Specify jobs in your pipeline

Azure DevOps Services | Azure DevOps Server 2022 - Azure DevOps Server 2019

You can organize your pipeline into jobs. Every pipeline has at least one job. A job is a series of steps that run sequentially as a unit. In other words, a job is the smallest unit of work that can be scheduled to run.

To learn about the key concepts and components that make up a pipeline, see Key concepts for new Azure Pipelines users.

Azure Pipelines does not support job priority for YAML pipelines. To control when jobs run, you can specify conditions and dependencies.

Define a single job

YAML

In the simplest case, a pipeline has a single job. In that case, you don't have to explicitly use the job keyword unless you're using a template. You can directly specify the steps in your YAML file.

This YAML file has a job that runs on a Microsoft-hosted agent and outputs Hello world.

pool:
  vmImage: 'ubuntu-latest'
steps:
- bash: echo "Hello world"

You might want to specify more properties on that job. In that case, you can use the job keyword.

jobs:
- job: myJob
  timeoutInMinutes: 10
  pool:
    vmImage: 'ubuntu-latest'
  steps:
  - bash: echo "Hello world"

Your pipeline could have multiple jobs. In that case, use the jobs keyword.

jobs:
- job: A
  steps:
  - bash: echo "A"

- job: B
  steps:
  - bash: echo "B"

Your pipeline may have multiple stages, each with multiple jobs. In that case, use the stages keyword.

stages:
- stage: A
  jobs:
  - job: A1
  - job: A2

- stage: B
  jobs:
  - job: B1
  - job: B2

The full syntax to specify a job is:

- job: string  # name of the job, A-Z, a-z, 0-9, and underscore
  displayName: string  # friendly name to display in the UI
  dependsOn: string | [ string ]
  condition: string
  strategy:
    parallel: # parallel strategy
    matrix: # matrix strategy
    maxParallel: number # maximum number simultaneous matrix legs to run
    # note: `parallel` and `matrix` are mutually exclusive
    # you may specify one or the other; including both is an error
    # `maxParallel` is only valid with `matrix`
  continueOnError: boolean  # 'true' if future jobs should run even if this job fails; defaults to 'false'
  pool: pool # agent pool
  workspace:
    clean: outputs | resources | all # what to clean up before the job runs
  container: containerReference # container to run this job inside
  timeoutInMinutes: number # how long to run the job before automatically cancelling
  cancelTimeoutInMinutes: number # how much time to give 'run always even if cancelled tasks' before killing them
  variables: { string: string } | [ variable | variableReference ] 
  steps: [ script | bash | pwsh | powershell | checkout | task | templateReference ]
  services: { string: string | container } # container resources to run as a service container
  uses: # Any resources (repos or pools) required by this job that are not already referenced
    repositories: [ string ] # Repository references to Azure Git repositories
    pools: [ string ] # Pool names, typically when using a matrix strategy for the job

If the primary intent of your job is to deploy your app (as opposed to build or test your app), then you can use a special type of job called deployment job.

The syntax for a deployment job is:

- deployment: string        # instead of job keyword, use deployment keyword
  pool:
    name: string
    demands: string | [ string ]
  environment: string
  strategy:
    runOnce:
      deploy:
        steps:
        - script: echo Hi!

Although you can add steps for deployment tasks in a job, we recommend that you instead use a deployment job. A deployment job has a few benefits. For example, you can deploy to an environment, which includes benefits such as being able to see the history of what you've deployed.

Classic

To add jobs to your build pipeline, edit the pipeline on the Pipelines page. Select ... to add a job.

To add jobs to your release pipeline, edit the pipeline from Pipelines > Releases. View the stage tasks in the stage where you want to add your job. Select ... to add a job.

Types of jobs

Jobs can be of different types, depending on where they run.

YAML

• Agent pool jobs run on an agent in an agent pool.
• Server jobs run on the Azure DevOps Server.
• Container jobs run in a container on an agent in an agent pool. For more information about choosing containers, see Define container jobs.

Classic

• Agent pool jobs run on an agent in an agent pool.
• Server jobs run on the Azure DevOps Server.
• Deployment group jobs run on machines in a deployment group. These jobs are only available in a release pipeline. For more information about defining groups of target servers for deployment, see Deployment group jobs.

