Contributing

Contributions are welcome and are greatly appreciated! Every little bit helps, and credit will always be given.

Types of Contributions

Report Bug

The best way to report a bug is to file an issue on GitHub. Please include:

Your operating system name and version.
Superset version.
Detailed steps to reproduce the bug.
Any details about your local setup that might be helpful in troubleshooting.

When posting Python stack traces, please quote them using Markdown blocks.

Submit Ideas or Feature Requests

The best way is to file an issue on GitHub:

Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)

For large features or major changes to codebase, please create Superset Improvement Proposal (SIP). See template from SIP-0

Fix Bugs

Look through the GitHub issues. Issues tagged with #bug is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues. Issues tagged with #feature is open to whoever wants to implement it.

Improve Documentation

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.

Add Translations

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.

Ask Questions

There is a dedicated apache-superset tag on StackOverflow. Please use it when asking questions.

Pull Request Guidelines

A philosophy we would like to strongly encourage is

Before creating a PR, create an issue.

The purpose is to separate problem from possible solutions.

Bug fixes: If you’re only fixing a small bug, it’s fine to submit a pull request right away but we highly recommend to file an issue detailing what you’re fixing. This is helpful in case we don’t accept that specific fix but want to keep track of the issue. Please keep in mind that the project maintainers reserve the rights to accept or reject incoming PRs, so it is better to separate the issue and the code to fix it from each other. In some cases, project maintainers may request you to create a separate issue from PR before proceeding.

Refactor: For small refactors, it can be a standalone PR itself detailing what you are refactoring and why. If there are concerns, project maintainers may request you to create a #SIP for the PR before proceeding.

Feature/Large changes: If you intend to change the public API, or make any non-trivial changes to the implementation, we requires you to file a new issue as #SIP (Superset Improvement Proposal). This lets us reach an agreement on your proposal before you put significant effort into it. You are welcome to submit a PR along with the SIP (sometimes necessary for demonstration), but we will not review/merge the code until the SIP is approved.

In general, small PRs are always easier to review than large PRs. The best practice is to break your work into smaller independent PRs and refer to the same issue. This will greatly reduce turnaround time.

Finally, never submit a PR that will put master branch in broken state. If the PR is part of multiple PRs to complete a large feature and cannot work on its own, you can create a feature branch and merge all related PRs into the feature branch before creating a PR from feature branch to master.

Protocol

Authoring

Fill in all sections of the PR template.
Add prefix [WIP] to title if not ready for review (WIP = work-in-progress). We recommend creating a PR with [WIP] first and remove it once you have passed CI test and read through your code changes at least once.
Screenshots/GIFs: Changes to user interface require before/after screenshots, or GIF for interactions
- Recommended capture tools (Kap, LICEcap, Skitch)
- If no screenshot is provided, the committers will mark the PR with need:screenshot label and will not review until screenshot is provided.
Dependencies: Be careful about adding new dependency and avoid unnecessary dependencies.
- For Python, include it in setup.py denoting any specific restrictions and in requirements.txt pinned to a specific version which ensures that the application build is deterministic.
- For Javascript, include new libraries in package.json
Tests: The pull request should include tests, either as doctests, unit tests, or both. Make sure to resolve all errors and test failures. See Testing for how to run tests.
Documentation: If the pull request adds functionality, the docs should be updated as part of the same PR. Doc string are often sufficient, make sure to follow the sphinx compatible standards.
CI: Reviewers will not review the code until all CI tests are passed. Sometimes there can be flaky tests. You can close and open PR to re-run CI test. Please report if the issue persists. After the CI fix has been deployed to master, please rebase your PR.
Code coverage: Please ensure that code coverage does not decrease.
Remove [WIP] when ready for review. Please note that it may be merged soon after approved so please make sure the PR is ready to merge and do not expect more time for post-approval edits.
If the PR was not ready for review and inactive for > 30 days, we will close it due to inactivity. The author is welcome to re-open and update.

