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.

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 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).

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 (community plugin)

  • Spark (community 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 (community plugin)

  • Trino (community plugin)

  • Spark (community plugin)

  • In-memory / Pandas

  • Custom offline store support

• Online Stores

  • DynamoDB

  • Redis

  • Datastore

  • SQLite

  • Azure Cache for Redis (community plugin)

  • Postgres (community 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)

  • REST API for browsing feature registry

  • Feature versioning

Dataset can be created from:

1. Results of historical retrieval

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.

Saved dataset can be later retrieved using get_saved_dataset method:

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.

• 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).

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.

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:

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).

Links & Resources

• • Design proposals in the form of Request for Comments (RFC).

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

How can I get help?

• Slack: Need to speak to a human? Come ask a question in our Slack channel (link above).

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

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:

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.

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.

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={},
)

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

Valid values for provider in feature_store.yaml are:

• local: use file source with SQLite/Redis

• gcp: use BigQuery/Snowflake with Google Cloud Datastore/Redis

• aws: use Redshift/Snowflake with DynamoDB/Redis

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

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

Step 3: Register feature definitions and deploy your feature store

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

feast apply

# This is an example feature definition file

from 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={},
)

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]
}

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!

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.

• 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.

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:

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.

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

• Snowflake source

• Redshift source

• BigQuery source

• Parquet file source

• Synapse source (community plugin)

• Hive (community plugin)

• Postgres (community plugin)

• Spark (community 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 (community plugin)

• Trino (community plugin)

• Spark (community plugin)

• In-memory / Pandas

• Custom offline store support

Online Stores

• DynamoDB

• Redis

• Datastore

• SQLite

• Azure Cache for Redis (community plugin)

• Postgres (community plugin)

• Custom online store support

• Bigtable (in progress)

• Cassandra

Deployments

• AWS Lambda (Alpha release. See guide and RFC)

• Kubernetes (See guide)

• Cloud Run

• KNative

Standards

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

1. The plugin must have tests. Ideally it would use the Feast universal tests (see this guide for an example), but custom tests are fine.

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.

Getting started

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

The quickstart is the easiest way to learn about Feast. For more detailed tutorials, please check out the tutorials page.

Concepts

What is the difference between feature tables and feature views?

Feature tables from Feast 0.9 have been renamed to feature views in Feast 0.10+. For more details, please see the discussion here.

Do feature views have to include entities?

No, there are feature views without entities.

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

The data source itself defines the underlying data warehouse table in which the features are stored. The offline store interface defines the APIs required to make an arbitrary compute layer work for Feast (e.g. pulling features given a set of feature views from their sources, exporting the data set results to different formats). Please see data sources and offline store for more details.

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

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

Functionality

Does Feast provide security or access control?

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

Does Feast support streaming sources?

Yes. In earlier versions of Feast, we used Feast Spark to manage ingestion from stream sources. In the current version of Feast, we support push based ingestion.

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?

Please see a detailed comparison of Feast vs. Tecton here. For another comparison, please see here.

What are the performance/latency characteristics of Feast?

Feast is designed to work at scale and support low latency online serving. Benchmarks (RFC) will 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)

  • Feast's implementation of online stores serializes features into Feast protocol buffers and supports list types (see reference)

• Sparse embeddings (e.g. one hot encodings)

  • One way to do this efficiently is to have a protobuf or string representation of https://www.tensorflow.org/guide/sparse_tensor

Does Feast support X storage engine?

The list of supported offline and online stores can be found here and here, respectively. The roadmap indicates the stores for which we are planning to add support. Finally, our Provider abstraction is built to be extensible, so you can plug in your own implementations of offline and online stores. Please see more details about custom providers here.

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?

Please follow the instructions here.

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.

Does Feast support S3 as a data source?

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

• Using Redshift as a data source via Spectrum (AWS tutorial), and then continuing with the Running Feast with Snowflake/GCP/AWS guide. See a presentation we did on this at our apply() meetup.

• 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?

Feast does not support Spark natively. However, you can create a custom provider that will support Spark, which can help with more scalable materialization and ingestion.

Is Feast planning on supporting X functionality?

Please see the roadmap.

Project

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

Feast 0.10+ is much lighter weight and more extensible than Feast 0.9. It is designed to be simple to install and use. Please see this document for more details.

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

Please see this document. If you have any questions or suggestions, feel free to leave a comment on the document!

How do I contribute to Feast?

For more details on contributing to the Feast community, see here and this here.

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

Feast Core and Feast Serving were both part of Feast Java. We plan to support Feast Serving. We will not support Feast Core; instead we will support our object store based registry. We will not support Feast Spark. For more details on what we plan on supporting, please see the roadmap.

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”)

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

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.

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

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.

Real-time Credit Scoring Example

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

• Deploying S3 with Parquet as your primary data source, containing both loan features and zip code features

• 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.

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):

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.

Driver Ranking Example

This tutorial guides you on how to use Feast with Scikit-learn. You will learn how to:

• Train a model locally (on your laptop) using data from BigQuery

• Test the model for online inference using SQLite (for fast iteration)

• Test the model for online inference using Firestore (for production use)

Try it and let us know what you think!

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.

Overview

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

Overview of typical production configuration is given below:

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

4. Ingest features from a stream source

5. Monitor your production deployment

1. Automatically deploying changes to your feature definitions

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

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

The contents of this repository are shown below:

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

The repository contains three sub-folders:

• staging/: This folder contains the staging feature_store.yaml and Feast objects. Users that want to make changes to the Feast deployment in the staging environment will commit changes to this directory.

• production/: This folder contains the production feature_store.yaml and Feast objects. Typically users would first test changes in staging before copying the feature definitions into the production folder, before committing the changes.

• .github: This folder is an example of a CI system that applies the changes in either the staging or production repositories using feast apply. This operation saves your feature definitions to a shared registry (for example, on GCS) and configures your infrastructure for serving features.

The feature_store.yaml contains the following:

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

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

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

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

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

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

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

2.1. Manual materializations

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

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

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

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

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

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

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

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

2.2. Automate periodic materializations

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

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

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

3. How to use Feast for model training

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

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

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

fs = FeatureStore(repo_path="production/")

Then, training data can be retrieved as follows:

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

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

model = ml.fit(training_df)

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

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

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

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

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

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

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

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

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

4. Retrieving online features for prediction

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

4.1. Use the Python SDK within an existing Python service

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

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

from feast import FeatureStore

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

fs = FeatureStore(repo_path="production/")

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

4.2. Consume features via HTTP API from Serverless Feature Server

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

Read more about this feature

4.3. Java based Feature Server deployed on Kubernetes

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

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

1. Install kubectl and helm 3

2. Add the Feast Helm repository and download the latest charts:

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

1. Run Helm Install

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

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

Load balancing

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

5. Ingesting features from a stream source

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

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

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

store = FeatureStore(...)

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

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

5.2. Push service (still under development)

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

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

6. Monitoring

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

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

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

Summary

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

Option #1 (currently preferred)

• Feast SDK is being triggered by CI (eg, Github Actions). It applies the latest changes from the feature repo to the Feast registry

• Airflow manages materialization jobs to ingest data from DWH to the online store periodically

• For the stream ingestion Feast Python SDK is used in the existing Spark / Beam pipeline

• Online features are served via either a Python feature server or a high performance Java feature server

  • Both the Java feature server and the transformation server are deployed on Kubernetes cluster (via Helm charts)

• Feast Python SDK is called locally to generate a training dataset

Option #2 (still in development)

Same as Option #1, except:

• Push service is deployed as AWS Lambda / Google Cloud Run and is configured as a sink for Kinesis or PubSub to ingest features directly from a stream broker. Lambda / Cloud Run is being managed by Feast SDK (from CI environment)

• Materialization jobs are managed inside Kubernetes via Kubernetes Job (currently not managed by Helm)

Option #3 (still in development)

Same as Option #2, except:

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

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

****

Overview

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

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

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

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

1. Defining an OfflineStore class.

2. Defining an OfflineStoreConfig class.

3. Defining a RetrievalJob class for this offline store.

4. Defining a DataSource class for the offline store

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

6. Testing the OfflineStore class.

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_historical_features() method. Typically, this method is used to retrieve features when training ML models.

• pull_all_from_table_or_query is a method that pulls all the data from an offline store from a specified start date to a specified end date.

