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Add Konveyor deployment and usage guide (#106)
* Add Konveyor deployment and usage guide * Update docs/en/solutions/How_to_Use_Konveyor.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update docs/en/solutions/How_to_Use_Konveyor.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix: updated violet tool url * fix: fix clean resource namespace * fix: set default cache pv size Add cache storage class and data volume size to deployment configuration. * fix: fix grammer --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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‎docs/en/solutions/How_to_Use_Konveyor.md‎

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---
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kind:
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- Solution
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products:
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- Alauda Application Services
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ProductsVersion:
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- 4.x
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---
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# How to Deploy and Use Konveyor
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## Overview
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Konveyor is a CNCF (Cloud Native Computing Foundation) project that provides a modular platform for application modernization. It supports the entire lifecycle of modernization: discovery, assessment, analysis, and execution. This guide covers deploying the Konveyor Hub (Tackle) platform and its core components.
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## Prerequisites
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- Kubernetes cluster with kubectl access
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- StorageClass that supports ReadWriteMany (RWX) access mode
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- StorageClass for RWO volumes (for databases)
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- (Optional) LoadBalancer or Ingress Controller for external access
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## Install Konveyor Operator
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Download the Konveyor Operator plugin from [Alauda Cloud Console](https://cloud.alauda.io/) Marketplace, and follow the [Upload Packages](https://docs.alauda.io/container_platform/4.2/extend/upload_package.html) guide to upload the plugin to the cluster.
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## Deploy Konveyor Hub (Tackle)
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### Create Tackle Instance
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Deploy the Tackle platform by creating a Tackle CR. The Tackle instance must be deployed in the same namespace as the konveyor-operator.
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```yaml
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cat << EOF | kubectl create -f -
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apiVersion: tackle.konveyor.io/v1alpha1
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kind: Tackle
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metadata:
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name: tackle
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namespace: konveyor-tackle
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spec:
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feature_auth_required: true
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feature_isolate_namespace: true
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feature_analysis_archiver: true
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hub_database_volume_size: 5Gi
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hub_bucket_volume_size: 100Gi
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rwx_supported: true
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hub_bucket_storage_class: nfs # Replace with your RWX StorageClass
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rwo_storage_class: sc-topolvm # Replace with your RWO StorageClass
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cache_storage_class: nfs
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cache_data_volume_size: 100Gi
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EOF
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```
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### Verify Deployment
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Check the status of the pods in the `konveyor-tackle` namespace:
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```bash
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kubectl get pods -n konveyor-tackle
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```
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Ensure all pods are in `Running` or `Completed` state before proceeding.
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> [!WARNING]
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> The Tackle instance must be deployed in the same namespace as the `konveyor-operator`. If you deploy it in a different namespace, some resources created by the operator (such as PersistentVolumeClaims, ConfigMaps, Secrets, and ServiceAccounts) might not be automatically deleted when the Tackle custom resource is removed. In that case, you must manually clean up these resources in the affected namespaces, for example:
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>
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> ```bash
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> # Delete common resources labeled for the Tackle instance
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> kubectl delete pvc,configmap,secret,sa -l app.kubernetes.io/instance=tackle -n konveyor-tackle
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> ```
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### Configuration Options
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| Name | Default | Description |
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| --- | --- | --- |
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| `spec.feature_auth_required` | `true` | Enable Keycloak authentication (set `false` for single user/no auth) |
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| `spec.feature_isolate_namespace` | `true` | Enable namespace isolation via network policies |
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| `spec.feature_analysis_archiver` | `true` | Automatically archive old analysis reports when a new one is created |
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| `spec.rwx_supported` | `true` | Whether RWX volumes are supported in the cluster |
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| `spec.hub_database_volume_size` | `5Gi` | Size requested for Hub database volume |
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| `spec.hub_bucket_volume_size` | `100Gi` | Size requested for Hub bucket volume |
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| `spec.keycloak_database_data_volume_size` | `1Gi` | Size requested for Keycloak DB volume |
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| `spec.cache_data_volume_size` | `100Gi` | Size requested for Tackle Cache volume |
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| `spec.cache_storage_class` | N/A | StorageClass requested for Tackle Cache volume |
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| `spec.hub_bucket_storage_class` | N/A | StorageClass requested for Tackle Hub Bucket volume (RWX) |
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| `spec.rwo_storage_class` | N/A | StorageClass requested for RWO database volumes |
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## Access Tackle UI
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### Quick Access via Port-Forward
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1. Set up port forwarding:
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```bash
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kubectl -n konveyor-tackle port-forward service/tackle-ui 8080:8080
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```
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2. Open [http://127.0.0.1:8080](http://127.0.0.1:8080) in your browser.
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### Initialize Admin Account
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The built-in Keycloak generates a random password on startup. This is the Keycloak root password, stored in the `tackle-keycloak-sso` secret.
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1. Retrieve Keycloak admin credentials:
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```bash
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# Get username (default: admin)
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kubectl -n konveyor-tackle get secret tackle-keycloak-sso -o jsonpath='{.data.username}' | base64 -d
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# Get password
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kubectl -n konveyor-tackle get secret tackle-keycloak-sso -o jsonpath='{.data.password}' | base64 -d
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```
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2. Login to Keycloak admin console at [http://127.0.0.1:8080/auth/admin/](http://127.0.0.1:8080/auth/admin/)
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3. Reset the Tackle admin password:
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- Select the **tackle** Realm from the dropdown (not Master Realm)
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- Click **Users** in the left menu
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- Find and select the **admin** user
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- Click the **Credentials** tab
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- Enter a new password (e.g., `admin@123`)
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- Disable **Temporary** toggle
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- Click **Reset Password**
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4. Login to Tackle at [http://127.0.0.1:8080](http://127.0.0.1:8080) using the admin user and new password.
