Affinity and Anti-Affinity Scheduling
A nodeSelector provides a very simple way to constrain pods to nodes with particular labels, as mentioned in DaemonSet. The affinity and anti-affinity feature greatly expands the types of constraints you can express.
Kubernetes supports node-level and pod-level affinity and anti-affinity. You can configure custom rules to achieve affinity and anti-affinity scheduling. For example, you can deploy frontend pods and backend pods together, deploy the same type of applications on a specific node, or deploy different applications on different nodes.
Node Affinity
Node affinity is conceptually similar to a nodeSelector as it allows you to constrain which nodes your pod is eligible to be scheduled on, based on labels on the node. The following output lists the labels of node 192.168.0.212.
$ kubectl describe node 192.168.0.212
Name: 192.168.0.212
Roles: <none>
Labels: beta.kubernetes.io/arch=amd64
beta.kubernetes.io/os=linux
failure-domain.beta.kubernetes.io/is-baremetal=false
failure-domain.beta.kubernetes.io/region=cn-east-3
failure-domain.beta.kubernetes.io/zone=cn-east-3a
kubernetes.io/arch=amd64
kubernetes.io/availablezone=cn-east-3a
kubernetes.io/eniquota=12
kubernetes.io/hostname=192.168.0.212
kubernetes.io/os=linux
node.kubernetes.io/subnetid=fd43acad-33e7-48b2-a85a-24833f362e0e
os.architecture=amd64
os.name=EulerOS_2.0_SP5
os.version=3.10.0-862.14.1.5.h328.eulerosv2r7.x86_64
These labels are automatically added by CCE during node creation. The following describes a few that are frequently used during scheduling.
- failure-domain.beta.kubernetes.io/region: region where the node is located. In the preceding output, the label value is cn-east-3, which indicates that the node is located in the CN East-Shanghai1 region.
- failure-domain.beta.kubernetes.io/zone: availability zone to which the node belongs.
- kubernetes.io/hostname: host name of the node.
In addition to these automatically added labels, you can tailor labels to your service requirements, as introduced in Label for Managing Pods. Generally, large Kubernetes clusters have various kinds of labels.
When you deploy pods, you can use a nodeSelector, as described in DaemonSet, to constrain pods to nodes with specific labels. The following example shows how to use a nodeSelector to deploy pods only on the nodes with the gpu=true label.
apiVersion: v1
kind: Pod
metadata:
name: nginx
spec:
nodeSelector: #Node selection. A pod is deployed on a node only when the node has the gpu=true label.
gpu: true
...
apiVersion: apps/v1
kind: Deployment
metadata:
name: gpu
labels:
app: gpu
spec:
selector:
matchLabels:
app: gpu
replicas: 3
template:
metadata:
labels:
app: gpu
spec:
containers:
- image: nginx:alpine
name: gpu
resources:
requests:
cpu: 100m
memory: 200Mi
limits:
cpu: 100m
memory: 200Mi
imagePullSecrets:
- name: default-secret
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu
operator: In
values:
- "true"
Even though the node affinity rule requires more lines, it is more expressive, which will be further described later.
requiredDuringSchedulingIgnoredDuringExecution seems to be complex, but it can be easily understood as a combination of two parts.
- requiredDuringScheduling indicates that pods can be scheduled to the node only when all the defined rules are met (required).
- IgnoredDuringExecution indicates that pods already running on the node do not need to meet the defined rules. That is, a label on the node is ignored, and pods that require the node to contain that label will not be re-scheduled.
In addition, the value of operator is In, indicating that the label value must be in the values list. Other available operator values are as follows:
- NotIn: The label value is not in a list.
- Exists: A specific label exists.
- DoesNotExist: A specific label does not exist.
- Gt: The label value is greater than a specified value (string comparison).
- Lt: The label value is less than a specified value (string comparison).
Note that there is no such thing as nodeAntiAffinity because operators NotIn and DoesNotExist provide the same function.