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

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

2. Defining an OfflineStoreConfig class

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

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

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

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

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

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

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

type: feast_custom_offline_store.file.CustomFileOfflineStore

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

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

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

3. Defining a RetrievalJob class

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

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

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

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

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

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

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

4. Defining a DataSource class for the offline store

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

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

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

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


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

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

        data_source_proto.timestamp_field = self.timestamp_field
        data_source_proto.created_timestamp_column = self.created_timestamp_column
        data_source_proto.date_partition_column = self.date_partition_column

        return data_source_proto

5. Using the custom offline store

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

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

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

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

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

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

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

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


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

6. Testing the OfflineStore class

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

make test

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

make test-python-universal

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

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

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

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

The original notebook and datasets for this tutorial can be found on GitHub.

0. Setup

Install Feast Python SDK and great expectations:

!pip install 'feast[ge]'

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

!pip install google-cloud-bigquery

import pyarrow.parquet

from google.cloud.bigquery import Client

bq_client = Client(project='kf-feast')

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

data_query = """SELECT
    taxi_id,
    TIMESTAMP_TRUNC(trip_start_timestamp, DAY) as day,
    SUM(trip_miles) as total_miles_travelled,
    SUM(trip_seconds) as total_trip_seconds,
    SUM(fare) as total_earned,
    COUNT(*) as trip_count
FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`
WHERE
    trip_miles > 0 AND trip_seconds > 60 AND
    trip_start_timestamp BETWEEN '2019-01-01' and '2020-12-31' AND
    trip_total < 1000
GROUP BY taxi_id, TIMESTAMP_TRUNC(trip_start_timestamp, DAY)"""

driver_stats_table = bq_client.query(data_query).to_arrow()

# Storing resulting dataset into parquet file
pyarrow.parquet.write_table(driver_stats_table, "trips_stats.parquet")

def entities_query(year):
    return f"""SELECT
    distinct taxi_id
FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`
WHERE
    trip_miles > 0 AND trip_seconds > 0 AND
    trip_start_timestamp BETWEEN '{year}-01-01' and '{year}-12-31'
"""

entities_2019_table = bq_client.query(entities_query(2019)).to_arrow()

# Storing entities (taxi ids) into parquet file
pyarrow.parquet.write_table(entities_2019_table, "entities.parquet")

2. Declaring features

import pyarrow.parquet
import pandas as pd

from feast import FeatureView, Entity, FeatureStore, Field
from feast.types import Float64, Int64
from feast.value_type import ValueType
from feast.data_format import ParquetFormat
from feast.on_demand_feature_view import on_demand_feature_view
from feast.infra.offline_stores.file_source import FileSource
from feast.infra.offline_stores.file import SavedDatasetFileStorage
from datetime import timedelta

batch_source = FileSource(
    timestamp_field="day",
    path="trips_stats.parquet",  # using parquet file that we created on previous step
    file_format=ParquetFormat()
)

taxi_entity = Entity(name='taxi', join_keys=['taxi_id'])

trips_stats_fv = FeatureView(
    name='trip_stats',
    entities=['taxi'],
    features=[
        Field(name="total_miles_travelled", dtype=Float64),
        Field(name="total_trip_seconds", dtype=Float64),
        Field(name="total_earned", dtype=Float64),
        Field(name="trip_count", dtype=Int64),

    ],
    ttl=timedelta(seconds=86400),
    source=batch_source,
)

Read more about feature views in Feast docs

@on_demand_feature_view(
    schema=[
        Field("avg_fare", Float64),
        Field("avg_speed", Float64),
        Field("avg_trip_seconds", Float64),
        Field("earned_per_hour", Float64),
    ],
    sources={
        "stats": trips_stats_fv
    }
)
def on_demand_stats(inp):
    out = pd.DataFrame()
    out["avg_fare"] = inp["total_earned"] / inp["trip_count"]
    out["avg_speed"] = 3600 * inp["total_miles_travelled"] / inp["total_trip_seconds"]
    out["avg_trip_seconds"] = inp["total_trip_seconds"] / inp["trip_count"]
    out["earned_per_hour"] = 3600 * inp["total_earned"] / inp["total_trip_seconds"]
    return out

Read more about on demand feature views here

store = FeatureStore(".")  # using feature_store.yaml that stored in the same directory

store.apply([taxi_entity, trips_stats_fv, on_demand_stats])  # writing to the registry

3. Generating training (reference) dataset

taxi_ids = pyarrow.parquet.read_table("entities.parquet").to_pandas()

Generating range of timestamps with daily frequency:

timestamps = pd.DataFrame()
timestamps["event_timestamp"] = pd.date_range("2019-06-01", "2019-07-01", freq='D')

