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Contributing

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

Table of Contents

Orientation

Here's a list of repositories that contain Superset-related packages:

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 are open to whoever wants to implement them.

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.

  • Title the PR with one of the following semantic prefixes (inspired by Karma):

    • feat (new feature)
    • fix (bug fix)
    • docs (changes to the documentation)
    • style (formatting, missing semi colons, etc; no application logic change)
    • refactor (refactoring code)
    • test (adding missing tests, refactoring tests; no application logic change)
    • chore (updating tasks etc; no application logic change)
    • perf (performance-related change)
    • build (build tooling, Docker configuration change)
    • ci (test runner, Github Actions workflow changes)
    • other (changes that don't correspond to the above -- should be rare!)
    • Examples:
      • feat: export charts as ZIP files
      • perf(api): improve API info performance
      • fix(chart-api): cached-indicator always shows value is cached

  • 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 TypeScript/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.

  • 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.

Reporting a Security Vulnerability

Please report security vulnerabilities to [email protected].

In the event a community member discovers a security flaw in Superset, it is important to follow the Apache Security Guidelines and release a fix as quickly as possible before public disclosure. Reporting security vulnerabilities through the usual GitHub Issues channel is not ideal as it will publicize the flaw before a fix can be applied.

Revert Guidelines

Reverting changes that are causing issues in the master branch is a normal and expected part of the development process. In an open source community, the ramifications of a change cannot always be fully understood. With that in mind, here are some considerations to keep in mind when considering a revert:

  • Availability of the PR author: If the original PR author or the engineer who merged the code is highly available and can provide a fix in a reasonable timeframe, this would counter-indicate reverting.
  • Severity of the issue: How severe is the problem on master? Is it keeping the project from moving forward? Is there user impact? What percentage of users will experience a problem?
  • Size of the change being reverted: Reverting a single small PR is a much lower-risk proposition than reverting a massive, multi-PR change.
  • Age of the change being reverted: Reverting a recently-merged PR will be more acceptable than reverting an older PR. A bug discovered in an older PR is unlikely to be causing widespread serious issues.
  • Risk inherent in reverting: Will the reversion break critical functionality? Is the medicine more dangerous than the disease?
  • Difficulty of crafting a fix: In the case of issues with a clear solution, it may be preferable to implement and merge a fix rather than a revert.

Should you decide that reverting is desirable, it is the responsibility of the Contributor performing the revert to:

  • Contact the interested parties: The PR's author and the engineer who merged the work should both be contacted and informed of the revert.
  • Provide concise reproduction steps: Ensure that the issue can be clearly understood and duplicated by the original author of the PR.
  • Put the revert through code review: The revert must be approved by another committer.

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 [email protected]:your-username/incubator-superset.git
cd incubator-superset

Documentation

The latest documentation and tutorial are available at https://superset.apache.org/.

The site is written using the Gatsby framework and docz for the documentation subsection. Find out more about it in docs/README.md

Images

If you're adding new images to the documentation, you'll notice that the images referenced in the rst, e.g.

.. image:: _static/images/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-frontend/images directory. When the docs are deployed to https://superset.incubator.apache.org/, images are copied from there to the _static/images directory, just like they're referenced in the docs.

For example, the image referenced above actually lives in superset-frontend/images/tutorial. Since the image is moved during the documentation build process, the docs reference the image in _static/images/tutorial instead.

Flask server

OS Dependencies

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

Ensure Python versions >3.7, Then proceed with:

# Create a virtual environemnt and activate it (recommended)
python3 -m venv venv # setup a python3 virtualenv
source venv/bin/activate

# Install external dependencies
pip install -r requirements/local.txt

# Install Superset in editable (development) mode
pip install -e .

# Create an admin user in your metadata database
superset fab create-admin

# 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 your virtualenv.
# Note that your page may not have css at this point.
# See instructions below how to build the front-end assets.
FLASK_ENV=development superset run -p 8088 --with-threads --reload --debugger

