Tested maximums that constrain workspace scaling

While there is no strict limit on the number of resources in a Kubernetes cluster, there are certain considerations for large clusters to remember.

OpenShift Container Platform, a certified distribution of Kubernetes, provides a set of tested maximums for various resources. These maximums can serve as an initial guideline for planning your environment:

For example, it is generally not recommended to have more than 10,000 namespaces due to potential performance and management cost. In Eclipse Che, each user is allocated a namespace. If you expect the user base to be large, consider spreading workloads across multiple "fit-for-purpose" clusters and potentially using solutions for multi-cluster orchestration.

When deploying Eclipse Che on Kubernetes, accurately calculate the resource requirements for each CDE, including memory and CPU or GPU needs. This determines the right sizing of the cluster. In general, the CDE size is limited by and cannot be bigger than the worker node size.

The resource requirements for CDEs can vary significantly based on the specific workloads and configurations. A simple CDE might require only a few hundred megabytes of memory. A more complex one might need several gigabytes of memory and multiple CPU cores.

For details about calculating resource requirements, see Additional resources.

Although cluster autoscaling is a powerful Kubernetes feature, you cannot always rely on it. Consider predictive scaling by analyzing load data to detect daily or weekly usage patterns.

If your workloads follow a pattern with dramatic peaks throughout the day, provision worker nodes accordingly. For example, if workspaces increase during business hours and decrease during off-hours, predictive scaling adjusts the number of worker nodes. This ensures enough resources are available during peak load while minimizing costs during off-peak hours.

You can also use open source solutions such as Karpenter for configuration and lifecycle management of the worker nodes. Karpenter can dynamically provision and optimize worker nodes based on the specific requirements of the workloads. This helps improve resource utilization and reduce costs.