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

entity_df = pd.merge(taxi_ids, timestamps, how='cross')
entity_df

156984 rows × 2 columns

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

job = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "trip_stats:total_miles_travelled",
        "trip_stats:total_trip_seconds",
        "trip_stats:total_earned",
        "trip_stats:trip_count",
        "on_demand_stats:avg_fare",
        "on_demand_stats:avg_trip_seconds",
        "on_demand_stats:avg_speed",
        "on_demand_stats:earned_per_hour",
    ]
)

store.create_saved_dataset(
    from_=job,
    name='my_training_ds',
    storage=SavedDatasetFileStorage(path='my_training_ds.parquet')
)

<SavedDataset(name = my_training_ds, features = ['trip_stats:total_miles_travelled', 'trip_stats:total_trip_seconds', 'trip_stats:total_earned', 'trip_stats:trip_count', 'on_demand_stats:avg_fare', 'on_demand_stats:avg_trip_seconds', 'on_demand_stats:avg_speed', 'on_demand_stats:earned_per_hour'], join_keys = ['taxi_id'], storage = <feast.infra.offline_stores.file_source.SavedDatasetFileStorage object at 0x1276e7950>, full_feature_names = False, tags = {}, _retrieval_job = <feast.infra.offline_stores.file.FileRetrievalJob object at 0x12716fed0>, min_event_timestamp = 2019-06-01 00:00:00, max_event_timestamp = 2019-07-01 00:00:00)>

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.

import numpy as np

from feast.dqm.profilers.ge_profiler import ge_profiler

from great_expectations.core.expectation_suite import ExpectationSuite
from great_expectations.dataset import PandasDataset

Loading saved dataset first and exploring the data:

ds = store.get_saved_dataset('my_training_ds')
ds.to_df()

156984 rows × 10 columns

Feast uses Great Expectations as a validation engine and ExpectationSuite as a dataset's profile. Hence, we need to develop a function that will generate ExpectationSuite. This function will receive instance of PandasDataset (wrapper around pandas.DataFrame) so we can utilize both Pandas DataFrame API and some helper functions from PandasDataset during profiling.

DELTA = 0.1  # controlling allowed window in fraction of the value on scale [0, 1]

@ge_profiler
def stats_profiler(ds: PandasDataset) -> ExpectationSuite:
    # simple checks on data consistency
    ds.expect_column_values_to_be_between(
        "avg_speed",
        min_value=0,
        max_value=60,
        mostly=0.99  # allow some outliers
    )

    ds.expect_column_values_to_be_between(
        "total_miles_travelled",
        min_value=0,
        max_value=500,
        mostly=0.99  # allow some outliers
    )

    # expectation of means based on observed values
    observed_mean = ds.trip_count.mean()
    ds.expect_column_mean_to_be_between("trip_count",
                                        min_value=observed_mean * (1 - DELTA),
                                        max_value=observed_mean * (1 + DELTA))

    observed_mean = ds.earned_per_hour.mean()
    ds.expect_column_mean_to_be_between("earned_per_hour",
                                        min_value=observed_mean * (1 - DELTA),
                                        max_value=observed_mean * (1 + DELTA))


    # expectation of quantiles
    qs = [0.5, 0.75, 0.9, 0.95]
    observed_quantiles = ds.avg_fare.quantile(qs)

    ds.expect_column_quantile_values_to_be_between(
        "avg_fare",
        quantile_ranges={
            "quantiles": qs,
            "value_ranges": [[None, max_value] for max_value in observed_quantiles]
        })

    return ds.get_expectation_suite()

Testing our profiler function:

ds.get_profile(profiler=stats_profiler)

02/02/2022 02:43:47 PM INFO:	5 expectation(s) included in expectation_suite. result_format settings filtered.
<GEProfile with expectations: [
  {
    "expectation_type": "expect_column_values_to_be_between",
    "kwargs": {
      "column": "avg_speed",
      "min_value": 0,
      "max_value": 60,
      "mostly": 0.99
    },
    "meta": {}
  },
  {
    "expectation_type": "expect_column_values_to_be_between",
    "kwargs": {
      "column": "total_miles_travelled",
      "min_value": 0,
      "max_value": 500,
      "mostly": 0.99
    },
    "meta": {}
  },
  {
    "expectation_type": "expect_column_mean_to_be_between",
    "kwargs": {
      "column": "trip_count",
      "min_value": 10.387244591346153,
      "max_value": 12.695521167200855
    },
    "meta": {}
  },
  {
    "expectation_type": "expect_column_mean_to_be_between",
    "kwargs": {
      "column": "earned_per_hour",
      "min_value": 52.320624975640214,
      "max_value": 63.94743052578249
    },
    "meta": {}
  },
  {
    "expectation_type": "expect_column_quantile_values_to_be_between",
    "kwargs": {
      "column": "avg_fare",
      "quantile_ranges": {
        "quantiles": [
          0.5,
          0.75,
          0.9,
          0.95
        ],
        "value_ranges": [
          [
            null,
            16.4
          ],
          [
            null,
            26.229166666666668
          ],
          [
            null,
            36.4375
          ],
          [
            null,
            42.0
          ]
        ]
      }
    },
    "meta": {}
  }
]>

