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1bc15caef7Adds tests/connection_budget.py, a helper that records which databases a block of code opens, reads, writes or ATTACHes, and tests/test_connection_budget.py which asserts budgets for ~60 endpoints, startup, sweeps and telemetry with 50 attached databases. Fixes for every all-database loop it found: - Index page: tables, views, hidden tables, columns, primary keys, FTS tables and foreign-key relationship counts come from the _internal catalog (a few queries for any number of databases). Row counts are only run when at most 20 databases need counting (COUNT_MAX_DATABASES); above that the page shows table counts plus counts already known. - Derived-table permissions: allowed_resources() no longer queries every database per page; dependency maps are computed by the SchemaWatcher from the rows it scans, cached per database, and read back from the catalog for databases restored from a persisted catalog. - catalog_tables gains a type column (PRAGMA table_list: table, virtual, shadow) so hidden shadow tables can be identified without a connection. - PaginatedResources.all() fetches pages of 1000 after the first. - /-/api and /-/debug/autocomplete use the catalog; /-/schema does one permission query and skips databases with nothing visible. - /-/versions computes SQLite details once per instance, without crossdb ATTACHes; detect_json1() caches its answer. - /-/databases only stats/hashes databases the actor can see. - Immutable table counts are computed when first needed, not at startup. - check_databases() (serve, --get) reuses the catalog scan instead of opening every database a second time, and reports "file is not a database" as a usage error. - SchemaWatcher: failed scans are retried when the file changes, not on every sweep; scan connections skip crossdb ATTACH; catalog_generation counter for caches. - The SQL write UI no longer forces a stat sweep of every database. 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.