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Visualize MariaDB Metrics with Grafana Dashboard

KubeDB exposes MariaDB metrics through a sidecar exporter. Once Prometheus scrapes those metrics, you can visualize them in Grafana using pre-built KubeDB dashboards. This tutorial walks through the full setup: deploying the monitoring stack, enabling monitoring on a MariaDB instance, and importing the Grafana dashboards.

Before You Begin

• You need a Kubernetes cluster with kubectl configured. If you do not already have a cluster, you can create one by using kind.

• KubeDB must be installed in your cluster with kubedb-metrics enabled. Follow the setup guide here and make sure to include the flag below during installation:

  --set kubedb-metrics.enabled=true
  

  kubedb-metrics creates MetricsConfiguration objects for each database type, which Panopticon (Step 2) uses to expose metrics to Prometheus.

• To keep monitoring resources isolated, we use a separate monitoring namespace and deploy the database in the 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/mariadb/monitoring folder in GitHub repository kubedb/docs.

Configuration

These two steps — deploying kube-prometheus-stack and installing Panopticon — are shared prerequisites for all KubeDB database monitoring guides. If you have already completed them in another guide, skip to Step 1.

Step 1: Deploy kube-prometheus-stack

kube-prometheus-stack installs Prometheus, Prometheus Operator, Alertmanager, and Grafana together. This is the recommended way to get the full monitoring stack on Kubernetes.

Add the prometheus-community Helm repo and install:

$ helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
$ helm repo update

$ helm upgrade --install prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --set grafana.image.tag=7.5.5

Wait for all pods to be ready:

$ kubectl get pods -n monitoring
NAME                                                   READY   STATUS    RESTARTS   AGE
alertmanager-prometheus-kube-prometheus-alertmanager-0 2/2     Running   0          2m
prometheus-grafana-xxxx                                3/3     Running   0          2m
prometheus-kube-prometheus-operator-xxxx               1/1     Running   0          2m
prometheus-kube-prometheus-prometheus-0                2/2     Running   0          2m
prometheus-kube-state-metrics-xxxx                     1/1     Running   0          2m

Find the serviceMonitorSelector label that Prometheus uses to pick up ServiceMonitor objects. You will need this label when enabling monitoring on the MariaDB instance.

$ kubectl get prometheus -n monitoring -o jsonpath='{.items[0].spec.serviceMonitorSelector}'
{"matchLabels":{"release":"prometheus"}}

The label is release: prometheus.

Step 2: Install Panopticon

Panopticon is the Appscode operator that reads MetricsConfiguration objects created by kubedb-metrics and exposes them to Prometheus. It must be installed before enabling kubedb-metrics.

$ helm repo add appscode https://charts.appscode.com/stable/
$ helm repo update

$ helm upgrade --install panopticon appscode/panopticon \
  --version v2026.4.30 \
  --namespace kubeops --create-namespace \
  --set monitoring.enabled=true \
  --set monitoring.agent=prometheus.io/operator \
  --set monitoring.serviceMonitor.labels.release=prometheus \
  --set-file license=/path/to/kubedb-license.txt \
  --wait --timeout 5m0s

Verify panopticon is running:

$ kubectl get pods -n kubeops
NAME                          READY   STATUS    RESTARTS   AGE
panopticon-xxxx               1/1     Running   0          1m

Setup

Step 1: Deploy MariaDB with Monitoring Enabled

Below is the MariaDB object with monitoring configured to use Prometheus Operator.

apiVersion: kubedb.com/v1
kind: MariaDB
metadata:
  name: mariadb-grafana-demo
  namespace: demo
spec:
  version: "11.5.2"
  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/operator tells KubeDB to create a ServiceMonitor for this instance.
• monitor.prometheus.serviceMonitor.labels must match the serviceMonitorSelector label of your Prometheus (release: prometheus).
• monitor.prometheus.serviceMonitor.interval sets the scrape interval to 10 seconds.

Create the MariaDB instance:

$ kubectl create -f https://github.com/kubedb/docs/raw/v2026.6.19/docs/examples/mariadb/monitoring/coreos-prom-mariadb.yaml
mariadb.kubedb.com/mariadb-grafana-demo created

Wait for it to be Ready:

$ kubectl get mariadb -n demo mariadb-grafana-demo
NAME                   VERSION   STATUS   AGE
mariadb-grafana-demo   11.5.2    Ready    2m

KubeDB creates a stats service named {mariadb-name}-stats for the exporter:

$ kubectl get svc -n demo --selector="app.kubernetes.io/instance=mariadb-grafana-demo"
NAME                         TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)     AGE
mariadb-grafana-demo         ClusterIP   10.96.10.1     <none>        3306/TCP    2m
mariadb-grafana-demo-stats   ClusterIP   10.96.10.2     <none>        9104/TCP    2m

KubeDB also creates a ServiceMonitor in the demo namespace:

$ kubectl get servicemonitor -n demo
NAME                         AGE
mariadb-grafana-demo-stats   2m

Verify it carries the correct label:

$ kubectl get servicemonitor -n demo mariadb-grafana-demo-stats -o jsonpath='{.metadata.labels}'
{"release":"prometheus", ...}

Step 2: Verify Prometheus is Scraping

Port-forward the Prometheus pod:

$ kubectl port-forward -n monitoring \
  prometheus-prometheus-kube-prometheus-prometheus-0 9090
Forwarding from 127.0.0.1:9090 -> 9090
Forwarding from [::1]:9090 -> 9090

Open http://localhost:9090/targets in your browser. Look for an entry whose service label matches mariadb-grafana-demo-stats. Its state should be UP.

If the target is missing, check that the ServiceMonitor label (release: prometheus) matches the Prometheus serviceMonitorSelector.

Step 3: Access Grafana

Port-forward the Grafana service:

$ kubectl port-forward -n monitoring svc/prometheus-grafana 3000:80
Forwarding from 127.0.0.1:3000 -> 80

Open http://localhost:3000. The username is admin. Retrieve the auto-generated password from the secret:

$ kubectl get secret -n monitoring prometheus-grafana \
  -o jsonpath='{.data.admin-password}' | base64 -d

After a successful login you will see the Grafana home page:

Step 4: Configure Prometheus as a Data Source

If you installed Grafana via kube-prometheus-stack, Prometheus is already configured as the default data source — skip to Step 5.

For a standalone Grafana installation:

1. Go to Connections → Data sources → Add new data source.

2. Select Prometheus.

3. Set the URL to your Prometheus service:

   http://prometheus-operated.monitoring.svc:9090
   

4. Click Save & test. You should see Data source is working.

Step 5: Import KubeDB MariaDB Dashboard

The KubeDB MariaDB dashboards are distributed as JSON files. Each JSON file is a complete dashboard definition — panels, queries, variables, and layout — that Grafana loads in one shot. Without importing, you would have to build every panel and write every PromQL query by hand. Importing lets you skip that entirely.

Four dashboards are available. Download all JSON files from the appscode/grafana-dashboards repository (mariadb/ folder):

The Galera Cluster dashboard is only relevant for MariaDB Galera cluster deployments (spec.topology.mode: GaleraCluster).

Import steps (repeat for each file):

1. In Grafana, click the + icon in the left sidebar.
2. Select Import from the menu.
3. Click Upload JSON file and select one of the downloaded .json files.
4. In the Prometheus dropdown that appears, select your Prometheus data source.
5. Click Import.

The import page looks like this — click Upload dashboard JSON file to select the file:

After importing all files, they will appear under Dashboards in the left sidebar.

Step 6: Explore the Dashboard

After opening a dashboard, use the dropdown filters at the top to focus on a specific instance.

KubeDB / MariaDB / Summary — start here for an instance overview:

• Database Status / Version — current health of the instance and MariaDB version running
• Require Secure Transport / Deletion Policy — whether TLS is enforced and the cleanup policy for the instance
• Total Nodes — number of replicas in the instance
• CPU / Memory / Storage Request & Limit — configured resource requests and limits
• CPU Info / CPU Quota — CPU usage over time and per-pod quota utilization
• Memory Info — memory usage over time and per-pod quota utilization

KubeDB / MariaDB / Pod — drill into a specific pod:

• Pod Summary — pod name, MySQL uptime, version, current QPS, InnoDB buffer pool size
• CPU, Memory and File Descriptor Stats — per-pod CPU usage, memory usage, and open file descriptors
• Connections — MySQL connections and aborted connections
• Client Threads — client thread activity and thread cache

KubeDB / MariaDB / Database — cluster and query metrics:

• Service Status / Uptime — per-pod health and how long each pod has been serving
• Cluster Size / Cluster Status — number of nodes and Galera cluster state (Primary/Non-Primary)
• Current QPS — query throughput
• MySQL Connections — current vs. max connections
• MySQL Disk Reads vs Writes — disk I/O throughput
• MySQL Network Received vs Sent — network throughput

KubeDB / MariaDB / Galera Cluster — Galera-specific metrics:

• Cluster Size — number of nodes in the cluster
• Local State — wsrep state per node (Synced, Donor, Joiner, etc.)
• wsrep_ready — whether each node is ready to accept queries
• Flow Control Paused — percentage of time replication was paused due to flow control
• Replication Bytes — bytes sent and received via Galera replication per node
• Local Commits / Cert Failures — commit throughput and certification conflict rate

Cleaning up

# Remove the MariaDB instance
kubectl delete mariadb -n demo mariadb-grafana-demo

# Remove namespaces
kubectl delete ns demo

# Uninstall monitoring stack (optional)
helm uninstall prometheus -n monitoring
helm uninstall panopticon -n kubeops
kubectl delete ns monitoring kubeops

Next Steps

• Monitor your MariaDB database with KubeDB using built-in Prometheus.
• Monitor your MariaDB database with KubeDB using Prometheus Operator.
• Want to hack on KubeDB? Check our contribution guidelines.