superset/docs/docs/faq.mdx

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# FAQ


## How big of a dataset can Superset handle?

Superset can work with even gigantic databases! Superset acts as a thin layer above your underlying
databases or data engines, which do all the processing.  Superset simply visualizes the results of
the query.

The key to achieving acceptable performance in Superset is whether your database can execute queries
and return results at a speed that is acceptable to your users. If you experience slow performance with
Superset, benchmark and tune your data warehouse.

## What are the computing specifications required to run Superset?

The specs of your Superset installation depend on how many users you have and what their activity is, not
on the size of your data.  Superset admins in the community have reported 8GB RAM, 2vCPUs as adequate to
run a moderately-sized instance. To develop Superset, e.g., compile code or build images, you may
need more power.

Monitor your resource usage and increase or decrease as needed. Note that Superset usage has a tendency
to occur in spikes, e.g., if everyone in a meeting loads the same dashboard at once.

Superset's application metadata does not require a very large database to store it, though
the log file grows over time.


## Can I join / query multiple tables at one time?

Not in the Explore or Visualization UI. A Superset SQLAlchemy datasource can only be a single table
or a view.

When working with tables, the solution would be to create a table that contains all the fields
needed for your analysis, most likely through some scheduled batch process.

A view is a simple logical layer that abstracts an arbitrary SQL queries as a virtual table. This can
allow you to join and union multiple tables and to apply some transformation using arbitrary SQL
expressions. The limitation there is your database performance, as Superset effectively will run a
query on top of your query (view). A good practice may be to limit yourself to joining your main
large table to one or many small tables only, and avoid using _GROUP BY_ where possible as Superset
will do its own _GROUP BY_ and doing the work twice might slow down performance.

Whether you use a table or a view, performance depends on how fast your database can deliver
the result to users interacting with Superset.

However, if you are using SQL Lab, there is no such limitation. You can write SQL queries to join
multiple tables as long as your database account has access to the tables.

## How do I create my own visualization?

We recommend reading the instructions in
[Creating Visualization Plugins](/docs/contributing/howtos#creating-visualization-plugins).

## Can I upload and visualize CSV data?

Absolutely! Read the instructions [here](/docs/using-superset/exploring-data) to learn
how to enable and use CSV upload.

## Why are my queries timing out?

There are many possible causes for why a long-running query might time out.

For running long query from Sql Lab, by default Superset allows it run as long as 6 hours before it
being killed by celery. If you want to increase the time for running query, you can specify the
timeout in configuration. For example:

```
SQLLAB_ASYNC_TIME_LIMIT_SEC = 60 * 60 * 6
```

If you are seeing timeouts (504 Gateway Time-out) when loading dashboard or explore slice, you are
probably behind gateway or proxy server (such as Nginx). If it did not receive a timely response
from Superset server (which is processing long queries), these web servers will send 504 status code
to clients directly. Superset has a client-side timeout limit to address this issue. If query didn’t
come back within client-side timeout (60 seconds by default), Superset will display warning message
to avoid gateway timeout message. If you have a longer gateway timeout limit, you can change the
timeout settings in **superset_config.py**:

```
SUPERSET_WEBSERVER_TIMEOUT = 60
```

## Why is the map not visible in the geospatial visualization?

You need to register a free account at [Mapbox.com](https://www.mapbox.com), obtain an API key, and add it
to **.env** at the key MAPBOX_API_KEY:

```
MAPBOX_API_KEY = "longstringofalphanumer1c"
```

## How to limit the timed refresh on a dashboard?

By default, the dashboard timed refresh feature allows you to automatically re-query every slice on
a dashboard according to a set schedule. Sometimes, however, you won’t want all of the slices to be
refreshed - especially if some data is slow moving, or run heavy queries. To exclude specific slices
from the timed refresh process, add the `timed_refresh_immune_slices` key to the dashboard JSON
Metadata field:

```
{
   "filter_immune_slices": [],
    "expanded_slices": {},
    "filter_immune_slice_fields": {},
    "timed_refresh_immune_slices": [324]
}
```

In the example above, if a timed refresh is set for the dashboard, then every slice except 324 will
be automatically re-queried on schedule.

