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.

Event timestamp

The timestamp on which an event occurred, as found in a feature view's data source. The entity 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.

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?

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 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.

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.

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).

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.

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.

The list below contains the functionality that contributors are planning to develop for Feast

• Items below that are in development (or planned for development) will be indicated in parentheses.

• We welcome contribution to all items in the roadmap!

• Want to influence our roadmap and prioritization? Submit your feedback to this form.

• Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using Calendly.

• Data Sources

  • Redshift source

  • BigQuery source

  • Parquet file source

  • Kafka source (Planned for Q4 2021)

  • HTTP source

  • Snowflake source

  • Synapse source

• Offline Stores

  • Redshift

  • BigQuery

  • In-memory / Pandas

  • Custom offline store support

  • Hive (community maintained)

  • Snowflake

  • Synapse

• Online Stores

  • DynamoDB

  • Redis

  • Datastore

  • SQLite

  • Custom online store support

  • Postgres

  • Bigtable

  • Cassandra

• Streaming

  • Custom streaming ingestion job support

  • Streaming ingestion on AWS (Planned for Q4 2021)

  • Streaming ingestion on GCP

• Feature Engineering

  • On-demand Transformations (Development in progress. See RFC)

  • Batch transformation (SQL)

  • Streaming transformation

• Deployments

  • AWS Lambda (Development in progress. See RFC)

  • Cloud Run

  • Kubernetes

  • KNative

• Feature Serving

  • Python Client

  • REST Feature Server (Python) (Development in progress. See RFC)

  • gRPC Feature Server (Java) (See #1497)

  • Java Client

  • Go Client

  • Push API

  • Delete API

  • Feature Logging (for training)

• Data Quality Management

  • Data profiling and validation (Great Expectations) (Planned for Q4 2021)

  • Metric production

  • Training-serving skew detection

  • Drift detection

  • Alerting

• Feature Discovery and Governance

  • Python SDK for browsing feature registry

  • CLI for browsing feature registry

  • Model-centric feature tracking (feature services)

  • REST API for browsing feature registry

  • Feast Web UI (Planned for Q4 2021)

  • Feature versioning

  • Amundsen integration

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).

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)

Links

• Zoom: https://zoom.us/j/6325193230

• Meeting notes: https://bit.ly/feast-notes

In this tutorial we will

1. Deploy a local feature store with a Parquet file offline store and Sqlite online store.

2. Build a training dataset using our time series features from our Parquet files.

3. Materialize feature values from the offline store into the online store.

4. 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:

1. 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.

   • *Upcoming: Feast alerts users to offline / online skew with data quality monitoring.

2. 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.

3. 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 GCP or AWS deployments, see Running Feast with 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 / SQLite

• gcp: use BigQuery / Google Cloud Datastore

• aws: use Redshift / DynamoDB

A custom setup (e.g. using the built-in support for Redis) can be made by following Creating 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 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!

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:

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.

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

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

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:

Or retrieving a specific feature view:

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).

Feature views

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.

• 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.

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.

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:

[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:

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.

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:

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

1. Building training datasets from time-series features.

2. Materializing (loading) features from the offline store into an online store in order to serve those features at low latency for prediction.

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.

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.

• 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.

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?

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?

Feast is actively working on this right now. Please reach out to the Feast team if you're interested in giving feedback!

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?

Feast is designed to work at scale and support low latency online serving. Benchmarks to be released soon, and active work is underway to support very latency sensitive use cases.

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?

How can I add a custom 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?

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

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

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

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!

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

The Feast CLI can be used to deploy a feature store to your infrastructure, spinning up any necessary persistent resources like buckets or tables in data stores. The deployment target and effects depend on the provider that has been configured in your feature_store.yaml file, as well as the feature definitions found in your feature repository.

Deploying

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

feast apply

# Processing example.py as example
# Done!

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.

feast teardown

****

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.

Fraud Detection Example

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):

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().

