FAQ
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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 GCP/AWS guide. See a presentation we did on this at our apply() meetup.
Using the
s3_endpoint_overridein aFileSourcedata 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.
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