A Cloud-Agnostic Query-Aware Performance Governance Framework for Distributed MongoDB and PostgreSQL Systems

Distributed database performance incidents in modern enterprises are increasingly driven by application-level query behavior rather than infrastructure-only bottlenecks. MongoDB and PostgreSQL deployments—often distributed across regions and cloud platforms—exhibit performance degradations such as query plan regressions, lock contention, replication lag, and I/O saturation that frequently surface only after user impact. Traditional database performance monitoring is typically reactive, threshold-driven, and fragmented across teams, creating systemic failure modes including alert fatigue, inconsistent incident handling, and prolonged application–database escalation cycles. This paper proposes a Cloud-Agnostic, Query-Aware Performance Governance Framework that combines intelligent observability with governed decision-making to operationalize database performance management at enterprise scale. The framework elevates monitoring from passive telemetry collection to a performance governance control plane that continuously reasons over query-level signals (e.g., query execution time distributions, wait events, lock waits, index usage, plan stability), correlates them with infrastructure telemetry and change events, and applies policy- and risk-aware response workflows with human-in-the-loop safeguards. Unlike vendor-centric monitoring solutions or ad-hoc runbooks, the framework is designed to be cloud-agnostic, supports both MongoDB and PostgreSQL performance primitives, and produces auditable decision artifacts suitable for regulated environments. We further present an applied case study demonstrating how query-aware reasoning reduces mean time to detection (MTTD) for query regressions, improves diagnostic precision during app-team escalations, and reduces operational toil by unifying signals and response pathways across heterogeneous database platforms. The outcome is a practical, defensible approach for enterprises to govern database performance reliably while preserving accountability and operational safety.