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 here.

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

Below is a matrix indicating which RetrievalJobs support what functionality.

Description

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.

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

project: my_feature_repo
registry: data/registry.db
provider: aws
offline_store:
  type: redshift
  region: us-west-2
  cluster_id: feast-cluster
  database: feast-database
  user: redshift-user
  s3_staging_location: s3://feast-bucket/redshift
  iam_role: arn:aws:iam::123456789012:role/redshift_s3_access_role

The full set of configuration options is available in RedshiftOfflineStoreConfig.

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 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.

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 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:

{
    "Statement": [
        {
            "Action": "s3:*",
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::feast-int-bucket",
                "arn:aws:s3:::feast-int-bucket/*"
            ]
        }
    ],
    "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"
    }
  ]
}

Redshift Serverless

In order to use AWS Redshift Serverless, specify a workgroup instead of a cluster_id and user.

project: my_feature_repo
registry: data/registry.db
provider: aws
offline_store:
  type: redshift
  region: us-west-2
  workgroup: feast-workgroup
  database: feast-database
  s3_staging_location: s3://feast-bucket/redshift
  iam_role: arn:aws:iam::123456789012:role/redshift_s3_access_role

Please note that the IAM policies above will need the redshift-serverless version, rather than the standard redshift.

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

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: COMPUTE_WH
  database: FEAST
  schema: PUBLIC

The full set of configuration options is available in SnowflakeOfflineStoreConfig.

Limitation

Please be aware that here is a restriction/limitation for using SQL query string in Feast with Snowflake. Try to avoid the usage of single quote in SQL query string. For example, the following query string will fail:

SELECT
    some_column
FROM
    some_table
WHERE
    other_column = 'value'

That 'value' will fail in Snowflake. Instead, please use pairs of dollar signs like $$value$$ as mentioned in Snowflake document.

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.

Description

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.

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

project: my_feature_repo
registry: gs://my-bucket/data/registry.db
provider: gcp
offline_store:
  type: bigquery
  dataset: feast_bq_dataset

The full set of configuration options is available in BigQueryOfflineStoreConfig.

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 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.

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

Description

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

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.

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

Description

The Spark offline store provides support for reading .

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

Description

The duckdb offline store provides support for reading . It can read both Parquet and Delta formats. DuckDB offline store uses under the hood to convert offline store operations to DuckDB queries.

• Entity dataframes can be provided as a Pandas dataframe.

Getting started

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

Example

Functionality Matrix

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

Description

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.

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

Functionality Matrix

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

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.

Description

The MsSQL offline store provides support for reading . Specifically, it is developed to read from 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 .

Disclaimer

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

Example

Functionality Matrix

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

Description

The Remote Offline Store is an Arrow Flight client for the offline store that implements the RemoteOfflineStore class using the existing OfflineStore interface. The client implements various methods, including get_historical_features, pull_latest_from_table_or_query, write_logged_features, and offline_write_batch.

How to configure the client

User needs to create client side feature_store.yaml file and set the offline_store type remote and provide the server connection configuration including adding the host and specifying the port (default is 8815) required by the Arrow Flight client to connect with the Arrow Flight server.

offline_store:
  type: remote
  host: localhost
  port: 8815

Client Example

The complete example can be find under remote-offline-store-example

How to configure the server

Please see the detail how to configure offline feature server offline-feature-server.md