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  • Introduction
  • Community
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Dataset
      • Entity
      • Feature view
      • Stream feature view
      • Feature retrieval
      • Point-in-time joins
      • Registry
    • Architecture
      • Overview
      • Feature repository
      • Registry
      • Offline store
      • Online store
      • Provider
    • Learning by example
    • Third party integrations
    • FAQ
  • Tutorials
    • Overview
    • Driver ranking
    • Fraud detection on GCP
    • Real-time credit scoring on AWS
    • Driver stats on Snowflake
    • Validating historical features with Great Expectations
    • Using Scalable Registry
    • Building streaming features
  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • 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
    • Running Feast in production
    • Deploying a Java feature server on Kubernetes
    • Upgrading from Feast 0.9
    • Adding a custom provider
    • Adding a new online store
    • Adding a new offline store
    • Adding or reusing tests
  • Reference
    • Codebase Structure
    • Data sources
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
    • Offline stores
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Spark (contrib)
      • PostgreSQL (contrib)
    • Online stores
      • SQLite
      • Redis
      • Datastore
      • DynamoDB
      • PostgreSQL (contrib)
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • Go-based feature retrieval
    • [Alpha] Web UI
    • [Alpha] Data quality monitoring
    • [Alpha] On demand feature view
    • [Alpha] AWS Lambda feature server
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
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  1. Getting started
  2. Concepts

Registry

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Last updated 2 years ago

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The Feast registry is where all applied Feast objects (e.g. Feature views, entities, etc) are stored. The registry exposes methods to apply, list, retrieve and delete these objects. The registry is abstraction, with multiple possible implementations.

By default, the registry Feast uses a file-based registry implementation, which stores the protobuf representation of the registry as a serialized file. This registry file can be stored in a local file system, or in cloud storage (in, say, S3 or GCS).

However, there's inherent limitations with a file-based registry, since changing a single field in the registry requires re-writing the whole registry file. With multiple concurrent writers, this presents a risk of data loss, or bottlenecks writes to the registry since all changes have to be serialized (e.g. when running materialization for multiple feature views or time ranges concurrently).

Alternatively, a can be used for a more scalable registry.

SQL Registry