Resource Propagating

The PropagationPolicy and ClusterPropagationPolicy APIs are provided to propagate resources. For the differences between the two APIs, please see here.

Here, we use PropagationPolicy as an example to describe how to propagate resources.

Before you start

Install Karmada and prepare the karmadactl command-line tool.

Deploy a simplest multi-cluster Deployment

Create a PropagationPolicy object

You can propagate a Deployment by creating a PropagationPolicy object defined in a YAML file. For example, this YAML file describes a Deployment object named nginx under default namespace need to be propagated to member1 cluster:

# propagationpolicy.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: example-policy # The default namespace is `default`.
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx # If no namespace is specified, the namespace is inherited from the parent object scope.
  placement:
    clusterAffinity:
      clusterNames:
        - member1

1. Create a propagationPolicy base on the YAML file:

kubectl apply -f propagationpolicy.yaml

2. Create a Deployment nginx resource:

kubectl create deployment nginx --image nginx

Note: The resource exists only as a template in karmada. After being propagated to a member cluster, the behavior of the resource is the same as that of a single kubernetes cluster.

Note: Resources and PropagationPolicy are created in no sequence. 3. Display information of the deployment:

karmadactl get deployment

The output is similar to this:

NAME    CLUSTER   READY   UP-TO-DATE   AVAILABLE   AGE   ADOPTION
nginx   member1   1/1     1            1           52s   Y

4. List the pods created by the deployment:

karmadactl get pod -l app=nginx

The output is similar to this:

NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
nginx-6799fc88d8-s7vv9   member1   1/1     Running   0          52s

Update PropagationPolicy

You can update the propagationPolicy by applying a new YAML file. This YAML file propagates the Deployment to the member2 cluster.

# propagationpolicy-update.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: example-policy
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
  placement:
    clusterAffinity:
      clusterNames: # Modify the selected cluster to propagate the Deployment.
        - member2

1. Apply the new YAML file:

kubectl apply -f propagationpolicy-update.yaml

2. Display information of the deployment (the output is similar to this):

NAME    CLUSTER   READY   UP-TO-DATE   AVAILABLE   AGE   ADOPTION
nginx   member2   1/1     1            1           5s    Y

3. List the pods of the deployment (the output is similar to this):

NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
nginx-6799fc88d8-8t8cc   member2   1/1     Running   0          17s

Update Deployment

You can update the deployment template. The changes will be automatically synchronized to the member clusters.

1. Update deployment replicas to 2
2. Display information of the deployment (the output is similar to this):

NAME    CLUSTER   READY   UP-TO-DATE   AVAILABLE   AGE     ADOPTION
nginx   member2   2/2     2            2           7m59s   Y

3. List the pods of the deployment (the output is similar to this):

NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
nginx-6799fc88d8-8t8cc   member2   1/1     Running   0          8m12s
nginx-6799fc88d8-zpl4j   member2   1/1     Running   0          17s

Delete a propagationPolicy

Delete the propagationPolicy by name:

kubectl delete propagationpolicy example-policy

Deleting a propagationPolicy does not delete deployments propagated to member clusters. You need to delete deployments in the karmada control-plane:

kubectl delete deployment nginx

Deploy deployment into a specified set of target clusters

.spec.placement.clusterAffinity field of PropagationPolicy represents scheduling restrictions on a certain set of clusters, without which any cluster can be scheduling candidates.

It has four fields to set:

• LabelSelector
• FieldSelector
• ClusterNames
• ExcludeClusters

LabelSelector

LabelSelector is a filter to select member clusters by labels. It uses *metav1.LabelSelector type. If it is non-nil and non-empty, only the clusters match this filter will be selected.

PropagationPolicy can be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: test-propagation
spec:
  #...
  placement:
    clusterAffinity:
      labelSelector:
        matchLabels:
          location: us
    #...

PropagationPolicy can also be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: test-propagation
spec:
  #...
  placement:
    clusterAffinity:
      labelSelector:
        matchExpressions:
        - key: location
          operator: In
          values:
          - us
    #...

For a description of matchLabels and matchExpressions, you can refer to Resources that support set-based requirements.

FieldSelector

FieldSelector is a filter to select member clusters by fields. If it is non-nil and non-empty, only the clusters match this filter will be selected.

PropagationPolicy can be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  #...
  placement:
    clusterAffinity:
      fieldSelector:
        matchExpressions:
        - key: provider
          operator: In
          values:
          - huaweicloud
        - key: region
          operator: NotIn
          values:
          - cn-south-1
    #...

If multiple matchExpressions are specified in the fieldSelector, the cluster must match all matchExpressions.

The key in matchExpressions now supports three values: provider, region, and zone, which correspond to the .spec.provider, .spec.region, and .spec.zone fields of the Cluster object, respectively.

The operator in matchExpressions now supports In and NotIn.

ClusterNames

Users can set the ClusterNames field to specify the selected clusters.

PropagationPolicy can be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  #...
  placement:
    clusterAffinity:
      clusterNames:
        - member1
        - member2
    #...

