2 posts tagged with "unified-observability"

It was 5:30 AM when Riya (name changed), VP of Engineering at a Series C e-commerce company, got the page. Morning traffic was climbing into triple digits and catalog latency had spiked to twelve seconds. Within minutes, Slack was flooded with alerts from three different monitoring tools, each painting a partial picture. The APM showed slow API calls. The infrastructure dashboard showed normal CPU and memory. The dedicated PostgreSQL monitoring tool showed elevated query times, but offered no correlation to what changed upstream. Riya watched as her on-call engineers spent the first forty minutes of the incident jumping between dashboards, arguing over whether this was a database problem or an application problem. By the time they traced the issue to a query introduced in the previous night's deployment, the checkout flow had been degraded for nearly ninety minutes. The postmortem would later reveal that all the data needed to diagnose the issue existed within five minutes of the alert firing. It was scattered across three tools, owned by two teams, and required manual timeline alignment to interpret. Riya realized the problem was not instrumentation. It was fragmentation.

Watch: Tracing a slow query from application latency to PostgreSQL stats with pgX.

Modern software systems do not fail along clean architectural boundaries. Application latency, database contention, infrastructure saturation, and user behavior are tightly coupled, yet most observability setups continue to treat them as separate concerns, creating silos between database monitoring and APM tools. PostgreSQL, despite being a core component in most production systems, is often monitored in isolation—through a separate tool, separate dashboards, and separate mental models.

This separation works when systems are small and traffic patterns are simple. As systems scale, however, PostgreSQL behavior becomes a direct function of application usage: query patterns change with features, load fluctuates with users, and database pressure reflects upstream design decisions. At this stage, isolating database monitoring from application and infrastructure observability actively slows down diagnosis and leads teams to optimize the wrong layer.

In-depth PostgreSQL monitoring is necessary—but depth alone is not sufficient. Metrics without context force engineers to manually correlate symptoms across tools, timelines, and data models. What is required instead is component-level observability—a unified database observability platform where PostgreSQL metrics live alongside application traces, infrastructure signals, and deployment events, sharing the same time axis and the same analytical surface.

This is why PostgreSQL observability belongs in the same place as application and infrastructure observability. When database behavior is observed as part of the system rather than as a standalone dependency, engineers can reason about causality instead of coincidence, and leaders gain confidence that performance issues are being addressed at their source-not just mitigated downstream.