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Spans created on a worker thread resolve their parent from that thread's ambient context, so without this every span produced below Database came back as an unparented root, disconnected from the request that caused it. Carrying the caller's context across each boundary is also what makes the thread-pool wait visible: db.query covers the full round trip, the new db.query.execute covers only the work inside the worker, and the gap between them is the queueing the old tracer folds invisibly into one number. - execute_fn()'s executor.submit() and execute_isolated_fn()'s run_in_executor() (immutable databases) now run the callable inside a contextvars.copy_context(). A *fresh* copy per submit is required: concurrently entering one shared Context raises "RuntimeError: cannot enter context ... already entered". - WriteTask carries the otel Context captured on the event loop at enqueue time plus an enqueued_at_ns timestamp (both need __slots__ entries, or they fail with AttributeError at runtime). _execute_writes attaches that context right after the _SHUTDOWN check and detaches it in a finally spanning all three execution branches - the write thread is persistent and shared, so a leaked token would grow its context stack for every write processed afterwards, and a wrong-token detach only logs rather than raising. - New spans: db.query.execute (read worker thread), db.write.queue_wait (explicit start/end timestamps, so its duration is the real enqueue -> dequeue wait rather than the microseconds spent building the span) and db.write.execute (skipped in the conn_exception branch, where fn never runs). db.query.execute honours log_sql_errors for the same reason db.query does: facet suggestion probes with log_sql_errors=False and would otherwise paint two red spans per text column on every table page. - The write-thread warm-up prepare_connection is left as a documented orphan root - no caller context exists that early. Tests assert actual parent/child span-id relationships in a shared trace, not just that spans exist, since an unparented root looks identical to a correct span if you only check presence. Note that copy_context() copies every ContextVar, not just OTel's, so Datasette's own context vars (_skip_permission_checks, _permission_check_cache, _in_datasette_client) now flow into worker threads where they previously did not. 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.