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Superset uses Flask-Caching for caching purposes. Configuring caching is as easy as providing a custom cache config in your that complies with the Flask-Caching specifications. Flask-Caching supports various caching backends, including Redis, Memcached, SimpleCache (in-memory), or the local filesystem. Custom cache backends are also supported. See here for specifics. The following cache configurations can be customized:

  • Metadata cache (optional): CACHE_CONFIG
  • Charting data queried from datasets (optional): DATA_CACHE_CONFIG
  • SQL Lab query results (optional): RESULTS_BACKEND. See Async Queries via Celery for details
  • Dashboard filter state (required): FILTER_STATE_CACHE_CONFIG.
  • Explore chart form data (required): EXPLORE_FORM_DATA_CACHE_CONFIG

Please note, that Dashboard and Explore caching is required. If 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:

"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
  • Redis (recommended): we recommend the redis Python package
  • Memcached: we recommend using pylibmc client library as python-memcached does not handle storing binary data correctly.

Both of these libraries can be installed using pip.

For chart data, Superset goes up a ‚Äútimeout search path‚ÄĚ, from a slice's configuration to the datasource‚Äôs, the database‚Äôs, then ultimately falls back to the global default defined in DATA_CACHE_CONFIG.

Celery beat‚Äč

Caching Thumbnails‚Äč

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


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:

from flask import Flask
from s3cache.s3cache import S3Cache


class CeleryConfig(object):
broker_url = "redis://localhost:6379/0"
imports = ("superset.sql_lab", "superset.tasks", "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
# Async selenium thumbnail task will use the following user

Using the above example cache keys for dashboards will be superset_thumb__dashboard__{ID}. You can override the base URL for selenium using:


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:

def auth_driver(driver: WebDriver, user: "User") -> WebDriver:

Then on configuration: