Cluster Administration

• 1: Certificates
• 2: Managing Resources
• 3: Cluster Networking
• 4: Logging Architecture
• 5: Metrics For Kubernetes System Components
• 6: System Logs
• 7: Traces For Kubernetes System Components
• 8: Proxies in Kubernetes
• 9: API Priority and Fairness
• 10: Installing Addons

1 - Certificates

To learn how to generate certificates for your cluster, see Certificates.

2 - Managing Resources

You've deployed your application and exposed it via a service. Now what? Kubernetes provides a number of tools to help you manage your application deployment, including scaling and updating. Among the features that we will discuss in more depth are configuration files and labels.

Organizing resource configurations

Many applications require multiple resources to be created, such as a Deployment and a Service. Management of multiple resources can be simplified by grouping them together in the same file (separated by --- in YAML). For example:

application/nginx-app.yaml

apiVersion: v1
kind: Service
metadata:
  name: my-nginx-svc
  labels:
    app: nginx
spec:
  type: LoadBalancer
  ports:
  - port: 80
  selector:
    app: nginx
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-nginx
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

Multiple resources can be created the same way as a single resource:

kubectl apply -f https://k8s.io/examples/application/nginx-app.yaml

service/my-nginx-svc created
deployment.apps/my-nginx created

The resources will be created in the order they appear in the file. Therefore, it's best to specify the service first, since that will ensure the scheduler can spread the pods associated with the service as they are created by the controller(s), such as Deployment.

kubectl apply also accepts multiple -f arguments:

kubectl apply -f https://k8s.io/examples/application/nginx/nginx-svc.yaml \
  -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml

It is a recommended practice to put resources related to the same microservice or application tier into the same file, and to group all of the files associated with your application in the same directory. If the tiers of your application bind to each other using DNS, you can deploy all of the components of your stack together.

A URL can also be specified as a configuration source, which is handy for deploying directly from configuration files checked into GitHub:

kubectl apply -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml

deployment.apps/my-nginx created

Bulk operations in kubectl

Resource creation isn't the only operation that kubectl can perform in bulk. It can also extract resource names from configuration files in order to perform other operations, in particular to delete the same resources you created:

kubectl delete -f https://k8s.io/examples/application/nginx-app.yaml

deployment.apps "my-nginx" deleted
service "my-nginx-svc" deleted

In the case of two resources, you can specify both resources on the command line using the resource/name syntax:

kubectl delete deployments/my-nginx services/my-nginx-svc

For larger numbers of resources, you'll find it easier to specify the selector (label query) specified using -l or --selector, to filter resources by their labels:

kubectl delete deployment,services -l app=nginx

deployment.apps "my-nginx" deleted
service "my-nginx-svc" deleted

Because kubectl outputs resource names in the same syntax it accepts, you can chain operations using $() or xargs:

kubectl get $(kubectl create -f docs/concepts/cluster-administration/nginx/ -o name | grep service)
kubectl create -f docs/concepts/cluster-administration/nginx/ -o name | grep service | xargs -i kubectl get {}

NAME           TYPE           CLUSTER-IP   EXTERNAL-IP   PORT(S)      AGE
my-nginx-svc   LoadBalancer   10.0.0.208   <pending>     80/TCP       0s

With the above commands, we first create resources under examples/application/nginx/ and print the resources created with -o name output format (print each resource as resource/name). Then we grep only the "service", and then print it with kubectl get.

If you happen to organize your resources across several subdirectories within a particular directory, you can recursively perform the operations on the subdirectories also, by specifying --recursive or -R alongside the --filename,-f flag.

For instance, assume there is a directory project/k8s/development that holds all of the manifests needed for the development environment, organized by resource type:

project/k8s/development
├── configmap
│   └── my-configmap.yaml
├── deployment
│   └── my-deployment.yaml
└── pvc
    └── my-pvc.yaml

By default, performing a bulk operation on project/k8s/development will stop at the first level of the directory, not processing any subdirectories. If we had tried to create the resources in this directory using the following command, we would have encountered an error:

kubectl apply -f project/k8s/development

error: you must provide one or more resources by argument or filename (.json|.yaml|.yml|stdin)

Instead, specify the --recursive or -R flag with the --filename,-f flag as such:

kubectl apply -f project/k8s/development --recursive

configmap/my-config created
deployment.apps/my-deployment created
persistentvolumeclaim/my-pvc created

The --recursive flag works with any operation that accepts the --filename,-f flag such as: kubectl {create,get,delete,describe,rollout} etc.

The --recursive flag also works when multiple -f arguments are provided:

kubectl apply -f project/k8s/namespaces -f project/k8s/development --recursive

namespace/development created
namespace/staging created
configmap/my-config created
deployment.apps/my-deployment created
persistentvolumeclaim/my-pvc created

If you're interested in learning more about kubectl, go ahead and read Command line tool (kubectl).

Using labels effectively

The examples we've used so far apply at most a single label to any resource. There are many scenarios where multiple labels should be used to distinguish sets from one another.

For instance, different applications would use different values for the app label, but a multi-tier application, such as the guestbook example, would additionally need to distinguish each tier. The frontend could carry the following labels:

labels:
   app: guestbook
   tier: frontend

while the Redis master and slave would have different tier labels, and perhaps even an additional role label:

labels:
   app: guestbook
   tier: backend
   role: master

and

labels:
   app: guestbook
   tier: backend
   role: slave

The labels allow us to slice and dice our resources along any dimension specified by a label:

kubectl apply -f examples/guestbook/all-in-one/guestbook-all-in-one.yaml
kubectl get pods -Lapp -Ltier -Lrole

NAME                           READY     STATUS    RESTARTS   AGE       APP         TIER       ROLE
guestbook-fe-4nlpb             1/1       Running   0          1m        guestbook   frontend   <none>
guestbook-fe-ght6d             1/1       Running   0          1m        guestbook   frontend   <none>
guestbook-fe-jpy62             1/1       Running   0          1m        guestbook   frontend   <none>
guestbook-redis-master-5pg3b   1/1       Running   0          1m        guestbook   backend    master
guestbook-redis-slave-2q2yf    1/1       Running   0          1m        guestbook   backend    slave
guestbook-redis-slave-qgazl    1/1       Running   0          1m        guestbook   backend    slave
my-nginx-divi2                 1/1       Running   0          29m       nginx       <none>     <none>
my-nginx-o0ef1                 1/1       Running   0          29m       nginx       <none>     <none>

kubectl get pods -lapp=guestbook,role=slave

NAME                          READY     STATUS    RESTARTS   AGE
guestbook-redis-slave-2q2yf   1/1       Running   0          3m
guestbook-redis-slave-qgazl   1/1       Running   0          3m

Canary deployments

Another scenario where multiple labels are needed is to distinguish deployments of different releases or configurations of the same component. It is common practice to deploy a canary of a new application release (specified via image tag in the pod template) side by side with the previous release so that the new release can receive live production traffic before fully rolling it out.

For instance, you can use a track label to differentiate different releases.

The primary, stable release would have a track label with value as stable:

name: frontend
replicas: 3
...
labels:
   app: guestbook
   tier: frontend
   track: stable
...
image: gb-frontend:v3

and then you can create a new release of the guestbook frontend that carries the track label with different value (i.e. canary), so that two sets of pods would not overlap:

name: frontend-canary
replicas: 1
...
labels:
   app: guestbook
   tier: frontend
   track: canary
...
image: gb-frontend:v4

The frontend service would span both sets of replicas by selecting the common subset of their labels (i.e. omitting the track label), so that the traffic will be redirected to both applications:

selector:
   app: guestbook
   tier: frontend

You can tweak the number of replicas of the stable and canary releases to determine the ratio of each release that will receive live production traffic (in this case, 3:1). Once you're confident, you can update the stable track to the new application release and remove the canary one.

For a more concrete example, check the tutorial of deploying Ghost.

Updating labels

Sometimes existing pods and other resources need to be relabeled before creating new resources. This can be done with kubectl label. For example, if you want to label all your nginx pods as frontend tier, run:

kubectl label pods -l app=nginx tier=fe

pod/my-nginx-2035384211-j5fhi labeled
pod/my-nginx-2035384211-u2c7e labeled
pod/my-nginx-2035384211-u3t6x labeled

This first filters all pods with the label "app=nginx", and then labels them with the "tier=fe". To see the pods you labeled, run:

kubectl get pods -l app=nginx -L tier

NAME                        READY     STATUS    RESTARTS   AGE       TIER
my-nginx-2035384211-j5fhi   1/1       Running   0          23m       fe
my-nginx-2035384211-u2c7e   1/1       Running   0          23m       fe
my-nginx-2035384211-u3t6x   1/1       Running   0          23m       fe

This outputs all "app=nginx" pods, with an additional label column of pods' tier (specified with -L or --label-columns).

For more information, please see labels and kubectl label.

Updating annotations

Sometimes you would want to attach annotations to resources. Annotations are arbitrary non-identifying metadata for retrieval by API clients such as tools, libraries, etc. This can be done with kubectl annotate. For example:

kubectl annotate pods my-nginx-v4-9gw19 description='my frontend running nginx'
kubectl get pods my-nginx-v4-9gw19 -o yaml

apiVersion: v1
kind: pod
metadata:
  annotations:
    description: my frontend running nginx
...

