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Kubernetes is a platform that eliminates the manual processes involved in deploying containerized applications. In this blog on Kubernetes Tutorial, you will go through all the concepts related to this multi-container management solution.
The following topics will be covered in this tutorial:
Now, before moving forward in this blog, let me just quickly brief you about containerization.
So, before containers came into existence, the developers and the testers always had a tiff between them. This usually, happened because what worked on the dev side, would not work on the testing side. Both of them existed in different environments. Now, to avoid such scenarios containers were introduced so that both the Developers and Testers were on the same page.
Handling a large number of containers all together was also a problem. Sometimes while running containers, on the product side, few issues were raised, which were not present at the development stage. This kind of scenarios introduced the Container Orchestration System.
Before I deep dive into the orchestration system, let me just quickly list down the challenges faced without this system.
As you can see in the above diagram when multiple services run inside containers, you may want to scale these containers. In large scale industries, this is really tough to do. That’s because it would increase the cost to maintain services, and the complexity to run them side by side.
This engine, lets us organize multiple containers, in such a way that all the underlying machines are launched, containers are healthy and distributed in a clustered environment. In today’s world, there are mainly two such engines: Kubernetes & Docker Swarm.
Kubernetes and Docker Swarm are leading container orchestration tools in today’s market. So before using them in prod, you should know what exactly they are and how they work.
Further, in the blog, I am going to deep dive into Kubernetes, but to know about Docker you can click here.
As you can refer to the above image, Kubernetes, when compared with Docker Swarm owns a great active community and empowers auto-scaling in many organizations. Similarly, Docker Swarm has an easy to start cluster when compared to Kubernetes, but it is limited to the Docker API’s capabilities.
Well, folks, these are not the only differences between these top tools. If you wish to know the detailed differences between both these container orchestration tools, you can click here.
If I could choose my pick between the two, then it would have to be Kubernetes since, containers need to be managed and connected to the outside world for tasks such as scheduling, load balancing, and distribution.
But, if you think logically, Docker Swarm would make a better option, as it runs on top of Docker right? If I were you, I would have definitely got confused about which tool to use. But hey, Kubernetes being an undisputed leader in the market and also does run on top of Docker containers with better functionalities.
Now, that you have understood the need for Kubernetes, it’s a good time, that I tell you What is Kubernetes?
Kubernetes is an open-source system that handles the work of scheduling containers onto a compute cluster and manages the workloads to ensure they run as the user intends. Being the Google’s brainchild, it offers excellent community and works brilliantly with all the cloud providers to become a multi-container management solution.
The features of Kubernetes, are as follows:
Kubernetes Architecture has the following main components:
I am going to discuss each one of them one by one. So, initially let’s start by understanding the Master Node.
The master node is responsible for the management of Kubernetes cluster. It is mainly the entry point for all administrative tasks. There can be more than one master node in the cluster to check for fault tolerance.
As you can see in the above diagram, the master node has various components like API Server, Controller Manager, Scheduler and ETCD.
Worker nodes contain all the necessary services to manage the networking between the containers, communicate with the master node, and assign resources to the scheduled containers.
As you can see in the above diagram, the worker node has various components like Docker Container, Kubelet, Kube-proxy, and Pods.
If you want a detailed explanation of all the components of Kubernetes Architecture, then you can refer to our blog on Kubernetes Architecture.
Yahoo! JAPAN is a web services provider headquartered in Sunnyvale, California. As the company aimed to virtualize the hardware, company started using OpenStack in 2012. Their internal environment changed very quickly. However, due to the progress of cloud and container technology, the company wanted the capability to launch services on various platforms.
Problem: How to create images for all required platforms from one application code, and deploy those images onto each platform?
For your better understanding, refer to the below image. When the code is changed at the code registry, then bare metal images, Docker containers, and VM images are created by continuous integration tools, pushed into the image registry, and then deployed to each infrastructure platform.
Now, let us focus on container workflow to understand how they used Kubernetes as a deployment platform. Refer to the below image to sneak peek into platform architecture.
OpenStack instances are used, with Docker, Kubernetes, Calico, etcd on top of it to perform various operations like Container Networking, Container Registry, and so on.
When you have a number of clusters, then it becomes hard to manage them right?
So, they just wanted to create a simple, base OpenStack cluster to provide the basic functionality needed for Kubernetes and make the OpenStack environment easier to manage.
By the combination of Image creation workflow and Kubernetes, they built the below toolchain which makes it easy from code push to deployment.
That’s how folks, Yahoo! JAPAN built an automation toolchain for “one-click” code deployment to Kubernetes running on OpenStack, with help from Google and Solinea.
In this Hands-On, I will show you how to create a deployment and a service. I am using an Amazon EC2 instance, to use Kubernetes. Well, Amazon has come up with Amazon Elastic Container Service for Kubernetes (Amazon EKS), which allows them to create Kubernetes clusters in the cloud very quickly and easily. If you wish to learn more about it, you can refer to the blog here.
Step 1: First create a folder inside which you will create your deployment and service. After that, use an editor and open a Deployment file.
mkdir handsOn cd handsOn vi Deploy.yaml
Step 2: Once you open the deployment file, mention all the specifications for the application you want to deploy. Here I am trying to deploy an httpd application.
apiVersion: apps/v1 #Defines the API Version kind: Deployment #Kinds parameter defines which kind of file is it, over here it is Deployment metadata: name: dep1 #Stores the name of the deployment spec: # Under Specifications, you mention all the specifications for the deployment replicas: 3 # Number of replicas would be 3 selector: matchLabels: app: httpd #Label name which would be searched is httpd template: metadata: labels: app: httpd #Template name would be httpd spec: # Under Specifications, you mention all the specifications for the containers containers: - name: httpd #Name of the containers would be httpd image: httpd:latest #The image which has to be downloaded is httpd:latest ports: - containerPort: 80 #The application would be exposed on port 80
Step 3: After you write your deployment file, apply the deployment using the following command.
kubectl apply -f Deploy.yaml
Here -f is a flag name used for the file name.
Step 4: Now, once the deployment is applied, get the list of pods running.
kubectl get pods -o wide
Here, -o wide are used to know on which node is the deployment running.
Step 5: After you have created a deployment, now you have to create a service. For that again use an editor and open a blank service.yaml file.
Step 6: Once you open a service file, mention all the specifications for the service.
apiVersion: v1 #Defines the API Version kind: Service #Kinds parameter defines which kind of file is it, over here it is Service metadata: name: netsvc #Stores the name of the service spec: # Under Specifications, you mention all the specifications for the service type: NodePort selector: app: httpd ports: -protocol: TCP port: 80 targetPort: 8084 #Target Port number is 8084
Step 7: After you write your service file, apply the service file using the following command.
kubectl apply -f service.yaml
Step 8: Now, once your service is applied to check whether the service is running or not use the following command.
kubectl get svc
Step 9: Now, to see the specifications of service, and check which Endpoint it is binded to, use the following command.
kubectl describe svc <name of the service>
Step 10: Now since we are using amazon ec2 instance, to fetch the webpage and check the output, use the following command.
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