Feast users use Feast to manage two important sets of configuration:

• Configuration about how to run Feast on your infrastructure
• Feature definitions

With Feast, the above configuration can be written declaratively and stored as code in a central location. This central location is called a feature repository. 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, deploy, and manage your feature store.

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.

{% hint style="info" %} Typically, users store their feature repositories in a Git repository, especially when working in teams. However, using Git is not a requirement. {% endhint %}

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 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:

{% code title="feature_store.yaml" %}

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

{% endcode %}

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

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

{% code title=".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

{% endcode %}

See .feastignore for more details.

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

{% code title="driver_features.py" %}

from datetime import timedelta

from feast import BigQuerySource, Entity, Feature, FeatureView, Field
from feast.types import Float32, Int64, String

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

driver = Entity(
    name="driver",
    description="driver id",
)

driver_locations = FeatureView(
    name="driver_locations",
    entities=[driver],
    ttl=timedelta(days=1),
    schema=[
        Field(name="lat", dtype=Float32),
        Field(name="lon", dtype=String),
        Field(name="driver", dtype=Int64),
    ],
    source=driver_locations_source,
)

{% endcode %}

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.

• See Create a feature repository to get started with an example feature repository.
• See feature_store.yaml, .feastignore, Registration inferencing, or Feature Views for more information on the configuration files that live in a feature registry.