Build and deploy a Docker image on Kubernetes using Tekton Pipelines

Tekton is an open source project to configure and run continuous integration (CI) and continuous delivery (CD) pipelines within a Kubernetes cluster. In this tutorial, I walk you through basic concepts used by Tekton Pipelines. Then, you get a chance to create a pipeline to build and deploy to a container registry. You also learn how to run the pipeline, check its status, and troubleshoot issues. But before you get started, you must set up a Kubernetes environment with Tekton installed.

Prerequisites

• Install the command-line interface (CLIs) to manage a cluster.

• Create a standard Kubernetes cluster in the IBM Cloud Kubernetes Service.

  Note:

  • Tekton requires Kubernetes version 1.15 or higher.
  • This tutorial uses a standard cluster because it supports dynamic provisioning of storage volumes.

• Create a private container registry in the IBM Cloud Container Registry.

• Install Tekton in your cluster. This tutorial was written using Tekton version 0.11.1. Be advised that if you use an older version, you may encounter some functional differences.

    kubectl apply --filename https://storage.googleapis.com/tekton-releases/pipeline/previous/v0.11.1/release.yaml
  

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  Monitor the installation using the following command until all components show a running status:

    kubectl get pods --namespace tekton-pipelines --watch
  

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• Install the Tekton Pipelines CLI.

Estimated time

Once you’ve completed the prerequisites, this tutorial takes about an hour to complete.

Steps

1. Understand Tekton Pipeline concepts
2. Clone the repository
3. Create a Task to clone the Git repository
4. Create a Task to build an image and push it to a container registry
5. Create a Task to deploy an image to a Kubernetes cluster
6. Create a pipeline
7. Define a ServiceAccount
8. Create a PipelineRun
9. Run the pipeline
10. Debug a failed PipelineRun

Understand Tekton Pipeline concepts

Tekton provides a set of extensions to Kubernetes, in the form of Custom Resources, for defining pipelines.

The following diagram shows the resources used in this tutorial. The arrows depict references from one resource to another resource.

The resources are used as follows:

• A PipelineRun defines an execution of a pipeline. It references the pipeline to run.
• A pipeline defines the set of Tasks that compose a pipeline.
• A Task defines a set of build steps, such as compiling code, running tests, and building and deploying images.

Don’t worry, I go into more detail about each resource throughout this tutorial.

Now it’s time to create a simple pipeline that:

• Builds a Docker image from source files and pushes it to your private container registry
• Deploys the image to your Kubernetes cluster

Clone the repository

You should clone this project to your workstation since you will need to edit some of the YAML files before applying them to your cluster. Make sure to check out the beta-update branch after cloning.

git clone https://github.com/IBM/tekton-tutorial
git checkout beta-update

Let’s work from the bottom up. First, define the Task resources needed to build and deploy the image. Then, define the pipeline resource that references the Tasks. Finally, create the PipelineRun resource needed to run the pipeline.

Create a Task to clone the Git repository

The first thing that the pipeline needs is a Task to clone the Git repository that the pipeline is building. This is such a common function that you don't need to write this Task yourself. Tekton provides a library of reusable Tasks called the Tekton Catalog. Within the catalog, you can find a description of the git-clone Task. Below is what the Task should look like:

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: git-clone
spec:
  workspaces:
  - name: output
    description: The git repo will be cloned onto the volume backing this workspace
  params:
  - name: url
    description: git url to clone
    type: string
  - name: revision
    description: git revision to checkout (branch, tag, sha, ref�)
    type: string
    default: master
  - name: submodules
    description: defines if the resource should initialize and fetch the submodules
    type: string
    default: "true"
  - name: depth
    description: performs a shallow clone where only the most recent commit(s) will be fetched
    type: string
    default: "1"
  - name: sslVerify
    description: defines if http.sslVerify should be set to true or false in the global git config
    type: string
    default: "true"
  - name: subdirectory
    description: subdirectory inside the "output" workspace to clone the git repo into
    type: string
    default: "src"
  - name: deleteExisting
    description: clean out the contents of the repo's destination directory (if it already exists) before trying to clone the repo there
    type: string
    default: "false"
  results:
  - name: commit
    description: The precise commit SHA that was fetched by this Task
  steps:
  - name: clone
    image: gcr.io/tekton-releases/github.com/tektoncd/pipeline/cmd/git-init:latest
    script: |
      CHECKOUT_DIR="$(workspaces.output.path)/$(params.subdirectory)"

