Chapter 2. Understanding OpenShift Pipelines

Task resources are the building blocks of a pipeline and consist of sequentially executed steps. It is essentially a function of inputs and outputs. A task can run individually or as a part of the pipeline. Tasks are reusable and can be used in multiple pipelines.

Steps are a series of commands that are sequentially executed by the task and achieve a specific goal, such as building an image. Every task runs as a pod, and each step runs as a container within that pod. Because steps run within the same pod, they can access the same volumes for caching files, config maps, and secrets.

The following example shows the apply-manifests task.

apiVersion: tekton.dev/v1 1
kind: Task 2
metadata:
  name: apply-manifests 3
spec: 4
  workspaces:
  - name: source
  params:
    - name: manifest_dir
      description: The directory in source that contains yaml manifests
      type: string
      default: "k8s"
  steps:
    - name: apply
      image: image-registry.openshift-image-registry.svc:5000/openshift/cli:latest
      workingDir: /workspace/source
      command: ["/bin/bash", "-c"]
      args:
        - |-
          echo Applying manifests in $(params.manifest_dir) directory
          oc apply -f $(params.manifest_dir)
          echo -----------------------------------

This task starts the pod and runs a container inside that pod using the specified image to run the specified commands.

spec:
  pipeline:
    disable-working-directory-overwrite: false
    disable-home-env-overwrite: false

When expressions guard task execution by setting criteria for the execution of tasks within a pipeline. They contain a list of components that allows a task to run only when certain criteria are met. When expressions are also supported in the final set of tasks that are specified using the finally field in the pipeline YAML file.

The key components of a when expression are as follows:

The declared when expressions are evaluated before the task is run. If the value of a when expression is True, the task is run. If the value of a when expression is False, the task is skipped.

You can use the when expressions in various use cases. For example, whether:

The following example shows the when expressions for a pipeline run. The pipeline run will execute the create-file task only if the following criteria are met: the path parameter is README.md, and the echo-file-exists task executed only if the exists result from the check-file task is yes.

apiVersion: tekton.dev/v1
kind: PipelineRun 1
metadata:
  generateName: guarded-pr-
spec:
  taskRunTemplate:
    serviceAccountName: pipeline
  pipelineSpec:
    params:
      - name: path
        type: string
        description: The path of the file to be created
    workspaces:
      - name: source
        description: |
          This workspace is shared among all the pipeline tasks to read/write common resources
    tasks:
      - name: create-file 2
        when:
          - input: "$(params.path)"
            operator: in
            values: ["README.md"]
        workspaces:
          - name: source
            workspace: source
        taskSpec:
          workspaces:
            - name: source
              description: The workspace to create the readme file in
          steps:
            - name: write-new-stuff
              image: ubuntu
              script: 'touch $(workspaces.source.path)/README.md'
      - name: check-file
        params:
          - name: path
            value: "$(params.path)"
        workspaces:
          - name: source
            workspace: source
        runAfter:
          - create-file
        taskSpec:
          params:
            - name: path
          workspaces:
            - name: source
              description: The workspace to check for the file
          results:
            - name: exists
              description: indicates whether the file exists or is missing
          steps:
            - name: check-file
              image: alpine
              script: |
                if test -f $(workspaces.source.path)/$(params.path); then
                  printf yes | tee /tekton/results/exists
                else
                  printf no | tee /tekton/results/exists
                fi
      - name: echo-file-exists
        when: 3
          - input: "$(tasks.check-file.results.exists)"
            operator: in
            values: ["yes"]
        taskSpec:
          steps:
            - name: echo
              image: ubuntu
              script: 'echo file exists'
...
      - name: task-should-be-skipped-1
        when: 4
          - input: "$(params.path)"
            operator: notin
            values: ["README.md"]
        taskSpec:
          steps:
            - name: echo
              image: ubuntu
              script: exit 1
...
    finally:
      - name: finally-task-should-be-executed
        when: 5
          - input: "$(tasks.echo-file-exists.status)"
            operator: in
            values: ["Succeeded"]
          - input: "$(tasks.status)"
            operator: in
            values: ["Succeeded"]
          - input: "$(tasks.check-file.results.exists)"
            operator: in
            values: ["yes"]
          - input: "$(params.path)"
            operator: in
            values: ["README.md"]
        taskSpec:
          steps:
            - name: echo
              image: ubuntu
              script: 'echo finally done'
  params:
    - name: path
      value: README.md
  workspaces:
    - name: source
      volumeClaimTemplate:
        spec:
          accessModes:
            - ReadWriteOnce
          resources:
            requests:
              storage: 16Mi

