Deep Reinforcement Learning-Enhanced Query Optimization Engine for Distributed and Federated Database Management Systems

Traditional query optimization struggles in distributed and federated database environments. The challenges come from having different types of data sources, network delays, and changing workloads. This research presents a new solution using a deep reinforcement learning (DRL) framework to enhance query execution in distributed systems. The solution uses a specialized LSTM-based agent that learns how to optimize through a method called proximal policy optimization, helping it adapt to various workload scenarios. Tests on three major industry benchmarks showed impressive improvements. The new method reduced query execution time by 42.7%, lowered network bandwidth usage by 39.8%, and achieved an 86.3% success rate in predicting the best join orders, compared to traditional cost-based optimizers. When implemented in real-world settings, it consistently improved throughput by 35-40%. This DRL-based optimization engine is a major advancement in solving the challenging problem of query optimization in distributed environments, specifically benefiting data-heavy applications that need fast, real-time analytics.