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docs: bifurcate documentation into user and admin sections (#38196)

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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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'll need to change configuration settings and install a suitable headless browser in your environment.
## Requirements
### Commons
#### In your `superset_config.py` or `superset_config_docker.py`
- `"ALERT_REPORTS"` [feature flag](/admin-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](/admin-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'll need to extend the Superset image to include a headless browser. Your options include:
- Use Playwright with Chrome: this is the recommended approach as of version 4.1.x or greater. A working example of a Dockerfile that installs these tools is provided under "Building your own production Docker image" on the [Docker Builds](/admin-docs/installation/docker-builds#building-your-own-production-docker-image) page. Read the code comments there as you'll also need to change a feature flag in your config.
- Use Firefox: you'll need to install geckodriver and Firefox.
- Use Chrome without Playwright: you'll need to install Chrome and set the value of `WEBDRIVER_TYPE` to `"chrome"` in your `superset_config.py`.
In Superset versions &lt;=4.0x, users installed Firefox or Chrome and that was documented here.
Only the worker container needs the browser.
### 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`.
#### Large Slack Workspaces (10k+ channels)
For workspaces with many channels, fetching the complete channel list can take several minutes and may encounter Slack API rate limits. Add the following to your `superset_config.py`:
```python
from datetime import timedelta
# Increase cache timeout to reduce API calls
# Default: 1 day (86400 seconds)
SLACK_CACHE_TIMEOUT = int(timedelta(days=2).total_seconds())
# Increase retry count for rate limit errors
# Default: 2
SLACK_API_RATE_LIMIT_RETRY_COUNT = 5
```
### 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](/admin-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 or Playwright with Chrome, you can stick with default values
# If you use Chrome and are *not* using Playwright, 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
```
## 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.
If you are handling the installation of the headless browser on your own, 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`.
### Duplicate report deliveries
In some deployment configurations a scheduled report can be delivered more than once around its planned time. This typically happens when more than one process is responsible for running the alerts & reports schedule (for example, multiple schedulers or Celery beat instances). To avoid duplicate emails or notifications:
- Ensure that only a **single scheduler/beat process** is configured to trigger alerts and reports for a given environment.
- If you run **multiple Celery workers**, verify that there is still only one component responsible for scheduling the report tasks (workers should execute tasks, not schedule them independently).
- Review your deployment/orchestration setup (for example systemd, Docker, or Kubernetes) to make sure the alerts & reports scheduler is **not started from multiple places by accident**.
## 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.
:::resources
- [Tutorial: Automated Alerts and Reporting via Slack/Email in Superset](https://dev.to/ngtduc693/apache-superset-topic-5-automated-alerts-and-reporting-via-slackemail-in-superset-2gbe)
- [Blog: Integrating Slack alerts and Apache Superset for better data observability](https://medium.com/affinityanswers-tech/integrating-slack-alerts-and-apache-superset-for-better-data-observability-fd2f9a12c350)
:::

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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 `BaseCache` (`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
```
:::resources
- [Blog: How to Set Up Global Async Queries (GAQ) in Apache Superset](https://medium.com/@ngigilevis/how-to-set-up-global-async-queries-gaq-in-apache-superset-a-complete-guide-9d2f4a047559)
:::

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---
title: Caching
hide_title: true
sidebar_position: 3
version: 1
---
# Caching
:::note
When a cache backend is configured, Superset expects it to remain available. Operations will
fail if the configured backend becomes unavailable rather than silently degrading. This
fail-fast behavior ensures operators are immediately aware of infrastructure issues.
:::
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](/admin-docs/configuration/async-queries-celery) for details.
## Caching Thumbnails
This is an optional feature that can be turned on by activating its [feature flag](/admin-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
```
## Signal Cache Backend
Superset supports an optional signal cache (`SIGNAL_CACHE_CONFIG`) for
high-performance distributed operations. This configuration enables:
- **Distributed locking**: Moves lock operations from the metadata database to Redis, improving
performance and reducing metastore load
- **Real-time event notifications**: Enables instant pub/sub messaging for task abort signals and
completion notifications instead of polling-based approaches
:::note
This requires Redis or Valkey specifically—it uses Redis-specific features (pub/sub, `SET NX EX`)
that are not available in general Flask-Caching backends.
