Elasticsearch caching deep dive: Boosting query speed one cache at a time

Elasticsearch employs multiple caching mechanisms to enhance data retrieval speed, focusing on page cache, shard-level request cache, and query cache. The page cache operates at the operating system level, storing frequently accessed data in memory to reduce disk reads, while the shard-level request cache stores full search responses, particularly useful for Kibana visualizations, to avoid redundant processing. The query cache is more granular, caching segments of queries that are repeatedly used across different searches, utilizing bit sets for efficient memory usage. These caches are designed to prevent stale data by aligning with the lifecycle of the data and are applicable whether Elasticsearch is self-hosted or used via Elastic Cloud. The article also highlights upcoming advancements in Linux and Java, such as io_uring and Project Loom, which could further optimize asynchronous I/O operations. Monitoring these caches is crucial to ensure they are effective and not frequently purged due to data changes, with Elasticsearch providing tools for observing cache usage and performance impact.