      cleandir() {
        # Delete any existing contents of the repo directory if it exists.
        #
        # We don't just "rm -rf $CHECKOUT_DIR" because $CHECKOUT_DIR might be "/"
        # or the root of a mounted volume.
        if [[ -d "$CHECKOUT_DIR" ]] ; then
          # Delete non-hidden files and directories
          rm -rf "$CHECKOUT_DIR"/*
          # Delete files and directories starting with . but excluding ..
          rm -rf "$CHECKOUT_DIR"/.[!.]*
          # Delete files and directories starting with .. plus any other character
          rm -rf "$CHECKOUT_DIR"/..?*
        fi
      }

      if [[ "$(params.deleteExisting)" == "true" ]] ; then
        cleandir
      fi

      /ko-app/git-init \
        -url "$(params.url)" \
        -revision "$(params.revision)" \
        -path "$CHECKOUT_DIR" \
        -sslVerify="$(params.sslVerify)" \
        -submodules="$(params.submodules)" \
        -depth="$(params.depth)"
      cd "$CHECKOUT_DIR"
      RESULT_SHA="$(git rev-parse HEAD | tr -d '\n')"
      EXIT_CODE="$?"
      if [ "$EXIT_CODE" != 0 ]
      then
        exit $EXIT_CODE
      fi
      # Make sure we don't add a trailing newline to the result!
      echo -n "$RESULT_SHA" > $(results.commit.path)

A Task can have one or more steps. Each step defines an image to run to perform the function of the step. This particular Task has one step that uses a Tekton-provided container to clone a Git repo.

A Task can also have parameters, which help to make it reusable. This Task accepts many parameters, including the URL of the Git repository to clone and the revision to check out.

Parameters can have default values provided by the Task or the values can be provided by the Pipeline and PipelineRun resources that you see later. Steps can reference parameter values by using the syntax $(params.name) where name is the name of the parameter. For example, this step uses $(params.url) to reference the url parameter value.

The Task requires a workspace where the clone is stored. From the point of view of the Task, a workspace provides a file system path where it can read or write data. Steps can reference the path using the syntax $(workspaces.name.path) where name is the name of the workspace. Later, you learn how the workspace becomes associated with a storage volume.

Now apply the file to your cluster to create the Task:

kubectl apply -f https://raw.githubusercontent.com/tektoncd/catalog/v1beta1/git/git-clone.yaml

Create a Task to build an image and push it to a container registry

The next function that the pipeline needs is a Task that builds a Docker image and pushes it to a container registry. The Tekton Catalog provides a kaniko Task which does this using Google's kaniko tool. The Task is reproduced below:

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: kaniko
spec:
  params:
  - name: IMAGE
    description: Name (reference) of the image to build.
  - name: DOCKERFILE
    description: Path to the Dockerfile to build.
    default: ./Dockerfile
  - name: CONTEXT
    description: The build context used by Kaniko.
    default: ./
  - name: EXTRA_ARGS
    default: ""
  - name: BUILDER_IMAGE
    description: The image on which builds will run
    default: gcr.io/kaniko-project/executor:latest
  workspaces:
  - name: source
  results:
  - name: IMAGE-DIGEST
    description: Digest of the image just built.

