96 lines
4.3 KiB
Markdown
96 lines
4.3 KiB
Markdown
---
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stage: Verify
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group: Continuous Integration
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info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
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type: reference
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---
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# Directed Acyclic Graph
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> - [Introduced](https://gitlab.com/gitlab-org/gitlab-foss/-/issues/47063) in GitLab 12.2.
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> - [Feature flag removed](https://gitlab.com/gitlab-org/gitlab/-/issues/206902) in GitLab 12.10.
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A [directed acyclic graph](https://www.techopedia.com/definition/5739/directed-acyclic-graph-dag) can be
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used in the context of a CI/CD pipeline to build relationships between jobs such that
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execution is performed in the quickest possible manner, regardless how stages may
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be set up.
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For example, you may have a specific tool or separate website that is built
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as part of your main project. Using a DAG, you can specify the relationship between
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these jobs and GitLab executes the jobs as soon as possible instead of waiting
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for each stage to complete.
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Unlike other DAG solutions for CI/CD, GitLab does not require you to choose one or the
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other. You can implement a hybrid combination of DAG and traditional
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stage-based operation within a single pipeline. Configuration is kept very simple,
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requiring a single keyword to enable the feature for any job.
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Consider a monorepo as follows:
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```plaintext
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./service_a
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./service_b
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./service_c
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./service_d
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```
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It has a pipeline that looks like the following:
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| build | test | deploy |
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| ----- | ---- | ------ |
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| build_a | test_a | deploy_a |
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| build_b | test_b | deploy_b |
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| build_c | test_c | deploy_c |
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| build_d | test_d | deploy_d |
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Using a DAG, you can relate the `_a` jobs to each other separately from the `_b` jobs,
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and even if service `a` takes a very long time to build, service `b` doesn't
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wait for it and finishes as quickly as it can. In this very same pipeline, `_c` and
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`_d` can be left alone and run together in staged sequence just like any normal
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GitLab pipeline.
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## Use cases
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A DAG can help solve several different kinds of relationships between jobs within
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a CI/CD pipeline. Most typically this would cover when jobs need to fan in or out,
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and/or merge back together (diamond dependencies). This can happen when you're
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handling multi-platform builds or complex webs of dependencies as in something like
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an operating system build or a complex deployment graph of independently deployable
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but related microservices.
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Additionally, a DAG can help with general speediness of pipelines and helping
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to deliver fast feedback. By creating dependency relationships that don't unnecessarily
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block each other, your pipelines run as quickly as possible regardless of
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pipeline stages, ensuring output (including errors) is available to developers
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as quickly as possible.
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## Usage
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Relationships are defined between jobs using the [`needs:` keyword](../yaml/README.md#needs).
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Note that `needs:` also works with the [parallel](../yaml/README.md#parallel) keyword,
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giving you powerful options for parallelization within your pipeline.
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## Limitations
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A directed acyclic graph is a complicated feature, and as of the initial MVC there
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are certain use cases that you may need to work around. For more information:
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- [`needs` requirements and limitations](../yaml/README.md#requirements-and-limitations).
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- Related epic [tracking planned improvements](https://gitlab.com/groups/gitlab-org/-/epics/1716).
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## Needs Visualization
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> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/issues/215517) in GitLab 13.1 as a [Beta feature](https://about.gitlab.com/handbook/product/#beta).
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> - It became a [standard feature](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/38517) in 13.3.
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> - [Feature flag removed](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/52208) in GitLab 13.9.
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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.
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To see the needs visualization, click on the **Needs** tab when viewing a pipeline that uses the `needs:` keyword.
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Clicking a node highlights all the job paths it depends on.
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