Understanding OpenShift Pipelines

 

Understanding OpenShift Pipelines

Red Hat OpenShift Pipelines is a cloud-native, continuous integration and continuous delivery (CI/CD) solution based on Kubernetes resources. It uses Tekton building blocks to automate deployments across multiple platforms by abstracting away the underlying implementation details. Tekton introduces a number of standard custom resource definitions (CRDs) for defining CI/CD pipelines that are portable across Kubernetes distributions.

Key features

  • Red Hat OpenShift Pipelines is a serverless CI/CD system that runs pipelines with all the required dependencies in isolated containers.

  • Red Hat OpenShift Pipelines are designed for decentralized teams that work on microservice-based architecture.

  • Red Hat OpenShift Pipelines use standard CI/CD pipeline definitions that are easy to extend and integrate with the existing Kubernetes tools, enabling you to scale on-demand.

  • You can use Red Hat OpenShift Pipelines to build images with Kubernetes tools such as Source-to-Image (S2I), Buildah, Buildpacks, and Kaniko that are portable across any Kubernetes platform.

  • You can use the OpenShift Container Platform Developer console to create Tekton resources, view logs of pipeline runs, and manage pipelines in your OpenShift Container Platform namespaces.

OpenShift Pipelines Concepts

This guide provides a detailed view of the various pipeline concepts.

Tasks

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 
kind: Task 
metadata:
  name: apply-manifests 
spec: 
  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 -----------------------------------
The task API version, v1.
The type of Kubernetes object, Task.
The unique name of this task.
The list of parameters and steps in the task and the workspace used by the task.

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

Starting with OpenShift Pipelines 1.6, the following defaults from the step YAML file are removed:

  • The HOME environment variable does not default to the /tekton/home directory

  • The workingDir field does not default to the /workspace directory

Instead, the container for the step defines the HOME environment variable and the workingDir field. However, you can override the default values by specifying the custom values in the YAML file for the step.

As a temporary measure, to maintain backward compatibility with the older OpenShift Pipelines versions, you can set the following fields in the TektonConfig custom resource definition to false:

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

When expression

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:

  • input: Specifies static inputs or variables such as a parameter, task result, and execution status. You must enter a valid input. If you do not enter a valid input, its value defaults to an empty string.

  • operator: Specifies the relationship of an input to a set of values. Enter in or notin as your operator values.

  • values: Specifies an array of string values. Enter a non-empty array of static values or variables such as parameters, results, and a bound state of a workspace.

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 result of a previous task is as expected.

  • A file in a Git repository has changed in the previous commits.

  • An image exists in the registry.

  • An optional workspace is available.

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 
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 
        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: 
          - input: "$(tasks.check-file.results.exists)"
            operator: in
            values: ["yes"]
        taskSpec:
          steps:
            - name: echo
              image: ubuntu
              script: 'echo file exists'
...
      - name: task-should-be-skipped-1
        when: 
          - input: "$(params.path)"
            operator: notin
            values: ["README.md"]
        taskSpec:
          steps:
            - name: echo
              image: ubuntu
              script: exit 1
...
    finally:
      - name: finally-task-should-be-executed
        when: 
          - 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
Specifies the type of Kubernetes object. In this example, PipelineRun.
Task create-file used in the pipeline.
when expression that specifies to execute the echo-file-exists task only if the exists result from the check-file task is yes.
when expression that specifies to skip the task-should-be-skipped-1 task only if the path parameter is README.md.
when expression that specifies to execute the finally-task-should-be-executed task only if the execution status of the echo-file-exists task and the task status is Succeeded, the exists result from the check-file task is yes, and the path parameter is README.md.

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

  • All the criteria are met: Tasks and the when expression symbol, which is represented by a diamond shape are green.

  • Any one of the criteria are not met: Task is skipped. Skipped tasks and the when expression symbol are grey.

  • None of the criteria are met: Task is skipped. Skipped tasks and the when expression symbol are grey.

  • Task run fails: Failed tasks and the when expression symbol are red.

Finally tasks

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 
spec:
  workspaces:
    - name: git-source 
  tasks:
    - name: clone-app-repo 
      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 
      taskRef: 
        name: cleanup-workspace
      workspaces: 
        - name: source
          workspace: git-source
    - name: check-git-commit
      params: 
        - name: commit
          value: $(tasks.clone-app-repo.results.commit)
      taskSpec: 
        params:
          - name: commit
        steps:
          - name: check-commit-initialized
            image: alpine
            script: |
              if [[ ! $(params.commit) ]]; then
                exit 1
              fi
Unique name of the pipeline.
The shared workspace where the git repository is cloned.
The task to clone the application repository to the shared workspace.
The task to clean-up the shared workspace.
A reference to the task that is to be executed in the task run.
A shared storage volume that a task in a pipeline needs at runtime to receive input or provide output.
A list of parameters required for a task. If a parameter does not have an implicit default value, you must explicitly set its value.
Embedded task definition.

