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).

What is Feast?

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

Problems Feast Solves

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

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

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

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

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

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

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

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

Problems Feast does not yet solve

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

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

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

What Feast is not

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

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

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

How can I get started?

Explore the following resources to get started with Feast:

• Quickstart is the fastest way to get started with Feast

• Concepts describes all important Feast API concepts

• Architecture describes Feast's overall architecture.

• Tutorials shows full examples of using Feast in machine learning applications.

• Running Feast with GCP/AWS provides a more in-depth guide to using Feast.

• Reference contains detailed API and design documents.

• Contributing contains resources for anyone who wants to contribute to Feast.

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

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

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

Entities should be reused across feature views.

Entity key

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

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

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

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.

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

2. Online feature availability: At inference time, models often need access to features that aren't readily available and need to be precomputed from other datasources.

   • Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and ensures necessary features are consistently available and freshly computed at inference time.

3. Feature reusability and model versioning: Different teams within an organization are often unable to reuse features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need to be versioned, for example when running A/B tests on model versions.

   • Feast enables discovery of and collaboration on previously used features and enables versioning of sets of features (via feature services).

   • Feast enables feature transformation so users can re-use transformation logic across online / offline usecases and across models.

Step 1: Install Feast

Install the Feast SDK and CLI using pip:

Step 2: Create a feature repository

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

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

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

Valid values for provider in feature_store.yaml are:

• local: use file source / SQLite

• gcp: use BigQuery / Google Cloud Datastore

• aws: use Redshift / DynamoDB

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

Step 3: Register feature definitions and deploy your feature store

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

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.

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).

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.

Next steps

Links & Resources

• : Feel free to ask questions or say hello!

• : We have both a user and developer mailing list.

  • Feast users should join group by clicking .

  • Feast developers should join group by clicking .

• : This folder is used as a central repository for all Feast resources. For example:

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

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

• : Find the complete Feast codebase on GitHub.

• : Our LFAI wiki page contains links to resources for contributors and maintainers.

How can I get help?

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

• GitHub Issues: Found a bug or need a feature? .

• StackOverflow: Need to ask a question on how to use Feast? We also monitor and respond to .

Community Calls

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

Frequency (alternating times every 2 weeks)

• Tuesday 18:00 pm to 18:30 pm (US, Asia)

• Tuesday 10:00 am to 10:30 am (US, Europe)

Links

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 .

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

• Data Sources

  • Kafka source (Planned for Q4 2021)

  • Snowflake source (Planned for Q4 2021)

  • HTTP source

• Offline Stores

  • Snowflake (Planned for Q4 2021)

  • Trino (Planned for Q4 2021)

• Online Stores

  • Bigtable

  • Cassandra

• Streaming

  • Streaming ingestion on AWS (Planned for Q4 2021)

  • Streaming ingestion on GCP

• Feature Engineering

  • On-demand Transformations (Alpha release. See )

  • Batch transformation (SQL)

  • Streaming transformation

• Deployments

  • AWS Lambda (Alpha release. See )

  • Cloud Run

  • Kubernetes

  • KNative

• Feature Serving

  • Python Client

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

  • gRPC Feature Server (Java) (See )

  • Java Client

  • Go Client

  • Push API

  • Delete API

  • Feature Logging (for training)

• Data Quality Management

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

  • Metric production

  • Training-serving skew detection

  • Drift detection

  • Alerting

• Feature Discovery and Governance

  • Python SDK for browsing feature registry

  • CLI for browsing feature registry

  • Model-centric feature tracking (feature services)

  • REST API for browsing feature registry

  • Feast Web UI (Planned for Q4 2021)

  • Feature versioning

  • Amundsen integration

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

Project

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

Feature views

A feature view is an object that represents a logical group of time-series feature data as it is found in a . Feature views consist of zero or more , one or more , and a . Feature views allow Feast to model your existing feature data in a consistent way in both an offline (training) and online (serving) environment. Feature views generally contain features that are properties of a specific object, in which case that object is defined as an entity and included in the feature view. If the features are not related to a specific object, the feature view might not have entities; see below.

Feature views are used during

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

• Loading of feature values into an online store. Feature views determine the storage schema in the online store.

• Retrieval of features from the online store. Feature views provide the schema definition to Feast in order to look up features from the online store.

Feature views without entities

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

Feature

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

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

[Alpha] On demand feature views

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

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”)

Feast users use Feast to manage two important sets of configuration:

• Configuration about how to run Feast on your infrastructure

• Feature definitions

With Feast, the above configuration can be written declaratively and stored as code in a central location. This central location is called a feature repository. The feature repository is the declarative source of truth for what the desired state of a feature store should be.

The Feast CLI uses the feature repository to configure, deploy, and manage your feature store.