:::
### Configuration
The signal cache uses Flask-Caching style configuration for consistency with other cache
backends. Configure `SIGNAL_CACHE_CONFIG` in `superset_config.py`:
```python
SIGNAL_CACHE_CONFIG = {
"CACHE_TYPE": "RedisCache",
"CACHE_REDIS_HOST": "localhost",
"CACHE_REDIS_PORT": 6379,
"CACHE_REDIS_DB": 0,
"CACHE_REDIS_PASSWORD": "", # Optional
}
```
For Redis Sentinel deployments:
```python
SIGNAL_CACHE_CONFIG = {
"CACHE_TYPE": "RedisSentinelCache",
"CACHE_REDIS_SENTINELS": [("sentinel1", 26379), ("sentinel2", 26379)],
"CACHE_REDIS_SENTINEL_MASTER": "mymaster",
"CACHE_REDIS_SENTINEL_PASSWORD": None, # Sentinel password (if different)
"CACHE_REDIS_PASSWORD": "", # Redis password
"CACHE_REDIS_DB": 0,
}
```
For SSL/TLS connections:
```python
SIGNAL_CACHE_CONFIG = {
"CACHE_TYPE": "RedisCache",
"CACHE_REDIS_HOST": "redis.example.com",
"CACHE_REDIS_PORT": 6380,
"CACHE_REDIS_SSL": True,
"CACHE_REDIS_SSL_CERTFILE": "/path/to/client.crt",
"CACHE_REDIS_SSL_KEYFILE": "/path/to/client.key",
"CACHE_REDIS_SSL_CA_CERTS": "/path/to/ca.crt",
}
```
### Distributed Lock TTL
You can configure the default lock TTL (time-to-live) in seconds. Locks automatically expire after
this duration to prevent deadlocks from crashed processes:
```python
DISTRIBUTED_LOCK_DEFAULT_TTL = 30 # Default: 30 seconds
```
Individual lock acquisitions can override this value when needed.
### Database-Only Mode
When `SIGNAL_CACHE_CONFIG` is not configured, Superset uses database-backed operations:
- **Locking**: Uses the KeyValue table with periodic cleanup of expired entries
- **Event notifications**: Uses database polling instead of pub/sub
While database-backed operations work reliably, the Redis backend is recommended for production
deployments where low latency and reduced database load are important.
:::resources
- [Blog: The Data Engineer's Guide to Lightning-Fast Superset Dashboards](https://preset.io/blog/the-data-engineers-guide-to-lightning-fast-apache-superset-dashboards/)
- [Blog: Accelerating Dashboards with Materialized Views](https://preset.io/blog/accelerating-apache-superset-dashboards-with-materialized-views/)
:::

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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](/admin-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](/admin-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'
}
}
]
```
## 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 the [Feature Flags](/admin-docs/configuration/feature-flags) documentation.
:::resources
- [Blog: Feature Flags in Apache Superset](https://preset.io/blog/feature-flags-in-apache-superset-and-preset/)
:::

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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: Feature Flags
hide_title: true
sidebar_position: 2
version: 1
---
import featureFlags from '@site/static/feature-flags.json';
export const FlagTable = ({flags}) => (
<table>
<thead>
<tr>
<th>Flag</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
{flags.map((flag) => (
<tr key={flag.name}>
<td><code>{flag.name}</code></td>
<td><code>{flag.default ? 'True' : 'False'}</code></td>
<td>
{flag.description}
{flag.docs && (
<> (<a href={flag.docs}>docs</a>)</>
)}
</td>
</tr>
))}
</tbody>
</table>
);
# Feature Flags
Superset uses feature flags to control the availability of features. Feature flags allow
gradual rollout of new functionality and provide a way to enable experimental features.
To enable a feature flag, add it to your `superset_config.py`:
```python
FEATURE_FLAGS = {
"ENABLE_TEMPLATE_PROCESSING": True,
}
```
## Lifecycle
Feature flags progress through lifecycle stages:
| Stage | Description |
|-------|-------------|
| **Development** | Experimental features under active development. May be incomplete or unstable. |
| **Testing** | Feature complete but undergoing testing. Usable but may contain bugs. |
| **Stable** | Production-ready features. Safe for all deployments. |
| **Deprecated** | Features scheduled for removal. Migrate away from these. |
---
## Development
These features are experimental and under active development. Use only in development environments.
<FlagTable flags={featureFlags.flags.development} />
---
## Testing
These features are complete but still being tested. They are usable but may have bugs.
<FlagTable flags={featureFlags.flags.testing} />
---
## Stable
These features are production-ready and safe to enable.
<FlagTable flags={featureFlags.flags.stable} />
---
## Deprecated
These features are scheduled for removal. Plan to migrate away from them.
<FlagTable flags={featureFlags.flags.deprecated} />
---
## Adding New Feature Flags
When adding a new feature flag to `superset/config.py`, include the following annotations:
```python
# Description of what the feature does
# @lifecycle: development | testing | stable | deprecated
# @docs: https://superset.apache.org/docs/... (optional)
# @category: runtime_config | path_to_deprecation (optional, for stable flags)
"MY_NEW_FEATURE": False,
```
This documentation is auto-generated from the annotations in
[config.py](https://github.com/apache/superset/blob/master/superset/config.py).

