Feast project structure

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

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).

Data ingestion

For offline use cases that only rely on batch data, Feast does not need to ingest data and can query your existing data (leveraging a compute engine, whether it be a data warehouse or (experimental) Spark / Trino). Feast can help manage pushing streaming features to a batch source to make features available for training.

For online use cases, Feast supports ingesting features from batch sources to make them available online (through a process called materialization), and pushing streaming features to make them available both offline / online. We explore this more in the next concept page (Data ingestion)

Feature registration and retrieval

Features are registered as code in a version controlled repository, and tie to data sources + model versions via the concepts of entities, feature views, and feature services. We explore these concepts more in the upcoming concept pages. These features are then stored in a registry, which can be accessed across users and services. The features can then be retrieved via SDK API methods or via a deployed feature server which exposes endpoints to query for online features (to power real time models).

Feast supports several patterns of feature retrieval.

Links & Resources

• GitHub Repository: Find the complete Feast codebase on GitHub.

• Slack: Feel free to ask questions or say hello! This is the main place where maintainers and contributors brainstorm and where users ask questions or discuss best practices.

  • Feast users should join #feast-general or #feast-beginners to ask questions

  • Feast developers / contributors should join #feast-development

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

  • Feast users should join feast-discuss@googlegroups.com group by clicking here.

  • Feast developers / contributors should join feast-dev@googlegroups.com group by clicking here.

• Community Calendar: Includes community calls and design meetings.

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

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

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

• Feast Linux Foundation Wiki: Our LFAI wiki page contains links to resources for contributors and maintainers.

How can I get help?

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

• GitHub Issues: Found a bug or need a feature? Create an issue on GitHub.

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

Community Calls

General community call (biweekly)

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

Frequency (every 2 weeks)

• Tuesday 10:00 am to 10:30 am PST

Links

• Zoom: https://zoom.us/j/6325193230

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

Developers call (biweekly)

We also have a #feast-development community call every two weeks, where we discuss contributions + brainstorm best practices.

Frequency (every 2 weeks)

• Tuesday 8:00 am to 8:30 am PST

Links

• Meeting notes (incl recordings): Feast Development Biweekly

• Zoom: https://zoom.us/j/93657748160?pwd=K3ZpdzhqejgrcXNhc3BlSjFMdzUxdz09

Overview

Generally, Feast supports several patterns of feature retrieval:

1. Training data generation (via feature_store.get_historical_features(...))

2. Offline feature retrieval for batch scoring (via feature_store.get_historical_features(...))

3. Online feature retrieval for real-time model predictions

   • via the SDK: feature_store.get_online_features(...)

   • via deployed feature server endpoints: requests.post('http://localhost:6566/get-online-features', data=json.dumps(online_request))

Each of these retrieval mechanisms accept:

• some way of specifying entities (to fetch features for)

• some way to specify the features to fetch (either via , which group features needed for a model version, or )

Before beginning, you need to instantiate a local FeatureStore object that knows how to parse the registry (see )

For code examples of how the below work, inspect the generated repository from feast init -t [YOUR TEMPLATE] (gcp, snowflake, and aws are the most fully fleshed).

Concepts

Before diving into how to retrieve features, we need to understand some high level concepts in Feast.

Feature Services

Feature services are used during

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

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

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

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

Retrieving from the online store with a feature service

Retrieving from the offline store with a feature service

Feature References

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

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

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

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.

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.

Retrieving historical features (for training data or batch scoring)

Feast abstracts away point-in-time join complexities with the get_historical_features API.

We go through the major steps, and also show example code. Note that the quickstart templates generally have end-to-end working examples for all these cases.

Step 1: Specifying Features

Feast accepts either: