ADR-0006: Kubernetes Operator

Status

Accepted

Context

As the Feast project grew, deploying a fully functional Feature Store in a production-like manner became increasingly difficult. Existing installers required many manual operations that led to configuration errors. Users needed a simpler way to install and maintain Feature Store environments, especially with features like RBAC.

The existing Helm-based operator had limitations in handling complex installation requirements. A more capable operator was needed to manage the full lifecycle of Feast deployments on Kubernetes.

Decision

Build a Go Operator using the operator-sdk framework with a cluster-scoped controller and a namespaced FeatureStore Custom Resource Definition (CRD).

FeatureStore CRD

The operator manages Feast through a single CRD that defines the entire feature store deployment:

apiVersion: feast.dev/v1alpha1
kind: FeatureStore
metadata:
  name: example
  namespace: feast
spec:
  feastProject: my-project
  auth:
    kubernetes:
      roles: [reader, writer]
  services:
    registry:
      replicas: 1
      persistence:
        file:
          pvc:
            capacity: 5Gi
    onlineStore:
      replicas: 2
      persistence:
        postgresql:
          secretRef: online-store-creds
    offlineStore:
      replicas: 3
  feastApplyJob:
    configMapRef: feast-definitions

Architecture

• Operator deploys Feast services (Registry, Online Store, Offline Store) as defined in the CR.
• Operator generates feature_store.yaml from the CR spec, including only relevant sections for each server type.
• Client ConfigMap is created automatically for remote connectivity.
• feast apply Job can be triggered from a ConfigMap or Git repo to initialize the registry.
• CR status.applied is the single source of truth for the deployed state.

Key Decisions

• Go over Python: Go is better suited for Kubernetes operators. Python is great for ML work but not for cloud-native infrastructure management.
• Single CRD (FeatureStore) instead of separate CRDs per service type. All services are part of a functioning Feature Store and should be managed together.
• Operator manages feature_store.yaml entirely to ensure consistency and validation (e.g., remote types are only used where appropriate).
• Data store deployments are out of scope: The operator assumes data stores are pre-provisioned and accessible.
• Deprecation of Helm-based operator: The existing Helm-based operator is deprecated in favor of the Go operator.

Persistence Options

• Default: Ephemeral file-based stores.
• File with PVC: For clusters supporting persistent volumes.
• PostgreSQL: Via referenced Kubernetes secrets for credentials.

Consequences

Positive

• Simplified, standardized deployment of Feast on Kubernetes.
• Full lifecycle management including RBAC, metrics, and feature store initialization.
• Supports multiple Feature Store deployments in a single cluster without conflict.
• Proper validation and consistency enforcement through the operator reconciliation loop.
• Deployable with kustomize; compatible with OLM and OperatorHub.

Negative

• Requires Kubernetes as the deployment platform.
• Data store management is left to users (intentionally out of scope).
• Initial release supports limited persistence backends; additional stores added incrementally.

References

• Original RFC: Feast RFC-042: Operator
• GitHub Issue: #4561
• Implementation: infra/feast-operator/