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### Secure Access via Ingress (Production)
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Port-forward is only for temporary access. For production, configure Ingress with TLS.
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#### Ingress Prerequisites
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- A domain name (e.g., `tackle.example.com`)
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- LoadBalancer service deployed (see [ALB deployment guide](https://docs.alauda.io/container_platform/4.1/configure/networking/how_to/alb/deploy_alb.html))
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- cert-manager installed
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#### Create TLS Certificate
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```yaml
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apiVersion: cert-manager.io/v1
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kind: Certificate
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metadata:
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name: tackle-ssl-cert
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namespace: konveyor-tackle
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spec:
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commonName: tackle.example.com
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dnsNames:
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- tackle.example.com
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issuerRef:
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kind: ClusterIssuer
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name: cpaas-ca # Replace with your Issuer
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secretName: tackle-tls-secret
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usages:
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- server auth
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- client auth
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```
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#### Create Ingress
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```yaml
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apiVersion: networking.k8s.io/v1
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kind: Ingress
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metadata:
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annotations:
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nginx.ingress.kubernetes.io/backend-protocol: HTTP
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nginx.ingress.kubernetes.io/ssl-redirect: "true"
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name: tackle-ui-tls-ingress
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namespace: konveyor-tackle
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spec:
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ingressClassName: nginx # Replace with your Ingress Class
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rules:
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- host: tackle.example.com
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http:
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paths:
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- backend:
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service:
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name: tackle-ui
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port:
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number: 8080
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path: /
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pathType: Prefix
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tls:
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- hosts:
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- tackle.example.com
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secretName: tackle-tls-secret
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```
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> [!NOTE]
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> Replace `tackle.example.com` with your actual domain.
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Access Tackle at `https://tackle.example.com`.
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## Enable KAI (Konveyor AI)
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KAI uses AI services to provide AI-powered code migration assistance. It supports multiple providers and models.
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### Supported Providers and Models
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| Provider (`kai_llm_provider`) | Model (`kai_llm_model`) |
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| --- | --- |
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| `openai` | `gpt-4`, `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo` |
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| `azure_openai` | `gpt-4`, `gpt-35-turbo` |
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| `bedrock` | `anthropic.claude-3-5-sonnet-20241022-v2:0`, `meta.llama3-1-70b-instruct-v1:0` |
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| `google` | `gemini-2.0-flash-exp`, `gemini-1.5-pro` |
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| `ollama` | `llama3.1`, `codellama`, `mistral` |
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| `groq` | `llama-3.1-70b-versatile`, `mixtral-8x7b-32768` |
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| `anthropic` | `claude-3-5-sonnet-20241022`, `claude-3-haiku-20240307` |
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### Enable KAI in Tackle
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1. Update the Tackle configuration:
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```yaml
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apiVersion: tackle.konveyor.io/v1alpha1
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kind: Tackle
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metadata:
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name: tackle
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namespace: konveyor-tackle
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spec:
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kai_solution_server_enabled: true
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kai_llm_provider: openai # Choose your provider
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kai_llm_model: gpt-4o-mini # Choose your model
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```
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2. Create API credentials secret:
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**For OpenAI:**
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```bash
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kubectl create secret generic kai-api-keys -n konveyor-tackle \
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--from-literal=OPENAI_API_BASE='https://api.openai.com/v1' \
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--from-literal=OPENAI_API_KEY='<YOUR_OPENAI_KEY>'
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```
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**For Google:**
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```bash
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kubectl create secret generic kai-api-keys -n konveyor-tackle \
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--from-literal=GOOGLE_API_KEY='<YOUR_GOOGLE_API_KEY>'
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```
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3. Force the operator to reconcile and pick up the new credentials:
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```bash
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kubectl patch tackle tackle -n konveyor-tackle --type=merge -p \
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'{"metadata":{"annotations":{"konveyor.io/force-reconcile":"'"$(date +%s)"'"}}}'
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```
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## Konveyor Components Overview
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Konveyor provides a modular architecture for application modernization:
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| Component | Description |
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| --- | --- |
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| **Konveyor Hub** | Central control plane providing unified application inventory, assessment module (risk evaluation), and analysis module (static code analysis). Implements RBAC with Administrator, Architect, and Migrator roles. |
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| **Kantra & Analyzer-LSP** | CLI tool for offline static analysis. Analyzer-LSP integrates into IDEs (VSCode) via Language Server Protocol for real-time migration issue detection. |
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| **Konveyor AI (KAI)** | RAG-based AI assistant for automated code remediation. Uses Solved Incident Store for context-aware code patch generation. |
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| **Move2Kube** | Automates conversion from Cloud Foundry/OpenShift to Kubernetes. Three phases: Collect, Plan, Transform. Generates Dockerfiles, K8s manifests, Helm Charts, and Tekton Pipelines. |
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| **Forklift** | VM migration tool for moving VMs from VMware vSphere, oVirt, or OpenStack to KubeVirt. |
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| **Crane** | Kubernetes-to-Kubernetes migration tool for cluster upgrades or cross-distribution migrations. Handles PV data sync with Restic or VolSync. |
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## Reference
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- [Konveyor Official Documentation](https://konveyor.io/docs/konveyor/)
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- [Konveyor Admin Tasks](https://konveyor.io/docs/konveyor/admintasks/)
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- [Konveyor Operator Repository](https://github.com/konveyor/operator)

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