Kubernetes provides an efficient, robust, and feature-rich platform for orchestrating containers at any scale. However, if not managed properly, Kubernetes can easily become heavy whether you are using a managed or self-hosted Kubernetes cluster. We take a look at some of the tools and services available for Kubernetes monitoring.
Monitoring plays a crucial role in the overall management process of Kubernetes, making it easy for users to identify performance bottlenecks and resolve any issues at the cluster or container level. There is a plethora of monitoring options available for Kunbernetes (aka K8). Let’s take a look at some of these options and best practices to implement when monitoring a Kubernetes cluster.
Kubernetes is a distributed environment with resources spread across multiple nodes and even multiple clusters. So having a unified monitoring solution is ideal for monitoring all aspects of the environment, from cluster metrics and container health to application logs.
The integrated Kubernetes dashboard is a great starting point for managing and monitoring K8. This will be more than sufficient for the management of small and medium size clusters. As a native application, users can easily deploy this dashboard to any cluster and start using it without the need for complex configurations. This dashboard provides most of the functionality needed to manage the cluster with the ability to deploy applications directly from the interface and modify cluster resources. You can create a complete monitoring solution by combining this dashboard with a notification manager.
Prometheus / Grafana
Prometheus and Grafana are separate tools used for Kubernetes monitoring. With its native support for K8, Prometheus is a metrics aggregator, while Grafana is a data visualization solution. However, these two tools go hand in hand when building a complete monitoring solution for K8s. Prometheus can target the Kubernetes API (Prometheus Kubernetes SD) to gather all K8 control plane, service, and node metrics, as well as use Autodiscover to collect application metrics. In addition, Prometheus comes with built-in modification mechanisms, and this modified data can then be visualized using Grafana for comprehensive monitoring.
ELK has become the industry standard for monitoring, especially when it comes to Kubernetes monitoring. ELK with Logstash, Elasticsearch and Kibana provides an all-in-one monitoring solution for metrics and logs in any environment. ELK can monitor Kubernetes and can also be integrated with the applications themselves, not only for sending logs, but also as a complete performance monitoring solution through Elastic Synthetics. In addition, some features of ELK further extend its functionality, such as Elastic Feet for easy deployments / integrations and Elastic Security for XDR. The downside to ELK is that there will be a deeper learning curve for setup and use. So investing in such a solution for a small scale deployment will not be an ideal solution while it will be an invaluable asset for large scale deployments.
When it comes to best practices for monitoring Kubernetes, most people forget that Kubernetes itself comes with a comprehensive monitoring toolkit. For example, the simple kubectl command line tool allows users to inspect and manage cluster resources and directly view logs without resorting to an external tool. Simple commands such as describe and cluster information dumps can be invaluable for troubleshooting. Additionally, tools like liveliness, prep, and starter probes can be used to monitor pod and container condition. For example, the Kubernetes activity probe provides a way to monitor the status of the pod using HTTP requests. Additionally, features like audit policies allow users to easily create auditable environments with the ability to store these events in a log or webhook backend. Therefore, always check if there is a native solution for your needs before turning to third-party solutions, as native solutions offer better compatibility and performance.
The effectiveness of monitoring will be greatly reduced if no alerts are configured. Alerts allow users to implement continuous monitoring and be immediately notified of any unusual behavior in the cluster. Resource metrics such as CPU, memory, disk usage, network performance, and network events for application events can be used as alert triggers. These alerts should then be passed on to the parties concerned so that they can quickly take the necessary action.
K8s monitoring is not limited to the cluster and the resources it contains and includes all external resources associated with it. This means that monitoring should also include the underlying hardware and software that powers the K8s cluster in a self-hosted environment. In a cloud environment, these external resources can be user events, network performance, and cost scans to manage cluster security, performance, and costs.
Occasionally, an error in an application results in errors within a cluster. For example, suppose an application drains all configured resources due to a resource management issue in the application. In this case, adding more resources to the cluster will not solve the problem. Thus, it is essential to view the applications deployed within a cluster as part of the overall monitoring process and to monitor the two as a single entity to better understand the overall workloads of each.
In conclusion, while Kubernetes monitoring is a large and complex subject, it is a must-have skill for any Kubernetes administrator. However, the scope and expected functionality of monitoring depends on the size and complexity of the cluster as well as the underlying workloads. Users can quickly implement a monitoring solution that exactly matches their needs by focusing on the best practices mentioned above.
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