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Version: 0.0.42

Kubernetes Cluster Deployment

Deploy Lakehousecat to production-grade Kubernetes clusters in the cloud or on-premise. This guide covers deployment to managed Kubernetes services and self-hosted clusters.

Overview​

Kubernetes cluster deployment is recommended for:

  • Production environments with high availability requirements
  • Multi-user deployments (teams and organizations)
  • Scalable workloads with dynamic resource needs
  • Enterprise deployments with compliance and security requirements

Supported Kubernetes Platforms​

Lakehousecat Operator runs on any standard Kubernetes 1.29+ cluster:

Managed Kubernetes Services​

PlatformStatusGuide
AWS EKS✅ Tested & Supported→ EKS Guide
Google GKE⚠️ Not yet tested→ GKE Guide
Azure AKS⚠️ Not yet tested→ AKS Guide

Self-Managed Kubernetes​

PlatformStatusGuide
On-Premise✅ Compatible→ On-Premise Guide
Bare Metal✅ CompatibleUse On-Premise Guide
Private Cloud✅ CompatibleUse On-Premise Guide

Prerequisites​

Before deploying to a Kubernetes cluster, ensure you have:

Cluster Requirements​

  • Kubernetes: Version 1.29 or higher
  • kubectl: Installed and configured for your cluster
  • Helm: Version 3.8 or higher
  • Persistent Storage: Dynamic volume provisioner (StorageClass)
  • LoadBalancer or Ingress Controller: For external access

Resource Requirements​

Minimum Production Configuration:

  • 16 CPU cores
  • 64 GB RAM
  • 500 GB persistent storage

Recommended Production Configuration:

  • 32+ CPU cores
  • 128+ GB RAM
  • 1+ TB persistent storage
  • Auto-scaling enabled

Network Requirements​

  • Outbound HTTPS access to:
    • license.lakehousecat.com (license validation)
    • AWS ECR (public and private) — container image pull; handled automatically by the operator
  • Ingress capability for external user access
  • TLS certificates for HTTPS (Let's Encrypt recommended)

License​

General Deployment Process​

The deployment process is consistent across all Kubernetes platforms:

Step 1: Provision Kubernetes Cluster​

Create a Kubernetes cluster on your chosen platform (EKS, GKE, AKS, or on-premise).

Key Configuration:

  • Kubernetes version: 1.29 or higher
  • Node pool: Appropriate instance types for your workload
  • Storage: Dynamic volume provisioner enabled
  • Networking: LoadBalancer or Ingress controller

See platform-specific guides for detailed instructions.

Step 2: Configure kubectl​

Ensure kubectl is configured to connect to your cluster:

# Verify cluster access
kubectl cluster-info
kubectl get nodes

# Check available storage classes
kubectl get storageclass

Step 3: Install Lakehousecat Operator​

Install the operator using Helm:

# Add Lakehousecat Helm repository (OCI registry)
helm pull oci://public.ecr.aws/lakehousecat/charts/lakehousecat-operator --version 0.0.42

# Or add traditional Helm repo
helm repo add lakehousecat https://charts.lakehousecat.com
helm repo update

# Install operator
helm install lhc-operator lakehousecat/lakehousecat-operator \
  --namespace lhc-operator \
  --create-namespace \
  --wait

Production Values (optional):

operator-values.yaml
replicaCount: 1

operator:
  logging:
    level: info
    format: json
  leaderElection:
    enabled: false

resources:
  requests:
    cpu: 500m
    memory: 512Mi
  limits:
    cpu: 2000m
    memory: 2Gi

metrics:
  serviceMonitor:
    enabled: true
    namespace: monitoring
    interval: 30s

networkPolicy:
  enabled: true

Install with custom values:

helm install lhc-operator lakehousecat/lakehousecat-operator \
  -n lhc-operator \
  --create-namespace \
  -f operator-values.yaml \
  --wait

Verify operator deployment:

kubectl get pods -n lhc-operator
kubectl logs -f deployment/lhc-operator -n lhc-operator

Step 4: Create Handshake Secret​

Create a Kubernetes secret with your handshake key:

kubectl create secret generic lhc-handshake-secret \
  --from-literal=handshake-key=YOUR_HANDSHAKE_KEY_FROM_PORTAL \
  -n lhc-operator

Verify secret creation:

kubectl get secret lhc-handshake-secret -n lhc-operator

Step 5: Create Required Secrets​

All sensitive values — model API keys, the Mapbox token, external S3 credentials, OAuth client secrets — are read by the operator from pre-created Kubernetes secrets, never from the CR. Create the secrets you need before deploying the instance.

See Required Secrets for the full list, the exact keys per provider, and ready-made helper scripts. At minimum you created the handshake secret in Step 4; add the optional secrets for any feature you intend to use (models, Mapbox, external S3, SSO).