An example structure of a feature repository is shown below:

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

1 directory, 4 files

For more details, see the Feature repository reference.

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.

Dataset

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

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

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

Feature References

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

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

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

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

Event timestamp

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

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

Functionality

• Create Batch Features: ELT/ETL systems like Spark and SQL are used to transform data in the batch store.

• Feast Apply: The user (or CI) publishes versioned controlled feature definitions using feast apply. This CLI command updates infrastructure and persists definitions in the object store registry.

• Feast Materialize: The user (or scheduler) executes feast materialize which loads features from the offline store into the online store.

• Model Training: A model training pipeline is launched. It uses the Feast Python SDK to retrieve a training dataset and trains a model.

• Get Historical Features: Feast exports a point-in-time correct training dataset based on the list of features and entity dataframe provided by the model training pipeline.

• Deploy Model: The trained model binary (and list of features) are deployed into a model serving system. This step is not executed by Feast.

• Prediction: A backend system makes a request for a prediction from the model serving service.

• Get Online Features: The model serving service makes a request to the Feast Online Serving service for online features using a Feast SDK.

Components

A complete Feast deployment contains the following components:

• Feast Registry: An object store (GCS, S3) based registry used to persist feature definitions that are registered with the feature store. Systems can discover feature data by interacting with the registry through the Feast SDK.

• Feast Python SDK/CLI: The primary user facing SDK. Used to:

  • Manage version controlled feature definitions.

  • Materialize (load) feature values into the online store.

  • Build and retrieve training datasets from the offline store.

  • Retrieve online features.

• Online Store: The online store is a database that stores only the latest feature values for each entity. The online store is populated by materialization jobs.

• Offline Store: The offline store persists batch data that has been ingested into Feast. This data is used for producing training datasets. Feast does not manage the offline store directly, but runs queries against it.

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

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

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

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

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

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

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

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

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

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

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

Getting started

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

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

Do feature views have to include entities?

No, there are .

Functionality

Does Feast provide security or access control?

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

Does Feast support streaming sources?

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

Does Feast support composite keys?

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

How does Feast compare with Tecton?

What are the performance/latency characteristics of Feast?

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

Does Feast support embeddings and list features?

Yes. Specifically:

• Simple lists / dense embeddings:

  • BigQuery supports list types natively

  • Redshift does not support list types, so you'll need to serialize these features into strings (e.g. json or protocol buffers)
• Sparse embeddings (e.g. one hot encodings)

Does Feast support X storage engine?

How can I add a custom online store?

Does Feast support S3 as a data source?

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

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

How can I use Spark with Feast?

Is Feast planning on supporting X functionality?

Project

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

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

How do I contribute to Feast?

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

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 as an offline store and 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 for more details.

Please see for configuring providers.

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.

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

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

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

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

Try it and let us know what you think!

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

• Kafka source (Planned for Q4 2021)

• Snowflake source (Planned for Q4 2021)

• HTTP source

Offline Stores

• Snowflake (Planned for Q4 2021)

• Trino (Planned for Q4 2021)

Online Stores

• Bigtable

• Cassandra

Deployments

• Cloud Run

• Kubernetes

• KNative

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

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

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

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

• Deploying S3 with Parquet as your primary data source, containing both and

• 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

Install Feast using :

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

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

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:

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

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

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

• Computing and backfilling feature data from raw data

• Building point-in-time correct training datasets from feature data and training a model

• Making online predictions from feature data

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

Overview

In this guide we will show you how to:

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

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

3. Use Feast for model training and serving

1. Automatically deploying changes to your feature definitions

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

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

The contents of this repository are shown below:

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

The repository contains three sub-folders:

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

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

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

The feature_store.yaml contains the following?

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

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

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

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

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

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

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

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

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

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

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

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

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

3. How to use Feast for model training and serving

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

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

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

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

Then, training data can be retrieved as follows:

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

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

model = ml.fit(training_df)

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

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

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

you can simply create a FeatureStore object, fetch the features, and then make a prediction:

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

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

# Create feature store object
prod_fs = FeatureStore(repo_path="production_feature_store.yaml")

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

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

Overview

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

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

• Launching custom streaming ingestion jobs (Spark, Beam)

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

• Adding custom validation to feature repositories during feast apply

• Adding custom infrastructure setup logic which runs during feast apply

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

Feast comes with built-in providers, e.g, LocalProvider, GcpProvider, and AwsProvider. However, users can develop their own providers by creating a class that implements the contract in the Provider class.

