Skip to content

Latest commit

 

History

History
2324 lines (1984 loc) · 82.7 KB

README.adoc

File metadata and controls

2324 lines (1984 loc) · 82.7 KB
Raw

micrometer: Micrometer QuickStart

The micrometer quickstart demonstrates the use of the Micrometer library in WildFly.

What is it?

Micrometer is a vendor-neutral facade that allows application developers to collect and report application and system metrics to the backend of their choice in an entirely portable manner. By simply replacing the MeterRegistry used, or combining them in Micrometer’s CompositeRegistry data can be exported a variety of monitoring systems with no application code changes.

Architecture

In this quickstart, we will build a small, simple application that shows the usage of a number of Micrometer’s Meter implementations. We will also demonstrate the means by which WildFly exports the metrics data, which is via the OpenTelemetry Protocol (OTLP) to the OpenTelemetry Collector. To provide simpler access to the published metrics, the Collector will be configured with a Prometheus endpoint, from which we can scrape data.

Prerequisites

To complete this guide, you will need:

  • less than 15 minutes

  • JDK 11+ installed with JAVA_HOME configured appropriately

  • Apache Maven 3.5.3+

  • Docker Compose, or alternatively Podman Compose

Use of the WILDFLY_HOME and QUICKSTART_HOME Variables

In the following instructions, replace WILDFLY_HOME with the actual path to your WildFly installation. The installation path is described in detail here: Use of WILDFLY_HOME and JBOSS_HOME Variables.

When you see the replaceable variable QUICKSTART_HOME, replace it with the path to the root directory of all of the quickstarts.

Steps

Start the WildFly Standalone Server

  1. Open a terminal and navigate to the root of the WildFly directory.

  2. Start the WildFly server with the default profile by typing the following command.

    $ WILDFLY_HOME/bin/standalone.sh
    Note
    		 For Windows, use the WILDFLY_HOME\bin\standalone.bat script.

Configure the Server

You enable Micrometer by running JBoss CLI commands. For your convenience, this quickstart batches the commands into a configure-micrometer.cli script provided in the root directory of this quickstart.

  1. Before you begin, make sure you do the following:

  2. Review the configure-micrometer.cli file in the root of this quickstart directory. This script adds the configuration that enables Micrometer for the quickstart components. Comments in the script describe the purpose of each block of commands.

  3. Open a new terminal, navigate to the root directory of this quickstart, and run the following command, replacing WILDFLY_HOME with the path to your server:

    $ WILDFLY_HOME/bin/jboss-cli.sh --connect --file=configure-micrometer.cli
    Note
    		 For Windows, use the WILDFLY_HOME\bin\jboss-cli.bat script.

    You should see the following result when you run the script:

    The batch executed successfully
process-state: reload-required

  4. You’ll need to reload the configuration after that:

    $ WILDFLY_HOME/bin/jboss-cli.sh --connect --commands=reload

Starting the OpenTelemetry Collector

By default, WildFly will publish metrics every 10 seconds, so you will soon start seeing errors about a refused connection.

This is because we told WildFly to publish to a server that is not there, so we need to fix that. To make that as simple as possible, you can use Docker Compose to start an instance of the OpenTelemetry Collector.

The Docker Compose configuration file is docker-compose.yaml:

version: "3"

services:
  otel-collector:
    image: otel/opentelemetry-collector:0.89.0
    command: [--config=/etc/otel-collector-config.yaml]
    volumes:
      - ./otel-collector-config.yaml:/etc/otel-collector-config.yaml:Z
    ports:
      - 1888:1888 # pprof extension
      - 8888:8888 # Prometheus metrics exposed by the collector
      - 8889:8889 # Prometheus exporter metrics
      - 13133:13133 # health_check extension
      - 4317:4317 # OTLP gRPC receiver
      - 4318:4318 # OTLP http receiver
      - 55679:55679 # zpages extension
      - 1234:1234 # /metrics endpoint