The Feast Python SDK allows users to retrieve feature values from an online store. This API is used to look up feature values at low latency during model serving in order to make online predictions.

Retrieving online features

1. Ensure that feature values have been loaded into the online store

Please ensure that you have materialized (loaded) your feature values into the online store before starting

2. Define feature references

Create a list of features that you would like to retrieve. This list typically comes from the model training step and should accompany the model binary.

features = [
    "driver_hourly_stats:conv_rate",
    "driver_hourly_stats:acc_rate"
]

3. Read online features

Next, we will create a feature store object and call get_online_features() which reads the relevant feature values directly from the online store.

fs = FeatureStore(repo_path="path/to/feature/repo")
online_features = fs.get_online_features(
    features=features,
    entity_rows=[
        {"driver_id": 1001},
        {"driver_id": 1002}]
).to_dict()

{
   "driver_hourly_stats__acc_rate":[
      0.2897740304470062,
      0.6447265148162842
   ],
   "driver_hourly_stats__conv_rate":[
      0.6508077383041382,
      0.14802511036396027
   ],
   "driver_id":[
      1001,
      1002
   ]
}

Overview

In this guide we will show you how to:

1. Deploy your feature store and keep your infrastructure in sync with your feature repository

2. Keep the data in your online store up to date

3. Use Feast for model training and serving

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.

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 loading of data into the feature store or retrieval of data.

2. How to keep the data in your online store up to date

In order 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.

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.

3. How to use Feast for model training and serving

Now that you have deployed a registry, provisioned your feature store, and loaded your data into your online store, your clients can start to consume features for training and inference.

For both model training and inferencing your clients will use the Feast Python SDK to retrieve features. In both cases it is necessary to create a FeatureStore object.

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.

prod_fs = FeatureStore(repo_path="production_feature_store.yaml")

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 = prod_fs.get_historical_features(
    entity_df=entity_df, 
    feature_refs=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)

you can simply create a FeatureStore object, fetch the 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
prod_fs = FeatureStore(repo_path="production_feature_store.yaml")

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

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

Overview

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

1. Defining an OfflineStore class.

2. Defining an OfflineStoreConfig class.

3. Defining a RetrievalJob class for this offline store.

4. Referencing the OfflineStore in a feature repo's feature_store.yaml file.

1. Defining an OfflineStore class

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.

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.

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 :

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

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.

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.

4. 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:

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.

Overview

All Feast operations execute through a provider. Operations like materializing data from the offline to the online store, updating infrastructure like databases, launching streaming ingestion jobs, building training datasets, and reading features from the online store.

Custom providers allow Feast users to extend Feast to execute any custom logic. Examples include:

• Launching custom streaming ingestion jobs (Spark, Beam)

• Launching custom batch ingestion (materialization) jobs (Spark, Beam)

• Adding custom validation to feature repositories during feast apply

• Adding custom infrastructure setup logic which runs during feast apply

• Extending Feast commands with in-house metrics, logging, or tracing

Guide

The fastest way to add custom logic to Feast is to extend an existing provider. The most generic provider is the LocalProvider which contains no cloud-specific logic. The guide that follows will extend the LocalProvider with operations that print text to the console. It is up to you as a developer to add your custom code to the provider methods, but the guide below will provide the necessary scaffolding to get you started.

Step 1: Define a Provider class

The first step is to define a custom provider class. We've created the MyCustomProvider below.

Notice how in the above provider we have only overwritten two of the methods on the LocalProvider, namely update_infra and materialize_single_feature_view. These two methods are convenient to replace if you are planning to launch custom batch or streaming jobs. update_infra can be used for launching idempotent streaming jobs, and materialize_single_feature_view can be used for launching batch ingestion jobs.

Step 2: Configuring Feast to use the provider

Notice how the provider field above points to the module and class where your provider can be found.

Step 3: Using the provider

Now you should be able to use your provider by running a Feast command:

It may also be necessary to add the module root path to your PYTHONPATH as follows:

That's it. You should not have a fully functional custom provider!