Agent pool jobs

These are the most common type of jobs and they run on an agent in an agent pool.

• When using Microsoft-hosted agents, each job in a pipeline gets a fresh agent.
• Use demands with self-hosted agents to specify what capabilities an agent must have to run your job. You may get the same agent for consecutive jobs, depending on whether there's more than one agent in your agent pool that matches your pipeline's demands. If there's only one agent in your pool that matches the pipeline's demands, the pipeline waits until this agent is available.

Note

Demands and capabilities are designed for use with self-hosted agents so that jobs can be matched with an agent that meets the requirements of the job. When using Microsoft-hosted agents, you select an image for the agent that matches the requirements of the job, so although it is possible to add capabilities to a Microsoft-hosted agent, you don't need to use capabilities with Microsoft-hosted agents.

YAML

pool:
  name: myPrivateAgents    # your job runs on an agent in this pool
  demands: agent.os -equals Windows_NT    # the agent must have this capability to run the job
steps:
- script: echo hello world

Or multiple demands:

pool:
  name: myPrivateAgents
  demands:
  - agent.os -equals Darwin
  - anotherCapability -equals somethingElse
steps:
- script: echo hello world

Classic

You have the option to specify demands in the pipeline, in the jobs, or both. If you specify demands in both the pipeline and in a job, the union of the two sets of demands is required for the system to select an agent.

Learn more about agent capabilities.

Server jobs

Tasks in a server job are orchestrated by and executed on the server (Azure Pipelines or TFS). A server job doesn't require an agent or any target computers. Only a few tasks are supported in a server job now. The maximum time for a server job is 30 days.

Agentless jobs supported tasks

Currently, only the following tasks are supported out of the box for agentless jobs:

• Delay task
• Invoke Azure Function task
• Invoke REST API task
• Manual Validation task
• Publish To Azure Service Bus task
• Query Azure Monitor Alerts task
• Query Work Items task

Because tasks are extensible, you can add more agentless tasks by using extensions. The default timeout for agentless jobs is 60 minutes.

YAML

The full syntax to specify a server job is:

jobs:
- job: string
  timeoutInMinutes: number
  cancelTimeoutInMinutes: number
  strategy:
    maxParallel: number
    matrix: { string: { string: string } }

  pool: server # note: the value 'server' is a reserved keyword which indicates this is an agentless job

You can also use the simplified syntax:

jobs:
- job: string
  pool: server # note: the value 'server' is a reserved keyword which indicates this is an agentless job

Classic

You add a server job in the editor by selecting '...' on the Pipeline channel in the Tasks tab of a pipeline. The properties for the server job are displayed when you select the job in the editor.

Dependencies

When you define multiple jobs in a single stage, you can specify dependencies between them. Pipelines must contain at least one job with no dependencies. By default Azure DevOps YAML pipeline jobs run in parallel unless the dependsOn value is set.

Note

Each agent can run only one job at a time. To run multiple jobs in parallel you must configure multiple agents. You also need sufficient parallel jobs.

YAML

The syntax for defining multiple jobs and their dependencies is:

jobs:
- job: string
  dependsOn: string
  condition: string

Example jobs that build sequentially:

jobs:
- job: Debug
  steps:
  - script: echo hello from the Debug build
- job: Release
  dependsOn: Debug
  steps:
  - script: echo hello from the Release build

Example jobs that build in parallel (no dependencies):

jobs:
- job: Windows
  pool:
    vmImage: 'windows-latest'
  steps:
  - script: echo hello from Windows
- job: macOS
  pool:
    vmImage: 'macOS-latest'
  steps:
  - script: echo hello from macOS
- job: Linux
  pool:
    vmImage: 'ubuntu-latest'
  steps:
  - script: echo hello from Linux

Example of fan out:

jobs:
- job: InitialJob
  steps:
  - script: echo hello from initial job
- job: SubsequentA
  dependsOn: InitialJob
  steps:
  - script: echo hello from subsequent A
- job: SubsequentB
  dependsOn: InitialJob
  steps:
  - script: echo hello from subsequent B

Example of fan-in:

jobs:
- job: InitialA
  steps:
  - script: echo hello from initial A
- job: InitialB
  steps:
  - script: echo hello from initial B
- job: Subsequent
  dependsOn:
  - InitialA
  - InitialB
  steps:
  - script: echo hello from subsequent

Classic

To add a new job, select '...' on the pipeline channel in the Tasks tab of the pipeline. The conditions and order of execution for a job are displayed when you select the job in the editor.