The Collector server configuration file is otel-collector-config.yaml:

extensions:
  health_check:
  pprof:
    endpoint: 0.0.0.0:1777
  zpages:
    endpoint: 0.0.0.0:55679

receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  batch:

exporters:
  prometheus:
    endpoint: "0.0.0.0:1234"

service:
  pipelines:
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [prometheus]

  extensions: [health_check, pprof, zpages]

We can now bring up the collector server instance:

$ docker-compose up

The service should be available almost immediately, which you can verify by looking at the Prometheus endpoint we’ve configured by pointing your browser at http://localhost:1234/metrics. You should see quite a few metrics listed, none of which are what our application has registered. What you’re seeing are the system and JVM metrics automatically registered and published by WildFly to give systems/applications administrators a comprehensive view of system health and performance.

Note

You may use Podman as alternative to Docker if you prefer, in such case the command should be podman-compose up.
Note

If your environment does not support Docker or Podman, please refer to Otel Collector documentation for alternatives on how to install and run the OpenTelemetry Collector. Please ensure the same OpenTelemetry version as the one in the docker-compose.yaml above is used, otherwise such configuration may fail to work.

Registering metrics

Micrometer uses a programmatic approach to metrics definition, as opposed the more declarative, annotation-based approach of other libraries. Because of that, we need to explicitly register our Meter s before they can be used:

@Path("/")
@ApplicationScoped
public class RootResource {
    // ...
    @Inject
    private MeterRegistry registry;

    private Counter performCheckCounter;
    private Counter originalCounter;
    private Counter duplicatedCounter;

    @PostConstruct
    private void createMeters() {
        Gauge.builder("prime.highestSoFar", () -> highestPrimeNumberSoFar)
                .description("Highest prime number so far.")
                .register(registry);
        performCheckCounter = Counter
                .builder("prime.performedChecks")
                .description("How many prime checks have been performed.")
                .register(registry);
        originalCounter = Counter
                .builder("prime.duplicatedCounter")
                .tags(List.of(Tag.of("type", "original")))
                .register(registry);
        duplicatedCounter = Counter
                .builder("prime.duplicatedCounter")
                .tags(List.of(Tag.of("type", "copy")))
                .register(registry);
    }
    // ...
}

Notice that we start by @Inject ing the MeterRegistry. This is a WildFly-managed instance, so all applications need to do it inject it and start using. Once we have that, we can use to build and register our meters, which we do in @PostConstuct private void createMeters()

Note

This must be done post-construction, as the MeterRegistry must be injected before it can be used to register the meters.

In this example, we register several different types to demonstrate their use. With those registered, we can start writing application logic:

@GET
@Path("/prime/{number}")
public String checkIfPrime(@PathParam("number") long number) throws Exception {
    performCheckCounter.increment();

    Timer timer = registry.timer("prime.timer");

    return timer.recordCallable(() -> {

        if (number < 1) {
            return "Only natural numbers can be prime numbers.";
        }

        if (number == 1) {
            return "1 is not prime.";
        }

        if (number == 2) {
            return "2 is prime.";
        }

        if (number % 2 == 0) {
            return number + " is not prime, it is divisible by 2.";
        }

        for (int i = 3; i < Math.floor(Math.sqrt(number)) + 1; i = i + 2) {
            try {
                Thread.sleep(10);
            } catch (InterruptedException e) {
                //
            }
            if (number % i == 0) {
                return number + " is not prime, is divisible by " + i + ".";
            }
        }

        if (number > highestPrimeNumberSoFar) {
            highestPrimeNumberSoFar = number;
        }

        return number + " is prime.";
    });
}

This method represents a simple REST endpoint that is able to determine whether the number passed as a path parameter is a prime number.

Build and Deploy the Quickstart

  1. Make sure WildFly server is started.

  2. Open a terminal and navigate to the root directory of this quickstart.

  3. Type the following command to build the quickstart.

    $ mvn clean package

  4. Type the following command to deploy the quickstart.

    $ mvn wildfly:deploy

This deploys the micrometer/target/micrometer.war to the running instance of the server.