TaskRun

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 
kind: TaskRun 
metadata:
  name: apply-manifests-taskrun 
spec: 
  taskRunTemplate:
    serviceAccountName: pipeline
  taskRef: 
    kind: Task
    name: apply-manifests
  workspaces: 
  - name: source
    persistentVolumeClaim:
      claimName: source-pvc
The task run API version v1.
Specifies the type of Kubernetes object. In this example, TaskRun.
Unique name to identify this task run.
Definition of the task run. For this task run, the task and the required workspace are specified.
Name of the task reference used for this task run. This task run executes the apply-manifests task.
Workspace used by the task run.

Pipelines

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 ClusterTask resource:

apiVersion: tekton.dev/v1 
kind: Pipeline 
metadata:
  name: build-and-deploy 
spec: 
  workspaces: 
  - name: shared-workspace
  params: 
  - 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-1.14"
  - name: IMAGE
    type: string
    description: image to be built from the code
  tasks: 
  - name: fetch-repository
    taskRef:
      name: git-clone
      kind: ClusterTask
    workspaces:
    - name: output
      workspace: shared-workspace
    params:
    - name: url
      value: $(params.git-url)
    - name: subdirectory
      value: ""
    - name: deleteExisting
      value: "true"
    - name: revision
      value: $(params.git-revision)
  - name: build-image 
    taskRef:
      name: buildah
      kind: ClusterTask
    params:
    - name: TLSVERIFY
      value: "false"
    - name: IMAGE
      value: $(params.IMAGE)
    workspaces:
    - name: source
      workspace: shared-workspace
    runAfter:
    - fetch-repository
  - name: apply-manifests 
    taskRef:
      name: apply-manifests
    workspaces:
    - name: source
      workspace: shared-workspace
    runAfter: 
    - 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
Pipeline API version v1.
Specifies the type of Kubernetes object. In this example, Pipeline.
Unique name of this pipeline.
Specifies the definition and structure of the pipeline.
Workspaces used across all the tasks in the pipeline.
Parameters used across all the tasks in the pipeline.
Specifies the list of tasks used in the pipeline.
Task build-image, which uses the buildah ClusterTask to build application images from a given Git repository.
Task apply-manifests, which uses a user-defined task with the same name.
Specifies the sequence in which tasks are run in a pipeline. In this example, the apply-manifests task is run only after the build-image task is completed.

The Red Hat OpenShift Pipelines Operator installs the Buildah cluster task and creates the pipeline service account with sufficient permission to build and push an image. The Buildah cluster task can fail when associated with a different service account with insufficient permissions.

PipelineRun

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 
kind: PipelineRun 
metadata:
  name: build-deploy-api-pipelinerun 
spec:
  pipelineRef:
    name: build-and-deploy 
  params: 
  - 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: 
  - name: shared-workspace
    volumeClaimTemplate:
      spec:
        accessModes:
          - ReadWriteOnce
        resources:
          requests:
            storage: 500Mi
Pipeline run API version v1.
The type of Kubernetes object. In this example, PipelineRun.
Unique name to identify this pipeline run.
Name of the pipeline to be run. In this example, build-and-deploy.
The list of parameters required to run the pipeline.
Workspace used by the pipeline run.
Additional resources

Workspaces

It is recommended that you use workspaces instead of the PipelineResource CRs in Red Hat OpenShift Pipelines, as PipelineResource CRs are difficult to debug, limited in scope, and make tasks less reusable.

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:

  • Store task inputs and outputs

  • Share data among tasks

  • Use it as a mount point for credentials held in secrets

  • Use it as a mount point for configurations held in config maps

  • Use it as a mount point for common tools shared by an organization

  • Create a cache of build artifacts that speed up jobs

You can specify workspaces in the TaskRun or PipelineRun using:

