# Lab: Tekton on OpenShift

## OpenShift Deployment using Tekton Pipelines

Tekton is an open source project to configure and run CI/CD pipelines within a OpenShift/Kubernetes cluster.

## Introduction

In this tutorial you'll learn

* what are the basic concepts used by Tekton pipelines
* how to create a pipeline to build and deploy a container
* how to run the pipeline, check its status and troubleshoot problems

Also, check out this [very good tutorial](https://github.com/openshift/pipelines-tutorial) by Red Hat.

## Prerequisites

Before you start the tutorial you must set up a OpenShift environment with Tekton installed.

Follow [this guide on installing OpenShift Pipelines](https://github.com/openshift/pipelines-tutorial/blob/master/install-operator.md)

Create a route for the OpenShift registry if you have not done so already.

```
oc patch configs.imageregistry.operator.openshift.io/cluster --patch '{"spec":{"defaultRoute":true}}' --type=merge
```

## Estimated time

1 hour

## Steps

### 1. Tekton pipeline concepts

Tekton provides a set of extensions to Kubernetes, in the form of [Custom Resources](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/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.

![crd](https://2816711705-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M1Bh2a26msY5M4vW3R9%2Fsync%2Fe35f23bcf19f40a2c73f17d0721353f2f3cd079d.png?generation=1591189046700596\&alt=media)

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.

We will go into more detail about each resource during the walkthrough of the example.

Let's 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

### 2. 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. Check out the `beta-update` branch after cloning.

```
git clone https://github.com/odrodrig/tekton-tutorial
cd tekton-tutorial
git checkout beta-update
```

We will work from the bottom-up, i.e. first we will define the Task resources needed to build and deploy the image, then we'll define the Pipeline resource that references the tasks, and finally we'll create the PipelineRun resource needed to run the pipeline.

### 3. 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](https://github.com/tektoncd/catalog). It provides a `git-clone` task which is described [here](https://github.com/tektoncd/catalog/tree/v1beta1/git#git-clone).

The task is reproduced below so that we can talk about it.

```
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 task has one step that uses a Tekton-provided container to clone a Git repo.

A task can have parameters. Parameters help to make a task reusable. This task accepts many parameters such as:

* the URL of the Git repository to clone
* 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 we'll see later. Steps can reference parameter values by using the syntax `$(params.name)` where `name` is the name of the parameter. For example the step uses `$(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. We'll see later how the workspace becomes associated with a storage volume.

Apply the file to your cluster to create the task.

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

### 4. 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 catalog provides a `kaniko` task which does this using Google's `kaniko` tool. The task is described [here](https://github.com/tektoncd/catalog/tree/v1beta1/kaniko).

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. The pipeline will provide 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`. We will see later 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 will be covered later on in the tutorial.

Apply the file to your cluster to create the task.

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

### 5. 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. You can find this yaml file at [tekton/tasks/deploy-using-kubectl.yaml](https://github.com/IBM/cloudpakforapplications-appmod-workshop/tree/65310d40c29b1e271b2b93e7ec52dc5204d223a9/workshop/generatedContent/tekton-tutorial-openshift/tekton/tasks/deploy-using-kubectl.yaml).

```
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. The 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.

You may be wondering about how the task authenticates to the cluster for permission to apply the resource(s) in the yaml file. This will be covered later on in the tutorial.

Apply the file to your cluster to create the task.

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

### 6. Create a pipeline

Below is a Tekton pipeline that runs the tasks we defined above. You can find this yaml file at [tekton/pipeline/build-and-deploy-pipeline.yaml](https://github.com/IBM/cloudpakforapplications-appmod-workshop/tree/65310d40c29b1e271b2b93e7ec52dc5204d223a9/workshop/generatedContent/tekton-tutorial-openshift/tekton/pipeline/build-and-deploy-pipeline.yaml).

```
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: 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: 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 via 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 and allows it to default inside the task.

This pipeline also shows how to take the result of one task and pass it to another task. We saw earlier that the `kaniko` task produces 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 so 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.

Apply the file to your cluster to create the pipeline.

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

### 7. Define a service account

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

The 3rd command will create a secret that contains credentials for accessing the internal OpenShift image registry

Next, run the `get routes` command to view the registry endpoint. Copy it and insert it into the command after that replacing `<Registry route>`. Then, replace  with the email address you used for the IBM Cloud account.