  steps:
  - name: build-and-push
    workingDir: $(workspaces.source.path)
    image: $(params.BUILDER_IMAGE)
    # specifying DOCKER_CONFIG is required to allow kaniko to detect docker credential
    # https://github.com/tektoncd/pipeline/pull/706
    env:
    - name: DOCKER_CONFIG
      value: /tekton/home/.docker
    command:
    - /kaniko/executor
    - $(params.EXTRA_ARGS)
    - --dockerfile=$(params.DOCKERFILE)
    - --context=$(workspaces.source.path)/$(params.CONTEXT)  # The user does not need to care the workspace and the source.
    - --destination=$(params.IMAGE)
    - --oci-layout-path=$(workspaces.source.path)/image-digest
    securityContext:
      runAsUser: 0
  - name: write-digest
    workingDir: $(workspaces.source.path)
    image: gcr.io/tekton-releases/github.com/tektoncd/pipeline/cmd/imagedigestexporter:v0.11.1
    # output of imagedigestexport [{"key":"digest","value":"sha256:eed29..660","resourceRef":{"name":"myrepo/myimage"}}]
    command: ["/ko-app/imagedigestexporter"]
    args:
    - -images=[{"name":"$(params.IMAGE)","type":"image","url":"$(params.IMAGE)","digest":"","OutputImageDir":"$(workspaces.source.path)/image-digest"}]
    - -terminationMessagePath=image-digested
  - name: digest-to-results
    workingDir: $(workspaces.source.path)
    image: stedolan/jq
    script: |
      cat image-digested | jq -j '.[0].value' | tee /tekton/results/IMAGE-DIGEST

You can see that this Task needs a workspace as well. This workspace has the source to build and the pipeline provides the same workspace that it used for the git-clone Task.

The kaniko Task also uses a feature called results. A result is a value produced by a Task which can then be used as a parameter value to other Tasks. This Task declares a result named IMAGE-DIGEST which it sets to the digest of the built image. A Task sets a result by writing it to a file named /tekton/results/name where name is the name of the result (in this case IMAGE-DIGEST). Later you learn how the pipeline uses this result.

You may be wondering about how the Task authenticates to the image repository for permission to push the image. This too will be covered later on in this tutorial.

Now apply the file to your cluster to create the Task:

kubectl apply -f https://raw.githubusercontent.com/tektoncd/catalog/v1beta1/kaniko/kaniko.yaml

Create a Task to deploy an image to a Kubernetes cluster

The final function that the pipeline needs is a Task that deploys a Docker image to a Kubernetes cluster. Below is a Tekton Task that does this:

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: deploy-using-kubectl
spec:
  workspaces:
    - name: git-source
      description: The git repo
  params:
    - name: pathToYamlFile
      description: The path to the yaml file to deploy within the git source
    - name: imageUrl
      description: Image name including repository
    - name: imageTag
      description: Image tag
      default: "latest"
    - name: imageDigest
      description: Digest of the image to be used.
  steps:
    - name: update-yaml
      image: alpine
      command: ["sed"]
      args:
        - "-i"
        - "-e"
        - "s;__IMAGE__;$(params.imageUrl):$(params.imageTag);g"
        - "-e"
        - "s;__DIGEST__;$(params.imageDigest);g"
        - "$(workspaces.git-source.path)/$(params.pathToYamlFile)"
    - name: run-kubectl
      image: lachlanevenson/k8s-kubectl
      command: ["kubectl"]
      args:
        - "apply"
        - "-f"
        - "$(workspaces.git-source.path)/$(params.pathToYamlFile)"

This Task has two steps.

1. The first step runs sed in an Alpine Linux container to update the YAML file used for deployment with the image that was built by the kaniko Task. This step requires the YAML file to have two character strings, __IMAGE__ and __DIGEST__, which are substituted with parameter values.

2. The second step runs kubectl using Lachlan Evenson's popular k8s-kubectl container image to apply the YAML file to the same cluster where the pipeline is running.

As was the case in the git-clone and kaniko Tasks, this Task makes use of parameters in order to make the Task as reusable as possible. It also needs the workspace to get the deployment YAML file.

Later in this tutorial, I address how the Task authenticates to the cluster for permission to apply the resource(s) in the YAML file.

