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101 | Running the tests | Once you have done this, you can run the Datasette unit tests from inside your datasette/ directory using pytest like so: pytest You can run the tests faster using multiple CPU cores with pytest-xdist like this: pytest -n auto -m "not serial" -n auto detects the number of available cores automatically. The -m "not serial" skips tests that don't work well in a parallel test environment. You can run those tests separately like so: pytest -m "serial" | 12 | |
102 | Using fixtures | To run Datasette itself, type datasette . You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests. You can create a copy of that database by running this command: python tests/fixtures.py fixtures.db Now you can run Datasette against the new fixtures database like so: datasette fixtures.db This will start a server at http://127.0.0.1:8001/ . Any changes you make in the datasette/templates or datasette/static folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript). If you want to change Datasette's Python code you can use the --reload option to cause Datasette to automatically reload any time the underlying code changes: datasette --reload fixtures.db You can also use the fixtures.py script to recreate the testing version of metadata.json used by the unit tests. To do that: python tests/fixtures.py fixtures.db fixtures-metadata.json Or to output the plugins used by the tests, run this: python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins Test tables written to fixtures.db - metadata written to fixtures-metadata.json Wrote plugin: fixtures-plugins/register_output_renderer.py Wrote plugin: fixtures-plugins/view_name.py Wrote plugin: fixtures-plugins/my_plugin.py Wrote plugin: fixtures-plugins/messages_output_renderer.py Wrote plugin: fixtures-plugins/my_plugin_2.py Then run Datasette like this: datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/ | 12 | |
103 | Debugging | Any errors that occur while Datasette is running while display a stack trace on the console. You can tell Datasette to open an interactive pdb debugger session if an error occurs using the --pdb option: datasette --pdb fixtures.db | 12 | |
104 | 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. | 12 | |
105 | Running Black | Black will be installed when you run pip install -e '.[test]' . To test that your code complies with Black, run the following in your root datasette repository checkout: black . --check All done! ✨ 🍰 ✨ 95 files would be left unchanged. If any of your code does not conform to Black you can run this to automatically fix those problems: black . reformatted ../datasette/setup.py All done! ✨ 🍰 ✨ 1 file reformatted, 94 files left unchanged. | 12 | |
106 | blacken-docs | The blacken-docs command applies Black formatting rules to code examples in the documentation. Run it like this: blacken-docs -l 60 docs/*.rst | 12 | |
107 | Prettier | To install Prettier, install Node.js and then run the following in the root of your datasette repository checkout: npm install This will install Prettier in a node_modules directory. You can then check that your code matches the coding style like so: npm run prettier -- --check > prettier > prettier 'datasette/static/*[!.min].js' "--check" Checking formatting... [warn] datasette/static/plugins.js [warn] Code style issues found in the above file(s). Forgot to run Prettier? You can fix any problems by running: npm run fix | 12 | |
108 | 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. | 12 | |
109 | Running Cog | Some pages of documentation (in particular the CLI reference ) are automatically updated using Cog . To update these pages, run the following command: cog -r docs/*.rst | 12 | |
110 | Continuously deployed demo instances | The demo instance at latest.datasette.io is re-deployed automatically to Google Cloud Run for every push to main that passes the test suite. This is implemented by the GitHub Actions workflow at .github/workflows/deploy-latest.yml . Specific branches can also be set to automatically deploy by adding them to the on: push: branches block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass. The Cloud Run URL for a branch demo can be found in the GitHub Actions logs. | 12 | |
111 | Release process | Datasette releases are performed using tags. When a new release is published on GitHub, a GitHub Action workflow will perform the following: Run the unit tests against all supported Python versions. If the tests pass... Build a Docker image of the release and push a tag to https://hub.docker.com/r/datasetteproject/datasette Re-point the "latest" tag on Docker Hub to the new image Build a wheel bundle of the underlying Python source code Push that new wheel up to PyPI: https://pypi.org/project/datasette/ If the release is an alpha, navigate to https://readthedocs.org/projects/datasette/versions/ and search for the tag name in the "Activate a version" filter, then mark that version as "active" to ensure it will appear on the public ReadTheDocs documentation site. To deploy new releases you will need to have push access to the main Datasette GitHub repository. Datasette follows Semantic Versioning : major.minor.patch We increment major for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always 0 . We increment minor for new features. We increment patch for bugfix releass. Alpha and beta releases may have an additional a0 or b0 prefix - the integer component will be incremented with each subsequent alpha or beta. To release a new version, first create a commit that updates the version number in datasette/version.py and the the changelog with highlights of the new version. An example commit can be seen here : # Update changelog git commit -m " Release 0.51a1 Ref… | 12 | |
112 | Alpha and beta releases | Alpha and beta releases are published to preview upcoming features that may not yet be stable - in particular to preview new plugin hooks. You are welcome to try these out, but please be aware that details may change before the final release. Please join discussions on the issue tracker to share your thoughts and experiences with on alpha and beta features that you try out. | 12 | |
113 | 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 | 12 | |
114 | Upgrading CodeMirror | Datasette bundles CodeMirror for the SQL editing interface, e.g. on this page . Here are the steps for upgrading to a new version of CodeMirror: Install the packages with: npm i codemirror @codemirror/lang-sql Build the bundle using the version number from package.json with: node_modules/.bin/rollup datasette/static/cm-editor-6.0.1.js \ -f iife \ -n cm \ -o datasette/static/cm-editor-6.0.1.bundle.js \ -p @rollup/plugin-node-resolve \ -p @rollup/plugin-terser Update the version reference in the codemirror.html template. | 12 | |
115 | CLI reference | The datasette CLI tool provides a number of commands. Running datasette without specifying a command runs the default command, datasette serve . See datasette serve for the full list of options for that command. [[[cog from datasette import cli from click.testing import CliRunner import textwrap def help(args): title = "datasette " + " ".join(args) cog.out("\n::\n\n") result = CliRunner().invoke(cli.cli, args) output = result.output.replace("Usage: cli ", "Usage: datasette ") cog.out(textwrap.indent(output, ' ')) cog.out("\n\n") ]]] [[[end]]] | 12 | |
116 | datasette --help | Running datasette --help shows a list of all of the available commands. [[[cog help(["--help"]) ]]] Usage: datasette [OPTIONS] COMMAND [ARGS]... Datasette is an open source multi-tool for exploring and publishing data About Datasette: https://datasette.io/ Full documentation: https://docs.datasette.io/ Options: --version Show the version and exit. --help Show this message and exit. Commands: serve* Serve up specified SQLite database files with a web UI create-token Create a signed API token for the specified actor ID inspect Generate JSON summary of provided database files install Install plugins and packages from PyPI into the same... package Package SQLite files into a Datasette Docker container plugins List currently installed plugins publish Publish specified SQLite database files to the internet... uninstall Uninstall plugins and Python packages from the Datasette... [[[end]]] Additional commands added by plugins that use the register_commands(cli) hook will be listed here as well. | 12 | |
