Tag: stages

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Stages explained in Azure Pipelines – Azure DevOps

Stages on Azure devops can be a powerful tool when it comes to complex environments as you can divide the deployment process into different logical units. For example you could have different stages for different environments like Uat, Dev, Production or you could separate functionality for different products or technology stacks like FrontEnd, Backend, Mobile etc.

In this article we will examine the dependsOn keyword that creates dependencies between various stages and indicates which should run first and what will be the sequence.

Main scenario
We have an application that is created from various components/microservices. Those components need to be compiled in one or more binaries and be exported for release in our platform/hosting provider. In order to deploy our application we will need to first compile all those dependencies, export them and later on use them in the release tasks.

Example 1
In the below example we have starting point which will be some initialization for our environment. Then we continue with the build steps that will be the components A, B, C and then we need to produce the artifacts. The artifacts stage need to wait for all three components stages to be completed so we use dependsOn and provide as a list all the component stages. After the artifact stage we evaluate the result and if we have a success we deploy the application in a new stage otherwise we perform a rollback. Rollback and deploy application will be executed only if the condition of the stage is true so as to create a branching logic.

When you need to depend ON more than one stages you can provide those as a list

Code

trigger:
- none

pool:
  vmImage: ubuntu-latest

stages:
- stage: Stage_Starting_Point
  displayName: Starting point
  jobs:
  - job: Starting_point_Job
    displayName:  Starting_point_Job
    steps:
    - script: echo pre processing
      displayName: pre processing

- stage: Stage_Comp_A
  dependsOn: Stage_Starting_Point
  displayName: Stage Component A
  jobs:
  - job: Job_Comp_A
    displayName:  Job Component A
    steps:
    - script: echo building Component A
      displayName: build component A

- stage: Stage_Comp_B
  
  displayName: Stage Component B
  dependsOn: Stage_Starting_Point
  jobs:
  - job: Job_Comp_B
    displayName:  Job Component B
    steps:
    - script: echo building Component B
      displayName: build component B

- stage: Stage_Comp_C
  dependsOn: Stage_Starting_Point
  displayName: Stage Component C
  jobs:
  - job: Job_Comp_C
    displayName:  Job Component C
    steps:
    - script: echo building Component C
      displayName: build component C

- stage: Stage_Artifacts
  dependsOn: 
  - Stage_Comp_A
  - Stage_Comp_B
  - Stage_Comp_C
  displayName: Produce artifacts
  jobs:
  - job: Job_Artifacts
    displayName:  Job Artifacts
    steps:
    - script: echo producing artifacts
      displayName: producing artifacts

- stage: Stage_Deploy_Prod
  dependsOn: Stage_Artifacts
  condition: succeeded('Stage_Artifacts')
  displayName: Deploy application Prod
  jobs:
  - job: Job_Deploy_Prod
    displayName:  Job Deployment
    steps:
    - script: echo deploying
      displayName: deploying application Prod

- stage: Stage_Rollback
  dependsOn: Stage_Artifacts
  condition: failed('Stage_Artifacts')
  displayName: Rolling back
  jobs:
  - job: Job_Rollback
    displayName:  Job Rollback
    steps:
    - script: echo rolling back application
      displayName: roll back

Example 2
The second example will be the same as previous one with one small difference. After the deploy for the production environment we want to deploy also on the Disaster recovery environment. For this scenario we depend on production stage and also the rollback stage, but as we see from the output we have the final stage skipped.

You can specify the conditions under which each stage, job, or step runs. By default, a job or stage runs if it does not depend on any other job or stage, or if all of the jobs or stages that it depends on have completed and succeeded

As a result deploy application DR stage will run only if we remove the dependency from the roll back stage. As the rollback stage is skipped, the final stage is also skipped.

