4.11. Installing a cluster on GCP in a restricted network with user-provisioned infrastructure

Procedure

1. Copy the template from the Deployment Manager template for the VPC section of this topic and save it as 01_vpc.py on your computer. This template describes the VPC that your cluster requires.

2. Create a 01_vpc.yaml resource definition file:

   $ cat <<EOF >01_vpc.yaml
   imports:
   - path: 01_vpc.py
   
   resources:
   - name: cluster-vpc
     type: 01_vpc.py
     properties:
       infra_id: '${INFRA_ID}' 1
       region: '${REGION}' 2
       master_subnet_cidr: '${MASTER_SUBNET_CIDR}' 3
       worker_subnet_cidr: '${WORKER_SUBNET_CIDR}' 4
   EOF

   1

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   2

   region is the region to deploy the cluster into, for example us-central1.

   3

   master_subnet_cidr is the CIDR for the master subnet, for example 10.0.0.0/19.

   4

   worker_subnet_cidr is the CIDR for the worker subnet, for example 10.0.32.0/19.

3. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-vpc --config 01_vpc.yaml

1. API load balancer: Provides a common endpoint for users, both human and machine, to interact with and configure the platform. Configure the following conditions:

   • Layer 4 load balancing only. This can be referred to as Raw TCP, SSL Passthrough, or SSL Bridge mode. If you use SSL Bridge mode, you must enable Server Name Indication (SNI) for the API routes.
   • A stateless load balancing algorithm. The options vary based on the load balancer implementation.
   注意

   Session persistence is not required for the API load balancer to function properly.

   Configure the following ports on both the front and back of the load balancers:

   表 4.47. API load balancer

   Port	Back-end machines (pool members)	Internal	External	Description
   6443	Bootstrap and control plane. You remove the bootstrap machine from the load balancer after the bootstrap machine initializes the cluster control plane. You must configure the /readyz endpoint for the API server health check probe.	X	X	Kubernetes API server
   22623	Bootstrap and control plane. You remove the bootstrap machine from the load balancer after the bootstrap machine initializes the cluster control plane.	X		Machine config server

   注意

   The load balancer must be configured to take a maximum of 30 seconds from the time the API server turns off the /readyz endpoint to the removal of the API server instance from the pool. Within the time frame after /readyz returns an error or becomes healthy, the endpoint must have been removed or added. Probing every 5 or 10 seconds, with two successful requests to become healthy and three to become unhealthy, are well-tested values.

2. Application Ingress load balancer: Provides an Ingress point for application traffic flowing in from outside the cluster. Configure the following conditions:

   • Layer 4 load balancing only. This can be referred to as Raw TCP, SSL Passthrough, or SSL Bridge mode. If you use SSL Bridge mode, you must enable Server Name Indication (SNI) for the Ingress routes.
   • A connection-based or session-based persistence is recommended, based on the options available and types of applications that will be hosted on the platform.

   Configure the following ports on both the front and back of the load balancers:

   表 4.48. Application Ingress load balancer

   Port	Back-end machines (pool members)	Internal	External	Description
   443	The machines that run the Ingress router pods, compute, or worker, by default.	X	X	HTTPS traffic
   80	The machines that run the Ingress router pods, compute, or worker, by default.	X	X	HTTP traffic

Procedure

1. Copy the template from the Deployment Manager template for the internal load balancer section of this topic and save it as 02_lb_int.py on your computer. This template describes the internal load balancing objects that your cluster requires.
2. For an external cluster, also copy the template from the Deployment Manager template for the external load balancer section of this topic and save it as 02_lb_ext.py on your computer. This template describes the external load balancing objects that your cluster requires.

