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Setting up a Development Environment

The documentation in this section is a bit of a patchwork of knowledge representing the multitude of ways that exist to run Superset (docker compose, just "docker", on "metal", using a Makefile).

note

Now we have evolved to recommend and support docker compose more actively as the main way to run Superset for development and preserve your sanity. Most people should stick to the first few sections - ("Fork & Clone", "docker compose" and "Installing Dev Tools")

Fork and Clone​

First, fork the repository on GitHub, then clone it.

Second, you can clone the main repository directly, but you won't be able to send pull requests.

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

Setting things up to squeeze a "hello world" into any part of Superset should be as simple as

# getting docker compose to fire up services, and rebuilding if some docker layers have changed
# using the `--build` suffix may be slower and optional if layers like py dependencies haven't changed
docker compose up --build

Note that:

  • this will pull/build docker images and run a cluster of services, including:
    • A Superset Flask web server, mounting the local python repo/code
    • A Superset Celery worker, also mounting the local python repo/code
    • A Superset Node service, mounting, compiling and bundling the JS/TS assets
    • A Superset Node websocket service to power the async backend
    • Postgres as the metadata database and to store example datasets, charts and dashboards which should be populated upon startup
    • Redis as the message queue for our async backend and caching backend
  • It'll load up examples into the database upon the first startup
  • all other details and pointers available in docker-compose.yml
  • The local repository is mounted within the services, meaning updating the code on the host will be reflected in the docker images
  • Superset is served at localhost:8088/
  • You can login with admin/admin
note

Installing and building Node modules for Apache Superset inside superset-node can take a significant amount of time. This is normal due to the size of the dependencies. Please be patient while the process completes, as long wait times do not indicate an issue with your setup. If delays seem excessive, check your internet connection or system resources.

caution

Since docker compose is primarily designed to run a set of containers on a single host and can't credibly support high availability as a result, we do not support nor recommend using our docker compose constructs to support production-type use-cases. For single host environments, we recommend using minikube along our installing on k8s documentation. configured to be secure.

Supported environment variables​

Affecting the Docker build process:

  • SUPERSET_BUILD_TARGET (default=dev): which --target to build, either lean or dev are commonly used
  • INCLUDE_FIREFOX (default=false): whether to include the Firefox headless browser in the build
  • INCLUDE_CHROMIUM (default=false): whether to include the Firefox headless browser in the build
  • BUILD_TRANSLATIONS(default=false): whether to compile the translations from the .po files available

For more env vars that affect your configuration, see this superset_config.py used in the docker compose context to assign env vars to the superset configuration.

Nuking the postgres database​

At times, it's possible to end up with your development database in a bad state, it's common while switching branches that contain migrations for instance, where the database version stamp that alembic manages is no longer available after switching branch.

In that case, the easy solution is to nuke the postgres db and start fresh. Note that the full state of the database will be gone after doing this, so be cautious.

# first stop docker-compose if it's running
docker-compose down
# delete the volume containing the database
docker volume rm superset_db_home
# restart docker-compose, which will init a fresh database and load examples
docker-compose up

Installing Development Tools​

note

While docker compose simplifies a lot of the setup, there are still many things you'll want to set up locally to power your IDE, and things like commit hooks, linters, and test-runners. Note that you can do these things inside docker images with commands like docker compose exec superset_app bash for instance, but many people like to run that tooling from their host.

Python environment​

Assuming you already have a way to setup your python environments like pyenv, virtualenv or something else, all you should have to do is to install our dev, pinned python requirements bundle

pip install -r requirements/development.txt

Git Hooks​

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

pre-commit install

This will install the hooks in your local repository. From now on, a series of checks will automatically run whenever you make a Git commit.

Running Pre-commit Manually​

You can also run the pre-commit checks manually in various ways:

  • Run pre-commit on all files (same as CI):

    To run the pre-commit checks across all files in your repository, use the following command:

    pre-commit run --all-files

    This is the same set of checks that will run during CI, ensuring your changes meet the project's standards.

  • Run pre-commit on a specific file:

    If you want to check or fix a specific file, you can do so by specifying the file path:

    pre-commit run --files path/to/your/file.py

    This will only run the checks on the file(s) you specify.