Now, check whether the node affinity rule takes effect. Add the gpu=true tag to the 192.168.0.212 node.
$ kubectl label node 192.168.0.212 gpu=true node/192.168.0.212 labeled $ kubectl get node -L gpu NAME STATUS ROLES AGE VERSION GPU 192.168.0.212 Ready <none> 13m v1.15.6-r1-20.3.0.2.B001-15.30.2 true 192.168.0.94 Ready <none> 13m v1.15.6-r1-20.3.0.2.B001-15.30.2 192.168.0.97 Ready <none> 13m v1.15.6-r1-20.3.0.2.B001-15.30.2
Create the Deployment. You can find that all pods are deployed on the 192.168.0.212 node.
$ kubectl create -f affinity.yaml deployment.apps/gpu created $ kubectl get pod -o wide NAME READY STATUS RESTARTS AGE IP NODE gpu-6df65c44cf-42xw4 1/1 Running 0 15s 172.16.0.37 192.168.0.212 gpu-6df65c44cf-jzjvs 1/1 Running 0 15s 172.16.0.36 192.168.0.212 gpu-6df65c44cf-zv5cl 1/1 Running 0 15s 172.16.0.38 192.168.0.212
Node Preference Rule
The preceding requiredDuringSchedulingIgnoredDuringExecution rule is a hard selection rule. There is another type of selection rule, that is, preferredDuringSchedulingIgnoredDuringExecution. It is used to specify which nodes are preferred during scheduling.
To demonstrate its effect, add a node to the cluster and ensure that the node is not in the same AZ with other nodes. After the node is created, query the AZ of the node. As shown in the following output, the newly added node is in cn-east-3c.
$ kubectl get node -L failure-domain.beta.kubernetes.io/zone,gpu NAME STATUS ROLES AGE VERSION ZONE GPU 192.168.0.100 Ready <none> 7h23m v1.15.6-r1-20.3.0.2.B001-15.30.2 cn-east-3c 192.168.0.212 Ready <none> 8h v1.15.6-r1-20.3.0.2.B001-15.30.2 cn-east-3a true 192.168.0.94 Ready <none> 8h v1.15.6-r1-20.3.0.2.B001-15.30.2 cn-east-3a 192.168.0.97 Ready <none> 8h v1.15.6-r1-20.3.0.2.B001-15.30.2 cn-east-3a
Define a Deployment. Use the preferredDuringSchedulingIgnoredDuringExecution rule to set the weight of nodes in cn-east-3a as 80 and nodes with the gpu=true label as 20. In this way, pods are preferentially deployed on the node in cn-east-3a.
apiVersion: apps/v1
kind: Deployment
metadata:
name: gpu
labels:
app: gpu
spec:
selector:
matchLabels:
app: gpu
replicas: 10
template:
metadata:
labels:
app: gpu
spec:
containers:
- image: nginx:alpine
name: gpu
resources:
requests:
cpu: 100m
memory: 200Mi
limits:
cpu: 100m
memory: 200Mi
imagePullSecrets:
- name: default-secret
affinity:
nodeAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 80
preference:
matchExpressions:
- key: failure-domain.beta.kubernetes.io/zone
operator: In
values:
- cn-east-3a
- weight: 20
preference:
matchExpressions:
- key: gpu
operator: In
values:
- "true"
After the deployment, you can find that five pods are deployed on the 192.168.0.212 node, and two pods are deployed on the 192.168.0.100 node.
