Deploy to Kubernetes using Github Actions (including Slack notification)

Deploy to Kubernetes using Github Actions (including Slack notification)

Deploy to Kubernetes using Github Actions

In this guide we'll cover the full cycle of deploying to Kubernetes using Github Actions. Batteries included:

  • Kubernetes cluster running in AWS EKS
  • Docker images stored in AWS ECR
  • Bonus: notification to Slack

The Github workflow will be triggered at every commit on pull request, and its steps are described as follows:

  • git checkout
  • login to AWS ECR (credentials needed)
  • build Docker image
  • push Docker image to ECR
  • deploy to EKS using kubectl
  • send notification to Slack (needs webhook URL)

Github Action

Let's place our file under .github/workflows/release.yml. Then, we start by configuring the workflow trigger:

name: Release

on:
  pull_request:
    branches: [main]

Such trigger will run right after we open and at every commit on the pull request.

Next, we define the env variables that will be used across steps:

env:
  RELEASE_REVISION: "pr-${{ github.event.pull_request.number }}-${{ github.event.pull_request.head.sha }}"
  AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
  AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
  AWS_REGION: ${{ secrets.AWS_REGION }}
  KUBE_CONFIG_DATA: ${{ secrets.KUBE_CONFIG_DATA }}
  KUBE_NAMESPACE: production
  ECR_REPOSITORY: my-cool-application
  SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}

Env explained:

Now, let's start writing the job, after which we'll declare the steps:

jobs:                                            
  release:                                       
    name: Release                                
    runs-on: ubuntu-latest                       
    steps:                                       
     ... [steps be at this level]

Step - Cancel Previous Runs

This step instructs Github to cancel any current run for this job on this very repository.

- name: Cancel Previous Runs               
  uses: styfle/cancel-workflow-action@0.4.1
  with:                                    
    access_token: ${{ github.token }}

Step - Checkout

Performs the git checkout at this specific commit.

- name: Checkout                                  
  uses: actions/checkout@v2                       
  with:                                           
    ref: ${{ github.event.pull_request.head.sha }}

Step - Configure AWS credentials

This steps uses the AWS credentials defined in the env section.

- name: Configure AWS credentials                          
  uses: aws-actions/configure-aws-credentials@v1           
  with:                                                    
    aws-access-key-id: ${{ env.AWS_ACCESS_KEY_ID }}        
    aws-secret-access-key: ${{ env.AWS_SECRET_ACCESS_KEY }}
    aws-region: ${{ env.AWS_REGION }}

Step - Login to AWS ECR

Performs the login to AWS ECR, using the AWS credentials configured in the previous step.

- name: Login to Amazon ECR            
  id: login-ecr                        
  uses: aws-actions/amazon-ecr-login@v1

Step - Setup Docker buildx cache

These two steps are very important for the performance of building image. A few key notes:

  • Github Actions, like every CI runner in the cloud, is ephemeral, which means a new instance is virtualized every time we perform a new workflow job
  • Due to this ephemerality, we cannot rely on the native Docker layer caching

The above constraints would make our build time very slow, since every layer in Dockerfile will be evaluated across builds.

But thanks to this action, we can make use of the buildkit CLI to cache Docker layers. Then, in combination with native Github actions cache, we can rely on this cache strategy, thus optimizing build time.

- name: Set up Docker Buildx                             
  id: buildx                                             
  uses: docker/setup-buildx-action@master                
- name: Docker cache layers                              
  uses: actions/cache@v2                                 
  with:                                                  
    path: /tmp/.buildx-cache                             
    key: ${{ runner.os }}-single-buildx-${{ github.sha }}
    restore-keys: |                                      
      ${{ runner.os }}-single-buildx

Step - Build & Push the image to the registry

This step covers building the Docker image with buildx to optimize build time, and pushing it to AWS ECR, which was previously configured in "Login to Amazon ECR".

The example assumes we have a target named "release" in the Dockerfile.

- name: Build & Push Image                                                                                      
  env:                                                                                                          
    ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}                                                       
    RELEASE_IMAGE: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:${{ env.RELEASE_REVISION }}
  run: |
    docker buildx create --use

    docker buildx build \                                
      --cache-from=type=local,src=/tmp/.buildx-cache \   
      --cache-to=type=local,dest=/tmp/.buildx-cache-new \
      --tag ${{ env.RELEASE_IMAGE }} \                           
      --target release \                                 
      --push \                                           
      .                                                  

    rm -rf /tmp/.buildx-cache
    mv /tmp/.buildx-cache-new /tmp/.buildx-cache

Step explained:

  • docker buildx create --use: creates a new build context for buildx and sets it as the current context
  • docker buildx build ...: builds the image using the cache configured/restored in the previous steps "Docker cache layers". After build, it uploads the image to the pre-configured registry using the --push option
  • rm | mv ...: we have to renew the cache at every run, otherwise we may reach the 5GB limit of storage on Githb Actions

Step - Deploy to Kubernetes cluster

Once we have the image uploaded to the registry, we can send a command to kubernetes to perform the deploy.

- name: Deploy to Kubernetes cluster                                                                            
  uses: kodermax/kubectl-aws-eks@master                                                                         
  env:                                                                                                          
    RELEASE_IMAGE: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:${{ env.RELEASE_REVISION }}
  with:                                                                                                         
    args: set image deployment/my-pod app=${{ env.RELEASE_IMAGE }} --record -n $KUBE_NAMESPACE

Here we are using kubectl set image but it could be kubectl rollout or any other command, as needed.

Additionally, we can include a step to check the deployment:

- name: Verify Kubernetes deployment                               
  uses: kodermax/kubectl-aws-eks@master                            
  with:                                                            
    args: rollout status deploy my-pod -n $KUBE_NAMESPACE

Step - Slack notification

After a succeeded deployment, we can use this action to send a notification to Slack.

- name: Slack notification                                
  uses: rtCamp/action-slack-notify@master                 
  env:                                                    
    SLACK_CHANNEL: my_cool_channel                   
    SLACK_MESSAGE: 'Just deployed our cool application!'
    SLACK_TITLE: 'Deploy'                         
    SLACK_USERNAME: 'Some Bot'                           
    SLACK_ICON: "[icon URL]"
    SLACK_COLOR: '#228B22'                                
    SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK_URL }}       
    MSG_MINIMAL: true

Wrapping up

In this guide we configured the full cycle of building a Docker image, uploading it to a registry, performing the deployment to Kubernetes and sending a notification to Slack.

In the upcoming posts we'll see how to optimize build time of dependencies' installation inside the Docker image (a.k.a bundle install for Ruby developers), using a cache strategy that relies on AWS S3.

 
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