Reviewing

Use constructive tone when writing reviews.
If there are changes required, state clearly what needs to be done before the PR can be approved.
If you are asked to update your pull request with some changes there's no need to create a new one. Push your changes to the same branch.
The committers reserve the right to reject any PR and in some cases may request the author to file an issue.

Merging

At least one approval is required for merging a PR.
PR is usually left open for at least 24 hours before merging.
After the PR is merged, close the corresponding issue(s).

Post-merge Responsibility

Project maintainers may contact the PR author if new issues are introduced by the PR.
Project maintainers may revert your changes if a critical issue is found, such as breaking master branch CI.

Managing Issues and PRs

To handle issues and PRs that are coming in, committers read issues/PRs and flag them with labels to categorize and help contributors spot where to take actions, as contributors usually have different expertises.

Triaging goals

For issues: Categorize, screen issues, flag required actions from authors.
For PRs: Categorize, flag required actions from authors. If PR is ready for review, flag required actions from reviewers.

First, add Category labels (a.k.a. hash labels). Every issue/PR must have one hash label (except spam entry). Labels that begin with # defines issue/PR type:

Label	for Issue	for PR
`#bug`	Bug report	Bug fix
`#code-quality`	Describe problem with code, architecture or productivity	Refactor, tests, tooling
`#feature`	New feature request	New feature implementation
`#refine`	Propose improvement that does not provide new features and is also not a bug fix nor refactor, such as adjust padding, refine UI style.	Implementation of improvement that does not provide new features and is also not a bug fix nor refactor, such as adjust padding, refine UI style.
`#doc`	Documentation	Documentation
`#question`	Troubleshooting: Installation, Running locally, Ask how to do something. Can be changed to `#bug` later.	N/A
`#SIP`	Superset Improvement Proposal	N/A
`#ASF`	Tasks related to Apache Software Foundation policy	Tasks related to Apache Software Foundation policy

Then add other types of labels as appropriate.

Descriptive labels (a.k.a. dot labels): These labels that begin with . describe the details of the issue/PR, such as .ui, .js, .install, .backend, etc. Each issue/PR can have zero or more dot labels.
Need labels: These labels have pattern need:xxx, which describe the work required to progress, such as need:rebase, need:update, need:screenshot.
Risk labels: These labels have pattern risk:xxx, which describe the potential risk on adopting the work, such as risk:db-migration. The intention was to better understand the impact and create awareness for PRs that need more rigorous testing.
Status labels: These labels describe the status (abandoned, wontfix, cant-reproduce, etc.) Issue/PRs that are rejected or closed without completion should have one or more status labels.
Version labels: These have the pattern vx.x such as v0.28. Version labels on issues describe the version the bug was reported on. Version labels on PR describe the first release that will include the PR.

Committers may also update title to reflect the issue/PR content if the author-provided title is not descriptive enough.

If the PR passes CI tests and does not have any need: labels, it is ready for review, add label review and/or design-review.

If an issue/PR has been inactive for >=30 days, it will be closed. If it does not have any status label, add inactive.

Setup Local Environment for Development

First, fork the repository on GitHub, then clone it. You can clone the main repository directly, but you won't be able to send pull requests.

git clone git@github.com:your-username/incubator-superset.git
cd incubator-superset

Documentation

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 http.server # Python2 users should use 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.

Images

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.

API documentation

Generate the API documentation with:

pip install -r docs/requirements.txt
python setup.py build_sphinx

Flask server

Make sure your machine meets the OS dependencies before following these steps.

# Create a virtual environemnt and activate it (recommended)
virtualenv -p python3 venv # setup a python3.6 virtualenv
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 from inside the `superset` dir at port 8088
# Note that your page may not have css at this point.
# See instructions below how to build the front-end assets.
cd superset
FLASK_ENV=development flask run -p 8088 --with-threads --reload --debugger

Logging to the browser console

This feature is only available on Python 3. When debugging your application, you can have the server logs sent directly to the browser console:

FLASK_ENV=development flask run -p 8088 --with-threads --reload --debugger --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

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.

