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PipelineRuns

Overview

A PipelineRun allows you to instantiate and execute a Pipeline on-cluster. A Pipeline specifies one or more Tasks in the desired order of execution. A PipelineRun executes the Tasks in the Pipeline in the order they are specified until all Tasks have executed successfully or a failure occurs.

Note: A PipelineRun automatically creates corresponding TaskRuns for every Task in your Pipeline.

The Status field tracks the current state of a PipelineRun, and can be used to monitor progress. This field contains the status of every TaskRun, as well as the full PipelineSpec used to instantiate this PipelineRun, for full auditability.

Configuring a PipelineRun

A PipelineRun definition supports the following fields:

  • Required:
    • apiVersion - Specifies the API version. For example tekton.dev/v1beta1.
    • kind - Indicates that this resource object is a PipelineRun object.
    • metadata - Specifies the metadata that uniquely identifies the PipelineRun object. For example, a name.
    • spec - Specifies the configuration information for this PipelineRun object.
  • Optional:
    • resources - Specifies the PipelineResources to provision for executing the target Pipeline.
    • params - Specifies the desired execution parameters for the Pipeline.
    • serviceAccountName - Specifies a ServiceAccount object that supplies specific execution credentials for the Pipeline.
    • serviceAccountNames - Maps specific serviceAccountName values to Tasks in the Pipeline. This overrides the credentials set for the entire Pipeline.
    • taskRunSpec - Specifies a list of PipelineRunTaskSpec which allows for setting ServiceAccountName and Pod template for each task. This overrides the Pod template set for the entire Pipeline.
    • timeout - Specifies the timeout before the PipelineRun fails.
    • podTemplate - Specifies a Pod template to use as the basis for the configuration of the Pod that executes each Task.

Specifying the target Pipeline

You must specify the target Pipeline that you want the PipelineRun to execute, either by referencing an existing Pipeline definition, or embedding a Pipeline definition directly in the PipelineRun.

To specify the target Pipeline by reference, use the pipelineRef field:

spec:
  pipelineRef:
    name: mypipeline

To embed a Pipeline definition in the PipelineRun, use the pipelineSpec field:

spec:
  pipelineSpec:
    tasks:
    - name: task1
      taskRef:
        name: mytask

The Pipeline in the pipelineSpec example example displays morning and evening greetings. Once you create and execute it, you can check the logs for its Pods:

kubectl logs $(kubectl get pods -o name | grep pipelinerun-echo-greetings-echo-good-morning)
Good Morning, Bob!

kubectl logs $(kubectl get pods -o name | grep pipelinerun-echo-greetings-echo-good-night)
Good Night, Bob!

You can also embed a Task definition the embedded Pipeline definition:

spec:
  pipelineSpec:
    tasks:
    - name: task1
      taskSpec:
        steps:
          ...

In the taskSpec in pipelineSpec example it's Tasks all the way down!

Specifying Resources

A Pipeline requires PipelineResources to provide inputs and store outputs for the Tasks that comprise it. You must provision those resources in the resources field in the spec section of the PipelineRun definition.

A Pipeline may require you to provision a number of different resources. For example:

  • When executing a Pipeline against a pull request, the triggering system must specify the commit-ish of a git resource.
  • When executing a Pipeline manually against your own environment, you must provision your GitHub fork using the git resource; your image registry using the image resource; and your Kubernetes cluster using the cluster resource.

You can reference a PipelineResources using the resourceRef field:

spec:
  resources:
    - name: source-repo
      resourceRef:
        name: skaffold-git
    - name: web-image
      resourceRef:
        name: skaffold-image-leeroy-web
    - name: app-image
      resourceRef:
        name: skaffold-image-leeroy-app

You can also embed a PipelineResource definition in the PipelineRun using the resourceSpec field:

spec:
  resources:
    - name: source-repo
      resourceSpec:
        type: git
        params:
          - name: revision
            value: v0.32.0
          - name: url
            value: https://github.com/GoogleContainerTools/skaffold
    - name: web-image
      resourceSpec:
        type: image
        params:
          - name: url
            value: gcr.io/christiewilson-catfactory/leeroy-web
    - name: app-image
      resourceSpec:
        type: image
        params:
          - name: url
            value: gcr.io/christiewilson-catfactory/leeroy-app

Note: All persistentVolumeClaims specified within a PipelineRun are bound until their respective Pods or the entire PipelineRun are deleted. This also applies to all persistentVolumeClaims generated internally.

