The article examines methods for optimizing PL/SQL queries in distributed banking databases, emphasizing the transition from static rule-based mechanisms to adaptive, learning-driven architectures. The study’s relevance is defined by the increasing complexity of financial data environments that require real-time consistency, fault tolerance, and intelligent workload distribution. The research synthesizes results from seven recent works published between 2021 and 2025, covering neural cost modeling, heuristic algorithms, hybrid plan enumeration, and visualization-based diagnostics. Special attention is devoted to learned cost models and metaheuristic strategies that enhance selectivity estimation, reduce latency, and stabilize throughput in distributed ledger systems. The methodological framework integrates comparative analysis, systematization, and critical evaluation of hybrid, heuristic, and learning-based optimizers. The findings reveal a multi-layered optimization model that combines probabilistic inference, robust plan selection, and heuristic refinement. The conclusions underscore the practical applicability of adaptive PL/SQL optimization for high-volume banking infrastructures and data-intensive financial analytics.

PL/SQL optimization, distributed databases, learned cost models, hybrid query planning

Anneser, C., Tatbul, N., Cohen, D., Xu, Z., Pandian, P., Laptev, N., & Marcus, R. (2023). AutoSteer: Learned query optimization for any SQL database. Proceedings of the VLDB Endowment, 16, 3515–3527. https://doi.org/10.14778/3611540.3611544

Du, Y., Cai, Z., & Ding, Z. (2024). Query optimization in distributed database based on improved artificial bee colony algorithm. Applied Sciences, 14(2), 846. https://doi.org/10.3390/app14020846

Gretscher, L., & Dittrich, J. (2025). How to optimize SQL queries? A comparison between split, holistic, and hybrid approaches. Proceedings of the VLDB Endowment, 18, 3910–3922. https://doi.org/10.14778/3749646.3749663

Heinrich, R., Li, X., Luthra, M., & Kaoudi, Z. (2025). Learned cost models for query optimization: From batch to streaming systems. Proceedings of the VLDB Endowment, 18, 5482–5487. https://www.vldb.org/pvldb/vol18/p5482-li.pdf

Milicevic, B., & Babovic, Z. (2024). A systematic review of deep learning applications in database query execution. Journal of Big Data, 11, 173. https://doi.org/10.1186/s40537-024-01025-1

Oracle. (2025). Real-time payments with Oracle globally distributed database [White paper]. https://www.oracle.com/a/ocom/docs/database/real-time-payments-with-oracle-distributed-database.pdf

Xiu, H., Li, Y., Yang, Q., Guo, W., Liu, Y., Agarwal, P. K., Roy, S., & Yang, J. (2025). Hint-QPT: Hints for robust query performance tuning. Proceedings of the VLDB Endowment, 18, 5327–5330. https://doi.org/10.14778/3750601.3750663

You, Z., Shen, Q., Yiu, M. L., & Tang, B. (2025). QOVIS: Understanding and diagnosing query optimizer via a visualization-assisted approach. Proceedings of the VLDB Endowment, 18, 1677–1690. https://doi.org/10.14778/3725688.3725698

Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., & Neumann, T. (2025). Still asking: How good are query optimizers, really? Proceedings of the VLDB Endowment, 18, 5531–5536. https://www.vldb.org/pvldb/vol18/p5531-viktor.pdf

Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., & Kraska, T. (2021). Bao: Making learned query optimization practical. In Proceedings of the 2021 ACM SIGMOD International Conference on Management of Data (pp. 1275–1288). https://people.csail.mit.edu/tatbul/publications/bao_sigmod21.pdf