For more information, see annotations and kubectl annotate document.

Scaling your application

When load on your application grows or shrinks, use kubectl to scale your application. For instance, to decrease the number of nginx replicas from 3 to 1, do:

kubectl scale deployment/my-nginx --replicas=1

deployment.apps/my-nginx scaled

Now you only have one pod managed by the deployment.

kubectl get pods -l app=nginx

NAME                        READY     STATUS    RESTARTS   AGE
my-nginx-2035384211-j5fhi   1/1       Running   0          30m

To have the system automatically choose the number of nginx replicas as needed, ranging from 1 to 3, do:

kubectl autoscale deployment/my-nginx --min=1 --max=3

horizontalpodautoscaler.autoscaling/my-nginx autoscaled

Now your nginx replicas will be scaled up and down as needed, automatically.

For more information, please see kubectl scale, kubectl autoscale and horizontal pod autoscaler document.

In-place updates of resources

Sometimes it's necessary to make narrow, non-disruptive updates to resources you've created.

kubectl apply

It is suggested to maintain a set of configuration files in source control (see configuration as code), so that they can be maintained and versioned along with the code for the resources they configure. Then, you can use kubectl apply to push your configuration changes to the cluster.

This command will compare the version of the configuration that you're pushing with the previous version and apply the changes you've made, without overwriting any automated changes to properties you haven't specified.

kubectl apply -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml

deployment.apps/my-nginx configured

Note that kubectl apply attaches an annotation to the resource in order to determine the changes to the configuration since the previous invocation. When it's invoked, kubectl apply does a three-way diff between the previous configuration, the provided input and the current configuration of the resource, in order to determine how to modify the resource.

Currently, resources are created without this annotation, so the first invocation of kubectl apply will fall back to a two-way diff between the provided input and the current configuration of the resource. During this first invocation, it cannot detect the deletion of properties set when the resource was created. For this reason, it will not remove them.

All subsequent calls to kubectl apply, and other commands that modify the configuration, such as kubectl replace and kubectl edit, will update the annotation, allowing subsequent calls to kubectl apply to detect and perform deletions using a three-way diff.

kubectl edit

Alternatively, you may also update resources with kubectl edit:

kubectl edit deployment/my-nginx

This is equivalent to first get the resource, edit it in text editor, and then apply the resource with the updated version:

kubectl get deployment my-nginx -o yaml > /tmp/nginx.yaml
vi /tmp/nginx.yaml
# do some edit, and then save the file

kubectl apply -f /tmp/nginx.yaml
deployment.apps/my-nginx configured

rm /tmp/nginx.yaml

This allows you to do more significant changes more easily. Note that you can specify the editor with your EDITOR or KUBE_EDITOR environment variables.

For more information, please see kubectl edit document.

kubectl patch

You can use kubectl patch to update API objects in place. This command supports JSON patch, JSON merge patch, and strategic merge patch. See Update API Objects in Place Using kubectl patch and kubectl patch.

Disruptive updates

In some cases, you may need to update resource fields that cannot be updated once initialized, or you may want to make a recursive change immediately, such as to fix broken pods created by a Deployment. To change such fields, use replace --force, which deletes and re-creates the resource. In this case, you can modify your original configuration file:

kubectl replace -f https://k8s.io/examples/application/nginx/nginx-deployment.yaml --force

deployment.apps/my-nginx deleted
deployment.apps/my-nginx replaced

Updating your application without a service outage

At some point, you'll eventually need to update your deployed application, typically by specifying a new image or image tag, as in the canary deployment scenario above. kubectl supports several update operations, each of which is applicable to different scenarios.

We'll guide you through how to create and update applications with Deployments.

Let's say you were running version 1.14.2 of nginx:

kubectl create deployment my-nginx --image=nginx:1.14.2

deployment.apps/my-nginx created

with 3 replicas (so the old and new revisions can coexist):

kubectl scale deployment my-nginx --current-replicas=1 --replicas=3

deployment.apps/my-nginx scaled

To update to version 1.16.1, change .spec.template.spec.containers[0].image from nginx:1.14.2 to nginx:1.16.1 using the previous kubectl commands.

kubectl edit deployment/my-nginx

That's it! The Deployment will declaratively update the deployed nginx application progressively behind the scene. It ensures that only a certain number of old replicas may be down while they are being updated, and only a certain number of new replicas may be created above the desired number of pods. To learn more details about it, visit Deployment page.

What's next

• Learn about how to use kubectl for application introspection and debugging.
• See Configuration Best Practices and Tips.

3 - Cluster Networking

Networking is a central part of Kubernetes, but it can be challenging to understand exactly how it is expected to work. There are 4 distinct networking problems to address:

1. Highly-coupled container-to-container communications: this is solved by Pods and localhost communications.
2. Pod-to-Pod communications: this is the primary focus of this document.
3. Pod-to-Service communications: this is covered by Services.
4. External-to-Service communications: this is also covered by Services.

Kubernetes is all about sharing machines between applications. Typically, sharing machines requires ensuring that two applications do not try to use the same ports. Coordinating ports across multiple developers is very difficult to do at scale and exposes users to cluster-level issues outside of their control.

Dynamic port allocation brings a lot of complications to the system - every application has to take ports as flags, the API servers have to know how to insert dynamic port numbers into configuration blocks, services have to know how to find each other, etc. Rather than deal with this, Kubernetes takes a different approach.

To learn about the Kubernetes networking model, see here.

How to implement the Kubernetes network model

The network model is implemented by the container runtime on each node. The most common container runtimes use Container Network Interface (CNI) plugins to manage their network and security capabilities. Many different CNI plugins exist from many different vendors. Some of these provide only basic features of adding and removing network interfaces, while others provide more sophisticated solutions, such as integration with other container orchestration systems, running multiple CNI plugins, advanced IPAM features etc.

See this page for a non-exhaustive list of networking addons supported by Kubernetes.

What's next

The early design of the networking model and its rationale, and some future plans are described in more detail in the networking design document.

4 - Logging Architecture

Application logs can help you understand what is happening inside your application. The logs are particularly useful for debugging problems and monitoring cluster activity. Most modern applications have some kind of logging mechanism. Likewise, container engines are designed to support logging. The easiest and most adopted logging method for containerized applications is writing to standard output and standard error streams.

However, the native functionality provided by a container engine or runtime is usually not enough for a complete logging solution.

For example, you may want to access your application's logs if a container crashes, a pod gets evicted, or a node dies.

In a cluster, logs should have a separate storage and lifecycle independent of nodes, pods, or containers. This concept is called cluster-level logging.

Cluster-level logging architectures require a separate backend to store, analyze, and query logs. Kubernetes does not provide a native storage solution for log data. Instead, there are many logging solutions that integrate with Kubernetes. The following sections describe how to handle and store logs on nodes.

Pod and container logs

Kubernetes captures logs from each container in a running Pod.

This example uses a manifest for a Pod with a container that writes text to the standard output stream, once per second.

debug/counter-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; i=$((i+1)); sleep 1; done']

To run this pod, use the following command:

kubectl apply -f https://k8s.io/examples/debug/counter-pod.yaml

The output is:

pod/counter created

To fetch the logs, use the kubectl logs command, as follows:

kubectl logs counter

The output is similar to:

0: Fri Apr  1 11:42:23 UTC 2022
1: Fri Apr  1 11:42:24 UTC 2022
2: Fri Apr  1 11:42:25 UTC 2022

You can use kubectl logs --previous to retrieve logs from a previous instantiation of a container. If your pod has multiple containers, specify which container's logs you want to access by appending a container name to the command, with a -c flag, like so:

kubectl logs counter -c count

See the kubectl logs documentation for more details.

How nodes handle container logs

A container runtime handles and redirects any output generated to a containerized application's stdout and stderr streams. Different container runtimes implement this in different ways; however, the integration with the kubelet is standardized as the CRI logging format.

By default, if a container restarts, the kubelet keeps one terminated container with its logs. If a pod is evicted from the node, all corresponding containers are also evicted, along with their logs.

The kubelet makes logs available to clients via a special feature of the Kubernetes API. The usual way to access this is by running kubectl logs.

Log rotation

You can configure the kubelet to rotate logs automatically.

If you configure rotation, the kubelet is responsible for rotating container logs and managing the logging directory structure. The kubelet sends this information to the container runtime (using CRI), and the runtime writes the container logs to the given location.

You can configure two kubelet configuration settings, containerLogMaxSize and containerLogMaxFiles, using the kubelet configuration file. These settings let you configure the maximum size for each log file and the maximum number of files allowed for each container respectively.

When you run kubectl logs as in the basic logging example, the kubelet on the node handles the request and reads directly from the log file. The kubelet returns the content of the log file.

System component logs

There are two types of system components: those that typically run in a container, and those components directly involved in running containers. For example:

• The kubelet and container runtime do not run in containers. The kubelet runs your containers (grouped together in pods)
• The Kubernetes scheduler, controller manager, and API server run within pods (usually static Pods). The etcd component runs in the control plane, and most commonly also as a static pod. If your cluster uses kube-proxy, you typically run this as a DaemonSet.

