tencent cloud


JVM Integration

Last updated: 2024-01-29 15:55:08


    When using the Java programming language, you need to monitor JVM performance. TMP collects the JVM monitoring data exposed by applications and provides an out-of-the-box Grafana dashboard for it.
    This document uses deploying a Java application in TKE as an example to describe how to use TMP to monitor the application status.
    If you have already used Spring Boot as the development framework, please see Spring Boot Integration.


    Create a TKE cluster.


    As a major programming language, Java has a comprehensive ecosystem, where Micrometer has been widely used as a metric timestamping SDK. This document uses Micrometer as an example to describe how to monitor JVM.

    Modifying application dependencies and configuration

    Step 1. Modify POM dependencies

    Add Maven dependencies to the pom.xml file and adjust the version as needed as follows:

    Step 2. Modify the code

    When the project is started, add the corresponding monitoring configuration. In addition, Micrometer also provides the collection of some common metrics, which are in the io.micrometer.core.instrument.binder package and can be added as needed as follows:
    public class Application {
    // It can be used in custom monitoring as a global variable
    public static final PrometheusMeterRegistry registry = new PrometheusMeterRegistry(PrometheusConfig.DEFAULT);
    static {
    // Add a global Prometheus label. We recommend you add the corresponding application name to it
    registry.config().commonTags("application", "java-demo");
    public static void main(String[] args) throws Exception {
    // Add JVM monitoring
    new ClassLoaderMetrics().bindTo(registry);
    new JvmMemoryMetrics().bindTo(registry);
    new JvmGcMetrics().bindTo(registry);
    new ProcessorMetrics().bindTo(registry);
    new JvmThreadMetrics().bindTo(registry);
    new UptimeMetrics().bindTo(registry);
    new FileDescriptorMetrics().bindTo(registry);
    System.gc(); // Test GC
    try {
    // Expose the Prometheus HTTP service. If it already exists, you can use the existing HTTP server
    HttpServer server = HttpServer.create(new InetSocketAddress(8080), 0);
    server.createContext("/metrics", httpExchange -> {
    String response = registry.scrape();
    httpExchange.sendResponseHeaders(200, response.getBytes().length);
    try (OutputStream os = httpExchange.getResponseBody()) {
    new Thread(server::start).start();
    } catch (IOException e) {
    throw new RuntimeException(e);
    As monitoring of JVM GC pauses is implemented through the GarbageCollector Notification mechanism, the monitoring data will be generated only after a GC occurs. The above sample actively calls System.gc() to make the test more straightforward.

    Step 3. Perform local verification

    After the application is started locally, you can access the metric data of the Prometheus protocol through http://localhost:8080/metrics.

    Releasing application to TKE

    Step 1. Configure a Docker image environment locally

    If you have already configured a Docker image environment locally, proceed to the next step; otherwise, configure one as instructed in Getting Started.

    Step 2. Package and upload the image

    1. Add Dockerfile in the root directory of the project. Please modify it based on your actual project conditions as follows:
    FROM openjdk:8-jdk
    WORKDIR /java-demo
    ADD target/java-demo-*.jar /java-demo/java-demo.jar
    CMD ["java","-jar","java-demo.jar"]
    2. Package the image by running the following command in the project root directory. You need to replace namespace, ImageName, and image tag as needed.
    mvn clean package
    docker build . -t ccr.ccs.tencentyun.com/[namespace]/[ImageName]:[image tag]
    docker push ccr.ccs.tencentyun.com/[namespace]/[ImageName]:[image tag]
    <b>Below is a sample:</b>
    mvn clean package
    docker build . -t ccr.ccs.tencentyun.com/prom_spring_demo/java-demo:latest
    docker push ccr.ccs.tencentyun.com/prom_spring_demo/-demo:latest

    Step 3. Deploy the application

    1. Log in to the TKE console and select the container cluster for deployment.
    2. Select Workload* > Deployment to enter the Deployment management page and select the corresponding namespace to deploy the service. Use the following YAML configuration to create the corresponding Deployment:
    If you want to create in the console, please see Spring Boot Integration.
    apiVersion: apps/v1
    kind: Deployment
    k8s-app: java-demo
    name: java-demo
    namespace: spring-demo
    replicas: 1
    k8s-app: java-demo
    k8s-app: java-demo
    - image: ccr.ccs.tencentyun.com/prom_spring_demo/java-demo
    imagePullPolicy: Always
    name: java-demo
    - containerPort: 8080
    name: metric-port
    terminationMessagePath: /dev/termination-log
    terminationMessagePolicy: File
    dnsPolicy: ClusterFirst
    - name: qcloudregistrykey
    restartPolicy: Always
    schedulerName: default-scheduler
    terminationGracePeriodSeconds: 30

    Step 4. Add a scrape task

    1. Log in to the TMP console and select the target TMP instance to enter the management page.
    2. Click a cluster ID in the TKE cluster list to enter the Integrate with TKE page.
    3. In Scrape Configuration, add Pod Monitor to define a Prometheus scrape task. Below is a sample YAML configuration:
    apiVersion: monitoring.coreos.com/v1
    kind: PodMonitor
    name: java-demo
    namespace: cm-prometheus
    - java-demo
    - interval: 30s
    path: /metrics
    port: metric-port
    k8s-app: java-demo

    Step 5. View the monitoring information

    1. In Integration Center in the target TMP instance, find JVM monitoring, install the corresponding Grafana dashboard, and then you can enable the JVM monitoring dashboard.
    2. Access the Grafana address of your TMP instance to view the application monitoring dashboard in Dashboards > Manage > Application.
    Application JVM: monitoring data of the status of all instances under an application. If you find a faulty instance, you can view its monitoring information at any time.
    Instance JVM: detailed monitoring data of a single instance JVM.
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