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  • Introduction
  • Quickstart
  • Getting started
    • Install Feast
    • Create a feature repository
    • Deploy a feature store
    • Build a training dataset
    • Load data into the online store
    • Read features from the online store
  • Community
  • Roadmap
  • Changelog
  • Concepts
    • Overview
    • Feature view
    • Data model
    • Online store
    • Offline store
    • Provider
    • Architecture
  • Reference
    • Data sources
      • BigQuery
      • File
    • Offline stores
      • File
      • BigQuery
    • Online stores
      • SQLite
      • Redis
      • Datastore
    • Providers
      • Local
      • Google Cloud Platform
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
        • Kubernetes (with Helm)
        • Amazon EKS (with Terraform)
        • Azure AKS (with Helm)
        • Azure AKS (with Terraform)
        • Google Cloud GKE (with Terraform)
        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
        • Feast CLI
      • Learn Feast
    • Concepts
      • Overview
      • Architecture
      • Entities
      • Sources
      • Feature Tables
      • Stores
    • Tutorials
      • Minimal Ride Hailing Example
    • User guide
      • Overview
      • Getting online features
      • Getting training features
      • Define and ingest features
      • Extending Feast
    • Reference
      • Configuration Reference
      • Feast and Spark
      • Metrics Reference
      • Limitations
      • API Reference
        • Go SDK
        • Java SDK
        • Core gRPC API
        • Python SDK
        • Serving gRPC API
        • gRPC Types
    • Advanced
      • Troubleshooting
      • Metrics
      • Audit Logging
      • Security
      • Upgrading Feast
  • Contributing
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
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  • Feast API
  • Ingestion
  • Storage

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  1. Feast on Kubernetes
  2. Reference

Limitations

Feast API

Limitation

Motivation

Features names and entity names cannot overlap in feature table definitions

Features and entities become columns in historical stores which may cause conflicts

The following field names are reserved in feature tables

  • event_timestamp

  • datetime

  • created_timestamp

  • ingestion_id

  • job_id

These keywords are used for column names when persisting metadata in historical stores

Ingestion

Limitation

Motivation

Once data has been ingested into Feast, there is currently no way to delete the data without manually going to the database and deleting it. However, during retrieval only the latest rows will be returned for a specific key (event_timestamp, entity) based on its created_timestamp.

This functionality simply doesn't exist yet as a Feast API

Storage

Limitation

Motivation

Feast does not support offline storage in Feast 0.8

As part of our re-architecture of Feast, we moved from GCP to cloud-agnostic deployments. Developing offline storage support that is available in all cloud environments is a pending action.

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Last updated 3 years ago

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