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Three corrections to the emitted data, bundled because changing what is on the wire after operators have built dashboards on it is a breaking change - so they belong in the first release that ships spans at all, not a later one. db.query is now SpanKind.CLIENT. Trace UIs key their database rendering off the span kind rather than off db.system, so the spans rendered as ordinary internal work despite carrying db.system and db.query.text. The three child spans stay INTERNAL on purpose: db.query.execute, db.write.execute and db.write.queue_wait are Datasette's decomposition of one logical query, not three database calls, and queue_wait touches no database at all - marking them CLIENT would make one query look like several to anything counting spans by kind. The instrumentation scope now carries the Datasette version and a schema URL, so a backend can tell which Datasette produced a span. The URL is 1.29.0 rather than the latest semconv release because that is the highest version at which every name emitted here is the current spelling: db.system was renamed to db.system.name in 1.30.0 and this code still emits the older form. Claiming a later schema would be false, and would stop a consumer translating that name forward, since the claim asserts the rename already happened. db.operation.name is the statement's leading keyword matched against a fixed allowlist, not a parse. On a public instance the SQL is attacker-controlled and this attribute is a candidate metric dimension in a later phase, so echoing back an arbitrary first token would let a visitor's typo mint a permanent series. Anything unrecognised gets no attribute rather than a wrong one. execute_write_script() does not set it at all, since semantic conventions say not to extract an operation name from query text that can hold several statements. db.collection.name comes only from a new table= argument on Database.execute(), and is never derived from the SQL: deriving it would be a parse, and on an instance where anyone can create a table the value set has no ceiling. It is passed from every query in the table and row views that targets exactly one user table. Internal-catalog reads and the row view's cross-table foreign key counts are deliberately left without it. 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.