$ kubectl create -f affinity2.yaml deployment.apps/gpu created $ kubectl get po -o wide NAME READY STATUS RESTARTS AGE IP NODE gpu-585455d466-5bmcz 1/1 Running 0 2m29s 172.16.0.44 192.168.0.212 gpu-585455d466-cg2l6 1/1 Running 0 2m29s 172.16.0.63 192.168.0.97 gpu-585455d466-f2bt2 1/1 Running 0 2m29s 172.16.0.79 192.168.0.100 gpu-585455d466-hdb5n 1/1 Running 0 2m29s 172.16.0.42 192.168.0.212 gpu-585455d466-hkgvz 1/1 Running 0 2m29s 172.16.0.43 192.168.0.212 gpu-585455d466-mngvn 1/1 Running 0 2m29s 172.16.0.48 192.168.0.97 gpu-585455d466-s26qs 1/1 Running 0 2m29s 172.16.0.62 192.168.0.97 gpu-585455d466-sxtzm 1/1 Running 0 2m29s 172.16.0.45 192.168.0.212 gpu-585455d466-t56cm 1/1 Running 0 2m29s 172.16.0.64 192.168.0.100 gpu-585455d466-t5w5x 1/1 Running 0 2m29s 172.16.0.41 192.168.0.212
In the preceding example, the node scheduling priority is as follows. Nodes with both cn-east-3a and gpu=true labels have the highest priority. Nodes with the cn-east-3a label but no gpu=true label have the second priority (weight: 80). Nodes with the gpu=true label but no cn-east-3a label have the third priority. Nodes without any of these two labels have the lowest priority.
From the preceding output, you can find that no pods of the Deployment are scheduled to node 192.168.0.94. This is because the node already has many pods on it and its resource usage is high. This also indicates that the preferredDuringSchedulingIgnoredDuringExecution rule defines a preference rather than a hard requirement.
Pod Affinity
Node affinity rules affect only the affinity between pods and nodes. Kubernetes also supports configuring inter-pod affinity rules. For example, the frontend and backend of an application can be deployed together on one node to reduce access latency. There are also two types of inter-pod affinity rules: requiredDuringSchedulingIgnoredDuringExecution and preferredDuringSchedulingIgnoredDuringExecution.
Assume that the backend of an application has been created and has the app=backend label.
$ kubectl get po -o wide NAME READY STATUS RESTARTS AGE IP NODE backend-658f6cb858-dlrz8 1/1 Running 0 2m36s 172.16.0.67 192.168.0.100
You can configure the following pod affinity rule to deploy the frontend pods of the application to the same node as the backend pods.
apiVersion: apps/v1
kind: Deployment
metadata:
name: frontend
labels:
app: frontend
spec:
selector:
matchLabels:
app: frontend
replicas: 3
template:
metadata:
labels:
app: frontend
spec:
containers:
- image: nginx:alpine
name: frontend
resources:
requests:
cpu: 100m
memory: 200Mi
limits:
cpu: 100m
memory: 200Mi
imagePullSecrets:
- name: default-secret
affinity:
podAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- topologyKey: kubernetes.io/hostname
labelSelector:
matchExpressions:
- key: app
operator: In
values:
- backend
Deploy the frontend and you can find that the frontend is deployed on the same node as the backend.
$ kubectl create -f affinity3.yaml deployment.apps/frontend created $ kubectl get po -o wide NAME READY STATUS RESTARTS AGE IP NODE backend-658f6cb858-dlrz8 1/1 Running 0 5m38s 172.16.0.67 192.168.0.100 frontend-67ff9b7b97-dsqzn 1/1 Running 0 6s 172.16.0.70 192.168.0.100 frontend-67ff9b7b97-hxm5t 1/1 Running 0 6s 172.16.0.71 192.168.0.100 frontend-67ff9b7b97-z8pdb 1/1 Running 0 6s 172.16.0.72 192.168.0.100
The topologyKey field specifies the selection range. The scheduler selects nodes within the range based on the affinity rule defined. The effect of topologyKey is not fully demonstrated in the preceding example because all the nodes have the kubernetes.io/hostname label, that is, all the nodes are within the range.
To see how topologyKey works, assume that the backend of the application has two pods, which are running on different nodes.
$ kubectl get po -o wide NAME READY STATUS RESTARTS AGE IP NODE backend-658f6cb858-5bpd6 1/1 Running 0 23m 172.16.0.40 192.168.0.97 backend-658f6cb858-dlrz8 1/1 Running 0 2m36s 172.16.0.67 192.168.0.100
Add the prefer=true label to nodes 192.168.0.97 and 192.168.0.94.
