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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
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 Sqlite online store.
To compare this set of functionality against other online stores, please see the full .
yes
readable by Java
no
readable by Go
yes
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
yes
support for on-demand transforms
yes
readable by Python SDK
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: sqlite
path: data/online_store.dbThis 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.
The registry is pointing to registry of remote feature store. If it is not accessible then should be configured to use remote registry.
ssl_cert_path is an optional configuration to the public certificate path when the online server starts in SSL mode. This may be needed if the online server is started with a self-signed certificate, typically this file ends with *.crt, *.cer, or *.pem.
Please refer the for more details on how to configure authentication and authorization.
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.
project: my-local-project
registry: /remote/data/registry.db
provider: local
online_store:
path: http://localhost:6566
type: remote
ssl_cert_path: /path/to/cert.pem
entity_key_serialization_version: 2
auth:
type: no_authpip install 'feast[aws]'feast init REPO_NAME -t awsThe full set of configuration options is available in DynamoDBOnlineStoreConfig.
Feast requires the following permissions in order to execute commands for DynamoDB online store:
Command
Permissions
Resources
Apply
dynamodb:CreateTable
dynamodb:DescribeTable
dynamodb:DeleteTable
arn:aws:dynamodb:<region>:<account_id>:table/*
Materialize
dynamodb.BatchWriteItem
arn:aws:dynamodb:<region>:<account_id>:table/*
Get Online Features
dynamodb.BatchGetItem
arn:aws:dynamodb:<region>:<account_id>:table/*
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 here.
The set of functionality supported by online stores is described in detail here. Below is a matrix indicating which functionality is supported by the DynamoDB online store.
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.
Make sure you have Python and pip installed.
Install the Feast SDK and CLI: pip install feast
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.
Go to https://inlined.io or email onboarding[at]inlined.io
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
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.
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 Datastore 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 here.
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.
To compare this set of functionality against other online stores, please see the full .
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
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.
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 SingleStore online store provides support for materializing feature values into a SingleStore database for serving online features.
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.
The set of functionality supported by online stores is described in detail . Below is a matrix indicating which functionality is supported by the SingleStore online store.
To compare this set of functionality against other online stores, please see the full .
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.
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 Bigtable online store.
To compare this set of functionality against other online stores, please see the full .
The 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 page for more details.
Each feature view is mapped one-to-one to a specific Hazelcast IMap
project: my_feature_repo
registry: data/registry.db
provider: aws
online_store:
type: dynamodb
region: us-west-2{
"Statement": [
{
"Action": [
"dynamodb:CreateTable",
"dynamodb:DescribeTable",
"dynamodb:DeleteTable",
"dynamodb:BatchWriteItem",
"dynamodb:BatchGetItem"
],
"Effect": "Allow",
"Resource": [
"arn:aws:dynamodb:<region>:<account_id>:table/*"
]
}
],
"Version": "2012-10-17"
}project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: ikv
account_id: secret
account_passkey: secret
store_name: your-store-name
mount_directory: /absolute/path/on/disk/for/ikv/embedded/indexreadable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
yes
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
no
collocated by feature service
no
collocated by entity key
yes
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
yes
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
yes
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
readable by Python SDK
project: my_feature_repo
registry: data/registry.db
provider: gcp
online_store:
type: datastore
project_id: my_gcp_project
namespace: my_datastore_namespaceproject: 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_PASSWORDproject: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: singlestore
host: DB_HOST
port: DB_PORT
database: DB_NAME
user: DB_USERNAME
password: DB_PASSWORDproject: my_feature_repo
registry: data/registry.db
provider: gcp
online_store:
type: bigtable
project_id: my_gcp_project
instance: my_bigtable_instanceThis 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.
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.
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 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 .
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:
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 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 .
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.
Install Feast with Cassandra support:
Create a new Feast project:
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.
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.
To compare this set of functionality against other online stores, please see the full .
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 .
Dragonfly 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.
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]'
Bootstrap a new feature repository:
Update feature_repo/feature_store.yaml with the below contents:
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
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:
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 .
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: hazelcast
cluster_name: dev
cluster_members: ["localhost:5701"]
key_ttl_seconds: 36000readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
yes
support for ttl (time to live) at retrieval
yes
support for deleting expired data
yes
collocated by feature view
no
collocated by feature service
no
collocated by entity key
yes
pip install 'feast[azure, redis]'yes
readable by Java
yes
readable by Go
yes
support for entityless feature views
yes
support for concurrent writing to the same key
yes
support for ttl (time to live) at retrieval
yes
support for deleting expired data
yes
collocated by feature view
no
collocated by feature service
no
collocated by entity key
yes
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
readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
yes
support for on-demand transforms
yes
readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
readable by Python SDK
yes
readable by Java
yes
readable by Go
yes
support for entityless feature views
yes
support for concurrent writing to the same key
yes
support for ttl (time to live) at retrieval
yes
support for deleting expired data
yes
collocated by feature view
no
collocated by feature service
no
collocated by entity key
yes
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
readable by Python SDK
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: redis
connection_string: "localhost:6379"project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: redis
redis_type: redis_cluster
connection_string: "redis1:6379,redis2:6379,ssl=true,password=my_password"project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: redis
redis_type: redis_sentinel
sentinel_master: mymaster
connection_string: "redis1:26379,ssl=true,password=my_password"project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: redis
key_ttl_seconds: 604800
connection_string: "localhost:6379"pip install "feast[cassandra]"feast init REPO_NAME -t cassandraproject: scylla_feature_repo
registry: data/registry.db
provider: local
online_store:
type: cassandra
hosts:
- 172.17.0.2
keyspace: feast
username: scylla
password: passwordproject: scylla_feature_repo
registry: data/registry.db
provider: local
online_store:
type: cassandra
hosts:
- node-0.aws_us_east_1.xxxxxxxx.clusters.scylla.cloud
- node-1.aws_us_east_1.xxxxxxxx.clusters.scylla.cloud
- node-2.aws_us_east_1.xxxxxxxx.clusters.scylla.cloud
keyspace: feast
username: scylla
password: passwordproject: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: snowflake.online
account: SNOWFLAKE_DEPLOYMENT_URL
user: SNOWFLAKE_USER
password: SNOWFLAKE_PASSWORD
role: SNOWFLAKE_ROLE
warehouse: SNOWFLAKE_WAREHOUSE
database: SNOWFLAKE_DATABASEdriver_stats_fv = FeatureView(
...
