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

  • Snowflake source

  • Redshift source

  • BigQuery source

  • Parquet file source

  • Synapse source (community plugin)

  • Hive (community plugin)

  • Postgres (contrib plugin)

  • Spark (contrib plugin)

  • Kafka / Kinesis sources (via push support into the online store)

  • HTTP source

• Offline Stores

  • Snowflake

  • Redshift

  • BigQuery

  • Synapse (community plugin)

  • Hive (community plugin)

  • Postgres (contrib plugin)

  • Trino (contrib plugin)

  • Spark (contrib plugin)

  • In-memory / Pandas

  • Custom offline store support

• Online Stores

  • DynamoDB

  • Redis

  • Datastore

  • SQLite

  • Azure Cache for Redis (community plugin)

  • Postgres (contrib plugin)

  • Custom online store support

  • Bigtable (in progress)

  • Cassandra

• Streaming

  • Custom streaming ingestion job support

  • Push based streaming data ingestion

  • Streaming ingestion on AWS

  • Streaming ingestion on GCP

• Feature Engineering

  • On-demand Transformations (Alpha release. See RFC)

  • Batch transformation (In progress. See RFC)

  • Streaming transformation

• Deployments

  • AWS Lambda (Alpha release. See RFC)

  • Kubernetes (See guide)

  • Cloud Run

  • KNative

• Feature Serving

  • Python Client

  • REST Feature Server (Python) (Alpha release. See RFC)

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

  • Push API

  • Java Client

  • Go Client

  • Delete API

  • Feature Logging (for training)

• Data Quality Management (See RFC)

  • Data profiling and validation (Great Expectations)

  • Training-serving skew detection (in progress)

  • Metric production

  • Drift detection

• Feature Discovery and Governance

  • Python SDK for browsing feature registry

  • CLI for browsing feature registry

  • Model-centric feature tracking (feature services)

  • Amundsen integration (see Feast extractor)

  • Feast Web UI (Alpha release. See documentation)

  • REST API for browsing feature registry

  • Feature versioning

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.

from feast import BigQuerySource, FeatureView, Field
from feast.types import Float32, Int64

driver_stats_fv = FeatureView(
    name="driver_activity",
    entities=["driver"],
    schema=[
        Field(name="trips_today", dtype=Int64),
        Field(name="rating", dtype=Float32),
    ],
    source=BigQuerySource(
        table="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.

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 (only event timestamps are needed for this feature view).

from feast import BigQuerySource, FeatureView, Field
from feast.types import Int64

global_stats_fv = FeatureView(
    name="global_stats",
    entities=[],
    schema=[
        Field(name="total_trips_today_by_all_drivers", dtype=Int64),
    ],
    source=BigQuerySource(
        table="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.

from feast import BigQuerySource, Entity, FeatureView, Field, ValueType
from feast.types import Int32

location = Entity(name="location", join_keys=["location_id"], value_type=ValueType.INT64)

location_stats_fv= FeatureView(
    name="location_stats",
    entities=["location"],
    schema=[
        Field(name="temperature", dtype=Int32)
    ],
    source=BigQuerySource(
        table="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:

from feast import Field
from feast.types import Float32

trips_today = Field(
    name="trips_today",
    dtype=Float32
)

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:

from feast import Field, RequestSource
from feast.types import Float64

# 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 = RequestSource(
    name="vals_to_add",
    schema=[
        Field(name="val_to_add", dtype=PrimitiveFeastType.INT64),
        Field(name="val_to_add_2": dtype=PrimitiveFeastType.INT64),
    ]
)

# Use the input data and feature view features to create new features
@on_demand_feature_view(
   sources=[
       driver_hourly_stats_view,
       input_request
   ],
   schema=[
     Field(name='conv_rate_plus_val1', dtype=Float64),
     Field(name='conv_rate_plus_val2', dtype=Float64)
   ]
)
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

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:

1. Results of historical retrieval

2. [planned] Logging request (including input for on demand transformation) and response during feature serving

3. [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.

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.

  • People interested in the Feast community newsletter should join feast-announce by clicking here.

• Community Calendar: Includes community calls and design meetings.

• 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 (every 2 weeks)

• Tuesday 10:00 am to 10:30 am PST

Links

• Zoom: https://zoom.us/j/6325193230

• Meeting notes (incl recordings): https://bit.ly/feast-notes

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 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.

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).

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.

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.

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”)

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 Services

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

from driver_ratings_feature_view import driver_ratings_fv
from driver_trips_feature_view import driver_stats_fv

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

Feature services are used during

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

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

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

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

Retrieving from the online store with a feature service

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

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

Retrieving from the offline store with a feature service

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

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

Feature References

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

Feature references uniquely identify feature values in Feast. The structure of a feature reference in string form is as follows: <feature_view>:<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=[
        # {join_key: entity_value}
        {'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 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.

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_keys=['driver_id'])

Entities are typically defined as part of feature views. Entity name is used to reference the entity from a feature view definition and join key is used to identify the physical 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).

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.

   • 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 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 datetime import timedelta

from feast import Entity, FeatureView, Field, FileSource, ValueType
from feast.types import Float32, Int64

# 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",
    timestamp_field="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.
# Entity has a name used for later reference (in a feature view, eg)
# and join_key to identify physical field name used in storages
driver = Entity(name="driver", value_type=ValueType.INT64, join_keys=["driver_id"], 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"],  # reference entity by name
    ttl=timedelta(seconds=86400 * 1),
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64),
    ],
    online=True,
    source=driver_hourly_stats,
    tags={},
)

driver_stats_fs = FeatureService(
    name="driver_activity",
    features=[driver_hourly_stats_view]
)

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.

Inspecting the raw data

The raw feature data we have in this demo is stored in a local parquet file. The dataset captures hourly stats of a driver in a ride-sharing app.

import pandas as pd
pd.read_parquet("data/driver_stats.parquet")

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 datetime import timedelta

from feast import Entity, FeatureView, Field, FileSource, ValueType
from feast.types import Float32, Int64

# 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",
    timestamp_field="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.
# Entity has a name used for later reference (in a feature view, eg)
# and join_key to identify physical field name used in storages
driver = Entity(name="driver", value_type=ValueType.INT64, join_keys=["driver_id"], 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"],  # reference entity by name
    ttl=timedelta(seconds=86400 * 1),
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64),
    ],
    online=True,
    source=driver_hourly_stats,
    tags={},
)

driver_stats_fs = FeatureService(
    name="driver_activity",
    features=[driver_hourly_stats_view]
)

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(
    {
        # entity's join key -> entity values
        "driver_id": [1001, 1002, 1003],

        # label name -> label values
        "label_driver_reported_satisfaction": [1, 5, 3],

        # "event_timestamp" (reserved key) -> timestamps
        "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=[
        # {join_key: entity_value}
        {"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]
}

Step 7: Using a feature service to fetch online features instead.

You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same api below. More information can be found here.

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=[
        # {join_key: entity_value}
        {"driver_id": 1004},
        {"driver_id": 1005},
    ],
).to_dict()

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

Step 8: Browse your features with the Web UI (experimental)

View all registered features, data sources, entities, and feature services with the Web UI.

One of the ways to view this is with the feast ui command.

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!

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:

from feast import FeatureView, Field, FileSource
from feast.types import Float32, Int64
from datetime import timedelta

driver_stats_fv = FeatureView(
    name="driver_hourly_stats",
    entities=["driver"],
    schema=[
        Field(name="trips_today", dtype=Int64),
        Field(name="earnings_today", dtype=Float32),
    ],
    ttl=timedelta(hours=2),
    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.

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.

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

Getting started

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

Concepts

Do feature views have to include entities?

How does Feast handle model or feature versioning?

