Kubernetes has established itself as a fundamental DevOps instrument for the execution and administration of containerized workloads. Its functionalities encompass the automation of diverse container-related processes, including the deployment, management, and configuration of containerized applications. Its application is prevalent within large-scale enterprise environments for the operation and management of microservices architectures.
The effective monitoring of your Kubernetes cluster’s performance is paramount to guaranteeing the optimal performance of your deployed services. Such monitoring provides you with essential visibility into the health of your cluster. By tracking uptime, cluster utilization metrics (encompassing disk, memory, and CPU utilization), and metrics of cluster components such as APIs, pods, and containers, you are enabled to identify underlying cluster issues. Furthermore, alert mechanisms assist operational teams in troubleshooting critical issues, including, but not limited to, unavailable nodes, pod crashes, control plane component failures, elevated resource utilization, and misconfigurations.
So, how do you go about monitoring the Kubernetes cluster performance?
Let’s find out.
Cluster and Node Metrics
A Kubernetes cluster usually has two types of nodes:
- Master node which hosts the control plane that manages the cluster, including the worker nodes and pods.
- Worker nodes that host the pods that run containerized workloads.
What is cluster monitoring?
Cluster monitoring denotes the comprehensive surveillance of the operational status of your entire Kubernetes cluster, encompassing both master nodes and worker nodes. Through this practice, you are afforded the capacity to monitor the health, performance, and resource utilization of the various constituent components of the cluster, including both the control plane and the worker nodes themselves. This process facilitates the acquisition of valuable insights about the control plane components, such as metrics related to the API server, scheduler, controller manager, and Etcd. Furthermore, you will obtain information regarding the health status of persistent volumes, enabling you to track storage I/O operations and monitor ingress and egress traffic.
Cluster monitoring further provides visibility into the operational status of containers, pods, nodes, and deployments, including Daemon Sets, Replica Sets, and Replication Controllers. For instance, you can ascertain the quantity of nodes, containers, and pods currently deployed. You can monitor the execution of applications within pods and observe pod and container metrics, such as memory and CPU utilization.
At the node level, you will acquire insights concerning individual nodes, encompassing their health and performance, the number of containers and pods operating on each node, storage metrics, network traffic I/O, and other relevant data points.
Container Metrics
Containerization has become the prevailing methodology for the distribution and deployment of applications, aimed at enhancing application portability and performance. Consequently, the monitoring of container metrics is essential to ensuring the operational integrity and performance of your applications.
Particular attention must be directed towards metrics such as memory and CPU usage per container, container state (i.e., Running, Waiting, Terminated), container restarts, and container logs for diagnostic purposes in the event of errors. These represent but a selection of the metrics that will enable you to proactively address potential issues and avert service interruptions.
Application Metrics
While the proper functioning of containers and nodes is a critical consideration, equally important is the health status of the applications residing within the pods. You must ensure applications operate optimally and utilize only the requisite computing resources. Limiting the quantity of computing resources (such as memory and CPU) allocated to each application is essential to prevent resource depletion on the node, which could adversely affect other applications and services.
To evaluate performance and address resource constraints and potential failures, you should consider monitoring metrics such as request latency and the frequency of application errors. Furthermore, depending on the specific nature of the application, you may monitor additional aspects such as database connections, network traffic I/O, queue size, and cache hit rate to gain visibility into your applications’ response times.
API Server Metrics
The API server constitutes a core component of the Kubernetes cluster, providing the Kubernetes API to users and other cluster components. It receives and processes API requests, thereby facilitating seamless communication between users and the various elements of the Kubernetes cluster.
As a central component of both the control plane and the cluster in general, proactive monitoring of the API server is of paramount importance to ensuring cluster health. A key metric to monitor is the latency of API requests directed to the cluster; latency is defined as the time required to service an API request. Tracking API request latencies enables you to ascertain the responsiveness of the API server. Elevated latencies are indicative of performance issues within the API server components.
The request rate represents another metric worthy of your attention, indicating the traffic processed by the API server. It is advisable to evaluate requests originating from various resources, including nodes, pods, deployments, namespaces, and other API services. These encompass HTTP requests such as POST and GET.
Furthermore, the monitoring of API server error logs is recommended. Errors such as 5xx errors may signify unavailable services, internal server errors, bad gateways, and similar issues. Error logs provide a convenient means of identifying issues related to the API server or the control plane.
Ingress Metrics
The monitoring of Ingress metrics is essential for ensuring effective communication between Kubernetes services and external endpoints. This process entails the monitoring of controller traffic metrics, which aids in tracking traffic statistics and the health of workloads.
In instances where you are utilizing the Nginx Ingress controller, it is considered best practice to monitor request rates, response times, error alerts, performance, and resource utilization. Elevated request latency and atypical traffic spikes may indicate an insufficient number of pod replicas or configuration discrepancies.
Network Metrics
Network metrics offer crucial insights into network performance and potential latencies that may affect the uptime and availability of services. Therefore, maintaining visibility of network latency is of paramount importance, as it provides you with information regarding potential causative factors and appropriate remedial actions.
Furthermore, it is advisable to evaluate traffic volume between pods and services and to monitor for dropped network packets to identify anomalies before they escalate and result in service disruptions.
These metrics will help monitor Kubernetes Cluster performance and help improve containerized applications.
How to get started with building containerized applications?
Enroll in Cognixia’s live instructor-led online Kubernetes training.
Learn Kubernetes online and enhance your career
Get certified in Kubernetes and improve your career prospects.
Kubernetes is an open-source orchestration system for automating the management, placement, scaling, and routing of containers. It provides an API to control how and where the containers would run. Docker is also an open-source container-file format for automating the deployment of applications as portable, self-sufficient containers that can run in the cloud or on-premises. Together, Kubernetes and Docker have become hugely popular among developers, especially in the DevOps world.
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