continuous deployment (cd) is an advanced part of the devops pipeline. it automatically pushes validated changes from version control straight to production. theres no need for manual approval in this process. unlike continuous delivery, which waits for human approval, continuous deployment uses automation to ensure faster releases.

key features of continuous deployment

following are the key features continuous deployment −

fully automated processes for building, testing, and deploying code.
strong testing and validation mechanisms like unit, integration, and performance testing.
tools and scripts that can handle infrastructure and application updates smoothly.

continuous deployment vs. continuous integration vs. continuous delivery

the following table highlights the key differences among continuous deployment, continuous integration, and continuous delivery −

aspect	continuous integration	continuous delivery	continuous deployment
definition	automates code changes into a shared repository.	keeps code always ready for release after passing tests.	fully automates code deployment to production, no manual step needed.
focus area	code merging and testing.	prepares builds for deployment.	automates deployment to production.
automation level	partly automated (builds and tests).	almost fully automated, but with manual approval for production.	fully automated deployment end-to-end.
key activities	merging code into main branch. running unit tests.	creating builds automatically. testing app readiness. packaging builds.	deploying to production. monitoring live systems. rolling back when problems occur.
tools	jenkins, github actions, gitlab ci/cd, circleci.	argocd, spinnaker, aws codepipeline.	kubernetes, terraform, jenkins (with plugins), gitops tools.
risk level	low, since it focuses on testing and code merging.	moderate, as production releases need manual approval.	high, as deployments go straight to production without human checks.
benefits	finds integration issues early. promotes frequent commits.	creates deployable builds. reduces delays for deployment readiness.	speeds up delivery. provides quick feedback. cuts down time-to-market.
challenges	needs high test coverage.	requires strong test and build automation. needs team coordination for releases.	requires strict automated testing. risky without proper validation for faulty deployments.

setting up a continuous deployment pipeline

we can set up a continuous deployment (cd) pipeline by automating the process of building, testing, and deploying code. let's go step by step to create this workflow.

step 1: choose and configure a version control system (vcs)

a version control system like git helps us manage code changes. when developers push their updates to the repository, the cd pipeline starts automatically.

example: create a new github repository −

# initialize a git repository locally
git init
  
# add a remote repository
git remote add origin https://github.com/user/project.git
  
# commit and push code
git add .  
git commit -m "initial commit"  
git push -u origin main

we can connect this repository to a ci/cd tool like jenkins or github actions using webhooks.

step 2: set up a ci / cd tool

we need a ci/cd tool like github actions, jenkins, or gitlab ci/cd. this tool automates steps like building, testing, and deploying.

example: (github actions configuration)

create a .github/workflows/deployment.yml file in the repository −

name: ci/cd pipeline
on:
  push:
    branches:
      - main
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - name: checkout code
        uses: actions/checkout@v2
      - name: set up node.js
        uses: actions/setup-node@v3
        with:
          node-version: 16
      - name: install dependencies
        run: npm install
      - name: run tests
        run: npm test
      - name: build application
        run: npm run build
  deploy:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: deploy to server
        run: |
          scp -r ./build user@your-server:/var/www/app

this pipeline does two things −

it builds and tests the app every time we push code to the main
it deploys the app to a server using scp.

step 3: automate testing

automated testing is important to ensure the code is stable. we can add unit tests, integration tests, and end-to-end tests.

example (jest for unit tests)

add a test script to package.json −

"scripts": {
  "test": "jest"
}

run these tests automatically in the pipeline using −

npm test

step 4: configure deployment automation

deployment automation helps move tested code to production without manual steps. tools like terraform make this process smooth.

example (terraform for aws deployment) −

provider "aws" {
   region = "us-west-2"
}
resource "aws_s3_bucket" "static_site" {
   bucket = "my-static-site"
   acl    = "public-read"
}
resource "aws_s3_bucket_object" "index" {
   bucket = aws_s3_bucket.static_site.bucket
   key    = "index.html"
   source = "build/index.html"
   content_type = "text/html"
}

run terraform commands to deploy −

terraform init  
terraform apply

step 5: integrate infrastructure as code (iac)

iac tools like kubernetes help us manage environments.

example (kubernetes deployment) −

write a deployment.yaml file −

apiversion: apps/v1
kind: deployment
metadata:
  name: app-deployment
spec:
  replicas: 3
  selector:
    matchlabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: app
        image: my-app-image:latest
        ports:
        - containerport: 80

apply this configuration.

kubectl apply -f deployment.yaml

step 6: implement monitoring and feedback loops

we need monitoring tools to check the app's performance. tools like prometheus and grafana are helpful.

example (prometheus configuration) −

scrape_configs:
  - job_name: "app"
    static_configs:
      - targets: ["localhost:9090"]

step 7: test the end-to-end pipeline

finally, push changes to the repository and make sure everything works:

build − compiles the application.
test − runs tests to find issues.
deploy − pushes the app to the server.

by combining version control, ci/cd tools, testing, and iac, we can make sure our pipeline is smooth and reliable. this helps deliver updates faster and with fewer errors.

implementing deployment strategies

the following table highlights the continuous deployment strategies in devops −

deployment strategy	description	use case / benefit
blue-green deployments	blue-green deployment uses two environments (blue and green). one environment (blue) runs the current version of the app, and the other (green) runs the new version. when the new version is ready, we switch the traffic to green. the blue environment stays for rollback if needed.	this method helps avoid downtime during deployment. it is great for apps that need zero downtime and fast rollback. it also ensures both environments are the same for reliable testing.
canary releases	canary releases roll out the new version to a small group of users first. we monitor the performance and issues of the new version. if everything works fine, we release it to more users.	this helps reduce risk. it lets us test the new version on a small group before going live. it's good for features that need testing in real user conditions.
rolling updates	rolling updates update the application on a few servers at a time. this ensures that some servers are always running the app. as we deploy new versions, old ones are gradually shut down. we continue this until all servers are updated.	this strategy reduces the risk of downtime. it's helpful when we need non-stop deployment, especially for apps that need to be always available.
feature toggles and flags	feature toggles (or feature flags) allow us to turn features on or off in the codebase without redeploying. it helps us release incomplete or experimental features and control them dynamically.	this is useful for turning features on/off quickly without deployment. it helps in a/b testing, managing feature rollouts, and working on different versions of features at the same time.

conclusion

in this chapter, we explained how to set up reliable deployment pipelines. we also looked at strategies like blue-green and canary releases. we discussed how to ensure security and compliance using automated scans and vulnerability checks.

by using these techniques and tools, development teams can make deployment processes smoother, reduce downtime, keep security high, and scale applications easily. this will help speed up the software delivery cycle and improve the overall operational performance.

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