Beginner’s Introduction to Monitoring in Kubernetes

With layers and layers of clusters, pods, and underlying issues, Kubernetes monitoring is a fairly complex issue that becomes a game of juggling several balls at once. From finding the issue to actually dealing with it, this platform presents a unique challenge when it comes to monitoring.

Within this article, we’ll be givingan introduction to Kubernetes monitoring, pointing you towards the main things to watch out for, while also outlining some common problems you’ll run into. Let’s get right into it.

Which Metrics Does Monitoring Include?

Kubernetes monitoring is split into two distinct levels, each of which covers a range of specific metrics. These two levels are:

• Cluster Monitoring -”This level of monitoring covers a more holistic approach, looking at complete clusters in the Kubernetes ecosystem. This involves everything from checking if specific nodes are working to seeing clusters and how their resource allocation is currently going.
• Pod Monitoring – At a pod level, this monitoring is about checking the health of individual pods. This could give information about the metrics produced by a specific application or how the pod is using its resources.

While these are the overall monitoring features within the platform, monitoring also takes place on a more intricate level.

Cluster Monitoring

Cluster Monitoring is typically split into three segments: Cluster Pods, Cluster Nodes, and Resource Utilization. Let’s break these down:

• Pods – How many total pods are running, reflecting overall workload and if your system can support it.
• Nodes – Total number of available nodes, helping you to decide the scale of cloud resources that you need to employ.
• Resources – Monitoring involves checking CPU, disk utilization, memory, and bandwidth that is being used by the does within a cluster.

Pod Monitoring

On a pod level, there are three main areas that you must be sure to monitor:

• Container Metrics – Accessed through metrics-server, this will be information about memory usage and CPU demand against the possible maximum.
• Application Metrics – Metrics that are specifically related to an application, like total users, or user experience.
• Scaling – Reflects how the orchestrator is handling a specific pod within your clusters. These reflect notions about network data, health, and development.

By familiarizing yourself with the different iterations of monitoring that you can use, you’ll be in a much better position to deal with any issues that may spring up when using Kubernetes.

Why can Kubernetes be difficult to monitor?

Kubernetes is a powerful tool that can simultaneously run millions of components, services, and servers in one location. Although this clustered design is one of its strengths, it also causes a lot of problems as users have an incredibly vast quantity of information to monitor at once.

There are three main issues when it comes to monitoring:

• Constant Change – Part of K8’s process is generating new pods with Deployments, Jobs, StatefulSets, and DaemonSets all creating pods regularly. This set of dockers will mold, change, and develop over time. As they are continually moved and reformed, they become almost impossible to track without tools.
• Observation Blocks’ – K8 uses a range of dynamic applications and microservices. While this ensures a thorough service, it also means that there can be such a layering of different functions that a user may not even know how to visualize all of their distinct micro-apps at once. K8’s dynasticism is also one of the core reasons that it is difficult to observe.
• Multilayered Solution’ – Within Kubernetes, there are a range of different aspects that you can control from the control plane. Everything from the cloud services that you’re using to the Kube-API is listed within this location. While this is great for getting a general overview, due to the compartmentalization of K8, this can quickly turn into a quantity of data that is simply impossible to visualize without help from effective Kubernetes monitoring tools. An additional feature known as ”’Pod Churn’ (when pods are created, recreated, destroyed, or changed) further multiplies the data that you’ll be looking at, making this a truly vast problem.

While these each present a unique set of issues, one of the most effective ways that you can monitor on Kubernetes is by turning to monitoring tools. These tools will break down the process and ensure that you’re able to visualize, track, and monitor the data that you’re creating.

Final Thoughts

Kubernetes is an incredibly complex tool that provides a range of utilities for software developers. However, it is this complexity that leads monitoring to form as a unique problem within this program. While complexity boosts utility and functionality, it also transforms Kubernetes into a platform that needs a little additional monitoring help.

Luckily, due to Kubernetes’ popularity, there are many monitoring tools that users can turn to. Each of these comes with different central benefits, providing a solution that matches exactly what you require.