You should see a message in the server log indicating that the archive deployed successfully.

Access the quickstart application

You can either access the application via your browser at http://localhost:8080/micrometer/prime/13, or from the command line:

$ curl http://localhost:8080/micrometer/prime/13

It should return a simple document:

13 is prime.

Once given enough time to allow WildFly to publish metrics updates, you now see your application’s meters reported in the Prometheus export. You can also view them via the command-line:

$ curl -s http://localhost:1234/metrics | grep "prime_"
# HELP prime_duplicatedCounter
# TYPE prime_duplicatedCounter counter
prime_duplicatedCounter{job="wildfly",type="copy"} 0
prime_duplicatedCounter{job="wildfly",type="original"} 0
# HELP prime_highestSoFar Highest prime number so far.
# TYPE prime_highestSoFar gauge
prime_highestSoFar{job="wildfly"} 13
# HELP prime_performedChecks How many prime checks have been performed.
# TYPE prime_performedChecks counter
prime_performedChecks{job="wildfly"} 1
# HELP prime_timer
# TYPE prime_timer histogram
prime_timer_bucket{job="wildfly",le="+Inf"} 1
prime_timer_sum{job="wildfly"} 10.941035
prime_timer_count{job="wildfly"} 1

Notice that all four meters registered in the @PostConstruct method as well as the Timer in our endpoint method have all been published.

Run the Integration Tests

This quickstart includes integration tests, which are located under the src/test/ directory. The integration tests verify that the quickstart runs correctly when deployed on the server.

Follow these steps to run the integration tests.

  1. Make sure WildFly server is started.

  2. Make sure the quickstart is deployed.

  3. Type the following command to run the verify goal with the integration-testing profile activated.

    $ mvn verify -Pintegration-testing

Undeploy the Quickstart

When you are finished testing the quickstart, follow these steps to undeploy the archive.

  1. Make sure WildFly server is started.

  2. Open a terminal and navigate to the root directory of this quickstart.

  3. Type this command to undeploy the archive:

    $ mvn wildfly:undeploy

Restore the WildFly Standalone Server Configuration

You can restore the original server configuration using either of the following methods.

Restore the WildFly Standalone Server Configuration by Running the JBoss CLI Script

  1. Start the WildFly server as described above.

  2. Open a new terminal, navigate to the root directory of this quickstart, and run the following command, replacing WILDFLY_HOME with the path to your server:

    $ WILDFLY_HOME/bin/jboss-cli.sh --connect --file=restore-configuration.cli
    Note
    		 For Windows, use the WILDFLY_HOME\bin\jboss-cli.bat script.

Restore the WildFly Standalone Server Configuration Manually

When you have completed testing the quickstart, you can restore the original server configuration by manually restoring the backup copy the configuration file.

  1. If it is running, stop the WildFly server.

  2. Replace the WILDFLY_HOME/standalone/configuration/standalone.xml file with the backup copy of the file.

Building and running the quickstart application with provisioned WildFly server

Instead of using a standard WildFly server distribution, you can alternatively provision a WildFly server to deploy and run the quickstart. The functionality is provided by the WildFly Maven Plugin, and you may find its configuration in the quickstart pom.xml:

        <profile>
            <id>provisioned-server</id>
            <activation>
                <activeByDefault>true</activeByDefault>
            </activation>
            <build>
                <plugins>
                    <plugin>
                        <groupId>org.wildfly.plugins</groupId>
                        <artifactId>wildfly-maven-plugin</artifactId>
                        <configuration>
                            <discover-provisioning-info>
                                <version>${version.server}</version>
                            </discover-provisioning-info>
                            <add-ons>...</add-ons>
                        </configuration>
                        <executions>
                            <execution>
                                <goals>
                                    <goal>package</goal>
                                </goals>
                            </execution>
                        </executions>
                    </plugin>
                    ...
                </plugins>
            </build>
        </profile>

When built, the provisioned WildFly server can be found in the target/server directory, and its usage is similar to a standard server distribution, with the simplification that there is never the need to specify the server configuration to be started.

