Skip to main content
Redhat Developers  Logo
  • Products

    Featured

    • Red Hat Enterprise Linux
      Red Hat Enterprise Linux Icon
    • Red Hat OpenShift AI
      Red Hat OpenShift AI
    • Red Hat Enterprise Linux AI
      Linux icon inside of a brain
    • Image mode for Red Hat Enterprise Linux
      RHEL image mode
    • Red Hat OpenShift
      Openshift icon
    • Red Hat Ansible Automation Platform
      Ansible icon
    • Red Hat Developer Hub
      Developer Hub
    • View All Red Hat Products
    • Linux

      • Red Hat Enterprise Linux
      • Image mode for Red Hat Enterprise Linux
      • Red Hat Universal Base Images (UBI)
    • Java runtimes & frameworks

      • JBoss Enterprise Application Platform
      • Red Hat build of OpenJDK
    • Kubernetes

      • Red Hat OpenShift
      • Microsoft Azure Red Hat OpenShift
      • Red Hat OpenShift Virtualization
      • Red Hat OpenShift Lightspeed
    • Integration & App Connectivity

      • Red Hat Build of Apache Camel
      • Red Hat Service Interconnect
      • Red Hat Connectivity Link
    • AI/ML

      • Red Hat OpenShift AI
      • Red Hat Enterprise Linux AI
    • Automation

      • Red Hat Ansible Automation Platform
      • Red Hat Ansible Lightspeed
    • Developer tools

      • Red Hat Trusted Software Supply Chain
      • Podman Desktop
      • Red Hat OpenShift Dev Spaces
    • Developer Sandbox

      Developer Sandbox
      Try Red Hat products and technologies without setup or configuration fees for 30 days with this shared Openshift and Kubernetes cluster.
    • Try at no cost
  • Technologies

    Featured

    • AI/ML
      AI/ML Icon
    • Linux
      Linux Icon
    • Kubernetes
      Cloud icon
    • Automation
      Automation Icon showing arrows moving in a circle around a gear
    • View All Technologies
    • Programming Languages & Frameworks

      • Java
      • Python
      • JavaScript
    • System Design & Architecture

      • Red Hat architecture and design patterns
      • Microservices
      • Event-Driven Architecture
      • Databases
    • Developer Productivity

      • Developer productivity
      • Developer Tools
      • GitOps
    • Secure Development & Architectures

      • Security
      • Secure coding
    • Platform Engineering

      • DevOps
      • DevSecOps
      • Ansible automation for applications and services
    • Automated Data Processing

      • AI/ML
      • Data Science
      • Apache Kafka on Kubernetes
      • View All Technologies
    • Start exploring in the Developer Sandbox for free

      sandbox graphic
      Try Red Hat's products and technologies without setup or configuration.
    • Try at no cost
  • Learn

    Featured

    • Kubernetes & Cloud Native
      Openshift icon
    • Linux
      Rhel icon
    • Automation
      Ansible cloud icon
    • Java
      Java icon
    • AI/ML
      AI/ML Icon
    • View All Learning Resources

    E-Books

    • GitOps Cookbook
    • Podman in Action
    • Kubernetes Operators
    • The Path to GitOps
    • View All E-books

    Cheat Sheets

    • Linux Commands
    • Bash Commands
    • Git
    • systemd Commands
    • View All Cheat Sheets

    Documentation

    • API Catalog
    • Product Documentation
    • Legacy Documentation
    • Red Hat Learning

      Learning image
      Boost your technical skills to expert-level with the help of interactive lessons offered by various Red Hat Learning programs.
    • Explore Red Hat Learning
  • Developer Sandbox

    Developer Sandbox

    • Access Red Hat’s products and technologies without setup or configuration, and start developing quicker than ever before with our new, no-cost sandbox environments.
    • Explore Developer Sandbox

    Featured Developer Sandbox activities

    • Get started with your Developer Sandbox
    • OpenShift virtualization and application modernization using the Developer Sandbox
    • Explore all Developer Sandbox activities

    Ready to start developing apps?

