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Kustomize to Helm

Prerequisites

  • Kubernetes v1.21 - 1.23.
  • Kubectl utility installed locally on Linux or macOS client host. The following guide does not support Windows client hosts.
  • Helm v3.8+ installed on the client host.
  • Yq installed on the client host.
  • Kubernetes cluster meets general Cognigy.AI prerequisites, including hardware resources.
  • Backup of Cognigy secrets for Kustomize installation (MongoDB and Redis connection strings) exists in the form of Kubernetes manifests.
  • Multi-replica MongoDB Helm Chart is used. Cognigy.AI Helm Chart is incompatible with the single-replica MongoDB (mongo-server) installation. If you have not migrated from single to multi-replica, follow migration guide.
  • Cognigy.AI Kustomize installation must be the same version as Cognigy.AI Helm Chart during migration.
  • Cognigy.AI Kustomize installation must be >= v4.38.
  • Snapshots/Backups of all PVCs/PVs (MongoDB, Redis-Persistent, flow-modules, flow-functions) are made before the migration starts.

Migration Checklist

There are 2 migration scenarios considered here:

  • Migration inside the existing cluster. Cognigy.AI Helm chart in the cognigy-ai namespace and MongoDB Helm Chart in the mongodb namespace are installed alongside the existing Kustomize installation. We strongly recommend this scenario as this process significantly simplifies the migration of the existing storage.
  • Migration to a new cluster. Cognigy.AI and MongoDB Helm Charts are installed in a new cluster. This scenario is more complex than the first one. You will either need to ensure that underlying storage for existing PVCs can be reattached to the new cluster or restore the data from snapshots in the new cluster.

Before starting migration, do the following steps:

  • Make sure backups (snapshots) for all PVCs are created in your Cloud Provider, including MongoDB, redis-persistent, flow-modules, functions.
  • Make sure a backup of Cognigy secrets for Kustomize installation is present.
  • Prepare values_prod.yaml values file for Cognigy.AI Helm Chart as described here. Ensure that all adjustments (patches) of the current Kustomize installation done form your side are properly migrated to values_prod.yaml file: ENV variables, resource request/limits, replica counts, etc.
  • Prepare the script from Rename MongoDB Databases section, fill in the required password values in advance.

Preparation for Migration

This section describes the procedure to prepare the migration of Cognigy.AI from Kustomize to Helm. These steps can be performed in advance and without bringing your Cognigy.AI installation down.

Secrets

During migration, Cognigy.AI product will be moved from default to a different namespace. In this document, we consider cognigy-ai as a target namespace, you can replace it with a namespace of your choice, but we strongly recommend using the cognigy-ai namespace. Hence, it is required to migrate the existing secrets to the new namespace and inform Helm release about the migrated secrets. To do so, execute the following steps:

  1. The migration scripts can be found in this repository. Clone the repository and checkout to your current Cognigy.AI version:
    git clone https://github.com/Cognigy/cognigy-ai-helm-chart.git
    git checkout tags/<release>
    cd scripts/kustomize-to-helm-migration-scripts
    
  2. Place a backup of existing secrets in the secrets folder.
  3. Copy the secrets folder into the kustomize-to-helm-migration-scripts folder
  4. Make sure that all the existing secrets are stored in the secrets folder before running the script.
  5. Execute the script, it will generate new secrets for the Helm installation in the migration-secrets folder:
    pip3 install -r requirements.txt
    python3 secret-migration.py -ns cognigy-ai
    
  6. Apply the secrets into a new cognigy-ai namespace:
    kubectl create ns cognigy-ai
    kubectl apply -f migration-secrets
    

Persistent Volumes

This subsection describes the migration of persistent volumes for AWS (EBS and EFS with efs-provisoner) and AZURE (Azure disk and Azure files). If your Cognigy.AI is deployed on a different cloud provider, you need to adapt the migration steps accordingly.

This subsection considers the Migration inside the existing cluster scenario. For the Migration to a new cluster scenario, you need to restore the data from snapshots of persistent volumes made in the old cluster. We do not provide any commands for the second case, as this process heavily depends on your cloud provider setup. Refer to your infrastructure data backup and restore processes and your cloud provider's documentation.

