Summary
In this blog, learn how we monitor GKE workloads using Prometheus and Grafana to ensure real-time visibility, performance optimization, and proactive issue resolution.
Table of contents
How We Monitor GKE Workloads Using Prometheus and Grafana
Monitoring is a crucial part of running workloads in Kubernetes, especially when operating at scale on Google Kubernetes Engine (GKE). To ensure application reliability, performance, and availability, we use Prometheus for metrics collection and Grafana for visualization. Combined with Alertmanager, this stack provides end-to-end observability.
Why Monitoring Matters in GKE
GKE abstracts infrastructure complexity, but applications still need monitoring to:
- Track CPU, memory, and storage usage.
- Monitor pod health and scaling events.
- Gain application-level insights (custom metrics).
- Detect and respond to incidents quickly.
Our Monitoring Stack
We deploy a Prometheus-Grafana-Alertmanager stack inside the GKE cluster using Helm charts for easy setup.
- Prometheus – Scrapes metrics from Kubernetes objects (nodes, pods, services) and application endpoints.
- Grafana – Provides interactive dashboards for metrics visualization.
- Alertmanager – Triggers alerts via email, Slack, or PagerDuty based on Prometheus rules.
Architecture Diagram
Here’s the high-level flow of how monitoring works:
+—————————–+
| GKE Workloads |
| (Pods, Nodes, Apps)|
+—————————–+
|
Expose Metrics
|
v
+——————–+
| Prometheus |
+——————–+
|
Scrapes & Store Metrics
|
v
+————————+
| Alertmanager |
| (Rules & Alerts) |
+———————–+
|
+———————v———————–+
| Grafana |
| (Dashboards & Visualizations |
+———————————————-+
Deployment steps
1. Install Prometheus & Grafana using Helm:
helm repo add prometheus-community
https://prometheus-community.github.io/helm-charts
helm install kube-prometheus-stack prometheus-community/kube-prometheus-stack
2. Expose Grafana Dashboard:
kubectl port-forward svc/kube-prometheus-stack-grafana 3000:80
Access Grafana at: http://localhost:3000
3. Create Dashboards for:
- Cluster & node health.
- Namespace & pod resource usage.
- Application latency, errors, and throughput.
4. ConfigureAlertsinPrometheus:
Example rule for pod crashlooping:
- alert: PodCrashLooping
expr: kube_pod_container_status_restarts_total > 3
for: 5m
labels:
severity: critical
annotations:
summary: "Pod {{ $labels.pod }} is crash looping"
Benefits We Achieved
- Proactive Monitoring – Issues detected before they impact customers.
- Scalability – Handles thousands of pods across namespaces.
- Custom Metrics – Applications expose business KPIs (e.g., API requests/sec).
- Unified View – Grafana dashboards combine system, cluster, and app metrics.
Conclusion
By integrating Prometheus, Grafana, and Alertmanager with GKE, we have built a powerful observability stack that provides real-time visibility, alerting, and insights. This setup helps us maintain healthy, reliable, and performant workloads.
Please contact us for any questions.