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Version: 0.18.9

SqliteDatasource

class great_expectations.datasource.fluent.SqliteDatasource(*, type: Literal['sqlite'] = 'sqlite', name: str, id: Optional[uuid.UUID] = None, assets: List[Union[great_expectations.datasource.fluent.sql_datasource.TableAsset, great_expectations.datasource.fluent.sql_datasource.QueryAsset]] = [], connection_string: Union[great_expectations.datasource.fluent.config_str.ConfigStr, great_expectations.datasource.fluent.sqlite_datasource.SqliteDsn], create_temp_table: bool = False, kwargs: Dict[str, Union[great_expectations.datasource.fluent.config_str.ConfigStr, Any]] = )#

Adds a sqlite datasource to the data context.

Parameters

  • name – The name of this sqlite datasource.

  • connection_string – The SQLAlchemy connection string used to connect to the sqlite database. For example: “sqlite:///path/to/file.db”

  • create_temp_table – Whether to leverage temporary tables during metric computation.

  • assets – An optional dictionary whose keys are TableAsset names and whose values are TableAsset objects.

add_query_asset(name: str, query: str, order_by: Optional[SortersDefinition] = None, batch_metadata: Optional[BatchMetadata] = None) → SqliteQueryAsset#

Adds a query asset to this datasource.

Parameters

  • name – The name of this table asset.

  • query – The SELECT query to selects the data to validate. It must begin with the “SELECT”.

  • order_by – A list of Sorters or Sorter strings.

  • batch_metadata – BatchMetadata we want to associate with this DataAsset and all batches derived from it.

Returns

The query asset that is added to the datasource. The type of this object will match the necessary type for this datasource. eg, it could be a QueryAsset or a SqliteQueryAsset.

add_table_asset(name: str, table_name: str = '', schema_name: Optional[str] = None, order_by: Optional[SortersDefinition] = None, batch_metadata: Optional[BatchMetadata] = None) → SqliteTableAsset#

Adds a table asset to this datasource.

Parameters

  • name – The name of this table asset.

  • table_name – The table where the data resides.

  • schema_name – The schema that holds the table.

  • order_by – A list of Sorters or Sorter strings.

  • batch_metadata – BatchMetadata we want to associate with this DataAsset and all batches derived from it.

Returns

The table asset that is added to the datasource. The type of this object will match the necessary type for this datasource. eg, it could be a TableAsset or a SqliteTableAsset.