Open Access
CONVERGENT DATABASE ARCHITECTURES: MULTI-MODEL DESIGN AND QUERY OPTIMIZATION IN NEWSQL SYSTEMS
4
Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, India
4
Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russia
Abstract
The contemporary data landscape, characterized by diverse data types and escalating volumes, has driven a significant evolution in database management systems. Traditional Relational Database Management Systems (RDBMS) often struggle with the scalability and flexibility required for modern applications, while NoSQL databases, though scalable, frequently compromise on transactional consistency. NewSQL systems have emerged as a hybrid solution, aiming to combine the ACID guarantees of RDBMS with the horizontal scalability of NoSQL. This report explores the critical need for multi-model capabilities within NewSQL DBMSs to efficiently manage heterogeneous data adhering to various data modelsâincluding relational, document, graph, and key-valueâwithin a single, unified database. It delves into the architectural considerations for supporting such diversity, examining storage paradigms like row-store, column-store, and hybrid approaches, alongside data distribution strategies such as sharding and partitioning. Furthermore, the paper investigates advancements in multi-model query languages, particularly SQL++ and extensions in SQL:2016, and discusses query optimization techniques essential for handling complex, hybrid workloads. Finally, it addresses the performance evaluation of these systems in big data environments, highlights current limitations, and outlines future research directions for achieving truly versatile and high-performing data management solutions.
Keywords
Convergent database architectures,
multi-model databases,
NewSQL systems,
query optimization
References
Guo Q, Zhang C, Zhang S, Lu J. Multi-model query languages: taming the variety of big data. Distributed and Parallel Databases, 2024, 42: 31â71.
Lu J, Holubova I. Multi-model Databases: A New Journey to Handle the Variety of Data. ACM Computing Surveys, 2019, Vol. 0, No. 0.
Michels J, Hare K, Kulkarni K, Zuzarte C, Liu Z H, Hammerschmidt B, Zemke F. The New and Improved SQL: 2016 Standard. SIGMOD Record, June 2018, Vol. 47, No. 2.
Ong K W, Papakonstantinou Y, Vernoux R. The SQL++ Unifying Semi-structured Query Language, and an Expressiveness Benchmark of SQL-on-Hadoop, NoSQL and NewSQL Databases. arXiv: 1405.3631, Dec. 2015.
Krishnappa M S, Harve B M, Jayaram V, Nagpal A, Ganeeb K K, Ingole B S. ORACLE 19C Sharding: A Comprehensive Guide to Modern Data Distribution. IJCET, Sep-Oct 2024, Volume 15, Issue 5.
Akinola S. Trends in Open Source RDBMS: Performance, Scalability and Security Insights. Journal of Research in Science and Engineering (JRSE), July 2024, Volume-6, Issue-7.
Miryala N K. Emerging Trends and Challenges in Modern Database Technologies: A Comprehensive Analysis. International Journal of Science and Research (IJSR), November 2024, Volume 13 Issue 11.
Muhammed A, Abdullah Z H, Ismail W, Aldailamy A Y, Radman A, Hendradi R, Afandi R R. A Survey of NewSQL DBMSs focusing on Taxonomy, Comparison and Open Issues. IJCSMC, December 2021, Volume 11, Issue 4.
Khasawneh T N, Alsahlee M, Safieh A. SQL, NewSQL, and NOSQL Databases: A Comparative Survey. In 2020 11th International Conference on Information and Communication Systems (ICICS).
Murazzo M, GĂłmez P, RodrĂguez N, Medel D. Database NewSQL Performance Evaluation for Big Data in the Public Cloud. In Book Communications in Computer and Information Science ((CCIS, volume 1050)), Naiouf, M., Chichizola, F., Rucci, E. (eds) Cloud Computing and Big Data. JCC&BD 2019.
Pavlo A, Aslett M. Whatâs Really New with NewSQL? SIGMOD Record, June 2016, Vol. 45, No. 2.
Maia F C M B O. Sharding by Hash Partitioning A database scalability pattern to achieve evenly sharded database clusters. 17th ICEIS At: Barcelona, Spain, April 2015.
Moniruzzaman A. NewSQL: Towards Next-Generation Scalable RDBMS for Online Transaction Processing (OLTP) for Big Data Management. arXiv preprint arXiv: 1411.7343, 2014.
Tankoano J. Providing in RDBMSs the flexibility to Work with Various Non-Relational Data Models. Global Journal of Computer Science and Technology: H Information & Technology, 2023, Volume 23 Issue 2 Version 1.0. DOI:(https://doi.org/10.34257/GJCSTHVOL23IS2PG1.
Chen P. The entity-relationship model - toward a unified view of data. ACM TODS, March 1976, Volume 1, Issue 1, pp 9â36.