117 | datasette serve | This command starts the Datasette web application running on your machine: datasette serve mydatabase.db Or since this is the default command you can run this instead: datasette mydatabase.db Once started you can access it at http://localhost:8001 [[[cog help(["serve", "--help"]) ]]] Usage: datasette serve [OPTIONS] [FILES]... Serve up specified SQLite database files with a web UI Options: -i, --immutable PATH Database files to open in immutable mode -h, --host TEXT Host for server. Defaults to 127.0.0.1 which means only connections from the local machine will be allowed. Use 0.0.0.0 to listen to all IPs and allow access from other machines. -p, --port INTEGER RANGE Port for server, defaults to 8001. Use -p 0 to automatically assign an available port. [0<=x<=65535] --uds TEXT Bind to a Unix domain socket --reload Automatically reload if code or metadata change detected - useful for development --cors Enable CORS by serving Access-Control-Allow- Origin: * --load-extension PATH:ENTRYPOINT? Path to a SQLite extension to load, and optional entrypoint --inspect-file TEXT Path to JSON file created using "datasette inspect" -m, --metadata FILENAME Path to JSON/YAML file containing license/source metadata --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files fr… | 12 | |
118 | datasette --get | The --get option to datasette serve (or just datasette ) specifies the path to a page within Datasette and causes Datasette to output the content from that path without starting the web server. This means that all of Datasette's functionality can be accessed directly from the command-line. For example: datasette --get '/-/versions.json' | jq . { "python": { "version": "3.8.5", "full": "3.8.5 (default, Jul 21 2020, 10:48:26) \n[Clang 11.0.3 (clang-1103.0.32.62)]" }, "datasette": { "version": "0.46+15.g222a84a.dirty" }, "asgi": "3.0", "uvicorn": "0.11.8", "sqlite": { "version": "3.32.3", "fts_versions": [ "FTS5", "FTS4", "FTS3" ], "extensions": { "json1": null }, "compile_options": [ "COMPILER=clang-11.0.3", "ENABLE_COLUMN_METADATA", "ENABLE_FTS3", "ENABLE_FTS3_PARENTHESIS", "ENABLE_FTS4", "ENABLE_FTS5", "ENABLE_GEOPOLY", "ENABLE_JSON1", "ENABLE_PREUPDATE_HOOK", "ENABLE_RTREE", "ENABLE_SESSION", "MAX_VARIABLE_NUMBER=250000", "THREADSAFE=1" ] } } You can use the --token TOKEN option to send an API token with the simulated request. Or you can make a request as a specific actor by passing a JSON representation of that actor to --actor : datasette --memory --actor '{"id": "root"}' --get '/-/actor.json' The exit code of datasette --get will be 0 if the request succeeds and 1 if the request produced an HTTP status code other than 200 - e.g. a 404 or 500 error. This lets you use datasette --get / to run tests against a Datasette application in a continuous integration environment such as GitHub Actions. | 12 | |
119 | datasette serve --help-settings | This command outputs all of the available Datasette settings . These can be passed to datasette serve using datasette serve --setting name value . [[[cog help(["--help-settings"]) ]]] Settings: default_page_size Default page size for the table view (default=100) max_returned_rows Maximum rows that can be returned from a table or custom query (default=1000) max_insert_rows Maximum rows that can be inserted at a time using the bulk insert API (default=100) num_sql_threads Number of threads in the thread pool for executing SQLite queries (default=3) sql_time_limit_ms Time limit for a SQL query in milliseconds (default=1000) default_facet_size Number of values to return for requested facets (default=30) facet_time_limit_ms Time limit for calculating a requested facet (default=200) facet_suggest_time_limit_ms Time limit for calculating a suggested facet (default=50) allow_facet Allow users to specify columns to facet using ?_facet= parameter (default=True) allow_download Allow users to download the original SQLite database files (default=True) allow_signed_tokens Allow users to create and use signed API tokens (default=True) default_allow_sql Allow anyone to run arbitrary SQL queries (default=True) max_signed_tokens_ttl Maximum allowed expiry time for signed API tokens (default=0) suggest_facets Calculate and display suggested facets (default… | 12 | |
120 | datasette plugins | Output JSON showing all currently installed plugins, their versions, whether they include static files or templates and which Plugin hooks they use. [[[cog help(["plugins", "--help"]) ]]] Usage: datasette plugins [OPTIONS] List currently installed plugins Options: --all Include built-in default plugins --requirements Output requirements.txt of installed plugins --plugins-dir DIRECTORY Path to directory containing custom plugins --help Show this message and exit. [[[end]]] Example output: [ { "name": "datasette-geojson", "static": false, "templates": false, "version": "0.3.1", "hooks": [ "register_output_renderer" ] }, { "name": "datasette-geojson-map", "static": true, "templates": false, "version": "0.4.0", "hooks": [ "extra_body_script", "extra_css_urls", "extra_js_urls" ] }, { "name": "datasette-leaflet", "static": true, "templates": false, "version": "0.2.2", "hooks": [ "extra_body_script", "extra_template_vars" ] } ] | 12 | |
121 | datasette install | Install new Datasette plugins. This command works like pip install but ensures that your plugins will be installed into the same environment as Datasette. This command: datasette install datasette-cluster-map Would install the datasette-cluster-map plugin. [[[cog help(["install", "--help"]) ]]] Usage: datasette install [OPTIONS] [PACKAGES]... Install plugins and packages from PyPI into the same environment as Datasette Options: -U, --upgrade Upgrade packages to latest version -r, --requirement PATH Install from requirements file -e, --editable TEXT Install a project in editable mode from this path --help Show this message and exit. [[[end]]] | 12 | |
122 | datasette uninstall | Uninstall one or more plugins. [[[cog help(["uninstall", "--help"]) ]]] Usage: datasette uninstall [OPTIONS] PACKAGES... Uninstall plugins and Python packages from the Datasette environment Options: -y, --yes Don't ask for confirmation --help Show this message and exit. [[[end]]] | 12 | |
123 | datasette publish | Shows a list of available deployment targets for publishing data with Datasette. Additional deployment targets can be added by plugins that use the publish_subcommand(publish) hook. [[[cog help(["publish", "--help"]) ]]] Usage: datasette publish [OPTIONS] COMMAND [ARGS]... Publish specified SQLite database files to the internet along with a Datasette-powered interface and API Options: --help Show this message and exit. Commands: cloudrun Publish databases to Datasette running on Cloud Run heroku Publish databases to Datasette running on Heroku [[[end]]] | 12 | |
124 | datasette publish cloudrun | See Publishing to Google Cloud Run . [[[cog help(["publish", "cloudrun", "--help"]) ]]] Usage: datasette publish cloudrun [OPTIONS] [FILES]... Publish databases to Datasette running on Cloud Run Options: -m, --metadata FILENAME Path to JSON/YAML file containing metadata to publish --extra-options TEXT Extra options to pass to datasette serve --branch TEXT Install datasette from a GitHub branch e.g. main --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/... --install TEXT Additional packages (e.g. plugins) to install --plugin-secret <TEXT TEXT TEXT>... Secrets to pass to plugins, e.g. --plugin- secret datasette-auth-github client_id xxx --version-note TEXT Additional note to show on /-/versions --secret TEXT Secret used for signing secure values, such as signed cookies --title TEXT Title for metadata --license TEXT License label for metadata --license_url TEXT License URL for metadata --source TEXT Source label for metadata --source_url TEXT Source URL for metadata --about TEXT About label for metadata --about_url TEXT About URL for metadata -n, --name TEXT Application name to use when building --service TEXT Cloud Run service to deploy (or over-write) --spatialite Enable SpatialLite extension --show-files Output the generated Dockerfile and metad… | 12 | |