When you specify multiple jobs in a build pipeline, they run in parallel by default. You can specify the order in which jobs must execute by configuring dependencies between jobs. Job dependencies aren't supported in release pipelines. Multiple jobs in a release pipeline run in sequence.

For example, the pipeline shown below divides the overall release execution into separate execution jobs by using two agent jobs and a server job.

In the example above:

1. The tasks in the first job of the release run on an agent and, after this job is complete, the agent is released.

2. The server job contains a Manual Intervention task that runs on the Azure Pipelines or TFS. The job doesn't execute on, or require, an agent or any target servers. The Manual Intervention task displays its message and waits for a "resume" or "reject" response from the user. In this example, if the configured timeout is reached, the task will automatically reject the deployment (set the timeout in the control options section to zero if you do not want an automated response to be generated).

3. If the release is resumed, tasks in the third job run - possibly on a different agent. If the release is rejected, this job doesn't run and the release is marked as failed.

It's important to understand some of the consequences of phased execution:

• Each job might use different agents. Don't assume that the state from an earlier job is available during subsequent jobs.

• The Continue on Error and Always run options for tasks in each job don't have any effect on tasks in subsequent jobs. For example, setting Always run on a task at the end of the first job won't guarantee that tasks in subsequent jobs run.

Conditions

You can specify the conditions under which each job runs. By default, a job runs if it doesn't depend on any other job, or if all of the jobs that it depends on have completed and succeeded. You can customize this behavior by forcing a job to run even if a previous job fails or by specifying a custom condition.

YAML

Example to run a job based upon the status of running a previous job:

jobs:
- job: A
  steps:
  - script: exit 1

- job: B
  dependsOn: A
  condition: failed()
  steps:
  - script: echo this will run when A fails

- job: C
  dependsOn:
  - A
  - B
  condition: succeeded('B')
  steps:
  - script: echo this will run when B runs and succeeds

Example of using a custom condition:

jobs:
- job: A
  steps:
  - script: echo hello

- job: B
  dependsOn: A
  condition: and(succeeded(), eq(variables['build.sourceBranch'], 'refs/heads/main'))
  steps:
  - script: echo this only runs for master

You can specify that a job run based on the value of an output variable set in a previous job. In this case, you can only use variables set in directly dependent jobs:

jobs:
- job: A
  steps:
  - script: "echo '##vso[task.setvariable variable=skipsubsequent;isOutput=true]false'"
    name: printvar

- job: B
  condition: and(succeeded(), ne(dependencies.A.outputs['printvar.skipsubsequent'], 'true'))
  dependsOn: A
  steps:
  - script: echo hello from B

Classic

Use the Run this job option on an agent or server job to run the tasks only when specific conditions are met. Select a predefined condition, or select "custom" and enter an expression that evaluates to true or false. Nested expressions can be used, and the expressions can access variables available in the release pipeline.

Timeouts

To avoid taking up resources when your job is unresponsive or waiting too long, it's a good idea to set a limit on how long your job is allowed to run. Use the job timeout setting to specify the limit in minutes for running the job. Setting the value to zero means that the job can run:

• Forever on self-hosted agents
• For 360 minutes (6 hours) on Microsoft-hosted agents with a public project and public repository
• For 60 minutes on Microsoft-hosted agents with a private project or private repository (unless additional capacity is paid for)

The timeout period begins when the job starts running. It doesn't include the time the job is queued or is waiting for an agent.

YAML

The timeoutInMinutes allows a limit to be set for the job execution time. When not specified, the default is 60 minutes. When 0 is specified, the maximum limit is used (described above).

