1.3. Understanding the monitoring stack - key concepts

By using the nodeSelector constraint with labeled nodes, you can move any of the monitoring stack components to specific nodes. By doing so, you can control the placement and distribution of the monitoring components across a cluster.

By controlling placement and distribution of monitoring components, you can optimize system resource use, improve performance, and separate workloads based on specific requirements or policies.

How node selectors work with other constraints

If you move monitoring components by using node selector constraints, be aware that other constraints to control pod scheduling might exist for a cluster:

When scheduling pods onto nodes, the pod scheduler tries to satisfy all existing constraints when determining pod placement. That is, all constraints compound when the pod scheduler determines which pods will be placed on which nodes.

Therefore, if you configure a node selector constraint but existing constraints cannot all be satisfied, the pod scheduler cannot match all constraints and will not schedule a pod for placement onto a node.

To maintain resilience and high availability for monitoring components, ensure that enough nodes are available and match all constraints when you configure a node selector constraint to move a component.

You can use pod topology spread constraints to control how the monitoring pods are spread across a network topology when OpenShift Dedicated pods are deployed in multiple availability zones.

Pod topology spread constraints are suitable for controlling pod scheduling within hierarchical topologies in which nodes are spread across different infrastructure levels, such as regions and zones within those regions. Additionally, by being able to schedule pods in different zones, you can improve network latency in certain scenarios.

You can configure pod topology spread constraints for all the pods deployed by the Cluster Monitoring Operator to control how pod replicas are scheduled to nodes across zones. This ensures that the pods are highly available and run more efficiently, because workloads are spread across nodes in different data centers or hierarchical infrastructure zones.

You can configure resource limits and requests for the following components that monitor user-defined projects:

By defining the resource limits, you limit a container’s resource usage, which prevents the container from exceeding the specified maximum values for CPU and memory resources.

By defining the resource requests, you specify that a container can be scheduled only on a node that has enough CPU and memory resources available to match the requested resources.