Prerequisite

Installing Dependencies

Install third-party dependencies listed in package.json:

# From the root of the repository
cd superset/assets

# Install dependencies from `package-lock.json`
npm ci

Building

You can run the Webpack dev server (in a separate terminal from Flask), which runs on port 9000 and proxies non-asset requests to the Flask server on port 8088. After pointing your browser to http://localhost:9000, 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

Alternatively you can use one of the following commands.

# Start a watcher that recompiles your assets as you modify them (but have to manually reload your browser to see changes.)
npm run dev

# Compile the Javascript and CSS in production/optimized mode for official releases
npm run prod

Updating NPM packages

Use npm in the prescribed way, making sure that superset/assets/package-lock.json is updated according to npm-prescribed best practices.

Feature flags

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',
}

superset/config.py contains DEFAULT_FEATURE_FLAGS which will be overwritten by those specified under FEATURE_FLAGS in superset_config.py. For example, DEFAULT_FEATURE_FLAGS = { 'FOO': True, 'BAR': False } in superset/config.py and FEATURE_FLAGS = { 'BAR': True, 'BAZ': True } in superset_config.py will result in combined feature flags of { 'FOO': True, 'BAR': True, 'BAZ': True }.

Linting

Lint the project with:

# for python
tox -e flake8

# for javascript
cd superset/assets
npm ci
npm run lint

Testing

Python Testing

All python tests are carried out in tox a standardized testing framework. All python tests can be run with any of the tox environments, via,

tox -e <environment>

For example,

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.

Typing

To ensure clarity, consistency, all readability, all new functions should use type hints and include a docstring using Sphinx documentation.

Note per PEP-484 no syntax for listing explicitly raised exceptions is proposed and thus the recommendation is to put this information in a docstring, i.e.,

import math
from typing import Union


def sqrt(x: Union[float, int]) -> Union[float, int]:
    """
    Return the square root of x.

    :param x: A number
    :returns: The square root of the given number
    :raises ValueError: If the number is negative
    """

    return math.sqrt(x)

JavaScript Testing

We use Jest and Enzyme to test Javascript. Tests can be run with:

cd superset/assets
npm run test

Integration Testing

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

Translating

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';

Enabling language selection

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'},
}

Extracting new strings for translation

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. You can use Poedit to translate the po file more conveniently. There are some tutorials in the wiki.

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). If you get a lot of [null,***] in messages.json, just delete all the null,. For example, "year":["年"] is correct while "year":[null,"年"]is incorrect.

Creating a new language dictionary

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.

Tips

Adding a new datasource

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.

Creating a new visualization type

Here's an example as a Github PR with comments that describe what the different sections of the code do: apache#3013

Adding a DB migration

Alter the model you want to change. This example will add a Column Annotations model.

Example commit
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.

Example commit

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.

Example commit

Merging DB migrations

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
```

SQL Lab Async

It's possible to configure a local database to operate in async mode, to work on async related features.

To do this, you'll need to:

Add an additional database entry. We recommend you copy the connection string from the database labeled main, and then enable SQL Lab and the features you want to use. Don't forget to check the Async box
Configure a results backend, here's a local FileSystemCache example, not recommended for production, but perfect for testing (stores cache in /tmp)
```
from werkzeug.contrib.cache import FileSystemCache
RESULTS_BACKEND = FileSystemCache('/tmp/sqllab')
```

Note that:

for changes that affect the worker logic, you'll have to restart the celery worker process for the changes to be reflected.
The message queue used is a sqlite database using the SQLAlchemy experimental broker. Ok for testing, but not recommended in production
In some cases, you may want to create a context that is more aligned to your production environment, and use the similar broker as well as results backend configuration

Files

CONTRIBUTING.md

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