The cancelTimeoutInMinutes allows a limit to be set for the job cancel time when the deployment task is set to keep running if a previous task failed. When not specified, the default is 5 minutes. The value should be in range from 1 to 35790 minutes.

jobs:
- job: Test
  timeoutInMinutes: 10 # how long to run the job before automatically cancelling
  cancelTimeoutInMinutes: 2 # how much time to give 'run always even if cancelled tasks' before stopping them

Classic

Select the job and then specify the timeout value.

On the Options tab, you can specify default values for all jobs in the pipeline. If you specify a nonzero value for the job timeout, then it overrides any value that is specified in the pipeline options. If you specify a zero value, then the timeout value from the pipeline options is used. If the pipeline value is also set to zero, then there's no timeout.

Timeouts have the following level of precedence.

1. On Microsoft-hosted agents, jobs are limited in how long they can run based on project type and whether they're run using a paid parallel job. When the Microsoft-hosted job timeout interval elapses, the job is terminated. On Microsoft-hosted agents, jobs can't run longer than this interval, regardless of any job level timeouts specified in the job.
2. The timeout configured at the job level specifies the maximum duration for the job to run. When the job level timeout interval elapses, the job is terminated. If the job is run on a Microsoft-hosted agent, setting the job level timeout to an interval greater than the built-in Microsoft-hosted job level timeout has no effect and the Microsoft-hosted job timeout is used.
3. You can also set the timeout for each task individually - see task control options. If the job level timeout interval elapses before the task completes, the running job is terminated, even if the task is configured with a longer timeout interval.

Multi-job configuration

From a single job you author, you can run multiple jobs on multiple agents in parallel. Some examples include:

• Multi-configuration builds: You can build multiple configurations in parallel. For example, you could build a Visual C++ app for both debug and release configurations on both x86 and x64 platforms. For more information, see Visual Studio Build - multiple configurations for multiple platforms.

• Multi-configuration deployments: You can run multiple deployments in parallel, for example, to different geographic regions.

• Multi-configuration testing: You can run test multiple configurations in parallel.

• Multi-configuration will always generate at least one job, even if a multi-configuration variable is empty.

YAML

The matrix strategy enables a job to be dispatched multiple times, with different variable sets. The maxParallel tag restricts the amount of parallelism. The following job is dispatched three times with the values of Location and Browser set as specified. However, only two jobs run at the same time.

jobs:
- job: Test
  strategy:
    maxParallel: 2
    matrix: 
      US_IE:
        Location: US
        Browser: IE
      US_Chrome:
        Location: US
        Browser: Chrome
      Europe_Chrome:
        Location: Europe
        Browser: Chrome

Note

Matrix configuration names (like US_IE above) must contain only basic Latin alphabet letters (A-Z, a-z), numbers, and underscores (_). They must start with a letter. Also, they must be 100 characters or less.

It's also possible to use output variables to generate a matrix. This can be handy if you need to generate the matrix using a script.

matrix accepts a runtime expression containing a stringified JSON object. That JSON object, when expanded, must match the matrixing syntax. In the example below, we've hard-coded the JSON string, but it could be generated by a scripting language or command-line program.

jobs:
- job: generator
  steps:
  - bash: echo "##vso[task.setVariable variable=legs;isOutput=true]{'a':{'myvar':'A'}, 'b':{'myvar':'B'}}"
    name: mtrx
  # This expands to the matrix
  #   a:
  #     myvar: A
  #   b:
  #     myvar: B
- job: runner
  dependsOn: generator
  strategy:
    matrix: $[ dependencies.generator.outputs['mtrx.legs'] ]
  steps:
  - script: echo $(myvar) # echos A or B depending on which leg is running

Classic

To run multiple jobs using multi-configuration option, you identify a variable named a multiplier, and specify a list of values for that multiplier. A separate job is run for each value in the list. To use multipliers for build or deployment, you must:

• Define one or more variables on the Variables tab of the pipeline or in a variable group. Each variable, known in this context as a multiplier variable, must be defined as a comma-delimited list of the values you want to pass individually to the agents.

• Enter the name of the multiplier variable, without the $ and parentheses, as the value of the Multipliers parameter. Using a secret variable as a multiplier variable isn't supported.

