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Multi-level caching

Multi-level caching is an architectural strategy that integrates multiple storage layers to optimize data retrieval speeds.^[600-developer__principle__cache.md] By combining fast, local memory with distributed network storage, systems aim to balance low latency with high capacity.

Architecture and Performance

The core principle of multi-level caching relies on the significant performance disparity between different storage mediums. Accessing data over a network is approximately 100 times slower than accessing it from local memory^[600-developer__principle__cache.md]. Similarly, mechanical latency is a major bottleneck; the time required for a hard disk to perform a single seek operation is roughly half the time required to read 1MB of data^[600-developer__principle__cache.md].

Implementation

A common implementation of this pattern in modern application development involves combining Caffeine (a high-performance, local caching library for Java) with Redis^[600-developer__principle__cache.md].

  • Caffeine: Typically serves as the L1 (Level 1) cache, storing data in the application's local memory for extremely fast access^[600-developer__principle__cache.md].
  • Redis: Functions as the L2 (Level 2) cache or a distributed store, handling larger datasets and sharing state across different application instances^[600-developer__principle__cache.md].
  • [[Latency]]
  • [[Distributed caching]]
  • [[Local storage]]

Sources

^[600-developer__principle__cache.md]