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

  • master 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.

Setting up a development environment

If you have Python 3.6 or higher installed on your computer (on OS X the easiest 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 just 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".

Once you have done this, you can run the Datasette unit tests from inside your datasette/ directory using pytest like so:

pytest

To run Datasette itself, just type datasette.

You're going to need at least one SQLite database. An easy 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

(You may need to delete fixtures.db before running this command.)

Then run Datasette like this:

datasette fixtures.db -m fixtures-metadata.json

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.

Release process

Datasette releases are performed using tags. When a new version tag is pushed to GitHub, a Travis CI task 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.

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

# Update changelog
git commit -m "Release notes for 0.43

Refs #581, #770, #729, #706, #751, #706, #744, #771, #773" -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.

For non-bugfix releases you may want to update the news section of README.md as part of the same commit.

To tag and push the releaes, run the following:

git tag 0.25.2
git push --tags

Final steps once the release has deployed to https://pypi.org/project/datasette/