Log locations

The way that the kubelet and container runtime write logs depends on the operating system that the node uses:

• Linux
• Windows

For Kubernetes cluster components that run in pods, these write to files inside the /var/log directory, bypassing the default logging mechanism (the components do not write to the systemd journal). You can use Kubernetes' storage mechanisms to map persistent storage into the container that runs the component.

For details about etcd and its logs, view the etcd documentation. Again, you can use Kubernetes' storage mechanisms to map persistent storage into the container that runs the component.

Cluster-level logging architectures

While Kubernetes does not provide a native solution for cluster-level logging, there are several common approaches you can consider. Here are some options:

• Use a node-level logging agent that runs on every node.
• Include a dedicated sidecar container for logging in an application pod.
• Push logs directly to a backend from within an application.

Using a node logging agent

You can implement cluster-level logging by including a node-level logging agent on each node. The logging agent is a dedicated tool that exposes logs or pushes logs to a backend. Commonly, the logging agent is a container that has access to a directory with log files from all of the application containers on that node.

Because the logging agent must run on every node, it is recommended to run the agent as a DaemonSet.

Node-level logging creates only one agent per node and doesn't require any changes to the applications running on the node.

Containers write to stdout and stderr, but with no agreed format. A node-level agent collects these logs and forwards them for aggregation.

Using a sidecar container with the logging agent

You can use a sidecar container in one of the following ways:

• The sidecar container streams application logs to its own stdout.
• The sidecar container runs a logging agent, which is configured to pick up logs from an application container.

Streaming sidecar container

By having your sidecar containers write to their own stdout and stderr streams, you can take advantage of the kubelet and the logging agent that already run on each node. The sidecar containers read logs from a file, a socket, or journald. Each sidecar container prints a log to its own stdout or stderr stream.

This approach allows you to separate several log streams from different parts of your application, some of which can lack support for writing to stdout or stderr. The logic behind redirecting logs is minimal, so it's not a significant overhead. Additionally, because stdout and stderr are handled by the kubelet, you can use built-in tools like kubectl logs.

For example, a pod runs a single container, and the container writes to two different log files using two different formats. Here's a manifest for the Pod:

admin/logging/two-files-counter-pod.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done      
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

It is not recommended to write log entries with different formats to the same log stream, even if you managed to redirect both components to the stdout stream of the container. Instead, you can create two sidecar containers. Each sidecar container could tail a particular log file from a shared volume and then redirect the logs to its own stdout stream.

Here's a manifest for a pod that has two sidecar containers:

admin/logging/two-files-counter-pod-streaming-sidecar.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done      
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-1
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/1.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-log-2
    image: busybox:1.28
    args: [/bin/sh, -c, 'tail -n+1 -F /var/log/2.log']
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  volumes:
  - name: varlog
    emptyDir: {}

Now when you run this pod, you can access each log stream separately by running the following commands:

kubectl logs counter count-log-1

The output is similar to:

0: Fri Apr  1 11:42:26 UTC 2022
1: Fri Apr  1 11:42:27 UTC 2022
2: Fri Apr  1 11:42:28 UTC 2022
...

kubectl logs counter count-log-2

The output is similar to:

Fri Apr  1 11:42:29 UTC 2022 INFO 0
Fri Apr  1 11:42:30 UTC 2022 INFO 0
Fri Apr  1 11:42:31 UTC 2022 INFO 0
...

If you installed a node-level agent in your cluster, that agent picks up those log streams automatically without any further configuration. If you like, you can configure the agent to parse log lines depending on the source container.

Even for Pods that only have low CPU and memory usage (order of a couple of millicores for cpu and order of several megabytes for memory), writing logs to a file and then streaming them to stdout can double how much storage you need on the node. If you have an application that writes to a single file, it's recommended to set /dev/stdout as the destination rather than implement the streaming sidecar container approach.

Sidecar containers can also be used to rotate log files that cannot be rotated by the application itself. An example of this approach is a small container running logrotate periodically. However, it's more straightforward to use stdout and stderr directly, and leave rotation and retention policies to the kubelet.

Sidecar container with a logging agent

If the node-level logging agent is not flexible enough for your situation, you can create a sidecar container with a separate logging agent that you have configured specifically to run with your application.

Here are two example manifests that you can use to implement a sidecar container with a logging agent. The first manifest contains a ConfigMap to configure fluentd.

admin/logging/fluentd-sidecar-config.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: fluentd-config
data:
  fluentd.conf: |
    <source>
      type tail
      format none
      path /var/log/1.log
      pos_file /var/log/1.log.pos
      tag count.format1
    </source>

    <source>
      type tail
      format none
      path /var/log/2.log
      pos_file /var/log/2.log.pos
      tag count.format2
    </source>

    <match **>
      type google_cloud
    </match>    

The second manifest describes a pod that has a sidecar container running fluentd. The pod mounts a volume where fluentd can pick up its configuration data.

admin/logging/two-files-counter-pod-agent-sidecar.yaml

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox:1.28
    args:
    - /bin/sh
    - -c
    - >
      i=0;
      while true;
      do
        echo "$i: $(date)" >> /var/log/1.log;
        echo "$(date) INFO $i" >> /var/log/2.log;
        i=$((i+1));
        sleep 1;
      done      
    volumeMounts:
    - name: varlog
      mountPath: /var/log
  - name: count-agent
    image: registry.k8s.io/fluentd-gcp:1.30
    env:
    - name: FLUENTD_ARGS
      value: -c /etc/fluentd-config/fluentd.conf
    volumeMounts:
    - name: varlog
      mountPath: /var/log
    - name: config-volume
      mountPath: /etc/fluentd-config
  volumes:
  - name: varlog
    emptyDir: {}
  - name: config-volume
    configMap:
      name: fluentd-config

Exposing logs directly from the application

Cluster-logging that exposes or pushes logs directly from every application is outside the scope of Kubernetes.

What's next

• Read about Kubernetes system logs
• Learn about Traces For Kubernetes System Components
• Learn how to customise the termination message that Kubernetes records when a Pod fails

5 - Metrics For Kubernetes System Components

System component metrics can give a better look into what is happening inside them. Metrics are particularly useful for building dashboards and alerts.

Kubernetes components emit metrics in Prometheus format. This format is structured plain text, designed so that people and machines can both read it.

Metrics in Kubernetes

In most cases metrics are available on /metrics endpoint of the HTTP server. For components that doesn't expose endpoint by default it can be enabled using --bind-address flag.

Examples of those components:

• kube-controller-manager
• kube-proxy
• kube-apiserver
• kube-scheduler
• kubelet

In a production environment you may want to configure Prometheus Server or some other metrics scraper to periodically gather these metrics and make them available in some kind of time series database.

Note that kubelet also exposes metrics in /metrics/cadvisor, /metrics/resource and /metrics/probes endpoints. Those metrics do not have same lifecycle.

If your cluster uses RBAC, reading metrics requires authorization via a user, group or ServiceAccount with a ClusterRole that allows accessing /metrics. For example:

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus
rules:
  - nonResourceURLs:
      - "/metrics"
    verbs:
      - get

Metric lifecycle

Alpha metric → Stable metric → Deprecated metric → Hidden metric → Deleted metric

Alpha metrics have no stability guarantees. These metrics can be modified or deleted at any time.

Stable metrics are guaranteed to not change. This means:

• A stable metric without a deprecated signature will not be deleted or renamed
• A stable metric's type will not be modified

Deprecated metrics are slated for deletion, but are still available for use. These metrics include an annotation about the version in which they became deprecated.

For example:

• Before deprecation

  # HELP some_counter this counts things
  # TYPE some_counter counter
  some_counter 0
  

• After deprecation

  # HELP some_counter (Deprecated since 1.15.0) this counts things
  # TYPE some_counter counter
  some_counter 0
  

Hidden metrics are no longer published for scraping, but are still available for use. To use a hidden metric, please refer to the Show hidden metrics section.

Deleted metrics are no longer published and cannot be used.

Show hidden metrics

As described above, admins can enable hidden metrics through a command-line flag on a specific binary. This intends to be used as an escape hatch for admins if they missed the migration of the metrics deprecated in the last release.

The flag show-hidden-metrics-for-version takes a version for which you want to show metrics deprecated in that release. The version is expressed as x.y, where x is the major version, y is the minor version. The patch version is not needed even though a metrics can be deprecated in a patch release, the reason for that is the metrics deprecation policy runs against the minor release.

The flag can only take the previous minor version as it's value. All metrics hidden in previous will be emitted if admins set the previous version to show-hidden-metrics-for-version. The too old version is not allowed because this violates the metrics deprecated policy.

Take metric A as an example, here assumed that A is deprecated in 1.n. According to metrics deprecated policy, we can reach the following conclusion:

• In release 1.n, the metric is deprecated, and it can be emitted by default.
• In release 1.n+1, the metric is hidden by default and it can be emitted by command line show-hidden-metrics-for-version=1.n.
• In release 1.n+2, the metric should be removed from the codebase. No escape hatch anymore.

If you're upgrading from release 1.12 to 1.13, but still depend on a metric A deprecated in 1.12, you should set hidden metrics via command line: --show-hidden-metrics=1.12 and remember to remove this metric dependency before upgrading to 1.14

Disable accelerator metrics

The kubelet collects accelerator metrics through cAdvisor. To collect these metrics, for accelerators like NVIDIA GPUs, kubelet held an open handle on the driver. This meant that in order to perform infrastructure changes (for example, updating the driver), a cluster administrator needed to stop the kubelet agent.

