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feat(docs): Docusaurus multi-versioning, Developer Portal starter kit (#34271)

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Co-authored-by: Claude <noreply@anthropic.com>
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2025-08-22 09:53:01 -07:00
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---
title: API
hide_title: true
sidebar_position: 10
---
import SwaggerUI from 'swagger-ui-react';
import openapi from '/resources/openapi.json';
import 'swagger-ui-react/swagger-ui.css';
import { Alert } from 'antd';
## API
Superset's public **REST API** follows the
[OpenAPI specification](https://swagger.io/specification/), and is
documented here. The docs below are generated using
[Swagger React UI](https://www.npmjs.com/package/swagger-ui-react).
<Alert
type="info"
message={
<div>
<strong>NOTE! </strong>
You can find an interactive version of this documentation on your local Superset
instance at <strong>/swagger/v1</strong> (unless disabled)
</div>
}
/>
<br />
<br />
<hr />
<div className="swagger-container">
<SwaggerUI spec={openapi} />
</div>

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---
title: Alerts and Reports
hide_title: true
sidebar_position: 2
version: 2
---
# Alerts and Reports
Users can configure automated alerts and reports to send dashboards or charts to an email recipient or Slack channel.
- *Alerts* are sent when a SQL condition is reached
- *Reports* are sent on a schedule
Alerts and reports are disabled by default. To turn them on, you need to do some setup, described here.
## Requirements
### Commons
#### In your `superset_config.py` or `superset_config_docker.py`
- `"ALERT_REPORTS"` [feature flag](/docs/configuration/configuring-superset#feature-flags) must be turned to True.
- `beat_schedule` in CeleryConfig must contain schedule for `reports.scheduler`.
- At least one of those must be configured, depending on what you want to use:
- emails: `SMTP_*` settings
- Slack messages: `SLACK_API_TOKEN`
- Users can customize the email subject by including date code placeholders, which will automatically be replaced with the corresponding UTC date when the email is sent. To enable this functionality, activate the `"DATE_FORMAT_IN_EMAIL_SUBJECT"` [feature flag](/docs/configuration/configuring-superset#feature-flags). This enables date formatting in email subjects, preventing all reporting emails from being grouped into the same thread (optional for the reporting feature).
- Use date codes from [strftime.org](https://strftime.org/) to create the email subject.
- If no date code is provided, the original string will be used as the email subject.
##### Disable dry-run mode
Screenshots will be taken but no messages actually sent as long as `ALERT_REPORTS_NOTIFICATION_DRY_RUN = True`, its default value in `docker/pythonpath_dev/superset_config.py`. To disable dry-run mode and start receiving email/Slack notifications, set `ALERT_REPORTS_NOTIFICATION_DRY_RUN` to `False` in [superset config](https://github.com/apache/superset/blob/master/docker/pythonpath_dev/superset_config.py).
#### In your `Dockerfile`
- You must install a headless browser, for taking screenshots of the charts and dashboards. Only Firefox and Chrome are currently supported.
> If you choose Chrome, you must also change the value of `WEBDRIVER_TYPE` to `"chrome"` in your `superset_config.py`.
Note: All the components required (Firefox headless browser, Redis, Postgres db, celery worker and celery beat) are present in the *dev* docker image if you are following [Installing Superset Locally](/docs/installation/docker-compose/).
All you need to do is add the required config variables described in this guide (See `Detailed Config`).
If you are running a non-dev docker image, e.g., a stable release like `apache/superset:3.1.0`, that image does not include a headless browser. Only the `superset_worker` container needs this headless browser to browse to the target chart or dashboard.
You can either install and configure the headless browser - see "Custom Dockerfile" section below - or when deploying via `docker compose`, modify your `docker-compose.yml` file to use a dev image for the worker container and a stable release image for the `superset_app` container.
*Note*: In this context, a "dev image" is the same application software as its corresponding non-dev image, just bundled with additional tools. So an image like `3.1.0-dev` is identical to `3.1.0` when it comes to stability, functionality, and running in production. The actual "in-development" versions of Superset - cutting-edge and unstable - are not tagged with version numbers on Docker Hub and will display version `0.0.0-dev` within the Superset UI.
### Slack integration
To send alerts and reports to Slack channels, you need to create a new Slack Application on your workspace.
1. Connect to your Slack workspace, then head to [https://api.slack.com/apps].
2. Create a new app.
3. Go to "OAuth & Permissions" section, and give the following scopes to your app:
- `incoming-webhook`
- `files:write`
- `chat:write`
- `channels:read`
- `groups:read`
4. At the top of the "OAuth and Permissions" section, click "install to workspace".
5. Select a default channel for your app and continue.
(You can post to any channel by inviting your Superset app into that channel).
6. The app should now be installed in your workspace, and a "Bot User OAuth Access Token" should have been created. Copy that token in the `SLACK_API_TOKEN` variable of your `superset_config.py`.
7. Ensure the feature flag `ALERT_REPORT_SLACK_V2` is set to True in `superset_config.py`
8. Restart the service (or run `superset init`) to pull in the new configuration.
Note: when you configure an alert or a report, the Slack channel list takes channel names without the leading '#' e.g. use `alerts` instead of `#alerts`.
### Kubernetes-specific
- You must have a `celery beat` pod running. If you're using the chart included in the GitHub repository under [helm/superset](https://github.com/apache/superset/tree/master/helm/superset), you need to put `supersetCeleryBeat.enabled = true` in your values override.
- You can see the dedicated docs about [Kubernetes installation](/docs/installation/kubernetes) for more details.
### Docker Compose specific
#### You must have in your `docker-compose.yml`
- A Redis message broker
- PostgreSQL DB instead of SQLlite
- One or more `celery worker`
- A single `celery beat`
This process also works in a Docker swarm environment, you would just need to add `Deploy:` to the Superset, Redis and Postgres services along with your specific configs for your swarm.
### Detailed config
The following configurations need to be added to the `superset_config.py` file. This file is loaded when the image runs, and any configurations in it will override the default configurations found in the `config.py`.
You can find documentation about each field in the default `config.py` in the GitHub repository under [superset/config.py](https://github.com/apache/superset/blob/master/superset/config.py).
You need to replace default values with your custom Redis, Slack and/or SMTP config.
Superset uses Celery beat and Celery worker(s) to send alerts and reports.
- The beat is the scheduler that tells the worker when to perform its tasks. This schedule is defined when you create the alert or report.
- The worker will process the tasks that need to be performed when an alert or report is fired.
In the `CeleryConfig`, only the `beat_schedule` is relevant to this feature, the rest of the `CeleryConfig` can be changed for your needs.
```python
from celery.schedules import crontab
FEATURE_FLAGS = {
"ALERT_REPORTS": True
}
REDIS_HOST = "superset_cache"
REDIS_PORT = "6379"
class CeleryConfig:
broker_url = f"redis://{REDIS_HOST}:{REDIS_PORT}/0"
imports = (
"superset.sql_lab",
"superset.tasks.scheduler",
)
result_backend = f"redis://{REDIS_HOST}:{REDIS_PORT}/0"
worker_prefetch_multiplier = 10
task_acks_late = True
task_annotations = {
"sql_lab.get_sql_results": {
"rate_limit": "100/s",
},
}
beat_schedule = {
"reports.scheduler": {
"task": "reports.scheduler",
"schedule": crontab(minute="*", hour="*"),
},
"reports.prune_log": {
"task": "reports.prune_log",
"schedule": crontab(minute=0, hour=0),
},
}
CELERY_CONFIG = CeleryConfig
SCREENSHOT_LOCATE_WAIT = 100
SCREENSHOT_LOAD_WAIT = 600
# Slack configuration
SLACK_API_TOKEN = "xoxb-"
# Email configuration
SMTP_HOST = "smtp.sendgrid.net" # change to your host
SMTP_PORT = 2525 # your port, e.g. 587
SMTP_STARTTLS = True
SMTP_SSL_SERVER_AUTH = True # If you're using an SMTP server with a valid certificate
SMTP_SSL = False
SMTP_USER = "your_user" # use the empty string "" if using an unauthenticated SMTP server
SMTP_PASSWORD = "your_password" # use the empty string "" if using an unauthenticated SMTP server
SMTP_MAIL_FROM = "noreply@youremail.com"
EMAIL_REPORTS_SUBJECT_PREFIX = "[Superset] " # optional - overwrites default value in config.py of "[Report] "
# WebDriver configuration
# If you use Firefox, you can stick with default values
# If you use Chrome, then add the following WEBDRIVER_TYPE and WEBDRIVER_OPTION_ARGS
WEBDRIVER_TYPE = "chrome"
WEBDRIVER_OPTION_ARGS = [
"--force-device-scale-factor=2.0",
"--high-dpi-support=2.0",
"--headless",
"--disable-gpu",
"--disable-dev-shm-usage",
"--no-sandbox",
"--disable-setuid-sandbox",
"--disable-extensions",
]
# This is for internal use, you can keep http
WEBDRIVER_BASEURL = "http://superset:8088" # When running using docker compose use "http://superset_app:8088'
# This is the link sent to the recipient. Change to your domain, e.g. https://superset.mydomain.com
WEBDRIVER_BASEURL_USER_FRIENDLY = "http://localhost:8088"
```
You also need
to specify on behalf of which username to render the dashboards. In general, dashboards and charts
are not accessible to unauthorized requests, that is why the worker needs to take over credentials
of an existing user to take a snapshot.
By default, Alerts and Reports are executed as the owner of the alert/report object. To use a fixed user account,
just change the config as follows (`admin` in this example):
```python
from superset.tasks.types import FixedExecutor
ALERT_REPORTS_EXECUTORS = [FixedExecutor("admin")]
```
Please refer to `ExecutorType` in the codebase for other executor types.
**Important notes**
- Be mindful of the concurrency setting for celery (using `-c 4`). Selenium/webdriver instances can
consume a lot of CPU / memory on your servers.
- In some cases, if you notice a lot of leaked geckodriver processes, try running your celery
processes with `celery worker --pool=prefork --max-tasks-per-child=128 ...`
- It is recommended to run separate workers for the `sql_lab` and `email_reports` tasks. This can be
done using the `queue` field in `task_annotations`.
- Adjust `WEBDRIVER_BASEURL` in your configuration file if celery workers can’t access Superset via
its default value of `http://0.0.0.0:8080/`.
It's also possible to specify a minimum interval between each report's execution through the config file:
``` python
# Set a minimum interval threshold between executions (for each Alert/Report)
# Value should be an integer
ALERT_MINIMUM_INTERVAL = int(timedelta(minutes=10).total_seconds())
REPORT_MINIMUM_INTERVAL = int(timedelta(minutes=5).total_seconds())
```
Alternatively, you can assign a function to `ALERT_MINIMUM_INTERVAL` and/or `REPORT_MINIMUM_INTERVAL`. This is useful to dynamically retrieve a value as needed:
``` python
def alert_dynamic_minimal_interval(**kwargs) -> int:
"""
Define logic here to retrieve the value dynamically
"""
ALERT_MINIMUM_INTERVAL = alert_dynamic_minimal_interval
```
## Custom Dockerfile
If you're running the dev version of a released Superset image, like `apache/superset:3.1.0-dev`, you should be set with the above.
But if you're building your own image, or starting with a non-dev version, a webdriver (and headless browser) is needed to capture screenshots of the charts and dashboards which are then sent to the recipient.
Here's how you can modify your Dockerfile to take the screenshots either with Firefox or Chrome.
### Using Firefox
```docker
FROM apache/superset:3.1.0
USER root
RUN apt-get update && \
apt-get install --no-install-recommends -y firefox-esr
ENV GECKODRIVER_VERSION=0.29.0
RUN wget -q https://github.com/mozilla/geckodriver/releases/download/v${GECKODRIVER_VERSION}/geckodriver-v${GECKODRIVER_VERSION}-linux64.tar.gz && \
tar -x geckodriver -zf geckodriver-v${GECKODRIVER_VERSION}-linux64.tar.gz -O > /usr/bin/geckodriver && \
chmod 755 /usr/bin/geckodriver && \
rm geckodriver-v${GECKODRIVER_VERSION}-linux64.tar.gz
RUN pip install --no-cache gevent psycopg2 redis
USER superset
```
### Using Chrome
```docker
FROM apache/superset:3.1.0
USER root
RUN apt-get update && \
apt-get install -y wget zip libaio1
RUN export CHROMEDRIVER_VERSION=$(curl --silent https://googlechromelabs.github.io/chrome-for-testing/LATEST_RELEASE_116) && \
wget -O google-chrome-stable_current_amd64.deb -q http://dl.google.com/linux/chrome/deb/pool/main/g/google-chrome-stable/google-chrome-stable_${CHROMEDRIVER_VERSION}-1_amd64.deb && \
apt-get install -y --no-install-recommends ./google-chrome-stable_current_amd64.deb && \
rm -f google-chrome-stable_current_amd64.deb
RUN export CHROMEDRIVER_VERSION=$(curl --silent https://googlechromelabs.github.io/chrome-for-testing/LATEST_RELEASE_116) && \
wget -q https://storage.googleapis.com/chrome-for-testing-public/${CHROMEDRIVER_VERSION}/linux64/chromedriver-linux64.zip && \
unzip -j chromedriver-linux64.zip -d /usr/bin && \
chmod 755 /usr/bin/chromedriver && \
rm -f chromedriver-linux64.zip
RUN pip install --no-cache gevent psycopg2 redis
USER superset
```
Don't forget to set `WEBDRIVER_TYPE` and `WEBDRIVER_OPTION_ARGS` in your config if you use Chrome.
## Troubleshooting
There are many reasons that reports might not be working. Try these steps to check for specific issues.
### Confirm feature flag is enabled and you have sufficient permissions
If you don't see "Alerts & Reports" under the *Manage* section of the Settings dropdown in the Superset UI, you need to enable the `ALERT_REPORTS` feature flag (see above). Enable another feature flag and check to see that it took effect, to verify that your config file is getting loaded.
Log in as an admin user to ensure you have adequate permissions.
### Check the logs of your Celery worker
This is the best source of information about the problem. In a docker compose deployment, you can do this with a command like `docker logs superset_worker --since 1h`.
### Check web browser and webdriver installation
To take a screenshot, the worker visits the dashboard or chart using a headless browser, then takes a screenshot. If you are able to send a chart as CSV or text but can't send as PNG, your problem may lie with the browser.
Superset docker images that have a tag ending with `-dev` have the Firefox headless browser and geckodriver already installed. You can test that these are installed and in the proper path by entering your Superset worker and running `firefox --headless` and then `geckodriver`. Both commands should start those applications.
If you are handling the installation of that software on your own, or wish to use Chromium instead, do your own verification to ensure that the headless browser opens successfully in the worker environment.
### Send a test email
One symptom of an invalid connection to an email server is receiving an error of `[Errno 110] Connection timed out` in your logs when the report tries to send.
Confirm via testing that your outbound email configuration is correct. Here is the simplest test, for an un-authenticated email SMTP email service running on port 25. If you are sending over SSL, for instance, study how [Superset's codebase sends emails](https://github.com/apache/superset/blob/master/superset/utils/core.py#L818) and then test with those commands and arguments.
Start Python in your worker environment, replace all example values, and run:
```python
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from_email = 'superset_emails@example.com'
to_email = 'your_email@example.com'
msg = MIMEMultipart()
msg['From'] = from_email
msg['To'] = to_email
msg['Subject'] = 'Superset SMTP config test'
message = 'It worked'
msg.attach(MIMEText(message))
mailserver = smtplib.SMTP('smtpmail.example.com', 25)
mailserver.sendmail(from_email, to_email, msg.as_string())
mailserver.quit()
```
This should send an email.
Possible fixes:
- Some cloud hosts disable outgoing unauthenticated SMTP email to prevent spam. For instance, [Azure blocks port 25 by default on some machines](https://learn.microsoft.com/en-us/azure/virtual-network/troubleshoot-outbound-smtp-connectivity). Enable that port or use another sending method.
- Use another set of SMTP credentials that you verify works in this setup.
### Browse to your report from the worker
The worker may be unable to reach the report. It will use the value of `WEBDRIVER_BASEURL` to browse to the report. If that route is invalid, or presents an authentication challenge that the worker can't pass, the report screenshot will fail.
Check this by attempting to `curl` the URL of a report that you see in the error logs of your worker. For instance, from the worker environment, run `curl http://superset_app:8088/superset/dashboard/1/`. You may get different responses depending on whether the dashboard exists - for example, you may need to change the `1` in that URL. If there's a URL in your logs from a failed report screenshot, that's a good place to start. The goal is to determine a valid value for `WEBDRIVER_BASEURL` and determine if an issue like HTTPS or authentication is redirecting your worker.
In a deployment with authentication measures enabled like HTTPS and Single Sign-On, it may make sense to have the worker navigate directly to the Superset application running in the same location, avoiding the need to sign in. For instance, you could use `WEBDRIVER_BASEURL="http://superset_app:8088"` for a docker compose deployment, and set `"force_https": False,` in your `TALISMAN_CONFIG`.
## Scheduling Queries as Reports
You can optionally allow your users to schedule queries directly in SQL Lab. This is done by adding
extra metadata to saved queries, which are then picked up by an external scheduled (like
[Apache Airflow](https://airflow.apache.org/)).
To allow scheduled queries, add the following to `SCHEDULED_QUERIES` in your configuration file:
```python
SCHEDULED_QUERIES = {
# This information is collected when the user clicks "Schedule query",
# and saved into the `extra` field of saved queries.
# See: https://github.com/mozilla-services/react-jsonschema-form
'JSONSCHEMA': {
'title': 'Schedule',
'description': (
'In order to schedule a query, you need to specify when it '
'should start running, when it should stop running, and how '
'often it should run. You can also optionally specify '
'dependencies that should be met before the query is '
'executed. Please read the documentation for best practices '
'and more information on how to specify dependencies.'
),
'type': 'object',
'properties': {
'output_table': {
'type': 'string',
'title': 'Output table name',
},
'start_date': {
'type': 'string',
'title': 'Start date',
# date-time is parsed using the chrono library, see
# https://www.npmjs.com/package/chrono-node#usage
'format': 'date-time',
'default': 'tomorrow at 9am',
},
'end_date': {
'type': 'string',
'title': 'End date',
# date-time is parsed using the chrono library, see
# https://www.npmjs.com/package/chrono-node#usage
'format': 'date-time',
'default': '9am in 30 days',
},
'schedule_interval': {
'type': 'string',
'title': 'Schedule interval',
},
'dependencies': {
'type': 'array',
'title': 'Dependencies',
'items': {
'type': 'string',
},
},
},
},
'UISCHEMA': {
'schedule_interval': {
'ui:placeholder': '@daily, @weekly, etc.',
},
'dependencies': {
'ui:help': (
'Check the documentation for the correct format when '
'defining dependencies.'
),
},
},
'VALIDATION': [
# ensure that start_date <= end_date
{
'name': 'less_equal',
'arguments': ['start_date', 'end_date'],
'message': 'End date cannot be before start date',
# this is where the error message is shown
'container': 'end_date',
},
],
# link to the scheduler; this example links to an Airflow pipeline
# that uses the query id and the output table as its name
'linkback': (
'https://airflow.example.com/admin/airflow/tree?'
'dag_id=query_${id}_${extra_json.schedule_info.output_table}'
),
}
```
This configuration is based on
[react-jsonschema-form](https://github.com/mozilla-services/react-jsonschema-form) and will add a
menu item called “Schedule” to SQL Lab. When the menu item is clicked, a modal will show up where
the user can add the metadata required for scheduling the query.
This information can then be retrieved from the endpoint `/api/v1/saved_query/` and used to
schedule the queries that have `schedule_info` in their JSON metadata. For schedulers other than
Airflow, additional fields can be easily added to the configuration file above.

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---
title: Async Queries via Celery
hide_title: true
sidebar_position: 4
version: 1
---
# Async Queries via Celery
## Celery
On large analytic databases, it’s common to run queries that execute for minutes or hours. To enable
support for long running queries that execute beyond the typical web request’s timeout (30-60
seconds), it is necessary to configure an asynchronous backend for Superset which consists of:
- one or many Superset workers (which is implemented as a Celery worker), and can be started with
the `celery worker` command, run `celery worker --help` to view the related options.
- a celery broker (message queue) for which we recommend using Redis or RabbitMQ
- a results backend that defines where the worker will persist the query results
Configuring Celery requires defining a `CELERY_CONFIG` in your `superset_config.py`. Both the worker
and web server processes should have the same configuration.
```python
class CeleryConfig(object):
broker_url = "redis://localhost:6379/0"
imports = (
"superset.sql_lab",
"superset.tasks.scheduler",
)
result_backend = "redis://localhost:6379/0"
worker_prefetch_multiplier = 10
task_acks_late = True
task_annotations = {
"sql_lab.get_sql_results": {
"rate_limit": "100/s",
},
}
CELERY_CONFIG = CeleryConfig
```
To start a Celery worker to leverage the configuration, run the following command:
```bash
celery --app=superset.tasks.celery_app:app worker --pool=prefork -O fair -c 4
```
To start a job which schedules periodic background jobs, run the following command:
```bash
celery --app=superset.tasks.celery_app:app beat
```
To setup a result backend, you need to pass an instance of a derivative of from
from flask_caching.backends.base import BaseCache to the RESULTS_BACKEND configuration key in your superset_config.py. You can
use Memcached, Redis, S3 (https://pypi.python.org/pypi/s3werkzeugcache), memory or the file system
(in a single server-type setup or for testing), or to write your own caching interface. Your
`superset_config.py` may look something like:
```python
# On S3
from s3cache.s3cache import S3Cache
S3_CACHE_BUCKET = 'foobar-superset'
S3_CACHE_KEY_PREFIX = 'sql_lab_result'
RESULTS_BACKEND = S3Cache(S3_CACHE_BUCKET, S3_CACHE_KEY_PREFIX)
# On Redis
from flask_caching.backends.rediscache import RedisCache
RESULTS_BACKEND = RedisCache(
host='localhost', port=6379, key_prefix='superset_results')
```
For performance gains, [MessagePack](https://github.com/msgpack/msgpack-python) and
[PyArrow](https://arrow.apache.org/docs/python/) are now used for results serialization. This can be
disabled by setting `RESULTS_BACKEND_USE_MSGPACK = False` in your `superset_config.py`, should any
issues arise. Please clear your existing results cache store when upgrading an existing environment.
**Important Notes**
- It is important that all the worker nodes and web servers in the Superset cluster _share a common
metadata database_. This means that SQLite will not work in this context since it has limited
support for concurrency and typically lives on the local file system.
- There should _only be one instance of celery beat running_ in your entire setup. If not,
background jobs can get scheduled multiple times resulting in weird behaviors like duplicate
delivery of reports, higher than expected load / traffic etc.
- SQL Lab will _only run your queries asynchronously if_ you enable **Asynchronous Query Execution**
in your database settings (Sources > Databases > Edit record).
## Celery Flower
Flower is a web based tool for monitoring the Celery cluster which you can install from pip:
```bash
pip install flower
```
You can run flower using:
```bash
celery --app=superset.tasks.celery_app:app flower
```

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---
title: Caching
hide_title: true
sidebar_position: 3
version: 1
---
# Caching
Superset uses [Flask-Caching](https://flask-caching.readthedocs.io/) for caching purposes.
Flask-Caching supports various caching backends, including Redis (recommended), Memcached,
SimpleCache (in-memory), or the local filesystem.
[Custom cache backends](https://flask-caching.readthedocs.io/en/latest/#custom-cache-backends)
are also supported.
Caching can be configured by providing dictionaries in
`superset_config.py` that comply with [the Flask-Caching config specifications](https://flask-caching.readthedocs.io/en/latest/#configuring-flask-caching).
The following cache configurations can be customized in this way:
- Dashboard filter state (required): `FILTER_STATE_CACHE_CONFIG`.
- Explore chart form data (required): `EXPLORE_FORM_DATA_CACHE_CONFIG`
- Metadata cache (optional): `CACHE_CONFIG`
- Charting data queried from datasets (optional): `DATA_CACHE_CONFIG`
For example, to configure the filter state cache using Redis:
```python
FILTER_STATE_CACHE_CONFIG = {
'CACHE_TYPE': 'RedisCache',
'CACHE_DEFAULT_TIMEOUT': 86400,
'CACHE_KEY_PREFIX': 'superset_filter_cache',
'CACHE_REDIS_URL': 'redis://localhost:6379/0'
}
```
## Dependencies
In order to use dedicated cache stores, additional python libraries must be installed
- For Redis: we recommend the [redis](https://pypi.python.org/pypi/redis) Python package
- Memcached: we recommend using [pylibmc](https://pypi.org/project/pylibmc/) client library as
`python-memcached` does not handle storing binary data correctly.
These libraries can be installed using pip.
## Fallback Metastore Cache
Note, that some form of Filter State and Explore caching are required. If either of these caches
are undefined, Superset falls back to using a built-in cache that stores data in the metadata
database. While it is recommended to use a dedicated cache, the built-in cache can also be used
to cache other data.
For example, to use the built-in cache to store chart data, use the following config:
```python
DATA_CACHE_CONFIG = {
"CACHE_TYPE": "SupersetMetastoreCache",
"CACHE_KEY_PREFIX": "superset_results", # make sure this string is unique to avoid collisions
"CACHE_DEFAULT_TIMEOUT": 86400, # 60 seconds * 60 minutes * 24 hours
}
```
## Chart Cache Timeout
The cache timeout for charts may be overridden by the settings for an individual chart, dataset, or
database. Each of these configurations will be checked in order before falling back to the default
value defined in `DATA_CACHE_CONFIG`.
Note, that by setting the cache timeout to `-1`, caching for charting data can be disabled, either
per chart, dataset or database, or by default if set in `DATA_CACHE_CONFIG`.
## SQL Lab Query Results
Caching for SQL Lab query results is used when async queries are enabled and is configured using
`RESULTS_BACKEND`.
Note that this configuration does not use a flask-caching dictionary for its configuration, but
instead requires a cachelib object.
See [Async Queries via Celery](/docs/configuration/async-queries-celery) for details.
## Caching Thumbnails
This is an optional feature that can be turned on by activating its [feature flag](/docs/configuration/configuring-superset#feature-flags) on config:
```
FEATURE_FLAGS = {
"THUMBNAILS": True,
"THUMBNAILS_SQLA_LISTENERS": True,
}
```
By default thumbnails are rendered per user, and will fall back to the Selenium user for anonymous users.
To always render thumbnails as a fixed user (`admin` in this example), use the following configuration:
```python
from superset.tasks.types import FixedExecutor
THUMBNAIL_EXECUTORS = [FixedExecutor("admin")]
```
For this feature you will need a cache system and celery workers. All thumbnails are stored on cache
and are processed asynchronously by the workers.
An example config where images are stored on S3 could be:
```python
from flask import Flask
from s3cache.s3cache import S3Cache
...
class CeleryConfig(object):
broker_url = "redis://localhost:6379/0"
imports = (
"superset.sql_lab",
"superset.tasks.thumbnails",
)
result_backend = "redis://localhost:6379/0"
worker_prefetch_multiplier = 10
task_acks_late = True
CELERY_CONFIG = CeleryConfig
def init_thumbnail_cache(app: Flask) -> S3Cache:
return S3Cache("bucket_name", 'thumbs_cache/')
THUMBNAIL_CACHE_CONFIG = init_thumbnail_cache
```
Using the above example cache keys for dashboards will be `superset_thumb__dashboard__{ID}`. You can
override the base URL for selenium using:
```
WEBDRIVER_BASEURL = "https://superset.company.com"
```
Additional selenium web drive configuration can be set using `WEBDRIVER_CONFIGURATION`. You can
implement a custom function to authenticate selenium. The default function uses the `flask-login`
session cookie. Here's an example of a custom function signature:
```python
def auth_driver(driver: WebDriver, user: "User") -> WebDriver:
pass
```
Then on configuration:
```
WEBDRIVER_AUTH_FUNC = auth_driver
```

