Artemis monitoring in OpenShift It is really simple to monitor the brokers deployed on OpenShift and show the metrics in grafana or configuring alerts based on the metrics. We are using the artemis version included in AMQ 7.9 deployed with the operator. Configuring user workload monitoring We can use a custom prometheus operator deployed on OpenShift, or just enable the user workload monitoring following the simple steps on this document
It has been ages since I was introduced in Apache Kafka and read the post from linkedin Benchmarking Apache Kafka: 2 Million Writes Per Second (On Three Cheap Machines). At that time, the post wanted to show why the new messaging platform fitted the linkedin use cases with better performance than other traditional brokers. Now that new kubernetes workloads are on the picture and it is fairly simple to deploy a kafka cluster using operators like strimzi, I was tempted to try to repeat the scenarios from the original post in a kubernetes cluster.
In previous article, we explanined how to develop a microservice in SpringBoot that uses infinispan as spring-cache implementation. We exposed the actuator prometheus endpoint to check in our local environment that it was running right. In this post, we are going to use the actuator endpoint to scrape it with prometheus and create a grafana dashboard to monitor the performance. Initial Set Up in kubernetes We assume that we have our microservice already deployed on your kubernetes namespace.