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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
There are two approaches to making dashboards publicly accessible:
**Option 1: Dataset-based access (simpler)**
1. Set `PUBLIC_ROLE_LIKE = "Public"` in `superset_config.py`
2. Grant the Public role access to the relevant datasets (Menu → Security → List Roles → Public)
3. All published dashboards using those datasets become visible to anonymous users
**Option 2: Dashboard-level access (selective control)**
1. Set `PUBLIC_ROLE_LIKE = "Public"` in `superset_config.py`
2. Add the `'DASHBOARD_RBAC': True` [Feature Flag](/admin-docs/configuration/feature-flags)
3. Edit each dashboard's properties and add the "Public" role
4. Only dashboards with the Public role explicitly assigned are visible to anonymous users
See the [Public role documentation](/admin-docs/security/security#public) for more details.
#### 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
```
### Hiding the Logout Button in Embedded Contexts
When Superset is embedded in an application that manages authentication via SSO (OAuth2, SAML, or JWT), the logout button should be hidden since session management is handled by the parent application.
To hide the logout button in embedded contexts, add to `superset_config.py`:
```python
FEATURE_FLAGS = {
"DISABLE_EMBEDDED_SUPERSET_LOGOUT": True,
}
```
This flag only hides the logout button when Superset detects it is running inside an iframe. Users accessing Superset directly (not embedded) will still see the logout button regardless of this setting.
:::note
When embedding with SSO, also set `SESSION_COOKIE_SAMESITE = 'None'` and `SESSION_COOKIE_SECURE = True`. See [Security documentation](/docs/security/securing_superset) for details.
:::
## 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
:::tip Looking to use SQL templating?
For a user-focused guide on writing Jinja templates in SQL Lab and virtual datasets, see the [SQL Templating User Guide](/user-docs/using-superset/sql-templating). This page covers administrator configuration options.
:::
## 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](/admin-docs/configuration/configuring-superset#feature-flags) needs to be enabled in `superset_config.py`.
:::warning[Security Warning]
While powerful, this feature executes template code on the server. Within the Superset security model, this is **intended functionality**, as users with permissions to edit charts and virtual datasets are considered **trusted users**.
If you grant these permissions to untrusted users, this feature can be exploited as a **Server-Side Template Injection (SSTI)** vulnerability. Do not enable `ENABLE_TEMPLATE_PROCESSING` unless you fully understand and accept the associated security risks.
:::
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). **Note:** Only available in virtual datasets when a time range filter is applied in Explore/Chart views—not available in standalone SQL Lab queries. (deprecated beginning in version 5.0, use `get_time_filter` instead)
- `to_dttm`: end `datetime` value from the selected time range (`None` if undefined). **Note:** Only available in virtual datasets when a time range filter is applied in Explore/Chart views—not available in standalone SQL Lab queries. (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.
### Understanding Context Availability
Some Jinja variables like `from_dttm`, `to_dttm`, and `filter` are **only available when a chart or dashboard provides them**. They are populated from:
- Time range filters applied in Explore/Chart views
- Dashboard native filters
- Filter components
**These variables are NOT available in standalone SQL Lab queries** because there's no filter context. If you try to use `{{ from_dttm }}` directly in SQL Lab, you'll get an "undefined parameter" error.
#### Testing Time-Filtered Queries in SQL Lab
To test queries that use time variables in SQL Lab, you have several options:
**Option 1: Use Jinja defaults (recommended)**
```sql
SELECT *
FROM tbl
WHERE dttm_col > '{{ from_dttm | default("2024-01-01", true) }}'
AND dttm_col < '{{ to_dttm | default("2024-12-31", true) }}'
```
**Option 2: Use SQL Lab Parameters**
Set parameters in the SQL Lab UI (Parameters menu):
```json
{
"from_dttm": "2024-01-01",
"to_dttm": "2024-12-31"
}
```
**Option 3: Use `{% set %}` for testing**
```sql
{% set from_dttm = "2024-01-01" %}
{% set to_dttm = "2024-12-31" %}
SELECT *
FROM tbl
WHERE dttm_col > '{{ from_dttm }}' AND dttm_col < '{{ to_dttm }}'
```
:::tip
When you save a SQL Lab query as a virtual dataset and use it in a chart with time filters,
the actual filter values will override any defaults or test values you set.
:::
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 %}
```
:::resources
- [Blog: Intro to Jinja Templating in Apache Superset](https://preset.io/blog/intro-jinja-templating-apache-superset/)
:::

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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 the theme configuration, allowing you to use branded or custom typefaces without rebuilding the application.
### Default Fonts
By default, Superset uses Inter and Fira Code fonts which are bundled with the application via `@fontsource` packages. These fonts work offline and require no external network calls.
