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Offline stores

Please see Offline Store for a conceptual explanation of offline stores.

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 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

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

Below is a matrix indicating which RetrievalJobs support what functionality.

File

Description

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

All data is downloaded and joined using Python and therefore may not scale to production workloads.

Example

The full set of configuration options is available in .

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 file offline store.

File

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

Snowflake

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.

Example

feature_store.yaml

project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: snowflake.offline
  account: snowflake_deployment.us-east-1
  user: user_login
  password: user_password
  role: sysadmin
  warehouse: demo_wh
  database: FEAST

The full set of configuration options is available in SnowflakeOfflineStoreConfig.

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 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 functionality matrix.

BigQuery

Description

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.

Getting started

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.

Example

The full set of configuration options is available in .

Functionality Matrix

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

Redshift

Description

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.

Getting started

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.

Example

Functionality Matrix

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

Permissions

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:

Spark (contrib)

Description

The Spark 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 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.

Example

Functionality Matrix

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

PostgreSQL (contrib)

Description

The PostgreSQL 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 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.

Example

Functionality Matrix

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

Trino (contrib)

Description

The Trino offline store provides support for reading TrinoSources.

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.

Disclaimer

The Trino 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[trino]'. You can then run feast init, then swap out feature_store.yaml with the below example to connect to Trino.

Example

feature_store.yaml

project: feature_repo
registry: data/registry.db
provider: local
offline_store:
    type: feast_trino.trino.TrinoOfflineStore
    host: localhost
    port: 8080
    catalog: memory
    connector:
        type: memory
online_store:
    path: data/online_store.db

The full set of configuration options is available in TrinoOfflineStoreConfig.

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 Trino offline store.

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

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

Azure Synapse + Azure SQL (contrib)

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

feature_store.yaml

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 here. 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 functionality matrix.

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 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 .

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

File

BigQuery

Snowflake

Redshift

Postgres

Spark

Trino

Below is a matrix indicating which RetrievalJobs support what functionality.

File

BigQuery

Snowflake

Redshift

Postgres

Spark

Trino

Redshift

Description

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.

Getting started

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.

Example

The full set of configuration options is available in .

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 Redshift offline store.

Redshift

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

Redshift

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

Permissions

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:

{
    "Statement": [
        {
            "Action": [
                "s3:ListBucket",
                "s3:PutObject",
                "s3:GetObject",
                "s3:DeleteObject"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::<bucket_name>/*",
                "arn:aws:s3:::<bucket_name>"
            ]
        },
        {
            "Action": [
                "redshift-data:DescribeTable",
                "redshift:GetClusterCredentials",
                "redshift-data:ExecuteStatement"
            ],
            "Effect": "Allow",
            "Resource": [
                "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>"
            ]
        },
        {
            "Action": [
                "redshift-data:DescribeStatement"
            ],
            "Effect": "Allow",
            "Resource": "*"
        }
    ],
    "Version": "2012-10-17"
}

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.

The following inline policy can be used to grant Redshift necessary permissions to access S3:

{
    "Statement": [
        {
            "Action": "s3:*",
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::feast-integration-tests",
                "arn:aws:s3:::feast-integration-tests/*"
            ]
        }
    ],
    "Version": "2012-10-17"
}

While the following trust relationship is necessary to make sure that Redshift, and only Redshift can assume this role:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "redshift.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}