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Analyzing the codebase of Caffeine: a high performance caching library | Adri’s Blog

adriacabeza.github.io · 4,400 words · saved by 1 readers

The other day, while wasting time reading reddit, I stumbled upon a blogpost mentioning S3 FIFO, a method claiming to outperform LRU (Least Recently Used) in terms of cache miss ratio. Notable companies like RedPandas, Rising Wave, and Cloudflare have already implemented it in various capacities, so this piqued my interest. Caches are a pretty darn interesting and at Datadog, we rely heavily on them in several services, so I knew I had to put S3 FIFO to the test, or at least, make sure I understood its core ideas.

The other day, while wasting time reading reddit, I stumbled upon a blogpost mentioning S3 FIFO , a method claiming to outperform LRU (Least Recently Used) in terms of cache miss ratio. Notable companies like RedPandas, Rising Wave, and Cloudflare have already implemented it in various capacities, so this piqued my interest. Caches are a pretty darn interesting and at Datadog, we rely heavily on them in several services, so I knew I had to put S3 FIFO to the test, or at least, make sure I understood its core ideas. However, diving into a new caching approach without a deep understanding of our

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