3. Export the variables that the deployment template uses:

   1. Export the cluster network location:

      $ export CLUSTER_NETWORK=(`gcloud compute networks describe ${INFRA_ID}-network --format json | jq -r .selfLink`)

   2. Export the control plane subnet location:

      $ export CONTROL_SUBNET=(`gcloud compute networks subnets describe ${INFRA_ID}-master-subnet --region=${REGION} --format json | jq -r .selfLink`)

   3. Export the three zones that the cluster uses:

      $ export ZONE_0=(`gcloud compute regions describe ${REGION} --format=json | jq -r .zones[0] | cut -d "/" -f9`)

      $ export ZONE_1=(`gcloud compute regions describe ${REGION} --format=json | jq -r .zones[1] | cut -d "/" -f9`)

      $ export ZONE_2=(`gcloud compute regions describe ${REGION} --format=json | jq -r .zones[2] | cut -d "/" -f9`)

4. Create a 02_infra.yaml resource definition file:

   $ cat <<EOF >02_infra.yaml
   imports:
   - path: 02_lb_ext.py
   - path: 02_lb_int.py 1
   resources:
   - name: cluster-lb-ext 2
     type: 02_lb_ext.py
     properties:
       infra_id: '${INFRA_ID}' 3
       region: '${REGION}' 4
   - name: cluster-lb-int
     type: 02_lb_int.py
     properties:
       cluster_network: '${CLUSTER_NETWORK}'
       control_subnet: '${CONTROL_SUBNET}' 5
       infra_id: '${INFRA_ID}'
       region: '${REGION}'
       zones: 6
       - '${ZONE_0}'
       - '${ZONE_1}'
       - '${ZONE_2}'
   EOF

   1 2

   Required only when deploying an external cluster.

   3

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   4

   region is the region to deploy the cluster into, for example us-central1.

   5

   control_subnet is the URI to the control subnet.

   6

   zones are the zones to deploy the control plane instances into, like us-east1-b, us-east1-c, and us-east1-d.

5. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-infra --config 02_infra.yaml

6. Export the cluster IP address:

   $ export CLUSTER_IP=(`gcloud compute addresses describe ${INFRA_ID}-cluster-ip --region=${REGION} --format json | jq -r .address`)

7. For an external cluster, also export the cluster public IP address:

   $ export CLUSTER_PUBLIC_IP=(`gcloud compute addresses describe ${INFRA_ID}-cluster-public-ip --region=${REGION} --format json | jq -r .address`)

Procedure

1. Copy the template from the Deployment Manager template for the private DNS section of this topic and save it as 02_dns.py on your computer. This template describes the private DNS objects that your cluster requires.

2. Create a 02_dns.yaml resource definition file:

   $ cat <<EOF >02_dns.yaml
   imports:
   - path: 02_dns.py
   
   resources:
   - name: cluster-dns
     type: 02_dns.py
     properties:
       infra_id: '${INFRA_ID}' 1
       cluster_domain: '${CLUSTER_NAME}.${BASE_DOMAIN}' 2
       cluster_network: '${CLUSTER_NETWORK}' 3
   EOF

   1

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   2

   cluster_domain is the domain for the cluster, for example openshift.example.com.

   3

   cluster_network is the selfLink URL to the cluster network.

3. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-dns --config 02_dns.yaml

4. The templates do not create DNS entries due to limitations of Deployment Manager, so you must create them manually:

   1. Add the internal DNS entries:

      $ if [ -f transaction.yaml ]; then rm transaction.yaml; fi
      $ gcloud dns record-sets transaction start --zone ${INFRA_ID}-private-zone
      $ gcloud dns record-sets transaction add ${CLUSTER_IP} --name api.${CLUSTER_NAME}.${BASE_DOMAIN}. --ttl 60 --type A --zone ${INFRA_ID}-private-zone
      $ gcloud dns record-sets transaction add ${CLUSTER_IP} --name api-int.${CLUSTER_NAME}.${BASE_DOMAIN}. --ttl 60 --type A --zone ${INFRA_ID}-private-zone
      $ gcloud dns record-sets transaction execute --zone ${INFRA_ID}-private-zone

   2. For an external cluster, also add the external DNS entries:

      $ if [ -f transaction.yaml ]; then rm transaction.yaml; fi
      $ gcloud dns record-sets transaction start --zone ${BASE_DOMAIN_ZONE_NAME}
      $ gcloud dns record-sets transaction add ${CLUSTER_PUBLIC_IP} --name api.${CLUSTER_NAME}.${BASE_DOMAIN}. --ttl 60 --type A --zone ${BASE_DOMAIN_ZONE_NAME}
      $ gcloud dns record-sets transaction execute --zone ${BASE_DOMAIN_ZONE_NAME}

Procedure

1. Copy the template from the Deployment Manager template for firewall rules section of this topic and save it as 03_firewall.py on your computer. This template describes the security groups that your cluster requires.