The Pipeline Run details page of the OpenShift Container Platform web console shows the status of the tasks and when expressions as follows:

The finally tasks are the final set of tasks specified using the finally field in the pipeline YAML file. A finally task always executes the tasks within the pipeline, irrespective of whether the pipeline runs are executed successfully. The finally tasks are executed in parallel after all the pipeline tasks are run, before the corresponding pipeline exits.

You can configure a finally task to consume the results of any task within the same pipeline. This approach does not change the order in which this final task is run. It is executed in parallel with other final tasks after all the non-final tasks are executed.

The following example shows a code snippet of the clone-cleanup-workspace pipeline. This code clones the repository into a shared workspace and cleans up the workspace. After executing the pipeline tasks, the cleanup task specified in the finally section of the pipeline YAML file cleans up the workspace.

apiVersion: tekton.dev/v1
kind: Pipeline
metadata:
  name: clone-cleanup-workspace 1
spec:
  workspaces:
    - name: git-source 2
  tasks:
    - name: clone-app-repo 3
      taskRef:
        name: git-clone-from-catalog
      params:
        - name: url
          value: https://github.com/tektoncd/community.git
        - name: subdirectory
          value: application
      workspaces:
        - name: output
          workspace: git-source
  finally:
    - name: cleanup 4
      taskRef: 5
        name: cleanup-workspace
      workspaces: 6
        - name: source
          workspace: git-source
    - name: check-git-commit
      params: 7
        - name: commit
          value: $(tasks.clone-app-repo.results.commit)
      taskSpec: 8
        params:
          - name: commit
        steps:
          - name: check-commit-initialized
            image: alpine
            script: |
              if [[ ! $(params.commit) ]]; then
                exit 1
              fi

A TaskRun instantiates a task for execution with specific inputs, outputs, and execution parameters on a cluster. It can be invoked on its own or as part of a pipeline run for each task in a pipeline.

A task consists of one or more steps that execute container images, and each container image performs a specific piece of build work. A task run executes the steps in a task in the specified order, until all steps execute successfully or a failure occurs. A TaskRun is automatically created by a PipelineRun for each task in a pipeline.

The following example shows a task run that runs the apply-manifests task with the relevant input parameters:

apiVersion: tekton.dev/v1 1
kind: TaskRun 2
metadata:
  name: apply-manifests-taskrun 3
spec: 4
  taskRunTemplate:
    serviceAccountName: pipeline
  taskRef: 5
    kind: Task
    name: apply-manifests
  workspaces: 6
  - name: source
    persistentVolumeClaim:
      claimName: source-pvc

A Pipeline is a collection of Task resources arranged in a specific order of execution. They are executed to construct complex workflows that automate the build, deployment and delivery of applications. You can define a CI/CD workflow for your application using pipelines containing one or more tasks.

A Pipeline resource definition consists of a number of fields or attributes, which together enable the pipeline to accomplish a specific goal. Each Pipeline resource definition must contain at least one Task resource, which ingests specific inputs and produces specific outputs. The pipeline definition can also optionally include Conditions, Workspaces, Parameters, or Resources depending on the application requirements.