ExcludeClusters

Users can set the ExcludeClusters fields to specify the clusters to be ignored.

PropagationPolicy can be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
 name: nginx-propagation
spec:
 #...
 placement:
  clusterAffinity:
   exclude:
    - member1
    - member3
  #...

Multiple cluster affinity groups

Users can set the ClusterAffinities field and declare multiple cluster groups in PropagationPolicy. The scheduler will evaluate these groups one by one in the order they appear in the spec, the group that does not satisfy scheduling restrictions will be ignored which means all clusters in this group will not be selected unless it also belongs to the next group(a cluster cloud belong to multiple groups).

If none of the groups satisfy the scheduling restrictions, the scheduling fails, which means no cluster will be selected.

Note:

1. ClusterAffinities can not co-exist with ClusterAffinity.
2. If both ClusterAffinity and ClusterAffinities are not set, any cluster can be scheduling candidates.

Potential use case 1: The private clusters in the local data center could be the main group, and the managed clusters provided by cluster providers could be the secondary group. So that the Karmada scheduler would prefer to schedule workloads to the main group and the second group will only be considered in case of the main group does not satisfy restrictions(like, lack of resources).

PropagationPolicy can be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
 name: test-propagation
spec:
 #...
 placement:
  clusterAffinities:
   - affinityName: local-clusters
     clusterNames:
      - local-member1
      - local-member2
   - affinityName: cloud-clusters
     clusterNames:
      - public-cloud-member1
      - public-cloud-member2
  #...

Potential use case 2: For the disaster recovery scenario, the clusters could be organized to primary and backup groups, the workloads would be scheduled to primary clusters firstly, and when primary cluster fails(like data center power off), Karmada scheduler could migrate workloads to the backup clusters.

PropagationPolicy can be configured as follows:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
 name: test-propagation
spec:
 #...
 placement:
  clusterAffinities:
   - affinityName: primary-clusters
     clusterNames:
      - member1
   - affinityName: backup-clusters
     clusterNames:
      - member1
      - member2
  #...

For more detailed design information, please refer to Multiple scheduling group.

Schedule based on Taints and Tolerations

.spec.placement.clusterTolerations field of PropagationPolicy represents the tolerations. Like kubernetes, tolerations need to be used in conjunction with taints on the clusters. After setting one or more taints on the cluster, workloads cannot be scheduled or run on these clusters unless the policy explicitly states that these taints are tolerated. Karmada currently supports taints whose effects are NoSchedule and NoExecute.

You can karmadactl taint to taint a cluster:

# Update cluster 'foo' with a taint with key 'dedicated' and value 'special-user' and effect 'NoSchedule'
# If a taint with that key and effect already exists, its value is replaced as specified
karmadactl taint clusters foo dedicated=special-user:NoSchedule

In order to schedule to the above cluster, you need to declare the following in the Policy:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  #...
  placement:
    clusterTolerations:
    - key: dedicated
      value: special-user
      Effect: NoSchedule 

NoExecute taints are also used in Multi-cluster Failover. See details here.

Multi region HA support

By leveraging the spread-by-region constraint, users are able to deploy workloads aross regions, e.g. people may want their workloads always running on different regions for HA purposes.

To enable multi region deployment, you should use the command below to customize the region of clusters.

kubectl --kubeconfig ~/.kube/karmada.config --context karmada-apiserver edit cluster/member1

...
spec:
  apiEndpoint: https://172.18.0.4:6443
  id: 257b5c81-dfae-4ae5-bc7c-6eaed9ed6a39
  impersonatorSecretRef:
    name: member1-impersonator
    namespace: karmada-cluster
  region: test
...

Then you need to restrict the maximum and minimum number of cluster groups to be selected. Assume there are two regions, you may want to deploy the workload in one cluster per region. You can refer to:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  #...
  placement:
    replicaScheduling:
     replicaSchedulingType: Duplicated  
    spreadConstraints:
      - spreadByField: region
        maxGroups: 2
        minGroups: 2
      - spreadByField: cluster
        maxGroups: 1
        minGroups: 1

note

If the replica division preference is StaticWeightList, the declaration specified by spread constraints will be ignored. If one of spread constraints are using SpreadByField, the SpreadByFieldCluster must be included. For example, when using SpreadByFieldRegion to specify region groups, at the meantime, you must use SpreadByFieldCluster to specify how many clusters should be selected.

Multiple strategies of replica Scheduling

.spec.placement.replicaScheduling represents the scheduling policy on dealing with the number of replicas when propagating resources that have replicas in spec (e.g. deployments, statefulsets and CRDs which can be interpreted by Customizing Resource Interpreter) to member clusters.

It has two replicaSchedulingTypes which determines how the replicas is scheduled when Karmada propagating a resource:

• Duplicated: duplicate the same replicas to each candidate member cluster from resources.
• Divided: divide replicas into parts according to numbers of valid candidate member clusters, and exact replicas for each cluster are determined by ReplicaDivisionPreference.

