SUMMARY:
XTIVIA leverages a unified observability architecture combining Prometheus, Grafana, and Alertmanager to deliver real-time performance insights and proactive alerting for Google Kubernetes Engine (GKE) workloads, ensuring reliability at scale.
- Prometheus serves as the primary engine for scraping metrics from Kubernetes objects and application endpoints, while Alertmanager automatically triggers notifications via channels like Slack or PagerDuty based on defined rules.
- The deployment process uses Helm charts to efficiently install the stack and configure interactive Grafana dashboards that visualize critical metrics, including cluster health, pod resources, and application latency.
- This integrated approach enables proactive monitoring, enabling teams to detect specific issues such as pod crash loops and resolve bottlenecks before they impact customers or disrupt services.
Adopting this open-source monitoring framework provides the granular visibility necessary to optimize cloud infrastructure and ensure enterprise-grade application reliability.
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.