Specifying Parameters

You can specify Parameters that you want to pass to the Pipeline during execution, including different values of the same parameter for different Tasks in the Pipeline.

Note: You must specify all the Parameters that the Pipeline expects. Parameters that have default values specified in Pipeline are not required to be provided by PipelineRun.

For example:

spec:
  params:
  - name: pl-param-x
    value: "100"
  - name: pl-param-y
    value: "500"

You can pass in extra Parameters if needed depending on your use cases. An example use case is when your CI system autogenerates PipelineRuns and it has Parameters it wants to provide to all PipelineRuns. Because you can pass in extra Parameters, you don't have to go through the complexity of checking each Pipeline and providing only the required params.

Specifying custom ServiceAccount credentials

You can execute the Pipeline in your PipelineRun with a specific set of credentials by specifying a ServiceAccount object name in the serviceAccountName field in your PipelineRun definition. If you do not explicitly specify this, the TaskRuns created by your PipelineRun will execute with the credentials specified in the configmap-defaults ConfigMap. If this default is not specified, the TaskRuns will execute with the default service account set for the target namespace.

For more information, see ServiceAccount.

Mapping ServiceAccount credentials to Tasks

If you require more granularity in specifying execution credentials, use the serviceAccountNames field to map a specific serviceAccountName value to a specific Task in the Pipeline. This overrides the global serviceAccountName you may have set for the Pipeline as described in the previous section.

For example, if you specify these mappings:

spec:
  serviceAccountName: sa-1
  serviceAccountNames:
    - taskName: build-task
      serviceAccountName: sa-for-build

for this Pipeline:

kind: Pipeline
spec:
  tasks:
    - name: build-task
      taskRef:
        name: build-push
    - name: test-task
      taskRef:
        name: test

then test-task will execute using the sa-1 account while build-task will execute with sa-for-build.

Specifying a Pod template

You can specify a Pod template configuration that will serve as the configuration starting point for the Pod in which the container images specified in your Tasks will execute. This allows you to customize the Pod configuration specifically for each TaskRun.

In the following example, the Task defines a volumeMount object named my-cache. The PipelineRun provisions this object for the Task using a persistentVolumeClaim and executes it as user 1001.

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: mytask
spec:
  steps:
    - name: writesomething
      image: ubuntu
      command: ["bash", "-c"]
      args: ["echo 'foo' > /my-cache/bar"]
      volumeMounts:
        - name: my-cache
          mountPath: /my-cache
---
apiVersion: tekton.dev/v1beta1
kind: Pipeline
metadata:
  name: mypipeline
spec:
  tasks:
    - name: task1
      taskRef:
        name: mytask
---
apiVersion: tekton.dev/v1beta1
kind: PipelineRun
metadata:
  name: mypipelinerun
spec:
  pipelineRef:
    name: mypipeline
  podTemplate:
    securityContext:
      runAsNonRoot: true
      runAsUser: 1001
    volumes:
    - name: my-cache
      persistentVolumeClaim:
        claimName: my-volume-claim

Specifying taskRunSpecs

Specifies a list of PipelineTaskRunSpec which contains TaskServiceAccountName, TaskPodTemplate and PipelineTaskName. Mapping the specs to the corresponding Task based upon the TaskName a PipelineTask will run with the configured TaskServiceAccountName and TaskPodTemplate overwriting the pipeline wide ServiceAccountName and podTemplate configuration, for example:

spec:
   podTemplate:
    securityContext:
      runAsUser: 1000
      runAsGroup: 2000
      fsGroup: 3000
  taskRunSpecs:
    - pipelineTaskName: build-task
      taskServiceAccountName: sa-for-build
      taskPodTemplate:
        nodeSelector:
          disktype: ssd

If used with this Pipeline, build-task will use the task specific PodTemplate (where nodeSelector has disktype equal to ssd).

Specifying Workspaces

If your Pipeline specifies one or more Workspaces, you must map those Workspaces to the corresponding physical volumes in your PipelineRun definition. For example, you can map a PersistentVolumeClaim volume to a Workspace as follows:

workspaces:
- name: myworkspace # must match workspace name in Task
  persistentVolumeClaim:
    claimName: mypvc # this PVC must already exist
  subPath: my-subdir

For more information, see the following topics:

Specifying LimitRange values

In order to only consume the bare minimum amount of resources needed to execute one Step at a time from the invoked Task, Tekton only requests the maximum values for CPU, memory, and ephemeral storage from within each Step. This is sufficient as Steps only execute one at a time in the Pod. Requests other than the maximum values are set to zero.