Now apply the file to your cluster to create the Task:

kubectl apply -f tekton/tasks/deploy-using-kubectl.yaml

Create a pipeline

Below is a Tekton Pipeline that runs the Tasks you defined above:

apiVersion: tekton.dev/v1beta1
kind: Pipeline
metadata:
  name: build-and-deploy-pipeline
spec:
  workspaces:
    - name: git-source
      description: The git repo
  params:
    - name: gitUrl
      description: Git repository url
    - name: gitRevision
      description: Git revision to check out
      default: master
    - name: pathToContext
      description: The path to the build context, used by Kaniko - within the workspace
      default: src
    - name: pathToYamlFile
      description: The path to the yaml file to deploy within the git source
    - name: imageUrl
      description: Image name including repository
    - name: imageTag
      description: Image tag
      default: "latest"
  tasks:
    - name: clone-repo
      taskRef:
        name: git-clone
      workspaces:
        - name: output
          workspace: git-source
      params:
        - name: url
          value: "$(params.gitUrl)"
        - name: revision
          value: "$(params.gitRevision)"
        - name: subdirectory
          value: "."
        - name: deleteExisting
          value: "true"
    - name: source-to-image
      taskRef:
        name: kaniko
      runAfter:
        - clone-repo
      workspaces:
        - name: source
          workspace: git-source
      params:
        - name: CONTEXT
          value: $(params.pathToContext)
        - name: IMAGE
          value: $(params.imageUrl):$(params.imageTag)
    - name: deploy-to-cluster
      taskRef:
        name: deploy-using-kubectl
      workspaces:
        - name: git-source
          workspace: git-source
      params:
        - name: pathToYamlFile
          value: $(params.pathToYamlFile)
        - name: imageUrl
          value: $(params.imageUrl)
        - name: imageTag
          value: $(params.imageTag)
        - name: imageDigest
          value: $(tasks.source-to-image.results.IMAGE-DIGEST)

A pipeline resource contains a list of Tasks to run. Each pipeline Task is assigned a name within the pipeline; here they are clone-repo, source-to-image, and deploy-using-kubectl.

The pipeline configures each Task through the Task's parameters. You can choose whether to expose a Task parameter as a pipeline parameter, set the value directly, or let the value default inside the Task (if it's an optional parameter). For example, this pipeline exposes the CONTEXT parameter from the kaniko Task (under a different name, pathToContext), but does not expose the DOCKERFILE parameter, allowing it to default inside the Task.

This pipeline also shows how to take the result of one Task and pass it to another Task. Earlier, the kaniko Task produced a result named IMAGE-DIGEST that holds the digest of the built image. The pipeline passes that value to the deploy-using-kubectl Task by using the syntax $(tasks.source-to-image.results.IMAGE-DIGEST), where source-to-image is the name used in the pipeline to run the kaniko Task.

By default, Tekton assumes that pipeline Tasks can be executed concurrently. In this pipeline, each pipeline Task depends on the previous one, meaning they must be executed sequentially. One way that dependencies between pipeline Tasks can be expressed is by using the runAfter key. It specifies that the Task must run after the given list of Tasks has completed. In this example, the pipeline specifies that the source-to-image pipeline Task must run after the clone-repo pipeline Task.

The deploy-using-kubectl pipeline Task must run after the source-to-image pipeline Task but it doesn't need to specify the runAfter key. This is because it references a Task result from the source-to-image pipeline Task and Tekton is smart enough to figure out that this means it must run after that Task.

Now apply the file to your cluster to create the pipeline:

kubectl apply -f tekton/pipeline/build-and-deploy-pipeline.yaml

Define a ServiceAccount

Before running the pipeline, you need to set up a service account so that it can access protected resources. The ServiceAccount ties together a couple of secrets containing credentials for authentication, along with role-based access control (RBAC) related resources for permission to create and modify certain Kubernetes resources.

First, you need to enable programmatic access to your private container registry by creating an IBM Cloud Identity and Access Management (IAM) API key. See the IBM Cloud documentation for automating access to the IBM Cloud Container Registry to learn how to create a user API key.