Slice refresh will also be staggered over the specified period. You can turn off this staggering by
setting the `stagger_refresh` to false and modify the stagger period by setting `stagger_time` to a
value in milliseconds in the JSON Metadata field:

```
{
    "stagger_refresh": false,
    "stagger_time": 2500
}
```

Here, the entire dashboard will refresh at once if periodic refresh is on. The stagger time of 2.5
seconds is ignored.

**Why does ‘flask fab’ or superset freezed/hung/not responding when started (my home directory is
NFS mounted)?**

By default, Superset creates and uses an SQLite database at `~/.superset/superset.db`. SQLite is
known to [not work well if used on NFS](https://www.sqlite.org/lockingv3.html) due to broken file
locking implementation on NFS.

You can override this path using the **SUPERSET_HOME** environment variable.

Another workaround is to change where superset stores the sqlite database by adding the following in
`superset_config.py`:

```
SQLALCHEMY_DATABASE_URI = 'sqlite:////new/location/superset.db?check_same_thread=false'
```

You can read more about customizing Superset using the configuration file
[here](/docs/configuration/configuring-superset).

## What if the table schema changed?

Table schemas evolve, and Superset needs to reflect that. It’s pretty common in the life cycle of a
dashboard to want to add a new dimension or metric. To get Superset to discover your new columns,
all you have to do is to go to **Data -> Datasets**, click the edit icon next to the dataset
whose schema has changed, and hit **Sync columns from source** from the **Columns** tab.
Behind the scene, the new columns will get merged. Following this, you may want to re-edit the
table afterwards to configure the Columns tab, check the appropriate boxes and save again.

## What database engine can I use as a backend for Superset?

To clarify, the database backend is an OLTP database used by Superset to store its internal
information like your list of users and dashboard definitions. While Superset supports a
[variety of databases as data *sources*](/docs/configuration/databases#installing-database-drivers),
only a few database engines are supported for use as the OLTP backend / metadata store.

Superset is tested using MySQL, PostgreSQL, and SQLite backends. It’s recommended you install
Superset on one of these database servers for production.  Installation on other OLTP databases
may work but isn’t tested.  It has been reported that [Microsoft SQL Server does *not*
work as a Superset backend](https://github.com/apache/superset/issues/18961). Column-store,
non-OLTP databases are not designed for this type of workload.

## How can I configure OAuth authentication and authorization?

You can take a look at this Flask-AppBuilder
[configuration example](https://github.com/dpgaspar/Flask-AppBuilder/blob/master/examples/oauth/config.py).

## Is there a way to force the dashboard to use specific colors?

It is possible on a per-dashboard basis by providing a mapping of labels to colors in the JSON
Metadata attribute using the `label_colors` key.

```
{
    "label_colors": {
        "Girls": "#FF69B4",
        "Boys": "#ADD8E6"
    }
}
```

## Does Superset work with [insert database engine here]?

The [Connecting to Databases section](/docs/configuration/databases) provides the best
overview for supported databases. Database engines not listed on that page may work too. We rely on
the community to contribute to this knowledge base.

For a database engine to be supported in Superset through the SQLAlchemy connector, it requires
having a Python compliant [SQLAlchemy dialect](https://docs.sqlalchemy.org/en/13/dialects/) as well
as a [DBAPI driver](https://www.python.org/dev/peps/pep-0249/) defined. Database that have limited
SQL support may work as well. For instance it’s possible to connect to Druid through the SQLAlchemy
connector even though Druid does not support joins and subqueries. Another key element for a
database to be supported is through the Superset Database Engine Specification interface. This
interface allows for defining database-specific configurations and logic that go beyond the
SQLAlchemy and DBAPI scope. This includes features like:

- date-related SQL function that allow Superset to fetch different time granularities when running
  time-series queries
- whether the engine supports subqueries. If false, Superset may run 2-phase queries to compensate
  for the limitation
- methods around processing logs and inferring the percentage of completion of a query
- technicalities as to how to handle cursors and connections if the driver is not standard DBAPI

Beyond the SQLAlchemy connector, it’s also possible, though much more involved, to extend Superset
and write your own connector. The only example of this at the moment is the Druid connector, which
is getting superseded by Druid’s growing SQL support and the recent availability of a DBAPI and
SQLAlchemy driver. If the database you are considering integrating has any kind of of SQL support,
it’s probably preferable to go the SQLAlchemy route. Note that for a native connector to be possible
the database needs to have support for running OLAP-type queries and should be able to do things that
are typical in basic SQL:

- aggregate data
- apply filters
- apply HAVING-type filters
- be schema-aware, expose columns and types

## Does Superset offer a public API?

Yes, a public REST API, and the surface of that API formal is expanding steadily. You can read more about this API and
interact with it using Swagger [here](/docs/api).

Some of the
original vision for the collection of endpoints under **/api/v1** was originally specified in
[SIP-17](https://github.com/apache/superset/issues/7259) and constant progress has been
made to cover more and more use cases.

The API available is documented using [Swagger](https://swagger.io/) and the documentation can be
made available under **/swagger/v1** by enabling the following flag in `superset_config.py`:

```
FAB_API_SWAGGER_UI = True
```

There are other undocumented [private] ways to interact with Superset programmatically that offer no
guarantees and are not recommended but may fit your use case temporarily:

- using the ORM (SQLAlchemy) directly
- using the internal FAB ModelView API (to be deprecated in Superset)
- altering the source code in your fork

## How can I see usage statistics (e.g., monthly active users)?

This functionality is not included with Superset, but you can extract and analyze Superset's application
metadata to see what actions have occurred.  By default, user activities are logged in the `logs` table
in Superset's metadata database.  One company has published a write-up of [how they analyzed Superset
usage, including example queries](https://engineering.hometogo.com/monitor-superset-usage-via-superset-c7f9fba79525).

## What Does Hours Offset in the Edit Dataset view do?

In the Edit Dataset view, you can specify a time offset. This field lets you configure the
number of hours to be added or subtracted from the time column.
This can be used, for example, to convert UTC time to local time.

## Does Superset collect any telemetry data?

Superset uses [Scarf](https://about.scarf.sh/) by default to collect basic telemetry data upon installing and/or running Superset. This data helps the maintainers of Superset better understand which versions of Superset are being used, in order to prioritize patch/minor releases and security fixes.
We use the [Scarf Gateway](https://docs.scarf.sh/gateway/) to sit in front of container registries, the [scarf-js](https://about.scarf.sh/package-sdks) package to track `npm` installations, and a Scarf pixel to gather anonymous analytics on Superset page views.
Scarf purges PII and provides aggregated statistics. Superset users can easily opt out of analytics in various ways documented [here](https://docs.scarf.sh/gateway/#do-not-track) and [here](https://docs.scarf.sh/package-analytics/#as-a-user-of-a-package-using-scarf-js-how-can-i-opt-out-of-analytics).
Superset maintainers can also opt out of telemetry data collection by setting the `SCARF_ANALYTICS` environment variable to `false` in the Superset container (or anywhere Superset/webpack are run).
Additional opt-out instructions for Docker users are available on the [Docker Installation](/docs/installation/docker-compose) page.

## Does Superset have an archive panel or trash bin from which a user can recover deleted assets?

No. Currently, there is no way to recover a deleted Superset dashboard/chart/dataset/database from the UI. However, there is an [ongoing discussion](https://github.com/apache/superset/discussions/18386) about implementing such a feature.

Hence, it is recommended to take periodic backups of the metadata database. For recovery, you can launch a recovery instance of a Superset server with the backed-up copy of the DB attached and use the Export Dashboard button in the Superset UI (or the `superset export-dashboards` CLI command). Then, take the .zip file and import it into the current Superset instance.

Alternatively, you can programmatically take regular exports of the assets as a backup.