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Over time, web applications have evolved from simple request/response-based systems into complex, distributed ones with lots of moving parts. If something goes wrong (and you can be sure it will), finding the cause can be nearly impossible. But this need not be the case: enter tracing. Tracing refers to the process of collecting detailed information about the execution of requests within an application, including function calls, execution time, and other relevant data.

When requests slow down across your microservices, tracing helps you understand where time is spent. Jaeger and Zipkin are two popular tools for distributed tracing, built to answer a simple question: where did the request go? If you're choosing between them or just exploring options, this guide breaks down the differences and when each one might be a better fit.

Podman is a modern, open-source container engine developed by Red Hat that serves as a powerful alternative to Docker. It's designed with security, composability, and simplicity in mind. Here’s why it stands out.

In a microservices setup, tracking a single request across services quickly gets complex. One service calls another, then a third, and your logs don’t line up. The traceparent header carries context between services, so all parts of a request connect back to the start. For example, when a frontend sends a request to an API, which then calls a database service, traceparent it links those calls in the trace. Without it, you’re left guessing how requests flow.

"All language is but a poor translation." — Franz Kafka This quote by Franz Kafka reminds me of the time when I used to look at metrics from “Apache Kafka” topics trying to figure out what was causing the huge lags and manually deleting the messages in certain partitions to get rid of polluted messages. Yep, pretty lost in translation. I wasn’t aware of the power of observability for a Kafka producer-topic-consumer system.

Once upon a time in the world of metrics, Wavefront was a pioneer. Before Prometheus took over and tools like OpenTelemetry unified tracing and metrics, Wavefront brought something novel to the table: human-readable metrics with real-time querying and tag-based dimensionality. In enterprise environments running VMware or early microservices, it offered a scalable way to understand a system's behavior. But as the telemetry landscape evolved, many systems that spoke Wavefront were left behind.

If you’ve ever wrangled sidecars or sprinkled instrumentation code just to get basic trace data, you know the setup overhead isn’t always worth the payoff. But what if it was… just easier? That’s where the OpenTelemetry Operator for Kubernetes steps in… and it plays great with Coralogix out of the box!

In a distributed system, things break in unexpected ways. That’s why observability isn’t optional—it’s how you understand what’s going on under the hood. If you’re comparing tools to instrument your services, OpenTelemetry and Micrometer are two names you’ll run into. Both are used to collect metrics, but they take very different approaches—especially when it comes to flexibility, vendor support, and what you can do with the data.

In this guide, we'll harness AppSignal to detect, diagnose, and remove performance bottlenecks and employ proper tracing in a Ruby on Rails application. From setting up tracing to capturing errors and logging, we’ve got you covered. We'll ensure our application runs smoother than ever, even under the heaviest loads! But first, let's quickly touch on how to define tracing and its benefits.