Real Time Monitoring and Continuous Optimization of MySQL Databases

MySQL database optimization in a big data environment is critical but still faces performance bottlenecks, inefficient query execution, and resource constraint issues by applying traditional approaches. This paper introduces realtime monitoring and continuous optimization of the MySQL database by integrating automated performance tuning into machine learning based predictive analysis. It monitors the key performance metrics in order to detect inefficiencies and optimize resource allocation, indexing strategies, and databases into executing efficiently. It also provides security through SQL pattern analysis and anomaly-based attack detection. Performance comparison is quite impressive where query response time decreased from 3-5 seconds to 1-2 seconds with a confidence level of $\mathbf{9 5 \%}$. CPU usage reduced from $70 \% \quad 90 {\%}$ to $40 \%-60 {\%}$ with a confidence level of $\mathbf{9 5 \%}$. Memory utilization reduced from $\mathbf{8 0 \% - 9 0 \%}$ to $\mathbf{5 0 \% - 7 0 \%}$ with a confidence level of $\mathbf{9 8 \%}$. The error rate has decreased from $\mathbf{3 - 5 \%}$ to less than 1 % with a confidence level of 98 %. As a result, the system provides important advice to DBAs for reducing human interaction and assuring the database's continual improvement. AI-powered, selfoptimizing database management systems that improve automation and scalability in dynamic data environments will be the main focus of future research. execution time, memory utilization security performance is fast.