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:

validation_reference = ds.as_reference(profiler=stats_profiler)

and test it against our existing retrieval job

_ = job.to_df(validation_reference=validation_reference)

02/02/2022 02:43:52 PM INFO: 5 expectation(s) included in expectation_suite. result_format settings filtered.
02/02/2022 02:43:53 PM INFO: Validating data_asset_name None with expectation_suite_name default

Validation successfully passed as no exception were raised.

5. Validating new historical retrieval

Creating new timestamps for Dec 2020:

from feast.dqm.errors import ValidationFailed

timestamps = pd.DataFrame()
timestamps["event_timestamp"] = pd.date_range("2020-12-01", "2020-12-07", freq='D')

entity_df = pd.merge(taxi_ids, timestamps, how='cross')
entity_df

35448 rows × 2 columns

job = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "trip_stats:total_miles_travelled",
        "trip_stats:total_trip_seconds",
        "trip_stats:total_earned",
        "trip_stats:trip_count",
        "on_demand_stats:avg_fare",
        "on_demand_stats:avg_trip_seconds",
        "on_demand_stats:avg_speed",
        "on_demand_stats:earned_per_hour",
    ]
)

Execute retrieval job with validation reference:

try:
    df = job.to_df(validation_reference=validation_reference)
except ValidationFailed as exc:
    print(exc.validation_report)

02/02/2022 02:43:58 PM INFO: 5 expectation(s) included in expectation_suite. result_format settings filtered.
02/02/2022 02:43:59 PM INFO: Validating data_asset_name None with expectation_suite_name default

[
  {
    "expectation_config": {
      "expectation_type": "expect_column_mean_to_be_between",
      "kwargs": {
        "column": "trip_count",
        "min_value": 10.387244591346153,
        "max_value": 12.695521167200855,
        "result_format": "COMPLETE"
      },
      "meta": {}
    },
    "meta": {},
    "result": {
      "observed_value": 6.692920555429092,
      "element_count": 35448,
      "missing_count": 31055,
      "missing_percent": 87.6071992778154
    },
    "exception_info": {
      "raised_exception": false,
      "exception_message": null,
      "exception_traceback": null
    },
    "success": false
  },
  {
    "expectation_config": {
      "expectation_type": "expect_column_mean_to_be_between",
      "kwargs": {
        "column": "earned_per_hour",
        "min_value": 52.320624975640214,
        "max_value": 63.94743052578249,
        "result_format": "COMPLETE"
      },
      "meta": {}
    },
    "meta": {},
    "result": {
      "observed_value": 68.99268345164135,
      "element_count": 35448,
      "missing_count": 31055,
      "missing_percent": 87.6071992778154
    },
    "exception_info": {
      "raised_exception": false,
      "exception_message": null,
      "exception_traceback": null
    },
    "success": false
  },
  {
    "expectation_config": {
      "expectation_type": "expect_column_quantile_values_to_be_between",
      "kwargs": {
        "column": "avg_fare",
        "quantile_ranges": {
          "quantiles": [
            0.5,
            0.75,
            0.9,
            0.95
          ],
          "value_ranges": [
            [
              null,
              16.4
            ],
            [
              null,
              26.229166666666668
            ],
            [
              null,
              36.4375
            ],
            [
              null,
              42.0
            ]
          ]
        },
        "result_format": "COMPLETE"
      },
      "meta": {}
    },
    "meta": {},
    "result": {
      "observed_value": {
        "quantiles": [
          0.5,
          0.75,
          0.9,
          0.95
        ],
        "values": [
          19.5,
          28.1,
          38.0,
          44.125
        ]
      },
      "element_count": 35448,
      "missing_count": 31055,
      "missing_percent": 87.6071992778154,
      "details": {
        "success_details": [
          false,
          false,
          false,
          false
        ]
      }
    },
    "exception_info": {
      "raised_exception": false,
      "exception_message": null,
      "exception_traceback": null
    },
    "success": false
  }
]