    • Try at no cost
  • Blog
  • Events
  • Videos

Connect Red Hat AMQ Streams to your Red Hat OpenShift 4 monitoring stack

April 19, 2021
David Kornel Jakub Stejskal
Related topics:
ContainersDeveloper ToolsJavaKubernetes
Related products:
Streams for Apache Kafka

Share:

    Monitoring systems in use is one of the greatest challenges in cloud environments. Users always want to know how their applications work in production. For example, they want to know how Red Hat OpenShift utilizes its resources; or how to monitor systems in use like Red Hat AMQ Streams.

    AMQ Streams, the enterprise version of Strimzi, exports many useful metrics from Apache Kafka clusters, Apache Zookeeper clusters, and other components. We can use Prometheus to scrape these metrics and display them in Grafana dashboards. Exporting AMQ Streams metrics to Grafana is quite easy, and using the existing monitoring stack on OpenShift 4 is easy, as well.

    This article shows you how to quickly set up a new or pre-existing AMQ Streams deployment with a default OpenShift 4 monitoring stack.

    Install AMQ Streams and Grafana

    All users can use an existing monitoring stack in one of two ways: Create a new namespace and deploy AMQ Streams from scratch or use a pre-existing AMQ Streams instance and update the configuration namespaces where it operates.

    Either way, the following example assumes you have cluster-wide AMQ Streams Operator in one namespace and Kafka clusters in different namespaces. We will not create a new namespace; instead, we'll use streams-cluster-operator for the AMQ Streams Operator and streams-Kafka-cluster for the Kafka cluster, and switch between them.

    Note: See Hello World for AMQ Streams on OpenShift for a detailed guide to installing AMQ Streams from scratch. You can follow all the steps there or check the Red Hat AMQ Streams documentation, which describes the same steps.

    Confirm your new AMQ Streams installation

    Once you are done setting up your AMQ Streams installation, you should see the strimzi-cluster-operator pod up and running:

    $ oc get pod -n streams-cluster-operator
    
    strimzi-cluster-operator-7bff5b4d65-jmgqr    1/1     Running   0          12m
    

    Deploy a Kafka cluster

    Next, we'll deploy a Kafka cluster with the metrics configured. During the AMQ Streams installation, you downloaded examples with installed files for Operators, Kafka, and metrics. An example of a Kafka custom resource with metrics was stored under examples/metrics/Kafka-metrics.yaml. The Kafka metrics are now stored in a new config map, which is referenced in the Kafka custom resource. The metrics format hasn't changed. Anytime that you change the metrics configuration, you will also need to change the config map.

    As an example, you can use the AMQ Streams default configuration and deploy everything by executing the following command:

    $ oc apply -f examples/metrics/kafka-metrics.yaml -n streams-kafka-cluster

    After a time, you should see the Kafka cluster up and running:

    $ oc get pod -n streams-kafka-cluster
    
    my-cluster-entity-operator-cf887b59-645zt    3/3     Running   0          6m
    my-cluster-kafka-0                           1/1     Running   0          9m
    my-cluster-kafka-1                           1/1     Running   0          9m
    my-cluster-kafka-2                           1/1     Running   0          9m
    my-cluster-kafka-exported-fsf343r            1/1     Running   0          5m
    my-cluster-zookeeper-0                       1/1     Running   0          11m
    my-cluster-zookeeper-1                       1/1     Running   0          11m
    my-cluster-zookeeper-2                       1/1     Running   0          11m
    

    You can change the name in the custom resource and deploy other clusters in different namespaces. As a result of the cluster-wide installation, the AMQ Streams Operator will serve all clusters deployed into an OpenShift cluster.

    Install the Grafana Operator

    In this section, we'll set up a Granfana instance. Grafana is by default installed in every OpenShift 4 instance. Unfortunately, the pre-installed Grafana instance is read-only, and you can only use predefined Grafana dashboards. As a result, we are forced to deploy our own Grafana instance into OpenShift.