Object Modeling Group. Unified Modeling Language Specification. October 2012, Version 2.5.
Valduriez P, Khoshajian S, Copeland G. Implementation Techniques of Complex Objects. 12th Int. Conference on Very Large Data Bases - Kyoto, August 1986.
Lahiri T, Abiteboul S, Widom J. Ozone: Integrating Structured and Semistructured Data. 7th Int. Workshop on Database Programming Languages: Research Issues in Structured and Semi-structured Database Programming, December 1999.
Natti, M. (2023). Reducing PostgreSQL read and write latencies through optimized fillfactor and HOT percentages for high-update applications. International Journal of Science and Research Archive, 9(2), 1059â1062. https://doi.org/10.30574/ijsra.2023.9.2.0657
Scholl M H. Extensions to the Relational Data Model. Available from:(https://www.researchgate.net/publication/2381217_Extensions_to_the_Relational_Data_Model) (accessed 29 March 2025).
Tankoano J. ModĂšle relationnel imbriquĂ©. In SGBD relationnels â Tome 2, Vers les Bases de donnĂ©es RĂ©parties, Objet, Objet-relationnelles, XML, ⊠Available from:(https://www.researchgate.net/publication/366548683_SGBD_relationnels_-_Tome_2_Vers_les_Bases_de_donnees_Reparties_Objet_Objet-relationnelles_XML) (accessed 29 March 2025).
Ozsoyoglu Z M, Yuan L Y. On the normalization in Nested Relational Databases. LNCS, 1989, volume 361.
Abadi D J, Madden S R, Hachem N. Column-Stores vs. Row-Stores: How Different Are They Really? SIGMOD'08, June 9â12, 2008, Vancouver, BC, Canada.
ORACLE. Oracle Database SQL Language. Reference 23ai, F47038-19, November 2024.
Comer D. The Ubiquitous B-Tree. Computing Surveys, June 1979, vol. 11, n° 2.
Valduriez P. Join Indices. ACM TODS, June 1987, Vol. 12, No. 2, Pages 218-246.
Mohod A P, Chaudhari M S. Improve Query Performance Using Effective Materialized View Selection and Maintenance: A Survey. IJCSMC, April 2013, Vol. 2, Issue. 4, pg. 485 â 490.
International Organization for Standardization (ISO). Information technology â Database languages SQL Part 16: Property Graph Queries (SQL/PGQ). (Edition 1, 2023), ISO/IEC 9075-16: 2023.
Costa C H, Filho J V B M, Lou Y, Lai L, Lyu B, Yang Y, Zhou X, Yu W, Zhang Y, Zhou J. Towards a Converged Relational-Graph Optimization Framework. Proc. ACM Manag. Data, Vol. 2, No. 6 (SIGMOD), December 2024.
Fagin R, Kolaitis P G, Nash A. Towards a Theory of Schema-Mapping Optimizationâ. PODSâ08, June 9â12, 2008, Vancouver, BC, Canada.
Bézivin J, Gerbé O. Towards a precise definition of the OMG/MDA framework. Proc. 16th Annual Int. Conf. on Automated Software Engineering (ASE 2001).
Asaad C, Ba K. NoSQL Databases: Seek for a Design Methodology. 8th Int. Conference, MEDI 2018, Marrakesh, Morocco, October 24â26, 2018.
Bondiombouy C, Valduriez P. Query Processing in Multistore Systems: an overview. RR-8890, INRIA Sophia Antipolis - Méditerranée. 2016, pp. 38. hal-01289759v2.
Atzeni P, Bugiotti F, Rossi L. Uniform Access to Non-relational Database Systems: The SOS Platform. J. RalytÂŽe et al. (Eds.): CAiSE 2012, LNCS 7328, pp. 160â174, 2012.
Vathy-Fogarassy Ă, HugyĂĄk T. Uniform data access platform for SQL and NoSQL database systems. Information Systems, September 2017, Volume 69, Pages 93-105.
Shin K, Hwang C, Jung H. NoSQL Database Design Using UML Conceptual Data Model Based on Peter Chenâs Framework. Int. Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 5 (2017) pp. 632-636.
Abdelhedi F, Brahim A A, Atigui F, Zurfluh G. Logical Unified Modeling For NoSQL DataBases. 19th ICEIS 2017, Apr 2017, Porto, Portugal. pp. 249-256. hal-01782574.
Stonebraker M. NoSQL and Enterprises. Cacm | august 2011 | vol. 54 | no.
Natti, M. (2023). Migrating from Oracle to PostgreSQL: Leveraging Open-Source to Reduce Database Costs and Enhance Flexibility. The Eastasouth Journal of Information System and Computer Science, 1(02), 109â112. https://doi.org/10.58812/esiscs.v1i02.433
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