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40a37307de* Add request.form() for multipart form data and file uploads New Request.form() method that handles both application/x-www-form-urlencoded and multipart/form-data content types with streaming parsing. Features: - Streaming multipart parser that doesn't buffer entire body in memory - Files spill to disk above 1MB threshold via SpooledTemporaryFile - files=False (default) discards file content, files=True stores them - Security limits: max_request_size, max_file_size, max_fields, max_files - FormData container with dict-like access and getlist() for multiple values - UploadedFile class with async read(), seek(), filename, content_type, size - Support for RFC 5987 filename* encoding for international filenames Uses multipart-form-data-conformance test suite for validation. * Update views to use request.form() and document new API - Migrate PermissionsDebugView, MessagesDebugView, and CreateTokenView from post_vars() to form() - Add documentation for request.form(), FormData, and UploadedFile classes Centralize multipart defaults and expose stricter limits via Request.form(). Enforce header, part, file, and disk space limits even when files are discarded; detect truncated bodies and client disconnects; and move blocking work off the event loop. Add FormData close/aclose context managers, update internals docs, and expand multipart tests (including len semantics and stricter conformance expectations). |
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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.