  • Run a specific pre-commit check:

    To run a specific check (hook) across all files or a particular file, use the following command:

    pre-commit run <hook_id> --all-files

    Or for a specific file:

    pre-commit run <hook_id> --files path/to/your/file.py

    Replace <hook_id> with the ID of the specific hook you want to run. You can find the list of available hooks in the .pre-commit-config.yaml file.

Alternatives to docker compose​

caution

This part of the documentation is a patchwork of information related to setting up development environments without docker compose and is documented/supported to varying degrees. It's been difficult to maintain this wide array of methods and insure they're functioning across environments.

Flask server​

OS Dependencies​

Make sure your machine meets the OS dependencies before following these steps. You also need to install MySQL.

Ensure that you are using Python version 3.9, 3.10 or 3.11, then proceed with:

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

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

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

# Initialize the database
superset db upgrade

# Create an admin user in your metadata database (use `admin` as username to be able to load the examples)
superset fab create-admin

# Create default roles and permissions
superset init

# Load some data to play with.
# Note: you MUST have previously created an admin user with the username `admin` for this command to work.
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 on how to build the front-end assets.
superset run -p 8088 --with-threads --reload --debugger --debug

Or you can install it via our Makefile

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

# install pip packages + pre-commit
$ make install

# Install superset pip packages and setup env only
$ make superset

# Setup pre-commit only
$ make pre-commit

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: superset run -p 8088 --reload --debugger --debug

Dependencies​

If you add a new requirement or update an existing requirement (per the install_requires section in setup.py) you must recompile (freeze) the Python dependencies to ensure that for CI, testing, etc. the build is deterministic. This can be achieved via,

$ python3 -m venv venv
$ source venv/bin/activate
$ python3 -m pip install -r requirements/development.txt
$ pip-compile-multi --no-upgrade

When upgrading the version number of a single package, you should run pip-compile-multi with the -P flag:

$ pip-compile-multi -P my-package

To bring all dependencies up to date as per the restrictions defined in setup.py and requirements/*.in, run pip-compile-multi` without any flags:

$ pip-compile-multi

This should be done periodically, but it is recommended to do thorough manual testing of the application to ensure no breaking changes have been introduced that aren't caught by the unit and integration tests.

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:

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​

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 frontend assets. Note that for some legacy pages 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.

Prerequisite​

nvm and node​

First, be sure you are using the following versions of Node.js and npm:

  • Node.js: Version 20
  • npm: Version 10

We recommend using nvm to manage your node environment:

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

in case it shows '-bash: nvm: command not found'
export NVM_DIR="$HOME/.nvm"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"  # This loads nvm
[ -s "$NVM_DIR/bash_completion" ] && \. "$NVM_DIR/bash_completion"  # This loads nvm bash_completion

cd superset-frontend
nvm install --lts
nvm use --lts

Or if you use the default macOS starting with Catalina shell zsh, try:

sh -c "$(curl -fsSL https://raw.githubusercontent.com/nvm-sh/nvm/v0.37.0/install.sh)"

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

Install dependencies​

Install third-party dependencies listed in package.json via:

# From the root of the repository
cd superset-frontend

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

Note that Superset uses Scarf to capture telemetry/analytics about versions being installed, including the scarf-js npm package and an analytics pixel. As noted elsewhere in this documentation, Scarf gathers aggregated stats for the sake of security/release strategy and does not capture/retain PII. You can read here about the scarf-js package, and various means to opt out of it, but you can opt out of the npm package and the pixel by setting the SCARF_ANALYTICS environment variable to false or opt out of the pixel by adding this setting in superset-frontent/package.json:

// your-package/package.json
{
  // ...
  "scarfSettings": {
    "enabled": false
  }
  // ...
}

Build assets​

There are three types of assets you can build:

  1. npm run build: the production assets, CSS/JSS minified and optimized
  2. npm run dev-server: local development assets, with sourcemaps and hot refresh support
  3. npm run build-instrumented: instrumented application code for collecting code coverage from Cypress tests

If while using the above commands you encounter an error related to the limit of file watchers:

Error: ENOSPC: System limit for number of file watchers reached

The error is thrown because the number of files monitored by the system has reached the limit. You can address this error by increasing the number of inotify watchers.

