docs/getting-started/faq.md
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The quickstart is the easiest way to learn about Feast. For more detailed tutorials, please check out the tutorials page.
No, there are feature views without entities.
Feast expects that each version of a model corresponds to a different feature service.
Feature views once they are used by a feature service are intended to be immutable and not deleted (until a feature service is removed). In the future, feast plan and feast apply will throw errors if it sees this kind of behavior.
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.
Yes, this is possible. For example, you can use BigQuery as an offline store and Redis as an online store.
get_historical_features without providing an entity dataframe?Feast does support fetching historical features without passing an entity dataframe with the request.
start_date and end_date parameters. Supported combinations:
start_date given → data from the start date to now.end_date given → data from (end_date minus feature view TTL) to end_date.We welcome contributions to add or improve entity-less retrieval. See GitHub issue #1611.
Feast currently does not support any access control other than the access control required for the Provider's environment (for example, GCP and AWS permissions).
It is a good idea though to lock down the registry file so only the CI/CD pipeline can modify it. That way data scientists and other users cannot accidentally modify the registry and lose other team's data.
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. Feast also defines a stream processor that allows a deeper integration with stream sources.
There are several kinds of transformations:
get_historical_features and at online serving time when you call `get_online_features.Yes. See documentation.
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.
Feast is designed to work at scale and support low latency online serving. See our benchmark blog post for details.
Yes. Specifically:
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 customizing Feast here.
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. You can override the offline and online stores to be in different clouds if you wish.
The data source and the offline store are closely tied, but separate concepts. The offline store controls how feast talks to a data store for historical feature retrieval, and the data source points to specific table (or query) within a data store. Offline stores are infrastructure-level connectors to data stores like Snowflake.
Additional differences:
Please follow the instructions here.
Yes. For example, the Postgres connector can be used as both an offline and online store (as well as the registry).
Yes. There are two ways to use S3 in Feast:
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.Please see the roadmap.
For more details on contributing to the Feast community, see here and this here.
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.
Please see this document. If you have any questions or suggestions, feel free to leave a comment on the document!
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.