After you have the API key, you can create the following secret:

kubectl create secret generic ibm-registry-secret --type="kubernetes.io/basic-auth" --from-literal=username=iamapikey --from-literal=password=<APIKEY>
kubectl annotate secret ibm-registry-secret tekton.dev/docker-0=<REGISTRY>

Please note the following:

• <APIKEY> is the API key that you created.
• <REGISTRY> is the domain name of your container registry, such as us.icr.io (you can find out the domain name of your registry using the command ibmcloud cr region).

This secret will be used to both push and pull images from your registry.

Great! Now you can create the ServiceAccount using the following YAML:

apiVersion: v1
kind: ServiceAccount
metadata:
  name: pipeline-account
secrets:
- name: ibm-registry-secret

---

apiVersion: v1
kind: Secret
metadata:
  name: kube-api-secret
  annotations:
    kubernetes.io/service-account.name: pipeline-account
type: kubernetes.io/service-account-token

---

kind: Role
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: pipeline-role
rules:
- apiGroups: [""]
  resources: ["services"]
  verbs: ["get", "create", "update", "patch"]
- apiGroups: ["apps"]
  resources: ["deployments"]
  verbs: ["get", "create", "update", "patch"]

---

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: pipeline-role-binding
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: pipeline-role
subjects:
- kind: ServiceAccount
  name: pipeline-account

This YAML creates the following Kubernetes resources:

• A ServiceAccount named pipeline-account. The ServiceAccount references the ibm-registry-secret secret so that the pipeline can authenticate to your private container registry when it pushes and pulls a container image.

• A secret named kube-api-secret which contains an API credential (generated by Kubernetes) for accessing the Kubernetes API. This allows the pipeline to use kubectl to talk to your cluster.

• A Role named pipeline-role and a RoleBinding named pipeline-role-binding. This provides the resource-based access control permissions needed for this pipeline to create and modify Kubernetes resources.

Now apply the file to your cluster to create the ServiceAccount and related resources:

kubectl apply -f tekton/pipeline-account.yaml

Create a PipelineRun

So far, you’ve defined reusable pipeline and Task resources for building and deploying an image. Now it’s time to look at how to run the pipeline. Below is a Tekton PipelineRun resource that runs the pipeline defined above. It should look something like this:

apiVersion: tekton.dev/v1beta1
kind: PipelineRun
metadata:
  generateName: picalc-pr-
spec:
  pipelineRef:
    name: build-and-deploy-pipeline
  params:
    - name: gitUrl
      value: https://github.com/IBM/tekton-tutorial
    - name: gitRevision
      value: beta-update
    - name: pathToYamlFile
      value: kubernetes/picalc.yaml
    - name: imageUrl
      value: <REGISTRY>/<NAMESPACE>/picalc
    - name: imageTag
      value: "1.0"
  serviceAccountName: pipeline-account
  workspaces:
    - name: git-source
      persistentVolumeClaim:
        claimName: picalc-source-pvc

Although this file is small, there is a lot going on here. Let's break it down from top to bottom:

• The PipelineRun does not have a fixed name. It uses generateName to generate a name each time it is created. This is because a particular PipelineRun resource executes the pipeline only once. If you want to run the pipeline again, you cannot modify an existing PipelineRun resource to request it to rerun. You must create a new PipelineRun resource. While you could use name to assign a unique name to your PipelineRun each time you create one, it is much easier to use generateName.

• The pipeline resource is identified under the pipelineRef key.

• Parameters exposed by the pipeline are set to specific values, such as the Git repository to clone, the image to build, and the YAML file to deploy. This example builds a Go program that calculates an approximation of Pi. The source includes a Dockerfile which runs tests, compiles the code, and builds an image for execution.

  You must edit the picalc-pipeline-run.yaml file to substitute the values of <REGISTRY> and <NAMESPACE> with the information for your private container registry.

  • To find the value for <REGISTRY>, enter the command ibmcloud cr region.
  • To find the value for <NAMESPACE>, enter the command ibmcloud cr namespace-list.

