This article will show how OpenTracing instrumentation can be used to collect Application Metrics, in addition to (but independent from) reported tracing data, from services deployed within Kubernetes.

Distributed applications and microservices required high level of observability.
If you followed one of the comprehensive install guides or you performed a custom installation and included the monitoring.yaml file in your installation, all of the observability features are already installed and you can skip down to the Create Elasticsearch Indices section. HTML5 UP theme, Future Imperfect with some extra goodies, ported by Julio Pescador. Installing logging, metrics, and traces. In this article we will integrate a Flask micro framework with Jaeger tracing tool.

OK, I Understand Code tasks.py I have skipped other CRUD operations for simplicity. Distributed Tracing Template¶. In the Kubernetes/OpenShift community everyone is talking about Istio service mesh, so I wanted to share my experience about the installation and running a sample microservice application with Istio on OpenShift 3.11 and 4.0. All code will be deployed to Kubernetes minikube cluster. We use cookies for various purposes including analytics. The Jaeger agent listens for spans from the client and sends them to the Jaeger collector. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. These Application Metrics can then be displayed in your monitoring dashboard and used to trigger alerts.

Flask Let’s build a simple task manager service using Flask framework. Illustrate the configuration for allowing distributed tracing using Jaeger.


The agent yaml is basically the same as the template with a few changes to add resource limits and to embed the configuration in a secret, which is better if you have sensitive configuration data, like an elasticsearch password. THE EXAMPLE APPLICATION In a recent article, we showed how a Spring Boot … Powered by Hugo
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