What this PostgreSQL Observability Lab Is – and What I’m Trying to Learn From It
Databases are still among the most critical systems we run, but they’re often the hardest to reason about when something goes wrong.
In many environments, database monitoring looks healthy right up until it isn’t. Dashboards stay green, alerts remain quiet, and latency appears acceptable—until a sudden change forces engineers into reactive mode. By the time a coherent explanation emerges, impact has already occurred.
Part of the challenge is that databases are not linear systems. Query performance, concurrency, memory pressure, I/O behavior, and workload shape interact in ways that don’t collapse cleanly into a single metric or threshold. What looks like a simple slowdown is often the emergent result of multiple subsystems influencing each other at once.
At the same time, modern observability tooling has largely evolved around stateless services and request/response models. While many of those concepts translate—metrics, logs, dashboards, alerts—their behavior changes when applied to stateful systems like databases. Signals that feel intuitive in application monitoring often behave differently when contention, caching, and storage dominate the performance profile.
This project exists to explore that gap.
I built this PostgreSQL observability lab as a way to make my assumptions about database monitoring explicit and testable. Rather than starting from a polished set of best practices, the goal is to create a small, runnable system where signals can be observed, stress can be applied, and mental models can be challenged. If an idea about observability only works on paper, this lab should make that obvious.
TL;DR
- Databases behave as complex systems, not linear pipelines
- Observability signals should be evaluated by how they reduce uncertainty
- This lab exists to test assumptions, not present best practices
How We Know the System Is Doing Its Job
A common failure mode in monitoring systems is mistaking presence for usefulness. Metrics exist. Dashboards render. Alerts fire. Yet when behavior changes, engineers still struggle to answer basic questions: What changed? Why now? Where should I look first?
In this lab, “working” doesn’t mean that every component is running or that every signal is populated. It means that each signal contributes meaningfully to understanding the system when conditions change.
To keep that honest, I evaluate the system by the questions it helps answer.
Metrics: Orientation, Not Diagnosis
Metrics are useful if they help orient the investigation. They should make it clear when behavior has shifted and which subsystems appear to be under new pressure. Metrics that only spike once users are already impacted may still be accurate, but they are not treated as early signals. Likewise, metrics that fluctuate constantly without correlating to meaningful change are treated as noise, even if they are technically correct.
Logs: Context After Attention
Logs exist to add context once attention has already been drawn to a change. They are considered effective if they help explain what changed around the time behavior shifted or whether the system is doing something unexpected. High log volume alone is not a success metric; unstructured or excessive logging often makes investigation slower rather than faster.
Dashboards: Shared Reasoning
Dashboards are treated as shared reasoning tools rather than static artifacts. A useful dashboard helps different roles arrive at the same mental model by surfacing relationships and tradeoffs—throughput versus latency, activity versus contention—rather than presenting isolated values.
Alerts: Decision Forcing
Alerts are the most expensive signal in the system. They interrupt people and carry implicit urgency. In this lab, alerts are evaluated not by how quickly they fire, but by whether they force a meaningful decision. If an alert fires but doesn’t change what someone does next, it’s treated as a design failure.
Ultimately, the system is only successful if these signals work together to reduce uncertainty when behavior changes. The goal isn’t certainty, but faster convergence on plausible explanations and next steps.
The Assumptions I’m Testing
With that framing in place, the rest of the lab is built around a small set of explicit assumptions.
This lab is built around a small set of working assumptions. They aren’t claims or best practices; they’re hypotheses shaped by operating real systems and by the gaps that tend to appear under stress.
One assumption is that some signals help us orient while others help us explain. Expecting a single metric to do both often leads to poor alerts and false confidence. In this lab, those roles are intentionally separated and evaluated based on how they’re used, not just whether they exist.
Another assumption is that early warning matters more than perfect diagnosis. Databases often fail gradually through rising contention, increasing variance, or subtle workload shifts. Signals that drift before impact are treated as more protective than signals that only become useful in hindsight.
I’m also operating under the assumption that observability should reduce uncertainty rather than eliminate it. In complex systems, ambiguity is unavoidable; what matters is whether signals help narrow the search space quickly enough to support good decisions.
Alerting is treated as a design problem rather than a coverage problem. Alerts are meant to force decisions, not report state. If an alert accurately reflects system behavior but doesn’t enable action, it isn’t helping.
Finally, I’m testing the idea that reliability lives at the intersection of performance, cost, and toil. Improvements in one dimension often create pressure in another. The purpose of observability here is to make those tradeoffs visible, not to optimize blindly for any single metric.
Exploring the Lab
The PostgreSQL observability lab that accompanies this post is available here:
- Repository: github.com/mdbdba/pgs_obs_demo
The README describes what the lab includes, what it deliberately avoids, and how to run it locally. Running the lab isn’t required to follow the series, but it provides a concrete reference point as the discussion moves from ideas to evidence.
Where This Goes Next
This post is intentionally foundational. Its purpose is to establish a way of thinking about database observability as a system of interacting signals rather than a collection of dashboards.
The next posts will ground these ideas more concretely, starting with what PostgreSQL metrics actually surface, how those signals behave under change, and where they help—or hinder—our ability to reason about system behavior. From there, we’ll look at how signals evolve over time, how alerting decisions shape on-call experience, and how these pieces can eventually support meaningful service-level objectives for database-backed systems.
This series is a work in progress by design. As assumptions are tested, some will hold up and others won’t. That feedback loop is part of the point—and part of what I’m hoping to learn by making the process visible.
Tags: postgresql observability sre databases