A directed acyclic graph can be used in the context of a CI/CD pipeline to build relationships between jobs such that execution is performed in the quickest possible manner, regardless how stages may be set up.
For example, you may have a specific tool or separate website that is built as part of your main project. Using a DAG, you can specify the relationship between these jobs and GitLab executes the jobs as soon as possible instead of waiting for each stage to complete.
Unlike other DAG solutions for CI/CD, GitLab does not require you to choose one or the other. You can implement a hybrid combination of DAG and traditional stage-based operation within a single pipeline. Configuration is kept very simple, requiring a single keyword to enable the feature for any job.
Consider a monorepo as follows:
./service_a ./service_b ./service_c ./service_d
It has a pipeline that looks like the following:
Using a DAG, you can relate the
_a jobs to each other separately from the
and even if service
a takes a very long time to build, service
wait for it and finishes as quickly as it can. In this very same pipeline,
_d can be left alone and run together in staged sequence just like any normal
A DAG can help solve several different kinds of relationships between jobs within a CI/CD pipeline. Most typically this would cover when jobs need to fan in or out, and/or merge back together (diamond dependencies). This can happen when you’re handling multi-platform builds or complex webs of dependencies as in something like an operating system build or a complex deployment graph of independently deployable but related microservices.
Additionally, a DAG can help with general speediness of pipelines and helping to deliver fast feedback. By creating dependency relationships that don’t unnecessarily block each other, your pipelines run as quickly as possible regardless of pipeline stages, ensuring output (including errors) is available to developers as quickly as possible.
Relationships are defined between jobs using the
needs: also works with the parallel keyword,
giving you powerful options for parallelization within your pipeline.
A directed acyclic graph is a complicated feature, and as of the initial MVC there are certain use cases that you may need to work around. For more information, check the:
The needs visualization makes it easier to visualize the relationships between dependent jobs in a DAG. This graph displays all the jobs in a pipeline that need or are needed by other jobs. Jobs with no relationships are not displayed in this view.
To see the needs visualization, click on the Needs tab when viewing a pipeline that uses the
Clicking a node highlights all the job paths it depends on.
You can also see
needs relationships in full pipeline graphs.