The current value of max watches can be checked with:

cat /proc/sys/fs/inotify/max_user_watches

Edit the file /etc/sysctl.conf to increase this value. The value needs to be decided based on the system memory (see this StackOverflow answer for more context).

Open the file in an editor and add a line at the bottom specifying the max watches values.

fs.inotify.max_user_watches=524288

Save the file and exit the editor. To confirm that the change succeeded, run the following command to load the updated value of max_user_watches from sysctl.conf:

sudo sysctl -p

Webpack dev server​

The dev server by default starts at http://localhost:9000 and proxies the backend requests to http://localhost:8088.

So a typical development workflow is the following:

  1. run Superset locally using Flask, on port 8088 — but don't access it directly,

    # Install Superset and dependencies, plus load your virtual environment first, as detailed above.
    superset run -p 8088 --with-threads --reload --debugger --debug

  2. in parallel, run the Webpack dev server locally on port 9000,

    npm run dev-server

  3. access http://localhost:9000 (the Webpack server, not Flask) in your web browser. This will use the hot-reloading front-end assets from the Webpack development server while redirecting back-end queries to Flask/Superset: your changes on Superset codebase — either front or back-end — will then be reflected live in the browser.

It's possible to change the Webpack server settings:

# Start the dev server at http://localhost:9000
npm run dev-server

# Run the dev server on a non-default port
npm run dev-server -- --port=9001

# Proxy backend requests to a Flask server running on a non-default port
npm run dev-server -- --env=--supersetPort=8081

# Proxy to a remote backend but serve local assets
npm run dev-server -- --env=--superset=https://superset-dev.example.com

The --superset= option is useful in case you want to debug a production issue or have to setup Superset behind a firewall. It allows you to run Flask server in another environment while keep assets building locally for the best developer experience.

Other npm commands​

Alternatively, there are other NPM commands you may find useful:

  1. npm run build-dev: build assets in development mode.
  2. npm run dev: built dev assets in watch mode, will automatically rebuild when a file changes

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-ui/core. 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 }.

The current status of the usability of each flag (stable vs testing, etc) can be found in RESOURCES/FEATURE_FLAGS.md.

Git Hooks​

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

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

A series of checks will now run when you make a git commit.

Linting​

See how tos

GitHub Actions and act​

tip

act compatibility of Superset's GHAs is not fully tested. Running act locally may or may not work for different actions, and may require fine tunning and local secret-handling. For those more intricate GHAs that are tricky to run locally, we recommend iterating directly on GHA's infrastructure, by pushing directly on a branch and monitoring GHA logs. For more targetted iteration, see the gh workflow run --ref {BRANCH} subcommand of the GitHub CLI.

For automation and CI/CD, Superset makes extensive use of GitHub Actions (GHA). You can find all of the workflows and other assets under the .github/ folder. This includes:

  • running the backend unit test suites (tests/)
  • running the frontend test suites (superset-frontend/src/**.*.test.*)
  • running our Cypress end-to-end tests (superset-frontend/cypress-base/)
  • linting the codebase, including all Python, Typescript and Javascript, yaml and beyond
  • checking for all sorts of other rules conventions

When you open a pull request (PR), the appropriate GitHub Actions (GHA) workflows will automatically run depending on the changes in your branch. It's perfectly reasonable (and required!) to rely on this automation. However, the downside is that it's mostly an all-or-nothing approach and doesn't provide much control to target specific tests or iterate quickly.

At times, it may be more convenient to run GHA workflows locally. For that purpose we use act, a tool that allows you to run GitHub Actions (GHA) workflows locally. It simulates the GitHub Actions environment, enabling developers to test and debug workflows on their local machines before pushing changes to the repository. More on how to use it in the next section.

note

In both GHA and act, we can run a more complex matrix for our tests, executing against different database engines (PostgreSQL, MySQL, SQLite) and different versions of Python. This enables us to ensure compatibility and stability across various environments.

