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Introduction

What is Feast?

Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).

Problems Feast Solves

Models need consistent access to data: Machine Learning (ML) systems built on traditional data infrastructure are often coupled to databases, object stores, streams, and files. A result of this coupling, however, is that any change in data infrastructure may break dependent ML systems. Another challenge is that dual implementations of data retrieval for training and serving can lead to inconsistencies in data, which in turn can lead to training-serving skew.

Feast decouples your models from your data infrastructure by providing a single data access layer that abstracts feature storage from feature retrieval. Feast also provides a consistent means of referencing feature data for retrieval, and therefore ensures that models remain portable when moving from training to serving.

Deploying new features into production is difficult: Many ML teams consist of members with different objectives. Data scientists, for example, aim to deploy features into production as soon as possible, while engineers want to ensure that production systems remain stable. These differing objectives can create an organizational friction that slows time-to-market for new features.

Feast addresses this friction by providing both a centralized registry to which data scientists can publish features and a battle-hardened serving layer. Together, these enable non-engineering teams to ship features into production with minimal oversight.

Models need point-in-time correct data: ML models in production require a view of data consistent with the one on which they are trained, otherwise the accuracy of these models could be compromised. Despite this need, many data science projects suffer from inconsistencies introduced by future feature values being leaked to models during training.

Feast solves the challenge of data leakage by providing point-in-time correct feature retrieval when exporting feature datasets for model training.

Features aren't reused across projects: Different teams within an organization are often unable to reuse features across projects. The siloed nature of development and the monolithic design of end-to-end ML systems contribute to duplication of feature creation and usage across teams and projects.

Feast addresses this problem by introducing feature reuse through a centralized registry. This registry enables multiple teams working on different projects not only to contribute features, but also to reuse these same features. With Feast, data scientists can start new ML projects by selecting previously engineered features from a centralized registry, and are no longer required to develop new features for each project.

Problems Feast does not yet solve

Feature engineering: We aim for Feast to support light-weight feature engineering as part of our API.

Feature discovery: We also aim for Feast to include a first-class user interface for exploring and discovering entities and features.

Feature validation: We additionally aim for Feast to improve support for statistics generation of feature data and subsequent validation of these statistics. Current support is limited.

What Feast is not

ETL or ELT system: Feast is not (and does not plan to become) a general purpose data transformation or pipelining system. Feast plans to include a light-weight feature engineering toolkit, but we encourage teams to integrate Feast with upstream ETL/ELT systems that are specialized in transformation.

Data warehouse: Feast is not a replacement for your data warehouse or the source of truth for all transformed data in your organization. Rather, Feast is a light-weight downstream layer that can serve data from an existing data warehouse (or other data sources) to models in production.

Data catalog: Feast is not a general purpose data catalog for your organization. Feast is purely focused on cataloging features for use in ML pipelines or systems, and only to the extent of facilitating the reuse of features.

How can I get started?

The best way to learn Feast is to use it. Head over to our Quickstart and try it out!

Explore the following resources to get started with Feast:

Quickstart is the fastest way to get started with Feast
Concepts describes all important Feast API concepts
Architecture describes Feast's overall architecture.
Tutorials shows full examples of using Feast in machine learning applications.
Running Feast with Snowflake/GCP/AWS provides a more in-depth guide to using Feast.
Reference contains detailed API and design documents.
Contributing contains resources for anyone who wants to contribute to Feast.

Community

Speak to us: Have a question, feature request, idea, or just looking to speak to a real person? Set up a meeting with a Feast maintainer over here!

Links & Resources

Slack: Feel free to ask questions or say hello!
Mailing list: We have both a user and developer mailing list.
- Feast users should join feast-discuss@googlegroups.com group by clicking here.
- Feast developers should join feast-dev@googlegroups.com group by clicking here.
Google Folder: This folder is used as a central repository for all Feast resources. For example:
- Design proposals in the form of Request for Comments (RFC).
- User surveys and meeting minutes.
- Slide decks of conferences our contributors have spoken at.
Feast GitHub Repository: Find the complete Feast codebase on GitHub.
Feast Linux Foundation Wiki: Our LFAI wiki page contains links to resources for contributors and maintainers.

How can I get help?

Slack: Need to speak to a human? Come ask a question in our Slack channel (link above).
GitHub Issues: Found a bug or need a feature? Create an issue on GitHub.
StackOverflow: Need to ask a question on how to use Feast? We also monitor and respond to StackOverflow.

Community Calls

We have a user and contributor community call every two weeks (Asia & US friendly).

Please join the above Feast user groups in order to see calendar invites to the community calls

Frequency (alternating times every 2 weeks)

Tuesday 18:00 pm to 18:30 pm (US, Asia)
Tuesday 10:00 am to 10:30 am (US, Europe)

Roadmap

Getting started

Quickstart

In this tutorial we will

Deploy a local feature store with a Parquet file offline store and Sqlite online store.
Build a training dataset using our time series features from our Parquet files.
Materialize feature values from the offline store into the online store.
Read the latest features from the online store for inference.

You can run this tutorial in Google Colab or run it on your localhost, following the guided steps below.

Overview

In this tutorial, we use feature stores to generate training data and power online model inference for a ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:

Training-serving skew and complex data joins: Feature values often exist across multiple tables. Joining these datasets can be complicated, slow, and error-prone.
- Feast joins these tables with battle-tested logic that ensures point-in-time correctness so future feature values do not leak to models.
- Feast alerts users to offline / online skew with data quality monitoring
Online feature availability: At inference time, models often need access to features that aren't readily available and need to be precomputed from other datasources.
- Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and ensures necessary features are consistently available and freshly computed at inference time.
Feature reusability and model versioning: Different teams within an organization are often unable to reuse features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need to be versioned, for example when running A/B tests on model versions.
- Feast enables discovery of and collaboration on previously used features and enables versioning of sets of features (via feature services).
- Feast enables feature transformation so users can re-use transformation logic across online / offline usecases and across models.

Step 1: Install Feast

Install the Feast SDK and CLI using pip:

In this tutorial, we focus on a local deployment. For a more in-depth guide on how to use Feast with Snowflake / GCP / AWS deployments, see Running Feast with Snowflake/GCP/AWS

pip install feast

Step 2: Create a feature repository

Bootstrap a new feature repository using feast init from the command line.

feast init feature_repo
cd feature_repo

Creating a new Feast repository in /home/Jovyan/feature_repo.

Let's take a look at the resulting demo repo itself. It breaks down into

data/ contains raw demo parquet data
example.py contains demo feature definitions
feature_store.yaml contains a demo setup configuring where data sources are

project: my_project
registry: data/registry.db
provider: local
online_store:
    path: data/online_store.db

# This is an example feature definition file

from google.protobuf.duration_pb2 import Duration

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

# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_hourly_stats = FileSource(
    path="/content/feature_repo/data/driver_stats.parquet",
    event_timestamp_column="event_timestamp",
    created_timestamp_column="created",
)

# Define an entity for the driver. You can think of entity as a primary key used to
# fetch features.
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)

# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_hourly_stats_view = FeatureView(
    name="driver_hourly_stats",
    entities=["driver_id"],
    ttl=Duration(seconds=86400 * 1),
    features=[
        Feature(name="conv_rate", dtype=ValueType.FLOAT),
        Feature(name="acc_rate", dtype=ValueType.FLOAT),
        Feature(name="avg_daily_trips", dtype=ValueType.INT64),
    ],
    online=True,
    batch_source=driver_hourly_stats,
    tags={},
)

The key line defining the overall architecture of the feature store is the provider. This defines where the raw data exists (for generating training data & feature values for serving), and where to materialize feature values to in the online store (for serving).

Valid values for provider in feature_store.yaml are:

local: use file source with SQLite/Redis
gcp: use BigQuery/Snowflake with Google Cloud Datastore/Redis
aws: use Redshift/Snowflake with DynamoDB/Redis

Note that there are many other sources Feast works with, including Azure, Hive, Trino, and PostgreSQL via community plugins. See Third party integrations for all supported datasources.

A custom setup can also be made by following adding a custom provider.

Step 3: Register feature definitions and deploy your feature store

The apply command scans python files in the current directory for feature view/entity definitions, registers the objects, and deploys infrastructure. In this example, it reads example.py (shown again below for convenience) and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by using the local provider in feature_store.yaml.

feast apply

# This is an example feature definition file

from google.protobuf.duration_pb2 import Duration

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

# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_hourly_stats = FileSource(
    path="/content/feature_repo/data/driver_stats.parquet",
    event_timestamp_column="event_timestamp",
    created_timestamp_column="created",
)

# Define an entity for the driver. You can think of entity as a primary key used to
# fetch features.
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)

# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_hourly_stats_view = FeatureView(
    name="driver_hourly_stats",
    entities=["driver_id"],
    ttl=Duration(seconds=86400 * 1),
    features=[
        Feature(name="conv_rate", dtype=ValueType.FLOAT),
        Feature(name="acc_rate", dtype=ValueType.FLOAT),
        Feature(name="avg_daily_trips", dtype=ValueType.INT64),
    ],
    online=True,
    batch_source=driver_hourly_stats,
    tags={},
)

Registered entity driver_id
Registered feature view driver_hourly_stats
Deploying infrastructure for driver_hourly_stats

Step 4: Generating training data

To train a model, we need features and labels. Often, this label data is stored separately (e.g. you have one table storing user survey results and another set of tables with feature values).