    Let’s start with a new namespace called streams-grafana. Then, we'll install Grafana from the OpenShift OperatorHub by creating proper operatorgroup and subscription. Here is the process to install the Grafana Operator:

    1. Make a new namespace:
      $ oc create namespace streams-grafana
    2. Create an Operator group:
      $ cat << EOF | oc apply -f -
      apiVersion: operators.coreos.com/v1
      kind: OperatorGroup
      metadata:
        name: grafana-group
        namespace: streams-grafana
        labels:
          app: grafana
      spec:
        targetNamespaces:
          - streams-grafana
      EOF
    3. Create a subscription for the Grafana Operator:
      $ cat << EOF | oc apply -f -
      apiVersion: operators.coreos.com/v1alpha1
      kind: Subscription
      metadata:
        name: grafana-operator
        namespace:  streams-grafana
      spec:
        channel: alpha
        installPlanApproval: Automatic
        name: grafana-operator
        source: community-operators
        sourceNamespace: openshift-marketplace
        startingCSV: grafana-operator.v3.9.0
      EOF
    4. Confirm the Grafana Operator is installed successfully:
      $ oc get pods -n streams-grafana
      
      NAME                               READY   STATUS    RESTARTS   AGE
      grafana-operator-957c6dcd9-wrljw   1/1     Running   0          65s
      

    Connect the AMQ Streams Operator and Kafka clusters to your monitoring stack

    During the OpenShift installation, the default OpenShift 4 monitoring stack is deployed in the openshift-monitoring namespace. With additional configuration, you can re-use it for monitoring your application.

    First, you need to allow user-workloads in your OpenShift cluster. You could achieve this by creating a new config map to the openshift-monitoring namespace. Here is the config map for our example:

    $ cat << EOF | oc apply -f -
    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: cluster-monitoring-config
      namespace: openshift-monitoring
    data:
      config.yaml: |
        enableUserWorkload: true
    EOF
    

    After applying the config map, you should see new pods in the openshift-user-workload-monitoring namespace:

    $ oc get po -n openshift-user-workload-monitoring
    
    NAME                                   READY   STATUS    RESTARTS   AGE
    prometheus-operator-868cd68496-jl44r   2/2     Running   0          118s
    prometheus-user-workload-0             5/5     Running   1          112s
    prometheus-user-workload-1             5/5     Running   1          112s
    thanos-ruler-user-workload-0           3/3     Running   0          111s
    thanos-ruler-user-workload-1           3/3     Running   0          111s
    

    Second, you have to deploy pod monitors for all AMQ Streams components. You can find the pod monitors YAML in examples/metrics/prometheus-install/strimzi-pod-monitor.yaml. You need to create a pod monitor for each namespace and component you use. In general, you need to have a pod monitor for Kafka in the kafka namespace, one for for cluster-operator in the cluster-operator namespace, and so on:

    $ cat examples/metrics/prometheus-install/strimzi-pod-monitor.yaml | sed "s#myproject#streams-kafka-cluster#g" | oc apply -n streams-kafka-cluster -f -
    $ cat examples/metrics/prometheus-install/strimzi-pod-monitor.yaml | sed "s#myproject#streams-cluster-operator#g" | oc apply -n streams-cluster-operator -f -
    

    Note that you can remove unused pod monitors, such as KafkaBridge.

    Check Prometheus for Strimzi or Kafka queries

    Finally, in the OpenShift administrator console, navigate to the Monitoring tab and open Metrics. Try a few Strimzi or Kafka-related queries. Figure 1 shows a query for strimzi_resources.

    Query prometheus in Openshift console
    Figure 1: Querying Prometheus in the OpenShift administrator console.

