- Resource Propagating
- Before you start
- Deploy a simplest multi-cluster Deployment
- Deploy deployment into a specified set of target clusters
- Multiple cluster affinity groups
- Schedule based on Taints and Tolerations
- Multi region HA support
- Multiple strategies of replica Scheduling
- Configure PropagationPolicy/ClusterPropagationPolicy priority
- 暂停与恢复资源分发
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
- Create a propagationPolicy base on the YAML file:
kubectl apply -f propagationpolicy.yaml
- 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.
- 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
- 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
- Apply the new YAML file:
kubectl apply -f propagationpolicy-update.yaml
- 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
- 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.
- Update deployment replicas to 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
- 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:
- ClusterAffinities can not co-exist with ClusterAffinity.
- 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
备注
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 byReplicaDivisionPreference
.
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 toWeightPreference
. There are two kinds ofWeightPreference
to set.StaticWeightList
statically allocates replicas to target clusters based on weight. Target clusters can be selected byClusterAffinity
.DynamicWeight
specifies the factor to generate the dynamic weight list. If specified,StaticWeightList
will be ignored. Karmada currently supports the factorAvailableReplicas
.
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”.
备注
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
.
暂停与恢复资源分发
PropagationPolicy
和 ClusterPropagationPolicy
中的 .spec.suspension
字段允许对一个或多个集群的资源分发进行暂停和恢复。
暂停向所有集群分发资源
为了暂停向所有成员集群分发资源,您可以使用以下配置:
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
name: nginx-propagation
spec:
#...
placement:
clusterAffinity:
exclude:
- member1
- member3
#...
suspension:
dispatching: true
在 Karmada 控制平面上对 nginx
Deployment 的更新将不会被同步到任何成员集群。
暂停向特定集群分发资源
为了暂停向个别的成员集群分发资源,您可以在 .spec.suspension.clusterNames
字段中指定集群名称:
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
name: nginx-propagation
spec:
#...
placement:
clusterAffinity:
exclude:
- member1
- member3
#...
suspension:
dispatchingOnClusters:
clusterNames:
- member3 # 修改此处的集群名称
在 Karmada 控制平面上对 nginx
Deployment 的更新将不会被同步到 member3
集群,但会被同步到所有其他集群。
恢复资源分发
为了恢复资源工作,您只需移除 .spec.suspension
配置:
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
name: nginx-propagation
spec:
#...
placement:
clusterAffinity:
exclude:
- member1
- member3
#...
Karmada 控制平面中的 nginx
Deployment 状态将被同步到所有成员集群,任何后续的更新也将被同步。