### Configuring Custom Fonts
To use custom fonts, add font URLs to your theme configuration using the `fontUrls` token:
```python
THEME_DEFAULT = {
"token": {
# Load fonts from external sources (e.g., Google Fonts, Adobe Fonts)
"fontUrls": [
"https://fonts.googleapis.com/css2?family=Roboto:wght@400;500;600;700&display=swap",
"https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;500&display=swap",
],
# Reference the loaded fonts
"fontFamily": "Roboto, -apple-system, BlinkMacSystemFont, sans-serif",
"fontFamilyCode": "JetBrains Mono, Monaco, monospace",
# ... other theme tokens
}
}
# 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"],
}
}
```
Or in the CRUD interface theme JSON:
```json
{
"token": {
"fontUrls": [
"https://fonts.googleapis.com/css2?family=Roboto:wght@400;500;600;700&display=swap"
],
"fontFamily": "Roboto, -apple-system, BlinkMacSystemFont, sans-serif",
"fontFamilyCode": "JetBrains Mono, Monaco, monospace"
}
}
```
:::note
Font URLs are validated against a configurable allowlist. By default, fonts from `fonts.googleapis.com`, `fonts.gstatic.com`, and `use.typekit.net` are allowed. Configure `THEME_FONT_URL_ALLOWED_DOMAINS` to customize the allowed domains.
:::
### 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!
## ECharts Configuration Overrides
:::note
Available since Superset 6.0
:::
Superset provides fine-grained control over ECharts visualizations through theme-level configuration overrides. This allows you to customize the appearance and behavior of all ECharts-based charts without modifying individual chart configurations.
### Global ECharts Overrides
Apply settings to all ECharts visualizations using `echartsOptionsOverrides`:
```python
THEME_DEFAULT = {
"token": {
"colorPrimary": "#2893B3",
# ... other Ant Design tokens
},
"echartsOptionsOverrides": {
"grid": {
"left": "10%",
"right": "10%",
"top": "15%",
"bottom": "15%"
},
"tooltip": {
"backgroundColor": "rgba(0, 0, 0, 0.8)",
"borderColor": "#ccc",
"textStyle": {
"color": "#fff"
}
},
"legend": {
"textStyle": {
"fontSize": 14,
"fontWeight": "bold"
}
}
}
}
```
### Chart-Specific Overrides
Target specific chart types using `echartsOptionsOverridesByChartType`:
```python
THEME_DEFAULT = {
"token": {
"colorPrimary": "#2893B3",
# ... other tokens
},
"echartsOptionsOverridesByChartType": {
"echarts_pie": {
"legend": {
"orient": "vertical",
"right": 10,
"top": "center"
}
},
"echarts_timeseries": {
"xAxis": {
"axisLabel": {
"rotate": 45,
"fontSize": 12
}
},
"dataZoom": [{
"type": "slider",
"show": True,
"start": 0,
"end": 100
}]
},
"echarts_bubble": {
"grid": {
"left": "15%",
"bottom": "20%"
}
}
}
}
```
### UI Configuration
You can also configure ECharts overrides through the theme CRUD interface:
```json
{
"token": {
"colorPrimary": "#2893B3"
},
"echartsOptionsOverrides": {
"grid": {
"left": "10%",
"right": "10%"
},
"tooltip": {
"backgroundColor": "rgba(0, 0, 0, 0.8)"
}
},
"echartsOptionsOverridesByChartType": {
"echarts_pie": {
"legend": {
"orient": "vertical",
"right": 10
}
}
}
}
```
### Override Precedence
The system applies overrides in the following order (last wins):
1. **Base ECharts theme** - Default Superset styling
2. **Plugin options** - Chart-specific configurations
3. **Global overrides** - `echartsOptionsOverrides`
4. **Chart-specific overrides** - `echartsOptionsOverridesByChartType[chartType]`
This ensures chart-specific overrides take precedence over global ones.
### Common Chart Types
Available chart types for `echartsOptionsOverridesByChartType`:
- `echarts_timeseries` - Time series/line charts
- `echarts_pie` - Pie and donut charts
- `echarts_bubble` - Bubble/scatter charts
- `echarts_funnel` - Funnel charts
- `echarts_gauge` - Gauge charts
- `echarts_radar` - Radar charts
- `echarts_boxplot` - Box plot charts
- `echarts_treemap` - Treemap charts
- `echarts_sunburst` - Sunburst charts
- `echarts_graph` - Network/graph charts
- `echarts_sankey` - Sankey diagrams
- `echarts_heatmap` - Heatmaps
- `echarts_mixed_timeseries` - Mixed time series
### Best Practices
1. **Start with global overrides** for consistent styling across all charts
2. **Use chart-specific overrides** for unique requirements per visualization type
3. **Test thoroughly** as overrides use deep merge - nested objects are combined, but arrays are completely replaced
4. **Document your overrides** to help team members understand custom styling
5. **Consider performance** - complex overrides may impact chart rendering speed
### Example: Corporate Branding
```python
# Complete corporate theme with ECharts customization
THEME_DEFAULT = {
"token": {
"colorPrimary": "#1B4D3E",
"fontFamily": "Corporate Sans, Arial, sans-serif"
},
"echartsOptionsOverrides": {
"grid": {
"left": "8%",
"right": "8%",
"top": "12%",
"bottom": "12%"
},
"textStyle": {
"fontFamily": "Corporate Sans, Arial, sans-serif"
},
"title": {
"textStyle": {
"color": "#1B4D3E",
"fontSize": 18,
"fontWeight": "bold"
}
}
},
"echartsOptionsOverridesByChartType": {
"echarts_timeseries": {
"xAxis": {
"axisLabel": {
"color": "#666",
"fontSize": 11
}
}
},
"echarts_pie": {
"legend": {
"textStyle": {
"fontSize": 12
},
"itemGap": 20
}
}
}
}
```
This feature provides powerful theming capabilities while maintaining the flexibility of ECharts' extensive configuration options.
