Contributions are welcome and are greatly appreciated! Every little bit helps, and credit will always be given.
Report bugs through GitHub. If you are reporting a bug, please include:
When posting Python stack traces, please quote them using Markdown blocks.
Look through the GitHub issues for bugs. Anything tagged with bug is open to whoever wants to implement it.
Look through the GitHub issues for features. Anything tagged with feature or starter_task is open to whoever wants to implement it.
Superset could always use better documentation, whether as part of the official Superset docs, in docstrings, docs/*.rst or even on the web as blog posts or articles. See Documentation for more details.
If you are proficient in a non-English language, you can help translate text strings from Superset's UI. You can jump in to the existing language dictionaries at superset/translations/<language_code>/LC_MESSAGES/messages.po, or even create a dictionary for a new language altogether. See Translating for more details.
The best way to send feedback is to file an issue on GitHub. If you are proposing a feature:
There is a dedicated apache-superset tag on StackOverflow. Please use it when asking questions.
Before you submit a pull request from your forked repo, check that it meets these guidelines:
tox and resolve all errors and test failures.setup.py denoting any specific restrictions and in requirements.txt pinned to a specific version which ensures that the application build is deterministic.First, fork the repository on GitHub, then clone it. You can clone the main repository directly instead, but you won't be able to send pull requests.
git clone git@github.com:your-username/incubator-superset.git cd incubator-superset
The latest documentation and tutorial are available at https://superset.incubator.apache.org/.
Contributing to the official documentation is relatively easy, once you‘ve setup your environment and done an edit end-to-end. The docs can be found in the docs/ subdirectory of the repository, and are written in the reStructuredText format (.rst). If you’ve written Markdown before, you'll find the reStructuredText format familiar.
Superset uses Sphinx to convert the rst files in docs/ to the final HTML output users see.
Finally, to make changes to the rst files and build the docs using Sphinx, you'll need to install a handful of dependencies from the repo you cloned:
pip install -r docs/requirements.txt
To get the feel for how to edit and build the docs, let‘s edit a file, build the docs and see our changes in action. First, you’ll want to create a new branch to work on your changes:
git checkout -b changes-to-docs
Now, go ahead and edit one of the files under docs/, say docs/tutorial.rst - change it however you want. Check out the ReStructuredText Primer for a reference on the formatting of the rst files.
Once you've made your changes, run this command to convert the docs into HTML:
make html
You‘ll see a lot of output as Sphinx handles the conversion. After it’s done, the HTML Sphinx generated should be in docs/_build/html. Navigate there and start a simple web server so we can check out the docs in a browser:
cd docs/_build/html python -m SimpleHTTPServer
This will start a small Python web server listening on port 8000. Point your browser to http://localhost:8000, find the file you edited earlier, and check out your changes!
If you‘ve made a change you’d like to contribute to the actual docs, just commit your code, push your new branch to Github:
git add docs/tutorial.rst git commit -m 'Awesome new change to tutorial' git push origin changes-to-docs
Then, open a pull request.
If you‘re adding new images to the documentation, you’ll notice that the images referenced in the rst, e.g.
.. image:: _static/img/tutorial/tutorial_01_sources_database.png
aren‘t actually stored in that directory. Instead, you should add and commit images (and any other static assets) to the superset/assets/images directory. When the docs are deployed to https://superset.incubator.apache.org/, images are copied from there to the _static/img directory, just like they’re referenced in the docs.
For example, the image referenced above actually lives in superset/assets/images/tutorial. Since the image is moved during the documentation build process, the docs reference the image in _static/img/tutorial instead.
Generate the API documentation with:
pip install -r docs/requirements.txt python setup.py build_sphinx
Make sure your machine meets the OS dependencies before following these steps.
# Create a virtual environemnt and activate it (recommended) virtualenv venv source venv/bin/activate # Install external dependencies pip install -r requirements.txt pip install -r requirements-dev.txt # Install Superset in editable (development) mode pip install -e . # Create an admin user fabmanager create-admin --app superset # Initialize the database superset db upgrade # Create default roles and permissions superset init # Load some data to play with superset load_examples # Start the Flask dev web server (but see below for frontend asset compilation) flask run -p 8080 --with-threads --reload --debugger
This feature is only available on Python 3. When debugging your application, you can have the server logs sent directly to the browser console:
superset runserver -d --console-log
You can log anything to the browser console, including objects:
from superset import app app.logger.error('An exception occurred!') app.logger.info(form_data)
Frontend assets (JavaScript, CSS, and images) must be compiled in order to properly display the web UI. The superset/assets directory contains all NPM-managed front end assets. Note that there are additional frontend assets bundled with Flask-Appbuilder (e.g. jQuery and bootstrap); these are not managed by NPM, and may be phased out in the future.
First, be sure you are using recent versions of NodeJS and npm. Using nvm to manage them is recommended.
Install third-party dependencies listed in package.json:
# From the root of the repository cd superset/assets # Install yarn, a replacement for `npm install` npm install -g yarn # Install dependencies yarn install
Finally, to compile frontend assets, run any of the following commands.
# Start a watcher that recompiles your assets as you modify them (reload your browser to see changes) npm run dev # Compile the Javascript and CSS in production/optimized mode for official releases npm run prod # Copy a conf file from the frontend to the backend npm run sync-backend
Alternatively, you can run the Webpack dev server, which runs on port 9000 and proxies non-asset requests to the Flask server on port 8088. After pointing your browser to it, updates to asset sources will be reflected in-browser without a refresh.
