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PostgreSQL offline store (contrib)

Description

The PostgreSQL offline store provides support for reading PostgreSQLSources.

  • 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

{% code title="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

{% endcode %}

Note that sslmode defaults to require, which encrypts the connection without certificate verification. To disable SSL (e.g. for local development), set sslmode: disable. For certificate verification, set sslmode to verify-ca or verify-full and provide the corresponding sslrootcert_path (and optionally sslcert_path and sslkey_path for mutual TLS). The full set of configuration options is available in PostgreSQLOfflineStoreConfig.

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

The set of functionality supported by offline stores is described in detail here. 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) no
write_logged_features (persist logged features to offline store) no

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 no
export to SQL yes
export to data lake (S3, GCS, etc.) yes
export to data warehouse yes
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 yes
read partitioned data yes

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