The user can query that table of labels with timestamps and pass that into Feast as an entity dataframe for training data generation. In many cases, Feast will also intelligently join relevant tables to create the relevant feature vectors.

Note that we include timestamps because want the features for the same driver at various timestamps to be used in a model.

from datetime import datetime, timedelta
import pandas as pd

from feast import FeatureStore

# The entity dataframe is the dataframe we want to enrich with feature values
entity_df = pd.DataFrame.from_dict(
    {
        "driver_id": [1001, 1002, 1003],
        "label_driver_reported_satisfaction": [1, 5, 3], 
        "event_timestamp": [
            datetime.now() - timedelta(minutes=11),
            datetime.now() - timedelta(minutes=36),
            datetime.now() - timedelta(minutes=73),
        ],
    }
)

store = FeatureStore(repo_path=".")

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

print("----- Feature schema -----\n")
print(training_df.info())

print()
print("----- Example features -----\n")
print(training_df.head())

----- Feature schema -----

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns (total 6 columns):
 #   Column                              Non-Null Count  Dtype              
---  ------                              --------------  -----              
 0   event_timestamp                     3 non-null      datetime64[ns, UTC]
 1   driver_id                           3 non-null      int64              
 2   label_driver_reported_satisfaction  3 non-null      int64              
 3   conv_rate                           3 non-null      float32            
 4   acc_rate                            3 non-null      float32            
 5   avg_daily_trips                     3 non-null      int32              
dtypes: datetime64[ns, UTC](1), float32(2), int32(1), int64(2)
memory usage: 132.0 bytes
None

----- Example features -----

                   event_timestamp  driver_id  ...  acc_rate  avg_daily_trips
0 2021-08-23 15:12:55.489091+00:00       1003  ...  0.120588              938
1 2021-08-23 15:49:55.489089+00:00       1002  ...  0.504881              635
2 2021-08-23 16:14:55.489075+00:00       1001  ...  0.138416              606

[3 rows x 6 columns]

Step 5: Load features into your online store

We now serialize the latest values of features since the beginning of time to prepare for serving (note: materialize-incremental serializes all new features since the last materialize call).

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME

Materializing 1 feature views to 2021-08-23 16:25:46+00:00 into the sqlite online 
store.

driver_hourly_stats from 2021-08-22 16:25:47+00:00 to 2021-08-23 16:25:46+00:00:
100%|████████████████████████████████████████████| 5/5 [00:00<00:00, 592.05it/s]

Step 6: Fetching feature vectors for inference

At inference time, we need to quickly read the latest feature values for different drivers (which otherwise might have existed only in batch sources) from the online feature store using get_online_features(). These feature vectors can then be fed to the model.

from pprint import pprint
from feast import FeatureStore

store = FeatureStore(repo_path=".")

feature_vector = store.get_online_features(
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips",
    ],
    entity_rows=[
        {"driver_id": 1004},
        {"driver_id": 1005},
    ],
).to_dict()

pprint(feature_vector)

{
 'acc_rate': [0.5732735991477966, 0.7828438878059387],
 'avg_daily_trips': [33, 984],
 'conv_rate': [0.15498852729797363, 0.6263588070869446],
 'driver_id': [1004, 1005]
}

Next steps

Read the Concepts page to understand the Feast data model.
Read the Architecture page.
Check out our Tutorials section for more examples on how to use Feast.
Follow our Running Feast with Snowflake/GCP/AWS guide for a more in-depth tutorial on using Feast.
Join other Feast users and contributors in Slack and become part of the community!

Concepts

Overview

The top-level namespace within Feast is a project. Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store.

Project

Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev, staging, prod).

Projects are currently being supported for backward compatibility reasons. Projects may change in the future as we simplify the Feast API.

Data source

The data source refers to raw underlying data (e.g. a table in BigQuery).

Feast uses a time-series data model to represent data. This data model is used to interpret feature data in data sources in order to build training datasets or when materializing features into an online store.

Below is an example data source with a single entity (driver) and two features (trips_today, and rating).

Entity

An entity is a collection of semantically related features. Users define entities to map to the domain of their use case. For example, a ride-hailing service could have customers and drivers as their entities, which group related features that correspond to these customers and drivers.

driver = Entity(name='driver', value_type=ValueType.STRING, join_key='driver_id')

Entities are typically defined as part of feature views. Entities are used to identify the primary key on which feature values should be stored and retrieved. These keys are used during the lookup of feature values from the online store and the join process in point-in-time joins. It is possible to define composite entities (more than one entity object) in a feature view. It is also possible for feature views to have zero entities. See feature view for more details.

Entities should be reused across feature views.

Entity key

A related concept is an entity key. These are one or more entity values that uniquely describe a feature view record. In the case of an entity (like a driver) that only has a single entity field, the entity is an entity key. However, it is also possible for an entity key to consist of multiple entity values. For example, a feature view with the composite entity of (customer, country) might have an entity key of (1001, 5).

Entity keys act as primary keys. They are used during the lookup of features from the online store, and they are also used to match feature rows across feature views during point-in-time joins.

Feature view

Feature views

A feature view is an object that represents a logical group of time-series feature data as it is found in a data source. Feature views consist of zero or more entities, one or more features, and a data source. Feature views allow Feast to model your existing feature data in a consistent way in both an offline (training) and online (serving) environment. Feature views generally contain features that are properties of a specific object, in which case that object is defined as an entity and included in the feature view. If the features are not related to a specific object, the feature view might not have entities; see feature views without entities below.

driver_stats_fv = FeatureView(
    name="driver_activity",
    entities=["driver"],
    features=[
        Feature(name="trips_today", dtype=ValueType.INT64),
        Feature(name="rating", dtype=ValueType.FLOAT),
    ],
    batch_source=BigQuerySource(
        table_ref="feast-oss.demo_data.driver_activity"
    )
)

Feature views are used during

The generation of training datasets by querying the data source of feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.
Loading of feature values into an online store. Feature views determine the storage schema in the online store. Feature values can be loaded from batch sources or from stream sources.
Retrieval of features from the online store. Feature views provide the schema definition to Feast in order to look up features from the online store.

Feast does not generate feature values. It acts as the ingestion and serving system. The data sources described within feature views should reference feature values in their already computed form.

Feature views without entities

If a feature view contains features that are not related to a specific entity, the feature view can be defined without entities.

global_stats_fv = FeatureView(
    name="global_stats",
    entities=[],
    features=[
        Feature(name="total_trips_today_by_all_drivers", dtype=ValueType.INT64),
    ],
    batch_source=BigQuerySource(
        table_ref="feast-oss.demo_data.global_stats"
    )
)

Feature inferencing

If the features parameter is not specified in the feature view creation, Feast will infer the features during feast apply by creating a feature for each column in the underlying data source except the columns corresponding to the entities of the feature view or the columns corresponding to the timestamp columns of the feature view's data source. The names and value types of the inferred features will use the names and data types of the columns from which the features were inferred.

Entity aliasing

"Entity aliases" can be specified to join entity_dataframe columns that do not match the column names in the source table of a FeatureView.

This could be used if a user has no control over these column names or if there are multiple entities are a subclass of a more general entity. For example, "spammer" and "reporter" could be aliases of a "user" entity, and "origin" and "destination" could be aliases of a "location" entity as shown below.

It is suggested that you dynamically specify the new FeatureView name using .with_name and join_key_map override using .with_join_key_map instead of needing to register each new copy.

location = Entity(name="location", join_key="location_id", value_type=ValueType.INT64)

location_stats_fv= FeatureView(
    name="location_stats",
    entities=["location"],
    features=[
        Feature(name="temperature", dtype=ValueType.INT32)
    ],
    batch_source=BigQuerySource(
        table_ref="feast-oss.demo_data.location_stats"
    ),
)

from location_stats_feature_view import location_stats_fv

temperatures_fs = FeatureService(
    name="temperatures",
    features=[
        location_stats_fv
            .with_name("origin_stats")
            .with_join_key_map(
                {"location_id": "origin_id"}
            ),
        location_stats_fv
            .with_name("destination_stats")
            .with_join_key_map(
                {"location_id": "destination_id"}
            ),
    ],
)

Feature

A feature is an individual measurable property. It is typically a property observed on a specific entity, but does not have to be associated with an entity. For example, a feature of a customer entity could be the number of transactions they have made on an average month, while a feature that is not observed on a specific entity could be the total number of posts made by all users in the last month.

Features are defined as part of feature views. Since Feast does not transform data, a feature is essentially a schema that only contains a name and a type:

trips_today = Feature(
    name="trips_today",
    dtype=ValueType.FLOAT
)

Together with data sources, they indicate to Feast where to find your feature values, e.g., in a specific parquet file or BigQuery table. Feature definitions are also used when reading features from the feature store, using feature references.

Feature names must be unique within a feature view.

