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Monitoring MongoDB Using Prometheus operator

Prometheus operator provides simple and Kubernetes native way to deploy and configure Prometheus server. This tutorial will show you how to use Prometheus operator to monitor MongoDB database deployed with KubeDB.

Before You Begin

  • At first, you need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. If you do not already have a cluster, you can create one by using kind.

  • To learn how Prometheus monitoring works with KubeDB in general, please visit here.

  • We need a Prometheus operator instance running. If you don’t already have a running instance, you can deploy one using this helm chart here.

  • To keep Prometheus resources isolated, we are going to use a separate namespace called monitoring to deploy the prometheus operator helm chart. We are going to deploy database in demo namespace.

    $ kubectl create ns monitoring
    namespace/monitoring created
    $ kubectl create ns demo
    namespace/demo created

Note: YAML files used in this tutorial are stored in docs/examples/mongodb folder in GitHub repository kubedb/docs.

Find out required labels for ServiceMonitor

We need to know the labels used to select ServiceMonitor by a Prometheus crd. We are going to provide these labels in spec.monitor.prometheus.serviceMonitor.labels field of MongoDB crd so that KubeDB creates ServiceMonitor object accordingly.

At first, let’s find out the available Prometheus server in our cluster.

$ kubectl get prometheus --all-namespaces
NAMESPACE    NAME                                    VERSION   REPLICAS   AGE
monitoring   prometheus-kube-prometheus-prometheus   v2.39.0   1          13d

If you don’t have any Prometheus server running in your cluster, deploy one following the guide specified in Before You Begin section.

Now, let’s view the YAML of the available Prometheus server prometheus in monitoring namespace.

$ kubectl get prometheus -n monitoring prometheus-kube-prometheus-prometheus -o yaml
kind: Prometheus
  annotations: prometheus monitoring
  creationTimestamp: "2022-10-11T07:12:20Z"
  generation: 1
    app: kube-prometheus-stack-prometheus prometheus Helm kube-prometheus-stack 40.5.0
    chart: kube-prometheus-stack-40.5.0
    heritage: Helm
    release: prometheus
  name: prometheus-kube-prometheus-prometheus
  namespace: monitoring
  resourceVersion: "490475"
  uid: 7e36caf3-228a-40f3-bff9-a1c0c78dedb0
      - apiVersion: v2
        name: prometheus-kube-prometheus-alertmanager
        namespace: monitoring
        pathPrefix: /
        port: http-web
  enableAdminAPI: false
  evaluationInterval: 30s
  externalUrl: http://prometheus-kube-prometheus-prometheus.monitoring:9090
  listenLocal: false
  logFormat: logfmt
  logLevel: info
  paused: false
  podMonitorNamespaceSelector: {}
      release: prometheus
  portName: http-web
  probeNamespaceSelector: {}
      release: prometheus
  replicas: 1
  retention: 10d
  routePrefix: /
  ruleNamespaceSelector: {}
      release: prometheus
  scrapeInterval: 30s
    fsGroup: 2000
    runAsGroup: 2000
    runAsNonRoot: true
    runAsUser: 1000
  serviceAccountName: prometheus-kube-prometheus-prometheus
  serviceMonitorNamespaceSelector: {}
      release: prometheus
  shards: 1
  version: v2.39.0
  walCompression: true

Notice the spec.serviceMonitorSelector section. Here, release: prometheus label is used to select ServiceMonitor crd. So, we are going to use this label in spec.monitor.prometheus.serviceMonitor.labels field of MongoDB crd.

Deploy MongoDB with Monitoring Enabled

At first, let’s deploy an MongoDB database with monitoring enabled. Below is the MongoDB object that we are going to create.

kind: MongoDB
  name: coreos-prom-mgo
  namespace: demo
  version: "4.4.26"
  terminationPolicy: WipeOut
    storageClassName: "standard"
    - ReadWriteOnce
        storage: 1Gi
          release: prometheus
        interval: 10s


  • monitor.agent: indicates that we are going to monitor this server using Prometheus operator.
  • monitor.prometheus.serviceMonitor.labels specifies that KubeDB should create ServiceMonitor with these labels.
  • monitor.prometheus.interval indicates that the Prometheus server should scrape metrics from this database with 10 seconds interval.

