feast.infra.offline_stores.contrib.athena_offline_store.tests package


feast.infra.offline_stores.contrib.athena_offline_store.tests.data_source module

class feast.infra.offline_stores.contrib.athena_offline_store.tests.data_source.AthenaDataSourceCreator(project_name: str, *args, **kwargs)[source]

Bases: tests.integration.feature_repos.universal.data_source_creator.DataSourceCreator

create_data_source(df: pandas.core.frame.DataFrame, destination_name: str, suffix: Optional[str] = None, timestamp_field='ts', created_timestamp_column='created_ts', field_mapping: Optional[Dict[str, str]] = None) feast.data_source.DataSource[source]

Create a data source based on the dataframe. Implementing this method requires the underlying implementation to persist the dataframe in offline store, using the destination string as a way to differentiate multiple dataframes and data sources.

  • df – The dataframe to be used to create the data source.

  • destination_name – This str is used by the implementing classes to isolate the multiple dataframes from each other.

  • event_timestamp_column – (Deprecated) Pass through for the underlying data source.

  • created_timestamp_column – Pass through for the underlying data source.

  • field_mapping – Pass through for the underlying data source.

  • timestamp_field – Pass through for the underlying data source.


A Data source object, pointing to a table or file that is uploaded/persisted for the purpose of the test.

create_logged_features_destination() feast.feature_logging.LoggingDestination[source]
create_offline_store_config() feast.repo_config.FeastConfigBaseModel[source]
create_saved_dataset_destination() feast.infra.offline_stores.contrib.athena_offline_store.athena_source.SavedDatasetAthenaStorage[source]
get_prefixed_table_name(suffix: str) str[source]
tables: List[str] = []

Module contents