- Python 79.4%
- JavaScript 11.4%
- HTML 6.2%
- CSS 2.7%
- Shell 0.1%
71d382bbd4
Outcome of a whole-stack review with the kit visible as one system: - otel_reset: an autouse fixture draining the span exporter and metric reader after every test. Without it a large suite accumulates hundreds of thousands of recorded spans in the session-scoped exporter - the likeliest amplifier of the slow-runner CI flakes - and plugins would inherit the same leak. - assert_registry_covered renamed to assert_spans_covered: the old name read as covering the whole registry, which is exactly wrong next to assert_metrics_covered. Public API is forever; renamed before anything ships, no alias. - The installers now verify their provider actually took: with a provider installed first (opentelemetry-instrument, an embedding app), set_*_provider() is silently ignored, and fixtures would assert against an exporter wired to nothing. They skip clearly instead. - UPDOWN_COUNTER registry kind, mapped to Sum with monotonicity checked both ways - a Counter must collect monotonic, an UpDownCounter must not. Previously an UpDownCounter's kind check was silently skipped. - The docs page now prescribes naming: scope = import package name (underscores), signal prefix = a name you own, never bare datasette.*; its own examples no longer teach the hyphenated outlier. Plus an observable-gauges pattern section and a prefix-overlap note. - assert_no_forbidden_values(): the enforcement half of the privacy rules - plant sentinel secrets in a workload and assert they never appear in any span name, attribute, event, status description or metric attribute, across all scopes by default. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA |
||
|---|---|---|
| .github | ||
| datasette | ||
| demos | ||
| docs | ||
| tests | ||
| .coveragerc | ||
| .dockerignore | ||
| .git-blame-ignore-revs | ||
| .gitattributes | ||
| .gitignore | ||
| .isort.cfg | ||
| .prettierrc | ||
| .readthedocs.yaml | ||
| CODE_OF_CONDUCT.md | ||
| codecov.yml | ||
| Dockerfile | ||
| Justfile | ||
| LICENSE | ||
| MANIFEST.in | ||
| package-lock.json | ||
| package.json | ||
| pyproject.toml | ||
| pytest.ini | ||
| README.md | ||
| ruff.toml | ||
| setup.cfg | ||
| test-in-pyodide-with-shot-scraper.sh | ||
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.