Amazon EKS (with Terraform)

Overview

This guide installs Feast on AWS using our reference Terraform configuration.

The Terraform configuration used here is a greenfield installation that neither assumes anything about, nor integrates with, existing resources in your AWS account. The Terraform configuration presents an easy way to get started, but you may want to customize this set up before using Feast in production.

This Terraform configuration creates the following resources:

  • Kubernetes cluster on Amazon EKS (3x r3.large nodes)

  • Kafka managed by Amazon MSK (2x kafka.t3.small nodes)

  • Postgres database for Feast metadata, using serverless Aurora (min capacity: 2)

  • Redis cluster, using Amazon Elasticache (1x cache.t2.micro)

  • Amazon EMR cluster to run Spark (3x spot m4.xlarge)

  • Staging S3 bucket to store temporary data

1. Requirements

2. Configure Terraform

Create a .tfvars file underfeast/infra/terraform/aws. Name the file. In our example, we use my_feast.tfvars. You can see the full list of configuration variables in variables.tf. At a minimum, you need to set name_prefix and an AWS region:

my_feast.tfvars
name_prefix = "my-feast"
region = "us-east-1"

3. Apply

After completing the configuration, initialize Terraform and apply:

$ cd feast/infra/terraform/aws
$ terraform init
$ terraform apply -var-file=my_feast.tfvars

Starting may take a minute. A kubectl configuration file is also created in this directory, and the file's name will start with kubeconfig_ and end with a random suffix.

4. Connect to Feast using Jupyter

After all pods are running, connect to the Jupyter Notebook Server running in the cluster.

To connect to the remote Feast server you just created, forward a port from the remote k8s cluster to your local machine. Replace kubeconfig_XXXXXXX below with the kubeconfig file name Terraform generates for you.

KUBECONFIG=kubeconfig_XXXXXXX kubectl port-forward \
$(kubectl get pod -o custom-columns=:metadata.name | grep jupyter) 8888:8888
Forwarding from 127.0.0.1:8888 -> 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.