Pipelines are the top-level component of continuous integration, deployment, and delivery.
Pipelines are the top-level component of continuous integration, delivery, and deployment.
Pipelines comprise:
- Jobs that define what to run. For example, code compilation or test runs.
- Stages that define when and how to run. For example, that test run after code compilation.
- Stages that define when and how to run. For example, that tests run only after code compilation.
Jobs in a stage are executed by [Runners](runners/README.md) in parallel, if there are enough concurrent [Runners](runners/README.md).
Multiple jobs in the same stage are executed by [Runners](runners/README.md) in parallel, if there are enough concurrent [Runners](runners/README.md).
If the jobs in a stage:
If all the jobs in a stage:
- Succeed, the pipeline moves on to the next stage.
- Fail, the next stage is not (usually) executed.
- Fail, the next stage is not (usually) executed and the pipeline ends early.
NOTE: **Note:**
If you have a [mirrored repository where GitLab pulls from](https://docs.gitlab.com/ee/workflow/repository_mirroring.html#pulling-from-a-remote-repository-starter),
If you have a [mirrored repository that GitLab pulls from](https://docs.gitlab.com/ee/workflow/repository_mirroring.html#pulling-from-a-remote-repository-starter),
you may need to enable pipeline triggering in your project's
**Settings > Repository > Pull from a remote repository > Trigger pipelines for mirror updates**.
### Simple example
### Simple pipeline example
As an example, imagine a pipeline consisting of four stages, executed in the following order:
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Pipelines can be complex structures with many sequential and parallel jobs.
To make it easier to understand the flow of a pipeline, GitLab has pipeline graphs for viewing pipeline
To make it easier to understand the flow of a pipeline, GitLab has pipeline graphs for viewing pipelines
and their statuses.
Pipeline graphs can be displayed in two different ways, depending on what page you
access the graph.
Pipeline graphs can be displayed in two different ways, depending on the page you
access the graph from.
NOTE: **Note:**
GitLab capitalizes the stages' names when shown in the [pipeline graphs](#pipeline-graphs).
### Regular pipeline graphs
Regular pipeline graphs that show the names of the jobs of each stage. Regular pipeline graphs can
Regular pipeline graphs show the names of the jobs of each stage. Regular pipeline graphs can
be found when you are on a [single pipeline page](#seeing-pipeline-status). For example:
![Pipelines example](img/pipelines.png)
### Pipeline mini graphs
Pipeline mini graphs takes less space and can give you a
quick glance if all jobs pass or something failed. The pipeline mini graph can
Pipeline mini graphs take less space and can tell you at a
quick glance if all jobs passed or something failed. The pipeline mini graph can
be found when you navigate to:
- The pipelines index page.
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### How pipeline duration is calculated
Total running time for a given pipeline excludes retries and pending
(queue) time.
(queued) time.
Each job is represented as a `Period`, which consists of:
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@@ -120,7 +120,7 @@ In the example:
- B begins at 2 and ends at 4.
- C begins at 6 and ends at 7.
Visually it can be viewed as:
Visually, it can be viewed as:
```text
0 1 2 3 4 5 6 7
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@@ -144,7 +144,7 @@ In particular:
- Jobs are the [basic configuration](yaml/README.html#introduction) component.
- Stages are defined using the [`stages`](yaml/README.html#stages) keyword.
For all available configuration options, see [GitLab CI/CD Pipeline Configuration Reference](yaml/README.md).
For all available configuration options, see the [GitLab CI/CD Pipeline Configuration Reference](yaml/README.md).
### Settings and schedules
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Pipeline status and test coverage report badges are available and configurable for each project.
For information on adding pipeline badges to project, see [Pipeline badges](../user/project/pipelines/settings.md#pipeline-badges).
For information on adding pipeline badges to projects, see [Pipeline badges](../user/project/pipelines/settings.md#pipeline-badges).
## Multi-project pipelines **[PREMIUM]**
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## Working with pipelines
Generally, pipelines are executed automatically and require no intervention once created.
In general, pipelines are executed automatically and require no intervention once created.
However, there are instances where you'll need to interact with pipelines. These are documented below.
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- In the pipeline widgets, in the merge requests and commit pages.
- In the job views, in the global and detailed views of a job.
In any case, if you hover over the failed job you can see the reason it failed.
In each place, if you hover over the failed job you can see the reason it failed.
![Pipeline detail](img/job_failure_reason.png)
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@@ -294,8 +294,8 @@ allow you to require manual interaction before moving forward in the pipeline.
You can do this straight from the pipeline graph. Just click on the play button
to execute that particular job.
For example, your entire pipeline could run automatically, but require manual action to
[deploy to production](environments.md#manually-deploying-to-environments). Below, the `production`
For example, your pipeline start automatically, but require manual action to
[deploy to production](environments.md#manually-deploying-to-environments). In the example below, the `production`
stage has a job with a manual action.
![Pipelines example](img/pipelines.png)
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This is especially useful for timed incremental rollout where new code is rolled out gradually.
For example, if you start rolling out new code and users:
For example, if you start rolling out new code and:
-Do not experience trouble, GitLab can automatically complete the deployment from 0% to 100%.
-Experience trouble with the new code, you can stop the timed incremental rollout by canceling the pipeline
and [rolling](environments.md#rolling-back-changes) back to last stable version.
-Users do not experience trouble, GitLab can automatically complete the deployment from 0% to 100%.
-Users experience trouble with the new code, you can stop the timed incremental rollout by canceling the pipeline
and [rolling](environments.md#rolling-back-changes) back to the last stable version.