2. Create a 03_firewall.yaml resource definition file:

   $ cat <<EOF >03_firewall.yaml
   imports:
   - path: 03_firewall.py
   
   resources:
   - name: cluster-firewall
     type: 03_firewall.py
     properties:
       allowed_external_cidr: '0.0.0.0/0' 1
       infra_id: '${INFRA_ID}' 2
       cluster_network: '${CLUSTER_NETWORK}' 3
       network_cidr: '${NETWORK_CIDR}' 4
   EOF

   1

   allowed_external_cidr is the CIDR range that can access the cluster API and SSH to the bootstrap host. For an internal cluster, set this value to ${NETWORK_CIDR}.

   2

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   3

   cluster_network is the selfLink URL to the cluster network.

   4

   network_cidr is the CIDR of the VPC network, for example 10.0.0.0/16.

3. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-firewall --config 03_firewall.yaml

Procedure

1. Copy the template from the Deployment Manager template for IAM roles section of this topic and save it as 03_iam.py on your computer. This template describes the IAM roles that your cluster requires.

2. Create a 03_iam.yaml resource definition file:

   $ cat <<EOF >03_iam.yaml
   imports:
   - path: 03_iam.py
   resources:
   - name: cluster-iam
     type: 03_iam.py
     properties:
       infra_id: '${INFRA_ID}' 1
   EOF

   1

   infra_id is the INFRA_ID infrastructure name from the extraction step.

3. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-iam --config 03_iam.yaml

4. Export the variable for the master service account:

   $ export MASTER_SERVICE_ACCOUNT=(`gcloud iam service-accounts list --filter "email~^${INFRA_ID}-m@${PROJECT_NAME}." --format json | jq -r '.[0].email'`)

5. Export the variable for the worker service account:

   $ export WORKER_SERVICE_ACCOUNT=(`gcloud iam service-accounts list --filter "email~^${INFRA_ID}-w@${PROJECT_NAME}." --format json | jq -r '.[0].email'`)

6. Export the variable for the subnet that hosts the compute machines:

   $ export COMPUTE_SUBNET=(`gcloud compute networks subnets describe ${INFRA_ID}-worker-subnet --region=${REGION} --format json | jq -r .selfLink`)

7. The templates do not create the policy bindings due to limitations of Deployment Manager, so you must create them manually:

   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${MASTER_SERVICE_ACCOUNT}" --role "roles/compute.instanceAdmin"
   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${MASTER_SERVICE_ACCOUNT}" --role "roles/compute.networkAdmin"
   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${MASTER_SERVICE_ACCOUNT}" --role "roles/compute.securityAdmin"
   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${MASTER_SERVICE_ACCOUNT}" --role "roles/iam.serviceAccountUser"
   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${MASTER_SERVICE_ACCOUNT}" --role "roles/storage.admin"
   
   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${WORKER_SERVICE_ACCOUNT}" --role "roles/compute.viewer"
   $ gcloud projects add-iam-policy-binding ${PROJECT_NAME} --member "serviceAccount:${WORKER_SERVICE_ACCOUNT}" --role "roles/storage.admin"

8. Create a service account key and store it locally for later use:

   $ gcloud iam service-accounts keys create service-account-key.json --iam-account=${MASTER_SERVICE_ACCOUNT}

Procedure

1. Obtain the RHCOS image from the RHCOS image mirror page.

   重要

   The RHCOS images might not change with every release of OpenShift Container Platform. You must download an image with the highest version that is less than or equal to the OpenShift Container Platform version that you install. Use the image version that matches your OpenShift Container Platform version if it is available.

   The file name contains the OpenShift Container Platform version number in the format rhcos-<version>-<arch>-gcp.<arch>.tar.gz.

2. Create the Google storage bucket:

   $ gsutil mb gs://<bucket_name>

3. Upload the RHCOS image to the Google storage bucket:

   $ gsutil cp <downloaded_image_file_path>/rhcos-<version>-x86_64-gcp.x86_64.tar.gz  gs://<bucket_name>

4. Export the uploaded RHCOS image location as a variable:

   $ export IMAGE_SOURCE=`gs://<bucket_name>/rhcos-<version>-x86_64-gcp.x86_64.tar.gz`

5. Create the cluster image:

   $ gcloud compute images create "${INFRA_ID}-rhcos-image" \
       --source-uri="${IMAGE_SOURCE}"

Procedure

1. Copy the template from the Deployment Manager template for the bootstrap machine section of this topic and save it as 04_bootstrap.py on your computer. This template describes the bootstrap machine that your cluster requires.