[Alpha] On demand feature views

On demand feature views allows users to use existing features and request time data (features only available at request time) to transform and create new features. Users define python transformation logic which is executed in both historical retrieval and online retrieval paths:

# Define a request data source which encodes features / information only 
# available at request time (e.g. part of the user initiated HTTP request)
input_request = RequestDataSource(
    name="vals_to_add",
    schema={
        "val_to_add": ValueType.INT64,
        "val_to_add_2": ValueType.INT64
    }
)

# Use the input data and feature view features to create new features
@on_demand_feature_view(
   inputs={
       'driver_hourly_stats': driver_hourly_stats_view,
       'vals_to_add': input_request
   },
   features=[
     Feature(name='conv_rate_plus_val1', dtype=ValueType.DOUBLE),
     Feature(name='conv_rate_plus_val2', dtype=ValueType.DOUBLE)
   ]
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame()
    df['conv_rate_plus_val1'] = (features_df['conv_rate'] + features_df['val_to_add'])
    df['conv_rate_plus_val2'] = (features_df['conv_rate'] + features_df['val_to_add_2'])
    return df

Feature service

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, allowing for tracking of the features used by models.

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 from the online store. 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 can be retrieved from the feature store, and referenced when retrieving features from the online store.

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]
)

Feature services can also be used when retrieving historical features from the offline store.

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 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.

Feature References

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

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

online_features = fs.get_online_features(
    features=[
        'driver_locations:lon',
        'drivers_activity:trips_today'
    ],
    entity_rows=[{'driver': 'driver_1001'}]
)

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

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.

Point-in-time joins

Feature values in Feast are modeled as time-series records. Below is an example of a driver feature view with two feature columns (trips_today, and earnings_today):

The above table can be registered with Feast through the following feature view:

driver_stats_fv = FeatureView(
    name="driver_hourly_stats",
    entities=["driver"],
    features=[
        Feature(name="trips_today", dtype=ValueType.INT64),
        Feature(name="earnings_today", dtype=ValueType.FLOAT),
    ],
    ttl=timedelta(hours=2),
    batch_source=FileSource(
        path="driver_hourly_stats.parquet"
    )
)

Feast is able to join features from one or more feature views onto an entity dataframe in a point-in-time correct way. This means Feast is able to reproduce the state of features at a specific point in the past.

Given the following entity dataframe, imagine a user would like to join the above driver_hourly_stats feature view onto it, while preserving the trip_success column:

The timestamps within the entity dataframe above are the events at which we want to reproduce the state of the world (i.e., what the feature values were at those specific points in time). In order to do a point-in-time join, a user would load the entity dataframe and run historical retrieval:

# Read in entity dataframe
entity_df = pd.read_csv("entity_df.csv")

training_df = store.get_historical_features(
    entity_df=entity_df,
    features = [
        'driver_hourly_stats:trips_today',
        'driver_hourly_stats:earnings_today'
    ],
)

For each row within the entity dataframe, Feast will query and join the selected features from the appropriate feature view data source. Feast will scan backward in time from the entity dataframe timestamp up to a maximum of the TTL time.

Please note that the TTL time is relative to each timestamp within the entity dataframe. TTL is not relative to the current point in time (when you run the query).

Below is the resulting joined training dataframe. It contains both the original entity rows and joined feature values:

Three feature rows were successfully joined to the entity dataframe rows. The first row in the entity dataframe was older than the earliest feature rows in the feature view and could not be joined. The last row in the entity dataframe was outside of the TTL window (the event happened 11 hours after the feature row) and also couldn't be joined.

Dataset

Feast datasets allow for conveniently saving dataframes that include both features and entities to be subsequently used for data analysis and model training. Data Quality Monitoring was the primary motivation for creating dataset concept.

Dataset's metadata is stored in the Feast registry and raw data (features, entities, additional input keys and timestamp) is stored in the offline store.

Dataset can be created from:

Results of historical retrieval
[planned] Logging request (including input for on demand transformation) and response during feature serving
[planned] Logging features during writing to online store (from batch source or stream)

Creating Saved Dataset from Historical Retrieval

To create a saved dataset from historical features for later retrieval or analysis, a user needs to call get_historical_features method first and then pass the returned retrieval job to create_saved_dataset method. create_saved_dataset will trigger provided retrieval job (by calling .persist() on it) to store the data using specified storage. Storage type must be the same as globally configured offline store (eg, it's impossible to persist data to Redshift with BigQuery source). create_saved_dataset will also create SavedDataset object with all related metadata and will write it to the registry.

from feast import FeatureStore
from feast.infra.offline_stores.bigquery_source import SavedDatasetBigQueryStorage

store = FeatureStore()

historical_job = store.get_historical_features(
    features=["driver:avg_trip"],
    entity_df=...,
)

dataset = store.create_saved_dataset(
    from_=historical_job,
    name='my_training_dataset',
    storage=SavedDatasetBigQueryStorage(table_ref='<gcp-project>.<gcp-dataset>.my_training_dataset'),
    tags={'author': 'oleksii'}
)

dataset.to_df()

Saved dataset can be later retrieved using get_saved_dataset method:

dataset = store.get_saved_dataset('my_training_dataset')
dataset.to_df()

Check out our tutorial on validating historical features to see how this concept can be applied in real-world use case.

Architecture

Overview

Functionality

Create Batch Features: ELT/ETL systems like Spark and SQL are used to transform data in the batch store.
Feast Apply: The user (or CI) publishes versioned controlled feature definitions using feast apply. This CLI command updates infrastructure and persists definitions in the object store registry.
Feast Materialize: The user (or scheduler) executes feast materialize which loads features from the offline store into the online store.
Model Training: A model training pipeline is launched. It uses the Feast Python SDK to retrieve a training dataset and trains a model.
Get Historical Features: Feast exports a point-in-time correct training dataset based on the list of features and entity dataframe provided by the model training pipeline.
Deploy Model: The trained model binary (and list of features) are deployed into a model serving system. This step is not executed by Feast.
Prediction: A backend system makes a request for a prediction from the model serving service.
Get Online Features: The model serving service makes a request to the Feast Online Serving service for online features using a Feast SDK.

Components

A complete Feast deployment contains the following components:

Feast Registry: An object store (GCS, S3) based registry used to persist feature definitions that are registered with the feature store. Systems can discover feature data by interacting with the registry through the Feast SDK.
Feast Python SDK/CLI: The primary user facing SDK. Used to:
- Manage version controlled feature definitions.
- Materialize (load) feature values into the online store.
- Build and retrieve training datasets from the offline store.
- Retrieve online features.
Online Store: The online store is a database that stores only the latest feature values for each entity. The online store is populated by materialization jobs and from stream ingestion.
Offline Store: The offline store persists batch data that has been ingested into Feast. This data is used for producing training datasets. Feast does not manage the offline store directly, but runs queries against it.

Java and Go Clients are also available for online feature retrieval.

Feature repository

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.

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

For more details, see the Feature repository reference.

Registry

The Feast feature registry is a central catalog of all the feature definitions and their related metadata. It allows data scientists to search, discover, and collaborate on new features.

Each Feast deployment has a single feature registry. Feast only supports file-based registries today, but supports three different backends

Local: Used as a local backend for storing the registry during development
S3: Used as a centralized backend for storing the registry on AWS
GCS: Used as a centralized backend for storing the registry on GCP

The feature registry is updated during different operations when using Feast. More specifically, objects within the registry (entities, feature views, feature services) are updated when running apply from the Feast CLI, but metadata about objects can also be updated during operations like materialization.

Users interact with a feature registry through the Feast SDK. Listing all feature views:

fs = FeatureStore("my_feature_repo/")
print(fs.list_feature_views())

Or retrieving a specific feature view:

fs = FeatureStore("my_feature_repo/")
fv = fs.get_feature_view(“my_fv1”)

The feature registry is a Protobuf representation of Feast metadata. This Protobuf file can be read programmatically from other programming languages, but no compatibility guarantees are made on the internal structure of the registry.

Offline store

Feast uses offline stores as storage and compute systems. Offline stores store historic time-series feature values. Feast does not generate these features, but instead uses the offline store as the interface for querying existing features in your organization.

Offline stores are used primarily for two reasons

Building training datasets from time-series features.
Materializing (loading) features from the offline store into an online store in order to serve those features at low latency for prediction.

Offline stores are configured through the feature_store.yaml. When building training datasets or materializing features into an online store, Feast will use the configured offline store along with the data sources you have defined as part of feature views to execute the necessary data operations.

It is not possible to query all data sources from all offline stores, and only a single offline store can be used at a time. For example, it is not possible to query a BigQuery table from a File offline store, nor is it possible for a BigQuery offline store to query files from your local file system.

Please see the Offline Stores reference for more details on configuring offline stores.

Online store

The Feast online store is used for low-latency online feature value lookups. Feature values are loaded into the online store from data sources in feature views using the materialize command.

The storage schema of features within the online store mirrors that of the data source used to populate the online store. One key difference between the online store and data sources is that only the latest feature values are stored per entity key. No historical values are stored.

Example batch data source

Once the above data source is materialized into Feast (using feast materialize), the feature values will be stored as follows:

Provider

A provider is an implementation of a feature store using specific feature store components (e.g. offline store, online store) targeting a specific environment (e.g. GCP stack).

