memorycaching

Memory caching is the practice of storing copies of data in fast-access memory to reduce latency and CPU load by avoiding repeated access to slower storage layers. It covers caches at multiple layers, from hardware CPUs to application-level caches in processes, to distributed in-memory caches used by scalable systems. It contrasts with disk-based caching.

Hardware caches: L1, L2, and L3 caches in CPUs are small, fast SRAM caches that store copies of recently used instructions and data. The operating system maintains a page cache to speed up block and file IO. These caches operate automatically and invisibly to applications.

Software caches: In-application caches reside in process memory, keyed by identifiers, caching objects or query results. External in-memory caches run as separate services (for example Redis or Memcached) and are used by multiple processes across machines for a shared cache. Content caches, such as those used by content delivery networks, are sometimes considered memory caches on edge nodes.

Techniques: eviction policies such as LRU, LFU, FIFO, and random, as well as hybrid approaches like ARC, govern what data is kept. Cache coherence and invalidation strategies include write-through, write-behind, or explicit invalidation. Cache expiration is commonly implemented with TTLs or max-age settings. Serialization overhead and memory usage are important practical considerations.

Challenges and best practices: Common issues include cache stampede, cache poisoning, and memory pressure. Monitoring hit rates, miss rates, and cache size helps tuning. Warming caches before full deployment, designing for eventual consistency when needed, and selecting appropriate eviction policies and TTL defaults are important. For distributed caches, consider consistent hashing and careful key design to balance load across nodes.