This plugin is no longer maintained since Feast now has postgres support.
This repo adds PostgreSQL offline and online stores to Feast
pip install feastpip install feast-postgresfeast init feature_repo
cd feature_repoTo configure the online store edit feature_store.yaml
project: feature_repo
registry: data/registry.db
provider: local
online_store:
type: feast_postgres.PostgreSQLOnlineStore # MUST be this value
host: localhost
port: 5432 # Optional, default is 5432
database: postgres
db_schema: feature_store # Optional, default is None
user: username
password: password
offline_store:
...When running feast apply, if db_schema is set then that value will be used when creating the schema, else the name of the schema will be the value in user. If the schema already exists then no schema is created, but the user must have privileges to create tables and indexes as well as dropping tables and indexes.
To configure the offline store edit feature_store.yaml
project: feature_repo
registry: data/registry.db
provider: local
online_store:
...
offline_store:
type: feast_postgres.PostgreSQLOfflineStore # MUST be this value
host: localhost
port: 5432 # Optional, default it 5432
database: postgres
db_schema: my_schema
user: username
password: passwordThe user will need to have privileges to create and drop tables in db_schema since temp tables will be created when querying for historical values.
To configure the registry edit feature_store.yaml
registry:
registry_store_type: feast_postgres.PostgreSQLRegistryStore
path: feast_registry # This will become the table name for the registry
host: localhost
port: 5432 # Optional, default is 5432
database: postgres
db_schema: my_schema
user: username
password: passwordIf the schema does not exists, the user will need to have privileges to create it. If the schema exists, the user will only need privileges to create the table.
Start by setting the values in feature_store.yaml. Then use copy_from_parquet_to_postgres.py to create a table and populate it with data from the parquet file that comes with Feast.
Then example.py can be used for the feature_store.
# This is an example feature definition file
from google.protobuf.duration_pb2 import Duration
from feast import Entity, Feature, FeatureView, ValueType
from feast_postgres import PostgreSQLSource
# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_hourly_stats = PostgreSQLSource(
query="SELECT * FROM driver_stats",
event_timestamp_column="event_timestamp",
created_timestamp_column="created",
)
# Define an entity for the driver. You can think of entity as a primary key used to
# fetch features.
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_hourly_stats_view = FeatureView(
name="driver_hourly_stats",
entities=["driver_id"],
ttl=Duration(seconds=86400 * 1),
features=[
Feature(name="conv_rate", dtype=ValueType.FLOAT),
Feature(name="acc_rate", dtype=ValueType.FLOAT),
Feature(name="avg_daily_trips", dtype=ValueType.INT64),
],
online=True,
batch_source=driver_hourly_stats,
tags={},
)Then run:
feast apply
feast materialize-incremental $(date -u +"%Y-%m-%dT%H:%M:%S")This will create the feature view table and populate if with data from the driver_stats table that we created in Postgres.