tags={"snowflake-online-store/online_path": '"FEAST"."ONLINE"'},
)feast init feast_dragonfly
cd feast_dragonfly/feature_repoproject: feast_dragonfly
registry: data/registry.db
provider: local
online_store:
type: redis
connection_string: "localhost:6379"....
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_v2online_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
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
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 issue for a discussion around the tradeoffs of each of these data models.
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 here.
Below is a matrix indicating which online stores support what functionality.
Sqlite
Redis
DynamoDB
Snowflake
Datastore
Postgres
Hbase
[ / ]
write feature values to the online store
yes
yes
yes
yes
yes
In order to use this online store, you'll need to run pip install 'feast[postgres]'. You can get started by then running feast init -t postgres.
The full set of configuration options is available in PostgreSQLOnlineStoreConfig.
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.
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 Postgres online store supports the use of PGVector for storing feature values. To enable PGVector, set vector_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.
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
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.
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.
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.
To compare this set of functionality against other online stores, please see the full .
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: postgres
host: DB_HOST
port: DB_PORT
database: DB_NAME
db_schema: DB_SCHEMA
user: DB_USERNAME
password: DB_PASSWORD
sslmode: verify-ca
sslkey_path: /path/to/client-key.pem
sslcert_path: /path/to/client-cert.pem
sslrootcert_path: /path/to/server-ca.pem
vector_enabled: false
vector_len: 512from feast import FeatureStore
from feast.infra.online_stores.postgres import retrieve_online_documents
feature_store = FeatureStore(repo_path=".")
query_vector = [0.1, 0.2, 0.3, 0.4, 0.5]
top_k = 5
feature_values = retrieve_online_documents(
feature_store=feature_store,
feature_view_name="document_fv:embedding_float",
query_vector=query_vector,
top_k=top_k,
)readable by Python SDK
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
yes
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
yes
yes
yes
yes
yes
yes
yes
yes
yes
generate a plan of infrastructure changes
yes
no
no
no
no
no
no
yes
no
support for on-demand transforms
yes
yes
yes
yes
yes
yes
yes
yes
yes
readable by Python SDK
yes
yes
yes
yes
yes
yes
yes
yes
yes
readable by Java
no
yes
no
no
no
no
no
no
no
readable by Go
yes
yes
no
no
no
no
no
no
no
support for entityless feature views
yes
yes
yes
yes
yes
yes
yes
yes
yes
support for concurrent writing to the same key
no
yes
no
no
no
no
no
no
yes
support for ttl (time to live) at retrieval
no
yes
no
no
no
no
no
no
no
support for deleting expired data
no
yes
no
no
no
no
no
no
no
collocated by feature view
yes
no
yes
yes
yes
yes
yes
yes
no
collocated by feature service
no
no
no
no
no
no
no
no
no
collocated by entity key
no
yes
no
no
no
no
no
no
yes
yes
readable by Java
no
readable by Go
no
support for entityless feature views
yes
support for concurrent writing to the same key
no
support for ttl (time to live) at retrieval
no
support for deleting expired data
no
collocated by feature view
yes
collocated by feature service
no
collocated by entity key
no
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
yes
support for on-demand transforms
yes
readable by Python SDK
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: cassandra
hosts:
- 192.168.1.1
- 192.168.1.2
- 192.168.1.3
keyspace: KeyspaceName
port: 9042 # optional
username: user # optional
password: secret # optional
protocol_version: 5 # optional
load_balancing: # optional
local_dc: 'datacenter1' # optional
load_balancing_policy: 'TokenAwarePolicy(DCAwareRoundRobinPolicy)' # optional
read_concurrency: 100 # optional
write_concurrency: 100 # optionalproject: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: cassandra
secure_bundle_path: /path/to/secure/bundle.zip
keyspace: KeyspaceName
username: Client_ID
password: Client_Secret
protocol_version: 4 # optional
load_balancing: # optional
local_dc: 'eu-central-1' # optional
load_balancing_policy: 'TokenAwarePolicy(DCAwareRoundRobinPolicy)' # optional
read_concurrency: 100 # optional
write_concurrency: 100 # optional