## 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.
:::resources
- [Video: Live Demo — Theming Apache Superset](https://www.youtube.com/watch?v=XsZAsO9tC3o)
- [CSS and Theming](https://docs.preset.io/docs/css-and-theming) - Additional theming techniques and CSS customization
- [Blog: Customizing Apache Superset Dashboards with CSS](https://preset.io/blog/customizing-superset-dashboards-with-css/)
- [Blog: Customizing Dashboards with CSS — Tips and Tricks](https://preset.io/blog/customizing-apache-superset-dashboards-with-css-additional-tips-and-tricks/)
- [Blog: Customizing Chart Colors](https://preset.io/blog/customizing-chart-colors-with-superset-and-preset/)
:::

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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](/admin-docs/databases#installing-drivers-in-docker) 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: Admin Documentation
description: Administrator guides for installing, configuring, and managing Apache Superset
---
<!--
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.
-->
# Admin Documentation
This section contains documentation for system administrators and operators who deploy and manage Apache Superset installations.
## What's in this section
- **[Installation](/admin-docs/installation/installation-methods)** - Deploy Superset using Docker, Kubernetes, or PyPI
- **[Configuration](/admin-docs/configuration/configuring-superset)** - Configure authentication, caching, feature flags, and more
- **[Security](/admin-docs/security/)** - Set up roles, permissions, and secure your deployment
## Related
- **[Database Drivers](/user-docs/databases/)** - See User Docs for database connection setup (admins may need to install drivers)
## Looking for something else?
- **[User Documentation](/user-docs/)** - Guides for analysts and business users
- **[Developer Documentation](/developer-docs)** - Contributing, extensions, and development guides

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---
title: Architecture
hide_title: true
sidebar_position: 1
version: 1
---
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](/admin-docs/configuration/alerts-reports)
- [Caching](/admin-docs/configuration/cache)
- [Async Queries](/admin-docs/configuration/async-queries-celery/)
- [Dashboard Thumbnails](/admin-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](/admin-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](/admin-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
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/admin-docs/installation/running-on-kubernetes)
documentation.
:::
As mentioned in our [quickstart guide](/user-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 match 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](/admin-docs/installation/architecture) 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](/admin-docs/installation/docker-compose)
**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](/admin-docs/installation/docker-builds/#building-your-own-production-docker-image).
## [Kubernetes (K8s)](/admin-docs/installation/kubernetes)
**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)](/admin-docs/installation/pypi)
**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](/admin-docs/databases#installing-database-drivers) 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](/admin-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/admin-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
```
:::resources
- [Tutorial: Mastering Data Visualization — Installing Superset on Kubernetes with Helm Chart](https://mahira-technology.medium.com/mastering-data-visualization-installing-superset-on-kubernetes-cluster-using-helm-chart-e4ec99199e1e)
- [Tutorial: Installing Apache Superset in Kubernetes](https://aws.plainenglish.io/installing-apache-superset-in-kubernetes-1aec192ac495)
:::

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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/admin-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.
## Breaking Changes
For a detailed list of breaking changes and migration notes for each version, see
[UPDATING.md](https://github.com/apache/superset/blob/master/UPDATING.md).
This file documents backwards-incompatible changes and provides guidance for migrating between
major versions, including:
- Configuration changes
- API changes
- Database migrations
- Deprecated features

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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: Securing Your Superset Installation for Production
sidebar_position: 3
---
> *This guide applies to Apache Superset version 4.0 and later and is an evolving set of best practices that administrators should adapt to their specific deployment architecture.*
The default Apache Superset configuration is optimized for ease of use and development, not for security. For any production deployment, it is **critical** that you review and apply the following security configurations to harden your instance, protect user data, and prevent unauthorized access.
This guide provides a comprehensive checklist of essential security configurations and best practices.
### **Critical Prerequisites: HTTPS/TLS Configuration**
Running Superset without HTTPS (TLS) is not secure. Without it, all network traffic—including user credentials, session tokens, and sensitive data—is sent in cleartext and can be easily intercepted.