125 | datasette publish heroku | See Publishing to Heroku . [[[cog help(["publish", "heroku", "--help"]) ]]] Usage: datasette publish heroku [OPTIONS] [FILES]... Publish databases to Datasette running on Heroku Options: -m, --metadata FILENAME Path to JSON/YAML file containing metadata to publish --extra-options TEXT Extra options to pass to datasette serve --branch TEXT Install datasette from a GitHub branch e.g. main --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/... --install TEXT Additional packages (e.g. plugins) to install --plugin-secret <TEXT TEXT TEXT>... Secrets to pass to plugins, e.g. --plugin- secret datasette-auth-github client_id xxx --version-note TEXT Additional note to show on /-/versions --secret TEXT Secret used for signing secure values, such as signed cookies --title TEXT Title for metadata --license TEXT License label for metadata --license_url TEXT License URL for metadata --source TEXT Source label for metadata --source_url TEXT Source URL for metadata --about TEXT About label for metadata --about_url TEXT About URL for metadata -n, --name TEXT Application name to use when deploying --tar TEXT --tar option to pass to Heroku, e.g. --tar=/usr/local/bin/gtar --generate-dir DIRECTORY Output generated application files and stop without deploying --h… | 12 | |
126 | datasette package | Package SQLite files into a Datasette Docker container, see datasette package . [[[cog help(["package", "--help"]) ]]] Usage: datasette package [OPTIONS] FILES... Package SQLite files into a Datasette Docker container Options: -t, --tag TEXT Name for the resulting Docker container, can optionally use name:tag format -m, --metadata FILENAME Path to JSON/YAML file containing metadata to publish --extra-options TEXT Extra options to pass to datasette serve --branch TEXT Install datasette from a GitHub branch e.g. main --template-dir DIRECTORY Path to directory containing custom templates --plugins-dir DIRECTORY Path to directory containing custom plugins --static MOUNT:DIRECTORY Serve static files from this directory at /MOUNT/... --install TEXT Additional packages (e.g. plugins) to install --spatialite Enable SpatialLite extension --version-note TEXT Additional note to show on /-/versions --secret TEXT Secret used for signing secure values, such as signed cookies -p, --port INTEGER RANGE Port to run the server on, defaults to 8001 [1<=x<=65535] --title TEXT Title for metadata --license TEXT License label for metadata --license_url TEXT License URL for metadata --source TEXT Source label for metadata --source_url TEXT Source URL for metadata --about TEXT About label for metadata --about_url TEXT About URL for metadata --help Show this message and exit. [[[end]]] | 12 | |
127 | datasette inspect | Outputs JSON representing introspected data about one or more SQLite database files. If you are opening an immutable database, you can pass this file to the --inspect-data option to improve Datasette's performance by allowing it to skip running row counts against the database when it first starts running: datasette inspect mydatabase.db > inspect-data.json datasette serve -i mydatabase.db --inspect-file inspect-data.json This performance optimization is used automatically by some of the datasette publish commands. You are unlikely to need to apply this optimization manually. [[[cog help(["inspect", "--help"]) ]]] Usage: datasette inspect [OPTIONS] [FILES]... Generate JSON summary of provided database files This can then be passed to "datasette --inspect-file" to speed up count operations against immutable database files. Options: --inspect-file TEXT --load-extension PATH:ENTRYPOINT? Path to a SQLite extension to load, and optional entrypoint --help Show this message and exit. [[[end]]] | 12 | |
128 | datasette create-token | Create a signed API token, see datasette create-token . [[[cog help(["create-token", "--help"]) ]]] Usage: datasette create-token [OPTIONS] ID Create a signed API token for the specified actor ID Example: datasette create-token root --secret mysecret To allow only "view-database-download" for all databases: datasette create-token root --secret mysecret \ --all view-database-download To allow "create-table" against a specific database: datasette create-token root --secret mysecret \ --database mydb create-table To allow "insert-row" against a specific table: datasette create-token root --secret myscret \ --resource mydb mytable insert-row Restricted actions can be specified multiple times using multiple --all, --database, and --resource options. Add --debug to see a decoded version of the token. Options: --secret TEXT Secret used for signing the API tokens [required] -e, --expires-after INTEGER Token should expire after this many seconds -a, --all ACTION Restrict token to this action -d, --database DB ACTION Restrict token to this action on this database -r, --resource DB RESOURCE ACTION Restrict token to this action on this database resource (a table, SQL view or named query) --debug Show decoded token --plugins-dir DIRECTORY Path to directory containing custom plugins --help Show this message and exit. [[[end]]] | 12 | |
129 | Metadata | Data loves metadata. Any time you run Datasette you can optionally include a YAML or JSON file with metadata about your databases and tables. Datasette will then display that information in the web UI. Run Datasette like this: datasette database1.db database2.db --metadata metadata.yaml Your metadata.yaml file can look something like this: [[[cog from metadata_doc import metadata_example metadata_example(cog, { "title": "Custom title for your index page", "description": "Some description text can go here", "license": "ODbL", "license_url": "https://opendatacommons.org/licenses/odbl/", "source": "Original Data Source", "source_url": "http://example.com/" }) ]]] [[[end]]] Choosing YAML over JSON adds support for multi-line strings and comments. The above metadata will be displayed on the index page of your Datasette-powered site. The source and license information will also be included in the footer of every page served by Datasette. Any special HTML characters in description will be escaped. If you want to include HTML in your description, you can use a description_html property instead. | 12 | |
130 | Per-database and per-table metadata | Metadata at the top level of the file will be shown on the index page and in the footer on every page of the site. The license and source is expected to apply to all of your data. You can also provide metadata at the per-database or per-table level, like this: [[[cog metadata_example(cog, { "databases": { "database1": { "source": "Alternative source", "source_url": "http://example.com/", "tables": { "example_table": { "description_html": "Custom <em>table</em> description", "license": "CC BY 3.0 US", "license_url": "https://creativecommons.org/licenses/by/3.0/us/" } } } } }) ]]] [[[end]]] Each of the top-level metadata fields can be used at the database and table level. | 12 | |
131 | Source, license and about | The three visible metadata fields you can apply to everything, specific databases or specific tables are source, license and about. All three are optional. source and source_url should be used to indicate where the underlying data came from. license and license_url should be used to indicate the license under which the data can be used. about and about_url can be used to link to further information about the project - an accompanying blog entry for example. For each of these you can provide just the *_url field and Datasette will treat that as the default link label text and display the URL directly on the page. | 12 | |
132 | Column descriptions | You can include descriptions for your columns by adding a "columns": {"name-of-column": "description-of-column"} block to your table metadata: [[[cog metadata_example(cog, { "databases": { "database1": { "tables": { "example_table": { "columns": { "column1": "Description of column 1", "column2": "Description of column 2" } } } } } }) ]]] [[[end]]] These will be displayed at the top of the table page, and will also show in the cog menu for each column. You can see an example of how these look at latest.datasette.io/fixtures/roadside_attractions . | 12 | |
133 | 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: [[[cog metadata_example(cog, { "databases": { "database1": { "tables": { "example_table": { "units": { "column1": "metres", "column2": "Hz" } } } } } }) ]]] [[[end]]] 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: [[[cog metadata_example(cog, { "custom_units": [ "decibel = [] = dB" ] }) ]]] [[[end]]] | 12 | |
134 | Setting a default sort order | 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: [[[cog metadata_example(cog, { "databases": { "mydatabase": { "tables": { "example_table": { "sort": "created" } } } } }) ]]] [[[end]]] Or use "sort_desc" to sort in descending order: [[[cog metadata_example(cog, { "databases": { "mydatabase": { "tables": { "example_table": { "sort_desc": "created" } } } } }) ]]] [[[end]]] | 12 | |