Follow these steps to run the quickstart using the provisioned server.

Procedure

  1. Make sure the server is provisioned.

    $ mvn clean package

  2. Start the WildFly provisioned server, using the WildFly Maven Plugin start goal.

    $ mvn wildfly:start

  3. Type the following command to run the integration tests.

    $ mvn verify -Pintegration-testing

  4. Shut down the WildFly provisioned server.

    $ mvn wildfly:shutdown

Building and Running the quickstart application in a bootable JAR

You can use the WildFly Maven Plugin to build a WildFly bootable JAR to run this quickstart.

The quickstart pom.xml file contains a Maven profile named bootable-jar, which activates the bootable JAR packaging when provisioning WildFly, through the <bootable-jar>true</bootable-jar> configuration element:

      <profile>
          <id>bootable-jar</id>
          <activation>
              <activeByDefault>true</activeByDefault>
          </activation>
          <build>
              <plugins>
                  <plugin>
                      <groupId>org.wildfly.plugins</groupId>
                      <artifactId>wildfly-maven-plugin</artifactId>
                      <configuration>
                          <discover-provisioning-info>
                              <version>${version.server}</version>
                          </discover-provisioning-info>
                          <bootable-jar>true</bootable-jar>
                          <add-ons>...</add-ons>
                      </configuration>
                      <executions>
                          <execution>
                              <goals>
                                  <goal>package</goal>
                              </goals>
                          </execution>
                      </executions>
                  </plugin>
                  ...
              </plugins>
          </build>
      </profile>

The bootable-jar profile is activate by default, and when built the WildFly bootable jar file is named micrometer-bootable.jar, and may be found in the target directory.

Procedure

  1. Ensure the bootable jar is built.

    $ mvn clean clean package

  2. Start the WildFly bootable jar use the WildFly Maven Plugin start-jar goal.

    $ mvn wildfly:start-jar
    Note

    You may also start the bootable jar without Maven, using the java command.

    $ java -jar target/micrometer-bootable.jar

  3. Run the integration tests use the verify goal, with the integration-testing profile activated.

    $ mvn verify -Pintegration-testing

  4. Shut down the WildFly bootable jar use the WildFly Maven Plugin shutdown goal.

    $ mvn wildfly:shutdown

Building and running the quickstart application with OpenShift

Build the WildFly Source-to-Image (S2I) Quickstart to OpenShift with Helm Charts

On OpenShift, the S2I build with Apache Maven uses an openshift Maven profile to provision a WildFly server, deploy and run the quickstart in OpenShift environment.

The server provisioning functionality is provided by the WildFly Maven Plugin, and you may find its configuration in the quickstart pom.xml:

        <profile>
            <id>openshift</id>
            <build>
                <plugins>
                    <plugin>
                        <groupId>org.wildfly.plugins</groupId>
                        <artifactId>wildfly-maven-plugin</artifactId>
                        <configuration>
                            <discover-provisioning-info>
                                <version>${version.server}</version>
                                <context>cloud</context>
                            </discover-provisioning-info>
                            <add-ons>...</add-ons>
                        </configuration>
                        <executions>
                            <execution>
                                <goals>
                                    <goal>package</goal>
                                </goals>
                            </execution>
                        </executions>
                    </plugin>
                    ...
                </plugins>
            </build>
        </profile>

You may note that unlike the provisioned-server profile it uses the cloud context which enables a configuration tuned for OpenShift environment.

The plugin uses WildFly Glow to discover the feature packs and layers required to run the application, and provisions a server containing those layers.

If you get an error or the server is missing some functionality which cannot be auto-discovered, you can download the WildFly Glow CLI and run the following command to see more information about what add-ons are available:

wildfly-glow show-add-ons

Getting Started with WildFly for OpenShift and Helm Charts

This section contains the basic instructions to build and deploy this quickstart to WildFly for OpenShift or WildFly for OpenShift Online using Helm Charts.

