Ghid de deployment MuleSoft Runtime Fabric (RTF)

# RTF Architecture Overview
# ========================
# Controller Nodes: Manage RTF operations (3 minimum for HA)
# Worker Nodes: Run Mule applications (scale based on workload)
# Components:
#   - rtf-agent: Manages communication with Anypoint Platform
#   - rtf-deployer: Handles application deployments
#   - rtf-resource-cache: Caches application resources
#   - rtf-persistence-gateway: Manages persistent storage
 
# Minimum Production Requirements
controller_nodes:
  count: 3
  cpu: 2 cores
  memory: 8GB
  disk: 60GB SSD
 
worker_nodes:
  count: 3  # Minimum for HA
  cpu: 2 cores
  memory: 15GB
  disk: 250GB SSD
 
networking:
  pod_cidr: "10.244.0.0/16"
  service_cidr: "10.96.0.0/12"
  required_ports:
    - 443   # HTTPS
    - 5044  # Filebeat
    - 32009 # RTF

#!/bin/bash
# RTF Installation Script
 
# 1. Download RTF installer
curl -L https://anypoint.mulesoft.com/runtimefabric/api/download/scripts/latest \
  --header "Authorization: Bearer $ANYPOINT_TOKEN" \
  -o rtfctl
 
chmod +x rtfctl
 
# 2. Generate activation data from Anypoint Platform
# This is done in Runtime Manager > Runtime Fabric > Create
 
# 3. Initialize cluster (on first controller node)
./rtfctl install \
  --activation-data "$RTF_ACTIVATION_DATA" \
  --mule-license "$MULE_LICENSE_KEY" \
  --controller-ips "10.0.1.10,10.0.1.11,10.0.1.12" \
  --worker-ips "10.0.2.10,10.0.2.11,10.0.2.12" \
  --http-proxy "http://proxy.company.com:8080" \
  --no-proxy "localhost,127.0.0.1,.internal.company.com"
 
# 4. Join additional nodes
# On each additional controller:
./rtfctl join \
  --role controller \
  --token "$JOIN_TOKEN" \
  --server "10.0.1.10:443"
 
# On each worker:
./rtfctl join \
  --role worker \
  --token "$JOIN_TOKEN" \
  --server "10.0.1.10:443"
 
# 5. Verify installation
./rtfctl status
./rtfctl appliance status

# rtf-config.yaml
apiVersion: rtf.mulesoft.com/v1
kind: RuntimeFabricConfig
metadata:
  name: production-rtf
spec:
  # Ingress configuration
  ingress:
    enabled: true
    # Use internal load balancer for private clusters
    serviceType: LoadBalancer
    annotations:
      service.beta.kubernetes.io/aws-load-balancer-internal: "true"
      service.beta.kubernetes.io/aws-load-balancer-cross-zone-load-balancing-enabled: "true"
 
  # Resource allocation defaults
  defaults:
    cpu:
      limit: "1000m"
      reserved: "500m"
    memory:
      limit: "2Gi"
      reserved: "1Gi"
 
  # Persistent storage
  persistence:
    storageClass: "gp3"
    accessMode: ReadWriteOnce
 
  # Log forwarding
  logForwarding:
    enabled: true
    type: "splunk"
    host: "splunk.company.com"
    port: 8088
    token: "${SPLUNK_HEC_TOKEN}"
    index: "mulesoft"
    source: "rtf"
    sourcetype: "_json"
 
  # Monitoring
  monitoring:
    enabled: true
    prometheus:
      enabled: true
      serviceMonitor: true

# deployment-settings.yaml
apiVersion: rtf.mulesoft.com/v1
kind: MuleApplication
metadata:
  name: order-api
  namespace: production
spec:
  # Application artifact
  artifact:
    groupId: com.company
    artifactId: order-api
    version: 1.2.0
    repository: exchange
 
  # Runtime configuration
  runtime:
    version: "4.6.0"
    java: "17"
    releaseChannel: "LTS"
 
  # Replica configuration
  replicas:
    count: 3
    updateStrategy:
      type: RollingUpdate
      maxSurge: 1
      maxUnavailable: 0
 
