The Redshift offline store provides support for reading RedshiftSources.
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
.
The full set of configuration options is available in RedshiftOfflineStoreConfig.
The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Redshift offline store.
Below is a matrix indicating which functionality is supported by RedshiftRetrievalJob
.
To compare this set of functionality against other offline stores, please see the full functionality matrix.
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:
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 UNLOAD and COPY commands. Once created, this IAM role needs to be configured in feature_store.yaml
file as offline_store: iam_role
.
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:
Redshift | |
---|---|
Redshift | |
---|---|
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
yes
export to SQL
yes
export to data lake (S3, GCS, etc.)
no
export to data warehouse
yes
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
Command
Permissions
Resources
Apply
redshift-data:DescribeTable
redshift:GetClusterCredentials
arn:aws:redshift:<region>:<account_id>:dbuser:<redshift_cluster_id>/<redshift_username>
arn:aws:redshift:<region>:<account_id>:dbname:<redshift_cluster_id>/<redshift_database_name>
arn:aws:redshift:<region>:<account_id>:cluster:<redshift_cluster_id>
Materialize
redshift-data:ExecuteStatement
arn:aws:redshift:<region>:<account_id>:cluster:<redshift_cluster_id>
Materialize
redshift-data:DescribeStatement
*
Materialize
s3:ListBucket
s3:GetObject
s3:DeleteObject
arn:aws:s3:::<bucket_name>
arn:aws:s3:::<bucket_name>/*
Get Historical Features
redshift-data:ExecuteStatement
redshift:GetClusterCredentials
arn:aws:redshift:<region>:<account_id>:dbuser:<redshift_cluster_id>/<redshift_username>
arn:aws:redshift:<region>:<account_id>:dbname:<redshift_cluster_id>/<redshift_database_name>
arn:aws:redshift:<region>:<account_id>:cluster:<redshift_cluster_id>
Get Historical Features
redshift-data:DescribeStatement
*
Get Historical Features
s3:ListBucket
s3:GetObject
s3:PutObject
s3:DeleteObject
arn:aws:s3:::<bucket_name>
arn:aws:s3:::<bucket_name>/*