Jump right into Kubernetes development with career-focused lessons and hands-on practice.

DevOps for Developers

Kubernetes is a powerful container management tool that's taking the world by storm. This detailed course will help you master it.

In this course, you'll start with the fundamentals of Kubernetes and what the main components of a cluster look like. You'll then learn how to use those components to build, test, deploy, and upgrade applications, as well as how to achieve state persistence once your application is deployed. You'll also learn how to secure your deployments and manage resources, which are crucial DevOps skills.

By the time you're done, you'll have a firm grasp on what Kubernetes is, as well as the skills to deploy your own clusters and applications with confidence.

A Practical Guide to Kubernetes

# Exploring the options

How did we come up with the current memory and CPU values? Why did we set the memory of the Mongo image to `100Mi`? Why not `50Mi` or `1Gi`? It is embarrassing to admit that the values we have right now are random. We guessed that the containers based on the `vfarcic/go-demo-2` image require fewer resources than the Mongo image, so their values are comparatively smaller. That was the only criteria we used to define the resources.

Before we put random values for resources, we should know that we do not have any metrics to back us up. Anybody’s guess is as good as ours.


The only way to truly know how much memory and CPU an application use is by retrieving metrics. We’ll use [Metrics Server](https://github.com/kubernetes-sigs/metrics-server) for that purpose.

Metrics Server collects and interprets various signals like compute resource usage, lifecycle events, etc. In our case, we’re interested only in the CPU and memory consumption of the containers we’re running in our cluster.

k3d clusters come with metrics-server already deployed as a system application.

The idea  of developing a Metrics Server as a tool for monitoring needs is mostly abandoned. Its primary focus is to serve as an internal tool required for some of the Kubernetes features.
 

Instead, I’d suggest a combination of [Prometheus](https://prometheus.io/) combined with the Kubernetes API as the source of metrics and [Alertmanager](https://prometheus.io/docs/alerting/alertmanager/) for our alerting needs. However, those tools are not in the scope of this chapter, so you might need to educate yourself from their documentation.

> **Note:** Use Metrics Server only as a quick-and-dirty way to retrieve metrics. Explore the combination of Prometheus and Alertmanager for your monitoring and alerting needs.

Now that we have clarified what Metrics Server is good for, as well as what it isn’t, we can proceed and confirm that it is indeed running inside our cluster. 

Learn how to measure the actual memory and CPU consumption.

Measuring the Actual Memory and CPU Consumption

Exploring the options