  # Resource allocation
  resources:
    cpu:
      reserved: "500m"
      limit: "1500m"
    memory:
      reserved: "1000Mi"
      limit: "2000Mi"
 
  # Properties and secrets
  properties:
    - name: "env"
      value: "production"
    - name: "api.timeout"
      value: "30000"
 
  secureProperties:
    - name: "db.password"
      secretRef: "order-api-secrets"
      key: "database-password"
    - name: "api.key"
      secretRef: "order-api-secrets"
      key: "api-key"
 
  # Clustering
  clustering:
    enabled: true
 
  # Auto-scaling
  autoscaling:
    enabled: true
    minReplicas: 2
    maxReplicas: 10
    metrics:
      - type: cpu
        targetAverageUtilization: 70
      - type: memory
        targetAverageUtilization: 80
 
  # Health checks
  healthCheck:
    readinessProbe:
      path: /api/health
      port: 8081
      initialDelaySeconds: 30
      periodSeconds: 10
      timeoutSeconds: 5
      failureThreshold: 3
    livenessProbe:
      path: /api/health
      port: 8081
      initialDelaySeconds: 60
      periodSeconds: 20
      timeoutSeconds: 10
      failureThreshold: 3
 
  # Networking
  networking:
    publicEndpoint:
      enabled: true
      pathPrefix: "/order"
    internalEndpoint:
      enabled: true

#!/bin/bash
# deploy-to-rtf.sh
 
set -e
 
# Configuration
APP_NAME="order-api"
ENVIRONMENT="production"
RTF_NAME="production-rtf"
VERSION="${1:-latest}"
 
# Anypoint Platform authentication
ANYPOINT_TOKEN=$(curl -s -X POST https://anypoint.mulesoft.com/accounts/login \
  -H "Content-Type: application/json" \
  -d '{
    "username": "'$ANYPOINT_USERNAME'",
    "password": "'$ANYPOINT_PASSWORD'"
  }' | jq -r '.access_token')
 
# Get organization and environment IDs
ORG_ID=$(curl -s https://anypoint.mulesoft.com/accounts/api/me \
  -H "Authorization: Bearer $ANYPOINT_TOKEN" | jq -r '.user.organizationId')
 
ENV_ID=$(curl -s "https://anypoint.mulesoft.com/accounts/api/organizations/$ORG_ID/environments" \
  -H "Authorization: Bearer $ANYPOINT_TOKEN" | jq -r ".data[] | select(.name==\"$ENVIRONMENT\") | .id")
 
# Get RTF target ID
RTF_ID=$(curl -s "https://anypoint.mulesoft.com/runtimefabric/api/organizations/$ORG_ID/fabrics" \
  -H "Authorization: Bearer $ANYPOINT_TOKEN" | jq -r ".[] | select(.name==\"$RTF_NAME\") | .id")
 
# Build deployment request
DEPLOYMENT_REQUEST=$(cat <<EOF
{
  "name": "$APP_NAME",
  "labels": ["production", "api"],
  "target": {
    "provider": "MC",
    "targetId": "$RTF_ID",
    "deploymentSettings": {
      "clustered": true,
      "enforceDeployingReplicasAcrossNodes": true,
      "http": {
        "inbound": {
          "publicUrl": "https://api.company.com/order"
        }
      },
      "jvm": {},
      "runtime": {
        "version": "4.6.0",
        "releaseChannel": "LTS",
        "java": "17"
      },
      "autoscaling": {
        "enabled": true,
        "minReplicas": 2,
        "maxReplicas": 10
      },
      "updateStrategy": "rolling",
      "resources": {
        "cpu": {
          "reserved": "500m",
          "limit": "1500m"
        },
        "memory": {
          "reserved": "1000Mi",
          "limit": "2000Mi"
        }
      },
      "disableAmLogForwarding": false,
      "persistentObjectStore": true
    },
    "replicas": 3
  },
  "application": {
    "ref": {
      "groupId": "com.company",
      "artifactId": "$APP_NAME",
      "version": "$VERSION",
      "packaging": "jar"
    },
    "desiredState": "STARTED",
    "configuration": {
      "mule.agent.application.properties.service": {
        "applicationName": "$APP_NAME",
        "properties": {
          "env": "production",
          "api.autodiscovery.id": "12345"
        },
        "secureProperties": {
          "db.password": "{{secure::db.password}}",
          "api.key": "{{secure::api.key}}"
        }
      }
    }
  }
}
EOF
)
 
