Vol.73, No.2, 2022, pp.3333-3350, doi:10.32604/cmc.2022.029566
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ARTICLE
Key-Value Store Coupled with an Operating System for Storing Large-Scale Values
  • Jeonghwan Im1, Hyuk-Yoon Kwon2,*
1 Graduate School of Data Science, Seoul National University of Science and Technology, Seoul, Korea
2 Department of Industrial Engineering, Graduate School of Data Science, Research Center for Electrical and Information Science, Seoul National University of Science and Technology, Seoul, Korea
* Corresponding Author: Hyuk-Yoon Kwon. Email:
Received 07 March 2022; Accepted 20 April 2022; Issue published 16 June 2022
Abstract
The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value pair. Various types of studies have been conducted to improve the performance of the key-value store while maintaining its flexibility. However, the research efforts storing the large-scale values such as multimedia data files (e.g., images or videos) in the key-value store were limited. In this study, we propose a new key-value store, WR-Store++ aiming to store the large-scale values stably. Specifically, it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file system. The utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage significantly. We also present chunk-based memory management and parallel processing of WR-Store++ to further improve its performance in the GET operation. Through the experiments, we show that WR-Store++ can store at least 32.74 times larger datasets than the existing baseline key-value store, WR-Store, which has the limitation in storing large-scale data sets. Furthermore, in terms of processing efficiency, we show that WR-Store++ outperforms not only WR-Store but also the other state-of-the-art key-value stores, LevelDB, RocksDB, and BerkeleyDB, for individual key-value operations and mixed workloads.
Keywords
Key-value stores; large-scale values; chunk-based memory management; parallel processing
Cite This Article
J. Im and H. Kwon, "Key-value store coupled with an operating system for storing large-scale values," Computers, Materials & Continua, vol. 73, no.2, pp. 3333–3350, 2022.
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