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Survey

Enhancing Storage Efficiency and Performance: A Survey of Data Partitioning Techniques

School of Information, Renmin University of China, Beijing 100872, China
Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education, Beijing 100872, China
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Abstract

Data partitioning techniques are pivotal for optimal data placement across storage devices, thereby enhancing resource utilization and overall system throughput. However, the design of effective partition schemes faces multiple challenges, including considerations of the cluster environment, storage device characteristics, optimization objectives, and the balance between partition quality and computational efficiency. Furthermore, dynamic environments necessitate robust partition detection mechanisms. This paper presents a comprehensive survey structured around partition deployment environments, outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are addressed. We discuss partitioning features pertaining to database schema, table data, workload, and runtime metrics. We then delve into the partition generation process, segmenting it into initialization and optimization stages. A comparative analysis of partition generation and update algorithms is provided, emphasizing their suitability for different scenarios and optimization objectives. Additionally, we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and solutions. This survey aims to foster the implementation, deployment, and updating of high-quality partitions for specific system scenarios.

Keywords

data partitioningsurveypartitioning featurepartition generationpartition update

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Journal of Computer Science and Technology
Volume 39 Issue 2,
March 2024
Pages 346-368
DOI: 10.1007/s11390-024-3538-1
Cite this article:
Liu P-J, Li C-P, Chen H. Enhancing Storage Efficiency and Performance: A Survey of Data Partitioning Techniques. Journal of Computer Science and Technology, 2024, 39(2): 346-368. https://doi.org/10.1007/s11390-024-3538-1

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Received: 21 June 2023
Accepted: 29 February 2024
Published: 30 March 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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