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Monitoring Elasticsearch 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 Elasticsearch 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.
To keep Prometheus resources isolated, we are going to use a separate namespace called
monitoringto deploy respective monitoring resources. We are going to deploy database indemonamespace.$ kubectl create ns monitoring namespace/monitoring created $ kubectl create ns demo namespace/demo createdWe need a Prometheus operator instance running. If you don’t already have a running instance, deploy one following the docs from here.
If you already don’t have a Prometheus server running, deploy one following tutorial from here.
Note: YAML files used in this tutorial are stored in docs/examples/elasticsearch 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.labels field of Elasticsearch 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 AGE
monitoring prometheus 18m
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 -o yaml
apiVersion: monitoring.coreos.com/v1
kind: Prometheus
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"monitoring.coreos.com/v1","kind":"Prometheus","metadata":{"annotations":{},"labels":{"prometheus":"prometheus"},"name":"prometheus","namespace":"monitoring"},"spec":{"replicas":1,"resources":{"requests":{"memory":"400Mi"}},"serviceAccountName":"prometheus","serviceMonitorSelector":{"matchLabels":{"release":"prometheus"}}}}
creationTimestamp: "2019-10-02T09:48:29Z"
generation: 1
labels:
prometheus: prometheus
name: prometheus
namespace: monitoring
resourceVersion: "74613"
selfLink: /apis/monitoring.coreos.com/v1/namespaces/monitoring/prometheuses/prometheus
uid: ca0db414-e4f9-11e9-b2b2-42010a940225
spec:
replicas: 1
resources:
requests:
memory: 400Mi
serviceAccountName: prometheus
serviceMonitorSelector:
matchLabels:
release: prometheus
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.labels field of Elasticsearch crd.
Deploy Elasticsearch with Monitoring Enabled
At first, let’s deploy an Elasticsearch database with monitoring enabled. Below is the Elasticsearch object that we are going to create.
apiVersion: kubedb.com/v1
kind: Elasticsearch
metadata:
name: coreos-prom-es
namespace: demo
spec:
version: xpack-8.11.1
deletionPolicy: WipeOut
storage:
storageClassName: "standard"
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
monitor:
agent: prometheus.io/operator
prometheus:
serviceMonitor:
labels:
release: prometheus
interval: 10s
Here,
monitor.agent: prometheus.io/operatorindicates that we are going to monitor this server using Prometheus operator.monitor.prometheus.namespace: monitoringspecifies that KubeDB should createServiceMonitorinmonitoringnamespace.monitor.prometheus.labelsspecifies that KubeDB should createServiceMonitorwith these labels.monitor.prometheus.intervalindicates that the Prometheus server should scrape metrics from this database with 10 seconds interval.
Let’s create the Elasticsearch object that we have shown above,
$ kubectl create -f https://github.com/kubedb/docs/raw/v2024.12.18/docs/examples/elasticsearch/monitoring/coreos-prom-es.yaml
elasticsearch.kubedb.com/coreos-prom-es created
Now, wait for the database to go into Running state.
$ kubectl get es -n demo coreos-prom-es
NAME VERSION STATUS AGE
coreos-prom-es 7.3.2 Running 85s
KubeDB will create a separate stats service with name {Elasticsearch crd name}-stats for monitoring purpose.
$ kubectl get svc -n demo --selector="app.kubernetes.io/instance=coreos-prom-es"
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
coreos-prom-es ClusterIP 10.0.1.56 <none> 9200/TCP 77s
coreos-prom-es-master ClusterIP 10.0.7.18 <none> 9300/TCP 77s
coreos-prom-es-stats ClusterIP 10.0.5.58 <none> 56790/TCP 19s
Here, coreos-prom-es-stats service has been created for monitoring purpose.
Let’s describe this stats service.
$ kubectl describe svc -n demo coreos-prom-es-stats
Name: coreos-prom-es-stats
Namespace: demo
Labels: app.kubernetes.io/name=elasticsearches.kubedb.com
app.kubernetes.io/instance=coreos-prom-es
kubedb.com/role=stats
Annotations: monitoring.appscode.com/agent: prometheus.io/operator
Selector: app.kubernetes.io/name=elasticsearches.kubedb.com,app.kubernetes.io/instance=coreos-prom-es
Type: ClusterIP
IP: 10.0.5.58
Port: prom-http 56790/TCP
TargetPort: prom-http/TCP
Endpoints: 10.4.0.50:56790
Session Affinity: None
Events: <none>
Notice the Labels and Port fields. ServiceMonitor will use these information to target its endpoints.
KubeDB will also create a ServiceMonitor crd in monitoring namespace that select the endpoints of coreos-prom-es-stats service. Verify that the ServiceMonitor crd has been created.
$ kubectl get servicemonitor -n monitoring
NAME AGE
kubedb-demo-coreos-prom-es 6m
Let’s verify that the ServiceMonitor has the label that we had specified in spec.monitor section of Elasticsearch crd.
$ kubectl get servicemonitor -n monitoring kubedb-demo-coreos-prom-es -o yaml
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
creationTimestamp: "2019-10-02T09:51:04Z"
generation: 1
labels:
release: prometheus
monitoring.appscode.com/service: coreos-prom-es-stats.demo
name: kubedb-demo-coreos-prom-es
namespace: monitoring
ownerReferences:
- apiVersion: v1
blockOwnerDeletion: true
kind: Service
name: coreos-prom-es-stats
uid: 25f91fcc-e4fa-11e9-b2b2-42010a940225
resourceVersion: "75305"
selfLink: /apis/monitoring.coreos.com/v1/namespaces/monitoring/servicemonitors/kubedb-demo-coreos-prom-es
uid: 2601a2ba-e4fa-11e9-b2b2-42010a940225
spec:
endpoints:
- honorLabels: true
interval: 10s
path: /metrics
port: prom-http
namespaceSelector:
matchNames:
- demo
selector:
matchLabels:
app.kubernetes.io/name: elasticsearches.kubedb.com
app.kubernetes.io/instance: coreos-prom-es
kubedb.com/role: stats
Notice that the ServiceMonitor has label release: prometheus that we had specified in Elasticsearch crd.
Also notice that the ServiceMonitor has selector which match the labels we have seen in the coreos-prom-es-stats service. It also, target the prom-http 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 -l=app=prometheus
NAME READY STATUS RESTARTS AGE
prometheus-prometheus-0 3/3 Running 1 63m
Prometheus server is listening to port 9090 of 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-0 pod,
$ kubectl port-forward -n monitoring prometheus-prometheus-0 9090
Forwarding from 127.0.0.1:9090 -> 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 prom-http endpoint of coreos-prom-es-stats service as one of the targets.
Check the endpoint and service labels marked by red rectangle. 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 beautiful dashboard with collected metrics.
Cleaning up
To cleanup the Kubernetes resources created by this tutorial, run following commands
# cleanup database
kubectl delete -n demo es/coreos-prom-es
# cleanup prometheus resources
kubectl delete -n monitoring prometheus prometheus
kubectl delete -n monitoring clusterrolebinding prometheus
kubectl delete -n monitoring clusterrole prometheus
kubectl delete -n monitoring serviceaccount prometheus
kubectl delete -n monitoring service prometheus-operated
# cleanup prometheus operator resources
kubectl delete -n monitoring deployment prometheus-operator
kubectl delete -n dmeo serviceaccount prometheus-operator
kubectl delete clusterrolebinding prometheus-operator
kubectl delete clusterrole prometheus-operator
# delete namespace
kubectl delete ns monitoring
kubectl delete ns demo
Next Steps
- Learn about backup & restore Elasticsearch database using Stash.
- Learn how to configure Elasticsearch Topology Cluster.
- Monitor your Elasticsearch database with KubeDB using
out-of-the-boxbuiltin-Prometheus. - Detail concepts of Elasticsearch object.
- Use private Docker registry to deploy Elasticsearch with KubeDB.
- Want to hack on KubeDB? Check our contribution guidelines.