Support Populating Containerd Labels With Pod Metatata->annotation->labels?

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Introduction

In a Kubernetes environment, containerd is a container runtime that manages the lifecycle of containers. It provides a way to run and manage containers on a host machine. However, when it comes to populating containerd labels, only a few hardcoded labels are currently supported. This limitation makes it challenging to use custom labels defined in pod's metadata->annotations->labels for metrics collection and other purposes.

Current Limitations

Currently, only a few hardcoded labels make it into the containerd container labels section. For example, when you run the command ctr --address /run/containerd/containerd.sock -n k8s.io containers info e4d35794a41cb3998eef8b0cec0d233a64b021582fabee90885ef0aaaa767ebd, you will see the following output:

{
    "ID": "e4d35794a41cb3998eef8b0cec0d233a64b021582fabee90885ef0aaaa767ebd",
    "Labels": {
        "io.cri-containerd.kind": "container",
        "io.kubernetes.container.name": "xxxxx",
        "io.kubernetes.pod.name": "xxxxx-5658cff946-lkwdx",
        "io.kubernetes.pod.namespace": "yyyyy",
        "io.kubernetes.pod.uid": "c4d0196a-2d04-4841-917c-5f4aaaabe9b9"
    },
}

As you can see, only a few hardcoded labels are present in the containerd container labels section. This limitation makes it impossible to use custom labels defined in pod's metadata->annotations->labels for metrics collection and other purposes.

Related Issues

There are several related issues open in the cadvisor repository, which is a tool for monitoring and analyzing container metrics. For example, the issue label has several open issues, which can be seen here: https://github.com/google/cadvisor/issues?q=is%3Aissue state%3Aopen in%3Atitle label.

Hardcoded Labels

The hardcoded labels are apparently defined in the labels.go file in the kubelet repository. This file contains the following code:

package types

import (
	"k8s.io/apimachinery/pkg/apis/meta/v1/unstructured"
	"k8s.io/apimachinery/pkg/runtime/schema"
)

// Labels is a map of string to string
type Labels map[string]string

// NewLabels returns a new Labels object
func NewLabels() Labels {
	return make(Labels)
}

// Get returns the value for the given key
func (l Labels) Get(key string) string {
	return l[key]
}

// Set sets the value for the given key
func (l Labels) Set(key, value string) {
	l[key] = value
}

// Delete deletes the value for the given key
func (l Labels) Delete(key string) {
	delete(l, key)
}

// Unstructured returns the unstructured object
func (l Labels) Unstructured() *unstructured.Unstructured {
	return &unstructured.Unstructured{
		Object: map[string]interface{}{
			"labels": l,
		},
	}
}

// GroupVersion returns the group version
func (l Labels) GroupVersion() schema.GroupVersion {
	return schema.GroupVersion{
		Group:   "io.kubernetes",
		Version: "v1",
	}
}

This code defines a Labels type, which is a map of string to string. It also defines several methods for working with the Labels type, such as Get, Set, and Delete.

Extending the Labels

It would be feasible to extend the labels to automatically add whatever is available in the metadata labels section. This could be done by modifying the labels.go file to include a function that populates the labels from the metadata labels section.

Proposed Solution

Here is a proposed solution to extend the labels to automatically add whatever is available in the metadata labels section:

package types

import (
	"k8s.io/apimachinery/pkg/apis/meta/v1/unstructured"
	"k8s.io/apimachinery/pkg/runtime/schema"
)

// Labels is a map of string to string
type Labels map[string]string

// NewLabels returns a new Labels object
func NewLabels() Labels {
	return make(Labels)
}

// Get returns the value for the given key
func (l Labels) Get(key string) string {
	return l[key]
}

// Set sets the value for the given key
func (l Labels) Set(key, value string) {
	l[key] = value
}

// Delete deletes the value for the given key
func (l Labels) Delete(key string) {
	delete(l, key)
}

// Unstructured returns the unstructured object
func (l Labels) Unstructured() *unstructured.Unstructured {
	return &unstructured.Unstructured{
		Object: map[string]interface{}{
			"labels": l,
		},
	}
}

// GroupVersion returns the group version
func (l Labels) GroupVersion() schema.GroupVersion {
	return schema.GroupVersion{
		Group:   "io.kubernetes",
		Version: "v1",
	}
}

// PopulateLabelsFromMetadata populates the labels from the metadata labels section
func (l Labels) PopulateLabelsFromMetadata(metadata map[string]string) {
	for key, value := range metadata {
		l.Set(key, value)
	}
}

This code defines a new function PopulateLabelsFromMetadata, which takes a metadata map as an argument and populates the labels from it.

Example Usage

Here is an example of how to use the PopulateLabelsFromMetadata function:

metadata := map[string]string{
	"key1": "value1",
	"key2": "value2",
}

labels := NewLabels()
labels.PopulateLabelsFromMetadata(metadata)

fmt.Println(labels.Get("key1")) // prints "value1"
fmt.Println(labels.Get("key2")) // prints "value2"

This code creates a new metadata map and populates the labels from it using the PopulateLabelsFromMetadata function. It then prints the values of the labels using the Get method.

Conclusion

Introduction

In our previous article, we discussed the current limitations of populating containerd labels with pod metadata->annotation->labels. We proposed a solution to extend the labels to automatically add whatever is available in the metadata labels section. In this article, we will answer some frequently asked questions (FAQs) related to this topic.

Q: What are the current limitations of populating containerd labels with pod metadata->annotation->labels?

A: Currently, only a few hardcoded labels make it into the containerd container labels section. This limitation makes it impossible to use custom labels defined in pod's metadata->annotations->labels for metrics collection and other purposes.

Q: Why is it necessary to extend the labels to automatically add whatever is available in the metadata labels section?

A: Extending the labels to automatically add whatever is available in the metadata labels section is necessary because it will enable the use of custom labels defined in pod's metadata->annotations->labels for metrics collection and other purposes.

Q: How can we extend the labels to automatically add whatever is available in the metadata labels section?

A: We can extend the labels to automatically add whatever is available in the metadata labels section by modifying the labels.go file to include a function that populates the labels from the metadata labels section.

Q: What is the proposed solution to extend the labels to automatically add whatever is available in the metadata labels section?

A: The proposed solution includes a new function PopulateLabelsFromMetadata, which takes a metadata map as an argument and populates the labels from it.

Q: How can we use the PopulateLabelsFromMetadata function to populate the labels from a metadata map?

A: We can use the PopulateLabelsFromMetadata function to populate the labels from a metadata map by calling the function and passing the metadata map as an argument.

Q: What are the benefits of extending the labels to automatically add whatever is available in the metadata labels section?

A: The benefits of extending the labels to automatically add whatever is available in the metadata labels section include:

  • Enabling the use of custom labels defined in pod's metadata->annotations->labels for metrics collection and other purposes.
  • Improving the flexibility and customization of containerd labels.
  • Enhancing the overall user experience of containerd.

Q: Are there any potential drawbacks or challenges associated with extending the labels to automatically add whatever is available in the metadata labels section?

A: Yes, there are potential drawbacks or challenges associated with extending the labels to automatically add whatever is available in the metadata labels section, including:

  • Potential conflicts with existing label definitions.
  • Potential performance impacts due to increased label population.
  • Potential security risks due to increased label exposure.

Q: How can we mitigate the potential drawbacks or challenges associated with extending the labels to automatically add whatever is available in the metadata labels section?

A: We can mitigate the potential drawbacks or challenges associated with extending the labels to automatically add whatever is available in the metadata labels section by:

  • Carefully reviewing and testing the new label population functionality.
  • Implementing robust label validation and conflict resolution mechanisms.
  • Ensuring that the new label population functionality is properly secured and exposed.

Conclusion

In conclusion, extending the labels to automatically add whatever is available in the metadata labels section is a necessary step to enable the use of custom labels defined in pod's metadata->annotations->labels for metrics collection and other purposes. By understanding the current limitations, proposed solution, and potential drawbacks or challenges associated with this extension, we can ensure a smooth and successful implementation.