Want to monitor your Kubernetes cluster like a pro? Grafana makes it simple to visualize metrics, identify issues, and optimize performance. By combining Grafana with Prometheus, you can track key Kubernetes data like CPU usage, memory consumption, and pod performance - all in real time.
By the end, you’ll know how to set up a monitoring stack that keeps your Kubernetes environment healthy and efficient.
Make sure all necessary components are set up before diving into Kubernetes metrics visualization. You'll need to configure your Kubernetes cluster, Grafana, and Prometheus in that order.
Before adding monitoring tools, confirm that your Kubernetes cluster is up and running. For production, managed services like Google Kubernetes Engine (GKE), Amazon EKS, or Azure AKS are great options. For local testing, tools like Minikube or Docker Desktop work well.
Key cluster requirements:
| Component | Minimum Specification |
|---|---|
| Memory | 512 MB |
| CPU | 1 core |
| Storage | 1 GB free space |
| Network | Port 3000 accessible |
You’ll also need to enable RBAC (Role-Based Access Control) in your cluster to allow Prometheus to collect metrics. Confirm your cluster is accessible by running:
.svg)
kubectl cluster-info
Install Grafana on your system. It works on Ubuntu, Red Hat, macOS, and Windows. Ensure your system meets these requirements:
"Grafana allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. Create, explore, and share beautiful dashboards with your team and foster a data-driven culture." - Grafana Labs
Once installed, connect Grafana to Prometheus to start pulling Kubernetes metrics.
Prometheus is essential for collecting Kubernetes metrics. Install it using Helm, the Kubernetes package manager. Start by creating a namespace for your monitoring stack:
kubectl create namespace monitoring
Use a values.yaml file to fine-tune the installation and manage resources effectively.
Key components to configure:
| Component | Purpose |
|---|---|
| Persistent Volume Claim (PVC) | Stores collected metrics |
| Service Account | Handles RBAC permissions |
| ConfigMap | Holds Prometheus configurations |
| Deployment | Manages Prometheus pods |
Learn how to configure Prometheus to gather Kubernetes metrics and monitor your cluster effectively.
Prometheus collects data from defined endpoints. To simplify setup, use the kube-prometheus-stack Helm chart, which includes:
| Component | Purpose | Key Metrics Collected |
|---|---|---|
| Node-Exporter | Metrics at the node level | CPU, memory, and disk usage |
| Kube-State-Metrics | Metrics for Kubernetes objects | Pod status, deployment state |
| Alertmanager | Manages alerts | Alert status, notification delivery |
To configure Prometheus for scraping, create a ServiceMonitor resource. Here's an example:
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: kubernetes-metrics
namespace: monitoring
spec:
endpoints:
- interval: 30s
port: metrics
selector:
matchLabels:
app: kubernetes
Once you set up metric collection, focus on tracking the most relevant performance indicators.
Keep an eye on these Kubernetes metrics to ensure efficient monitoring:
| Metric Type | Description | Warning Threshold |
|---|---|---|
| Node CPU Usage | CPU usage per node | Above 80% |
| Node Memory Active | Active memory usage in bytes | Above 90% |
| Pod Resource Usage | Resource consumption per container | Above 85% of limits |
| API Server Latency | API request response time | Above 500ms |
After identifying these metrics, configure service discovery to make monitoring more dynamic.
Prometheus uses Kubernetes service discovery to monitor all components automatically. Start by creating a ClusterRole:
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: prometheus-k8s
rules:
- apiGroups: [""]
resources:
- nodes
- services
- endpoints
- pods
verbs: ["get", "list", "watch"]
To enable automatic discovery, annotate Kubernetes services with the following:
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "9090"
prometheus.io/path: "/metrics"
In production testing with a 100-node cluster, kube-state-metrics latency was observed as follows:
Once Prometheus and Grafana are set up, you can connect them to start creating dashboards for Kubernetes metrics.
To link Prometheus in Grafana, follow these steps:
Prometheus and click Add new data source.Use the following configuration settings:
| Setting | Value | Description |
|---|---|---|
| Name | prometheus | A unique name for the data source |
| URL | http://prometheus:9090 | Address of your Prometheus server |
| Scrape interval | 30s | Matches the scrape interval in Prometheus |
| HTTP Method | GET | The method used for requests |
When running in containers, use service names instead of localhost to avoid connection issues.
After configuring the data source:
up to check if metrics are being retrieved.If the connection is successful, you'll see the metric data displayed in the Explore view. Once verified, you can start creating Kubernetes dashboards.
If you encounter problems, these tips might help:
| Issue | Solution | Why It Matters |
|---|---|---|
| Network Access | Check firewall rules and container networking | Ensures services can communicate |
| URL Configuration | Verify hostnames and port numbers | Connects Grafana to the correct backend |
| Performance | Reduce data points and adjust scrape intervals | Keeps dashboards responsive |
For secure environments, enable TLS authentication to encrypt data between Prometheus and Grafana. Add a TLS configuration like this:
apiVersion: v1
kind: Secret
metadata:
name: prometheus-tls
type: kubernetes.io/tls
data:
tls.crt: base64_encoded_cert
tls.key: base64_encoded_key
This setup ensures your metrics data remains secure during transmission.
