Methods For Optimizing PL/SQL Queries in Distributed Banking Databases

Abstract

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.