The Spark offline store provides support for reading SparkSources.
Entity dataframes can be provided as a SQL query, Pandas dataframe or can be provided as a Pyspark dataframe. A Pandas dataframes will be converted to a Spark dataframe and processed as a temporary view.
The Spark 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[spark]'
. You can get started by then running feast init -t spark
.
The full set of configuration options is available in SparkOfflineStoreConfig.
The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Spark offline store.
Below is a matrix indicating which functionality is supported by SparkRetrievalJob
.
To compare this set of functionality against other offline stores, please see the full functionality matrix.
Spark | |
---|---|
Spark | |
---|---|
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
no
export to data lake (S3, GCS, etc.)
no
export to data warehouse
no
export as Spark dataframe
yes
local execution of Python-based on-demand transforms
no
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