The aim of this article is to demonstrate how you can instrument a Java application using Opentelementry and Jaeger. In this example, we will be instrumenting our Java application using OpenTelemetry and the OpenTelemetry Java client, and the tracing data will be exported and visualized using Jaeger. We will use the Logz.io Jaeger backend as it is compatible with common tracing standards like Zipkin, OpenTelemetry, and OpenTracing.

Serverless applications streamline development by allowing you to focus on writing and deploying code rather than managing and provisioning infrastructure. To help you monitor the performance of your serverless applications, last year we released distributed tracing for AWS Lambda to provide comprehensive visibility across your serverless applications.

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 the beginning, there was the Log – or to be a bit more precise, there were application logs. At least that's how it was in the early days of application development, when raw log data itself was more often than not the point where troubleshooting began. Now, of course, the starting point for troubleshooting with cloud-based applications is much more likely to be an automatically-generated alert, or an indication on a monitoring dashboard that something isn't quite right.

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.

Microservice architectures are everywhere these days. Even internal enterprise applications—which have typically been structured as self-contained monoliths—are now being designed using a microservices architecture. There are definite advantages to a microservices architecture. Breaking an application into discrete, independent chunks—basically mini apps—gives you enormous flexibility. But this flexibility dramatically increases complexity, especially when things go wrong.

The OpenTelemetry project is an ambitious endeavor with of goal of bringing together various technologies to form a vendor neutral observability platform. Within the past year, many of the biggest names in tech provide native support within their commercial projects.