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:

@pytest.mark.integration
@pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
def test_historical_features(environment, universal_data_sources, full_feature_names):
    store = environment.feature_store

    (entities, datasets, data_sources) = universal_data_sources
    feature_views = construct_universal_feature_views(data_sources)

    customer_df, driver_df, orders_df, global_df, entity_df = (
        datasets["customer"],
        datasets["driver"],
        datasets["orders"],
        datasets["global"],
        datasets["entity"],
    )
    # ... more test code

    customer_fv, driver_fv, driver_odfv, order_fv, global_fv = (
        feature_views["customer"],
        feature_views["driver"],
        feature_views["driver_odfv"],
        feature_views["order"],
        feature_views["global"],
    )

    feature_service = FeatureService(
        "convrate_plus100",
        features=[
            feature_views["driver"][["conv_rate"]],
            feature_views["driver_odfv"]
        ],
    )

    feast_objects = []
    feast_objects.extend(
        [
            customer_fv,
            driver_fv,
            driver_odfv,
            order_fv,
            global_fv,
            driver(),
            customer(),
            feature_service,
        ]
    )
    store.apply(feast_objects)
    # ... more test code

    job_from_df = store.get_historical_features(
        entity_df=entity_df_with_request_data,
        features=[
            "driver_stats:conv_rate",
            "driver_stats:avg_daily_trips",
            "customer_profile:current_balance",
            "customer_profile:avg_passenger_count",
            "customer_profile:lifetime_trip_count",
            "conv_rate_plus_100:conv_rate_plus_100",
            "conv_rate_plus_100:conv_rate_plus_val_to_add",
            "order:order_is_success",
            "global_stats:num_rides",
            "global_stats:avg_ride_length",
        ],
        full_feature_names=full_feature_names,
    )
    actual_df_from_df_entities = job_from_df.to_df()
    # ... more test code

    assert_frame_equal(
        expected_df, actual_df_from_df_entities, check_dtype=False,
    )
    # ... more test code

The key fixtures are the environment and universal_data_sources fixtures, which are defined in the feature_repos directories. This by default pulls in a standard dataset with driver and customer entities, certain feature views, and feature values. By including the environment as a parameter, the test automatically parametrizes across other offline / online store combinations.

Writing a new test or reusing existing tests

To add a new test to an existing test file

• Use the same function signatures as an existing test (e.g. use environment as an argument) to include the relevant test fixtures.

• If possible, expand an individual test instead of writing a new test, due to the cost of standing up offline / online stores.

To test a new offline / online store from a plugin repo

• Install Feast in editable mode with pip install -e.

• The core tests for offline / online store behavior are parametrized by the FULL_REPO_CONFIGS variable defined in feature_repos/repo_configuration.py. To overwrite this variable without modifying the Feast repo, create your own file that contains a FULL_REPO_CONFIGS (which will require adding a new IntegrationTestRepoConfig or two) and set the environment variable FULL_REPO_CONFIGS_MODULE to point to that file. Then the core offline / online store tests can be run with make test-python-universal.

• See the custom offline store demo and the custom online store demo for examples.

To include a new offline / online store in the main Feast repo

• Extend data_source_creator.py for your offline store.

• In repo_configuration.py add a newIntegrationTestRepoConfig or two (depending on how many online stores you want to test).

• Run the full test suite with make test-python-integration.

Including a new offline / online store in the main Feast repo from external plugins with community maintainers.

• This folder is for plugins that are officially maintained with community owners. Place the APIs in feast/infra/offline_stores/contrib/.

• Extend data_source_creator.py for your offline store and implement the required APIs.

• In contrib_repo_configuration.py add a new IntegrationTestRepoConfig (depending on how many online stores you want to test).

• Run the test suite on the contrib test suite with make test-python-contrib-universal.

To include a new online store

• In repo_configuration.py add a new config that maps to a serialized version of configuration you need in feature_store.yaml to setup the online store.

• In repo_configuration.py, add newIntegrationTestRepoConfig for offline stores you want to test.

• Run the full test suite with make test-python-integration