Key metrics to focus on when managing kubernetes clusters

Pokemon per second processing of Go cloud data storage (expected vs Actual)

This can happen, and you should be prepared for it as well. This is also the article in this series to mention. In this series of tutorials we'll show you what you need to track, why you're tracking them, and what you need to do to deal with possible root causes.

We'll show you each indicator, how to track it, and what you can do to take action. We will use different tools to collect and analyze this data. The tutorial does not cover too much detail, but it provides outreach links so that you can get more information. Don't say much, let's get started.

Metrics: For monitoring, more than monitoring

This series of articles focuses on how to monitor and run kubernetes clusters. The use of logs is a good idea, but in the case of large-scale deployments, logs can have a significant impact on post-mortem analysis, but it is difficult to constantly warn operators of increasingly serious problems that are occurring in the process. Metrics Server can monitor the CPU and memory usage of the container, as well as the node where the container is running.

This allows operations personnel to set up and monitor KPIs (key performance indicators). These operations define levels of things that can provide operations teams with a way to determine when an application or node is unhealthy. They also provide them with all the data they need to see the problem.

In addition, the Metrics Server

https://kubernetes.io/docs/tasks/debug-application-cluster/core-metrics-pipeline/) Allow kubernetes to enable horizontal Pod autoscaling

(https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/). This feature allows Kubernetes to scale up when expanding the number of pod instances, based on the metrics reported by the Kubernetes Metrics API and the number of API objects reflected by those metrics.

in the rancher kubernetes cluster Setting up Metrics Server

Starting with Kubernetes version 1.8, Metrics server kubernetes monitoring Architecture

(https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/ MONITORING_ARCHITECTURE.MD) plug-in has become the standard for fetching container metrics. Before the standard appeared, the default was to use Heapster, which was now deprecated and began to support metrics Server.

Soon, Metrics server will be able to run on the Kubernetes cluster configured in Rancher 2.0. You can check out the latest release of Rancher 2.0 in rancher's GitHub repo and look forward to:https://github.com/rancher/rancher/releases.

If you want to make metric server work, you must modify the cluster definition through the Rancher Server API. This allows the rancher server to modify the Kubelet and Kubeapi parameters so that they contain the markup required for the metrics server to function properly.

For instructions on how to do this on the rancher provisioned cluster and to modify other hyperkube-based clusters, refer to this link on GitHub:https://github.com/ jasonvanbrackel/metrics-server-on-rancher-2.0.2.

Key metrics to focus on when managing kubernetes clusters