A cache layer is an intermediate storage tier interposed between a data consumer and an authoritative data source, holding frequently or recently accessed data in faster storage to reduce latency, decrease load on origin systems, and improve overall system throughput. Cache layers operate at multiple levels of a computing stack—CPU caches, in-process memory caches, distributed shared caches, and edge CDN caches—each exploiting temporal or spatial locality of access patterns. Correctness requires coherence protocols or explicit invalidation strategies to prevent stale data from being served.
Content
- Caching as a concept predates computing—postal sorting offices and library card catalogues are physical analogues. In computing, CPU instruction and data caches emerged in the 1960s with IBM System/360 models. Application-level caching became widespread in the web era when relational databases could not sustain the read amplification of high-traffic websites; sites like LiveJournal popularised memcached in 2003, which was rapidly adopted across the industry.
- A cache layer introduces the fundamental challenge of coherence: when the authoritative source changes, cached copies must be invalidated or updated. Common strategies include time-to-live (TTL) expiry, write-through (update cache synchronously with origin), write-behind (update cache immediately, propagate to origin asynchronously), and cache-aside (application checks cache first, loads from origin on miss). Eviction policies—LRU, LFU, ARC—determine which entries are ejected when the cache reaches capacity, balanced against hit-rate objectives.
- In modern AI inference systems, the KV Cache is a specialised cache layer that stores key-value attention tensors between auto-regressive generation steps, dramatically reducing per-token computation. This is distinct from but structurally analogous to application caches. Distributed cache layers (Redis Cluster, Hazelcast, Apache Ignite) enable horizontal scaling of stateful cache capacity. Multi-tier caching hierarchies—local in-process cache backed by distributed cache backed by database—reduce cross-network round trips at each level.
- By 2024–2025 cache layers face new pressures from LLM-serving infrastructure: prompt caching and KV cache sharing across users are critical cost-reduction techniques in large-scale inference. Semantic caches that match semantically similar queries (not just exact key matches) are emerging for AI applications. Meanwhile, cache security—preventing cache poisoning, side-channel timing attacks, and cache-based data leakage between tenants—is an active area in multi-tenant cloud environments.