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---
title: Configuring Superset
hide_title: true
sidebar_position: 1
version: 1
---
# Configuring Superset
## superset_config.py
Superset exposes hundreds of configurable parameters through its
[config.py module](https://github.com/apache/superset/blob/master/superset/config.py). The
variables and objects exposed act as a public interface of the bulk of what you may want
to configure, alter and interface with. In this python module, you'll find all these
parameters, sensible defaults, as well as rich documentation in the form of comments
To configure your application, you need to create your own configuration module, which
will allow you to override few or many of these parameters. Instead of altering the core module,
you'll want to define your own module (typically a file named `superset_config.py`).
Add this file to your `PYTHONPATH` or create an environment variable
`SUPERSET_CONFIG_PATH` specifying the full path of the `superset_config.py`.
For example, if deploying on Superset directly on a Linux-based system where your
`superset_config.py` is under `/app` directory, you can run:
```bash
export SUPERSET_CONFIG_PATH=/app/superset_config.py
```
If you are using your own custom Dockerfile with the official Superset image as base image,
then you can add your overrides as shown below:
```bash
COPY --chown=superset superset_config.py /app/
ENV SUPERSET_CONFIG_PATH /app/superset_config.py
```
Docker compose deployments handle application configuration differently using specific conventions.
Refer to the [docker compose tips & configuration](/docs/installation/docker-compose#docker-compose-tips--configuration)
for details.
The following is an example of just a few of the parameters you can set in your `superset_config.py` file:
```
# Superset specific config
ROW_LIMIT = 5000
# Flask App Builder configuration
# Your App secret key will be used for securely signing the session cookie
# and encrypting sensitive information on the database
# Make sure you are changing this key for your deployment with a strong key.
# Alternatively you can set it with `SUPERSET_SECRET_KEY` environment variable.
# You MUST set this for production environments or the server will refuse
# to start and you will see an error in the logs accordingly.
SECRET_KEY = 'YOUR_OWN_RANDOM_GENERATED_SECRET_KEY'
# The SQLAlchemy connection string to your database backend
# This connection defines the path to the database that stores your
# superset metadata (slices, connections, tables, dashboards, ...).
# Note that the connection information to connect to the datasources
# you want to explore are managed directly in the web UI
# The check_same_thread=false property ensures the sqlite client does not attempt
# to enforce single-threaded access, which may be problematic in some edge cases
SQLALCHEMY_DATABASE_URI = 'sqlite:////path/to/superset.db?check_same_thread=false'
# Flask-WTF flag for CSRF
WTF_CSRF_ENABLED = True
# Add endpoints that need to be exempt from CSRF protection
WTF_CSRF_EXEMPT_LIST = []
# A CSRF token that expires in 1 year
WTF_CSRF_TIME_LIMIT = 60 * 60 * 24 * 365
# Set this API key to enable Mapbox visualizations
MAPBOX_API_KEY = ''
```
:::tip
Note that it is typical to copy and paste [only] the portions of the
core [superset/config.py](https://github.com/apache/superset/blob/master/superset/config.py) that
you want to alter, along with the related comments into your own `superset_config.py` file.
:::
All the parameters and default values defined
in [superset/config.py](https://github.com/apache/superset/blob/master/superset/config.py)
can be altered in your local `superset_config.py`. Administrators will want to read through the file
to understand what can be configured locally as well as the default values in place.
Since `superset_config.py` acts as a Flask configuration module, it can be used to alter the
settings of Flask itself, as well as Flask extensions that Superset bundles like
`flask-wtf`, `flask-caching`, `flask-migrate`,
and `flask-appbuilder`. Each one of these extensions offers intricate configurability.
Flask App Builder, the web framework used by Superset, also offers many
configuration settings. Please consult the
[Flask App Builder Documentation](https://flask-appbuilder.readthedocs.org/en/latest/config.html)
for more information on how to configure it.
At the very least, you'll want to change `SECRET_KEY` and `SQLALCHEMY_DATABASE_URI`. Continue reading for more about each of these.
## Specifying a SECRET_KEY
### Adding an initial SECRET_KEY
Superset requires a user-specified SECRET_KEY to start up. This requirement was [added in version 2.1.0 to force secure configurations](https://preset.io/blog/superset-security-update-default-secret_key-vulnerability/). Add a strong SECRET_KEY to your `superset_config.py` file like:
```python
SECRET_KEY = 'YOUR_OWN_RANDOM_GENERATED_SECRET_KEY'
```
You can generate a strong secure key with `openssl rand -base64 42`.
:::caution Use a strong secret key
This key will be used for securely signing session cookies and encrypting sensitive information stored in Superset's application metadata database.
Your deployment must use a complex, unique key.
:::
### Rotating to a newer SECRET_KEY
If you wish to change your existing SECRET_KEY, add the existing SECRET_KEY to your `superset_config.py` file as
`PREVIOUS_SECRET_KEY =`and provide your new key as `SECRET_KEY =`. You can find your current SECRET_KEY with these
commands - if running Superset with Docker, execute from within the Superset application container:
```python
superset shell
from flask import current_app; print(current_app.config["SECRET_KEY"])
```
Save your `superset_config.py` with these values and then run `superset re-encrypt-secrets`.
## Setting up a production metadata database
Superset needs a database to store the information it manages, like the definitions of
charts, dashboards, and many other things.
By default, Superset is configured to use [SQLite](https://www.sqlite.org/),
a self-contained, single-file database that offers a simple and fast way to get started
(without requiring any installation). However, for production environments,
using SQLite is highly discouraged due to security, scalability, and data integrity reasons.
It's important to use only the supported database engines and consider using a different
database engine on a separate host or container.
Superset supports the following database engines/versions:
| Database Engine | Supported Versions |
| ----------------------------------------- | ---------------------------------------- |
| [PostgreSQL](https://www.postgresql.org/) | 10.X, 11.X, 12.X, 13.X, 14.X, 15.X, 16.X |
| [MySQL](https://www.mysql.com/) | 5.7, 8.X |
Use the following database drivers and connection strings:
| Database | PyPI package | Connection String |
| ----------------------------------------- | ------------------------- | ---------------------------------------------------------------------- |
| [PostgreSQL](https://www.postgresql.org/) | `pip install psycopg2` | `postgresql://<UserName>:<DBPassword>@<Database Host>/<Database Name>` |
| [MySQL](https://www.mysql.com/) | `pip install mysqlclient` | `mysql://<UserName>:<DBPassword>@<Database Host>/<Database Name>` |
:::tip
Properly setting up metadata store is beyond the scope of this documentation. We recommend
using a hosted managed service such as [Amazon RDS](https://aws.amazon.com/rds/) or
[Google Cloud Databases](https://cloud.google.com/products/databases?hl=en) to handle
service and supporting infrastructure and backup strategy.
:::
To configure Superset metastore set `SQLALCHEMY_DATABASE_URI` config key on `superset_config`
to the appropriate connection string.
## Running on a WSGI HTTP Server
While you can run Superset on NGINX or Apache, we recommend using Gunicorn in async mode. This
enables impressive concurrency even and is fairly easy to install and configure. Please refer to the
documentation of your preferred technology to set up this Flask WSGI application in a way that works
well in your environment. Here’s an async setup known to work well in production:
```
-w 10 \
-k gevent \
--worker-connections 1000 \
--timeout 120 \
-b 0.0.0.0:6666 \
--limit-request-line 0 \
--limit-request-field_size 0 \
--statsd-host localhost:8125 \
"superset.app:create_app()"
```
Refer to the [Gunicorn documentation](https://docs.gunicorn.org/en/stable/design.html) for more
information. _Note that the development web server (`superset run` or `flask run`) is not intended
for production use._
If you're not using Gunicorn, you may want to disable the use of `flask-compress` by setting
`COMPRESS_REGISTER = False` in your `superset_config.py`.
Currently, the Google BigQuery Python SDK is not compatible with `gevent`, due to some dynamic monkeypatching on python core library by `gevent`.
So, when you use `BigQuery` datasource on Superset, you have to use `gunicorn` worker type except `gevent`.
## HTTPS Configuration
You can configure HTTPS upstream via a load balancer or a reverse proxy (such as nginx) and do SSL/TLS Offloading before traffic reaches the Superset application. In this setup, local traffic from a Celery worker taking a snapshot of a chart for Alerts & Reports can access Superset at a `http://` URL, from behind the ingress point.
You can also configure [SSL in Gunicorn](https://docs.gunicorn.org/en/stable/settings.html#ssl) (the Python webserver) if you are using an official Superset Docker image.
## Configuration Behind a Load Balancer
If you are running superset behind a load balancer or reverse proxy (e.g. NGINX or ELB on AWS), you
may need to utilize a healthcheck endpoint so that your load balancer knows if your superset
instance is running. This is provided at `/health` which will return a 200 response containing “OK”
if the webserver is running.
If the load balancer is inserting `X-Forwarded-For/X-Forwarded-Proto` headers, you should set
`ENABLE_PROXY_FIX = True` in the superset config file (`superset_config.py`) to extract and use the
headers.
In case the reverse proxy is used for providing SSL encryption, an explicit definition of the
`X-Forwarded-Proto` may be required. For the Apache webserver this can be set as follows:
```
RequestHeader set X-Forwarded-Proto "https"
```
## Configuring the application root
*Please be advised that this feature is in BETA.*
Superset supports running the application under a non-root path. The root path
prefix can be specified in one of two ways:
- Setting the `SUPERSET_APP_ROOT` environment variable to the desired prefix.
- Customizing the [Flask entrypoint](https://github.com/apache/superset/blob/master/superset/app.py#L29)
by passing the `superset_app_root` variable.
Note, the prefix should start with a `/`.
### Customizing the Flask entrypoint
To configure a prefix, e.g `/analytics`, pass the `superset_app_root` argument to
`create_app` when calling flask run either through the `FLASK_APP`
environment variable:
```sh
FLASK_APP="superset:create_app(superset_app_root='/analytics')"
```
or as part of the `--app` argument to `flask run`:
```sh
flask --app "superset.app:create_app(superset_app_root='/analytics')"
```
### Docker builds
The [docker compose](/docs/installation/docker-compose#configuring-further) developer
configuration includes an additional environmental variable,
[`SUPERSET_APP_ROOT`](https://github.com/apache/superset/blob/master/docker/.env),
to simplify the process of setting up a non-default root path across the services.
In `docker/.env-local` set `SUPERSET_APP_ROOT` to the desired prefix and then bring the
services up with `docker compose up --detach`.
## Custom OAuth2 Configuration
Superset is built on Flask-AppBuilder (FAB), which supports many providers out of the box
(GitHub, Twitter, LinkedIn, Google, Azure, etc). Beyond those, Superset can be configured to connect
with other OAuth2 Authorization Server implementations that support “code” authorization.
Make sure the pip package [`Authlib`](https://authlib.org/) is installed on the webserver.
First, configure authorization in Superset `superset_config.py`.
```python
from flask_appbuilder.security.manager import AUTH_OAUTH
# Set the authentication type to OAuth
AUTH_TYPE = AUTH_OAUTH
OAUTH_PROVIDERS = [
{ 'name':'egaSSO',
'token_key':'access_token', # Name of the token in the response of access_token_url
'icon':'fa-address-card', # Icon for the provider
'remote_app': {
'client_id':'myClientId', # Client Id (Identify Superset application)
'client_secret':'MySecret', # Secret for this Client Id (Identify Superset application)
'client_kwargs':{
'scope': 'read' # Scope for the Authorization
},
'access_token_method':'POST', # HTTP Method to call access_token_url
'access_token_params':{ # Additional parameters for calls to access_token_url
'client_id':'myClientId'
},
'jwks_uri':'https://myAuthorizationServe/adfs/discovery/keys', # may be required to generate token
'access_token_headers':{ # Additional headers for calls to access_token_url
'Authorization': 'Basic Base64EncodedClientIdAndSecret'
},
'api_base_url':'https://myAuthorizationServer/oauth2AuthorizationServer/',
'access_token_url':'https://myAuthorizationServer/oauth2AuthorizationServer/token',
'authorize_url':'https://myAuthorizationServer/oauth2AuthorizationServer/authorize'
}
}
]
# Will allow user self registration, allowing to create Flask users from Authorized User
AUTH_USER_REGISTRATION = True
# The default user self registration role
AUTH_USER_REGISTRATION_ROLE = "Public"
```
In case you want to assign the `Admin` role on new user registration, it can be assigned as follows:
```python
AUTH_USER_REGISTRATION_ROLE = "Admin"
```
If you encounter the [issue](https://github.com/apache/superset/issues/13243) of not being able to list users from the Superset main page settings, although a newly registered user has an `Admin` role, please re-run `superset init` to sync the required permissions. Below is the command to re-run `superset init` using docker compose.
```
docker-compose exec superset superset init
```
Then, create a `CustomSsoSecurityManager` that extends `SupersetSecurityManager` and overrides
`oauth_user_info`:
```python
import logging
from superset.security import SupersetSecurityManager
class CustomSsoSecurityManager(SupersetSecurityManager):
def oauth_user_info(self, provider, response=None):
logging.debug("Oauth2 provider: {0}.".format(provider))
if provider == 'egaSSO':
# As example, this line request a GET to base_url + '/' + userDetails with Bearer Authentication,
# and expects that authorization server checks the token, and response with user details
me = self.appbuilder.sm.oauth_remotes[provider].get('userDetails').data
logging.debug("user_data: {0}".format(me))
return { 'name' : me['name'], 'email' : me['email'], 'id' : me['user_name'], 'username' : me['user_name'], 'first_name':'', 'last_name':''}
...
```
This file must be located in the same directory as `superset_config.py` with the name
`custom_sso_security_manager.py`. Finally, add the following 2 lines to `superset_config.py`:
```
from custom_sso_security_manager import CustomSsoSecurityManager
CUSTOM_SECURITY_MANAGER = CustomSsoSecurityManager
```
**Notes**
- The redirect URL will be `https://<superset-webserver>/oauth-authorized/<provider-name>`
When configuring an OAuth2 authorization provider if needed. For instance, the redirect URL will
be `https://<superset-webserver>/oauth-authorized/egaSSO` for the above configuration.
- If an OAuth2 authorization server supports OpenID Connect 1.0, you could configure its configuration
document URL only without providing `api_base_url`, `access_token_url`, `authorize_url` and other
required options like user info endpoint, jwks uri etc. For instance:
```python
OAUTH_PROVIDERS = [
{ 'name':'egaSSO',
'token_key':'access_token', # Name of the token in the response of access_token_url
'icon':'fa-address-card', # Icon for the provider
'remote_app': {
'client_id':'myClientId', # Client Id (Identify Superset application)
'client_secret':'MySecret', # Secret for this Client Id (Identify Superset application)
'server_metadata_url': 'https://myAuthorizationServer/.well-known/openid-configuration'
}
}
]
```
### Keycloak-Specific Configuration using Flask-OIDC
If you are using Keycloak as OpenID Connect 1.0 Provider, the above configuration based on [`Authlib`](https://authlib.org/) might not work. In this case using [`Flask-OIDC`](https://pypi.org/project/flask-oidc/) is a viable option.
Make sure the pip package [`Flask-OIDC`](https://pypi.org/project/flask-oidc/) is installed on the webserver. This was successfully tested using version 2.2.0. This package requires [`Flask-OpenID`](https://pypi.org/project/Flask-OpenID/) as a dependency.
The following code defines a new security manager. Add it to a new file named `keycloak_security_manager.py`, placed in the same directory as your `superset_config.py` file.
```python
from flask_appbuilder.security.manager import AUTH_OID
from superset.security import SupersetSecurityManager
from flask_oidc import OpenIDConnect
from flask_appbuilder.security.views import AuthOIDView
from flask_login import login_user
from urllib.parse import quote
from flask_appbuilder.views import ModelView, SimpleFormView, expose
from flask import (
redirect,
request
)
import logging
class OIDCSecurityManager(SupersetSecurityManager):
def __init__(self, appbuilder):
super(OIDCSecurityManager, self).__init__(appbuilder)
if self.auth_type == AUTH_OID:
self.oid = OpenIDConnect(self.appbuilder.get_app)
self.authoidview = AuthOIDCView
class AuthOIDCView(AuthOIDView):
@expose('/login/', methods=['GET', 'POST'])
def login(self, flag=True):
sm = self.appbuilder.sm
oidc = sm.oid
@self.appbuilder.sm.oid.require_login
def handle_login():
user = sm.auth_user_oid(oidc.user_getfield('email'))
if user is None:
info = oidc.user_getinfo(['preferred_username', 'given_name', 'family_name', 'email'])
user = sm.add_user(info.get('preferred_username'), info.get('given_name'), info.get('family_name'),
info.get('email'), sm.find_role('Gamma'))
login_user(user, remember=False)
return redirect(self.appbuilder.get_url_for_index)
return handle_login()
@expose('/logout/', methods=['GET', 'POST'])
def logout(self):
oidc = self.appbuilder.sm.oid
oidc.logout()
super(AuthOIDCView, self).logout()
redirect_url = request.url_root.strip('/') + self.appbuilder.get_url_for_login
return redirect(
oidc.client_secrets.get('issuer') + '/protocol/openid-connect/logout?redirect_uri=' + quote(redirect_url))
```
Then add to your `superset_config.py` file:
```python
from keycloak_security_manager import OIDCSecurityManager
from flask_appbuilder.security.manager import AUTH_OID, AUTH_REMOTE_USER, AUTH_DB, AUTH_LDAP, AUTH_OAUTH
import os
AUTH_TYPE = AUTH_OID
SECRET_KEY: 'SomethingNotEntirelySecret'
OIDC_CLIENT_SECRETS = '/path/to/client_secret.json'
OIDC_ID_TOKEN_COOKIE_SECURE = False
OIDC_OPENID_REALM: '<myRealm>'
OIDC_INTROSPECTION_AUTH_METHOD: 'client_secret_post'
CUSTOM_SECURITY_MANAGER = OIDCSecurityManager
# Will allow user self registration, allowing to create Flask users from Authorized User
AUTH_USER_REGISTRATION = True
# The default user self registration role
AUTH_USER_REGISTRATION_ROLE = 'Public'
```
Store your client-specific OpenID information in a file called `client_secret.json`. Create this file in the same directory as `superset_config.py`:
```json
{
"<myOpenIDProvider>": {
"issuer": "https://<myKeycloakDomain>/realms/<myRealm>",
"auth_uri": "https://<myKeycloakDomain>/realms/<myRealm>/protocol/openid-connect/auth",
"client_id": "https://<myKeycloakDomain>",
"client_secret": "<myClientSecret>",
"redirect_uris": [
"https://<SupersetWebserver>/oauth-authorized/<myOpenIDProvider>"
],
"userinfo_uri": "https://<myKeycloakDomain>/realms/<myRealm>/protocol/openid-connect/userinfo",
"token_uri": "https://<myKeycloakDomain>/realms/<myRealm>/protocol/openid-connect/token",
"token_introspection_uri": "https://<myKeycloakDomain>/realms/<myRealm>/protocol/openid-connect/token/introspect"
}
}
```
## LDAP Authentication
FAB supports authenticating user credentials against an LDAP server.
To use LDAP you must install the [python-ldap](https://www.python-ldap.org/en/latest/installing.html) package.
See [FAB's LDAP documentation](https://flask-appbuilder.readthedocs.io/en/latest/security.html#authentication-ldap)
for details.
## Mapping LDAP or OAUTH groups to Superset roles
AUTH_ROLES_MAPPING in Flask-AppBuilder is a dictionary that maps from LDAP/OAUTH group names to FAB roles.
It is used to assign roles to users who authenticate using LDAP or OAuth.
### Mapping OAUTH groups to Superset roles
The following `AUTH_ROLES_MAPPING` dictionary would map the OAUTH group "superset_users" to the Superset roles "Gamma" as well as "Alpha", and the OAUTH group "superset_admins" to the Superset role "Admin".
```python
AUTH_ROLES_MAPPING = {
"superset_users": ["Gamma","Alpha"],
"superset_admins": ["Admin"],
}
```
### Mapping LDAP groups to Superset roles
The following `AUTH_ROLES_MAPPING` dictionary would map the LDAP DN "cn=superset_users,ou=groups,dc=example,dc=com" to the Superset roles "Gamma" as well as "Alpha", and the LDAP DN "cn=superset_admins,ou=groups,dc=example,dc=com" to the Superset role "Admin".
```python
AUTH_ROLES_MAPPING = {
"cn=superset_users,ou=groups,dc=example,dc=com": ["Gamma","Alpha"],
"cn=superset_admins,ou=groups,dc=example,dc=com": ["Admin"],
}
```
Note: This requires `AUTH_LDAP_SEARCH` to be set. For more details, please see the [FAB Security documentation](https://flask-appbuilder.readthedocs.io/en/latest/security.html).
### Syncing roles at login
You can also use the `AUTH_ROLES_SYNC_AT_LOGIN` configuration variable to control how often Flask-AppBuilder syncs the user's roles with the LDAP/OAUTH groups. If `AUTH_ROLES_SYNC_AT_LOGIN` is set to True, Flask-AppBuilder will sync the user's roles each time they log in. If `AUTH_ROLES_SYNC_AT_LOGIN` is set to False, Flask-AppBuilder will only sync the user's roles when they first register.
## Flask app Configuration Hook
`FLASK_APP_MUTATOR` is a configuration function that can be provided in your environment, receives
the app object and can alter it in any way. For example, add `FLASK_APP_MUTATOR` into your
`superset_config.py` to setup session cookie expiration time to 24 hours:
```python
from flask import session
from flask import Flask
def make_session_permanent():
'''
Enable maxAge for the cookie 'session'
'''
session.permanent = True
# Set up max age of session to 24 hours
PERMANENT_SESSION_LIFETIME = timedelta(hours=24)
def FLASK_APP_MUTATOR(app: Flask) -> None:
app.before_request_funcs.setdefault(None, []).append(make_session_permanent)
```
## Feature Flags
To support a diverse set of users, Superset has some features that are not enabled by default. For
example, some users have stronger security restrictions, while some others may not. So Superset
allows users to enable or disable some features by config. For feature owners, you can add optional
functionalities in Superset, but will be only affected by a subset of users.
You can enable or disable features with flag from `superset_config.py`:
```python
FEATURE_FLAGS = {
'PRESTO_EXPAND_DATA': False,
}
```
A current list of feature flags can be found in [RESOURCES/FEATURE_FLAGS.md](https://github.com/apache/superset/blob/master/RESOURCES/FEATURE_FLAGS.md).

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---
title: Country Map Tools
sidebar_position: 10
version: 1
---
import countriesData from '../../../data/countries.json';
# The Country Map Visualization
The Country Map visualization allows you to plot lightweight choropleth maps of
your countries by province, states, or other subdivision types. It does not rely
on any third-party map services but would require you to provide the
[ISO-3166-2](https://en.wikipedia.org/wiki/ISO_3166-2) codes of your country's
top-level subdivisions. Comparing to a province or state's full names, the ISO
code is less ambiguous and is unique to all regions in the world.
## Included Maps
The current list of countries can be found in the src
[legacy-plugin-chart-country-map/src/countries.ts](https://github.com/apache/superset/blob/master/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries.ts)
The Country Maps visualization already ships with the maps for the following countries:
<ul style={{columns: 3}}>
{countriesData.countries.map((country, index) => (
<li key={index}>{country}</li>
))}
</ul>
## Adding a New Country
To add a new country to the list, you'd have to edit files in
[@superset-ui/legacy-plugin-chart-country-map](https://github.com/apache/superset/tree/master/superset-frontend/plugins/legacy-plugin-chart-country-map).
1. Generate a new GeoJSON file for your country following the guide in [this Jupyter notebook](https://github.com/apache/superset/blob/master/superset-frontend/plugins/legacy-plugin-chart-country-map/scripts/Country%20Map%20GeoJSON%20Generator.ipynb).
2. Edit the countries list in [legacy-plugin-chart-country-map/src/countries.ts](https://github.com/apache/superset/blob/master/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries.ts).
3. Install superset-frontend dependencies: `cd superset-frontend && npm install`
4. Verify your countries in Superset plugins storybook: `npm run plugins:storybook`.
5. Build and install Superset from source code.

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---
title: Event Logging
sidebar_position: 9
version: 1
---
# Logging
## Event Logging
Superset by default logs special action events in its internal database (DBEventLogger). These logs can be accessed
on the UI by navigating to **Security > Action Log**. You can freely customize these logs by
implementing your own event log class.
**When custom log class is enabled DBEventLogger is disabled and logs
stop being populated in UI logs view.**
To achieve both, custom log class should extend built-in DBEventLogger log class.
Here's an example of a simple JSON-to-stdout class:
```python
def log(self, user_id, action, *args, **kwargs):
records = kwargs.get('records', list())
dashboard_id = kwargs.get('dashboard_id')
slice_id = kwargs.get('slice_id')
duration_ms = kwargs.get('duration_ms')
referrer = kwargs.get('referrer')
for record in records:
log = dict(
action=action,
json=record,
dashboard_id=dashboard_id,
slice_id=slice_id,
duration_ms=duration_ms,
referrer=referrer,
user_id=user_id
)
print(json.dumps(log))
```
End by updating your config to pass in an instance of the logger you want to use:
```
EVENT_LOGGER = JSONStdOutEventLogger()
```
## StatsD Logging
Superset can be configured to log events to [StatsD](https://github.com/statsd/statsd)
if desired. Most endpoints hit are logged as
well as key events like query start and end in SQL Lab.
To setup StatsD logging, it’s a matter of configuring the logger in your `superset_config.py`.
If not already present, you need to ensure that the `statsd`-package is installed in Superset's python environment.
```python
from superset.stats_logger import StatsdStatsLogger
STATS_LOGGER = StatsdStatsLogger(host='localhost', port=8125, prefix='superset')
```
Note that it’s also possible to implement your own logger by deriving
`superset.stats_logger.BaseStatsLogger`.

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---
title: Importing and Exporting Datasources
hide_title: true
sidebar_position: 11
version: 1
---
# Importing and Exporting Datasources
The superset cli allows you to import and export datasources from and to YAML. Datasources include
databases. The data is expected to be organized in the following hierarchy:
```text
├──databases
| ├──database_1
| | ├──table_1
| | | ├──columns
| | | | ├──column_1
| | | | ├──column_2
| | | | └──... (more columns)
| | | └──metrics
| | | ├──metric_1
| | | ├──metric_2
| | | └──... (more metrics)
| | └── ... (more tables)
| └── ... (more databases)
```
## Exporting Datasources to YAML
You can print your current datasources to stdout by running:
```bash
superset export_datasources
```
To save your datasources to a ZIP file run:
```bash
superset export_datasources -f <filename>
```
By default, default (null) values will be omitted. Use the -d flag to include them. If you want back
references to be included (e.g. a column to include the table id it belongs to) use the -b flag.
Alternatively, you can export datasources using the UI:
1. Open **Sources -> Databases** to export all tables associated to a single or multiple databases.
(**Tables** for one or more tables)
2. Select the items you would like to export.
3. Click **Actions -> Export** to YAML
4. If you want to import an item that you exported through the UI, you will need to nest it inside
its parent element, e.g. a database needs to be nested under databases a table needs to be nested
inside a database element.
In order to obtain an **exhaustive list of all fields** you can import using the YAML import run:
```bash
superset export_datasource_schema
```
As a reminder, you can use the `-b` flag to include back references.
## Importing Datasources
In order to import datasources from a ZIP file, run:
```bash
superset import_datasources -p <path / filename>
```
The optional username flag **-u** sets the user used for the datasource import. The default is 'admin'. Example:
```bash
superset import_datasources -p <path / filename> -u 'admin'
```
## Legacy Importing Datasources
### From older versions of Superset to current version
When using Superset version 4.x.x to import from an older version (2.x.x or 3.x.x) importing is supported as the command `legacy_import_datasources` and expects a JSON or directory of JSONs. The options are `-r` for recursive and `-u` for specifying a user. Example of legacy import without options:
```bash
superset legacy_import_datasources -p <path or filename>
```
### From older versions of Superset to older versions
When using an older Superset version (2.x.x & 3.x.x) of Superset, the command is `import_datasources`. ZIP and YAML files are supported and to switch between them the feature flag `VERSIONED_EXPORT` is used. When `VERSIONED_EXPORT` is `True`, `import_datasources` expects a ZIP file, otherwise YAML. Example:
```bash
superset import_datasources -p <path or filename>
```
When `VERSIONED_EXPORT` is `False`, if you supply a path all files ending with **yaml** or **yml** will be parsed. You can apply
additional flags (e.g. to search the supplied path recursively):
```bash
superset import_datasources -p <path> -r
```
The sync flag **-s** takes parameters in order to sync the supplied elements with your file. Be
careful this can delete the contents of your meta database. Example:
```bash
superset import_datasources -p <path / filename> -s columns,metrics
```
This will sync all metrics and columns for all datasources found in the `<path /filename>` in the
Superset meta database. This means columns and metrics not specified in YAML will be deleted. If you
would add tables to columns,metrics those would be synchronised as well.
If you don’t supply the sync flag (**-s**) importing will only add and update (override) fields.
E.g. you can add a verbose_name to the column ds in the table random_time_series from the example
datasets by saving the following YAML to file and then running the **import_datasources** command.
```yaml
databases:
- database_name: main
tables:
- table_name: random_time_series
columns:
- column_name: ds
verbose_name: datetime
```

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---
title: Map Tiles
sidebar_position: 12
version: 1
---
# Map tiles
Superset uses OSM and Mapbox tiles by default. OSM is free but you still need setting your MAPBOX_API_KEY if you want to use mapbox maps.
## Setting map tiles
Map tiles can be set with `DECKGL_BASE_MAP` in your `superset_config.py` or `superset_config_docker.py`
For adding your own map tiles, you can use the following format.
```python
DECKGL_BASE_MAP = [
['tile://https://your_personal_url/{z}/{x}/{y}.png', 'MyTile']
]
```
Openstreetmap tiles url can be added without prefix.
```python
DECKGL_BASE_MAP = [
['https://c.tile.openstreetmap.org/{z}/{x}/{y}.png', 'OpenStreetMap']
]
```
Default values are:
```python
DECKGL_BASE_MAP = [
['https://tile.openstreetmap.org/{z}/{x}/{y}.png', 'Streets (OSM)'],
['https://tile.osm.ch/osm-swiss-style/{z}/{x}/{y}.png', 'Topography (OSM)'],
['mapbox://styles/mapbox/streets-v9', 'Streets'],
['mapbox://styles/mapbox/dark-v9', 'Dark'],
['mapbox://styles/mapbox/light-v9', 'Light'],
['mapbox://styles/mapbox/satellite-streets-v9', 'Satellite Streets'],
['mapbox://styles/mapbox/satellite-v9', 'Satellite'],
['mapbox://styles/mapbox/outdoors-v9', 'Outdoors'],
]
```
It is possible to set only mapbox by removing osm tiles and other way around.
:::warning
Setting `DECKGL_BASE_MAP` overwrite default values
:::
After defining your map tiles, set them in these variables:
- `CORS_OPTIONS`
- `connect-src` of `TALISMAN_CONFIG` and `TALISMAN_CONFIG_DEV` variables.
```python
ENABLE_CORS = True
CORS_OPTIONS: dict[Any, Any] = {
"origins": [
"https://tile.openstreetmap.org",
"https://tile.osm.ch",
"https://your_personal_url/{z}/{x}/{y}.png",
]
}
.
.
TALISMAN_CONFIG = {
"content_security_policy": {
...
"connect-src": [
"'self'",
"https://api.mapbox.com",
"https://events.mapbox.com",
"https://tile.openstreetmap.org",
"https://tile.osm.ch",
"https://your_personal_url/{z}/{x}/{y}.png",
],
...
}
```

