Overview

Functionality

Here are the methods exposed by the OfflineStore interface, along with the core functionality supported by the method:

• get_historical_features: point-in-time correct join to retrieve historical features

• pull_latest_from_table_or_query: retrieve latest feature values for materialization into the online store

• pull_all_from_table_or_query: retrieve a saved dataset

• offline_write_batch: persist dataframes to the offline store, primarily for push sources

• write_logged_features: persist logged features to the offline store, for feature logging

The first three of these methods all return a RetrievalJob specific to an offline store, such as a SnowflakeRetrievalJob. Here is a list of functionality supported by RetrievalJobs:

• export to dataframe

• export to arrow table

• export to arrow batches (to handle large datasets in memory)

• export to SQL

• export to data lake (S3, GCS, etc.)

• export to data warehouse

• export as Spark dataframe

• local execution of Python-based on-demand transforms

• remote execution of Python-based on-demand transforms

• persist results in the offline store

• preview the query plan before execution (RetrievalJobs are lazily executed)

• read partitioned data

Functionality Matrix

There are currently four core offline store implementations: FileOfflineStore, BigQueryOfflineStore, SnowflakeOfflineStore, and RedshiftOfflineStore. There are several additional implementations contributed by the Feast community (PostgreSQLOfflineStore, SparkOfflineStore, and TrinoOfflineStore), which are not guaranteed to be stable or to match the functionality of the core implementations. Details for each specific offline store, such as how to configure it in a feature_store.yaml, can be found here.

Below is a matrix indicating which offline stores support which methods.

	File	BigQuery	Snowflake	Redshift	Postgres	Spark	Trino
get_historical_features	yes	yes	yes	yes	yes	yes	yes
pull_latest_from_table_or_query	yes	yes	yes	yes	yes	yes	yes
pull_all_from_table_or_query	yes	yes	yes	yes	yes	yes	yes
offline_write_batch	yes	yes	yes	yes	no	no	no
write_logged_features	yes	yes	yes	yes	no	no	no

Below is a matrix indicating which RetrievalJobs support what functionality.

	File	BigQuery	Snowflake	Redshift	Postgres	Spark	Trino
export to dataframe	yes	yes	yes	yes	yes	yes	yes
export to arrow table	yes	yes	yes	yes	yes	yes	yes
export to arrow batches	no	no	no	yes	no	no	no
export to SQL	no	yes	no	yes	yes	no	yes
export to data lake (S3, GCS, etc.)	no	no	yes	no	yes	no	no
export to data warehouse	no	yes	yes	yes	yes	no	no
export as Spark dataframe	no	no	no	no	no	yes	no
local execution of Python-based on-demand transforms	yes	yes	yes	yes	yes	no	yes
remote execution of Python-based on-demand transforms	no	no	no	no	no	no	no
persist results in the offline store	yes	yes	yes	yes	yes	yes	no
preview the query plan before execution	yes	yes	yes	yes	yes	yes	yes
read partitioned data	yes	yes	yes	yes	yes	yes	yes