Code

trigger:
- none

pool:
  vmImage: ubuntu-latest

stages:
- stage: Stage_Starting_Point
  displayName: Starting point
  jobs:
  - job: Starting_point_Job
    displayName:  Starting_point_Job
    steps:
    - script: echo pre processing
      displayName: pre processing

- stage: Stage_Comp_A
  dependsOn: Stage_Starting_Point
  displayName: Stage Component A
  jobs:
  - job: Job_Comp_A
    displayName:  Job Component A
    steps:
    - script: echo building Component A
      displayName: build component A

- stage: Stage_Comp_B
  
  displayName: Stage Component B
  dependsOn: Stage_Starting_Point
  jobs:
  - job: Job_Comp_B
    displayName:  Job Component B
    steps:
    - script: echo building Component B
      displayName: build component B

- stage: Stage_Comp_C
  dependsOn: Stage_Starting_Point
  displayName: Stage Component C
  jobs:
  - job: Job_Comp_C
    displayName:  Job Component C
    steps:
    - script: echo building Component C
      displayName: build component C

- stage: Stage_Artifacts
  dependsOn: 
  - Stage_Comp_A
  - Stage_Comp_B
  - Stage_Comp_C
  displayName: Produce artifacts
  jobs:
  - job: Job_Artifacts
    displayName:  Job Artifacts
    steps:
    - script: echo producing artifacts
      displayName: producing artifacts

- stage: Stage_Deploy_Prod
  dependsOn: Stage_Artifacts
  condition: succeeded('Stage_Artifacts')
  displayName: Deploy application Prod
  jobs:
  - job: Job_Deploy_Prod
    displayName:  Job Deployment
    steps:
    - script: echo deploying
      displayName: deploying application Prod

- stage: Stage_Rollback
  dependsOn: Stage_Artifacts
  condition: failed('Stage_Artifacts')
  displayName: Rolling back
  jobs:
  - job: Job_Rollback
    displayName:  Job Rollback
    steps:
    - script: echo rolling back application
      displayName: roll back

- stage: Stage_Deploy_DR
  dependsOn: 
  - Stage_Rollback
  - Stage_Deploy_Prod
  displayName: Deploy application DR
  jobs:
  - job: Job_Deploy_DR
    displayName:  Job Deployment
    steps:
    - script: echo deploying
      displayName: deploying application DR

Microsoft Docs:
https://learn.microsoft.com/en-us/azure/devops/pipelines/process/conditions?view=azure-devops&tabs=yaml

Youtube video:

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Conditional execution for stage – Azure DevOps

Conditions on Azure DevOps provide the flexibility to execute logic based on different environments and setups. You can use the template expression to execute a specific stage based on a variable or parameter that is provided from user input.

Lets take for example the below scenario. We need to deploy on two different environments the uat and production. Logic for these environments is included in the template files that are located inside the repository under pipelines/templates folder.

Based on user input only the stage for the selected environment will be executed.

main.yml

trigger:
- none

parameters:
  - name: environment
    type: string
    values:
      - production
      - uat

pool:
  vmImage: ubuntu-latest

stages: 

- ${{ if eq(parameters.environment, 'production') }}:
  - template: pipelines/templates/production.yml
- ${{ elseif eq(parameters.environment, 'uat') }}:
  - template: pipelines/templates/uat.yml

production.yml

stages:
- stage: production
  displayName: running production deployment
  jobs:    
  - job: job1
    steps:
    - script: echo "inside production environment"  
      displayName: print message production task

uat.yml

stages:
- stage: uat
  displayName: running uat deployment
  jobs:    
  - job: job1
    steps:
      - script: echo "inside uat environment"
        displayName: print message uat task

Video tutorial on Youtube:

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Azure DevOps best practices – jobs and stages

Azure DevOps stages and jobs on build pipelines could save you a lot of minutes when you deal with more complex setups and heavy projects. In this article I will analyze how you can use jobs and stages to maximize performance and parallelism.

