---
title: Caching
hide_title: true
sidebar_position: 5
version: 1
---

## Caching

Superset uses [Flask-Caching](https://flask-caching.readthedocs.io/) for caching purpose. For security reasons,
there are two separate cache configs for Superset's own metadata (`CACHE_CONFIG`) and charting data queried from
connected datasources (`DATA_CACHE_CONFIG`). However, Query results from SQL Lab are stored in another backend
called `RESULTS_BACKEND`, See [Async Queries via Celery](/docs/installation/async-queries-celery) for details.

Configuring caching is as easy as providing `CACHE_CONFIG` and `DATA_CACHE_CONFIG` in your
`superset_config.py` that complies with [the Flask-Caching specifications](https://flask-caching.readthedocs.io/en/latest/#configuring-flask-caching).

Flask-Caching supports various caching backends, including Redis, Memcached, SimpleCache (in-memory), or the
local filesystem.

- Memcached: we recommend using [pylibmc](https://pypi.org/project/pylibmc/) client library as
  `python-memcached` does not handle storing binary data correctly.
- Redis: we recommend the [redis](https://pypi.python.org/pypi/redis) Python package

Both of these libraries can be installed using pip.

For chart data, Superset goes up a “timeout search path”, from a slice's configuration
to the datasource’s, the database’s, then ultimately falls back to the global default
defined in `DATA_CACHE_CONFIG`.

```
DATA_CACHE_CONFIG = {
    'CACHE_TYPE': 'redis',
    'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24, # 1 day default (in secs)
    'CACHE_KEY_PREFIX': 'superset_results',
    'CACHE_REDIS_URL': 'redis://localhost:6379/0',
}
```

Custom cache backends are also supported. See [here](https://flask-caching.readthedocs.io/en/latest/#custom-cache-backends) for specifics.

Superset has a Celery task that will periodically warm up the cache based on different strategies.
To use it, add the following to the `CELERYBEAT_SCHEDULE` section in `config.py`:

```python
CELERYBEAT_SCHEDULE = {
    'cache-warmup-hourly': {
        'task': 'cache-warmup',
        'schedule': crontab(minute=0, hour='*'),  # hourly
        'kwargs': {
            'strategy_name': 'top_n_dashboards',
            'top_n': 5,
            'since': '7 days ago',
        },
    },
}
```

This will cache all the charts in the top 5 most popular dashboards every hour. For other
strategies, check the `superset/tasks/cache.py` file.

### Caching Thumbnails

This is an optional feature that can be turned on by activating it’s feature flag on config:

```
FEATURE_FLAGS = {
    "THUMBNAILS": True,
    "THUMBNAILS_SQLA_LISTENERS": True,
}
```

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"
    CELERY_IMPORTS = ("superset.sql_lab", "superset.tasks", "superset.tasks.thumbnails")
    CELERY_RESULT_BACKEND = "redis://localhost:6379/0"
    CELERYD_PREFETCH_MULTIPLIER = 10
    CELERY_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
# Async selenium thumbnail task will use the following user
THUMBNAIL_SELENIUM_USER = "Admin"
```

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
```