This remote online store will let you interact with remote feature server. At this moment this only supports the read operation. You can use this online store and able retrieve online features store.get_online_features from remote feature server.
Examples
The registry is pointing to registry of remote feature store. If it is not accessible then should be configured to use remote registry.
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
update: update infrastructure (e.g. tables) in the online store
teardown: teardown infrastructure (e.g. tables) in the online store
plan: generate a plan of infrastructure changes based on feature repo changes
There is also additional functionality not properly captured by these interface methods:
support for on-demand transforms
readable by Python SDK
readable by Java
readable by Go
Finally, there are multiple data models for storing the features in the online store. For example, features could be:
collocated by feature view
collocated by feature service
collocated by entity key
See this for a discussion around the tradeoffs of each of these data models.
Functionality Matrix
There are currently five core online store implementations: SqliteOnlineStore, RedisOnlineStore, DynamoDBOnlineStore, SnowflakeOnlineStore, and DatastoreOnlineStore. There are several additional implementations contributed by the Feast community (PostgreSQLOnlineStore, HbaseOnlineStore, CassandraOnlineStore and IKVOnlineStore), which are not guaranteed to be stable or to match the functionality of the core implementations. Details for each specific online store, such as how to configure it in a feature_store.yaml, can be found .
Below is a matrix indicating which online stores support what functionality.
SingleStore (contrib)
Description
The SingleStore online store provides support for materializing feature values into a SingleStore database for serving online features.
Getting started
support for entityless feature views
support for concurrent writing to the same key
support for ttl (time to live) at retrieval
support for deleting expired data
yes
yes
yes
read feature values from the online store
yes
yes
yes
yes
yes
yes
yes
yes
yes
update infrastructure (e.g. tables) in the online store
yes
yes
yes
yes
yes
yes
yes
yes
yes
teardown infrastructure (e.g. tables) in the online store
In order to use this online store, you'll need to run pip install 'feast[singlestore]'. You can get started by then running feast init and then setting the feature_store.yaml as described below.
Example
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 SingleStore online store.
SingleStore
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
Hazelcast (contrib)
Description
The Hazelcast online store provides support for materializing feature values into a Hazelcast cluster for serving online features in real-time. In order to use Hazelcast as an online store, you need to have a running Hazelcast cluster. See this getting started page for more details.
Each feature view is mapped one-to-one to a specific Hazelcast IMap
This implementation inherits all strengths of Hazelcast such as high availability, fault-tolerance, and data distribution.
Secure TSL/SSL connection is supported by Hazelcast online store.
You can set TTL (Time-To-Live) setting for your features in Hazelcast cluster.
Each feature view corresponds to an IMap in Hazelcast cluster and the entries in that IMap correspond to features of entities. Each feature value stored separately and can be retrieved individually.
Getting started
In order to use Hazelcast online store, you'll need to run pip install 'feast[hazelcast]'. You can then get started with the command feast init REPO_NAME -t hazelcast.
Examples
Functionality Matrix
Hazelcast
To compare this set of functionality against other online stores, please see the full .
SQLite
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
The full set of configuration options is available in .
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.
Sqlite
To compare this set of functionality against other online stores, please see the full .
Rockset (contrib)
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
Example
Snowflake
Description
The online store provides support for materializing feature values into a Snowflake Transient Table for serving online features.
Only the latest feature values are persisted
Dragonfly
Description
is a modern in-memory datastore that implements novel algorithms and data structures on top of a multi-threaded, share-nothing architecture. Thanks to its API compatibility, Dragonfly can act as a drop-in replacement for Redis. Due to Dragonfly's hardware efficiency, you can run a single node instance on a small 8GB instance or scale vertically to large 768GB machines with 64 cores. This greatly reduces infrastructure costs as well as architectural complexity.
Similar to Redis, Dragonfly can be used as an online feature store for Feast.
IKV
Description
is a fully-managed embedded key-value store, primarily designed for storing ML features. Most key-value stores (think Redis or Cassandra) need a remote database cluster, whereas IKV allows you to utilize your existing application infrastructure to store data (cost efficient) and access it without any network calls (better performance). See detailed performance benchmarks and cost comparison with Redis on . IKV can be used as an online-store in Feast, the rest of this guide goes over the setup.
Datastore
Description
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 .
project: my_feature_app
registry: data/registry.db
provider: local
online_store:
## Basic Configs ##
# If apikey or host is left blank the driver will try to pull
# these values from environment variables ROCKSET_APIKEY and
# ROCKSET_APISERVER respectively.
type: rockset
api_key: <your_api_key_here>
host: <your_region_endpoint_here>
## Advanced Configs ##
# Batch size of records that will be turned per page when
# paginating a batched read.
#
# read_pagination_batch_size: 100
# The amount of time, in seconds, we will wait for the
# collection to become visible to the API.
#
# collection_created_timeout_secs: 60
# The amount of time, in seconds, we will wait for the
# collection to enter READY state.
#
# collection_ready_timeout_secs: 1800
# Whether to wait for all writes to be flushed from log
# and queryable before returning write as completed. If
# False, documents that are written may not be seen
# immediately in subsequent reads.
#
# fence_all_writes: True
# The amount of time we will wait, in seconds, for the
# write fence to be passed
#
# fence_timeout_secs: 600
# Initial backoff, in seconds, we will wait between
# requests when polling for a response.
#
# initial_request_backoff_secs: 2
# Initial backoff, in seconds, we will wait between
# requests when polling for a response.
# max_request_backoff_secs: 30
# The max amount of times we will retry a failed request.
# max_request_attempts: 10000
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)
Getting started
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.
