GitLab CI/CD Pipeline Patterns#

GitLab CI/CD runs pipelines defined in a .gitlab-ci.yml file at the repository root. Every push, merge request, or tag triggers a pipeline consisting of stages that contain jobs. The pipeline configuration is version-controlled alongside your code, so the build process evolves with the application.

Basic .gitlab-ci.yml Structure#

A minimal pipeline defines stages and jobs. Stages run sequentially; jobs within the same stage run in parallel:

stages:
  - build
  - test
  - deploy

build-app:
  stage: build
  image: golang:1.22
  script:
    - go build -o myapp ./cmd/myapp
  artifacts:
    paths:
      - myapp
    expire_in: 1 hour

unit-tests:
  stage: test
  image: golang:1.22
  script:
    - go test ./... -v -coverprofile=coverage.out
  artifacts:
    reports:
      coverage_report:
        coverage_format: cobertura
        path: coverage.out

deploy-staging:
  stage: deploy
  image: bitnami/kubectl:latest
  script:
    - kubectl set image deployment/myapp myapp=$CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
  environment:
    name: staging
    url: https://staging.example.com
  rules:
    - if: $CI_COMMIT_BRANCH == "main"

Every job must have a stage and a script. The image field specifies the Docker image the job runs inside. If omitted, it falls back to the pipeline-level default image or the runner’s default.

GitOps and Infrastructure as Code#

GitOps says: the desired state is in Git. A controller continuously reconciles the real state to match. Infrastructure as Code says: the desired state is in code. A human (or agent) runs apply to push changes.

These two paradigms overlap but do not align perfectly. Kubernetes resources fit the GitOps model well — ArgoCD/Flux watch Git, detect differences, and apply changes continuously. Cloud infrastructure (VPCs, databases, IAM roles) fits the IaC model better — Terraform tracks state, computes diffs, and applies on command.

Integrating Infrastructure as Code with CI/CD#

Running Terraform locally works for one person. It breaks down when multiple people (or agents) modify infrastructure concurrently, when changes need review before applying, and when environments (dev/staging/prod) need synchronized promotion. CI/CD pipelines solve this by making the plan-review-apply cycle automated, auditable, and safe.

This article covers the patterns for integrating Terraform into CI/CD — from the basic plan-on-PR flow to multi-directory monorepos with dependency ordering and environment promotion.

Jenkins Debugging#

Jenkins failures fall into a few categories: builds stuck waiting, cryptic pipeline errors, performance degradation, and Kubernetes agent pods that refuse to launch.

Builds Stuck in Queue#

When a build sits in the queue and never starts, check the queue tooltip in the UI – it tells you why. Common causes:

No agents with matching labels. The pipeline requests agent { label 'docker-arm64' } but no agent has that label. Check Manage Jenkins > Nodes to see available labels.

Jenkins Kubernetes Integration#

The kubernetes plugin gives Jenkins elastic build capacity. Each build spins up a pod, runs its work, and the pod is deleted. No idle agents, no capacity planning, no snowflake build servers.

The Kubernetes Plugin#

The plugin creates agent pods on demand. When a pipeline requests an agent, a pod is created from a template, its JNLP container connects back to Jenkins, the build runs, and the pod is deleted.

Jenkins Pipeline Patterns#

Jenkins pipelines define your build, test, and deploy process as code in a Jenkinsfile stored alongside your application source. This eliminates configuration drift and makes CI/CD reproducible across branches.

Declarative vs Scripted#

Declarative is the standard choice. It has a fixed structure, better error reporting, and supports the Blue Ocean visual editor. Scripted is raw Groovy – more flexible, but harder to read and maintain. Use declarative unless you need control flow that declarative cannot express.

Jenkins Setup and Configuration#

Jenkins is a self-hosted automation server. Unlike managed CI services, you own the infrastructure, which means you control everything from plugin versions to executor capacity. This guide covers the three main installation methods and the configuration patterns that make Jenkins manageable at scale.

Installation with Docker#

The fastest way to run Jenkins locally or in a VM:

docker run -d \
  --name jenkins \
  -p 8080:8080 \
  -p 50000:50000 \
  -v jenkins_home:/var/jenkins_home \
  jenkins/jenkins:lts-jdk17

Port 8080 is the web UI. Port 50000 is the JNLP agent port for inbound agent connections. The volume mount is critical – without it, all configuration and build history is lost when the container restarts.

Release Management Patterns#

Releasing software is more than merging to main and deploying. A disciplined release process ensures that every version is identifiable, every change is documented, every deployment is reversible, and failures are contained before they reach all users. This operational sequence walks through each phase of a production release workflow.

Phase 1 – Semantic Versioning#

Step 1: Adopt Semantic Versioning#

Semantic versioning (semver) communicates the impact of changes through the version number itself: MAJOR.MINOR.PATCH.

Tekton Pipelines#

Tekton is a Kubernetes-native CI/CD framework. Every pipeline concept – tasks, runs, triggers – is a Kubernetes Custom Resource. Pipelines execute as pods. There is no central server, no UI-driven configuration, no special runtime. If you know Kubernetes, you know how to operate Tekton.

Core Concepts#

Tekton has four primary resources:

• Task: A sequence of steps that run in a single pod. Each step is a container.
• TaskRun: An instantiation of a Task with specific inputs. Creating a TaskRun executes the Task.
• Pipeline: An ordered collection of Tasks with dependencies, parameter passing, and conditional execution.
• PipelineRun: An instantiation of a Pipeline. Creating a PipelineRun executes the entire pipeline.

The separation between definition (Task/Pipeline) and execution (TaskRun/PipelineRun) means you define your CI/CD process once and trigger it many times with different inputs.

Testing Strategies in CI Pipelines#

Every CI pipeline faces the same tension: run enough tests to catch bugs before they merge, but not so many that developers wait twenty minutes for feedback on a one-line change. The answer is not running everything everywhere. It is running the right tests at the right time.

The Three Test Tiers#

Tests divide into three tiers based on execution speed, failure signal quality, and infrastructure requirements.