Introduction

As a Checkmk extension developer and enthusiast, and most recently OpenTelemetry Certified, I wanted to put observability into practice on a real-world project.

• Context
• Solution
• Output
• Benefits
• Conclusion

For Lynxmind , simple uptime checks were no longer enough. We needed more than a binary view of “up or down.” The goal was to understand the actual health of the system: how it performs under load, how resources are consumed, and where bottlenecks emerge.

Solution

I instrumented our Node.js and Astro stack with the OpenTelemetry SDK and exported metrics into the Checkmk 2.4 OpenTelemetry Collector (OTLP/HTTP). This pipeline was designed to capture essential runtime signals, including CPU usage, memory and heap statistics, garbage collection pauses, event loop latency, and HTTP client/server performance. Using service name mapping with Checkmk’s Dynamic Configuration Daemon (DCD), hosts such as Lynxmind website were created automatically. Custom dashboards in Checkmk then turned this raw telemetry into actionable insights.

Output

The resulting dashboards now provide:

• Real-time CPU and memory consumption
• Heap pressure indicators (for example, ~88% usage)
• Event loop responsiveness at the 95th and 99th percentiles
• Garbage collection breakdown by type
• HTTP request rates, latency distributions, and error tracking

Benefits

This observability stack delivers tangible value:

• Early anomaly detection before users are impacted
• Data-driven capacity planning and scaling decisions
• Clear identification of bottlenecks such as memory pressure and GC lag
• Shared visibility across development and operations teams
• Stronger stability and reliability for lynxmind.com

Conclusion

With OpenTelemetry and Checkmk we no longer just monitor if Lynxmind is available. We monitor how healthy it is, how it behaves under stress, and exactly where optimization is needed. In other words, it is like being able to see the cake from the inside while it is still baking.