API

class pipeline.Message(*, kind: Kind = Kind.Message, id: str = None, created: datetime = None, logs: List[Log] = [], content: Dict[str, Any] = {})

as_model(model_class: Type[BaseModel], mappings: Optional[Dict[str, str]] = None) → BaseModel

return content as another BaseModel instance

Parameters

• param model_class: return class type

• type model_class: class

• return: BaseModel

• rtype: BaseModel

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

get(key: str, default: Optional[Any] = None) → Any

access any field in message content

Parameters

• param key: field name

• type key: str

• return: value

• rtype: Any

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

update_content(other: BaseModel) → KeysView[str]

add fields from other model to update message’s content

Parameters

• param other: other BaseModel object

• type other: BaseModel

• return: list of keys updated

• rtype: KeysView[str]

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

class pipeline.Producer(settings: ~pipeline.worker.ProducerSettings, output_class: ~typing.Type[~pydantic.main.BaseModel], logger: ~logging.Logger = <Logger pipeline (INFO)>)

Producer is a worker to generate new messages. For example, a webcrawler can be a producer. It reads no input, and produce outputs until it exits.

Parameters

• param settings: settings

• type settings: ProducerSettings

• param output_class: output class

• type output_class: Type[BaseModel]

• param logger: logger

• type logger: Logger

Usage:

>>> from pydantic import BaseModel
>>>
>>> class Output(BaseModel):
...     pass
>>>
>>> settings = ProducerSettings(name='', version='', description='', out_kind='MEM')
>>> producer = Producer(settings, output_class=Output)
>>> producer.parse_args()
>>> #producer.start()

duplicate_destination(topic)

helper function

generate() → Iterator[BaseModel]

a producer to generate dict.

parse_args(args: Union[List[str], str] = ['-T', '-E', '-W', '--keep-going', '-b', 'html', '-d', '_build/doctrees', '-D', 'language=en', '.', '/home/docs/checkouts/readthedocs.org/user_builds/tanbih-pipeline/checkouts/latest/_readthedocs//html']) → None

parse command line arguments args: a list of arguments or a command line arguments string

setup() → None

loading code goes here

shutdown() → None

clean up code goes here

step() → Tuple[int, Message]

make new message and write to destination rtype: Tuple[int, Message]

class pipeline.Splitter(settings: ~pipeline.worker.SplitterSettings, logger: ~logging.Logger = <Logger pipeline (INFO)>)

Splitter will write to a topic whose name is based on a function

duplicate_destination(topic)

helper function

parse_args(args: Union[List[str], str] = ['-T', '-E', '-W', '--keep-going', '-b', 'html', '-d', '_build/doctrees', '-D', 'language=en', '.', '/home/docs/checkouts/readthedocs.org/user_builds/tanbih-pipeline/checkouts/latest/_readthedocs//html']) → None

parse command line arguments args: a list of arguments or a command line arguments string

setup() → None

loading code goes here

shutdown() → None

clean up code goes here

class pipeline.Processor(settings: ~pipeline.worker.ProcessorSettings, input_class: ~typing.Type[~pydantic.main.BaseModel], output_class: ~typing.Type[~pydantic.main.BaseModel], logger: ~logging.Logger = <Logger pipeline (INFO)>)

Processor is a worker which will process incoming messages and output new messages

duplicate_destination(topic)

helper function

parse_args(args: Union[List[str], str] = ['-T', '-E', '-W', '--keep-going', '-b', 'html', '-d', '_build/doctrees', '-D', 'language=en', '.', '/home/docs/checkouts/readthedocs.org/user_builds/tanbih-pipeline/checkouts/latest/_readthedocs//html']) → None

parse command line arguments args: a list of arguments or a command line arguments string

process(message_content: BaseModel, message_id: str) → BaseModel

process function to be overridden by users, for streaming processing, this function needs to do in-place update on msg.dct and return an error or a list of errors (for batch processing). Message has been terminated though .terminates() will be skipped in output.

A typical process definition will be:

newValue = msg.value
return OutputModel(value=newValue)

setup() → None

loading code goes here

shutdown() → None

clean up code goes here

start() → None

start processing.

class pipeline.Pipeline(**kwargs: Any)

Pipeline manages SourceTap and DestinationTap when you don’t want to use predefined worker logic. Instead, you have access to SourceTap and DestinationTap directly.

Usage:

from .tap import TapKind, MemorySourceSettings, MemoryDestinationSettings
in_settings = MemorySourceSettings()
out_settings = MemoryDestinationSettings()
pipeline = Pipeline(
  in_kind=TapKind.MEM,
  in_settings=in_settings,
  out_kind=TapKind.MEM,
  out_settings=out_settings
)

Take command line arguments:

pipeline = Pipeline(args=sys.argv)

Take environment settings:

pipeline = Pipeline()

add_destination_topic(name: str) → None

Add a new DestinationTap with a defined topic(queue) name

Parameters

name – a name given for the destination topic

add_source_topic(name: str) → None

Add a new SourceTap with a defined topic(queue) name

Parameters

name – a name given for the source topic

destination_of(name: str) → DestinationTap

Return the DestinationTap of specified topic(queue) name

source_of(name: str) → SourceTap

Return the SourceTap of specified topic(queue) name