• The ServiceAccount named pipeline-account, which you created earlier, is specified to provide the credentials needed for the pipeline to run successfully.

• The workspace used by the pipeline to clone the Git repository is mapped to a persistent volume claim which is a request for a storage volume.

Before you run the pipeline for the first time, you must create the persistent volume claim for the workspace:

kubectl create -f tekton/picalc-pipeline-pvc.yaml

The persistent volume claim requests Kubernetes to obtain a storage volume. Since each PipelineRun references the same claim and thus the same volume, the PipelineRun can only be run consecutively to avoid conflicting use of the volume. However, new functionality is being worked on to allow each PipelineRun to create its own persistent volume claim and thus use its own volume.

Before continuing, check to see that the persistent volume claim is bound:

$ kubectl get pvc picalc-source-pvc
NAME                STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS       AGE
picalc-source-pvc   Bound    pvc-662946bc-57f2-4ba5-982c-b0fa9db1d065   20Gi       RWO            ibmc-file-bronze   2m

Run the pipeline

All the pieces are in place to run the pipeline:

$ kubectl create -f tekton/run/picalc-pipeline-run.yaml
pipelinerun.tekton.dev/picalc-pr-c7hsb created

Note that you’re using kubectl create here instead of kubectl apply. As mentioned previously, a given PipelineRun resource can run a pipeline only once. This means you need to create a new one each time you want to run the pipeline. kubectl will respond with the generated name of the PipelineRun resource.

Let's use the tkn CLI to check the status of the PipelineRun. While you can check the status of the pipeline using the kubectl describe command, the tkn cli provides a much nicer output:

$ tkn pipelinerun describe picalc-pr-c7hsb
Name:              picalc-pr-c7hsb
Namespace:         default
Pipeline Ref:      build-and-deploy-pipeline
Service Account:   pipeline-account

Status

STARTED         DURATION   STATUS
2 minutes ago   ---        Running

Resources

 No resources

Params

 NAME             VALUE
 gitUrl           https://github.com/IBM/tekton-tutorial
 pathToYamlFile   kubernetes/picalc.yaml
 imageUrl         us.icr.io/gregd/picalc
 imageTag         1.0

Taskruns

 NAME                                    TASK NAME         STARTED          DURATION   STATUS
 picalc-pr-c7hsb-source-to-image-s8rrg   source-to-image   56 seconds ago   ---        Running
 picalc-pr-c7hsb-clone-repo-pvbsk        clone-repo        2 minutes ago    1 minute   Succeeded

This tells you that the pipeline is running. The clone-repo pipeline Task was completely successfully and the source-to-image pipeline Task is currently running.

Continue to rerun the command to check the status. If the pipeline runs successfully, the description eventually should look like this:

$ tkn pipelinerun describe picalc-pr-c7hsb
Name:              picalc-pr-c7hsb
Namespace:         default
Pipeline Ref:      build-and-deploy-pipeline
Service Account:   pipeline-account

Status

STARTED          DURATION    STATUS
12 minutes ago   2 minutes   Succeeded

Resources

 No resources

Params

 NAME             VALUE
 gitUrl           https://github.com/IBM/tekton-tutorial
 pathToYamlFile   kubernetes/picalc.yaml
 imageUrl         us.icr.io/gregd/picalc
 imageTag         1.0

Taskruns

 NAME                                      TASK NAME           STARTED          DURATION     STATUS
 picalc-pr-c7hsb-deploy-to-cluster-mwvfs   deploy-to-cluster   9 minutes ago    10 seconds   Succeeded
 picalc-pr-c7hsb-source-to-image-s8rrg     source-to-image     10 minutes ago   1 minute     Succeeded
 picalc-pr-c7hsb-clone-repo-pvbsk          clone-repo          12 minutes ago   1 minute     Succeeded

Check the status of the Kubernetes deployment. It should be ready.

$ kubectl get deploy picalc
NAME     READY   UP-TO-DATE   AVAILABLE   AGE
picalc   1/1     1            1           9m

You can curl the application using its NodePort service. First, display the nodes and choose one of the node's external IP addresses. Then, display the service to get its NodePort.

