The file offline store provides support for reading FileSources. It uses Dask as the compute engine.
All data is downloaded and joined using Python and therefore may not scale to production workloads.
The full set of configuration options is available in .
The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the file offline store.
Below is a matrix indicating which functionality is supported by FileRetrievalJob.
To compare this set of functionality against other offline stores, please see the full .
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
yes
read partitioned data
yes
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
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
project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
type: file