extract.incremental

Incremental Objects

@configspec
class Incremental(ItemTransform[TDataItem], BaseConfiguration,
                  Generic[TCursorValue])

[view_source]

Adds incremental extraction for a resource by storing a cursor value in persistent state.

The cursor could for example be a timestamp for when the record was created and you can use this to load only new records created since the last run of the pipeline.

To use this the resource function should have an argument either type annotated with Incremental or a default Incremental instance. For example:

When the resource has a primary_key specified this is used to deduplicate overlapping items with the same cursor value.

Alternatively you can use this class as transform step and add it to any resource. For example:

@dlt.resource(primary_key='id')
def some_data(created_at=dlt.sources.incremental('created_at', '2023-01-01T00:00:00Z'):
   yield from request_data(created_after=created_at.last_value)

@dlt.resource
def some_data():
    last_value = dlt.sources.incremental.from_existing_state("some_data", "item.ts")
    ...

r = some_data().add_step(dlt.sources.incremental("item.ts", initial_value=now, primary_key="delta"))
info = p.run(r, destination="duckdb")

Arguments:

• cursor_path - The name or a JSON path to an cursor field. Uses the same names of fields as in your JSON document, before they are normalized to store in the database.
• initial_value - Optional value used for last_value when no state is available, e.g. on the first run of the pipeline. If not provided last_value will be None on the first run.
• last_value_func - Callable used to determine which cursor value to save in state. It is called with a list of the stored state value and all cursor vals from currently processing items. Default is max
• primary_key - Optional primary key used to deduplicate data. If not provided, a primary key defined by the resource will be used. Pass a tuple to define a compound key. Pass empty tuple to disable unique checks
• end_value - Optional value used to load a limited range of records between initial_value and end_value. Use in conjunction with initial_value, e.g. load records from given month incremental(initial_value="2022-01-01T00:00:00Z", end_value="2022-02-01T00:00:00Z") Note, when this is set the incremental filtering is stateless and initial_value always supersedes any previous incremental value in state.
• row_order - Declares that data source returns rows in descending (desc) or ascending (asc) order as defined by last_value_func. If row order is know, Incremental class is able to stop requesting new rows by closing pipe generator. This prevents getting more data from the source. Defaults to None, which means that row order is not known.
• allow_external_schedulers - If set to True, allows dlt to look for external schedulers from which it will take "initial_value" and "end_value" resulting in loading only specified range of data. Currently Airflow scheduler is detected: "data_interval_start" and "data_interval_end" are taken from the context and passed Incremental class. The values passed explicitly to Incremental will be ignored. Note that if logical "end date" is present then also "end_value" will be set which means that resource state is not used and exactly this range of date will be loaded

from_existing_state

@classmethod
def from_existing_state(cls, resource_name: str,
                        cursor_path: str) -> "Incremental[TCursorValue]"

[view_source]

Create Incremental instance from existing state.

merge

def merge(other: "Incremental[TCursorValue]") -> "Incremental[TCursorValue]"

[view_source]

Create a new incremental instance which merges the two instances. Only properties which are not None from other override the current instance properties.

This supports use cases with partial overrides, such as:

def my_resource(updated=incremental('updated', initial_value='1970-01-01'))
    ...

my_resource(updated=incremental(initial_value='2023-01-01', end_value='2023-02-01'))

get_state

def get_state() -> IncrementalColumnState

[view_source]

Returns an Incremental state for a particular cursor column

get_incremental_value_type

def get_incremental_value_type() -> Type[Any]

[view_source]

Infers the type of incremental value from a class of an instance if those preserve the Generic arguments information.

bind

def bind(pipe: SupportsPipe) -> "Incremental[TCursorValue]"

[view_source]

Called by pipe just before evaluation

can_close

def can_close() -> bool

[view_source]

Checks if incremental is out of range and can be closed.

Returns true only when row_order was set and

1. results are ordered ascending and are above upper bound (end_value)
2. results are ordered descending and are below or equal lower bound (start_value)

IncrementalResourceWrapper Objects

class IncrementalResourceWrapper(ItemTransform[TDataItem])

[view_source]

__init__

def __init__(
        primary_key: Optional[TTableHintTemplate[TColumnNames]] = None
) -> None

[view_source]

Creates a wrapper over a resource function that accepts Incremental instance in its argument to perform incremental loading.

The wrapper delays instantiation of the Incremental to the moment of actual execution and is currently used by dlt.resource decorator. The wrapper explicitly (via resource_name) parameter binds the Incremental state to a resource state. Note that wrapper implements FilterItem transform interface and functions as a processing step in the before-mentioned resource pipe.

Arguments:

• primary_key TTableHintTemplate[TColumnKey], optional - A primary key to be passed to Incremental Instance at execution. Defaults to None.

wrap

def wrap(sig: inspect.Signature, func: TFun) -> TFun

[view_source]

Wrap the callable to inject an Incremental object configured for the resource.

allow_external_schedulers

@property
def allow_external_schedulers() -> bool

[view_source]

Allows the Incremental instance to get its initial and end values from external schedulers like Airflow