{"componentChunkName":"component---src-templates-tag-js","path":"/tags/kubernetes/","result":{"data":{"site":{"siteMetadata":{"title":"LoginRadius Blog"}},"allMarkdownRemark":{"totalCount":2,"edges":[{"node":{"fields":{"slug":"/engineering/rest-api-kubernetes/"},"html":"

This blog will help you get started on deploying your REST API in Kubernetes. First, we'll set up a local Kubernetes cluster, then create a simple API to deploy.

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There are already a lot of free resources available explaining basic Kubernetes concepts, so go check those out first if you haven't already. This blog is intended for beginners but assumes you already have a basic understanding of Kubernetes and Docker concepts.

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1. Set Up Local Kubernetes

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There's a couple options for running Kubernetes locally, with the most popular ones including minikube, k3s, kind, microk8s. In this guide, any of these will work, but we will be using k3s because of the lightweight installation.

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Install k3d, which is a utility for running k3s. k3s will be running in Docker, so make sure you have that installed as well. We used k3d v4.0 in this blog.

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curl -s https://raw.githubusercontent.com/rancher/k3d/main/install.sh | bash
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Set up a cluster named test:

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k3d cluster create test -p "80:80@loadbalancer"
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Optionally, check that your kubeconfig got updated and the current context is correct:

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kubectl config view\nkubectl config current-context
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Optionally, confirm that k3s is running in Docker. There should be two containers up, one for k3s and the other for load balancing:

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docker ps
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Make sure that all the pods are running. If they are stuck in pending status, it may be that there is not enough disk space on your machine. You can get more information by using the describe command:

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kubectl get pods -A\nkubectl describe pods -A
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There's a lot of kubectl commands you can try, so I recommend checking out the list of resources and being aware of their short names:

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kubectl api-resources
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2. Create a Simple API

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We will create a simple API using Express.js.

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Set up the project:

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mkdir my-backend-api && cd my-backend-api\ntouch server.js\nnpm init\nnpm i express --save
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// server.js\nconst express = require("express");\nconst app = express();\n\napp.get("/user/:id", (req, res) => {\n  const id = req.params.id;\n  res.json({\n    id,\n    name: `John Doe #${id}`\n  });\n});\n\napp.listen(80, () => {\n  console.log("Server running on port 80");\n});
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Optionally, you can try running it if you have Node.js installed and test the endpoint /user/{id} with curl:

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node server.js\n\n// request:\ncurl http://localhost:80/user/123\n\n// response: {"id":"123","name":"John Doe #123"}
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Next, add a Dockerfile and .dockerignore:

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// Dockerfile\nFROM node:12\n\nWORKDIR /usr/src/app\nCOPY package*.json ./\nRUN npm i\nCOPY . .\n\nEXPOSE 80\nCMD ["node", "server.js"]
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// .dockerignore\nnode_modules
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Then, build the image and push it to the Docker Hub registry:

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docker build -t <YOUR_DOCKER_ID>/my-backend-api .\ndocker push <YOUR_DOCKER_ID>/my-backend-api
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3. Deploy

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Now, we deploy the image to our local Kubernetes cluster. We use the default namespace.

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Create a deployment:

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kubectl create deploy my-backend-api --image=andyy5/my-backend-api
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kubectl create -f deployment.yaml
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// deployment.yaml\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n  name: my-backend-api\n  labels:\n    app: my-backend-api\nspec:\n  replicas: 1\n  selector:\n    matchLabels:\n      app: my-backend-api\n  template:\n    metadata:\n      labels:\n        app: my-backend-api\n    spec:\n      containers:\n      - name: my-backend-api\n        image: andyy5/my-backend-api
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Create a service:

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kubectl expose deploy my-backend-api --type=ClusterIP --port=80
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kubectl create -f service.yaml
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// service.yaml\napiVersion: v1\nkind: Service\nmetadata:\n  name: my-backend-api\n  labels:\n    app: my-backend-api\nspec:\n  type: ClusterIP\n  ports:\n  - port: 80\n    protocol: TCP\n    targetPort: 80\n  selector:\n    app: my-backend-api
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Check that everything was created and the pod is running:

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kubectl get deploy -A\nkubectl get svc -A\nkubectl get pods -A
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Once the pod is running, the API is accessible within the cluster only. One quick way to verify the deployment from our localhost is by doing port forwarding:

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kubectl port-forward my-backend-api-84bb9d79fc-m9ddn 3000:80
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curl http://localhost:3000/user/123
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To correctly manage external access to the services in a cluster, we need to use ingress. Close the port-forwarding and let's expose our API by creating an ingress resource.

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Create an Ingress resource with the following YAML file:

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kubectl create -f ingress.yaml\nkubectl get ing -A
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// ingress.yaml\napiVersion: networking.k8s.io/v1\nkind: Ingress\nmetadata:\n  name: my-backend-api\n  annotations:\n    ingress.kubernetes.io/ssl-redirect: "false"\nspec:\n  rules:\n  - http:\n      paths:\n      - path: /user/\n        pathType: Prefix\n        backend:\n          service:\n            name: my-backend-api\n            port:\n              number: 80
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curl http://localhost:80/user/123
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If you want to learn more on how to deploy using a managed Kubernetes service in the cloud, such as Google Kubernetes Engine, then check out the excellent guides on the official Kubernetes docs.

