ScyllaDB

Description

ScyllaDB is a distributed real-time NoSQL database with vector search support. This integration uses the native scylla-driver Python driver for optimised performance and supports materializing feature values into a ScyllaDB Cloud cluster for real-time online feature serving.

Getting started

Install Feast with the scylladb extra, which pulls in scylla-driver automatically:

pip install feast[scylladb]

Example (ScyllaDB)

feature_store.yaml

project: scylla_feature_repo
registry: data/registry.db
provider: local
online_store:
    type: scylladb
    hosts:
        - 172.17.0.2
    keyspace: feast
    username: scylla
    password: password

Example (ScyllaDB Cloud)

feature_store.yaml

project: scylla_feature_repo
registry: data/registry.db
provider: local
online_store:
    type: scylladb
    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: xxxxxx
    local_dc: AWS_US_EAST_1

Configuration options

Parameter	Type	Default	Description
hosts	list[str]	(required)	Contact-point host addresses.
port	int	9042	CQL port.
keyspace	str	feast_keyspace	Target ScyllaDB keyspace.
username	str	None	Auth username.
password	str	None	Auth password.
local_dc	str	None	Local datacenter name for DC-aware load balancing.
request_timeout	float	None	Driver request timeout in seconds.
read_concurrency	int	100	concurrency argument passed to the driver's execute_concurrent_with_args for reads. Controls how many CQL statements are in-flight at once.
write_concurrency	int	100	concurrency argument passed to the driver's execute_concurrent_with_args for writes. Controls how many CQL statements are in-flight at once.
vector_similarity_function	str	COSINE	Default similarity function for vector indexes. Supported: COSINE, DOT_PRODUCT, EUCLIDEAN. Can be overridden per-feature via the similarity_function Field tag.

Storage specifications can be found at docs/specs/online_store_format.md.

Vector Search

ScyllaDB Cloud supports approximate nearest-neighbour (ANN) vector search. To enable it for a feature view, tag the embedding Field with vector_index=true and specify the number of dimensions:

feature_definitions.py

from feast import FeatureView, Field
from feast.types import Array, Float32, String

documents_fv = FeatureView(
    name="documents",
    entities=[item],
    schema=[
        Field(name="text", dtype=String),
        Field(
            name="embedding",
            dtype=Array(Float32),
            tags={
                "vector_index": "true",
                "dimensions": "768",
                "similarity_function": "COSINE",  # COSINE | DOT_PRODUCT | EUCLIDEAN
            },
        ),
    ],
    online=True,
    source=push_source,
)

When feast apply runs, the store automatically creates the necessary tables and HNSW ANN index for any feature view with vector-tagged fields.

To query the top-k most similar documents:

result = store.retrieve_online_documents_v2(
    features=["documents:text", "documents:embedding"],
    query=[0.1, 0.2, ...],   # your query embedding
    top_k=10,
    distance_metric="COSINE",
)

Metadata filtering (OpenAI-compatible)

ScyllaDB supports vector similarity search, but OpenAI-style metadata filtering is not supported yet. Passing filters to retrieve_online_documents_v2 or the OpenAI-compatible search endpoint raises NotImplementedError.

For filtered vector search today, use one of the backends that implement metadata filters (for example Milvus, Elasticsearch, Postgres, SQLite, or MongoDB). See Alpha Vector Database.

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 ScyllaDB online store.

	ScyllaDB
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	yes
support for deleting expired data	yes
collocated by feature view	yes
collocated by feature service	no
collocated by entity key	no

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

Resources

• ScyllaDB Vector Search documentation
• ScyllaDB website
• ScyllaDB Cloud documentation