2. Export the location of the Red Hat Enterprise Linux CoreOS (RHCOS) image that the installation program requires:

   $ export CLUSTER_IMAGE=(`gcloud compute images describe ${INFRA_ID}-rhcos-image --format json | jq -r .selfLink`)

3. Create a bucket and upload the bootstrap.ign file:

   $ gsutil mb gs://${INFRA_ID}-bootstrap-ignition
   $ gsutil cp <installation_directory>/bootstrap.ign gs://${INFRA_ID}-bootstrap-ignition/

4. Create a signed URL for the bootstrap instance to use to access the Ignition config. Export the URL from the output as a variable:

   $ export BOOTSTRAP_IGN=`gsutil signurl -d 1h service-account-key.json gs://${INFRA_ID}-bootstrap-ignition/bootstrap.ign | grep "^gs:" | awk '{print $5}'`

5. Create a 04_bootstrap.yaml resource definition file:

   $ cat <<EOF >04_bootstrap.yaml
   imports:
   - path: 04_bootstrap.py
   
   resources:
   - name: cluster-bootstrap
     type: 04_bootstrap.py
     properties:
       infra_id: '${INFRA_ID}' 1
       region: '${REGION}' 2
       zone: '${ZONE_0}' 3
   
       cluster_network: '${CLUSTER_NETWORK}' 4
       control_subnet: '${CONTROL_SUBNET}' 5
       image: '${CLUSTER_IMAGE}' 6
       machine_type: 'n1-standard-4' 7
       root_volume_size: '128' 8
   
       bootstrap_ign: '${BOOTSTRAP_IGN}' 9
   EOF

   1

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   2

   region is the region to deploy the cluster into, for example us-central1.

   3

   zone is the zone to deploy the bootstrap instance into, for example us-central1-b.

   4

   cluster_network is the selfLink URL to the cluster network.

   5

   control_subnet is the selfLink URL to the control subnet.

   6

   image is the selfLink URL to the RHCOS image.

   7

   machine_type is the machine type of the instance, for example n1-standard-4.

   8

   root_volume_size is the boot disk size for the bootstrap machine.

   9

   bootstrap_ign is the URL output when creating a signed URL.

6. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-bootstrap --config 04_bootstrap.yaml

7. The templates do not manage load balancer membership due to limitations of Deployment Manager, so you must add the bootstrap machine manually.

   1. Add the bootstrap instance to the internal load balancer instance group:

      $ gcloud compute instance-groups unmanaged add-instances \
          ${INFRA_ID}-bootstrap-instance-group --zone=${ZONE_0} --instances=${INFRA_ID}-bootstrap

   2. Add the bootstrap instance group to the internal load balancer backend service:

      $ gcloud compute backend-services add-backend \
          ${INFRA_ID}-api-internal-backend-service --region=${REGION} --instance-group=${INFRA_ID}-bootstrap-instance-group --instance-group-zone=${ZONE_0}

Procedure

1. Copy the template from the Deployment Manager template for control plane machines section of this topic and save it as 05_control_plane.py on your computer. This template describes the control plane machines that your cluster requires.

2. Export the following variable required by the resource definition:

   $ export MASTER_IGNITION=`cat <installation_directory>/master.ign`

3. Create a 05_control_plane.yaml resource definition file:

   $ cat <<EOF >05_control_plane.yaml
   imports:
   - path: 05_control_plane.py
   
   resources:
   - name: cluster-control-plane
     type: 05_control_plane.py
     properties:
       infra_id: '${INFRA_ID}' 1
       zones: 2
       - '${ZONE_0}'
       - '${ZONE_1}'
       - '${ZONE_2}'
   
       control_subnet: '${CONTROL_SUBNET}' 3
       image: '${CLUSTER_IMAGE}' 4
       machine_type: 'n1-standard-4' 5
       root_volume_size: '128'
       service_account_email: '${MASTER_SERVICE_ACCOUNT}' 6
   
       ignition: '${MASTER_IGNITION}' 7
   EOF

   1

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   2

   zones are the zones to deploy the control plane instances into, for example us-central1-a, us-central1-b, and us-central1-c.

