Feature retrieval

Overview

Generally, Feast supports several patterns of feature retrieval:

1. Training data generation (via feature_store.get_historical_features(...))

2. Offline feature retrieval for batch scoring (via feature_store.get_historical_features(...))

3. Online feature retrieval for real-time model predictions

   • via the SDK: feature_store.get_online_features(...)

   • via deployed feature server endpoints: requests.post('http://localhost:6566/get-online-features', data=json.dumps(online_request))

Each of these retrieval mechanisms accept:

• some way of specifying entities (to fetch features for)

• some way to specify the features to fetch (either via feature services, which group features needed for a model version, or feature references)

Before beginning, you need to instantiate a local FeatureStore object that knows how to parse the registry (see more details)

For code examples of how the below work, inspect the generated repository from feast init -t [YOUR TEMPLATE] (gcp, snowflake, and aws are the most fully fleshed).

Concepts

Before diving into how to retrieve features, we need to understand some high level concepts in Feast.

Feature Services

A feature service is an object that represents a logical group of features from one or more feature views. Feature Services allows features from within a feature view to be used as needed by an ML model. Users can expect to create one feature service per model version, allowing for tracking of the features used by models.

driver_trips_feature_service.py

from driver_ratings_feature_view import driver_ratings_fv
from driver_trips_feature_view import driver_stats_fv

driver_stats_fs = FeatureService(
    name="driver_activity",
    features=[driver_stats_fv, driver_ratings_fv[["lifetime_rating"]]]
)

Feature services are used during

• The generation of training datasets when querying feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.

• Retrieval of features for batch scoring from the offline store (e.g. with an entity dataframe where all timestamps are now())

• Retrieval of features from the online store for online inference (with smaller batch sizes). The features retrieved from the online store may also belong to multiple feature views.

Applying a feature service does not result in an actual service being deployed.

Feature services enable referencing all or some features from a feature view.

Retrieving from the online store with a feature service

from feast import FeatureStore
feature_store = FeatureStore('.')  # Initialize the feature store

feature_service = feature_store.get_feature_service("driver_activity")
features = feature_store.get_online_features(
    features=feature_service, entity_rows=[entity_dict]
)

Retrieving from the offline store with a feature service

from feast import FeatureStore
feature_store = FeatureStore('.')  # Initialize the feature store

feature_service = feature_store.get_feature_service("driver_activity")
feature_store.get_historical_features(features=feature_service, entity_df=entity_df)

Feature References

This mechanism of retrieving features is only recommended as you're experimenting. Once you want to launch experiments or serve models, feature services are recommended.

Feature references uniquely identify feature values in Feast. The structure of a feature reference in string form is as follows: <feature_view>[@version]:<feature>

The @version part is optional. When omitted, the latest (active) version is used. You can specify a version like @v2 to read from a specific historical version snapshot.

Feature references are used for the retrieval of features from Feast:

online_features = fs.get_online_features(
    features=[
        'driver_locations:lon',                # latest version (default)
        'drivers_activity:trips_today',        # latest version (default)
        'drivers_activity@v2:trips_today',     # specific version
        'drivers_activity@latest:trips_today', # explicit latest
    ],
    entity_rows=[
        # {join_key: entity_value}
        {'driver': 'driver_1001'}
    ]
)

Version-qualified reads (@v<N>) require enable_online_feature_view_versioning: true in your registry config and are currently supported only on the SQLite online store. See the feature view versioning docs for details.

It is possible to retrieve features from multiple feature views with a single request, and Feast is able to join features from multiple tables in order to build a training dataset. However, it is not possible to reference (or retrieve) features from multiple projects at the same time.

Note, if you're using Feature views without entities, then those features can be added here without additional entity values in the entity_rows parameter.

Event timestamp

The timestamp on which an event occurred, as found in a feature view's data source. The event timestamp describes the event time at which a feature was observed or generated.

Event timestamps are used during point-in-time joins to ensure that the latest feature values are joined from feature views onto entity rows. Event timestamps are also used to ensure that old feature values aren't served to models during online serving.

Why event_timestamp is required in the entity dataframe

When calling get_historical_features(), the entity_df must include an event_timestamp column. This timestamp acts as the upper bound (inclusive) for which feature values are allowed to be retrieved for each entity row. Feast performs a point-in-time join (also called a "last known good value" temporal join): for each entity row, it retrieves the latest feature values with a timestamp at or before the entity row's event_timestamp.

This ensures point-in-time correctness, which is critical to prevent data leakage during model training. Without this constraint, features generated after the prediction time could leak into training data—effectively letting the model "see the future"—leading to inflated offline metrics that do not translate to real-world performance.

For example, if you want to predict whether a driver will be rated well on April 12 at 10:00 AM, the entity dataframe row should have event_timestamp = datetime(2021, 4, 12, 10, 0, 0). Feast will then only join feature values observed on or before that time, excluding any data generated after 10:00 AM.

Retrieving features without an entity dataframe

While the entity dataframe is the standard way to retrieve historical features, Feast also supports entity-less historical feature retrieval by datetime range. This is useful when:

• You are training time-series or population-level models and don't have a pre-defined list of entity IDs.

• You want all features in a time window for exploratory analysis or batch training on full history.

• Constructing an entity dataframe upfront is unnecessarily complex or expensive.

Instead of passing entity_df, you specify a time window with start_date and/or end_date:

from datetime import datetime

training_df = store.get_historical_features(
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips",
    ],
    start_date=datetime(2025, 7, 1),
    end_date=datetime(2025, 7, 2),
).to_df()

If start_date is omitted, it defaults to end_date minus the feature view TTL. If end_date is omitted, it defaults to the current time. Point-in-time correctness is still preserved.