135 | Setting a custom page size | 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 : [[[cog metadata_example(cog, { "databases": { "mydatabase": { "tables": { "example_table": { "size": 10 } } } } }) ]]] [[[end]]] This size can still be over-ridden by passing e.g. ?_size=50 in the query string. | 12 | |
136 | Setting which columns can be used for sorting | Datasette allows any column to be used for sorting by default. If you need to control which columns are available for sorting you can do so using the optional sortable_columns key: [[[cog metadata_example(cog, { "databases": { "database1": { "tables": { "example_table": { "sortable_columns": [ "height", "weight" ] } } } } }) ]]] [[[end]]] This will restrict sorting of example_table to just the height and weight columns. You can also disable sorting entirely by setting "sortable_columns": [] You can use sortable_columns to enable specific sort orders for a view called name_of_view in the database my_database like so: [[[cog metadata_example(cog, { "databases": { "my_database": { "tables": { "name_of_view": { "sortable_columns": [ "clicks", "impressions" ] } } } } }) ]]] [[[end]]] | 12 | |
137 | 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: [[[cog metadata_example(cog, { "databases": { "database1": { "tables": { "example_table": { "label_column": "title" } } } } }) ]]] [[[end]]] | 12 | |
138 | Hiding tables | You can hide tables from the database listing view (in the same way that FTS and SpatiaLite tables are automatically hidden) using "hidden": true : [[[cog metadata_example(cog, { "databases": { "database1": { "tables": { "example_table": { "hidden": True } } } } }) ]]] [[[end]]] | 12 | |
139 | Metadata reference | A full reference of every supported option in a metadata.json or metadata.yaml file. | 12 | |
140 | Top-level metadata | "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. The following are the full list of allowed top-level metadata fields: title description description_html license license_url source source_url | 12 | |
141 | Database-level metadata | "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. The following are the full list of allowed database-level metadata fields: source source_url license license_url about about_url | 12 | |
142 | Table-level metadata | "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. The following are the full list of allowed table-level metadata fields: source source_url license license_url about about_url hidden sort/sort_desc size sortable_columns label_column facets fts_table fts_pk searchmode columns | 12 | |
143 | 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. See Controlling the ability to execute arbitrary SQL . 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. | 12 | |
144 | Named parameters | Datasette has special support for SQLite named parameters. Consider a SQL query like this: select * from Street_Tree_List where "PermitNotes" like :notes and "qSpecies" = :species If you execute this query using the custom query editor, Datasette will extract the two named parameters and use them to construct form fields for you to provide values. You can also provide values for these fields by constructing a URL: /mydatabase?sql=select...&species=44 SQLite string escaping rules will be applied to values passed using named parameters - they will be wrapped in quotes and their content will be correctly escaped. Values from named parameters are treated as SQLite strings. If you need to perform numeric comparisons on them you should cast them to an integer or float first using cast(:name as integer) or cast(:name as real) , for example: select * from Street_Tree_List where latitude > cast(:min_latitude as real) and latitude < cast(:max_latitude as real) Datasette disallows custom SQL queries containing the string PRAGMA (with a small number of exceptions ) as SQLite pragma statements can be used to change database settings at runtime. If you need to include the string "pragma" in a query you can do so safely using a named parameter. | 12 | |
145 | 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> You can also use the sqlite-utils tool to create a view : sqlite-utils create-view sf-trees.db demo_view "select qSpecies from Street_Tree_List" | 12 | |
146 | Canned queries | As an alternative to adding views to your database, you can define canned queries inside your datasette.yaml file. Here's an example: [[[cog from metadata_doc import config_example, config_example config_example(cog, { "databases": { "sf-trees": { "queries": { "just_species": { "sql": "select qSpecies from Street_Tree_List" } } } } }) ]]] [[[end]]] Then run Datasette like this: datasette sf-trees.db -m metadata.json Each canned query will be listed on the database index page, and will also get its own URL at: /database-name/canned-query-name For the above example, that URL would be: /sf-trees/just_species You can optionally include "title" and "description" keys to show a title and description on the canned query page. As with regular table metadata you can alternatively specify "description_html" to have your description rendered as HTML (rather than having HTML special characters escaped). | 12 | |
147 | Canned query parameters | Canned queries support named parameters, so if you include those in the SQL you will then be able to enter them using the form fields on the canned query page or by adding them to the URL. This means canned queries can be used to create custom JSON APIs based on a carefully designed SQL statement. Here's an example of a canned query with a named parameter: select neighborhood, facet_cities.name, state from facetable join facet_cities on facetable.city_id = facet_cities.id where neighborhood like '%' || :text || '%' order by neighborhood; In the canned query configuration looks like this: [[[cog config_example(cog, """ databases: fixtures: queries: neighborhood_search: title: Search neighborhoods sql: |- select neighborhood, facet_cities.name, state from facetable join facet_cities on facetable.city_id = facet_cities.id where neighborhood like '%' || :text || '%' order by neighborhood """) ]]] [[[end]]] Note that we are using SQLite string concatenation here - the || operator - to add wildcard % characters to the string provided by the user. You can try this canned query out here: https://latest.datasette.io/fixtures/neighborhood_search?text=town In this example the :text named parameter is automatically extracted from the query using a regular expression. You can alternatively provide an explicit list of named parameters using the "params" key, like this: [[[cog config_example(cog, """ databases: fixtures: queries: neighborhood_search: title: Search neighborhoods params: - text sql: |- select neighborhood, facet_cities.name, state from facetable join facet_cities on facetable.city_id = facet_cities.id where neighborhood like … | 12 | |
148 | Additional canned query options | Additional options can be specified for canned queries in the YAML or JSON configuration. | 12 | |
149 | hide_sql | Canned queries default to displaying their SQL query at the top of the page. If the query is extremely long you may want to hide it by default, with a "show" link that can be used to make it visible. Add the "hide_sql": true option to hide the SQL query by default. | 12 | |
150 | fragment | Some plugins, such as datasette-vega , can be configured by including additional data in the fragment hash of the URL - the bit that comes after a # symbol. You can set a default fragment hash that will be included in the link to the canned query from the database index page using the "fragment" key. This example demonstrates both fragment and hide_sql : [[[cog config_example(cog, """ databases: fixtures: queries: neighborhood_search: fragment: fragment-goes-here hide_sql: true sql: |- select neighborhood, facet_cities.name, state from facetable join facet_cities on facetable.city_id = facet_cities.id where neighborhood like '%' || :text || '%' order by neighborhood; """) ]]] [[[end]]] See here for a demo of this in action. | 12 | |
