Application of Database Performance Optimization Technology in Large-Scale AI Infrastructure

Large-scale AI infrastructure presents significant challenges to database systems, particularly in managing high concurrency, minimizing response latency, and ensuring high availability. This article focuses on addressing three critical performance bottlenecks: query efficiency, storage I/O throughput, and concurrency control mechanisms. To tackle these challenges, we propose a comprehensive suite of performance acceleration techniques, including structural reconstruction of database schemas, hierarchical layering of hot and cold data to optimize access patterns, and advanced transaction scheduling strategies to reduce conflicts and improve throughput. These optimization methods are rigorously validated through application in representative AI scenarios such as large-scale model training and real-time online inference services. Experimental results demonstrate that the integrated optimization framework significantly enhances database performance, providing more robust and scalable data support for complex AI workloads, ultimately enabling more efficient and reliable AI infrastructure operations.