    Connect a Granfana instance to OpenShift 4 Prometheus

    Now, we are ready to create a Grafana instance. Because the Grafana Operator is up and running, we just need to deploy the Grafana instance using the following commands:

    $ cat << EOF | oc apply -f -
    apiVersion: integreatly.org/v1alpha1
    kind: Grafana
    metadata:
      name: grafana
      namespace: streams-grafana
    spec:
      ingress:
        enabled: True
      config:
        log:
          mode: "console"
          level: "warn"
        security:
          admin_user: "admin"
          admin_password: "admin"
        auth:
          disable_login_form: False
          disable_signout_menu: True
        auth.anonymous:
          enabled: True
      dashboardLabelSelector:
        - matchExpressions:
            - { key: app, operator: In, values: [strimzi] }
      resources:
        limits:
          cpu: 2000m
          memory: 8000Mi
        requests:
          cpu: 100m
          memory: 200Mi
    EOF

    With the Grafana instance running, we need to create a data source from Prometheus. Before we can do that, we need to create a ServiceAccount and ClusterRoleBinding for Grafana. Here is the ServiceAccount:

    $ cat << EOF | oc apply -f -
    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: grafana-serviceaccount
      labels:
        app: strimzi
        namespace: streams-grafana
    EOF
    

    And here is the ClusterRoleBinding:

    $ cat << EOF | oc apply -f -
    apiVersion: rbac.authorization.k8s.io/v1
    kind: ClusterRoleBinding
    metadata:
      name: grafana-cluster-monitoring-binding
      labels:
        app: strimzi
    subjects:
      - kind: ServiceAccount
        name: grafana-serviceaccount
        namespace: streams-grafana
    roleRef:
      kind: ClusterRole
      name: cluster-monitoring-view
      apiGroup: rbac.authorization.k8s.io
    EOF

    Now, we can get a token to grant access for Grafana into Prometheus:

    $ export TOKEN=$(oc serviceaccounts get-token grafana-serviceaccount -n streams-grafana)

    When you have the token, run the following command to create a data source and pass the token to it:

    $ cat << EOF | oc apply -f -
    apiVersion: integreatly.org/v1alpha1
    kind: GrafanaDataSource
    metadata:
      name: grafanadatasource
      namespace: streams-grafana
    spec:
      name: middleware.yaml
      datasources:
        - name: Prometheus
          type: prometheus
          access: proxy
          url: https://thanos-querier.openshift-monitoring.svc.cluster.local:9091
          basicAuth: false
          basicAuthUser: internal
          isDefault: true
          version: 1
          editable: true
          jsonData:
            tlsSkipVerify: true
            timeInterval: "5s"
            httpHeaderName1: "Authorization"
          secureJsonData:
            httpHeaderValue1: "Bearer $TOKEN"
    EOF
    

    Set up your AMQ Streams dashboard in the Grafana instance

    Example dashboards for AMQ Streams are available in the examples/metrics/grafana-dashboards folder. There are two ways to include a dashboard in a Grafana instance. One option is to use the Grafana user interface (UI) and navigate to the Dashboards page. You can include available dashboards and select a proper data source. The second option is to create a GrafanaDashboard custom resource with the dashboard JSON inside it.

    For this example, we'll take the second option. Below is a script that gets the JSON from the examples folder and makes a collection runner. Go to the Grafana dashboards that you want to use and replace the .spec.name of your JSON dashboard and the .metadata.name of your custom resource (CR) definition, as shown here:

    $ cat << EOF > /tmp/dashboard.yaml
    apiVersion: integreatly.org/v1alpha1
    kind: GrafanaDashboard
    metadata:
      labels:
        app: strimzi
        monitoring-key: middleware
      name: strimzi-operators
      namespace: streams-grafana
    spec:
      name: strimzi-operators.json
      json: |
        PLACEHOLDER
    EOF
    
    $ DATA="$(jq 'del(.__inputs,.__requires)'  examples/metrics/grafana-dashboards/strimzi-operators.json)" yq eval ".spec.json = strenv(DATA)" /tmp/dashboard.yaml | sed -e '/DS_PROMETHEUS/d' | oc apply -f -

    You can use the same commands for other dashboards, as well; just keep in mind that you have to change .spec.name and .metadata.name if you do that.

    Finally, you can access the Grafana UI via the exported route:

    $ oc get route grafana-route -n streams-grafana -o=jsonpath='{.spec.host}'

    Once you are in the UI, you can see your imported dashboards. Figure 2 shows how the AMQ Streams example dashboards represent metrics from the installed Operators.