The responsibility for collecting accelerator metrics now belongs to the vendor rather than the kubelet. Vendors must provide a container that collects metrics and exposes them to the metrics service (for example, Prometheus).

The DisableAcceleratorUsageMetrics feature gate disables metrics collected by the kubelet, with a timeline for enabling this feature by default.

Component metrics

kube-controller-manager metrics

Controller manager metrics provide important insight into the performance and health of the controller manager. These metrics include common Go language runtime metrics such as go_routine count and controller specific metrics such as etcd request latencies or Cloudprovider (AWS, GCE, OpenStack) API latencies that can be used to gauge the health of a cluster.

Starting from Kubernetes 1.7, detailed Cloudprovider metrics are available for storage operations for GCE, AWS, Vsphere and OpenStack. These metrics can be used to monitor health of persistent volume operations.

For example, for GCE these metrics are called:

cloudprovider_gce_api_request_duration_seconds { request = "instance_list"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_insert"}
cloudprovider_gce_api_request_duration_seconds { request = "disk_delete"}
cloudprovider_gce_api_request_duration_seconds { request = "attach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "detach_disk"}
cloudprovider_gce_api_request_duration_seconds { request = "list_disk"}

kube-scheduler metrics

The scheduler exposes optional metrics that reports the requested resources and the desired limits of all running pods. These metrics can be used to build capacity planning dashboards, assess current or historical scheduling limits, quickly identify workloads that cannot schedule due to lack of resources, and compare actual usage to the pod's request.

The kube-scheduler identifies the resource requests and limits configured for each Pod; when either a request or limit is non-zero, the kube-scheduler reports a metrics timeseries. The time series is labelled by:

• namespace
• pod name
• the node where the pod is scheduled or an empty string if not yet scheduled
• priority
• the assigned scheduler for that pod
• the name of the resource (for example, cpu)
• the unit of the resource if known (for example, cores)

Once a pod reaches completion (has a restartPolicy of Never or OnFailure and is in the Succeeded or Failed pod phase, or has been deleted and all containers have a terminated state) the series is no longer reported since the scheduler is now free to schedule other pods to run. The two metrics are called kube_pod_resource_request and kube_pod_resource_limit.

The metrics are exposed at the HTTP endpoint /metrics/resources and require the same authorization as the /metrics endpoint on the scheduler. You must use the --show-hidden-metrics-for-version=1.20 flag to expose these alpha stability metrics.

Disabling metrics

You can explicitly turn off metrics via command line flag --disabled-metrics. This may be desired if, for example, a metric is causing a performance problem. The input is a list of disabled metrics (i.e. --disabled-metrics=metric1,metric2).

Metric cardinality enforcement

Metrics with unbounded dimensions could cause memory issues in the components they instrument. To limit resource use, you can use the --allow-label-value command line option to dynamically configure an allow-list of label values for a metric.

In alpha stage, the flag can only take in a series of mappings as metric label allow-list. Each mapping is of the format <metric_name>,<label_name>=<allowed_labels> where <allowed_labels> is a comma-separated list of acceptable label names.

The overall format looks like:

--allow-label-value <metric_name>,<label_name>='<allow_value1>, <allow_value2>...', <metric_name2>,<label_name>='<allow_value1>, <allow_value2>...', ...

Here is an example:

--allow-label-value number_count_metric,odd_number='1,3,5', number_count_metric,even_number='2,4,6', date_gauge_metric,weekend='Saturday,Sunday'

What's next

• Read about the Prometheus text format for metrics
• See the list of stable Kubernetes metrics
• Read about the Kubernetes deprecation policy

6 - System Logs

System component logs record events happening in cluster, which can be very useful for debugging. You can configure log verbosity to see more or less detail. Logs can be as coarse-grained as showing errors within a component, or as fine-grained as showing step-by-step traces of events (like HTTP access logs, pod state changes, controller actions, or scheduler decisions).

Klog

klog is the Kubernetes logging library. klog generates log messages for the Kubernetes system components.

For more information about klog configuration, see the Command line tool reference.

Kubernetes is in the process of simplifying logging in its components. The following klog command line flags are deprecated starting with Kubernetes 1.23 and will be removed in a future release:

• --add-dir-header
• --alsologtostderr
• --log-backtrace-at
• --log-dir
• --log-file
• --log-file-max-size
• --logtostderr
• --one-output
• --skip-headers
• --skip-log-headers
• --stderrthreshold

Output will always be written to stderr, regardless of the output format. Output redirection is expected to be handled by the component which invokes a Kubernetes component. This can be a POSIX shell or a tool like systemd.

In some cases, for example a distroless container or a Windows system service, those options are not available. Then the kube-log-runner binary can be used as wrapper around a Kubernetes component to redirect output. A prebuilt binary is included in several Kubernetes base images under its traditional name as /go-runner and as kube-log-runner in server and node release archives.

This table shows how kube-log-runner invocations correspond to shell redirection:

Klog output

An example of the traditional klog native format:

I1025 00:15:15.525108       1 httplog.go:79] GET /api/v1/namespaces/kube-system/pods/metrics-server-v0.3.1-57c75779f-9p8wg: (1.512ms) 200 [pod_nanny/v0.0.0 (linux/amd64) kubernetes/$Format 10.56.1.19:51756]

The message string may contain line breaks:

I1025 00:15:15.525108       1 example.go:79] This is a message
which has a line break.

Structured Logging

Structured logging introduces a uniform structure in log messages allowing for programmatic extraction of information. You can store and process structured logs with less effort and cost. The code which generates a log message determines whether it uses the traditional unstructured klog output or structured logging.

The default formatting of structured log messages is as text, with a format that is backward compatible with traditional klog:

<klog header> "<message>" <key1>="<value1>" <key2>="<value2>" ...

Example:

I1025 00:15:15.525108       1 controller_utils.go:116] "Pod status updated" pod="kube-system/kubedns" status="ready"

Strings are quoted. Other values are formatted with %+v, which may cause log messages to continue on the next line depending on the data.

I1025 00:15:15.525108       1 example.go:116] "Example" data="This is text with a line break\nand \"quotation marks\"." someInt=1 someFloat=0.1 someStruct={StringField: First line,
second line.}

Contextual Logging

Contextual logging builds on top of structured logging. It is primarily about how developers use logging calls: code based on that concept is more flexible and supports additional use cases as described in the Contextual Logging KEP.

If developers use additional functions like WithValues or WithName in their components, then log entries contain additional information that gets passed into functions by their caller.

Currently this is gated behind the StructuredLogging feature gate and disabled by default. The infrastructure for this was added in 1.24 without modifying components. The component-base/logs/example command demonstrates how to use the new logging calls and how a component behaves that supports contextual logging.

$ cd $GOPATH/src/k8s.io/kubernetes/staging/src/k8s.io/component-base/logs/example/cmd/
$ go run . --help
...
      --feature-gates mapStringBool  A set of key=value pairs that describe feature gates for alpha/experimental features. Options are:
                                     AllAlpha=true|false (ALPHA - default=false)
                                     AllBeta=true|false (BETA - default=false)
                                     ContextualLogging=true|false (ALPHA - default=false)
$ go run . --feature-gates ContextualLogging=true
...
I0404 18:00:02.916429  451895 logger.go:94] "example/myname: runtime" foo="bar" duration="1m0s"
I0404 18:00:02.916447  451895 logger.go:95] "example: another runtime" foo="bar" duration="1m0s"

The example prefix and foo="bar" were added by the caller of the function which logs the runtime message and duration="1m0s" value, without having to modify that function.

With contextual logging disable, WithValues and WithName do nothing and log calls go through the global klog logger. Therefore this additional information is not in the log output anymore:

$ go run . --feature-gates ContextualLogging=false
...
I0404 18:03:31.171945  452150 logger.go:94] "runtime" duration="1m0s"
I0404 18:03:31.171962  452150 logger.go:95] "another runtime" duration="1m0s"

JSON log format

The --logging-format=json flag changes the format of logs from klog native format to JSON format. Example of JSON log format (pretty printed):

{
   "ts": 1580306777.04728,
   "v": 4,
   "msg": "Pod status updated",
   "pod":{
      "name": "nginx-1",
      "namespace": "default"
   },
   "status": "ready"
}

Keys with special meaning:

• ts - timestamp as Unix time (required, float)
• v - verbosity (only for info and not for error messages, int)
• err - error string (optional, string)
• msg - message (required, string)

List of components currently supporting JSON format:

• kube-controller-manager
• kube-apiserver
• kube-scheduler
• kubelet

Log verbosity level

The -v flag controls log verbosity. Increasing the value increases the number of logged events. Decreasing the value decreases the number of logged events. Increasing verbosity settings logs increasingly less severe events. A verbosity setting of 0 logs only critical events.

Log location

There are two types of system components: those that run in a container and those that do not run in a container. For example:

• The Kubernetes scheduler and kube-proxy run in a container.
• The kubelet and container runtime do not run in containers.

