Practical Top-down Resource Monitoring of a Kubernetes Cluster With Metrics Server

Let’s probe the Kubernetes magic that makes the beautiful CPU and memory dials tick on the monitoring dashboards.

You might have previously used observability tools such as Prometheus, Azure Monitor, AWS Container Insight, or commercial products such as Logic Monitor to monitor your Kubernetes cluster. Let’s probe the Kubernetes magic that makes the beautiful CPU and memory dials tick on the monitoring dashboards.

Kubernetes has a built-in Metrics API (see spec) and a simple CLI query, kubectl top ( documentation), that you can use to fetch a snapshot of the CPU and memory consumption of a Kubernetes object. The Kubernetes Metrics API is dependent on the Metrics Server cluster add-on that gathers resource usage from the Kubelets of the cluster. The primary consumer of the Metrics API is the Horizontal Pod Autoscaler. The Horizontal Pod Autoscaler (HPA) uses the metrics served by the Metrics API to scale the number of pods based on the observed resource metrics values. Apart from the Metrics API, HPA is designed to also consume metrics from your application running on the cluster (custom metrics) and services outside the cluster (external metrics) to autoscale pods. Some examples of external metrics provider to HPA are the popular open-source events-based autoscaling service KEDA and soon Logic Monitor. Similar to HPA, Vertical Pod Autoscaler (VPA) relies on Metrics Server as well. VPA allows you to automatically scale the CPU and memory constraints of the containers in a pod.

#devops #aws #kubernetes #kubernetes cluster

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50+ Useful Kubernetes Tools for 2020 - Part 2

Introduction

Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.

According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.

(State of Kubernetes and Container Security, 2020)

And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.

(State of Kubernetes and Container Security, 2020)

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How to Deploy Metrics Servers in Kubernetes Cluster for Resource Monitoring

This article will guide you quickly on How to Deploy Metrics servers in Kubernetes cluster which will monitor resource utilization of the resources such as CPU, Memory, network and disk utilization of your POD and kubernetes cluster nodes.

Once you deploy your kubernetes cluster you may need to monitor the same on utilization front. Like you need to gather the current utilization of the resources of the cluster nodes and PODS. There are a number of open-source solutions available today, such as the Metrics-Server, Prometheus,Elastic Stack, and proprietary solutions like Datadog and Dynatrace.

However in this article we are going to learn How to Deploy Metrics servers in Kubernetes cluster which will monitor your resources.

#devops #how to deploy metrics servers in kubernetes #kubernetes metrics server

Kubernetes in the Cloud: Strategies for Effective Multi Cloud Implementations

Kubernetes is a highly popular container orchestration platform. Multi cloud is a strategy that leverages cloud resources from multiple vendors. Multi cloud strategies have become popular because they help prevent vendor lock-in and enable you to leverage a wide variety of cloud resources. However, multi cloud ecosystems are notoriously difficult to configure and maintain.

This article explains how you can leverage Kubernetes to reduce multi cloud complexities and improve stability, scalability, and velocity.

Kubernetes: Your Multi Cloud Strategy

Maintaining standardized application deployments becomes more challenging as your number of applications and the technologies they are based on increase. As environments, operating systems, and dependencies differ, management and operations require more effort and extensive documentation.

In the past, teams tried to get around these difficulties by creating isolated projects in the data center. Each project, including its configurations and requirements were managed independently. This required accurately predicting performance and the number of users before deployment and taking down applications to update operating systems or applications. There were many chances for error.

Kubernetes can provide an alternative to the old method, enabling teams to deploy applications independent of the environment in containers. This eliminates the need to create resource partitions and enables teams to operate infrastructure as a unified whole.

In particular, Kubernetes makes it easier to deploy a multi cloud strategy since it enables you to abstract away service differences. With Kubernetes deployments you can work from a consistent platform and optimize services and applications according to your business needs.

The Compelling Attributes of Multi Cloud Kubernetes

Multi cloud Kubernetes can provide multiple benefits beyond a single cloud deployment. Below are some of the most notable advantages.

Stability

In addition to the built-in scalability, fault tolerance, and auto-healing features of Kubernetes, multi cloud deployments can provide service redundancy. For example, you can mirror applications or split microservices across vendors. This reduces the risk of a vendor-related outage and enables you to create failovers.

#kubernetes #multicloud-strategy #kubernetes-cluster #kubernetes-top-story #kubernetes-cluster-install #kubernetes-explained #kubernetes-infrastructure #cloud

Top Kubernetes Health Metrics You Must Monitor

Kubernetes is one of the most popular choices for container management and automation today. A highly efficient Kubernetes setup generates innumerable new metrics every day, making monitoring cluster health quite challenging. You might find yourself sifting through several different metrics without being entirely sure which ones are the most insightful and warrant utmost attention.

As daunting a task as this may seem, you can hit the ground running by knowing which of these metrics provide the right kind of insights into the health of your Kubernetes clusters. Although there are observability platforms to help you monitor your Kubernetes clusters’ right metrics, knowing exactly which ones to watch will help you stay on top of your monitoring needs. In this article, we take you through a few Kubernetes health metrics that top our list.

Crash Loops

A crash loop is the last thing you’d want to go undetected. During a crash loop, your application breaks down as a pod starts and keeps crashing and restarting in a circle. Multiple reasons can lead to a crash loop, making it tricky to identify the root cause. Being alerted when a crash loop occurs can help you quickly narrow down the list of causes and take emergency measures to keep your application active.

#devops #kubernetes #monitoring #observability #kubernetes health monitoring #monitoring for kubernetes

Practical Top-down Resource Monitoring of a Kubernetes Cluster With Metrics Server

Let’s probe the Kubernetes magic that makes the beautiful CPU and memory dials tick on the monitoring dashboards.

You might have previously used observability tools such as Prometheus, Azure Monitor, AWS Container Insight, or commercial products such as Logic Monitor to monitor your Kubernetes cluster. Let’s probe the Kubernetes magic that makes the beautiful CPU and memory dials tick on the monitoring dashboards.

Kubernetes has a built-in Metrics API (see spec) and a simple CLI query, kubectl top ( documentation), that you can use to fetch a snapshot of the CPU and memory consumption of a Kubernetes object. The Kubernetes Metrics API is dependent on the Metrics Server cluster add-on that gathers resource usage from the Kubelets of the cluster. The primary consumer of the Metrics API is the Horizontal Pod Autoscaler. The Horizontal Pod Autoscaler (HPA) uses the metrics served by the Metrics API to scale the number of pods based on the observed resource metrics values. Apart from the Metrics API, HPA is designed to also consume metrics from your application running on the cluster (custom metrics) and services outside the cluster (external metrics) to autoscale pods. Some examples of external metrics provider to HPA are the popular open-source events-based autoscaling service KEDA and soon Logic Monitor. Similar to HPA, Vertical Pod Autoscaler (VPA) relies on Metrics Server as well. VPA allows you to automatically scale the CPU and memory constraints of the containers in a pod.

#devops #aws #kubernetes #kubernetes cluster