  • A read-only config map or secret

  • An existing persistent volume claim shared with other tasks

  • A persistent volume claim from a provided volume claim template

  • An emptyDir that is discarded when the task run completes

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: 
  - name: shared-workspace
  params:
...
  tasks: 
  - name: build-image
    taskRef:
      name: buildah
      kind: ClusterTask
    params:
    - name: TLSVERIFY
      value: "false"
    - name: IMAGE
      value: $(params.IMAGE)
    workspaces: 
    - name: source 
      workspace: shared-workspace 
    runAfter:
    - fetch-repository
  - name: apply-manifests
    taskRef:
      name: apply-manifests
    workspaces: 
    - name: source
      workspace: shared-workspace
    runAfter:
      - build-image
...
List of workspaces shared between the tasks defined in the pipeline. A pipeline can define as many workspaces as required. In this example, only one workspace named shared-workspace is declared.
Definition of tasks used in the pipeline. This snippet defines two tasks, build-image and apply-manifests, which share a common workspace.
List of workspaces used in the build-image task. A task definition can include as many workspaces as it requires. However, it is recommended that a task uses at most one writable workspace.
Name that uniquely identifies the workspace used in the task. This task uses one workspace named source.
Name of the pipeline workspace used by the task. Note that the workspace source in turn uses the pipeline workspace named shared-workspace.
List of workspaces used in the apply-manifests task. Note that this task shares the source workspace with the build-image task.

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: 
  - name: shared-workspace 
    volumeClaimTemplate: 
      spec:
        accessModes:
          - ReadWriteOnce
        resources:
          requests:
            storage: 500Mi
Specifies the list of pipeline workspaces for which volume binding will be provided in the pipeline run.
The name of the workspace in the pipeline for which the volume is being provided.
Specifies a volume claim template that creates a persistent volume claim to define the storage volume for the workspace.

Triggers

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:

  • The TriggerBinding resource extracts the fields from an event payload and stores them as parameters.

    The following example shows a code snippet of the TriggerBinding resource, which extracts the Git repository information from the received event payload:

    apiVersion: triggers.tekton.dev/v1beta1 
kind: TriggerBinding 
metadata:
  name: vote-app 
spec:
  params: 
  - name: git-repo-url
    value: $(body.repository.url)
  - name: git-repo-name
    value: $(body.repository.name)
  - name: git-revision
    value: $(body.head_commit.id)
    The API version of the TriggerBinding resource. In this example, v1beta1.
    Specifies the type of Kubernetes object. In this example, TriggerBinding.
    Unique name to identify the TriggerBinding resource.
    List of parameters which will be extracted from the received event payload and passed to the TriggerTemplate resource. In this example, the Git repository URL, name, and revision are extracted from the body of the event payload.

  • The TriggerTemplate resource acts as a standard for the way resources must be created. It specifies the way parameterized data from the TriggerBinding resource should be used. A trigger template receives input from the trigger binding, and then performs a series of actions that results in creation of new pipeline resources, and initiation of a new pipeline run.

    The following example shows a code snippet of a TriggerTemplate resource, which creates a pipeline run using the Git repository information received from the TriggerBinding resource you just created:

    apiVersion: triggers.tekton.dev/v1beta1 
kind: TriggerTemplate 
metadata:
  name: vote-app 
spec:
  params: 
  - name: git-repo-url
    description: The git repository url
  - name: git-revision
    description: The git revision
    default: pipelines-1.14
  - name: git-repo-name
    description: The name of the deployment to be created / patched

  resourcetemplates: 
  - apiVersion: tekton.dev/v1
    kind: PipelineRun
    metadata:
      name: build-deploy-$(tt.params.git-repo-name)-$(uid)
    spec:
      taskRunTemplate:
        serviceAccountName: pipeline
      pipelineRef:
        name: build-and-deploy
      params:
      - name: deployment-name
        value: $(tt.params.git-repo-name)
      - name: git-url
        value: $(tt.params.git-repo-url)
      - name: git-revision
        value: $(tt.params.git-revision)
      - name: IMAGE
        value: image-registry.openshift-image-registry.svc:5000/pipelines-tutorial/$(tt.params.git-repo-name)
      workspaces:
      - name: shared-workspace
        volumeClaimTemplate:
         spec:
          accessModes:
           - ReadWriteOnce
          resources:
            requests:
              storage: 500Mi
    The API version of the TriggerTemplate resource. In this example, v1beta1.
    Specifies the type of Kubernetes object. In this example, TriggerTemplate.
    Unique name to identify the TriggerTemplate resource.
    Parameters supplied by the TriggerBinding resource.
    List of templates that specify the way resources must be created using the parameters received through the TriggerBinding or EventListener resources.

  • The Trigger resource combines the TriggerBinding and TriggerTemplate resources, and optionally, the interceptors event processor.

    Interceptors process all the events for a specific platform that runs before the TriggerBinding resource. You can use interceptors to filter the payload, verify events, define and test trigger conditions, and implement other useful processing. Interceptors use secret for event verification. After the event data passes through an interceptor, it then goes to the trigger before you pass the payload data to the trigger binding. You can also use an interceptor to modify the behavior of the associated trigger referenced in the EventListener specification.