On machines with systemd, the kubelet and container runtime write to journald. Otherwise, they write to .log files in the /var/log directory. System components inside containers always write to .log files in the /var/log directory, bypassing the default logging mechanism. Similar to the container logs, you should rotate system component logs in the /var/log directory. In Kubernetes clusters created by the kube-up.sh script, log rotation is configured by the logrotate tool. The logrotate tool rotates logs daily, or once the log size is greater than 100MB.

What's next

• Read about the Kubernetes Logging Architecture
• Read about Structured Logging
• Read about Contextual Logging
• Read about deprecation of klog flags
• Read about the Conventions for logging severity

7 - Traces For Kubernetes System Components

System component traces record the latency of and relationships between operations in the cluster.

Kubernetes components emit traces using the OpenTelemetry Protocol with the gRPC exporter and can be collected and routed to tracing backends using an OpenTelemetry Collector.

Trace Collection

For a complete guide to collecting traces and using the collector, see Getting Started with the OpenTelemetry Collector. However, there are a few things to note that are specific to Kubernetes components.

By default, Kubernetes components export traces using the grpc exporter for OTLP on the IANA OpenTelemetry port, 4317. As an example, if the collector is running as a sidecar to a Kubernetes component, the following receiver configuration will collect spans and log them to standard output:

receivers:
  otlp:
    protocols:
      grpc:
exporters:
  # Replace this exporter with the exporter for your backend
  logging:
    logLevel: debug
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [logging]

Component traces

kube-apiserver traces

The kube-apiserver generates spans for incoming HTTP requests, and for outgoing requests to webhooks, etcd, and re-entrant requests. It propagates the W3C Trace Context with outgoing requests but does not make use of the trace context attached to incoming requests, as the kube-apiserver is often a public endpoint.

Enabling tracing in the kube-apiserver

To enable tracing, enable the APIServerTracing feature gate on the kube-apiserver. Also, provide the kube-apiserver with a tracing configuration file with --tracing-config-file=<path-to-config>. This is an example config that records spans for 1 in 10000 requests, and uses the default OpenTelemetry endpoint:

apiVersion: apiserver.config.k8s.io/v1alpha1
kind: TracingConfiguration
# default value
#endpoint: localhost:4317
samplingRatePerMillion: 100

For more information about the TracingConfiguration struct, see API server config API (v1alpha1).

kubelet traces

The kubelet CRI interface and authenticated http servers are instrumented to generate trace spans. As with the apiserver, the endpoint and sampling rate are configurable. Trace context propagation is also configured. A parent span's sampling decision is always respected. A provided tracing configuration sampling rate will apply to spans without a parent. Enabled without a configured endpoint, the default OpenTelemetry Collector receiver address of "localhost:4317" is set.

Enabling tracing in the kubelet

To enable tracing, enable the KubeletTracing feature gate on the kubelet. Also, provide the kubelet with a tracing configuration. This is an example snippet of a kubelet config that records spans for 1 in 10000 requests, and uses the default OpenTelemetry endpoint:

apiVersion: kubelet.config.k8s.io/v1beta1
kind: KubeletConfiguration
featureGates:
  KubeletTracing: true
tracing:
  # default value
  #endpoint: localhost:4317
  samplingRatePerMillion: 100

Stability

Tracing instrumentation is still under active development, and may change in a variety of ways. This includes span names, attached attributes, instrumented endpoints, etc. Until this feature graduates to stable, there are no guarantees of backwards compatibility for tracing instrumentation.

What's next

• Read about Getting Started with the OpenTelemetry Collector

8 - Proxies in Kubernetes

This page explains proxies used with Kubernetes.

Proxies

There are several different proxies you may encounter when using Kubernetes:

1. The kubectl proxy:

   • runs on a user's desktop or in a pod
   • proxies from a localhost address to the Kubernetes apiserver
   • client to proxy uses HTTP
   • proxy to apiserver uses HTTPS
   • locates apiserver
   • adds authentication headers

2. The apiserver proxy:

   • is a bastion built into the apiserver
   • connects a user outside of the cluster to cluster IPs which otherwise might not be reachable
   • runs in the apiserver processes
   • client to proxy uses HTTPS (or http if apiserver so configured)
   • proxy to target may use HTTP or HTTPS as chosen by proxy using available information
   • can be used to reach a Node, Pod, or Service
   • does load balancing when used to reach a Service

3. The kube proxy:

   • runs on each node
   • proxies UDP, TCP and SCTP
   • does not understand HTTP
   • provides load balancing
   • is only used to reach services

4. A Proxy/Load-balancer in front of apiserver(s):

   • existence and implementation varies from cluster to cluster (e.g. nginx)
   • sits between all clients and one or more apiservers
   • acts as load balancer if there are several apiservers.

5. Cloud Load Balancers on external services:

   • are provided by some cloud providers (e.g. AWS ELB, Google Cloud Load Balancer)
   • are created automatically when the Kubernetes service has type LoadBalancer
   • usually supports UDP/TCP only
   • SCTP support is up to the load balancer implementation of the cloud provider
   • implementation varies by cloud provider.

Kubernetes users will typically not need to worry about anything other than the first two types. The cluster admin will typically ensure that the latter types are set up correctly.

Requesting redirects

Proxies have replaced redirect capabilities. Redirects have been deprecated.

9 - API Priority and Fairness

Controlling the behavior of the Kubernetes API server in an overload situation is a key task for cluster administrators. The kube-apiserver has some controls available (i.e. the --max-requests-inflight and --max-mutating-requests-inflight command-line flags) to limit the amount of outstanding work that will be accepted, preventing a flood of inbound requests from overloading and potentially crashing the API server, but these flags are not enough to ensure that the most important requests get through in a period of high traffic.

The API Priority and Fairness feature (APF) is an alternative that improves upon aforementioned max-inflight limitations. APF classifies and isolates requests in a more fine-grained way. It also introduces a limited amount of queuing, so that no requests are rejected in cases of very brief bursts. Requests are dispatched from queues using a fair queuing technique so that, for example, a poorly-behaved controller need not starve others (even at the same priority level).

This feature is designed to work well with standard controllers, which use informers and react to failures of API requests with exponential back-off, and other clients that also work this way.

Enabling/Disabling API Priority and Fairness

The API Priority and Fairness feature is controlled by a feature gate and is enabled by default. See Feature Gates for a general explanation of feature gates and how to enable and disable them. The name of the feature gate for APF is "APIPriorityAndFairness". This feature also involves an API Group with: (a) a v1alpha1 version and a v1beta1 version, disabled by default, and (b) v1beta2 and v1beta3 versions, enabled by default. You can disable the feature gate and API group beta versions by adding the following command-line flags to your kube-apiserver invocation:

kube-apiserver \
--feature-gates=APIPriorityAndFairness=false \
--runtime-config=flowcontrol.apiserver.k8s.io/v1beta2=false,flowcontrol.apiserver.k8s.io/v1beta3=false \
 # …and other flags as usual

Alternatively, you can enable the v1alpha1 and v1beta1 versions of the API group with --runtime-config=flowcontrol.apiserver.k8s.io/v1alpha1=true,flowcontrol.apiserver.k8s.io/v1beta1=true.

The command-line flag --enable-priority-and-fairness=false will disable the API Priority and Fairness feature, even if other flags have enabled it.

Concepts

There are several distinct features involved in the API Priority and Fairness feature. Incoming requests are classified by attributes of the request using FlowSchemas, and assigned to priority levels. Priority levels add a degree of isolation by maintaining separate concurrency limits, so that requests assigned to different priority levels cannot starve each other. Within a priority level, a fair-queuing algorithm prevents requests from different flows from starving each other, and allows for requests to be queued to prevent bursty traffic from causing failed requests when the average load is acceptably low.

Priority Levels

Without APF enabled, overall concurrency in the API server is limited by the kube-apiserver flags --max-requests-inflight and --max-mutating-requests-inflight. With APF enabled, the concurrency limits defined by these flags are summed and then the sum is divided up among a configurable set of priority levels. Each incoming request is assigned to a single priority level, and each priority level will only dispatch as many concurrent requests as its particular limit allows.

The default configuration, for example, includes separate priority levels for leader-election requests, requests from built-in controllers, and requests from Pods. This means that an ill-behaved Pod that floods the API server with requests cannot prevent leader election or actions by the built-in controllers from succeeding.

The concurrency limits of the priority levels are periodically adjusted, allowing under-utilized priority levels to temporarily lend concurrency to heavily-utilized levels. These limits are based on nominal limits and bounds on how much concurrency a priority level may lend and how much it may borrow, all derived from the configuration objects mentioned below.

Seats Occupied by a Request

The above description of concurrency management is the baseline story. In it, requests have different durations but are counted equally at any given moment when comparing against a priority level's concurrency limit. In the baseline story, each request occupies one unit of concurrency. The word "seat" is used to mean one unit of concurrency, inspired by the way each passenger on a train or aircraft takes up one of the fixed supply of seats.

But some requests take up more than one seat. Some of these are list requests that the server estimates will return a large number of objects. These have been found to put an exceptionally heavy burden on the server, among requests that take a similar amount of time to run. For this reason, the server estimates the number of objects that will be returned and considers the request to take a number of seats that is proportional to that estimated number.

Execution time tweaks for watch requests

API Priority and Fairness manages watch requests, but this involves a couple more excursions from the baseline behavior. The first concerns how long a watch request is considered to occupy its seat. Depending on request parameters, the response to a watch request may or may not begin with create notifications for all the relevant pre-existing objects. API Priority and Fairness considers a watch request to be done with its seat once that initial burst of notifications, if any, is over.

