Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (5): 37-47.doi: 10.3969/j.issn.1000-5641.2021.05.004

• Key Technologies for System • Previous Articles     Next Articles

Optimization of LSM-tree storage systems based on non-volatile memory

Yang YU, Huiqi HU*(), Xuan ZHOU   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2021-07-27 Online:2021-09-25 Published:2021-09-28
  • Contact: Huiqi HU


With the advent of the big data era, the financial industry has been generating increasing volumn of data, exerting pressure on database systems. LevelDB is a key-value database, developed by Google, based on the LSM-tree architecture. It offers fast writing and a small footprint, and is widely used in the financial industry. In this paper, we propose a design method for the L0layer, based on non-volatile memory and machine learning, with the aim of addressing the shortcomings of the LSM-tree architecture, including write pause, write amplification, and unfriendly reading. The proposed solution can slow down or even solve the aforementioned problems; the experimental results demonstrate that the design can achieve better read and write performance.

Key words: NVM, machine learning, LSM-tree architecture

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