Providers orchestrate various components (offline store, online store, infrastructure, compute) inside an environment. For example, the gcp provider supports BigQuery as an offline store and Datastore as an online store, ensuring that these components can work together seamlessly. Feast has three built-in providers (local, gcp, and aws) with default configurations that make it easy for users to start a feature store in a specific environment. These default configurations can be overridden easily. For instance, you can use the gcp provider but use Redis as the online store instead of Datastore.

If the built-in providers are not sufficient, you can create your own custom provider. Please see this guide for more details.

Please see feature_store.yaml for configuring providers.

Third party integrations

FAQ

Don't see your question?

Getting started

Do you have any examples of how Feast should be used?

Concepts

What is the difference between feature tables and feature views?

Do feature views have to include entities?

What is the difference between data sources and the offline store?

Is it possible to have offline and online stores from different providers?

Yes, this is possible. For example, you can use BigQuery as an offline store and Redis as an online store.

Functionality

Does Feast provide security or access control?

Feast currently does not support any access control other than the access control required for the Provider's environment (for example, GCP and AWS permissions).

Does Feast support streaming sources?

Does Feast support composite keys?

A feature view can be defined with multiple entities. Since each entity has a unique join_key, using multiple entities will achieve the effect of a composite key.

How does Feast compare with Tecton?

What are the performance/latency characteristics of Feast?

Does Feast support embeddings and list features?

Yes. Specifically:

Simple lists / dense embeddings:
- BigQuery supports list types natively
- Redshift does not support list types, so you'll need to serialize these features into strings (e.g. json or protocol buffers)
Sparse embeddings (e.g. one hot encodings)

Does Feast support X storage engine?

Does Feast support using different clouds for offline vs online stores?

Yes. Using a GCP or AWS provider in feature_store.yaml primarily sets default offline / online stores and configures where the remote registry file can live (Using the AWS provider also allows for deployment to AWS Lambda). You can override the offline and online stores to be in different clouds if you wish.

How can I add a custom online store?

Can the same storage engine be used for both the offline and online store?

Does Feast support S3 as a data source?

Yes. There are two ways to use S3 in Feast:

Using the s3_endpoint_override in a FileSource data source. This endpoint is more suitable for quick proof of concepts that won't necessarily scale for production use cases.

How can I use Spark with Feast?

Is Feast planning on supporting X functionality?

Project

What is the difference between Feast 0.9 and Feast 0.10+?

How do I migrate from Feast 0.9 to Feast 0.10+?

How do I contribute to Feast?

What are the plans for Feast Core, Feast Serving, and Feast Spark?

Tutorials

Overview

These Feast tutorials showcase how to use Feast to simplify end to end model training / serving.

Driver ranking

Making a prediction using a linear regression model is a common use case in ML. This model predicts if a driver will complete a trip based on features ingested into Feast.

In this example, you'll learn how to use some of the key functionality in Feast. The tutorial runs in both local mode and on the Google Cloud Platform (GCP). For GCP, you must have access to a GCP project already, including read and write permissions to BigQuery.

Try it and let us know what you think!

Fraud detection on GCP

A common use case in machine learning, this tutorial is an end-to-end, production-ready fraud prediction system. It predicts in real-time whether a transaction made by a user is fraudulent.

Throughout this tutorial, we’ll walk through the creation of a production-ready fraud prediction system. A prediction is made in real-time as the user makes the transaction, so we need to be able to generate a prediction at low latency.

Our end-to-end example will perform the following workflows:

Computing and backfilling feature data from raw data
Building point-in-time correct training datasets from feature data and training a model
Making online predictions from feature data

Here's a high-level picture of our system architecture on Google Cloud Platform (GCP):

Real-time credit scoring on AWS

Credit scoring models are used to approve or reject loan applications. In this tutorial we will build a real-time credit scoring system on AWS.

When individuals apply for loans from banks and other credit providers, the decision to approve a loan application is often made through a statistical model. This model uses information about a customer to determine the likelihood that they will repay or default on a loan, in a process called credit scoring.

In this example, we will demonstrate how a real-time credit scoring system can be built using Feast and Scikit-Learn on AWS, using feature data from S3.

This real-time system accepts a loan request from a customer and responds within 100ms with a decision on whether their loan has been approved or rejected.

This end-to-end tutorial will take you through the following steps:

Deploying Redshift as the interface Feast uses to build training datasets
Registering your features with Feast and configuring DynamoDB for online serving
Building a training dataset with Feast to train your credit scoring model
Loading feature values from S3 into DynamoDB
Making online predictions with your credit scoring model using features from DynamoDB

Driver stats on Snowflake

Initial demonstration of Snowflake as an offline store with Feast, using the Snowflake demo template.

In the steps below, we will set up a sample Feast project that leverages Snowflake as an offline store.

Starting with data in a Snowflake table, we will register that table to the feature store and define features associated with the columns in that table. From there, we will generate historical training data based on those feature definitions and then materialize the latest feature values into the online store. Lastly, we will retrieve the materialized feature values.

Our template will generate new data containing driver statistics. From there, we will show you code snippets that will call to the offline store for generating training datasets, and then the code for calling the online store to serve you the latest feature values to serve models in production.

Snowflake Offline Store Example

Install feast-snowflake

Get a Snowflake Trial Account (Optional)

Create a feature repository

The following files will automatically be created in your project folder:

feature_store.yaml -- This is your main configuration file
driver_repo.py -- This is your main feature definition file
test.py -- This is a file to test your feature store configuration

Inspect `feature_store.yaml`

Here you will see the information that you entered. This template will use Snowflake as an offline store and SQLite as the online store. The main thing to remember is by default, Snowflake objects have ALL CAPS names unless lower case was specified.

Run our test python script `test.py`

What we did in `test.py`

Initialize our Feature Store

Create a dummy training dataframe, then call our offline store to add additional columns

Materialize the latest feature values into our online store

Retrieve the latest values from our online store based on our entity key

Validating historical features with Great Expectations

In this tutorial, we will use the public dataset of Chicago taxi trips to present data validation capabilities of Feast.

The original dataset is stored in BigQuery and consists of raw data for each taxi trip (one row per trip) since 2013.
We will generate several training datasets (aka historical features in Feast) for different periods and evaluate expectations made on one dataset against another.

Types of features we're ingesting and generating:

Features that aggregate raw data with daily intervals (eg, trips per day, average fare or speed for a specific day, etc.).
Features using SQL while pulling data from BigQuery (like total trips time or total miles travelled).
Features calculated on the fly when requested using Feast's on-demand transformations

Our plan:

Prepare environment
Pull data from BigQuery (optional)
Declare & apply features and feature views in Feast
Generate reference dataset
Develop & test profiler function
Run validation on different dataset using reference dataset & profiler

0. Setup

Install Feast Python SDK and great expectations:

1. Dataset preparation (Optional)

You can skip this step if you don't have GCP account. Please use parquet files that are coming with this tutorial instead

Running some basic aggregations while pulling data from BigQuery. Grouping by taxi_id and day:

2. Declaring features

3. Generating training (reference) dataset

Generating range of timestamps with daily frequency:

Cross merge (aka relation multiplication) produces entity dataframe with each taxi_id repeated for each timestamp:

156984 rows × 2 columns

Retrieving historical features for resulting entity dataframe and persisting output as a saved dataset:

4. Developing dataset profiler

Dataset profiler is a function that accepts dataset and generates set of its characteristics. This charasteristics will be then used to evaluate (validate) next datasets.

Important: datasets are not compared to each other! Feast use a reference dataset and a profiler function to generate a reference profile. This profile will be then used during validation of the tested dataset.

Loading saved dataset first and exploring the data:

156984 rows × 10 columns

Testing our profiler function:

Verify that all expectations that we coded in our profiler are present here. Otherwise (if you can't find some expectations) it means that it failed to pass on the reference dataset (do it silently is default behavior of Great Expectations).

Now we can create validation reference from dataset and profiler function:

and test it against our existing retrieval job

Validation successfully passed as no exception were raised.

5. Validating new historical retrieval

Creating new timestamps for Dec 2020:

35448 rows × 2 columns

Execute retrieval job with validation reference:

Validation failed since several expectations didn't pass:

Trip count (mean) decreased more than 10% (which is expected when comparing Dec 2020 vs June 2019)
Average Fare increased - all quantiles are higher than expected
Earn per hour (mean) increased more than 10% (most probably due to increased fare)

How-to Guides

Running Feast with Snowflake/GCP/AWS

Install Feast

Install Feast with Snowflake dependencies (required when using Snowflake):

Install Feast with GCP dependencies (required when using BigQuery or Firestore):

Install Feast with AWS dependencies (required when using Redshift or DynamoDB):

Install Feast with Redis dependencies (required when using Redis, either through AWS Elasticache or independently):

Create a feature repository

The easiest way to create a new feature repository to use feast init command:

The init command creates a Python file with feature definitions, sample data, and a Feast configuration file for local development:

Enter the directory:

You can now use this feature repository for development. You can try the following:

Run feast apply to apply these definitions to Feast.
Edit the example feature definitions in example.py and run feast apply again to change feature definitions.
Initialize a git repository in the same directory and checking the feature repository into version control.

Deploy a feature store

Deploying

To have Feast deploy your infrastructure, run feast apply from your command line while inside a feature repository:

Depending on whether the feature repository is configured to use a local provider or one of the cloud providers like GCP or AWS, it may take from a couple of seconds to a minute to run to completion.