151 | Writable canned queries | Canned queries by default are read-only. You can use the "write": true key to indicate that a canned query can write to the database. See Access to specific canned queries for details on how to add permission checks to canned queries, using the "allow" key. [[[cog config_example(cog, { "databases": { "mydatabase": { "queries": { "add_name": { "sql": "INSERT INTO names (name) VALUES (:name)", "write": True } } } } }) ]]] [[[end]]] This configuration will create a page at /mydatabase/add_name displaying a form with a name field. Submitting that form will execute the configured INSERT query. You can customize how Datasette represents success and errors using the following optional properties: on_success_message - the message shown when a query is successful on_success_message_sql - alternative to on_success_message : a SQL query that should be executed to generate the message on_success_redirect - the path or URL the user is redirected to on success on_error_message - the message shown when a query throws an error on_error_redirect - the path or URL the user is redirected to on error For example: [[[cog config_example(cog, { "databases": { "mydatabase": { "queries": { "add_name": { "sql": "INSERT INTO names (name) VALUES (:name)", "params": ["name"], "write": Tru… | 12 | |
152 | Magic parameters | Named parameters that start with an underscore are special: they can be used to automatically add values created by Datasette that are not contained in the incoming form fields or query string. These magic parameters are only supported for canned queries: to avoid security issues (such as queries that extract the user's private cookies) they are not available to SQL that is executed by the user as a custom SQL query. Available magic parameters are: _actor_* - e.g. _actor_id , _actor_name Fields from the currently authenticated Actors . _header_* - e.g. _header_user_agent Header from the incoming HTTP request. The key should be in lower case and with hyphens converted to underscores e.g. _header_user_agent or _header_accept_language . _cookie_* - e.g. _cookie_lang The value of the incoming cookie of that name. _now_epoch The number of seconds since the Unix epoch. _now_date_utc The date in UTC, e.g. 2020-06-01 _now_datetime_utc The ISO 8601 datetime in UTC, e.g. 2020-06-24T18:01:07Z _random_chars_* - e.g. … | 12 | |
153 | JSON API for writable canned queries | Writable canned queries can also be accessed using a JSON API. You can POST data to them using JSON, and you can request that their response is returned to you as JSON. To submit JSON to a writable canned query, encode key/value parameters as a JSON document: POST /mydatabase/add_message {"message": "Message goes here"} You can also continue to submit data using regular form encoding, like so: POST /mydatabase/add_message message=Message+goes+here There are three options for specifying that you would like the response to your request to return JSON data, as opposed to an HTTP redirect to another page. Set an Accept: application/json header on your request Include ?_json=1 in the URL that you POST to Include "_json": 1 in your JSON body, or &_json=1 in your form encoded body The JSON response will look like this: { "ok": true, "message": "Query executed, 1 row affected", "redirect": "/data/add_name" } The "message" and "redirect" values here will take into account on_success_message , on_success_message_sql , on_success_redirect , on_error_message and on_error_redirect , if they have been set. | 12 | |
154 | 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. | 12 | |
155 | Cross-database queries | SQLite has the ability to run queries that join across multiple databases. Up to ten databases can be attached to a single SQLite connection and queried together. Datasette can execute joins across multiple databases if it is started with the --crossdb option: datasette fixtures.db extra_database.db --crossdb If it is started in this way, the /_memory page can be used to execute queries that join across multiple databases. References to tables in attached databases should be preceded by the database name and a period. For example, this query will show a list of tables across both of the above databases: select 'fixtures' as database, * from [fixtures].sqlite_master union select 'extra_database' as database, * from [extra_database].sqlite_master Try that out here . | 12 | |
156 | JavaScript plugins | Datasette can run custom JavaScript in several different ways: Datasette plugins written in Python can use the extra_js_urls() or extra_body_script() plugin hooks to inject JavaScript into a page Datasette instances with custom templates can include additional JavaScript in those templates The extra_js_urls key in datasette.yaml can be used to include extra JavaScript 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. 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. | 12 | |
157 | The datasette_init event | 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. Your JavaScript code can listen out for this event using document.addEventListener() like this: document.addEventListener("datasette_init", function (evt) { const manager = evt.detail; console.log("Datasette version:", manager.VERSION); }); | 12 | |
158 | datasetteManager | The datasetteManager object VERSION - string The version of Datasette plugins - Map() A Map of currently loaded plugin names to plugin implementations registerPlugin(name, implementation) Call this to register a plugin, passing its name and implementation selectors - object An object providing named aliases to useful CSS selectors, listed below | 12 | |
159 | JavaScript plugin objects | JavaScript plugins are blocks of code that can be registered with Datasette using the registerPlugin() method on the datasetteManager object. 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: | 12 | |
160 | makeAboveTablePanelConfigs() | 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: id - string A unique string ID for the panel, for example map-panel label - string A human-readable label for the panel render(node) - function A function that will be called with a DOM node to render the panel into This example shows how a plugin might define a single panel: document.addEventListener('datasette_init', function(ev) { ev.detail.registerPlugin('panel-plugin', { version: 0.1, makeAboveTablePanelConfigs: () => { return [ { id: 'first-panel', label: 'First panel', render: node => { node.innerHTML = '<h2>My custom panel</h2><p>This is a custom panel that I added using a JavaScript plugin</p>'; } } ] } }); }); 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. | 12 | |
161 | makeColumnActions(columnDetails) | 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. The method will be called with a columnDetails object with the following keys: columnName - string The name of the column columnNotNull - boolean True if the column is defined as NOT NULL columnType - string The SQLite data type of the column isPk - boolean True if the column is part of the primary key It should return a JavaScript array of objects each with a label and onClick property: label - string The human-readable label for the action onClick(evt) - function A function that will be called when the action is clicked The evt object passed to the onClick is the standard browser event object that triggered the click. This example plugin adds two menu items - one to copy … | 12 | |
162 | Selectors | These are available on the selectors property of the datasetteManager object. const DOM_SELECTORS = { /** Should have one match */ jsonExportLink: ".export-links a[href*=json]", /** Event listeners that go outside of the main table, e.g. existing scroll listener */ tableWrapper: ".table-wrapper", table: "table.rows-and-columns", aboveTablePanel: ".above-table-panel", // These could have multiple matches /** Used for selecting table headers. Use makeColumnActions if you want to add menu items. */ tableHeaders: `table.rows-and-columns th`, /** Used to add "where" clauses to query using direct manipulation */ filterRows: ".filter-row", /** Used to show top available enum values for a column ("facets") */ facetResults: ".facet-results [data-column]", }; | 12 | |
163 | Custom pages and templates | Datasette provides a number of ways of customizing the way data is displayed. | 12 | |
164 | 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> <td class="col-name">SMITH</td> </tr> </tbody> </table> | 12 | |
