3 types of GitLab CI/CD pipelines and managing them effectively

What is a GitLab CI/CD pipeline?

GitLab CI/CD is a platform that provides version control, build management, and basic Continuous Delivery capabilities. A GitLab CI/CD pipeline automates the software development process by integrating code, running tests, and deploying releases. GitLab’s CI/CD platform enables developers to manage their project workflows, reducing manual tasks and potential human errors.

A GitLab pipeline uses a series of steps known as jobs, defined in the .gitlab-ci.yml file. Each job is a script executed by GitLab runners. These jobs can be configured to run in stages, ensuring specified tasks are completed before progressing to the next step. This setup provides a structured workflow that is both repeatable and scalable.

Understanding GitLab CI/CD pipeline architecture

1. Basic pipelines

Basic pipelines in GitLab provide a straightforward configuration for managing stages like build, test, and deploy sequentially. In this setup, all jobs in a given stage execute concurrently, and the next stage begins only after all jobs in the current stage have completed. This approach is suitable for smaller projects with fewer complexities but can become less efficient as the number of jobs and dependencies grows.

Here’s an example configuration for a basic pipeline:

stages:
  - build
  - test
  - deploy

default:
  image: alpine

build_a:
  stage: build
  script:
    - echo "This job builds component A."

build_b:
  stage: build
  script:
    - echo "This job builds component B."

test_a:
  stage: test
  script:
    - echo "This job tests component A after build jobs are complete."

test_b:
  stage: test
  script:
    - echo "This job tests component B after build jobs are complete."

deploy_a:
  stage: deploy
  script:
    - echo "This job deploys component A after test jobs are complete."
  environment: production

deploy_b:
  stage: deploy
  script:
    - echo "This job deploys component B after test jobs are complete."
  environment: production

In this setup:

• Jobs in the same stage run concurrently: build_a and build_b run simultaneously, followed by test_a and test_b, and so on.
• Stages execute sequentially: The pipeline progresses only after all jobs in the current stage finish.

2. Pipelines with the needs keyword

The needs keyword enables faster pipelines by explicitly defining dependencies between jobs, allowing jobs to start as soon as their dependencies are complete, even if other jobs in the same stage are still running.

Here’s an example pipeline using needs:

stages:
  - build
  - test
  - deploy

default:
  image: alpine

build_a:
  stage: build
  script:
    - echo "Building component A quickly."

build_b:
  stage: build
  script:
    - echo "Building component B slowly."

test_a:
  stage: test
  needs: [build_a]
  script:
    - echo "Testing component A immediately after build_a finishes."

test_b:
  stage: test
  needs: [build_b]
  script:
    - echo "Testing component B immediately after build_b finishes."

deploy_a:
  stage: deploy
  needs: [test_a]
  script:
    - echo "Deploying component A without waiting for build_b or test_b."
  environment: production

deploy_b:
  stage: deploy
  needs: [test_b]
  script:
    - echo "Deploying component B without dependency on component A."
  environment: production

Key benefits:

• Jobs like test_a can start as soon as build_a completes, without waiting for build_b.
• Stages no longer strictly control execution; dependencies dictate the order, enabling greater parallelism.

3. Parent-child pipelines

Parent-child pipelines provide a way to manage complex workflows by splitting a pipeline into smaller, independent pipelines. This modular structure improves readability and maintainability and allows dynamic pipeline behavior. Child pipelines can be triggered conditionally and configured with their own set of jobs and stages.

Parent pipeline example:

stages:
  - triggers

trigger_a:
  stage: triggers
  trigger:
    include: a/.gitlab-ci.yml
  rules:
    - changes:
        - a/*

trigger_b:
  stage: triggers
  trigger:
    include: b/.gitlab-ci.yml
  rules:
    - changes:
        - b/*

Child pipeline A (/a/.gitlab-ci.yml):

stages:
  - build
  - test
  - deploy

default:
  image: alpine

build_a:
  stage: build
  script:
    - echo "Building component A."

test_a:
  stage: test
  needs: [build_a]
  script:
    - echo "Testing component A."

deploy_a:
  stage: deploy
  needs: [test_a]
  script:
    - echo "Deploying component A."
  environment: production

Child pipeline B (/b/.gitlab-ci.yml):

stages:
  - build
  - test
  - deploy

default:
  image: alpine

build_b:
  stage: build
  script:
    - echo "Building component B."

test_b:
  stage: test
  needs: [build_b]
  script:
    - echo "Testing component B."

deploy_b:
  stage: deploy
  needs: [test_b]
  script:
    - echo "Deploying component B."
  environment: production

Advantages of parent-child pipelines:

• Modularity: Configurations are split into smaller files, reducing complexity.
• Dynamic Behavior: Child pipelines can be triggered based on rules, e.g., changes in specific directories.
• Efficiency: Combining needs with parent-child pipelines optimizes execution and reduces redundant tasks.

GitLab CI/CD pipeline best practices

1. General best practices

When designing GitLab CI/CD pipelines, follow practices that improve maintainability, reliability, and scalability. Start by modularizing pipeline configurations using reusable templates and the include keyword, enabling consistency across multiple projects. Use environments and deploy targets to ensure the deployment process aligns with the organization’s workflow.

Adopt a fail-fast approach by setting allow_failure: false for critical jobs to prevent unnecessary execution of subsequent jobs when errors occur early in the pipeline. To reduce complexity, use descriptive names for jobs, stages, and scripts, ensuring clarity for everyone involved in the project. Finally, implement pipeline linting and use the GitLab CI/CD lint tool to validate configurations before committing changes.

2. Use failures to improve processes

Pipeline failures are an opportunity to refine processes and improve overall quality. Start by setting up notifications or alerts for failed jobs, ensuring developers are immediately informed. Review job logs and identify recurring issues, addressing them through automation or adjustments in configuration.

For testing stages, track flaky tests and prioritize fixing them, as they can erode confidence in the pipeline. Use GitLab’s test reports and analytics features to monitor test success rates and identify problem areas. By consistently analyzing pipeline metrics, teams can identify bottlenecks or inefficiencies and take steps to optimize workflows.

3\ Ensure the test environment mirrors production

To maximize reliability, the test environment should closely replicate the production environment. Use the same operating system, dependencies, and configuration files for both environments. This reduces the likelihood of environment-specific issues surfacing after deployment.

Use Docker containers or virtual machine images to ensure a consistent runtime across environments. Tools like GitLab’s variables can be used to define environment-specific values, ensuring that jobs remain adaptable without requiring changes to the pipeline configuration itself. Additionally, consider using feature flags to test changes incrementally in production-like conditions.

4. Standardize the keyword order in jobs

Standardizing the keyword order in job definitions enhances readability and consistency, particularly when collaborating across teams. A consistent structure makes it easier for developers to understand and troubleshoot pipelines. A commonly recommended order is:

1. stage
2. image
3. variables
4. before_script
5. script
6. after_script
7. rules or only/except
8. artifacts
9. needs
10. environment

This order groups related configurations logically, making it easier to identify specific sections at a glance. Adopting this standard minimizes confusion for new contributors and helps align pipelines with industry best practices.

5. Build stageless pipelines

Stageless pipelines eliminate rigid stage definitions, allowing jobs to define their dependencies independently using the needs keyword. This approach improves flexibility, enabling jobs to start as soon as their prerequisites are complete, regardless of stage.

For example, instead of grouping jobs into build, test, and deploy stages, use needs to create a network of dependencies. This reduces idle time in the pipeline and speeds up execution by running independent jobs in parallel. Stageless pipelines are particularly useful for complex workflows with many interdependent components, as they optimize execution order and resource use.

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