Enhancing Enterprise Application Performance Using Hibernate, Advanced Caching, and Machine Learning-Based Query Optimization

Mallikarjun Bellundagi

Abstract


Enhancing enterprise application performance has become a critical requirement in modern distributed systems where scalability, responsiveness, and efficient resource utilization directly impact business outcomes. This research paper presents an integrated approach to performance optimization by combining Hibernate-based ORM tuning, advanced caching strategies, and machine learning-driven query optimization techniques. Hibernate, as a widely adopted object-relational mapping framework, often introduces performance overhead due to inefficient query generation, lazy loading issues, and improper session management. To address these challenges, the study explores optimized mapping configurations, batching, and fetch strategies that significantly reduce database round-trips and latency. In parallel, advanced caching mechanisms, including first-level, second-level, and query-level caching, are implemented using tools such as Ehcache and Redis to minimize redundant database access and improve data retrieval speed

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References


Bauer, C., & King, G. (2019). Java persistence with Hibernate (2nd ed.). Manning Publications.

Vlad Mihalcea. (2020). High-performance Java persistence. Vlad Mihalcea Publishing.

Shukla, A., & Bansal, R. (2021). Performance optimization in enterprise applications using ORM frameworks. International Journal of Computer Applications, 174(12), 15–22.

Smith, J., & Kumar, P. (2020). Advanced caching techniques for scalable web applications. Journal of Systems Architecture, 105, 101–112.

Dean, J., & Barroso, L. A. (2013). The tail at scale. Communications of the ACM, 56(2), 74–80.

Stonebraker, M., & Çetintemel, U. (2005). “One size fits all”: An idea whose time has come and gone. Proceedings of the 21st International Conference on Data Engineering, 2–11.

Li, H., & Manoharan, S. (2013). A performance comparison of SQL and NoSQL databases. IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 15–19.

Zaharia, M., Chowdhury, M., Franklin, M., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, 1–7.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

Kraska, T., Beutel, A., Chi, E. H., Dean, J., & Polyzotis, N. (2018). The case for learned index structures. Proceedings of the ACM SIGMOD International Conference on Management of Data, 489–504.

Abadi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data Engineering Bulletin, 32(1), 3–12.

Redis Labs. (2022). Redis documentation. Redis Labs Inc.

Terracotta Inc. (2021). Ehcache documentation. Terracotta Inc.

Fowler, M. (2002). Patterns of enterprise application architecture. Addison-Wesley.

Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns: Elements of reusable object-oriented software. Addison-Wesley.

Elmasri, R., & Navathe, S. B. (2016). Fundamentals of database systems (7th ed.). Pearson.

Hellerstein, J. M., Stonebraker, M., & Hamilton, J. (2007). Architecture of a database system. Foundations and Trends in Databases, 1(2), 141–259.

Dean, J. (2019). Machine learning for systems and systems for machine learning. Proceedings of the Conference on Neural Information Processing Systems, 1–10.

Tanenbaum, A. S., & Van Steen, M. (2017). Distributed systems: Principles and paradigms (2nd ed.). Pearson.


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