Prerequisites

  • You must be logged in OpenShift and have an oc client to connect to OpenShift

  • Helm must be installed to deploy the backend on OpenShift.

Once you have installed Helm, you need to add the repository that provides Helm Charts for WildFly.

$ helm repo add wildfly https://docs.wildfly.org/wildfly-charts/
"wildfly" has been added to your repositories
$ helm search repo wildfly
NAME                    CHART VERSION   APP VERSION     DESCRIPTION
wildfly/wildfly         ...             ...            Build and Deploy WildFly applications on OpenShift
wildfly/wildfly-common  ...             ...            A library chart for WildFly-based applications
Install OpenTelemetry Collector on OpenShift

The functionality of this quickstart depends on a running instance of the OpenTelemetry Collector.

To deploy and configure the OpenTelemetry Collector, you will need to apply a set of configurations to your OpenShift cluster:

---
apiVersion: v1
kind: ConfigMap
metadata:
  name: collector-config
data:
  collector.yml: |
    receivers:
      otlp:
        protocols:
          grpc:
          http:
    processors:
    exporters:
      logging:
        verbosity: detailed
      prometheus:
        endpoint: "0.0.0.0:1234"
    service:
      pipelines:
        metrics:
          receivers: [otlp]
          processors: []
          exporters: [logging,prometheus]
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: opentelemetrycollector
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: opentelemetrycollector
  template:
    metadata:
      labels:
        app.kubernetes.io/name: opentelemetrycollector
    spec:
      containers:
        - name: otelcol
          args:
            - --config=/conf/collector.yml
          image: otel/opentelemetry-collector:0.89.0
          volumeMounts:
            - mountPath: /conf
              name: collector-config
      volumes:
        - configMap:
            items:
              - key: collector.yml
                path: collector.yml
            name: collector-config
          name: collector-config
---
apiVersion: v1
kind: Service
metadata:
  name: opentelemetrycollector
spec:
  ports:
    - name: otlp-grpc
      port: 4317
      protocol: TCP
      targetPort: 4317
    - name: otlp-http
      port: 4318
      protocol: TCP
      targetPort: 4318
    - name: prometheus
      port: 1234
      protocol: TCP
      targetPort: 1234
  selector:
    app.kubernetes.io/name: opentelemetrycollector
  type: ClusterIP
---
apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: otelcol-otlp-grpc
  labels:
    app.kubernetes.io/name: microprofile
spec:
  port:
    targetPort: otlp-grpc
  to:
    kind: Service
    name: opentelemetrycollector
  tls:
    termination: edge
    insecureEdgeTerminationPolicy: Redirect
  wildcardPolicy: None
---
apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: otelcol-otlp-http
  labels:
    app.kubernetes.io/name: microprofile
spec:
  port:
    targetPort: otlp-http
  to:
    kind: Service
    name: opentelemetrycollector
  tls:
    termination: edge
    insecureEdgeTerminationPolicy: Redirect
  wildcardPolicy: None
---
apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: otelcol-prometheus
  labels:
    app.kubernetes.io/name: microprofile
spec:
  port:
    targetPort: prometheus
  to:
    kind: Service
    name: opentelemetrycollector
  tls:
    termination: edge
    insecureEdgeTerminationPolicy: Redirect
  wildcardPolicy: None

To make things simpler, you can find these commands in charts/opentelemetry-collector-openshift.yaml, and to apply them run the following command in your terminal:

$ oc apply -f charts/opentelemetry-collector-openshift.yaml
Note

When done with the quickstart, the oc delete -f charts/opentelemetry-collector-openshift.yaml command may be used to revert the applied changes.

Deploy the WildFly Source-to-Image (S2I) Quickstart to OpenShift with Helm Charts

Log in to your OpenShift instance using the oc login command. The backend will be built and deployed on OpenShift with a Helm Chart for WildFly.

