Deployment

This guide covers three ways to deploy AIObserve Stack. Choose the method that fits your environment:

Method	Best for
Docker Compose	Local testing, development, PoC
Kubernetes (Helm)	Production, scalable environments
Manual	Custom setups, existing infrastructure

Each method deploys the core components: OpenTelemetry Collector, Apache Doris, and Grafana with Doris App Plugin. For data collection configuration.

Docker Compose

Best for local testing, development, and proof of concepts (PoC).

AIObserve Stack - Docker Compose

Prerequisites

• Docker Engine (v20.10+)
• Docker Compose (v2.0+)

Deploy

1. Clone the repository:

   git clone https://github.com/velodb/ai-observe-stack.git
   cd ai-observe-stack/docker

2. If you already have an existing Apache Doris cluster, configure the connection and start in external mode:

   cp .env.example .env

   Edit .env with your Doris connection details:

   DORIS_FE_HTTP_ENDPOINT=http://<DORIS_FE_HOST>:<FE_HTTP_PORT>
   DORIS_FE_MYSQL_ENDPOINT=<DORIS_FE_HOST>:<FE_MYSQL_PORT>
   DORIS_USERNAME=root
   DORIS_PASSWORD=

   Then start the services (OTel Collector + Grafana only):

   docker compose -f docker-compose-without-doris.yaml up -d

3. If you don't have a Doris cluster, simply start the full stack with the built-in Doris:

   docker compose up -d

4. Verify the services are running:

   docker compose ps

   All services should show running status.

5. Access Grafana at http://localhost:3000 and log in with admin / admin.

Service endpoints

Service	Endpoint	Credentials
Grafana	http://localhost:3000	admin / admin
Doris FE UI	http://localhost:8030	root / (empty)
Doris MySQL	localhost:9030	root / (empty)
OTel gRPC	localhost:4317	-
OTel HTTP	localhost:4318	-

Note: Doris FE UI and Doris MySQL endpoints are only available with the built-in Doris. When using an external Doris, access your existing cluster directly.

Stop and clean up

To stop services while preserving data:

docker compose down

To stop services and remove all data:

docker compose down -v

Kubernetes (Helm)

Best for production deployments, development environments, and scalable setups.

AIObserve Stack Helm Chart

Prerequisites

• Kubernetes cluster (v1.20+)
• Helm (v3.0+)
• kubectl configured to access your cluster
• PersistentVolume provisioner (for data persistence)

Deploy

1. Add the AIObserve Stack Helm repository:

   helm repo add ai-observe-stack https://charts.velodb.io
   helm repo update

2. Create a namespace for AIObserve Stack:

   kubectl create namespace ai-observe-stack

3. Install AIObserve Stack:

   helm install my-aiobs ai-observe-stack/ai-observe-stack -n ai-observe-stack

   If you have an existing Doris cluster, use external mode instead:

   helm install my-aiobs ai-observe-stack/ai-observe-stack -n ai-observe-stack \
   --set doris.mode=external \
   --set doris.external.host=<DORIS_FE_HOST> \
   --set doris.external.port=9030 \
   --set doris.external.feHttpPort=8030 \
   --set doris.internal.operator.enabled=false

4. Verify all pods are running:

   kubectl get pods -n ai-observe-stack

   Wait until all pods show Running status.

5. Access Grafana:

   kubectl port-forward svc/my-aiobs-grafana 3000:3000 -n ai-observe-stack

   Open http://localhost:3000 and log in with admin / admin.

Service endpoints

Service	Port-forward command
Grafana	kubectl port-forward svc/my-aiobs-grafana 3000:3000 -n ai-observe-stack
Doris FE UI	kubectl port-forward svc/my-aiobs-doris-fe 8030:8030 -n ai-observe-stack
Doris MySQL	kubectl port-forward svc/my-aiobs-doris-fe 9030:9030 -n ai-observe-stack

Uninstall

helm uninstall my-aiobs -n ai-observe-stack
kubectl delete namespace ai-observe-stack

Manually

Best for custom setups or integrating with existing infrastructure.

1. Deploy Apache Doris following the Doris deployment documentation. Skip this step if you already have a Doris cluster.

2. Deploy OpenTelemetry Collector following the installation guide.

3. Deploy Grafana following the Grafana installation documentation.

4. Install the AIObserve Stack Grafana plugin. See Plugin Installation.

After completing all steps, access Grafana at http://localhost:3000.