* **Use a Reverse Proxy:** Your Superset instance should always be deployed behind a reverse proxy (e.g., Nginx, Traefik) or a load balancer (e.g., AWS ALB, Google Cloud Load Balancer) that is configured to handle HTTPS termination.
* **Enforce Modern TLS:** Configure your proxy to enforce TLS 1.2 or higher with strong, industry-standard cipher suites.
* **Implement HSTS:** Use the HTTP Strict Transport Security (HSTS) header to ensure browsers only connect to your Superset instance over HTTPS. This can be configured in your reverse proxy or within Superset's Talisman settings.
### **`SUPERSET_SECRET_KEY` Management (CRITICAL)**
This is the most critical security setting for your Superset instance. It is used to sign all session cookies and encrypt sensitive information in the metadata database, such as database connection credentials.
* **Generate a Unique, Strong Key:** A unique key must be generated for every Superset instance. Use a cryptographically secure method to create it.
```bash
# Example using openssl to generate a strong key
openssl rand -base64 42
```
* **Store the Key Securely:** The key must be kept confidential. The recommended approach is to store it as an environment variable or in a secrets management system (e.g., AWS Secrets Manager, HashiCorp Vault). **Do not hardcode the key in `superset_config.py` or commit it to version control.**
```python
# In superset_config.py
import os
SECRET_KEY = os.environ.get('SUPERSET_SECRET_KEY')
```
> #### ⚠️ Warning: Your `SUPERSET_SECRET_KEY` Must Be Unique
>
> **NEVER** reuse the same `SUPERSET_SECRET_KEY` across different environments (e.g., development, staging, production) or different Superset instances. Reusing a key allows cryptographically signed session cookies to be used across those instances, which can lead to a full authentication bypass if a cookie is compromised. Treat this key like a master password.
### **Session Management Security (CRITICAL)**
Properly configuring user sessions is essential to prevent session hijacking and ensure that sessions are terminated correctly.
#### **Use a Server-Side Session Backend (Strongly Recommended for Production)**
The default stateless cookie-based session handling presents challenges for immediate session invalidation upon logout. For all production deployments, we strongly recommend configuring an optional server-side session backend like Redis, Memcached, or a database. This ensures that session data is stored securely on the server and can be instantly destroyed upon logout, rendering any copied session cookies immediately useless.
**Example `superset_config.py` for Redis:**
```python
# superset_config.py
from redis import Redis
import os
# 1. Enable server-side sessions
SESSION_SERVER_SIDE = True
# 2. Choose your backend (e.g., 'redis', 'memcached', 'filesystem', 'sqlalchemy')
SESSION_TYPE = 'redis'
# 3. Configure your Redis connection
# Use environment variables for sensitive details
SESSION_REDIS = Redis(
host=os.environ.get('REDIS_HOST', 'localhost'),
port=int(os.environ.get('REDIS_PORT', 6379)),
password=os.environ.get('REDIS_PASSWORD'),
db=int(os.environ.get('REDIS_DB', 0)),
ssl=os.environ.get('REDIS_SSL_ENABLED', 'True').lower() == 'true',
ssl_cert_reqs='required' # Or another appropriate SSL setting
)
# 4. Ensure the session cookie is signed for integrity
SESSION_USE_SIGNER = True
```
#### **Configure Session Lifetime and Cookie Security Flags**
This is mandatory for *all* deployments, whether stateless or server-side.
```python
# superset_config.py
from datetime import timedelta
# Set a short absolute session timeout
# The default is 31 days, which is NOT recommended for production.
PERMANENT_SESSION_LIFETIME = timedelta(hours=8)
# Enforce secure cookie flags to prevent browser-based attacks
SESSION_COOKIE_SECURE = True # Transmit cookie only over HTTPS
SESSION_COOKIE_HTTPONLY = True # Prevent client-side JS from accessing the cookie
SESSION_COOKIE_SAMESITE = 'Lax' # Provide protection against CSRF attacks
```
> ##### Note on iFrame Embedding and `SESSION_COOKIE_SAMESITE`
>The recommended default setting `'Lax'` provides good CSRF protection for most use cases. However, if you need to embed Superset dashboards into other applications using an iFrame, you will need to change this setting to `'None'`.
SESSION_COOKIE_SAMESITE = 'None'
Setting SameSite to 'None' requires that SESSION_COOKIE_SECURE is also set to True. Be aware that this configuration disables some of the browser's built-in CSRF protections to allow for cross-domain functionality, so it should only be used when iFrame embedding is necessary.
### **Authentication and Authorization**
While Superset's built-in database authentication is convenient, for production it's highly recommended to integrate with an enterprise-grade identity provider (IdP).
* **Use an Enterprise IdP:** Configure authentication via OAuth or LDAP to leverage your organization's existing identity management system. This provides benefits like Single Sign-On (SSO), Multi-Factor Authentication (MFA), and centralized user provisioning/deprovisioning.