# Run the dev server npm run dev-server # Run the dev server on a non-default port npm run dev-server -- --port=9001 # Run the dev server proxying to a Flask server on a non-default port npm run dev-server -- --supersetPort=8081
After adding or upgrading an NPM package by changing package.json, you must run yarn install, which will regenerate the yarn.lock file. Then, be sure to commit the new yarn.lock so that other users' builds are reproducible. See the Yarn docs for more information.
Superset supports a server-wide feature flag system, which eases the incremental development of features. To add a new feature flag, simply modify superset_config.py with something like the following:
FEATURE_FLAGS = {
'SCOPED_FILTER': True,
}
If you want to use the same flag in the client code, also add it to the FeatureFlag TypeScript enum in superset/assets/src/featureFlags.ts. For example,
export enum FeatureFlag {
SCOPED_FILTER = 'SCOPED_FILTER',
}
All tests are carried out in tox a standardized testing framework mostly for Python (though we also used it for Javascript). All python tests can be run with any of the tox environments, via,
tox -e <environment>
i.e.,
tox -e py27 tox -e py36
Alternatively, you can run all tests in a single file via,
tox -e <environment> -- tests/test_file.py
or for a specific test via,
tox -e <environment> -- tests/test_file.py:TestClassName.test_method_name
Note that the test environment uses a temporary directory for defining the SQLite databases which will be cleared each time before the group of test commands are invoked.
We use Jest and Enzyme to test Javascript. Tests can be run with:
cd superset/assets/spec npm install npm run test
We use Cypress for integration tests. Tests can be run by tox -e cypress. To open Cypress and explore tests first setup and run test server:
export SUPERSET_CONFIG=tests.superset_test_config superset db upgrade superset init superset load_test_users superset load_examples superset runserver
Run Cypress tests:
cd /superset/superset/assets npm run build npm run cypress run
Lint the project with:
# for python tox -e flake8 # for javascript tox -e eslint
We use Babel to translate Superset. In Python files, we import the magic _ function using:
from flask_babel import lazy_gettext as _
then wrap our translatable strings with it, e.g. _('Translate me'). During extraction, string literals passed to _ will be added to the generated .po file for each language for later translation. At runtime, the _ function will return the translation of the given string for the current language, or the given string itself if no translation is available.
In JavaScript, the technique is similar: we import t (simple translation), tn (translation containing a number).
import {t, tn } from '@superset-ui/translation';
Add the LANGUAGES variable to your superset_config.py. Having more than one option inside will add a language selection dropdown to the UI on the right side of the navigation bar.
LANGUAGES = { 'en': {'flag': 'us', 'name': 'English'}, 'fr': {'flag': 'fr', 'name': 'French'}, 'zh': {'flag': 'cn', 'name': 'Chinese'}, }
fabmanager babel-extract --target superset/translations --output superset/translations/messages.pot --config superset/translations/babel.cfg -k _ -k __ -k t -k tn -k tct
You can then translate the strings gathered in files located under superset/translation, where there's one per language. For the translations to take effect:
# In the case of JS translation, we need to convert the PO file into a JSON file, and we need the global download of the npm package po2json. npm install -g po2json fabmanager babel-compile --target superset/translations # Convert the en PO file into a JSON file po2json -d superset -f jed1.x superset/translations/en/LC_MESSAGES/messages.po superset/translations/en/LC_MESSAGES/messages.json
If you get errors running po2json, you might be running the Ubuntu package with the same name, rather than the NodeJS package (they have a different format for the arguments). If there is a conflict, you may need to update your PATH environment variable or fully qualify the executable path (e.g. /usr/local/bin/po2json instead of po2json).
To create a dictionary for a new language, run the following, where LANGUAGE_CODE is replaced with the language code for your target language, e.g. es (see Flask AppBuilder i18n documentation for more details):
pip install -r superset/translations/requirements.txt pybabel init -i superset/translations/messages.pot -d superset/translations -l LANGUAGE_CODE
Then, extract strings for the new language.
Create Models and Views for the datasource, add them under superset folder, like a new my_models.py with models for cluster, datasources, columns and metrics and my_views.py with clustermodelview and datasourcemodelview.
Create DB migration files for the new models
Specify this variable to add the datasource model and from which module it is from in config.py:
For example:
ADDITIONAL_MODULE_DS_MAP = {'superset.my_models': ['MyDatasource', 'MyOtherDatasource']}
This means it'll register MyDatasource and MyOtherDatasource in superset.my_models module in the source registry.
Here's an example as a Github PR with comments that describe what the different sections of the code do: https://github.com/apache/incubator-superset/pull/3013
Alter the model you want to change. This example will add a Column Annotations model.
Generate the migration file
superset db migrate -m 'add_metadata_column_to_annotation_model.py'
This will generate a file in migrations/version/{SHA}_this_will_be_in_the_migration_filename.py.
Upgrade the DB
superset db upgrade
The output should look like this:
INFO [alembic.runtime.migration] Context impl SQLiteImpl. INFO [alembic.runtime.migration] Will assume transactional DDL. INFO [alembic.runtime.migration] Running upgrade 1a1d627ebd8e -> 40a0a483dd12, add_metadata_column_to_annotation_model.py
Add column to view
Since there is a new column, we need to add it to the AppBuilder Model view.
When two DB migrations collide, you'll get an error message like this one:
alembic.util.exc.CommandError: Multiple head revisions are present for given argument 'head'; please specify a specific target revision, '<branchname>@head' to narrow to a specific head, or 'heads' for all heads`
To fix it:
Get the migration heads
superset db heads
This should list two or more migration hashes.
Create a new merge migration
superset db merge {HASH1} {HASH2}
Upgrade the DB to the new checkpoint
superset db upgrade