Navigate to the root directory of this quickstart and run the following command:

$ helm install micrometer -f charts/helm.yaml wildfly/wildfly --wait --timeout=10m0s 
NAME: micrometer
...
STATUS: deployed
REVISION: 1

This command will return once the application has successfully deployed. In case of a timeout, you can check the status of the application with the following command in another terminal:

oc get deployment micrometer

The Helm Chart for this quickstart contains all the information to build an image from the source code using S2I on Java 17:

build:
  uri: https://github.com/wildfly/quickstart.git
  ref: main
  contextDir: micrometer
deploy:
  replicas: 1
  env:
    - name: OTEL_COLLECTOR_HOST
      value: "opentelemetrycollector"

This will create a new deployment on OpenShift and deploy the application.

If you want to see all the configuration elements to customize your deployment you can use the following command:

$ helm show readme wildfly/wildfly

Get the URL of the route to the deployment.

$ oc get route micrometer -o jsonpath="{.spec.host}"

Access the application in your web browser using the displayed URL.

Run the Integration Tests with OpenShift

The integration tests included with this quickstart, which verify that the quickstart runs correctly, may also be run with the quickstart running on OpenShift.

Note

The integration tests expect a deployed application, so make sure you have deployed the quickstart on OpenShift before you begin.

Run the integration tests using the following command to run the verify goal with the integration-testing profile activated and the proper URL:

$ mvn verify -Pintegration-testing -Dserver.host=https://$(oc get route micrometer --template='{{ .spec.host }}')
Note

The tests are using SSL to connect to the quickstart running on OpenShift. So you need the certificates to be trusted by the machine the tests are run from.

Undeploy the WildFly Source-to-Image (S2I) Quickstart from OpenShift with Helm Charts

$ helm uninstall micrometer

Building and running the quickstart application with Kubernetes

Build the WildFly Quickstart to Kubernetes with Helm Charts

For Kubernetes, the build with Apache Maven uses an openshift Maven profile to provision a WildFly server, suitable for running on Kubernetes.

The server provisioning functionality is provided by the WildFly Maven Plugin, and you may find its configuration in the quickstart pom.xml:

        <profile>
            <id>openshift</id>
            <build>
                <plugins>
                    <plugin>
                        <groupId>org.wildfly.plugins</groupId>
                        <artifactId>wildfly-maven-plugin</artifactId>
                        <configuration>
                            <discover-provisioning-info>
                                <version>${version.server}</version>
                                <context>cloud</context>
                            </discover-provisioning-info>
                            <add-ons>...</add-ons>
                        </configuration>
                        <executions>
                            <execution>
                                <goals>
                                    <goal>package</goal>
                                </goals>
                            </execution>
                        </executions>
                    </plugin>
                    ...
                </plugins>
            </build>
        </profile>

You may note that unlike the provisioned-server profile it uses the cloud context which enables a configuration tuned for Kubernetes environment.

The plugin uses WildFly Glow to discover the feature packs and layers required to run the application, and provisions a server containing those layers.

If you get an error or the server is missing some functionality which cannot be auto-discovered, you can download the WildFly Glow CLI and run the following command to see more information about what add-ons are available:

wildfly-glow show-add-ons

Getting Started with Kubernetes and Helm Charts

This section contains the basic instructions to build and deploy this quickstart to Kubernetes using Helm Charts.

Install Kubernetes

In this example we are using Minikube as our Kubernetes provider. See the Minikube Getting Started guide for how to install it. After installing it, we start it with 4GB of memory.

minikube start --memory='4gb'

The above command should work if you have Docker installed on your machine. If, you are using Podman instead of Docker, you will also need to pass in --driver=podman, as covered in the Minikube documentation.

