PostgreSQL

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

• sslmode, sslkey_path, sslcert_path, and sslrootcert_path are optional

Getting started

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.

Example

feature_store.yaml

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

The full set of configuration options is available in PostgreSQLOnlineStoreConfig.

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 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
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

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 vector_enabled: true in the online store configuration.

The vector_length parameter can be used to specify the length of the vector in the Field.

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.

python

from feast import FeatureStore
from feast.infra.online_stores.postgres_online_store 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,
)