Graceful by design

Missing optional secrets do not block the deployment — the affected feature is simply skipped and reported via a status condition. You can add secrets later and re-trigger a reconcile.

Step 5b: Configure Storage​

Lakehousecat requires persistent storage for databases and object storage.

Option 1: Cloud Object Storage​

Use cloud-native S3-compatible storage. Credentials come from the lhc-storage-s3-secret (see Required Secrets), and the CR references it:

spec:
  objectStorage:
    type: s3
    region: "us-east-1"
    bucketPrefix: "my-company-lakehousecat"
    credentialsSecretName: "lhc-storage-s3-secret"   # keys: access-key, secret-key

Option 2: In-Cluster SeaweedFS (Default)​

Deploy SeaweedFS within the cluster (managed by the operator, no credentials needed):

spec:
  objectStorage:
    type: seaweedfs
    volume:
      persistence:
        size: "500Gi"
        storageClass: "gp3"  # Use your cluster's storage class

Step 6: Deploy Lakehousecat Instance​

Create a Lakehousecat Custom Resource with production configuration:

lakehousecat-production.yaml
apiVersion: lhc.lakehousecat.com/v1alpha2
kind: Lakehousecat
metadata:
  name: production
spec:
  license:
    handshakeSecretName: "lhc-handshake-secret"

  customer:
    namespace: "lakehousecat-prod"
    adminemail: "admin@company.com"

  version: "0.0.42"
  architecture: "amd64"

  # Storage configuration — external S3 (credentials via pre-created secret).
  # Omit this block entirely to use the default in-cluster SeaweedFS.
  objectStorage:
    type: s3
    region: "us-east-1"
    bucketPrefix: "my-company-lakehousecat"
    credentialsSecretName: "lhc-storage-s3-secret"   # keys: access-key, secret-key

  # Domain configuration with TLS
  domain:
    baseDomain: "lakehousecat.company.com"
    tls:
      enabled: true
      letsEncrypt: true
      email: "admin@company.com"

  # Production resource allocations
  analytics:
    replicaCount: 3
    autoscaling:
      enabled: true
      minReplicas: 3
      maxReplicas: 10
      targetCPUUtilizationPercentage: 70
    resources:
      requests:
        cpu: "2"
        memory: "8Gi"
      limits:
        cpu: "4"
        memory: "16Gi"

  lhc:
    replicaCount: 2
    resources:
      requests:
        cpu: "1"
        memory: "4Gi"
      limits:
        cpu: "2"
        memory: "8Gi"

  llm:
    replicaCount: 2
    resources:
      requests:
        cpu: "1"
        memory: "4Gi"

  ui:
    replicaCount: 2
    resources:
      requests:
        cpu: "500m"
        memory: "1Gi"

  postgresql:
    persistence:
      size: "100Gi"
      storageClass: "gp3"
    resources:
      requests:
        cpu: "2"
        memory: "8Gi"

  clickhouse:
    shards: 2
    replicaCount: 2
    persistence:
      size: "500Gi"
      storageClass: "gp3"
    resources:
      requests:
        cpu: "4"
        memory: "32Gi"

  valkey:
    persistence:
      size: "20Gi"

  # Airflow configuration
  airflow:
    worker:
      replicaCount: 3
      autoscaling:
        enabled: true
        minReplicas: 3
        maxReplicas: 10
      resources:
        requests:
          cpu: "2"
          memory: "8Gi"

  # Monitoring — exposes /metrics endpoints on backend services
  # for scraping by the customer's existing monitoring stack.
  # The Operator does NOT deploy Prometheus or Grafana.
  monitoring:
    metricsEnabled: true

Apply the configuration:

kubectl apply -f lakehousecat-production.yaml

Step 7: Monitor Deployment​

Watch the deployment progress:

# Check instance status
kubectl get lakehousecat -w

# Check all pods
kubectl get pods -n lakehousecat-prod -w

# View operator logs
kubectl logs -f deployment/lhc-operator -n lhc-operator

# Check specific service
kubectl describe lakehousecat production

Step 8: Configure DNS and Ingress​

Once deployed, configure DNS to point to your cluster's LoadBalancer or Ingress:

Get LoadBalancer IP/Hostname:

kubectl get svc -n lakehousecat-prod lakehousecat-ui

Configure DNS: Create A or CNAME records:

lakehousecat.company.com → <LOADBALANCER_IP_OR_HOSTNAME>

Verify TLS:

curl -I https://lakehousecat.company.com

Step 9: Access Lakehousecat​

Open your browser to your configured domain:

https://lakehousecat.company.com

Create your admin account and begin onboarding users.

