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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
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.
| File | BigQuery | Snowflake | Redshift | Postgres | Spark | Trino | |
|---|---|---|---|---|---|---|---|
| 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
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
yes
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
yes
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
The file offline store provides support for reading FileSources. It uses Dask as the compute engine.
All data is downloaded and joined using Python and therefore may not scale to production workloads.
The full set of configuration options is available in FileOfflineStoreConfig.
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 functionality matrix.
| File | |
|---|---|
| File | |
|---|---|
get_historical_features (point-in-time correct join)
yes
pull_latest_from_table_or_query (retrieve latest feature values)
yes
pull_all_from_table_or_query (retrieve a saved dataset)
yes
offline_write_batch (persist dataframes to offline store)
yes
write_logged_features (persist logged features to offline store)
yes
export to dataframe
yes
export to arrow table
yes
export to arrow batches
no
export to SQL
no
export to data lake (S3, GCS, etc.)
no
export to data warehouse
no
export as Spark dataframe
no
local execution of Python-based on-demand transforms
yes
remote execution of Python-based on-demand transforms
no
persist results in the offline store
yes
preview the query plan before execution
yes
read partitioned data
yes
The BigQuery offline store provides support for reading BigQuerySources.
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]'. You can get started by then running feast init -t gcp.
The full set of configuration options is available in BigQueryOfflineStoreConfig.
The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the BigQuery offline store.
Below is a matrix indicating which functionality is supported by BigQueryRetrievalJob.
*See GitHub issue for details on proposed solutions for enabling the BigQuery offline store to understand tables that use _PARTITIONTIME as the partition column.
To compare this set of functionality against other offline stores, please see the full functionality matrix.
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]'. You can get started by then running feast init -t aws.
Below is a matrix indicating which functionality is supported by RedshiftRetrievalJob.
Feast requires the following permissions in order to execute commands for Redshift offline store:
The following inline policy can be used to grant Feast the necessary permissions:
The following inline policy can be used to grant Redshift necessary permissions to access S3:
While the following trust relationship is necessary to make sure that Redshift, and only Redshift can assume this role:
| BigQuery | |
|---|---|
| BigQuery | |
|---|---|
The Redshift offline store provides support for reading .
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 Redshift offline store.
| Redshift |
|---|
| Redshift |
|---|
To compare this set of functionality against other offline stores, please see the full .
In addition to this, Redshift offline store requires an IAM role that will be used by Redshift itself to interact with S3. More concretely, Redshift has to use this IAM role to run and commands. Once created, this IAM role needs to be configured in feature_store.yaml file as offline_store: iam_role.
In order to use , specify a workgroup instead of a cluster_id and user.
Please note that the IAM policies above will need the version, rather than the standard .