Using act​

First, install act -> https://nektosact.com/

To list the workflows, simply:

act --list

To run a specific workflow:

act pull_request --job {workflow_name} --secret GITHUB_TOKEN=$GITHUB_TOKEN --container-architecture linux/amd64

In the example above, notice that:

  • we target a specific workflow, using --job
  • we pass a secret using --secret, as many jobs require read access (public) to the repo
  • we simulate a pull_request event by specifying it as the first arg, similarly, we could simulate a push event or something else
  • we specify --container-architecture, which tends to emulate GHA more reliably
note

act is a rich tool that offers all sorts of features, allowing you to simulate different events (pull_request, push, ...), semantics around passing secrets where required and much more. For more information, refer to act's documentation

note

Some jobs require secrets to interact with external systems and accounts that you may not have in your possession. In those cases you may have to rely on remote CI or parameterize the job further to target a different environment/sandbox or your own alongside the related secrets.

Testing​

Python Testing​

Unit Tests​

For unit tests located in tests/unit_tests/, it's usually easy to simply run the script locally using:

pytest tests/unit_tests/*

Integration Tests​

For more complex pytest-defined integration tests (not to be confused with our end-to-end Cypress tests), many tests will require having a working test environment. Some tests require a database, Celery, and potentially other services or libraries installed.

Running Tests with act​

To run integration tests locally using act, ensure you have followed the setup instructions from the GitHub Actions and act section. You can run specific workflows or jobs that include integration tests. For example:

act --job test-python-38 --secret GITHUB_TOKEN=$GITHUB_TOKEN --event pull_request --container-architecture linux/amd64

Running locally using a test script​

There is also a utility script included in the Superset codebase to run Python integration tests. The readme can be found here.

There is also a utility script included in the Superset codebase to run python integration tests. The readme can be found here

To run all integration tests, for example, run this script from the root directory:

scripts/tests/run.sh

You can run unit tests found in ./tests/unit_tests with pytest. It is a simple way to run an isolated test that doesn't need any database setup:

pytest ./link_to_test.py

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. To open Cypress and explore tests first setup and run test server:

export SUPERSET_CONFIG=tests.integration_tests.superset_test_config
export SUPERSET_TESTENV=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/e2e/explore/link.test.ts

# run specific file with video capture
npm run cypress-run-chrome -- --spec cypress/e2e/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 ensure a fresh database instance: docker compose down -v

Launch environment:

CYPRESS_CONFIG=true docker compose up --build

It will serve the backend and frontend on port 8088.

Run Cypress tests:

cd cypress-base
npm install
npm run cypress open

Debugging Server App​

Local​

For debugging locally using VSCode, you can configure a launch configuration file .vscode/launch.json such as

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Python: Flask",
      "type": "python",
      "request": "launch",
      "module": "flask",
      "env": {
        "FLASK_APP": "superset",
        "SUPERSET_ENV": "development"
      },
      "args": ["run", "-p 8088", "--with-threads", "--reload", "--debugger"],
      "jinja": true,
      "justMyCode": true
    }
  ]
}

Raw Docker (without docker compose)​

Follow these instructions to debug the Flask app running inside a docker container. Note that this will run a barebones Superset web server,

First, add the following to the ./docker-compose.yaml file

superset:
    env_file: docker/.env
    image: *superset-image
    container_name: superset_app
    command: ["/app/docker/docker-bootstrap.sh", "app"]
    restart: unless-stopped
+   cap_add:
+     - SYS_PTRACE
    ports:
      - 8088:8088
+     - 5678:5678
    user: "root"
    depends_on: *superset-depends-on
    volumes: *superset-volumes
    environment:
      CYPRESS_CONFIG: "${CYPRESS_CONFIG}"

Start Superset as usual

docker compose up --build

Install the required libraries and packages to the docker container

Enter the superset_app container

docker exec -it superset_app /bin/bash
root@39ce8cf9d6ab:/app#

Run the following commands inside the container

apt update
apt install -y gdb
apt install -y net-tools
pip install debugpy

Find the PID for the Flask process. Make sure to use the first PID. The Flask app will re-spawn a sub-process every time you change any of the python code. So it's important to use the first PID.

