Summary:
**NOTE**: this also needs to be back-ported to 6.4.6 and possibly older branches if further releases from them is envisaged.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6081
Differential Revision: D18710107
Pulled By: zhichao-cao
fbshipit-source-id: 03260f9316566e2bfc12c7d702d6338bb7941e01
Summary:
This PR adds support in block cache trace analyzer to read from human readable trace file. This is needed when a user does not have access to the binary trace file.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5679
Test Plan: USE_CLANG=1 make check -j32
Differential Revision: D16697239
Pulled By: HaoyuHuang
fbshipit-source-id: f2e29d7995816c389b41458f234ec8e184a924db
Summary:
This PR adds four more eviction policies.
- OPT [1]
- Hyperbolic caching [2]
- ARC [3]
- GreedyDualSize [4]
[1] L. A. Belady. 1966. A Study of Replacement Algorithms for a Virtual-storage Computer. IBM Syst. J. 5, 2 (June 1966), 78-101. DOI=http://dx.doi.org/10.1147/sj.52.0078
[2] Aaron Blankstein, Siddhartha Sen, and Michael J. Freedman. 2017. Hyperbolic caching: flexible caching for web applications. In Proceedings of the 2017 USENIX Conference on Usenix Annual Technical Conference (USENIX ATC '17). USENIX Association, Berkeley, CA, USA, 499-511.
[3] Nimrod Megiddo and Dharmendra S. Modha. 2003. ARC: A Self-Tuning, Low Overhead Replacement Cache. In Proceedings of the 2nd USENIX Conference on File and Storage Technologies (FAST '03). USENIX Association, Berkeley, CA, USA, 115-130.
[4] N. Young. The k-server dual and loose competitiveness for paging. Algorithmica, June 1994, vol. 11,(no.6):525-41. Rewritten version of ''On-line caching as cache size varies'', in The 2nd Annual ACM-SIAM Symposium on Discrete Algorithms, 241-250, 1991.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5644
Differential Revision: D16548817
Pulled By: HaoyuHuang
fbshipit-source-id: 838f76db9179f07911abaab46c97e1c929cfcd63
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
