Adaptive Machine Learning Techniques for PostgreSQL Performance Optimization: A PostgreSQL Case Study

This article presents a framework for integrating advanced machine learning models within PostgreSQL to optimize query performance and manage workloads dynamically. The integration creates a paradigm shift from static, rulebased optimization to adaptive, data-driven approaches that respond to changing conditions. PostgreSQL's extensible architecture provides an ideal foundation for implementing ML-enhanced components without modifying core database code. The framework encompasses four key areas: query optimizer enhancement using gradient boosting and neural networks, adaptive indexing mechanisms that automatically adjust to workload patterns, dynamic resource allocation through workload classification and forecasting, and a comprehensive model training pipeline. Experimental evaluations across analytical, transactional, and hybrid workloads demonstrate significant improvements in cardinality estimation accuracy, execution plan quality, resource utilization, and administrative overhead reduction. The modular design enables incremental adoption in production environments while maintaining compatibility with existing applications, illustrating how traditional relational database systems can evolve to meet modern data challenges through machine learning integration.