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I wonder if dormando who sometimes comes around would care to run memcache with the same traces as are used in this paper, which are available at https://github.com/twitter/cache-trace. I'm not sure I care about a cache that can scale to 24 cores, as in my experience I usually end up with hundreds of caches each with a few cores rather than fewer, bigger cache servers, but it still would be interesting to see what memcached can do.
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InfluxDB
Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.
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According to https://github.com/Cyan4973/xxHash, the best hash function can only do 100s M hashes per second, how can a local cache run at such throughput? I assume when measuring cache throughput, one need to calculate hash, look up, (maybe compare keys), and copy the data.
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A multi-threaded benchmark of a cache should be fully populated and use a scrambled Zipfian distribution. This emulates hot/cold entries and highlights the areas of contention (locks, CASes, etc). A lock-free read benefits thanks to cpu cache efficiency causing super linear growth.
This shows if the implementation could be a bottleneck and scales well enough, after which the hit rate and other factors are more important than raw throughput. I would rather sacrifice a few nanos on a read than suffer much lower hit rates or have long pauses on a write due to eviction inefficiencies.
[1] https://github.com/ben-manes/caffeine/wiki/Benchmarks#read-1...