The normal notifications are sent in a concurrent burst to all relevant watch response streams whenever the server is notified of an object create/update/delete. To account for this work, API Priority and Fairness considers every write request to spend some additional time occupying seats after the actual writing is done. The server estimates the number of notifications to be sent and adjusts the write request's number of seats and seat occupancy time to include this extra work.

Queuing

Even within a priority level there may be a large number of distinct sources of traffic. In an overload situation, it is valuable to prevent one stream of requests from starving others (in particular, in the relatively common case of a single buggy client flooding the kube-apiserver with requests, that buggy client would ideally not have much measurable impact on other clients at all). This is handled by use of a fair-queuing algorithm to process requests that are assigned the same priority level. Each request is assigned to a flow, identified by the name of the matching FlowSchema plus a flow distinguisher — which is either the requesting user, the target resource's namespace, or nothing — and the system attempts to give approximately equal weight to requests in different flows of the same priority level. To enable distinct handling of distinct instances, controllers that have many instances should authenticate with distinct usernames

After classifying a request into a flow, the API Priority and Fairness feature then may assign the request to a queue. This assignment uses a technique known as shuffle sharding, which makes relatively efficient use of queues to insulate low-intensity flows from high-intensity flows.

The details of the queuing algorithm are tunable for each priority level, and allow administrators to trade off memory use, fairness (the property that independent flows will all make progress when total traffic exceeds capacity), tolerance for bursty traffic, and the added latency induced by queuing.

Exempt requests

Some requests are considered sufficiently important that they are not subject to any of the limitations imposed by this feature. These exemptions prevent an improperly-configured flow control configuration from totally disabling an API server.

Resources

The flow control API involves two kinds of resources. PriorityLevelConfigurations define the available priority levels, the share of the available concurrency budget that each can handle, and allow for fine-tuning queuing behavior. FlowSchemas are used to classify individual inbound requests, matching each to a single PriorityLevelConfiguration. There is also a v1alpha1 version of the same API group, and it has the same Kinds with the same syntax and semantics.

PriorityLevelConfiguration

A PriorityLevelConfiguration represents a single priority level. Each PriorityLevelConfiguration has an independent limit on the number of outstanding requests, and limitations on the number of queued requests.

The nominal oncurrency limit for a PriorityLevelConfiguration is not specified in an absolute number of seats, but rather in "nominal concurrency shares." The total concurrency limit for the API Server is distributed among the existing PriorityLevelConfigurations in proportion to these shares, to give each level its nominal limit in terms of seats. This allows a cluster administrator to scale up or down the total amount of traffic to a server by restarting kube-apiserver with a different value for --max-requests-inflight (or --max-mutating-requests-inflight), and all PriorityLevelConfigurations will see their maximum allowed concurrency go up (or down) by the same fraction.

The bounds on how much concurrency a priority level may lend and how much it may borrow are expressed in the PriorityLevelConfiguration as percentages of the level's nominal limit. These are resolved to absolute numbers of seats by multiplying with the nominal limit / 100.0 and rounding. The dynamically adjusted concurrency limit of a priority level is constrained to lie between (a) a lower bound of its nominal limit minus its lendable seats and (b) an upper bound of its nominal limit plus the seats it may borrow. At each adjustment the dynamic limits are derived by each priority level reclaiming any lent seats for which demand recently appeared and then jointly fairly responding to the recent seat demand on the priority levels, within the bounds just described.

When the volume of inbound requests assigned to a single PriorityLevelConfiguration is more than its permitted concurrency level, the type field of its specification determines what will happen to extra requests. A type of Reject means that excess traffic will immediately be rejected with an HTTP 429 (Too Many Requests) error. A type of Queue means that requests above the threshold will be queued, with the shuffle sharding and fair queuing techniques used to balance progress between request flows.

The queuing configuration allows tuning the fair queuing algorithm for a priority level. Details of the algorithm can be read in the enhancement proposal, but in short:

• Increasing queues reduces the rate of collisions between different flows, at the cost of increased memory usage. A value of 1 here effectively disables the fair-queuing logic, but still allows requests to be queued.

• Increasing queueLengthLimit allows larger bursts of traffic to be sustained without dropping any requests, at the cost of increased latency and memory usage.

• Changing handSize allows you to adjust the probability of collisions between different flows and the overall concurrency available to a single flow in an overload situation.

  Note: A larger handSize makes it less likely for two individual flows to collide (and therefore for one to be able to starve the other), but more likely that a small number of flows can dominate the apiserver. A larger handSize also potentially increases the amount of latency that a single high-traffic flow can cause. The maximum number of queued requests possible from a single flow is handSize * queueLengthLimit.

Following is a table showing an interesting collection of shuffle sharding configurations, showing for each the probability that a given mouse (low-intensity flow) is squished by the elephants (high-intensity flows) for an illustrative collection of numbers of elephants. See https://play.golang.org/p/Gi0PLgVHiUg , which computes this table.

FlowSchema

A FlowSchema matches some inbound requests and assigns them to a priority level. Every inbound request is tested against every FlowSchema in turn, starting with those with numerically lowest --- which we take to be the logically highest --- matchingPrecedence and working onward. The first match wins.

A FlowSchema matches a given request if at least one of its rules matches. A rule matches if at least one of its subjects and at least one of its resourceRules or nonResourceRules (depending on whether the incoming request is for a resource or non-resource URL) matches the request.

For the name field in subjects, and the verbs, apiGroups, resources, namespaces, and nonResourceURLs fields of resource and non-resource rules, the wildcard * may be specified to match all values for the given field, effectively removing it from consideration.

A FlowSchema's distinguisherMethod.type determines how requests matching that schema will be separated into flows. It may be either ByUser, in which case one requesting user will not be able to starve other users of capacity, or ByNamespace, in which case requests for resources in one namespace will not be able to starve requests for resources in other namespaces of capacity, or it may be blank (or distinguisherMethod may be omitted entirely), in which case all requests matched by this FlowSchema will be considered part of a single flow. The correct choice for a given FlowSchema depends on the resource and your particular environment.

Defaults

Each kube-apiserver maintains two sorts of APF configuration objects: mandatory and suggested.

Mandatory Configuration Objects

The four mandatory configuration objects reflect fixed built-in guardrail behavior. This is behavior that the servers have before those objects exist, and when those objects exist their specs reflect this behavior. The four mandatory objects are as follows.

• The mandatory exempt priority level is used for requests that are not subject to flow control at all: they will always be dispatched immediately. The mandatory exempt FlowSchema classifies all requests from the system:masters group into this priority level. You may define other FlowSchemas that direct other requests to this priority level, if appropriate.

• The mandatory catch-all priority level is used in combination with the mandatory catch-all FlowSchema to make sure that every request gets some kind of classification. Typically you should not rely on this catch-all configuration, and should create your own catch-all FlowSchema and PriorityLevelConfiguration (or use the suggested global-default priority level that is installed by default) as appropriate. Because it is not expected to be used normally, the mandatory catch-all priority level has a very small concurrency share and does not queue requests.

Suggested Configuration Objects

The suggested FlowSchemas and PriorityLevelConfigurations constitute a reasonable default configuration. You can modify these and/or create additional configuration objects if you want. If your cluster is likely to experience heavy load then you should consider what configuration will work best.

The suggested configuration groups requests into six priority levels:

• The node-high priority level is for health updates from nodes.

• The system priority level is for non-health requests from the system:nodes group, i.e. Kubelets, which must be able to contact the API server in order for workloads to be able to schedule on them.

• The leader-election priority level is for leader election requests from built-in controllers (in particular, requests for endpoints, configmaps, or leases coming from the system:kube-controller-manager or system:kube-scheduler users and service accounts in the kube-system namespace). These are important to isolate from other traffic because failures in leader election cause their controllers to fail and restart, which in turn causes more expensive traffic as the new controllers sync their informers.

• The workload-high priority level is for other requests from built-in controllers.

• The workload-low priority level is for requests from any other service account, which will typically include all requests from controllers running in Pods.

• The global-default priority level handles all other traffic, e.g. interactive kubectl commands run by nonprivileged users.

The suggested FlowSchemas serve to steer requests into the above priority levels, and are not enumerated here.

Maintenance of the Mandatory and Suggested Configuration Objects

Each kube-apiserver independently maintains the mandatory and suggested configuration objects, using initial and periodic behavior. Thus, in a situation with a mixture of servers of different versions there may be thrashing as long as different servers have different opinions of the proper content of these objects.

Each kube-apiserver makes an initial maintenance pass over the mandatory and suggested configuration objects, and after that does periodic maintenance (once per minute) of those objects.

For the mandatory configuration objects, maintenance consists of ensuring that the object exists and, if it does, has the proper spec. The server refuses to allow a creation or update with a spec that is inconsistent with the server's guardrail behavior.

Maintenance of suggested configuration objects is designed to allow their specs to be overridden. Deletion, on the other hand, is not respected: maintenance will restore the object. If you do not want a suggested configuration object then you need to keep it around but set its spec to have minimal consequences. Maintenance of suggested objects is also designed to support automatic migration when a new version of the kube-apiserver is rolled out, albeit potentially with thrashing while there is a mixed population of servers.

Maintenance of a suggested configuration object consists of creating it --- with the server's suggested spec --- if the object does not exist. OTOH, if the object already exists, maintenance behavior depends on whether the kube-apiservers or the users control the object. In the former case, the server ensures that the object's spec is what the server suggests; in the latter case, the spec is left alone.

The question of who controls the object is answered by first looking for an annotation with key apf.kubernetes.io/autoupdate-spec. If there is such an annotation and its value is true then the kube-apiservers control the object. If there is such an annotation and its value is false then the users control the object. If neither of those condtions holds then the metadata.generation of the object is consulted. If that is 1 then the kube-apiservers control the object. Otherwise the users control the object. These rules were introduced in release 1.22 and their consideration of metadata.generation is for the sake of migration from the simpler earlier behavior. Users who wish to control a suggested configuration object should set its apf.kubernetes.io/autoupdate-spec annotation to false.

Maintenance of a mandatory or suggested configuration object also includes ensuring that it has an apf.kubernetes.io/autoupdate-spec annotation that accurately reflects whether the kube-apiservers control the object.

Maintenance also includes deleting objects that are neither mandatory nor suggested but are annotated apf.kubernetes.io/autoupdate-spec=true.

Health check concurrency exemption

The suggested configuration gives no special treatment to the health check requests on kube-apiservers from their local kubelets --- which tend to use the secured port but supply no credentials. With the suggested config, these requests get assigned to the global-default FlowSchema and the corresponding global-default priority level, where other traffic can crowd them out.

If you add the following additional FlowSchema, this exempts those requests from rate limiting.

priority-and-fairness/health-for-strangers.yaml

apiVersion: flowcontrol.apiserver.k8s.io/v1beta3
kind: FlowSchema
metadata:
  name: health-for-strangers
spec:
  matchingPrecedence: 1000
  priorityLevelConfiguration:
    name: exempt
  rules:
    - nonResourceRules:
      - nonResourceURLs:
          - "/healthz"
          - "/livez"
          - "/readyz"
        verbs:
          - "*"
      subjects:
        - kind: Group
          group:
            name: "system:unauthenticated"

Diagnostics

Every HTTP response from an API server with the priority and fairness feature enabled has two extra headers: X-Kubernetes-PF-FlowSchema-UID and X-Kubernetes-PF-PriorityLevel-UID, noting the flow schema that matched the request and the priority level to which it was assigned, respectively. The API objects' names are not included in these headers in case the requesting user does not have permission to view them, so when debugging you can use a command like

kubectl get flowschemas -o custom-columns="uid:{metadata.uid},name:{metadata.name}"
kubectl get prioritylevelconfigurations -o custom-columns="uid:{metadata.uid},name:{metadata.name}"

to get a mapping of UIDs to names for both FlowSchemas and PriorityLevelConfigurations.

Observability

Metrics

When you enable the API Priority and Fairness feature, the kube-apiserver exports additional metrics. Monitoring these can help you determine whether your configuration is inappropriately throttling important traffic, or find poorly-behaved workloads that may be harming system health.

• apiserver_flowcontrol_rejected_requests_total is a counter vector (cumulative since server start) of requests that were rejected, broken down by the labels flow_schema (indicating the one that matched the request), priority_level (indicating the one to which the request was assigned), and reason. The reason label will be have one of the following values:

  • queue-full, indicating that too many requests were already queued,
  • concurrency-limit, indicating that the PriorityLevelConfiguration is configured to reject rather than queue excess requests, or
  • time-out, indicating that the request was still in the queue when its queuing time limit expired.
  • cancelled, indicating that the request is not purge locked and has been ejected from the queue.

• apiserver_flowcontrol_dispatched_requests_total is a counter vector (cumulative since server start) of requests that began executing, broken down by the labels flow_schema (indicating the one that matched the request) and priority_level (indicating the one to which the request was assigned).

• apiserver_current_inqueue_requests is a gauge vector of recent high water marks of the number of queued requests, grouped by a label named request_kind whose value is mutating or readOnly. These high water marks describe the largest number seen in the one second window most recently completed. These complement the older apiserver_current_inflight_requests gauge vector that holds the last window's high water mark of number of requests actively being served.

• apiserver_flowcontrol_read_vs_write_current_requests is a histogram vector of observations, made at the end of every nanosecond, of the number of requests broken down by the labels phase (which takes on the values waiting and executing) and request_kind (which takes on the values mutating and readOnly). Each observed value is a ratio, between 0 and 1, of a number of requests divided by the corresponding limit on the number of requests (queue volume limit for waiting and concurrency limit for executing).

• apiserver_flowcontrol_current_inqueue_requests is a gauge vector holding the instantaneous number of queued (not executing) requests, broken down by the labels priority_level and flow_schema.

• apiserver_flowcontrol_current_executing_requests is a gauge vector holding the instantaneous number of executing (not waiting in a queue) requests, broken down by the labels priority_level and flow_schema.

• apiserver_flowcontrol_request_concurrency_in_use is a gauge vector holding the instantaneous number of occupied seats, broken down by the labels priority_level and flow_schema.

• apiserver_flowcontrol_priority_level_request_utilization is a histogram vector of observations, made at the end of each nanosecond, of the number of requests broken down by the labels phase (which takes on the values waiting and executing) and priority_level. Each observed value is a ratio, between 0 and 1, of a number of requests divided by the corresponding limit on the number of requests (queue volume limit for waiting and concurrency limit for executing).

• apiserver_flowcontrol_priority_level_seat_utilization is a histogram vector of observations, made at the end of each nanosecond, of the utilization of a priority level's concurrency limit, broken down by priority_level. This utilization is the fraction (number of seats occupied) / (concurrency limit). This metric considers all stages of execution (both normal and the extra delay at the end of a write to cover for the corresponding notification work) of all requests except WATCHes; for those it considers only the initial stage that delivers notifications of pre-existing objects. Each histogram in the vector is also labeled with phase: executing (there is no seat limit for the waiting phase).

• apiserver_flowcontrol_request_queue_length_after_enqueue is a histogram vector of queue lengths for the queues, broken down by the labels priority_level and flow_schema, as sampled by the enqueued requests. Each request that gets queued contributes one sample to its histogram, reporting the length of the queue immediately after the request was added. Note that this produces different statistics than an unbiased survey would.

  Note: An outlier value in a histogram here means it is likely that a single flow (i.e., requests by one user or for one namespace, depending on configuration) is flooding the API server, and being throttled. By contrast, if one priority level's histogram shows that all queues for that priority level are longer than those for other priority levels, it may be appropriate to increase that PriorityLevelConfiguration's concurrency shares.

• apiserver_flowcontrol_request_concurrency_limit is the same as apiserver_flowcontrol_nominal_limit_seats. Before the introduction of concurrency borrowing between priority levels, this was always equal to apiserver_flowcontrol_current_limit_seats (which did not exist as a distinct metric).

• apiserver_flowcontrol_nominal_limit_seats is a gauge vector holding each priority level's nominal concurrency limit, computed from the API server's total concurrency limit and the priority level's configured nominal concurrency shares.

• apiserver_flowcontrol_lower_limit_seats is a gauge vector holding the lower bound on each priority level's dynamic concurrency limit.

• apiserver_flowcontrol_upper_limit_seats is a gauge vector holding the upper bound on each priority level's dynamic concurrency limit.

• apiserver_flowcontrol_demand_seats is a histogram vector counting observations, at the end of every nanosecond, of each priority level's ratio of (seat demand) / (nominal concurrency limit). A priority level's seat demand is the sum, over both queued requests and those in the initial phase of execution, of the maximum of the number of seats occupied in the request's initial and final execution phases.

• apiserver_flowcontrol_demand_seats_high_watermark is a gauge vector holding, for each priority level, the maximum seat demand seen during the last concurrency borrowing adjustment period.

• apiserver_flowcontrol_demand_seats_average is a gauge vector holding, for each priority level, the time-weighted average seat demand seen during the last concurrency borrowing adjustment period.

• apiserver_flowcontrol_demand_seats_stdev is a gauge vector holding, for each priority level, the time-weighted population standard deviation of seat demand seen during the last concurrency borrowing adjustment period.

• apiserver_flowcontrol_demand_seats_smoothed is a gauge vector holding, for each priority level, the smoothed enveloped seat demand determined at the last concurrency adjustment.

• apiserver_flowcontrol_target_seats is a gauge vector holding, for each priority level, the concurrency target going into the borrowing allocation problem.

• apiserver_flowcontrol_seat_fair_frac is a gauge holding the fair allocation fraction determined in the last borrowing adjustment.

• apiserver_flowcontrol_current_limit_seats is a gauge vector holding, for each priority level, the dynamic concurrency limit derived in the last adjustment.

• apiserver_flowcontrol_request_wait_duration_seconds is a histogram vector of how long requests spent queued, broken down by the labels flow_schema (indicating which one matched the request), priority_level (indicating the one to which the request was assigned), and execute (indicating whether the request started executing).

  Note: Since each FlowSchema always assigns requests to a single PriorityLevelConfiguration, you can add the histograms for all the FlowSchemas for one priority level to get the effective histogram for requests assigned to that priority level.

