Directed Acyclic Graph
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:
| build | test | deploy |
|---|---|---|
build_a
| test_a
| deploy_a
|
build_b
| test_b
| deploy_b
|
build_c
| test_c
| deploy_c
|
build_d
| test_d
| deploy_d
|
Using a DAG, you can relate the _a jobs to each other separately from the _b jobs,
and even if service a takes a very long time to build, service b doesn’t
wait for it and finishes as quickly as it can. In this very same pipeline, _c and
_d can be left alone and run together in staged sequence just like any standard
GitLab pipeline.
Use cases
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.
Usage
Relationships are defined between jobs using the needs keyword.
The needs keyword also works with the parallel keyword,
giving you powerful options for parallelization within your pipeline.
Limitations
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:
-
needsadditional details. - Related epic tracking planned improvements.
Needs Visualization
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, select Needs when viewing a pipeline that uses the needs keyword.
Selecting a node highlights all the job paths it depends on.
You can also see needs relationships in full pipeline graphs.