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

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

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Data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhanc-ing 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 ro-bust 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 optimiza-tion stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitabili-ty for different scenarios and optimization objectives.Additionally,we illustrate the applications of partitioning in preva-lent database products and suggest potential future research directions and solutions.This survey aims to foster the imple-mentation,deployment,and updating of high-quality partitions for specific system scenarios.

data partitioningsurveypartitioning featurepartition generationpartition update

刘鹏举、李翠平、陈红

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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

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaBeijing Natural Science Foundation

2023YFB45036036207246062076245621724244212022

2024

10.1007/s11390-024-3538-1

计算机科学技术学报(英文版)
中国计算机学会

计算机科学技术学报(英文版)

CSTPCD
影响因子：0.432
ISSN：1000-9000
年,卷(期)：2024.39(2)
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