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---
title: Network and Security Settings
sidebar_position: 7
version: 1
---
# Network and Security Settings
## CORS
:::note
In Superset versions prior to `5.x` you have to install to install `flask-cors` with `pip install flask-cors` to enable CORS support.
:::
The following keys in `superset_config.py` can be specified to configure CORS:
- `ENABLE_CORS`: Must be set to `True` in order to enable CORS
- `CORS_OPTIONS`: options passed to Flask-CORS
([documentation](https://flask-cors.readthedocs.io/en/latest/api.html#extension))
## HTTP headers
Note that Superset bundles [flask-talisman](https://pypi.org/project/talisman/)
Self-described as a small Flask extension that handles setting HTTP headers that can help
protect against a few common web application security issues.
## HTML Embedding of Dashboards and Charts
There are two ways to embed a dashboard: Using the [SDK](https://www.npmjs.com/package/@superset-ui/embedded-sdk) or embedding a direct link. Note that in the latter case everybody who knows the link is able to access the dashboard.
### Embedding a Public Direct Link to a Dashboard
This works by first changing the content security policy (CSP) of [flask-talisman](https://github.com/GoogleCloudPlatform/flask-talisman) to allow for certain domains to display Superset content. Then a dashboard can be made publicly accessible, i.e. **bypassing authentication**. Once made public, the dashboard's URL can be added to an iframe in another website's HTML code.
#### Changing flask-talisman CSP
Add to `superset_config.py` the entire `TALISMAN_CONFIG` section from `config.py` and include a `frame-ancestors` section:
```python
TALISMAN_ENABLED = True
TALISMAN_CONFIG = {
"content_security_policy": {
...
"frame-ancestors": ["*.my-domain.com", "*.another-domain.com"],
...
```
Restart Superset for this configuration change to take effect.
#### Making a Dashboard Public
1. Add the `'DASHBOARD_RBAC': True` [Feature Flag](https://github.com/apache/superset/blob/master/RESOURCES/FEATURE_FLAGS.md) to `superset_config.py`
2. Add the `Public` role to your dashboard as described [here](https://superset.apache.org/docs/using-superset/creating-your-first-dashboard/#manage-access-to-dashboards)
#### Embedding a Public Dashboard
Now anybody can directly access the dashboard's URL. You can embed it in an iframe like so:
```html
<iframe
width="600"
height="400"
seamless
frameBorder="0"
scrolling="no"
src="https://superset.my-domain.com/superset/dashboard/10/?standalone=1&height=400"
>
</iframe>
```
#### Embedding a Chart
A chart's embed code can be generated by going to a chart's edit view and then clicking at the top right on `...` > `Share` > `Embed code`
### Enabling Embedding via the SDK
Clicking on `...` next to `EDIT DASHBOARD` on the top right of the dashboard's overview page should yield a drop-down menu including the entry "Embed dashboard".
To enable this entry, add the following line to the `.env` file:
```text
SUPERSET_FEATURE_EMBEDDED_SUPERSET=true
```
## CSRF settings
Similarly, [flask-wtf](https://flask-wtf.readthedocs.io/en/0.15.x/config/) is used to manage
some CSRF configurations. If you need to exempt endpoints from CSRF (e.g. if you are
running a custom auth postback endpoint), you can add the endpoints to `WTF_CSRF_EXEMPT_LIST`:
## SSH Tunneling
1. Turn on feature flag
- Change [`SSH_TUNNELING`](https://github.com/apache/superset/blob/eb8386e3f0647df6d1bbde8b42073850796cc16f/superset/config.py#L489) to `True`
- If you want to add more security when establishing the tunnel we allow users to overwrite the `SSHTunnelManager` class [here](https://github.com/apache/superset/blob/eb8386e3f0647df6d1bbde8b42073850796cc16f/superset/config.py#L507)
- You can also set the [`SSH_TUNNEL_LOCAL_BIND_ADDRESS`](https://github.com/apache/superset/blob/eb8386e3f0647df6d1bbde8b42073850796cc16f/superset/config.py#L508) this the host address where the tunnel will be accessible on your VPC
2. Create database w/ ssh tunnel enabled
- With the feature flag enabled you should now see ssh tunnel toggle.
- Click the toggle to enable SSH tunneling and add your credentials accordingly.
- Superset allows for two different types of authentication (Basic + Private Key). These credentials should come from your service provider.
3. Verify data is flowing
- Once SSH tunneling has been enabled, go to SQL Lab and write a query to verify data is properly flowing.
## Domain Sharding
:::note
Domain Sharding is deprecated as of Superset 5.0.0, and will be removed in Superset 6.0.0. Please Enable HTTP2 to keep more open connections per domain.
:::
Chrome allows up to 6 open connections per domain at a time. When there are more than 6 slices in
dashboard, a lot of time fetch requests are queued up and wait for next available socket.
[PR 5039](https://github.com/apache/superset/pull/5039) adds domain sharding to Superset,
and this feature will be enabled by configuration only (by default Superset doesn’t allow
cross-domain request).
Add the following setting in your `superset_config.py` file:
- `SUPERSET_WEBSERVER_DOMAINS`: list of allowed hostnames for domain sharding feature.
Please create your domain shards as subdomains of your main domain for authorization to
work properly on new domains. For Example:
- `SUPERSET_WEBSERVER_DOMAINS=['superset-1.mydomain.com','superset-2.mydomain.com','superset-3.mydomain.com','superset-4.mydomain.com']`
or add the following setting in your `superset_config.py` file if domain shards are not subdomains of main domain.
- `SESSION_COOKIE_DOMAIN = '.mydomain.com'`
## Middleware
Superset allows you to add your own middleware. To add your own middleware, update the
`ADDITIONAL_MIDDLEWARE` key in your `superset_config.py`. `ADDITIONAL_MIDDLEWARE` should be a list
of your additional middleware classes.
For example, to use `AUTH_REMOTE_USER` from behind a proxy server like nginx, you have to add a
simple middleware class to add the value of `HTTP_X_PROXY_REMOTE_USER` (or any other custom header
from the proxy) to Gunicorn’s `REMOTE_USER` environment variable.

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---
title: SQL Templating
hide_title: true
sidebar_position: 5
version: 1
---
# SQL Templating
## Jinja Templates
SQL Lab and Explore supports [Jinja templating](https://jinja.palletsprojects.com/en/2.11.x/) in queries.
To enable templating, the `ENABLE_TEMPLATE_PROCESSING` [feature flag](/docs/configuration/configuring-superset#feature-flags) needs to be enabled in
`superset_config.py`. When templating is enabled, python code can be embedded in virtual datasets and
in Custom SQL in the filter and metric controls in Explore. By default, the following variables are
made available in the Jinja context:
- `columns`: columns which to group by in the query
- `filter`: filters applied in the query
- `from_dttm`: start `datetime` value from the selected time range (`None` if undefined) (deprecated beginning in version 5.0, use `get_time_filter` instead)
- `to_dttm`: end `datetime` value from the selected time range (`None` if undefined). (deprecated beginning in version 5.0, use `get_time_filter` instead)
- `groupby`: columns which to group by in the query (deprecated)
- `metrics`: aggregate expressions in the query
- `row_limit`: row limit of the query
- `row_offset`: row offset of the query
- `table_columns`: columns available in the dataset
- `time_column`: temporal column of the query (`None` if undefined)
- `time_grain`: selected time grain (`None` if undefined)
For example, to add a time range to a virtual dataset, you can write the following:
```sql
SELECT *
FROM tbl
WHERE dttm_col > '{{ from_dttm }}' and dttm_col < '{{ to_dttm }}'
```
You can also use [Jinja's logic](https://jinja.palletsprojects.com/en/2.11.x/templates/#tests)
to make your query robust to clearing the timerange filter:
```sql
SELECT *
FROM tbl
WHERE (
{% if from_dttm is not none %}
dttm_col > '{{ from_dttm }}' AND
{% endif %}
{% if to_dttm is not none %}
dttm_col < '{{ to_dttm }}' AND
{% endif %}
1 = 1
)
```
The `1 = 1` at the end ensures a value is present for the `WHERE` clause even when
the time filter is not set. For many database engines, this could be replaced with `true`.
Note that the Jinja parameters are called within _double_ brackets in the query and with
_single_ brackets in the logic blocks.
To add custom functionality to the Jinja context, you need to overload the default Jinja
context in your environment by defining the `JINJA_CONTEXT_ADDONS` in your superset configuration
(`superset_config.py`). Objects referenced in this dictionary are made available for users to use
where the Jinja context is made available.
```python
JINJA_CONTEXT_ADDONS = {
'my_crazy_macro': lambda x: x*2,
}
```
Default values for jinja templates can be specified via `Parameters` menu in the SQL Lab user interface.
In the UI you can assign a set of parameters as JSON
```json
{
"my_table": "foo"
}
```
The parameters become available in your SQL (example: `SELECT * FROM {{ my_table }}` ) by using Jinja templating syntax.
SQL Lab template parameters are stored with the dataset as `TEMPLATE PARAMETERS`.
There is a special ``_filters`` parameter which can be used to test filters used in the jinja template.
```json
{
"_filters": [
{
"col": "action_type",
"op": "IN",
"val": ["sell", "buy"]
}
]
}
```
```sql
SELECT action, count(*) as times
FROM logs
WHERE action in {{ filter_values('action_type')|where_in }}
GROUP BY action
```
Note ``_filters`` is not stored with the dataset. It's only used within the SQL Lab UI.
Besides default Jinja templating, SQL lab also supports self-defined template processor by setting
the `CUSTOM_TEMPLATE_PROCESSORS` in your superset configuration. The values in this dictionary
overwrite the default Jinja template processors of the specified database engine. The example below
configures a custom presto template processor which implements its own logic of processing macro
template with regex parsing. It uses the `$` style macro instead of `{{ }}` style in Jinja
templating.
By configuring it with `CUSTOM_TEMPLATE_PROCESSORS`, a SQL template on a presto database is
processed by the custom one rather than the default one.
```python
def DATE(
ts: datetime, day_offset: SupportsInt = 0, hour_offset: SupportsInt = 0
) -> str:
"""Current day as a string."""
day_offset, hour_offset = int(day_offset), int(hour_offset)
offset_day = (ts + timedelta(days=day_offset, hours=hour_offset)).date()
return str(offset_day)
class CustomPrestoTemplateProcessor(PrestoTemplateProcessor):
"""A custom presto template processor."""
engine = "presto"
def process_template(self, sql: str, **kwargs) -> str:
"""Processes a sql template with $ style macro using regex."""
# Add custom macros functions.
macros = {
"DATE": partial(DATE, datetime.utcnow())
} # type: Dict[str, Any]
# Update with macros defined in context and kwargs.
macros.update(self.context)
macros.update(kwargs)
def replacer(match):
"""Expand $ style macros with corresponding function calls."""
macro_name, args_str = match.groups()
args = [a.strip() for a in args_str.split(",")]
if args == [""]:
args = []
f = macros[macro_name[1:]]
return f(*args)
macro_names = ["$" + name for name in macros.keys()]
pattern = r"(%s)\s*\(([^()]*)\)" % "|".join(map(re.escape, macro_names))
return re.sub(pattern, replacer, sql)
CUSTOM_TEMPLATE_PROCESSORS = {
CustomPrestoTemplateProcessor.engine: CustomPrestoTemplateProcessor
}
```
SQL Lab also includes a live query validation feature with pluggable backends. You can configure
which validation implementation is used with which database engine by adding a block like the
following to your configuration file:
```python
FEATURE_FLAGS = {
'SQL_VALIDATORS_BY_ENGINE': {
'presto': 'PrestoDBSQLValidator',
}
}
```
The available validators and names can be found in
[sql_validators](https://github.com/apache/superset/tree/master/superset/sql_validators).
## Available Macros
In this section, we'll walkthrough the pre-defined Jinja macros in Superset.
**Current Username**
The `{{ current_username() }}` macro returns the `username` of the currently logged in user.
If you have caching enabled in your Superset configuration, then by default the `username` value will be used
by Superset when calculating the cache key. A cache key is a unique identifier that determines if there's a
cache hit in the future and Superset can retrieve cached data.
You can disable the inclusion of the `username` value in the calculation of the
cache key by adding the following parameter to your Jinja code:
```python
{{ current_username(add_to_cache_keys=False) }}
```
**Current User ID**
The `{{ current_user_id() }}` macro returns the account ID of the currently logged in user.
If you have caching enabled in your Superset configuration, then by default the account `id` value will be used
by Superset when calculating the cache key. A cache key is a unique identifier that determines if there's a
cache hit in the future and Superset can retrieve cached data.
You can disable the inclusion of the account `id` value in the calculation of the
cache key by adding the following parameter to your Jinja code:
```python
{{ current_user_id(add_to_cache_keys=False) }}
```
**Current User Email**
The `{{ current_user_email() }}` macro returns the email address of the currently logged in user.
If you have caching enabled in your Superset configuration, then by default the email address value will be used
by Superset when calculating the cache key. A cache key is a unique identifier that determines if there's a
cache hit in the future and Superset can retrieve cached data.
You can disable the inclusion of the email value in the calculation of the
cache key by adding the following parameter to your Jinja code:
```python
{{ current_user_email(add_to_cache_keys=False) }}
```
**Current User Roles**
The `{{ current_user_roles() }}` macro returns an array of roles for the logged in user.
If you have caching enabled in your Superset configuration, then by default the roles value will be used
by Superset when calculating the cache key. A cache key is a unique identifier that determines if there's a
cache hit in the future and Superset can retrieve cached data.
You can disable the inclusion of the roles value in the calculation of the
cache key by adding the following parameter to your Jinja code:
```python
{{ current_user_roles(add_to_cache_keys=False) }}
```
You can json-stringify the array by adding `|tojson` to your Jinja code:
```python
{{ current_user_roles()|tojson }}
```
You can use the `|where_in` filter to use your roles in a SQL statement. For example, if `current_user_roles()` returns `['admin', 'viewer']`, the following template:
```python
SELECT * FROM users WHERE role IN {{ current_user_roles()|where_in }}
```
Will be rendered as:
```sql
SELECT * FROM users WHERE role IN ('admin', 'viewer')
```
**Current User RLS Rules**
The `{{ current_user_rls_rules() }}` macro returns an array of RLS rules applied to the current dataset for the logged in user.
If you have caching enabled in your Superset configuration, then the list of RLS Rules will be used
by Superset when calculating the cache key. A cache key is a unique identifier that determines if there's a
cache hit in the future and Superset can retrieve cached data.
**Custom URL Parameters**
The `{{ url_param('custom_variable') }}` macro lets you define arbitrary URL
parameters and reference them in your SQL code.
Here's a concrete example:
- You write the following query in SQL Lab:
```sql
SELECT count(*)
FROM ORDERS
WHERE country_code = '{{ url_param('countrycode') }}'
```
- You're hosting Superset at the domain www.example.com and you send your
coworker in Spain the following SQL Lab URL `www.example.com/superset/sqllab?countrycode=ES`
and your coworker in the USA the following SQL Lab URL `www.example.com/superset/sqllab?countrycode=US`
- For your coworker in Spain, the SQL Lab query will be rendered as:
```sql
SELECT count(*)
FROM ORDERS
WHERE country_code = 'ES'
```
- For your coworker in the USA, the SQL Lab query will be rendered as:
```sql
SELECT count(*)
FROM ORDERS
WHERE country_code = 'US'
```
**Explicitly Including Values in Cache Key**
The `{{ cache_key_wrapper() }}` function explicitly instructs Superset to add a value to the
accumulated list of values used in the calculation of the cache key.
This function is only needed when you want to wrap your own custom function return values
in the cache key. You can gain more context
[here](https://github.com/apache/superset/blob/efd70077014cbed62e493372d33a2af5237eaadf/superset/jinja_context.py#L133-L148).
Note that this function powers the caching of the `user_id` and `username` values
in the `current_user_id()` and `current_username()` function calls (if you have caching enabled).
**Filter Values**
You can retrieve the value for a specific filter as a list using `{{ filter_values() }}`.
This is useful if:
- You want to use a filter component to filter a query where the name of filter component column doesn't match the one in the select statement
- You want to have the ability to filter inside the main query for performance purposes
Here's a concrete example:
```sql
SELECT action, count(*) as times
FROM logs
WHERE
action in {{ filter_values('action_type')|where_in }}
GROUP BY action
```
There `where_in` filter converts the list of values from `filter_values('action_type')` into a string suitable for an `IN` expression.
**Filters for a Specific Column**
The `{{ get_filters() }}` macro returns the filters applied to a given column. In addition to
returning the values (similar to how `filter_values()` does), the `get_filters()` macro
returns the operator specified in the Explore UI.
This is useful if:
- You want to handle more than the IN operator in your SQL clause
- You want to handle generating custom SQL conditions for a filter
- You want to have the ability to filter inside the main query for speed purposes
Here's a concrete example:
```sql
WITH RECURSIVE
superiors(employee_id, manager_id, full_name, level, lineage) AS (
SELECT
employee_id,
manager_id,
full_name,
1 as level,
employee_id as lineage
FROM
employees
WHERE
1=1
{# Render a blank line #}
{%- for filter in get_filters('full_name', remove_filter=True) -%}
{%- if filter.get('op') == 'IN' -%}
AND
full_name IN {{ filter.get('val')|where_in }}
{%- endif -%}
{%- if filter.get('op') == 'LIKE' -%}
AND
full_name LIKE {{ "'" + filter.get('val') + "'" }}
{%- endif -%}
{%- endfor -%}
UNION ALL
SELECT
e.employee_id,
e.manager_id,
e.full_name,
s.level + 1 as level,
s.lineage
FROM
employees e,
superiors s
WHERE s.manager_id = e.employee_id
)
SELECT
employee_id, manager_id, full_name, level, lineage
FROM
superiors
order by lineage, level
```
**Time Filter**
The `{{ get_time_filter() }}` macro returns the time filter applied to a specific column. This is useful if you want
to handle time filters inside the virtual dataset, as by default the time filter is placed on the outer query. This can
considerably improve performance, as many databases and query engines are able to optimize the query better
if the temporal filter is placed on the inner query, as opposed to the outer query.
The macro takes the following parameters:
- `column`: Name of the temporal column. Leave undefined to reference the time range from a Dashboard Native Time Range
filter (when present).
- `default`: The default value to fall back to if the time filter is not present, or has the value `No filter`
- `target_type`: The target temporal type as recognized by the target database (e.g. `TIMESTAMP`, `DATE` or
`DATETIME`). If `column` is defined, the format will default to the type of the column. This is used to produce
the format of the `from_expr` and `to_expr` properties of the returned `TimeFilter` object.
- `strftime`: format using the `strftime` method of `datetime` for custom time formatting.
([see docs for valid format codes](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes)).
When defined `target_type` will be ignored.
- `remove_filter`: When set to true, mark the filter as processed, removing it from the outer query. Useful when a
filter should only apply to the inner query.
The return type has the following properties:
- `from_expr`: the start of the time filter (if any)
- `to_expr`: the end of the time filter (if any)
- `time_range`: The applied time range
Here's a concrete example using the `logs` table from the Superset metastore:
```
{% set time_filter = get_time_filter("dttm", remove_filter=True) %}
{% set from_expr = time_filter.from_expr %}
{% set to_expr = time_filter.to_expr %}
{% set time_range = time_filter.time_range %}
SELECT
*,
'{{ time_range }}' as time_range
FROM logs
{% if from_expr or to_expr %}WHERE 1 = 1
{% if from_expr %}AND dttm >= {{ from_expr }}{% endif %}
{% if to_expr %}AND dttm < {{ to_expr }}{% endif %}
{% endif %}
```
Assuming we are creating a table chart with a simple `COUNT(*)` as the metric with a time filter `Last week` on the
`dttm` column, this would render the following query on Postgres (note the formatting of the temporal filters, and
the absence of time filters on the outer query):
```
SELECT COUNT(*) AS count
FROM
(SELECT *,
'Last week' AS time_range
FROM public.logs
WHERE 1 = 1
AND dttm >= TO_TIMESTAMP('2024-08-27 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')
AND dttm < TO_TIMESTAMP('2024-09-03 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US')) AS virtual_table
ORDER BY count DESC
LIMIT 1000;
```
When using the `default` parameter, the templated query can be simplified, as the endpoints will always be defined
(to use a fixed time range, you can also use something like `default="2024-08-27 : 2024-09-03"`)
```
{% set time_filter = get_time_filter("dttm", default="Last week", remove_filter=True) %}
SELECT
*,
'{{ time_filter.time_range }}' as time_range
FROM logs
WHERE
dttm >= {{ time_filter.from_expr }}
AND dttm < {{ time_filter.to_expr }}
```
**Datasets**
It's possible to query physical and virtual datasets using the `dataset` macro. This is useful if you've defined computed columns and metrics on your datasets, and want to reuse the definition in adhoc SQL Lab queries.
To use the macro, first you need to find the ID of the dataset. This can be done by going to the view showing all the datasets, hovering over the dataset you're interested in, and looking at its URL. For example, if the URL for a dataset is https://superset.example.org/explore/?dataset_type=table&dataset_id=42 its ID is 42.
Once you have the ID you can query it as if it were a table:
```sql
SELECT * FROM {{ dataset(42) }} LIMIT 10
```
If you want to select the metric definitions as well, in addition to the columns, you need to pass an additional keyword argument:
```sql
SELECT * FROM {{ dataset(42, include_metrics=True) }} LIMIT 10
```
Since metrics are aggregations, the resulting SQL expression will be grouped by all non-metric columns. You can specify a subset of columns to group by instead:
```sql
SELECT * FROM {{ dataset(42, include_metrics=True, columns=["ds", "category"]) }} LIMIT 10
```
**Metrics**
The `{{ metric('metric_key', dataset_id) }}` macro can be used to retrieve the metric SQL syntax from a dataset. This can be useful for different purposes:
- Override the metric label in the chart level
- Combine multiple metrics in a calculation
- Retrieve a metric syntax in SQL lab
- Re-use metrics across datasets
This macro avoids copy/paste, allowing users to centralize the metric definition in the dataset layer.
The `dataset_id` parameter is optional, and if not provided Superset will use the current dataset from context (for example, when using this macro in the Chart Builder, by default the `macro_key` will be searched in the dataset powering the chart).
The parameter can be used in SQL Lab, or when fetching a metric from another dataset.
## Available Filters
Superset supports [builtin filters from the Jinja2 templating package](https://jinja.palletsprojects.com/en/stable/templates/#builtin-filters). Custom filters have also been implemented:
**Where In**
Parses a list into a SQL-compatible statement. This is useful with macros that return an array (for example the `filter_values` macro):
```
Dashboard filter with "First", "Second" and "Third" options selected
{{ filter_values('column') }} => ["First", "Second", "Third"]
{{ filter_values('column')|where_in }} => ('First', 'Second', 'Third')
```
By default, this filter returns `()` (as a string) in case the value is null. The `default_to_none` parameter can be se to `True` to return null in this case:
```
Dashboard filter without any value applied
{{ filter_values('column') }} => ()
{{ filter_values('column')|where_in(default_to_none=True) }} => None
```
**To Datetime**
Loads a string as a `datetime` object. This is useful when performing date operations. For example:
```
{% set from_expr = get_time_filter("dttm", strftime="%Y-%m-%d").from_expr %}
{% set to_expr = get_time_filter("dttm", strftime="%Y-%m-%d").to_expr %}
{% if (to_expr|to_datetime(format="%Y-%m-%d") - from_expr|to_datetime(format="%Y-%m-%d")).days > 100 %}
do something
{% else %}
do something else
{% endif %}
```

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---
title: Theming
hide_title: true
sidebar_position: 12
version: 1
---
# Theming Superset
:::note
apache-superset>=6.0
:::
Superset now rides on **Ant Design v5's token-based theming**.
Every Antd token works, plus a handful of Superset-specific ones for charts and dashboard chrome.
## Managing Themes via UI
Superset includes a built-in **Theme Management** interface accessible from the admin menu under **Settings > Themes**.
### Creating a New Theme
1. Navigate to **Settings > Themes** in the Superset interface
2. Click **+ Theme** to create a new theme
3. Use the [Ant Design Theme Editor](https://ant.design/theme-editor) to design your theme:
- Design your palette, typography, and component overrides
- Open the `CONFIG` modal and copy the JSON configuration
4. Paste the JSON into the theme definition field in Superset
5. Give your theme a descriptive name and save
You can also extend with Superset-specific tokens (documented in the default theme object) before you import.
### System Theme Administration
When `ENABLE_UI_THEME_ADMINISTRATION = True` is configured, administrators can manage system-wide themes directly from the UI:
#### Setting System Themes
- **System Default Theme**: Click the sun icon on any theme to set it as the system-wide default
- **System Dark Theme**: Click the moon icon on any theme to set it as the system dark mode theme
- **Automatic OS Detection**: When both default and dark themes are set, Superset automatically detects and applies the appropriate theme based on OS preferences
#### Managing System Themes
- System themes are indicated with special badges in the theme list
- Only administrators with write permissions can modify system theme settings
- Removing a system theme designation reverts to configuration file defaults
### Applying Themes to Dashboards
Once created, themes can be applied to individual dashboards:
- Edit any dashboard and select your custom theme from the theme dropdown
- Each dashboard can have its own theme, allowing for branded or context-specific styling
## Configuration Options
### Python Configuration
Configure theme behavior via `superset_config.py`:
```python
# Enable UI-based theme administration for admins
ENABLE_UI_THEME_ADMINISTRATION = True
# Optional: Set initial default themes via configuration
# These can be overridden via the UI when ENABLE_UI_THEME_ADMINISTRATION = True
THEME_DEFAULT = {
"token": {
"colorPrimary": "#2893B3",
"colorSuccess": "#5ac189",
# ... your theme JSON configuration
}
}
# Optional: Dark theme configuration
THEME_DARK = {
"algorithm": "dark",
"token": {
"colorPrimary": "#2893B3",
# ... your dark theme overrides
}
}
# To force a single theme on all users, set THEME_DARK = None
# When both themes are defined (via UI or config):
# - Users can manually switch between themes
# - OS preference detection is automatically enabled
```
### Migration from Configuration to UI
When `ENABLE_UI_THEME_ADMINISTRATION = True`:
1. System themes set via the UI take precedence over configuration file settings
2. The UI shows which themes are currently set as system defaults
3. Administrators can change system themes without restarting Superset
4. Configuration file themes serve as fallbacks when no UI themes are set
### Copying Themes Between Systems
To export a theme for use in configuration files or another instance:
1. Navigate to **Settings > Themes** and click the export icon on your desired theme
2. Extract the JSON configuration from the exported YAML file
3. Use this JSON in your `superset_config.py` or import it into another Superset instance
## Theme Development Workflow
1. **Design**: Use the [Ant Design Theme Editor](https://ant.design/theme-editor) to iterate on your design
2. **Test**: Create themes in Superset's CRUD interface for testing
3. **Apply**: Assign themes to specific dashboards or configure instance-wide
4. **Iterate**: Modify theme JSON directly in the CRUD interface or re-import from the theme editor
## Custom Fonts
Superset supports custom fonts through runtime configuration, allowing you to use branded or custom typefaces without rebuilding the application.
### Configuring Custom Fonts
Add font URLs to your `superset_config.py`:
```python
# Load fonts from Google Fonts, Adobe Fonts, or self-hosted sources
CUSTOM_FONT_URLS = [
"https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap",
"https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500&display=swap",
]
# Update CSP to allow font sources
TALISMAN_CONFIG = {
"content_security_policy": {
"font-src": ["'self'", "https://fonts.googleapis.com", "https://fonts.gstatic.com"],
"style-src": ["'self'", "'unsafe-inline'", "https://fonts.googleapis.com"],
}
}
```
### Using Custom Fonts in Themes
Once configured, reference the fonts in your theme configuration:
```python
THEME_DEFAULT = {
"token": {
"fontFamily": "Inter, -apple-system, BlinkMacSystemFont, sans-serif",
"fontFamilyCode": "JetBrains Mono, Monaco, monospace",
# ... other theme tokens
}
}
```
Or in the CRUD interface theme JSON:
```json
{
"token": {
"fontFamily": "Inter, -apple-system, BlinkMacSystemFont, sans-serif",
"fontFamilyCode": "JetBrains Mono, Monaco, monospace"
}
}
```
### Font Sources
- **Google Fonts**: Free, CDN-hosted fonts with wide variety
- **Adobe Fonts**: Premium fonts (requires subscription and kit ID)
- **Self-hosted**: Place font files in `/static/assets/fonts/` and reference via CSS
This feature works with the stock Docker image - no custom build required!
## Advanced Features
- **System Themes**: Manage system-wide default and dark themes via UI or configuration
- **Per-Dashboard Theming**: Each dashboard can have its own visual identity
- **JSON Editor**: Edit theme configurations directly within Superset's interface
- **Custom Fonts**: Load external fonts via configuration without rebuilding
- **OS Dark Mode Detection**: Automatically switches themes based on system preferences
- **Theme Import/Export**: Share themes between instances via YAML files
## API Access
For programmatic theme management, Superset provides REST endpoints:
- `GET /api/v1/theme/` - List all themes
- `POST /api/v1/theme/` - Create a new theme
- `PUT /api/v1/theme/{id}` - Update a theme
- `DELETE /api/v1/theme/{id}` - Delete a theme
- `PUT /api/v1/theme/{id}/set_system_default` - Set as system default theme (admin only)
- `PUT /api/v1/theme/{id}/set_system_dark` - Set as system dark theme (admin only)
- `DELETE /api/v1/theme/unset_system_default` - Remove system default designation
- `DELETE /api/v1/theme/unset_system_dark` - Remove system dark designation
- `GET /api/v1/theme/export/` - Export themes as YAML
- `POST /api/v1/theme/import/` - Import themes from YAML
These endpoints require appropriate permissions and are subject to RBAC controls.

