Our Go service behaved like a sealed box—until it didn’t. When latency spiked or errors surged, we had theories, not answers. OpenTelemetry gave us the sensors; SigNoz gave us the control room. In this guide, we’ll wire a small Go service end-to-end—spans, metrics, and logs—then use SigNoz to turn “it feels slow” into “the DB insert is the bottleneck” (with receipts).
As our team scaled, we encountered a frustrating reality: our Go-based order-processing service worked perfectly—until suddenly it didn't. We had opaque systems that give little insight into their internal state. The problem was that we couldn’t explore what was happening inside our services; why our services we failing; since when have our services been failing; how do we know what to fix.
Without observability, we couldn’t answer critical questions like:
We had logs from our Go services, but they alone proved insufficient. Plus, manually scanning through the logs to find errors was tedious. We needed to see the whole picture: traces, metrics, and errors connected in context.
In this detailed guide, we’ll look at how we instrumented our backend service with OpenTelemetry (OTel) and visualized the data in SigNoz. By the end, you'll know how to trace requests, measure performance, and quickly identify failures in your services.