After successfully integrating Prometheus, it's time to create dashboards that provide clear insights into your Kubernetes environment.
Grafana includes pre-built dashboards that make Kubernetes monitoring easier. These templates save you time by offering detailed visualizations right out of the box.
To import a pre-made dashboard:
If these templates don’t fully meet your needs, you can create custom dashboards tailored to your specific metrics.
Custom dashboards allow you to focus on the metrics that matter most. Start by clicking Dashboards > New > New Dashboard in Grafana.
| Panel Type | Ideal For | Key Metrics |
|---|---|---|
| Time series | Tracking trends | CPU, memory, network usage |
| Heatmap | Identifying patterns | Request latency, pod scaling |
| Table | Viewing detailed data | Container states, error rates |
| Stat | Highlighting single values | Uptime, availability |
Steps for building custom panels:
Follow these tips to ensure your dashboards are both effective and user-friendly:
For infrastructure monitoring, consider using the USE method (Utilization, Saturation, Errors) for resource metrics and the RED method (Rate, Errors, Duration) for service-level indicators.
Dashboard variables help make Grafana visualizations more interactive and efficient. They let you filter data across multiple panels at once, streamlining workflows and keeping dashboards organized.
To set up dashboard variables:
label_values(kube_pod_info, namespace)
label_values(kube_pod_info{namespace="$namespace"}, pod)
With this setup, you can easily drill down from namespaces to individual pods, simplifying troubleshooting. Once your variables are ready, you can move on to defining alert rules to monitor these metrics.
Dynamic dashboards become even more useful with alert rules that ensure you're always on top of critical issues. Here's an example of how to set up alerts:
| Alert Type | Threshold Example | Alert Channel |
|---|---|---|
| CPU Usage | >80% for 15 minutes | Slack |
| Memory Saturation | >90% for 5 minutes | |
| Pod Restarts | >3 in 1 hour | PagerDuty |
These alerts allow you to act quickly when thresholds are breached, ensuring smooth operations.
For complete observability, Grafana lets you integrate metrics, logs, and traces into one platform. The LGTM Stack (Loki, Grafana, Tempo, Mimir) from Grafana Labs is designed to make this integration seamless.
Steps to Integrate:
This approach transforms isolated metrics into a unified observability system.
"Grafana Labs is really aiming to do now is make Grafana into a full observability platform. That means not only showing users their metrics, logs, and traces, but also providing them with the ability to correlate between them and be able to use all three of those pillars to quickly solve their issues." - Andrej Ocenas, Full Stack Developer, Grafana Labs
OptiAPM goes beyond basic configurations to provide enterprise-level support for Kubernetes monitoring. Their approach enhances Grafana visualizations, offering deeper insights and helping teams address potential issues before they escalate.
| Monitoring Aspect | Key Benefits | Business Impact |
|---|---|---|
| Resource Utilization | Tracks CPU, memory, and storage in real time | Helps allocate resources efficiently and cut cloud costs |
| Performance Metrics | Monitors application response times | Boosts user experience and system reliability |
| Cluster Health | Detects node issues and pod distribution automatically | Minimizes downtime and ensures availability |
With OptiAPM, teams can monitor resource usage across clusters, keep tabs on storage volume changes, analyze energy consumption, and use machine learning to predict CPU and memory requirements.
OptiAPM also provides expert support to help maintain peak system performance. Their services include:
These solutions simplify complex metrics, turning them into actionable insights through Grafana, while ensuring your clusters run efficiently and reliably.
Using Grafana to visualize Kubernetes metrics combines powerful tools into a straightforward process. For Docker setups, replace localhost with http://host.docker.internal:9090. Grafana dashboards provide real-time visibility into Kubernetes environments, with over 900,000 active installations proving its reliability for enterprise use. By keeping track of these metrics, you can strengthen your Kubernetes deployments and improve how they run. These basics lay the groundwork for further improvements.
Take your setup to the next level with these advanced strategies:
| Focus Area | Key Actions | Benefits |
|---|---|---|
| Resource Management | Adjust resource quotas and enable predictive autoscaling | Avoid over-allocation and improve resource efficiency |
| Cost Optimization | Monitor resource usage and use dynamic storage management | Lower operational costs and boost efficiency |
| Advanced Monitoring | Combine logs, metrics, and traces | Gain a more complete view of your system |
For businesses aiming to get the most out of Kubernetes monitoring, you might want to look into Grafana Enterprise Metrics, which offers better scalability and governance features. Tools like Grafana Mimir or VictoriaMetrics can also help solve issues with long-term data storage and query performance.
To stay ahead, focus on proactive monitoring and keep your dashboards updated regularly. If your infrastructure needs more advanced observability, professional support options are worth considering.
