great_expectations.experimental.datasources.sources
¶
Module Contents¶
Classes¶
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Contains a collection of datasource factory methods in the format .add_<TYPE_NAME>() |
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great_expectations.experimental.datasources.sources.
SourceFactoryFn
¶
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great_expectations.experimental.datasources.sources.
LOGGER
¶
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exception
great_expectations.experimental.datasources.sources.
TypeRegistrationError
¶ Bases:
TypeError
Inappropriate argument type.
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class
great_expectations.experimental.datasources.sources.
_SourceFactories
(data_context: Union[DataContext, GXDataContext])¶ Contains a collection of datasource factory methods in the format .add_<TYPE_NAME>()
Contains a .type_lookup dict-like two way mapping between previously registered Datasource or DataAsset types and a simplified name for those types.
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type_lookup
:ClassVar¶
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__source_factories
:ClassVar[Dict[str, SourceFactoryFn]]¶
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_data_context
:Union[DataContext, GXDataContext]¶
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classmethod
register_types_and_ds_factory
(cls, ds_type: Type[Datasource], factory_fn: SourceFactoryFn)¶ Add/Register a datasource factory function and all related Datasource, DataAsset and ExecutionEngine types.
Creates mapping table between the DataSource/DataAsset classes and their declared type string.
Example
An .add_pandas() pandas factory method will be added to context.sources.
>>> class PandasDatasource(Datasource): >>> type: str = 'pandas'` >>> asset_types = [FileAsset] >>> execution_engine: PandasExecutionEngine
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classmethod
_register_datasource_and_factory_method
(cls, ds_type: Type[Datasource], factory_fn: SourceFactoryFn, ds_type_name: str, datasource_type_lookup: TypeLookup)¶ Register the Datasource class and add a factory method for the class on sources. The method name is pulled from the Datasource.type attribute.
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classmethod
_register_assets
(cls, ds_type: Type[Datasource], asset_type_lookup: TypeLookup)¶
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property
factories
(self)¶
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__getattr__
(self, attr_name: str)¶
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__dir__
(self)¶ Preserves autocompletion for dynamic attributes.
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