  1. Create snapshots of existing Cognigy.AI PVCs: flow-modules, functions, redis-persistent
  2. To avoid loss of PVs during the migration, set Reclaim Policy to retain for underlying PVs of 3 PVCs mentioned above and note down the corresponding PV names:
    # get the PV names for PVs attached to flow-modules`, `functions`, `redis-persistent` PVCs of Kustomize installation:
    kubectl get pv 
    # patch the reclaim policy for PV, set <pv-name> to the NAME from the previous command, repeat for all 3 PVCs:
    kubectl patch pv <pv-name> -p '{"spec":{"persistentVolumeReclaimPolicy":"Retain"}}'
    # check that reclaim policy has changed to Retain: 
    kubectl get pv
    
  3. Get the PVs IDs and note them down:
    kubectl get pv | grep -E 'redis-persistent|flow-modules|functions'
    for i in $(kubectl get pv | grep -E 'redis-persistent|flow-modules|functions' | awk '{print $1}')
    do
    echo $i
    done
    
  4. (AWS only) Get the IDs of underlying Volumes (EFS files shares) for all 2 PVs mentioned above and note them down. You will need to use these IDs in the following steps:

    ## Get details of the PVs, set <pv-name> to the NAME of PV attached flow-modules, functions, and redis-persistent PVCs:
    kubectl describe pv <pv-name> 
    ## Example for `flow modules` and `functions` PVs on AWS: 
    Source:
    Type:      NFS (an NFS mount that lasts the lifetime of a pod)
    Server:    fs-000000000001a.efs.eu-central-1.amazonaws.com
    

  5. (AWS only): Set the IDs of flow-modules and functions volumes obtained in the previous step in your values_prod.yaml for Cognigy.AI Helm Chart:

    # Example for AWS:
    efs:
      flowModules:
        id: "fs-000000000001a"
      functions:
        id: "fs-000000000001b"
    

  6. (AWS only): For the Migration inside the existing cluster scenario, add annotations and labels to existing flow-modules and functions storage classes and related rolebindings:
    ## annotate `flow-modules` and `functions` StorageClasses
    kubectl annotate storageclass flow-modules meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
    kubectl label storageclass flow-modules app.kubernetes.io/managed-by=Helm
    kubectl annotate storageclass functions meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
    kubectl label storageclass functions app.kubernetes.io/managed-by=Helm
    
    ## AWS only: annotate related ClusterRoleBindings
    kubectl annotate clusterrolebindings efs-provisioner-flow-modules meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
    kubectl label clusterrolebindings efs-provisioner-flow-modules app.kubernetes.io/managed-by=Helm
    kubectl annotate clusterrolebindings efs-provisioner-functions meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
    kubectl label clusterrolebindings efs-provisioner-functions app.kubernetes.io/managed-by=Helm
    
  7. Save backups of PVC manifests for Kustomize and Helm installations:
    kubectl get pvc -n=default redis-persistent -o yaml > redis-persistent-pvc-kustomize.yaml
    kubectl get pvc -n=default flow-modules -o yaml > flow-modules-pvc-kustomize.yaml
    kubectl get pvc -n=default functions -o yaml > functions-pvc-kustomize.yaml
    
  8. Create another copy of PVC manifests which will be modified in next step:

kubectl get pvc -n=default redis-persistent -o yaml > redis-persistent-pvc.yaml
kubectl get pvc -n=default flow-modules -o yaml > flow-modules-pvc.yaml
kubectl get pvc -n=default functions -o yaml > functions-pvc.yaml
9. Remove unnecessary fields from PVC:
for i in redis-persistent-pvc flow-modules-pvc functions-pvc
do
    yq -i 'del(.metadata.annotations, .metadata.finalizers, .metadata.labels,  .metadata.creationTimestamp, .metadata.resourceVersion, .metadata.uid, .status)' $i.yaml
done
10. Edit PVC manifests saved in Step 8 for all 3 PVCs in the following way:

  1. Change metadata.namespace to cognigy-ai.
  2. Add meta.helm.sh/release-name: cognigy-ai and meta.helm.sh/release-namespace: cognigy-ai under metadata.annotations.
  3. Add app.kubernetes.io/managed-by: Helm under metadata.labels.
  4. Change spec.volumeName to the name of the respective PVs from Step 2.

