basic installation Datasette Desktop for Mac Using Homebrew Using pip Advanced installation options Using pipx Installing plugins using pipx Upgrading packages using pipx Using Docker Loading SpatiaLite Installing plugins A note about extensions | [] | [] |
performance:performance-hashed-urls | performance | performance-hashed-urls | datasette-hashed-urls | If you open a database file in immutable mode using the -i option, you can be assured that the content of that database will not change for the lifetime of the Datasette server. The datasette-hashed-urls plugin implements an optimization where your database is served with part of the SHA-256 hash of the database contents baked into the URL. A database at /fixtures will instead be served at /fixtures-aa7318b , and a year-long cache expiry header will be returned with those pages. This will then be cached by both browsers and caching proxies such as Cloudflare or Fastly, providing a potentially significant performance boost. To install the plugin, run the following: datasette install datasette-hashed-urls Prior to Datasette 0.61 hashed URL mode was a core Datasette feature, enabled using the hash_urls setting. This implementation has now been removed in favor of the datasette-hashed-urls plugin. Prior to Datasette 0.28 hashed URL mode was the default behaviour for Datasette, since all database files were assumed to be immutable and unchanging. From 0.28 onwards the default has been to treat database files as mutable unless explicitly configured otherwise. | ["Performance and caching"] | [{"href": "https://datasette.io/plugins/datasette-hashed-urls", "label": "datasette-hashed-urls plugin"}] |
sql_queries:sql-views | sql_queries | sql-views | Views | If you want to bundle some pre-written SQL queries with your Datasette-hosted database you can do so in two ways. The first is to include SQL views in your database - Datasette will then list those views on your database index page. The quickest way to create views is with the SQLite command-line interface: $ sqlite3 sf-trees.db SQLite version 3.19.3 2017-06-27 16:48:08 Enter ".help" for usage hints. sqlite> CREATE VIEW demo_view AS select qSpecies from Street_Tree_List; <CTRL+D> | ["Running SQL queries"] | [] |
changelog:v0-29-medium-changes | changelog | v0-29-medium-changes | Easier custom templates for table rows | If you want to customize the display of individual table rows, you can do so using a _table.html template include that looks something like this: {% for row in display_rows %} <div> <h2>{{ row["title"] }}</h2> <p>{{ row["description"] }}<lp> <p>Category: {{ row.display("category_id") }}</p> </div> {% endfor %} This is a backwards incompatible change . If you previously had a custom template called _rows_and_columns.html you need to rename it to _table.html . See Custom templates for full details. | ["Changelog", "0.29 (2019-07-07)"] | [] |
installation:installing-plugins | installation | installing-plugins | Installing plugins | If you want to install plugins into your local Datasette Docker image you can do so using the following recipe. This will install the plugins and then save a brand new local image called datasette-with-plugins : docker run datasetteproject/datasette \ pip install datasette-vega docker commit $(docker ps -lq) datasette-with-plugins You can now run the new custom image like so: docker run -p 8001:8001 -v `pwd`:/mnt \ datasette-with-plugins \ datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db You can confirm that the plugins are installed by visiting http://127.0.0.1:8001/-/plugins Some plugins such as datasette-ripgrep may need additional system packages. You can install these by running apt-get install inside the container: docker run datasette-057a0 bash -c ' apt-get update && apt-get install ripgrep && pip install datasette-ripgrep' docker commit $(docker ps -lq) datasette-with-ripgrep | ["Installation", "Advanced installation options", "Using Docker"] | [{"href": "http://127.0.0.1:8001/-/plugins", "label": "http://127.0.0.1:8001/-/plugins"}, {"href": "https://datasette.io/plugins/datasette-ripgrep", "label": "datasette-ripgrep"}] |
facets:id2 | facets | id2 | Facet by JSON array | If your SQLite installation provides the json1 extension (you can check using /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns. This is useful for modelling things like tags without needing to break them out into a new table. Example here: latest.datasette.io/fixtures/facetable?_facet_array=tags | ["Facets"] | [{"href": "https://latest.datasette.io/fixtures/facetable?_facet_array=tags", "label": "latest.datasette.io/fixtures/facetable?_facet_array=tags"}] |
internals:internals-tracer-trace-child-tasks | internals | internals-tracer-trace-child-tasks | Tracing child tasks | If your code uses a mechanism such as asyncio.gather() to execute code in additional tasks you may find that some of the traces are missing from the display. You can use the trace_child_tasks() context manager to ensure these child tasks are correctly handled. from datasette import tracer with tracer.trace_child_tasks(): results = await asyncio.gather( # ... async tasks here ) This example uses the register_routes() plugin hook to add a page at /parallel-queries which executes two SQL queries in parallel using asyncio.gather() and returns their results. from datasette import hookimpl from datasette import tracer @hookimpl def register_routes(): async def parallel_queries(datasette): db = datasette.get_database() with tracer.trace_child_tasks(): one, two = await asyncio.gather( db.execute("select 1"), db.execute("select 2"), ) return Response.json( { "one": one.single_value(), "two": two.single_value(), } ) return [ (r"/parallel-queries$", parallel_queries), ] Adding ?_trace=1 will show that the trace covers both of those child tasks. | ["Internals for plugins", "datasette.tracer"] | [] |