```
kubectl get routes -n openshift-image-registry

export REGISTRY=<Registry route>
export EMAIL=<your IBM ID>

kubectl create secret generic ibm-registry-secret --type="kubernetes.io/basic-auth" --from-literal=username=$(oc whoami) --from-literal=password=$(oc whoami -t)

kubectl create secret docker-registry pull-secret --docker-username=$(oc whoami) --docker-password=$(oc whoami -t) --docker-email=$EMAIL --docker-server=$REGISTRY
```

```
kubectl annotate secret ibm-registry-secret tekton.dev/docker-0=$REGISTRY
```

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

Now you can create the service account using the following yaml. You can find this yaml file at [tekton/pipeline-account.yaml](https://github.com/IBM/cloudpakforapplications-appmod-workshop/tree/65310d40c29b1e271b2b93e7ec52dc5204d223a9/workshop/generatedContent/tekton-tutorial-openshift/tekton/pipeline-account.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 service account 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` which provide the resource-based access control permissions needed for this pipeline to create and modify Kubernetes resources.

Apply the file to your cluster to create the service account and related resources.

```
kubectl apply -f tekton/pipeline-account.yaml
```

### 8. Create a PipelineRun

We've defined reusable Pipeline and Task resources for building and deploying an image. It is now time to look at how one runs the pipeline.

Below is a Tekton PipelineRun resource that runs the pipeline we defined above. You can find this yaml file at [tekton/run/picalc-pipeline-run.yaml](https://github.com/IBM/cloudpakforapplications-appmod-workshop/tree/65310d40c29b1e271b2b93e7ec52dc5204d223a9/workshop/generatedContent/tekton-tutorial-openshift/tekton/run/picalc-pipeline-run.yaml).

```
apiVersion: tekton.dev/v1beta1
kind: PipelineRun
metadata:
  generateName: picalc-pr-
spec:
  pipelineRef:
    name: build-and-deploy-pipeline
  params:
    - name: gitUrl
      value: https://github.com/odrodrig/tekton-tutorial
    - name: pathToYamlFile
      value: kubernetes/picalc.yaml
    - name: imageUrl
      value: <REGISTRY>/default/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 re-run -- 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](https://github.com/IBM/cloudpakforapplications-appmod-workshop/tree/65310d40c29b1e271b2b93e7ec52dc5204d223a9/workshop/generatedContent/tekton-tutorial-openshift/src/picalc.go). The source includes a [Dockerfile](https://github.com/IBM/cloudpakforapplications-appmod-workshop/tree/65310d40c29b1e271b2b93e7ec52dc5204d223a9/workshop/generatedContent/tekton-tutorial-openshift/src/Dockerfile/README.md) 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>` with the information for your private container registry.

    * To find the value for `<REGISTRY>`, enter the command `oc get routes -n openshift-image-registry`.
* The service account named `pipeline-account` which we 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, this means the PipelineRun can only be run serially to avoid conflicting use of the volume. There is [funtionality coming in Tekton](https://github.com/tektoncd/pipeline/pull/2326) to allow each PipelineRun to create its own persistent volume claim and thus use its own volume.

Check that the persistent volume claim is bound before continuing.

```
$ 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
```

### 9. 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 we're using `kubectl create` here instead of `kubectl apply`. As mentioned previously a given PipelineRun resource can run a pipeline only once so 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 view the status of our pipeline run

```
kubectl get pipelinerun

NAME              SUCCEEDED   REASON    STARTTIME   COMPLETIONTIME
picalc-pr-dqwqb   Unknown     Running   5s
```

The pipeline will be successful when `SUCCEEDED` is `True`.

```
NAME              SUCCEEDED   REASON      STARTTIME   COMPLETIONTIME
picalc-pr-dqwqb   True        Succeeded   7m26s       4m49s
```

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
```

#### Debugging a failed PipelineRun

Let's take a look at what a PipelineRun failure would look like. 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 (&#34;step-clone&#34; exited with code 1 (image: &#34;gcr.io/tekton-releases/github.com/tektoncd/pipeline/cmd/git-init@sha256:bee98bfe6807e8f4e0a31b4e786fd1f7f459e653ed1a22b1a25999f33fa9134a&#34;); 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 us that the `clone-repo` pipeline task failed. The `Message` also tells us 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 will 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.

### 10. Check it out in OpenShift Console

OpenShift provides a nice UI for the pipelines and the applications deployed. From the OpenShift console, click **developer** in the upper left drop-down to get to the developer view. Then click **Topology** to view your running app.

![OpenShift Topology](https://2816711705-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M1Bh2a26msY5M4vW3R9%2Fsync%2Fb788271e751a27329224fc7ae964c182aa163a4c.png?generation=1591192257721745\&alt=media)

Click **Pipelines** to explore the pipline your created and explore the PipelineRuns

![OpenShift Pipelines](https://2816711705-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M1Bh2a26msY5M4vW3R9%2Fsync%2Fc017edf0d7bdea3ec3ac1b31cf6d962aee0f4dc7.png?generation=1591192257995078\&alt=media)

## 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 building your own pipelines. There are more features available and many more planned for upcoming releases.
