Predictive Maintenance for Database Systems

Abstract

Predictive maintenance (PdM) re-organizes the work of a database as a forecasting / decision problem: identify potential precursors of faults and performance failures that are weak, and then plan the minimal risk intervention before SLAs are violated. The paper suggests a framework, which clearly distinguishes sensing, learning, and control. The data layer merges the high-granularity telemetry query traces, percentiles of latency, lock/wait graphs, buffer and I/O counters, log growth, index health and hardware SMART signals into seasonality-encoded time-aligned feature store with change-point signals. The model layer is an integration of unsupervised anomaly detection (unsupervised), short-horizon sequence forecasting (unsupervised), supervised failure category classifiers (supervised), and survival analysis (remaining time to breach) models are calibrated and drift-observed and incident- and operator-supplied labeling data is used to train and run the models. The control layer maps risk to actions statistics refresh, index-targeted maintenance, plan pinning, throttling, file pre-growth, or storage rebalancing is performed subject to guardrail (confidence thresholds, rate limits, maintenance window, rollback) starts. The assessments of synthetic failures and TPC-like benchmarks have demonstrated previous warning level, increased alertness, and fewer emergency measures compared to calendar-based maintenance system and also a decline in unintended downtime and workforce. Privacy-preserving telemetry, explainability, auditability are all considerations of governance that are implemented to maintain operator trust. The framework is engine-agnostic (SQL/NoSQL, on-prem/cloud) and promotes a secure evolution of advisory insights to selective autonomy, growing database reliability out of reactive firefighting to proactive and constantly learning operations