    Metrics from the AMQ Streams Operators
    Figure 2: Metrics from the AMQ Streams Operators.

    Figure 3 shows Kafka metrics inside the example dashboard.

    A Kafka dashboard with collected data
    Figure 3: A Kafka dashboard with collected data.

    Finally, Figure 4 shows the Kafka Exporter dashboard. This dashboard shows various topics and the clients attached to them.

    The Kafka Exporter dashboard with collected data
    Figure 4: The Kafka Exporter dashboard with collected data.

    Set up your AMQ Streams dashboard in OpenShift 4

    It is also possible to use OpenShift dashboards without installing Grafana. The OpenShift dashboards are limited compared to Grafana, but they're enough for a quick overview. You will find the OpenShift dashboards in the admin console under Monitoring. As an example, you could use the following commands to import a Strimzi dashboard:

    $ cat << EOF > /tmp/file.yaml
    kind: ConfigMap
    apiVersion: v1
    metadata:
      name: strimzi-operators-dashboard
      namespace: openshift-config-managed
    labels:
      console.openshift.io/dashboard: 'true'
    data:
      strimzi-operators: |-
        PLACEHOLDER
    EOF
    
    $ DATA="$(jq 'del(.__inputs,.__requires)'  strimzi-examples/examples/metrics/grafana-dashboards/strimzi-operators.json)" yq eval ".data.strimzi-operators = env(DATA)" /tmp/file.yaml | sed -e 's/DS_PROMETHEUS/d' | oc apply -f -
    

    Now, you should be able to list the strimzi-operators dashboard in the OpenShift UI and see something similar to what's shown in Figure 5.

    An AMQ Streams dashboard in the OpenShift 4 dashboards view
    Figure 5: An AMQ Streams dashboard in the OpenShift 4 dashboards view.

    Conclusion

    In this article, you've seen how to set up and use the OpenShift 4 monitoring stack to monitor AMQ Streams. You can monitor existing AMQ Streams clusters without needing to redeploy AMQ Streams, and you can also set up your own Grafana dashboard for more detailed metrics. Everything you need to apply proper custom resources is available from the oc client. If you want to avoid manual copying and pasting, you can use the yq command.

    Last updated: October 14, 2022

    Related Posts

    • HTTP-based Kafka messaging with Red Hat AMQ Streams

    • Hello World for AMQ Streams on OpenShift

    Recent Posts

    • Expand Model-as-a-Service for secure enterprise AI

    • OpenShift LACP bonding performance expectations

    • Build container images in CI/CD with Tekton and Buildpacks

    • How to deploy OpenShift AI & Service Mesh 3 on one cluster

    • JVM tuning for Red Hat Data Grid on Red Hat OpenShift 4

    Red Hat Developers logo LinkedIn YouTube Twitter Facebook

    Products

    • Red Hat Enterprise Linux
    • Red Hat OpenShift
    • Red Hat Ansible Automation Platform

    Build

    • Developer Sandbox
    • Developer Tools
    • Interactive Tutorials
    • API Catalog

    Quicklinks

    • Learning Resources
    • E-books
    • Cheat Sheets
    • Blog
    • Events
    • Newsletter

    Communicate

    • About us
    • Contact sales
    • Find a partner
    • Report a website issue
    • Site Status Dashboard
    • Report a security problem

    RED HAT DEVELOPER

    Build here. Go anywhere.

    We serve the builders. The problem solvers who create careers with code.

    Join us if you’re a developer, software engineer, web designer, front-end designer, UX designer, computer scientist, architect, tester, product manager, project manager or team lead.

    Sign me up

    Red Hat legal and privacy links

    • About Red Hat
    • Jobs
    • Events
    • Locations
    • Contact Red Hat
    • Red Hat Blog
    • Inclusion at Red Hat
    • Cool Stuff Store
    • Red Hat Summit
    © 2025 Red Hat

    Red Hat legal and privacy links

    • Privacy statement
    • Terms of use
    • All policies and guidelines
    • Digital accessibility

    Report a website issue