Improvement of RocksDB Performance via Large-Scale Parameter Analysis and Optimization


Huijun Jin, Won Gi Choi, Jonghwan Choi, Hanseung Sung, Sanghyun Park, Journal of Information Processing Systems Vol. 18, No. 3, pp. 374-388, Jun. 2022  

10.3745/JIPS.04.0244
Keywords: database, genetic algorithm, Log-Structured Merge-Tree, Optimization, Random Forest, Space Amplification, Write Amplification
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Abstract

Database systems usually have many parameters that must be configured by database administrators and users. RocksDB achieves fast data writing performance using a log-structured merged tree. This database has many parameters associated with write and space amplifications. Write amplification degrades the database performance, and space amplification leads to an increased storage space owing to the storage of unwanted data. Previously, it was proven that significant performance improvements can be achieved by tuning the database parameters. However, tuning the multiple parameters of a database is a laborious task owing to the large number of potential configuration combinations. To address this problem, we selected the important parameters that affect the performance of RocksDB using random forest. We then analyzed the effects of the selected parameters on write and space amplifications using analysis of variance. We used a genetic algorithm to obtain optimized values of the major parameters. The experimental results indicate an insignificant reduction (-5.64%) in the execution time when using these optimized values; however, write amplification, space amplification, and data processing rates improved considerably by 20.65%, 54.50%, and 89.68%, respectively, as compared to the performance when using the default settings.


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Cite this article
[APA Style]
Huijun Jin, Won Gi Choi, Jonghwan Choi, Hanseung Sung, & Sanghyun Park (2022). Improvement of RocksDB Performance via Large-Scale Parameter Analysis and Optimization. Journal of Information Processing Systems, 18(3), 374-388. DOI: 10.3745/JIPS.04.0244.

[IEEE Style]
H. Jin, W. G. Choi, J. Choi, H. Sung and S. Park, "Improvement of RocksDB Performance via Large-Scale Parameter Analysis and Optimization," Journal of Information Processing Systems, vol. 18, no. 3, pp. 374-388, 2022. DOI: 10.3745/JIPS.04.0244.

[ACM Style]
Huijun Jin, Won Gi Choi, Jonghwan Choi, Hanseung Sung, and Sanghyun Park. 2022. Improvement of RocksDB Performance via Large-Scale Parameter Analysis and Optimization. Journal of Information Processing Systems, 18, 3, (2022), 374-388. DOI: 10.3745/JIPS.04.0244.