Entity-less retrieval is currently supported for the Postgres, Dask, Spark, and Ray offline stores. You cannot mix entity_df with start_date/end_date in the same call.

For more details, see the FAQ and this blog post on entity-less historical feature retrieval.

Dataset

A dataset is a collection of rows that is produced by a historical retrieval from Feast in order to train a model. A dataset is produced by a join from one or more feature views onto an entity dataframe. Therefore, a dataset may consist of features from multiple feature views.

Dataset vs Feature View: Feature views contain the schema of data and a reference to where data can be found (through its data source). Datasets are the actual data manifestation of querying those data sources.

Dataset vs Data Source: Datasets are the output of historical retrieval, whereas data sources are the inputs. One or more data sources can be used in the creation of a dataset.

Retrieving historical features (for training data or batch scoring)

Feast abstracts away point-in-time join complexities with the get_historical_features API.

We go through the major steps, and also show example code. Note that the quickstart templates generally have end-to-end working examples for all these cases.

Full example: generate training data

entity_df = pd.DataFrame.from_dict(
    {
        "driver_id": [1001, 1002, 1003, 1004, 1001],
        "event_timestamp": [
            datetime(2021, 4, 12, 10, 59, 42),
            datetime(2021, 4, 12, 8, 12, 10),
            datetime(2021, 4, 12, 16, 40, 26),
            datetime(2021, 4, 12, 15, 1, 12),
            datetime.now()
        ]
    }
)
training_df = store.get_historical_features(
    entity_df=entity_df,
    features=store.get_feature_service("model_v1"),
).to_df()
print(training_df.head())

Full example: retrieve offline features for batch scoring

The main difference here compared to training data generation is how to handle timestamps in the entity dataframe. You want to pass in the current time to get the latest feature values for all your entities.

from feast import FeatureStore

store = FeatureStore(repo_path=".")

# Get the latest feature values for unique entities
entity_sql = f"""
    SELECT
        driver_id,
        CURRENT_TIMESTAMP() as event_timestamp
    FROM {store.get_data_source("driver_hourly_stats_source").get_table_query_string()}
    WHERE event_timestamp BETWEEN '2021-01-01' and '2021-12-31'
    GROUP BY driver_id
"""
batch_scoring_features = store.get_historical_features(
    entity_df=entity_sql,
    features=store.get_feature_service("model_v2"),
).to_df()
# predictions = model.predict(batch_scoring_features)

Step 1: Specifying Features

Feast accepts either:

• feature services, which group features needed for a model version

• feature references

Example: querying a feature service (recommended)

training_df = store.get_historical_features(
    entity_df=entity_df,
    features=store.get_feature_service("model_v1"),
).to_df()

Example: querying a list of feature references

training_df = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven"
    ],
).to_df()

Step 2: Specifying Entities

Feast accepts either a Pandas dataframe as the entity dataframe (including entity keys and timestamps) or a SQL query to generate the entities.

Both approaches must specify the full entity key needed as well as the timestamps. Feast then joins features onto this dataframe.

Example: entity dataframe for generating training data

entity_df = pd.DataFrame.from_dict(
    {
        "driver_id": [1001, 1002, 1003, 1004, 1001],
        "event_timestamp": [
            datetime(2021, 4, 12, 10, 59, 42),
            datetime(2021, 4, 12, 8, 12, 10),
            datetime(2021, 4, 12, 16, 40, 26),
            datetime(2021, 4, 12, 15, 1, 12),
            datetime.now()
        ]
    }
)
training_df = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven"
    ],
).to_df()

Example: entity SQL query for generating training data

You can also pass a SQL string to generate the above dataframe. This is useful for getting all entities in a timeframe from some data source.

entity_sql = f"""
    SELECT
        driver_id,
        event_timestamp
    FROM {store.get_data_source("driver_hourly_stats_source").get_table_query_string()}
    WHERE event_timestamp BETWEEN '2021-01-01' and '2021-12-31'
"""
training_df = store.get_historical_features(
    entity_df=entity_sql,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven"
    ],
).to_df()

Retrieving online features (for model inference)

Feast will ensure the latest feature values for registered features are available. At retrieval time, you need to supply a list of entities and the corresponding features to be retrieved. Similar to get_historical_features, we recommend using feature services as a mechanism for grouping features in a model version.

Note: unlike get_historical_features, the entity_rows do not need timestamps since you only want one feature value per entity key.

There are several options for retrieving online features: through the SDK, or through a feature server

Full example: retrieve online features for real-time model inference (Python SDK)

from feast import RepoConfig, FeatureStore
from feast.repo_config import RegistryConfig

repo_config = RepoConfig(
    registry=RegistryConfig(path="gs://feast-test-gcs-bucket/registry.pb"),
    project="feast_demo_gcp",
    provider="gcp",
)
store = FeatureStore(config=repo_config)

features = store.get_online_features(
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven",
    ],
    entity_rows=[
        {
            "driver_id": 1001,
        }
    ],
).to_dict()

Full example: retrieve online features for real-time model inference (Feature Server)

This approach requires you to deploy a feature server (see Python feature server).

import requests
import json

online_request = {
    "features": [
        "driver_hourly_stats:conv_rate",
    ],
    "entities": {"driver_id": [1001, 1002]},
}
r = requests.post('http://localhost:6566/get-online-features', data=json.dumps(online_request))
print(json.dumps(r.json(), indent=4, sort_keys=True))