Thomas Lively
ABSTRACT
Modern persistent key-value stores typically use a log-structured merge-tree (LSM-tree) design, which allows for high write throughput. Our observation is that the LSM-tree, however, has suboptimal performance during read-intensive workload windows with non-uniform key access distributions. To address this shortcoming, we propose and analyze a simple decision scheme that can be added to any LSM-based key-value store and dramatically reduce the number of disk I/Os for these classes of workloads. The key insight is that copying a frequently accessed key to the top of an LSM-tree ("splaying'') allows cheaper reads on that key in the near future.
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Digital Library
- N. Dayan, M. Athanassoulis, and S. Idreos. Monkey: Optimal Navigable Key-Value Store. In SIGMOD, 2017. Google ScholarDigital Library
- Facebook. RocksDB. https://github.com/facebook/rocksdb.Google Scholar
- P. E. O'Neil, E. Cheng, D. Gawlick, and E. J. O'Neil. The log-structured merge-tree (LSM-tree). Acta Informatica, 33(4): 351--385, 1996. Google ScholarDigital Library
- D. D. Sleator and R. E. Tarjan. Self-Adjusting Binary Search Trees. Journal of the Association for Computing Machinery, 32 (3): 652--686, 1985. Google ScholarDigital Library
Index Terms
- Splaying Log-Structured Merge-Trees
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