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990c8782dbRead path: the leased pool replaces the thread-local cache. Pooled connections record Database._conn_generation when opened; a stale one is discarded on checkout or return, and _invalidate_connections() (called by the SchemaWatcher on a replaced/deleted file) closes that database's idle connections at once. Leased ones are only ever closed by their own thread on release. Write path: the idle-exit write thread keeps its lock protocol; its connection lives in a _WriteConnection holder that is reopened when the generation moves, before the next task. Post-write schema_version check and _after_write catalog await are kept for every write API in both modes. Non-threaded write connections are reopened on a generation change and never cached half-prepared. Deleted files: write connections use mode=rw once Datasette has seen the file exist (_file_seen), so a deleted file is never silently recreated, while a database added before its file exists is still created by its first write. Settings: max_open_connections (clamped to >= 4 x num_sql_threads), connection_idle_timeout_ms (30000, read connections and write threads), schema_watch_interval_ms, default_schema_watch (external|owned). The spike-only knobs (connection_idle_timeout, connection_pool_wait_ms, pool_read_connections, write_thread_idle_timeout_ms) are gone, along with the pool's wait/disable code paths. The reaper falls back to checkout-time reaping where threads cannot be started (Pyodide). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EwkLo2rJWidpkDVT3HkvNv |
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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 3.10 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.