Guide

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

Step 1: Define a Provider class

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

from datetime import datetime
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union

from feast.entity import Entity
from feast.feature_table import FeatureTable
from feast.feature_view import FeatureView
from feast.infra.local import LocalProvider
from feast.infra.offline_stores.offline_store import RetrievalJob
from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
from feast.protos.feast.types.Value_pb2 import Value as ValueProto
from feast.registry import Registry
from feast.repo_config import RepoConfig


class MyCustomProvider(LocalProvider):
    def __init__(self, config: RepoConfig, repo_path):
        super().__init__(config)
        # Add your custom init code here. This code runs on every Feast operation.

    def update_infra(
        self,
        project: str,
        tables_to_delete: Sequence[Union[FeatureTable, FeatureView]],
        tables_to_keep: Sequence[Union[FeatureTable, FeatureView]],
        entities_to_delete: Sequence[Entity],
        entities_to_keep: Sequence[Entity],
        partial: bool,
    ):
        super().update_infra(
            project,
            tables_to_delete,
            tables_to_keep,
            entities_to_delete,
            entities_to_keep,
            partial,
        )
        print("Launching custom streaming jobs is pretty easy...")

    def materialize_single_feature_view(
        self,
        config: RepoConfig,
        feature_view: FeatureView,
        start_date: datetime,
        end_date: datetime,
        registry: Registry,
        project: str,
        tqdm_builder: Callable[[int], tqdm],
    ) -> None:
        super().materialize_single_feature_view(
            config, feature_view, start_date, end_date, registry, project, tqdm_builder
        )
        print("Launching custom batch jobs is pretty easy...")

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

It is possible to overwrite all the methods on the provider class. In fact, it isn't even necessary to subclass an existing provider like LocalProvider. The only requirement for the provider class is that it follows the Provider contract.

Step 2: Configuring Feast to use the provider

Configure your feature_store.yaml file to point to your new provider class:

project: repo
registry: registry.db
provider: feast_custom_provider.custom_provider.MyCustomProvider
online_store:
    type: sqlite
    path: online_store.db
offline_store:
    type: file

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

Step 3: Using the provider

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

feast apply

Registered entity driver_id
Registered feature view driver_hourly_stats
Deploying infrastructure for driver_hourly_stats
Launching custom streaming jobs is pretty easy...

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

PYTHONPATH=$PYTHONPATH:/home/my_user/my_custom_provider feast apply

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

Next steps

Have a look at the custom provider demo repository for a fully functional example of a custom provider. Feel free to fork it when creating your own custom provider!

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

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

feast init

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

feast init -t gcp

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

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

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

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

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

1 directory, 3 files

Enter the directory:

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

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

• Run feast apply to apply these definitions to Feast.

• Edit the example feature definitions in example.py and run feast apply again to change feature definitions.

• Initialize a git repository in the same directory and checking the feature repository into version control.

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

****

Description

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

Example

from feast import FileSource
from feast.data_format import ParquetFormat

parquet_file_source = FileSource(
    file_format=ParquetFormat(),
    file_url="file:///feast/customer.parquet",
)

Configuration options are available here.

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 as is:

$ 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 sets up pytest fixtures for other tests. Crucially, this parametrizes on different offline stores, different online stores, etc and abstracts 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:

• Include a new offline store:

  • 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

  • See more guidelines at https://github.com/feast-dev/feast/issues/1892

• 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

  • See more guidelines at https://github.com/feast-dev/feast/issues/1892

• Add a new test to an existing test file:

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

  • We prefer to expand what an individual test covers due to the cost of standing up offline / online stores

• Using custom data in a new test:

  • This is used in several places such as test_universal_types.py

@pytest.mark.integration
def your_test(environment: Environment):
    df = #...#
    data_source = environment.data_source_creator.create_data_source(
        df,
        destination_name=environment.feature_store.project
    )
    your_fv = driver_feature_view(data_source)
    entity = driver(value_type=ValueType.UNKNOWN)
    fs.apply([fv, entity])

    # ... run test

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

1. Defining an OfflineStore class

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

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

• pull_latest_from_table_or_query is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pull data from the offline store, and the FeatureStore class takes care of writing this data into the online store.

• get_historical_features is invoked when reading values from the offline store using the FeatureStore.get_historica_features() method. Typically, this method is used to retrieve features when training ML models.

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

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

2. Defining an OfflineStoreConfig class

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

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

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. 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

Overview

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

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

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

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

1. Defining the OnlineStore class.

2. Defining the OnlineStoreConfig class.

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

1. Defining an OnlineStore class

The OnlineStore class broadly contains two sets of methods

• One set deals with managing infrastructure that the online store needed for operations

• One set deals with writing data into the store, and reading data from the store.

1.1 Infrastructure Methods

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

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

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

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

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

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

        project = config.project

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

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


    def teardown(
        self,
        config: RepoConfig,
        tables: Sequence[Union[FeatureTable, FeatureView]],
        entities: Sequence[Entity],
    ):
        """

        """
        conn = self._get_conn(config)
        cur = conn.cursor(buffered=True)
        project = config.project

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