• If you want to execute the job for more than one multiplier variable, enter the variable names as a comma-delimited list - omitting the $ and parentheses for each one.

• If you want to limit the number of agents used during the deployment to a number less than you've configured for your subscription, enter that value as the Maximum number of agents parameter.

For example, you might define two variables named Location and Browser as follows:

• Location = US,Europe
• Browser = IE,Chrome,Edge,Firefox

The following configuration executes the deployment eight times using a maximum of four agents at any one time:

• Multipliers = Location,Browser
• Maximum number of agents = 4

With multi-configuration you can run multiple jobs, each with a different value for one, or more variables (multipliers). If you want to run the same job on multiple agents, then you can use multi-agent option of parallelism. The test slicing example above can be accomplished through multi-agent option.

Slicing

An agent job can be used to run a suite of tests in parallel. For example, you can run a large suite of 1000 tests on a single agent. Or, you can use two agents and run 500 tests on each one in parallel.

To apply slicing, the tasks in the job should be smart enough to understand the slice they belong to.

The Visual Studio Test task is one such task that supports test slicing. If you installed multiple agents, you can specify how the Visual Studio Test task runs in parallel on these agents.

YAML

The parallel strategy enables a job to be duplicated many times. Variables System.JobPositionInPhase and System.TotalJobsInPhase are added to each job. The variables can then be used within your scripts to divide work among the jobs. See Parallel and multiple execution using agent jobs.

The following job is dispatched five times with the values of System.JobPositionInPhase and System.TotalJobsInPhase set appropriately.

jobs:
- job: Test
  strategy:
    parallel: 5

Classic

Specify the multi-agent option on an agent job to apply slicing. The job is dispatched as many times as the number of agents you specify, and the variables System.JobPositionInPhase and System.TotalJobsInPhase are automatically set in each job.

Job variables

If you're using YAML, variables can be specified on the job. The variables can be passed to task inputs using the macro syntax $(variableName), or accessed within a script using the stage variable.

YAML

Here's an example of defining variables in a job and using them within tasks.

variables:
  mySimpleVar: simple var value
  "my.dotted.var": dotted var value
  "my var with spaces": var with spaces value

steps:
- script: echo Input macro = $(mySimpleVar). Env var = %MYSIMPLEVAR%
  condition: eq(variables['agent.os'], 'Windows_NT')
- script: echo Input macro = $(mySimpleVar). Env var = $MYSIMPLEVAR
  condition: in(variables['agent.os'], 'Darwin', 'Linux')
- bash: echo Input macro = $(my.dotted.var). Env var = $MY_DOTTED_VAR
- powershell: Write-Host "Input macro = $(my var with spaces). Env var = $env:MY_VAR_WITH_SPACES"

Classic

Job variables aren't yet supported in the web editor.

For information about using a condition, see Specify conditions.

Workspace

When you run an agent pool job, it creates a workspace on the agent. The workspace is a directory in which it downloads the source, runs steps, and produces outputs. The workspace directory can be referenced in your job using Pipeline.Workspace variable. Under this, various subdirectories are created:

• Build.SourcesDirectory is where tasks download the application's source code.
• Build.ArtifactStagingDirectory is where tasks download artifacts needed for the pipeline or upload artifacts before they're published.
• Build.BinariesDirectory is where tasks write their outputs.
• Common.TestResultsDirectory is where tasks upload their test results.

YAML

The $(Build.ArtifactStagingDirectory) and $(Common.TestResultsDirectory) are always deleted and recreated prior to every build.

When you run a pipeline on a self-hosted agent, by default, none of the subdirectories other than $(Build.ArtifactStagingDirectory) and $(Common.TestResultsDirectory) are cleaned in between two consecutive runs. As a result, you can do incremental builds and deployments, provided that tasks are implemented to make use of that. You can override this behavior using the workspace setting on the job.

Important

The workspace clean options are applicable only for self-hosted agents. Jobs are always run on a new agent with Microsoft-hosted agents.

