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Visualize PgBouncer Metrics with Grafana Dashboard

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

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.

• PgBouncer sits in front of a PostgreSQL server. Prepare a KubeDB Postgres instance (for example ha-postgres in the demo namespace) following the streaming replication guide, or use any externally managed Postgres.

• 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/pgbouncer/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 PgBouncer 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 PgBouncer with Monitoring Enabled

Below is the PgBouncer object pointing at the ha-postgres backend, with monitoring configured to use Prometheus Operator.

apiVersion: kubedb.com/v1
kind: PgBouncer
metadata:
  name: pb-grafana-demo
  namespace: demo
spec:
  replicas: 1
  version: "1.24.0"
  database:
    syncUsers: true
    databaseName: "postgres"
    databaseRef:
      name: "ha-postgres"
      namespace: demo
  connectionPool:
    poolMode: session
    port: 5432
  deletionPolicy: WipeOut
  monitor:
    agent: prometheus.io/operator
    prometheus:
      serviceMonitor:
        labels:
          release: prometheus
        interval: 10s

Here,

• database.databaseRef points at the backend Postgres instance PgBouncer pools connections for.
• 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 PgBouncer instance:

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

Wait for it to be Ready:

$ kubectl get pb -n demo pb-grafana-demo
NAME             TYPE           VERSION   STATUS   AGE
pb-grafana-demo  kubedb.com/v1  1.24.0    Ready    65s

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

$ kubectl get svc -n demo --selector="app.kubernetes.io/instance=pb-grafana-demo"
NAME                  TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)             AGE
pb-grafana-demo       ClusterIP   10.96.201.180   <none>        5432/TCP            2m
pb-grafana-demo-pods  ClusterIP   None            <none>        5432/TCP            2m
pb-grafana-demo-stats ClusterIP   10.96.73.22     <none>        9719/TCP            2m

KubeDB also creates a ServiceMonitor in the demo namespace:

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

Verify it carries the correct label:

$ kubectl get servicemonitor -n demo pb-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 pb-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 PgBouncer Dashboard

The KubeDB PgBouncer 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.

Three dashboards are available. Download all three JSON files from the appscode/grafana-dashboards repository (pgbouncer/ folder):

Import steps (repeat for each of the three files):

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 three files, they will appear under Dashboards in the left sidebar.

Step 6: Explore the Dashboard

After opening a dashboard, you will see dropdown filters at the top. These control which data is shown across all panels — change them to focus on a specific instance without editing any queries.

Once you set these, all panels update automatically. Below is what each dashboard shows:

KubeDB / PgBouncer / Summary — start here for a pool-wide overview:

• Total / Client / Server Connections — connections across all pools
• Active Pools — number of connection pools to the backend
• Queries per Second — query throughput routed through PgBouncer
• Waiting Clients — clients queued waiting for a server connection (sustained non-zero indicates pool exhaustion)
• Avg Query Time — average time queries spend waiting + executing
• CPU / Memory / Storage — resource consumption vs. requests and limits

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

• Client / Server Connections — connections held on this pod
• Active Pools — pools managed by this pod
• Queries per Second — per-pod query throughput
• Waiting Clients — queued clients on this pod
• CPU / Memory — per-pod resource usage

KubeDB / PgBouncer / Database — per-database pool metrics:

• Pool State — cl_active, cl_waiting, sv_active, sv_idle per database
• Active / Idle Server Connections — backend connection usage per database
• Max Client Connections — configured limit per database
• Query Rate — queries per second per database
• Total Xact / Query Count — transaction and query counters per database

Cleaning up

# Remove the PgBouncer instance
kubectl delete pb -n demo pb-grafana-demo

# Remove the backend Postgres instance
kubectl delete pg -n demo ha-postgres

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