Once Minikube has started, we need to enable its registry since that is where we will push the image needed to deploy the quickstart, and where we will tell the Helm charts to download it from.

minikube addons enable registry

In order to be able to push images to the registry we need to make it accessible from outside Kubernetes. How we do this depends on your operating system. All the below examples will expose it at localhost:5000

# On Mac:
docker run --rm -it --network=host alpine ash -c "apk add socat && socat TCP-LISTEN:5000,reuseaddr,fork TCP:$(minikube ip):5000"

# On Linux:
kubectl port-forward --namespace kube-system service/registry 5000:80 &

# On Windows:
kubectl port-forward --namespace kube-system service/registry 5000:80
docker run --rm -it --network=host alpine ash -c "apk add socat && socat TCP-LISTEN:5000,reuseaddr,fork TCP:host.docker.internal:5000"

Prerequisites

  • Helm must be installed to deploy the backend on Kubernetes.

Once you have installed Helm, you need to add the repository that provides Helm Charts for WildFly.

$ helm repo add wildfly https://docs.wildfly.org/wildfly-charts/
"wildfly" has been added to your repositories
$ helm search repo wildfly
NAME                    CHART VERSION   APP VERSION     DESCRIPTION
wildfly/wildfly         ...             ...            Build and Deploy WildFly applications on OpenShift
wildfly/wildfly-common  ...             ...            A library chart for WildFly-based applications
Install OpenTelemetry Collector on OpenShift

The functionality of this quickstart depends on a running instance of the OpenTelemetry Collector.

To deploy and configure the OpenTelemetry Collector, you will need to apply a set of configurations to your Kubernetes cluster:

---
apiVersion: v1
kind: ConfigMap
metadata:
  name: collector-config
data:
  collector.yml: |
    receivers:
      otlp:
        protocols:
          grpc:
          http:
    processors:
    exporters:
      logging:
        verbosity: detailed
      prometheus:
        endpoint: "0.0.0.0:1234"
    service:
      pipelines:
        metrics:
          receivers: [otlp]
          processors: []
          exporters: [logging,prometheus]
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: opentelemetrycollector
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: opentelemetrycollector
  template:
    metadata:
      labels:
        app.kubernetes.io/name: opentelemetrycollector
    spec:
      containers:
        - name: otelcol
          args:
            - --config=/conf/collector.yml
          image: otel/opentelemetry-collector:0.89.0
          volumeMounts:
            - mountPath: /conf
              name: collector-config
      volumes:
        - configMap:
            items:
              - key: collector.yml
                path: collector.yml
            name: collector-config
          name: collector-config
---
apiVersion: v1
kind: Service
metadata:
  name: opentelemetrycollector
spec:
  ports:
    - name: otlp-grpc
      port: 4317
      protocol: TCP
      targetPort: 4317
    - name: otlp-http
      port: 4318
      protocol: TCP
      targetPort: 4318
    - name: prometheus
      port: 1234
      protocol: TCP
      targetPort: 1234
  selector:
    app.kubernetes.io/name: opentelemetrycollector
  type: ClusterIP
---
apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: otelcol-otlp-grpc
  labels:
    app.kubernetes.io/name: microprofile
spec:
  port:
    targetPort: otlp-grpc
  to:
    kind: Service
    name: opentelemetrycollector
  tls:
    termination: edge
    insecureEdgeTerminationPolicy: Redirect
  wildcardPolicy: None
---
apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: otelcol-otlp-http
  labels:
    app.kubernetes.io/name: microprofile
spec:
  port:
    targetPort: otlp-http
  to:
    kind: Service
    name: opentelemetrycollector
  tls:
    termination: edge
    insecureEdgeTerminationPolicy: Redirect
  wildcardPolicy: None
---
apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: otelcol-prometheus
  labels:
    app.kubernetes.io/name: microprofile
spec:
  port:
    targetPort: prometheus
  to:
    kind: Service
    name: opentelemetrycollector
  tls:
    termination: edge
    insecureEdgeTerminationPolicy: Redirect
  wildcardPolicy: None

To make things simpler, you can find these commands in charts/opentelemetry-collector-openshift.yaml, and to apply them run the following command in your terminal:

$ kubectl apply -f charts/opentelemetry-collector-kubernetes.yaml
Note

When done with the quickstart, the kubectl delete -f charts/opentelemetry-collector-kubernetes.yaml command may be used to revert the applied changes.

