Tekton custom tasks aren’t just about packaging scripts; they’re a way to embed arbitrary control flow directly into your CI/CD pipelines, managed by Kubernetes controllers.
Let’s watch a custom task in action. Imagine we have a git-clone task that uses a custom controller to fetch code. This isn’t just a git clone command; the controller handles authentication, potential retries, and even provides status updates via Kubernetes events.
Here’s a simplified Task definition for this hypothetical git-clone controller:
apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
name: custom-git-clone
spec:
params:
- name: url
type: string
description: The Git repository URL
- name: revision
type: string
default: main
description: The Git revision to checkout
results:
- name: commit
description: The commit hash of the checked out revision
steps:
- name: clone
image: gcr.io/tekton-releases/github.com/tektoncd/pipeline/cmd/git-sync:v0.35.0 # Example image
script: |
#!/usr/bin/env bash
# This script would normally be executed by the Tekton runner.
# However, with a custom controller, the controller itself
# orchestrates the actual git operation based on the TaskRun.
echo "Initiating custom git clone for $(params.url) at $(params.revision)"
# The actual clone logic is handled by the custom controller.
# We're just defining the inputs and expected outputs here.
# The controller will update the TaskRun status and results.
exit 0
And here’s how a Pipeline might use it:
apiVersion: tekton.dev/v1beta1
kind: Pipeline
metadata:
name: custom-git-pipeline
spec:
tasks:
- name: fetch-code
taskRef:
name: custom-git-clone
params:
- name: url
value: "https://github.com/tektoncd/pipeline.git"
- name: revision
value: "main"
results:
- name: commit
name: commit-hash # Alias for the result
The real magic isn’t in the steps section of the Task definition above; that’s often a placeholder or a minimal script. The custom-git-clone Task is recognized by Kubernetes because a custom controller is watching for Task resources with a specific annotation or name convention, or perhaps it’s registered as a CustomResourceDefinition (CRD) that Tekton knows how to interpret. When a TaskRun is created for custom-git-clone, the custom controller intercepts it. It reads the params and spec, performs the actual git clone using its own logic (which might involve a dedicated pod, a sidecar, or even direct API calls), and then updates the TaskRun’s status, conditions, and results fields to signal completion and store the commit hash.
The problem this solves is the limitation of Tekton’s built-in steps. While steps are great for running containers, they don’t inherently provide complex orchestration, external system integration, or state management beyond pod lifecycle. A custom controller, however, can be written in any language, leverage any SDK, and interact with any API. It can manage long-running operations, perform complex state transitions, and report back to Tekton with rich status information. Think of it as extending Tekton’s DSL with your own domain-specific languages, implemented as Kubernetes controllers. You might use this for custom Git operations, cloud resource provisioning, or even integrating with proprietary CI/CD systems.
The controller understands the Task structure and the TaskRun’s desired state. It translates the TaskRun’s parameters into actions. For our custom-git-clone, the controller might spin up a temporary pod with a specific Git client image, mount a volume for the repository, execute git clone --depth 1 --single-branch --branch $(params.revision) $(params.url) /workspace/output, capture the git rev-parse HEAD output as the commit result, and then update the TaskRun’s status to Succeeded. The steps array in the Task definition itself might not even be executed by the standard Tekton runner; it’s more of a schema declaration for the controller.
A common pattern is for the custom controller to watch TaskRun objects. When it sees a TaskRun for a Task it manages (identified by a label, annotation, or kind if it’s a CRD), it creates its own Kubernetes resources (like Pods) to perform the work. It then monitors these resources and updates the TaskRun’s status.conditions and status.results fields as it progresses. The key is that the custom controller owns the execution lifecycle for that specific Task type, not the default Tekton controller.
The most surprising aspect is that the steps defined within the Task manifest for a custom task are often not executed by the standard Tekton pipeline runner. Instead, they serve primarily as a declaration of inputs, outputs, and potentially a fallback or illustrative script. The custom controller, which is a separate Kubernetes controller watching for these specific TaskRuns, is responsible for interpreting the Task definition and orchestrating the actual execution logic, which might involve creating entirely different pods or interacting with external services.
The next step is understanding how to build and deploy your own custom controllers that integrate with Tekton.