Journal of East China Normal University(Natural Science) >

2021 , Vol. 2021 >Issue 5: 37 - 47

DOI: https://doi.org/10.3969/j.issn.1000-5641.2021.05.004

Key Technologies for System

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

  • Yang YU ,
  • Huiqi HU ,
  • Xuan ZHOU
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  • School of Data Science and Engineering, East China Normal University, Shanghai　200062, China

Received date: 2021-07-27

  Online published: 2021-09-28

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Abstract

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

Cite this article

Yang YU , Huiqi HU , Xuan ZHOU . Optimization of LSM-tree storage systems based on non-volatile memory[J]. Journal of East China Normal University(Natural Science), 2021 , 2021(5) : 37 -47 . DOI: 10.3969/j.issn.1000-5641.2021.05.004

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