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docs(caching): Restructure and improve caching docs (#22687)

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Reid Beels
2023-01-12 22:48:56 -08:00
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@@ -7,19 +7,49 @@ version: 1
## Caching
Superset uses [Flask-Caching](https://flask-caching.readthedocs.io/) 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](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. Custom cache backends are also supported. See [here](https://flask-caching.readthedocs.io/en/latest/#custom-cache-backends) 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](/docs/installation/async-queries-celery) for details
Superset uses [Flask-Caching](https://flask-caching.readthedocs.io/) for caching purposes.
Flask-Caching supports various caching backends, including Redis (recommended), Memcached,
SimpleCache (in-memory), or the local filesystem.
[Custom cache backends](https://flask-caching.readthedocs.io/en/latest/#custom-cache-backends)
are also supported.
Caching can be configured by providing a dictionaries in
`superset_config.py` that comply with[the Flask-Caching config specifications](https://flask-caching.readthedocs.io/en/latest/#configuring-flask-caching).
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:
```python
FILTER_STATE_CACHE_CONFIG = {
'CACHE_TYPE': 'RedisCache',
'CACHE_DEFAULT_TIMEOUT': 86400,
'CACHE_KEY_PREFIX': 'superset_filter_cache',
'CACHE_REDIS_URL': 'redis://localhost:6379/0'
}
```
### Dependencies
In order to use dedicated cache stores, additional python libraries must be installed
- For Redis: we recommend the [redis](https://pypi.python.org/pypi/redis) Python package
- Memcached: we recommend using [pylibmc](https://pypi.org/project/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.
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:
```python
@@ -30,17 +60,21 @@ DATA_CACHE_CONFIG = {
}
```
- Redis (recommended): we recommend the [redis](https://pypi.python.org/pypi/redis) Python package
- Memcached: we recommend using [pylibmc](https://pypi.org/project/pylibmc/) client library as
`python-memcached` does not handle storing binary data correctly.
### Chart Cache Timeout
Both of these libraries can be installed using pip.
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.
For chart data, Superset goes up a “timeout search path”, from a slice's (chart's) configuration
to the dataset’s, the database’s, then ultimately falls back to the global default
defined in `DATA_CACHE_CONFIG`.
### SQL Lab Query Results
## Celery beat
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](/docs/installation/async-queries-celery) for details.
### Caching Thumbnails