- job: myJob
  workspace:
    clean: outputs | resources | all # what to clean up before the job runs

When you specify one of the clean options, they're interpreted as follows:

• outputs: Delete Build.BinariesDirectory before running a new job.
• resources: Delete Build.SourcesDirectory before running a new job.
• all: Delete the entire Pipeline.Workspace directory before running a new job.

  jobs:
  - deployment: MyDeploy
    pool:
      vmImage: 'ubuntu-latest'
    workspace:
      clean: all
    environment: staging

Note

Depending on your agent capabilities and pipeline demands, each job may be routed to a different agent in your self-hosted pool. As a result, you may get a new agent for subsequent pipeline runs (or stages or jobs in the same pipeline), so not cleaning is not a guarantee that subsequent runs, jobs, or stages will be able to access outputs from previous runs, jobs, or stages. You can configure agent capabilities and pipeline demands to specify which agents are used to run a pipeline job, but unless there is only a single agent in the pool that meets the demands, there is no guarantee that subsequent jobs will use the same agent as previous jobs. For more information, see Specify demands.

In addition to workspace clean, you can also configure cleaning by configuring the Clean setting in the pipeline settings UI. When the Clean setting is true, which is also its default value, it's equivalent to specifying clean: true for every checkout step in your pipeline. When you specify clean: true, you'll run git clean -ffdx followed by git reset --hard HEAD before git fetching. To configure the Clean setting:

1. Edit your pipeline, choose ..., and select Triggers.

   

2. Select YAML, Get sources, and configure your desired Clean setting. The default is true.

   

Classic

When you run a pipeline on a self-hosted agent, by default, none of the subdirectories are cleaned in between two consecutive runs. As a result, you can run incremental builds and deployments, provided that tasks are implemented to do that. However, you can override this behavior using the Clean build option under Get sources task. The options vary depending on the type of repository that you use.

• GitHub
• Azure Repos Git
• TFVC

Artifact download

This example YAML file publishes the artifact WebSite and then downloads the artifact to $(Pipeline.Workspace). The Deploy job only runs if the Build job is successful.

YAML

# test and upload my code as an artifact named WebSite
jobs:
- job: Build
  pool:
    vmImage: 'ubuntu-latest'
  steps:
  - script: npm test
  - task: PublishBuildArtifacts@1
    inputs:
      pathtoPublish: '$(System.DefaultWorkingDirectory)'
      artifactName: WebSite

# download the artifact and deploy it only if the build job succeeded
- job: Deploy
  pool:
    vmImage: 'ubuntu-latest'
  steps:
  - checkout: none #skip checking out the default repository resource
  - task: DownloadBuildArtifacts@0
    displayName: 'Download Build Artifacts'
    inputs:
      artifactName: WebSite
      downloadPath: $(Pipeline.Workspace)

  dependsOn: Build
  condition: succeeded()

Classic

In a release pipeline, you may choose to skip the download of artifacts during the job execution. Use this option if you want to implement your own custom logic for downloading artifacts by using tasks, or if the tasks in a particular job don't rely on the artifacts.

Alternatively, you can choose to download specific artifacts during the job execution in a release. Use this option if the tasks in a particular job rely on only specific artifacts.

For information about using dependsOn and condition, see Specify conditions.

Access to OAuth token

You can allow scripts running in a job to access the current Azure Pipelines or TFS OAuth security token. The token can be used to authenticate to the Azure Pipelines REST API.

YAML

The OAuth token is always available to YAML pipelines. It must be explicitly mapped into the task or step using env. Here's an example:

steps:
- powershell: |
    $url = "$($env:SYSTEM_TEAMFOUNDATIONCOLLECTIONURI)$env:SYSTEM_TEAMPROJECTID/_apis/build/definitions/$($env:SYSTEM_DEFINITIONID)?api-version=4.1-preview"
    Write-Host "URL: $url"
    $pipeline = Invoke-RestMethod -Uri $url -Headers @{
      Authorization = "Bearer $env:SYSTEM_ACCESSTOKEN"
    }
    Write-Host "Pipeline = $($pipeline | ConvertTo-Json -Depth 100)"
  env:
    SYSTEM_ACCESSTOKEN: $(system.accesstoken)

Classic

Select the Allow scripts to access OAuth token option in the control options for the job. The token is available as the environment variable SYSTEM_ACCESSTOKEN.

What's next

• Deployment group jobs
• Conditions