- Python 86.7%
- HTML 7.5%
- JavaScript 4.1%
- CSS 1.3%
- Shell 0.2%
bb59c61c9f
Adds a per-request cache for permission check results, plus wiring that resolves action permissions in bulk before plugin hooks need them: - New _permission_check_cache contextvar, set to a fresh dict for each request by DatasetteRouter and reset when the request ends. Keys include the full serialized actor, so actors differing in any field (e.g. token restrictions) never share entries. SkipPermissions mode bypasses the cache entirely. - datasette.allowed_many() now consults the cache and stores its results there, so repeated datasette.allowed() checks within one request resolve without further SQL. - Table pages resolve all registered table-level actions against the current table and all database-level actions against its database (database pages likewise) in batched queries before invoking the table_actions/database_actions plugin hooks - allowed() calls made inside those hooks are then served from the cache with no plugin changes required. Actions with no permission rules from any plugin are resolved to False without touching the database. Benchmarks (benchmarks/) with a simulated 12-plugin ecosystem making 18 checks per table page show 34 -> 13 internal-DB queries per page; with 2ms-per-query internal DB latency (modelling Datasette Cloud) table page time drops from 77.9ms to 27.6ms - the caching layer accounts for ~91% of that improvement over allowed_many() alone. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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An open source multi-tool for exploring and publishing data
Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API.
Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world.
Explore a demo, watch a video about the project or try it out on GitHub Codespaces.
- datasette.io is the official project website
- Latest Datasette News
- Comprehensive documentation: https://docs.datasette.io/
- Examples: https://datasette.io/examples
- Live demo of current
mainbranch: https://latest.datasette.io/ - Questions, feedback or want to talk about the project? Join our Discord
Want to stay up-to-date with the project? Subscribe to the Datasette newsletter for tips, tricks and news on what's new in the Datasette ecosystem.
Installation
If you are on a Mac, Homebrew is the easiest way to install Datasette:
brew install datasette
You can also install it using pip or pipx:
pip install datasette
Datasette requires Python 3.8 or higher. We also have detailed installation instructions covering other options such as Docker.
Basic usage
datasette serve path/to/database.db
This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface.
serve is the default subcommand, you can omit it if you like.
Use Chrome on OS X? You can run datasette against your browser history like so:
datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock
Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data:
metadata.json
If you want to include licensing and source information in the generated datasette website you can do so using a JSON file that looks something like this:
{
"title": "Five Thirty Eight",
"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"
}
Save this in metadata.json and run Datasette like so:
datasette serve fivethirtyeight.db -m metadata.json
The license and source information will be displayed on the index page and in the footer. They will also be included in the JSON produced by the API.
datasette publish
If you have Heroku or Google Cloud Run configured, Datasette can deploy one or more SQLite databases to the internet with a single command:
datasette publish heroku database.db
Or:
datasette publish cloudrun database.db
This will create a docker image containing both the datasette application and the specified SQLite database files. It will then deploy that image to Heroku or Cloud Run and give you a URL to access the resulting website and API.
See Publishing data in the documentation for more details.
Datasette Lite
Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. Read more about that in the Datasette Lite documentation.