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b74231c190
Datasette has a family of callers that run a query under a tiny time limit and treat "did not finish" as a usable answer. table_counts() is the loudest: the homepage counts every table with a 10ms budget and stores None for the ones that blow it. The QueryInterrupted handler on the db.query span was unconditional, so on a two-table database that produced four ERROR spans - two db.query and two db.query.execute - on every homepage hit. Measured on a 30MB two-table database: 4 red spans before, 0 after. Honouring log_sql_errors here would have silenced none of it. Only the three ArrayFacet json_type() probes pass log_sql_errors=False, and they are not the queries that time out; table_counts() and ColumnFacet.suggest both leave it at its True default. The signal that does separate the two cases is the budget itself: a caller asking for less time than sql_time_limit_ms is saying the query may not finish. Keying off that needs no new API and no changes outside database.py. A query that runs out the instance-wide limit is still an error. datasette.interrupted is still set in every case - it is the signal worth having, and only the ERROR status becomes conditional. Its registry description said the status is "also set to ERROR" full stop, which is now wrong, and that string is published in docs/internals.rst. The inner db.query.execute span carried the same bug through set_status_on_exception=log_sql_errors, so its exception handling is now explicit, matching the db.query span above it. The context manager's flags apply to every exception type alike and this span has to tell two apart. test_query_interrupted_sets_error_status forced its timeout with ?_timelimit=5, which is exactly the signal now reclassified as expected. It now forces one via sql_time_limit_ms so it still tests what it was written to test. Also documents, at the copy_context() sites, that context propagation carries Datasette's non-OTel ContextVars into worker threads too. Verified harmless: nothing reads _skip_permission_checks, _permission_check_cache or _in_datasette_client off the event loop, and Context.run() restores the thread's previous context on return, so no value can reach the next task on the shared pool. 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.