Survey on performance optimization for database systems

Huang, Shiyue; Qin, Yanzhao; Zhang, Xinyi; Tu, Yaofeng; Li, Zhongliang; Cui, Bin

doi:10.1007/s11432-021-3578-6

Cited by 11 publications

(2 citation statements)

References 89 publications

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“…The analysis of anomalies can diagnose the ground cause introducing performance regression. Tuning operations improve the performance by adjusting influencing factors such as the indexes, views, stored procedures, and queries' designs [22].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

Bonteanu,

Tudose

2024

Applied Sciences

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The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

Bonteanu,

Tudose

2024

Applied Sciences

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“…The relational paradigm, which emerged in the 1970s and was advocated by E.F. Codd, brought about a significant transformation in data management. It introduced Structured Query Language (SQL) and relational algebra (Huang et al, 2023). The relational model was the foundation for many DBMS implementations and paved the way for the digital transformation era.…”

Section: Introduction To Database Management System and Solutionsmentioning

confidence: 99%

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Clavijo-López,

Navarrete,

Velásquez

et al. 2024

JISIS

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Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

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LATuner: An LLM-Enhanced Database Tuning System Based on Adaptive Surrogate Model

Fan,

Pan,

Sun

et al. 2024

Lecture Notes in Computer Science

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Survey on performance optimization for database systems

Cited by 11 publications

References 89 publications

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

LATuner: An LLM-Enhanced Database Tuning System Based on Adaptive Surrogate Model

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