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---
title: Timezones
hide_title: true
sidebar_position: 6
version: 1
---
# Timezones
There are four distinct timezone components which relate to Apache Superset,
1. The timezone that the underlying data is encoded in.
2. The timezone of the database engine.
3. The timezone of the Apache Superset backend.
4. The timezone of the Apache Superset client.
where if a temporal field (`DATETIME`, `TIME`, `TIMESTAMP`, etc.) does not explicitly define a timezone it defaults to the underlying timezone of the component.
To help make the problem somewhat tractable—given that Apache Superset has no control on either how the data is ingested (1) or the timezone of the client (4)—from a consistency standpoint it is highly recommended that both (2) and (3) are configured to use the same timezone with a strong preference given to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) to ensure temporal fields without an explicit timestamp are not incorrectly coerced into the wrong timezone. Actually Apache Superset currently has implicit assumptions that timestamps are in UTC and thus configuring (3) to a non-UTC timezone could be problematic.
To strive for data consistency (regardless of the timezone of the client) the Apache Superset backend tries to ensure that any timestamp sent to the client has an explicit (or semi-explicit as in the case with [Epoch time](https://en.wikipedia.org/wiki/Unix_time) which is always in reference to UTC) timezone encoded within.
The challenge however lies with the slew of [database engines](/docs/configuration/databases#installing-drivers-in-docker-images) which Apache Superset supports and various inconsistencies between their [Python Database API (DB-API)](https://www.python.org/dev/peps/pep-0249/) implementations combined with the fact that we use [Pandas](https://pandas.pydata.org/) to read SQL into a DataFrame prior to serializing to JSON. Regrettably Pandas ignores the DB-API [type_code](https://www.python.org/dev/peps/pep-0249/#type-objects) relying by default on the underlying Python type returned by the DB-API. Currently only a subset of the supported database engines work correctly with Pandas, i.e., ensuring timestamps without an explicit timestamp are serializd to JSON with the server timezone, thus guaranteeing the client will display timestamps in a consistent manner irrespective of the client's timezone.
For example the following is a comparison of MySQL and Presto,
```python
import pandas as pd
from sqlalchemy import create_engine
pd.read_sql_query(
sql="SELECT TIMESTAMP('2022-01-01 00:00:00') AS ts",
con=create_engine("mysql://root@localhost:3360"),
).to_json()
pd.read_sql_query(
sql="SELECT TIMESTAMP '2022-01-01 00:00:00' AS ts",
con=create_engine("presto://localhost:8080"),
).to_json()
```
which outputs `{"ts":{"0":1640995200000}}` (which infers the UTC timezone per the Epoch time definition) and `{"ts":{"0":"2022-01-01 00:00:00.000"}}` (without an explicit timezone) respectively and thus are treated differently in JavaScript:
```js
new Date(1640995200000)
> Sat Jan 01 2022 13:00:00 GMT+1300 (New Zealand Daylight Time)
new Date("2022-01-01 00:00:00.000")
> Sat Jan 01 2022 00:00:00 GMT+1300 (New Zealand Daylight Time)
```

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---
title: Contributing to Superset
sidebar_position: 1
version: 1
---
# Contributing to Superset
Superset is an [Apache Software foundation](https://www.apache.org/theapacheway/index.html) project.
The core contributors (or committers) to Superset communicate primarily in the following channels (
which can be joined by anyone):
- [Mailing list](https://lists.apache.org/list.html?dev@superset.apache.org)
- [Apache Superset Slack community](http://bit.ly/join-superset-slack)
- [GitHub issues](https://github.com/apache/superset/issues)
- [GitHub pull requests](https://github.com/apache/superset/pulls)
- [GitHub discussions](https://github.com/apache/superset/discussions)
- [Superset Community Calendar](https://superset.apache.org/community)
More references:
- [Superset Wiki (code guidelines and additional resources)](https://github.com/apache/superset/wiki)
## Orientation
Here's a list of repositories that contain Superset-related packages:
- [apache/superset](https://github.com/apache/superset)
is the main repository containing the `apache_superset` Python package
distributed on
[pypi](https://pypi.org/project/apache_superset/). This repository
also includes Superset's main TypeScript/JavaScript bundles and react apps under
the [superset-frontend](https://github.com/apache/superset/tree/master/superset-frontend)
folder.
- [github.com/apache-superset](https://github.com/apache-superset) is the
GitHub organization under which we manage Superset-related
small tools, forks and Superset-related experimental ideas.
## 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](https://help.github.com/articles/creating-and-highlighting-code-blocks/).
_Please note that feature requests opened as GitHub Issues will be moved to Discussions._
### Submit Ideas or Feature Requests
The best way is to start an ["Ideas" Discussion thread](https://github.com/apache/superset/discussions/categories/ideas) 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 your contributions are as welcome as anyone's :)
To propose large features or major changes to codebase, and help usher in those changes, please create a **Superset Improvement Proposal (SIP)**. See template from [SIP-0](https://github.com/apache/superset/issues/5602)
### 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` are open to whoever wants to implement them.
### 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](/docs/contributing/howtos#contributing-to-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 into 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](howtos#contributing-translations) for more details.
### Ask Questions
There is a dedicated [`apache-superset` tag](https://stackoverflow.com/questions/tagged/apache-superset) on [StackOverflow](https://stackoverflow.com/). Please use it when asking questions.
## Types of Contributors
Following the project governance model of the Apache Software Foundation (ASF), Apache Superset has a specific set of contributor roles:
### PMC Member
A Project Management Committee (PMC) member is a person who has been elected by the PMC to help manage the project. PMC members are responsible for the overall health of the project, including community development, release management, and project governance. PMC members are also responsible for the technical direction of the project.
For more information about Apache Project PMCs, please refer to https://www.apache.org/foundation/governance/pmcs.html
### Committer
A committer is a person who has been elected by the PMC to have write access (commit access) to the code repository. They can modify the code, documentation, and website and accept contributions from others.
The official list of committers and PMC members can be found [here](https://projects.apache.org/committee.html?superset).
### Contributor
A contributor is a person who has contributed to the project in any way, including but not limited to code, tests, documentation, issues, and discussions.
> You can also review the Superset project's guidelines for PMC member promotion here: https://github.com/apache/superset/wiki/Guidelines-for-promoting-Superset-Committers-to-the-Superset-PMC
### Security Team
The security team is a selected subset of PMC members, committers and non-committers who are responsible for handling security issues.
New members of the security team are selected by the PMC members in a vote. You can request to be added to the team by sending a message to private@superset.apache.org. However, the team should be small and focused on solving security issues, so the requests will be evaluated on a case-by-case basis and the team size will be kept relatively small, limited to only actively security-focused contributors.
This security team must follow the [ASF vulnerability handling process](https://apache.org/security/committers.html#asf-project-security-for-committers).
Each new security issue is tracked as a JIRA ticket on the [ASF's JIRA Superset security project](https://issues.apache.org/jira/secure/RapidBoard.jspa?rapidView=588&projectKey=SUPERSETSEC)
Security team members must:
- Have an [ICLA](https://www.apache.org/licenses/contributor-agreements.html) signed with Apache Software Foundation.
- Not reveal information about pending and unfixed security issues to anyone (including their employers) unless specifically authorised by the security team members, e.g., if the security team agrees that diagnosing and solving an issue requires the involvement of external experts.
A release manager, the contributor overseeing the release of a specific version of Apache Superset, is by default a member of the security team. However, they are not expected to be active in assessing, discussing, and fixing security issues.
Security team members should also follow these general expectations:
- Actively participate in assessing, discussing, fixing, and releasing security issues in Superset.
- Avoid discussing security fixes in public forums. Pull request (PR) descriptions should not contain any information about security issues. The corresponding JIRA ticket should contain a link to the PR.
- Security team members who contribute to a fix may be listed as remediation developers in the CVE report, along with their job affiliation (if they choose to include it).

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---
title: Guidelines
sidebar_position: 2
version: 1
---
## 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 filing 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 require 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.
If you wish to share your work which is not ready to merge yet, create a [Draft PR](https://github.blog/2019-02-14-introducing-draft-pull-requests/). This will enable maintainers and the CI runner to prioritize mature PR's.
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](http://karma-runner.github.io/0.10/dev/git-commit-msg.html])):
- `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.
- If you believe your PR contributes a potentially breaking change, put a `!` after the semantic prefix but before the colon in the PR title, like so: `feat!: Added foo functionality to bar`
- **Screenshots/GIFs:** Changes to user interface require before/after screenshots, or GIF for interactions
- Recommended capture tools ([Kap](https://getkap.co/), [LICEcap](https://www.cockos.com/licecap/), [Skitch](https://download.cnet.com/Skitch/3000-13455_4-189876.html))
- 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 `pyproject.toml` 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](/docs/contributing/howtos#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.
#### Test Environments
- Members of the Apache GitHub org can launch an ephemeral test environment directly on a pull request by creating a comment containing (only) the command `/testenv up`.
- Note that org membership must be public in order for this validation to function properly.
- Feature flags may be set for a test environment by specifying the flag name (prefixed with `FEATURE_`) and value after the command.
- Format: `/testenv up FEATURE_<feature flag name>=true|false`
- Example: `/testenv up FEATURE_DASHBOARD_NATIVE_FILTERS=true`
- Multiple feature flags may be set in single command, separated by whitespace
- A comment will be created by the workflow script with the address and login information for the ephemeral environment.
- Test environments may be created once the Docker build CI workflow for the PR has completed successfully.
- Test environments do not currently update automatically when new commits are added to a pull request.
- Test environments do not currently support async workers, though this is planned.
- Running test environments will be shutdown upon closing the pull request.
#### 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)](https://help.github.com/articles/closing-issues-using-keywords/).
#### 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 such as adjusting padding or refining UI style, excluding new features, bug fixes, and refactoring. | Implementation of improvement such as adjusting padding or refining UI style, excluding new features, bug fixes, and refactoring. |
| `#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`.
When creating a PR, if you're aiming to have it included in a specific release, please tag it with the version label. For example, to have a PR considered for inclusion in Superset 1.1 use the label `v1.1`.
## 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 time frame, 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.
## Design Guidelines
### Capitalization guidelines
#### Sentence case
Use sentence-case capitalization for everything in the UI (except these \*\*).
Sentence case is predominantly lowercase. Capitalize only the initial character of the first word, and other words that require capitalization, like:
- **Proper nouns.** Objects in the product _are not_ considered proper nouns e.g. dashboards, charts, saved queries etc. Proprietary feature names eg. SQL Lab, Preset Manager _are_ considered proper nouns
- **Acronyms** (e.g. CSS, HTML)
- When referring to **UI labels that are themselves capitalized** from sentence case (e.g. page titles - Dashboards page, Charts page, Saved queries page, etc.)
- User input that is reflected in the UI. E.g. a user-named a dashboard tab
**Sentence case vs. Title case:**
Title case: "A Dog Takes a Walk in Paris"
Sentence case: "A dog takes a walk in Paris"
**Why sentence case?**
- It’s generally accepted as the quickest to read
- It’s the easiest form to distinguish between common and proper nouns
#### How to refer to UI elements
When writing about a UI element, use the same capitalization as used in the UI.
For example, if an input field is labeled “Name” then you refer to this as the “Name input field”. Similarly, if a button has the label “Save” in it, then it is correct to refer to the “Save button”.
Where a product page is titled “Settings”, you refer to this in writing as follows:
“Edit your personal information on the Settings page”.
Often a product page will have the same title as the objects it contains. In this case, refer to the page as it appears in the UI, and the objects as common nouns:
- Upload a dashboard on the Dashboards page
- Go to Dashboards
- View dashboard
- View all dashboards
- Upload CSS templates on the CSS templates page
- Queries that you save will appear on the Saved queries page
- Create custom queries in SQL Lab then create dashboards
#### \*\*Exceptions to sentence case
- Input labels, buttons and UI tabs are all caps
- User input values (e.g. column names, SQL Lab tab names) should be in their original case
## Programming Language Conventions
### Python
Parameters in the `config.py` (which are accessible via the Flask app.config dictionary) are
assumed to always be defined and thus should be accessed directly via,
```python
blueprints = app.config["BLUEPRINTS"]
```
rather than,
```python
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 / Types Hints
To ensure clarity, consistency, all readability, _all_ new functions should use
[type hints](https://docs.python.org/3/library/typing.html) and include a
docstring.
Note per [PEP-484](https://www.python.org/dev/peps/pep-0484/#exceptions) no
syntax for listing explicitly raised exceptions is proposed and thus the
recommendation is to put this information in a docstring, i.e.,
```python
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](https://github.com/apache/superset/pull/9162) and [#9180](https://github.com/apache/superset/pull/9180).