• apiserver_flowcontrol_request_execution_seconds is a histogram vector of how long requests took to actually execute, broken down by the labels flow_schema (indicating which one matched the request) and priority_level (indicating the one to which the request was assigned).

• apiserver_flowcontrol_watch_count_samples is a histogram vector of the number of active WATCH requests relevant to a given write, broken down by flow_schema and priority_level.

• apiserver_flowcontrol_work_estimated_seats is a histogram vector of the number of estimated seats (maximum of initial and final stage of execution) associated with requests, broken down by flow_schema and priority_level.

• apiserver_flowcontrol_request_dispatch_no_accommodation_total is a counter vec of the number of events that in principle could have led to a request being dispatched but did not, due to lack of available concurrency, broken down by flow_schema and priority_level. The relevant sorts of events are arrival of a request and completion of a request.

Debug endpoints

When you enable the API Priority and Fairness feature, the kube-apiserver serves the following additional paths at its HTTP[S] ports.

• /debug/api_priority_and_fairness/dump_priority_levels - a listing of all the priority levels and the current state of each. You can fetch like this:

  kubectl get --raw /debug/api_priority_and_fairness/dump_priority_levels
  

  The output is similar to this:

  PriorityLevelName, ActiveQueues, IsIdle, IsQuiescing, WaitingRequests, ExecutingRequests, DispatchedRequests, RejectedRequests, TimedoutRequests, CancelledRequests
  catch-all,         0,            true,   false,       0,               0,                 1,                  0,                0,                0
  exempt,            <none>,       <none>, <none>,      <none>,          <none>,            <none>,             <none>,           <none>,           <none>
  global-default,    0,            true,   false,       0,               0,                 46,                 0,                0,                0
  leader-election,   0,            true,   false,       0,               0,                 4,                  0,                0,                0
  node-high,         0,            true,   false,       0,               0,                 34,                 0,                0,                0
  system,            0,            true,   false,       0,               0,                 48,                 0,                0,                0
  workload-high,     0,            true,   false,       0,               0,                 500,                0,                0,                0
  workload-low,      0,            true,   false,       0,               0,                 0,                  0,                0,                0
  

• /debug/api_priority_and_fairness/dump_queues - a listing of all the queues and their current state. You can fetch like this:

  kubectl get --raw /debug/api_priority_and_fairness/dump_queues
  

  The output is similar to this:

  PriorityLevelName, Index,  PendingRequests, ExecutingRequests, VirtualStart,
  workload-high,     0,      0,               0,                 0.0000,
  workload-high,     1,      0,               0,                 0.0000,
  workload-high,     2,      0,               0,                 0.0000,
  ...
  leader-election,   14,     0,               0,                 0.0000,
  leader-election,   15,     0,               0,                 0.0000,
  

• /debug/api_priority_and_fairness/dump_requests - a listing of all the requests that are currently waiting in a queue. You can fetch like this:

  kubectl get --raw /debug/api_priority_and_fairness/dump_requests
  

  The output is similar to this:

  PriorityLevelName, FlowSchemaName, QueueIndex, RequestIndexInQueue, FlowDistingsher,       ArriveTime,
  exempt,            <none>,         <none>,     <none>,              <none>,                <none>,
  system,            system-nodes,   12,         0,                   system:node:127.0.0.1, 2020-07-23T15:26:57.179170694Z,
  

  In addition to the queued requests, the output includes one phantom line for each priority level that is exempt from limitation.

  You can get a more detailed listing with a command like this:

  kubectl get --raw '/debug/api_priority_and_fairness/dump_requests?includeRequestDetails=1'
  

  The output is similar to this:

  PriorityLevelName, FlowSchemaName, QueueIndex, RequestIndexInQueue, FlowDistingsher,       ArriveTime,                     UserName,              Verb,   APIPath,                                                     Namespace, Name,   APIVersion, Resource, SubResource,
  system,            system-nodes,   12,         0,                   system:node:127.0.0.1, 2020-07-23T15:31:03.583823404Z, system:node:127.0.0.1, create, /api/v1/namespaces/scaletest/configmaps,
  system,            system-nodes,   12,         1,                   system:node:127.0.0.1, 2020-07-23T15:31:03.594555947Z, system:node:127.0.0.1, create, /api/v1/namespaces/scaletest/configmaps,
  

Debug logging

At -v=3 or more verbose the server outputs an httplog line for every request, and it includes the following attributes.

• apf_fs: the name of the flow schema to which the request was classified.
• apf_pl: the name of the priority level for that flow schema.
• apf_iseats: the number of seats determined for the initial (normal) stage of execution of the request.
• apf_fseats: the number of seats determined for the final stage of execution (accounting for the associated WATCH notifications) of the request.
• apf_additionalLatency: the duration of the final stage of execution of the request.

At higher levels of verbosity there will be log lines exposing details of how APF handled the request, primarily for debug purposes.

Response headers

APF adds the following two headers to each HTTP response message.

• X-Kubernetes-PF-FlowSchema-UID holds the UID of the FlowSchema object to which the corresponding request was classified.
• X-Kubernetes-PF-PriorityLevel-UID holds the UID of the PriorityLevelConfiguration object associated with that FlowSchema.

What's next

For background information on design details for API priority and fairness, see the enhancement proposal. You can make suggestions and feature requests via SIG API Machinery or the feature's slack channel.

10 - Installing Addons

Add-ons extend the functionality of Kubernetes.

This page lists some of the available add-ons and links to their respective installation instructions. The list does not try to be exhaustive.

Networking and Network Policy

• ACI provides integrated container networking and network security with Cisco ACI.
• Antrea operates at Layer 3/4 to provide networking and security services for Kubernetes, leveraging Open vSwitch as the networking data plane. Antrea is a CNCF project at the Sandbox level.
• Calico is a networking and network policy provider. Calico supports a flexible set of networking options so you can choose the most efficient option for your situation, including non-overlay and overlay networks, with or without BGP. Calico uses the same engine to enforce network policy for hosts, pods, and (if using Istio & Envoy) applications at the service mesh layer.
• Canal unites Flannel and Calico, providing networking and network policy.
• Cilium is a networking, observability, and security solution with an eBPF-based data plane. Cilium provides a simple flat Layer 3 network with the ability to span multiple clusters in either a native routing or overlay/encapsulation mode, and can enforce network policies on L3-L7 using an identity-based security model that is decoupled from network addressing. Cilium can act as a replacement for kube-proxy; it also offers additional, opt-in observability and security features. Cilium is a CNCF project at the Incubation level.
• CNI-Genie enables Kubernetes to seamlessly connect to a choice of CNI plugins, such as Calico, Canal, Flannel, or Weave. CNI-Genie is a CNCF project at the Sandbox level.
• Contiv provides configurable networking (native L3 using BGP, overlay using vxlan, classic L2, and Cisco-SDN/ACI) for various use cases and a rich policy framework. Contiv project is fully open sourced. The installer provides both kubeadm and non-kubeadm based installation options.
• Contrail, based on Tungsten Fabric, is an open source, multi-cloud network virtualization and policy management platform. Contrail and Tungsten Fabric are integrated with orchestration systems such as Kubernetes, OpenShift, OpenStack and Mesos, and provide isolation modes for virtual machines, containers/pods and bare metal workloads.
• Flannel is an overlay network provider that can be used with Kubernetes.
• Knitter is a plugin to support multiple network interfaces in a Kubernetes pod.
• Multus is a Multi plugin for multiple network support in Kubernetes to support all CNI plugins (e.g. Calico, Cilium, Contiv, Flannel), in addition to SRIOV, DPDK, OVS-DPDK and VPP based workloads in Kubernetes.
• OVN-Kubernetes is a networking provider for Kubernetes based on OVN (Open Virtual Network), a virtual networking implementation that came out of the Open vSwitch (OVS) project. OVN-Kubernetes provides an overlay based networking implementation for Kubernetes, including an OVS based implementation of load balancing and network policy.
• Nodus is an OVN based CNI controller plugin to provide cloud native based Service function chaining(SFC).
• NSX-T Container Plug-in (NCP) provides integration between VMware NSX-T and container orchestrators such as Kubernetes, as well as integration between NSX-T and container-based CaaS/PaaS platforms such as Pivotal Container Service (PKS) and OpenShift.
• Nuage is an SDN platform that provides policy-based networking between Kubernetes Pods and non-Kubernetes environments with visibility and security monitoring.
• Romana is a Layer 3 networking solution for pod networks that also supports the NetworkPolicy API.
• Weave Net provides networking and network policy, will carry on working on both sides of a network partition, and does not require an external database.

Service Discovery

• CoreDNS is a flexible, extensible DNS server which can be installed as the in-cluster DNS for pods.

Visualization & Control

• Dashboard is a dashboard web interface for Kubernetes.
• Weave Scope is a tool for graphically visualizing your containers, pods, services etc. Use it in conjunction with a Weave Cloud account or host the UI yourself.

Infrastructure

• KubeVirt is an add-on to run virtual machines on Kubernetes. Usually run on bare-metal clusters.
• The node problem detector runs on Linux nodes and reports system issues as either Events or Node conditions.

Legacy Add-ons

There are several other add-ons documented in the deprecated cluster/addons directory.

Well-maintained ones should be linked to here. PRs welcome!