Note: the FLASK_APP env var should not need to be set, as it's currently controlled via .flaskenv, however if needed, it should be set to superset.app:create_app()

If you have made changes to the FAB-managed templates, which are not built the same way as the newer, React-powered front-end assets, you need to start the app without the --with-threads argument like so: FLASK_ENV=development superset run -p 8088 --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 using the ConsoleLog package. You need to mutate the app, by adding the following to your config.py or superset_config.py:

from console_log import ConsoleLog

def FLASK_APP_MUTATOR(app):
    app.wsgi_app = ConsoleLog(app.wsgi_app, app.logger)

Then make sure you run your WSGI server using the right worker type:

FLASK_ENV=development gunicorn "superset.app:create_app()" -k "geventwebsocket.gunicorn.workers.GeventWebSocketWorker" -b 127.0.0.1:8088 --reload

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 (TypeScript, JavaScript, CSS, and images) must be compiled in order to properly display the web UI. The superset-frontend 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.

nvm and node

First, be sure you are using recent versions of NodeJS and npm. We recommend using nvm to manage your node environment:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.37.0/install.sh | bash

cd superset-frontend
nvm install
nvm use

For those interested, you may also try out avn to automatically switch to the node version that is required to run Superset frontend.

Prerequisite

Installing Dependencies

Install third-party dependencies listed in package.json:

# From the root of the repository
cd superset-frontend

# 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 -- --devserverPort=9001

# Run the dev server proxying to a Flask server on a non-default port
npm run dev-server -- --supersetPort=8081

# Or proxy it to a remote backend so you can test frontend changes without
# starting the backend locally
npm run dev-server -- --superset=https://superset-dev.example.com

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 TypeScript/JavaScript and CSS in production/optimized mode for official releases
npm run prod

If you run this service from somewhere other than your local machine, you may need to add hostname value to webpack.config.js at .devServer.public specifying the endpoint at which you will access the app. For example: myhost:9001. For convenience you may want to install webpack, webpack-cli and webpack-dev-server globally so that you can run them directly:

npm install --global webpack webpack-cli webpack-dev-server

Docker (docker-compose)

See docs here

Updating NPM packages

Use npm in the prescribed way, making sure that superset-frontend/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-frontend/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 }.

Git Hooks

Superset uses Git pre-commit hooks courtesy of pre-commit. To install run the following:

pip3 install -r requirements/integration.txt
pre-commit install

Linting

Lint the project with:

# for python
tox -e flake8

# for frontend
cd superset-frontend
npm ci
npm run lint

The Python code is auto-formatted using Black which is configured as a pre-commit hook. There are also numerous editor integrations.

Conventions

Python

Parameters in the config.py (which are accessible via the Flask app.config dictionary) are assummed to always be defined and thus should be accessed directly via,

blueprints = app.config["BLUEPRINTS"]

rather than,

blueprints = app.config.get("BLUEPRINTS")

or similar as the later will cause typing issues. The former is of type List[Callable] whereas the later is of type Optional[List[Callable]].

Typing

Python

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

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)

TypeScript

TypeScript is fully supported and is the recommended language for writing all new frontend components. When modifying existing functions/components, migrating to TypeScript is appreciated, but not required. Examples of migrating functions/components to TypeScript can be found in #9162 and #9180.

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.

Frontend Testing

We use Jest and Enzyme to test TypeScript/JavaScript. Tests can be run with:

cd superset-frontend
npm run test

To run a single test file:

npm run test -- path/to/file.js

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
export SUPERSET_TESTENV=true
export ENABLE_REACT_CRUD_VIEWS=true
export CYPRESS_BASE_URL="http://localhost:8081"
superset db upgrade
superset load_test_users
superset load_examples --load-test-data
superset init
superset run --port 8081

Run Cypress tests:

cd superset-frontend
npm run build-instrumented

cd cypress-base
npm install

# run tests via headless Chrome browser (requires Chrome 64+)
npm run cypress-run-chrome

# run tests from a specific file
npm run cypress-run-chrome -- --spec cypress/integration/explore/link.test.js

# run specific file with video capture
npm run cypress-run-chrome -- --spec cypress/integration/dashboard/index.test.js --config video=true

# to open the cypress ui
npm run cypress-debug

# to point cypress to a url other than the default (http://localhost:8088) set the environment variable before running the script
# e.g., CYPRESS_BASE_URL="http://localhost:9000"
CYPRESS_BASE_URL=<your url> npm run cypress open

See superset-frontend/cypress_build.sh.