Next steps

Overview

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

1. Defining the OnlineStore class.

2. Defining the OnlineStoreConfig class.

3. Referencing the OnlineStore in a feature repo's feature_store.yaml file.

1. Defining an OnlineStore class

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.

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.

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.

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 :

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

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.

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:

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.

A feature repository is a directory that contains the configuration of the feature store and individual features. This configuration is written as code (Python/YAML) and it's highly recommended that teams track it centrally using git. See Feature Repository for a detailed explanation of feature repositories.

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

feast init

Creating a new Feast repository in /<...>/tiny_pika.

feast init -t gcp

Creating a new Feast repository in /<...>/tiny_pika.

feast init -t aws
AWS Region (e.g. us-west-2): ...
Redshift Cluster ID: ...
Redshift Database Name: ...
Redshift User Name: ...
Redshift S3 Staging Location (s3://*): ...
Redshift IAM Role for S3 (arn:aws:iam::*:role/*): ...
Should I upload example data to Redshift (overwriting 'feast_driver_hourly_stats' table)? (Y/n): 

Creating a new Feast repository in /<...>/tiny_pika.

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

$ tree
.
└── tiny_pika
    ├── data
    │   └── driver_stats.parquet
    ├── example.py
    └── feature_store.yaml

1 directory, 3 files

Enter the directory:

# Replace "tiny_pika" with your auto-generated dir name
cd tiny_pika

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.

Feast allows users to load their feature data into an online store in order to serve the latest features to models for online prediction.

Materializing features

1. Register feature views

Before proceeding, please ensure that you have applied (registered) the feature views that should be materialized.

2.a Materialize

The materialize command allows users to materialize features over a specific historical time range into the online store.

feast materialize 2021-04-07T00:00:00 2021-04-08T00:00:00

The above command will query the batch sources for all feature views over the provided time range, and load the latest feature values into the configured online store.

It is also possible to materialize for specific feature views by using the -v / --views argument.

feast materialize 2021-04-07T00:00:00 2021-04-08T00:00:00 \
--views driver_hourly_stats

The materialize command is completely stateless. It requires the user to provide the time ranges that will be loaded into the online store. This command is best used from a scheduler that tracks state, like Airflow.

2.b Materialize Incremental (Alternative)

For simplicity, Feast also provides a materialize command that will only ingest new data that has arrived in the offline store. Unlike materialize, materialize-incremental will track the state of previous ingestion runs inside of the feature registry.

The example command below will load only new data that has arrived for each feature view up to the end date and time (2021-04-08T00:00:00).

feast materialize-incremental 2021-04-08T00:00:00

The materialize-incremental command functions similarly to materialize in that it loads data over a specific time range for all feature views (or the selected feature views) into the online store.

Unlike materialize, materialize-incremental automatically determines the start time from which to load features from batch sources of each feature view. The first time materialize-incremental is executed it will set the start time to the oldest timestamp of each data source, and the end time as the one provided by the user. For each run of materialize-incremental, the end timestamp will be tracked.

Subsequent runs of materialize-incremental will then set the start time to the end time of the previous run, thus only loading new data that has arrived into the online store. Note that the end time that is tracked for each run is at the feature view level, not globally for all feature views, i.e, different feature views may have different periods that have been materialized into the online store.

Description

BigQuery data sources allow for the retrieval of historical feature values from BigQuery for building training datasets as well as materializing features into an online store.

• Either a table reference or a SQL query can be provided.

• No performance guarantees can be provided over SQL query-based sources. Please use table references where possible.

Examples

Using a table reference

Using a query

Description

File data sources allow for the retrieval of historical feature values from files on disk for building training datasets, as well as for materializing features into an online store.

Example

Description

Redshift data sources allow for the retrieval of historical feature values from Redshift for building training datasets as well as materializing features into an online store.

• Either a table name or a SQL query can be provided.