Traefik

If you use the Traefik reverse-proxy shipped with Cognigy.AI installation by default, you need to execute the following commands. You do not need to execute these commands if you use a 3rd-party reverse-proxy:

kubectl annotate clusterrole traefik meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
kubectl label clusterrole traefik app.kubernetes.io/managed-by=Helm
kubectl annotate clusterrolebindings traefik meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
kubectl label clusterrolebindings traefik app.kubernetes.io/managed-by=Helm
kubectl annotate ingressclass traefik meta.helm.sh/release-name=cognigy-ai meta.helm.sh/release-namespace=cognigy-ai
kubectl label ingressclass traefik app.kubernetes.io/managed-by=Helm

Migration

This section describes the actual migration of Cognigy.AI from Kustomize to Helm. The migration will require downtime of your Cognigy.AI installation. Plan a maintenance window for at least 2 hours accordingly.

Rename MongoDB Databases

  1. Scale down the current installation:
    for i in $(kubectl get deployment --namespace default --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
    do
        kubectl --namespace default scale --replicas=0 deployment $i
    done
    
  2. Rename the databases and create new users. In Cognigy.AI Helm Chart, we have renamed service-analytics-collector-provider database to service-analytics-collector and service-analytics-conversation-collector-provider to service-analytics-conversation. To rename the databases, execute the following script, fill in the password values in advance (see the comments inside the script). Check the root username for MongoDB Helm installation (root or admin) and use that as while migrating the databases.
MongoDB Migration Script Compatibility

The script below is compatible with the cognigy-mongodb-helm-chart only. If you are using any other MongoDB service (for example, MongoDB Atlas), you need to find compatible commands for your database service to rename the databases.

kubectl exec -it -n mongodb mongodb-0 bash

# rename the service-analytics-collector-provider, set admin root password in <password>
mongodump -u <root_username> -p <password> --authenticationDatabase admin --host "mongodb-0.mongodb-headless.mongodb.svc.cluster.local:27017,mongodb-1.mongodb-headless.mongodb.svc.cluster.local:27017,mongodb-2.mongodb-headless.mongodb.svc.cluster.local:27017" --archive --db=service-analytics-collector-provider | mongorestore -u admin -p <password> --authenticationDatabase admin --archive --nsFrom='service-analytics-collector-provider.*' --nsTo='service-analytics-collector.*'

# rename the service-analytics-conversation-collector-provider, set admin root password in <password>
mongodump -u <root_username> -p <password> --authenticationDatabase admin --host "mongodb-0.mongodb-headless.mongodb.svc.cluster.local:27017,mongodb-1.mongodb-headless.mongodb.svc.cluster.local:27017,mongodb-2.mongodb-headless.mongodb.svc.cluster.local:27017" --archive --db=service-analytics-conversation-collector-provider | mongorestore -u admin -p <password> --authenticationDatabase admin --archive --nsFrom='service-analytics-conversation-collector-provider.*' --nsTo='service-analytics-conversation.*'

# Create service-analytics-collector user in service-analytics-collector db
# Get the existing password from `cognigy-service-analytics-collector-provider` secret and put it into <password-service-analytics-collector>:
mongo -u <root_username> -p $MONGODB_ROOT_PASSWORD --authenticationDatabase admin
use service-analytics-collector
db.createUser({
 user: "service-analytics-collector",
 pwd: "<password-service-analytics-collector>",
 roles: [
     { role: "readWrite", db: "service-analytics-collector" }
 ]
});

# Create service-analytics-conversation user in service-analytics-conversation db
# Get the existing password from `cognigy-service-analytics-conversation-collector-provider` secret and put it into <password-service-analytics-conversation>:
use service-analytics-conversation
db.createUser({
 user: "service-analytics-conversation",
 pwd: "<password-service-analytics-conversation>",
 roles: [
     { role: "readWrite", db: "service-analytics-conversation" }
 ]
});

exit
exit

Migrate Persistent Volumes for Cognigy.AI

  1. Attach PVCs of flow-modules, functions and redis-persistent of Cognigy.AI Helm release to the existing PVs of Kustomize installation:

    ## delete dynamically provisioned PVCs for flow-modules, functions and redis-persistent during Kustomization deployment
    kubectl delete pvc -n=default flow-modules 
    kubectl delete pvc -n=default functions 
    kubectl delete pvc -n=default redis-persistent  
    ## verify that dynamic PVCs are removed and that PVs from Kustomize installation still exist
    kubectl get pvc
    kubectl get pvc -n=cognigy-ai
    kubectl get pv
    # edit PVs for `flow-modules`, `functions` and `redis-persistent` and remove `spec.claimRef:` section completely
    kubectl patch pv <pv-name> -p '{"spec":{"claimRef": null}}' 
    # check that PVs status has changed from `Released` to `Available`
    kubectl get pv
    

  2. Deploy the PVCs manifests, which have been modified in Prepare Persistent Volumes section.

# apply modified PVCs to the cluster
kubectl apply -f redis-persistent-pvc.yaml
kubectl apply -f flow-modules-pvc.yaml
kubectl apply -f functions-pvc.yaml
# check that status of PVs and PVCs has changed to Bound:
kubectl get pv
kubectl get pvc -n=cognigy-ai

Migrate Cognigy.AI from Kustomize to Helm

Perform the following steps for Cognigy.AI migration:

  1. Bring back the deployments of Cognigy.AI Helm Release:

helm registry login cognigy.azurecr.io \
--username <your-username> \
--password <your-password>

helm upgrade --install --namespace cognigy-ai cognigy-ai oci://cognigy.azurecr.io/helm/cognigy.ai --version HELM_CHART_VERSION --values values_prod.yaml
2. Verify that all deployments are in a ready state:
kubectl get deployments -n=cognigy-ai
3. (Traefik as reverse-proxy only) In case EXTERNAL-IP for traefik service of type LoadBalancer changes, update the DNS records to point to the new EXTERNAL-IP of traefik Service. If you're using Traefik Ingress with AWS Classic Load Balancer, change the CNAME of the DNS entries to the new EXTERNAL-IP. Check the new external IP/CNAME record with:
kubectl get service -n=cognigy-ai traefik

Rollback

In case Cognigy.AI Helm release does not function properly and rollback is required, perform the following steps:

  1. Scale down the Cognigy.AI Helm Release deployments
    for i in $(kubectl get deployment --namespace cognigy-ai --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
    do
        kubectl --namespace cognigy-ai scale --replicas=0 deployment $i
    done
    
  2. Delete PVCs for Helm Release:
    kubectl delete pvc -n=cognigy-ai flow-modules 
    kubectl delete pvc -n=cognigy-ai functions 
    kubectl delete pvc -n=cognigy-ai redis-persistent  
    
  3. Restore PVCs for Kustomize installation:
    kubectl apply -f redis-persistent-pvc-kustomize.yaml
    kubectl apply -f flow-modules-pvc-kustomize.yaml
    kubectl apply -f functions-pvc-kustomize.yaml
    
  4. Bring back Kustomize installation:
    cd kubernetes/core/<environment>/product
    kubectl apply -k ./
    
  5. After Cognigy.AI Kustomize installation is up and running, you can clean up the Helm release by completely removing cognigy-ai namespace (the namespace of Helm release):
    kubectl delete namespace cognigy-ai
    

Clean-up

After Cognigy.AI Helm release is up and running properly, you can clean up the Kustomize installation, for this execute following steps:

  1. Drop old databases in MongoDB (set MONGODB_ROOT_USER to root or admin in accordance with values_prod.yaml in MongoDB Helm Chart):

    kubectl exec -it -n mongodb mongodb-0 -- bash
    mongo -u $MONGODB_ROOT_USER -p $MONGODB_ROOT_PASSWORD --authenticationDatabase admin
    
    # Drop service-analytics-collector-provider
    use service-analytics-collector-provider
    db.dropDatabase()
    
    # Drop service-analytics-conversation-collector-provider
    use service-analytics-conversation-collector-provider
    db.dropDatabase()
    

  2. Delete the Kustomize deployments running in the default namespace:

    for i in $(kubectl get deployment --namespace default --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')             
    do
        kubectl --namespace default delete deployment $i
    done
    

  3. Delete the services in the default namespace:

    for i in $(kubectl get service --namespace default --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}' | grep service-)
    do
        kubectl --namespace default delete service $i
    done
    # delete rabbitmq, redis, redis-persistent, and traefik
    kubectl --namespace default delete svc rabbitmq redis redis-persistent traefik
    
    Be careful while deleting service, do not delete the kubernetes service.

  4. Delete the ingresses in the default namespace:

    for i in $(kubectl get ingress --namespace default --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
    do
        kubectl delete ingress $i --namespace default
    done
    

  5. Delete PVCs from default namespace (if still present):

    kubectl delete pvc -n=default flow-modules
    kubectl delete pvc -n=default functions
    kubectl delete pvc -n=default redis-persistent
    

  6. (Optional) Delete PVC for single replica MongoDB setup in case of single-replica to multi-replica MongoDB migration:

    kubectl delete pvc mongodb -n default