Please see for a conceptual explanation of offline stores.

Description

The file offline store provides support for reading FileSources. It uses Dask as the compute engine.

Example

project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: file

The full set of configuration options is available in FileOfflineStoreConfig.

Functionality Matrix

The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the file offline store.

Below is a matrix indicating which functionality is supported by FileRetrievalJob.

To compare this set of functionality against other offline stores, please see the full .

Description

The MsSQL offline store provides support for reading MsSQL Sources. Specifically, it is developed to read from Synapse SQL on Microsoft Azure

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[azure]'. You can get started by then following this tutorial.

Disclaimer

The MsSQL offline store does not achieve full test coverage. Please do not assume complete stability.

Example

registry:
  registry_store_type: AzureRegistryStore
  path: ${REGISTRY_PATH} # Environment Variable
project: production
provider: azure
online_store:
    type: redis
    connection_string: ${REDIS_CONN} # Environment Variable
offline_store:
    type: mssql
    connection_string: ${SQL_CONN}  # Environment Variable

Functionality Matrix

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the Spark offline store.

Below is a matrix indicating which functionality is supported by MsSqlServerRetrievalJob.

To compare this set of functionality against other offline stores, please see the full .

Description

The Spark offline store provides support for reading SparkSources.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be converted to a Spark dataframe and processed as a temporary view.

Disclaimer

The Spark offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[spark]'. You can get started by then running feast init -t spark.

The full set of configuration options is available in .

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the Spark offline store.

Below is a matrix indicating which functionality is supported by SparkRetrievalJob.

To compare this set of functionality against other offline stores, please see the full .

Description

The PostgreSQL offline store provides support for reading PostgreSQLSources.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Postgres as a table in order to complete join operations.

Disclaimer

The PostgreSQL offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[postgres]'. You can get started by then running feast init -t postgres.

Note that sslmode, sslkey_path, sslcert_path, and sslrootcert_path are optional parameters. The full set of configuration options is available in .

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the PostgreSQL offline store.

Below is a matrix indicating which functionality is supported by PostgreSQLRetrievalJob.

To compare this set of functionality against other offline stores, please see the full .

The BigQuery offline store provides support for reading .

• All joins happen within BigQuery.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to BigQuery as a table (marked for expiration) in order to complete join operations.

In order to use this offline store, you'll need to run pip install 'feast[gcp]'

The Trino offline store provides support for reading .

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Trino as a table in order to complete join operations.

The Trino offline store does not achieve full test coverage. Please do not assume complete stability.

In order to use this offline store, you'll need to run pip install 'feast[trino]'. You can then run feast init

Description

The Snowflake offline store provides support for reading SnowflakeSources.

• All joins happen within Snowflake.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Snowflake as a temporary table in order to complete join operations.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[snowflake]'.

If you're using a file based registry, then you'll also need to install the relevant cloud extra (pip install 'feast[snowflake, CLOUD]' where CLOUD is one of aws, gcp, azure)

You can get started by then running feast init -t snowflake.

The full set of configuration options is available in .

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the Snowflake offline store.

Below is a matrix indicating which functionality is supported by SnowflakeRetrievalJob.

To compare this set of functionality against other offline stores, please see the full .

The Redshift offline store provides support for reading .

• All joins happen within Redshift.

• Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Redshift temporarily in order to complete join operations.

In order to use this offline store, you'll need to run pip install 'feast[aws]'

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)

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 .

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

Below is a matrix indicating which RetrievalJobs support what functionality.