Contributing

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.

General guidelines

  • 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 3.7 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 git@github.com:YOURNAME/datasette

If you want to get started without creating your own fork, you can do this instead:

git clone git@github.com: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:

pytest

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"

Using fixtures

To run Datasette itself, type datasette.

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:

datasette fixtures.db

This will start a server at http://127.0.0.1:8001/.

Any changes you make in the datasette/templates or datasette/static folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript).

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/

Debugging

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 --pdb option:

datasette --pdb fixtures.db

Code formatting

Datasette uses opinionated code formatters: Black for Python and Prettier for JavaScript.

These formatters are enforced by Datasette's continuous integration: if a commit includes Python or JavaScript code that does not match the style enforced by those tools, the tests will fail.

When developing locally, you can verify and correct the formatting of your code using these tools.

Running Black

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.

blacken-docs

The blacken-docs command applies Black formatting rules to code examples in the documentation. Run it like this:

blacken-docs -l 60 docs/*.rst

Prettier

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 and sphinx_rtd_theme in your Datasette development environment and then running make html directly in the docs/ directory:

# 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:

open _build/html/index.html

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:

make livehtml

Now browse to http://localhost:8000/ to view the documentation. Any edits you make should be instantly reflected in your browser.

Continuously deployed demo instances

The demo instance at latest.datasette.io is re-deployed automatically to Google Cloud Run for every push to main that passes the test suite. This is implemented by the GitHub Actions workflow at .github/workflows/deploy-latest.yml.

Specific branches can also be set to automatically deploy by adding them to the on: push: branches block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass.

The Cloud Run URL for a branch demo can be found in the GitHub Actions logs.

Release process

Datasette releases are performed using tags. When a new release is published on GitHub, a GitHub Action workflow will perform the following:

To deploy new releases you will need to have push access to the main Datasette GitHub repository.

Datasette follows Semantic Versioning:

major.minor.patch

We increment major for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always 0.

We increment minor for new features.

We increment patch for bugfix releass.

Alpha and beta releases may have an additional a0 or b0 prefix - the integer component will be incremented with each subsequent alpha or beta.

To release a new version, first create a commit that updates the version number in datasette/version.py and the the changelog with highlights of the new version. An example commit can be seen here:

# 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.

Finally, post a news item about the release on datasette.io by editing the news.yaml file in that site's repository.

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 main:

git checkout main
git cherry-pick COMMIT
git push

Upgrading CodeMirror

Datasette bundles CodeMirror for the SQL editing interface, e.g. on this page. Here are the steps for upgrading to a new version of CodeMirror:

  • Download and extract latest CodeMirror zip file from https://codemirror.net/codemirror.zip

  • Rename lib/codemirror.js to codemirror-5.57.0.js (using latest version number)

  • Rename lib/codemirror.css to codemirror-5.57.0.css

  • Rename mode/sql/sql.js to codemirror-5.57.0-sql.js

  • Edit both JavaScript files to make the top license comment a /* */ block instead of multiple // lines

  • Minify the JavaScript files like this:

    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
    
  • Edit the _codemirror.html template to reference the new files

  • git rm the old files, git add the new files