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---
title: Development How-tos
hide_title: true
sidebar_position: 4
version: 1
---
# Development How-tos
## Contributing to Documentation
The latest documentation and tutorial are available at https://superset.apache.org/.
The documentation site is built using [Docusaurus 3](https://docusaurus.io/), a modern
static website generator, the source for which resides in `./docs`.
### Local Development
To set up a local development environment with hot reloading for the documentation site:
```shell
cd docs
yarn install # Installs NPM dependencies
yarn start # Starts development server at http://localhost:3000
```
### Build
To create and serve a production build of the documentation site:
```shell
yarn build
yarn serve
```
### Deployment
Commits to `master` trigger a rebuild and redeploy of the documentation site. Submit pull requests that modify the documentation with the `docs:` prefix.
## Creating Visualization Plugins
Visualizations in Superset are implemented in JavaScript or TypeScript. Superset
comes preinstalled with several visualizations types (hereafter "viz plugins") that
can be found under the `superset-frontend/plugins` directory. Viz plugins are added to
the application in the `superset-frontend/src/visualizations/presets/MainPreset.js`.
The Superset project is always happy to review proposals for new high quality viz
plugins. However, for highly custom viz types it is recommended to maintain a fork
of Superset, and add the custom built viz plugins by hand.
**Note:** Additional community-generated resources about creating and deploying custom visualization plugins can be found on the [Superset Wiki](https://github.com/apache/superset/wiki/Community-Resource-Library#creating-custom-data-visualizations)
### Prerequisites
In order to create a new viz plugin, you need the following:
- Run MacOS or Linux (Windows is not officially supported, but may work)
- Node.js 16
- npm 7 or 8
A general familiarity with [React](https://reactjs.org/) and the npm/Node system is
also recommended.
### Creating a simple Hello World viz plugin
To get started, you need the Superset Yeoman Generator. It is recommended to use the
version of the template that ships with the version of Superset you are using. This
can be installed by doing the following:
```bash
npm i -g yo
cd superset-frontend/packages/generator-superset
npm i
npm link
```
After this you can proceed to create your viz plugin. Create a new directory for your
viz plugin with the prefix `superset-plugin-chart` and run the Yeoman generator:
```bash
mkdir /tmp/superset-plugin-chart-hello-world
cd /tmp/superset-plugin-chart-hello-world
```
Initialize the viz plugin:
```bash
yo @superset-ui/superset
```
After that the generator will ask a few questions (the defaults should be fine):
```bash
$ yo @superset-ui/superset
_-----_ ╭──────────────────────────╮
| | │ Welcome to the │
|--(o)--| │ generator-superset │
`---------´ │ generator! │
( _´U`_ ) ╰──────────────────────────╯
/___A___\ /
| ~ |
__'.___.'__
´ ` |° ´ Y `
? Package name: superset-plugin-chart-hello-world
? Description: Hello World
? What type of chart would you like? Time-series chart
create package.json
create .gitignore
create babel.config.js
create jest.config.js
create README.md
create tsconfig.json
create src/index.ts
create src/plugin/buildQuery.ts
create src/plugin/controlPanel.ts
create src/plugin/index.ts
create src/plugin/transformProps.ts
create src/types.ts
create src/SupersetPluginChartHelloWorld.tsx
create test/index.test.ts
create test/__mocks__/mockExportString.js
create test/plugin/buildQuery.test.ts
create test/plugin/transformProps.test.ts
create types/external.d.ts
create src/images/thumbnail.png
```
To build the viz plugin, run the following commands:
```bash
npm i --force
npm run build
```
Alternatively, to run the viz plugin in development mode (=rebuilding whenever changes
are made), start the dev server with the following command:
```bash
npm run dev
```
To add the package to Superset, go to the `superset-frontend` subdirectory in your
Superset source folder run
```bash
npm i -S /tmp/superset-plugin-chart-hello-world
```
If you publish your package to npm, you can naturally install directly from there, too.
After this edit the `superset-frontend/src/visualizations/presets/MainPreset.js`
and make the following changes:
```js
import { SupersetPluginChartHelloWorld } from 'superset-plugin-chart-hello-world';
```
to import the viz plugin and later add the following to the array that's passed to the
`plugins` property:
```js
new SupersetPluginChartHelloWorld().configure({ key: 'ext-hello-world' }),
```
After that the viz plugin should show up when you run Superset, e.g. the development
server:
```bash
npm run dev-server
```
## Testing
### Python Testing
`pytest`, backend by docker-compose is how we recommend running tests locally.
For a more complex test matrix (against different database backends, python versions, ...) you
can rely on our GitHub Actions by simply opening a draft pull request.
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.
There is also a utility script included in the Superset codebase to run python integration tests. The [readme can be
found here](https://github.com/apache/superset/tree/master/scripts/tests)
To run all integration tests for example, run this script from the root directory:
```bash
scripts/tests/run.sh
```
You can run unit tests found in './tests/unit_tests' for example with pytest. It is a simple way to run an isolated test that doesn't need any database setup
```bash
pytest ./link_to_test.py
```
#### Testing with local Presto connections
If you happen to change db engine spec for Presto/Trino, you can run a local Presto cluster with Docker:
```bash
docker run -p 15433:15433 starburstdata/presto:350-e.6
```
Then update `SUPERSET__SQLALCHEMY_EXAMPLES_URI` to point to local Presto cluster:
```bash
export SUPERSET__SQLALCHEMY_EXAMPLES_URI=presto://localhost:15433/memory/default
```
### Frontend Testing
We use [Jest](https://jestjs.io/) and [Enzyme](https://airbnb.io/enzyme/) to test TypeScript/JavaScript. Tests can be run with:
```bash
cd superset-frontend
npm run test
```
To run a single test file:
```bash
npm run test -- path/to/file.js
```
### E2E Integration Testing
For E2E testing, we recommend that you use a `docker compose` backend
```bash
CYPRESS_CONFIG=true docker compose up --build
```
`docker compose` will get to work and expose a Cypress-ready Superset app.
This app uses a different database schema (`superset_cypress`) to keep it isolated from
your other dev environmen(s)t, a specific set of examples, and a set of configurations that
aligns with the expectations within the end-to-end tests. Also note that it's served on a
different port than the default port for the backend (`8088`).
Now in another terminal, let's get ready to execute some Cypress commands. First, tell cypress
to connect to the Cypress-ready Superset backend.
```
CYPRESS_BASE_URL=http://localhost:8081
```
```bash
# superset-frontend/cypress-base is the base folder for everything Cypress-related
# It's essentially its own npm app, with its own dependencies, configurations and utilities
cd superset-frontend/cypress-base
npm install
# use interactive mode to run tests, while keeping memory usage contained
# this will fire up an interactive Cypress UI
# as you alter the code, the tests will re-run automatically, and you can visualize each
# and every step for debugging purposes
npx cypress open --config numTestsKeptInMemory=5
# to run the test suite on the command line using chrome (same as CI)
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
```
See [`superset-frontend/cypress_build.sh`](https://github.com/apache/superset/blob/master/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:
```bash
cd cypress-base
npm install
npm run cypress open
```
### Debugging Server App
Follow these instructions to debug the Flask app running inside a docker container.
First add the following to the ./docker-compose.yaml file
```diff
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: "${CYPRESS_CONFIG}"
```
Start Superset as usual
```bash
docker compose up
```
Install the required libraries and packages to the docker container
Enter the superset_app container
```bash
docker exec -it superset_app /bin/bash
root@39ce8cf9d6ab:/app#
```
Run the following commands inside the container
```bash
apt update
apt install -y gdb
apt install -y net-tools
pip install debugpy
```
Find the PID for the Flask process. Make sure to use the first PID. The Flask app will re-spawn a sub-process every time you change any of the python code. So it's important to use the first PID.
```bash
ps -ef
UID PID PPID C STIME TTY TIME CMD
root 1 0 0 14:09 ? 00:00:00 bash /app/docker/docker-bootstrap.sh app
root 6 1 4 14:09 ? 00:00:04 /usr/local/bin/python /usr/bin/flask run -p 8088 --with-threads --reload --debugger --host=0.0.0.0
root 10 6 7 14:09 ? 00:00:07 /usr/local/bin/python /usr/bin/flask run -p 8088 --with-threads --reload --debugger --host=0.0.0.0
```
Inject debugpy into the running Flask process. In this case PID 6.
```bash
python3 -m debugpy --listen 0.0.0.0:5678 --pid 6
```
Verify that debugpy is listening on port 5678
```bash
netstat -tunap
Active Internet connections (servers and established)
Proto Recv-Q Send-Q Local Address Foreign Address State PID/Program name
tcp 0 0 0.0.0.0:5678 0.0.0.0:* LISTEN 462/python
tcp 0 0 0.0.0.0:8088 0.0.0.0:* LISTEN 6/python
```
You are now ready to attach a debugger to the process. Using VSCode you can configure a launch configuration file .vscode/launch.json like so.
```json
{
"version": "0.2.0",
"configurations": [
{
"name": "Attach to Superset App in Docker Container",
"type": "python",
"request": "attach",
"connect": {
"host": "127.0.0.1",
"port": 5678
},
"pathMappings": [
{
"localRoot": "${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 "wakeup" 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.
```yaml
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.
```bash
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 to 127.0.0.1:5678 which is forwarded by kubectl to your remote kubernetes POD.
### Storybook
Superset includes a [Storybook](https://storybook.js.org/) to preview the layout/styling of various Superset components, and variations thereof. To open and view the Storybook:
```bash
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.
## Contributing Translations
We use [Flask-Babel](https://python-babel.github.io/flask-babel/) to translate Superset.
In Python files, we use the following
[translation functions](https://python-babel.github.io/flask-babel/#using-translations)
from `Flask-Babel`:
- `gettext` and `lazy_gettext` (usually aliased to `_`): for translating singular
strings.
- `ngettext`: for translating strings that might become plural.
```python
from flask_babel import lazy_gettext as _
```
then wrap the 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).
```javascript
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.
```python
LANGUAGES = {
'en': {'flag': 'us', 'name': 'English'},
'fr': {'flag': 'fr', 'name': 'French'},
'zh': {'flag': 'cn', 'name': 'Chinese'},
}
```
### Creating a new language dictionary
First check if the language code for your target language already exists. Check if the
[two letter ISO 639-1 code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)
for your target language already exists in the `superset/translations` directory:
```bash
ls superset/translations | grep -E "^[a-z]{2}\/"
```
If your language already has a preexisting translation, skip to the next section
The following languages are already supported by Flask AppBuilder, and will make it
easier to translate the application to your target language:
[Flask AppBuilder i18n documentation](https://flask-appbuilder.readthedocs.io/en/latest/i18n.html)
To create a dictionary for a new language, first make sure the necessary dependencies are installed:
```bash
pip install -r superset/translations/requirements.txt
```
Then run the following, where `LANGUAGE_CODE` is replaced with the language code for your target
language:
```bash
pybabel init -i superset/translations/messages.pot -d superset/translations -l LANGUAGE_CODE
```
For instance, to add a translation for Finnish (language code `fi`), run the following:
```bash
pybabel init -i superset/translations/messages.pot -d superset/translations -l fi
```
### Extracting new strings for translation
Periodically, when working on translations, we need to extract the strings from both the
backend and the frontend to compile a list of all strings to be translated. It doesn't
happen automatically and is a required step to gather the strings and get them into the
`.po` files where they can be translated, so that they can then be compiled.
This script does just that:
```bash
./scripts/translations/babel_update.sh
```
### Updating language files
Run the following command to update the language files with the new extracted strings.
```bash
pybabel update -i superset/translations/messages.pot -d superset/translations --ignore-obsolete
```
You can then translate the strings gathered in files located under
`superset/translation`, where there's one folder per language. You can use [Poedit](https://poedit.net/features)
to translate the `po` file more conveniently.
Here is [a tutorial](https://web.archive.org/web/20220517065036/https://wiki.lxde.org/en/Translate_*.po_files_with_Poedit).
To perform the translation on MacOS, you can install `poedit` via Homebrew:
```bash
brew install poedit
```
After this, just start the `poedit` application and open the `messages.po` file. In the
case of the Finnish translation, this would be `superset/translations/fi/LC_MESSAGES/messages.po`.
### Applying translations
To make the translations available on the frontend, we need to convert the PO file into
a collection of JSON files. To convert all PO files to formatted JSON files you can use
the `build-translation` script
```bash
# Install dependencies if you haven't already
cd superset-frontend/ && npm ci
# Compile translations for the frontend
npm run build-translation
```
Finally, for the translations to take effect we need to compile translation catalogs into
binary MO files for the backend using `pybabel`.
```bash
# inside the project root
pybabel compile -d superset/translations
```
## Linting
### Python
We use [ruff](https://github.com/astral-sh/ruff) for linting which can be invoked via:
```
# auto-reformat using ruff
ruff format
# lint check with ruff
ruff check
# lint fix with ruff
ruff check --fix
```
Ruff configuration is located in our
(pyproject.toml)[https://github.com/apache/superset/blob/master/pyproject.toml] file
All this is configured to run in pre-commit hooks, which we encourage you to setup
with `pre-commit install`
### TypeScript
```bash
cd superset-frontend
npm ci
# run eslint checks
npm run eslint -- .
# run tsc (typescript) checks
npm run type
```
If using the eslint extension with vscode, put the following in your workspace `settings.json` file:
```json
"eslint.workingDirectories": [
"superset-frontend"
]
```
## GitHub Ephemeral Environments
On any given pull request on GitHub, it's possible to create a temporary environment/deployment
by simply adding the label `testenv-up` to the PR. Once you add the `testenv-up` label, a
GitHub Action will be triggered that will:
- build a docker image
- deploy it in EC2 (sponsored by the folks at [Preset](https://preset.io))
- write a comment on the PR with a link to the ephemeral environment
For more advanced use cases, it's possible to set a feature flag on the PR body, which will
take effect on the ephemeral environment. For example, if you want to set the `TAGGING_SYSTEM`
feature flag to `true`, you can add the following line to the PR body/description:
```
FEATURE_TAGGING_SYSTEM=true
```
Simarly, it's possible to disable feature flags with:
```
FEATURE_TAGGING_SYSTEM=false
```

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# Miscellaneous
## Reporting a Security Vulnerability
Please report security vulnerabilities to private@superset.apache.org.
In the event a community member discovers a security flaw in Superset, it is important to follow the [Apache Security Guidelines](https://www.apache.org/security/committers.html) 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.
## 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`)
```python
from flask_caching.backends.filesystemcache import FileSystemCache
RESULTS_BACKEND = FileSystemCache('/tmp/sqllab')
```
- Start up a celery worker
```shell script
celery --app=superset.tasks.celery_app:app worker -O fair
```
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
## Async Chart Queries
It's possible to configure database queries for charts to operate in `async` mode. This is especially useful for dashboards with many charts that may otherwise be affected by browser connection limits. To enable async queries for dashboards and Explore, the following dependencies are required:
- Redis 5.0+ (the feature utilizes [Redis Streams](https://redis.io/topics/streams-intro))
- Cache backends enabled via the `CACHE_CONFIG` and `DATA_CACHE_CONFIG` config settings
- Celery workers configured and running to process async tasks

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import InteractiveSVG from '../../../src/components/InteractiveERDSVG';
import Mermaid from '@theme/Mermaid';
# Resources
## High Level Architecture
<div style={{ maxWidth: "600px", margin: "0 auto", marginLeft: 0, marginRight: "auto" }}>
```mermaid
flowchart TD
%% Top Level
LB["<b>Load Balancer(s)</b><br/>(optional)"]
LB -.-> WebServers
%% Web Servers
subgraph WebServers ["<b>Web Server(s)</b>"]
WS1["<b>Frontend</b><br/>(React, AntD, ECharts, AGGrid)"]
WS2["<b>Backend</b><br/>(Python, Flask, SQLAlchemy, Pandas, ...)"]
end
%% Infra
subgraph InfraServices ["<b>Infra</b>"]
DB[("<b>Metadata Database</b><br/>(Postgres / MySQL)")]
subgraph Caching ["<b>Caching Subservices<br/></b>(Redis, memcache, S3, ...)"]
direction LR
DummySpace[" "]:::invisible
QueryCache["<b>Query Results Cache</b><br/>(Accelerated Dashboards)"]
CsvCache["<b>CSV Exports Cache</b>"]
ThumbnailCache["<b>Thumbnails Cache</b>"]
AlertImageCache["<b>Alert/Report Images Cache</b>"]
QueryCache -- " " --> CsvCache
linkStyle 1 stroke:transparent;
ThumbnailCache -- " " --> AlertImageCache
linkStyle 2 stroke:transparent;
end
Broker(("<b>Message Queue</b><br/>(Redis / RabbitMQ / SQS)"))
end
AsyncBackend["<b>Async Workers (Celery)</b><br>required for Alerts & Reports, thumbnails, CSV exports, long-running workloads, ..."]
%% External DBs
subgraph ExternalDatabases ["<b>Analytics Databases</b>"]
direction LR
BigQuery[(BigQuery)]
Snowflake[(Snowflake)]
Redshift[(Redshift)]
Postgres[(Postgres)]
Postgres[(... any ...)]
end
%% Connections
LB -.-> WebServers
WebServers --> DB
WebServers -.-> Caching
WebServers -.-> Broker
WebServers -.-> ExternalDatabases
Broker -.-> AsyncBackend
AsyncBackend -.-> ExternalDatabases
AsyncBackend -.-> Caching
%% Legend styling
classDef requiredNode stroke-width:2px,stroke:black;
class Required requiredNode;
class Optional optionalNode;
%% Hide real arrow
linkStyle 0 stroke:transparent;
%% Styling
classDef optionalNode stroke-dasharray: 5 5, opacity:0.9;
class LB optionalNode;
class Caching optionalNode;
class AsyncBackend optionalNode;
class Broker optionalNode;
class QueryCache optionalNode;
class CsvCache optionalNode;
class ThumbnailCache optionalNode;
class AlertImageCache optionalNode;
class Celery optionalNode;
classDef invisible fill:transparent,stroke:transparent;
```
</div>
## Entity-Relationship Diagram
Here is our interactive ERD:
<InteractiveSVG />
<br />
[Download the .svg](https://github.com/apache/superset/tree/master/docs/static/img/erd.svg)

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# FAQ
## How big of a dataset can Superset handle?
Superset can work with even gigantic databases! Superset acts as a thin layer above your underlying
databases or data engines, which do all the processing. Superset simply visualizes the results of
the query.
The key to achieving acceptable performance in Superset is whether your database can execute queries
and return results at a speed that is acceptable to your users. If you experience slow performance with
Superset, benchmark and tune your data warehouse.
## What are the computing specifications required to run Superset?
The specs of your Superset installation depend on how many users you have and what their activity is, not
on the size of your data. Superset admins in the community have reported 8GB RAM, 2vCPUs as adequate to
run a moderately-sized instance. To develop Superset, e.g., compile code or build images, you may
need more power.
Monitor your resource usage and increase or decrease as needed. Note that Superset usage has a tendency
to occur in spikes, e.g., if everyone in a meeting loads the same dashboard at once.
Superset's application metadata does not require a very large database to store it, though
the log file grows over time.
## Can I join / query multiple tables at one time?
Not in the Explore or Visualization UI. A Superset SQLAlchemy datasource can only be a single table
or a view.
When working with tables, the solution would be to create a table that contains all the fields
needed for your analysis, most likely through some scheduled batch process.
A view is a simple logical layer that abstracts an arbitrary SQL queries as a virtual table. This can
allow you to join and union multiple tables and to apply some transformation using arbitrary SQL
expressions. The limitation there is your database performance, as Superset effectively will run a
query on top of your query (view). A good practice may be to limit yourself to joining your main
large table to one or many small tables only, and avoid using _GROUP BY_ where possible as Superset
will do its own _GROUP BY_ and doing the work twice might slow down performance.
Whether you use a table or a view, performance depends on how fast your database can deliver
the result to users interacting with Superset.
However, if you are using SQL Lab, there is no such limitation. You can write SQL queries to join
multiple tables as long as your database account has access to the tables.
## How do I create my own visualization?
We recommend reading the instructions in
[Creating Visualization Plugins](/docs/contributing/howtos#creating-visualization-plugins).
## Can I upload and visualize CSV data?
Absolutely! Read the instructions [here](/docs/using-superset/exploring-data) to learn
how to enable and use CSV upload.
## Why are my queries timing out?
There are many possible causes for why a long-running query might time out.
For running long query from Sql Lab, by default Superset allows it run as long as 6 hours before it
being killed by celery. If you want to increase the time for running query, you can specify the
timeout in configuration. For example:
```python
SQLLAB_ASYNC_TIME_LIMIT_SEC = 60 * 60 * 6
```
If you are seeing timeouts (504 Gateway Time-out) when loading dashboard or explore slice, you are
probably behind gateway or proxy server (such as Nginx). If it did not receive a timely response
from Superset server (which is processing long queries), these web servers will send 504 status code
to clients directly. Superset has a client-side timeout limit to address this issue. If query didn’t
come back within client-side timeout (60 seconds by default), Superset will display warning message
to avoid gateway timeout message. If you have a longer gateway timeout limit, you can change the
timeout settings in **superset_config.py**:
```python
SUPERSET_WEBSERVER_TIMEOUT = 60
```
## Why is the map not visible in the geospatial visualization?
You need to register a free account at [Mapbox.com](https://www.mapbox.com), obtain an API key, and add it
to **.env** at the key MAPBOX_API_KEY:
```python
MAPBOX_API_KEY = "longstringofalphanumer1c"
```
## How to limit the timed refresh on a dashboard?
By default, the dashboard timed refresh feature allows you to automatically re-query every slice on
a dashboard according to a set schedule. Sometimes, however, you won’t want all of the slices to be
refreshed - especially if some data is slow moving, or run heavy queries. To exclude specific slices
from the timed refresh process, add the `timed_refresh_immune_slices` key to the dashboard JSON
Metadata field:
```json
{
"filter_immune_slices": [],
"expanded_slices": {},
"filter_immune_slice_fields": {},
"timed_refresh_immune_slices": [324]
}
```
In the example above, if a timed refresh is set for the dashboard, then every slice except 324 will
be automatically re-queried on schedule.
Slice refresh will also be staggered over the specified period. You can turn off this staggering by
setting the `stagger_refresh` to false and modify the stagger period by setting `stagger_time` to a
value in milliseconds in the JSON Metadata field:
```json
{
"stagger_refresh": false,
"stagger_time": 2500
}
```
Here, the entire dashboard will refresh at once if periodic refresh is on. The stagger time of 2.5
seconds is ignored.
**Why does ‘flask fab’ or superset freeze/hang/not responding when started (my home directory is
NFS mounted)?**
By default, Superset creates and uses an SQLite database at `~/.superset/superset.db`. SQLite is
known to [not work well if used on NFS](https://www.sqlite.org/lockingv3.html) due to broken file
locking implementation on NFS.
You can override this path using the **SUPERSET_HOME** environment variable.
Another workaround is to change where superset stores the sqlite database by adding the following in
`superset_config.py`:
```python
SQLALCHEMY_DATABASE_URI = 'sqlite:////new/location/superset.db?check_same_thread=false'
```
You can read more about customizing Superset using the configuration file
[here](/docs/configuration/configuring-superset).
## What if the table schema changed?
Table schemas evolve, and Superset needs to reflect that. It’s pretty common in the life cycle of a
dashboard to want to add a new dimension or metric. To get Superset to discover your new columns,
all you have to do is to go to **Data -> Datasets**, click the edit icon next to the dataset
whose schema has changed, and hit **Sync columns from source** from the **Columns** tab.
Behind the scene, the new columns will get merged. Following this, you may want to re-edit the
table afterwards to configure the Columns tab, check the appropriate boxes and save again.
## What database engine can I use as a backend for Superset?
To clarify, the database backend is an OLTP database used by Superset to store its internal
information like your list of users and dashboard definitions. While Superset supports a
[variety of databases as data _sources_](/docs/configuration/databases#installing-database-drivers),
only a few database engines are supported for use as the OLTP backend / metadata store.
Superset is tested using MySQL, PostgreSQL, and SQLite backends. It’s recommended you install
Superset on one of these database servers for production. Installation on other OLTP databases
may work but isn’t tested. It has been reported that [Microsoft SQL Server does _not_
work as a Superset backend](https://github.com/apache/superset/issues/18961). Column-store,
non-OLTP databases are not designed for this type of workload.
## How can I configure OAuth authentication and authorization?
You can take a look at this Flask-AppBuilder
[configuration example](https://github.com/dpgaspar/Flask-AppBuilder/blob/master/examples/oauth/config.py).
## Is there a way to force the dashboard to use specific colors?
It is possible on a per-dashboard basis by providing a mapping of labels to colors in the JSON
Metadata attribute using the `label_colors` key. You can use either the full hex color, a named color,
like `red`, `coral` or `lightblue`, or the index in the current color palette (0 for first color, 1 for
second etc). Example:
```json
{
"label_colors": {
"foo": "#FF69B4",
"bar": "lightblue",
"baz": 0
}
}
```
## Does Superset work with [insert database engine here]?
The [Connecting to Databases section](/docs/configuration/databases) provides the best
overview for supported databases. Database engines not listed on that page may work too. We rely on
the community to contribute to this knowledge base.
For a database engine to be supported in Superset through the SQLAlchemy connector, it requires
having a Python compliant [SQLAlchemy dialect](https://docs.sqlalchemy.org/en/13/dialects/) as well
as a [DBAPI driver](https://www.python.org/dev/peps/pep-0249/) defined. Database that have limited
SQL support may work as well. For instance it’s possible to connect to Druid through the SQLAlchemy
connector even though Druid does not support joins and subqueries. Another key element for a
database to be supported is through the Superset Database Engine Specification interface. This
interface allows for defining database-specific configurations and logic that go beyond the
SQLAlchemy and DBAPI scope. This includes features like:
- date-related SQL function that allow Superset to fetch different time granularities when running
time-series queries
- whether the engine supports subqueries. If false, Superset may run 2-phase queries to compensate
for the limitation
- methods around processing logs and inferring the percentage of completion of a query
- technicalities as to how to handle cursors and connections if the driver is not standard DBAPI
Beyond the SQLAlchemy connector, it’s also possible, though much more involved, to extend Superset
and write your own connector. The only example of this at the moment is the Druid connector, which
is getting superseded by Druid’s growing SQL support and the recent availability of a DBAPI and
SQLAlchemy driver. If the database you are considering integrating has any kind of SQL support,
it’s probably preferable to go the SQLAlchemy route. Note that for a native connector to be possible
the database needs to have support for running OLAP-type queries and should be able to do things that
are typical in basic SQL:
- aggregate data
- apply filters
- apply HAVING-type filters
- be schema-aware, expose columns and types
## Does Superset offer a public API?
Yes, a public REST API, and the surface of that API formal is expanding steadily. You can read more about this API and
interact with it using Swagger [here](/docs/api).
Some of the
original vision for the collection of endpoints under **/api/v1** was originally specified in
[SIP-17](https://github.com/apache/superset/issues/7259) and constant progress has been
made to cover more and more use cases.
The API available is documented using [Swagger](https://swagger.io/) and the documentation can be
made available under **/swagger/v1** by enabling the following flag in `superset_config.py`:
```python
FAB_API_SWAGGER_UI = True
```
There are other undocumented [private] ways to interact with Superset programmatically that offer no
guarantees and are not recommended but may fit your use case temporarily:
- using the ORM (SQLAlchemy) directly
- using the internal FAB ModelView API (to be deprecated in Superset)
- altering the source code in your fork
## How can I see usage statistics (e.g., monthly active users)?
This functionality is not included with Superset, but you can extract and analyze Superset's application
metadata to see what actions have occurred. By default, user activities are logged in the `logs` table
in Superset's metadata database. One company has published a write-up of [how they analyzed Superset
usage, including example queries](https://engineering.hometogo.com/monitor-superset-usage-via-superset-c7f9fba79525).
## What Does Hours Offset in the Edit Dataset view do?
In the Edit Dataset view, you can specify a time offset. This field lets you configure the
number of hours to be added or subtracted from the time column.
This can be used, for example, to convert UTC time to local time.
## Does Superset collect any telemetry data?
Superset uses [Scarf](https://about.scarf.sh/) by default to collect basic telemetry data upon installing and/or running Superset. This data helps the maintainers of Superset better understand which versions of Superset are being used, in order to prioritize patch/minor releases and security fixes.
We use the [Scarf Gateway](https://docs.scarf.sh/gateway/) to sit in front of container registries, the [scarf-js](https://about.scarf.sh/package-sdks) package to track `npm` installations, and a Scarf pixel to gather anonymous analytics on Superset page views.
Scarf purges PII and provides aggregated statistics. Superset users can easily opt out of analytics in various ways documented [here](https://docs.scarf.sh/gateway/#do-not-track) and [here](https://docs.scarf.sh/package-analytics/#as-a-user-of-a-package-using-scarf-js-how-can-i-opt-out-of-analytics).
Superset maintainers can also opt out of telemetry data collection by setting the `SCARF_ANALYTICS` environment variable to `false` in the Superset container (or anywhere Superset/webpack are run).
Additional opt-out instructions for Docker users are available on the [Docker Installation](/docs/installation/docker-compose) page.
## Does Superset have an archive panel or trash bin from which a user can recover deleted assets?
No. Currently, there is no way to recover a deleted Superset dashboard/chart/dataset/database from the UI. However, there is an [ongoing discussion](https://github.com/apache/superset/discussions/18386) about implementing such a feature.
Hence, it is recommended to take periodic backups of the metadata database. For recovery, you can launch a recovery instance of a Superset server with the backed-up copy of the DB attached and use the Export Dashboard button in the Superset UI (or the `superset export-dashboards` CLI command). Then, take the .zip file and import it into the current Superset instance.
Alternatively, you can programmatically take regular exports of the assets as a backup.
## I ran a security scan of the Superset container image and it showed dozens of "high" and "critical" vulnerabilities! Can you release a version of Superset without these?
You are talking about dependency CVEs: identified vulnerabilities in software that Superset uses. Most of these CVEs are in the Linux kernel or Python, both of which have many other people working on their security.
We address these dependency CVEs as best we can by regularly updating our dependencies to newer versions. We use bots to assist with that and cheerfully welcome pull requests from humans that fix dependency CVEs.
The Superset [security team](https://superset.apache.org/docs/security/#reporting-security-vulnerabilities) focuses primarily on vulnerabilities _in Superset itself_. See our [CVEs page](https://superset.apache.org/docs/security/cves) for a list of past Superset CVEs.

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import useBaseUrl from "@docusaurus/useBaseUrl";
# Architecture
This page is meant to give new administrators an understanding of Superset's components.
## Components
A Superset installation is made up of these components:
1. The Superset application itself
2. A metadata database
3. A caching layer (optional, but necessary for some features)
4. A worker & beat (optional, but necessary for some features)
### Optional components and associated features
The optional components above are necessary to enable these features:
- [Alerts and Reports](/docs/configuration/alerts-reports)
- [Caching](/docs/configuration/cache)
- [Async Queries](/docs/configuration/async-queries-celery/)
- [Dashboard Thumbnails](/docs/configuration/cache/#caching-thumbnails)
If you install with Kubernetes or Docker Compose, all of these components will be created.
However, installing from PyPI only creates the application itself. Users installing from PyPI will need to configure a caching layer, worker, and beat on their own if they wish to enable the above features. Configuration of those components for a PyPI install is not currently covered in this documentation.
Here are further details on each component.
### The Superset Application
This is the core application. Superset operates like this:
- A user visits a chart or dashboard
- That triggers a SQL query to the data warehouse holding the underlying dataset
- The resulting data is served up in a data visualization
- The Superset application is comprised of the Python (Flask) backend application (server), API layer, and the React frontend, built via Webpack, and static assets needed for the application to work
### Metadata Database
This is where chart and dashboard definitions, user information, logs, etc. are stored. Superset is tested to work with PostgreSQL and MySQL databases as the metadata database (not be confused with a data source like your data warehouse, which could be a much greater variety of options like Snowflake, Redshift, etc.).
Some installation methods like our Quickstart and PyPI come configured by default to use a SQLite on-disk database. And in a Docker Compose installation, the data would be stored in a PostgreSQL container volume. Neither of these cases are recommended for production instances of Superset.
For production, a properly-configured, managed, standalone database is recommended. No matter what database you use, you should plan to back it up regularly.
### Caching Layer
The caching layer serves two main functions:
- Store the results of queries to your data warehouse so that when a chart is loaded twice, it pulls from the cache the second time, speeding up the application and reducing load on your data warehouse.
- Act as a message broker for the worker, enabling the Alerts & Reports, async queries, and thumbnail caching features.
Most people use Redis for their cache, but Superset supports other options too. See the [cache docs](/docs/configuration/cache/) for more.
### Worker and Beat
This is one or more workers who execute tasks like run async queries or take snapshots of reports and send emails, and a "beat" that acts as the scheduler and tells workers when to perform their tasks. Most installations use Celery for these components.
## Other components
Other components can be incorporated into Superset. The best place to learn about additional configurations is the [Configuration page](/docs/configuration/configuring-superset). For instance, you could set up a load balancer or reverse proxy to implement HTTPS in front of your Superset application, or specify a Mapbox URL to enable geospatial charts, etc.
Superset won't even start without certain configuration settings established, so it's essential to review that page.

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---
title: Docker Builds
hide_title: true
sidebar_position: 7
version: 1
---
# Docker builds, images and tags
The Apache Superset community extensively uses Docker for development, release,
and productionizing Superset. This page details our Docker builds and tag naming
schemes to help users navigate our offerings.
Images are built and pushed to the [Superset Docker Hub repository](
https://hub.docker.com/r/apache/superset) using GitHub Actions.
Different sets of images are built and/or published at different times:
- **Published releases** (`release`): published using
tags like `5.0.0` and the `latest` tag.
- **Pull request iterations** (`pull_request`): for each pull request, while
we actively build the docker to validate the build, we do
not publish those images for security reasons, we simply `docker build --load`
- **Merges to the main branch** (`push`): resulting in new SHAs, with tags
prefixed with `master` for the latest `master` version.
## Build presets
We have a set of build "presets" that each represent a combination of
parameters for the build, mostly pointing to either different target layer
for the build, and/or base image.
Here are the build presets that are exposed through the `supersetbot docker` utility:
- `lean`: The default Docker image, including both frontend and backend. Tags
without a build_preset are lean builds (ie: `latest`, `5.0.0`, `4.1.2`, ...). `lean`
builds do not contain database
drivers, meaning you need to install your own. That applies to analytics databases **AND
the metadata database**. You'll likely want to layer either `mysqlclient` or `psycopg2-binary`
depending on the metadata database you choose for your installation, plus the required