Let’s create the MongoDB object that we have shown above,

$ kubectl create -f created

Now, wait for the database to go into Running state.

$ kubectl get mg -n demo coreos-prom-mgo
NAME              VERSION   STATUS    AGE
coreos-prom-mgo   4.4.26     Ready     34s

KubeDB will create a separate stats service with name {MongoDB crd name}-stats for monitoring purpose.

$ kubectl get svc -n demo --selector=""
NAME                    TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)     AGE
coreos-prom-mgo         ClusterIP   <none>        27017/TCP   84s
coreos-prom-mgo-pods    ClusterIP   None            <none>        27017/TCP   84s
coreos-prom-mgo-stats   ClusterIP    <none>        56790/TCP   64s

Here, coreos-prom-mgo-stats service has been created for monitoring purpose.

Let’s describe this stats service.

$ kubectl describe svc -n demo coreos-prom-mgo-stats
Name:              coreos-prom-mgo-stats
Namespace:         demo
Type:              ClusterIP
IP Family Policy:  SingleStack
IP Families:       IPv4
Port:              metrics  56790/TCP
TargetPort:        metrics/TCP
Session Affinity:  None
Events:            <none>

Notice the Labels and Port fields. ServiceMonitor will use this information to target its endpoints.

KubeDB will also create a ServiceMonitor crd in demo namespace that select the endpoints of coreos-prom-mgo-stats service. Verify that the ServiceMonitor crd has been created.

$ kubectl get servicemonitor -n demo
NAME                    AGE
coreos-prom-mgo-stats   2m40s

Let’s verify that the ServiceMonitor has the label that we had specified in spec.monitor section of MongoDB crd.

$ kubectl get servicemonitor -n demo coreos-prom-mgo-stats -o yaml
kind: ServiceMonitor
  creationTimestamp: "2022-10-24T11:51:08Z"
  generation: 1
  labels: database coreos-prom-mgo
    release: prometheus
  name: coreos-prom-mgo-stats
  namespace: demo
    - apiVersion: v1
      blockOwnerDeletion: true
      controller: true
      kind: Service
      name: coreos-prom-mgo-stats
      uid: 68b0e8c4-cba4-4dcb-9016-4e1901ca1fd0
  resourceVersion: "528373"
  uid: 56eb596b-d2cf-4d2c-a204-c43dbe8fe896
    - bearerTokenSecret:
        key: ""
      honorLabels: true
      interval: 10s
      path: /metrics
      port: metrics
      - demo
    matchLabels: database coreos-prom-mgo stats

Notice that the ServiceMonitor has label release: prometheus that we had specified in MongoDB crd.

Also notice that the ServiceMonitor has selector which match the labels we have seen in the coreos-prom-mgo-stats service. It also, target the metrics port that we have seen in the stats service.

Verify Monitoring Metrics

At first, let’s find out the respective Prometheus pod for prometheus Prometheus server.

$ kubectl get pod -n monitoring
NAME                                                 READY   STATUS    RESTARTS   AGE
prometheus-prometheus-kube-prometheus-prometheus-0   2/2     Running   1          13d

Prometheus server is listening to port 9090 of prometheus-prometheus-kube-prometheus-prometheus-0 pod. We are going to use port forwarding to access Prometheus dashboard.

Run following command on a separate terminal to forward the port 9090 of prometheus-prometheus-kube-prometheus-prometheus-0 pod,

$ kubectl port-forward -n monitoring prometheus-prometheus-kube-prometheus-prometheus-0 9090
Forwarding from -> 9090
Forwarding from [::1]:9090 -> 9090

Now, we can access the dashboard at localhost:9090. Open http://localhost:9090 in your browser. You should see metrics endpoint of coreos-prom-mgo-stats service as one of the targets.

  Prometheus Target

Check the endpoint and service labels marked by the red rectangles. It verifies that the target is our expected database. Now, you can view the collected metrics and create a graph from homepage of this Prometheus dashboard. You can also use this Prometheus server as data source for Grafana and create a beautiful dashboard with collected metrics.

Cleaning up

To cleanup the Kubernetes resources created by this tutorial, run following commands

kubectl delete -n demo mg/coreos-prom-mgo
kubectl delete ns demo

Next Steps