ps -ef

UID        PID  PPID  C STIME TTY          TIME CMD
root         1     0  0 14:09 ?        00:00:00 bash /app/docker/docker-bootstrap.sh app
root         6     1  4 14:09 ?        00:00:04 /usr/local/bin/python /usr/bin/flask run -p 8088 --with-threads --reload --debugger --host=0.0.0.0
root        10     6  7 14:09 ?        00:00:07 /usr/local/bin/python /usr/bin/flask run -p 8088 --with-threads --reload --debugger --host=0.0.0.0

Inject debugpy into the running Flask process. In this case PID 6.

python3 -m debugpy --listen 0.0.0.0:5678 --pid 6

Verify that debugpy is listening on port 5678

netstat -tunap

Active Internet connections (servers and established)
Proto Recv-Q Send-Q Local Address           Foreign Address         State       PID/Program name
tcp        0      0 0.0.0.0:5678            0.0.0.0:*               LISTEN      462/python
tcp        0      0 0.0.0.0:8088            0.0.0.0:*               LISTEN      6/python

You are now ready to attach a debugger to the process. Using VSCode you can configure a launch configuration file .vscode/launch.json like so.

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Attach to Superset App in Docker Container",
      "type": "python",
      "request": "attach",
      "connect": {
        "host": "127.0.0.1",
        "port": 5678
      },
      "pathMappings": [
        {
          "localRoot": "${workspaceFolder}",
          "remoteRoot": "/app"
        }
      ]
    }
  ]
}

VSCode will not stop on breakpoints right away. We've attached to PID 6 however it does not yet know of any sub-processes. In order to "wake up" the debugger you need to modify a python file. This will trigger Flask to reload the code and create a new sub-process. This new sub-process will be detected by VSCode and breakpoints will be activated.

Debugging Server App in Kubernetes Environment​

To debug Flask running in POD inside a kubernetes cluster, you'll need to make sure the pod runs as root and is granted the SYS_TRACE capability.These settings should not be used in production environments.

  securityContext:
    capabilities:
      add: ["SYS_PTRACE"]

See (set capabilities for a container)[https://kubernetes.io/docs/tasks/configure-pod-container/security-context/#set-capabilities-for-a-container] for more details.

Once the pod is running as root and has the SYS_PTRACE capability it will be able to debug the Flask app.

You can follow the same instructions as in docker compose. Enter the pod and install the required library and packages; gdb, netstat and debugpy.

Often in a Kubernetes environment nodes are not addressable from outside the cluster. VSCode will thus be unable to remotely connect to port 5678 on a Kubernetes node. In order to do this you need to create a tunnel that port forwards 5678 to your local machine.

kubectl port-forward  pod/superset-<some random id> 5678:5678

You can now launch your VSCode debugger with the same config as above. VSCode will connect to 127.0.0.1:5678 which is forwarded by kubectl to your remote kubernetes POD.

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.

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.

Visualization Plugins​

The topic of authoring new plugins, whether you'd like to contribute it back or not has been well documented in the the documentation, and in this blog post.

To contribute a plugin to Superset, 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 theme variables via Emotion, as passed in by the ThemeProvider
  • The plugin should provide adequate error handling (no data returned, malformed 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'

    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. E.g.

    1412ec1e5a7b (head)
    67da9ef1ef9c (head)

  2. Pick one of them as the parent revision, open the script for the other revision and update Revises and down_revision to the new parent revision. E.g.:

    --- a/67da9ef1ef9c_add_hide_left_bar_to_tabstate.py
    +++ b/67da9ef1ef9c_add_hide_left_bar_to_tabstate.py
    @@ -17,14 +17,14 @@
    """add hide_left_bar to tabstate

    Revision ID: 67da9ef1ef9c
    -Revises: c501b7c653a3
    +Revises: 1412ec1e5a7b
    Create Date: 2021-02-22 11:22:10.156942

    """

    # revision identifiers, used by Alembic.
    revision = "67da9ef1ef9c"
    -down_revision = "c501b7c653a3"
    +down_revision = "1412ec1e5a7b"

    import sqlalchemy as sa
    from alembic import op

    Alternatively, you may also run superset db merge to create a migration script just for merging the heads.

    superset db merge {HASH1} {HASH2}

  3. Upgrade the DB to the new checkpoint

    superset db upgrade