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.
The PostgreSQL offline store does not achieve full test coverage. Please do not assume complete stability.
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
.
Note that sslmode
, sslkey_path
, sslcert_path
, and sslrootcert_path
are optional parameters. The full set of configuration options is available in PostgreSQLOfflineStoreConfig.
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.
Below is a matrix indicating which functionality is supported by PostgreSQLRetrievalJob
.
To compare this set of functionality against other offline stores, please see the full functionality matrix.
Postgres | |
---|---|
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
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