debian-mirror-gitlab/doc/ci/pipelines/pipeline_architectures.md

stage	group	info	type
Verify	Pipeline Execution	To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments	reference

Pipeline architecture (FREE)

Pipelines are the fundamental building blocks for CI/CD in GitLab. This page documents some of the important concepts related to them.

You can structure your pipelines with different methods, each with their own advantages. These methods can be mixed and matched if needed:

• Basic: Good for straightforward projects where all the configuration is in one easy to find place.

• Directed Acyclic Graph: Good for large, complex projects that need efficient execution.

• Parent-child pipelines: Good for monorepos and projects with lots of independently defined components.

  For an overview, see the Parent-Child Pipelines feature demo.

• Multi-project pipelines: Good for larger products that require cross-project interdependencies, like those with a microservices architecture.

  For example, you might deploy your web application from three different GitLab projects. With multi-project pipelines you can trigger a pipeline in each project, where each has its own build, test, and deploy process. You can visualize the connected pipelines in one place, including all cross-project interdependencies.

  For an overview, see the Multi-project pipelines demo.

Basic Pipelines

This is the simplest pipeline in GitLab. It runs everything in the build stage concurrently, and once all of those finish, it runs everything in the test and subsequent stages the same way. It's not the most efficient, and if you have lots of steps it can grow quite complex, but it's easier to maintain:

graph LR
  subgraph deploy stage
    deploy --> deploy_a
    deploy --> deploy_b
  end
  subgraph test stage
    test --> test_a
    test --> test_b
  end
  subgraph build stage
    build --> build_a
    build --> build_b
  end
  build_a -.-> test
  build_b -.-> test
  test_a -.-> deploy
  test_b -.-> deploy

Example basic /.gitlab-ci.yml pipeline configuration matching the diagram:

stages:
  - build
  - test
  - deploy

image: alpine

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

build_b:
  stage: build
  script:
    - echo "This job builds something else."

test_a:
  stage: test
  script:
    - echo "This job tests something. It will only run when all jobs in the"
    - echo "build stage are complete."

test_b:
  stage: test
  script:
    - echo "This job tests something else. It will only run when all jobs in the"
    - echo "build stage are complete too. It will start at about the same time as test_a."

deploy_a:
  stage: deploy
  script:
    - echo "This job deploys something. It will only run when all jobs in the"
    - echo "test stage complete."
  environment: production

deploy_b:
  stage: deploy
  script:
    - echo "This job deploys something else. It will only run when all jobs in the"
    - echo "test stage complete. It will start at about the same time as deploy_a."
  environment: production

Directed Acyclic Graph Pipelines

If efficiency is important to you and you want everything to run as quickly as possible, you can use Directed Acyclic Graphs (DAG). Use the needs keyword to define dependency relationships between your jobs. When GitLab knows the relationships between your jobs, it can run everything as fast as possible, and even skips into subsequent stages when possible.

In the example below, if build_a and test_a are much faster than build_b and test_b, GitLab starts deploy_a even if build_b is still running.

graph LR
  subgraph Pipeline using DAG
    build_a --> test_a --> deploy_a
    build_b --> test_b --> deploy_b
  end

Example DAG /.gitlab-ci.yml configuration matching the diagram:

stages:
  - build
  - test
  - deploy

image: alpine

build_a:
  stage: build
  script:
    - echo "This job builds something quickly."

build_b:
  stage: build
  script:
    - echo "This job builds something else slowly."

test_a:
  stage: test
  needs: [build_a]
  script:
    - echo "This test job will start as soon as build_a finishes."
    - echo "It will not wait for build_b, or other jobs in the build stage, to finish."

test_b:
  stage: test
  needs: [build_b]
  script:
    - echo "This test job will start as soon as build_b finishes."
    - echo "It will not wait for other jobs in the build stage to finish."

deploy_a:
  stage: deploy
  needs: [test_a]
  script:
    - echo "Since build_a and test_a run quickly, this deploy job can run much earlier."
    - echo "It does not need to wait for build_b or test_b."
  environment: production

deploy_b:
  stage: deploy
  needs: [test_b]
  script:
    - echo "Since build_b and test_b run slowly, this deploy job will run much later."
  environment: production

Parent-child pipelines

As pipelines grow more complex, a few related problems start to emerge:

• The staged structure, where all steps in a stage must complete before the first job in next stage begins, causes waits that slow things down.
• Configuration for the single global pipeline becomes hard to manage.
• Imports with include increase the complexity of the configuration, and can cause namespace collisions where jobs are unintentionally duplicated.
• Pipeline UX has too many jobs and stages to work with.

Additionally, sometimes the behavior of a pipeline needs to be more dynamic. The ability to choose to start sub-pipelines (or not) is a powerful ability, especially if the YAML is dynamically generated.

In the basic pipeline and directed acyclic graph examples above, there are two packages that could be built independently. These cases are ideal for using parent-child pipelines. It separates out the configuration into multiple files, keeping things simpler. You can combine parent-child pipelines with:

• The rules keyword: For example, have the child pipelines triggered only when there are changes to that area.
• The include keyword: Bring in common behaviors, ensuring you are not repeating yourself.
• DAG pipelines inside of child pipelines, achieving the benefits of both.

graph LR
  subgraph Parent pipeline
    trigger_a -.-> build_a
  trigger_b -.-> build_b
    subgraph child pipeline B
    build_b --> test_b --> deploy_b
    end

    subgraph child pipeline A
      build_a --> test_a --> deploy_a
    end
  end

Example /.gitlab-ci.yml configuration for the parent pipeline matching the diagram:

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/*

Example child a pipeline configuration, located in /a/.gitlab-ci.yml, making use of the DAG needs keyword:

stages:
  - build
  - test
  - deploy

image: alpine

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

test_a:
  stage: test
  needs: [build_a]
  script:
    - echo "This job tests something."

deploy_a:
  stage: deploy
  needs: [test_a]
  script:
    - echo "This job deploys something."
  environment: production

Example child b pipeline configuration, located in /b/.gitlab-ci.yml, making use of the DAG needs keyword:

stages:
  - build
  - test
  - deploy

image: alpine

build_b:
  stage: build
  script:
    - echo "This job builds something else."

test_b:
  stage: test
  needs: [build_b]
  script:
    - echo "This job tests something else."

deploy_b:
  stage: deploy
  needs: [test_b]
  script:
    - echo "This job deploys something else."
  environment: production

It's also possible to set jobs to run before or after triggering child pipelines, for example if you have common setup steps or a unified deployment at the end.