• No performance guarantees can be provided over SQL query-based sources. Please use table references where possible.

Examples

Using a table name

from feast import RedshiftSource

my_redshift_source = RedshiftSource(
    table="redshift_table",
)

Using a query

from feast import RedshiftSource

my_redshift_source = RedshiftSource(
    query="SELECT timestamp as ts, created, f1, f2 "
          "FROM redshift_table",
)

Configuration options are available here.

Description

The File offline store provides support for reading FileSources.

• Only Parquet files are currently supported.

• All data is downloaded and joined using Python and may not scale to production workloads.

Example

project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: file

Configuration options are available here.

Please see Offline Store for an explanation of offline stores.

Please see Online Store for an explanation of online stores.

Description

The Redshift offline store provides support for reading RedshiftSources.

• Redshift tables and views are allowed as sources.

• All joins happen within Redshift.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. Pandas dataframes will be uploaded to Redshift in order to complete join operations.

• A RedshiftRetrievalJob is returned when calling get_historical_features().

Example

project: my_feature_repo
registry: data/registry.db
provider: aws
offline_store:
  type: redshift
  region: us-west-2
  cluster_id: feast-cluster
  database: feast-database
  user: redshift-user
  s3_staging_location: s3://feast-bucket/redshift
  iam_role: arn:aws:iam::123456789012:role/redshift_s3_access_role

Configuration options are available here.

Permissions

Feast requires the following permissions in order to execute commands for Redshift offline store:

The following inline policy can be used to grant Feast the necessary permissions:

{
    "Statement": [
        {
            "Action": [
                "s3:ListBucket",
                "s3:PutObject",
                "s3:GetObject",
                "s3:DeleteObject"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::<bucket_name>/*",
                "arn:aws:s3:::<bucket_name>"
            ]
        },
        {
            "Action": [
                "redshift-data:DescribeTable",
                "redshift:GetClusterCredentials",
                "redshift-data:ExecuteStatement"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:redshift:<region>:<account_id>:dbuser:<redshift_cluster_id>/<redshift_username>",
                "arn:aws:redshift:<region>:<account_id>:dbname:<redshift_cluster_id>/<redshift_database_name>",
                "arn:aws:redshift:<region>:<account_id>:cluster:<redshift_cluster_id>"
            ]
        },
        {
            "Action": [
                "redshift-data:DescribeStatement"
            ],
            "Effect": "Allow",
            "Resource": "*"
        }
    ],
    "Version": "2012-10-17"
}

In addition to this, Redshift offline store requires an IAM role that will be used by Redshift itself to interact with S3. More concretely, Redshift has to use this IAM role to run UNLOAD and COPY commands. Once created, this IAM role needs to be configured in feature_store.yaml file as offline_store: iam_role.

The following inline policy can be used to grant Redshift necessary permissions to access S3:

{
    "Statement": [
        {
            "Action": "s3:*",
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::feast-integration-tests",
                "arn:aws:s3:::feast-integration-tests/*"
            ]
        }
    ],
    "Version": "2012-10-17"
}

While the following trust relationship is necessary to make sure that Redshift, and only Redshift can assume this role:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "redshift.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Description

• BigQuery tables and views are allowed as sources.

• All joins happen within BigQuery.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. Pandas dataframes will be uploaded to BigQuery in order to complete join operations.

Example

Description

The Datastore online store provides support for materializing feature values into Cloud Datastore. The data model used to store feature values in Datastore is described in more detail here.

Example

project: my_feature_repo
registry: data/registry.db
provider: gcp
online_store:
  type: datastore
  project_id: my_gcp_project
  namespace: my_datastore_namespace

Configuration options are available here.

Description

The SQLite online store provides support for materializing feature values into an SQLite database for serving online features.

• All feature values are stored in an on-disk SQLite database

• Only the latest feature values are persisted

Example

project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
  type: sqlite
  path: data/online_store.db

Configuration options are available here.

