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.
Text
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.
Text
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

Text	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

Text	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

Last modified 1mo ago