# 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 `RetrievalJob`s:

* 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 (`RetrievalJob`s are lazily executed)
* read partitioned data

## Functionality Matrix

There are currently four core offline store implementations: `DaskOfflineStore`, `BigQueryOfflineStore`, `SnowflakeOfflineStore`, and `RedshiftOfflineStore`. There are several additional implementations contributed by the Feast community (`PostgreSQLOfflineStore`, `SparkOfflineStore`, `TrinoOfflineStore`, and `RayOfflineStore`), 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](https://docs.feast.dev/reference/offline-stores).

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

|| | Dask | BigQuery | Snowflake | Redshift | Postgres | Spark | Trino | Couchbase | Ray | || :-------------------------------- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | :-- | || `get_historical_features` | yes | yes | yes | yes | yes | yes | yes | yes | yes | || `pull_latest_from_table_or_query` | yes | yes | yes | yes | yes | yes | yes | yes | yes | || `pull_all_from_table_or_query` | yes | yes | yes | yes | yes | yes | yes | yes | yes | || `offline_write_batch` | yes | yes | yes | yes | no | no | no | no | yes | || `write_logged_features` | yes | yes | yes | yes | no | no | no | no | yes |

Below is a matrix indicating which `RetrievalJob`s support what functionality.

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