Caching in Apache Superset involves temporarily storing query results and metadata to reduce redundant computations, thus improving performance. It utilizes two types of caches: data cache for query results and metadata cache for schema information.
Data caching benefits include faster dashboard loading, reduced database load, and improved user experience. Metadata caching speeds up exploration by minimizing repetitive requests for schema details.
To configure caching for optimal performance:
1. Set up a caching backend like Redis or Memcached.
2. Configure the CACHE_CONFIG variable in superset_config.py with appropriate settings (e.g., CACHE_TYPE, CACHE_KEY_PREFIX, etc.).
3. Adjust DATA_CACHE_CONFIG parameters such as TIMEOUT and CACHE_DEFAULT_TIMEOUT based on dataset freshness requirements.
4. Fine-tune the granularity of caching using EXTRA_CACHE_KEYS_FUNC to add custom keys.
5. Enable/disable caching per chart or dashboard using “Cache Timeout” setting.
6. Monitor cache usage and adjust capacity accordingly.
7. Regularly review and update cache configurations to maintain optimal performance.
