LogoLogo
v0.11-branch
v0.11-branch
  • 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
Powered by GitBook
On this page
  • What is a feature repository?
  • Structure of a feature repository
  • The feature_store.yaml configuration file
  • The .feastignore file
  • Feature definitions
  • Next steps

Was this helpful?

Edit on Git
Export as PDF
  1. Reference

Feature repository

Feast manages two important sets of configuration: feature definitions, and configuration about how to run the feature store. With Feast, this configuration can be written declaratively and stored as code in a central location. This central location is called a feature repository, and it's essentially just a directory that contains some code files.

The feature repository is the declarative source of truth for what the desired state of a feature store should be. The Feast CLI uses the feature repository to configure your infrastructure, e.g., migrate tables.

What is a feature repository?

A feature repository consists of:

  • A collection of Python files containing feature declarations.

  • A feature_store.yaml file containing infrastructural configuration.

  • A .feastignore file containing paths in the feature repository to ignore.

Typically, users store their feature repositories in a Git repository, especially when working in teams. However, using Git is not a requirement.

Structure of a feature repository

The structure of a feature repository is as follows:

  • The root of the repository should contain a feature_store.yaml file and may contain a .feastignore file.

  • The repository should contain Python files that contain feature definitions.

  • The repository can contain other files as well, including documentation and potentially data files.

An example structure of a feature repository is shown below:

$ tree -a
.
├── data
│   └── driver_stats.parquet
├── driver_features.py
├── feature_store.yaml
└── .feastignore

1 directory, 4 files

A couple of things to note about the feature repository:

  • Feast reads all Python files recursively when feast apply is ran, including subdirectories, even if they don't contain feature definitions.

  • It's recommended to add .feastignore and add paths to all imperative scripts if you need to store them inside the feature registry.

The feature_store.yaml configuration file

The configuration for a feature store is stored in a file named feature_store.yaml , which must be located at the root of a feature repository. An example feature_store.yaml file is shown below:

feature_store.yaml
project: my_feature_repo_1
registry: data/metadata.db
provider: local
online_store:
    path: data/online_store.db

The .feastignore file

This file contains paths that should be ignored when running feast apply. An example .feastignore is shown below:

.feastignore
# Ignore virtual environment
venv

# Ignore a specific Python file
scripts/foo.py

# Ignore all Python files directly under scripts directory
scripts/*.py

# Ignore all "foo.py" anywhere under scripts directory
scripts/**/foo.py

Feature definitions

A feature repository can also contain one or more Python files that contain feature definitions. An example feature definition file is shown below:

driver_features.py
from datetime import timedelta

from feast import BigQuerySource, Entity, Feature, FeatureView, ValueType

driver_locations_source = BigQuerySource(
    table_ref="rh_prod.ride_hailing_co.drivers",
    event_timestamp_column="event_timestamp",
    created_timestamp_column="created_timestamp",
)

driver = Entity(
    name="driver",
    value_type=ValueType.INT64,
    description="driver id",
)

driver_locations = FeatureView(
    name="driver_locations",
    entities=["driver"],
    ttl=timedelta(days=1),
    features=[
        Feature(name="lat", dtype=ValueType.FLOAT),
        Feature(name="lon", dtype=ValueType.STRING),
    ],
    input=driver_locations_source,
)

Next steps

PreviousGoogle Cloud PlatformNextfeature_store.yaml

Last updated 3 years ago

Was this helpful?

The feature_store.yaml file configures how the feature store should run. See for more details.

See for more details.

To declare new feature definitions, just add code to the feature repository, either in existing files or in a new file. For more information on how to define features, see .

See to get started with an example feature repository.

See , , or for more information on the configuration files that live in a feature registry.

feature_store.yaml
.feastignore
Create a feature repository
feature_store.yaml
.feastignore
Feature Views
Feature Views