- Python 79.4%
- JavaScript 11.4%
- HTML 6.2%
- CSS 2.7%
- Shell 0.1%
f28db54eda
A survey of five plugin OTel plans (datasette-paper, -agent, -litestream,
-accounts, -cron) found every one hand-copying the same core machinery:
the registry classes, the conformance-test harness, the pytest fixtures,
the bucket boundaries and the detached-root-with-Link recipe. This makes
that machinery importable instead:
- The registry classes are documented public API. Attribute gains
values= (a closed enum the conformance helpers enforce - what makes an
attribute safe as a metric dimension); SpanName gains prefix=True for
span families like "chat {model}" whose names share a fixed prefix,
matched by span_for() after exact names. span_for()/attribute helpers
accept a spans= tuple so plugin registries can use them.
- datasette.telemetry.linked_root_span_kwargs(): the root-span-with-Link
shape for work a request caused without containing - background jobs,
scheduled ticks, block=False writes. Core's own write thread now uses
it instead of building the kwargs inline.
- datasette.telemetry_testing: the session provider fixtures, otel_spans
/ otel_metrics, a two-way registry conformance checker (including enum
and prefix handling, filtered by instrumentation scope) and an
assert_package_never_imports_sdk() guard. Core's conftest now imports
these instead of defining them, so the suite consumes the kit exactly
as a plugin's would.
- New "Telemetry for plugin authors" docs page: scope discipline,
registry usage, privacy/cardinality rules, named-callable guidance,
request_span(), the background root-with-link convention (one root per
tick, always emitted), provider-ordering facts and known caveats.
request_span() is now documented public API.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
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|---|---|---|
| .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.