Caching
Superset uses Flask-Caching for caching purposes. Configuring caching is as easy as providing a custom cache config in your
superset_config.py
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:
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
}
- 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 “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
.
Celery beat
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:
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
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:
def auth_driver(driver: WebDriver, user: "User") -> WebDriver:
pass
Then on configuration:
WEBDRIVER_AUTH_FUNC = auth_driver