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

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Khan, Wisal, Kumar, Teerath, Zhang, Cheng, Raj, Kislay, Roy, Arunabha M., Luo, Bin·2023·Big Data and Cognitive Computing
DOI 10.3390/bdcc7020097
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Abstract

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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  • A consolidated analysis of empirical studies shows measurable differences in execution time, query complexity, and memory usage between OO, OOR, SQL, and NoSQL database paradigms under small, medium, and large workloads.

    “A consolidated analysis of empirical studies (Kaufmann & Meier, 2023;de Oliveira et al., 2021;Khan et al., 2023;Hassan, 2021;Győrödi et al., 2020;Matallah et al., 2021) shows measurable differences in execution time, query complexity, and memory usage between Object-Oriented (OO), Object-Relational (OOR), Relational (SQL), and NoSQL database paradigms under small, medium, and large workloads.”

    From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Khan et al., 2023

    Section 4, “Discussion and Classification” (page unavailable), says the review analyzed approximately 140 studies comparing SQL and NoSQL databases, and the RQ1/RQ2 section reports differences in query response speed. The cited paper does not establish the claimed four-way comparison of OO, OOR, SQL, and NoSQL across execution time, query complexity, and memory usage under small, medium, and large workloads. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • In storage models, OO-DBMSs score 4/5, can store objects without ORM, reducing transformation overhead by as much as 25% on iterative builds.

    “In storage models, OO-DBMSs score 4/5, can store objects without Object-Relational Mapping (ORM), thereby reducing the transformation overhead by as much as 25% on iterative builds (Khan et al., 2023).”

    From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Khan et al., 2023

    Section 4, “Discussion and Classification” (page unavailable), says only that the object data model has seen limited adoption. Searches for OO-DBMS, ORM, direct object storage, a 4/5 score, and a 25% reduction in transformation overhead found no passage addressing those claims. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • OO-DBMSs score maximum 5/5 on strengths because of improved semantic fidelity and less ORM.

    “but a maximum of 5/5 on strengths because of improved semantic fidelity and less ORM (Khan et al., 2023).”

    From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Khan et al., 2023

    Section 4, “Discussion and Classification” (page unavailable), mentions the object data model but does not assess OO-DBMS strengths. Searches for “5/5,” “semantic fidelity,” and “ORM” found no passage supporting the claimed score or its explanation; the paper’s comparison table instead covers RDBMS and NoSQL features. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • AI-driven optimisation comes with the highest cost efficiency (40%), attributed to lower administration overhead, which Khan et al. (2023) found to yield substantial savings in AI-augmented deployments in PostgreSQL.

    “However, compared to multi-model databases, this gain is not extreme; the fact that it comes hand in hand with the highest cost efficiency (40%) makes it even more promising, as it can be attributed to lower administration overhead, which Khan et al. (2023) found to yield substantial savings in AI-augmented deployments in PostgreSQL.”

    From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Khan et al. (2023)

    The discussion of DBMS comparisons lists PostgreSQL, and the Open Virtualization section discusses cloud database costs generally. Neither passage addresses AI-augmented PostgreSQL deployments, reduced administration overhead, or the claimed 40% cost efficiency; searches for those terms found no supporting passage in our copy. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

  • AI-driven optimisation has shown a performance improvement of 25%, especially for adaptive indexing, self-tuning query execution plans, and anomaly detection in workload patterns.

    “One of the most promising areas is AI-driven optimisation, which has shown a performance improvement of 25%, especially when it comes to adaptive indexing, self-tuning query execution plans and anomaly detection in workload patterns (Trivedi & Jobdas, 2021;Khan et al., 2023).”

    From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Khan et al., 2023

    Section 4.2 (page unavailable) describes using Gaussian Naïve Bayes to predict database-name occurrences, not a measured gain from AI-driven database optimisation. Searches for a 25% performance improvement, adaptive indexing, self-tuning query plans, and workload anomaly detection found no passage addressing the attributed claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cites

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Cited by

Sorted by citations

Authors
Khan, W., Kumar, T., Zhang, C., Raj, K., Roy, A. M., & Luo, B.
Affiliations
  • Anhui University (CN)
  • Dublin City University (IE)
  • University of Michigan (US)
Corresponding author
  • Zhang, Cheng
  • Luo, Bin
ORCID
Venue
Big Data and Cognitive Computing
Publisher
MDPI AG
Published
2023-05-12
Volume
7
Issue
2
Pages
97
ISSN
2504-2289 (ISSN-L)
DOI
10.3390/bdcc7020097
OpenAlex
W4376274168
Cited by
143
References
2
Citations by year
2026: 34 · 2025: 49 · 2024: 43 · 2023: 16 · 2022: 1
FWCI
5.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Big Data and Cognitive Computing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • the National High Technology Research and Development Program of China (863 programs) (2014AA012204, 61671018)
  • NSFC (2014AA012204, 61671018)
  • the Chinese Government Scholarship (CSC) (2014AA012204, 61671018)
Topics
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Keywords
SQL database, NoSQL databases, database performance, interoperability, big data processing, horizontal scaling, sharding, Oracle database, MongoDB, document-oriented databases, online transaction processing, big data analytics, cloud service providers

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