Cleaning up

If you need to clean up the infrastructure created by feast apply, use the teardown command.

Warning: teardown is an irreversible command and will remove all feature store infrastructure. Proceed with caution!

****

Build a training dataset

Feast allows users to build a training dataset from time-series feature data that already exists in an offline store. Users are expected to provide a list of features to retrieve (which may span multiple feature views), and a dataframe to join the resulting features onto. Feast will then execute a point-in-time join of multiple feature views onto the provided dataframe, and return the full resulting dataframe.

Retrieving historical features

1. Register your feature views

Please ensure that you have created a feature repository and that you have registered (applied) your feature views with Feast.

2. Define feature references

Start by defining the feature references (e.g., driver_trips:average_daily_rides) for the features that you would like to retrieve from the offline store. These features can come from multiple feature tables. The only requirement is that the feature tables that make up the feature references have the same entity (or composite entity), and that they aren't located in the same offline store.

feature_refs = [
    "driver_trips:average_daily_rides",
    "driver_trips:maximum_daily_rides",
    "driver_trips:rating",
    "driver_trips:rating:trip_completed",
]

3. Create an entity dataframe

An entity dataframe is the target dataframe on which you would like to join feature values. The entity dataframe must contain a timestamp column called event_timestamp and all entities (primary keys) necessary to join feature tables onto. All entities found in feature views that are being joined onto the entity dataframe must be found as column on the entity dataframe.

It is possible to provide entity dataframes as either a Pandas dataframe or a SQL query.

Pandas:

In the example below we create a Pandas based entity dataframe that has a single row with an event_timestamp column and a driver_id entity column. Pandas based entity dataframes may need to be uploaded into an offline store, which may result in longer wait times compared to a SQL based entity dataframe.

import pandas as pd
from datetime import datetime

entity_df = pd.DataFrame(
    {
        "event_timestamp": [pd.Timestamp(datetime.now(), tz="UTC")],
        "driver_id": [1001]
    }
)

SQL (Alternative):

Below is an example of an entity dataframe built from a BigQuery SQL query. It is only possible to use this query when all feature views being queried are available in the same offline store (BigQuery).

entity_df = "SELECT event_timestamp, driver_id FROM my_gcp_project.table"

4. Launch historical retrieval

from feast import FeatureStore

fs = FeatureStore(repo_path="path/to/your/feature/repo")

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

Once the feature references and an entity dataframe are defined, it is possible to call get_historical_features(). This method launches a job that executes a point-in-time join of features from the offline store onto the entity dataframe. Once completed, a job reference will be returned. This job reference can then be converted to a Pandas dataframe by calling to_df().

Reference

Project

Running Feast in production

Overview

After learning about Feast concepts and playing with Feast locally, you're now ready to use Feast in production. This guide aims to help with the transition from a sandbox project to production-grade deployment in the cloud or on-premise.

Overview of typical production configuration is given below:

Important note: We're trying to keep Feast modular. With the exception of the core, most of the Feast blocks are loosely connected and can be used independently. Hence, you are free to build your own production configuration. For example, you might not have a stream source and, thus, no need to write features in real-time to an online store. Or you might not need to retrieve online features.

Furthermore, there's no single "true" approach. As you will see in this guide, Feast usually provides several options for each problem. It's totally up to you to pick a path that's better suited to your needs.

In this guide we will show you how to:

Deploy your feature store and keep your infrastructure in sync with your feature repository
Keep the data in your online store up to date
Use Feast for model training and serving
Ingest features from a stream source
Monitor your production deployment

1. Automatically deploying changes to your feature definitions

The first step to setting up a deployment of Feast is to create a Git repository that contains your feature definitions. The recommended way to version and track your feature definitions is by committing them to a repository and tracking changes through commits.

Most teams will need to have a feature store deployed to more than one environment. We have created an example repository (Feast Repository Example) which contains two Feast projects, one per environment.

The contents of this repository are shown below:

├── .github
│   └── workflows
│       ├── production.yml
│       └── staging.yml
│
├── staging
│   ├── driver_repo.py
│   └── feature_store.yaml
│
└── production
    ├── driver_repo.py
    └── feature_store.yaml

The repository contains three sub-folders:

staging/: This folder contains the staging feature_store.yaml and Feast objects. Users that want to make changes to the Feast deployment in the staging environment will commit changes to this directory.
production/: This folder contains the production feature_store.yaml and Feast objects. Typically users would first test changes in staging before copying the feature definitions into the production folder, before committing the changes.
.github: This folder is an example of a CI system that applies the changes in either the staging or production repositories using feast apply. This operation saves your feature definitions to a shared registry (for example, on GCS) and configures your infrastructure for serving features.

The feature_store.yaml contains the following:

project: staging
registry: gs://feast-ci-demo-registry/staging/registry.db
provider: gcp

Notice how the registry has been configured to use a Google Cloud Storage bucket. All changes made to infrastructure using feast apply are tracked in the registry.db. This registry will be accessed later by the Feast SDK in your training pipelines or model serving services in order to read features.

It is important to note that the CI system above must have access to create, modify, or remove infrastructure in your production environment. This is unlike clients of the feature store, who will only have read access.

If your organization consists of many independent data science teams or a single group is working on several projects that could benefit from sharing features, entities, sources, and transformations, then we encourage you to utilize Python packages inside each environment:

└── production
    ├── common
    │    ├── __init__.py
    │    ├── sources.py
    │    └── entities.py
    ├── ranking
    │    ├── __init__.py
    │    ├── views.py
    │    └── transformations.py
    ├── segmentation
    │    ├── __init__.py
    │    ├── views.py
    │    └── transformations.py
    └── feature_store.yaml

In summary, once you have set up a Git based repository with CI that runs feast apply on changes, your infrastructure (offline store, online store, and cloud environment) will automatically be updated to support the loading of data into the feature store or retrieval of data.

2. How to load data into your online store and keep it up to date

To keep your online store up to date, you need to run a job that loads feature data from your feature view sources into your online store. In Feast, this loading operation is called materialization.

2.1. Manual materializations

The simplest way to schedule materialization is to run an incremental materialization using the Feast CLI:

feast materialize-incremental 2022-01-01T00:00:00

The above command will load all feature values from all feature view sources into the online store up to the time 2022-01-01T00:00:00.

A timestamp is required to set the end date for materialization. If your source is fully up to date then the end date would be the current time. However, if you are querying a source where data is not yet available, then you do not want to set the timestamp to the current time. You would want to use a timestamp that ends at a date for which data is available. The next time materialize-incremental is run, Feast will load data that starts from the previous end date, so it is important to ensure that the materialization interval does not overlap with time periods for which data has not been made available. This is commonly the case when your source is an ETL pipeline that is scheduled on a daily basis.

An alternative approach to incremental materialization (where Feast tracks the intervals of data that need to be ingested), is to call Feast directly from your scheduler like Airflow. In this case, Airflow is the system that tracks the intervals that have been ingested.

feast materialize -v driver_hourly_stats 2020-01-01T00:00:00 2020-01-02T00:00:00

In the above example we are materializing the source data from the driver_hourly_stats feature view over a day. This command can be scheduled as the final operation in your Airflow ETL, which runs after you have computed your features and stored them in the source location. Feast will then load your feature data into your online store.

The timestamps above should match the interval of data that has been computed by the data transformation system.

2.2. Automate periodic materializations

It is up to you which orchestration/scheduler to use to periodically run $ feast materialize. Feast keeps the history of materialization in its registry so that the choice could be as simple as a unix cron util. Cron util should be sufficient when you have just a few materialization jobs (it's usually one materialization job per feature view) triggered infrequently. However, the amount of work can quickly outgrow the resources of a single machine. That happens because the materialization job needs to repackage all rows before writing them to an online store. That leads to high utilization of CPU and memory. In this case, you might want to use a job orchestrator to run multiple jobs in parallel using several workers. Kubernetes Jobs or Airflow are good choices for more comprehensive job orchestration.

If you are using Airflow as a scheduler, Feast can be invoked through the BashOperator after the Python SDK has been installed into a virtual environment and your feature repo has been synced:

materialize = BashOperator(
    task_id='materialize',
    bash_command=f'feast materialize-incremental {datetime.datetime.now().replace(microsecond=0).isoformat()}',
)

Important note: Airflow worker must have read and write permissions to the registry file on GS / S3 since it pulls configuration and updates materialization history.

3. How to use Feast for model training

After we've defined our features and data sources in the repository, we can generate training datasets.

The first thing we need to do in our training code is to create a FeatureStore object with a path to the registry.