   3

   control_subnet is the selfLink URL to the control subnet.

   4

   image is the selfLink URL to the RHCOS image.

   5

   machine_type is the machine type of the instance, for example n1-standard-4.

   6

   service_account_email is the email address for the master service account that you created.

   7

   ignition is the contents of the master.ign file.

4. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-control-plane --config 05_control_plane.yaml

5. The templates do not manage load balancer membership due to limitations of Deployment Manager, so you must add the control plane machines manually.

   • Run the following commands to add the control plane machines to the appropriate instance groups:

     $ gcloud compute instance-groups unmanaged add-instances ${INFRA_ID}-master-${ZONE_0}-instance-group --zone=${ZONE_0} --instances=${INFRA_ID}-m-0
     $ gcloud compute instance-groups unmanaged add-instances ${INFRA_ID}-master-${ZONE_1}-instance-group --zone=${ZONE_1} --instances=${INFRA_ID}-m-1
     $ gcloud compute instance-groups unmanaged add-instances ${INFRA_ID}-master-${ZONE_2}-instance-group --zone=${ZONE_2} --instances=${INFRA_ID}-m-2

   • For an external cluster, you must also run the following commands to add the control plane machines to the target pools:

     $ gcloud compute target-pools add-instances ${INFRA_ID}-api-target-pool --instances-zone="${ZONE_0}" --instances=${INFRA_ID}-m-0
     $ gcloud compute target-pools add-instances ${INFRA_ID}-api-target-pool --instances-zone="${ZONE_1}" --instances=${INFRA_ID}-m-1
     $ gcloud compute target-pools add-instances ${INFRA_ID}-api-target-pool --instances-zone="${ZONE_2}" --instances=${INFRA_ID}-m-2

Procedure

1. Change to the directory that contains the installation program and run the following command:

   $ ./openshift-install wait-for bootstrap-complete --dir=<installation_directory> \ 1
       --log-level info 2

   1

   For <installation_directory>, specify the path to the directory that you stored the installation files in.

   2

   To view different installation details, specify warn, debug, or error instead of info.

   If the command exits without a FATAL warning, your production control plane has initialized.

2. Delete the bootstrap resources:

   $ gcloud compute backend-services remove-backend ${INFRA_ID}-api-internal-backend-service --region=${REGION} --instance-group=${INFRA_ID}-bootstrap-instance-group --instance-group-zone=${ZONE_0}
   $ gsutil rm gs://${INFRA_ID}-bootstrap-ignition/bootstrap.ign
   $ gsutil rb gs://${INFRA_ID}-bootstrap-ignition
   $ gcloud deployment-manager deployments delete ${INFRA_ID}-bootstrap

Procedure

1. Copy the template from the Deployment Manager template for worker machines section of this topic and save it as 06_worker.py on your computer. This template describes the worker machines that your cluster requires.

2. Export the variables that the resource definition uses.

   1. Export the subnet that hosts the compute machines:

      $ export COMPUTE_SUBNET=(`gcloud compute networks subnets describe ${INFRA_ID}-worker-subnet --region=${REGION} --format json | jq -r .selfLink`)

   2. Export the email address for your service account:

      $ export WORKER_SERVICE_ACCOUNT=(`gcloud iam service-accounts list --filter "email~^${INFRA_ID}-w@${PROJECT_NAME}." --format json | jq -r '.[0].email'`)

