Enhance Observability on Amazon EKS with Prometheus, Grafana, and OpenSearch


Our client, an organization running their infrastructure on the Amazon Cloud, relies on Kubernetes for efficient container orchestration of their development, testing and production environments. They have an EKS cluster hosting their microservices-based e-commerce application. With their critical e-commerce application hosted on an EKS cluster, they sought a solution that would enable comprehensive monitoring and observability using open-source tools.


Monitoring and observability in AWS Elastic Kubernetes Service (EKS) environments can present various challenges that require careful consideration. Here are some common challenges when establishing observability for EKS:

  1. Data Volume and Scalability: EKS clusters dynamically scale based on workload demands, resulting in a substantial volume of monitoring data generated by multiple nodes, containers, and microservices. Efficiently handling and analyzing this massive amount of data necessitates robust data collection, storage, and analysis mechanisms capable of scaling with the cluster.
  2. Node-Level Monitoring: Gaining visibility into the health and performance of individual worker nodes within an EKS cluster is crucial for identifying performance issues and resource utilization. However, due to the distributed nature of the cluster, effectively monitoring and correlating data at the node level poses a challenge.
  3. Application-Level Monitoring: In addition to monitoring the cluster infrastructure, tracking the health, performance, and resource usage of individual applications and microservices deployed within the EKS cluster is essential. Accomplishing this requires capturing metrics and logs at the application level, ensuring proper instrumentation, and effectively correlating the data with cluster-level metrics.
  4. Distributed Tracing and Service Dependencies: EKS clusters typically consist of interconnected microservices, making it vital to understand the flow of requests and dependencies across these services. Implementing distributed tracing mechanisms and managing service dependencies becomes complex, particularly in dynamic and rapidly changing environments.
  5. Data Aggregation and Visualization: EKS generates various types of monitoring data, including metrics, logs, and traces. Aggregating and visualizing this data to gain insights into cluster behavior, application performance, and infrastructure utilization necessitates efficient data aggregation mechanisms and effective visualization tools. Selecting and configuring the appropriate tools and designing meaningful dashboards and visualizations can be challenging.
  6. Security and Compliance: Ensuring that monitoring and observability solutions adhere to security best practices and compliance requirements is crucial. Securing data collection, storage, and transmission, implementing proper access controls, and complying with standards such as HIPAA or PCI-DSS can be complex in EKS environments.
  7. Cost Management: Extensive monitoring and observability can generate a significant amount of data, leading to increased costs. Striking a balance between comprehensive monitoring and cost optimization requires a thoughtful selection of metrics, data retention policies, and optimization of storage and analysis resources.


To overcome the challenges mentioned above, we collaborated closely with the client to identify the optimal solution. After careful evaluation, we recommended implementing the following tools for AWS EKS monitoring and observability:

  1. Prometheus Deployment:

Our team deployed Prometheus, a robust open-source monitoring and alerting tool, within their EKS cluster. Prometheus collects and stores metrics from various sources, including Kubernetes resources, applications, and infrastructure components. It scrapes metrics at regular intervals, enabling real-time monitoring.

  1. Grafana Integration:

To create rich and customizable dashboards and visualizations of monitoring data, we seamlessly integrated Grafana, an open-source visualization and analytics platform, with Prometheus. Grafana connects to Prometheus as a data source, empowering users to create custom dashboards and visualize metrics in real-time. Our team designed intuitive dashboards to display key performance indicators, application health metrics, and infrastructure utilization.

  1. Alerting and Notification:

Leveraging Prometheus, we set up alerting rules based on predefined thresholds or patterns in the collected metrics. Critical metrics such as high CPU usage, low disk space, or application errors trigger alerts. When an alert is triggered, Prometheus sends notifications to relevant stakeholders via email, text messages, or other communication channels, ensuring timely responses to potential issues.

  1. Log Aggregation with OpenSearch:

In addition to metrics-based monitoring, we employed OpenSearch, a powerful open-source distributed search and analytics engine, to perform log aggregation and analysis. Our team configured the client’s applications to send logs to OpenSearch, which indexes and stores them. This enables advanced log searches, visualizations, and deeper insights into application behaviour for effective troubleshooting.

Benefits and Conclusion

By implementing Prometheus, Grafana, and OpenSearch together, our client achieved centralized monitoring and observability for their e-commerce application running on Amazon EKS. The benefits they gained are as follows:

  1. Real-time Visibility: The client gained real-time insights into the health and performance of their microservices, enabling proactive issue identification and resolution.
  2. Infrastructure Utilization: Monitoring metrics at the node level facilitated efficient resource utilization and identified performance bottlenecks.
  3. Application Performance: Monitoring individual applications and microservices empowered the client to optimize their performance and ensure a better user experience.
  4. Troubleshooting: Distributed tracing mechanisms and log aggregation capabilities enabled efficient troubleshooting by identifying and resolving performance issues and dependencies.
  5. Customizable Dashboards: Grafana’s integration allowed the creation of custom dashboards tailored to the client’s specific needs, providing a comprehensive view of the application and infrastructure.
  6. Compliance and Security: The implemented solution adhered to security best practices and compliance standards, ensuring secure data collection, storage, and transmission.
  7. Cost Optimization: Thoughtful selection of metrics, data retention policies, and storage and analysis resources enabled a balance between comprehensive monitoring and cost management.

With the collective expertise of our team in Amazon EKS and the meticulous selection and implementation of Prometheus, Grafana, and OpenSearch, High Plains Computing effectively tackled the challenges pertaining to EKS observability faced by our client. The implemented solutions significantly improved visibility, facilitated proactive monitoring, and enhanced troubleshooting capabilities, culminating in optimized application performance and an exceptional user experience for their valuable customers.

Need help in adding observability to your AWS EKS Clusters? The High Plains team is very experienced and has done this for many clients. We can help you as well.

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