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.

Below is a matrix indicating which RetrievalJobs support what functionality.

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