One way to ensure your production clients have access to the feature store is to provide a copy of the feature_store.yaml to those pipelines. This feature_store.yaml file will have a reference to the feature store registry, which allows clients to retrieve features from offline or online stores.

fs = FeatureStore(repo_path="production/")

Then, training data can be retrieved as follows:

feature_refs = [
    'driver_hourly_stats:conv_rate',
    'driver_hourly_stats:acc_rate',
    'driver_hourly_stats:avg_daily_trips'
]

training_df = fs.get_historical_features(
    entity_df=entity_df, 
    features=feature_refs,
).to_df()

model = ml.fit(training_df)

The most common way to productionize ML models is by storing and versioning models in a "model store", and then deploying these models into production. When using Feast, it is recommended that the list of feature references also be saved alongside the model. This ensures that models and the features they are trained on are paired together when being shipped into production:

# Save model
model.save('my_model.bin')

# Save features
open('feature_refs.json', 'w') as f:
    json.dump(feature_refs, f)

To test your model locally, you can simply create a FeatureStore object, fetch online features, and then make a prediction:

# Load model
model = ml.load('my_model.bin')

# Load feature references
with open('feature_refs.json', 'r') as f:
    feature_refs = json.load(f)

# Create feature store object
fs = FeatureStore(repo_path="production/")

# Read online features
feature_vector = fs.get_online_features(
    features=feature_refs,
    entity_rows=[{"driver_id": 1001}]
).to_dict()

# Make a prediction
prediction = model.predict(feature_vector)

It is important to note that both the training pipeline and model serving service need only read access to the feature registry and associated infrastructure. This prevents clients from accidentally making changes to the feature store.

4. Retrieving online features for prediction

Once you have successfully loaded (or in Feast terminology materialized) your data from batch sources into the online store, you can start consuming features for model inference. There are three approaches for that purpose sorted from the most simple one (in an operational sense) to the most performant (benchmarks to be published soon):

4.1. Use the Python SDK within an existing Python service

This approach is the most convenient to keep your infrastructure as minimalistic as possible and avoid deploying extra services. The Feast Python SDK will connect directly to the online store (Redis, Datastore, etc), pull the feature data, and run transformations locally (if required). The obvious drawback is that your service must be written in Python to use the Feast Python SDK. A benefit of using a Python stack is that you can enjoy production-grade services with integrations with many existing data science tools.

To integrate online retrieval into your service use the following code:

from feast import FeatureStore

with open('feature_refs.json', 'r') as f:
    feature_refs = json.loads(f)

fs = FeatureStore(repo_path="production/")

# Read online features
feature_vector = fs.get_online_features(
    features=feature_refs,
    entity_rows=[{"driver_id": 1001}]
).to_dict()

4.2. Consume features via HTTP API from Serverless Feature Server

If you don't want to add the Feast Python SDK as a dependency, or your feature retrieval service is written in a non-Python language, Feast can deploy a simple feature server on serverless infrastructure (eg, AWS Lambda, Google Cloud Run) for you. This service will provide an HTTP API with JSON I/O, which can be easily used with any programming language.

4.3. Java based Feature Server deployed on Kubernetes

For users with very latency-sensitive and high QPS use-cases, Feast offers a high-performance Java feature server. Besides the benefits of running on JVM, this implementation also provides a gRPC API, which guarantees good connection utilization and small request / response body size (compared to JSON). You will need the Feast Java SDK to retrieve features from this service. This SDK wraps all the gRPC logic for you and provides more convenient APIs.

The Java based feature server can be deployed to Kubernetes cluster via Helm charts in a few simple steps:

Install kubectl and helm 3
Add the Feast Helm repository and download the latest charts:

helm repo add feast-charts https://feast-helm-charts.storage.googleapis.com
helm repo update

Run Helm Install

helm install feast-release feast-charts/feast \
    --set global.registry.path=s3://feast/registries/prod \
    --set global.project=<project name>

This chart will deploy two services: feature-server and transformation-service. Both must have read access to the registry file on cloud storage. Both will keep a copy of the registry in their memory and periodically refresh it, so expect some delays in update propagation in exchange for better performance.

Load balancing

The next step would be to install an L7 Load Balancer (eg, Envoy) in front of the Java feature server. For seamless integration with Kubernetes (including services created by Feast Helm chart) we recommend using Istio as Envoy's orchestrator.

5. Ingesting features from a stream source

Recently Feast added functionality for stream ingestion. Please note that this is still in an early phase and new incompatible changes may be introduced.

5.1. Using Python SDK in your Apache Spark / Beam pipeline

The default option to write features from a stream is to add the Python SDK into your existing PySpark / Beam pipeline. Feast SDK provides writer implementation that can be called from foreachBatch stream writer in PySpark like this:

store = FeatureStore(...)

def feast_writer(spark_df):
    pandas_df = spark_df.to_pandas()
    store.write_to_online_store("driver_hourly_stats", pandas_df)

streamingDF.writeStream.foreachBatch(feast_writer).start()

5.2. Push service (still under development)

Alternatively, if you want to ingest features directly from a broker (eg, Kafka or Kinesis), you can use the "push service", which will write to an online store. This service will expose an HTTP API or when deployed on Serverless platforms like AWS Lambda or Google Cloud Run, this service can be directly connected to Kinesis or PubSub.

If you are using Kafka, HTTP Sink could be utilized as a middleware. In this case, the "push service" can be deployed on Kubernetes or as a Serverless function.

6. Monitoring

Feast services can report their metrics to a StatsD-compatible collector. To activate this function, you'll need to provide a StatsD IP address and a port when deploying the helm chart (in future, this will be added to feature_store.yaml).

We use an InfluxDB-style extension for StatsD format to be able to send tags along with metrics. Keep that in mind while selecting the collector (telegraph will work for sure).

We chose StatsD since it's a de-facto standard with various implementations (eg, 1, 2) and metrics can be easily exported to Prometheus, InfluxDB, AWS CloudWatch, etc.

Summary

Summarizing it all together we want to show several options of architecture that will be most frequently used in production:

Option #1 (currently preferred)

Feast SDK is being triggered by CI (eg, Github Actions). It applies the latest changes from the feature repo to the Feast registry
Airflow manages materialization jobs to ingest data from DWH to the online store periodically
For the stream ingestion Feast Python SDK is used in the existing Spark / Beam pipeline
Online features are served via either a Python feature server or a high performance Java feature server
- Both the Java feature server and the transformation server are deployed on Kubernetes cluster (via Helm charts)
Feast Python SDK is called locally to generate a training dataset

Option #2 (still in development)

Same as Option #1, except:

Push service is deployed as AWS Lambda / Google Cloud Run and is configured as a sink for Kinesis or PubSub to ingest features directly from a stream broker. Lambda / Cloud Run is being managed by Feast SDK (from CI environment)
Materialization jobs are managed inside Kubernetes via Kubernetes Job (currently not managed by Helm)

Option #3 (still in development)

Same as Option #2, except:

Push service is deployed on Kubernetes cluster and exposes an HTTP API that can be used as a sink for Kafka (via kafka-http connector) or accessed directly.

Adding a new online store

Overview

Feast makes adding support for a new online store (database) easy. Developers can simply implement the OnlineStore interface to add support for a new store (other than the existing stores like Redis, DynamoDB, SQLite, and Datastore).

In this guide, we will show you how to integrate with MySQL as an online store. While we will be implementing a specific store, this guide should be representative for adding support for any new online store.

The full working code for this guide can be found at feast-dev/feast-custom-online-store-demo.

The process of using a custom online store consists of 3 steps:

Defining the OnlineStore class.
Defining the OnlineStoreConfig class.
Referencing the OnlineStore in a feature repo's feature_store.yaml file.
Testing the OnlineStore class.

1. Defining an OnlineStore class

OnlineStore class names must end with the OnlineStore suffix!

The OnlineStore class broadly contains two sets of methods

One set deals with managing infrastructure that the online store needed for operations
One set deals with writing data into the store, and reading data from the store.

1.1 Infrastructure Methods

There are two methods that deal with managing infrastructure for online stores, update and teardown

update is invoked when users run feast apply as a CLI command, or the FeatureStore.apply() sdk method.

The update method should be used to perform any operations necessary before data can be written to or read from the store. The update method can be used to create MySQL tables in preparation for reads and writes to new feature views.

teardown is invoked when users run feast teardown or FeatureStore.teardown().

The teardown method should be used to perform any clean-up operations. teardown can be used to drop MySQL indices and tables corresponding to the feature views being deleted.

feast_custom_online_store/mysql.py

def update(
    self,
    config: RepoConfig,
    tables_to_delete: Sequence[Union[FeatureTable, FeatureView]],
    tables_to_keep: Sequence[Union[FeatureTable, FeatureView]],
    entities_to_delete: Sequence[Entity],
    entities_to_keep: Sequence[Entity],
    partial: bool,
):
    """
    An example of creating manging the tables needed for a mysql-backed online store.
    """
    conn = self._get_conn(config)
    cur = conn.cursor(buffered=True)

    project = config.project

    for table in tables_to_keep:
        cur.execute(
            f"CREATE TABLE IF NOT EXISTS {_table_id(project, table)} (entity_key VARCHAR(512), feature_name VARCHAR(256), value BLOB, event_ts timestamp, created_ts timestamp,  PRIMARY KEY(entity_key, feature_name))"
        )
        cur.execute(
            f"CREATE INDEX {_table_id(project, table)}_ek ON {_table_id(project, table)} (entity_key);"
        )

    for table in tables_to_delete:
        cur.execute(
            f"DROP INDEX {_table_id(project, table)}_ek ON {_table_id(project, table)};"
        )
        cur.execute(f"DROP TABLE IF EXISTS {_table_id(project, table)}")


def teardown(
    self,
    config: RepoConfig,
    tables: Sequence[Union[FeatureTable, FeatureView]],
    entities: Sequence[Entity],
):
    """
    