* **Principle of Least Privilege:** Assign users to the most restrictive roles necessary for their jobs. Avoid over-provisioning users with Admin or Alpha roles, and ensure row-level security is applied where appropriate.
* **Admin Accounts:** Delete or disable the default admin user after a new administrative account has been configured.
### **Content Security Policy (CSP) and Other Headers**
Superset can use Flask-Talisman to set security headers. However, it must be explicitly enabled.
> #### ⚠️ Important: Talisman is Disabled by Default
>
> In Superset 4.0 and later, Talisman is disabled by default (`TALISMAN_ENABLED = False`). You **must** explicitly enable it in your `superset_config.py` for the security headers defined in `TALISMAN_CONFIG` to take effect.
Here's the documentation section how how to set up Talisman: https://superset.apache.org/admin-docs/security/#content-security-policy-csp
### **Database Security**
> #### ❗ Superset is Not a Database Firewall
>
> It is essential to understand that **Apache Superset is a data visualization and exploration platform, not a database firewall or a comprehensive security solution for your data warehouse.** While Superset provides features to help manage data access, the ultimate responsibility for securing your underlying databases lies with your database administrators (DBAs) and security teams. This includes managing network access, user privileges, and fine-grained permissions directly within the database. The configurations below are an important secondary layer of security but should not be your only line of defense.
* **Use a Dedicated Database User:** The database connection configured in Superset should use a dedicated, limited-privilege database user. This user should only have the minimum required permissions (e.g., `SELECT` on specific schemas) for the data sources it needs to query. It should **not** have `INSERT`, `UPDATE`, `DELETE`, or administrative privileges.
* **Restrict Dangerous SQL Functions:** To mitigate potential SQL injection risks, configure the `DISALLOWED_SQL_FUNCTIONS` list in your `superset_config.py`. Be aware that this is a defense-in-depth measure, not a substitute for proper database permissions.
### **Additional Security Layers**
* **Web Application Firewall (WAF):** Deploying Superset behind a WAF (e.g., Cloudflare, AWS WAF) is strongly recommended. A WAF with a standard ruleset (like the OWASP Core Rule Set) provides a critical layer of defense against common attacks like SQL Injection, XSS, and remote code execution.
### **Monitoring and Logging**
* **Configure Structured Logging:** Set up a robust logging configuration to capture important security events.
* **Centralize Logs:** Ship logs from all Superset components (frontend, worker, etc.) to a centralized SIEM (Security Information and Event Management) system for analysis and alerting.
* **Monitor Key Events:** Create alerts for suspicious activities, including:
* Multiple failed login attempts for a single user or from a single IP address.
* Changes to user roles or permissions.
* Creation or deletion of high-privilege users.
* Attempts to use disallowed SQL functions.
-----
### **Appendix A: Production Deployment Checklist**
#### **Initial Setup:**
- [ ] HTTPS/TLS is configured and enforced via a reverse proxy.
- [ ] A unique, strong `SUPERSET_SECRET_KEY` is generated and secured in an environment variable or secrets vault.
- [ ] Server-side session management is configured (e.g., Redis).
- [ ] `PERMANENT_SESSION_LIFETIME` is set to a short duration (e.g., 8 hours).
- [ ] All session cookie security flags (`Secure`, `HttpOnly`, `SameSite`) are enabled.
- [ ] `DEBUG` mode is set to `False`.
- [ ] Talisman is explicitly enabled and configured with a strict Content Security Policy.
- [ ] Database connections use dedicated, limited-privilege accounts.
- [ ] Authentication is integrated with an enterprise identity provider (OAuth/LDAP).
- [ ] A Web Application Firewall (WAF) is deployed in front of Superset.
- [ ] Logging is configured and logs are shipped to a central monitoring system.
#### **Ongoing Maintenance:**
- [ ] Regularly update to the latest major or minor versions of Superset. Those versions receive up-to-date security patches.
- [ ] Rotate the `SUPERSET_SECRET_KEY` periodically (e.g., quarterly) and after any potential security incident.
- [ ] Conduct quarterly access reviews for all users.
- [ ] Assuming logging and monitoring is in place, review security monitoring alerts weekly.