Deploy the WildFly Source-to-Image (S2I) Quickstart to Kubernetes with Helm Charts

The backend will be built and deployed on Kubernetes with a Helm Chart for WildFly.

Navigate to the root directory of this quickstart and run the following commands:

mvn -Popenshift package wildfly:image

This will use the openshift Maven profile we saw earlier to build the application, and create a Docker image containing the WildFly server with the application deployed. The name of the image will be micrometer.

Next we need to tag the image and make it available to Kubernetes. You can push it to a registry like quay.io. In this case we tag as localhost:5000/micrometer:latest and push it to the internal registry in our Kubernetes instance:

# Tag the image
docker tag micrometer localhost:5000/micrometer:latest
# Push the image to the registry
docker push localhost:5000/micrometer:latest

In the below call to helm install which deploys our application to Kubernetes, we are passing in some extra arguments to tweak the Helm build:

  • --set build.enabled=false - This turns off the s2i build for the Helm chart since Kubernetes, unlike OpenShift, does not have s2i. Instead, we are providing the image to use.

  • --set deploy.route.enabled=false - This disables route creation normally performed by the Helm chart. On Kubernetes we will use port-forwards instead to access our application, since routes are an OpenShift specific concept and thus not available on Kubernetes.

  • --set image.name="localhost:5000/micrometer" - This tells the Helm chart to use the image we built, tagged and pushed to Kubernetes' internal registry above.

$ helm install micrometer -f charts/helm.yaml wildfly/wildfly --wait --timeout=10m0s --set build.enabled=false --set deploy.route.enabled=false --set image.name="localhost:5000/micrometer"
NAME: micrometer
...
STATUS: deployed
REVISION: 1

This command will return once the application has successfully deployed. In case of a timeout, you can check the status of the application with the following command in another terminal:

kubectl get deployment micrometer

The Helm Chart for this quickstart contains all the information to build an image from the source code using S2I on Java 17:

build:
  uri: https://github.com/wildfly/quickstart.git
  ref: main
  contextDir: micrometer
deploy:
  replicas: 1
  env:
    - name: OTEL_COLLECTOR_HOST
      value: "opentelemetrycollector"

This will create a new deployment on Kubernetes and deploy the application.

If you want to see all the configuration elements to customize your deployment you can use the following command:

$ helm show readme wildfly/wildfly

To be able to connect to our application running in Kubernetes from outside, we need to set up a port-forward to the micrometer service created for us by the Helm chart.

This service will run on port 8080, and we set up the port forward to also run on port 8080:

kubectl port-forward service/micrometer 8080:8080

The server can now be accessed via http://localhost:8080 from outside Kubernetes. Note that the command to create the port-forward will not return, so it is easiest to run this in a separate terminal.

Run the Integration Tests with Kubernetes

The integration tests included with this quickstart, which verify that the quickstart runs correctly, may also be run with the quickstart running on Kubernetes.

Note

The integration tests expect a deployed application, so make sure you have deployed the quickstart on Kubernetes before you begin.

Run the integration tests using the following command to run the verify goal with the integration-testing profile activated and the proper URL:

$ mvn verify -Pintegration-testing -Dserver.host=http://localhost:8080

Undeploy the WildFly Source-to-Image (S2I) Quickstart from Kubernetes with Helm Charts

$ helm uninstall micrometer

To stop the port forward you created earlier use:

$ kubectl port-forward service/micrometer 8080:8080

Conclusion

Micrometer provides a de facto standard way of capturing and publishing metrics to the monitoring solution of your choice. WildFly provides a convenient, out-of-the-box integration of Micrometer to make it easier to capture those metrics and monitor your application’s health and performance. For more information on Micrometer, please refer to the project’s website.