#TektonPipeline

TektonPipeline is a custom resource provided by the Tekton Operator that allows you to install, update, and manage Tekton Pipelines in your Kubernetes cluster. It provides a declarative way to configure and maintain your Tekton Pipeline components without manually applying manifests.

#Terminology Explanation

#Why We Need TektonPipeline

#The Challenge of Managing Tekton Components

Manually installing and maintaining Tekton Pipeline components involves:

• Applying multiple manifests for different components
• Keeping track of component versions and their compatibility
• Managing upgrades and rollbacks safely
• Configuring components consistently across environments
• Handling custom configurations for specific deployments

This approach leads to:

• Complex management procedures
• Potential for misconfiguration
• Difficulty in tracking the deployed state
• Challenges in implementing consistent upgrades

#How TektonPipeline Addresses These Problems

The TektonPipeline resource provides a declarative, Kubernetes-native way to:

1. Simplify installation: Install all Pipeline components with a single resource
2. Centralize configuration: Manage all component settings in one place
3. Automate upgrades: Easily upgrade to new versions by updating the resource
4. Ensure consistency: Apply the same configuration across different environments
5. Support customization: Customize Pipeline components based on specific requirements

This approach enables better management of Tekton Pipeline components while maintaining the flexibility to customize as needed.

#Advantages

• Simplified management: Single resource to manage all Pipeline components
• Declarative configuration: Define the desired state and let the operator handle implementation details
• Version control: Easy tracking of configuration changes through GitOps practices
• Automated reconciliation: Operator ensures the actual state matches the desired state
• Configuration validation: Built-in validation of configuration parameters
• Streamlined upgrades: Simplified process for upgrading to new versions
• Kubernetes-native: Integrates seamlessly with the Kubernetes ecosystem

#Applicable Scenarios

TektonPipeline is essential in the following scenarios:

1. Initial Deployment: Setting up Tekton Pipeline in a new Kubernetes cluster.

2. Configuration Management: Managing Pipeline configuration changes over time.

3. Version Upgrades: Upgrading Tekton Pipeline to newer versions.

4. Multi-Cluster Deployments: Ensuring consistent Pipeline deployment across multiple clusters.

5. Custom Deployments: Implementing specific Pipeline configurations for different environments.

6. GitOps Workflows: Including Pipeline configuration in GitOps deployment processes.

#Constraints and Limitations

• Requires the Tekton Operator to be installed in the cluster
• Some advanced configurations may require other Tekton Operator resources
• Changes to certain fields may require component restarts
• Must follow Kubernetes resource management principles

#Principles

#TektonPipeline Structure

A TektonPipeline resource has the following structure:

apiVersion: operator.tekton.dev/v1alpha1
kind: TektonPipeline
metadata:
  name: pipeline
spec:
  targetNamespace: tekton-pipelines
  await-sidecar-readiness: true
  coschedule: workspaces
  disable-affinity-assistant: false
  disable-creds-init: false
  disable-home-env-overwrite: true
  disable-working-directory-overwrite: true
  disable-inline-spec: "taskrun,pipelinerun,pipeline"
  enable-api-fields: beta
  enable-bundles-resolver: true
  enable-cel-in-whenexpression: false
  enable-cluster-resolver: true
  enable-custom-tasks: true
  enable-git-resolver: true
  enable-hub-resolver: true
  enable-param-enum: false
  enable-provenance-in-status: true
  enable-step-actions: false
  enforce-nonfalsifiability: none
  keep-pod-on-cancel: false
  max-result-size: 4096
  metrics.count.enable-reason: false
  metrics.pipelinerun.duration-type: histogram
  metrics.pipelinerun.level: pipeline
  metrics.taskrun.duration-type: histogram
  metrics.taskrun.level: task
  require-git-ssh-secret-known-hosts: false
  results-from: termination-message
  running-in-environment-with-injected-sidecars: true
  send-cloudevents-for-runs: false
  set-security-context: false
  trusted-resources-verification-no-match-policy: ignore
  performance:
    disable-ha: false
    buckets: 1
    replicas: 1
    threads-per-controller: 2
    kube-api-qps: 5.0
    kube-api-burst: 10
    statefulset-ordinals: false
  options:
    disabled: false
    configMaps: {}
    deployments: {}

#Key Components and Their Relationships

1. targetNamespace: Where Tekton Pipeline components will be installed

   • Default is "tekton-pipelines"
   • All Pipeline components will be created in this namespace
   • Make sure you have permissions to create resources in this namespace

2. version: The version of Tekton Pipeline to install

   • Can specify a specific version like "v0.45.0"
   • Omitting this field will install the latest version supported by the operator

3. Feature flags: Many configuration options to control Pipeline behavior

   • disable-affinity-assistant: Controls affinity assistant behavior
   • disable-creds-init: Controls credential initialization
   • enable-api-fields: Controls which API fields are enabled
   • enable-custom-tasks: Enables the use of custom tasks
   • And many more as shown in the full structure above

4. performance: Settings for high-availability and performance tuning

   • replicas: Number of controller replicas
   • buckets: Number of buckets for the controller
   • threads-per-controller: Number of worker threads per controller
   • statefulset-ordinals: Enables StatefulSet Ordinals mode

#Configuration Examples

#Basic Installation

apiVersion: operator.tekton.dev/v1alpha1
kind: TektonPipeline
metadata:
  name: pipeline
spec:
  targetNamespace: tekton-pipelines

#Customized Installation with Feature Flags

apiVersion: operator.tekton.dev/v1alpha1
kind: TektonPipeline
metadata:
  name: pipeline
spec:
  targetNamespace: tekton-pipelines
  disable-affinity-assistant: true
  disable-creds-init: true
  enable-api-fields: beta
  enable-custom-tasks: true
  results-from: sidecar-logs
  max-result-size: 4096

#High Availability Configuration

apiVersion: operator.tekton.dev/v1alpha1
kind: TektonPipeline
metadata:
  name: pipeline
spec:
  targetNamespace: tekton-pipelines
  performance:
    disable-ha: false
    buckets: 10
    replicas: 3
    threads-per-controller: 4
    kube-api-qps: 10.0
    kube-api-burst: 20

#Important Parameter Explanations Related to TektonPipeline

#Feature Flags

Feature flags control specific behaviors of the Pipeline controller.

#Applicable Scenarios

• Disabling the affinity assistant for performance reasons
• Enabling or disabling specific Pipeline features
• Configuring environment-specific settings

#Constraints and Limitations

• Some feature flags might be deprecated in future versions
• Changing certain flags may require pipeline reruns
• Compatibility considerations between different flags

#Principles/Parameter Explanation

Common feature flags include:

• disable-affinity-assistant (Default: false): Setting this flag to "true" will prevent Tekton from creating an Affinity Assistant for every TaskRun sharing a PVC workspace.

• disable-creds-init (Default: false): Setting this flag to "true" will prevent Tekton scanning attached service accounts and injecting any credentials it finds into your Steps.

• await-sidecar-readiness (Default: true): Controls whether to wait for sidecar containers to be running before starting a TaskRun's first step.

• running-in-environment-with-injected-sidecars (Default: true): Should be set to false when Pipelines is running in a cluster that does not use injected sidecars such as Istio.

• enable-custom-tasks (Default: false): Enables the use of custom tasks from within pipelines.

#Metrics Properties

Metrics properties control how metrics are collected and reported.

#Applicable Scenarios

• Setting up monitoring for Tekton pipelines
• Customizing metric collection behavior
• Integrating with observability systems

#Configuration Examples

apiVersion: operator.tekton.dev/v1alpha1
kind: TektonPipeline
metadata:
  name: pipeline
spec:
  targetNamespace: tekton-pipelines
  metrics.pipelinerun.duration-type: histogram
  metrics.pipelinerun.level: pipeline
  metrics.taskrun.duration-type: histogram
  metrics.taskrun.level: task
  metrics.count.enable-reason: true

#Performance Configuration

The performance section allows for tuning the controller's performance characteristics.

#Applicable Scenarios

• Scaling for high-volume pipeline execution
• Optimizing resource usage
• Implementing high-availability configurations

#Configuration Examples

apiVersion: operator.tekton.dev/v1alpha1
kind: TektonPipeline
metadata:
  name: pipeline
spec:
  targetNamespace: tekton-pipelines
  performance:
    disable-ha: false
    buckets: 5
    replicas: 2
    threads-per-controller: 4
    kube-api-qps: 10.0
    kube-api-burst: 20
    statefulset-ordinals: true

#Reference Materials

• Tekton Operator Documentation
• TektonPipeline Official Documentation
• Tekton Pipeline Documentation
• Kubernetes Operator Pattern