Summary:
Some files miss headers. Also some headers are irregular. Fix them to make an internal checkup tool happy.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/10519
Reviewed By: jay-zhuang
Differential Revision: D38603291
fbshipit-source-id: 13b1bbd6d48f5ee15ba20da67544396de48238f1
Summary:
This PR updated the python script to plot graphs for stats output from block cache analyzer.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5673
Test Plan: Manually run the script to generate graphs.
Differential Revision: D16657145
Pulled By: HaoyuHuang
fbshipit-source-id: fd510b5fd4307835f9a986fac545734dbe003d28
Summary:
This PR implements cache eviction using reinforcement learning. It includes two implementations:
1. An implementation of Thompson Sampling for the Bernoulli Bandit [1].
2. An implementation of LinUCB with disjoint linear models [2].
The idea is that a cache uses multiple eviction policies, e.g., MRU, LRU, and LFU. The cache learns which eviction policy is the best and uses it upon a cache miss.
Thompson Sampling is contextless and does not include any features.
LinUCB includes features such as level, block type, caller, column family id to decide which eviction policy to use.
[1] Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen. 2018. A Tutorial on Thompson Sampling. Found. Trends Mach. Learn. 11, 1 (July 2018), 1-96. DOI: https://doi.org/10.1561/2200000070
[2] Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web (WWW '10). ACM, New York, NY, USA, 661-670. DOI=http://dx.doi.org/10.1145/1772690.1772758
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5610
Differential Revision: D16435067
Pulled By: HaoyuHuang
fbshipit-source-id: 6549239ae14115c01cb1e70548af9e46d8dc21bb