$ kubectl get nodes -o wide
NAME           STATUS   ROLES    AGE     VERSION       INTERNAL-IP    EXTERNAL-IP      OS-IMAGE             KERNEL-VERSION      CONTAINER-RUNTIME
10.221.22.11   Ready    <none>   7d23h   v1.16.8+IKS   10.221.22.11   150.238.236.26   Ubuntu 18.04.4 LTS   4.15.0-96-generic   containerd://1.3.3
10.221.22.49   Ready    <none>   7d23h   v1.16.8+IKS   10.221.22.49   150.238.236.21   Ubuntu 18.04.4 LTS   4.15.0-96-generic   containerd://1.3.3

$ kubectl get svc picalc
NAME     TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
picalc   NodePort   172.21.199.71   <none>        8080:30925/TCP   9m

$ curl 150.238.236.26:30925?iterations=20000000
3.1415926036

Debug a failed PipelineRun

Let's take a look at what a PipelineRun failure would look like. To begin, edit the PipelineRun YAML and change the gitUrl parameter to a non-existent Git repository to force a failure. Then, create a new PipelineRun and describe it after letting it run for a minute or two.

$ kubectl create -f tekton/picalc-pipeline-run.yaml
pipelinerun.tekton.dev/picalc-pr-sk7md created

$ tkn pipelinerun describe picalc-pr-sk7md
Name:              picalc-pr-sk7md
Namespace:         default
Pipeline Ref:      build-and-deploy-pipeline
Service Account:   pipeline-account

Status

STARTED         DURATION     STATUS
2 minutes ago   41 seconds   Failed

Message

TaskRun picalc-pr-sk7md-clone-repo-8gs25 has failed ("step-clone" exited with code 1 (image: "gcr.io/tekton-releases/github.com/tektoncd/pipeline/cmd/git-init@sha256:bee98bfe6807e8f4e0a31b4e786fd1f7f459e653ed1a22b1a25999f33fa9134a"); for logs run: kubectl -n default logs picalc-pr-sk7md-clone-repo-8gs25-pod-v7fg8 -c step-clone)

Resources

 No resources

Params

 NAME             VALUE
 gitUrl           https://github.com/IBM/tekton-tutorial-not-there
 pathToYamlFile   kubernetes/picalc.yaml
 imageUrl         us.icr.io/gregd/picalc
 imageTag         1.0

Taskruns

 NAME                               TASK NAME    STARTED         DURATION     STATUS
 picalc-pr-sk7md-clone-repo-8gs25   clone-repo   2 minutes ago   41 seconds   Failed

The output tells you that the clone-repo pipeline Task failed. The Message also tells you how to get the logs from the pod which was used to run the Task:

for logs run: kubectl -n default logs picalc-pr-sk7md-clone-repo-8gs25-pod-v7fg8 -c step-clone

If you run that kubectl logs command, you see that there is a failure trying to fetch the non-existing Git repository. An even easier way to get the logs from a PipelineRun is to use the tkn CLI:

tkn pipelinerun logs picalc-pr-sk7md-clone-repo-8gs25-pod-v7fg8 -t clone-repo

If you omit the -t flag, then the command will get the logs for all pipeline Tasks that executed. You can also get the logs for the last PipelineRun for a particular pipeline using this command:

tkn pipeline logs build-and-deploy-pipeline -L

You should delete a PipelineRun when you no longer have a need to reference its logs. Deleting the PipelineRun deletes the pods that were used to run the pipeline Tasks.

Summary

Tekton provides simple, easy to learn features for constructing CI/CD pipelines that run on Kubernetes. This tutorial covered the basics to get you started with building your own pipelines. There are more features available and many more planned for upcoming releases.

Next steps

If you’d like to continue your learning, start by familiarizing yourself with Tekton Pipeline's newest version release. Next, take a hands-on approach and complete the Tekton Triggers 101 tutorial.