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e24a2c122f
A block=False write returns without awaiting the reply future, so the enclosing db.query span finishes - and exports - before db.write.queue_wait and db.write.execute even exist. They were still parented to it, which produced a child bar ending ~50ms after its already-closed parent: legal OpenTelemetry, but it renders as nonsense in a trace UI. Parenting asserts containment; a link asserts causation without containment. The enqueueing request causes the write without containing it, which is exactly what a span link is for. So for block=False both write spans are now roots - started with an explicit empty Context, so the write thread's ambient context cannot supply a parent either - each carrying one link back to the enqueueing span. block=True is untouched, since there the caller really does await the reply and containment is accurate. The link carries no attributes. There is only one kind of link here, so naming the relationship would be a constant conveying nothing the link's existence does not already say. Accepted trade-off: a linked span will not appear inside the request's waterfall in most trace UIs. It shows up as its own trace with a "linked from" reference rather than a bar under the request. For a fire-and-forget write whose latency the request never pays, that is the right trade - correctness over at-a-glance nesting for a case the request-latency view was never accurate for anyway. This does add root traces, which looks like it cuts against the startup span work that spent its whole diff removing them. The difference is reachability: those roots were orphans, whereas these are reachable from the request that caused them via the link. Nothing in core issues block=False writes today - it is a plugin-facing path - so this changes no trace Datasette produces on its own. 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.