165 | Serving static files | Datasette can serve static files for you, using the --static option. Consider the following directory structure: metadata.json static-files/styles.css static-files/app.js You can start Datasette using --static assets:static-files/ to serve those files from the /assets/ mount point: datasette --config datasette.yaml --static assets:static-files/ --memory The following URLs will now serve the content from those CSS and JS files: http://localhost:8001/assets/styles.css http://localhost:8001/assets/app.js You can reference those files from datasette.yaml like this, see custom CSS and JavaScript for more details: [[[cog from metadata_doc import config_example config_example(cog, """ extra_css_urls: - /assets/styles.css extra_js_urls: - /assets/app.js """) ]]] [[[end]]] | 12 | |
166 | Publishing static assets | The datasette publish command can be used to publish your static assets, using the same syntax as above: datasette publish cloudrun mydb.db --static assets:static-files/ This will upload the contents of the static-files/ directory as part of the deployment, and configure Datasette to correctly serve the assets from /assets/ . | 12 | |
167 | Custom templates | By default, Datasette uses default templates that ship with the package. You can over-ride these templates by specifying a custom --template-dir like this: datasette mydb.db --template-dir=mytemplates/ Datasette will now first look for templates in that directory, and fall back on the defaults if no matches are found. It is also possible to over-ride templates on a per-database, per-row or per- table basis. The lookup rules Datasette uses are as follows: Index page (/): index.html Database page (/mydatabase): database-mydatabase.html database.html Custom query page (/mydatabase?sql=...): query-mydatabase.html query.html Canned query page (/mydatabase/canned-query): query-mydatabase-canned-query.html query-mydatabase.html query.html Table page (/mydatabase/mytable): table-mydatabase-mytable.html table.html Row page (/mydatabase/mytable/id): row-mydatabase-mytable.html row.html Table of rows and columns include on table page: _table-table-mydatabase-mytable.html _table-mydatabase-mytable.html _table.html Table of rows and columns include on row page: _table-row-mydatabase-mytable.html _table-mydatabase-mytable.html _table.html If a table name has spaces or other unexpected characters in it, the template filename will follow the same rules as our custom <body> CSS classes - for example, a table called "Food Trucks" will attempt to load the following templates: table-mydatabase-Food-Trucks-399138.html table.html You can find out which templates were considered for a specific page by viewing source on that page and looking for an HTML comment at the bottom. The comment will look something like this: <!-… | 12 | |
168 | Custom pages | You can add templated pages to your Datasette instance by creating HTML files in a pages directory within your templates directory. For example, to add a custom page that is served at http://localhost/about you would create a file in templates/pages/about.html , then start Datasette like this: datasette mydb.db --template-dir=templates/ You can nest directories within pages to create a nested structure. To create a http://localhost:8001/about/map page you would create templates/pages/about/map.html . | 12 | |
169 | Path parameters for pages | You can define custom pages that match multiple paths by creating files with {variable} definitions in their filenames. For example, to capture any request to a URL matching /about/* , you would create a template in the following location: templates/pages/about/{slug}.html A hit to /about/news would render that template and pass in a variable called slug with a value of "news" . If you use this mechanism don't forget to return a 404 if the referenced content could not be found. You can do this using {{ raise_404() }} described below. Templates defined using custom page routes work particularly well with the sql() template function from datasette-template-sql or the graphql() template function from datasette-graphql . | 12 | |
170 | Custom headers and status codes | Custom pages default to being served with a content-type of text/html; charset=utf-8 and a 200 status code. You can change these by calling a custom function from within your template. For example, to serve a custom page with a 418 I'm a teapot HTTP status code, create a file in pages/teapot.html containing the following: {{ custom_status(418) }} <html> <head><title>Teapot</title></head> <body> I'm a teapot </body> </html> To serve a custom HTTP header, add a custom_header(name, value) function call. For example: {{ custom_status(418) }} {{ custom_header("x-teapot", "I am") }} <html> <head><title>Teapot</title></head> <body> I'm a teapot </body> </html> You can verify this is working using curl like this: curl -I 'http://127.0.0.1:8001/teapot' HTTP/1.1 418 date: Sun, 26 Apr 2020 18:38:30 GMT server: uvicorn x-teapot: I am content-type: text/html; charset=utf-8 | 12 | |
171 | Returning 404s | To indicate that content could not be found and display the default 404 page you can use the raise_404(message) function: {% if not rows %} {{ raise_404("Content not found") }} {% endif %} If you call raise_404() the other content in your template will be ignored. | 12 | |
172 | Custom redirects | You can use the custom_redirect(location) function to redirect users to another page, for example in a file called pages/datasette.html : {{ custom_redirect("https://github.com/simonw/datasette") }} Now requests to http://localhost:8001/datasette will result in a redirect. These redirects are served with a 302 Found status code by default. You can send a 301 Moved Permanently code by passing 301 as the second argument to the function: {{ custom_redirect("https://github.com/simonw/datasette", 301) }} | 12 | |
173 | 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. | 12 | |
174 | Writing plugins | You can write one-off plugins that apply to just one Datasette instance, or you can write plugins which can be installed using pip and can be shipped to the Python Package Index ( PyPI ) for other people to install. Want to start by looking at an example? The Datasette plugins directory lists more than 90 open source plugins with code you can explore. The plugin hooks page includes links to example plugins for each of the documented hooks. | 12 | |
175 | Tracing plugin hooks | The DATASETTE_TRACE_PLUGINS environment variable turns on detailed tracing showing exactly which hooks are being run. This can be useful for understanding how Datasette is using your plugin. DATASETTE_TRACE_PLUGINS=1 datasette mydb.db Example output: actor_from_request: { 'datasette': <datasette.app.Datasette object at 0x100bc7220>, 'request': <asgi.Request method="GET" url="http://127.0.0.1:4433/">} Hook implementations: [ <HookImpl plugin_name='codespaces', plugin=<module 'datasette_codespaces' from '.../site-packages/datasette_codespaces/__init__.py'>>, <HookImpl plugin_name='datasette.actor_auth_cookie', plugin=<module 'datasette.actor_auth_cookie' from '.../datasette/datasette/actor_auth_cookie.py'>>, <HookImpl plugin_name='datasette.default_permissions', plugin=<module 'datasette.default_permissions' from '.../datasette/default_permissions.py'>>] Results: [{'id': 'root'}] | 12 | |
176 | Writing one-off plugins | The quickest way to start writing a plugin is to create a my_plugin.py file and drop it into your plugins/ directory. Here is an example plugin, which adds a new custom SQL function called hello_world() which takes no arguments and returns the string Hello world! . from datasette import hookimpl @hookimpl def prepare_connection(conn): conn.create_function( "hello_world", 0, lambda: "Hello world!" ) If you save this in plugins/my_plugin.py you can then start Datasette like this: datasette serve mydb.db --plugins-dir=plugins/ Now you can navigate to http://localhost:8001/mydb and run this SQL: select hello_world(); To see the output of your plugin. | 12 | |
177 | Starting an installable plugin using cookiecutter | Plugins that can be installed should be written as Python packages using a setup.py file. The quickest way to start writing one an installable plugin is to use the datasette-plugin cookiecutter template. This creates a new plugin structure for you complete with an example test and GitHub Actions workflows for testing and publishing your plugin. Install cookiecutter and then run this command to start building a plugin using the template: cookiecutter gh:simonw/datasette-plugin Read a cookiecutter template for writing Datasette plugins for more information about this template. | 12 | |
