PostgreSQL (contrib)

Description

The PostgreSQL offline store provides support for reading .

Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Postgres as a table in order to complete join operations.

Disclaimer

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

Getting started

In order to use this offline 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_project
registry: data/registry.db
provider: local
offline_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
  entity_select_mode: temp_table
online_store:
    path: data/online_store.db

Additionally, a new optional parameter entity_select_mode was added to tell how Postgres should load the entity data. By default(temp_table), a temporary table is created and the entity data frame or sql is loaded into that table. A new value of embed_query was added to allow directly loading the SQL query into a CTE, providing improved performance and skipping the need to CREATE and DROP the temporary table.

Functionality Matrix

Postgres

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)

write_logged_features (persist logged features to offline store)

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

Postgres

export to dataframe

yes

export to arrow table

yes

export to arrow batches

export to SQL

yes

export to data lake (S3, GCS, etc.)

yes

export to data warehouse

yes

export as Spark dataframe

local execution of Python-based on-demand transforms

yes

remote execution of Python-based on-demand transforms

persist results in the offline store

yes

preview the query plan before execution

yes

read partitioned data

yes

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Example

feature_store.yaml

project: my_project
registry: data/registry.db
provider: local
offline_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
  entity_select_mode: temp_table
online_store:
    path: data/online_store.db

Note that sslmode, sslkey_path, sslcert_path, and sslrootcert_path are optional parameters. The full set of configuration options is available in .

Functionality Matrix

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

Postgres

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)

write_logged_features (persist logged features to offline store)

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

Postgres

export to dataframe

yes

export to arrow table

yes

export to arrow batches

export to SQL

yes

export to data lake (S3, GCS, etc.)

yes

export to data warehouse

yes

export as Spark dataframe

local execution of Python-based on-demand transforms

yes

remote execution of Python-based on-demand transforms

persist results in the offline store

yes

preview the query plan before execution

yes

read partitioned data

yes

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