Description

• Both Redis and Redis Cluster are supported

Examples

Connecting to a single Redis instance

Connecting to a Redis Cluster with SSL enabled and password authentication

Description

• Offline Store: Uses the File offline store by default. Also supports BigQuery as the offline store.

• Online Store: Uses the Sqlite online store by default. Also supports Redis and Datastore as online stores.

Example

Description

Example

Permissions

Feast requires the following permissions in order to execute commands for DynamoDB online store:

The following inline policy can be used to grant Feast the necessary permissions:

Description

• Offline Store: Uses the BigQuery offline store by default. Also supports File as the offline store.

• Online Store: Uses the Datastore online store by default. Also supports Sqlite as an online store.

Example

project: my_feature_repo
registry: gs://my-bucket/data/registry.db
provider: gcp

Permissions

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.

What is a feature repository?

A feature repository consists of:

• A collection of Python files containing feature declarations.

• A feature_store.yaml file containing infrastructural configuration.

• A .feastignore file containing paths in the feature repository to ignore.

Structure of a feature repository

The structure of a feature repository is as follows:

• The root of the repository should contain a feature_store.yaml file and may contain a .feastignore file.

• The repository should contain Python files that contain feature definitions.

• The repository can contain other files as well, including documentation and potentially data files.

An example structure of a feature repository is shown below:

$ tree -a
.
├── data
│   └── driver_stats.parquet
├── driver_features.py
├── feature_store.yaml
└── .feastignore

1 directory, 4 files

A couple of things to note about the feature repository:

• Feast reads all Python files recursively when feast apply is ran, including subdirectories, even if they don't contain feature definitions.

• It's recommended to add .feastignore and add paths to all imperative scripts if you need to store them inside the feature registry.

The feature_store.yaml configuration file

The configuration for a feature store is stored in a file named feature_store.yaml , which must be located at the root of a feature repository. An example feature_store.yaml file is shown below:

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

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

The .feastignore file

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

# Ignore virtual environment
venv

# Ignore a specific Python file
scripts/foo.py

# Ignore all Python files directly under scripts directory
scripts/*.py

# Ignore all "foo.py" anywhere under scripts directory
scripts/**/foo.py

See .feastignore for more details.

Feature definitions

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

from datetime import timedelta

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

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

driver = Entity(
    name="driver",
    value_type=ValueType.INT64,
    description="driver id",
)

driver_locations = FeatureView(
    name="driver_locations",
    entities=["driver"],
    ttl=timedelta(days=1),
    features=[
        Feature(name="lat", dtype=ValueType.FLOAT),
        Feature(name="lon", dtype=ValueType.STRING),
    ],
    batch_source=driver_locations_source,
)

To declare new feature definitions, just add code to the feature repository, either in existing files or in a new file. For more information on how to define features, see Feature Views.

Next steps

• See Create a feature repository to get started with an example feature repository.

• See feature_store.yaml, .feastignore, or Feature Views for more information on the configuration files that live in a feature registry.

Overview

Options

The following top-level configuration options exist in the feature_store.yaml file.

• provider — Configures the environment in which Feast will deploy and operate.

• registry — Configures the location of the feature registry.

• online_store — Configures the online store.

• offline_store — Configures the offline store.

• project — Defines a namespace for the entire feature store. Can be used to isolate multiple deployments in a single installation of Feast. Should only contain letters, numbers, and underscores.

Overview

.feastignore is a file that is placed at the root of the Feature Repository. This file contains paths that should be ignored when running feast apply. An example .feastignore is shown below:

# Ignore virtual environment
venv

# Ignore a specific Python file
scripts/foo.py

# Ignore all Python files directly under scripts directory
scripts/*.py

# Ignore all "foo.py" anywhere under scripts directory
scripts/**/foo.py

.feastignore file is optional. If the file can not be found, every Python file in the feature repo directory will be parsed by feast apply.

Feast Ignore Patterns

Warning: This is an experimental feature. It's intended for early testing and feedback, and could change without warnings in future releases.