    """
    conn = self._get_conn(config)
    cur = conn.cursor(buffered=True)
    project = config.project

    for table in tables:
        cur.execute(
            f"DROP INDEX {_table_id(project, table)}_ek ON {_table_id(project, table)};"
        )
        cur.execute(f"DROP TABLE IF EXISTS {_table_id(project, table)}")

1.2 Read/Write Methods

There are two methods that deal with writing data to and from the online stores.online_write_batch and online_read.

online_write_batch is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method.
online_read is invoked when reading values from the online store using the FeatureStore.get_online_features() method.

feast_custom_online_store/mysql.py

def online_write_batch(
    self,
    config: RepoConfig,
    table: Union[FeatureTable, FeatureView],
    data: List[
        Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
    ],
    progress: Optional[Callable[[int], Any]],
) -> None:
    conn = self._get_conn(config)
    cur = conn.cursor(buffered=True)

    project = config.project

    for entity_key, values, timestamp, created_ts in data:
        entity_key_bin = serialize_entity_key(entity_key).hex()
        timestamp = _to_naive_utc(timestamp)
        if created_ts is not None:
            created_ts = _to_naive_utc(created_ts)

        for feature_name, val in values.items():
            self.write_to_table(created_ts, cur, entity_key_bin, feature_name, project, table, timestamp, val)
        self._conn.commit()
        if progress:
            progress(1)

def online_read(
    self,
    config: RepoConfig,
    table: Union[FeatureTable, FeatureView],
    entity_keys: List[EntityKeyProto],
    requested_features: Optional[List[str]] = None,
) -> List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]]:
    conn = self._get_conn(config)
    cur = conn.cursor(buffered=True)

    result: List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]] = []

    project = config.project
    for entity_key in entity_keys:
        entity_key_bin = serialize_entity_key(entity_key).hex()
        print(f"entity_key_bin: {entity_key_bin}")

        cur.execute(
            f"SELECT feature_name, value, event_ts FROM {_table_id(project, table)} WHERE entity_key = %s",
            (entity_key_bin,),
        )

        res = {}
        res_ts = None
        for feature_name, val_bin, ts in cur.fetchall():
            val = ValueProto()
            val.ParseFromString(val_bin)
            res[feature_name] = val
            res_ts = ts

        if not res:
            result.append((None, None))
        else:
            result.append((res_ts, res))
    return result

2. Defining an OnlineStoreConfig class

Additional configuration may be needed to allow the OnlineStore to talk to the backing store. For example, MySQL may need configuration information like the host at which the MySQL instance is running, credentials for connecting to the database, etc.

To facilitate configuration, all OnlineStore implementations are required to also define a corresponding OnlineStoreConfig class in the same file. This OnlineStoreConfig class should inherit from the FeastConfigBaseModel class, which is defined here.

The FeastConfigBaseModel is a pydantic class, which parses yaml configuration into python objects. Pydantic also allows the model classes to define validators for the config classes, to make sure that the config classes are correctly defined.

This config class must container a type field, which contains the fully qualified class name of its corresponding OnlineStore class.

Additionally, the name of the config class must be the same as the OnlineStore class, with the Config suffix.

An example of the config class for MySQL :

feast_custom_online_store/mysql.py

class MySQLOnlineStoreConfig(FeastConfigBaseModel):
    type: Literal["feast_custom_online_store.mysql.MySQLOnlineStore"] = "feast_custom_online_store.mysql.MySQLOnlineStore"

    host: Optional[StrictStr] = None
    user: Optional[StrictStr] = None
    password: Optional[StrictStr] = None
    database: Optional[StrictStr] = None

This configuration can be specified in the feature_store.yaml as follows:

feature_repo/feature_store.yaml

online_store:
    type: feast_custom_online_store.mysql.MySQLOnlineStore
    user: foo
    password: bar

This configuration information is available to the methods of the OnlineStore, via theconfig: RepoConfig parameter which is passed into all the methods of the OnlineStore interface, specifically at the config.online_store field of the config parameter.

feast_custom_online_store/mysql.py

def online_write_batch(
        self,
        config: RepoConfig,
        table: Union[FeatureTable, FeatureView],
        data: List[
            Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
        ],
        progress: Optional[Callable[[int], Any]],
) -> None:

    online_store_config = config.online_store
    assert isinstance(online_store_config, MySQLOnlineStoreConfig)

    connection = mysql.connector.connect(
        host=online_store_config.host or "127.0.0.1",
        user=online_store_config.user or "root",
        password=online_store_config.password,
        database=online_store_config.database or "feast",
        autocommit=True
    )

3. Using the custom online store

After implementing both these classes, the custom online store can be used by referencing it in a feature repo's feature_store.yaml file, specifically in the online_store field. The value specified should be the fully qualified class name of the OnlineStore.

As long as your OnlineStore class is available in your Python environment, it will be imported by Feast dynamically at runtime.

To use our MySQL online store, we can use the following feature_store.yaml:

feature_repo/feature_store.yaml

project: test_custom
registry: data/registry.db
provider: local
online_store: 
    type: feast_custom_online_store.mysql.MySQLOnlineStore
    user: foo
    password: bar

If additional configuration for the online store is **not **required, then we can omit the other fields and only specify the type of the online store class as the value for the online_store.

feature_repo/feature_store.yaml

project: test_custom
registry: data/registry.db
provider: local
online_store: feast_custom_online_store.mysql.MySQLOnlineStore

4. Testing the OnlineStore class

Even if you have created the OnlineStore class in a separate repo, you can still test your implementation against the Feast test suite, as long as you have Feast as a submodule in your repo. In the Feast submodule, we can run all the unit tests with:

make test

The universal tests, which are integration tests specifically intended to test offline and online stores, can be run with:

make test-python-universal

The unit tests should succeed, but the universal tests will likely fail. The tests are parametrized based on the FULL_REPO_CONFIGS variable defined in sdk/python/tests/integration/feature_repos/repo_configuration.py. To overwrite these configurations, you can simply create your own file that contains a FULL_REPO_CONFIGS, and point Feast to that file by setting the environment variable FULL_REPO_CONFIGS_MODULE to point to that file. In this repo, the file that overwrites FULL_REPO_CONFIGS is feast_custom_online_store/feast_tests.py, so you would run

export FULL_REPO_CONFIGS_MODULE='feast_custom_online_store.feast_tests'
make test-python-universal

to test the MySQL online store against the Feast universal tests. You should notice that some of the tests actually fail; this indicates that there is a mistake in the implementation of this online store!

Adding a new offline store

Overview

Feast makes adding support for a new offline store (database) easy. Developers can simply implement the OfflineStore interface to add support for a new store (other than the existing stores like Parquet files, Redshift, and Bigquery).

In this guide, we will show you how to extend the existing File offline store and use in a feature repo. While we will be implementing a specific store, this guide should be representative for adding support for any new offline store.

The full working code for this guide can be found at feast-dev/feast-custom-offline-store-demo.

The process for using a custom offline store consists of 4 steps:

Defining an OfflineStore class.
Defining an OfflineStoreConfig class.
Defining a RetrievalJob class for this offline store.
Defining a DataSource class for the offline store
Referencing the OfflineStore in a feature repo's feature_store.yaml file.
Testing the OfflineStore class.

1. Defining an OfflineStore class

OfflineStore class names must end with the OfflineStore suffix!

The OfflineStore class contains a couple of methods to read features from the offline store. Unlike the OnlineStore class, Feast does not manage any infrastructure for the offline store.

There are two methods that deal with reading data from the offline storesget_historical_featuresand pull_latest_from_table_or_query.

pull_latest_from_table_or_query is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pull data from the offline store, and the FeatureStore class takes care of writing this data into the online store.
get_historical_features is invoked when reading values from the offline store using the FeatureStore.get_historica_features() method. Typically, this method is used to retrieve features when training ML models.

feast_custom_offline_store/file.py

    def get_historical_features(self,
                                config: RepoConfig,
                                feature_views: List[FeatureView],
                                feature_refs: List[str],
                                entity_df: Union[pd.DataFrame, str],
                                registry: Registry, project: str,
                                full_feature_names: bool = False) -> RetrievalJob:
        print("Getting historical features from my offline store")
        return super().get_historical_features(config,
                                               feature_views,
                                               feature_refs,
                                               entity_df,
                                               registry,
                                               project,
                                               full_feature_names)

    def pull_latest_from_table_or_query(self,
                                        config: RepoConfig,
                                        data_source: DataSource,
                                        join_key_columns: List[str],
                                        feature_name_columns: List[str],
                                        event_timestamp_column: str,
                                        created_timestamp_column: Optional[str],
                                        start_date: datetime,
                                        end_date: datetime) -> RetrievalJob:
        print("Pulling latest features from my offline store")
        return super().pull_latest_from_table_or_query(config,
                                                       data_source,
                                                       join_key_columns,
                                                       feature_name_columns,
                                                       event_timestamp_column,
                                                       created_timestamp_column,
                                                       start_date,
                                                       end_date)

2. Defining an OfflineStoreConfig class

Additional configuration may be needed to allow the OfflineStore to talk to the backing store. For example, Redshift needs configuration information like the connection information for the Redshift instance, credentials for connecting to the database, etc.

