Optimized Database Sharding Techniques for High-Performance MySQL Applications
DOI:
https://doi.org/10.22399/ijcesen.4340Keywords:
Database Sharding, MySQL Optimization, Distributed Systems, Query Routing, ScalabilityAbstract
With the ever-growing data-intensive applications, the classical monolithic MySQL databases are usually unable to satisfy the requirements of the high-throughput, low-latency, and real-time applications. The technique of horizontally dividing data in more than one database is called database sharding, though it has proven to be a strong tool to overcome these problems. The paper provides an extensive overview of optimized forms of database sharding that are specific to MySQL applications. It discusses the basic Sharding concepts, comparisons of various models, and advanced optimization techniques, i.e., consistent hashing, query-aware routing, dynamically re-sharding, and caching. The paper also introduces patterns of deployment of architecture that can be adapted to the cloud-native environment and provides comprehensive results of performance benchmarking that can be used to measure the benefits and drawbacks of diverse approaches. The paper will offer a roadmap to help the architects and engineers create scalable, reliable, and high-performance MySQL infrastructures by combining the existing best practices with scholarly work. The results highlight that sharding used and tuned properly can be of major benefit to a database in terms of performance, operational efficiency, and scalability of the system.
References
[1] Quan, B. L. Y., Wahab, N. H. A., Al-Dhaqm, A., Alshammari, A., Aqarni, A., Abd Razak, S., & Wei, K. T. (2024). Recent advances in sharding techniques for scalable blockchain networks: A review. IEEE Access.
[2] Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D., & Koziris, N. (2011, October). On the elasticity of NoSQL databases over cloud management platforms. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (pp. 2385-2388).
[3] Cao, W., Yu, F., & Xie, J. (2014). Realization of the low-cost and high-performance MySQL cloud database. Proceedings of the VLDB Endowment, 7(13), 1742-1747.
[4] Nookala, G. (2023). Microservices and Data Architecture: Aligning Scalability with Data Flow. International Journal of Digital Innovation, 4(1).
[5] Jinka, P. (2025). Database Evolution: The Transformation of Data Partitioning and Indexing in the Cloud Era. Journal of Computer Science and Technology Studies, 7(5), 16-22.
[6] Gubala Hari Babu, L. S., & Dodla, S. N. S. (2024). Comparative Analysis of Oracle and MySQL Databases: A Study on Query Execution and Scalability.
[7] Dhulavvagol, P. M., & Totad, S. G. (2023). Performance enhancement of a distributed system using HDFS federation and sharding. Procedia Computer Science, 218, 2830-2841.
[8] Zimmermann, R., Ku, W. S., & Chu, W. C. (2004, November). Efficient query routing in distributed spatial databases. In Proceedings of the 12th annual ACM international workshop on Geographic information systems (pp. 176-183).
[9] Archer, A., Aydin, K., Bateni, M. H., Mirrokni, V., Schild, A., Yang, R., & Zhuang, R. (2019). Cache-aware load balancing of data centre applications. Proceedings of the VLDB Endowment, 12(6), 709-723.
[10] Pandey, R. (2020). Performance benchmarking and comparison of cloud-based databases, MongoDB (NoSQL) vs MySQL (Relational), using YCSB. Nat. College Ireland, Dublin, Ireland, Tech. Rep.
[11] Mansouri, Y., Ullah, F., Dhingra, S., & Babar, M. A. (2023). Design and implementation of fragmented clouds for the evaluation of distributed databases. IEEE Transactions on Cloud Computing.
[12] Lee, S., Guo, Z., Sunercan, O., Ying, J., Kooburat, T., Biswal, S., ... & Tang, C. (2021, October). Shard manager: A generic shard management framework for geo-distributed applications. In Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles (pp. 553-569).
[13] Kaluba, Z., & Nyirenda, M. Database Migration Service With A Microservice Architecture.
[14] Song, H., Zhou, W., Cui, H., Peng, X., & Li, F. (2024). A survey on hybrid transactional and analytical processing. The VLDB Journal, 33(5), 1485-1515.
[15] Shethiya, A. S. (2025). Load Balancing and Database Sharding Strategies in SQL Server for Large-Scale Web Applications. Journal of Selected Topics in Academic Research, 1(1).
[16] Annamalai, M., Ravichandran, K., Srinivas, H., Zinkovsky, I., Pan, L., Savor, T., ... & Stumm, M. (2018). Sharding the shards: managing datastore locality at scale with Akkio. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) (pp. 445-460).
[17] Nwosu, K. C., Kamara, I., Abdulgader, M., & Hu, Y. H. (2024, December). Data Partitioning and Storage Strategies for Artificial Intelligence and Machine Learning Applications: A Review of Techniques. In 2024 International Conference on Computer and Applications (ICCA) (pp. 1-10). IEEE.
[18] Arnqvist, A. (2023). Evaluating Failover and Recovery of Replicated SQL Databases.
[19] Afra, W. M. (2019). Sharding as a Method of Data Storage.
[20] Ferretti, L., Pierazzi, F., Colajanni, M., & Marchetti, M. (2014). Performance and cost evaluation of an adaptive encryption architecture for cloud databases. IEEE Transactions on Cloud Computing, 2(2), 143-155.
[21] Pham, C., Wang, L., Tak, B. C., Baset, S., Tang, C., Kalbarczyk, Z., & Iyer, R. K. (2016). Failure diagnosis for distributed systems using targeted fault injection. IEEE Transactions on Parallel and Distributed Systems, 28(2), 503-516.
[22] Böhm, S., & Wirtz, G. (2022). Cloud-edge orchestration for smart cities: A review of Kubernetes-based orchestration architectures. EAI Endorsed Trans. Smart Cities, 6(18), e2.
[23] Al-Said Ahmad, A., Al-Qora’n, L. F., & Zayed, A. (2024). Exploring the impact of chaos engineering with various user loads on cloud native applications: an exploratory empirical study. Computing, 106(7), 2389-2425.
[24] Abu-Libdeh, H., Geng, H., & van Renesse, R. (2011). Elastic replication for scalable, consistent service. SOSP (extended abstract), Cascais, Portugal.
[25] Solat, S. (2024). Sharding distributed databases: A critical review. arXiv preprint arXiv:2404.04384.
[26] Tapia-Fernández, S., García-García, D., & García-Hernández, P. (2022). Key Concepts, Weaknesses, and Benchmarks on Hash Table Data Structures. Algorithms, 15(3), 100.
[27] Yu, G., Wang, X., Yu, K., Ni, W., Zhang, J. A., & Liu, R. P. (2020). Survey: Sharding in blockchains. IEEE Access, 8, 14155-14181.
[28] Abdelhafiz, B. M. (2020, December). Distributed database using a sharding database architecture. In 2020, IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-17). IEEE.
[29] Kim, G., & Lee, W. (2022). In-network leaderless replication for distributed data stores. Proceedings of the VLDB Endowment, 15(7), 1337-1349.
[30] Ceri, S., Negri, M., & Pelagatti, G. (1982, June). Horizontal data partitioning in database design. In Proceedings of the 1982 ACM SIGMOD International Conference on Management of Data (pp. 128-136).
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