Azure AKS (with Helm)


This guide installs Feast on Azure Kubernetes cluster (known as AKS), and ensures the following services are running:

  • Feast Core

  • Feast Online Serving

  • Postgres

  • Redis

  • Spark

  • Kafka

  • Feast Jupyter (Optional)

  • Prometheus (Optional)

1. Requirements

  1. Install and configure Azure CLI

  2. Install and configure Kubectl

  3. Install Helm 3

2. Preparation

Create an AKS cluster with Azure CLI. The detailed steps can be found here, and a high-level walk through includes:

az group create --name myResourceGroup --location eastus
az acr create --resource-group myResourceGroup --name feast-AKS-ACR --sku Basic
az aks create -g myResourceGroup -n feast-AKS --location eastus --attach-acr feast-AKS-ACR --generate-ssh-keys
az aks install-cli
az aks get-credentials --resource-group myResourceGroup --name feast-AKS

Add the Feast Helm repository and download the latest charts:

helm version # make sure you have the latest Helm installed
helm repo add feast-charts
helm repo update

Feast includes a Helm chart that installs all necessary components to run Feast Core, Feast Online Serving, and an example Jupyter notebook.

Feast Core requires Postgres to run, which requires a secret to be set on Kubernetes:

kubectl create secret generic feast-postgresql --from-literal=postgresql-password=password

3. Feast installation

Install Feast using Helm. The pods may take a few minutes to initialize.

helm install feast-release feast-charts/feast

4. Spark operator installation

Follow the documentation to install Spark operator on Kubernetes , and Feast documentation to configure Spark roles

helm repo add spark-operator
helm install my-release spark-operator/spark-operator --set --set image.tag=v1beta2-1.1.2-2.4.5

and ensure the service account used by Feast has permissions to manage Spark Application resources. This depends on your k8s setup, but typically you'd need to configure a Role and a RoleBinding like the one below:

cat <<EOF | kubectl apply -f -
kind: Role
name: use-spark-operator
namespace: <REPLACE ME>
- apiGroups: [""]
resources: ["sparkapplications"]
verbs: ["create", "delete", "deletecollection", "get", "list", "update", "watch", "patch"]
kind: RoleBinding
name: use-spark-operator
namespace: <REPLACE ME>
kind: Role
name: use-spark-operator
- kind: ServiceAccount
name: default

5. Use Jupyter to connect to Feast

After all the pods are in a RUNNING state, port-forward to the Jupyter Notebook Server in the cluster:

kubectl port-forward \
$(kubectl get pod -o | grep jupyter) 8888:8888
Forwarding from -> 8888
Forwarding from [::1]:8888 -> 8888

You can now connect to the bundled Jupyter Notebook Server at localhost:8888 and follow the example Jupyter notebook.

6. Environment variables

If you are running the Minimal Ride Hailing Example, you may want to make sure the following environment variables are correctly set:

demo_data_location = "wasbs://<container_name>@<storage_account_name>"
os.environ["FEAST_AZURE_BLOB_ACCOUNT_NAME"] = "<storage_account_name>"
os.environ["FEAST_AZURE_BLOB_ACCOUNT_ACCESS_KEY"] = <Insert your key here>
os.environ["FEAST_HISTORICAL_FEATURE_OUTPUT_LOCATION"] = "wasbs://<container_name>@<storage_account_name>"
os.environ["FEAST_SPARK_STAGING_LOCATION"] = "wasbs://<container_name>@<storage_account_name>"
os.environ["FEAST_SPARK_LAUNCHER"] = "k8s"
os.environ["FEAST_SPARK_K8S_NAMESPACE"] = "default"
os.environ["FEAST_REDIS_HOST"] = "feast-release-redis-master.default.svc.cluster.local"
os.environ["DEMO_KAFKA_BROKERS"] = "feast-release-kafka.default.svc.cluster.local:9092"

7. Further Reading