cachingrelated

Cachingrelated refers to the study and application of caching techniques and architectures used to improve data access latency and reduce load on primary data sources. It encompasses software and hardware caches, as well as policies for invalidation, consistency, and eviction.

Caches exist at multiple levels, including CPU caches (L1/L2/L3), in-memory application caches (Redis, Memcached), web caches (browsers, reverse proxies, content delivery networks), and database or query caches. Each layer trades storage size and speed against data freshness and complexity.

Common strategies include eviction policies (least recently used, least frequently used, ARC, FIFO), and write policies (write-through, write-back, write-around). Caching patterns such as read-through, lazy loading, and prefetching help keep useful data available. Proper partitioning and sizing are essential for performance.

Consistency and invalidation are central challenges. Caches must stay coherent with the source of truth, via TTL expirations, explicit invalidation on updates, or more complex coherence protocols. In distributed systems, eventual consistency and cache warm-up can affect data accuracy and latency.

Performance is often measured by hit rate, miss rate, and latency reduction, along with throughput and memory usage. Design considerations include data volatility, access patterns, scalability, security, and privacy concerns related to cached sensitive information.