    The following example shows a code snippet of a Trigger resource, named vote-trigger that connects the TriggerBinding and TriggerTemplate resources, and the interceptors event processor.

    apiVersion: triggers.tekton.dev/v1beta1 
kind: Trigger 
metadata:
  name: vote-trigger 
spec:
  taskRunTemplate:
    serviceAccountName: pipeline 
  interceptors:
    - ref:
        name: "github" 
      params: 
        - name: "secretRef"
          value:
            secretName: github-secret
            secretKey: secretToken
        - name: "eventTypes"
          value: ["push"]
  bindings:
    - ref: vote-app 
  template: 
     ref: vote-app
---
apiVersion: v1
kind: Secret 
metadata:
  name: github-secret
type: Opaque
stringData:
  secretToken: "1234567"
    The API version of the Trigger resource. In this example, v1beta1.
    Specifies the type of Kubernetes object. In this example, Trigger.
    Unique name to identify the Trigger resource.
    Service account name to be used.
    Interceptor name to be referenced. In this example, github.
    Desired parameters to be specified.
    Name of the TriggerBinding resource to be connected to the TriggerTemplate resource.
    Name of the TriggerTemplate resource to be connected to the TriggerBinding resource.
    Secret to be used to verify events.

  • The EventListener resource provides an endpoint, or an event sink, that listens for incoming HTTP-based events with a JSON payload. It extracts event parameters from each TriggerBinding resource, and then processes this data to create Kubernetes resources as specified by the corresponding TriggerTemplate resource. The EventListener resource also performs lightweight event processing or basic filtering on the payload using event interceptors, which identify the type of payload and optionally modify it. Currently, pipeline triggers support five types of interceptors: Webhook Interceptors, GitHub Interceptors, GitLab Interceptors, Bitbucket Interceptors, and Common Expression Language (CEL) Interceptors.

    The following example shows an EventListener resource, which references the Trigger resource named vote-trigger.

    apiVersion: triggers.tekton.dev/v1beta1 
kind: EventListener 
metadata:
  name: vote-app 
spec:
  taskRunTemplate:
    serviceAccountName: pipeline 
  triggers:
    - triggerRef: vote-trigger
    The API version of the EventListener resource. In this example, v1beta1.
    Specifies the type of Kubernetes object. In this example, EventListener.
    Unique name to identify the EventListener resource.
    Service account name to be used.
    Name of the Trigger resource referenced by the EventListener resource.

Additional resources

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Terraform

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  Terraform is an infrastructure as code tool that lets you build, change, and version cloud and on-prem resources safely and efficiently. HashiCorp Terraform is an infrastructure as code tool that lets you define both cloud and on-prem resources in human-readable configuration files that you can version, reuse, and share. You can then use a consistent workflow to provision and manage all of your infrastructure throughout its lifecycle. Terraform can manage low-level components like compute, storage, and networking resources, as well as high-level components like DNS entries and SaaS features. How does Terraform work? Terraform creates and manages resources on cloud platforms and other services through their application programming interfaces (APIs). Providers enable Terraform to work with virtually any platform or service with an accessible API. HashiCorp and the Terraform community have already written  thousands of providers  to manage many different types of resources and services.
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Scrum Master Interview help - Bootcamp

Understanding the MoSCoW prioritization | How to implement it into your project Projects, irrespective of their size or complexity, often juggle numerous tasks and requirements. In such a scenario,  MoSCoW prioritization  becomes essential to ensure the successful completion of the project.  Besides, it is also a very effective way to manage project priorities. With a straightforward and adaptable approach, the MoSCoW method helps manage stakeholder expectations and improve project outcomes. What is the MoSCoW prioritization technique?  The MoSCoW method is a prioritization matrix widely used in project management and software development. The term MoSCoW is an acronym that stands for  Must have ,  Should have ,  Could have , and  Won’t have , each denoting a level of priority. Here is the breakdown of the MoSCoW method:  Must have:  These are critical requirements that the project cannot be completed without them. If these are not fulfilled, the project is con
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Kubernetes

  Using containers to host applications and other processes has gone mainstream. As more and more workload is moved into containers, management systems are needed to handle the demands of containerized applications at scale. One of the most popular options for managing  container-based workload is Kubernetes . Kubernetes combines container management automation with an extensible API to create a cloud-native application management powerhouse. At its core, Kubernetes manages the placement of pods, which can consist of one or more containers, on a Kubernetes cluster node. Additionally, if one of these pods crashes, Kubernetes can create a new instance of it. If a cluster node is removed, Kubernetes can move any affected workload to a different node in the cluster. On top of that, Kubernetes pods can be scaled to provide more or less throughput to meet scale demands and these scale operations can be triggered manually or automatically using Kuberentes horizontal pod auto-scaling. Finally,
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