SQL or NoSQL—Practical Aspect and Rational behind Choosing Data Stores  

SQL or NoSQL—Practical Aspect and Rational behind Choosing Data Stores

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作  者:Sourabh Sethi Sarah Panda Sourabh Sethi;Sarah Panda(Digital Experience, Infosys Limited, New York, USA;Research and Incubation, Microsoft Inc., Seattle, USA)

机构地区:[1]Digital Experience, Infosys Limited, New York, USA [2]Research and Incubation, Microsoft Inc., Seattle, USA

出  处:《Journal of Computer and Communications》2024年第8期1-20,共20页电脑和通信(英文)

摘  要:Data storage solutions are a crucial aspect of any application, significantly impacting data management and system performance. This article explores the rationale behind utilizing both SQL and NoSQL databases, addressing key questions about when each type is preferable. The background emphasizes the importance of selecting the appropriate database technology to meet specific application requirements. The purpose of this research is to provide a comprehensive guide for choosing between SQL and NoSQL databases based on various factors, including workload characteristics, scalability needs, and consistency requirements. To achieve this, we examine different strategies for implementing SQL and NoSQL databases in large-scale distributed applications and systems. The research method involves a comparative analysis of the features, advantages, and limitations of both database types. We specifically focus on scenarios involving read-heavy versus write-heavy systems and the trade-offs between availability and consistency. The results of this research indicate that SQL databases, with their relational structure and ACID compliance, are ideal for applications requiring complex queries and data integrity. In contrast, NoSQL databases, offering schema flexibility and horizontal scalability, are better suited for managing extensive datasets and high-velocity data ingestion. In conclusion, the selection of a database depends on the specific needs of the application. SQL databases are preferred for transactional systems with complex relationships, while NoSQL databases excel in scenarios demanding flexibility and scalability. The study provides insights into hybrid approaches, leveraging both database types to optimize system performance.Data storage solutions are a crucial aspect of any application, significantly impacting data management and system performance. This article explores the rationale behind utilizing both SQL and NoSQL databases, addressing key questions about when each type is preferable. The background emphasizes the importance of selecting the appropriate database technology to meet specific application requirements. The purpose of this research is to provide a comprehensive guide for choosing between SQL and NoSQL databases based on various factors, including workload characteristics, scalability needs, and consistency requirements. To achieve this, we examine different strategies for implementing SQL and NoSQL databases in large-scale distributed applications and systems. The research method involves a comparative analysis of the features, advantages, and limitations of both database types. We specifically focus on scenarios involving read-heavy versus write-heavy systems and the trade-offs between availability and consistency. The results of this research indicate that SQL databases, with their relational structure and ACID compliance, are ideal for applications requiring complex queries and data integrity. In contrast, NoSQL databases, offering schema flexibility and horizontal scalability, are better suited for managing extensive datasets and high-velocity data ingestion. In conclusion, the selection of a database depends on the specific needs of the application. SQL databases are preferred for transactional systems with complex relationships, while NoSQL databases excel in scenarios demanding flexibility and scalability. The study provides insights into hybrid approaches, leveraging both database types to optimize system performance.

关 键 词:SQLData Stores NO-SQLData Stores ACID BASE RUM Conjecture 

分 类 号:TP3[自动化与计算机技术—计算机科学与技术]

 

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