drivers to connect to your analytics database(s).
- `dev`: For development, with a headless browser, dev-related utilities and root access. This
includes some commonly used database drivers like `mysqlclient`, `psycopg2-binary` and
some other used for development/CI
- `py311`, e.g., Py311: Similar to lean but with a different Python version (in this example, 3.11).
- `ci`: For certain CI workloads.
- `websocket`: For Superset clusters supporting advanced features.
- `dockerize`: Used by Helm in initContainers to wait for database dependencies to be available.
## Key tags examples
- `latest`: The latest official release build
- `latest-dev`: the `-dev` image of the latest official release build, with a
headless browser and root access.
- `master`: The latest build from the `master` branch, implicitly the lean build
preset
- `master-dev`: Similar to `master` but includes a headless browser and root access.
- `pr-5252`: The latest commit in PR 5252.
- `30948dc401b40982cb7c0dbf6ebbe443b2748c1b-dev`: A build for
this specific SHA, which could be from a `master` merge, or release.
- `websocket-latest`: The WebSocket image for use in a Superset cluster.
For insights or modifications to the build matrix and tagging conventions,
check the [supersetbot docker](https://github.com/apache-superset/supersetbot)
subcommand and the [docker.yml](https://github.com/apache/superset/blob/master/.github/workflows/docker.yml)
GitHub action.
## Building your own production Docker image
Every Superset deployment will require its own set of drivers depending on the data warehouse(s),
etc. so we recommend that users build their own Docker image by extending the `lean` image.
Here's an example Dockerfile that does this. Follow the in-line comments to customize it for
your desired Superset version and database drivers. The comments also note that a certain feature flag will
have to be enabled in your config file.
You would build the image with `docker build -t mysuperset:latest .` or `docker build -t ourcompanysuperset:5.0.0 .`
```Dockerfile
# change this to apache/superset:5.0.0 or whatever version you want to build from;
# otherwise the default is the latest commit on GitHub master branch
FROM apache/superset:master
USER root
# Set environment variable for Playwright
ENV PLAYWRIGHT_BROWSERS_PATH=/usr/local/share/playwright-browsers
# Install packages using uv into the virtual environment
# Superset started using uv after the 4.1 branch; if you are building from apache/superset:4.1.x or an older version,
# replace the first two lines with RUN pip install \
RUN . /app/.venv/bin/activate && \
uv pip install \
# install psycopg2 for using PostgreSQL metadata store - could be a MySQL package if using that backend:
psycopg2-binary \
# add the driver(s) for your data warehouse(s), in this example we're showing for Microsoft SQL Server:
pymssql \
# package needed for using single-sign on authentication:
Authlib \
# openpyxl to be able to upload Excel files
openpyxl \
# Pillow for Alerts & Reports to generate PDFs of dashboards
Pillow \
# install Playwright for taking screenshots for Alerts & Reports. This assumes the feature flag PLAYWRIGHT_REPORTS_AND_THUMBNAILS is enabled
# That feature flag will default to True starting in 6.0.0
# Playwright works only with Chrome.
# If you are still using Selenium instead of Playwright, you would instead install here the selenium package and a headless browser & webdriver
playwright \
&& playwright install-deps \
&& PLAYWRIGHT_BROWSERS_PATH=/usr/local/share/playwright-browsers playwright install chromium
# Switch back to the superset user
USER superset
CMD ["/app/docker/entrypoints/run-server.sh"]
```
## Key ARGs in Dockerfile
- `BUILD_TRANSLATIONS`: whether to build the translations into the image. For the
frontend build this tells webpack to strip out all locales other than `en` from
the `moment-timezone` library. For the backendthis skips compiling the
`*.po` translation files
- `DEV_MODE`: whether to skip the frontend build, this is used by our `docker-compose` dev setup
where we mount the local volume and build using `webpack` in `--watch` mode, meaning as you
alter the code in the local file system, webpack, from within a docker image used for this
purpose, will constantly rebuild the frontend as you go. This ARG enables the initial
`docker-compose` build to take much less time and resources
- `INCLUDE_CHROMIUM`: whether to include chromium in the backend build so that it can be
used as a headless browser for workloads related to "Alerts & Reports" and thumbnail generation
- `INCLUDE_FIREFOX`: same as above, but for firefox
- `PY_VER`: specifying the base image for the python backend, we don't recommend altering
this setting if you're not working on forwards or backwards compatibility
## Caching
To accelerate builds, we follow Docker best practices and use `apache/superset-cache`.
## About database drivers
Our docker images come with little to zero database driver support since
each environment requires different drivers, and maintaining a build with
wide database support would be both challenging (dozens of databases,
python drivers, and os dependencies) and inefficient (longer
build times, larger images, lower layer cache hit rate, ...).
For production use cases, we recommend that you derive our `lean` image(s) and
add database support for the database you need.
## On supporting different platforms (namely arm64 AND amd64)
Currently all automated builds are multi-platform, supporting both `linux/arm64`
and `linux/amd64`. This enables higher level constructs like `helm` and
`docker compose` to point to these images and effectively be multi-platform
as well.
Pull requests and master builds
are one-image-per-platform so that they can be parallelized and the
build matrix for those is more sparse as we don't need to build every
build preset on every platform, and generally can be more selective here.
For those builds, we suffix tags with `-arm` where it applies.
### Working with Apple silicon
Apple's current generation of computers uses ARM-based CPUs, and Docker
running on MACs seem to require `linux/arm64/v8` (at least one user's M2 was
configured in that way). Setting the environment
variable `DOCKER_DEFAULT_PLATFORM` to `linux/amd64` seems to function in
term of leveraging, and building upon the Superset builds provided here.
```bash
export DOCKER_DEFAULT_PLATFORM=linux/amd64
```
Presumably, `linux/arm64/v8` would be more optimized for this generation
of chips, but less compatible across the ARM ecosystem.

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title: Docker Compose
hide_title: true
sidebar_position: 5
version: 1
---
import useBaseUrl from "@docusaurus/useBaseUrl";
# Using Docker Compose
<img src={useBaseUrl("/img/docker-compose.webp" )} width="150" />
<br /><br />
:::caution
Since `docker compose` is primarily designed to run a set of containers on **a single host**
and can't support requirements for **high availability**, 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](https://minikube.sigs.k8s.io/docs/start/) along
with our [installing on k8s](https://superset.apache.org/docs/installation/running-on-kubernetes)
documentation.
:::
As mentioned in our [quickstart guide](/docs/quickstart), the fastest way to try
Superset locally is using Docker Compose on a Linux or Mac OSX
computer. Superset does not have official support for Windows. It's also the easiest
way to launch a fully functioning **development environment** quickly.
Note that there are 4 major ways we support to run `docker compose`:
1. **docker-compose.yml:** for interactive development, where we mount your local folder with the
frontend/backend files that you can edit and experience the changes you
make in the app in real time
1. **docker-compose-light.yml:** a lightweight configuration with minimal services (database,
Superset app, and frontend dev server) for development. Uses in-memory caching instead of Redis
and is designed for running multiple instances simultaneously
1. **docker-compose-non-dev.yml** where we just build a more immutable image based on the
local branch and get all the required images running. Changes in the local branch
at the time you fire this up will be reflected, but changes to the code
while `up` won't be reflected in the app
1. **docker-compose-image-tag.yml** where we fetch an image from docker-hub say for the
`5.0.0` release for instance, and fire it up so you can try it. Here what's in
the local branch has no effects on what's running, we just fetch and run
pre-built images from docker-hub. For `docker compose` to work along with the
Postgres image it boots up, you'll want to point to a `-dev`-suffixed TAG, as in
`export TAG=5.0.0-dev` or `export TAG=4.1.2-dev`, with `latest-dev` being the default.
The `dev` builds include the `psycopg2-binary` required to connect
to the Postgres database launched as part of the `docker compose` builds.
More on these approaches after setting up the requirements for either.
## Requirements
Note that this documentation assumes that you have [Docker](https://www.docker.com) and
[git](https://git-scm.com/) installed. Note also that we used to use `docker-compose` but that
is on the path to deprecation so we now use `docker compose` instead.
## 1. Clone Superset's GitHub repository
[Clone Superset's repo](https://github.com/apache/superset) in your terminal with the
following command:
```bash
git clone --depth=1 https://github.com/apache/superset.git
```
Once that command completes successfully, you should see a new `superset` folder in your
current directory.
## 2. Launch Superset Through Docker Compose
First let's assume you're familiar with `docker compose` mechanics. Here we'll refer generally
to `docker compose up` even though in some cases you may want to force a check for newer remote
images using `docker compose pull`, force a build with `docker compose build` or force a build
on latest base images using `docker compose build --pull`. In most cases though, the simple
`up` command should do just fine. Refer to docker compose docs for more information on the topic.
### Option #1 - for an interactive development environment
```bash
# The --build argument insures all the layers are up-to-date
docker compose up --build
```
:::tip
When running in development mode the `superset-node`
container needs to finish building assets in order for the UI to render properly. If you would just
like to try out Superset without making any code changes follow the steps documented for
`production` or a specific version below.
:::
:::tip
By default, we mount the local superset-frontend folder here and run `npm install` as well
as `npm run dev` which triggers webpack to compile/bundle the frontend code. Depending
on your local setup, especially if you have less than 16GB of memory, it may be very slow to
perform those operations. In this case, we recommend you set the env var
`BUILD_SUPERSET_FRONTEND_IN_DOCKER` to `false`, and to run this locally instead in a terminal.
Simply trigger `npm i && npm run dev`, this should be MUCH faster.
:::
:::tip
Sometimes, your npm-related state can get out-of-wack, running `npm run prune` from
the `superset-frontend/` folder will nuke the various' packages `node_module/` folders
and help you start fresh. In the context of `docker compose` setting
`export NPM_RUN_PRUNE=true` prior to running `docker compose up` will trigger that
from within docker. This will slow down the startup, but will fix various npm-related issues.
:::
### Option #2 - lightweight development with multiple instances
For a lighter development setup that uses fewer resources and supports running multiple instances:
```bash
# Single lightweight instance (default port 9001)
docker compose -f docker-compose-light.yml up
# Multiple instances with different ports
NODE_PORT=9001 docker compose -p superset-1 -f docker-compose-light.yml up
NODE_PORT=9002 docker compose -p superset-2 -f docker-compose-light.yml up
NODE_PORT=9003 docker compose -p superset-3 -f docker-compose-light.yml up
```
This configuration includes:
- PostgreSQL database (internal network only)
- Superset application server
- Frontend development server with webpack hot reloading
- In-memory caching (no Redis)
- Isolated volumes and networks per instance
Access each instance at `http://localhost:{NODE_PORT}` (e.g., `http://localhost:9001`).
### Option #3 - build a set of immutable images from the local branch
```bash
docker compose -f docker-compose-non-dev.yml up
```
### Option #4 - boot up an official release
```bash
# Set the version you want to run
export TAG=5.0.0
# Fetch the tag you're about to check out (assuming you shallow-cloned the repo)
git fetch --depth=1 origin tag $TAG
# Could also fetch all tags too if you've got bandwidth to spare
# git fetch --tags
# Checkout the corresponding git ref
git checkout $TAG
# Fire up docker compose
docker compose -f docker-compose-image-tag.yml up
```
Here various release tags, github SHA, and latest `master` can be referenced by the TAG env var.
Refer to the docker-related documentation to learn more about existing tags you can point to
from Docker Hub.
:::note
For option #2 and #3, we recommend checking out the release tag from the git repository
(ie: `git checkout 5.0.0`) for more guaranteed results. This ensures that the `docker-compose.*.yml`
configurations and that the mounted `docker/` scripts are in sync with the image you are
looking to fire up.
:::
## `docker compose` tips & configuration
:::caution
All of the content belonging to a Superset instance - charts, dashboards, users, etc. - is stored in
its metadata database. In production, this database should be backed up. The default installation
with docker compose will store that data in a PostgreSQL database contained in a Docker
[volume](https://docs.docker.com/storage/volumes/), which is not backed up.
Again, **THE DOCKER-COMPOSE INSTALLATION IS NOT PRODUCTION-READY OUT OF THE BOX.**
:::
You should see a stream of logging output from the containers being launched on your machine. Once
this output slows, you should have a running instance of Superset on your local machine! To avoid
the wall of text on future runs, add the `-d` option to the end of the `docker compose up` command.
### Configuring Further
The following is for users who want to configure how Superset runs in Docker Compose; otherwise, you
can skip to the next section.
You can install additional python packages and apply config overrides by following the steps
mentioned in [docker/README.md](https://github.com/apache/superset/tree/master/docker#configuration)
Note that `docker/.env` sets the default environment variables for all the docker images
used by `docker compose`, and that `docker/.env-local` can be used to override those defaults.
Also note that `docker/.env-local` is referenced in our `.gitignore`,
preventing developers from risking committing potentially sensitive configuration to the repository.
One important variable is `SUPERSET_LOAD_EXAMPLES` which determines whether the `superset_init`
container will populate example data and visualizations into the metadata database. These examples
are helpful for learning and testing out Superset but unnecessary for experienced users and
production deployments. The loading process can sometimes take a few minutes and a good amount of
CPU, so you may want to disable it on a resource-constrained device.
For more advanced or dynamic configurations that are typically managed in a `superset_config.py` file
located in your `PYTHONPATH`, note that it can be done by providing a
`docker/pythonpath_dev/superset_config_docker.py` that will be ignored by git
(preventing you to commit/push your local configuration back to the repository).
The mechanics of this are in `docker/pythonpath_dev/superset_config.py` where you can see
that the logic runs a `from superset_config_docker import *`
:::note
Users often want to connect to other databases from Superset. Currently, the easiest way to
do this is to modify the `docker-compose-non-dev.yml` file and add your database as a service that
the other services depend on (via `x-superset-depends-on`). Others have attempted to set
`network_mode: host` on the Superset services, but these generally break the installation,
because the configuration requires use of the Docker Compose DNS resolver for the service names.
If you have a good solution for this, let us know!
:::
:::note
Superset uses [Scarf Gateway](https://about.scarf.sh/scarf-gateway) to collect telemetry
data. Knowing the installation counts for different Superset versions informs the project's
decisions about patching and long-term support. Scarf purges personally identifiable information
(PII) and provides only aggregated statistics.
To opt-out of this data collection for packages downloaded through the Scarf Gateway by your docker
compose based installation, edit the `x-superset-image:` line in your `docker-compose.yml` and
`docker-compose-non-dev.yml` files, replacing `apachesuperset.docker.scarf.sh/apache/superset` with
`apache/superset` to pull the image directly from Docker Hub.
To disable the Scarf telemetry pixel, set the `SCARF_ANALYTICS` environment variable to `False` in
your terminal and/or in your `docker/.env` file.
:::
## 3. Log in to Superset
Your local Superset instance also includes a Postgres server to store your data and is already
pre-loaded with some example datasets that ship with Superset. You can access Superset now via your
web browser by visiting `http://localhost:8088`. Note that many browsers now default to `https` - if
yours is one of them, please make sure it uses `http`.
Log in with the default username and password:
```bash
username: admin
```
```bash
password: admin
```
## 4. Connecting Superset to your local database instance
When running Superset using `docker` or `docker compose` it runs in its own docker container, as if
the Superset was running in a separate machine entirely. Therefore attempts to connect to your local
database with the hostname `localhost` won't work as `localhost` refers to the docker container
Superset is running in, and not your actual host machine. Fortunately, docker provides an easy way
to access network resources in the host machine from inside a container, and we will leverage this
capability to connect to our local database instance.
Here the instructions are for connecting to postgresql (which is running on your host machine) from
Superset (which is running in its docker container). Other databases may have slightly different
configurations but gist would be same and boils down to 2 steps -
1. **(Mac users may skip this step)** Configuring the local postgresql/database instance to accept
public incoming connections. By default, postgresql only allows incoming connections from
`localhost` and under Docker, unless you use `--network=host`, `localhost` will refer to different
endpoints on the host machine and in a docker container respectively. Allowing postgresql to accept
connections from the Docker involves making one-line changes to the files `postgresql.conf` and
`pg_hba.conf`; you can find helpful links tailored to your OS / PG version on the web easily for
this task. For Docker it suffices to only whitelist IPs `172.0.0.0/8` instead of `*`, but in any
case you are _warned_ that doing this in a production database _may_ have disastrous consequences as
you are opening your database to the public internet.
1. Instead of `localhost`, try using `host.docker.internal` (Mac users, Ubuntu) or `172.18.0.1`
(Linux users) as the hostname when attempting to connect to the database. This is a Docker internal
detail -- what is happening is that, in Mac systems, Docker Desktop creates a dns entry for the
hostname `host.docker.internal` which resolves to the correct address for the host machine, whereas
in Linux this is not the case (at least by default). If neither of these 2 hostnames work then you
may want to find the exact hostname you want to use, for that you can do `ifconfig` or
`ip addr show` and look at the IP address of `docker0` interface that must have been created by
Docker for you. Alternately if you don't even see the `docker0` interface try (if needed with sudo)
`docker network inspect bridge` and see if there is an entry for `"Gateway"` and note the IP
address.
## 4. To build or not to build
When running `docker compose up`, docker will build what is required behind the scene, but
may use the docker cache if assets already exist. Running `docker compose build` prior to
`docker compose up` or the equivalent shortcut `docker compose up --build` ensures that your
docker images matche the definition in the repository. This should only apply to the main
docker-compose.yml file (default) and not to the alternative methods defined above.

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title: Installation Methods
hide_title: true
sidebar_position: 2
version: 1
---
import useBaseUrl from "@docusaurus/useBaseUrl";
# Installation Methods
How should you install Superset? Here's a comparison of the different options. It will help if you've first read the [Architecture](/docs/installation/architecture.mdx) page to understand Superset's different components.
The fundamental trade-off is between you needing to do more of the detail work yourself vs. using a more complex deployment route that handles those details.
## [Docker Compose](/docs/installation/docker-compose.mdx)
**Summary:** This takes advantage of containerization while remaining simpler than Kubernetes. This is the best way to try out Superset; it's also useful for developing & contributing back to Superset.
If you're not just demoing the software, you'll need a moderate understanding of Docker to customize your deployment and avoid a few risks. Even when fully-optimized this is not as robust a method as Kubernetes when it comes to large-scale production deployments.
You manage a superset-config.py file and a docker-compose.yml file. Docker Compose brings up all the needed services - the Superset application, a Postgres metadata DB, Redis cache, Celery worker and beat. They are automatically connected to each other.
**Responsibilities**
You will need to back up your metadata DB. That could mean backing up the service running as a Docker container and its volume; ideally you are running Postgres as a service outside of that container and backing up that service.
You will also need to extend the Superset docker image. The default `lean` images do not contain drivers needed to access your metadata database (Postgres or MySQL), nor to access your data warehouse, nor the headless browser needed for Alerts & Reports. You could run a `-dev` image while demoing Superset, which has some of this, but you'll still need to install the driver for your data warehouse. The `-dev` images run as root, which is not recommended for production.
Ideally you will build your own image of Superset that extends `lean`, adding what your deployment needs. See [Building your own production Docker image](/docs/installation/docker-builds/#building-your-own-production-docker-image).
## [Kubernetes (K8s)](/docs/installation/kubernetes.mdx)
**Summary:** This is the best-practice way to deploy a production instance of Superset, but has the steepest skill requirement - someone who knows Kubernetes.
You will deploy Superset into a K8s cluster. The most common method is using the community-maintained Helm chart, though work is now underway to implement [SIP-149 - a Kubernetes Operator for Superset](https://github.com/apache/superset/issues/31408).
A K8s deployment can scale up and down based on usage and deploy rolling updates with zero downtime - features that big deployments appreciate.
**Responsibilities**
You will need to build your own Docker image, and back up your metadata DB, both as described in Docker Compose above. You'll also need to customize your Helm chart values and deploy and maintain your Kubernetes cluster.
## [PyPI (Python)](/docs/installation/pypi.mdx)
**Summary:** This is the only method that requires no knowledge of containers. It requires the most hands-on work to deploy, connect, and maintain each component.
You install Superset as a Python package and run it that way, providing your own metadata database. Superset has documentation on how to install this way, but it is updated infrequently.
If you want caching, you'll set up Redis or RabbitMQ. If you want Alerts & Reports, you'll set up Celery.
**Responsibilities**
You will need to get the component services running and communicating with each other. You'll need to arrange backups of your metadata database.
When upgrading, you'll need to manage the system environment and packages and ensure all components have functional dependencies.

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title: Kubernetes
hide_title: true
sidebar_position: 3
version: 1
---
import useBaseUrl from "@docusaurus/useBaseUrl";
# Installing on Kubernetes
<img src={useBaseUrl("/img/k8s.png" )} width="150" />
<br /><br />
Running Superset on Kubernetes is supported with the provided [Helm](https://helm.sh/) chart
found in the official [Superset helm repository](https://apache.github.io/superset/index.yaml).
## Prerequisites
- A Kubernetes cluster
- Helm installed
:::note
For simpler, single host environments, we recommend using
[minikube](https://minikube.sigs.k8s.io/docs/start/) which is easy to setup on many platforms
and works fantastically well with the Helm chart referenced here.
:::
## Running
1. Add the Superset helm repository
```sh
helm repo add superset https://apache.github.io/superset
"superset" has been added to your repositories
```
2. View charts in repo
```sh
helm search repo superset
NAME CHART VERSION APP VERSION DESCRIPTION
superset/superset 0.1.1 1.0 Apache Superset is a modern, enterprise-ready b...
```
3. Configure your setting overrides
Just like any typical Helm chart, you'll need to craft a `values.yaml` file that would define/override any of the values exposed into the default [values.yaml](https://github.com/apache/superset/tree/master/helm/superset/values.yaml), or from any of the dependent charts it depends on:
- [bitnami/redis](https://artifacthub.io/packages/helm/bitnami/redis)
- [bitnami/postgresql](https://artifacthub.io/packages/helm/bitnami/postgresql)
More info down below on some important overrides you might need.
4. Install and run
```sh
helm upgrade --install --values my-values.yaml superset superset/superset
```
You should see various pods popping up, such as:
```sh
kubectl get pods
NAME READY STATUS RESTARTS AGE
superset-celerybeat-7cdcc9575f-k6xmc 1/1 Running 0 119s
superset-f5c9c667-dw9lp 1/1 Running 0 4m7s
superset-f5c9c667-fk8bk 1/1 Running 0 4m11s
superset-init-db-zlm9z 0/1 Completed 0 111s
superset-postgresql-0 1/1 Running 0 6d20h
superset-redis-master-0 1/1 Running 0 6d20h
superset-worker-75b48bbcc-jmmjr 1/1 Running 0 4m8s
superset-worker-75b48bbcc-qrq49 1/1 Running 0 4m12s
```
The exact list will depend on some of your specific configuration overrides but you should generally expect:
- N `superset-xxxx-yyyy` and `superset-worker-xxxx-yyyy` pods (depending on your `supersetNode.replicaCount` and `supersetWorker.replicaCount` values)
- 1 `superset-postgresql-0` depending on your postgres settings
- 1 `superset-redis-master-0` depending on your redis settings
- 1 `superset-celerybeat-xxxx-yyyy` pod if you have `supersetCeleryBeat.enabled = true` in your values overrides
1. Access it
The chart will publish appropriate services to expose the Superset UI internally within your k8s cluster. To access it externally you will have to either:
- Configure the Service as a `LoadBalancer` or `NodePort`
- Set up an `Ingress` for it - the chart includes a definition, but will need to be tuned to your needs (hostname, tls, annotations etc...)
- Run `kubectl port-forward superset-xxxx-yyyy :8088` to directly tunnel one pod's port into your localhost
Depending how you configured external access, the URL will vary. Once you've identified the appropriate URL you can log in with:
- user: `admin`
- password: `admin`
## Important settings
### Security settings
Default security settings and passwords are included but you **MUST** update them to run `prod` instances, in particular:
```yaml
postgresql:
postgresqlPassword: superset
```
Make sure, you set a unique strong complex alphanumeric string for your SECRET_KEY and use a tool to help you generate
a sufficiently random sequence.
- To generate a good key you can run, `openssl rand -base64 42`
```yaml
configOverrides:
secret: |
SECRET_KEY = 'YOUR_OWN_RANDOM_GENERATED_SECRET_KEY'
```
If you want to change the previous secret key then you should rotate the keys.
Default secret key for kubernetes deployment is `thisISaSECRET_1234`
```yaml
configOverrides:
my_override: |
PREVIOUS_SECRET_KEY = 'YOUR_PREVIOUS_SECRET_KEY'
SECRET_KEY = 'YOUR_OWN_RANDOM_GENERATED_SECRET_KEY'
init:
command:
- /bin/sh
- -c
- |
. {{ .Values.configMountPath }}/superset_bootstrap.sh
superset re-encrypt-secrets
. {{ .Values.configMountPath }}/superset_init.sh
```
:::note
Superset uses [Scarf Gateway](https://about.scarf.sh/scarf-gateway) to collect telemetry data. Knowing the installation counts for different Superset versions informs the project's decisions about patching and long-term support. Scarf purges personally identifiable information (PII) and provides only aggregated statistics.
To opt-out of this data collection in your Helm-based installation, edit the `repository:` line in your `helm/superset/values.yaml` file, replacing `apachesuperset.docker.scarf.sh/apache/superset` with `apache/superset` to pull the image directly from Docker Hub.
:::
### Dependencies
Install additional packages and do any other bootstrap configuration in the bootstrap script.
For production clusters it's recommended to build own image with this step done in CI.
:::note
Superset requires a Python DB-API database driver and a SQLAlchemy
dialect to be installed for each datastore you want to connect to.
See [Install Database Drivers](/docs/configuration/databases) for more information.
It is recommended that you refer to versions listed in
[pyproject.toml](https://github.com/apache/superset/blob/master/pyproject.toml)
instead of hard-coding them in your bootstrap script, as seen below.
:::
The following example installs the drivers for BigQuery and Elasticsearch, allowing you to connect to these data sources within your Superset setup:
```yaml
bootstrapScript: |
#!/bin/bash
uv pip install .[postgres] \
.[bigquery] \
.[elasticsearch] &&\
if [ ! -f ~/bootstrap ]; then echo "Running Superset with uid {{ .Values.runAsUser }}" > ~/bootstrap; fi
```
### superset_config.py
The default `superset_config.py` is fairly minimal and you will very likely need to extend it. This is done by specifying one or more key/value entries in `configOverrides`, e.g.:
```yaml
configOverrides:
my_override: |
# This will make sure the redirect_uri is properly computed, even with SSL offloading
ENABLE_PROXY_FIX = True
FEATURE_FLAGS = {
"DYNAMIC_PLUGINS": True
}
```
Those will be evaluated as Helm templates and therefore will be able to reference other `values.yaml` variables e.g. `{{ .Values.ingress.hosts[0] }}` will resolve to your ingress external domain.
The entire `superset_config.py` will be installed as a secret, so it is safe to pass sensitive parameters directly... however it might be more readable to use secret env variables for that.
Full python files can be provided by running `helm upgrade --install --values my-values.yaml --set-file configOverrides.oauth=set_oauth.py`
### Environment Variables
Those can be passed as key/values either with `extraEnv` or `extraSecretEnv` if they're sensitive. They can then be referenced from `superset_config.py` using e.g. `os.environ.get("VAR")`.
```yaml
extraEnv:
SMTP_HOST: smtp.gmail.com
SMTP_USER: user@gmail.com
SMTP_PORT: "587"
SMTP_MAIL_FROM: user@gmail.com
extraSecretEnv:
SMTP_PASSWORD: xxxx
configOverrides:
smtp: |
import ast
SMTP_HOST = os.getenv("SMTP_HOST","localhost")
SMTP_STARTTLS = ast.literal_eval(os.getenv("SMTP_STARTTLS", "True"))
SMTP_SSL = ast.literal_eval(os.getenv("SMTP_SSL", "False"))
SMTP_USER = os.getenv("SMTP_USER","superset")
SMTP_PORT = os.getenv("SMTP_PORT",25)
SMTP_PASSWORD = os.getenv("SMTP_PASSWORD","superset")
```
### System packages
If new system packages are required, they can be installed before application startup by overriding the container's `command`, e.g.:
```yaml
supersetWorker:
command:
- /bin/sh
- -c
- |
apt update
apt install -y somepackage
apt autoremove -yqq --purge
apt clean
# Run celery worker
. {{ .Values.configMountPath }}/superset_bootstrap.sh; celery --app=superset.tasks.celery_app:app worker
```
### Data sources
Data source definitions can be automatically declared by providing key/value yaml definitions in `extraConfigs`:
```yaml
extraConfigs:
import_datasources.yaml: |
databases:
- allow_file_upload: true
allow_ctas: true
allow_cvas: true
database_name: example-db
extra: "{\r\n \"metadata_params\": {},\r\n \"engine_params\": {},\r\n \"\
metadata_cache_timeout\": {},\r\n \"schemas_allowed_for_file_upload\": []\r\n\
}"
sqlalchemy_uri: example://example-db.local
tables: []
```
Those will also be mounted as secrets and can include sensitive parameters.
## Configuration Examples
### Setting up OAuth
:::note
OAuth setup requires that the [authlib](https://authlib.org/) Python library is installed. This can
be done using `pip` by updating the `bootstrapScript`. See the [Dependencies](#dependencies) section
for more information.
:::
```yaml
extraEnv:
AUTH_DOMAIN: example.com
extraSecretEnv:
GOOGLE_KEY: xxxxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.apps.googleusercontent.com
GOOGLE_SECRET: xxxxxxxxxxxxxxxxxxxxxxxx
configOverrides:
enable_oauth: |
# This will make sure the redirect_uri is properly computed, even with SSL offloading
ENABLE_PROXY_FIX = True
from flask_appbuilder.security.manager import AUTH_OAUTH
AUTH_TYPE = AUTH_OAUTH
OAUTH_PROVIDERS = [
{
"name": "google",
"icon": "fa-google",
"token_key": "access_token",
"remote_app": {
"client_id": os.getenv("GOOGLE_KEY"),
"client_secret": os.getenv("GOOGLE_SECRET"),
"api_base_url": "https://www.googleapis.com/oauth2/v2/",
"client_kwargs": {"scope": "email profile"},
"request_token_url": None,
"access_token_url": "https://accounts.google.com/o/oauth2/token",
"authorize_url": "https://accounts.google.com/o/oauth2/auth",
"authorize_params": {"hd": os.getenv("AUTH_DOMAIN", "")}
},
}
]
# Map Authlib roles to superset roles
AUTH_ROLE_ADMIN = 'Admin'
AUTH_ROLE_PUBLIC = 'Public'
# Will allow user self registration, allowing to create Flask users from Authorized User
AUTH_USER_REGISTRATION = True
# The default user self registration role
AUTH_USER_REGISTRATION_ROLE = "Admin"
```
### Enable Alerts and Reports
For this, as per the [Alerts and Reports doc](/docs/configuration/alerts-reports), you will need to:
#### Install a supported webdriver in the Celery worker
This is done either by using a custom image that has the webdriver pre-installed, or installing at startup time by overriding the `command`. Here's a working example for `chromedriver`:
```yaml
supersetWorker:
command:
- /bin/sh
- -c
- |
# Install chrome webdriver
# See https://github.com/apache/superset/blob/4fa3b6c7185629b87c27fc2c0e5435d458f7b73d/docs/src/pages/docs/installation/email_reports.mdx
apt-get update
apt-get install -y wget
wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
apt-get install -y --no-install-recommends ./google-chrome-stable_current_amd64.deb
wget https://chromedriver.storage.googleapis.com/88.0.4324.96/chromedriver_linux64.zip
apt-get install -y zip
unzip chromedriver_linux64.zip
chmod +x chromedriver
mv chromedriver /usr/bin
apt-get autoremove -yqq --purge
apt-get clean
rm -f google-chrome-stable_current_amd64.deb chromedriver_linux64.zip
# Run
. {{ .Values.configMountPath }}/superset_bootstrap.sh; celery --app=superset.tasks.celery_app:app worker
```
#### Run the Celery beat
This pod will trigger the scheduled tasks configured in the alerts and reports UI section:
```yaml
supersetCeleryBeat:
enabled: true
```
#### Configure the appropriate Celery jobs and SMTP/Slack settings
```yaml
extraEnv:
SMTP_HOST: smtp.gmail.com
SMTP_USER: user@gmail.com
SMTP_PORT: "587"
SMTP_MAIL_FROM: user@gmail.com
extraSecretEnv:
SLACK_API_TOKEN: xoxb-xxxx-yyyy
SMTP_PASSWORD: xxxx-yyyy
configOverrides:
feature_flags: |
import ast
FEATURE_FLAGS = {
"ALERT_REPORTS": True
}
SMTP_HOST = os.getenv("SMTP_HOST","localhost")
SMTP_STARTTLS = ast.literal_eval(os.getenv("SMTP_STARTTLS", "True"))
SMTP_SSL = ast.literal_eval(os.getenv("SMTP_SSL", "False"))
SMTP_USER = os.getenv("SMTP_USER","superset")
SMTP_PORT = os.getenv("SMTP_PORT",25)
SMTP_PASSWORD = os.getenv("SMTP_PASSWORD","superset")
SMTP_MAIL_FROM = os.getenv("SMTP_MAIL_FROM","superset@superset.com")
SLACK_API_TOKEN = os.getenv("SLACK_API_TOKEN",None)
celery_conf: |
from celery.schedules import crontab
class CeleryConfig:
broker_url = f"redis://{env('REDIS_HOST')}:{env('REDIS_PORT')}/0"
imports = (
"superset.sql_lab",
"superset.tasks.cache",
"superset.tasks.scheduler",
)
result_backend = f"redis://{env('REDIS_HOST')}:{env('REDIS_PORT')}/0"
task_annotations = {
"sql_lab.get_sql_results": {
"rate_limit": "100/s",
},
}
beat_schedule = {
"reports.scheduler": {
"task": "reports.scheduler",
"schedule": crontab(minute="*", hour="*"),
},
"reports.prune_log": {
"task": "reports.prune_log",
'schedule': crontab(minute=0, hour=0),
},
'cache-warmup-hourly': {
"task": "cache-warmup",
"schedule": crontab(minute="*/30", hour="*"),
"kwargs": {
"strategy_name": "top_n_dashboards",
"top_n": 10,
"since": "7 days ago",
},
}
}
CELERY_CONFIG = CeleryConfig
reports: |
EMAIL_PAGE_RENDER_WAIT = 60
WEBDRIVER_BASEURL = "http://{{ template "superset.fullname" . }}:{{ .Values.service.port }}/"
WEBDRIVER_BASEURL_USER_FRIENDLY = "https://www.example.com/"
WEBDRIVER_TYPE= "chrome"
WEBDRIVER_OPTION_ARGS = [
"--force-device-scale-factor=2.0",
"--high-dpi-support=2.0",
"--headless",
"--disable-gpu",
"--disable-dev-shm-usage",
# This is required because our process runs as root (in order to install pip packages)
"--no-sandbox",
"--disable-setuid-sandbox",
"--disable-extensions",
]
```
### Load the Examples data and dashboards
If you are trying Superset out and want some data and dashboards to explore, you can load some examples by creating a `my_values.yaml` and deploying it as described above in the **Configure your setting overrides** step of the **Running** section.
To load the examples, add the following to the `my_values.yaml` file:
```yaml
init:
loadExamples: true
```

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---
title: PyPI
hide_title: true
sidebar_position: 4
version: 1
---
import useBaseUrl from "@docusaurus/useBaseUrl";
# Installing Superset from PyPI
<img src={useBaseUrl("/img/pypi.png" )} width="150" />
<br /><br />
This page describes how to install Superset using the `apache_superset` package [published on PyPI](https://pypi.org/project/apache_superset/).
## OS Dependencies
Superset stores database connection information in its metadata database. For that purpose, we use
the cryptography Python library to encrypt connection passwords. Unfortunately, this library has OS
level dependencies.
**Debian and Ubuntu**
Ubuntu **24.04** uses python 3.12 per default, which currently is not supported by Superset. You need to add a second python installation of 3.11 and install the required additional dependencies.
```bash
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install python3.11 python3.11-dev python3.11-venv build-essential libssl-dev libffi-dev libsasl2-dev libldap2-dev default-libmysqlclient-dev
```
In Ubuntu **20.04 and 22.04** the following command will ensure that the required dependencies are installed:
```bash
sudo apt-get install build-essential libssl-dev libffi-dev python3-dev python3-pip libsasl2-dev libldap2-dev default-libmysqlclient-dev
```
In Ubuntu **before 20.04** the following command will ensure that the required dependencies are installed:
```bash
sudo apt-get install build-essential libssl-dev libffi-dev python-dev python-pip libsasl2-dev libldap2-dev default-libmysqlclient-dev
```
**Fedora and RHEL-derivative Linux distributions**
Install the following packages using the `yum` package manager:
```bash
sudo yum install gcc gcc-c++ libffi-devel python-devel python-pip python-wheel openssl-devel cyrus-sasl-devel openldap-devel
