> For the complete documentation index, see [llms.txt](https://docs.feast.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feast.dev/master/reference/online-stores/scylladb.md). # ScyllaDB ## Description [ScyllaDB](https://www.scylladb.com/) 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](https://www.scylladb.com/product/scylla-cloud/) cluster for real-time online feature serving. ## Getting started Install Feast with the `scylladb` extra, which pulls in `scylla-driver` automatically: ```bash pip install feast[scylladb] ``` ### Example (ScyllaDB) {% code title="feature\_store.yaml" %} ```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 ``` {% endcode %} ### Example (ScyllaDB Cloud) {% code title="feature\_store.yaml" %} ```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 ``` {% endcode %} ## 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: {% code title="feature\_definitions.py" %} ```python 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, ) ``` {% endcode %} 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: ```python 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](/master/reference/alpha-vector-database.md#supported-online-stores). ## Functionality Matrix The set of functionality supported by online stores is described in detail [here](/master/reference/online-stores/overview.md#functionality). 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](/master/reference/online-stores/overview.md#functionality-matrix). ## Resources * [ScyllaDB Vector Search documentation](https://cloud.docs.scylladb.com/stable/vector-search/) * [ScyllaDB website](https://www.scylladb.com/) * [ScyllaDB Cloud documentation](https://cloud.docs.scylladb.com/stable/) --- # Agent Instructions This documentation is published with GitBook. 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