The service mesh dashboard glowed green across the board for the payment processing service, a testament to the new automated dependency analysis system. Then the calls started coming in from merchants reporting intermittent transaction timeouts, a problem the green dashboard promised was not happening.
Uber's system, as reported by InfoQ, classifies service-mesh relationships as fail-close, fail-open, or unknown, an attempt to bring clarity to complex interactions. This kind of automation in dependency analysis is meant to be a safety net, helping engineering and ops teams understand how services depend on each other and how they will behave under stress.
However, the precision of these automated labels, while valuable, can become a blind spot. A 'fail-close' dependency, for instance, implies a clean cutoff, a clear boundary where one service stops affecting another. This is often an ideal, not a reality.
Many times, a failing dependency does not simply 'close'; it degrades slowly, returning corrupted data, or introduces latency that falls outside the defined failure threshold. Such partial or slow failures do not always trigger the 'fail-close' label accurately, yet they can still starve upstream services or corrupt business processes, leading to a cascading failure.
Engineers and product teams, seeing a green dashboard with an 'okay' label, naturally shift their focus away from that area. They trust the automation, which is the point of its deployment, but this trust can inadvertently reduce deep observability into the actual runtime behavior, especially for low-frequency, high-impact incidents that bypass initial failure detection.
The 'unknown' label is perhaps the most honest, forcing teams to investigate and add the necessary telemetry. Yet even then, the push for clear classification can lead to premature labeling of complex or poorly understood dependencies as 'fail-open' or 'fail-close' without sufficient data quality validation, increasing risk.
Leaders in engineering need to foster a culture of healthy skepticism, understanding that automation provides a powerful lens but not a perfect mirror. The labels are a starting point for risk assessment, not the final word on systemic resilience; they demand continuous validation against real-world production metrics and customer experiences.
In the medical imaging system I worked on, a similar situation arose where a 'fail-open' dependency on an external metadata service was believed to be harmless. The system was designed to default to cached data. What it actually did was fall back to stale data that led to patient studies not being linked correctly, creating a quiet but critical data integrity issue.
The incident revealed that the automated dependency analysis, while technically correct in its fail-open classification, had masked a deeper semantic failure. The system was 'open' to continue processing, but the data it was processing became unreliable, leading to a cascade of errors that only careful manual observation eventually uncovered. The green light on the dashboard was telling a partial truth, and that made it a dangerous one.