```
In more recent versions of CentOS and Fedora, you may need to install a slightly different set of packages using `dnf`:
```bash
sudo dnf install gcc gcc-c++ libffi-devel python3-devel python3-pip python3-wheel openssl-devel cyrus-sasl-devel openldap-devel
```
Also, on CentOS, you may need to upgrade pip for the install to work:
```bash
pip3 install --upgrade pip
```
**Mac OS X**
If you're not on the latest version of OS X, we recommend upgrading because we've found that many
issues people have run into are linked to older versions of Mac OS X. After updating, install the
latest version of XCode command line tools:
```bash
xcode-select --install
```
We don't recommend using the system installed Python. Instead, first install the
[homebrew](https://brew.sh/) manager and then run the following commands:
```bash
brew install readline pkg-config libffi openssl mysql postgresql@14
```
You should install a recent version of Python. Refer to the
[pyproject.toml](https://github.com/apache/superset/blob/master/pyproject.toml) file for a list of Python
versions officially supported by Superset. We'd recommend using a Python version manager
like [pyenv](https://github.com/pyenv/pyenv)
(and also [pyenv-virtualenv](https://github.com/pyenv/pyenv-virtualenv)).
Let's also make sure we have the latest version of `pip` and `setuptools`:
```bash
pip install --upgrade setuptools pip
```
Lastly, you may need to set LDFLAGS and CFLAGS for certain Python packages to properly build. You can export these variables with:
```bash
export LDFLAGS="-L$(brew --prefix openssl)/lib"
export CFLAGS="-I$(brew --prefix openssl)/include"
```
These will now be available when pip installing requirements.
## Python Virtual Environment
We highly recommend installing Superset inside of a virtual environment.
You can create and activate a virtual environment using the following commands. Ensure you are using a compatible version of python. You might have to explicitly use for example `python3.11` instead of `python3`.
```bash
# virtualenv is shipped in Python 3.6+ as venv instead of pyvenv.
# See https://docs.python.org/3.6/library/venv.html
python3 -m venv venv
. venv/bin/activate
```
Or with pyenv-virtualenv:
```bash
# Here we name the virtual env 'superset'
pyenv virtualenv superset
pyenv activate superset
```
Once you activated your virtual environment, all of the Python packages you install or uninstall
will be confined to this environment. You can exit the environment by running `deactivate` on the
command line.
### Installing and Initializing Superset
First, start by installing `apache_superset`:
```bash
pip install apache_superset
```
Then, define mandatory configurations, SECRET_KEY and FLASK_APP:
```bash
export SUPERSET_SECRET_KEY=YOUR-SECRET-KEY # For production use, make sure this is a strong key, for example generated using `openssl rand -base64 42`. See https://superset.apache.org/docs/configuration/configuring-superset#specifying-a-secret_key
export FLASK_APP=superset
```
Then, you need to initialize the database:
```bash
superset db upgrade
```
Finish installing by running through the following commands:
```bash
# Create an admin user in your metadata database (use `admin` as username to be able to load the examples)
superset fab create-admin
# Load some data to play with
superset load_examples
# Create default roles and permissions
superset init
# To start a development web server on port 8088, use -p to bind to another port
superset run -p 8088 --with-threads --reload --debugger
```
If everything worked, you should be able to navigate to `hostname:port` in your browser (e.g.
locally by default at `localhost:8088`) and login using the username and password you created.

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---
title: Upgrading Superset
hide_title: true
sidebar_position: 6
version: 1
---
# Upgrading Superset
## Docker Compose
First, make sure to shut down the running containers in Docker Compose:
```bash
docker compose down
```
Next, update the folder that mirrors the `superset` repo through git:
```bash
git pull origin master
```
Then, restart the containers and any changed Docker images will be automatically pulled down:
```bash
docker compose up
```
## Updating Superset Manually
To upgrade superset in a native installation, run the following commands:
```bash
pip install apache_superset --upgrade
```
## Upgrading the Metadata Database
Migrate the metadata database by running:
```bash
superset db upgrade
superset init
```
While upgrading superset should not delete your charts and dashboards, we recommend following best
practices and to backup your metadata database before upgrading. Before upgrading production, we
recommend upgrading in a staging environment and upgrading production finally during off-peak usage.

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hide_title: true
sidebar_position: 1
---
<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
# Superset
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/license/apache-2-0)
[![Latest Release on Github](https://img.shields.io/github/v/release/apache/superset?sort=semver)](https://github.com/apache/superset/releases/latest)
[![Build Status](https://github.com/apache/superset/actions/workflows/superset-python-unittest.yml/badge.svg)](https://github.com/apache/superset/actions)
[![PyPI version](https://badge.fury.io/py/apache_superset.svg)](https://badge.fury.io/py/apache_superset)
[![Coverage Status](https://codecov.io/github/apache/superset/coverage.svg?branch=master)](https://codecov.io/github/apache/superset)
[![PyPI](https://img.shields.io/pypi/pyversions/apache_superset.svg?maxAge=2592000)](https://pypi.python.org/pypi/apache_superset)
[![Get on Slack](https://img.shields.io/badge/slack-join-orange.svg)](http://bit.ly/join-superset-slack)
[![Documentation](https://img.shields.io/badge/docs-apache.org-blue.svg)](https://superset.apache.org)
<picture width="500">
<source
width="600"
media="(prefers-color-scheme: dark)"
src="https://superset.apache.org/img/superset-logo-horiz-dark.svg"
alt="Superset logo (dark)"
/>
<img
width="600"
src="https://superset.apache.org/img/superset-logo-horiz-apache.svg"
alt="Superset logo (light)"
/>
</picture>
A modern, enterprise-ready business intelligence web application.
[**Why Superset?**](#why-superset) |
[**Supported Databases**](#supported-databases) |
[**Installation and Configuration**](#installation-and-configuration) |
[**Release Notes**](https://github.com/apache/superset/blob/master/RELEASING/README.md#release-notes-for-recent-releases) |
[**Get Involved**](#get-involved) |
[**Contributor Guide**](#contributor-guide) |
[**Resources**](#resources) |
[**Organizations Using Superset**](https://github.com/apache/superset/blob/master/RESOURCES/INTHEWILD.md)
## Why Superset?
Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.
Superset provides:
- A **no-code interface** for building charts quickly
- A powerful, web-based **SQL Editor** for advanced querying
- A **lightweight semantic layer** for quickly defining custom dimensions and metrics
- Out of the box support for **nearly any SQL** database or data engine
- A wide array of **beautiful visualizations** to showcase your data, ranging from simple bar charts to geospatial visualizations
- Lightweight, configurable **caching layer** to help ease database load
- Highly extensible **security roles and authentication** options
- An **API** for programmatic customization
- A **cloud-native architecture** designed from the ground up for scale
## Screenshots & Gifs
**Video Overview**
<!-- File hosted here https://github.com/apache/superset-site/raw/lfs/superset-video-4k.mp4 -->
[superset-video-1080p.webm](https://github.com/user-attachments/assets/b37388f7-a971-409c-96a7-90c4e31322e6)
<br/>
**Large Gallery of Visualizations**
<kbd><img title="Gallery" src="https://superset.apache.org/img/screenshots/gallery.jpg"/></kbd><br/>
**Craft Beautiful, Dynamic Dashboards**
<kbd><img title="View Dashboards" src="https://superset.apache.org/img/screenshots/slack_dash.jpg"/></kbd><br/>
**No-Code Chart Builder**
<kbd><img title="Slice & dice your data" src="https://superset.apache.org/img/screenshots/explore.jpg"/></kbd><br/>
**Powerful SQL Editor**
<kbd><img title="SQL Lab" src="https://superset.apache.org/img/screenshots/sql_lab.jpg"/></kbd><br/>
## Supported Databases
Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, [and more](https://superset.apache.org/docs/configuration/databases)) that has a Python DB-API driver and a SQLAlchemy dialect.
Here are some of the major database solutions that are supported:
<p align="center">
<img src="https://superset.apache.org/img/databases/redshift.png" alt="redshift" border="0" width="200"/>
<img src="https://superset.apache.org/img/databases/google-biquery.png" alt="google-bigquery" border="0" width="200"/>
<img src="https://superset.apache.org/img/databases/snowflake.png" alt="snowflake" border="0" width="200"/>
<img src="https://superset.apache.org/img/databases/trino.png" alt="trino" border="0" width="150" />
<img src="https://superset.apache.org/img/databases/presto.png" alt="presto" border="0" width="200"/>
<img src="https://superset.apache.org/img/databases/databricks.png" alt="databricks" border="0" width="160" />
<img src="https://superset.apache.org/img/databases/druid.png" alt="druid" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/firebolt.png" alt="firebolt" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/timescale.png" alt="timescale" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/postgresql.png" alt="postgresql" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/mysql.png" alt="mysql" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/mssql-server.png" alt="mssql-server" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/ibm-db2.svg" alt="db2" border="0" width="220" />
<img src="https://superset.apache.org/img/databases/sqlite.png" alt="sqlite" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/sybase.png" alt="sybase" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/mariadb.png" alt="mariadb" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/vertica.png" alt="vertica" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/oracle.png" alt="oracle" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/firebird.png" alt="firebird" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/greenplum.png" alt="greenplum" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/clickhouse.png" alt="clickhouse" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/exasol.png" alt="exasol" border="0" width="160" />
<img src="https://superset.apache.org/img/databases/monet-db.png" alt="monet-db" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/apache-kylin.png" alt="apache-kylin" border="0" width="80"/>
<img src="https://superset.apache.org/img/databases/hologres.png" alt="hologres" border="0" width="80"/>
<img src="https://superset.apache.org/img/databases/netezza.png" alt="netezza" border="0" width="80"/>
<img src="https://superset.apache.org/img/databases/pinot.png" alt="pinot" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/teradata.png" alt="teradata" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/yugabyte.png" alt="yugabyte" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/databend.png" alt="databend" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/starrocks.png" alt="starrocks" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/doris.png" alt="doris" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/oceanbase.svg" alt="oceanbase" border="0" width="220" />
<img src="https://superset.apache.org/img/databases/sap-hana.png" alt="sap-hana" border="0" width="220" />
<img src="https://superset.apache.org/img/databases/denodo.png" alt="denodo" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/ydb.svg" alt="ydb" border="0" width="200" />
<img src="https://superset.apache.org/img/databases/tdengine.png" alt="TDengine" border="0" width="200" />
</p>
**A more comprehensive list of supported databases** along with the configuration instructions can be found [here](https://superset.apache.org/docs/configuration/databases).
Want to add support for your datastore or data engine? Read more [here](https://superset.apache.org/docs/frequently-asked-questions#does-superset-work-with-insert-database-engine-here) about the technical requirements.
## Installation and Configuration
Try out Superset's [quickstart](https://superset.apache.org/docs/quickstart/) guide or learn about [the options for production deployments](https://superset.apache.org/docs/installation/architecture/).
## Get Involved
- Ask and answer questions on [StackOverflow](https://stackoverflow.com/questions/tagged/apache-superset) using the **apache-superset** tag
- [Join our community's Slack](http://bit.ly/join-superset-slack)
and please read our [Slack Community Guidelines](https://github.com/apache/superset/blob/master/CODE_OF_CONDUCT.md#slack-community-guidelines)
- [Join our dev@superset.apache.org Mailing list](https://lists.apache.org/list.html?dev@superset.apache.org). To join, simply send an email to [dev-subscribe@superset.apache.org](mailto:dev-subscribe@superset.apache.org)
- If you want to help troubleshoot GitHub Issues involving the numerous database drivers that Superset supports, please consider adding your name and the databases you have access to on the [Superset Database Familiarity Rolodex](https://docs.google.com/spreadsheets/d/1U1qxiLvOX0kBTUGME1AHHi6Ywel6ECF8xk_Qy-V9R8c/edit#gid=0)
- Join Superset's Town Hall and [Operational Model](https://preset.io/blog/the-superset-operational-model-wants-you/) recurring meetings. Meeting info is available on the [Superset Community Calendar](https://superset.apache.org/community)
## Contributor Guide
Interested in contributing? Check out our
[CONTRIBUTING.md](https://github.com/apache/superset/blob/master/CONTRIBUTING.md)
to find resources around contributing along with a detailed guide on
how to set up a development environment.
## Resources
- [Superset "In the Wild"](https://github.com/apache/superset/blob/master/RESOURCES/INTHEWILD.md) - open a PR to add your org to the list!
- [Feature Flags](https://github.com/apache/superset/blob/master/RESOURCES/FEATURE_FLAGS.md) - the status of Superset's Feature Flags.
- [Standard Roles](https://github.com/apache/superset/blob/master/RESOURCES/STANDARD_ROLES.md) - How RBAC permissions map to roles.
- [Superset Wiki](https://github.com/apache/superset/wiki) - Tons of additional community resources: best practices, community content and other information.
- [Superset SIPs](https://github.com/orgs/apache/projects/170) - The status of Superset's SIPs (Superset Improvement Proposals) for both consensus and implementation status.
Understanding the Superset Points of View
- [The Case for Dataset-Centric Visualization](https://preset.io/blog/dataset-centric-visualization/)
- [Understanding the Superset Semantic Layer](https://preset.io/blog/understanding-superset-semantic-layer/)
- Getting Started with Superset
- [Superset in 2 Minutes using Docker Compose](https://superset.apache.org/docs/installation/docker-compose#installing-superset-locally-using-docker-compose)
- [Installing Database Drivers](https://superset.apache.org/docs/configuration/databases#installing-database-drivers)
- [Building New Database Connectors](https://preset.io/blog/building-database-connector/)
- [Create Your First Dashboard](https://superset.apache.org/docs/using-superset/creating-your-first-dashboard/)
- [Comprehensive Tutorial for Contributing Code to Apache Superset
](https://preset.io/blog/tutorial-contributing-code-to-apache-superset/)
- [Resources to master Superset by Preset](https://preset.io/resources/)
- Deploying Superset
- [Official Docker image](https://hub.docker.com/r/apache/superset)
- [Helm Chart](https://github.com/apache/superset/tree/master/helm/superset)
- Recordings of Past [Superset Community Events](https://preset.io/events)
- [Mixed Time Series Charts](https://preset.io/events/mixed-time-series-visualization-in-superset-workshop/)
- [How the Bing Team Customized Superset for the Internal Self-Serve Data & Analytics Platform](https://preset.io/events/how-the-bing-team-heavily-customized-superset-for-their-internal-data/)
- [Live Demo: Visualizing MongoDB and Pinot Data using Trino](https://preset.io/events/2021-04-13-visualizing-mongodb-and-pinot-data-using-trino/)
- [Introduction to the Superset API](https://preset.io/events/introduction-to-the-superset-api/)
- [Building a Database Connector for Superset](https://preset.io/events/2021-02-16-building-a-database-connector-for-superset/)
- Visualizations
- [Creating Viz Plugins](https://superset.apache.org/docs/contributing/creating-viz-plugins/)
- [Managing and Deploying Custom Viz Plugins](https://medium.com/nmc-techblog/apache-superset-manage-custom-viz-plugins-in-production-9fde1a708e55)
- [Why Apache Superset is Betting on Apache ECharts](https://preset.io/blog/2021-4-1-why-echarts/)
- [Superset API](https://superset.apache.org/docs/rest-api)
## Repo Activity
<a href="https://next.ossinsight.io/widgets/official/compose-last-28-days-stats?repo_id=39464018" target="_blank" align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://next.ossinsight.io/widgets/official/compose-last-28-days-stats/thumbnail.png?repo_id=39464018&image_size=auto&color_scheme=dark" width="655" height="auto" />
<img alt="Performance Stats of apache/superset - Last 28 days" src="https://next.ossinsight.io/widgets/official/compose-last-28-days-stats/thumbnail.png?repo_id=39464018&image_size=auto&color_scheme=light" width="655" height="auto" />
</picture>
</a>
<!-- Made with [OSS Insight](https://ossinsight.io/) -->
<!-- telemetry/analytics pixel: -->
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=bc1c90cd-bc04-4e11-8c7b-289fb2839492" />

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---
title: Quickstart
hide_title: false
sidebar_position: 2
---
**Ready to try Apache Superset?** This quickstart guide will help you
get up and running on your local machine in **3 simple steps**. Note that
it assumes that you have [Docker](https://www.docker.com),
[Docker Compose](https://docs.docker.com/compose/), and
[Git](https://git-scm.com/) installed.
:::caution
Although we recommend using `Docker Compose` for a quick start in a sandbox-type
environment and for other development-type use cases, **we
do not recommend this setup for production**. For this purpose please
refer to our
[Installing on Kubernetes](/docs/installation/kubernetes/)
page.
:::
### 1. Get Superset
```bash
git clone https://github.com/apache/superset
```
### 2. Start the latest official release of Superset
```bash
# Enter the repository you just cloned
$ cd superset
# Set the repo to the state associated with the latest official version
$ git checkout tags/5.0.0
# Fire up Superset using Docker Compose
$ docker compose -f docker-compose-image-tag.yml up
```
This may take a moment as Docker Compose will fetch the underlying
container images and will load up some examples. Once all containers
are downloaded and the output settles, you're ready to log in.
⚠️ If you get an error message like `validating superset\docker-compose-image-tag.yml: services.superset-worker-beat.env_file.0 must be a string`, you need to update your version of `docker-compose`.
Note that `docker-compose` is on the path to deprecation and you should now use `docker compose` instead.
### 3. Log into Superset
Now head over to [http://localhost:8088](http://localhost:8088) and log in with the default created account:
```bash
username: admin
password: admin
```
#### 🎉 Congratulations! Superset is now up and running on your machine! 🎉
### Wrapping Up
Once you're done with Superset, you can stop and delete just like any other container environment:
```bash
docker compose down
```
:::tip
You can use the same environment more than once, as Superset will persist data locally. However, make sure to properly stop all
processes by running Docker Compose `stop` command. By doing so, you can avoid data corruption and/or loss of data.
:::
## What's next?
From this point on, you can head on to:
- [Create your first Dashboard](/docs/using-superset/creating-your-first-dashboard)
- [Connect to a Database](/docs/configuration/databases)
- [Using Docker Compose](/docs/installation/docker-compose)
- [Configure Superset](/docs/configuration/configuring-superset/)
- [Installing on Kubernetes](/docs/installation/kubernetes/)
Or just explore our [Documentation](https://superset.apache.org/docs/intro)!

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---
title: CVEs fixed by release
sidebar_position: 2
---
#### Version 5.0.0
| CVE | Title | Affected |
|:---------------|:-----------------------------------------------------------------------------------|---------:|
| CVE-2025-55673 | Exposure of Sensitive Information to an Unauthorized Actor | < 5.0.0 |
| CVE-2025-55674 | Improper Neutralization of Special Elements used in an SQL Command | < 5.0.0 |
| CVE-2025-55675 | Improper Access Control leading to Information Disclosure | < 5.0.0 |
#### Version 4.1.3
| CVE | Title | Affected |
|:---------------|:-----------------------------------------------------------------------------------|---------:|
| CVE-2025-55672 | Improper Neutralization of Input During Web Page Generation | < 4.1.3 |
#### Version 4.1.2
| CVE | Title | Affected |
|:---------------|:-----------------------------------------------------------------------------------|---------:|
| CVE-2025-27696 | Improper authorization leading to resource ownership takeover | < 4.1.2 |
| CVE-2025-48912 | Improper authorization bypass on row level security via SQL Injection | < 4.1.2 |
#### Version 4.1.0
| CVE | Title | Affected |
|:---------------|:-----------------------------------------------------------------------------------|---------:|
| CVE-2024-53947 | Improper SQL authorisation, parse for specific postgres functions | < 4.1.0 |
| CVE-2024-53948 | Error verbosity exposes metadata in analytics databases | < 4.1.0 |
| CVE-2024-53949 | Lower privilege users are able to create Role when FAB_ADD_SECURITY_API is enabled | < 4.1.0 |
| CVE-2024-55633 | SQLLab Improper readonly query validation allows unauthorized write access | < 4.1.0 |
#### Version 4.0.2
| CVE | Title | Affected |
|:---------------|:----------------------------|---------:|
| CVE-2024-39887 | Improper SQL authorization | < 4.0.1 |
#### Version 3.1.3, 4.0.1
| CVE | Title | Affected |
|:---------------|:----------------------------|----------------------------:|
| CVE-2024-34693 | Server arbitrary file read | < 3.1.3, >= 4.0.0, < 4.0.1 |
#### Version 3.1.2
| CVE | Title | Affected |
|:---------------|:--------------------------------------------------------|---------:|
| CVE-2024-28148 | Incorrect datasource authorization on explore REST API | < 3.1.2 |
#### Version 3.0.4, 3.1.1
| CVE | Title | Affected |
|:---------------|:-----------------------------------------------------------------------------|----------------------------:|
| CVE-2024-27315 | Improper error handling on alerts | < 3.0.4, >= 3.1.0, < 3.1.1 |
| CVE-2024-24773 | Improper validation of SQL statements allows for unauthorized access to data | < 3.0.4, >= 3.1.0, < 3.1.1 |
| CVE-2024-24772 | Improper Neutralisation of custom SQL on embedded context | < 3.0.4, >= 3.1.0, < 3.1.1 |
| CVE-2024-24779 | Improper data authorization when creating a new dataset | < 3.0.4, >= 3.1.0, < 3.1.1 |
| CVE-2024-26016 | Improper authorization validation on dashboards and charts import | < 3.0.4, >= 3.1.0, < 3.1.1 |
#### Version 3.0.3
| CVE | Title | Affected |
|:---------------|:----------------------------------------------|---------:|
| CVE-2023-49657 | Stored XSS in Dashboard Title and Chart Title | < 3.0.3 |
#### Version 3.0.2, 2.1.3
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------|---------------------------:|
| CVE-2023-46104 | Allows for uncontrolled resource consumption via a ZIP bomb | < 2.1.3, >= 3.0.0, < 3.0.2 |
| CVE-2023-49736 | SQL Injection on where_in JINJA macro | < 2.1.3, >= 3.0.0, < 3.0.2 |
| CVE-2023-49734 | Privilege Escalation Vulnerability | < 2.1.3, >= 3.0.0, < 3.0.2 |
#### Version 3.0.0
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------------------|---------:|
| CVE-2023-42502 | Open Redirect Vulnerability | < 3.0.0 |
| CVE-2023-42505 | Sensitive information disclosure on db connection details | < 3.0.0 |
#### Version 2.1.3
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------------------|---------:|
| CVE-2023-42504 | Lack of rate limiting allows for possible denial of service | < 2.1.3 |
#### Version 2.1.2
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------------------|---------:|
| CVE-2023-40610 | Privilege escalation with default examples database | < 2.1.2 |
| CVE-2023-42501 | Unnecessary read permissions within the Gamma role | < 2.1.2 |
| CVE-2023-43701 | Stored XSS on API endpoint | < 2.1.2 |
#### Version 2.1.1
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------------------|---------:|
| CVE-2023-36387 | Improper API permission for low privilege users | < 2.1.1 |
| CVE-2023-36388 | Improper API permission for low privilege users allows for SSRF | < 2.1.1 |
| CVE-2023-27523 | Improper data permission validation on Jinja templated queries | < 2.1.1 |
| CVE-2023-27526 | Improper Authorization check on import charts | < 2.1.1 |
| CVE-2023-39264 | Stack traces enabled by default | < 2.1.1 |
| CVE-2023-39265 | Possible Unauthorized Registration of SQLite Database Connections | < 2.1.1 |
| CVE-2023-37941 | Metadata db write access can lead to remote code execution | < 2.1.1 |
| CVE-2023-32672 | SQL parser edge case bypasses data access authorization | < 2.1.1 |
#### Version 2.1.0
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------------------|---------:|
| CVE-2023-25504 | Possible SSRF on import datasets | < 2.1.0 |
| CVE-2023-27524 | Session validation vulnerability when using provided default SECRET_KEY | < 2.1.0 |
| CVE-2023-27525 | Incorrect default permissions for Gamma role | < 2.1.0 |
| CVE-2023-30776 | Database connection password leak | < 2.1.0 |
#### Version 2.0.1
| CVE | Title | Affected |
|:---------------|:------------------------------------------------------------|------------------: |
| CVE-2022-41703 | SQL injection vulnerability in adhoc clauses | < 2.0.1 or < 1.5.2 |
| CVE-2022-43717 | Cross-Site Scripting on dashboards | < 2.0.1 or < 1.5.2 |
| CVE-2022-43718 | Cross-Site Scripting vulnerability on upload forms | < 2.0.1 or < 1.5.2 |
| CVE-2022-43719 | Cross Site Request Forgery (CSRF) on accept, request access | < 2.0.1 or < 1.5.2 |
| CVE-2022-43720 | Improper rendering of user input | < 2.0.1 or < 1.5.2 |
| CVE-2022-43721 | Open Redirect Vulnerability | < 2.0.1 or < 1.5.2 |
| CVE-2022-45438 | Dashboard metadata information leak | < 2.0.1 or < 1.5.2 |

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---
title: Security Configurations
sidebar_position: 1
---
Authentication and authorization in Superset is handled by Flask AppBuilder (FAB), an application development framework
built on top of Flask. FAB provides authentication, user management, permissions and roles.
Please read its [Security documentation](https://flask-appbuilder.readthedocs.io/en/latest/security.html).
### Provided Roles
Superset ships with a set of roles that are handled by Superset itself. You can assume
that these roles will stay up-to-date as Superset evolves (and as you update Superset versions).
Even though **Admin** users have the ability, we don't recommend altering the
permissions associated with each role (e.g. by removing or adding permissions to them). The permissions
associated with each role will be re-synchronized to their original values when you run
the **superset init** command (often done between Superset versions).
A table with the permissions for these roles can be found at [/RESOURCES/STANDARD_ROLES.md](https://github.com/apache/superset/blob/master/RESOURCES/STANDARD_ROLES.md).
### Admin
Admins have all possible rights, including granting or revoking rights from other
users and altering other people’s slices and dashboards.
### Alpha
Alpha users have access to all data sources, but they cannot grant or revoke access
from other users. They are also limited to altering the objects that they own. Alpha users can add and alter data sources.
### Gamma
Gamma users have limited access. They can only consume data coming from data sources
they have been given access to through another complementary role. They only have access to
view the slices and dashboards made from data sources that they have access to. Currently Gamma
users are not able to alter or add data sources. We assume that they are mostly content consumers, though they can create slices and dashboards.
Also note that when Gamma users look at the dashboards and slices list view, they will
only see the objects that they have access to.
### sql_lab
The **sql_lab** role grants access to SQL Lab. Note that while **Admin** users have access
to all databases by default, both **Alpha** and **Gamma** users need to be given access on a per database basis.
### Public
To allow logged-out users to access some Superset features, you can use the `PUBLIC_ROLE_LIKE` config setting and assign it to another role whose permissions you want passed to this role.
For example, by setting `PUBLIC_ROLE_LIKE = "Gamma"` in your `superset_config.py` file, you grant
public role the same set of permissions as for the **Gamma** role. This is useful if one
wants to enable anonymous users to view dashboards. Explicit grant on specific datasets is
still required, meaning that you need to edit the **Public** role and add the public data sources to the role manually.
### Managing Data Source Access for Gamma Roles
Here’s how to provide users access to only specific datasets. First make sure the users with
limited access have [only] the Gamma role assigned to them. Second, create a new role (Menu -> Security -> List Roles) and click the + sign.
This new window allows you to give this new role a name, attribute it to users and select the
tables in the **Permissions** dropdown. To select the data sources you want to associate with this role, simply click on the dropdown and use the typeahead to search for your table names.
You can then confirm with users assigned to the **Gamma** role that they see the
objects (dashboards and slices) associated with the tables you just extended them.
### SQL Execution Security Considerations
Apache Superset includes features designed to provide safeguards when interacting with connected databases, such as the `DISALLOWED_SQL_FUNCTIONS` configuration setting. This aims to prevent the execution of potentially harmful database functions or system variables directly from Superset interfaces like SQL Lab.
However, it is crucial to understand the following:
**Superset is Not a Database Firewall**: Superset's built-in checks, like `DISALLOWED_SQL_FUNCTIONS`, provide a layer of protection but cannot guarantee complete security against all database-level threats or advanced bypass techniques (like specific comment injection methods). They should be viewed as a supplement to, not a replacement for, robust database security.
**Configuration is Key**: The effectiveness of Superset's safeguards heavily depends on proper configuration by the Superset administrator. This includes maintaining the `DISALLOWED_SQL_FUNCTIONS` list, carefully managing feature flags (like `ENABLE_TEMPLATE_PROCESSING`), and configuring other security settings appropriately.
**Database Security is Paramount**: The ultimate responsibility for securing database access, controlling permissions, and preventing unauthorized function execution lies with the database administrators (DBAs) and security teams managing the underlying database instance.
**Recommended Database Practices**: We strongly recommend implementing security best practices at the database level, including:
* **Least Privilege**: Connecting Superset using dedicated database user accounts with the minimum permissions required for Superset's operation (typically read-only access to necessary schemas/tables).
* **Database Roles & Permissions**: Utilizing database-native roles and permissions to restrict access to sensitive functions, system variables (like `@@hostname`), schemas, or tables.
* **Network Security**: Employing network-level controls like database firewalls or proxies to restrict connections.
* **Auditing**: Enabling database-level auditing to monitor executed queries and access patterns.
By combining Superset's configurable safeguards with strong database-level security practices, you can achieve a more robust and layered security posture.
### REST API for user & role management
Flask-AppBuilder supports a REST API for user CRUD,
but this feature is in beta and is not enabled by default in Superset.
To enable this feature, set the following in your Superset configuration:
```python
FAB_ADD_SECURITY_API = True
```
Once configured, the documentation for additional "Security" endpoints will be visible in Swagger for you to explore.
### Customizing Permissions
The permissions exposed by FAB are very granular and allow for a great level of
customization. FAB creates many permissions automatically for each model that is
created (can_add, can_delete, can_show, can_edit, …) as well as for each view.
On top of that, Superset can expose more granular permissions like **all_datasource_access**.
**We do not recommend altering the 3 base roles as there are a set of assumptions that
Superset is built upon**. It is possible though for you to create your own roles, and union them to existing ones.
### Permissions
Roles are composed of a set of permissions, and Superset has many categories of
permissions. Here are the different categories of permissions:
- Model & Action: models are entities like Dashboard, Slice, or User. Each model has
a fixed set of permissions, like **can_edit**, **can_show**, **can_delete**, **can_list**, **can_add**,
and so on. For example, you can allow a user to delete dashboards by adding **can_delete** on
Dashboard entity to a role and granting this user that role.
- Views: views are individual web pages, like the Explore view or the SQL Lab view.
When granted to a user, they will see that view in its menu items, and be able to load that page.
- Data source: For each data source, a permission is created. If the user does not have the
`all_datasource_access permission` granted, the user will only be able to see Slices or explore the data sources that are granted to them
- Database: Granting access to a database allows for the user to access all
data sources within that database, and will enable the user to query that
database in SQL Lab, provided that the SQL Lab specific permission have been granted to the user
### Restricting Access to a Subset of Data Sources
We recommend giving a user the **Gamma** role plus any other roles that would add
access to specific data sources. We recommend that you create individual roles for
each access profile. For example, the users on the Finance team might have access to a set of
databases and data sources; these permissions can be consolidated in a single role.
Users with this profile then need to be assigned the **Gamma** role as a foundation to
the models and views they can access, and that Finance role that is a collection of permissions to data objects.
A user can have multiple roles associated with them. For example, an executive on the Finance
team could be granted **Gamma**, **Finance**, and the **Executive** roles. The **Executive**
role could provide access to a set of data sources and dashboards made available only to executives.
In the **Dashboards** view, a user can only see the ones they have access to
based on the roles and permissions that were attributed.
### Row Level Security
Using Row Level Security filters (under the **Security** menu) you can create filters
that are assigned to a particular table, as well as a set of roles.
If you want members of the Finance team to only have access to
rows where `department = "finance"`, you could:
- Create a Row Level Security filter with that clause (`department = "finance"`)
- Then assign the clause to the **Finance** role and the table it applies to
The **clause** field, which can contain arbitrary text, is then added to the generated
SQL statement’s WHERE clause. So you could even do something like create a filter
for the last 30 days and apply it to a specific role, with a clause
like `date_field > DATE_SUB(NOW(), INTERVAL 30 DAY)`. It can also support
multiple conditions: `client_id = 6` AND `advertiser="foo"`, etc.
All relevant Row level security filters will be combined together (under the hood,
the different SQL clauses are combined using AND statements). This means it's
possible to create a situation where two roles conflict in such a way as to limit a table subset to empty.
For example, the filters `client_id=4` and `client_id=5`, applied to a role,
will result in users of that role having `client_id=4` AND `client_id=5`
added to their query, which can never be true.
### User Sessions
Superset uses [Flask](https://pypi.org/project/Flask/)
and [Flask-Login](https://pypi.org/project/Flask-Login/) for user session management.
Session cookies are used to maintain session info and user state between requests,
although they do not contain personal user information they serve the purpose of identifying
a user session on the server side.
The session cookie is encrypted with the application `SECRET_KEY` and cannot be read by the client.
So it's very important to keep the `SECRET_KEY` secret and set to a secure unique complex random value.
Flask and Flask-Login offer a number of configuration options to control session behavior.
- Relevant Flask settings:
`SESSION_COOKIE_HTTPONLY`: (default: `False`): Controls if cookies should be set with the `HttpOnly` flag.
`SESSION_COOKIE_SECURE`: (default: `False`) Browsers will only send cookies with requests over
HTTPS if the cookie is marked “secure”. The application must be served over HTTPS for this to make sense.
`SESSION_COOKIE_SAMESITE`: (default: "Lax") Prevents the browser from sending this cookie along with cross-site requests.
`PERMANENT_SESSION_LIFETIME`: (default: "31 days") The lifetime of a permanent session as a `datetime.timedelta` object.
#### Switching to server side sessions
Server side sessions offer benefits over client side sessions on security and performance.
By enabling server side sessions, the session data is stored server side and only a session ID
is sent to the client. When a user logs in, a session is created server side and the session ID
is sent to the client in a cookie. The client will send the session ID with each request and the
server will use it to retrieve the session data.
On logout, the session is destroyed server side and the session cookie is deleted on the client side.
This reduces the risk for replay attacks and session hijacking.
Superset uses [Flask-Session](https://flask-session.readthedocs.io/en/latest/) to manage server side sessions.
To enable this extension you have to set:
``` python
SESSION_SERVER_SIDE = True
```
Flask-Session offers multiple backend session interfaces for Flask, here's an example for Redis:
``` python
from redis import Redis
SESSION_TYPE = "redis"
SESSION_REDIS = Redis(host="redis", port=6379, db=0)
# sign the session cookie sid
SESSION_USE_SIGNER = True
```
### Content Security Policy (CSP)
Superset uses the [Talisman](https://pypi.org/project/flask-talisman/) extension to enable implementation of a
[Content Security Policy (CSP)](https://developer.mozilla.org/en-US/docs/Web/HTTP/CSP), an added
layer of security that helps to detect and mitigate certain types of attacks, including
Cross-Site Scripting (XSS) and data injection attacks.
A CSP makes it possible for server administrators to reduce or eliminate the vectors by which XSS can
occur by specifying the domains that the browser should consider to be valid sources of executable scripts.
A CSP-compatible browser will then only execute scripts loaded in source files received from those allowed domains,
ignoring all other scripts (including inline scripts and event-handling HTML attributes).
A policy is described using a series of policy directives, each of which describes the policy for