   3. Export the location of the compute machine Ignition config file:

      $ export WORKER_IGNITION=`cat <installation_directory>/worker.ign`

3. Create a 06_worker.yaml resource definition file:

   $ cat <<EOF >06_worker.yaml
   imports:
   - path: 06_worker.py
   
   resources:
   - name: 'worker-0' 1
     type: 06_worker.py
     properties:
       infra_id: '${INFRA_ID}' 2
       zone: '${ZONE_0}' 3
       compute_subnet: '${COMPUTE_SUBNET}' 4
       image: '${CLUSTER_IMAGE}' 5
       machine_type: 'n1-standard-4' 6
       root_volume_size: '128'
       service_account_email: '${WORKER_SERVICE_ACCOUNT}' 7
       ignition: '${WORKER_IGNITION}' 8
   - name: 'worker-1'
     type: 06_worker.py
     properties:
       infra_id: '${INFRA_ID}' 9
       zone: '${ZONE_1}' 10
       compute_subnet: '${COMPUTE_SUBNET}' 11
       image: '${CLUSTER_IMAGE}' 12
       machine_type: 'n1-standard-4' 13
       root_volume_size: '128'
       service_account_email: '${WORKER_SERVICE_ACCOUNT}' 14
       ignition: '${WORKER_IGNITION}' 15
   EOF

   1

   name is the name of the worker machine, for example worker-0.

   2 9

   infra_id is the INFRA_ID infrastructure name from the extraction step.

   3 10

   zone is the zone to deploy the worker machine into, for example us-central1-a.

   4 11

   compute_subnet is the selfLink URL to the compute subnet.

   5 12

   image is the selfLink URL to the RHCOS image.

   6 13

   machine_type is the machine type of the instance, for example n1-standard-4.

   7 14

   service_account_email is the email address for the worker service account that you created.

   8 15

   ignition is the contents of the worker.ign file.

4. Optional: If you want to launch additional instances, include additional resources of type 06_worker.py in your 06_worker.yaml resource definition file.

5. Create the deployment by using the gcloud CLI:

   $ gcloud deployment-manager deployments create ${INFRA_ID}-worker --config 06_worker.yaml

Procedure

1. Export the kubeadmin credentials:

   $ export KUBECONFIG=<installation_directory>/auth/kubeconfig 1

   1

   For <installation_directory>, specify the path to the directory that you stored the installation files in.

2. Verify you can run oc commands successfully using the exported configuration:

   $ oc whoami

   Example output

   system:admin

Procedure

1. Confirm that the cluster recognizes the machines:

   $ oc get nodes

   Example output

   NAME      STATUS    ROLES   AGE  VERSION
   master-0  Ready     master  63m  v1.18.3
   master-1  Ready     master  63m  v1.18.3
   master-2  Ready     master  64m  v1.18.3
   worker-0  NotReady  worker  76s  v1.18.3
   worker-1  NotReady  worker  70s  v1.18.3

   The output lists all of the machines that you created.

2. Review the pending CSRs and ensure that you see the client requests with the Pending or Approved status for each machine that you added to the cluster:

   $ oc get csr

   Example output

   NAME        AGE     REQUESTOR                                                                   CONDITION
   csr-8b2br   15m     system:serviceaccount:openshift-machine-config-operator:node-bootstrapper   Pending
   csr-8vnps   15m     system:serviceaccount:openshift-machine-config-operator:node-bootstrapper   Pending
   ...

   In this example, two machines are joining the cluster. You might see more approved CSRs in the list.

3. If the CSRs were not approved, after all of the pending CSRs for the machines you added are in Pending status, approve the CSRs for your cluster machines:

   注意

   Because the CSRs rotate automatically, approve your CSRs within an hour of adding the machines to the cluster. If you do not approve them within an hour, the certificates will rotate, and more than two certificates will be present for each node. You must approve all of these certificates. Once the client CSR is approved, the Kubelet creates a secondary CSR for the serving certificate, which requires manual approval. Then, subsequent serving certificate renewal requests are automatically approved by the machine-approver if the Kubelet requests a new certificate with identical parameters.

   • To approve them individually, run the following command for each valid CSR:

     $ oc adm certificate approve <csr_name> 1

     1

     <csr_name> is the name of a CSR from the list of current CSRs.

   • To approve all pending CSRs, run the following command:

     $ oc get csr -o go-template='{{range .items}}{{if not .status}}{{.metadata.name}}{{"\n"}}{{end}}{{end}}' | xargs --no-run-if-empty oc adm certificate approve

4. Now that your client requests are approved, you must review the server requests for each machine that you added to the cluster:

   $ oc get csr

   Example output

   NAME        AGE     REQUESTOR                                                                   CONDITION
   csr-bfd72   5m26s   system:node:ip-10-0-50-126.us-east-2.compute.internal                       Pending
   csr-c57lv   5m26s   system:node:ip-10-0-95-157.us-east-2.compute.internal                       Pending
   ...