### **Appendix B: `SECRET_KEY` Rotation and Compromise Response**
**Why and When to Rotate the `SECRET_KEY`**
Rotating the `SUPERSET_SECRET_KEY` is a critical security procedure. It is mandatory after a known or suspected compromise and is a best practice when an employee with access to the key departs. While periodic rotation can limit the window of exposure for an unknown leak, it is a high-impact operation that will invalidate all user sessions and requires careful execution to avoid breaking your instance. The principles behind managing this key align with general best practices for cryptographic storage, which are further detailed in the OWASP Cryptographic Storage Cheat Sheet here: https://cheatsheetseries.owasp.org/cheatsheets/Cryptographic_Storage_Cheat_Sheet.html
**Procedure for Rotating the Key**
The procedure for safely rotating the SECRET_KEY must be followed precisely to avoid locking yourself out of your instance. The official Apache Superset documentation maintains the correct, up-to-date procedure. Please follow the official guide here:
https://superset.apache.org/admin-docs/configuration/configuring-superset/#rotating-to-a-newer-secret_key
:::resources
- [Blog: Running Apache Superset on the Open Internet](https://preset.io/blog/running-apache-superset-on-the-open-internet-a-report-from-the-fireline/)
- [Blog: How Security Vulnerabilities are Reported & Handled in Apache Superset](https://preset.io/blog/how-security-vulnerabilities-are-reported-and-handled-in-apache-superset/)
:::

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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
The **Public** role is the most restrictive built-in role, designed specifically for anonymous/unauthenticated
users who need to view dashboards. It provides minimal read-only access for:
- Viewing dashboards and charts
- Using interactive dashboard filters
- Accessing dashboard and chart permalinks
- Reading embedded dashboards
- Viewing annotations on charts
The Public role explicitly excludes:
- Any write permissions on dashboards, charts, or datasets
- SQL Lab access
- Share functionality
- User profile or admin features
- Menu access to most Superset features
Anonymous users are automatically assigned the Public role when `AUTH_ROLE_PUBLIC` is configured
(a Flask-AppBuilder setting). The `PUBLIC_ROLE_LIKE` setting is **optional** and controls what
permissions are synced to the Public role when you run `superset init`:
```python
# Optional: Sync sensible default permissions to the Public role
PUBLIC_ROLE_LIKE = "Public"
# Alternative: Copy permissions from Gamma for broader access
# PUBLIC_ROLE_LIKE = "Gamma"
```
If you prefer to manually configure the Public role's permissions (or use `DASHBOARD_RBAC` to
grant access at the dashboard level), you do not need to set `PUBLIC_ROLE_LIKE`.
**Important notes:**
- **Data access is still required:** The Public role only grants UI/API permissions. You must
also grant access to specific datasets necessary to view a dashboard. As with other roles,
this can be done in two ways:
- **Without `DASHBOARD_RBAC`:** Dashboards only appear in the list and are accessible if
the user has permission to at least one of their datasets. Grant dataset access by editing
the Public role in the Superset UI (Menu → Security → List Roles → Public) and adding the
relevant data sources. All published dashboards using those datasets become visible.
- **With `DASHBOARD_RBAC` enabled:** Anonymous users will only see dashboards where the
"Public" role has been explicitly added in the dashboard's properties. Dataset permissions
are not required—DASHBOARD_RBAC handles the cascading permissions check. This provides
fine-grained control over which dashboards are publicly visible.
- **Role synchronization:** Built-in role permissions (Admin, Alpha, Gamma, sql_lab, and Public
when `PUBLIC_ROLE_LIKE = "Public"`) are synchronized when you run `superset init`. Any manual
permission edits to these roles may be overwritten during upgrades. To customize the Public
role permissions, you can either:
- Edit the Public role directly and avoid setting `PUBLIC_ROLE_LIKE` (permissions won't be
overwritten by `superset init`)
- Copy the Public role via "Copy Role" in the Superset web UI, save it under a different name
(e.g., "Public_Custom"), customize the permissions, then update **both** configs:
`PUBLIC_ROLE_LIKE = "Public_Custom"` and `AUTH_ROLE_PUBLIC = "Public_Custom"`
### 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.
### Dashboard Access Control
Access to dashboards is managed via owners (users that have edit permissions to the dashboard).
Non-owner user access can be managed in two ways. Note that dashboards must be published to be
visible to other users.
#### Dataset-Based Access (Default)
By default, users can view published dashboards if they have access to at least one dataset
used in that dashboard. Grant dataset access by adding the relevant data source permissions
to a role (Menu → Security → List Roles).
This is the simplest approach but provides all-or-nothing access based on dataset permissions—
if a user has access to a dataset, they can see all published dashboards using that dataset.
#### Dashboard-Level Access (DASHBOARD_RBAC)
For fine-grained control over which dashboards specific roles can access, enable the
`DASHBOARD_RBAC` feature flag:
```python
FEATURE_FLAGS = {
"DASHBOARD_RBAC": True,
}
```
With this enabled, you can assign specific roles to each dashboard in its properties. Users
will only see dashboards where their role is explicitly added.
**Important considerations:**
- Dashboard access **bypasses** dataset-level checks—granting a role access to a dashboard
implicitly grants read access to all charts and datasets in that dashboard
- Dashboards without any assigned roles fall back to dataset-based access
- The dashboard must still be published to be visible
This feature is particularly useful for:
- Making specific dashboards public while keeping others private
- Granting access to dashboards without exposing the underlying datasets for other uses
- Creating dashboard-specific access patterns that don't align with dataset ownership
### 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/admin-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.