Datasette is an open source project. We welcome contributions!
This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new Plugins.
- main should always be releasable. Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released.
- The ideal commit should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue.
- New plugin hooks should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.
Setting up a development environment¶
If you have Python 3.6 or higher installed on your computer (on OS X the quickest way to do this is using homebrew) you can install an editable copy of Datasette using the following steps.
If you want to use GitHub to publish your changes, first create a fork of datasette under your own GitHub account.
Now clone that repository somewhere on your computer:
git clone email@example.com:YOURNAME/datasette
If you want to get started without creating your own fork, you can do this instead:
git clone firstname.lastname@example.org:simonw/datasette
The next step is to create a virtual environment for your project and use it to install Datasette's dependencies:
cd datasette # Create a virtual environment in ./venv python3 -m venv ./venv # Now activate the virtual environment, so pip can install into it source venv/bin/activate # Install Datasette and its testing dependencies python3 -m pip install -e .[test]
That last line does most of the work:
pip install -e means "install this package in a way that allows me to edit the source code in place". The
.[test] option means "use the setup.py in this directory and install the optional testing dependencies as well".
Running the tests¶
Once you have done this, you can run the Datasette unit tests from inside your
datasette/ directory using pytest like so:
You can run the tests faster using multiple CPU cores with pytest-xdist like this:
pytest -n auto -m "not serial"
-n auto detects the number of available cores automatically. The
-m "not serial" skips tests that don't work well in a parallel test environment. You can run those tests separately like so:
pytest -m "serial"
To run Datasette itself, type
You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests.
You can create a copy of that database by running this command:
python tests/fixtures.py fixtures.db
Now you can run Datasette against the new fixtures database like so:
This will start a server at
Any changes you make in the
If you want to change Datasette's Python code you can use the
--reload option to cause Datasette to automatically reload any time the underlying code changes:
datasette --reload fixtures.db
You can also use the
fixtures.py script to recreate the testing version of
metadata.json used by the unit tests. To do that:
python tests/fixtures.py fixtures.db fixtures-metadata.json
Or to output the plugins used by the tests, run this:
python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins Test tables written to fixtures.db - metadata written to fixtures-metadata.json Wrote plugin: fixtures-plugins/register_output_renderer.py Wrote plugin: fixtures-plugins/view_name.py Wrote plugin: fixtures-plugins/my_plugin.py Wrote plugin: fixtures-plugins/messages_output_renderer.py Wrote plugin: fixtures-plugins/my_plugin_2.py
Then run Datasette like this:
datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/
Any errors that occur while Datasette is running while display a stack trace on the console.
You can tell Datasette to open an interactive
pdb debugger session if an error occurs using the
datasette --pdb fixtures.db
When developing locally, you can verify and correct the formatting of your code using these tools.
Black will be installed when you run
pip install -e '.[test]'. To test that your code complies with Black, run the following in your root
datasette repository checkout:
$ black . --check All done! ✨ 🍰 ✨ 95 files would be left unchanged.
If any of your code does not conform to Black you can run this to automatically fix those problems:
$ black . reformatted ../datasette/setup.py All done! ✨ 🍰 ✨ 1 file reformatted, 94 files left unchanged.
To install Prettier, install Node.js and then run the following in the root of your
datasette repository checkout:
$ npm install
This will install Prettier in a
node_modules directory. You can then check that your code matches the coding style like so:
$ npm run prettier -- --check > prettier > prettier 'datasette/static/*[!.min].js' "--check" Checking formatting... [warn] datasette/static/plugins.js [warn] Code style issues found in the above file(s). Forgot to run Prettier?
You can fix any problems by running:
$ npm run fix
Editing and building the documentation¶
Datasette's documentation lives in the
docs/ directory and is deployed automatically using Read The Docs.
The documentation is written using reStructuredText. You may find this article on The subset of reStructuredText worth committing to memory useful.
You can build it locally by installing
sphinx_rtd_theme in your Datasette development environment and then running
make html directly in the
# You may first need to activate your virtual environment: source venv/bin/activate # Install the dependencies needed to build the docs pip install -e .[docs] # Now build the docs cd docs/ make html
This will create the HTML version of the documentation in
docs/_build/html. You can open it in your browser like so:
Any time you make changes to a
.rst file you can re-run
make html to update the built documents, then refresh them in your browser.
For added productivity, you can use use sphinx-autobuild to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor.
sphinx-autobuild will have been installed when you ran
pip install -e .[docs]. In your
docs/ directory you can start the server by running the following:
Now browse to
http://localhost:8000/ to view the documentation. Any edits you make should be instantly reflected in your browser.
Datasette releases are performed using tags. When a new release is published on GitHub, a GitHub Action workflow will perform the following:
- Run the unit tests against all supported Python versions. If the tests pass...
- Build a Docker image of the release and push a tag to https://hub.docker.com/r/datasetteproject/datasette
- Re-point the "latest" tag on Docker Hub to the new image
- Build a wheel bundle of the underlying Python source code
- Push that new wheel up to PyPI: https://pypi.org/project/datasette/
To deploy new releases you will need to have push access to the main Datasette GitHub repository.
Datasette follows Semantic Versioning:
major for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always
minor for new features.
patch for bugfix releass.
Alpha and beta releases may have an additional
b0 prefix - the integer component will be incremented with each subsequent alpha or beta.
# Update changelog git commit -m " Release 0.51a1 Refs #1056, #1039, #998, #1045, #1033, #1036, #1034, #976, #1057, #1058, #1053, #1064, #1066" -a git push
Referencing the issues that are part of the release in the commit message ensures the name of the release shows up on those issue pages, e.g. here.
You can generate the list of issue references for a specific release by copying and pasting text from the release notes or GitHub changes-since-last-release view into this Extract issue numbers from pasted text tool.
To create the tag for the release, create a new release on GitHub matching the new version number. You can convert the release notes to Markdown by copying and pasting the rendered HTML into this Paste to Markdown tool.
Alpha and beta releases¶
Alpha and beta releases are published to preview upcoming features that may not yet be stable - in particular to preview new plugin hooks.
You are welcome to try these out, but please be aware that details may change before the final release.
Please join discussions on the issue tracker to share your thoughts and experiences with on alpha and beta features that you try out.
Releasing bug fixes from a branch¶
If it's necessary to publish a bug fix release without shipping new features that have landed on
main a release branch can be used.
Create it from the relevant last tagged release like so:
git branch 0.52.x 0.52.4 git checkout 0.52.x
Next cherry-pick the commits containing the bug fixes:
git cherry-pick COMMIT
Write the release notes in the branch, and update the version number in
version.py. Then push the branch:
git push -u origin 0.52.x
Once the tests have completed, publish the release from that branch target using the GitHub Draft a new release form.
Finally, cherry-pick the commit with the release notes and version number bump across to
git checkout main git cherry-pick COMMIT git push
Download and extract latest CodeMirror zip file from https://codemirror.net/codemirror.zip
codemirror-5.57.0.js(using latest version number)
/* */block instead of multiple
npx uglify-js codemirror-5.57.0.js -o codemirror-5.57.0.min.js --comments '/LICENSE/' npx uglify-js codemirror-5.57.0-sql.js -o codemirror-5.57.0-sql.min.js --comments '/LICENSE/'
Check that the LICENSE comment did indeed survive minification
Minify the CSS file like this:
npx clean-css-cli codemirror-5.57.0.css -o codemirror-5.57.0.min.css
_codemirror.htmltemplate to reference the new files
git rmthe old files,
git addthe new files