$ kubectl label node 192.168.0.97 prefer=true node/192.168.0.97 labeled $ kubectl label node 192.168.0.94 prefer=true node/192.168.0.94 labeled $ kubectl get node -L prefer NAME STATUS ROLES AGE VERSION PREFER 192.168.0.100 Ready <none> 44m v1.15.6-r1-20.3.0.2.B001-15.30.2 192.168.0.212 Ready <none> 91m v1.15.6-r1-20.3.0.2.B001-15.30.2 192.168.0.94 Ready <none> 91m v1.15.6-r1-20.3.0.2.B001-15.30.2 true 192.168.0.97 Ready <none> 91m v1.15.6-r1-20.3.0.2.B001-15.30.2 true
Define topologyKey in the podAffinity section as prefer.
affinity:
podAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- topologyKey: prefer
labelSelector:
matchExpressions:
- key: app
operator: In
values:
- backend
The scheduler recognizes the nodes with the prefer label, that is, 192.168.0.97 and 192.168.0.94, and then find the pods with the app=backend label. In this way, all frontend pods are deployed onto 192.168.0.97.
$ kubectl create -f affinity3.yaml deployment.apps/frontend created $ kubectl get po -o wide NAME READY STATUS RESTARTS AGE IP NODE backend-658f6cb858-5bpd6 1/1 Running 0 26m 172.16.0.40 192.168.0.97 backend-658f6cb858-dlrz8 1/1 Running 0 5m38s 172.16.0.67 192.168.0.100 frontend-67ff9b7b97-dsqzn 1/1 Running 0 6s 172.16.0.70 192.168.0.97 frontend-67ff9b7b97-hxm5t 1/1 Running 0 6s 172.16.0.71 192.168.0.97 frontend-67ff9b7b97-z8pdb 1/1 Running 0 6s 172.16.0.72 192.168.0.97
Pod Anti-affinity
Unlike the scenarios in which pods are preferred to be scheduled onto the same node, sometimes, it could be the exact opposite. For example, if certain pods are deployed together, they will affect the performance.
The following example defines an inter-pod anti-affinity rule, which specifies that pods must not be scheduled to nodes that already have pods with the app=frontend label, that is, to deploy the pods of the frontend to different nodes with each node has only one replica.
apiVersion: apps/v1
kind: Deployment
metadata:
name: frontend
labels:
app: frontend
spec:
selector:
matchLabels:
app: frontend
replicas: 5
template:
metadata:
labels:
app: frontend
spec:
containers:
- image: nginx:alpine
name: frontend
resources:
requests:
cpu: 100m
memory: 200Mi
limits:
cpu: 100m
memory: 200Mi
imagePullSecrets:
- name: default-secret
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- topologyKey: kubernetes.io/hostname
labelSelector:
matchExpressions:
- key: app
operator: In
values:
- frontend
Deploy the frontend and query the deployment results. You can find that each node has only one frontend pod and one pod of the Deployment is Pending. This is because when the scheduler is deploying the fifth pod, all nodes already have one pod with the app=frontend label on them. There is no available node. Therefore, the fifth pod will remain in the Pending status.
$ kubectl create -f affinity4.yaml deployment.apps/frontend created $ kubectl get po -o wide NAME READY STATUS RESTARTS AGE IP NODE frontend-6f686d8d87-8dlsc 1/1 Running 0 18s 172.16.0.76 192.168.0.100 frontend-6f686d8d87-d6l8p 0/1 Pending 0 18s <none> <none> frontend-6f686d8d87-hgcq2 1/1 Running 0 18s 172.16.0.54 192.168.0.97 frontend-6f686d8d87-q7cfq 1/1 Running 0 18s 172.16.0.47 192.168.0.212 frontend-6f686d8d87-xl8hx 1/1 Running 0 18s 172.16.0.23 192.168.0.94
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