Worker HA

This Kubernetes deployment requires a single Control Plane node while workload is distributed across multiple Worker nodes across sites. The solution continues to work even if the Control Plane is down. However, Control Plane functions such as rescheduling of the workload cannot be performed during the outage of the Control Plane.

The CX solution will go on a partial or full failure when the Control Plane and any Worker node(s) are down simultaneously.

Suitable when

• Only two physical sites are available in the infrastructure for the Kubernetes deployment.

• ETCD or MySQL cluster is not available in the customer infrastructure that this Kubernetes deployment may use to maintain status.

• High availability of workload is needed and a temporary failure of Control Plane is acceptable.

Recommendations

• Provide a cloud-native storage such as NFS, VSAN, or any CNCF certified cloud-native storage accessible at both sites.

• Do a snapshot backup of the Control Plane to a neutral S3 compatible storage. In case of the failure of the site hosting Control Plane node, a restore of Control Plane on any available node may be performed.

Limitations

• Same subnet is available on both Site-A and Site-B ( extended subnet )

• In the case of the Control Plane failure, a manual intervention will be required to re-bootstrap the control-plane from last known good backup 

• If a site hosting Control Plane and one or more Worker nodes are down then the solution may be partially or completely down.  

• rescheduling of the worker node cannot be done until the Control Plane node is up again.

What-if Scenarios

See the What if scenarios below to learn more about the behaviour of this cluster.

Deployment Prerequisites

• At least three nodes (typically virtual machines) - one for Control Plane and two or more for worker nodes.

• One free additional IP for loadbalancer.

• Full network connectivity between all nodes

• Minimum resource requirement for each node is mentioned below:-

Installation Steps

This guide covers steps to install a single control-plane multi-worker deployment of an RKE2 cluster.

Step-1 Install RKE2 Control-plane

Install RKE2 control-plane RKE2 Control plane Deployment

Step-2 Install metallb

For the Installation of MetalLB you will need 1 free additional IP or an IP Pool to assign to this load balancer as mentioned in prerequisites

kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.14.3/config/manifests/metallb-native.yaml

Add additional IP pool for metallb

cat <<"EOF" | kubectl apply -f -
apiVersion: metallb.io/v1beta1
kind: IPAddressPool
metadata:
  name: first-pool
  namespace: metallb-system
spec:
  addresses:
  - <Additional IP>/32
  
EOF

Add Layer2 configuration for Metallb

cat <<"EOF"| kubectl apply -f -
apiVersion: metallb.io/v1beta1
kind: L2Advertisement
metadata:
  name: example
  namespace: metallb-system
spec:
  ipAddressPools:
  - first-pool
  nodeSelectors:
  - matchLabels:
      kubernetes.io/hostname: <Worker node hostname>
  - matchLabels:
      kubernetes.io/hostname:  <Second worker node hostname>
EOF

Step- 3 deploy nginx-ingress-controller

download the ingress-nginx-controller helm chart

helm pull --untar oci://registry-1.docker.io/bitnamicharts/nginx-ingress-controller
cd nginx-ingress-controller

create values file for the nginx-ingress-controller

cat<EOF |tee -a nginx-values.yaml
initContainers: 
  - name: ef-set-sysctl
    image: busybox
    securityContext:
      privileged: true
    command:
    - sh
    - -c
    - |
      sysctl -w net.core.somaxconn=65535
      sysctl -w net.ipv4.ip_local_port_range="1024 65535"
config:
  use-forwarded-headers: "true"
  keep-alive-requests: "10000"
  upstream-keepalive-requests: "1000"
  worker-processes: "auto"
  max-worker-connections: "65535"
  use-gzip: "true"
allowSnippetAnnotations: true
EOF

deploy the nginx-ingress-controller using

helm upgrade --install --namespace ingress-nginx --create-namespace --values ./nginx-values.yaml  nginx-ingress .

Step-4 Get Control-plane token

On the control-plane node, run the following command to get the control-plane token to join worker(s) with this control-plane.

cat /var/lib/rancher/rke2/server/node-token
# It will display the node-token as something like the following 
K10e2bfc647bbf0839a7997cdcbee8754b3cd841e85e4250686161893f2b139c7d8::server:a342ef5189711287fb48f05c05346b89

Step-5 Add Worker(s)

Follow the Deployment Prerequisites from RKE2 Control plane Deployment for each worker node before deployment i.e disable firewall on all worker nodes.

On each worker node,

1. Run the following command to install RKE2 agent on the worker.

   BASH

   curl -sfL https://get.rke2.io | INSTALL_RKE2_TYPE="agent" sh -

2. Enable the rke2-agent service by using the following command.

   BASH

   systemctl enable rke2-agent.service

3. Create a directory by running the following commands.

   BASH

   mkdir -p /etc/rancher/rke2/

4. Add/edit /etc/rancher/rke2/config.yaml and update the following fields.

   1. <Control-Plane-IP> This is the IP for the control-plane node.

   2. <Control-Plane-TOKEN> This is the token which can be extracted from first control-plane by running cat /var/lib/rancher/rke2/server/node-token

      BASH

      server: https://<Control-Plane-IP>:9345
      token: <Control-Plane-TOKEN>

5. Start the service by using follow command.

   BASH

   systemctl start rke2-agent.service

Step 6: Verify

On the control-plane node run the following command to verify that the worker(s) have been added.

kubectl get nodes -o wide

Sample output:-

Step 7: Tainting control-plane

Taint control-plane Tainting a Control Plane Node

Step 8: Draining control-plane

On control-plane node, run the following command to evict pods from control-plane.

kubectl drain <control-plane hostname> --delete-emptydir-data --ignore-daemonsets

Limitation

1. After a node goes down if pod remain terminating state you need to force full delete all the terminating pods after 5 mins by using the following command.

for p in $(kubectl get pods -n ef-external | grep Terminating | awk '{print $1}'); do kubectl delete pod -n ef-external $p --grace-period=0 --force;done
for p in $(kubectl get pods -n expertflow | grep Terminating | awk '{print $1}'); do kubectl delete pod -n expertflow $p --grace-period=0 --force;done
for p in $(kubectl get pods -n monitoring | grep Terminating | awk '{print $1}'); do kubectl delete pod -n monitoring $p --grace-period=0 --force;done
for p in $(kubectl get pods -n kube-system | grep Terminating | awk '{print $1}'); do kubectl delete pod -n kube-system $p --grace-period=0 --force;done

Note: Do not delete terminating pods from “nfs-client“ and “ingress-nginx“ namespaces. It may lead to potential failure.

Next Steps

Choose Storage

Use a cloud native storage for a Worker HA setup. For available storage options, see Storage Solution - Getting Started

For multi-node (Worker HA) you can use local storage with node affinity. But, this will impose a restriction on worker nodes that a workload will have to be provisioned from the same node it was setup initially.

Setup CX on Kubernetes

To deploy Expertflow CX on this node, see CX Deployment on Kubernetes