extract.resource

with_table_name

def with_table_name(item: TDataItems, table_name: str) -> DataItemWithMeta

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Marks item to be dispatched to table table_name when yielded from resource function.

with_hints

def with_hints(item: TDataItems, hints: TResourceHints) -> DataItemWithMeta

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Marks item to update the resource with specified hints.

Create TResourceHints with make_hints. Setting table_name will dispatch the item to a specified table, like with_table_name

DltResource Objects

class DltResource(Iterable[TDataItem], DltResourceHints)

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Implements dlt resource. Contains a data pipe that wraps a generating item and table schema that can be adjusted

source_name

Name of the source that contains this instance of the source, set when added to DltResourcesDict

section

A config section name

name

@property
def name() -> str

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Resource name inherited from the pipe

with_name

def with_name(new_name: str) -> "DltResource"

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Clones the resource with a new name. Such resource keeps separate state and loads data to new_name table by default.

is_transformer

@property
def is_transformer() -> bool

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Checks if the resource is a transformer that takes data from another resource

requires_args

@property
def requires_args() -> bool

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Checks if resource has unbound arguments

incremental

@property
def incremental() -> IncrementalResourceWrapper

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Gets incremental transform if it is in the pipe

validator

@property
def validator() -> Optional[ValidateItem]

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Gets validator transform if it is in the pipe

validator

@validator.setter
def validator(validator: Optional[ValidateItem]) -> None

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Add/remove or replace the validator in pipe

pipe_data_from

def pipe_data_from(data_from: Union["DltResource", Pipe]) -> None

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Replaces the parent in the transformer resource pipe from which the data is piped.

add_pipe

def add_pipe(data: Any) -> None

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Creates additional pipe for the resource from the specified data

select_tables

def select_tables(*table_names: Iterable[str]) -> "DltResource"

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For resources that dynamically dispatch data to several tables allows to select tables that will receive data, effectively filtering out other data items.

Both with_table_name marker and data-based (function) table name hints are supported.

add_map

def add_map(item_map: ItemTransformFunc[TDataItem],
            insert_at: int = None) -> "DltResource"

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Adds mapping function defined in item_map to the resource pipe at position inserted_at

item_map receives single data items, dlt will enumerate any lists of data items automatically

Arguments:

• item_map ItemTransformFunc[TDataItem] - A function taking a single data item and optional meta argument. Returns transformed data item.
• insert_at int, optional - At which step in pipe to insert the mapping. Defaults to None which inserts after last step

Returns:

• "DltResource" - returns self

add_yield_map

def add_yield_map(item_map: ItemTransformFunc[Iterator[TDataItem]],
                  insert_at: int = None) -> "DltResource"

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Adds generating function defined in item_map to the resource pipe at position inserted_at

item_map receives single data items, dlt will enumerate any lists of data items automatically. It may yield 0 or more data items and be used to ie. pivot an item into sequence of rows.

Arguments:

• item_map ItemTransformFunc[Iterator[TDataItem]] - A function taking a single data item and optional meta argument. Yields 0 or more data items.
• insert_at int, optional - At which step in pipe to insert the generator. Defaults to None which inserts after last step

Returns:

• "DltResource" - returns self

add_filter

def add_filter(item_filter: ItemTransformFunc[bool],
               insert_at: int = None) -> "DltResource"

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Adds filter defined in item_filter to the resource pipe at position inserted_at

item_filter receives single data items, dlt will enumerate any lists of data items automatically

Arguments:

• item_filter ItemTransformFunc[bool] - A function taking a single data item and optional meta argument. Returns bool. If True, item is kept
• insert_at int, optional - At which step in pipe to insert the filter. Defaults to None which inserts after last step

Returns:

• "DltResource" - returns self

add_limit

def add_limit(max_items: int) -> "DltResource"

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Adds a limit max_items to the resource pipe

This mutates the encapsulated generator to stop after max_items items are yielded. This is useful for testing and debugging. It is a no-op for transformers. Those should be limited by their input data.

Arguments:

• max_items int - The maximum number of items to yield

Returns:

• "DltResource" - returns self

parallelize

def parallelize() -> "DltResource"

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Wraps the resource to execute each item in a threadpool to allow multiple resources to extract in parallel.

The resource must be a generator or generator function or a transformer function.

bind

def bind(*args: Any, **kwargs: Any) -> "DltResource"

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Binds the parametrized resource to passed arguments. Modifies resource pipe in place. Does not evaluate generators or iterators.

explicit_args

@property
def explicit_args() -> StrAny

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Returns a dictionary of arguments used to parametrize the resource. Does not include defaults and injected args.

state

@property
def state() -> StrAny

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Gets resource-scoped state from the active pipeline. PipelineStateNotAvailable is raised if pipeline context is not available

__call__

def __call__(*args: Any, **kwargs: Any) -> "DltResource"

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Binds the parametrized resources to passed arguments. Creates and returns a bound resource. Generators and iterators are not evaluated.

__or__

def __or__(transform: Union["DltResource", AnyFun]) -> "DltResource"

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Allows to pipe data from across resources and transform functions with | operator This is the LEFT side OR so the self may be resource or transformer

__ror__

def __ror__(data: Union[Iterable[Any], Iterator[Any]]) -> "DltResource"

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Allows to pipe data from across resources and transform functions with | operator This is the RIGHT side OR so the self may not be a resource and the LEFT must be an object that does not implement | ie. a list

__iter__

def __iter__() -> Iterator[TDataItem]

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Opens iterator that yields the data items from the resources in the same order as in Pipeline class.

A read-only state is provided, initialized from active pipeline state. The state is discarded after the iterator is closed.