How do you implement a CI/CD pipeline for microservices using Tekton?

Think of a Tekton CI/CD pipeline for microservices like an automotive assembly line. Each station on the line performs a specialized task: welding, painting, quality inspection, and final assembly. Parts move between stations on a conveyor belt. In Tekton, each station is a Task, the conveyor belt is a workspace, quality gates are when expressions, and the factory floor manager who decides when to start the line is a Trigger. The result is a fully automated, repeatable process that transforms source code into a running, tested, production-ready service.

The pipeline architecture for a microservice like payments-api typically consists of six to eight sequential and parallel Tasks. The first Task, git-clone from the Tekton Catalog, checks out the source code into a shared workspace. The next stage fans out into parallel Tasks: a lint-and-format Task checks code style, a unit-test Task runs the test suite, and a dependency-audit Task scans for vulnerable dependencies. These three can run simultaneously because they all read from the same workspace without conflicting writes. After all three parallel Tasks succeed, the pipeline converges on a build-and-push Task that uses Kaniko to create a container image and push it to the registry, emitting the image digest as a Tekton Result. A security-scan Task then runs Trivy or Grype against the built image using the digest result, and emits a scan-status result. Finally, a deploy Task uses kubectl or Helm to update the Kubernetes Deployment with the new image digest, but only if the security scan passed, enforced by a when expression.

Workspace design is critical for microservice pipelines. A single PersistentVolumeClaim workspace shared across all Tasks provides the source code, build artifacts, and test reports. Using a volumeClaimTemplate in the PipelineRun automatically provisions a fresh PVC for each run, ensuring isolation between concurrent pipeline executions for different services. The access mode must be ReadWriteOnce for most cloud providers, which means all Tasks sharing the workspace must run on the same node. If your cluster uses multiple availability zones, configure a StorageClass with volumeBindingMode: WaitForFirstConsumer to ensure the PVC is created in the zone where the first Task pod is scheduled. For the order-processing-service, which has a large test data directory, allocating a 5Gi PVC prevents out-of-space errors during parallel test execution.

In a microservices architecture with services like payments-api, user-auth-service, order-processing-service, inventory-sync, and checkout-service, you need a strategy for managing multiple pipelines efficiently. The recommended approach is creating a parameterized Pipeline template that accepts the repository URL, image name, deployment name, and namespace as parameters. Each service uses the same Pipeline definition with different PipelineRun parameters, avoiding pipeline duplication. Tekton Triggers handles the automation: a single EventListener receives webhooks from all repositories, CEL interceptors route events to the correct TriggerTemplate based on the repository name, and each TriggerTemplate creates a PipelineRun with service-specific parameters. This architecture means adding a new microservice to the CI/CD system requires only a new TriggerTemplate and TriggerBinding, not a new Pipeline.

Production hardening involves several critical considerations. Set resource requests and limits on every Task step to prevent build pods from consuming excessive cluster resources and starving other workloads. Configure timeouts at both the Task and Pipeline levels to prevent hung builds from occupying cluster resources indefinitely. Use the finally block in the Pipeline to define cleanup Tasks that run regardless of pipeline success or failure, such as sending Slack notifications, updating deployment status in a dashboard, or cleaning up temporary cloud resources. Implement Tekton Chains for supply chain security, which automatically signs TaskRun and PipelineRun attestations and generates SLSA provenance documents. For the checkout-service handling financial transactions, this provenance chain provides auditable proof that every deployed image was built from a specific commit, passed all tests, cleared the security scan, and was deployed by an authorized pipeline execution.