LogoLogo
v0.49-branch
v0.49-branch
  • Introduction
  • Blog
  • Community & getting help
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Architecture
      • Overview
      • Language
      • Push vs Pull Model
      • Write Patterns
      • Feature Transformation
      • Feature Serving and Model Inference
      • Role-Based Access Control (RBAC)
    • Concepts
      • Overview
      • Project
      • Data ingestion
      • Entity
      • Feature view
      • Feature retrieval
      • Point-in-time joins
      • [Alpha] Saved dataset
      • Permission
      • Tags
    • Use Cases
    • Components
      • Overview
      • Registry
      • Offline store
      • Online store
      • Feature server
      • Batch Materialization Engine
      • Provider
      • Authorization Manager
      • OpenTelemetry Integration
    • Third party integrations
    • FAQ
  • Tutorials
    • Sample use-case tutorials
      • Driver ranking
      • Fraud detection on GCP
      • Real-time credit scoring on AWS
      • Driver stats on Snowflake
    • Validating historical features with Great Expectations
    • Building streaming features
    • Retrieval Augmented Generation (RAG) with Feast
  • 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
      • Scaling Feast
      • Structuring Feature Repos
    • Running Feast in production (e.g. on Kubernetes)
    • Customizing Feast
      • Adding a custom batch materialization engine
      • Adding a new offline store
      • Adding a new online store
      • Adding a custom provider
    • Adding or reusing tests
    • Starting Feast servers in TLS(SSL) Mode
  • Reference
    • Codebase Structure
    • Type System
    • Data sources
      • Overview
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
      • Couchbase (contrib)
    • Offline stores
      • Overview
      • Dask
      • Snowflake
      • BigQuery
      • Redshift
      • DuckDB
      • Couchbase Columnar (contrib)
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
      • Clickhouse (contrib)
      • Remote Offline
    • Online stores
      • Overview
      • SQLite
      • Snowflake
      • Redis
      • Dragonfly
      • IKV
      • Datastore
      • DynamoDB
      • Bigtable
      • Remote
      • PostgreSQL
      • Cassandra + Astra DB
      • Couchbase
      • MySQL
      • Hazelcast
      • ScyllaDB
      • SingleStore
      • Milvus
    • Registries
      • Local
      • S3
      • GCS
      • SQL
      • Snowflake
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
      • Azure
    • Batch Materialization Engines
      • Snowflake
      • AWS Lambda (alpha)
      • Spark (contrib)
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • Offline Feature Server
      • Registry server
    • [Beta] Web UI
    • [Beta] On demand feature view
    • [Alpha] Vector Database
    • [Alpha] Data quality monitoring
    • [Alpha] Streaming feature computation with Denormalized
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Backwards Compatibility Policy
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
Powered by GitBook
On this page
  • Description
  • Getting started
  • Example
  • Functionality Matrix

Was this helpful?

Edit on GitHub
Export as PDF
  1. Reference
  2. Online stores

Datastore

PreviousIKVNextDynamoDB

Last updated 26 days ago

Was this helpful?

Description

The online store provides support for materializing feature values into Cloud Datastore. The data model used to store feature values in Datastore is described in more detail .

Getting started

In order to use this online store, you'll need to run pip install 'feast[gcp]'. You can then get started with the command feast init REPO_NAME -t gcp.

Example

feature_store.yaml
project: my_feature_repo
registry: data/registry.db
provider: gcp
online_store:
  type: datastore
  project_id: my_gcp_project
  namespace: my_datastore_namespace

The full set of configuration options is available in .

Functionality Matrix

Datastore

write feature values to the online store

yes

read feature values from the online store

yes

update infrastructure (e.g. tables) in the online store

yes

teardown infrastructure (e.g. tables) in the online store

yes

generate a plan of infrastructure changes

no

support for on-demand transforms

yes

readable by Python SDK

yes

readable by Java

no

readable by Go

no

support for entityless feature views

yes

support for concurrent writing to the same key

no

support for ttl (time to live) at retrieval

no

support for deleting expired data

no

collocated by feature view

yes

collocated by feature service

no

collocated by entity key

no

The set of functionality supported by online stores is described in detail . Below is a matrix indicating which functionality is supported by the Datastore online store.

To compare this set of functionality against other online stores, please see the full .

Datastore
here
DatastoreOnlineStoreConfig
here
functionality matrix