The following example shows the build-and-deploy pipeline, which builds an application image from a Git repository using the buildah task provided in the openshift-pipelines namespace:

apiVersion: tekton.dev/v1 1
kind: Pipeline 2
metadata:
  name: build-and-deploy 3
spec: 4
  workspaces: 5
  - name: shared-workspace
  params: 6
  - name: deployment-name
    type: string
    description: name of the deployment to be patched
  - name: git-url
    type: string
    description: url of the git repo for the code of deployment
  - name: git-revision
    type: string
    description: revision to be used from repo of the code for deployment
    default: "pipelines-1.16"
  - name: IMAGE
    type: string
    description: image to be built from the code
  tasks: 7
  - name: fetch-repository
    taskRef:
      resolver: cluster
      params:
      - name: kind
        value: task
      - name: name
        value: git-clone
      - name: namespace
        value: openshift-pipelines
    workspaces:
    - name: output
      workspace: shared-workspace
    params:
    - name: URL
      value: $(params.git-url)
    - name: SUBDIRECTORY
      value: ""
    - name: DELETE_EXISTING
      value: "true"
    - name: REVISION
      value: $(params.git-revision)
  - name: build-image 8
    taskRef:
      resolver: cluster
      params:
      - name: kind
        value: task
      - name: name
        value: buildah
      - name: namespace
        value: openshift-pipelines
    workspaces:
    - name: source
      workspace: shared-workspace
    params:
    - name: TLSVERIFY
      value: "false"
    - name: IMAGE
      value: $(params.IMAGE)
    runAfter:
    - fetch-repository
  - name: apply-manifests 9
    taskRef:
      name: apply-manifests
    workspaces:
    - name: source
      workspace: shared-workspace
    runAfter: 10
    - build-image
  - name: update-deployment
    taskRef:
      name: update-deployment
    workspaces:
    - name: source
      workspace: shared-workspace
    params:
    - name: deployment
      value: $(params.deployment-name)
    - name: IMAGE
      value: $(params.IMAGE)
    runAfter:
    - apply-manifests

A PipelineRun is a type of resource that binds a pipeline, workspaces, credentials, and a set of parameter values specific to a scenario to run the CI/CD workflow.

A pipeline run is the running instance of a pipeline. It instantiates a pipeline for execution with specific inputs, outputs, and execution parameters on a cluster. It also creates a task run for each task in the pipeline run.

The pipeline runs the tasks sequentially until they are complete or a task fails. The status field tracks and the progress of each task run and stores it for monitoring and auditing purposes.

The following example runs the build-and-deploy pipeline with relevant resources and parameters:

apiVersion: tekton.dev/v1 1
kind: PipelineRun 2
metadata:
  name: build-deploy-api-pipelinerun 3
spec:
  pipelineRef:
    name: build-and-deploy 4
  params: 5
  - name: deployment-name
    value: vote-api
  - name: git-url
    value: https://github.com/openshift-pipelines/vote-api.git
  - name: IMAGE
    value: image-registry.openshift-image-registry.svc:5000/pipelines-tutorial/vote-api
  workspaces: 6
  - name: shared-workspace
    volumeClaimTemplate:
      spec:
        accessModes:
          - ReadWriteOnce
        resources:
          requests:
            storage: 500Mi

Workspaces declare shared storage volumes that a task in a pipeline needs at runtime to receive input or provide output. Instead of specifying the actual location of the volumes, workspaces enable you to declare the filesystem or parts of the filesystem that would be required at runtime. A task or pipeline declares the workspace and you must provide the specific location details of the volume. It is then mounted into that workspace in a task run or a pipeline run. This separation of volume declaration from runtime storage volumes makes the tasks reusable, flexible, and independent of the user environment.

With workspaces, you can:

You can specify workspaces in the TaskRun or PipelineRun using:

The following example shows a code snippet of the build-and-deploy pipeline, which declares a shared-workspace workspace for the build-image and apply-manifests tasks as defined in the pipeline.

apiVersion: tekton.dev/v1
kind: Pipeline
metadata:
  name: build-and-deploy
spec:
  workspaces: 1
  - name: shared-workspace
  params:
...
  tasks: 2
  - name: build-image
    taskRef:
      resolver: cluster
      params:
      - name: kind
        value: task
      - name: name
        value: buildah
      - name: namespace
        value: openshift-pipelines
    workspaces: 3
    - name: source 4
      workspace: shared-workspace 5
    params:
    - name: TLSVERIFY
      value: "false"
    - name: IMAGE
      value: $(params.IMAGE)
    runAfter:
    - fetch-repository
  - name: apply-manifests
    taskRef:
      name: apply-manifests
    workspaces: 6
    - name: source
      workspace: shared-workspace
    runAfter:
      - build-image
...

