File

Description

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.

Example

feature_store.yaml
project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: file

The full set of configuration options is available in FileOfflineStoreConfig.

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 file offline store.

File

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

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

File

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

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.

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