As an alternative you can use docker-compose environment for testing:

Make sure you have added below line to your /etc/hosts file: 127.0.0.1 db

If you already have launched Docker environment please use the following command to assure a fresh database instance: docker-compose down -v

Launch environment:

CYPRESS_CONFIG=true docker-compose up

It will serve backend and frontend on port 8088.

Run Cypress tests:

cd cypress-base
npm install
npm run cypress open

Storybook

Superset includes a Storybook to preview the layout/styling of various Superset components, and variations thereof. To open and view the Storybook:

cd superset-frontend
npm run storybook

When contributing new React components to Superset, please try to add a Story alongside the component's jsx/tsx file.

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 TypeScript/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

flask fab 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
flask fab 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

  1. 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.

  2. Create DB migration files for the new models

  3. 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.

Improving visualizations

To edit the frontend code for visualizations, you will have to check out a copy of apache-superset/superset-ui:

git clone https://github.com/apache-superset/superset-ui.git
cd superset-ui
yarn
yarn build

Then use npm link to create symlinks of the plugins/superset-ui packages you want to edit in superset-frontend/node_modules:

cd incubator-superset/superset-frontend
npm link ../../superset-ui/plugins/[PLUGIN NAME]

# Or to link all core superset-ui and plugin packages:
# npm link ../../superset-ui/{packages,plugins}/*

# Start developing
npm run dev-server

When superset-ui packages are linked with npm link, the dev server will automatically load a package's source code from its /src directory, instead of the built modules in lib/ or esm/.

Note that every time you do npm install, you will lose the symlink(s) and may have to run npm link again.

Visualization Plugins

The topic of authoring new plugins, whether you'd like to contribute it back or not has been well documented in the So, You Want to Build a Superset Viz Plugin... blog post

To contribute a plugin to Superset-UI, your plugin must meet the following criteria:

  • The plugin should be applicable to the community at large, not a particularly specialized use case
  • The plugin should be written with TypeScript
  • The plugin should contain sufficient unit/e2e tests
  • The plugin should use appropriate namespacing, e.g. a folder name of plugin-chart-whatever and a package name of @superset-ui/plugin-chart-whatever
  • The plugin should use them variables via Emotion, as passed in by the ThemeProvider
  • The plugin should provide adequate error handling (no data returned, malformatted data, invalid controls, etc.)
  • The plugin should contain documentation in the form of a populated README.md file
  • The plugin should have a meaningful and unique icon
  • Above all else, the plugin should come with a commitment to maintenance from the original author(s)

Submissions will be considered for submission (or removal) on a case-by-case basis.

Adding a DB migration

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

    Example commit

  2. 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

  3. 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

  4. Add column to view

    Since there is a new column, we need to add it to the AppBuilder Model view.

    Example commit

  5. Test the migration's down method

    superset db downgrade

    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 downgrade 40a0a483dd12 -> 1a1d627ebd8e, add_metadata_column_to_annotation_model.py

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:

  1. Get the migration heads

    superset db heads

    This should list two or more migration hashes.

  2. Create a new merge migration

    superset db merge {HASH1} {HASH2}

  3. 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 cachelib.file import FileSystemCache
RESULTS_BACKEND = FileSystemCache('/tmp/sqllab')

  • Start up a celery worker

    celery worker --app=superset.tasks.celery_app:app -Ofair

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

Chart Parameters

Chart parameters are stored as a JSON encoded string the slices.params column and are often referenced throughout the code as form-data. Currently the form-data is neither versioned nor typed as thus is somewhat free-formed. Note in the future there may be merit in using something like JSON Schema to both annotate and validate the JSON object in addition to using a Mypy TypedDict (introduced in Python 3.8) for typing the form-data in the backend. This section serves as a potential primer for that work.

