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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