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Superset uses Flask-Caching for caching purposes. Flask-Caching supports various caching backends, including Redis (recommended), Memcached, SimpleCache (in-memory), or the local filesystem. Custom cache backends are also supported.

Caching can be configured by providing a dictionaries in that comply withthe Flask-Caching config specifications.

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:

'CACHE_TYPE': 'RedisCache',
'CACHE_KEY_PREFIX': 'superset_filter_cache',
'CACHE_REDIS_URL': 'redis://localhost:6379/0'


In order to use dedicated cache stores, additional python libraries must be installed

  • For Redis: we recommend the redis Python package
  • Memcached: we recommend using 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:

"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 for details.

Caching Thumbnails

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


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:

from superset.tasks.types import ExecutorType


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 = (
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: