Dask

Description

The Dask offline store provides support for reading FileSources.

When an entity dataframe is supplied to get_historical_features, entity rows and their additional columns (such as training labels) are retained even if all matching feature records are outside the feature view's TTL window or after the entity timestamp. Features from that view are returned as null; valid values from other views are retained. The TTL boundary is inclusive.

Retrieval without an entity dataframe continues to return only entities with eligible feature records for the requested snapshot.

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: dask

The full set of configuration options is available in DaskOfflineStoreConfig.

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 dask offline store.

	Dask
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 DaskRetrievalJob.

	Dask
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.