\n","frontmatter":{"date":"February 03, 2021","updated_date":null,"title":"How to Deploy a REST API in Kubernetes","tags":["Kubernetes"],"coverImage":{"childImageSharp":{"fluid":{"aspectRatio":1.492537313432836,"src":"/static/efa8ecb370a0a94f380c24981ede2913/ee604/cover.png","srcSet":"/static/efa8ecb370a0a94f380c24981ede2913/69585/cover.png 200w,\n/static/efa8ecb370a0a94f380c24981ede2913/497c6/cover.png 400w,\n/static/efa8ecb370a0a94f380c24981ede2913/ee604/cover.png 800w,\n/static/efa8ecb370a0a94f380c24981ede2913/f3583/cover.png 1200w","sizes":"(max-width: 800px) 100vw, 800px"}}},"author":{"id":"Andy Yeung","github":null,"avatar":null}}}},{"node":{"fields":{"slug":"/engineering/understanding-kubernetes/"},"html":"

Kubernetes is a standard container orchestration open-source platform, that is, for managing applications constructed from several, often self-contained runtimes called containers.

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Since the Docker containerization project launched in 2013, containers have become increasingly common, but massive distributed containerized apps can become increasingly difficult to coordinate.

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Kubernetes became a central part of the container revolution by making containerized systems significantly more straightforward to handle at scale.

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This blog targets the way modern applications are deployed and consumed. It introduces the importance of Kubernetes, a container Orchestration tool on a basic level, and how it is making Developers more convenient.

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What is Kubernetes

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Kubernetes (also known as k8s) is a container orchestration tool widely used to deploy and update the application without any downtime. It enables auto-scaling of resources in case of an increase/decrease in requests to the server.\nAlso, it provides intelligent management, allocation of resources, and services, which saves a lot of money for us.

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Benefits of Kubernetes

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By using Kubernetes, developers can build cloud-native applications with Kubernetes as a runtime framework. Patterns are the tools a developer of Kubernetes requires to construct applications and services based on containers.

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Here are a few benefits of Kubernetes:

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Kubernetes vs. Docker

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Often mistaken as a choice between one or the other, Kubernetes and Docker for running containerized applications are different but complementary technologies.

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Docker lets you put everything you need into a box that can be stored and opened when and where it is required to run your application. It would help if you had a way to handle them once you start boxing up your applications, and that's what Kubernetes does.

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  1. With or without the Docker, Kubernetes can be used.
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  2. The distinction between Docker and Kubernetes refers to the role each plays in your applications' containerization and execution.
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  3. Docker is an open industry standard to package and distribute container applications.
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  4. To deploy, manage, and scale containerized software, Kubernetes uses Docker.
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Why do you need Kubernetes?

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Kubernetes will assist you in deploying and managing containerized, legacy, and cloud-native applications, as well as microservice, refactors.

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To meet evolving business demands, the development team needs to build new products and services rapidly. The cloud-native architecture starts with container microservices, allowing faster development and making it easier to transform and optimize existing software.

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That is where Kubernetes comes into the picture. It handles all the above requirements effectively and reduces a lot of burden from the developer's shoulders.

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Technical Aspect

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Kubernetes was designed as a highly available cluster of computers connected to work as a single unit. This abstraction level allows us to deploy the application without thinking much about the machines on which the application would run. Kubernetes' role is to automate the distribution of application containers on computer clusters efficiently.

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A typical computer cluster in Kubernetes consists of:

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The Master is responsible for managing the cluster. The master nodes will coordinate all the activities in your cluster, such as scheduling applications, maintaining their desired state, scaling applications, and rolling new updates. Whereas a Node is a VM or a physical computer that is used as a worker machine in a Kubernetes cluster to actually, run the application

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A master node contains processes that continuously watch the deployment for any CPU or Memory requirement and smartly allocated it in the form of new pods to auto-scale the application, which handles the vast amount of requests coming to the server.

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Most of the current cloud services like Amazon Web Service(AWS), Microsoft Azure, and Google Cloud come with automatic support for Kubernetes to provide ease in deploying and maintaining application containers.

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A Kubernetes cluster can be deployed on either virtual or physical machines. A software named Minikube can be installed on your local machine that creates a VM on your local machine and deploys a simple cluster containing only one node.\nMinikube provides a CLI through which we can execute commands & simulate a small-scale Kubernetes cluster.

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Some of the basic Kubernetes CLI commands are:

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Conclusion

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There are many benefits of learning Kubernetes as it releases a lot of deployment stress from the developer's shoulders. It helps in auto-scaling, intelligent utilization of available resources, and enables low downtime for maintenance.

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Without k8s, a lot of human effort and money is gone into these things, but with the help of k8s, everything can be automated.

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Since K8s is a vast technology, it is impossible to wrap all things in just one blog. To simplify things, I will be publishing more blogs soon related to k8s Deployments, ReplicaSets, Services, Ingress Controller, Master and Worker Processes, Kubectl, etc. Stay connected and keep learning.

\n","frontmatter":{"date":"October 26, 2020","updated_date":null,"title":"What is Kubernetes? - A Basic Guide","tags":["Kubernetes","Deployment","Distributed Systems"],"coverImage":{"childImageSharp":{"fluid":{"aspectRatio":1.5037593984962405,"src":"/static/c0eaad61b9cb15dfe35f7a6d2d0f665a/ee604/image3.png","srcSet":"/static/c0eaad61b9cb15dfe35f7a6d2d0f665a/69585/image3.png 200w,\n/static/c0eaad61b9cb15dfe35f7a6d2d0f665a/497c6/image3.png 400w,\n/static/c0eaad61b9cb15dfe35f7a6d2d0f665a/ee604/image3.png 800w,\n/static/c0eaad61b9cb15dfe35f7a6d2d0f665a/f3583/image3.png 1200w","sizes":"(max-width: 800px) 100vw, 800px"}}},"author":{"id":"Prabhat Kumar","github":"prabhatkgupta","avatar":null}}}}]}},"pageContext":{"tag":"Kubernetes"}},"staticQueryHashes":["1171199041","1384082988","2100481360","23180105","528864852"]}