Athena (contrib)

Description

The Athena offline store provides support for reading AthenaSources.

  • Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.

Disclaimer

The Athena offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[aws]'.

Example

feature_store.yaml
project: my_project
registry: data/registry.db
provider: local
offline_store:
  type: athena
  data_source: AwsDataCatalog
  region: us-east-1
  database: my_database
  workgroup: primary
online_store:
    path: data/online_store.db

The full set of configuration options is available in AthenaOfflineStoreConfigarrow-up-right.

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 Athena offline store.

Athena

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)

yes

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

Athena

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.)

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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