Analyzing and Comparison of NoSQL DBMS

Kuzochkina, Anna; Shirokopetleva, Mariya; Дударь, Зоя Владимировна

doi:10.1109/infocommst.2018.8632133

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Articles [70,85] and [86] compared the efficiency of several NoSQL databases (including Couchbase, MongoDB, Re-thinkDB, and in-memory) when used with real-world data. While planning the trials they've proposed, the authors considered several factors, such as the response time of various databases.…”

Section: Empirical Studies Analysismentioning

confidence: 99%

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan

Kumar

Cheng

et al. 2023

BDCC

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The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.

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Section: Empirical Studies Analysismentioning

confidence: 99%

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan

Kumar

Cheng

et al. 2023

BDCC

View full text Add to dashboard Cite

show abstract

“…For this work, it was necessary to choose an example of what kind of NoSQL data model will be further consideration of the material. Considering the information about today's most popular and widely used NoSQL databases [19], it was found that the document-oriented database model MongoDB falls under such criteria. In the overall ranking of databases according to the resource [20], it is in the top 10, being in the fifth position, and ranks first in the NoSQL ranking, since all the previous ones are relational databases.…”

Section: Fig 3 Heterogeneous Model-heterogeneous Data Base Migrationmentioning

confidence: 99%

Research of Methods to Support Data Migration Between Relational and Document Data Storage Models

Peretiatko

Shirokopetleva

Lesna

2022

ITSSI

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The subject matter of the article is heterogeneous model-inhomogeneous data migration between relational and document-oriented data storage models, existing strategies and methods to support such migrations, the use of relational algebra and set theory in the context of databases in building a new data migration algorithm. The goal of the work is to consider the features and procedure of data migration, explore methods to support data migration between relational and documentary data models, build a mathematical model and algorithm for data migration. The following methods were used: analysis and comparison of existing approaches to data migration, choice of strategy for further use in compiling the migration algorithm, mathematical modeling of the algorithm of heterogeneous model-inhomogeneous data migration, formalization of the data migration algorithm. The following tasks were solved in the article: consideration of the concept and types of data migration, justification for choosing a document-oriented data model as a target for data migration, analysis of existing literature sources on methods and strategies of heterogeneous model data migration from relational to document-oriented data model, highlighting advantages and disadvantages existing methods, choosing an approach to the formation of the data migration algorithm, compiling and describing a mathematical model of data migration using relational algebra and set theory, presentation of the data migration algorithm, which is based on the focus on data queries. The following results were obtained: the possibilities of relational algebra and set theory in the context of data models and queries are used, as well as in model redesign, the strategy of migration of data models is chosen, which provides relational and document-oriented data models, the algorithm of application of this method is described. Conclusions: because of the work, the main methods of migration support for different data storage models are analyzed, with the help of relational algebra, set theory a mathematical model is built, and an algorithm for transforming a relational data model into a document-oriented data model is taken into account. The obtained algorithm is suitable for use in real examples, and is the subject of further research and possible improvements, analysis of efficiency in comparison with other methods.

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“…Fast and widespread distribution of NoSQL (DB) databases is due to the ease of working with them. NoSQL DB is convenient to use for many modern applications that aim to use scalable databases with high performance, wide functionality, ability to provide maximum usability [4,6]. For such as mobile, gaming, Internet applications, etc.…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

“…The current recommendations and approaches do not give developers for NoSQL systems any knowledge about how to model entities and relationships effectively for a particular data model, which data indexes work best, and so on [2,6,8]. For example, MongoDB recommends using the "Manual reference" method to create similar logic to links [10 -11].…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Nosql Database Logic Design Methods for Mongodb and Neo4j

Mazurova

Syvolovskyi

Syvolovska

2022

ITSSI

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Modern developers of gaming mobile and Internet applications almost do not imagine themselves without the use of NoSQL databases, if they pursue the goal of creating scalable databases with high-performance and wide functionality. When designing a database for any NoSQL system, the developer needs a clear understanding of the logic of such databases and the capabilities of the tools offered by the corresponding DBMS. However, unfortunately, they do not have unified methods of logical design of such models, as in relational databases. Thus, there is a problem of developing effective methods for the logical design of such databases that would provide the necessary performance when implementing the business logic of the corresponding applications. The subject of the research is approaches to the logical design of NoSQL document and graph databases. The goal of the work is to propose unified logical modeling methods for MongoDB and Neo4j NoSQL systems based on an experimental study of their performance. The following tasks are solved in the work: analysis of current approaches to the logical design of document and graph databases\, the development of logical design methods for them; planning and experimental study of the performance of the proposed methods on the example of models developed with their help. The following methods are used: database design methods, database performance evaluation methods, development methods are based on MongoDB 5.0.5, Neo4j 4.4.3 DBMS, Visual Studio 2022 development environment. The following results are obtained: unified logical design methods for MongoDB and Neo4j NoSQL systems are proposed; on their basis, the corresponding logical models have been developed; experimental measurements of the number of resources required working with the developed models; recommendations on the proposed methods are formed. Conclusions: The proposed modeling methods for MongoDB have their own aspects of their effective use for different types of applications. The strengths and weaknesses of both methods were identified, but a mixed method based on a combination of modeling through normalization and denormalization was recommended. Even though Neo4j lost out to MongoDB in terms of consumed resources in most experiments, both DBMS's' demonstrate good productivity, taking into account the orientation to different tasks.

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Analyzing and Comparison of NoSQL DBMS

Cited by 5 publications

References 9 publications

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Research of Methods to Support Data Migration Between Relational and Document Data Storage Models

Nosql Database Logic Design Methods for Mongodb and Neo4j

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