178 | Packaging a plugin | Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at https://github.com/simonw/datasette-plugin-demos The example consists of two files: a setup.py file that defines the plugin: from setuptools import setup VERSION = "0.1" setup( name="datasette-plugin-demos", description="Examples of plugins for Datasette", author="Simon Willison", url="https://github.com/simonw/datasette-plugin-demos", license="Apache License, Version 2.0", version=VERSION, py_modules=["datasette_plugin_demos"], entry_points={ "datasette": [ "plugin_demos = datasette_plugin_demos" ] }, install_requires=["datasette"], ) And a Python module file, datasette_plugin_demos.py , that implements the plugin: from datasette import hookimpl import random @hookimpl def prepare_jinja2_environment(env): env.filters["uppercase"] = lambda u: u.upper() @hookimpl def prepare_connection(conn): conn.create_function( "random_integer", 2, random.randint ) Having built a plugin in this way you can turn it into an installable package using the following command: python3 setup.py sdist This will create a .tar.gz file in the dist/ directory. You can then install your new plugin into a Datasette virtual environment or Docker container using pip : pip install datasette-plugin-demos-0.1.tar.gz To learn how to upload your plugin to PyPI for use by other people, read the PyPA guide to Packaging and distributing projects . | 12 | |
179 | Static assets | If your plugin has a static/ directory, Datasette will automatically configure itself to serve those static assets from the following path: /-/static-plugins/NAME_OF_PLUGIN_PACKAGE/yourfile.js Use the datasette.urls.static_plugins(plugin_name, path) method to generate URLs to that asset that take the base_url setting into account, see datasette.urls . To bundle the static assets for a plugin in the package that you publish to PyPI, add the following to the plugin's setup.py : package_data = ( { "datasette_plugin_name": [ "static/plugin.js", ], }, ) Where datasette_plugin_name is the name of the plugin package (note that it uses underscores, not hyphens) and static/plugin.js is the path within that package to the static file. datasette-cluster-map is a useful example of a plugin that includes packaged static assets in this way. | 12 | |
180 | Custom templates | If your plugin has a templates/ directory, Datasette will attempt to load templates from that directory before it uses its own default templates. The priority order for template loading is: templates from the --template-dir argument, if specified templates from the templates/ directory in any installed plugins default templates that ship with Datasette See Custom pages and templates for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI. Templates should be bundled for distribution using the same package_data mechanism in setup.py described for static assets above, for example: package_data = ( { "datasette_plugin_name": [ "templates/my_template.html", ], }, ) You can also use wildcards here such as templates/*.html . See datasette-edit-schema for an example of this pattern. | 12 | |
181 | Writing plugins that accept configuration | When you are writing plugins, you can access plugin configuration like this using the datasette plugin_config() method. If you know you need plugin configuration for a specific table, you can access it like this: plugin_config = datasette.plugin_config( "datasette-cluster-map", database="sf-trees", table="Street_Tree_List" ) This will return the {"latitude_column": "lat", "longitude_column": "lng"} in the above example. If there is no configuration for that plugin, the method will return None . If it cannot find the requested configuration at the table layer, it will fall back to the database layer and then the root layer. For example, a user may have set the plugin configuration option inside datasette.yaml like so: [[[cog from metadata_doc import metadata_example metadata_example(cog, { "databases": { "sf-trees": { "plugins": { "datasette-cluster-map": { "latitude_column": "xlat", "longitude_column": "xlng" } } } } }) ]]] [[[end]]] In this case, the above code would return that configuration for ANY table within the sf-trees database. The plugin configuration could also be set at the top level of datasette.yaml : [[[cog metadata_example(cog, { "plugins": { "datasette-cluster-map": { "latitude_column": "xlat", "longitude_column": "xlng" } } }) ]]] [[[end]]] Now that datasette-cluster-map plugin configuration will apply to every table in every database. | 12 | |
182 | Designing URLs for your plugin | You can register new URL routes within Datasette using the register_routes(datasette) plugin hook. Datasette's default URLs include these: /dbname - database page /dbname/tablename - table page /dbname/tablename/pk - row page See Pages and API endpoints and Introspection for more default URL routes. To avoid accidentally conflicting with a database file that may be loaded into Datasette, plugins should register URLs using a /-/ prefix. For example, if your plugin adds a new interface for uploading Excel files you might register a URL route like this one: /-/upload-excel Try to avoid registering URLs that clash with other plugins that your users might have installed. There is no central repository of reserved URL paths (yet) but you can review existing plugins by browsing the plugins directory . If your plugin includes functionality that relates to a specific database you could also register a URL route like this: /dbname/-/upload-excel Or for a specific table like this: /dbname/tablename/-/modify-table-schema Note that a row could have a primary key of - and this URL scheme will still work, because Datasette row pages do not ever have a trailing slash followed by additional path components. | 12 | |
183 | Building URLs within plugins | Plugins that define their own custom user interface elements may need to link to other pages within Datasette. This can be a bit tricky if the Datasette instance is using the base_url configuration setting to run behind a proxy, since that can cause Datasette's URLs to include an additional prefix. The datasette.urls object provides internal methods for correctly generating URLs to different pages within Datasette, taking any base_url configuration into account. This object is exposed in templates as the urls variable, which can be used like this: Back to the <a href="{{ urls.instance() }}">Homepage</a> See datasette.urls for full details on this object. | 12 | |
184 | Plugins that define new plugin hooks | Plugins can define new plugin hooks that other plugins can use to further extend their functionality. datasette-graphql is one example of a plugin that does this. It defines a new hook called graphql_extra_fields , described here , which other plugins can use to define additional fields that should be included in the GraphQL schema. To define additional hooks, add a file to the plugin called datasette_your_plugin/hookspecs.py with content that looks like this: from pluggy import HookspecMarker hookspec = HookspecMarker("datasette") @hookspec def name_of_your_hook_goes_here(datasette): "Description of your hook." You should define your own hook name and arguments here, following the documentation for Pluggy specifications . Make sure to pick a name that is unlikely to clash with hooks provided by any other plugins. Then, to register your plugin hooks, add the following code to your datasette_your_plugin/__init__.py file: from datasette.plugins import pm from . import hookspecs pm.add_hookspecs(hookspecs) This will register your plugin hooks as part of the datasette plugin hook namespace. Within your plugin code you can trigger the hook using this pattern: from datasette.plugins import pm for ( plugin_return_value ) in pm.hook.name_of_your_hook_goes_here( datasette=datasette ): # Do something with plugin_return_value pass Other plugins will then be able to register their own implementations of your hook using this syntax: from datasette import hookimpl @hookimpl def name_of_your_hook_goes_here(datasette): return "Response from this plugin hook" These plugin implementations can accept 0 or more of the named arguments that you defined in your hook specification. | 12 | |
185 | Deploying Datasette | The quickest way to deploy a Datasette instance on the internet is to use the datasette publish command, described in Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel. You can deploy Datasette to other hosting providers using the instructions on this page. | 12 | |
