Oracle offline store (contrib)

Description

The Oracle offline store provides support for reading OracleSources.

Entity dataframes can be provided as a SQL query or as a Pandas dataframe.
Uses the ibis Oracle backend (ibis.oracle) for all database interactions.
Only one of service_name, sid, or dsn may be set in the configuration.

Disclaimer

The Oracle offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

Install the Oracle extras:

bash

pip install 'feast[oracle]'

Example

{% code title="feature_store.yaml" %}

yaml

project: my_project
registry: data/registry.db
provider: local
offline_store:
  type: oracle
  host: DB_HOST
  port: 1521
  user: DB_USERNAME
  password: DB_PASSWORD
  service_name: ORCL
online_store:
  path: data/online_store.db

{% endcode %}

Connection can alternatively use sid or dsn instead of service_name:

yaml

# Using SID
offline_store:
  type: oracle
  host: DB_HOST
  port: 1521
  user: DB_USERNAME
  password: DB_PASSWORD
  sid: ORCL

# Using DSN
offline_store:
  type: oracle
  host: DB_HOST
  port: 1521
  user: DB_USERNAME
  password: DB_PASSWORD
  dsn: "(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST=DB_HOST)(PORT=1521))(CONNECT_DATA=(SERVICE_NAME=ORCL)))"

Configuration reference

Parameter	Required	Default	Description
`type`	yes	—	Must be set to `oracle`
`user`	yes	—	Oracle database user
`password`	yes	—	Oracle database password
`host`	no	`localhost`	Oracle database host
`port`	no	`1521`	Oracle database port
`service_name`	no	—	Oracle service name (mutually exclusive with sid and dsn)
`sid`	no	—	Oracle SID (mutually exclusive with service_name and dsn)
`database`	no	—	Oracle database name
`dsn`	no	—	Oracle DSN string (mutually exclusive with service_name and sid)

Functionality Matrix

The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Oracle offline store.

	Oracle
`get_historical_features` (point-in-time correct join)	yes
`pull_latest_from_table_or_query` (retrieve latest feature values)	yes
`pull_all_from_table_or_query` (retrieve a saved dataset)	yes
`offline_write_batch` (persist dataframes to offline store)	yes
`write_logged_features` (persist logged features to offline store)	yes

Below is a matrix indicating which functionality is supported by OracleRetrievalJob.

	Oracle
export to dataframe	yes
export to arrow table	yes
export to arrow batches	no
export to SQL	no
export to data lake (S3, GCS, etc.)	no
export to data warehouse	no
export as Spark dataframe	no
local execution of Python-based on-demand transforms	yes
remote execution of Python-based on-demand transforms	no
persist results in the offline store	yes
preview the query plan before execution	no
read partitioned data	no

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