A cache is a fast, temporary storage layer that holds copies of frequently accessed data to reduce latency and load on slower backing stores (main memory, disk, or remote services).

Cache is characterized by three key components:

• Hierarchy & placement: CPU caches (L1/L2/L3), in-memory caches (Redis, Memcached), and CDN/edge caches each operate at different latencies and scopes.
• Eviction & coherence policies: policies like LRU, LFU, TTL govern eviction; coherence is critical for CPU caches and distributed caches.
• Tradeoffs & consistency: caching improves latency and throughput but introduces staleness and complexity for consistency and invalidation.

Caching is one of the most effective levers for improving application responsiveness and reducing backend load. On Arm-based servers (e.g., Ampere Altra), CPU cache sizes and memory subsystems impact real-world throughput and should be considered when tuning cache-heavy applications like web proxies or in-memory stores.

• Cache (computing):Wikipedia
• Redis: in-memory datastore
• Memcached
• Performance guidance on memory/cache behavior for Ampere Altra