27 December 2020

Any enterprise application can't be completely successful on production without good monitoring solution. For years vendors and developers provided custom tooling for it. Since Eclipse Microprofile Metrics specification we have a unified way to export monitoring data to the management agents and unified Java API, that developers can use to expose their telemetry data.

Wildfly application server provides microprofile metrics support, but unfortunately only for standalone configurations yet. In case domain mode you can provide necessary dependencies

dependencies {
    compile group: 'org.eclipse.microprofile.metrics', name: 'microprofile-metrics-api', version: '2.3'
    compile group: 'io.smallrye', name: 'smallrye-metrics', version: '2.4.0'

and then expose application scope metrics through custom endpoint like

import io.smallrye.metrics.exporters.JsonExporter;
import io.smallrye.metrics.exporters.OpenMetricsExporter;
public class MetricsTestResource {

  private OpenMetricsExporter openMetricsExporter = new OpenMetricsExporter();
  private JsonExporter jsonExporter = new JsonExporter();

  public String prometheus() {
    return openMetricsExporter.exportAllScopes().toString();

  public String json() {
    return jsonExporter.exportAllScopes().toString();

JVM and subsystems metrics will not be available by endpoint above, but them you can obtain through old good JMX.

Standalone server from the box provides metrics in prometheus format for all scopes over management interface (port 9990 ) using org.wildfly.extension.microprofile.metrics-smallrye extension and microprofile-metrics-smallrye subsystem.

kostenko@kostenko:$ curl
# HELP base_classloader_loadedClasses_count Displays the number of classes that are currently loaded in the Java virtual machine.
# TYPE base_classloader_loadedClasses_count gauge
base_classloader_loadedClasses_count 11826.0
# HELP base_cpu_availableProcessors Displays the number of processors available to the Java virtual machine. This value may change during a particular invocation of the virtual machine.
# TYPE base_cpu_availableProcessors gauge
base_cpu_availableProcessors 8.0

For developers available next annotations (sorry for the low output examples values):

Prometheus is a free software application used for event monitoring and alerting. It records real-time metrics in a time series database (allowing for high dimensionality) built using a HTTP pull model, with flexible queries and real-time alerting.

Let's setup above and check how metrics monitoring with Prometheus looks on practice:

wget https://github.com/prometheus/prometheus/releases/download/v2.23.0/prometheus-2.23.0.linux-amd64.tar.gz
tar xvfz prometheus-*.tar.gz
cd prometheus-*

To provide path to the metrics endpoint edit prometheus.yml and provide correct metrics_path and targets

# Here it's Prometheus itself.
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    - targets: ['']

This is it! http://localhost:9090/graph :

Now our metrics is collecting and can be visualized over standard prometheus UI(shown above) or easy integrated with grafana

Source code of custom metrics endpoint example available on GitHub