A Beginner’s Guide To Kubernetes Metrics And What They Mean
Originally designed by Google, Kubernetes has grown into the go-to management system for platforms and applications. Anything that needs multiple different containers typically turns to Kubernetes, with the tool being perfect for developing, monitoring, and scaling an application.
Part of what makes Kubernetes so effective is the range of different metrics within the software. By moving through the various different metrics available, you’re able to build up a holistic understanding of the app, containers, and the performance of individual nodes. With this, you’re able to address problems when they arise, allocate resources effectively, and understand exactly how your system works.
In this article, we’ll be exploring Kubernetes metrics, pointing towards the most important metrics to follow and why you should take a note of them.
Which Kubernetes Metrics Should I Monitor?
If you’re looking to create healthy clusters, then monitoring them by following certain metrics is a vital practice. Considering the popularity of Kubernetes, it’s no wonder that there are a range of different metrics that you can trace within the system. Typically, these fall into two broad categories:
Cluster Metrics – Whether it be able to general health and efficiency of the cluster itself, its workload and progress, or even information about the individual nodes that are used by the cluster, Kubernetes has metrics that you can access.
Deployments and Pods – Any pods or deployments that come from the Kubernetes system itself will have a range of different metrics for you to monitor.
Kubernetes Cluster Metrics
To give yourself a holistic understanding of how a cluster within Kubernetes is working, you’ll need to know the number of resources that the cluster is using, how interconnected it is in terms of which applications are on each node, and information about the capacity of each individual node in your system.
Due to the sheer scope of Kubernetes, supporting up to 5,000 nodes per cluster, it’s important to understand how the system is working. Considering the potential size of your ecosystem, this would be nearly impossible without the cluster metrics.
These metrics come in a few different forms:
Nodes – Within this section, anything that relates to nodes within a cluster will be located. This pertains to information like CPU, memory or disk utilization, network bandwidth usage, and more will all be here. With these metrics, you’re able to deduce the strain on individual nodes, allowing you to run your system more efficiently by reallocating resources to different pods. Equally, if a pod is restarting or crashing, you’ll be able to find out why by checking the strain on the related nodes.
Pods Per Node – Within Kubernetes, you will have to pay your cloud provider for the use of certain clusters. This considered, metrics related to this section will tell you exactly what a cluster is doing, how many nodes are available to it, and information about the cost of that particular cluster to your business.
Memory and CPU – Kubernetes allows a node’s kubelet to be allocated to different containers. Within this section, you’ll be able to set minimum and maximum limits on the number of resources that are allocated. Equally, metrics within this section will detail the memory requirements of all of the different pods connected to your system, giving you a holistic overview of your entire ecosystem’s memory usage.
Node Resources – Sometimes within Kubernetes, nodes will fail. Without these node resource metrics, it would be difficult to explain why. This section allows you to track the workload of individual pods, double-checking the size of different nodes available and if this will be enough to sustain the system as a whole. Additionally, you can use the information about the number of pods per node
While the cluster metrics are much more complex than pod and deployment metrics, they also give much more information about the system as a whole, allowing you to understand where resources are being allocated and how efficiently your ecosystem is running.
Pod and Deployment Metrics
These metrics are incredibly vast, covering 10s of different individual factors and allocations. However, they can be summarized into three distinct groups, each of which has a range of different metrics within them.
The three pod and deployment metric groups that you’ll encounter within Kubernetes are:
General Metrics – Anything that is related to the health of the pods within your Kubernetes ecosystem falls into this category. From information about the quantity of instances that a pod is supporting to details about any pods that have failed or have restarted, this section will help you find information about resource allocation, the strain on your pods, and their general health.
Container Metrics – Pertaining to the general resource limits of each of your clusters, you’ll be able to monitor the CPU of each container, its memory allowance and utilization, and additional information about the data that is being processed by the container. Anything related to the day-to-day actions of a container will be found here.
App Metrics – Finally, application metrics relate to the specific performance of the apps that your Kubernetes ecosystem is built upon. From the current runtime of an application to information about its performance or latency, these metrics break down the most important information about applications within Kubernetes.
Alongside these, there are always a range of Kubernetes metrics monitoring tools that you can use to create a more comprehensive overview of your system.
Kubernetes provides the framework that allows developers to create behemoth application ecosystems. Due to the sheer scope of the platform, it’s important to know which metrics you should be paying attention to, helping you to ensure that everything is running smoothly.
If you start to monitor the metrics and metric areas detailed in this article, you’ll be well on your way to having an efficient Kubernetes ecosystem.