Pierre Blanc

Start using dci-pipeline


dci-openshift-agent and dci-openshift-app-agent have long been the most used method to deploy and test OpenShift with DCI.

dci-pipeline was released to offer a way to chain jobs.

We have been using it now for years, it is stable and offers definitely more flexibility than agents.

That is why dci-pipeline is now the recommended way to deploy and test OpenShift with DCI.

With dci-pipeline you can easily :

  • Deploy OpenShift, as you did it with dci-openshift-agent.
  • Test your workload, as you did it with dci-openshift-app-agent.
  • Chain multiple jobs, deployment and/or test.
  • Share information between jobs.
  • Split the declaration of your job in multiple files.
  • And more…

Another blog post posted a few months ago describes a dci-pipeline use case, we definitely recommend reading it. In this present blog post, we will explain how to use it from the beginning. No prerequisites are necessary.


dci-pipeline is an Open Source project hosted in github The repository provides you a way to install it from sources.

To simplify access to the tools, rpm package for RHEL 7, 8 and 9 are also available here.

Always refer you to the official documentation to know the updated way to install dci-pipeline.


The command line

Once installed, we can start to play with:

    $ dci-pipeline --help
    Usage: /usr/bin/dci-pipeline [<jobdef name>:<key>=<value>...] [<pipeline file>]

The command only accepts two type of arguments:

  • Pipeline : which is the declaration of the job, multiple files can be used.
  • Variables : to easily override a variable on these pipeline files.

It is a good practice to have in the name of the pipeline the prefix -pipeline.yml.

And also using the absolute path of your pipelines.

It may be also wise to use the job name as the name of the file. Example:

    $ grep name deploy-ocp-pipeline.yml
    - name: deploy-ocp

If you use multiple files to configure one job, you can ignore this advice.

Pipeline files

A pipeline file is the declaration in YAML of one or several job definitions.

Even if we can have only one file to declare multiple jobs, it is often more sensible to have one file per job or even split your job into multiple jobs regarding your use case.

Let’s see how these famous files are composed:

    - name: openshift-vanilla ¹
      stage: ocp ²
      ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml ³
      ansible_inventory: /etc/dci-pipeline/inventory ⁴
      dci_credentials: /etc/dci-openshift-agent/dci_credentials.yml ⁵
      topic: OCP-4.11 ⁶
      components: ⁷
        - ocp
  • ¹ : This is the name of the job. If you use multiple pipeline files with the same name, it will be merged in only one job.
  • ² : Used to order jobs, will be explain below.
  • ³ : Playbook used, you can modify this parameter to use dci-openshift-app-agent playbook.
  • ⁴ : Inventory used.
  • ⁵ : Credential to access to DCI, it contents DCI_ and quay_ variables.
  • ⁶ : OCP release used.
  • ⁷ : List of components needed.

This file contains one job named openshift-vanilla which will install OpenShift 4.11.

If you want to split your job in multiple files, you just need to use the same job name.

    $ cat openshift-custom-pipeline.yaml
    - name: openshift-custom
      stage: ocp
      ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
      ansible_inventory: /etc/dci-pipeline/inventory
      dci_credentials: /etc/dci-openshift-agent/dci_credentials.yml
    $ cat ocp-4.10-pipeline.yaml
    - name: openshift-custom
      topic: OCP-4.10
        - ocp

Now, to execute this job, we just have to specify these two pipeline files:

    $ dci-pipeline ~/openshift-custom-pipeline.yaml ~/ocp-4.10-pipeline.yaml

This feature can be very useful to avoid duplications of configuration.

Override pipeline

First, any part in the pipeline files can be overridden on the command line using the syntax :=. For example if you want to change the topic version in the openshift-vanilla job from the previous example, use:

    $ dci-pipeline openshift-vanilla:topic=OCP-12 ~/ocp-vanilla-pipeline.yaml

Also we often need to customize variables of the playbook, in this case use the key ansible_extravars.

    - name: openshift-vanilla
      stage: ocp
      ansible_playbook: /usr/share/dci-openshift-agent/dci-openshift-agent.yml
      ansible_inventory: /etc/dci-pipeline/inventory
        variable1: value1
      dci_credentials: /etc/dci-openshift-agent/dci_credentials.yml
      topic: OCP-4.11
        - ocp

In the official documentation it also explains how to customize environment variables, use the variables files and more.

Order your jobs

To create an order on the execution of your jobs two configuration keys are used:

  • stage : to define the actual step (string)
  • prev_stages: to specify what are the previous job (array)

Let's now go through an example of a common use case:

simple pipelinediagram

We shrunk the jobs by just showing the important part for the configuration

So, the first job deploys OpenShift.

    $ cat openshift-vanilla-pipeline.yaml
    - name: openshift-vanilla
      stage: ocp

This second job installs the workload on the infrastructure, the prev_stages value specifies what is the previous job.

    $ cat deploy-app1-pipeline.yaml
    - name: deploy-app1
      stage: workflow
      prev_stages: [ocp]

The final job doing test of the workload.

    $ cat http-test-pipeline.yaml
    - name: http-test
      stage: test
      prev_stages: [ocp, workflow]

This job needs to be executed in the last, prev_stages contains all previous stages.

Well, now maybe you want to pass variables between jobs. Perfect, a mechanism exists to share information between jobs with the output/input configuration keys, it is explainen in detail in this section.


To summarize, dci-pipeline is the recommended way to deploy and test OpenShift with DCI, it replaces the agents and offers much more flexibility to create complex workflows.

This blog post partially covered its major behaviors, others posts will follow with more advanced scenarios and features.