Workspaces help tasks share data, and allow you to specify one or more volumes that each task in the pipeline requires during execution. You can create a persistent volume claim or provide a volume claim template that creates a persistent volume claim for you.

The following code snippet of the build-deploy-api-pipelinerun pipeline run uses a volume claim template to create a persistent volume claim for defining the storage volume for the shared-workspace workspace used in the build-and-deploy pipeline.

apiVersion: tekton.dev/v1
kind: PipelineRun
metadata:
  name: build-deploy-api-pipelinerun
spec:
  pipelineRef:
    name: build-and-deploy
  params:
...

  workspaces: 1
  - name: shared-workspace 2
    volumeClaimTemplate: 3
      spec:
        accessModes:
          - ReadWriteOnce
        resources:
          requests:
            storage: 500Mi

A step is a part of a task. If you define a step in a task, you cannot reference this step from another task.

However, you can optionally define a step action in a StepAction custom resource (CR). This CR contains the action that a step performs. You can reference a StepAction object from a step to create a step that performs the action. You can also use resolvers to reference a StepAction definition that is available from an external source.

The following examples shows a StepAction CR named apply-manifests-action. This step action applies manifests from a source tree to your OpenShift Container Platform environment:

apiVersion: tekton.dev/v1
kind: StepAction
metadata:
  name: apply-manifests-action
spec:
  params:
  - name: working_dir
    description: The working directory where the source is located
    type: string 1
    default: "/workspace/source"
  - name: manifest_dir
    description: The directory in source that contains yaml manifests
    default: "k8s"
  results:
  - name: output
    description: The output of the oc apply command
  image: image-registry.openshift-image-registry.svc:5000/openshift/cli:latest
  env:
  - name: MANIFEST_DIR
    value: $(params.manifest_dir)
  workingDir: $(params.working_dir)
  script: |
      #!/usr/bin/env bash
      oc apply -f "$MANIFEST_DIR" | tee $(results.output)

The StepAction CR does not include definitions of workspaces. Instead, the step action expects that the task that includes the action also provides the mounted source tree, typically using a workspace.

A StepAction object can define parameters and results. When you reference this object, you must specify the values for the parameters of the StepAction object in the definition of the step. The results of the StepAction object automatically become the results of the step.

The following example task includes a step that references the apply-manifests-action step action, provides the necessary parameters, and uses the result:

apiVersion: tekton.dev/v1
kind: Task
metadata:
  name: apply-manifests-with-action
spec:
  workspaces:
  - name: source
  params:
  - name: manifest_dir
    description: The directory in source that contains yaml manifests
    type: string
    default: "k8s"
  steps:
  - name: apply
    ref:
      name: apply-manifests-action
    params:
    - name: working_dir
      value: "/workspace/source"
    - name: manifest_dir
      value: $(params.manifest_dir)
  - name: display_result
    script: 'echo $(step.apply.results.output)'

Use Triggers in conjunction with pipelines to create a full-fledged CI/CD system where Kubernetes resources define the entire CI/CD execution. Triggers capture the external events, such as a Git pull request, and process them to extract key pieces of information. Mapping this event data to a set of predefined parameters triggers a series of tasks that can then create and deploy Kubernetes resources and instantiate the pipeline.

For example, you define a CI/CD workflow using Red Hat OpenShift Pipelines for your application. The pipeline must start for any new changes to take effect in the application repository. Triggers automate this process by capturing and processing any change event and by triggering a pipeline run that deploys the new image with the latest changes.

Triggers consist of the following main resources that work together to form a reusable, decoupled, and self-sustaining CI/CD system: