Applying Machine Learning to Detect and Prevent Performance Regression in Production Databases

Performance regression is a significant operational problem in production databases, particularly in large-scale data-intensive systems, where a single performance degradation can propagate to significant service failures.The old way of monitoring database performance through traditional monitoring with primarily fixed thresholds and rule-based alerts is not applicable in dynamic workloads, particularly in capturing the complex and dynamic patterns of performance.As previously mentioned, this study describes how machine learning can be utilized to forecast and prevent the decline in the performance of production database systems.A theoretical framework is presented, which assumes the use of telemetry collection on a continuous basis, feature engineering, anomaly detection, predictive modelling and automated response mechanisms.The structure assists in detecting regressions and proactively preventing them by learning normal performance behaviour and predicting potential degradations prior to the user facing the effect of said degradation.This paper summarizes recent research on machine learning-powered system monitoring, examines the types of models that should be used when processing data on database performance and provides the difficulties in practice, such as drift in data, model explainability and operational cost.The findings suggest that machine learning-focused solutions may be more generalized, detect countermeasures earlier and have a lower false positive rate than traditional monitoring systems.Therefore, they represent an option to consider when managing database performance in modern environments.