5. If the remaining CSRs are not approved, and are in the Pending status, approve the CSRs for your cluster machines:

   • To approve them individually, run the following command for each valid CSR:

     $ oc adm certificate approve <csr_name> 1

     1

     <csr_name> is the name of a CSR from the list of current CSRs.

   • To approve all pending CSRs, run the following command:

     $ oc get csr -o go-template='{{range .items}}{{if not .status}}{{.metadata.name}}{{"\n"}}{{end}}{{end}}' | xargs oc adm certificate approve

6. After all client and server CSRs have been approved, the machines have the Ready status. Verify this by running the following command:

   $ oc get nodes

   Example output

   NAME      STATUS    ROLES   AGE  VERSION
   master-0  Ready     master  73m  v1.20.0
   master-1  Ready     master  73m  v1.20.0
   master-2  Ready     master  74m  v1.20.0
   worker-0  Ready     worker  11m  v1.20.0
   worker-1  Ready     worker  11m  v1.20.0

   注意

   It can take a few minutes after approval of the server CSRs for the machines to transition to the Ready status.

Procedure

1. Wait for the Ingress router to create a load balancer and populate the EXTERNAL-IP field:

   $ oc -n openshift-ingress get service router-default

   Example output

   NAME             TYPE           CLUSTER-IP      EXTERNAL-IP      PORT(S)                      AGE
   router-default   LoadBalancer   172.30.18.154   35.233.157.184   80:32288/TCP,443:31215/TCP   98

2. Add the A record to your zones:

   • To use A records:

     1. Export the variable for the router IP address:

        $ export ROUTER_IP=`oc -n openshift-ingress get service router-default --no-headers | awk '{print $4}'`

     2. Add the A record to the private zones:

        $ if [ -f transaction.yaml ]; then rm transaction.yaml; fi
        $ gcloud dns record-sets transaction start --zone ${INFRA_ID}-private-zone
        $ gcloud dns record-sets transaction add ${ROUTER_IP} --name \*.apps.${CLUSTER_NAME}.${BASE_DOMAIN}. --ttl 300 --type A --zone ${INFRA_ID}-private-zone
        $ gcloud dns record-sets transaction execute --zone ${INFRA_ID}-private-zone

     3. For an external cluster, also add the A record to the public zones:

        $ if [ -f transaction.yaml ]; then rm transaction.yaml; fi
        $ gcloud dns record-sets transaction start --zone ${BASE_DOMAIN_ZONE_NAME}
        $ gcloud dns record-sets transaction add ${ROUTER_IP} --name \*.apps.${CLUSTER_NAME}.${BASE_DOMAIN}. --ttl 300 --type A --zone ${BASE_DOMAIN_ZONE_NAME}
        $ gcloud dns record-sets transaction execute --zone ${BASE_DOMAIN_ZONE_NAME}

   • To add explicit domains instead of using a wildcard, create entries for each of the cluster’s current routes:

     $ oc get --all-namespaces -o jsonpath='{range .items[*]}{range .status.ingress[*]}{.host}{"\n"}{end}{end}' routes

     Example output

     oauth-openshift.apps.your.cluster.domain.example.com
     console-openshift-console.apps.your.cluster.domain.example.com
     downloads-openshift-console.apps.your.cluster.domain.example.com
     alertmanager-main-openshift-monitoring.apps.your.cluster.domain.example.com
     grafana-openshift-monitoring.apps.your.cluster.domain.example.com
     prometheus-k8s-openshift-monitoring.apps.your.cluster.domain.example.com

Procedure

1. Complete the cluster installation:

   $ ./openshift-install --dir=<installation_directory> wait-for install-complete 1

   Example output

   INFO Waiting up to 30m0s for the cluster to initialize...

   1

   For <installation_directory>, specify the path to the directory that you stored the installation files in.

   重要

   The Ignition config files that the installation program generates contain certificates that expire after 24 hours, which are then renewed at that time. If the cluster is shut down before renewing the certificates and the cluster is later restarted after the 24 hours have elapsed, the cluster automatically recovers the expired certificates. The exception is that you must manually approve the pending node-bootstrapper certificate signing requests (CSRs) to recover kubelet certificates. See the documentation for Recovering from expired control plane certificates for more information.