Overview

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.

Currently, these transformations are executed locally. Future milestones include building a Feature Transformation Server for executing transformations at higher scale.

CLI

There are new CLI commands:

• feast on-demand-feature-views list lists all registered on demand feature view after feast apply is run

• feast on-demand-feature-views describe [NAME] describes the definition of an on demand feature view

Example

See https://github.com/feast-dev/on-demand-feature-views-demo for an example on how to use on demand feature views.

Registering transformations

We register RequestDataSource inputs and the transform in on_demand_feature_view:

# 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 retrieval

And then to retrieve historical or online features, we can call this in a feature service or reference individual features:

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",
        "transformed_conv_rate:conv_rate_plus_val1",
        "transformed_conv_rate:conv_rate_plus_val2",
    ],
).to_df()

Warning: This is an experimental feature. It's intended for early testing and feedback, and could change without warnings in future releases.

Overview

Feature Server is an HTTP endpoint that serves features with JSON I/O. This enables users to get features from Feast using any programming language that can make HTTP requests. As of now, it's only possible to run the server locally. A remote feature server is work in progress.

CLI

There is a new CLI command that starts the server: feast serve. By default Feast uses port 6566, however it can be overriden by a --port flag.

Example

Here's the local feature server usage example with the local template:

After the server starts, we can execute cURL commands from another terminal tab:

It's also possible to specify a feature service name instead of the list of features:

Description

• Offline Store: Uses the Redshift offline store by default. Also supports File as the offline store.

• Online Store: Uses the DynamoDB online store by default. Also supports Sqlite as an online store.

Example

project: my_feature_repo
registry: data/registry.db
provider: aws
online_store:
  type: dynamodb
  region: us-west-2
offline_store:
  type: redshift
  region: us-west-2
  cluster_id: feast-cluster
  database: feast-database
  user: redshift-user
  s3_staging_location: s3://feast-bucket/redshift
  iam_role: arn:aws:iam::123456789012:role/redshift_s3_access_role

We use RFCs and GitHub issues to communicate development ideas. The simplest way to contribute to Feast is to leave comments in our RFCs in the Feast Google Drive or our GitHub issues. You will need to join our Google Group in order to get access.

We follow a process of lazy consensus. If you believe you know what the project needs then just start development. If you are unsure about which direction to take with development then please communicate your ideas through a GitHub issue or through our Slack Channel before starting development.

Please submit a PR to the master branch of the Feast repository once you are ready to submit your contribution. Code submission to Feast (including submission from project maintainers) require review and approval from maintainers or code owners.

PRs that are submitted by the general public need to be identified as ok-to-test. Once enabled, Prow will run a range of tests to verify the submission, after which community members will help to review the pull request.

How Feast SDK usage is measured

The Feast project logs anonymous usage statistics and errors in order to inform our planning. Several client methods are tracked, beginning in Feast 0.9. Users are assigned a UUID which is sent along with the name of the method, the Feast version, the OS (using sys.platform), and the current time.

The source code is available here.

How to disable usage logging

Set the environment variable FEAST_USAGE to False.

Overview

The Feast CLI comes bundled with the Feast Python package. It is immediately available after installing Feast.

Usage: feast [OPTIONS] COMMAND [ARGS]...

  Feast CLI

  For more information, see our public docs at https://docs.feast.dev/

  For any questions, you can reach us at https://slack.feast.dev/

Options:
  -c, --chdir TEXT  Switch to a different feature repository directory before
                    executing the given subcommand.

  --help            Show this message and exit.

Commands:
  apply                    Create or update a feature store deployment
  entities                 Access entities
  feature-views            Access feature views
  init                     Create a new Feast repository
  materialize              Run a (non-incremental) materialization job to...
  materialize-incremental  Run an incremental materialization job to ingest...
  registry-dump            Print contents of the metadata registry
  teardown                 Tear down deployed feature store infrastructure
  version                  Display Feast SDK version