a certain resource type or policy area. You can check possible directives
[here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy).
It's extremely important to correctly configure a Content Security Policy when deploying Superset to
prevent many types of attacks. Superset provides two variables in `config.py` for deploying a CSP:
- `TALISMAN_ENABLED` defaults to `True`; set this to `False` in order to disable CSP
- `TALISMAN_CONFIG` holds the actual the policy definition (*see example below*) as well as any
other arguments to be passed to Talisman.
When running in production mode, Superset will check at startup for the presence
of a CSP. If one is not found, it will issue a warning with the security risks. For environments
where CSP policies are defined outside of Superset using other software, administrators can disable
this warning using the `CONTENT_SECURITY_POLICY_WARNING` key in `config.py`.
#### CSP Requirements
- Superset needs the `style-src unsafe-inline` CSP directive in order to operate.
```
style-src 'self' 'unsafe-inline'
```
- Only scripts marked with a [nonce](https://content-security-policy.com/nonce/) can be loaded and executed.
Nonce is a random string automatically generated by Talisman on each page load.
You can get current nonce value by calling jinja macro `csp_nonce()`.
```html
<script nonce="{{ csp_nonce() }}">
/* my script */
</script>
```
- Some dashboards load images using data URIs and require `data:` in their `img-src`
```
img-src 'self' data:
```
- MapBox charts use workers and need to connect to MapBox servers in addition to the Superset origin
```
worker-src 'self' blob:
connect-src 'self' https://api.mapbox.com https://events.mapbox.com
```
- Cartodiagram charts request map data (image and json) from external resources that can be edited by users,
and therefore either require a list of allowed domains to request from or a wildcard (`'*'`) for `img-src` and `connect-src`.
- Other CSP directives default to `'self'` to limit content to the same origin as the Superset server.
In order to adjust provided CSP configuration to your needs, follow the instructions and examples provided in
[Content Security Policy Reference](https://content-security-policy.com/)
#### Other Talisman security considerations
Setting `TALISMAN_ENABLED = True` will invoke Talisman's protection with its default arguments,
of which `content_security_policy` is only one. Those can be found in the
[Talisman documentation](https://pypi.org/project/flask-talisman/) under *Options*.
These generally improve security, but administrators should be aware of their existence.
In particular, the option of `force_https = True` (`False` by default) may break Superset's Alerts & Reports
if workers are configured to access charts via a `WEBDRIVER_BASEURL` beginning
with `http://`. As long as a Superset deployment enforces https upstream, e.g.,
through a load balancer or application gateway, it should be acceptable to keep this
option disabled. Otherwise, you may want to enable `force_https` like this:
```python
TALISMAN_CONFIG = {
"force_https": True,
"content_security_policy": { ...
```
#### Configuring Talisman in Superset
Talisman settings in Superset can be modified using superset_config.py. If you need to adjust security policies, you can override the default configuration.
Example: Overriding Talisman Configuration in superset_config.py for loading images form s3 or other external sources.
```python
TALISMAN_CONFIG = {
"content_security_policy": {
"base-uri": ["'self'"],
"default-src": ["'self'"],
"img-src": [
"'self'",
"blob:",
"data:",
"https://apachesuperset.gateway.scarf.sh",
"https://static.scarf.sh/",
# "https://cdn.brandfolder.io", # Uncomment when SLACK_ENABLE_AVATARS is True # noqa: E501
"ows.terrestris.de",
"aws.s3.com", # Add Your Bucket or external data source
],
"worker-src": ["'self'", "blob:"],
"connect-src": [
"'self'",
"https://api.mapbox.com",
"https://events.mapbox.com",
],
"object-src": "'none'",
"style-src": [
"'self'",
"'unsafe-inline'",
],
"script-src": ["'self'", "'strict-dynamic'"],
},
"content_security_policy_nonce_in": ["script-src"],
"force_https": False,
"session_cookie_secure": False,
}
```
For more information on setting up Talisman, please refer to
https://superset.apache.org/docs/configuration/networking-settings/#changing-flask-talisman-csp.
### Reporting Security Vulnerabilities
Apache Software Foundation takes a rigorous standpoint in annihilating the security issues in its
software projects. Apache Superset is highly sensitive and forthcoming to issues pertaining to its
features and functionality.
If you have apprehensions regarding Superset security or you discover vulnerability or potential
threat, don’t hesitate to get in touch with the Apache Security Team by dropping a mail at
security@apache.org. In the mail, specify the project name Superset with the description of the
issue or potential threat. You are also urged to recommend the way to reproduce and replicate the
issue. The security team and the Superset community will get back to you after assessing and
analysing the findings.
PLEASE PAY ATTENTION to report the security issue on the security email before disclosing it on
public domain. The ASF Security Team maintains a page with the description of how vulnerabilities
and potential threats are handled, check [their web page](https://apache.org/security/committers.html)
for more details.

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---
title: Creating Your First Dashboard
hide_title: true
sidebar_position: 1
version: 1
---
import useBaseUrl from "@docusaurus/useBaseUrl";
## Creating Your First Dashboard
This section is focused on documentation for end-users who will be using Superset
for the data analysis and exploration workflow
(data analysts, business analysts, data
scientists, etc).
:::tip
In addition to this site, [Preset.io](http://preset.io/) maintains an updated set of end-user
documentation at [docs.preset.io](https://docs.preset.io/).
:::
This tutorial targets someone who wants to create charts and dashboards in Superset. We’ll show you
how to connect Superset to a new database and configure a table in that database for analysis.
You’ll also explore the data you’ve exposed and add a visualization to a dashboard so that you get a
feel for the end-to-end user experience.
### Connecting to a new database
Superset itself doesn't have a storage layer to store your data but instead pairs with
your existing SQL-speaking database or data store.
First things first, we need to add the connection credentials to your database to be able
to query and visualize data from it. If you're using Superset locally via
[Docker compose](/docs/installation/docker-compose), you can
skip this step because a Postgres database, named **examples**, is included and
pre-configured in Superset for you.
Under the **+** menu in the top right, select Data, and then the _Connect Database_ option:
<img src={useBaseUrl("/img/tutorial/tutorial_01_add_database_connection.png")} width="600" />{" "} <br/><br/>
Then select your database type in the resulting modal:
<img src={useBaseUrl("/img/tutorial/tutorial_02_select_database.png" )} width="600" />{" "} <br/><br/>
Once you've selected a database, you can configure a number of advanced options in this window,
or for the purposes of this walkthrough, you can click the link below all these fields:
<img src={useBaseUrl("/img/tutorial/tutorial_03a_database_connection_string_link.png" )} width="600" />{" "} <br/><br/>
Please note, if you are trying to connect to another locally running database (whether on host or another container), and you get the message `The port is closed.`, then you need to adjust the HOST to `host.docker.internal`
Once you've clicked that link you only need to specify two things (the database name and SQLAlchemy URI):
<img src={useBaseUrl("/img/tutorial/tutorial_03b_connection_string_details.png" )} width="600" />{" "} <br/><br/>
As noted in the text below the form, you should refer to the SQLAlchemy documentation on
[creating new connection URIs](https://docs.sqlalchemy.org/en/12/core/engines.html#database-urls)
for your target database.
Click the **Test Connection** button to confirm things work end to end. If the connection looks good, save the configuration
by clicking the **Connect** button in the bottom right corner of the modal window:
Congratulations, you've just added a new data source in Superset!
### Registering a new table
Now that you’ve configured a data source, you can select specific tables (called **Datasets** in Superset)
that you want exposed in Superset for querying.
Navigate to **Data ‣ Datasets** and select the **+ Dataset** button in the top right corner.
<img src={useBaseUrl("/img/tutorial/tutorial_08_sources_tables.png" )} />
A modal window should pop up in front of you. Select your **Database**,
**Schema**, and **Table** using the drop downs that appear. In the following example,
we register the **cleaned_sales_data** table from the **examples** database.
<img src={useBaseUrl("/img/tutorial/tutorial_09_add_new_table.png" )} />
To finish, click the **Add** button in the bottom right corner. You should now see your dataset in the list of datasets.
### Customizing column properties
Now that you've registered your dataset, you can configure column properties
for how the column should be treated in the Explore workflow:
- Is the column temporal? (should it be used for slicing & dicing in time series charts?)
- Should the column be filterable?
- Is the column dimensional?
- If it's a datetime column, how should Superset parse
the datetime format? (using the [ISO-8601 string pattern](https://en.wikipedia.org/wiki/ISO_8601))
<img src={useBaseUrl("/img/tutorial/tutorial_column_properties.png" )} />
### Superset semantic layer
Superset has a thin semantic layer that adds many quality of life improvements for analysts.
The Superset semantic layer can store 2 types of computed data:
1. Virtual metrics: you can write SQL queries that aggregate values
from multiple column (e.g. `SUM(recovered) / SUM(confirmed)`) and make them
available as columns for (e.g. `recovery_rate`) visualization in Explore.
Aggregate functions are allowed and encouraged for metrics.
<img src={useBaseUrl("/img/tutorial/tutorial_sql_metric.png" )} />
You can also certify metrics if you'd like for your team in this view.
1. Virtual calculated columns: you can write SQL queries that
customize the appearance and behavior
of a specific column (e.g. `CAST(recovery_rate as float)`).
Aggregate functions aren't allowed in calculated columns.
<img src={useBaseUrl("/img/tutorial/tutorial_calculated_column.png" )} />
### Creating charts in Explore view
Superset has 2 main interfaces for exploring data:
- **Explore**: no-code viz builder. Select your dataset, select the chart,
customize the appearance, and publish.
- **SQL Lab**: SQL IDE for cleaning, joining, and preparing data for Explore workflow
We'll focus on the Explore view for creating charts right now.
To start the Explore workflow from the **Datasets** tab, start by clicking the name
of the dataset that will be powering your chart.
<img src={useBaseUrl("/img/tutorial/tutorial_launch_explore.png" )} /><br/><br/>
You're now presented with a powerful workflow for exploring data and iterating on charts.
- The **Dataset** view on the left-hand side has a list of columns and metrics,
scoped to the current dataset you selected.
- The **Data** preview below the chart area also gives you helpful data context.
- Using the **Data** tab and **Customize** tabs, you can change the visualization type,
select the temporal column, select the metric to group by, and customize
the aesthetics of the chart.
As you customize your chart using drop-down menus, make sure to click the **Run** button
to get visual feedback.
<img src={useBaseUrl("/img/tutorial/tutorial_explore_run.jpg" )} />
In the following screenshot, we craft a grouped Time-series Bar Chart to visualize
our quarterly sales data by product line just by clicking options in drop-down menus.
<img src={useBaseUrl("/img/tutorial/tutorial_explore_settings.jpg" )} />
### Creating a slice and dashboard
To save your chart, first click the **Save** button. You can either:
- Save your chart and add it to an existing dashboard
- Save your chart and add it to a new dashboard
In the following screenshot, we save the chart to a new "Superset Duper Sales Dashboard":
<img src={useBaseUrl("/img/tutorial/tutorial_save_slice.png" )} />
To publish, click **Save and goto Dashboard**.
Behind the scenes, Superset will create a slice and store all the information needed
to create your chart in its thin data layer
(the query, chart type, options selected, name, etc).
<img src={useBaseUrl("/img/tutorial/tutorial_first_dashboard.png" )} style={{width: "100%", maxWidth: "500px"}} />
To resize the chart, start by clicking the Edit Dashboard button in the top right corner.
<img src={useBaseUrl("/img/tutorial/tutorial_edit_button.png" )} width="300" />
Then, click and drag the bottom right corner of the chart until the chart layout snaps
into a position you like onto the underlying grid.
<img src={useBaseUrl("/img/tutorial/tutorial_chart_resize.png" )} style={{width: "100%", maxWidth: "500px"}} />
Click **Save** to persist the changes.
Congrats! You’ve successfully linked, analyzed, and visualized data in Superset. There are a wealth
of other table configuration and visualization options, so please start exploring and creating
slices and dashboards of your own.
### Manage access to Dashboards
Access to dashboards is managed via owners (users that have edit permissions to the dashboard).
Non-owner users access can be managed in two different ways. The dashboard needs to be published to be visible to other users.
1. Dataset permissions - if you add to the relevant role permissions to datasets it automatically grants implicit access to all dashboards that uses those permitted datasets.
2. Dashboard roles - if you enable [**DASHBOARD_RBAC** feature flag](/docs/configuration/configuring-superset#feature-flags) then you will be able to manage which roles can access the dashboard
- Granting a role access to a dashboard will bypass dataset level checks. Having dashboard access implicitly grants read access to all the featured charts in the dashboard, and thereby also all the associated datasets.
- If no roles are specified for a dashboard, regular **Dataset permissions** will apply.
<img src={useBaseUrl("/img/tutorial/tutorial_dashboard_access.png" )} />
### Publishing a Dashboard
If you would like to make your dashboard available to other users, click on the `Draft` button next to the
title of your dashboard.
<img src={useBaseUrl("/img/tutorial/publish_button_dashboard.png" )} />
:::warning
Draft dashboards are only visible to the dashboard owners and admins. Published dashboards are visible to all users with access to the underlying datasets or if RBAC is enabled, to the roles that have been granted access to the dashboard.
:::
### Mark a Dashboard as Favorite
You can mark a dashboard as a favorite by clicking on the star icon next to the title of your dashboard. This makes it easier to find it in the list of dashboards or on the home page.
### Customizing dashboard
The following URL parameters can be used to modify how the dashboard is rendered:
- `standalone`:
- `0` (default): dashboard is displayed normally
- `1`: Top Navigation is hidden
- `2`: Top Navigation + title is hidden
- `3`: Top Navigation + title + top level tabs are hidden
- `show_filters`:
- `0`: render dashboard without Filter Bar
- `1` (default): render dashboard with Filter Bar if native filters are enabled
- `expand_filters`:
- (default): render dashboard with Filter Bar expanded if there are native filters
- `0`: render dashboard with Filter Bar collapsed
- `1`: render dashboard with Filter Bar expanded
For example, when running the local development build, the following will disable the
Top Nav and remove the Filter Bar:
`http://localhost:8088/superset/dashboard/my-dashboard/?standalone=1&show_filters=0`

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---
title: Exploring Data in Superset
hide_title: true
sidebar_position: 2
version: 1
---
import useBaseUrl from "@docusaurus/useBaseUrl";
## Exploring Data in Superset
In this tutorial, we will introduce key concepts in Apache Superset through the exploration of a
real dataset which contains the flights made by employees of a UK-based organization in 2011. The
following information about each flight is given:
- The traveler’s department. For the purposes of this tutorial the departments have been renamed
Orange, Yellow and Purple.
- The cost of the ticket.
- The travel class (Economy, Premium Economy, Business and First Class).
- Whether the ticket was a single or return.
- The date of travel.
- Information about the origin and destination.
- The distance between the origin and destination, in kilometers (km).
### Enabling Data Upload Functionality
You may need to enable the functionality to upload a CSV or Excel file to your database. The following section
explains how to enable this functionality for the examples database.
In the top menu, select **Settings ‣ Data ‣ Database Connections**. Find the **examples** database in the list and
select the **Edit** button.
<img src={useBaseUrl("/img/tutorial/edit-record.png" )} />
In the resulting modal window, switch to the **Advanced** tab and open **Security** section.
Then, tick the checkbox for **Allow file uploads to database**. End by clicking the **Finish** button.
<img src={useBaseUrl("/img/tutorial/allow-file-uploads.png" )} />
### Loading CSV Data
Download the CSV dataset to your computer from
[GitHub](https://raw.githubusercontent.com/apache-superset/examples-data/master/tutorial_flights.csv).
In the top menu, select **Settings ‣ Data ‣ Database Connections**. Then, **Upload file to database ‣ Upload CSV**.
<img src={useBaseUrl("/img/tutorial/upload_a_csv.png" )} />
Then, select select the CSV file from your computer, select **Database** and **Schema**, and enter the **Table Name**
as _tutorial_flights_.
<img src={useBaseUrl("/img/tutorial/csv_to_database_configuration.png" )} />
Next enter the text _Travel Date_ into the **File settings ‣ Columns to be parsed as dates** field.
<img src={useBaseUrl("/img/tutorial/parse_dates_column.png" )} />
Leaving all the other options in their default settings, select **Upload** at the bottom of the page.
### Table Visualization
You should now see _tutorial_flights_ as a dataset in the **Datasets** tab. Click on the entry to
launch an Explore workflow using this dataset.
In this section, we'll create a table visualization
to show the number of flights and cost per travel class.
By default, Apache Superset only shows the last week of data. In our example, we want to visualize all
of the data in the dataset. Click the **Time ‣ Time Range** section and change
the **Range Type** to **No Filter**.
<img src={useBaseUrl("/img/tutorial/no_filter_on_time_filter.png" )} />
Click **Apply** to save.
Now, we want to specify the rows in our table by using the **Group by** option. Since in this
example, we want to understand different Travel Classes, we select **Travel Class** in this menu.
Next, we can specify the metrics we would like to see in our table with the **Metrics** option.
- `COUNT(*)`, which represents the number of rows in the table
(in this case, quantity of flights in each Travel Class)
- `SUM(Cost)`, which represents the total cost spent by each Travel Class
<img src={useBaseUrl("/img/tutorial/sum_cost_column.png" )} />
Finally, select **Run Query** to see the results of the table.
<img src={useBaseUrl("/img/tutorial/tutorial_table.png" )} />
To save the visualization, click on **Save** in the top left of the screen. In the following modal,
- Select the **Save as**
option and enter the chart name as Tutorial Table (you will be able to find it again through the
**Charts** screen, accessible in the top menu).
- Select **Add To Dashboard** and enter
Tutorial Dashboard. Finally, select **Save & Go To Dashboard**.
<img src={useBaseUrl("/img/tutorial/save_tutorial_table.png" )} />
### Dashboard Basics
Next, we are going to explore the dashboard interface. If you’ve followed the previous section, you
should already have the dashboard open. Otherwise, you can navigate to the dashboard by selecting
Dashboards on the top menu, then Tutorial dashboard from the list of dashboards.
On this dashboard you should see the table you created in the previous section. Select **Edit
dashboard** and then hover over the table. By selecting the bottom right hand corner of the table
(the cursor will change too), you can resize it by dragging and dropping.
<img src={useBaseUrl("/img/tutorial/resize_tutorial_table_on_dashboard.png" )} />
Finally, save your changes by selecting Save changes in the top right.
### Pivot Table
In this section, we will extend our analysis using a more complex visualization, Pivot Table. By the
end of this section, you will have created a table that shows the monthly spend on flights for the
first six months, by department, by travel class.
Create a new chart by selecting **+ ‣ Chart** from the top right corner. Choose
tutorial_flights again as a datasource, then click on the visualization type to get to the
visualization menu. Select the **Pivot Table** visualization (you can filter by entering text in the
search box) and then **Create New Chart**.
<img src={useBaseUrl("/img/tutorial/create_pivot.png" )} />
In the **Time** section, keep the Time Column as Travel Date (this is selected automatically as we
only have one time column in our dataset). Then select Time Grain to be month as having daily data
would be too granular to see patterns from. Then select the time range to be the first six months of
2011 by click on Last week in the Time Range section, then in Custom selecting a Start / end of 1st
January 2011 and 30th June 2011 respectively by either entering directly the dates or using the
calendar widget (by selecting the month name and then the year, you can move more quickly to far
away dates).
<img src={useBaseUrl("/img/tutorial/select_dates_pivot_table.png" )} />
Next, within the **Query** section, remove the default COUNT(\*) and add Cost, keeping the default
SUM aggregate. Note that Apache Superset will indicate the type of the metric by the symbol on the
left hand column of the list (ABC for string, # for number, a clock face for time, etc.).
In **Group by**, select **Time**: this will automatically use the Time Column and Time Grain
selections we defined in the Time section.
Within **Columns**, first select Department and then Travel Class. All set – let’s **Run Query** to
see some data!
<img src={useBaseUrl("/img/tutorial/tutorial_pivot_table.png" )} />
You should see months in the rows and Department and Travel Class in the columns. Publish this chart
to your existing Tutorial Dashboard you created earlier.
### Line Chart
In this section, we are going to create a line chart to understand the average price of a ticket by
month across the entire dataset.
In the Time section, as before, keep the Time Column as Travel Date and Time Grain as month but this
time for the Time range select No filter as we want to look at entire dataset.
Within Metrics, remove the default `COUNT(*)` metric and instead add `AVG(Cost)`, to show the mean value.
<img src={useBaseUrl("/img/tutorial/average_aggregate_for_cost.png" )} />
Next, select **Run Query** to show the data on the chart.
How does this look? Well, we can see that the average cost goes up in December. However, perhaps it
doesn’t make sense to combine both single and return tickets, but rather show two separate lines for
each ticket type.
Let’s do this by selecting Ticket Single or Return in the Group by box, and the selecting **Run
Query** again. Nice! We can see that on average single tickets are cheaper than returns and that the
big spike in December is caused by return tickets.
Our chart is looking pretty good already, but let’s customize some more by going to the Customize
tab on the left hand pane. Within this pane, try changing the Color Scheme, removing the range
filter by selecting No in the Show Range Filter drop down and adding some labels using X Axis Label
and Y Axis Label.
<img src={useBaseUrl("/img/tutorial/tutorial_line_chart.png" )} />
Once you’re done, publish the chart in your Tutorial Dashboard.
### Markup
In this section, we will add some text to our dashboard. If you’re there already, you can navigate
to the dashboard by selecting Dashboards on the top menu, then Tutorial dashboard from the list of
dashboards. Got into edit mode by selecting **Edit dashboard**.
Within the Insert components pane, drag and drop a Markdown box on the dashboard. Look for the blue
lines which indicate the anchor where the box will go.
<img src={useBaseUrl("/img/tutorial/blue_bar_insert_component.png" )} />
Now, to edit the text, select the box. You can enter text, in markdown format (see
[this Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) for
more information about this format). You can toggle between Edit and Preview using the menu on the
top of the box.
<img src={useBaseUrl("/img/tutorial/markdown.png" )} />
To exit, select any other part of the dashboard. Finally, don’t forget to keep your changes using
**Save changes**.
### Publishing Your Dashboard
If you have followed all of the steps outlined in the previous section, you should have a dashboard
that looks like the below. If you would like, you can rearrange the elements of the dashboard by
selecting **Edit dashboard** and dragging and dropping.
If you would like to make your dashboard available to other users, simply select Draft next to the
title of your dashboard on the top left to change your dashboard to be in Published state. You can
also favorite this dashboard by selecting the star.
<img src={useBaseUrl("/img/tutorial/publish_dashboard.png" )} />
### Annotations
Annotations allow you to add additional context to your chart. In this section, we will add an
annotation to the Tutorial Line Chart we made in a previous section. Specifically, we will add the
dates when some flights were cancelled by the UK’s Civil Aviation Authority in response to the
eruption of the Grímsvötn volcano in Iceland (23-25 May 2011).
First, add an annotation layer by navigating to Manage ‣ Annotation Layers. Add a new annotation
layer by selecting the green plus sign to add a new record. Enter the name Volcanic Eruptions and
save. We can use this layer to refer to a number of different annotations.
Next, add an annotation by navigating to Manage ‣ Annotations and then create a new annotation by
selecting the green plus sign. Then, select the Volcanic Eruptions layer, add a short description
Grímsvötn and the eruption dates (23-25 May 2011) before finally saving.
<img src={useBaseUrl("/img/tutorial/edit_annotation.png" )} />
Then, navigate to the line chart by going to Charts then selecting Tutorial Line Chart from the
list. Next, go to the Annotations and Layers section and select Add Annotation Layer. Within this
dialogue:
- Name the layer as Volcanic Eruptions
- Change the Annotation Layer Type to Event
- Set the Annotation Source as Superset annotation
- Specify the Annotation Layer as Volcanic Eruptions
<img src={useBaseUrl("/img/tutorial/annotation_settings.png" )} />
Select **Apply** to see your annotation shown on the chart.
<img src={useBaseUrl("/img/tutorial/annotation.png" )} />
If you wish, you can change how your annotation looks by changing the settings in the Display
configuration section. Otherwise, select **OK** and finally **Save** to save your chart. If you keep
the default selection to overwrite the chart, your annotation will be saved to the chart and also
appear automatically in the Tutorial Dashboard.
### Advanced Analytics
In this section, we are going to explore the Advanced Analytics feature of Apache Superset that
allows you to apply additional transformations to your data. The three types of transformation are:
**Setting up the base chart**
In this section, we’re going to set up a base chart which we can then apply the different **Advanced
Analytics** features to. Start off by creating a new chart using the same _tutorial_flights_
datasource and the **Line Chart** visualization type. Within the Time section, set the Time Range as
1st October 2011 and 31st October 2011.
Next, in the query section, change the Metrics to the sum of Cost. Select **Run Query** to show the
chart. You should see the total cost per day for each month in October 2011.
<img src={useBaseUrl("/img/tutorial/advanced_analytics_base.png" )} />
Finally, save the visualization as Tutorial Advanced Analytics Base, adding it to the Tutorial
Dashboard.
### Rolling Mean
There is quite a lot of variation in the data, which makes it difficult to identify any trend. One
approach we can take is to show instead a rolling average of the time series. To do this, in the
**Moving Average** subsection of **Advanced Analytics**, select mean in the **Rolling** box and
enter 7 into both Periods and Min Periods. The period is the length of the rolling period expressed
as a multiple of the Time Grain. In our example, the Time Grain is day, so the rolling period is 7
days, such that on the 7th October 2011 the value shown would correspond to the first seven days of
October 2011. Lastly, by specifying Min Periods as 7, we ensure that our mean is always calculated
on 7 days and we avoid any ramp up period.
After displaying the chart by selecting **Run Query** you will see that the data is less variable
and that the series starts later as the ramp up period is excluded.
<img src={useBaseUrl("/img/tutorial/rolling_mean.png" )} />
Save the chart as Tutorial Rolling Mean and add it to the Tutorial Dashboard.
### Time Comparison
In this section, we will compare values in our time series to the value a week before. Start off by
opening the Tutorial Advanced Analytics Base chart, by going to **Charts** in the top menu and then
selecting the visualization name in the list (alternatively, find the chart in the Tutorial
Dashboard and select Explore chart from the menu for that visualization).
Next, in the Time Comparison subsection of **Advanced Analytics**, enter the Time Shift by typing in
“minus 1 week” (note this box accepts input in natural language). Run Query to see the new chart,
which has an additional series with the same values, shifted a week back in time.
<img src={useBaseUrl("/img/tutorial/time_comparison_two_series.png" )} />
Then, change the **Calculation type** to Absolute difference and select **Run Query**. We can now
see only one series again, this time showing the difference between the two series we saw
previously.
<img src={useBaseUrl("/img/tutorial/time_comparison_absolute_difference.png" )} />
Save the chart as Tutorial Time Comparison and add it to the Tutorial Dashboard.
### Resampling the data
In this section, we’ll resample the data so that rather than having daily data we have weekly data.
As in the previous section, reopen the Tutorial Advanced Analytics Base chart.
Next, in the Python Functions subsection of **Advanced Analytics**, enter 7D, corresponding to seven
days, in the Rule and median as the Method and show the chart by selecting **Run Query**.
<img src={useBaseUrl("/img/tutorial/resample.png" )} />
Note that now we have a single data point every 7 days. In our case, the value showed corresponds to
the median value within the seven daily data points. For more information on the meaning of the
various options in this section, refer to the
[Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html).
Lastly, save your chart as Tutorial Resample and add it to the Tutorial Dashboard. Go to the
tutorial dashboard to see the four charts side by side and compare the different outputs.

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---
title: Issue Codes
sidebar_position: 5
version: 1
---
# Issue Code Reference
This page lists issue codes that may be displayed in
Superset and provides additional context.
## Issue 1000
```
The datasource is too large to query.
```
It's likely your datasource has grown too large to run the current
query, and is timing out. You can resolve this by reducing the
size of your datasource or by modifying your query to only process a
subset of your data.
## Issue 1001
```
The database is under an unusual load.
```
Your query may have timed out because of unusually high load on the
database engine. You can make your query simpler, or wait until the
database is under less load and try again.
## Issue 1002
```
The database returned an unexpected error.
```
Your query failed because of an error that occurred on the database.
This may be due to a syntax error, a bug in your query, or some other
internal failure within the database. This is usually not an
issue within Superset, but instead a problem with the underlying
database that serves your query.
## Issue 1003
```
There is a syntax error in the SQL query. Perhaps there was a misspelling or a typo.
```
Your query failed because of a syntax error within the underlying query. Please
validate that all columns or tables referenced within the query exist and are spelled
correctly.
## Issue 1004
```
The column was deleted or renamed in the database.
```
Your query failed because it is referencing a column that no longer exists in
the underlying datasource. You should modify the query to reference the
replacement column, or remove this column from your query.
## Issue 1005
```
The table was deleted or renamed in the database.
```
Your query failed because it is referencing a table that no longer exists in
the underlying database. You should modify your query to reference the correct
table.
## Issue 1006
```
One or more parameters specified in the query are missing.
```
Your query was not submitted to the database because it's missing one or more
parameters. You should define all the parameters referenced in the query in a
valid JSON document. Check that the parameters are spelled correctly and that
the document has a valid syntax.
## Issue 1007
```
The hostname provided can't be resolved.
```
The hostname provided when adding a new database is invalid and cannot be
resolved. Please check that there are no typos in the hostname.
## Issue 1008
```
The port is closed.
```
The port provided when adding a new database is not open. Please check that
the port number is correct, and that the database is running and listening on
that port.
## Issue 1009
```
The host might be down, and cannot be reached on the provided port.
```
The host provided when adding a new database doesn't seem to be up.
Additionally, it cannot be reached on the provided port. Please check that
there are no firewall rules preventing access to the host.
## Issue 1010
```
Superset encountered an error while running a command.
```
Something unexpected happened, and Superset encountered an error while
running a command. Please reach out to your administrator.
## Issue 1011
```
Superset encountered an unexpected error.
```
Something unexpected happened in the Superset backend. Please reach out
to your administrator.
## Issue 1012
```
The username provided when connecting to a database is not valid.
```
The user provided a username that doesn't exist in the database. Please check
that the username is typed correctly and exists in the database.
## Issue 1013
```
The password provided when connecting to a database is not valid.
```
The user provided a password that is incorrect. Please check that the
password is typed correctly.
## Issue 1014
```
Either the username or the password used are incorrect.
```
Either the username provided does not exist or the password was written incorrectly. Please
check that the username and password were typed correctly.
## Issue 1015
```
Either the database is spelled incorrectly or does not exist.
```
Either the database was written incorrectly or it does not exist. Check that it was typed correctly.
## Issue 1016
```
The schema was deleted or renamed in the database.
```
The schema was either removed or renamed. Check that the schema is typed correctly and exists.
## Issue 1017
```
The user doesn't have the proper permissions to connect to the database
```
We were unable to connect to your database. Please confirm that your service account has the Viewer and Job User roles on the project.
## Issue 1018
```
One or more parameters needed to configure a database are missing.
```
Not all parameters required to test, create, or edit a database were present. Please double check which parameters are needed, and that they are present.
## Issue 1019
```
The submitted payload has the incorrect format.
```
Please check that the request payload has the correct format (eg, JSON).
## Issue 1020
```
The submitted payload has the incorrect schema.
```
Please check that the request payload has the expected schema.
## Issue 1021
```
Results backend needed for asynchronous queries is not configured.
```
Your instance of Superset doesn't have a results backend configured, which is needed for asynchronous queries. Please contact an administrator for further assistance.
## Issue 1022
```
Database does not allow data manipulation.
```
Only `SELECT` statements are allowed against this database. Please contact an administrator if you need to run DML (data manipulation language) on this database.
## Issue 1023
```
CTAS (create table as select) doesn't have a SELECT statement at the end.
```
The last statement in a query run as CTAS (create table as select) MUST be a SELECT statement. Please make sure the last statement in the query is a SELECT.
## Issue 1024
```
CVAS (create view as select) query has more than one statement.
```
When running a CVAS (create view as select) the query should have a single statement. Please make sure the query has a single statement, and no extra semi-colons other than the last one.
## Issue 1025
```
CVAS (create view as select) query is not a SELECT statement.
```
When running a CVAS (create view as select) the query should be a SELECT statement. Please make sure the query has a single statement and it's a SELECT statement.
## Issue 1026
```
Query is too complex and takes too long to run.
```
The submitted query might be too complex to run under the time limit defined by your Superset administrator. Please double check your query and verify if it can be optimized. Alternatively, contact your administrator to increase the timeout period.
## Issue 1027
```
The database is currently running too many queries.
```
The database might be under heavy load, running too many queries. Please try again later, or contact an administrator for further assistance.
## Issue 1028
```
One or more parameters specified in the query are malformed.
```
The query contains one or more malformed template parameters. Please check your query and confirm that all template parameters are surround by double braces, for example, "\{\{ ds \}\}". Then, try running your query again.
## Issue 1029
```
The object does not exist in this database.
```
Either the schema, column, or table do not exist in the database.
## Issue 1030
```
The query potentially has a syntax error.
```
The query might have a syntax error. Please check and run again.
## Issue 1031
```
The results backend no longer has the data from the query.
```
The results from the query might have been deleted from the results backend after some period. Please re-run your query.
## Issue 1032
```
The query associated with the results was deleted.
```
The query associated with the stored results no longer exists. Please re-run your query.
## Issue 1033
```
The results stored in the backend were stored in a different format, and no longer can be deserialized.
```
The query results were stored in a format that is no longer supported. Please re-run your query.
## Issue 1034
```
The database port provided is invalid.
```
Please check that the provided database port is an integer between 0 and 65535 (inclusive).
## Issue 1035
```
Failed to start remote query on a worker.
```
The query was not started by an asynchronous worker. Please reach out to your administrator for further assistance.
## Issue 1036
```
The database was deleted.
```
The operation failed because the database referenced no longer exists. Please reach out to your administrator for further assistance.