# Check if application exists
EXISTING_APP=$(curl -s "https://anypoint.mulesoft.com/amc/application-manager/api/v2/organizations/$ORG_ID/environments/$ENV_ID/deployments" \
  -H "Authorization: Bearer $ANYPOINT_TOKEN" | jq -r ".items[] | select(.name==\"$APP_NAME\") | .id")
 
if [ -n "$EXISTING_APP" ]; then
  echo "Updating existing deployment: $EXISTING_APP"
  HTTP_METHOD="PATCH"
  DEPLOYMENT_URL="https://anypoint.mulesoft.com/amc/application-manager/api/v2/organizations/$ORG_ID/environments/$ENV_ID/deployments/$EXISTING_APP"
else
  echo "Creating new deployment"
  HTTP_METHOD="POST"
  DEPLOYMENT_URL="https://anypoint.mulesoft.com/amc/application-manager/api/v2/organizations/$ORG_ID/environments/$ENV_ID/deployments"
fi
 
# Deploy
RESPONSE=$(curl -s -X $HTTP_METHOD "$DEPLOYMENT_URL" \
  -H "Authorization: Bearer $ANYPOINT_TOKEN" \
  -H "Content-Type: application/json" \
  -d "$DEPLOYMENT_REQUEST")
 
DEPLOYMENT_ID=$(echo $RESPONSE | jq -r '.id')
echo "Deployment initiated: $DEPLOYMENT_ID"
 
# Wait for deployment to complete
echo "Waiting for deployment to complete..."
while true; do
  STATUS=$(curl -s "https://anypoint.mulesoft.com/amc/application-manager/api/v2/organizations/$ORG_ID/environments/$ENV_ID/deployments/$DEPLOYMENT_ID" \
    -H "Authorization: Bearer $ANYPOINT_TOKEN" | jq -r '.application.status')
 
  echo "Current status: $STATUS"
 
  case $STATUS in
    "RUNNING")
      echo "Deployment successful!"
      exit 0
      ;;
    "DEPLOYMENT_FAILED"|"FAILED")
      echo "Deployment failed!"
      exit 1
      ;;
  esac
 
  sleep 10
done

# resource-quota.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
  name: mule-apps-quota
  namespace: production
spec:
  hard:
    requests.cpu: "20"
    requests.memory: "40Gi"
    limits.cpu: "40"
    limits.memory: "80Gi"
    pods: "50"
 
---
# priority-class.yaml
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: mule-critical
value: 1000000
globalDefault: false
description: "Critical Mule applications"
 
---
# node-affinity.yaml
apiVersion: rtf.mulesoft.com/v1
kind: DeploymentOverride
metadata:
  name: order-api-override
spec:
  selector:
    matchLabels:
      app: order-api
  template:
    spec:
      priorityClassName: mule-critical
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: workload-type
                    operator: In
                    values:
                      - mule-production
        podAntiAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
            - weight: 100
              podAffinityTerm:
                labelSelector:
                  matchExpressions:
                    - key: app
                      operator: In
                      values:
                        - order-api
                topologyKey: kubernetes.io/hostname
      tolerations:
        - key: "dedicated"
          operator: "Equal"
          value: "mule"
          effect: "NoSchedule"

# prometheus-rules.yaml
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: mule-rtf-alerts
  namespace: monitoring
spec:
  groups:
    - name: mule-application-alerts
      rules:
        - alert: MuleAppHighCPU
          expr: |
            avg(rate(container_cpu_usage_seconds_total{
              namespace=~".*mule.*",
              container!=""
            }[5m])) by (pod) > 0.8
          for: 5m
          labels:
            severity: warning
          annotations:
            summary: "High CPU usage on Mule app {{ $labels.pod }}"
            description: "CPU usage is above 80% for 5 minutes"
 
        - alert: MuleAppHighMemory
          expr: |
            container_memory_working_set_bytes{
              namespace=~".*mule.*",
              container!=""
            } / container_spec_memory_limit_bytes > 0.85
          for: 5m
          labels:
            severity: warning
          annotations:
            summary: "High memory usage on Mule app {{ $labels.pod }}"
 