The following tables provide a non-exhausive list of the various fields which can be present in the JSON object grouped by the Explorer pane sections. These values were obtained by extracting the distinct fields from a legacy deployment consisting of tens of thousands of charts and thus some fields may be missing whilst others may be deprecated.

Note not all fields are correctly catagorized. The fields vary based on visualization type and may apprear in different sections depending on the type. Verified deprecated columns may indicate a missing migration and/or prior migrations which were unsucessful and thus future work may be required to clean up the form-data.

Datasource & Chart Type

Field Type Notes
database_name string Deprecated?
datasource string <datasouce_id>__<datasource_type>
datasource_id string Deprecated? See datasource
datasource_name string Deprecated?
datasource_type string Deprecated? See datasource
viz_type string The Visualization Type widget

Time

Field Type Notes
druid_time_origin string The Druid Origin widget
granularity string The Druid Time Granularity widget
granularity_sqla string The SQLA Time Column widget
time_grain_sqla string The SQLA Time Grain widget
time_range string The Time range widget

GROUP BY

Field Type Notes
metrics array(string) See Query section
order_asc - See Query section
row_limit - See Query section
timeseries_limit_metric - See Query section

NOT GROUPED BY

Field Type Notes
order_by_cols array(string) The Ordering widget
row_limit - See Query section

Y Axis 1

Field Type Notes
metric - The Left Axis Metric widget. See Query section
y_axis_format - See Y Axis section

Y Axis 2

Field Type Notes
metric_2 - The Right Axis Metric widget. See Query section

Query

Field Type Notes
adhoc_filters array(object) The Filters widget
columns array(string) The Breakdowns widget
groupby array(string) The Group by or Series widget
limit number The Series Limit widget
metric
metric_2
metrics
percent_mertics
secondary_metric
size
x
y		 string,object,array(string),array(object) The metric(s) depending on the visualization type
order_asc boolean The Sort Descending widget
row_limit number The Row limit widget
timeseries_limit_metric object The Sort By widget

The metric (or equivalent) and timeseries_limit_metric fields are all composed of either metric names or the JSON representation of the AdhocMetric TypeScript type. The adhoc_filters is composed of the JSON represent of the AdhocFilter TypeScript type (which can comprise of columns or metrics depending on whether it is a WHERE or HAVING clause). The all_columns, all_columns_x, columns, groupby, and order_by_cols fields all represent column names.

Chart Options

Field Type Notes
color_picker object The Fixed Color widget
label_colors object The Color Scheme widget
normalized boolean The Normalized widget

Y Axis

Field Type Notes
y_axis_2_label N/A Deprecated?
y_axis_format string The Y Axis Format widget
y_axis_zero N/A Deprecated?

Note the y_axis_format is defined under various section for some charts.

Other

Field Type Notes
color_scheme string

Unclassified

Field Type Notes
add_to_dash N/A
code N/A
collapsed_fieldsets N/A
comparison type N/A
country_fieldtype N/A
default_filters N/A
entity N/A
expanded_slices N/A
extra_filters N/A
filter_immune_slice_fields N/A
filter_immune_slices N/A
flt_col_0 N/A
flt_col_1 N/A
flt_eq_0 N/A
flt_eq_1 N/A
flt_op_0 N/A
flt_op_1 N/A
goto_dash N/A
import_time N/A
label N/A
linear_color_scheme N/A
new_dashboard_name N/A
new_slice_name N/A
num_period_compare N/A
period_ratio_type N/A
perm N/A
rdo_save N/A
refresh_frequency N/A
remote_id N/A
resample_fillmethod N/A
resample_how N/A
rose_area_proportion N/A
save_to_dashboard_id N/A
schema N/A
series N/A
show_bubbles N/A
slice_name N/A
timed_refresh_immune_slices N/A
userid N/A