Distributed Tracing In Go: Build Observability Into Your...

As a best-selling author, I invite you to explore my books on Amazon. Don't forget to follow me on Medium and show your support. Thank you! Your support means the world!

When you have a system built from many small services, figuring out what went wrong or what's slow can feel like searching for a needle in a haystack. A request might bounce through five, ten, or even twenty different services. If it fails or slows down, which link in the chain is the problem? This is where distributed tracing comes in. It's like giving a unique passport to each request as it enters your system, stamping it at every service it visits. Later, you can gather all those stamps to see the complete journey.

I think of a "trace" as the entire story of one request. Each step in that story, like a call to a database or another service, is a "span." By collecting these spans, we can see the whole picture: how long each part took, if it failed, and how the services are connected.

Let's talk about how to build this in Go. The goal is to create something that adds clarity without slowing everything down. We need a few core parts: a way to start and track traces, a method to pass trace information between services, a smart system to decide which requests to record, and a place to store and analyze the data.

First, we establish a tracer. This is the main object that manages the tracing lifecycle. We'll use the OpenTelemetry project as a foundation because it provides excellent standards and tools.

We initialize it by connecting to a backend like Jaeger, which will collect and visualize our traces. We also set up a "propagator." This is the crucial piece that knows how to pack trace information into HTTP headers or other message formats to send it to the next service.

Now, the magic of propagation. When Service A calls Service B, it must pass along the trace context. We do this by injecting the data into the HTTP headers before the call.

In practice, this means before Service A makes an HTTP request to B, it calls InjectTrace on the headers. Service B, upon receiving the request, immediately calls ExtractTrace to retrieve the context and link its work back to the original trace.

You can't trace every single request in a high-volume system. The overhead would be too great. This is where sampling is essential. You might only record 1 or 10 out of every 100 requests. The key is to sample intelligently.

A simple sampler might just use a random percentage. A more advanced one can adap

Source: Dev.to