- Python 79.4%
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4ebec0b1ea
invoke_startup() runs before any request exists, so nothing it does has an ambient span to nest under. Measured on a fresh instance: 19 distinct traces, 19 of them single- or few-span roots - the register_* hook dispatches, the internal catalog's db.query reads and its db.write.* catalog writes. In a trace UI that is nineteen pieces of noise sitting next to every real trace, which for an operator opening Jaeger for the first time is the difference between "this works" and "this is unusable". Bracketing the whole method body in one datasette.startup span takes that to 1. This is not a propagation fix - ticket 04's context propagation was already correct, it simply had nothing to propagate. The bulk of the app.py diff is re-indentation; `git diff -w` shows the real change (plus one line-length rewrap black applied to the StartupError raise). register_output_renderer and asgi_wrapper stay orphans deliberately: both are dispatched from Datasette.__init__ / .app(), before invoke_startup() exists to be called, and wrapping them would mean holding a span open across object construction in library code that may never serve a request. Suppressing instrumentation during warm-up was rejected as an alternative: a slow prepare_connection runs on every connection, not just at startup, and is exactly what tracing should reveal. Also corrects the stale write-thread warm-up comment in database.py. It is still a root, but for a reason worth stating precisely: a raw threading.Thread does not inherit the starting thread's context, so the datasette.startup span current on the event loop does not reach it. Read connections do warm up under copy_context() and nest correctly. Co-Authored-By: Claude Opus 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.