Feast expects that each version of a model corresponds to a different feature service.

Feature views once they are used by a feature service are intended to be immutable and not deleted (until a feature service is removed). In the future, feast plan and `feast apply will throw errors if it sees this kind of behavior.

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

How do I run get_historical_features without providing an entity dataframe?

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).

It is a good idea though to lock down the registry file so only the CI/CD pipeline can modify it. That way data scientists and other users cannot accidentally modify the registry and lose other team's data.

Does Feast support streaming sources?

Does Feast support feature transformation?

There are several kinds of transformations:

• • These transformations are Pandas transformations run on batch data when you call get_historical_features and at online serving time when you call `get_online_features.

  • Note that if you use push sources to ingest streaming features, these transformations will execute on the fly as well

• • These will include SQL + PySpark based transformations on batch data sources.

• Streaming transformations (RFC in progress)

Does Feast have a Web UI?

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?

Yes. For example, the Postgres connector can be used as both an offline and online store (as well as the registry).

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

How do I contribute to Feast?

Feast 0.9 (legacy)

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

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

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

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:

Features can also be written to the online store via push sources

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.

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

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):

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!

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).

This workshop aims to teach users about Feast.

We explain concepts & best practices by example, and also showcase how to address common use cases.

Pre-requisites

This workshop assumes you have the following installed:

• A local development environment that supports running Jupyter notebooks (e.g. VSCode with Jupyter plugin)

• Python 3.7+

• Java 11 (for Spark, e.g. brew install java11)

• pip

• Docker & Docker Compose (e.g. brew install docker docker-compose)

• Terraform (docs)

• AWS CLI

• An AWS account setup with credentials via aws configure (e.g see AWS credentials quickstart)

Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.

Caveats

• M1 Macbook development is untested with this flow. See also How to run / develop for Feast on M1 Macs.

• Windows development has only been tested with WSL. You will need to follow this guide to have Docker play nicely.

Modules

See also: Feast quickstart, Feast x Great Expectations tutorial

These are meant mostly to be done in order, with examples building on previous concepts.

We integrate with a wide set of tools and technologies so you can make Feast work in your existing stack. Many of these integrations are maintained as plugins to the main Feast repo.

Integrations

Data Sources

• HTTP source

Offline Stores

Online Stores

• Bigtable (in progress)

• Cassandra

Deployments

• Cloud Run

• KNative

Standards

In order for a plugin integration to be highlighted on this page, it must meet the following requirements:

2. The plugin must have some basic documentation on how it should be used.

3. The author must work with a maintainer to pass a basic code review (e.g. to ensure that the implementation roughly matches the core Feast implementations).

In order for a plugin integration to be merged into the main Feast repo, it must meet the following requirements:

1. The PR must pass all integration tests. The universal tests (tests specifically designed for custom integrations) must be updated to test the integration.

2. There is documentation and a tutorial on how to use the integration.

3. The author (or someone else) agrees to take ownership of all the files, and maintain those files going forward.

4. If the plugin is being contributed by an organization, and not an individual, the organization should provide the infrastructure (or credits) for integration tests.

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

****

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

pip install 'feast[snowflake]'

Get a Snowflake Trial Account (Optional)

Snowflake Trial Account

Create a feature repository

feast init -t snowflake {feature_repo_name}
Snowflake Deployment URL (exclude .snowflakecomputing.com):
Snowflake User Name::
Snowflake Password::
Snowflake Role Name (Case Sensitive)::
Snowflake Warehouse Name (Case Sensitive)::
Snowflake Database Name (Case Sensitive)::
Should I upload example data to Snowflake (overwrite table)? [Y/n]: Y
cd {feature_repo_name}

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.

project: ...
registry: ...
provider: local
offline_store:
    type: snowflake.offline
    account: SNOWFLAKE_DEPLOYMENT_URL #drop .snowflakecomputing.com
    user: USERNAME
    password: PASSWORD
    role: ROLE_NAME #case sensitive
    warehouse: WAREHOUSE_NAME #case sensitive
    database: DATABASE_NAME #case cap sensitive

