Please see for an explanation of online stores.

Description

The SQLite online store provides support for materializing feature values into an SQLite database for serving online features.

• All feature values are stored in an on-disk SQLite database

• Only the latest feature values are persisted

Example

project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
  type: sqlite
  path: data/online_store.db

The full set of configuration options is available in SqliteOnlineStoreConfig.

Functionality Matrix

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

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

Description

The Bigtable online store provides support for materializing feature values into Cloud Bigtable. The data model used to store feature values in Bigtable is described in more detail here.

Getting started

In order to use this online store, you'll need to run pip install 'feast[gcp]'. You can then get started with the command feast init REPO_NAME -t gcp.

Example

project: my_feature_repo
registry: data/registry.db
provider: gcp
online_store:
  type: bigtable
  project_id: my_gcp_project
  instance: my_bigtable_instance

The full set of configuration options is available in BigtableOnlineStoreConfig.

Functionality Matrix

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

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

The PostgreSQL online store provides support for materializing feature values into a PostgreSQL database for serving online features.

• Only the latest feature values are persisted

• sslmode, sslkey_path, sslcert_path, and sslrootcert_path are optional

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

The online store provides support for materializing feature values into Cloud Datastore. The data model used to store feature values in Datastore is described in more detail .

In order to use this online store, you'll need to run pip install 'feast[gcp]'. You can then get started with the command feast init REPO_NAME -t gcp.

The full set of configuration options is available in .

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

Description

The Snowflake online store provides support for materializing feature values into a Snowflake Transient Table for serving online features.

• Only the latest feature values are persisted

The data model for using a Snowflake Transient Table as an online store follows a tall format (one row per feature)):

• "entity_feature_key" (BINARY) -- unique key used when reading specific feature_view x entity combination

• "entity_key" (BINARY) -- repeated key currently unused for reading entity_combination

• "feature_name" (VARCHAR)

• "value" (BINARY)

• "event_ts" (TIMESTAMP)

• "created_ts" (TIMESTAMP)

(This model may be subject to change when Snowflake Hybrid Tables are released)

In order to use this online store, you'll need to run pip install 'feast[snowflake]'. You can then get started with the command feast init REPO_NAME -t snowflake.

"snowflake-online-store/online_path": Adding the "snowflake-online-store/online_path" key to a FeatureView tags parameter allows you to choose the online table path for the online serving table (ex. "{database}"."{schema}").

The full set of configuration options is available in .

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

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

Here are the methods exposed by the OnlineStore interface, along with the core functionality supported by the method:

• online_write_batch: write feature values to the online store

• online_read: read feature values from the online store

Description

The MySQL online store provides support for materializing feature values into a MySQL database for serving online features.

• Only the latest feature values are persisted

Getting started

In order to use this online store, you'll need to run pip install 'feast[mysql]'. You can get started by then running feast init and then setting the feature_store.yaml as described below.

Example

project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
    type: mysql
    host: DB_HOST
    port: DB_PORT
    database: DB_NAME
    user: DB_USERNAME
    password: DB_PASSWORD

The full set of configuration options is available in .

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

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

The [Cassandra / Astra DB] online store provides support for materializing feature values into an Apache Cassandra / Astra DB database for online features.

• The whole project is contained within a Cassandra keyspace

• Each feature view is mapped one-to-one to a specific Cassandra table

Description

The Redis online store provides support for materializing feature values into Redis.

• Both Redis and Redis Cluster are supported.

• The data model used to store feature values in Redis is described in more detail here.

Getting started

In order to use this online store, you'll need to install the redis extra (along with the dependency needed for the offline store of choice). E.g.

• pip install 'feast[gcp, redis]'

• pip install 'feast[snowflake, redis]'

• pip install 'feast[aws, redis]'

You can get started by using any of the other templates (e.g. feast init -t gcp or feast init -t snowflake or feast init -t aws), and then swapping in Redis as the online store as seen below in the examples.

Connecting to a single Redis instance:

Connecting to a Redis Cluster with SSL enabled and password authentication:

Additionally, the redis online store also supports automatically deleting data via a TTL mechanism. The TTL is applied at the entity level, so feature values from any associated feature views for an entity are removed together. This TTL can be set in the feature_store.yaml, using the key_ttl_seconds field in the online store. For example:

The full set of configuration options is available in .

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

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

Description

The DynamoDB online store provides support for materializing feature values into AWS DynamoDB.

Getting started

In order to use this online store, you'll need to run pip install 'feast[aws]'. You can then get started with the command feast init REPO_NAME -t aws.

Example

project: my_feature_repo
registry: data/registry.db
provider: aws
online_store:
  type: dynamodb
  region: us-west-2

The full set of configuration options is available in DynamoDBOnlineStoreConfig.

Permissions

Feast requires the following permissions in order to execute commands for DynamoDB online store:

The following inline policy can be used to grant Feast the necessary permissions:

Lastly, this IAM role needs to be associated with the desired Redshift cluster. Please follow the official AWS guide for the necessary steps .

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

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

Description

In Alpha Development.

The Rockset online store provides support for materializing feature values within a Rockset collection in order to serve features in real-time.

• Each document is uniquely identified by its '_id' value. Repeated inserts into the same document '_id' will result in an upsert.

Rockset indexes all columns allowing for quick per feature look up and also allows for a dynamic typed schema that can change based on any new requirements. API Keys can be found in the Rockset console. You can also find host urls on the same tab by clicking "View Region Endpoint Urls".

Data Model Used Per Doc