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

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

See .feastignore for more details.

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,
)

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 Feature Views.

Next steps