To facilitate configuration, all OfflineStore implementations are required to also define a corresponding OfflineStoreConfig class in the same file. This OfflineStoreConfig class should inherit from the FeastConfigBaseModel class, which is defined here.

This config class must container a type field, which contains the fully qualified class name of its corresponding OfflineStore class.

Additionally, the name of the config class must be the same as the OfflineStore class, with the Config suffix.

An example of the config class for the custom file offline store :

feast_custom_offline_store/file.py

class CustomFileOfflineStoreConfig(FeastConfigBaseModel):
    """ Custom offline store config for local (file-based) store """

    type: Literal["feast_custom_offline_store.file.CustomFileOfflineStore"] \
        = "feast_custom_offline_store.file.CustomFileOfflineStore"

This configuration can be specified in the feature_store.yaml as follows:

feature_repo/feature_store.yaml

type: feast_custom_offline_store.file.CustomFileOfflineStore

This configuration information is available to the methods of the OfflineStore, via theconfig: RepoConfig parameter which is passed into the methods of the OfflineStore interface, specifically at the config.offline_store field of the config parameter.

feast_custom_offline_store/file.py

    def get_historical_features(self,
                                config: RepoConfig,
                                feature_views: List[FeatureView],
                                feature_refs: List[str],
                                entity_df: Union[pd.DataFrame, str],
                                registry: Registry, project: str,
                                full_feature_names: bool = False) -> RetrievalJob:

        offline_store_config = config.offline_store
        assert isinstance(offline_store_config, CustomFileOfflineStoreConfig)
        store_type = offline_store_config.type

3. Defining a RetrievalJob class

The offline store methods aren't expected to perform their read operations eagerly. Instead, they are expected to execute lazily, and they do so by returning a RetrievalJob instance, which represents the execution of the actual query against the underlying store.

Custom offline stores may need to implement their own instances of the RetrievalJob interface.

The RetrievalJob interface exposes two methods - to_df and to_arrow. The expectation is for the retrieval job to be able to return the rows read from the offline store as a parquet DataFrame, or as an Arrow table respectively.

feast_custom_offline_store/file.py

class CustomFileRetrievalJob(RetrievalJob):
    def __init__(self, evaluation_function: Callable):
        """Initialize a lazy historical retrieval job"""

        # The evaluation function executes a stored procedure to compute a historical retrieval.
        self.evaluation_function = evaluation_function

    def to_df(self):
        # Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.
        print("Getting a pandas DataFrame from a File is easy!")
        df = self.evaluation_function()
        return df

    def to_arrow(self):
        # Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.
        print("Getting a pandas DataFrame from a File is easy!")
        df = self.evaluation_function()
        return pyarrow.Table.from_pandas(df)

4. Defining a DataSource class for the offline store

Before this offline store can be used as the batch source for a feature view in a feature repo, a subclass of the DataSource base class needs to be defined. This class is responsible for holding information needed by specific feature views to support reading historical values from the offline store. For example, a feature view using Redshift as the offline store may need to know which table contains historical feature values.

The data source class should implement two methods - from_proto, and to_proto.

For custom offline stores that are not being implemented in the main feature repo, the custom_options field should be used to store any configuration needed by the data source. In this case, the implementer is responsible for serializing this configuration into bytes in the to_proto method and reading the value back from bytes in the from_proto method.

feast_custom_offline_store/file.py

class CustomFileDataSource(FileSource):
    """Custom data source class for local files"""
    def __init__(
        self,
        event_timestamp_column: Optional[str] = "",
        path: Optional[str] = None,
        field_mapping: Optional[Dict[str, str]] = None,
        created_timestamp_column: Optional[str] = "",
        date_partition_column: Optional[str] = "",
    ):
        super(CustomFileDataSource, self).__init__(
            event_timestamp_column,
            created_timestamp_column,
            field_mapping,
            date_partition_column,
        )
        self._path = path


    @staticmethod
    def from_proto(data_source: DataSourceProto):
        custom_source_options = str(
            data_source.custom_options.configuration, encoding="utf8"
        )
        path = json.loads(custom_source_options)["path"]
        return CustomFileDataSource(
            field_mapping=dict(data_source.field_mapping),
            path=path,
            event_timestamp_column=data_source.event_timestamp_column,
            created_timestamp_column=data_source.created_timestamp_column,
            date_partition_column=data_source.date_partition_column,
        )

    def to_proto(self) -> DataSourceProto:
        config_json = json.dumps({"path": self.path})
        data_source_proto = DataSourceProto(
            type=DataSourceProto.CUSTOM_SOURCE,
            custom_options=DataSourceProto.CustomSourceOptions(
                configuration=bytes(config_json, encoding="utf8")
            ),
        )

        data_source_proto.event_timestamp_column = self.event_timestamp_column
        data_source_proto.created_timestamp_column = self.created_timestamp_column
        data_source_proto.date_partition_column = self.date_partition_column

        return data_source_proto

5. Using the custom offline store

After implementing these classes, the custom offline store can be used by referencing it in a feature repo's feature_store.yaml file, specifically in the offline_store field. The value specified should be the fully qualified class name of the OfflineStore.

As long as your OfflineStore class is available in your Python environment, it will be imported by Feast dynamically at runtime.

To use our custom file offline store, we can use the following feature_store.yaml:

feature_repo/feature_store.yaml

project: test_custom
registry: data/registry.db
provider: local
offline_store: 
    type: feast_custom_offline_store.file.CustomFileOfflineStore

If additional configuration for the offline store is **not **required, then we can omit the other fields and only specify the type of the offline store class as the value for the offline_store.

feature_repo/feature_store.yaml

project: test_custom
registry: data/registry.db
provider: local
offline_store: feast_custom_offline_store.file.CustomFileOfflineStore

Finally, the custom data source class can be use in the feature repo to define a data source, and refer to in a feature view definition.

feature_repo/repo.py

pdriver_hourly_stats = CustomFileDataSource(
    path="feature_repo/data/driver_stats.parquet",
    event_timestamp_column="event_timestamp",
    created_timestamp_column="created",
)


driver_hourly_stats_view = FeatureView(
    batch_source=driver_hourly_stats,
    ...
)

6. Testing the OfflineStore class

Even if you have created the OfflineStore class in a separate repo, you can still test your implementation against the Feast test suite, as long as you have Feast as a submodule in your repo. In the Feast submodule, we can run all the unit tests with:

make test

The universal tests, which are integration tests specifically intended to test offline and online stores, can be run with:

make test-python-universal

The unit tests should succeed, but the universal tests will likely fail. The tests are parametrized based on the FULL_REPO_CONFIGS variable defined in sdk/python/tests/integration/feature_repos/repo_configuration.py. To overwrite these configurations, you can simply create your own file that contains a FULL_REPO_CONFIGS, and point Feast to that file by setting the environment variable FULL_REPO_CONFIGS_MODULE to point to that file. The main challenge there will be to write a DataSourceCreator for the offline store. In this repo, the file that overwrites FULL_REPO_CONFIGS is feast_custom_offline_store/feast_tests.py, so you would run

export FULL_REPO_CONFIGS_MODULE='feast_custom_offline_store.feast_tests'
make test-python-universal

to test the offline store against the Feast universal tests. You should notice that some of the tests actually fail; this indicates that there is a mistake in the implementation of this offline store!

v0.18-branch

Introduction

What is Feast?

Problems Feast Solves

Problems Feast does not yet solve

What Feast is not

How can I get started?

Community

Links & Resources

How can I get help?

Community Calls

Frequency (alternating times every 2 weeks)

Links

Roadmap

Getting started

Quickstart

Overview

Step 1: Install Feast

Step 2: Create a feature repository

Step 3: Register feature definitions and deploy your feature store

Step 4: Generating training data

Step 5: Load features into your online store

Step 6: Fetching feature vectors for inference

Next steps

Concepts

Overview

Project

Data source

Entity

Entity key

Feature view

Feature views

Feature views without entities

Feature inferencing

Entity aliasing

Feature

[Alpha] On demand feature views

Feature service

Feature retrieval

Dataset

Feature References

Event timestamp

Point-in-time joins

Dataset

Creating Saved Dataset from Historical Retrieval

Architecture

Overview

Functionality

Components

Feature repository

Registry

Offline store

Online store

Provider

Third party integrations

Integrations

Data Sources

Offline Stores

Online Stores

Deployments

Standards

FAQ

Getting started

Do you have any examples of how Feast should be used?

Concepts

What is the difference between feature tables and feature views?

Do feature views have to include entities?

What is the difference between data sources and the offline store?

Is it possible to have offline and online stores from different providers?

Functionality

Does Feast provide security or access control?

Does Feast support streaming sources?

Does Feast support composite keys?

How does Feast compare with Tecton?

What are the performance/latency characteristics of Feast?

Does Feast support embeddings and list features?

Does Feast support X storage engine?

Does Feast support using different clouds for offline vs online stores?

How can I add a custom online store?

Can the same storage engine be used for both the offline and online store?

Inspect `feature_store.yaml`

Run our test python script `test.py`

What we did in `test.py`