Hybrid Offline Store

Description

The HybridOfflineStore allows routing offline feature operations to different offline store backends based on the batch_source of the FeatureView. This enables a single Feast deployment to support multiple offline store backends, each configured independently and selected dynamically at runtime.

Getting started

To use the HybridOfflineStore, install Feast with all required offline store dependencies (e.g., BigQuery, Snowflake, etc.) for the stores you plan to use. For example:

bash

pip install 'feast[spark,snowflake]'

Example

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

yaml

project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: hybrid_offline_store.HybridOfflineStore
  offline_stores:
    - type: spark
      conf:
        spark_master: local[*]
        spark_app_name: feast_spark_app
    - type: snowflake
      conf:
        account: my_snowflake_account
        user: feast_user
        password: feast_password
        database: feast_database
        schema: feast_schema

{% endcode %}

Example FeatureView

python

from feast import FeatureView, Entity, ValueType
from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
    SparkSource,
)
from feast.infra.offline_stores.snowflake_source import SnowflakeSource


entity = Entity(name="user_id", value_type=ValueType.INT64, join_keys=["user_id"])
feature_view1 = FeatureView(
    name="user_features",
    entities=["user_id"],
    ttl=None,
    features=[
        # Define your features here
    ],
    source=SparkSource(
        path="s3://my-bucket/user_features_data",
    ),
)

feature_view2 = FeatureView(
    name="user_activity",
    entities=["user_id"],
    ttl=None,
    features=[
        # Define your features here
    ],
    source=SnowflakeSource(
        path="s3://my-bucket/user_activity_data",
    ),
)

Then you can use materialize API to materialize the data from the specified offline store based on the batch_source of the FeatureView.

python

from feast import FeatureStore
store = FeatureStore(repo_path=".")
store.materialize(
    start_date="2025-01-01",
    end_date="2025-07-31",
    feature_views=[feature_view1, feature_view2],
)

Functionality Matrix

Feature/Functionality	Supported
pull_latest_from_table_or_query	Yes
pull_all_from_table_or_query	Yes
offline_write_batch	Yes
validate_data_source	Yes
get_table_column_names_and_types_from_data_source	Yes
write_logged_features	No
get_historical_features	Only with same data source