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How to use k8s and gitlab to achieve automated deployment

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2023-04-10 09:43:342328browse

With the increasing development of the Internet industry, software development has become more and more complex. How to complete software deployment quickly, efficiently and reliably has become an important issue that developers need to solve. Automated deployment is just such a solution, which plays an irreplaceable role in accelerating software rollout and improving deployment quality. This article will introduce how to use k8s and gitlab to achieve automated deployment.

1. What is k8s?

k8s (Kubernetes) is a container orchestration platform open sourced by Google in 2014. It can automate container orchestration and scheduling, helping developers deploy and manage containerized applications quickly, efficiently, and reliably. Kubernetes supports multiple container runtimes, including Docker and Rocket. It also provides automated deployment, self-healing, scaling and other functions.

2. What is Gitlab?

Gitlab is a git-based code hosting and collaboration platform that provides various tools needed by developers such as code warehouse, code review, CI/CD automated deployment, etc. Gitlab also provides a visual interface, making team collaboration more convenient.

3. Why should we combine k8s with Gitlab?

In traditional software development, developers usually need to complete software deployment manually. This method is prone to errors, and the deployment speed is slow and inefficient. Combining k8s with Gitlab can realize automated deployment and continuous delivery, greatly improving deployment speed and efficiency.

4. How to realize automated deployment?

1. Create a k8s cluster

First you need to create a k8s cluster on a server. You can use minikube, kubeadm and other tools to quickly create a k8s cluster. After creating the cluster, you need to create a namespace within the cluster to deploy your own applications.

2. Create a Gitlab project

Create a new project on Gitlab. Create a Gitlab CI/CD configuration file in the project. This file tells Gitlab how to perform CI/CD automated deployment. The sample configuration file is as follows:

image: docker:latest
services:
  - docker:dind
stages:
  - build
  - deploy
build:
  stage: build
  script:
    - docker build -t registry.gitlab.com/<group>/<project>:$CI_COMMIT_SHA .
    - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN registry.gitlab.com
    - docker push registry.gitlab.com/<group>/<project>:$CI_COMMIT_SHA
  tags:
    - docker
deploy:
  stage: deploy
  script:
    - kubectl set image deployments/<deployment_name> <container_name>=registry.gitlab.com/<group>/<project>:$CI_COMMIT_SHA -n <namespace>
  tags:
    - k8s

3. Deploy application

Deploy your own application in the k8s cluster. You can use kubectl create deployment to create a deployment object. Among them, the image and container name need to be specified. Examples are as follows:

kubectl create deployment <deployment_name> --image=registry.gitlab.com/<group>/<project>:$CI_COMMIT_SHA -n <namespace>

4. Create Service

Create a Service object and expose the deployment to external users. An example is as follows:

kubectl expose deployment <deployment_name> --type=LoadBalancer --port=80 --target-port=8080 -n <namespace>

Here we are using a Service based on the LoadBalancer type, which will create a public IP address outside the cluster for users to access the application.

5. Complete automated deployment

After completing the above steps, whenever new code is submitted to the Gitlab warehouse, Gitlab will automatically build the image based on the CI/CD configuration file and deploy it to In k8s cluster. Users can access the application through the public IP address exposed by the Service.

5. Summary

By combining k8s with Gitlab, automated deployment and continuous delivery can be achieved. This not only improves deployment speed and efficiency, but also avoids deployment errors caused by human error. Enterprises can choose which automated deployment solution to use based on their own needs to improve software development efficiency and quality.

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