186 | Deployment fundamentals | Datasette can be deployed as a single datasette process that listens on a port. Datasette is not designed to be run as root, so that process should listen on a higher port such as port 8000. If you want to serve Datasette on port 80 (the HTTP default port) or port 443 (for HTTPS) you should run it behind a proxy server, such as nginx, Apache or HAProxy. The proxy server can listen on port 80/443 and forward traffic on to Datasette. | 12 | |
187 | Running Datasette using systemd | You can run Datasette on Ubuntu or Debian systems using systemd . First, ensure you have Python 3 and pip installed. On Ubuntu you can use sudo apt-get install python3 python3-pip . You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use sudo pip3 install datasette . Now create a folder for your Datasette databases, for example using mkdir /home/ubuntu/datasette-root . You can copy a test database into that folder like so: cd /home/ubuntu/datasette-root curl -O https://latest.datasette.io/fixtures.db Create a file at /etc/systemd/system/datasette.service with the following contents: [Unit] Description=Datasette After=network.target [Service] Type=simple User=ubuntu Environment=DATASETTE_SECRET= WorkingDirectory=/home/ubuntu/datasette-root ExecStart=datasette serve . -h 127.0.0.1 -p 8000 Restart=on-failure [Install] WantedBy=multi-user.target Add a random value for the DATASETTE_SECRET - this will be used to sign Datasette cookies such as the CSRF token cookie. You can generate a suitable value like so: python3 -c 'import secrets; print(secrets.token_hex(32))' This configuration will run Datasette against all database files contained in the /home/ubuntu/datasette-root directory. If that directory contains a metadata.yml (or .json ) file or a templates/ or plugins/ sub-directory those will automatically be loaded by Datasette - see Configuration directory mode for details. You can start the Datasette process running using the following: sudo systemctl daemon-reload sudo systemctl start datasette.service You will need to restart the Datasette service after making changes to its metadata.json configuration or adding a new database file to that directory. You can do that using: sudo systemctl restart datasette.service Once the … | 12 | |
188 | Running Datasette using OpenRC | OpenRC is the service manager on non-systemd Linux distributions like Alpine Linux and Gentoo . Create an init script at /etc/init.d/datasette with the following contents: #!/sbin/openrc-run name="datasette" command="datasette" command_args="serve -h 0.0.0.0 /path/to/db.db" command_background=true pidfile="/run/${RC_SVCNAME}.pid" You then need to configure the service to run at boot and start it: rc-update add datasette rc-service datasette start | 12 | |
189 | Deploying using buildpacks | Some hosting providers such as Heroku , DigitalOcean App Platform and Scalingo support the Buildpacks standard for deploying Python web applications. Deploying Datasette on these platforms requires two files: requirements.txt and Procfile . The requirements.txt file lets the platform know which Python packages should be installed. It should contain datasette at a minimum, but can also list any Datasette plugins you wish to install - for example: datasette datasette-vega The Procfile lets the hosting platform know how to run the command that serves web traffic. It should look like this: web: datasette . -h 0.0.0.0 -p $PORT --cors The $PORT environment variable is provided by the hosting platform. --cors enables CORS requests from JavaScript running on other websites to your domain - omit this if you don't want to allow CORS. You can add additional Datasette Settings options here too. These two files should be enough to deploy Datasette on any host that supports buildpacks. Datasette will serve any SQLite files that are included in the root directory of the application. If you want to build SQLite files or download them as part of the deployment process you can do so using a bin/post_compile file. For example, the following bin/post_compile will download an example database that will then be served by Datasette: wget https://fivethirtyeight.datasettes.com/fivethirtyeight.db simonw/buildpack-datasette-demo is an example GitHub repository showing a Datasette configuration that can be deployed to a buildpack-supporting host. | 12 | |
190 | Running Datasette behind a proxy | You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site. You can use the base_url configuration setting to tell Datasette to serve traffic with a specific URL prefix. For example, you could run Datasette like this: datasette my-database.db --setting base_url /my-datasette/ -p 8009 This will run Datasette with the following URLs: http://127.0.0.1:8009/my-datasette/ - the Datasette homepage http://127.0.0.1:8009/my-datasette/my-database - the page for the my-database.db database http://127.0.0.1:8009/my-datasette/my-database/some_table - the page for the some_table table You can now set your nginx or Apache server to proxy the /my-datasette/ path to this Datasette instance. | 12 | |
191 | 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/ . | 12 | |
192 | 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. | 12 | |
193 | Introspection | Datasette includes some pages and JSON API endpoints for introspecting the current instance. These can be used to understand some of the internals of Datasette and to see how a particular instance has been configured. Each of these pages can be viewed in your browser. Add .json to the URL to get back the contents as JSON. | 12 | |
194 | /-/metadata | Shows the contents of the metadata.json file that was passed to datasette serve , if any. Metadata example : { "license": "CC Attribution 4.0 License", "license_url": "http://creativecommons.org/licenses/by/4.0/", "source": "fivethirtyeight/data on GitHub", "source_url": "https://github.com/fivethirtyeight/data", "title": "Five Thirty Eight", "databases": { } } | 12 | |
195 | /-/versions | Shows the version of Datasette, Python and SQLite. Versions example : { "datasette": { "version": "0.60" }, "python": { "full": "3.8.12 (default, Dec 21 2021, 10:45:09) \n[GCC 10.2.1 20210110]", "version": "3.8.12" }, "sqlite": { "extensions": { "json1": null }, "fts_versions": [ "FTS5", "FTS4", "FTS3" ], "compile_options": [ "COMPILER=gcc-6.3.0 20170516", "ENABLE_FTS3", "ENABLE_FTS4", "ENABLE_FTS5", "ENABLE_JSON1", "ENABLE_RTREE", "THREADSAFE=1" ], "version": "3.37.0" } } | 12 | |
196 | /-/plugins | Shows a list of currently installed plugins and their versions. Plugins example : [ { "name": "datasette_cluster_map", "static": true, "templates": false, "version": "0.10", "hooks": ["extra_css_urls", "extra_js_urls", "extra_body_script"] } ] Add ?all=1 to include details of the default plugins baked into Datasette. | 12 | |
197 | /-/settings | Shows the Settings for this instance of Datasette. Settings example : { "default_facet_size": 30, "default_page_size": 100, "facet_suggest_time_limit_ms": 50, "facet_time_limit_ms": 1000, "max_returned_rows": 1000, "sql_time_limit_ms": 1000 } | 12 | |
198 | /-/config | 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 : { "settings": { "template_debug": true, "trace_debug": true, "force_https_urls": true } } 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 . | 12 | |
199 | /-/databases | Shows currently attached databases. Databases example : [ { "hash": null, "is_memory": false, "is_mutable": true, "name": "fixtures", "path": "fixtures.db", "size": 225280 } ] | 12 | |
200 | /-/threads | Shows details of threads and asyncio tasks. Threads example : { "num_threads": 2, "threads": [ { "daemon": false, "ident": 4759197120, "name": "MainThread" }, { "daemon": true, "ident": 123145319682048, "name": "Thread-1" }, ], "num_tasks": 3, "tasks": [ "<Task pending coro=<RequestResponseCycle.run_asgi() running at uvicorn/protocols/http/httptools_impl.py:385> cb=[set.discard()]>", "<Task pending coro=<Server.serve() running at uvicorn/main.py:361> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x10365c3d0>()]> cb=[run_until_complete.<locals>.<lambda>()]>", "<Task pending coro=<LifespanOn.main() running at uvicorn/lifespan/on.py:48> wait_for=<Future pending cb=[<TaskWakeupMethWrapper object at 0x10364f050>()]>>" ] } | 12 | |
201 | /-/actor | Shows the currently authenticated actor. Useful for debugging Datasette authentication plugins. { "actor": { "id": 1, "username": "some-user" } } | 12 |