Lets take the starter pipeline example on Azure DevOps (code below). Although this is perfect for a single task scenario, it will execute tasks on a row without parallelism. The output of this pipeline will be two printed messages on your debug window.

trigger:
- main

pool:
  vmImage: ubuntu-latest

steps:
- script: echo Hello, world!
  displayName: 'Run a one-line script'

- script: |
    echo Add other tasks to build, test, and deploy your project.
    echo See https://aka.ms/yaml
  displayName: 'Run a multi-line script'

The two tasks that are included (script task) are located under steps. Those steps are part of a job that is not present as it is the only one.

The hierarchy of a pipeline can include the below: Stages -> Jobs -> Steps -> Tasks

On the starter pipeline two tasks are included under steps that belong on a single job. Jobs big advantage is that they can run in parallel. Take for example a big pipeline that includes a lot of jobs 30 and more. It would be time killing to wait all these jobs execute one by one. Keep in mind that one job can also fail and you will have to start the process again.

If you have a single job and all tasks are included on it, you have to use continueOnError if you do not want to stop the pipeline on a task failure.

The below example shows two jobs that can be executed on parallel based on your Azure DevOps configuration (this may include more costs on your subscription). As you can see the failure of the first job will not affect the second job that will be executed. If you are also eligible for parallel jobs, these can run simultaneously if you do not have constraints between them. 

pool:
  vmImage: ubuntu-latest

trigger: none
pr: none  

jobs:
  - job:
    displayName: Install npm and yarn tools 
    steps:
      - task: Npm@1
        displayName: Install npm
        inputs:
          command: 'install'
          workingDir: '$(Pipeline.Workspace)/s'

      - task: PowerShell@2
        displayName: Install yarn
        inputs:
          targetType: 'inline'
          script: |
            npm install -g yarn

  - job:
    displayName: build code and publish artifact
    steps:

      - task: PowerShell@2
        displayName: build code
        inputs:
          targetType: 'inline'
          script: |
            Write-Host "this task will be executed"
      - task: PowerShell@2
        displayName: this task will fail
        inputs:
          targetType: 'inline'
          script: |
            fail

Lets now examine the power of stages. The stage includes multiple jobs as you can see from the example below. A typical production environment will include stages for QA -> DEV -> Production deployments.

This approach big advantage is that you can rerun failed jobs separately and also rerun the whole stage in separation from each other. As a typical build pipeline may take a lot of minutes to complete by using stages you do not have to rerun the whole pipeline on a task failure.

trigger:
- none

pool:
  vmImage: ubuntu-latest

stages:
- stage: BuildApp
  displayName: Build Apps
  jobs:
  - job: BuildFrontendApp
    displayName: Build Frontend App
    steps:
    - script: echo building frontend app
      displayName: build frontend app
    - script: echo running unit tests for frontend app
      displayName: unit tests frontend

  - job: BuildBackendApp
    displayName: Build Backend App
    steps:
    - script: echo building backend app
      displayName: build backend app
    - script: echo running unit tests for backend app
      displayName: unit tests backend

- stage: DeployDev
  displayName: Deploy to DEV environment 
  jobs:
  - job: DeployFrontendDev
    displayName: Deploy frontend to DEV
    steps:
    - checkout: none
    - script: echo deploying frontend app to DEV
      displayName: deploy frontend app to DEV

  - job: DeployBackendDev
    displayName: Deploy backend to DEV
    steps:
    - checkout: none
    - script: echo deploying backend app to DEV
      displayName: deploy backend app to DEV

- stage: DeployProd
  displayName: Deploy to PROD environment
  jobs:
  - job: Failjob
    displayName: Running this job will fail
    steps:
    - checkout: none
    - script: kati
      displayName: deploy frontend app to PROD

  - job: DeployBackendProd
    displayName: Deploy backend to PROD
    steps:
    - checkout: none
    - script: echo deploying backend app to PROD
      displayName: deploy backend app to PROD

https://docs.microsoft.com/en-us/azure/devops/pipelines/licensing/concurrent-jobs?view=azure-devops&tabs=ms-hosted

Video tutorial on YouTube: