General CI/CD pipeline examples
A general Continuous Integration/Continuous Deployment (CI/CD) pipeline is an automated workflow designed to streamline software delivery, from a developer's initial code commit to the application's deployment in a production environment.
The pipeline typically consists of several core stages:
General CI/CD Pipeline Stages
| Stage | Description | Key Activities | Example Tools |
|---|---|---|---|
| Source/Commit | The process is triggered when a developer pushes code changes to a shared repository. | Code commit, version control, manage branches. | Git, GitHub, GitLab, Bitbucket |
| Build | The committed code is fetched, compiled into an executable artifact (like a container image), and its dependencies are linked. | Compiling code, packaging application, running static analysis (SAST), creating container images (Docker). | Maven, Gradle, Docker, Jenkins, AWS CodeBuild |
| Test | Automated tests are run to validate the new code and ensure it doesn't break existing functionality. | Unit testing, integration testing, security testing (DAST, IAST), performance testing, regression testing. | JUnit, Selenium, Jest, Pytest, Katalon, SonarQube |
| Staging/Deploy | The application artifact is deployed to a staging or pre-production environment that mirrors the production setting. | Environment provisioning, configuration, user acceptance testing (UAT). | Ansible, Chef, Spinnaker, Kubernetes, AWS CodeDeploy |
| Production | If all tests pass and stakeholders approve (in CD, this is automatic), the application is automatically deployed to the live production environment for end-users. | Automated deployment, canary releases, blue-green deployments. | Kubernetes, Docker, Jenkins, AWS CodePipeline, Azure DevOps |
| Monitor/Feedback | Post-deployment, the application's health, performance, and user feedback are continuously tracked. | Tracking performance, error reporting, collecting user feedback. | Prometheus, Grafana, New Relic, PagerDuty |
Real-World Examples by Platform
Specific implementations combine the general stages using different toolchains and configurations:
- Jenkins Pipeline: A highly flexible, open-source example often uses Jenkins to orchestrate the entire process, integrating various plugins for tasks like building a Java web app with Maven, testing with JUnit, and deploying with Docker and Kubernetes.
- GitLab CI/CD: GitLab offers built-in CI/CD features configured via a
.gitlab-ci.ymlfile in the repository. A typical pipeline might involve pushing Python code to a repository, automatically triggering linting and Pytest jobs, followed by an automatic deployment to a service like Heroku upon success. - AWS CodePipeline: An example within the Amazon ecosystem uses AWS services exclusively. A developer commits a Node.js application to AWS CodeCommit, which triggers a CodePipeline workflow that uses CodeBuild for building and CodeDeploy for deployment to various AWS environments.
- GitHub Actions: With GitHub Actions, workflows are triggered by events like a pull request. The workflow can automatically build and test the code in a virtual environment and then deploy the merged request to production.