High Availability Configuration​

For mission-critical deployments, configure high availability:

Multi-Replica Services​

spec:
  analytics:
    replicaCount: 3
  lhc:
    replicaCount: 3
  llm:
    replicaCount: 2
  ui:
    replicaCount: 3

Database Replication​

spec:
  postgresql:
    replicaCount: 3
    replication:
      enabled: true

  clickhouse:
    shards: 3
    replicaCount: 2  # 2 replicas per shard

Pod Disruption Budgets​

The operator automatically creates Pod Disruption Budgets (PDBs) for critical services to ensure availability during cluster maintenance.

Security Best Practices​

Network Policies​

Enable network policies to isolate namespaces:

# Operator values
networkPolicy:
  enabled: true

RBAC​

The operator creates minimal RBAC permissions. Review and adjust as needed:

kubectl get rolebindings -n lakehousecat-prod
kubectl get clusterrolebindings | grep lakehousecat

Secrets Management​

Use external secret management for sensitive data:

  • AWS: AWS Secrets Manager or Parameter Store
  • Google Cloud: Secret Manager
  • Azure: Key Vault

Integrate using Kubernetes External Secrets Operator.

TLS/HTTPS​

Always enable TLS for production:

spec:
  domain:
    tls:
      enabled: true
      letsEncrypt: true  # Or provide your own certificates

Monitoring and Observability​

The Operator does not deploy a monitoring stack. It exposes observability data through open interfaces — connect your own tooling.

Prometheus Metrics — /metrics Endpoint​

Each backend service (lhc, llm, analytics, semantic, audio) exposes a /metrics endpoint in the Prometheus exposition format. The endpoint is unauthenticated by design (cluster-internal, follows Prometheus convention) and toggled via monitoring.metricsEnabled (default true).

For customers running prometheus-operator, a ServiceMonitor such as the following scrapes Lakehousecat services:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: lakehousecat-services
  namespace: lakehousecat-prod
spec:
  selector:
    matchLabels:
      app.kubernetes.io/part-of: lakehousecat
  endpoints:
    - port: http
      path: /metrics
      interval: 30s

Datadog, New Relic and similar agents support equivalent auto-discovery via Kubernetes service annotations — consult your agent's documentation.

Logging​

Centralize logs using your preferred logging solution. All backend services produce structured application logs that are written to the SeaweedFS backend_logs/ bucket and to stdout/stderr. Compatible collectors include:

  • AWS: CloudWatch Logs
  • Google Cloud: Cloud Logging
  • Azure: Azure Monitor
  • ELK Stack: Elasticsearch, Logstash, Kibana
  • Loki: Grafana Loki
  • Datadog / New Relic / Splunk: via their respective Kubernetes agents

Backup and Disaster Recovery​

Database Backups​

Configure automated backups for PostgreSQL and ClickHouse:

spec:
  postgresql:
    backup:
      enabled: true
      schedule: "0 2 * * *"  # Daily at 2 AM
      retention: 30  # Keep 30 days

  clickhouse:
    backup:
      enabled: true
      schedule: "0 3 * * *"
      retention: 30

Object Storage Backups​

Enable versioning on your S3/Cloud Storage bucket for data protection.

Disaster Recovery Plan​

  1. Regular backups of all databases
  2. GitOps for Lakehousecat CR configurations
  3. Multi-region deployment for critical workloads
  4. RTO/RPO definitions and testing

Scaling​

Scale your deployment as needs grow. See the Scaling documentation for detailed guidance.

Quick scaling example:

# Scale analytics service
kubectl patch lakehousecat production --type=merge \
  -p '{"spec":{"analytics":{"replicaCount":5}}}'

# Enable autoscaling
kubectl patch lakehousecat production --type=merge \
  -p '{"spec":{"analytics":{"autoscaling":{"enabled":true,"minReplicas":3,"maxReplicas":10}}}}'

Upgrading​

Operator Upgrade​

helm upgrade lhc-operator lakehousecat/lakehousecat-operator \
  -n lhc-operator \
  --version 0.0.42

Instance Upgrade​

kubectl patch lakehousecat production --type=merge \
  -p '{"spec":{"version":"0.0.42"}}'

The operator handles rolling upgrades with zero downtime.

Platform-Specific Guides​

For detailed, platform-specific deployment instructions:

Troubleshooting​

Pods Not Starting​

# Check pod events
kubectl describe pod -n lakehousecat-prod <pod-name>

# Check operator logs
kubectl logs -f deployment/lhc-operator -n lhc-operator

# Check instance status
kubectl describe lakehousecat production

License Validation Failures​

# Verify handshake secret
kubectl get secret lhc-handshake-secret -n lhc-operator -o yaml

# Check network connectivity to provider backend
kubectl run -it --rm debug --image=alpine --restart=Never -- \
  wget -O- https://license.lakehousecat.com/health

Storage Issues​

# Check PVCs
kubectl get pvc -n lakehousecat-prod

# Check storage class
kubectl get storageclass

# Describe PVC for events
kubectl describe pvc -n lakehousecat-prod <pvc-name>

Support​