Example
Tags KWARGs Actions:
"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 set of functionality supported by online stores is described in detail here. Below is a matrix indicating which functionality is supported by the Snowflake online store.
Snowflake
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
Using Dragonfly as a drop-in Feast online store instead of Redis
Make sure you have Python and pip installed.
Install the Feast SDK and CLI
pip install feast
In order to use Dragonfly as the online store, you'll need to install the redis extra:
pip install 'feast[redis]'
1. Create a feature repository
Bootstrap a new feature repository:
Update feature_repo/feature_store.yaml with the below contents:
2. Start Dragonfly
There are several options available to get Dragonfly up and running quickly. We will be using Docker for this tutorial.
docker run --network=host --ulimit memlock=-1 docker.dragonflydb.io/dragonflydb/dragonfly
3. Register feature definitions and deploy your feature store
feast apply
The apply command scans python files in the current directory (example_repo.py in this case) for feature view/entity definitions, registers the objects, and deploys infrastructure. You should see the following output:
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 Redis online store.
Redis
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
In order to use this online store, you'll need to install the IKV extra (along with the dependency needed for the offline store of choice). E.g.
pip install 'feast[gcp, ikv]'
pip install 'feast[snowflake, ikv]'
pip install 'feast[aws, ikv]'
pip install 'feast[azure, ikv]'
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 IKV as the online store as seen below in the examples.
Update my_feature_repo/feature_store.yaml with the below contents:
After provisioning an IKV account/store, you should have an account id, passkey and store-name. Additionally you must specify a mount-directory - where IKV will pull/update (maintain) a copy of the index for online reads (IKV is an embedded database). It can be skipped only if you don't plan to read any data from this container. The mount directory path usually points to a location on local/remote disk.
The full set of configuration options is available in IKVOnlineStoreConfig at sdk/python/feast/infra/online_stores/contrib/ikv_online_store/ikv.py
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 IKV online store.
IKV
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full 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 Datastore online store.
Datastore
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
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
The full set of configuration options is available in .
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 Mys online store.
Mys
To compare this set of functionality against other online stores, please see the full .
ScyllaDB (contrib)
Description
ScyllaDB is a low-latency and high-performance Cassandra-compatible (uses CQL) database. You can use the existing Cassandra connector to use ScyllaDB as an online store in Feast.
The ScyllaDB online store provides support for materializing feature values into a ScyllaDB or ScyllaDB Cloud cluster for serving online features real-time.
Getting started
Install Feast with Cassandra support:
Create a new Feast project:
Example (ScyllaDB)
Example (ScyllaDB Cloud)
The full set of configuration options is available in . For a full explanation of configuration options please look at file sdk/python/feast/infra/online_stores/contrib/cassandra_online_store/README.md.
Storage specifications can be found at docs/specs/online_store_format.md.
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 Cassandra plugin.
Cassandra
To compare this set of functionality against other online stores, please see the full .
Resources
Cassandra + Astra DB (contrib)
Description
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
This implementation inherits all strengths of Cassandra such as high availability, fault-tolerance, and data distribution
Getting started
In order to use this online store, you'll need to run pip install 'feast[cassandra]'. You can then get started with the command feast init REPO_NAME -t cassandra.
Example (Cassandra)
Example (Astra DB)
The full set of configuration options is available in . For a full explanation of configuration options please look at file sdk/python/feast/infra/online_stores/contrib/cassandra_online_store/README.md.
Storage specifications can be found at docs/specs/online_store_format.md.
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 Cassandra online store.
Cassandra
To compare this set of functionality against other online stores, please see the full .
PostgreSQL (contrib)
Description
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
Bigtable
Description
The 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 .
....
Created entity driver
Created feature view driver_hourly_stats_fresh
Created feature view driver_hourly_stats
Created on demand feature view transformed_conv_rate
Created on demand feature view transformed_conv_rate_fresh
Created feature service driver_activity_v1
Created feature service driver_activity_v3
Created feature service driver_activity_v2
The set of functionality supported by online stores is described in detail here. Below is a matrix indicating which functionality is supported by the Postgres online store.
Postgres
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
PGVector
The Postgres online store supports the use of PGVector for storing feature values. To enable PGVector, set pgvector_enabled: true in the online store configuration.
The vector_len parameter can be used to specify the length of the vector. The default value is 512.
Please make sure to follow the instructions in the repository, which, as the time of this writing, requires you to run CREATE EXTENSION vector; in the database.
Then you can use retrieve_online_documents to retrieve the top k closest vectors to a query vector. For the Retrieval Augmented Generation (RAG) use-case, you have to embed the query prior to passing the query vector.
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.
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.
Bigtable
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
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]'
pip install 'feast[azure, 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.
Examples
Connecting to a single Redis instance:
Connecting to a Redis Cluster with SSL enabled and password authentication:
Connecting to a Redis Sentinel 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 set of functionality supported by online stores is described in detail here. Below is a matrix indicating which functionality is supported by the Redis online store.
Redis
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
To compare this set of functionality against other online stores, please see the full functionality matrix.
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
The full set of configuration options is available in .
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 .
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 DynamoDB online store.
DynamoDB
To compare this set of functionality against other online stores, please see the full .