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How to Retrieve Resource Usage Metrics for Pods and Nodes in Kubernetes with Go Client?

Linda Hamilton
Release: 2024-11-03 07:46:30
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How to Retrieve Resource Usage Metrics for Pods and Nodes in Kubernetes with Go Client?

Retrieving Resource Usage of Pods and Nodes in Kubernetes with Go Client

The standard Kubernetes Go client (client-go) lacks integrated methods for fetching resource usage metrics for pods and nodes. However, the metrics package within the Kubernetes repository features a pregenerated client that provides this functionality.

Retrieving Resource Usage with the Metrics Client

To access the metrics client, you need to first generate a configuration and pass it to the clientset:

<code class="go">import (
    "k8s.io/client-go/tools/clientcmd"
    metrics "k8s.io/metrics/pkg/client/clientset/versioned"
    metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
)



func main() {
    config, err := clientcmd.BuildConfigFromFlags("", "")
    if err != nil {
        panic(err)
    }

    mc, err := metrics.NewForConfig(config)
    if err != nil {
        panic(err)
    }
}</code>
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Once you have created the metrics client (mc), you can use the following methods to retrieve resource usage data:

Getting Node Metrics:

  • mc.MetricsV1beta1().NodeMetricses().Get("node_name", metav1.GetOptions{})
  • mc.MetricsV1beta1().NodeMetricses().List(metav1.ListOptions{})

Getting Pod Metrics:

  • mc.MetricsV1beta1().PodMetricses(metav1.NamespaceAll).List(metav1.ListOptions{})
  • mc.MetricsV1beta1().PodMetricses(metav1.NamespaceAll).Get("pod_name", metav1.GetOptions{})

Return Values:

Each of these methods returns an appropriate structure containing the resource usage information for the specified node or pod. The structure returned will vary depending on the type of metric being retrieved.

Conclusion:

The metrics package in the Kubernetes repository provides a convenient way to access resource usage metrics for pods and nodes in Go programs. Developers can use these metrics to gain valuable insights into the health and utilization of their Kubernetes clusters.

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