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Version: 0.5.4

common.libs.pydantic

DltConfig Objects

class DltConfig(TypedDict)

[view_source]

dlt configuration that can be attached to Pydantic model

Example below removes nested field from the resulting dlt schema.

class ItemModel(BaseModel):
b: bool
nested: Dict[str, Any]
dlt_config: ClassVar[DltConfig] = {"skip_complex_types": True}

skip_complex_types

If True, columns of complex types (dict, list, BaseModel) will be excluded from dlt schema generated from the model

pydantic_to_table_schema_columns

def pydantic_to_table_schema_columns(
model: Union[BaseModel, Type[BaseModel]]) -> TTableSchemaColumns

[view_source]

Convert a pydantic model to a table schema columns dict

See also DltConfig for more control over how the schema is created

Arguments:

  • model - The pydantic model to convert. Can be a class or an instance.

Returns:

  • TTableSchemaColumns - table schema columns dict

apply_schema_contract_to_model

def apply_schema_contract_to_model(
model: Type[_TPydanticModel],
column_mode: TSchemaEvolutionMode,
data_mode: TSchemaEvolutionMode = "freeze") -> Type[_TPydanticModel]

[view_source]

Configures or re-creates model so it behaves according to column_mode and data_mode settings.

column_mode sets the model behavior when unknown field is found. data_mode sets model behavior when known field does not validate. currently evolve and freeze are supported here.

discard_row is implemented in validate_item.

create_list_model

def create_list_model(
model: Type[_TPydanticModel],
data_mode: TSchemaEvolutionMode = "freeze"
) -> Type[ListModel[_TPydanticModel]]

[view_source]

Creates a model from model for validating list of items in batch according to data_mode

Currently only freeze is supported. See comments in the code

validate_and_filter_items

def validate_and_filter_items(
table_name: str, list_model: Type[ListModel[_TPydanticModel]],
items: List[TDataItem], column_mode: TSchemaEvolutionMode,
data_mode: TSchemaEvolutionMode) -> List[_TPydanticModel]

[view_source]

Validates list of item with list_model and returns parsed Pydantic models. If column_mode and data_mode are set this function will remove non validating items (discard_row) or raise on the first non-validating items (freeze). Note that the model itself may be configured to remove non validating or extra items as well.

list_model should be created with create_list_model and have items field which this function returns.

validate_and_filter_item

def validate_and_filter_item(
table_name: str, model: Type[_TPydanticModel], item: TDataItems,
column_mode: TSchemaEvolutionMode,
data_mode: TSchemaEvolutionMode) -> Optional[_TPydanticModel]

[view_source]

Validates item against model model and returns an instance of it. If column_mode and data_mode are set this function will return None (discard_row) or raise on non-validating items (freeze). Note that the model itself may be configured to remove non validating or extra items as well.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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