When a LimitRange parameter is present in the namespace in which PipelineRuns are executing and minimum values are specified for container resource requests, Tekton searches through all LimitRange values present in the namespace and uses the minimums instead of 0.

For more information, see the LimitRange code example.

Configuring a failure timeout

You can use the timeout field to set the PipelineRun's desired timeout value in minutes. If you do not specify this value in the PipelineRun, the global default timeout value applies. If you set the timeout to 0, the PipelineRun fails immediately upon encountering an error.

The global default timeout is set to 60 minutes when you first install Tekton. You can set a different global default timeout value using the default-timeout-minutes field in config/config-defaults.yaml.

The timeout value is a duration conforming to Go's ParseDuration format. For example, valid values are 1h30m, 1h, 1m, and 60s. If you set the global timeout to 0, all PipelineRuns that do not have an individual timeout set will fail immediately upon encountering an error.

Monitoring execution status

As your PipelineRun executes, its status field accumulates information on the execution of each TaskRun as well as the PipelineRun as a whole. This information includes the name of the pipeline Task associated to a TaskRun, the complete status of the TaskRun and details about Conditions that may be associated to a TaskRun.

The following example shows an extract from the status field of a PipelineRun that has executed successfully:

completionTime: "2020-05-04T02:19:14Z"
conditions:
- lastTransitionTime: "2020-05-04T02:19:14Z"
  message: 'Tasks Completed: 4, Skipped: 0'
  reason: Succeeded
  status: "True"
  type: Succeeded
startTime: "2020-05-04T02:00:11Z"
taskRuns:
  triggers-release-nightly-frwmw-build-ng2qk:
    pipelineTaskName: build
    status:
      completionTime: "2020-05-04T02:10:49Z"
      conditions:
      - lastTransitionTime: "2020-05-04T02:10:49Z"
        message: All Steps have completed executing
        reason: Succeeded
        status: "True"
        type: Succeeded
      podName: triggers-release-nightly-frwmw-build-ng2qk-pod-8vj99
      resourcesResult:
      - key: commit
        resourceRef:
          name: git-source-triggers-frwmw
        value: 9ab5a1234166a89db352afa28f499d596ebb48db
      startTime: "2020-05-04T02:05:07Z"
      steps:
      - container: step-build
        imageID: docker-pullable://golang@sha256:a90f2671330831830e229c3554ce118009681ef88af659cd98bfafd13d5594f9
        name: build
        terminated:
          containerID: docker://6b6471f501f59dbb7849f5cdde200f4eeb64302b862a27af68821a7fb2c25860
          exitCode: 0
          finishedAt: "2020-05-04T02:10:45Z"
          reason: Completed
          startedAt: "2020-05-04T02:06:24Z"

The following tables shows how to read the overall status of a PipelineRun:

status reason completionTime is set Description
Unknown Started No The PipelineRun has just been picked up by the controller.
Unknown Running No The PipelineRun has been validate and started to perform its work.
Unknown PipelineRunCancelled No The user requested the PipelineRun to be cancelled. Cancellation has not be done yet.
True Succeeded Yes The PipelineRun completed successfully.
True Completed Yes The PipelineRun completed successfully, one or more Tasks were skipped.
False Failed Yes The PipelineRun failed because one of the TaskRuns failed.
False [Error message] No The PipelineRun encountered an non-permanent error, but it's still running and it may ultimately succeed.
False [Error message] Yes The PipelineRun failed with a permanent error (usually validation).
False PipelineRunCancelled Yes The PipelineRun was cancelled successfully.
False PipelineRunTimeout Yes The PipelineRun timed out.

When a PipelineRun changes status, events are triggered accordingly.

Cancelling a PipelineRun

To cancel a PipelineRun that's currently executing, update its definition to mark it as cancelled. When you do so, the spawned TaskRuns are also marked as cancelled and all associated Pods are deleted. For example:

apiVersion: tekton.dev/v1beta1
kind: PipelineRun
metadata:
  name: go-example-git
spec:
  # […]
  status: "PipelineRunCancelled"

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License.