        - alert: MuleAppReplicasDown
          expr: |
            kube_deployment_status_replicas_available{
              namespace=~".*mule.*"
            } < kube_deployment_spec_replicas
          for: 2m
          labels:
            severity: critical
          annotations:
            summary: "Mule app {{ $labels.deployment }} has unavailable replicas"
 
        - alert: MuleAppRestartLoop
          expr: |
            increase(kube_pod_container_status_restarts_total{
              namespace=~".*mule.*"
            }[1h]) > 5
          labels:
            severity: critical
          annotations:
            summary: "Mule app {{ $labels.pod }} is in restart loop"
 
    - name: rtf-infrastructure-alerts
      rules:
        - alert: RTFNodeNotReady
          expr: |
            kube_node_status_condition{
              condition="Ready",
              status="true"
            } == 0
          for: 5m
          labels:
            severity: critical
          annotations:
            summary: "RTF node {{ $labels.node }} is not ready"
 
        - alert: RTFAgentDown
          expr: |
            up{job="rtf-agent"} == 0
          for: 2m
          labels:
            severity: critical
          annotations:
            summary: "RTF agent is down"

{
  "dashboard": {
    "title": "MuleSoft RTF Overview",
    "panels": [
      {
        "title": "Application Status",
        "type": "stat",
        "targets": [
          {
            "expr": "count(kube_deployment_status_replicas_available{namespace=~\".*mule.*\"} > 0)",
            "legendFormat": "Running Apps"
          }
        ]
      },
      {
        "title": "CPU Usage by Application",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(rate(container_cpu_usage_seconds_total{namespace=~\".*mule.*\", container!=\"\"}[5m])) by (pod)",
            "legendFormat": "{{ pod }}"
          }
        ]
      },
      {
        "title": "Memory Usage by Application",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(container_memory_working_set_bytes{namespace=~\".*mule.*\", container!=\"\"}) by (pod) / 1024 / 1024",
            "legendFormat": "{{ pod }} (MB)"
          }
        ]
      },
      {
        "title": "Request Rate",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(rate(http_server_requests_seconds_count{namespace=~\".*mule.*\"}[5m])) by (app)",
            "legendFormat": "{{ app }}"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(rate(http_server_requests_seconds_count{namespace=~\".*mule.*\", status=~\"5..\"}[5m])) by (app) / sum(rate(http_server_requests_seconds_count{namespace=~\".*mule.*\"}[5m])) by (app) * 100",
            "legendFormat": "{{ app }} error %"
          }
        ]
      }
    ]
  }
}

# production-checklist.yaml
production_readiness:
  infrastructure:
    - name: "High Availability"
      requirements:
        - "Minim 3 noduri controller distribuite pe zone de disponibilitate"
        - "Minim 3 noduri worker distribuite pe zone de disponibilitate"
        - "Load balancer extern cu health checks"
        - "Stocare persistenta cu replicare"
 
    - name: "Securitate"
      requirements:
        - "Network policies care restrictioneaza traficul pod-to-pod"
        - "Secrete stocate in vault extern (HashiCorp, AWS Secrets)"
        - "TLS termination la nivelul ingress"
        - "Pod security policies activate"
        - "Scanarea imaginilor in pipeline-ul CI/CD"
 
    - name: "Monitorizare"
      requirements:
        - "Logging centralizat (Splunk/ELK)"
        - "Colectare de metrici (Prometheus)"
        - "Alerte configurate si testate"
        - "Dashboard pentru echipa de operatiuni"
 
  application:
    - name: "Deployment"
      requirements:
        - "Minim 2 replici pentru HA"
        - "Pod anti-affinity configurat"
        - "Limite de resurse definite"
        - "Health checks configurate"
        - "Strategie de rolling update"
 
    - name: "Configurare"
      requirements:
        - "Configurare externalizata"
        - "Proprietati securizate in secrets"
        - "Configurari specifice pe mediu"
        - "API autodiscovery activat"
 
    - name: "Testare"
      requirements:
        - "Testele de integrare trec"
        - "Load testing finalizat"
        - "Testarea failover finalizata"
        - "Procedura de rollback documentata"