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Reducing resource consumption of OpenShift Pipelines

If you use clusters in multi-tenant environments you must control the consumption of CPU, memory, and storage resources for each project and Kubernetes object. This helps prevent any one application from consuming too many resources and affecting other applications.

To define the final resource limits that are set on the resulting pods, Red Hat OpenShift Pipelines use resource quota limits and limit ranges of the project in which they are executed.

To restrict resource consumption in your project, you can:

Understanding resource consumption in pipelines

Each task consists of a number of required steps to be executed in a particular order defined in the steps field of the Task resource. Every task runs as a pod, and each step runs as a container within that pod.

The Resources field in the steps spec specifies the limits for resource consumption. By default, the resource requests for the CPU, memory, and ephemeral storage are set to BestEffort (zero) values or to the minimums set through limit ranges in that project.

Example configuration of resource requests and limits for a step
spec:
  steps:
  - name: <step_name>
    computeResources:
      requests:
        memory: 2Gi
        cpu: 600m
      limits:
        memory: 4Gi
        cpu: 900m

When the LimitRange parameter and the minimum values for container resource requests are specified in the project in which the pipeline and task runs are executed, Red Hat OpenShift Pipelines looks at all the LimitRange values in the project and uses the minimum values instead of zero.

Example configuration of limit range parameters at a project level
apiVersion: v1
kind: LimitRange
metadata:
  name: <limit_container_resource>
spec:
  limits:
  - max:
      cpu: "600m"
      memory: "2Gi"
    min:
      cpu: "200m"
      memory: "100Mi"
    default:
      cpu: "500m"
      memory: "800Mi"
    defaultRequest:
      cpu: "100m"
      memory: "100Mi"
    type: Container
...

Mitigating extra resource consumption in pipelines

When you have resource limits set on the containers in your pod, OpenShift Container Platform sums up the resource limits requested as all containers run simultaneously.

To consume the minimum amount of resources needed to execute one step at a time in the invoked task, Red Hat OpenShift Pipelines requests the maximum CPU, memory, and ephemeral storage as specified in the step that requires the most amount of resources. This ensures that the resource requirements of all the steps are met. Requests other than the maximum values are set to zero.

However, this behavior can lead to higher resource usage than required. If you use resource quotas, this could also lead to unschedulable pods.

For example, consider a task with two steps that uses scripts, and that does not define any resource limits and requests. The resulting pod has two init containers (one for entrypoint copy, the other for writing scripts) and two containers, one for each step.

OpenShift Container Platform uses the limit range set up for the project to compute required resource requests and limits. For this example, set the following limit range in the project:

apiVersion: v1
kind: LimitRange
metadata:
  name: mem-min-max-demo-lr
spec:
  limits:
  - max:
      memory: 1Gi
    min:
      memory: 500Mi
    type: Container

In this scenario, each init container uses a request memory of 1Gi (the max limit of the limit range), and each container uses a request memory of 500Mi. Thus, the total memory request for the pod is 2Gi.

If the same limit range is used with a task of ten steps, the final memory request is 5Gi, which is higher than what each step actually needs, that is 500Mi (since each step runs after the other).

Thus, to reduce resource consumption of resources, you can:

  • Reduce the number of steps in a given task by grouping different steps into one bigger step, using the script feature, and the same image. This reduces the minimum requested resource.

  • Distribute steps that are relatively independent of each other and can run on their own to multiple tasks instead of a single task. This lowers the number of steps in each task, making the request for each task smaller, and the scheduler can then run them when the resources are available.

Additional resources