With your application deployed to Red Hat OpenShift, you can then create a ServiceMonitor definition that is used to tell Prometheus how to scrape your application. This is required as the Grafana packaged with Red Hat OpenShift is read-only and doesn’t allow you to import your own Dashboards. ![]() You will also need to install your own Grafana instance in to Red Hat OpenShift 4.5 using the instructions in this blog post. This will allow Prometheus to scrape your application which is disabled by default. You will need to configure Red Hat OpenShift to monitor applications using the instructions in the documentation. Example Red Hat OpenShift 4.5 configuration This configuration results in the Liberty metrics being published on which can then be scraped by Prometheus. javaagent:/opt/ibm/wlp/usr/shared/resources/jmx_exporter/jmx_prometheus_javaagent-0.11.0.jar=9081:/opt/ibm/wlp/usr/shared/resources/jmx_exporter/jmx-config.yml An example jvm.options file is shown below. Liberty uses the jvm.options file to provide JVM parameters. In order to connect the JMX exporter and configuration file to Liberty a javaagent parameter must be provided to the JVM on start up. These files are typically placed in the /opt/ibm/wlp/usr/shared/resources/jmx_exporter folder on your Docker image - startDelaySeconds: 0 ssl: false lowercaseOutputName: false lowercaseOutputLabelNames: false The configuration file used for the Customer Order Services application is shown below. The JMX exporter is made up of a JAR file and a configuration file. In order to use the JMX Exporter the monitor-1.0 feature should be enabled in server.xml as shown below monitor-1.0. The Prometheus JMX exporter connects to any MXBeans on a JVM, retrieves their data and exposes the results on a /metrics endpoint so that the data can be scraped by Prometheus. This is where the Prometheus JMX exporter comes in. Prometheus however cannot natively scrape MXBeans. WebSphere Liberty provides a monitor-1.0 feature which enables the server Performance Monitoring Infrastructure (PMI) and makes the monitoring data available via MXBeans. There is however a solution to this problem which utilizes the Prometheus JMX exporter. This means that there are many applications that therefore can’t use the mpMetrics feature and it is these applications that are the focus of this article. However, the mpMetrics feature requires the cdi-1.2 or newer feature which conflicts with JavaEE6 applications or applications that require the cdi-1.0 feature. ![]() WebSphere Liberty provides a mpMetrics feature which publishes application server metrics on a /metrics endpoint that can be read by Prometheus. Metrics related to connection pools, thread pools, CPU usage, memory usage and JVM heap usage are more useful for a Java application than the basic pod metrics available from cluster monitoring. It is the responsibility of the application to make their metrics available on a /metrics endpoint which Prometheus scrapes and stores the data in its database for later visualization using Grafana. Prometheus can pull metrics data from applications using scraping. This data is useful for an overall cluster view but it doesn’t provide application specific data. Individual pod data is also available including CPU and RAM usage. Red Hat OpenShift monitoring is focused on cluster monitoring and provides data related to the overall cluster state such as pod state (running, pending, error etc), cluster CPU usage and cluster RAM usage. ![]() Prometheus is a monitoring system that collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. Prometheus and Grafana are widely used in Kubernetes to monitor applications.
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