Run our test python script test.py

python test.py

What we did in test.py

Initialize our Feature Store

from datetime import datetime, timedelta

import pandas as pd
from driver_repo import driver, driver_stats_fv

from feast import FeatureStore

fs = FeatureStore(repo_path=".")

fs.apply([driver, driver_stats_fv])

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

entity_df = pd.DataFrame(
    {
        "event_timestamp": [
            pd.Timestamp(dt, unit="ms", tz="UTC").round("ms")
            for dt in pd.date_range(
                start=datetime.now() - timedelta(days=3),
                end=datetime.now(),
                periods=3,
            )
        ],
        "driver_id": [1001, 1002, 1003],
    }
)

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

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

Materialize the latest feature values into our online store

fs.materialize_incremental(end_date=datetime.now())

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

online_features = fs.get_online_features(
    features=features,
    entity_rows=[
      # {join_key: entity_value}
      {"driver_id": 1001},
      {"driver_id": 1002}
    ],
).to_dict()

Install Feast using pip:

pip install feast

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

pip install 'feast[snowflake]'

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

pip install 'feast[gcp]'

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

pip install 'feast[aws]'

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

pip install 'feast[redis]'

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 snowflake
Snowflake Deployment URL: ...
Snowflake User Name: ...
Snowflake Password: ...
Snowflake Role Name: ...
Snowflake Warehouse Name: ...
Snowflake Database Name: ...

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.

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=[
        # {join_key: entity_value, ...}
        {"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
   ]
}

This tutorial guides you on how to:

• Define features and data sources in Feast using the Feast CLI

• Materialize features to a Redis cluster deployed on Kubernetes.

• Deploy a Feast Java feature server into a Kubernetes cluster using the Feast helm charts

• Retrieve features using the gRPC API exposed by the Feast Java server

Try it and let us know what you think!

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.

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:

1. Prepare environment

2. Pull data from BigQuery (optional)

3. Declare & apply features and feature views in Feast

4. Generate reference dataset

5. Develop & test profiler function

6. 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)

Overview

This guide will go over:

1. how Feast tests are setup

2. how to extend the test suite to test new functionality

3. how to use the existing test suite to test a new custom offline / online store.

Test suite overview

Let's inspect the test setup in sdk/python/tests/integration:

$ tree

.
├── e2e
│   └── test_universal_e2e.py
├── feature_repos
│   ├── repo_configuration.py
│   └── universal
│       ├── data_source_creator.py
│       ├── data_sources
│       │   ├── bigquery.py
│       │   ├── file.py
│       │   └── redshift.py
│       ├── entities.py
│       └── feature_views.py
├── offline_store
│   ├── test_s3_custom_endpoint.py
│   └── test_universal_historical_retrieval.py
├── online_store
│   ├── test_e2e_local.py
│   ├── test_feature_service_read.py
│   ├── test_online_retrieval.py
│   └── test_universal_online.py
├── registration
│   ├── test_cli.py
│   ├── test_cli_apply_duplicated_featureview_names.py
│   ├── test_cli_chdir.py
│   ├── test_feature_service_apply.py
│   ├── test_feature_store.py
│   ├── test_inference.py
│   ├── test_registry.py
│   ├── test_universal_odfv_feature_inference.py
│   └── test_universal_types.py
└── scaffolding
    ├── test_init.py
    ├── test_partial_apply.py
    ├── test_repo_config.py
    └── test_repo_operations.py

8 directories, 27 files

feature_repos has setup files for most tests in the test suite and pytest fixtures for other tests. These fixtures parametrize on different offline stores, online stores, etc. and thus abstract away store specific implementations so tests don't need to rewrite e.g. uploading dataframes to a specific store for setup.

Understanding an example test

Let's look at a sample test using the universal repo: