Understanding and effectively using Kubernetes resource requests and limits is crucial for managing your applications' performance and stability. Not only can you ensure the optimal operation of your Kubernetes workloads, but also conserve costs in the long run. Here are some tips to help you.

What Are Kubernetes Resource Requests?

Kubernetes Resource Requests are part of the Kubernetes Pod definition. They tell the Kubernetes scheduler about the resources required for a Pod to operate correctly, and help Kubernetes manage the resources on a node, ensuring the stability and efficiency of the applications running on it.

Resource requests are essential for Kubernetes as they help the system know how to allocate resources and where to place Pods. Without resource requests, Kubernetes would have no way of knowing how much CPU or memory a specific Pod requires, leading to potential resource starvation or wastage.

When you specify a resource request, you're telling Kubernetes that your Pod needs a certain amount of resources to function correctly. Kubernetes then ensures that these resources are reserved for your Pod. In essence, Kubernetes Resource Requests are about guaranteeing the minimum resources available for your Pods.

Types of Resource Requests in Kubernetes 

CPU Resource Requests

CPU resource requests are one of the two primary types of resource requests in Kubernetes. These requests specify the amount of CPU that a Pod needs to operate correctly. When you set a CPU resource request, Kubernetes schedules your Pod to a Node with sufficient free CPU resources.

The CPU resource request is specified in CPU units. One CPU unit in Kubernetes is equivalent to one AWS vCPU, one GCP Core, one Azure vCore, or one hyperthread on a bare-metal Intel processor.

It's essential to set appropriate CPU resource requests because if a Pod exceeds its CPU request, Kubernetes may throttle the Pod's CPU usage. In contrast, if the Pod's CPU usage is less than the requested amount, the CPU cycles could go unused, leading to wastage of resources.

Memory Resource Requests

Memory resource requests are the other primary type of resource requests in Kubernetes. These requests specify the amount of memory (RAM) that a Pod needs to operate correctly. When you set a memory resource request, Kubernetes schedules your Pod to a Node with sufficient free memory resources.

The memory resource request is specified in bytes. However, you can also specify it in multiples of bytes, like kilobytes (K), megabytes (M), gigabytes (G), and so on.

Memory resource requests are crucial because if a Pod exceeds its memory request, Kubernetes may kill the Pod's processes to reclaim the memory, causing the Pod to restart. On the other hand, if the Pod's memory usage is less than the requested amount, the memory could go unused, leading to wastage of resources.

Difference between Resource Requests and Resource Limits 

While resource requests tell Kubernetes the minimum resources a Pod needs, resource limits define the maximum resources that a Pod can use. Both are specified in the Pod's definition and play a critical role in resource management in Kubernetes.

If a Pod's resource usage reaches its limit, Kubernetes takes action to prevent it from consuming more. For instance, if a Pod hits its CPU limit, its CPU usage is throttled. If a Pod hits its memory limit, its processes may get killed.

In contrast, resource requests are about guaranteeing that a Pod gets the minimum resources it needs. Kubernetes schedules Pods on Nodes based on their resource requests and ensures that these requested resources are reserved for the Pods.

Working with Resource Requests and Limits

To work with resource requests and limits in Kubernetes, you need to specify them in the Pod's specification. This can be done in the resources field under the container's specification.

Here is a simple example:

apiVersion: v1
kind: Pod
metadata:
  name: sample-pod
spec:
  containers:
  - name: sample-container
    image: sample-image
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
      limits:
        memory: "128Mi"
        cpu: "500m"

In this example, the Pod requests 64 MiB of memory and 250 milliCPU units. Kubernetes will ensure that these resources are reserved for the Pod. Additionally, the Pod has a limit of 128 MiB of memory and 500 milliCPU units. If the Pod tries to use more than these resources, Kubernetes will throttle or terminate its processes to ensure the limits are not exceeded.

You can also use Kubernetes namespaces to define default resource requests and limits for all Pods in the namespace, which can be a useful way to manage resources at a higher level.

For more complex scenarios, or to troubleshoot problems with Kubernetes requests and limits, you can use tools like Komodor or Kubernetes Lens. These types of tools can provide visibility over resource requests and limits in your Kubernetes clusters, and help you optimize them.

How Can You Conserve Kubernetes Costs with Resource Requests and Limits?

There are several ways to use resource requests and limits to conserve Kubernetes costs:

• Set appropriate requests and limits: Avoid setting resource requests or limits too high or too low. Overestimating resources can lead to unused capacity, and underestimating can lead to performance issues. Use monitoring tools to understand your Pods' resource usage patterns and set requests and limits accordingly.

• Use namespace defaults: If you have many Pods with similar resource requirements, you can set default requests and limits at the namespace level to ensure that all Pods within the namespace adhere to these defaults.

• Utilize autoscaling: Kubernetes supports autoscaling, which automatically adjusts the number of Pods based on the current resource utilization. By using Horizontal Pod Autoscaler (HPA), you can ensure that you're only using and therefore paying for the resources you need.

• Implement resource quotas: You can use resource quotas to limit the total amount of resources a namespace can use. This can help prevent one namespace from using up all of a cluster's resources and driving up costs.

Conclusion

Understanding and effectively using Kubernetes resource requests and limits is crucial for managing your applications' performance and stability. They allow you to specify the resources your applications need, help Kubernetes to schedule your applications efficiently, and protect your nodes from resource starvation. By using resource requests and limits effectively, you can not only ensure the optimal operation of your Kubernetes workloads but also conserve costs in the long run. Always remember to monitor your applications' resource usage to inform your decisions about setting these values.

Image by creativeart on Freepik 

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