2. Observe the running state of your cluster.

   1. Run the following command to view the current cluster version and status:

      $ oc get clusterversion

      Example output

      NAME      VERSION   AVAILABLE   PROGRESSING   SINCE   STATUS
      version             False       True          24m     Working towards 4.5.4: 99% complete

   2. Run the following command to view the Operators managed on the control plane by the Cluster Version Operator (CVO):

      $ oc get clusteroperators

      Example output

      NAME                                       VERSION   AVAILABLE   PROGRESSING   DEGRADED   SINCE
      authentication                             4.5.4     True        False         False      7m56s
      cloud-credential                           4.5.4     True        False         False      31m
      cluster-autoscaler                         4.5.4     True        False         False      16m
      console                                    4.5.4     True        False         False      10m
      csi-snapshot-controller                    4.5.4     True        False         False      16m
      dns                                        4.5.4     True        False         False      22m
      etcd                                       4.5.4     False       False         False      25s
      image-registry                             4.5.4     True        False         False      16m
      ingress                                    4.5.4     True        False         False      16m
      insights                                   4.5.4     True        False         False      17m
      kube-apiserver                             4.5.4     True        False         False      19m
      kube-controller-manager                    4.5.4     True        False         False      20m
      kube-scheduler                             4.5.4     True        False         False      20m
      kube-storage-version-migrator              4.5.4     True        False         False      16m
      machine-api                                4.5.4     True        False         False      22m
      machine-config                             4.5.4     True        False         False      22m
      marketplace                                4.5.4     True        False         False      16m
      monitoring                                 4.5.4     True        False         False      10m
      network                                    4.5.4     True        False         False      23m
      node-tuning                                4.5.4     True        False         False      23m
      openshift-apiserver                        4.5.4     True        False         False      17m
      openshift-controller-manager               4.5.4     True        False         False      15m
      openshift-samples                          4.5.4     True        False         False      16m
      operator-lifecycle-manager                 4.5.4     True        False         False      22m
      operator-lifecycle-manager-catalog         4.5.4     True        False         False      22m
      operator-lifecycle-manager-packageserver   4.5.4     True        False         False      18m
      service-ca                                 4.5.4     True        False         False      23m
      service-catalog-apiserver                  4.5.4     True        False         False      23m
      service-catalog-controller-manager         4.5.4     True        False         False      23m
      storage                                    4.5.4     True        False         False      17m

   3. Run the following command to view your cluster pods:

      $ oc get pods --all-namespaces

      Example output

      NAMESPACE                                               NAME                                                                READY     STATUS      RESTARTS   AGE
      kube-system                                             etcd-member-ip-10-0-3-111.us-east-2.compute.internal                1/1       Running     0          35m
      kube-system                                             etcd-member-ip-10-0-3-239.us-east-2.compute.internal                1/1       Running     0          37m
      kube-system                                             etcd-member-ip-10-0-3-24.us-east-2.compute.internal                 1/1       Running     0          35m
      openshift-apiserver-operator                            openshift-apiserver-operator-6d6674f4f4-h7t2t                       1/1       Running     1          37m
      openshift-apiserver                                     apiserver-fm48r                                                     1/1       Running     0          30m
      openshift-apiserver                                     apiserver-fxkvv                                                     1/1       Running     0          29m
      openshift-apiserver                                     apiserver-q85nm                                                     1/1       Running     0          29m
      ...
      openshift-service-ca-operator                           openshift-service-ca-operator-66ff6dc6cd-9r257                      1/1       Running     0          37m
      openshift-service-ca                                    apiservice-cabundle-injector-695b6bcbc-cl5hm                        1/1       Running     0          35m
      openshift-service-ca                                    configmap-cabundle-injector-8498544d7-25qn6                         1/1       Running     0          35m
      openshift-service-ca                                    service-serving-cert-signer-6445fc9c6-wqdqn                         1/1       Running     0          35m
      openshift-service-catalog-apiserver-operator            openshift-service-catalog-apiserver-operator-549f44668b-b5q2w       1/1       Running     0          32m
      openshift-service-catalog-controller-manager-operator   openshift-service-catalog-controller-manager-operator-b78cr2lnm     1/1       Running     0          31m

   When the current cluster version is AVAILABLE, the installation is complete.