Previously, I wrote a Beginner’s Guide to Jaeger + OpenTracing Instrumentation for Go providing guidance on manually instrumenting Go services. This is useful for cases where we want fine-grained tracing of specific functions. However, what if all we want is to trace a service’s inbound and outbound calls with little to no additional code?

Like a bratty teenager, traditional monitoring answers your questions, but does so in a terse, unhelpful manner: Why is my page slow? Guess it’s the API call. It’s a 504 thing — you wouldn’t understand. Ok, so why is the API call slow? Ask your DB query. Gosh! You need a better conversation with your code — one which gives you contextual clues about your application’s performance.

In a previous blog post, "Monitoring Kafka Performance with Splunk," we discussed key performance metrics to monitor different components in Kafka. This blog is focused on how to collect and monitor Kafka performance metrics with Splunk Infrastructure Monitoring using OpenTelemetry, a vendor-neutral and open framework to export telemetry data. In this step-by-step getting-started blog, we will.

If you already had some experience with Kamon, you probably saw Kamon create Spans automatically for a lot of stuff, including HTTP server requests, database calls, actor messages, and more. But what happens when you want to create Spans for methods or code blocks that Kamon doesn’t instrument automatically? Let’s look at the two simplest ways to create Spans programmatically with Kamon.

Logz.io is always looking to improve the user experience when it comes to Kubernetes and monitoring your K8s architecture. We’ve taken another step with that, adding OpenTelemetry instrumentation with Helm charts. We have made Helm charts available before, previously with editions suitable for Metricbeat and for Prometheus operators.