ReplicaDivisionPreference determines the replicas is divided when ReplicaSchedulingType is Divided.

• Aggregated: divide replicas into clusters as few as possible, while respecting clusters' resource availabilities during the division. See details in Schedule based on Cluster Resource Modeling.
• Weighted: divide replicas by weight according to WeightPreference. There are two kinds of WeightPreference to set. StaticWeightList statically allocates replicas to target clusters based on weight. Target clusters can be selected by ClusterAffinity. DynamicWeight specifies the factor to generate the dynamic weight list. If specified, StaticWeightList will be ignored. Karmada currently supports the factor AvailableReplicas.

The following gives two simple examples:

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  #...
  placement:
    replicaScheduling:
      replicaDivisionPreference: Weighted
      replicaSchedulingType: Divided
      weightPreference:
        staticWeightList:
          - targetCluster:
              clusterNames:
                - member1
            weight: 1
          - targetCluster:
              clusterNames:
                - member2
            weight: 1

It means replicas will be evenly propagated to member1 and member2.

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  #...
  placement:
    replicaScheduling:
      replicaDivisionPreference: Weighted
      replicaSchedulingType: Divided
      weightPreference:
        dynamicWeight: AvailableReplicas 

It means replicas will be propagated based on available replicas in member clusters. For example, the scheduler selected 3 cluster(A/B/C) and should divide 12 replicas to them. Based on cluster resource modeling, we get that the max available replica of A, B, C is 6, 12, 18. Therefore, the weight of cluster A:B:C will be 6:12:18 (equal to 1:2:3). At last, the assignment would be "A: 2, B: 4, C: 6".

note

If ReplicaDivisionPreference is set to Weighted and WeightPreference is not set, the default strategy is to weight all clusters averagely.

Configure PropagationPolicy/ClusterPropagationPolicy priority

If a PropagationPolicy and a ClusterPropagationPolicy match the workload, Karmada will select the PropagationPolicy. If multiple PropagationPolicies match the workload, Karmada will select the one with the highest priority. A PropagationPolicy supports implicit and explicit priorities. The same goes for ClusterPropagationPolicy.

The following takes PropagationPolicy as an example.

Configure explicit priority

The spec.priority in a PropagationPolicy represents the explicit priority. A greater value means a higher priority.

Note: If not specified, defaults to 0.

Assume there are multiple policies:

# highexplicitpriority.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-high-explicit-priority
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      labelSelector:
        matchLabels:
          app: nginx
  priority: 2
  placement:
    clusterAffinity:
      clusterNames:
        - member1
---
# lowexplicitpriority.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-low-explicit-priority
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      labelSelector:
        matchLabels:
          app: nginx
  priority: 1
  placement:
    clusterAffinity:
      clusterNames:
        - member2
---
# defaultexplicitpriority.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-low-explicit-priority
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      labelSelector:
        matchLabels:
          app: nginx
  placement:
    clusterAffinity:
      clusterNames:
        - member3

The nginx deployment in default namespace will be propagated to cluster member1.

Configure implicit priority

The spec.resourceSelectors in a PropagationPolicy represents the implicit priority. The priority order (from low to high) is as follows:

• The PropagationPolicy resourceSelector whose name and labelSelector are empty matches the workload.
• The labelSelector of PropagationPolicy resourceSelector matches the workload.
• The name of PropagationPolicy resourceSelector matches the workload.

Assume there are multiple policies:

# emptymatch.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-emptymatch
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
  placement:
    clusterAffinity:
      clusterNames:
        - member1
---
# labelselectormatch.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-labelselectormatch
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      labelSelector:
        matchLabels:
          app: nginx
  placement:
    clusterAffinity:
      clusterNames:
        - member2
---
# namematch.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-namematch
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
  placement:
    clusterAffinity:
      clusterNames:
        - member3

The nginx deployment in default namespace will be propagated to cluster member3.

Choose from same-priority PropagationPolicies

If multiple PropagationPolicies with the same explicit priority match the workload, the one with the highest implicit priority will be selected.

Assume there are multiple policies:

# explicit-labelselectormatch.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-explicit-labelselectormatch
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      labelSelector:
        matchLabels:
          app: nginx
  priority: 3
  placement:
    clusterAffinity:
      clusterNames:
        - member1
---
# explicit-namematch.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-explicit-namematch
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
  priority: 3
  placement:
    clusterAffinity:
      clusterNames:
        - member3

The nginx deployment in default namespace will be propagated to cluster member3.

If both explicit and implicit priorities are the same, Karmada applies the PropagationPolicy in an ascending alphabetical order, for example, choosing xxx-a-xxx instead of xxx-b-xxx.

Assume there are multiple policies:

# higher-alphabetical.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-b-higher-alphabetical
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
  priority: 3
  placement:
    clusterAffinity:
      clusterNames:
        - member1
---
# lower-alphabetical.yaml
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: propagation-a-lower-alphabetical
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
  priority: 3
  placement:
    clusterAffinity:
      clusterNames:
        - member2

The nginx deployment in default namespace will be propagated to cluster member2.